diff options
502 files changed, 27084 insertions, 11649 deletions
diff --git a/.gitignore b/.gitignore index e1f64a1133..b3c4363af0 100644 --- a/.gitignore +++ b/.gitignore @@ -41,3 +41,4 @@ derby.log dist/ spark-*-bin.tar.gz unit-tests.log +lib/ @@ -12,9 +12,8 @@ This README file only contains basic setup instructions. ## Building -Spark requires Scala 2.9.3 (Scala 2.10 is not yet supported). The project is -built using Simple Build Tool (SBT), which is packaged with it. To build -Spark and its example programs, run: +Spark requires Scala 2.10. The project is built using Simple Build Tool (SBT), +which is packaged with it. To build Spark and its example programs, run: sbt/sbt assembly @@ -55,7 +54,7 @@ versions without YARN, use: # Cloudera CDH 4.2.0 with MapReduce v1 $ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt/sbt assembly -For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions +For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, also set `SPARK_YARN=true`: # Apache Hadoop 2.0.5-alpha @@ -64,12 +63,12 @@ with YARN, also set `SPARK_YARN=true`: # Cloudera CDH 4.2.0 with MapReduce v2 $ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt/sbt assembly -For convenience, these variables may also be set through the `conf/spark-env.sh` file -described below. + # Apache Hadoop 2.2.X and newer + $ SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt assembly When developing a Spark application, specify the Hadoop version by adding the "hadoop-client" artifact to your project's dependencies. For example, if you're -using Hadoop 1.0.1 and build your application using SBT, add this entry to +using Hadoop 1.2.1 and build your application using SBT, add this entry to `libraryDependencies`: "org.apache.hadoop" % "hadoop-client" % "1.2.1" diff --git a/assembly/pom.xml b/assembly/pom.xml index d62332137a..fc2adc1fbb 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -26,7 +26,7 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-assembly</artifactId> + <artifactId>spark-assembly_2.10</artifactId> <name>Spark Project Assembly</name> <url>http://spark.incubator.apache.org/</url> @@ -41,27 +41,27 @@ <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-bagel</artifactId> + <artifactId>spark-bagel_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-mllib</artifactId> + <artifactId>spark-mllib_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-repl</artifactId> + <artifactId>spark-repl_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-streaming</artifactId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> @@ -79,7 +79,7 @@ <artifactId>maven-shade-plugin</artifactId> <configuration> <shadedArtifactAttached>false</shadedArtifactAttached> - <outputFile>${project.build.directory}/scala-${scala.version}/${project.artifactId}-${project.version}-hadoop${hadoop.version}.jar</outputFile> + <outputFile>${project.build.directory}/scala-${scala.binary.version}/${project.artifactId}-${project.version}-hadoop${hadoop.version}.jar</outputFile> <artifactSet> <includes> <include>*:*</include> @@ -104,13 +104,13 @@ </goals> <configuration> <transformers> - <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/> + <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" /> <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"> <resource>META-INF/services/org.apache.hadoop.fs.FileSystem</resource> </transformer> </transformers> <transformers> - <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/> + <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" /> <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"> <resource>reference.conf</resource> </transformer> @@ -128,7 +128,7 @@ <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-yarn</artifactId> + <artifactId>spark-yarn_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> </dependencies> diff --git a/bagel/pom.xml b/bagel/pom.xml index c4ce006085..cb8e79f225 100644 --- a/bagel/pom.xml +++ b/bagel/pom.xml @@ -26,7 +26,7 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-bagel</artifactId> + <artifactId>spark-bagel_2.10</artifactId> <packaging>jar</packaging> <name>Spark Project Bagel</name> <url>http://spark.incubator.apache.org/</url> @@ -34,7 +34,7 @@ <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> @@ -43,18 +43,18 @@ </dependency> <dependency> <groupId>org.scalatest</groupId> - <artifactId>scalatest_${scala.version}</artifactId> + <artifactId>scalatest_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> <dependency> <groupId>org.scalacheck</groupId> - <artifactId>scalacheck_${scala.version}</artifactId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> </dependencies> <build> - <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> - <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> <plugins> <plugin> <groupId>org.scalatest</groupId> diff --git a/bin/pyspark b/bin/pyspark index 392a92b670..d6810f4686 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -23,7 +23,7 @@ FWDIR="$(cd `dirname $0`/..; pwd)" # Export this as SPARK_HOME export SPARK_HOME="$FWDIR" -SCALA_VERSION=2.9.3 +SCALA_VERSION=2.10 # Exit if the user hasn't compiled Spark if [ ! -f "$FWDIR/RELEASE" ]; then @@ -59,8 +59,12 @@ if [ -n "$IPYTHON_OPTS" ]; then fi if [[ "$IPYTHON" = "1" ]] ; then - IPYTHON_OPTS=${IPYTHON_OPTS:--i} - exec ipython "$IPYTHON_OPTS" -c "%run $PYTHONSTARTUP" + # IPython <1.0.0 doesn't honor PYTHONSTARTUP, while 1.0.0+ does. + # Hence we clear PYTHONSTARTUP and use the -c "%run $IPYTHONSTARTUP" command which works on all versions + # We also force interactive mode with "-i" + IPYTHONSTARTUP=$PYTHONSTARTUP + PYTHONSTARTUP= + exec ipython "$IPYTHON_OPTS" -i -c "%run $IPYTHONSTARTUP" else exec "$PYSPARK_PYTHON" "$@" fi diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd index bb8e624c2f..95791095ec 100644 --- a/bin/pyspark2.cmd +++ b/bin/pyspark2.cmd @@ -17,7 +17,7 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem -set SCALA_VERSION=2.9.3 +set SCALA_VERSION=2.10 rem Figure out where the Spark framework is installed set FWDIR=%~dp0..\ diff --git a/bin/run-example b/bin/run-example index ded08a843a..f2699c38a9 100755 --- a/bin/run-example +++ b/bin/run-example @@ -17,7 +17,12 @@ # limitations under the License. # -SCALA_VERSION=2.9.3 +cygwin=false +case "`uname`" in + CYGWIN*) cygwin=true;; +esac + +SCALA_VERSION=2.10 # Figure out where the Scala framework is installed FWDIR="$(cd `dirname $0`/..; pwd)" @@ -59,6 +64,11 @@ fi CLASSPATH=`$FWDIR/sbin/compute-classpath.sh` CLASSPATH="$SPARK_EXAMPLES_JAR:$CLASSPATH" +if $cygwin; then + CLASSPATH=`cygpath -wp $CLASSPATH` + export SPARK_EXAMPLES_JAR=`cygpath -w $SPARK_EXAMPLES_JAR` +fi + # Find java binary if [ -n "${JAVA_HOME}" ]; then RUNNER="${JAVA_HOME}/bin/java" diff --git a/bin/run-example2.cmd b/bin/run-example2.cmd index e250a92965..6861334cb0 100644 --- a/bin/run-example2.cmd +++ b/bin/run-example2.cmd @@ -17,7 +17,7 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem -set SCALA_VERSION=2.9.3 +set SCALA_VERSION=2.10 rem Figure out where the Spark framework is installed set FWDIR=%~dp0..\ diff --git a/bin/spark-shell b/bin/spark-shell index 6717fe7049..bc7386db4d 100755 --- a/bin/spark-shell +++ b/bin/spark-shell @@ -23,7 +23,11 @@ # if those two env vars are set in spark-env.sh but MASTER is not. # Options: # -c <cores> Set the number of cores for REPL to use -# + +cygwin=false +case "`uname`" in + CYGWIN*) cygwin=true;; +esac # Enter posix mode for bash set -o posix @@ -79,7 +83,18 @@ if [[ ! $? ]]; then saved_stty="" fi -$FWDIR/sbin/spark-class $OPTIONS org.apache.spark.repl.Main "$@" +if $cygwin; then + # Workaround for issue involving JLine and Cygwin + # (see http://sourceforge.net/p/jline/bugs/40/). + # If you're using the Mintty terminal emulator in Cygwin, may need to set the + # "Backspace sends ^H" setting in "Keys" section of the Mintty options + # (see https://github.com/sbt/sbt/issues/562). + stty -icanon min 1 -echo > /dev/null 2>&1 + $FWDIR/sbin/spark-class -Djline.terminal=unix $OPTIONS org.apache.spark.repl.Main "$@" + stty icanon echo > /dev/null 2>&1 +else + $FWDIR/sbin/spark-class $OPTIONS org.apache.spark.repl.Main "$@" +fi # record the exit status lest it be overwritten: # then reenable echo and propagate the code. diff --git a/conf/metrics.properties.template b/conf/metrics.properties.template index ae10f615d1..1c3d94e1b0 100644 --- a/conf/metrics.properties.template +++ b/conf/metrics.properties.template @@ -80,6 +80,14 @@ # /metrics/aplications/json # App information # /metrics/master/json # Master information +# org.apache.spark.metrics.sink.GraphiteSink +# Name: Default: Description: +# host NONE Hostname of Graphite server +# port NONE Port of Graphite server +# period 10 Poll period +# unit seconds Units of poll period +# prefix EMPTY STRING Prefix to prepend to metric name + ## Examples # Enable JmxSink for all instances by class name #*.sink.jmx.class=org.apache.spark.metrics.sink.JmxSink diff --git a/core/pom.xml b/core/pom.xml index 8359fefdb4..aac0a9d11e 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -17,218 +17,219 @@ --> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> - <modelVersion>4.0.0</modelVersion> - <parent> - <groupId>org.apache.spark</groupId> - <artifactId>spark-parent</artifactId> - <version>0.9.0-incubating-SNAPSHOT</version> - <relativePath>../pom.xml</relativePath> - </parent> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>org.apache.spark</groupId> + <artifactId>spark-parent</artifactId> + <version>0.9.0-incubating-SNAPSHOT</version> + <relativePath>../pom.xml</relativePath> + </parent> - <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> - <packaging>jar</packaging> - <name>Spark Project Core</name> - <url>http://spark.incubator.apache.org/</url> + <groupId>org.apache.spark</groupId> + <artifactId>spark-core_2.10</artifactId> + <packaging>jar</packaging> + <name>Spark Project Core</name> + <url>http://spark.incubator.apache.org/</url> - <dependencies> - <dependency> - <groupId>org.apache.hadoop</groupId> - <artifactId>hadoop-client</artifactId> - </dependency> - <dependency> - <groupId>net.java.dev.jets3t</groupId> - <artifactId>jets3t</artifactId> - <version>0.7.1</version> - </dependency> - <dependency> - <groupId>org.apache.avro</groupId> - <artifactId>avro</artifactId> - </dependency> - <dependency> - <groupId>org.apache.avro</groupId> - <artifactId>avro-ipc</artifactId> - </dependency> - <dependency> - <groupId>org.eclipse.jetty</groupId> - <artifactId>jetty-server</artifactId> - </dependency> - <dependency> - <groupId>com.google.guava</groupId> - <artifactId>guava</artifactId> - </dependency> - <dependency> - <groupId>com.google.code.findbugs</groupId> - <artifactId>jsr305</artifactId> - </dependency> - <dependency> - <groupId>org.slf4j</groupId> - <artifactId>slf4j-api</artifactId> - </dependency> - <dependency> - <groupId>com.ning</groupId> - <artifactId>compress-lzf</artifactId> - </dependency> - <dependency> - <groupId>org.xerial.snappy</groupId> - <artifactId>snappy-java</artifactId> - </dependency> - <dependency> - <groupId>org.ow2.asm</groupId> - <artifactId>asm</artifactId> - </dependency> - <dependency> - <groupId>com.google.protobuf</groupId> - <artifactId>protobuf-java</artifactId> - </dependency> - <dependency> - <groupId>com.twitter</groupId> - <artifactId>chill_2.9.3</artifactId> - <version>0.3.1</version> - </dependency> - <dependency> - <groupId>com.twitter</groupId> - <artifactId>chill-java</artifactId> - <version>0.3.1</version> - </dependency> - <dependency> - <groupId>com.typesafe.akka</groupId> - <artifactId>akka-actor</artifactId> - </dependency> - <dependency> - <groupId>com.typesafe.akka</groupId> - <artifactId>akka-remote</artifactId> - </dependency> - <dependency> - <groupId>com.typesafe.akka</groupId> - <artifactId>akka-slf4j</artifactId> - </dependency> - <dependency> - <groupId>org.scala-lang</groupId> - <artifactId>scalap</artifactId> - </dependency> - <dependency> - <groupId>org.scala-lang</groupId> - <artifactId>scala-library</artifactId> - </dependency> - <dependency> - <groupId>net.liftweb</groupId> - <artifactId>lift-json_2.9.2</artifactId> - </dependency> - <dependency> - <groupId>it.unimi.dsi</groupId> - <artifactId>fastutil</artifactId> - </dependency> - <dependency> - <groupId>colt</groupId> - <artifactId>colt</artifactId> - </dependency> - <dependency> - <groupId>com.github.scala-incubator.io</groupId> - <artifactId>scala-io-file_2.9.2</artifactId> - </dependency> - <dependency> - <groupId>org.apache.mesos</groupId> - <artifactId>mesos</artifactId> - </dependency> - <dependency> - <groupId>io.netty</groupId> - <artifactId>netty-all</artifactId> - </dependency> - <dependency> - <groupId>log4j</groupId> - <artifactId>log4j</artifactId> - </dependency> - <dependency> - <groupId>com.codahale.metrics</groupId> - <artifactId>metrics-core</artifactId> - </dependency> - <dependency> - <groupId>com.codahale.metrics</groupId> - <artifactId>metrics-jvm</artifactId> - </dependency> - <dependency> - <groupId>com.codahale.metrics</groupId> - <artifactId>metrics-json</artifactId> - </dependency> - <dependency> - <groupId>com.codahale.metrics</groupId> - <artifactId>metrics-ganglia</artifactId> - </dependency> - <dependency> - <groupId>org.apache.derby</groupId> - <artifactId>derby</artifactId> - <scope>test</scope> - </dependency> - <dependency> - <groupId>org.scalatest</groupId> - <artifactId>scalatest_${scala.version}</artifactId> - <scope>test</scope> - </dependency> - <dependency> - <groupId>org.scalacheck</groupId> - <artifactId>scalacheck_${scala.version}</artifactId> - <scope>test</scope> - </dependency> - <dependency> - <groupId>org.easymock</groupId> - <artifactId>easymock</artifactId> - <scope>test</scope> - </dependency> - <dependency> - <groupId>com.novocode</groupId> - <artifactId>junit-interface</artifactId> - <scope>test</scope> - </dependency> - <dependency> - <groupId>org.slf4j</groupId> - <artifactId>slf4j-log4j12</artifactId> - <scope>test</scope> - </dependency> - </dependencies> - <build> - <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> - <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> - <plugins> - <plugin> - <groupId>org.apache.maven.plugins</groupId> - <artifactId>maven-antrun-plugin</artifactId> - <executions> - <execution> - <phase>test</phase> - <goals> - <goal>run</goal> - </goals> - <configuration> - <exportAntProperties>true</exportAntProperties> - <tasks> - <property name="spark.classpath" refid="maven.test.classpath"/> - <property environment="env"/> - <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry."> - <condition> - <not> - <or> - <isset property="env.SCALA_HOME"/> - <isset property="env.SCALA_LIBRARY_PATH"/> - </or> - </not> - </condition> - </fail> - </tasks> - </configuration> - </execution> - </executions> - </plugin> - <plugin> - <groupId>org.scalatest</groupId> - <artifactId>scalatest-maven-plugin</artifactId> - <configuration> - <environmentVariables> - <SPARK_HOME>${basedir}/..</SPARK_HOME> - <SPARK_TESTING>1</SPARK_TESTING> - </environmentVariables> - </configuration> - </plugin> - </plugins> - </build> + <dependencies> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + </dependency> + <dependency> + <groupId>net.java.dev.jets3t</groupId> + <artifactId>jets3t</artifactId> + </dependency> + <dependency> + <groupId>org.apache.avro</groupId> + <artifactId>avro</artifactId> + </dependency> + <dependency> + <groupId>org.apache.avro</groupId> + <artifactId>avro-ipc</artifactId> + </dependency> + <dependency> + <groupId>org.apache.zookeeper</groupId> + <artifactId>zookeeper</artifactId> + </dependency> + <dependency> + <groupId>org.eclipse.jetty</groupId> + <artifactId>jetty-server</artifactId> + </dependency> + <dependency> + <groupId>com.google.guava</groupId> + <artifactId>guava</artifactId> + </dependency> + <dependency> + <groupId>com.google.code.findbugs</groupId> + <artifactId>jsr305</artifactId> + </dependency> + <dependency> + <groupId>org.slf4j</groupId> + <artifactId>slf4j-api</artifactId> + </dependency> + <dependency> + <groupId>com.ning</groupId> + <artifactId>compress-lzf</artifactId> + </dependency> + <dependency> + <groupId>org.xerial.snappy</groupId> + <artifactId>snappy-java</artifactId> + </dependency> + <dependency> + <groupId>org.ow2.asm</groupId> + <artifactId>asm</artifactId> + </dependency> + <dependency> + <groupId>com.twitter</groupId> + <artifactId>chill_${scala.binary.version}</artifactId> + <version>0.3.1</version> + </dependency> + <dependency> + <groupId>com.twitter</groupId> + <artifactId>chill-java</artifactId> + <version>0.3.1</version> + </dependency> + <dependency> + <groupId>${akka.group}</groupId> + <artifactId>akka-remote_${scala.binary.version}</artifactId> + </dependency> + <dependency> + <groupId>${akka.group}</groupId> + <artifactId>akka-slf4j_${scala.binary.version}</artifactId> + </dependency> + <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> + </dependency> + <dependency> + <groupId>net.liftweb</groupId> + <artifactId>lift-json_${scala.binary.version}</artifactId> + </dependency> + <dependency> + <groupId>it.unimi.dsi</groupId> + <artifactId>fastutil</artifactId> + </dependency> + <dependency> + <groupId>colt</groupId> + <artifactId>colt</artifactId> + </dependency> + <dependency> + <groupId>org.apache.mesos</groupId> + <artifactId>mesos</artifactId> + </dependency> + <dependency> + <groupId>io.netty</groupId> + <artifactId>netty-all</artifactId> + </dependency> + <dependency> + <groupId>log4j</groupId> + <artifactId>log4j</artifactId> + </dependency> + <dependency> + <groupId>com.clearspring.analytics</groupId> + <artifactId>stream</artifactId> + </dependency> + <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-core</artifactId> + </dependency> + <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-jvm</artifactId> + </dependency> + <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-json</artifactId> + </dependency> + <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-ganglia</artifactId> + </dependency> + <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-graphite</artifactId> + </dependency> + <dependency> + <groupId>org.apache.derby</groupId> + <artifactId>derby</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>commons-io</groupId> + <artifactId>commons-io</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalacheck</groupId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.easymock</groupId> + <artifactId>easymock</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.novocode</groupId> + <artifactId>junit-interface</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.slf4j</groupId> + <artifactId>slf4j-log4j12</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + <build> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-antrun-plugin</artifactId> + <executions> + <execution> + <phase>test</phase> + <goals> + <goal>run</goal> + </goals> + <configuration> + <exportAntProperties>true</exportAntProperties> + <tasks> + <property name="spark.classpath" refid="maven.test.classpath" /> + <property environment="env" /> + <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry."> + <condition> + <not> + <or> + <isset property="env.SCALA_HOME" /> + <isset property="env.SCALA_LIBRARY_PATH" /> + </or> + </not> + </condition> + </fail> + </tasks> + </configuration> + </execution> + </executions> + </plugin> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + <configuration> + <environmentVariables> + <SPARK_HOME>${basedir}/..</SPARK_HOME> + <SPARK_TESTING>1</SPARK_TESTING> + <SPARK_CLASSPATH>${spark.classpath}</SPARK_CLASSPATH> + </environmentVariables> + </configuration> + </plugin> + </plugins> + </build> </project> diff --git a/core/src/main/java/org/apache/spark/network/netty/FileClient.java b/core/src/main/java/org/apache/spark/network/netty/FileClient.java index 20a7a3aa8c..46d61503bc 100644 --- a/core/src/main/java/org/apache/spark/network/netty/FileClient.java +++ b/core/src/main/java/org/apache/spark/network/netty/FileClient.java @@ -19,22 +19,25 @@ package org.apache.spark.network.netty; import io.netty.bootstrap.Bootstrap; import io.netty.channel.Channel; -import io.netty.channel.ChannelFuture; -import io.netty.channel.ChannelFutureListener; import io.netty.channel.ChannelOption; +import io.netty.channel.EventLoopGroup; import io.netty.channel.oio.OioEventLoopGroup; import io.netty.channel.socket.oio.OioSocketChannel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; +import java.util.concurrent.TimeUnit; + class FileClient { private Logger LOG = LoggerFactory.getLogger(this.getClass().getName()); - private FileClientHandler handler = null; + private final FileClientHandler handler; private Channel channel = null; private Bootstrap bootstrap = null; - private int connectTimeout = 60*1000; // 1 min + private EventLoopGroup group = null; + private final int connectTimeout; + private final int sendTimeout = 60; // 1 min public FileClient(FileClientHandler handler, int connectTimeout) { this.handler = handler; @@ -42,8 +45,9 @@ class FileClient { } public void init() { + group = new OioEventLoopGroup(); bootstrap = new Bootstrap(); - bootstrap.group(new OioEventLoopGroup()) + bootstrap.group(group) .channel(OioSocketChannel.class) .option(ChannelOption.SO_KEEPALIVE, true) .option(ChannelOption.TCP_NODELAY, true) @@ -58,6 +62,7 @@ class FileClient { // ChannelFuture cf = channel.closeFuture(); //cf.addListener(new ChannelCloseListener(this)); } catch (InterruptedException e) { + LOG.warn("FileClient interrupted while trying to connect", e); close(); } } @@ -73,16 +78,21 @@ class FileClient { public void sendRequest(String file) { //assert(file == null); //assert(channel == null); - channel.write(file + "\r\n"); + try { + // Should be able to send the message to network link channel. + boolean bSent = channel.writeAndFlush(file + "\r\n").await(sendTimeout, TimeUnit.SECONDS); + if (!bSent) { + throw new RuntimeException("Failed to send"); + } + } catch (InterruptedException e) { + LOG.error("Error", e); + } } public void close() { - if(channel != null) { - channel.close(); - channel = null; - } - if ( bootstrap!=null) { - bootstrap.shutdown(); + if (group != null) { + group.shutdownGracefully(); + group = null; bootstrap = null; } } diff --git a/core/src/main/java/org/apache/spark/network/netty/FileClientChannelInitializer.java b/core/src/main/java/org/apache/spark/network/netty/FileClientChannelInitializer.java index 65ee15d63b..fb61be1c12 100644 --- a/core/src/main/java/org/apache/spark/network/netty/FileClientChannelInitializer.java +++ b/core/src/main/java/org/apache/spark/network/netty/FileClientChannelInitializer.java @@ -17,15 +17,13 @@ package org.apache.spark.network.netty; -import io.netty.buffer.BufType; import io.netty.channel.ChannelInitializer; import io.netty.channel.socket.SocketChannel; import io.netty.handler.codec.string.StringEncoder; - class FileClientChannelInitializer extends ChannelInitializer<SocketChannel> { - private FileClientHandler fhandler; + private final FileClientHandler fhandler; public FileClientChannelInitializer(FileClientHandler handler) { fhandler = handler; @@ -35,7 +33,7 @@ class FileClientChannelInitializer extends ChannelInitializer<SocketChannel> { public void initChannel(SocketChannel channel) { // file no more than 2G channel.pipeline() - .addLast("encoder", new StringEncoder(BufType.BYTE)) + .addLast("encoder", new StringEncoder()) .addLast("handler", fhandler); } } diff --git a/core/src/main/java/org/apache/spark/network/netty/FileClientHandler.java b/core/src/main/java/org/apache/spark/network/netty/FileClientHandler.java index c4aa2669e0..63d3d92725 100644 --- a/core/src/main/java/org/apache/spark/network/netty/FileClientHandler.java +++ b/core/src/main/java/org/apache/spark/network/netty/FileClientHandler.java @@ -19,10 +19,11 @@ package org.apache.spark.network.netty; import io.netty.buffer.ByteBuf; import io.netty.channel.ChannelHandlerContext; -import io.netty.channel.ChannelInboundByteHandlerAdapter; +import io.netty.channel.SimpleChannelInboundHandler; +import org.apache.spark.storage.BlockId; -abstract class FileClientHandler extends ChannelInboundByteHandlerAdapter { +abstract class FileClientHandler extends SimpleChannelInboundHandler<ByteBuf> { private FileHeader currentHeader = null; @@ -33,16 +34,10 @@ abstract class FileClientHandler extends ChannelInboundByteHandlerAdapter { } public abstract void handle(ChannelHandlerContext ctx, ByteBuf in, FileHeader header); - public abstract void handleError(String blockId); + public abstract void handleError(BlockId blockId); @Override - public ByteBuf newInboundBuffer(ChannelHandlerContext ctx) { - // Use direct buffer if possible. - return ctx.alloc().ioBuffer(); - } - - @Override - public void inboundBufferUpdated(ChannelHandlerContext ctx, ByteBuf in) { + public void channelRead0(ChannelHandlerContext ctx, ByteBuf in) { // get header if (currentHeader == null && in.readableBytes() >= FileHeader.HEADER_SIZE()) { currentHeader = FileHeader.create(in.readBytes(FileHeader.HEADER_SIZE())); diff --git a/core/src/main/java/org/apache/spark/network/netty/FileServer.java b/core/src/main/java/org/apache/spark/network/netty/FileServer.java index 666432474d..aea7534459 100644 --- a/core/src/main/java/org/apache/spark/network/netty/FileServer.java +++ b/core/src/main/java/org/apache/spark/network/netty/FileServer.java @@ -20,16 +20,14 @@ package org.apache.spark.network.netty; import java.net.InetSocketAddress; import io.netty.bootstrap.ServerBootstrap; -import io.netty.channel.Channel; import io.netty.channel.ChannelFuture; import io.netty.channel.ChannelOption; +import io.netty.channel.EventLoopGroup; import io.netty.channel.oio.OioEventLoopGroup; import io.netty.channel.socket.oio.OioServerSocketChannel; - import org.slf4j.Logger; import org.slf4j.LoggerFactory; - /** * Server that accept the path of a file an echo back its content. */ @@ -37,7 +35,8 @@ class FileServer { private Logger LOG = LoggerFactory.getLogger(this.getClass().getName()); - private ServerBootstrap bootstrap = null; + private EventLoopGroup bossGroup = null; + private EventLoopGroup workerGroup = null; private ChannelFuture channelFuture = null; private int port = 0; private Thread blockingThread = null; @@ -46,8 +45,11 @@ class FileServer { InetSocketAddress addr = new InetSocketAddress(port); // Configure the server. - bootstrap = new ServerBootstrap(); - bootstrap.group(new OioEventLoopGroup(), new OioEventLoopGroup()) + bossGroup = new OioEventLoopGroup(); + workerGroup = new OioEventLoopGroup(); + + ServerBootstrap bootstrap = new ServerBootstrap(); + bootstrap.group(bossGroup, workerGroup) .channel(OioServerSocketChannel.class) .option(ChannelOption.SO_BACKLOG, 100) .option(ChannelOption.SO_RCVBUF, 1500) @@ -90,13 +92,19 @@ class FileServer { public void stop() { // Close the bound channel. if (channelFuture != null) { - channelFuture.channel().close(); + channelFuture.channel().close().awaitUninterruptibly(); channelFuture = null; } - // Shutdown bootstrap. - if (bootstrap != null) { - bootstrap.shutdown(); - bootstrap = null; + + // Shutdown event groups + if (bossGroup != null) { + bossGroup.shutdownGracefully(); + bossGroup = null; + } + + if (workerGroup != null) { + workerGroup.shutdownGracefully(); + workerGroup = null; } // TODO: Shutdown all accepted channels as well ? } diff --git a/core/src/main/java/org/apache/spark/network/netty/FileServerChannelInitializer.java b/core/src/main/java/org/apache/spark/network/netty/FileServerChannelInitializer.java index 833af1632d..3f15ff898f 100644 --- a/core/src/main/java/org/apache/spark/network/netty/FileServerChannelInitializer.java +++ b/core/src/main/java/org/apache/spark/network/netty/FileServerChannelInitializer.java @@ -23,7 +23,6 @@ import io.netty.handler.codec.DelimiterBasedFrameDecoder; import io.netty.handler.codec.Delimiters; import io.netty.handler.codec.string.StringDecoder; - class FileServerChannelInitializer extends ChannelInitializer<SocketChannel> { PathResolver pResolver; @@ -36,7 +35,7 @@ class FileServerChannelInitializer extends ChannelInitializer<SocketChannel> { public void initChannel(SocketChannel channel) { channel.pipeline() .addLast("framer", new DelimiterBasedFrameDecoder(8192, Delimiters.lineDelimiter())) - .addLast("strDecoder", new StringDecoder()) + .addLast("stringDecoder", new StringDecoder()) .addLast("handler", new FileServerHandler(pResolver)); } } diff --git a/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java b/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java index d3d57a0255..e2d9391b4c 100644 --- a/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java +++ b/core/src/main/java/org/apache/spark/network/netty/FileServerHandler.java @@ -21,54 +21,55 @@ import java.io.File; import java.io.FileInputStream; import io.netty.channel.ChannelHandlerContext; -import io.netty.channel.ChannelInboundMessageHandlerAdapter; +import io.netty.channel.SimpleChannelInboundHandler; import io.netty.channel.DefaultFileRegion; +import org.apache.spark.storage.BlockId; +import org.apache.spark.storage.FileSegment; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; -class FileServerHandler extends ChannelInboundMessageHandlerAdapter<String> { +class FileServerHandler extends SimpleChannelInboundHandler<String> { - PathResolver pResolver; + private Logger LOG = LoggerFactory.getLogger(this.getClass().getName()); + + private final PathResolver pResolver; public FileServerHandler(PathResolver pResolver){ this.pResolver = pResolver; } @Override - public void messageReceived(ChannelHandlerContext ctx, String blockId) { - String path = pResolver.getAbsolutePath(blockId); - // if getFilePath returns null, close the channel - if (path == null) { + public void channelRead0(ChannelHandlerContext ctx, String blockIdString) { + BlockId blockId = BlockId.apply(blockIdString); + FileSegment fileSegment = pResolver.getBlockLocation(blockId); + // if getBlockLocation returns null, close the channel + if (fileSegment == null) { //ctx.close(); return; } - File file = new File(path); + File file = fileSegment.file(); if (file.exists()) { if (!file.isFile()) { - //logger.info("Not a file : " + file.getAbsolutePath()); ctx.write(new FileHeader(0, blockId).buffer()); ctx.flush(); return; } - long length = file.length(); + long length = fileSegment.length(); if (length > Integer.MAX_VALUE || length <= 0) { - //logger.info("too large file : " + file.getAbsolutePath() + " of size "+ length); ctx.write(new FileHeader(0, blockId).buffer()); ctx.flush(); return; } int len = new Long(length).intValue(); - //logger.info("Sending block "+blockId+" filelen = "+len); - //logger.info("header = "+ (new FileHeader(len, blockId)).buffer()); ctx.write((new FileHeader(len, blockId)).buffer()); try { - ctx.sendFile(new DefaultFileRegion(new FileInputStream(file) - .getChannel(), 0, file.length())); + ctx.write(new DefaultFileRegion(new FileInputStream(file) + .getChannel(), fileSegment.offset(), fileSegment.length())); } catch (Exception e) { - //logger.warning("Exception when sending file : " + file.getAbsolutePath()); - e.printStackTrace(); + LOG.error("Exception: ", e); } } else { - //logger.warning("File not found: " + file.getAbsolutePath()); ctx.write(new FileHeader(0, blockId).buffer()); } ctx.flush(); @@ -76,7 +77,7 @@ class FileServerHandler extends ChannelInboundMessageHandlerAdapter<String> { @Override public void exceptionCaught(ChannelHandlerContext ctx, Throwable cause) { - cause.printStackTrace(); + LOG.error("Exception: ", cause); ctx.close(); } } diff --git a/core/src/main/java/org/apache/spark/network/netty/PathResolver.java b/core/src/main/java/org/apache/spark/network/netty/PathResolver.java index 94c034cad0..9f7ced44cf 100755 --- a/core/src/main/java/org/apache/spark/network/netty/PathResolver.java +++ b/core/src/main/java/org/apache/spark/network/netty/PathResolver.java @@ -17,13 +17,10 @@ package org.apache.spark.network.netty;
+import org.apache.spark.storage.BlockId;
+import org.apache.spark.storage.FileSegment;
public interface PathResolver {
- /**
- * Get the absolute path of the file
- *
- * @param fileId
- * @return the absolute path of file
- */
- public String getAbsolutePath(String fileId);
+ /** Get the file segment in which the given block resides. */
+ public FileSegment getBlockLocation(BlockId blockId);
}
diff --git a/core/src/main/resources/org/apache/spark/default-log4j.properties b/core/src/main/resources/org/apache/spark/default-log4j.properties new file mode 100644 index 0000000000..d72dbadc39 --- /dev/null +++ b/core/src/main/resources/org/apache/spark/default-log4j.properties @@ -0,0 +1,8 @@ +# Set everything to be logged to the console +log4j.rootCategory=INFO, console +log4j.appender.console=org.apache.log4j.ConsoleAppender +log4j.appender.console.layout=org.apache.log4j.PatternLayout +log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN diff --git a/core/src/main/scala/org/apache/hadoop/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/hadoop/mapred/SparkHadoopMapRedUtil.scala index f87460039b..0c47afae54 100644 --- a/core/src/main/scala/org/apache/hadoop/mapred/SparkHadoopMapRedUtil.scala +++ b/core/src/main/scala/org/apache/hadoop/mapred/SparkHadoopMapRedUtil.scala @@ -17,20 +17,29 @@ package org.apache.hadoop.mapred +private[apache] trait SparkHadoopMapRedUtil { def newJobContext(conf: JobConf, jobId: JobID): JobContext = { - val klass = firstAvailableClass("org.apache.hadoop.mapred.JobContextImpl", "org.apache.hadoop.mapred.JobContext"); - val ctor = klass.getDeclaredConstructor(classOf[JobConf], classOf[org.apache.hadoop.mapreduce.JobID]) + val klass = firstAvailableClass("org.apache.hadoop.mapred.JobContextImpl", + "org.apache.hadoop.mapred.JobContext") + val ctor = klass.getDeclaredConstructor(classOf[JobConf], + classOf[org.apache.hadoop.mapreduce.JobID]) ctor.newInstance(conf, jobId).asInstanceOf[JobContext] } def newTaskAttemptContext(conf: JobConf, attemptId: TaskAttemptID): TaskAttemptContext = { - val klass = firstAvailableClass("org.apache.hadoop.mapred.TaskAttemptContextImpl", "org.apache.hadoop.mapred.TaskAttemptContext") + val klass = firstAvailableClass("org.apache.hadoop.mapred.TaskAttemptContextImpl", + "org.apache.hadoop.mapred.TaskAttemptContext") val ctor = klass.getDeclaredConstructor(classOf[JobConf], classOf[TaskAttemptID]) ctor.newInstance(conf, attemptId).asInstanceOf[TaskAttemptContext] } - def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = { + def newTaskAttemptID( + jtIdentifier: String, + jobId: Int, + isMap: Boolean, + taskId: Int, + attemptId: Int) = { new TaskAttemptID(jtIdentifier, jobId, isMap, taskId, attemptId) } diff --git a/core/src/main/scala/org/apache/hadoop/mapreduce/SparkHadoopMapReduceUtil.scala b/core/src/main/scala/org/apache/hadoop/mapreduce/SparkHadoopMapReduceUtil.scala index 93180307fa..32429f01ac 100644 --- a/core/src/main/scala/org/apache/hadoop/mapreduce/SparkHadoopMapReduceUtil.scala +++ b/core/src/main/scala/org/apache/hadoop/mapreduce/SparkHadoopMapReduceUtil.scala @@ -17,9 +17,10 @@ package org.apache.hadoop.mapreduce -import org.apache.hadoop.conf.Configuration import java.lang.{Integer => JInteger, Boolean => JBoolean} +import org.apache.hadoop.conf.Configuration +private[apache] trait SparkHadoopMapReduceUtil { def newJobContext(conf: Configuration, jobId: JobID): JobContext = { val klass = firstAvailableClass( @@ -37,23 +38,31 @@ trait SparkHadoopMapReduceUtil { ctor.newInstance(conf, attemptId).asInstanceOf[TaskAttemptContext] } - def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = { - val klass = Class.forName("org.apache.hadoop.mapreduce.TaskAttemptID"); + def newTaskAttemptID( + jtIdentifier: String, + jobId: Int, + isMap: Boolean, + taskId: Int, + attemptId: Int) = { + val klass = Class.forName("org.apache.hadoop.mapreduce.TaskAttemptID") try { - // first, attempt to use the old-style constructor that takes a boolean isMap (not available in YARN) + // First, attempt to use the old-style constructor that takes a boolean isMap + // (not available in YARN) val ctor = klass.getDeclaredConstructor(classOf[String], classOf[Int], classOf[Boolean], - classOf[Int], classOf[Int]) - ctor.newInstance(jtIdentifier, new JInteger(jobId), new JBoolean(isMap), new JInteger(taskId), new - JInteger(attemptId)).asInstanceOf[TaskAttemptID] + classOf[Int], classOf[Int]) + ctor.newInstance(jtIdentifier, new JInteger(jobId), new JBoolean(isMap), new JInteger(taskId), + new JInteger(attemptId)).asInstanceOf[TaskAttemptID] } catch { case exc: NoSuchMethodException => { - // failed, look for the new ctor that takes a TaskType (not available in 1.x) - val taskTypeClass = Class.forName("org.apache.hadoop.mapreduce.TaskType").asInstanceOf[Class[Enum[_]]] - val taskType = taskTypeClass.getMethod("valueOf", classOf[String]).invoke(taskTypeClass, if(isMap) "MAP" else "REDUCE") + // If that failed, look for the new constructor that takes a TaskType (not available in 1.x) + val taskTypeClass = Class.forName("org.apache.hadoop.mapreduce.TaskType") + .asInstanceOf[Class[Enum[_]]] + val taskType = taskTypeClass.getMethod("valueOf", classOf[String]).invoke( + taskTypeClass, if(isMap) "MAP" else "REDUCE") val ctor = klass.getDeclaredConstructor(classOf[String], classOf[Int], taskTypeClass, classOf[Int], classOf[Int]) - ctor.newInstance(jtIdentifier, new JInteger(jobId), taskType, new JInteger(taskId), new - JInteger(attemptId)).asInstanceOf[TaskAttemptID] + ctor.newInstance(jtIdentifier, new JInteger(jobId), taskType, new JInteger(taskId), + new JInteger(attemptId)).asInstanceOf[TaskAttemptID] } } } diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala index 6e922a612a..5f73d234aa 100644 --- a/core/src/main/scala/org/apache/spark/Accumulators.scala +++ b/core/src/main/scala/org/apache/spark/Accumulators.scala @@ -41,7 +41,7 @@ class Accumulable[R, T] ( @transient initialValue: R, param: AccumulableParam[R, T]) extends Serializable { - + val id = Accumulators.newId @transient private var value_ = initialValue // Current value on master val zero = param.zero(initialValue) // Zero value to be passed to workers @@ -113,7 +113,7 @@ class Accumulable[R, T] ( def setValue(newValue: R) { this.value = newValue } - + // Called by Java when deserializing an object private def readObject(in: ObjectInputStream) { in.defaultReadObject() @@ -177,7 +177,7 @@ class GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Ser def zero(initialValue: R): R = { // We need to clone initialValue, but it's hard to specify that R should also be Cloneable. // Instead we'll serialize it to a buffer and load it back. - val ser = new JavaSerializer().newInstance() + val ser = new JavaSerializer(new SparkConf(false)).newInstance() val copy = ser.deserialize[R](ser.serialize(initialValue)) copy.clear() // In case it contained stuff copy @@ -215,7 +215,7 @@ private object Accumulators { val originals = Map[Long, Accumulable[_, _]]() val localAccums = Map[Thread, Map[Long, Accumulable[_, _]]]() var lastId: Long = 0 - + def newId: Long = synchronized { lastId += 1 return lastId diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala index 3ef402926e..1a2ec55876 100644 --- a/core/src/main/scala/org/apache/spark/Aggregator.scala +++ b/core/src/main/scala/org/apache/spark/Aggregator.scala @@ -17,43 +17,42 @@ package org.apache.spark -import java.util.{HashMap => JHashMap} +import org.apache.spark.util.AppendOnlyMap -import scala.collection.JavaConversions._ - -/** A set of functions used to aggregate data. - * - * @param createCombiner function to create the initial value of the aggregation. - * @param mergeValue function to merge a new value into the aggregation result. - * @param mergeCombiners function to merge outputs from multiple mergeValue function. - */ +/** + * A set of functions used to aggregate data. + * + * @param createCombiner function to create the initial value of the aggregation. + * @param mergeValue function to merge a new value into the aggregation result. + * @param mergeCombiners function to merge outputs from multiple mergeValue function. + */ case class Aggregator[K, V, C] ( createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C) { def combineValuesByKey(iter: Iterator[_ <: Product2[K, V]]) : Iterator[(K, C)] = { - val combiners = new JHashMap[K, C] - for (kv <- iter) { - val oldC = combiners.get(kv._1) - if (oldC == null) { - combiners.put(kv._1, createCombiner(kv._2)) - } else { - combiners.put(kv._1, mergeValue(oldC, kv._2)) - } + val combiners = new AppendOnlyMap[K, C] + var kv: Product2[K, V] = null + val update = (hadValue: Boolean, oldValue: C) => { + if (hadValue) mergeValue(oldValue, kv._2) else createCombiner(kv._2) + } + while (iter.hasNext) { + kv = iter.next() + combiners.changeValue(kv._1, update) } combiners.iterator } def combineCombinersByKey(iter: Iterator[(K, C)]) : Iterator[(K, C)] = { - val combiners = new JHashMap[K, C] - iter.foreach { case(k, c) => - val oldC = combiners.get(k) - if (oldC == null) { - combiners.put(k, c) - } else { - combiners.put(k, mergeCombiners(oldC, c)) - } + val combiners = new AppendOnlyMap[K, C] + var kc: (K, C) = null + val update = (hadValue: Boolean, oldValue: C) => { + if (hadValue) mergeCombiners(oldValue, kc._2) else kc._2 + } + while (iter.hasNext) { + kc = iter.next() + combiners.changeValue(kc._1, update) } combiners.iterator } diff --git a/core/src/main/scala/org/apache/spark/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/BlockStoreShuffleFetcher.scala index 908ff56a6b..d9ed572da6 100644 --- a/core/src/main/scala/org/apache/spark/BlockStoreShuffleFetcher.scala +++ b/core/src/main/scala/org/apache/spark/BlockStoreShuffleFetcher.scala @@ -22,13 +22,17 @@ import scala.collection.mutable.HashMap import org.apache.spark.executor.{ShuffleReadMetrics, TaskMetrics} import org.apache.spark.serializer.Serializer -import org.apache.spark.storage.BlockManagerId +import org.apache.spark.storage.{BlockId, BlockManagerId, ShuffleBlockId} import org.apache.spark.util.CompletionIterator private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Logging { - override def fetch[T](shuffleId: Int, reduceId: Int, metrics: TaskMetrics, serializer: Serializer) + override def fetch[T]( + shuffleId: Int, + reduceId: Int, + context: TaskContext, + serializer: Serializer) : Iterator[T] = { @@ -45,12 +49,12 @@ private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Loggin splitsByAddress.getOrElseUpdate(address, ArrayBuffer()) += ((index, size)) } - val blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])] = splitsByAddress.toSeq.map { + val blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])] = splitsByAddress.toSeq.map { case (address, splits) => - (address, splits.map(s => ("shuffle_%d_%d_%d".format(shuffleId, s._1, reduceId), s._2))) + (address, splits.map(s => (ShuffleBlockId(shuffleId, s._1, reduceId), s._2))) } - def unpackBlock(blockPair: (String, Option[Iterator[Any]])) : Iterator[T] = { + def unpackBlock(blockPair: (BlockId, Option[Iterator[Any]])) : Iterator[T] = { val blockId = blockPair._1 val blockOption = blockPair._2 blockOption match { @@ -58,9 +62,8 @@ private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Loggin block.asInstanceOf[Iterator[T]] } case None => { - val regex = "shuffle_([0-9]*)_([0-9]*)_([0-9]*)".r blockId match { - case regex(shufId, mapId, _) => + case ShuffleBlockId(shufId, mapId, _) => val address = statuses(mapId.toInt)._1 throw new FetchFailedException(address, shufId.toInt, mapId.toInt, reduceId, null) case _ => @@ -74,7 +77,7 @@ private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Loggin val blockFetcherItr = blockManager.getMultiple(blocksByAddress, serializer) val itr = blockFetcherItr.flatMap(unpackBlock) - CompletionIterator[T, Iterator[T]](itr, { + val completionIter = CompletionIterator[T, Iterator[T]](itr, { val shuffleMetrics = new ShuffleReadMetrics shuffleMetrics.shuffleFinishTime = System.currentTimeMillis shuffleMetrics.remoteFetchTime = blockFetcherItr.remoteFetchTime @@ -83,7 +86,9 @@ private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Loggin shuffleMetrics.totalBlocksFetched = blockFetcherItr.totalBlocks shuffleMetrics.localBlocksFetched = blockFetcherItr.numLocalBlocks shuffleMetrics.remoteBlocksFetched = blockFetcherItr.numRemoteBlocks - metrics.shuffleReadMetrics = Some(shuffleMetrics) + context.taskMetrics.shuffleReadMetrics = Some(shuffleMetrics) }) + + new InterruptibleIterator[T](context, completionIter) } } diff --git a/core/src/main/scala/org/apache/spark/CacheManager.scala b/core/src/main/scala/org/apache/spark/CacheManager.scala index 68b99ca125..519ecde50a 100644 --- a/core/src/main/scala/org/apache/spark/CacheManager.scala +++ b/core/src/main/scala/org/apache/spark/CacheManager.scala @@ -18,7 +18,7 @@ package org.apache.spark import scala.collection.mutable.{ArrayBuffer, HashSet} -import org.apache.spark.storage.{BlockManager, StorageLevel} +import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, RDDBlockId} import org.apache.spark.rdd.RDD @@ -26,28 +26,29 @@ import org.apache.spark.rdd.RDD sure a node doesn't load two copies of an RDD at once. */ private[spark] class CacheManager(blockManager: BlockManager) extends Logging { - private val loading = new HashSet[String] + + /** Keys of RDD splits that are being computed/loaded. */ + private val loading = new HashSet[RDDBlockId]() /** Gets or computes an RDD split. Used by RDD.iterator() when an RDD is cached. */ def getOrCompute[T](rdd: RDD[T], split: Partition, context: TaskContext, storageLevel: StorageLevel) : Iterator[T] = { - val key = "rdd_%d_%d".format(rdd.id, split.index) - logInfo("Cache key is " + key) + val key = RDDBlockId(rdd.id, split.index) + logDebug("Looking for partition " + key) blockManager.get(key) match { - case Some(cachedValues) => - // Partition is in cache, so just return its values - logInfo("Found partition in cache!") - return cachedValues.asInstanceOf[Iterator[T]] + case Some(values) => + // Partition is already materialized, so just return its values + return new InterruptibleIterator(context, values.asInstanceOf[Iterator[T]]) case None => // Mark the split as loading (unless someone else marks it first) loading.synchronized { if (loading.contains(key)) { - logInfo("Loading contains " + key + ", waiting...") + logInfo("Another thread is loading %s, waiting for it to finish...".format(key)) while (loading.contains(key)) { try {loading.wait()} catch {case _ : Throwable =>} } - logInfo("Loading no longer contains " + key + ", so returning cached result") + logInfo("Finished waiting for %s".format(key)) // See whether someone else has successfully loaded it. The main way this would fail // is for the RDD-level cache eviction policy if someone else has loaded the same RDD // partition but we didn't want to make space for it. However, that case is unlikely @@ -55,9 +56,9 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging { // downside of the current code is that threads wait serially if this does happen. blockManager.get(key) match { case Some(values) => - return values.asInstanceOf[Iterator[T]] + return new InterruptibleIterator(context, values.asInstanceOf[Iterator[T]]) case None => - logInfo("Whoever was loading " + key + " failed; we'll try it ourselves") + logInfo("Whoever was loading %s failed; we'll try it ourselves".format(key)) loading.add(key) } } else { @@ -66,13 +67,13 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging { } try { // If we got here, we have to load the split - logInfo("Computing partition " + split) + logInfo("Partition %s not found, computing it".format(key)) val computedValues = rdd.computeOrReadCheckpoint(split, context) // Persist the result, so long as the task is not running locally if (context.runningLocally) { return computedValues } val elements = new ArrayBuffer[Any] elements ++= computedValues - blockManager.put(key, elements, storageLevel, true) + blockManager.put(key, elements, storageLevel, tellMaster = true) return elements.iterator.asInstanceOf[Iterator[T]] } finally { loading.synchronized { diff --git a/core/src/main/scala/org/apache/spark/FutureAction.scala b/core/src/main/scala/org/apache/spark/FutureAction.scala new file mode 100644 index 0000000000..c6b4ac5192 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/FutureAction.scala @@ -0,0 +1,250 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark + +import scala.concurrent._ +import scala.concurrent.duration.Duration +import scala.util.Try + +import org.apache.spark.scheduler.{JobSucceeded, JobWaiter} +import org.apache.spark.scheduler.JobFailed +import org.apache.spark.rdd.RDD + + +/** + * A future for the result of an action. This is an extension of the Scala Future interface to + * support cancellation. + */ +trait FutureAction[T] extends Future[T] { + // Note that we redefine methods of the Future trait here explicitly so we can specify a different + // documentation (with reference to the word "action"). + + /** + * Cancels the execution of this action. + */ + def cancel() + + /** + * Blocks until this action completes. + * @param atMost maximum wait time, which may be negative (no waiting is done), Duration.Inf + * for unbounded waiting, or a finite positive duration + * @return this FutureAction + */ + override def ready(atMost: Duration)(implicit permit: CanAwait): FutureAction.this.type + + /** + * Awaits and returns the result (of type T) of this action. + * @param atMost maximum wait time, which may be negative (no waiting is done), Duration.Inf + * for unbounded waiting, or a finite positive duration + * @throws Exception exception during action execution + * @return the result value if the action is completed within the specific maximum wait time + */ + @throws(classOf[Exception]) + override def result(atMost: Duration)(implicit permit: CanAwait): T + + /** + * When this action is completed, either through an exception, or a value, applies the provided + * function. + */ + def onComplete[U](func: (Try[T]) => U)(implicit executor: ExecutionContext) + + /** + * Returns whether the action has already been completed with a value or an exception. + */ + override def isCompleted: Boolean + + /** + * The value of this Future. + * + * If the future is not completed the returned value will be None. If the future is completed + * the value will be Some(Success(t)) if it contains a valid result, or Some(Failure(error)) if + * it contains an exception. + */ + override def value: Option[Try[T]] + + /** + * Blocks and returns the result of this job. + */ + @throws(classOf[Exception]) + def get(): T = Await.result(this, Duration.Inf) +} + + +/** + * The future holding the result of an action that triggers a single job. Examples include + * count, collect, reduce. + */ +class SimpleFutureAction[T] private[spark](jobWaiter: JobWaiter[_], resultFunc: => T) + extends FutureAction[T] { + + override def cancel() { + jobWaiter.cancel() + } + + override def ready(atMost: Duration)(implicit permit: CanAwait): SimpleFutureAction.this.type = { + if (!atMost.isFinite()) { + awaitResult() + } else jobWaiter.synchronized { + val finishTime = System.currentTimeMillis() + atMost.toMillis + while (!isCompleted) { + val time = System.currentTimeMillis() + if (time >= finishTime) { + throw new TimeoutException + } else { + jobWaiter.wait(finishTime - time) + } + } + } + this + } + + @throws(classOf[Exception]) + override def result(atMost: Duration)(implicit permit: CanAwait): T = { + ready(atMost)(permit) + awaitResult() match { + case scala.util.Success(res) => res + case scala.util.Failure(e) => throw e + } + } + + override def onComplete[U](func: (Try[T]) => U)(implicit executor: ExecutionContext) { + executor.execute(new Runnable { + override def run() { + func(awaitResult()) + } + }) + } + + override def isCompleted: Boolean = jobWaiter.jobFinished + + override def value: Option[Try[T]] = { + if (jobWaiter.jobFinished) { + Some(awaitResult()) + } else { + None + } + } + + private def awaitResult(): Try[T] = { + jobWaiter.awaitResult() match { + case JobSucceeded => scala.util.Success(resultFunc) + case JobFailed(e: Exception, _) => scala.util.Failure(e) + } + } +} + + +/** + * A FutureAction for actions that could trigger multiple Spark jobs. Examples include take, + * takeSample. Cancellation works by setting the cancelled flag to true and interrupting the + * action thread if it is being blocked by a job. + */ +class ComplexFutureAction[T] extends FutureAction[T] { + + // Pointer to the thread that is executing the action. It is set when the action is run. + @volatile private var thread: Thread = _ + + // A flag indicating whether the future has been cancelled. This is used in case the future + // is cancelled before the action was even run (and thus we have no thread to interrupt). + @volatile private var _cancelled: Boolean = false + + // A promise used to signal the future. + private val p = promise[T]() + + override def cancel(): Unit = this.synchronized { + _cancelled = true + if (thread != null) { + thread.interrupt() + } + } + + /** + * Executes some action enclosed in the closure. To properly enable cancellation, the closure + * should use runJob implementation in this promise. See takeAsync for example. + */ + def run(func: => T)(implicit executor: ExecutionContext): this.type = { + scala.concurrent.future { + thread = Thread.currentThread + try { + p.success(func) + } catch { + case e: Exception => p.failure(e) + } finally { + thread = null + } + } + this + } + + /** + * Runs a Spark job. This is a wrapper around the same functionality provided by SparkContext + * to enable cancellation. + */ + def runJob[T, U, R]( + rdd: RDD[T], + processPartition: Iterator[T] => U, + partitions: Seq[Int], + resultHandler: (Int, U) => Unit, + resultFunc: => R) { + // If the action hasn't been cancelled yet, submit the job. The check and the submitJob + // command need to be in an atomic block. + val job = this.synchronized { + if (!cancelled) { + rdd.context.submitJob(rdd, processPartition, partitions, resultHandler, resultFunc) + } else { + throw new SparkException("Action has been cancelled") + } + } + + // Wait for the job to complete. If the action is cancelled (with an interrupt), + // cancel the job and stop the execution. This is not in a synchronized block because + // Await.ready eventually waits on the monitor in FutureJob.jobWaiter. + try { + Await.ready(job, Duration.Inf) + } catch { + case e: InterruptedException => + job.cancel() + throw new SparkException("Action has been cancelled") + } + } + + /** + * Returns whether the promise has been cancelled. + */ + def cancelled: Boolean = _cancelled + + @throws(classOf[InterruptedException]) + @throws(classOf[scala.concurrent.TimeoutException]) + override def ready(atMost: Duration)(implicit permit: CanAwait): this.type = { + p.future.ready(atMost)(permit) + this + } + + @throws(classOf[Exception]) + override def result(atMost: Duration)(implicit permit: CanAwait): T = { + p.future.result(atMost)(permit) + } + + override def onComplete[U](func: (Try[T]) => U)(implicit executor: ExecutionContext): Unit = { + p.future.onComplete(func)(executor) + } + + override def isCompleted: Boolean = p.isCompleted + + override def value: Option[Try[T]] = p.future.value +} diff --git a/core/src/main/scala/org/apache/spark/HttpServer.scala b/core/src/main/scala/org/apache/spark/HttpServer.scala index cdfc9dd54e..69a738dc44 100644 --- a/core/src/main/scala/org/apache/spark/HttpServer.scala +++ b/core/src/main/scala/org/apache/spark/HttpServer.scala @@ -46,6 +46,7 @@ private[spark] class HttpServer(resourceBase: File) extends Logging { if (server != null) { throw new ServerStateException("Server is already started") } else { + logInfo("Starting HTTP Server") server = new Server() val connector = new SocketConnector connector.setMaxIdleTime(60*1000) diff --git a/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala b/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala new file mode 100644 index 0000000000..56e0b8d2c0 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala @@ -0,0 +1,30 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark + +/** + * An iterator that wraps around an existing iterator to provide task killing functionality. + * It works by checking the interrupted flag in TaskContext. + */ +class InterruptibleIterator[+T](val context: TaskContext, val delegate: Iterator[T]) + extends Iterator[T] { + + def hasNext: Boolean = !context.interrupted && delegate.hasNext + + def next(): T = delegate.next() +} diff --git a/core/src/main/scala/org/apache/spark/Logging.scala b/core/src/main/scala/org/apache/spark/Logging.scala index 6a973ea495..d519fc5a29 100644 --- a/core/src/main/scala/org/apache/spark/Logging.scala +++ b/core/src/main/scala/org/apache/spark/Logging.scala @@ -17,8 +17,8 @@ package org.apache.spark -import org.slf4j.Logger -import org.slf4j.LoggerFactory +import org.apache.log4j.{LogManager, PropertyConfigurator} +import org.slf4j.{Logger, LoggerFactory} /** * Utility trait for classes that want to log data. Creates a SLF4J logger for the class and allows @@ -33,6 +33,7 @@ trait Logging { // Method to get or create the logger for this object protected def log: Logger = { if (log_ == null) { + initializeIfNecessary() var className = this.getClass.getName // Ignore trailing $'s in the class names for Scala objects if (className.endsWith("$")) { @@ -89,7 +90,37 @@ trait Logging { log.isTraceEnabled } - // Method for ensuring that logging is initialized, to avoid having multiple - // threads do it concurrently (as SLF4J initialization is not thread safe). - protected def initLogging() { log } + private def initializeIfNecessary() { + if (!Logging.initialized) { + Logging.initLock.synchronized { + if (!Logging.initialized) { + initializeLogging() + } + } + } + } + + private def initializeLogging() { + // If Log4j doesn't seem initialized, load a default properties file + val log4jInitialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements + if (!log4jInitialized) { + val defaultLogProps = "org/apache/spark/default-log4j.properties" + val classLoader = this.getClass.getClassLoader + Option(classLoader.getResource(defaultLogProps)) match { + case Some(url) => PropertyConfigurator.configure(url) + case None => System.err.println(s"Spark was unable to load $defaultLogProps") + } + log.info(s"Using Spark's default log4j profile: $defaultLogProps") + } + Logging.initialized = true + + // Force a call into slf4j to initialize it. Avoids this happening from mutliple threads + // and triggering this: http://mailman.qos.ch/pipermail/slf4j-dev/2010-April/002956.html + log + } +} + +object Logging { + @volatile private var initialized = false + val initLock = new Object() } diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index ae7cf2a893..cdae167aef 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -20,31 +20,28 @@ package org.apache.spark import java.io._ import java.util.zip.{GZIPInputStream, GZIPOutputStream} -import scala.collection.mutable.HashMap import scala.collection.mutable.HashSet +import scala.concurrent.Await +import scala.concurrent.duration._ import akka.actor._ -import akka.dispatch._ import akka.pattern.ask -import akka.remote._ -import akka.util.Duration - import org.apache.spark.scheduler.MapStatus import org.apache.spark.storage.BlockManagerId -import org.apache.spark.util.{Utils, MetadataCleaner, TimeStampedHashMap} - +import org.apache.spark.util.{AkkaUtils, MetadataCleaner, MetadataCleanerType, TimeStampedHashMap, Utils} private[spark] sealed trait MapOutputTrackerMessage private[spark] case class GetMapOutputStatuses(shuffleId: Int, requester: String) extends MapOutputTrackerMessage private[spark] case object StopMapOutputTracker extends MapOutputTrackerMessage -private[spark] class MapOutputTrackerActor(tracker: MapOutputTracker) extends Actor with Logging { +private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster) + extends Actor with Logging { def receive = { case GetMapOutputStatuses(shuffleId: Int, requester: String) => logInfo("Asked to send map output locations for shuffle " + shuffleId + " to " + requester) - sender ! tracker.getSerializedLocations(shuffleId) + sender ! tracker.getSerializedMapOutputStatuses(shuffleId) case StopMapOutputTracker => logInfo("MapOutputTrackerActor stopped!") @@ -53,32 +50,39 @@ private[spark] class MapOutputTrackerActor(tracker: MapOutputTracker) extends Ac } } -private[spark] class MapOutputTracker extends Logging { +private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { + + private val timeout = AkkaUtils.askTimeout(conf) - private val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") - // Set to the MapOutputTrackerActor living on the driver - var trackerActor: ActorRef = _ + var trackerActor: Either[ActorRef, ActorSelection] = _ - private var mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]] + protected val mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]] // Incremented every time a fetch fails so that client nodes know to clear // their cache of map output locations if this happens. - private var epoch: Long = 0 - private val epochLock = new java.lang.Object + protected var epoch: Long = 0 + protected val epochLock = new java.lang.Object - // Cache a serialized version of the output statuses for each shuffle to send them out faster - var cacheEpoch = epoch - private val cachedSerializedStatuses = new TimeStampedHashMap[Int, Array[Byte]] - - val metadataCleaner = new MetadataCleaner("MapOutputTracker", this.cleanup) + private val metadataCleaner = + new MetadataCleaner(MetadataCleanerType.MAP_OUTPUT_TRACKER, this.cleanup, conf) // Send a message to the trackerActor and get its result within a default timeout, or // throw a SparkException if this fails. - def askTracker(message: Any): Any = { + private def askTracker(message: Any): Any = { try { - val future = trackerActor.ask(message)(timeout) - return Await.result(future, timeout) + /* + The difference between ActorRef and ActorSelection is well explained here: + http://doc.akka.io/docs/akka/2.2.3/project/migration-guide-2.1.x-2.2.x.html#Use_actorSelection_instead_of_actorFor + In spark a map output tracker can be either started on Driver where it is created which + is an ActorRef or it can be on executor from where it is looked up which is an + actorSelection. + */ + val future = trackerActor match { + case Left(a: ActorRef) => a.ask(message)(timeout) + case Right(b: ActorSelection) => b.ask(message)(timeout) + } + Await.result(future, timeout) } catch { case e: Exception => throw new SparkException("Error communicating with MapOutputTracker", e) @@ -86,50 +90,12 @@ private[spark] class MapOutputTracker extends Logging { } // Send a one-way message to the trackerActor, to which we expect it to reply with true. - def communicate(message: Any) { + private def communicate(message: Any) { if (askTracker(message) != true) { throw new SparkException("Error reply received from MapOutputTracker") } } - def registerShuffle(shuffleId: Int, numMaps: Int) { - if (mapStatuses.putIfAbsent(shuffleId, new Array[MapStatus](numMaps)).isDefined) { - throw new IllegalArgumentException("Shuffle ID " + shuffleId + " registered twice") - } - } - - def registerMapOutput(shuffleId: Int, mapId: Int, status: MapStatus) { - var array = mapStatuses(shuffleId) - array.synchronized { - array(mapId) = status - } - } - - def registerMapOutputs( - shuffleId: Int, - statuses: Array[MapStatus], - changeEpoch: Boolean = false) { - mapStatuses.put(shuffleId, Array[MapStatus]() ++ statuses) - if (changeEpoch) { - incrementEpoch() - } - } - - def unregisterMapOutput(shuffleId: Int, mapId: Int, bmAddress: BlockManagerId) { - var arrayOpt = mapStatuses.get(shuffleId) - if (arrayOpt.isDefined && arrayOpt.get != null) { - var array = arrayOpt.get - array.synchronized { - if (array(mapId) != null && array(mapId).location == bmAddress) { - array(mapId) = null - } - } - incrementEpoch() - } else { - throw new SparkException("unregisterMapOutput called for nonexistent shuffle ID") - } - } - // Remembers which map output locations are currently being fetched on a worker private val fetching = new HashSet[Int] @@ -159,16 +125,16 @@ private[spark] class MapOutputTracker extends Logging { fetching += shuffleId } } - + if (fetchedStatuses == null) { // We won the race to fetch the output locs; do so logInfo("Doing the fetch; tracker actor = " + trackerActor) - val hostPort = Utils.localHostPort() + val hostPort = Utils.localHostPort(conf) // This try-finally prevents hangs due to timeouts: try { val fetchedBytes = askTracker(GetMapOutputStatuses(shuffleId, hostPort)).asInstanceOf[Array[Byte]] - fetchedStatuses = deserializeStatuses(fetchedBytes) + fetchedStatuses = MapOutputTracker.deserializeMapStatuses(fetchedBytes) logInfo("Got the output locations") mapStatuses.put(shuffleId, fetchedStatuses) } finally { @@ -186,7 +152,7 @@ private[spark] class MapOutputTracker extends Logging { else{ throw new FetchFailedException(null, shuffleId, -1, reduceId, new Exception("Missing all output locations for shuffle " + shuffleId)) - } + } } else { statuses.synchronized { return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, statuses) @@ -194,9 +160,8 @@ private[spark] class MapOutputTracker extends Logging { } } - private def cleanup(cleanupTime: Long) { + protected def cleanup(cleanupTime: Long) { mapStatuses.clearOldValues(cleanupTime) - cachedSerializedStatuses.clearOldValues(cleanupTime) } def stop() { @@ -206,15 +171,7 @@ private[spark] class MapOutputTracker extends Logging { trackerActor = null } - // Called on master to increment the epoch number - def incrementEpoch() { - epochLock.synchronized { - epoch += 1 - logDebug("Increasing epoch to " + epoch) - } - } - - // Called on master or workers to get current epoch number + // Called to get current epoch number def getEpoch: Long = { epochLock.synchronized { return epoch @@ -228,14 +185,63 @@ private[spark] class MapOutputTracker extends Logging { epochLock.synchronized { if (newEpoch > epoch) { logInfo("Updating epoch to " + newEpoch + " and clearing cache") - // mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]] - mapStatuses.clear() epoch = newEpoch + mapStatuses.clear() + } + } + } +} + +private[spark] class MapOutputTrackerMaster(conf: SparkConf) + extends MapOutputTracker(conf) { + + // Cache a serialized version of the output statuses for each shuffle to send them out faster + private var cacheEpoch = epoch + private val cachedSerializedStatuses = new TimeStampedHashMap[Int, Array[Byte]] + + def registerShuffle(shuffleId: Int, numMaps: Int) { + if (mapStatuses.putIfAbsent(shuffleId, new Array[MapStatus](numMaps)).isDefined) { + throw new IllegalArgumentException("Shuffle ID " + shuffleId + " registered twice") + } + } + + def registerMapOutput(shuffleId: Int, mapId: Int, status: MapStatus) { + val array = mapStatuses(shuffleId) + array.synchronized { + array(mapId) = status + } + } + + def registerMapOutputs(shuffleId: Int, statuses: Array[MapStatus], changeEpoch: Boolean = false) { + mapStatuses.put(shuffleId, Array[MapStatus]() ++ statuses) + if (changeEpoch) { + incrementEpoch() + } + } + + def unregisterMapOutput(shuffleId: Int, mapId: Int, bmAddress: BlockManagerId) { + val arrayOpt = mapStatuses.get(shuffleId) + if (arrayOpt.isDefined && arrayOpt.get != null) { + val array = arrayOpt.get + array.synchronized { + if (array(mapId) != null && array(mapId).location == bmAddress) { + array(mapId) = null + } } + incrementEpoch() + } else { + throw new SparkException("unregisterMapOutput called for nonexistent shuffle ID") + } + } + + def incrementEpoch() { + epochLock.synchronized { + epoch += 1 + logDebug("Increasing epoch to " + epoch) } } - def getSerializedLocations(shuffleId: Int): Array[Byte] = { + def getSerializedMapOutputStatuses(shuffleId: Int): Array[Byte] = { var statuses: Array[MapStatus] = null var epochGotten: Long = -1 epochLock.synchronized { @@ -247,13 +253,13 @@ private[spark] class MapOutputTracker extends Logging { case Some(bytes) => return bytes case None => - statuses = mapStatuses(shuffleId) + statuses = mapStatuses.getOrElse(shuffleId, Array[MapStatus]()) epochGotten = epoch } } // If we got here, we failed to find the serialized locations in the cache, so we pulled - // out a snapshot of the locations as "locs"; let's serialize and return that - val bytes = serializeStatuses(statuses) + // out a snapshot of the locations as "statuses"; let's serialize and return that + val bytes = MapOutputTracker.serializeMapStatuses(statuses) logInfo("Size of output statuses for shuffle %d is %d bytes".format(shuffleId, bytes.length)) // Add them into the table only if the epoch hasn't changed while we were working epochLock.synchronized { @@ -261,13 +267,35 @@ private[spark] class MapOutputTracker extends Logging { cachedSerializedStatuses(shuffleId) = bytes } } - return bytes + bytes } + protected override def cleanup(cleanupTime: Long) { + super.cleanup(cleanupTime) + cachedSerializedStatuses.clearOldValues(cleanupTime) + } + + override def stop() { + super.stop() + cachedSerializedStatuses.clear() + } + + override def updateEpoch(newEpoch: Long) { + // This might be called on the MapOutputTrackerMaster if we're running in local mode. + } + + def has(shuffleId: Int): Boolean = { + cachedSerializedStatuses.get(shuffleId).isDefined || mapStatuses.contains(shuffleId) + } +} + +private[spark] object MapOutputTracker { + private val LOG_BASE = 1.1 + // Serialize an array of map output locations into an efficient byte format so that we can send // it to reduce tasks. We do this by compressing the serialized bytes using GZIP. They will // generally be pretty compressible because many map outputs will be on the same hostname. - private def serializeStatuses(statuses: Array[MapStatus]): Array[Byte] = { + def serializeMapStatuses(statuses: Array[MapStatus]): Array[Byte] = { val out = new ByteArrayOutputStream val objOut = new ObjectOutputStream(new GZIPOutputStream(out)) // Since statuses can be modified in parallel, sync on it @@ -278,18 +306,11 @@ private[spark] class MapOutputTracker extends Logging { out.toByteArray } - // Opposite of serializeStatuses. - def deserializeStatuses(bytes: Array[Byte]): Array[MapStatus] = { + // Opposite of serializeMapStatuses. + def deserializeMapStatuses(bytes: Array[Byte]): Array[MapStatus] = { val objIn = new ObjectInputStream(new GZIPInputStream(new ByteArrayInputStream(bytes))) - objIn.readObject(). - // // drop all null's from status - not sure why they are occuring though. Causes NPE downstream in slave if present - // comment this out - nulls could be due to missing location ? - asInstanceOf[Array[MapStatus]] // .filter( _ != null ) + objIn.readObject().asInstanceOf[Array[MapStatus]] } -} - -private[spark] object MapOutputTracker { - private val LOG_BASE = 1.1 // Convert an array of MapStatuses to locations and sizes for a given reduce ID. If // any of the statuses is null (indicating a missing location due to a failed mapper), @@ -300,7 +321,7 @@ private[spark] object MapOutputTracker { statuses: Array[MapStatus]): Array[(BlockManagerId, Long)] = { assert (statuses != null) statuses.map { - status => + status => if (status == null) { throw new FetchFailedException(null, shuffleId, -1, reduceId, new Exception("Missing an output location for shuffle " + shuffleId)) diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala index 0e2c987a59..31b0773bfe 100644 --- a/core/src/main/scala/org/apache/spark/Partitioner.scala +++ b/core/src/main/scala/org/apache/spark/Partitioner.scala @@ -17,8 +17,10 @@ package org.apache.spark -import org.apache.spark.util.Utils +import scala.reflect.ClassTag + import org.apache.spark.rdd.RDD +import org.apache.spark.util.Utils /** * An object that defines how the elements in a key-value pair RDD are partitioned by key. @@ -50,7 +52,7 @@ object Partitioner { for (r <- bySize if r.partitioner != None) { return r.partitioner.get } - if (System.getProperty("spark.default.parallelism") != null) { + if (rdd.context.conf.contains("spark.default.parallelism")) { return new HashPartitioner(rdd.context.defaultParallelism) } else { return new HashPartitioner(bySize.head.partitions.size) @@ -72,7 +74,7 @@ class HashPartitioner(partitions: Int) extends Partitioner { case null => 0 case _ => Utils.nonNegativeMod(key.hashCode, numPartitions) } - + override def equals(other: Any): Boolean = other match { case h: HashPartitioner => h.numPartitions == numPartitions @@ -85,10 +87,10 @@ class HashPartitioner(partitions: Int) extends Partitioner { * A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly equal ranges. * Determines the ranges by sampling the RDD passed in. */ -class RangePartitioner[K <% Ordered[K]: ClassManifest, V]( +class RangePartitioner[K <% Ordered[K]: ClassTag, V]( partitions: Int, @transient rdd: RDD[_ <: Product2[K,V]], - private val ascending: Boolean = true) + private val ascending: Boolean = true) extends Partitioner { // An array of upper bounds for the first (partitions - 1) partitions diff --git a/core/src/main/scala/org/apache/spark/ShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/ShuffleFetcher.scala index 307c383a89..a85aa50a9b 100644 --- a/core/src/main/scala/org/apache/spark/ShuffleFetcher.scala +++ b/core/src/main/scala/org/apache/spark/ShuffleFetcher.scala @@ -27,7 +27,10 @@ private[spark] abstract class ShuffleFetcher { * Fetch the shuffle outputs for a given ShuffleDependency. * @return An iterator over the elements of the fetched shuffle outputs. */ - def fetch[T](shuffleId: Int, reduceId: Int, metrics: TaskMetrics, + def fetch[T]( + shuffleId: Int, + reduceId: Int, + context: TaskContext, serializer: Serializer = SparkEnv.get.serializerManager.default): Iterator[T] /** Stop the fetcher */ diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala new file mode 100644 index 0000000000..98343e9532 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -0,0 +1,189 @@ +package org.apache.spark + +import scala.collection.JavaConverters._ +import scala.collection.mutable.HashMap + +import com.typesafe.config.ConfigFactory + +/** + * Configuration for a Spark application. Used to set various Spark parameters as key-value pairs. + * + * Most of the time, you would create a SparkConf object with `new SparkConf()`, which will load + * values from both the `spark.*` Java system properties and any `spark.conf` on your application's + * classpath (if it has one). In this case, system properties take priority over `spark.conf`, and + * any parameters you set directly on the `SparkConf` object take priority over both of those. + * + * For unit tests, you can also call `new SparkConf(false)` to skip loading external settings and + * get the same configuration no matter what is on the classpath. + * + * All setter methods in this class support chaining. For example, you can write + * `new SparkConf().setMaster("local").setAppName("My app")`. + * + * Note that once a SparkConf object is passed to Spark, it is cloned and can no longer be modified + * by the user. Spark does not support modifying the configuration at runtime. + * + * @param loadDefaults whether to load values from the system properties and classpath + */ +class SparkConf(loadDefaults: Boolean) extends Serializable with Cloneable { + + /** Create a SparkConf that loads defaults from system properties and the classpath */ + def this() = this(true) + + private val settings = new HashMap[String, String]() + + if (loadDefaults) { + ConfigFactory.invalidateCaches() + val typesafeConfig = ConfigFactory.systemProperties() + .withFallback(ConfigFactory.parseResources("spark.conf")) + for (e <- typesafeConfig.entrySet().asScala if e.getKey.startsWith("spark.")) { + settings(e.getKey) = e.getValue.unwrapped.toString + } + } + + /** Set a configuration variable. */ + def set(key: String, value: String): SparkConf = { + if (key == null) { + throw new NullPointerException("null key") + } + if (value == null) { + throw new NullPointerException("null value") + } + settings(key) = value + this + } + + /** + * The master URL to connect to, such as "local" to run locally with one thread, "local[4]" to + * run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster. + */ + def setMaster(master: String): SparkConf = { + set("spark.master", master) + } + + /** Set a name for your application. Shown in the Spark web UI. */ + def setAppName(name: String): SparkConf = { + set("spark.app.name", name) + } + + /** Set JAR files to distribute to the cluster. */ + def setJars(jars: Seq[String]): SparkConf = { + set("spark.jars", jars.mkString(",")) + } + + /** Set JAR files to distribute to the cluster. (Java-friendly version.) */ + def setJars(jars: Array[String]): SparkConf = { + setJars(jars.toSeq) + } + + /** + * Set an environment variable to be used when launching executors for this application. + * These variables are stored as properties of the form spark.executorEnv.VAR_NAME + * (for example spark.executorEnv.PATH) but this method makes them easier to set. + */ + def setExecutorEnv(variable: String, value: String): SparkConf = { + set("spark.executorEnv." + variable, value) + } + + /** + * Set multiple environment variables to be used when launching executors. + * These variables are stored as properties of the form spark.executorEnv.VAR_NAME + * (for example spark.executorEnv.PATH) but this method makes them easier to set. + */ + def setExecutorEnv(variables: Seq[(String, String)]): SparkConf = { + for ((k, v) <- variables) { + setExecutorEnv(k, v) + } + this + } + + /** + * Set multiple environment variables to be used when launching executors. + * (Java-friendly version.) + */ + def setExecutorEnv(variables: Array[(String, String)]): SparkConf = { + setExecutorEnv(variables.toSeq) + } + + /** + * Set the location where Spark is installed on worker nodes. + */ + def setSparkHome(home: String): SparkConf = { + set("spark.home", home) + } + + /** Set multiple parameters together */ + def setAll(settings: Traversable[(String, String)]) = { + this.settings ++= settings + this + } + + /** Set a parameter if it isn't already configured */ + def setIfMissing(key: String, value: String): SparkConf = { + if (!settings.contains(key)) { + settings(key) = value + } + this + } + + /** Remove a parameter from the configuration */ + def remove(key: String): SparkConf = { + settings.remove(key) + this + } + + /** Get a parameter; throws a NoSuchElementException if it's not set */ + def get(key: String): String = { + settings.getOrElse(key, throw new NoSuchElementException(key)) + } + + /** Get a parameter, falling back to a default if not set */ + def get(key: String, defaultValue: String): String = { + settings.getOrElse(key, defaultValue) + } + + /** Get a parameter as an Option */ + def getOption(key: String): Option[String] = { + settings.get(key) + } + + /** Get all parameters as a list of pairs */ + def getAll: Array[(String, String)] = settings.clone().toArray + + /** Get a parameter as an integer, falling back to a default if not set */ + def getInt(key: String, defaultValue: Int): Int = { + getOption(key).map(_.toInt).getOrElse(defaultValue) + } + + /** Get a parameter as a long, falling back to a default if not set */ + def getLong(key: String, defaultValue: Long): Long = { + getOption(key).map(_.toLong).getOrElse(defaultValue) + } + + /** Get a parameter as a double, falling back to a default if not set */ + def getDouble(key: String, defaultValue: Double): Double = { + getOption(key).map(_.toDouble).getOrElse(defaultValue) + } + + /** Get all executor environment variables set on this SparkConf */ + def getExecutorEnv: Seq[(String, String)] = { + val prefix = "spark.executorEnv." + getAll.filter{case (k, v) => k.startsWith(prefix)} + .map{case (k, v) => (k.substring(prefix.length), v)} + } + + /** Does the configuration contain a given parameter? */ + def contains(key: String): Boolean = settings.contains(key) + + /** Copy this object */ + override def clone: SparkConf = { + new SparkConf(false).setAll(settings) + } + + /** + * Return a string listing all keys and values, one per line. This is useful to print the + * configuration out for debugging. + */ + def toDebugString: String = { + settings.toArray.sorted.map{case (k, v) => k + "=" + v}.mkString("\n") + } +} diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index ce7c4feaf6..84bd0f7ffd 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -19,95 +19,126 @@ package org.apache.spark import java.io._ import java.net.URI -import java.util.Properties +import java.util.{UUID, Properties} import java.util.concurrent.atomic.AtomicInteger -import scala.collection.Map +import scala.collection.{Map, Set} import scala.collection.generic.Growable -import scala.collection.JavaConversions._ -import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap + +import scala.collection.mutable.{ArrayBuffer, HashMap} +import scala.reflect.{ClassTag, classTag} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.hadoop.io.ArrayWritable -import org.apache.hadoop.io.BooleanWritable -import org.apache.hadoop.io.BytesWritable -import org.apache.hadoop.io.DoubleWritable -import org.apache.hadoop.io.FloatWritable -import org.apache.hadoop.io.IntWritable -import org.apache.hadoop.io.LongWritable -import org.apache.hadoop.io.NullWritable -import org.apache.hadoop.io.Text -import org.apache.hadoop.io.Writable -import org.apache.hadoop.mapred.FileInputFormat -import org.apache.hadoop.mapred.InputFormat -import org.apache.hadoop.mapred.JobConf -import org.apache.hadoop.mapred.SequenceFileInputFormat -import org.apache.hadoop.mapred.TextInputFormat -import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} -import org.apache.hadoop.mapreduce.{Job => NewHadoopJob} +import org.apache.hadoop.io.{ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable, +FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable} +import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf, SequenceFileInputFormat, +TextInputFormat} +import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, Job => NewHadoopJob} import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} - import org.apache.mesos.MesosNativeLibrary -import org.apache.spark.deploy.LocalSparkCluster +import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil} import org.apache.spark.partial.{ApproximateEvaluator, PartialResult} import org.apache.spark.rdd._ import org.apache.spark.scheduler._ -import org.apache.spark.scheduler.cluster.{StandaloneSchedulerBackend, SparkDeploySchedulerBackend, - ClusterScheduler} -import org.apache.spark.scheduler.local.LocalScheduler +import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, + SparkDeploySchedulerBackend, SimrSchedulerBackend} import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend} -import org.apache.spark.storage.{StorageUtils, BlockManagerSource} +import org.apache.spark.scheduler.local.LocalBackend +import org.apache.spark.storage.{BlockManagerSource, RDDInfo, StorageStatus, StorageUtils} import org.apache.spark.ui.SparkUI -import org.apache.spark.util.{ClosureCleaner, Utils, MetadataCleaner, TimeStampedHashMap} -import org.apache.spark.scheduler.StageInfo -import org.apache.spark.storage.RDDInfo -import org.apache.spark.storage.StorageStatus +import org.apache.spark.util.{Utils, TimeStampedHashMap, MetadataCleaner, MetadataCleanerType, +ClosureCleaner} /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster. * - * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). - * @param appName A name for your application, to display on the cluster web UI. - * @param sparkHome Location where Spark is installed on cluster nodes. - * @param jars Collection of JARs to send to the cluster. These can be paths on the local file - * system or HDFS, HTTP, HTTPS, or FTP URLs. - * @param environment Environment variables to set on worker nodes. + * @param config a Spark Config object describing the application configuration. Any settings in + * this config overrides the default configs as well as system properties. + * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on. Can + * be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]] + * from a list of input files or InputFormats for the application. */ class SparkContext( - val master: String, - val appName: String, - val sparkHome: String = null, - val jars: Seq[String] = Nil, - val environment: Map[String, String] = Map(), - // This is used only by yarn for now, but should be relevant to other cluster types (mesos, etc) too. - // This is typically generated from InputFormatInfo.computePreferredLocations .. host, set of data-local splits on host - val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] = scala.collection.immutable.Map()) + config: SparkConf, + // This is used only by YARN for now, but should be relevant to other cluster types (Mesos, etc) + // too. This is typically generated from InputFormatInfo.computePreferredLocations. It contains + // a map from hostname to a list of input format splits on the host. + val preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()) extends Logging { - // Ensure logging is initialized before we spawn any threads - initLogging() + /** + * Alternative constructor that allows setting common Spark properties directly + * + * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). + * @param appName A name for your application, to display on the cluster web UI + * @param conf a [[org.apache.spark.SparkConf]] object specifying other Spark parameters + */ + def this(master: String, appName: String, conf: SparkConf) = + this(SparkContext.updatedConf(conf, master, appName)) - // Set Spark driver host and port system properties - if (System.getProperty("spark.driver.host") == null) { - System.setProperty("spark.driver.host", Utils.localHostName()) + /** + * Alternative constructor that allows setting common Spark properties directly + * + * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). + * @param appName A name for your application, to display on the cluster web UI. + * @param sparkHome Location where Spark is installed on cluster nodes. + * @param jars Collection of JARs to send to the cluster. These can be paths on the local file + * system or HDFS, HTTP, HTTPS, or FTP URLs. + * @param environment Environment variables to set on worker nodes. + */ + def this( + master: String, + appName: String, + sparkHome: String = null, + jars: Seq[String] = Nil, + environment: Map[String, String] = Map(), + preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()) = + { + this(SparkContext.updatedConf(new SparkConf(), master, appName, sparkHome, jars, environment), + preferredNodeLocationData) + } + + private[spark] val conf = config.clone() + + /** + * Return a copy of this SparkContext's configuration. The configuration ''cannot'' be + * changed at runtime. + */ + def getConf: SparkConf = conf.clone() + + if (!conf.contains("spark.master")) { + throw new SparkException("A master URL must be set in your configuration") } - if (System.getProperty("spark.driver.port") == null) { - System.setProperty("spark.driver.port", "0") + if (!conf.contains("spark.app.name")) { + throw new SparkException("An application must be set in your configuration") } + // Set Spark driver host and port system properties + conf.setIfMissing("spark.driver.host", Utils.localHostName()) + conf.setIfMissing("spark.driver.port", "0") + + val jars: Seq[String] = if (conf.contains("spark.jars")) { + conf.get("spark.jars").split(",").filter(_.size != 0) + } else { + null + } + + val master = conf.get("spark.master") + val appName = conf.get("spark.app.name") + val isLocal = (master == "local" || master.startsWith("local[")) // Create the Spark execution environment (cache, map output tracker, etc) - private[spark] val env = SparkEnv.createFromSystemProperties( + private[spark] val env = SparkEnv.create( + conf, "<driver>", - System.getProperty("spark.driver.host"), - System.getProperty("spark.driver.port").toInt, - true, - isLocal) + conf.get("spark.driver.host"), + conf.get("spark.driver.port").toInt, + isDriver = true, + isLocal = isLocal) SparkEnv.set(env) // Used to store a URL for each static file/jar together with the file's local timestamp @@ -116,9 +147,10 @@ class SparkContext( // Keeps track of all persisted RDDs private[spark] val persistentRdds = new TimeStampedHashMap[Int, RDD[_]] - private[spark] val metadataCleaner = new MetadataCleaner("SparkContext", this.cleanup) + private[spark] val metadataCleaner = + new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, conf) - // Initalize the Spark UI + // Initialize the Spark UI private[spark] val ui = new SparkUI(this) ui.bind() @@ -126,109 +158,38 @@ class SparkContext( // Add each JAR given through the constructor if (jars != null) { - jars.foreach { addJar(_) } + jars.foreach(addJar) } + private[spark] val executorMemory = conf.getOption("spark.executor.memory") + .orElse(Option(System.getenv("SPARK_MEM"))) + .map(Utils.memoryStringToMb) + .getOrElse(512) + // Environment variables to pass to our executors private[spark] val executorEnvs = HashMap[String, String]() // Note: SPARK_MEM is included for Mesos, but overwritten for standalone mode in ExecutorRunner - for (key <- Seq("SPARK_LIBRARY_PATH", "SPARK_JAVA_OPTS", "SPARK_TESTING")) { - val value = System.getenv(key) - if (value != null) { - executorEnvs(key) = value - } + for (key <- Seq("SPARK_CLASSPATH", "SPARK_LIBRARY_PATH", "SPARK_JAVA_OPTS", "SPARK_TESTING"); + value <- Option(System.getenv(key))) { + executorEnvs(key) = value } // Since memory can be set with a system property too, use that - executorEnvs("SPARK_MEM") = SparkContext.executorMemoryRequested + "m" - if (environment != null) { - executorEnvs ++= environment + executorEnvs("SPARK_MEM") = executorMemory + "m" + executorEnvs ++= conf.getExecutorEnv + + // Set SPARK_USER for user who is running SparkContext. + val sparkUser = Option { + Option(System.getProperty("user.name")).getOrElse(System.getenv("SPARK_USER")) + }.getOrElse { + SparkContext.SPARK_UNKNOWN_USER } + executorEnvs("SPARK_USER") = sparkUser // Create and start the scheduler - private var taskScheduler: TaskScheduler = { - // Regular expression used for local[N] master format - val LOCAL_N_REGEX = """local\[([0-9]+)\]""".r - // Regular expression for local[N, maxRetries], used in tests with failing tasks - val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+)\s*,\s*([0-9]+)\]""".r - // Regular expression for simulating a Spark cluster of [N, cores, memory] locally - val LOCAL_CLUSTER_REGEX = """local-cluster\[\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*]""".r - // Regular expression for connecting to Spark deploy clusters - val SPARK_REGEX = """(spark://.*)""".r - //Regular expression for connection to Mesos cluster - val MESOS_REGEX = """(mesos://.*)""".r - - master match { - case "local" => - new LocalScheduler(1, 0, this) - - case LOCAL_N_REGEX(threads) => - new LocalScheduler(threads.toInt, 0, this) - - case LOCAL_N_FAILURES_REGEX(threads, maxFailures) => - new LocalScheduler(threads.toInt, maxFailures.toInt, this) - - case SPARK_REGEX(sparkUrl) => - val scheduler = new ClusterScheduler(this) - val backend = new SparkDeploySchedulerBackend(scheduler, this, sparkUrl, appName) - scheduler.initialize(backend) - scheduler - - case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) => - // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang. - val memoryPerSlaveInt = memoryPerSlave.toInt - if (SparkContext.executorMemoryRequested > memoryPerSlaveInt) { - throw new SparkException( - "Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format( - memoryPerSlaveInt, SparkContext.executorMemoryRequested)) - } - - val scheduler = new ClusterScheduler(this) - val localCluster = new LocalSparkCluster( - numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt) - val sparkUrl = localCluster.start() - val backend = new SparkDeploySchedulerBackend(scheduler, this, sparkUrl, appName) - scheduler.initialize(backend) - backend.shutdownCallback = (backend: SparkDeploySchedulerBackend) => { - localCluster.stop() - } - scheduler - - case "yarn-standalone" => - val scheduler = try { - val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClusterScheduler") - val cons = clazz.getConstructor(classOf[SparkContext]) - cons.newInstance(this).asInstanceOf[ClusterScheduler] - } catch { - // TODO: Enumerate the exact reasons why it can fail - // But irrespective of it, it means we cannot proceed ! - case th: Throwable => { - throw new SparkException("YARN mode not available ?", th) - } - } - val backend = new StandaloneSchedulerBackend(scheduler, this.env.actorSystem) - scheduler.initialize(backend) - scheduler - - case _ => - if (MESOS_REGEX.findFirstIn(master).isEmpty) { - logWarning("Master %s does not match expected format, parsing as Mesos URL".format(master)) - } - MesosNativeLibrary.load() - val scheduler = new ClusterScheduler(this) - val coarseGrained = System.getProperty("spark.mesos.coarse", "false").toBoolean - val masterWithoutProtocol = master.replaceFirst("^mesos://", "") // Strip initial mesos:// - val backend = if (coarseGrained) { - new CoarseMesosSchedulerBackend(scheduler, this, masterWithoutProtocol, appName) - } else { - new MesosSchedulerBackend(scheduler, this, masterWithoutProtocol, appName) - } - scheduler.initialize(backend) - scheduler - } - } + private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master, appName) taskScheduler.start() - @volatile private var dagScheduler = new DAGScheduler(taskScheduler) + @volatile private[spark] var dagScheduler = new DAGScheduler(taskScheduler) dagScheduler.start() ui.start() @@ -236,21 +197,24 @@ class SparkContext( /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */ val hadoopConfiguration = { val env = SparkEnv.get - val conf = env.hadoop.newConfiguration() + val hadoopConf = SparkHadoopUtil.get.newConfiguration() // Explicitly check for S3 environment variables - if (System.getenv("AWS_ACCESS_KEY_ID") != null && System.getenv("AWS_SECRET_ACCESS_KEY") != null) { - conf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID")) - conf.set("fs.s3n.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID")) - conf.set("fs.s3.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY")) - conf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY")) + if (System.getenv("AWS_ACCESS_KEY_ID") != null && + System.getenv("AWS_SECRET_ACCESS_KEY") != null) { + hadoopConf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID")) + hadoopConf.set("fs.s3n.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID")) + hadoopConf.set("fs.s3.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY")) + hadoopConf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY")) } // Copy any "spark.hadoop.foo=bar" system properties into conf as "foo=bar" - for (key <- System.getProperties.toMap[String, String].keys if key.startsWith("spark.hadoop.")) { - conf.set(key.substring("spark.hadoop.".length), System.getProperty(key)) + conf.getAll.foreach { case (key, value) => + if (key.startsWith("spark.hadoop.")) { + hadoopConf.set(key.substring("spark.hadoop.".length), value) + } } - val bufferSize = System.getProperty("spark.buffer.size", "65536") - conf.set("io.file.buffer.size", bufferSize) - conf + val bufferSize = conf.get("spark.buffer.size", "65536") + hadoopConf.set("io.file.buffer.size", bufferSize) + hadoopConf } private[spark] var checkpointDir: Option[String] = None @@ -260,6 +224,12 @@ class SparkContext( override protected def childValue(parent: Properties): Properties = new Properties(parent) } + private[spark] def getLocalProperties: Properties = localProperties.get() + + private[spark] def setLocalProperties(props: Properties) { + localProperties.set(props) + } + def initLocalProperties() { localProperties.set(new Properties()) } @@ -279,15 +249,46 @@ class SparkContext( Option(localProperties.get).map(_.getProperty(key)).getOrElse(null) /** Set a human readable description of the current job. */ + @deprecated("use setJobGroup", "0.8.1") def setJobDescription(value: String) { setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, value) } + /** + * Assigns a group id to all the jobs started by this thread until the group id is set to a + * different value or cleared. + * + * Often, a unit of execution in an application consists of multiple Spark actions or jobs. + * Application programmers can use this method to group all those jobs together and give a + * group description. Once set, the Spark web UI will associate such jobs with this group. + * + * The application can also use [[org.apache.spark.SparkContext.cancelJobGroup]] to cancel all + * running jobs in this group. For example, + * {{{ + * // In the main thread: + * sc.setJobGroup("some_job_to_cancel", "some job description") + * sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.count() + * + * // In a separate thread: + * sc.cancelJobGroup("some_job_to_cancel") + * }}} + */ + def setJobGroup(groupId: String, description: String) { + setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, description) + setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, groupId) + } + + /** Clear the job group id and its description. */ + def clearJobGroup() { + setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, null) + setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, null) + } + // Post init taskScheduler.postStartHook() - val dagSchedulerSource = new DAGSchedulerSource(this.dagScheduler, this) - val blockManagerSource = new BlockManagerSource(SparkEnv.get.blockManager, this) + private val dagSchedulerSource = new DAGSchedulerSource(this.dagScheduler, this) + private val blockManagerSource = new BlockManagerSource(SparkEnv.get.blockManager, this) def initDriverMetrics() { SparkEnv.get.metricsSystem.registerSource(dagSchedulerSource) @@ -299,19 +300,19 @@ class SparkContext( // Methods for creating RDDs /** Distribute a local Scala collection to form an RDD. */ - def parallelize[T: ClassManifest](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { + def parallelize[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]()) } /** Distribute a local Scala collection to form an RDD. */ - def makeRDD[T: ClassManifest](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { + def makeRDD[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { parallelize(seq, numSlices) } /** Distribute a local Scala collection to form an RDD, with one or more * location preferences (hostnames of Spark nodes) for each object. * Create a new partition for each collection item. */ - def makeRDD[T: ClassManifest](seq: Seq[(T, Seq[String])]): RDD[T] = { + def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = { val indexToPrefs = seq.zipWithIndex.map(t => (t._2, t._1._2)).toMap new ParallelCollectionRDD[T](this, seq.map(_._1), seq.size, indexToPrefs) } @@ -326,7 +327,7 @@ class SparkContext( } /** - * Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf giving its InputFormat and any + * Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and any * other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable, * etc). */ @@ -337,6 +338,8 @@ class SparkContext( valueClass: Class[V], minSplits: Int = defaultMinSplits ): RDD[(K, V)] = { + // Add necessary security credentials to the JobConf before broadcasting it. + SparkHadoopUtil.get.addCredentials(conf) new HadoopRDD(this, conf, inputFormatClass, keyClass, valueClass, minSplits) } @@ -347,14 +350,22 @@ class SparkContext( keyClass: Class[K], valueClass: Class[V], minSplits: Int = defaultMinSplits - ) : RDD[(K, V)] = { - val conf = new JobConf(hadoopConfiguration) - FileInputFormat.setInputPaths(conf, path) - new HadoopRDD(this, conf, inputFormatClass, keyClass, valueClass, minSplits) + ): RDD[(K, V)] = { + // A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it. + val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration)) + val setInputPathsFunc = (jobConf: JobConf) => FileInputFormat.setInputPaths(jobConf, path) + new HadoopRDD( + this, + confBroadcast, + Some(setInputPathsFunc), + inputFormatClass, + keyClass, + valueClass, + minSplits) } /** - * Smarter version of hadoopFile() that uses class manifests to figure out the classes of keys, + * Smarter version of hadoopFile() that uses class tags to figure out the classes of keys, * values and the InputFormat so that users don't need to pass them directly. Instead, callers * can just write, for example, * {{{ @@ -362,17 +373,17 @@ class SparkContext( * }}} */ def hadoopFile[K, V, F <: InputFormat[K, V]](path: String, minSplits: Int) - (implicit km: ClassManifest[K], vm: ClassManifest[V], fm: ClassManifest[F]) + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]) : RDD[(K, V)] = { hadoopFile(path, - fm.erasure.asInstanceOf[Class[F]], - km.erasure.asInstanceOf[Class[K]], - vm.erasure.asInstanceOf[Class[V]], + fm.runtimeClass.asInstanceOf[Class[F]], + km.runtimeClass.asInstanceOf[Class[K]], + vm.runtimeClass.asInstanceOf[Class[V]], minSplits) } /** - * Smarter version of hadoopFile() that uses class manifests to figure out the classes of keys, + * Smarter version of hadoopFile() that uses class tags to figure out the classes of keys, * values and the InputFormat so that users don't need to pass them directly. Instead, callers * can just write, for example, * {{{ @@ -380,17 +391,17 @@ class SparkContext( * }}} */ def hadoopFile[K, V, F <: InputFormat[K, V]](path: String) - (implicit km: ClassManifest[K], vm: ClassManifest[V], fm: ClassManifest[F]): RDD[(K, V)] = + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = hadoopFile[K, V, F](path, defaultMinSplits) /** Get an RDD for a Hadoop file with an arbitrary new API InputFormat. */ def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]](path: String) - (implicit km: ClassManifest[K], vm: ClassManifest[V], fm: ClassManifest[F]): RDD[(K, V)] = { + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = { newAPIHadoopFile( path, - fm.erasure.asInstanceOf[Class[F]], - km.erasure.asInstanceOf[Class[K]], - vm.erasure.asInstanceOf[Class[V]]) + fm.runtimeClass.asInstanceOf[Class[F]], + km.runtimeClass.asInstanceOf[Class[K]], + vm.runtimeClass.asInstanceOf[Class[V]]) } /** @@ -448,11 +459,11 @@ class SparkContext( * IntWritable). The most natural thing would've been to have implicit objects for the * converters, but then we couldn't have an object for every subclass of Writable (you can't * have a parameterized singleton object). We use functions instead to create a new converter - * for the appropriate type. In addition, we pass the converter a ClassManifest of its type to + * for the appropriate type. In addition, we pass the converter a ClassTag of its type to * allow it to figure out the Writable class to use in the subclass case. */ def sequenceFile[K, V](path: String, minSplits: Int = defaultMinSplits) - (implicit km: ClassManifest[K], vm: ClassManifest[V], + (implicit km: ClassTag[K], vm: ClassTag[V], kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]) : RDD[(K, V)] = { val kc = kcf() @@ -471,7 +482,7 @@ class SparkContext( * slow if you use the default serializer (Java serialization), though the nice thing about it is * that there's very little effort required to save arbitrary objects. */ - def objectFile[T: ClassManifest]( + def objectFile[T: ClassTag]( path: String, minSplits: Int = defaultMinSplits ): RDD[T] = { @@ -480,17 +491,17 @@ class SparkContext( } - protected[spark] def checkpointFile[T: ClassManifest]( + protected[spark] def checkpointFile[T: ClassTag]( path: String ): RDD[T] = { new CheckpointRDD[T](this, path) } /** Build the union of a list of RDDs. */ - def union[T: ClassManifest](rdds: Seq[RDD[T]]): RDD[T] = new UnionRDD(this, rdds) + def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = new UnionRDD(this, rdds) /** Build the union of a list of RDDs passed as variable-length arguments. */ - def union[T: ClassManifest](first: RDD[T], rest: RDD[T]*): RDD[T] = + def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = new UnionRDD(this, Seq(first) ++ rest) // Methods for creating shared variables @@ -538,14 +549,13 @@ class SparkContext( val uri = new URI(path) val key = uri.getScheme match { case null | "file" => env.httpFileServer.addFile(new File(uri.getPath)) - case _ => path + case "local" => "file:" + uri.getPath + case _ => path } addedFiles(key) = System.currentTimeMillis - // Fetch the file locally in case a job is executed locally. - // Jobs that run through LocalScheduler will already fetch the required dependencies, - // but jobs run in DAGScheduler.runLocally() will not so we must fetch the files here. - Utils.fetchFile(path, new File(SparkFiles.getRootDirectory)) + // Fetch the file locally in case a job is executed using DAGScheduler.runLocally(). + Utils.fetchFile(path, new File(SparkFiles.getRootDirectory), conf) logInfo("Added file " + path + " at " + key + " with timestamp " + addedFiles(key)) } @@ -632,12 +642,11 @@ class SparkContext( /** * Adds a JAR dependency for all tasks to be executed on this SparkContext in the future. * The `path` passed can be either a local file, a file in HDFS (or other Hadoop-supported - * filesystems), or an HTTP, HTTPS or FTP URI. + * filesystems), an HTTP, HTTPS or FTP URI, or local:/path for a file on every worker node. */ def addJar(path: String) { if (path == null) { - logWarning("null specified as parameter to addJar", - new SparkException("null specified as parameter to addJar")) + logWarning("null specified as parameter to addJar") } else { var key = "" if (path.contains("\\")) { @@ -646,12 +655,27 @@ class SparkContext( } else { val uri = new URI(path) key = uri.getScheme match { + // A JAR file which exists only on the driver node case null | "file" => - if (env.hadoop.isYarnMode()) { - logWarning("local jar specified as parameter to addJar under Yarn mode") - return + if (SparkHadoopUtil.get.isYarnMode()) { + // In order for this to work on yarn the user must specify the --addjars option to + // the client to upload the file into the distributed cache to make it show up in the + // current working directory. + val fileName = new Path(uri.getPath).getName() + try { + env.httpFileServer.addJar(new File(fileName)) + } catch { + case e: Exception => { + logError("Error adding jar (" + e + "), was the --addJars option used?") + throw e + } + } + } else { + env.httpFileServer.addJar(new File(uri.getPath)) } - env.httpFileServer.addJar(new File(uri.getPath)) + // A JAR file which exists locally on every worker node + case "local" => + "file:" + uri.getPath case _ => path } @@ -701,15 +725,7 @@ class SparkContext( * (in that order of preference). If neither of these is set, return None. */ private[spark] def getSparkHome(): Option[String] = { - if (sparkHome != null) { - Some(sparkHome) - } else if (System.getProperty("spark.home") != null) { - Some(System.getProperty("spark.home")) - } else if (System.getenv("SPARK_HOME") != null) { - Some(System.getenv("SPARK_HOME")) - } else { - None - } + conf.getOption("spark.home").orElse(Option(System.getenv("SPARK_HOME"))) } /** @@ -718,20 +734,20 @@ class SparkContext( * flag specifies whether the scheduler can run the computation on the driver rather than * shipping it out to the cluster, for short actions like first(). */ - def runJob[T, U: ClassManifest]( + def runJob[T, U: ClassTag]( rdd: RDD[T], func: (TaskContext, Iterator[T]) => U, partitions: Seq[Int], allowLocal: Boolean, resultHandler: (Int, U) => Unit) { val callSite = Utils.formatSparkCallSite + val cleanedFunc = clean(func) logInfo("Starting job: " + callSite) val start = System.nanoTime - val result = dagScheduler.runJob(rdd, func, partitions, callSite, allowLocal, resultHandler, - localProperties.get) + dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal, + resultHandler, localProperties.get) logInfo("Job finished: " + callSite + ", took " + (System.nanoTime - start) / 1e9 + " s") rdd.doCheckpoint() - result } /** @@ -739,7 +755,7 @@ class SparkContext( * allowLocal flag specifies whether the scheduler can run the computation on the driver rather * than shipping it out to the cluster, for short actions like first(). */ - def runJob[T, U: ClassManifest]( + def runJob[T, U: ClassTag]( rdd: RDD[T], func: (TaskContext, Iterator[T]) => U, partitions: Seq[Int], @@ -754,7 +770,7 @@ class SparkContext( * Run a job on a given set of partitions of an RDD, but take a function of type * `Iterator[T] => U` instead of `(TaskContext, Iterator[T]) => U`. */ - def runJob[T, U: ClassManifest]( + def runJob[T, U: ClassTag]( rdd: RDD[T], func: Iterator[T] => U, partitions: Seq[Int], @@ -766,21 +782,21 @@ class SparkContext( /** * Run a job on all partitions in an RDD and return the results in an array. */ - def runJob[T, U: ClassManifest](rdd: RDD[T], func: (TaskContext, Iterator[T]) => U): Array[U] = { + def runJob[T, U: ClassTag](rdd: RDD[T], func: (TaskContext, Iterator[T]) => U): Array[U] = { runJob(rdd, func, 0 until rdd.partitions.size, false) } /** * Run a job on all partitions in an RDD and return the results in an array. */ - def runJob[T, U: ClassManifest](rdd: RDD[T], func: Iterator[T] => U): Array[U] = { + def runJob[T, U: ClassTag](rdd: RDD[T], func: Iterator[T] => U): Array[U] = { runJob(rdd, func, 0 until rdd.partitions.size, false) } /** * Run a job on all partitions in an RDD and pass the results to a handler function. */ - def runJob[T, U: ClassManifest]( + def runJob[T, U: ClassTag]( rdd: RDD[T], processPartition: (TaskContext, Iterator[T]) => U, resultHandler: (Int, U) => Unit) @@ -791,7 +807,7 @@ class SparkContext( /** * Run a job on all partitions in an RDD and pass the results to a handler function. */ - def runJob[T, U: ClassManifest]( + def runJob[T, U: ClassTag]( rdd: RDD[T], processPartition: Iterator[T] => U, resultHandler: (Int, U) => Unit) @@ -818,6 +834,42 @@ class SparkContext( } /** + * Submit a job for execution and return a FutureJob holding the result. + */ + def submitJob[T, U, R]( + rdd: RDD[T], + processPartition: Iterator[T] => U, + partitions: Seq[Int], + resultHandler: (Int, U) => Unit, + resultFunc: => R): SimpleFutureAction[R] = + { + val cleanF = clean(processPartition) + val callSite = Utils.formatSparkCallSite + val waiter = dagScheduler.submitJob( + rdd, + (context: TaskContext, iter: Iterator[T]) => cleanF(iter), + partitions, + callSite, + allowLocal = false, + resultHandler, + localProperties.get) + new SimpleFutureAction(waiter, resultFunc) + } + + /** + * Cancel active jobs for the specified group. See [[org.apache.spark.SparkContext.setJobGroup]] + * for more information. + */ + def cancelJobGroup(groupId: String) { + dagScheduler.cancelJobGroup(groupId) + } + + /** Cancel all jobs that have been scheduled or are running. */ + def cancelAllJobs() { + dagScheduler.cancelAllJobs() + } + + /** * Clean a closure to make it ready to serialized and send to tasks * (removes unreferenced variables in $outer's, updates REPL variables) */ @@ -828,23 +880,15 @@ class SparkContext( /** * Set the directory under which RDDs are going to be checkpointed. The directory must - * be a HDFS path if running on a cluster. If the directory does not exist, it will - * be created. If the directory exists and useExisting is set to true, then the - * exisiting directory will be used. Otherwise an exception will be thrown to - * prevent accidental overriding of checkpoint files in the existing directory. + * be a HDFS path if running on a cluster. */ - def setCheckpointDir(dir: String, useExisting: Boolean = false) { - val env = SparkEnv.get - val path = new Path(dir) - val fs = path.getFileSystem(env.hadoop.newConfiguration()) - if (!useExisting) { - if (fs.exists(path)) { - throw new Exception("Checkpoint directory '" + path + "' already exists.") - } else { - fs.mkdirs(path) - } + def setCheckpointDir(directory: String) { + checkpointDir = Option(directory).map { dir => + val path = new Path(dir, UUID.randomUUID().toString) + val fs = path.getFileSystem(hadoopConfiguration) + fs.mkdirs(path) + fs.getFileStatus(path).getPath().toString } - checkpointDir = Some(dir) } /** Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD). */ @@ -873,7 +917,12 @@ class SparkContext( * various Spark features. */ object SparkContext { - val SPARK_JOB_DESCRIPTION = "spark.job.description" + + private[spark] val SPARK_JOB_DESCRIPTION = "spark.job.description" + + private[spark] val SPARK_JOB_GROUP_ID = "spark.jobGroup.id" + + private[spark] val SPARK_UNKNOWN_USER = "<unknown>" implicit object DoubleAccumulatorParam extends AccumulatorParam[Double] { def addInPlace(t1: Double, t2: Double): Double = t1 + t2 @@ -897,14 +946,16 @@ object SparkContext { // TODO: Add AccumulatorParams for other types, e.g. lists and strings - implicit def rddToPairRDDFunctions[K: ClassManifest, V: ClassManifest](rdd: RDD[(K, V)]) = + implicit def rddToPairRDDFunctions[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]) = new PairRDDFunctions(rdd) - implicit def rddToSequenceFileRDDFunctions[K <% Writable: ClassManifest, V <% Writable: ClassManifest]( + implicit def rddToAsyncRDDActions[T: ClassTag](rdd: RDD[T]) = new AsyncRDDActions(rdd) + + implicit def rddToSequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable: ClassTag]( rdd: RDD[(K, V)]) = new SequenceFileRDDFunctions(rdd) - implicit def rddToOrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest]( + implicit def rddToOrderedRDDFunctions[K <% Ordered[K]: ClassTag, V: ClassTag]( rdd: RDD[(K, V)]) = new OrderedRDDFunctions[K, V, (K, V)](rdd) @@ -929,16 +980,16 @@ object SparkContext { implicit def stringToText(s: String) = new Text(s) - private implicit def arrayToArrayWritable[T <% Writable: ClassManifest](arr: Traversable[T]): ArrayWritable = { + private implicit def arrayToArrayWritable[T <% Writable: ClassTag](arr: Traversable[T]): ArrayWritable = { def anyToWritable[U <% Writable](u: U): Writable = u - new ArrayWritable(classManifest[T].erasure.asInstanceOf[Class[Writable]], + new ArrayWritable(classTag[T].runtimeClass.asInstanceOf[Class[Writable]], arr.map(x => anyToWritable(x)).toArray) } // Helper objects for converting common types to Writable - private def simpleWritableConverter[T, W <: Writable: ClassManifest](convert: W => T) = { - val wClass = classManifest[W].erasure.asInstanceOf[Class[W]] + private def simpleWritableConverter[T, W <: Writable: ClassTag](convert: W => T) = { + val wClass = classTag[W].runtimeClass.asInstanceOf[Class[W]] new WritableConverter[T](_ => wClass, x => convert(x.asInstanceOf[W])) } @@ -957,11 +1008,11 @@ object SparkContext { implicit def stringWritableConverter() = simpleWritableConverter[String, Text](_.toString) implicit def writableWritableConverter[T <: Writable]() = - new WritableConverter[T](_.erasure.asInstanceOf[Class[T]], _.asInstanceOf[T]) + new WritableConverter[T](_.runtimeClass.asInstanceOf[Class[T]], _.asInstanceOf[T]) /** * Find the JAR from which a given class was loaded, to make it easy for users to pass - * their JARs to SparkContext + * their JARs to SparkContext. */ def jarOfClass(cls: Class[_]): Seq[String] = { val uri = cls.getResource("/" + cls.getName.replace('.', '/') + ".class") @@ -978,28 +1029,181 @@ object SparkContext { } } - /** Find the JAR that contains the class of a particular object */ + /** + * Find the JAR that contains the class of a particular object, to make it easy for users + * to pass their JARs to SparkContext. In most cases you can call jarOfObject(this) in + * your driver program. + */ def jarOfObject(obj: AnyRef): Seq[String] = jarOfClass(obj.getClass) - /** Get the amount of memory per executor requested through system properties or SPARK_MEM */ - private[spark] val executorMemoryRequested = { - // TODO: Might need to add some extra memory for the non-heap parts of the JVM - Option(System.getProperty("spark.executor.memory")) - .orElse(Option(System.getenv("SPARK_MEM"))) - .map(Utils.memoryStringToMb) - .getOrElse(512) + /** + * Creates a modified version of a SparkConf with the parameters that can be passed separately + * to SparkContext, to make it easier to write SparkContext's constructors. This ignores + * parameters that are passed as the default value of null, instead of throwing an exception + * like SparkConf would. + */ + private def updatedConf( + conf: SparkConf, + master: String, + appName: String, + sparkHome: String = null, + jars: Seq[String] = Nil, + environment: Map[String, String] = Map()): SparkConf = + { + val res = conf.clone() + res.setMaster(master) + res.setAppName(appName) + if (sparkHome != null) { + res.setSparkHome(sparkHome) + } + if (!jars.isEmpty) { + res.setJars(jars) + } + res.setExecutorEnv(environment.toSeq) + res + } + + /** Creates a task scheduler based on a given master URL. Extracted for testing. */ + private def createTaskScheduler(sc: SparkContext, master: String, appName: String) + : TaskScheduler = + { + // Regular expression used for local[N] master format + val LOCAL_N_REGEX = """local\[([0-9]+)\]""".r + // Regular expression for local[N, maxRetries], used in tests with failing tasks + val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+)\s*,\s*([0-9]+)\]""".r + // Regular expression for simulating a Spark cluster of [N, cores, memory] locally + val LOCAL_CLUSTER_REGEX = """local-cluster\[\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*]""".r + // Regular expression for connecting to Spark deploy clusters + val SPARK_REGEX = """spark://(.*)""".r + // Regular expression for connection to Mesos cluster by mesos:// or zk:// url + val MESOS_REGEX = """(mesos|zk)://.*""".r + // Regular expression for connection to Simr cluster + val SIMR_REGEX = """simr://(.*)""".r + + // When running locally, don't try to re-execute tasks on failure. + val MAX_LOCAL_TASK_FAILURES = 1 + + master match { + case "local" => + val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) + val backend = new LocalBackend(scheduler, 1) + scheduler.initialize(backend) + scheduler + + case LOCAL_N_REGEX(threads) => + val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) + val backend = new LocalBackend(scheduler, threads.toInt) + scheduler.initialize(backend) + scheduler + + case LOCAL_N_FAILURES_REGEX(threads, maxFailures) => + val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true) + val backend = new LocalBackend(scheduler, threads.toInt) + scheduler.initialize(backend) + scheduler + + case SPARK_REGEX(sparkUrl) => + val scheduler = new TaskSchedulerImpl(sc) + val masterUrls = sparkUrl.split(",").map("spark://" + _) + val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls, appName) + scheduler.initialize(backend) + scheduler + + case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) => + // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang. + val memoryPerSlaveInt = memoryPerSlave.toInt + if (sc.executorMemory > memoryPerSlaveInt) { + throw new SparkException( + "Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format( + memoryPerSlaveInt, sc.executorMemory)) + } + + val scheduler = new TaskSchedulerImpl(sc) + val localCluster = new LocalSparkCluster( + numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt) + val masterUrls = localCluster.start() + val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls, appName) + scheduler.initialize(backend) + backend.shutdownCallback = (backend: SparkDeploySchedulerBackend) => { + localCluster.stop() + } + scheduler + + case "yarn-standalone" => + val scheduler = try { + val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClusterScheduler") + val cons = clazz.getConstructor(classOf[SparkContext]) + cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl] + } catch { + // TODO: Enumerate the exact reasons why it can fail + // But irrespective of it, it means we cannot proceed ! + case th: Throwable => { + throw new SparkException("YARN mode not available ?", th) + } + } + val backend = new CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + scheduler.initialize(backend) + scheduler + + case "yarn-client" => + val scheduler = try { + val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientClusterScheduler") + val cons = clazz.getConstructor(classOf[SparkContext]) + cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl] + + } catch { + case th: Throwable => { + throw new SparkException("YARN mode not available ?", th) + } + } + + val backend = try { + val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend") + val cons = clazz.getConstructor(classOf[TaskSchedulerImpl], classOf[SparkContext]) + cons.newInstance(scheduler, sc).asInstanceOf[CoarseGrainedSchedulerBackend] + } catch { + case th: Throwable => { + throw new SparkException("YARN mode not available ?", th) + } + } + + scheduler.initialize(backend) + scheduler + + case mesosUrl @ MESOS_REGEX(_) => + MesosNativeLibrary.load() + val scheduler = new TaskSchedulerImpl(sc) + val coarseGrained = sc.conf.get("spark.mesos.coarse", "false").toBoolean + val url = mesosUrl.stripPrefix("mesos://") // strip scheme from raw Mesos URLs + val backend = if (coarseGrained) { + new CoarseMesosSchedulerBackend(scheduler, sc, url, appName) + } else { + new MesosSchedulerBackend(scheduler, sc, url, appName) + } + scheduler.initialize(backend) + scheduler + + case SIMR_REGEX(simrUrl) => + val scheduler = new TaskSchedulerImpl(sc) + val backend = new SimrSchedulerBackend(scheduler, sc, simrUrl) + scheduler.initialize(backend) + scheduler + + case _ => + throw new SparkException("Could not parse Master URL: '" + master + "'") + } } } /** * A class encapsulating how to convert some type T to Writable. It stores both the Writable class * corresponding to T (e.g. IntWritable for Int) and a function for doing the conversion. - * The getter for the writable class takes a ClassManifest[T] in case this is a generic object + * The getter for the writable class takes a ClassTag[T] in case this is a generic object * that doesn't know the type of T when it is created. This sounds strange but is necessary to * support converting subclasses of Writable to themselves (writableWritableConverter). */ private[spark] class WritableConverter[T]( - val writableClass: ClassManifest[T] => Class[_ <: Writable], + val writableClass: ClassTag[T] => Class[_ <: Writable], val convert: Writable => T) extends Serializable diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 29968c273c..634a94f0a7 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -20,18 +20,18 @@ package org.apache.spark import collection.mutable import serializer.Serializer -import akka.actor.{Actor, ActorRef, Props, ActorSystemImpl, ActorSystem} +import akka.actor._ import akka.remote.RemoteActorRefProvider import org.apache.spark.broadcast.BroadcastManager import org.apache.spark.metrics.MetricsSystem -import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.storage.{BlockManagerMasterActor, BlockManager, BlockManagerMaster} import org.apache.spark.network.ConnectionManager import org.apache.spark.serializer.{Serializer, SerializerManager} import org.apache.spark.util.{Utils, AkkaUtils} import org.apache.spark.api.python.PythonWorkerFactory +import com.google.common.collect.MapMaker /** * Holds all the runtime environment objects for a running Spark instance (either master or worker), @@ -40,7 +40,7 @@ import org.apache.spark.api.python.PythonWorkerFactory * objects needs to have the right SparkEnv set. You can get the current environment with * SparkEnv.get (e.g. after creating a SparkContext) and set it with SparkEnv.set. */ -class SparkEnv ( +class SparkEnv private[spark] ( val executorId: String, val actorSystem: ActorSystem, val serializerManager: SerializerManager, @@ -54,24 +54,16 @@ class SparkEnv ( val connectionManager: ConnectionManager, val httpFileServer: HttpFileServer, val sparkFilesDir: String, - val metricsSystem: MetricsSystem) { + val metricsSystem: MetricsSystem, + val conf: SparkConf) { private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]() - val hadoop = { - val yarnMode = java.lang.Boolean.valueOf(System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) - if(yarnMode) { - try { - Class.forName("org.apache.spark.deploy.yarn.YarnSparkHadoopUtil").newInstance.asInstanceOf[SparkHadoopUtil] - } catch { - case th: Throwable => throw new SparkException("Unable to load YARN support", th) - } - } else { - new SparkHadoopUtil - } - } + // A general, soft-reference map for metadata needed during HadoopRDD split computation + // (e.g., HadoopFileRDD uses this to cache JobConfs and InputFormats). + private[spark] val hadoopJobMetadata = new MapMaker().softValues().makeMap[String, Any]() - def stop() { + private[spark] def stop() { pythonWorkers.foreach { case(key, worker) => worker.stop() } httpFileServer.stop() mapOutputTracker.stop() @@ -83,9 +75,11 @@ class SparkEnv ( actorSystem.shutdown() // Unfortunately Akka's awaitTermination doesn't actually wait for the Netty server to shut // down, but let's call it anyway in case it gets fixed in a later release - actorSystem.awaitTermination() + // UPDATE: In Akka 2.1.x, this hangs if there are remote actors, so we can't call it. + //actorSystem.awaitTermination() } + private[spark] def createPythonWorker(pythonExec: String, envVars: Map[String, String]): java.net.Socket = { synchronized { val key = (pythonExec, envVars) @@ -114,33 +108,35 @@ object SparkEnv extends Logging { /** * Returns the ThreadLocal SparkEnv. */ - def getThreadLocal : SparkEnv = { + def getThreadLocal: SparkEnv = { env.get() } - def createFromSystemProperties( + private[spark] def create( + conf: SparkConf, executorId: String, hostname: String, port: Int, isDriver: Boolean, isLocal: Boolean): SparkEnv = { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, port) + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, port, + conf = conf) // Bit of a hack: If this is the driver and our port was 0 (meaning bind to any free port), // figure out which port number Akka actually bound to and set spark.driver.port to it. if (isDriver && port == 0) { - System.setProperty("spark.driver.port", boundPort.toString) + conf.set("spark.driver.port", boundPort.toString) } // set only if unset until now. - if (System.getProperty("spark.hostPort", null) == null) { + if (!conf.contains("spark.hostPort")) { if (!isDriver){ // unexpected Utils.logErrorWithStack("Unexpected NOT to have spark.hostPort set") } Utils.checkHost(hostname) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + conf.set("spark.hostPort", hostname + ":" + boundPort) } val classLoader = Thread.currentThread.getContextClassLoader @@ -148,61 +144,66 @@ object SparkEnv extends Logging { // Create an instance of the class named by the given Java system property, or by // defaultClassName if the property is not set, and return it as a T def instantiateClass[T](propertyName: String, defaultClassName: String): T = { - val name = System.getProperty(propertyName, defaultClassName) + val name = conf.get(propertyName, defaultClassName) Class.forName(name, true, classLoader).newInstance().asInstanceOf[T] } val serializerManager = new SerializerManager val serializer = serializerManager.setDefault( - System.getProperty("spark.serializer", "org.apache.spark.serializer.JavaSerializer")) + conf.get("spark.serializer", "org.apache.spark.serializer.JavaSerializer"), conf) val closureSerializer = serializerManager.get( - System.getProperty("spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer")) + conf.get("spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer"), + conf) - def registerOrLookup(name: String, newActor: => Actor): ActorRef = { + def registerOrLookup(name: String, newActor: => Actor): Either[ActorRef, ActorSelection] = { if (isDriver) { logInfo("Registering " + name) - actorSystem.actorOf(Props(newActor), name = name) + Left(actorSystem.actorOf(Props(newActor), name = name)) } else { - val driverHost: String = System.getProperty("spark.driver.host", "localhost") - val driverPort: Int = System.getProperty("spark.driver.port", "7077").toInt + val driverHost: String = conf.get("spark.driver.host", "localhost") + val driverPort: Int = conf.get("spark.driver.port", "7077").toInt Utils.checkHost(driverHost, "Expected hostname") - val url = "akka://spark@%s:%s/user/%s".format(driverHost, driverPort, name) + val url = "akka.tcp://spark@%s:%s/user/%s".format(driverHost, driverPort, name) logInfo("Connecting to " + name + ": " + url) - actorSystem.actorFor(url) + Right(actorSystem.actorSelection(url)) } } val blockManagerMaster = new BlockManagerMaster(registerOrLookup( "BlockManagerMaster", - new BlockManagerMasterActor(isLocal))) - val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster, serializer) + new BlockManagerMasterActor(isLocal, conf)), conf) + val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster, serializer, conf) val connectionManager = blockManager.connectionManager - val broadcastManager = new BroadcastManager(isDriver) + val broadcastManager = new BroadcastManager(isDriver, conf) val cacheManager = new CacheManager(blockManager) // Have to assign trackerActor after initialization as MapOutputTrackerActor // requires the MapOutputTracker itself - val mapOutputTracker = new MapOutputTracker() + val mapOutputTracker = if (isDriver) { + new MapOutputTrackerMaster(conf) + } else { + new MapOutputTracker(conf) + } mapOutputTracker.trackerActor = registerOrLookup( "MapOutputTracker", - new MapOutputTrackerActor(mapOutputTracker)) + new MapOutputTrackerMasterActor(mapOutputTracker.asInstanceOf[MapOutputTrackerMaster])) val shuffleFetcher = instantiateClass[ShuffleFetcher]( "spark.shuffle.fetcher", "org.apache.spark.BlockStoreShuffleFetcher") val httpFileServer = new HttpFileServer() httpFileServer.initialize() - System.setProperty("spark.fileserver.uri", httpFileServer.serverUri) + conf.set("spark.fileserver.uri", httpFileServer.serverUri) val metricsSystem = if (isDriver) { - MetricsSystem.createMetricsSystem("driver") + MetricsSystem.createMetricsSystem("driver", conf) } else { - MetricsSystem.createMetricsSystem("executor") + MetricsSystem.createMetricsSystem("executor", conf) } metricsSystem.start() @@ -216,7 +217,7 @@ object SparkEnv extends Logging { } // Warn about deprecated spark.cache.class property - if (System.getProperty("spark.cache.class") != null) { + if (conf.contains("spark.cache.class")) { logWarning("The spark.cache.class property is no longer being used! Specify storage " + "levels using the RDD.persist() method instead.") } @@ -235,6 +236,7 @@ object SparkEnv extends Logging { connectionManager, httpFileServer, sparkFilesDir, - metricsSystem) + metricsSystem, + conf) } } diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala index 2bab9d6e3d..103a1c2051 100644 --- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala +++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala @@ -17,14 +17,14 @@ package org.apache.hadoop.mapred -import org.apache.hadoop.fs.FileSystem -import org.apache.hadoop.fs.Path - +import java.io.IOException import java.text.SimpleDateFormat import java.text.NumberFormat -import java.io.IOException import java.util.Date +import org.apache.hadoop.fs.FileSystem +import org.apache.hadoop.fs.Path + import org.apache.spark.Logging import org.apache.spark.SerializableWritable @@ -36,7 +36,11 @@ import org.apache.spark.SerializableWritable * Saves the RDD using a JobConf, which should contain an output key class, an output value class, * a filename to write to, etc, exactly like in a Hadoop MapReduce job. */ -class SparkHadoopWriter(@transient jobConf: JobConf) extends Logging with SparkHadoopMapRedUtil with Serializable { +private[apache] +class SparkHadoopWriter(@transient jobConf: JobConf) + extends Logging + with SparkHadoopMapRedUtil + with Serializable { private val now = new Date() private val conf = new SerializableWritable(jobConf) @@ -83,13 +87,11 @@ class SparkHadoopWriter(@transient jobConf: JobConf) extends Logging with SparkH } getOutputCommitter().setupTask(getTaskContext()) - writer = getOutputFormat().getRecordWriter( - fs, conf.value, outputName, Reporter.NULL) + writer = getOutputFormat().getRecordWriter(fs, conf.value, outputName, Reporter.NULL) } def write(key: AnyRef, value: AnyRef) { - if (writer!=null) { - //println (">>> Writing ("+key.toString+": " + key.getClass.toString + ", " + value.toString + ": " + value.getClass.toString + ")") + if (writer != null) { writer.write(key, value) } else { throw new IOException("Writer is null, open() has not been called") @@ -179,6 +181,7 @@ class SparkHadoopWriter(@transient jobConf: JobConf) extends Logging with SparkH } } +private[apache] object SparkHadoopWriter { def createJobID(time: Date, id: Int): JobID = { val formatter = new SimpleDateFormat("yyyyMMddHHmm") diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala index c2c358c7ad..cae983ed4c 100644 --- a/core/src/main/scala/org/apache/spark/TaskContext.scala +++ b/core/src/main/scala/org/apache/spark/TaskContext.scala @@ -17,21 +17,30 @@ package org.apache.spark -import executor.TaskMetrics import scala.collection.mutable.ArrayBuffer +import org.apache.spark.executor.TaskMetrics + class TaskContext( val stageId: Int, - val splitId: Int, + val partitionId: Int, val attemptId: Long, val runningLocally: Boolean = false, - val taskMetrics: TaskMetrics = TaskMetrics.empty() + @volatile var interrupted: Boolean = false, + private[spark] val taskMetrics: TaskMetrics = TaskMetrics.empty() ) extends Serializable { - @transient val onCompleteCallbacks = new ArrayBuffer[() => Unit] + @deprecated("use partitionId", "0.8.1") + def splitId = partitionId + + // List of callback functions to execute when the task completes. + @transient private val onCompleteCallbacks = new ArrayBuffer[() => Unit] - // Add a callback function to be executed on task completion. An example use - // is for HadoopRDD to register a callback to close the input stream. + /** + * Add a callback function to be executed on task completion. An example use + * is for HadoopRDD to register a callback to close the input stream. + * @param f Callback function. + */ def addOnCompleteCallback(f: () => Unit) { onCompleteCallbacks += f } diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala index 03bf268863..faf6dcd618 100644 --- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala +++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala @@ -46,6 +46,10 @@ private[spark] case class ExceptionFailure( metrics: Option[TaskMetrics]) extends TaskEndReason -private[spark] case class OtherFailure(message: String) extends TaskEndReason +/** + * The task finished successfully, but the result was lost from the executor's block manager before + * it was fetched. + */ +private[spark] case object TaskResultLost extends TaskEndReason -private[spark] case class TaskResultTooBigFailure() extends TaskEndReason +private[spark] case object TaskKilled extends TaskEndReason diff --git a/core/src/main/scala/org/apache/spark/TaskState.scala b/core/src/main/scala/org/apache/spark/TaskState.scala index 19ce8369d9..0bf1e4a5e2 100644 --- a/core/src/main/scala/org/apache/spark/TaskState.scala +++ b/core/src/main/scala/org/apache/spark/TaskState.scala @@ -19,8 +19,7 @@ package org.apache.spark import org.apache.mesos.Protos.{TaskState => MesosTaskState} -private[spark] object TaskState - extends Enumeration("LAUNCHING", "RUNNING", "FINISHED", "FAILED", "KILLED", "LOST") { +private[spark] object TaskState extends Enumeration { val LAUNCHING, RUNNING, FINISHED, FAILED, KILLED, LOST = Value diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala index 5fd1fab580..da30cf619a 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala @@ -17,18 +17,23 @@ package org.apache.spark.api.java +import scala.reflect.ClassTag + import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext.doubleRDDToDoubleRDDFunctions import org.apache.spark.api.java.function.{Function => JFunction} import org.apache.spark.util.StatCounter import org.apache.spark.partial.{BoundedDouble, PartialResult} import org.apache.spark.storage.StorageLevel + import java.lang.Double import org.apache.spark.Partitioner +import scala.collection.JavaConverters._ + class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, JavaDoubleRDD] { - override val classManifest: ClassManifest[Double] = implicitly[ClassManifest[Double]] + override val classTag: ClassTag[Double] = implicitly[ClassTag[Double]] override val rdd: RDD[Double] = srdd.map(x => Double.valueOf(x)) @@ -42,12 +47,25 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */ def cache(): JavaDoubleRDD = fromRDD(srdd.cache()) - /** + /** * Set this RDD's storage level to persist its values across operations after the first time * it is computed. Can only be called once on each RDD. */ def persist(newLevel: StorageLevel): JavaDoubleRDD = fromRDD(srdd.persist(newLevel)) + /** + * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. + * This method blocks until all blocks are deleted. + */ + def unpersist(): JavaDoubleRDD = fromRDD(srdd.unpersist()) + + /** + * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. + * + * @param blocking Whether to block until all blocks are deleted. + */ + def unpersist(blocking: Boolean): JavaDoubleRDD = fromRDD(srdd.unpersist(blocking)) + // first() has to be overriden here in order for its return type to be Double instead of Object. override def first(): Double = srdd.first() @@ -81,8 +99,19 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav fromRDD(srdd.coalesce(numPartitions, shuffle)) /** + * Return a new RDD that has exactly numPartitions partitions. + * + * Can increase or decrease the level of parallelism in this RDD. Internally, this uses + * a shuffle to redistribute data. + * + * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, + * which can avoid performing a shuffle. + */ + def repartition(numPartitions: Int): JavaDoubleRDD = fromRDD(srdd.repartition(numPartitions)) + + /** * Return an RDD with the elements from `this` that are not in `other`. - * + * * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ @@ -158,6 +187,44 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav /** (Experimental) Approximate operation to return the sum within a timeout. */ def sumApprox(timeout: Long): PartialResult[BoundedDouble] = srdd.sumApprox(timeout) + + /** + * Compute a histogram of the data using bucketCount number of buckets evenly + * spaced between the minimum and maximum of the RDD. For example if the min + * value is 0 and the max is 100 and there are two buckets the resulting + * buckets will be [0,50) [50,100]. bucketCount must be at least 1 + * If the RDD contains infinity, NaN throws an exception + * If the elements in RDD do not vary (max == min) always returns a single bucket. + */ + def histogram(bucketCount: Int): Pair[Array[scala.Double], Array[Long]] = { + val result = srdd.histogram(bucketCount) + (result._1, result._2) + } + + /** + * Compute a histogram using the provided buckets. The buckets are all open + * to the left except for the last which is closed + * e.g. for the array + * [1,10,20,50] the buckets are [1,10) [10,20) [20,50] + * e.g 1<=x<10 , 10<=x<20, 20<=x<50 + * And on the input of 1 and 50 we would have a histogram of 1,0,0 + * + * Note: if your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched + * from an O(log n) inseration to O(1) per element. (where n = # buckets) if you set evenBuckets + * to true. + * buckets must be sorted and not contain any duplicates. + * buckets array must be at least two elements + * All NaN entries are treated the same. If you have a NaN bucket it must be + * the maximum value of the last position and all NaN entries will be counted + * in that bucket. + */ + def histogram(buckets: Array[scala.Double]): Array[Long] = { + srdd.histogram(buckets, false) + } + + def histogram(buckets: Array[Double], evenBuckets: Boolean): Array[Long] = { + srdd.histogram(buckets.map(_.toDouble), evenBuckets) + } } object JavaDoubleRDD { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index a6518abf45..55c87450ac 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -22,6 +22,7 @@ import java.util.Comparator import scala.Tuple2 import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import com.google.common.base.Optional import org.apache.hadoop.io.compress.CompressionCodec @@ -43,13 +44,13 @@ import org.apache.spark.rdd.OrderedRDDFunctions import org.apache.spark.storage.StorageLevel -class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManifest[K], - implicit val vManifest: ClassManifest[V]) extends JavaRDDLike[(K, V), JavaPairRDD[K, V]] { +class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kClassTag: ClassTag[K], + implicit val vClassTag: ClassTag[V]) extends JavaRDDLike[(K, V), JavaPairRDD[K, V]] { override def wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V] = JavaPairRDD.fromRDD(rdd) - override val classManifest: ClassManifest[(K, V)] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K, V]]] + override val classTag: ClassTag[(K, V)] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[Tuple2[K, V]]] import JavaPairRDD._ @@ -58,13 +59,26 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */ def cache(): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.cache()) - /** + /** * Set this RDD's storage level to persist its values across operations after the first time * it is computed. Can only be called once on each RDD. */ def persist(newLevel: StorageLevel): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.persist(newLevel)) + /** + * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. + * This method blocks until all blocks are deleted. + */ + def unpersist(): JavaPairRDD[K, V] = wrapRDD(rdd.unpersist()) + + /** + * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. + * + * @param blocking Whether to block until all blocks are deleted. + */ + def unpersist(blocking: Boolean): JavaPairRDD[K, V] = wrapRDD(rdd.unpersist(blocking)) + // Transformations (return a new RDD) /** @@ -95,6 +109,17 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif fromRDD(rdd.coalesce(numPartitions, shuffle)) /** + * Return a new RDD that has exactly numPartitions partitions. + * + * Can increase or decrease the level of parallelism in this RDD. Internally, this uses + * a shuffle to redistribute data. + * + * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, + * which can avoid performing a shuffle. + */ + def repartition(numPartitions: Int): JavaPairRDD[K, V] = fromRDD(rdd.repartition(numPartitions)) + + /** * Return a sampled subset of this RDD. */ def sample(withReplacement: Boolean, fraction: Double, seed: Int): JavaPairRDD[K, V] = @@ -114,14 +139,14 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif override def first(): (K, V) = rdd.first() // Pair RDD functions - + /** - * Generic function to combine the elements for each key using a custom set of aggregation - * functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a - * "combined type" C * Note that V and C can be different -- for example, one might group an - * RDD of type (Int, Int) into an RDD of type (Int, List[Int]). Users provide three + * Generic function to combine the elements for each key using a custom set of aggregation + * functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a + * "combined type" C * Note that V and C can be different -- for example, one might group an + * RDD of type (Int, Int) into an RDD of type (Int, List[Int]). Users provide three * functions: - * + * * - `createCombiner`, which turns a V into a C (e.g., creates a one-element list) * - `mergeValue`, to merge a V into a C (e.g., adds it to the end of a list) * - `mergeCombiners`, to combine two C's into a single one. @@ -133,8 +158,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif mergeValue: JFunction2[C, V, C], mergeCombiners: JFunction2[C, C, C], partitioner: Partitioner): JavaPairRDD[K, C] = { - implicit val cm: ClassManifest[C] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[C]] + implicit val cm: ClassTag[C] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[C]] fromRDD(rdd.combineByKey( createCombiner, mergeValue, @@ -171,14 +195,14 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif /** Count the number of elements for each key, and return the result to the master as a Map. */ def countByKey(): java.util.Map[K, Long] = mapAsJavaMap(rdd.countByKey()) - /** + /** * (Experimental) Approximate version of countByKey that can return a partial result if it does * not finish within a timeout. */ def countByKeyApprox(timeout: Long): PartialResult[java.util.Map[K, BoundedDouble]] = rdd.countByKeyApprox(timeout).map(mapAsJavaMap) - /** + /** * (Experimental) Approximate version of countByKey that can return a partial result if it does * not finish within a timeout. */ @@ -234,7 +258,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif /** * Return an RDD with the elements from `this` that are not in `other`. - * + * * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ @@ -291,15 +315,14 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)}) } - /** + /** * Simplified version of combineByKey that hash-partitions the resulting RDD using the existing * partitioner/parallelism level. */ def combineByKey[C](createCombiner: JFunction[V, C], mergeValue: JFunction2[C, V, C], mergeCombiners: JFunction2[C, C, C]): JavaPairRDD[K, C] = { - implicit val cm: ClassManifest[C] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[C]] + implicit val cm: ClassTag[C] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[C]] fromRDD(combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(rdd))) } @@ -390,8 +413,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * this also retains the original RDD's partitioning. */ def mapValues[U](f: JFunction[V, U]): JavaPairRDD[K, U] = { - implicit val cm: ClassManifest[U] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] + implicit val cm: ClassTag[U] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] fromRDD(rdd.mapValues(f)) } @@ -402,8 +424,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairRDD[K, U] = { import scala.collection.JavaConverters._ def fn = (x: V) => f.apply(x).asScala - implicit val cm: ClassManifest[U] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] + implicit val cm: ClassTag[U] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] fromRDD(rdd.flatMapValues(fn)) } @@ -568,6 +589,20 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif } /** + * Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling + * `collect` or `save` on the resulting RDD will return or output an ordered list of records + * (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in + * order of the keys). + */ + def sortByKey(comp: Comparator[K], ascending: Boolean, numPartitions: Int): JavaPairRDD[K, V] = { + class KeyOrdering(val a: K) extends Ordered[K] { + override def compare(b: K) = comp.compare(a, b) + } + implicit def toOrdered(x: K): Ordered[K] = new KeyOrdering(x) + fromRDD(new OrderedRDDFunctions[K, V, (K, V)](rdd).sortByKey(ascending, numPartitions)) + } + + /** * Return an RDD with the keys of each tuple. */ def keys(): JavaRDD[K] = JavaRDD.fromRDD[K](rdd.map(_._1)) @@ -576,26 +611,71 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * Return an RDD with the values of each tuple. */ def values(): JavaRDD[V] = JavaRDD.fromRDD[V](rdd.map(_._2)) + + /** + * Return approximate number of distinct values for each key in this RDD. + * The accuracy of approximation can be controlled through the relative standard deviation + * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in + * more accurate counts but increase the memory footprint and vise versa. Uses the provided + * Partitioner to partition the output RDD. + */ + def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): JavaRDD[(K, Long)] = { + rdd.countApproxDistinctByKey(relativeSD, partitioner) + } + + /** + * Return approximate number of distinct values for each key this RDD. + * The accuracy of approximation can be controlled through the relative standard deviation + * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in + * more accurate counts but increase the memory footprint and vise versa. The default value of + * relativeSD is 0.05. Hash-partitions the output RDD using the existing partitioner/parallelism + * level. + */ + def countApproxDistinctByKey(relativeSD: Double = 0.05): JavaRDD[(K, Long)] = { + rdd.countApproxDistinctByKey(relativeSD) + } + + + /** + * Return approximate number of distinct values for each key in this RDD. + * The accuracy of approximation can be controlled through the relative standard deviation + * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in + * more accurate counts but increase the memory footprint and vise versa. HashPartitions the + * output RDD into numPartitions. + * + */ + def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaRDD[(K, Long)] = { + rdd.countApproxDistinctByKey(relativeSD, numPartitions) + } } object JavaPairRDD { - def groupByResultToJava[K, T](rdd: RDD[(K, Seq[T])])(implicit kcm: ClassManifest[K], - vcm: ClassManifest[T]): RDD[(K, JList[T])] = + def groupByResultToJava[K, T](rdd: RDD[(K, Seq[T])])(implicit kcm: ClassTag[K], + vcm: ClassTag[T]): RDD[(K, JList[T])] = rddToPairRDDFunctions(rdd).mapValues(seqAsJavaList _) - def cogroupResultToJava[W, K, V](rdd: RDD[(K, (Seq[V], Seq[W]))])(implicit kcm: ClassManifest[K], - vcm: ClassManifest[V]): RDD[(K, (JList[V], JList[W]))] = rddToPairRDDFunctions(rdd).mapValues((x: (Seq[V], - Seq[W])) => (seqAsJavaList(x._1), seqAsJavaList(x._2))) + def cogroupResultToJava[W, K, V](rdd: RDD[(K, (Seq[V], Seq[W]))])(implicit kcm: ClassTag[K], + vcm: ClassTag[V]): RDD[(K, (JList[V], JList[W]))] = rddToPairRDDFunctions(rdd) + .mapValues((x: (Seq[V], Seq[W])) => (seqAsJavaList(x._1), seqAsJavaList(x._2))) def cogroupResult2ToJava[W1, W2, K, V](rdd: RDD[(K, (Seq[V], Seq[W1], - Seq[W2]))])(implicit kcm: ClassManifest[K]) : RDD[(K, (JList[V], JList[W1], + Seq[W2]))])(implicit kcm: ClassTag[K]) : RDD[(K, (JList[V], JList[W1], JList[W2]))] = rddToPairRDDFunctions(rdd).mapValues( (x: (Seq[V], Seq[W1], Seq[W2])) => (seqAsJavaList(x._1), seqAsJavaList(x._2), seqAsJavaList(x._3))) - def fromRDD[K: ClassManifest, V: ClassManifest](rdd: RDD[(K, V)]): JavaPairRDD[K, V] = + def fromRDD[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd) implicit def toRDD[K, V](rdd: JavaPairRDD[K, V]): RDD[(K, V)] = rdd.rdd + + + /** Convert a JavaRDD of key-value pairs to JavaPairRDD. */ + def fromJavaRDD[K, V](rdd: JavaRDD[(K, V)]): JavaPairRDD[K, V] = { + implicit val cmk: ClassTag[K] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val cmv: ClassTag[V] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] + new JavaPairRDD[K, V](rdd.rdd) + } + } diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala index eec58abdd6..037cd1c774 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala @@ -17,12 +17,14 @@ package org.apache.spark.api.java +import scala.reflect.ClassTag + import org.apache.spark._ import org.apache.spark.rdd.RDD import org.apache.spark.api.java.function.{Function => JFunction} import org.apache.spark.storage.StorageLevel -class JavaRDD[T](val rdd: RDD[T])(implicit val classManifest: ClassManifest[T]) extends +class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) extends JavaRDDLike[T, JavaRDD[T]] { override def wrapRDD(rdd: RDD[T]): JavaRDD[T] = JavaRDD.fromRDD(rdd) @@ -41,9 +43,17 @@ JavaRDDLike[T, JavaRDD[T]] { /** * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. + * This method blocks until all blocks are deleted. */ def unpersist(): JavaRDD[T] = wrapRDD(rdd.unpersist()) + /** + * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. + * + * @param blocking Whether to block until all blocks are deleted. + */ + def unpersist(blocking: Boolean): JavaRDD[T] = wrapRDD(rdd.unpersist(blocking)) + // Transformations (return a new RDD) /** @@ -74,6 +84,17 @@ JavaRDDLike[T, JavaRDD[T]] { rdd.coalesce(numPartitions, shuffle) /** + * Return a new RDD that has exactly numPartitions partitions. + * + * Can increase or decrease the level of parallelism in this RDD. Internally, this uses + * a shuffle to redistribute data. + * + * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, + * which can avoid performing a shuffle. + */ + def repartition(numPartitions: Int): JavaRDD[T] = rdd.repartition(numPartitions) + + /** * Return a sampled subset of this RDD. */ def sample(withReplacement: Boolean, fraction: Double, seed: Int): JavaRDD[T] = @@ -104,12 +125,13 @@ JavaRDDLike[T, JavaRDD[T]] { */ def subtract(other: JavaRDD[T], p: Partitioner): JavaRDD[T] = wrapRDD(rdd.subtract(other, p)) + + override def toString = rdd.toString } object JavaRDD { - implicit def fromRDD[T: ClassManifest](rdd: RDD[T]): JavaRDD[T] = new JavaRDD[T](rdd) + implicit def fromRDD[T: ClassTag](rdd: RDD[T]): JavaRDD[T] = new JavaRDD[T](rdd) implicit def toRDD[T](rdd: JavaRDD[T]): RDD[T] = rdd.rdd } - diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index 7a3568c5ef..924d8af060 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -20,6 +20,7 @@ package org.apache.spark.api.java import java.util.{List => JList, Comparator} import scala.Tuple2 import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import com.google.common.base.Optional import org.apache.hadoop.io.compress.CompressionCodec @@ -35,7 +36,7 @@ import org.apache.spark.storage.StorageLevel trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def wrapRDD(rdd: RDD[T]): This - implicit val classManifest: ClassManifest[T] + implicit val classTag: ClassTag[T] def rdd: RDD[T] @@ -71,7 +72,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return a new RDD by applying a function to each partition of this RDD, while tracking the index * of the original partition. */ - def mapPartitionsWithIndex[R: ClassManifest]( + def mapPartitionsWithIndex[R: ClassTag]( f: JFunction2[Int, java.util.Iterator[T], java.util.Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R] = new JavaRDD(rdd.mapPartitionsWithIndex(((a,b) => f(a,asJavaIterator(b))), @@ -87,7 +88,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return a new RDD by applying a function to all elements of this RDD. */ def map[K2, V2](f: PairFunction[T, K2, V2]): JavaPairRDD[K2, V2] = { - def cm = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K2, V2]]] + def cm = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[Tuple2[K2, V2]]] new JavaPairRDD(rdd.map(f)(cm))(f.keyType(), f.valueType()) } @@ -118,7 +119,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def flatMap[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairRDD[K2, V2] = { import scala.collection.JavaConverters._ def fn = (x: T) => f.apply(x).asScala - def cm = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K2, V2]]] + def cm = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[Tuple2[K2, V2]]] JavaPairRDD.fromRDD(rdd.flatMap(fn)(cm))(f.keyType(), f.valueType()) } @@ -158,18 +159,16 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * elements (a, b) where a is in `this` and b is in `other`. */ def cartesian[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, U] = - JavaPairRDD.fromRDD(rdd.cartesian(other.rdd)(other.classManifest))(classManifest, - other.classManifest) + JavaPairRDD.fromRDD(rdd.cartesian(other.rdd)(other.classTag))(classTag, other.classTag) /** * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements * mapping to that key. */ def groupBy[K](f: JFunction[T, K]): JavaPairRDD[K, JList[T]] = { - implicit val kcm: ClassManifest[K] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]] - implicit val vcm: ClassManifest[JList[T]] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[JList[T]]] + implicit val kcm: ClassTag[K] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val vcm: ClassTag[JList[T]] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[JList[T]]] JavaPairRDD.fromRDD(groupByResultToJava(rdd.groupBy(f)(f.returnType)))(kcm, vcm) } @@ -178,10 +177,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * mapping to that key. */ def groupBy[K](f: JFunction[T, K], numPartitions: Int): JavaPairRDD[K, JList[T]] = { - implicit val kcm: ClassManifest[K] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]] - implicit val vcm: ClassManifest[JList[T]] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[JList[T]]] + implicit val kcm: ClassTag[K] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val vcm: ClassTag[JList[T]] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[JList[T]]] JavaPairRDD.fromRDD(groupByResultToJava(rdd.groupBy(f, numPartitions)(f.returnType)))(kcm, vcm) } @@ -209,7 +207,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * a map on the other). */ def zip[U](other: JavaRDDLike[U, _]): JavaPairRDD[T, U] = { - JavaPairRDD.fromRDD(rdd.zip(other.rdd)(other.classManifest))(classManifest, other.classManifest) + JavaPairRDD.fromRDD(rdd.zip(other.rdd)(other.classTag))(classTag, other.classTag) } /** @@ -224,7 +222,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def fn = (x: Iterator[T], y: Iterator[U]) => asScalaIterator( f.apply(asJavaIterator(x), asJavaIterator(y)).iterator()) JavaRDD.fromRDD( - rdd.zipPartitions(other.rdd)(fn)(other.classManifest, f.elementType()))(f.elementType()) + rdd.zipPartitions(other.rdd)(fn)(other.classTag, f.elementType()))(f.elementType()) } // Actions (launch a job to return a value to the user program) @@ -247,6 +245,17 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { } /** + * Return an array that contains all of the elements in a specific partition of this RDD. + */ + def collectPartitions(partitionIds: Array[Int]): Array[JList[T]] = { + // This is useful for implementing `take` from other language frontends + // like Python where the data is serialized. + import scala.collection.JavaConversions._ + val res = context.runJob(rdd, (it: Iterator[T]) => it.toArray, partitionIds, true) + res.map(x => new java.util.ArrayList(x.toSeq)).toArray + } + + /** * Reduces the elements of this RDD using the specified commutative and associative binary operator. */ def reduce(f: JFunction2[T, T, T]): T = rdd.reduce(f) @@ -356,7 +365,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Creates tuples of the elements in this RDD by applying `f`. */ def keyBy[K](f: JFunction[T, K]): JavaPairRDD[K, T] = { - implicit val kcm: ClassManifest[K] = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]] + implicit val kcm: ClassTag[K] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] JavaPairRDD.fromRDD(rdd.keyBy(f)) } @@ -435,4 +444,15 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[T]] takeOrdered(num, comp) } + + /** + * Return approximate number of distinct elements in the RDD. + * + * The accuracy of approximation can be controlled through the relative standard deviation + * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in + * more accurate counts but increase the memory footprint and vise versa. The default value of + * relativeSD is 0.05. + */ + def countApproxDistinct(relativeSD: Double = 0.05): Long = rdd.countApproxDistinct(relativeSD) + } diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala index 8869e072bf..0680a065a3 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala @@ -21,6 +21,7 @@ import java.util.{Map => JMap} import scala.collection.JavaConversions import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import org.apache.hadoop.conf.Configuration import org.apache.hadoop.mapred.InputFormat @@ -28,17 +29,22 @@ import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import com.google.common.base.Optional -import org.apache.spark.{Accumulable, AccumulableParam, Accumulator, AccumulatorParam, SparkContext} +import org.apache.spark._ import org.apache.spark.SparkContext.IntAccumulatorParam import org.apache.spark.SparkContext.DoubleAccumulatorParam import org.apache.spark.broadcast.Broadcast import org.apache.spark.rdd.RDD +import scala.Tuple2 /** * A Java-friendly version of [[org.apache.spark.SparkContext]] that returns [[org.apache.spark.api.java.JavaRDD]]s and * works with Java collections instead of Scala ones. */ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWorkaround { + /** + * @param conf a [[org.apache.spark.SparkConf]] object specifying Spark parameters + */ + def this(conf: SparkConf) = this(new SparkContext(conf)) /** * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). @@ -49,6 +55,14 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork /** * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). * @param appName A name for your application, to display on the cluster web UI + * @param conf a [[org.apache.spark.SparkConf]] object specifying other Spark parameters + */ + def this(master: String, appName: String, conf: SparkConf) = + this(conf.setMaster(master).setAppName(appName)) + + /** + * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). + * @param appName A name for your application, to display on the cluster web UI * @param sparkHome The SPARK_HOME directory on the slave nodes * @param jarFile JAR file to send to the cluster. This can be a path on the local file system * or an HDFS, HTTP, HTTPS, or FTP URL. @@ -82,8 +96,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork /** Distribute a local Scala collection to form an RDD. */ def parallelize[T](list: java.util.List[T], numSlices: Int): JavaRDD[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices) } @@ -94,10 +107,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork /** Distribute a local Scala collection to form an RDD. */ def parallelizePairs[K, V](list: java.util.List[Tuple2[K, V]], numSlices: Int) : JavaPairRDD[K, V] = { - implicit val kcm: ClassManifest[K] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]] - implicit val vcm: ClassManifest[V] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V]] + implicit val kcm: ClassTag[K] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val vcm: ClassTag[V] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] JavaPairRDD.fromRDD(sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices)) } @@ -132,16 +143,16 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork valueClass: Class[V], minSplits: Int ): JavaPairRDD[K, V] = { - implicit val kcm = ClassManifest.fromClass(keyClass) - implicit val vcm = ClassManifest.fromClass(valueClass) + implicit val kcm: ClassTag[K] = ClassTag(keyClass) + implicit val vcm: ClassTag[V] = ClassTag(valueClass) new JavaPairRDD(sc.sequenceFile(path, keyClass, valueClass, minSplits)) } /**Get an RDD for a Hadoop SequenceFile. */ def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]): JavaPairRDD[K, V] = { - implicit val kcm = ClassManifest.fromClass(keyClass) - implicit val vcm = ClassManifest.fromClass(valueClass) + implicit val kcm: ClassTag[K] = ClassTag(keyClass) + implicit val vcm: ClassTag[V] = ClassTag(valueClass) new JavaPairRDD(sc.sequenceFile(path, keyClass, valueClass)) } @@ -153,8 +164,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork * that there's very little effort required to save arbitrary objects. */ def objectFile[T](path: String, minSplits: Int): JavaRDD[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] sc.objectFile(path, minSplits)(cm) } @@ -166,8 +176,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork * that there's very little effort required to save arbitrary objects. */ def objectFile[T](path: String): JavaRDD[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] sc.objectFile(path)(cm) } @@ -183,8 +192,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork valueClass: Class[V], minSplits: Int ): JavaPairRDD[K, V] = { - implicit val kcm = ClassManifest.fromClass(keyClass) - implicit val vcm = ClassManifest.fromClass(valueClass) + implicit val kcm: ClassTag[K] = ClassTag(keyClass) + implicit val vcm: ClassTag[V] = ClassTag(valueClass) new JavaPairRDD(sc.hadoopRDD(conf, inputFormatClass, keyClass, valueClass, minSplits)) } @@ -199,8 +208,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork keyClass: Class[K], valueClass: Class[V] ): JavaPairRDD[K, V] = { - implicit val kcm = ClassManifest.fromClass(keyClass) - implicit val vcm = ClassManifest.fromClass(valueClass) + implicit val kcm: ClassTag[K] = ClassTag(keyClass) + implicit val vcm: ClassTag[V] = ClassTag(valueClass) new JavaPairRDD(sc.hadoopRDD(conf, inputFormatClass, keyClass, valueClass)) } @@ -212,8 +221,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork valueClass: Class[V], minSplits: Int ): JavaPairRDD[K, V] = { - implicit val kcm = ClassManifest.fromClass(keyClass) - implicit val vcm = ClassManifest.fromClass(valueClass) + implicit val kcm: ClassTag[K] = ClassTag(keyClass) + implicit val vcm: ClassTag[V] = ClassTag(valueClass) new JavaPairRDD(sc.hadoopFile(path, inputFormatClass, keyClass, valueClass, minSplits)) } @@ -224,8 +233,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork keyClass: Class[K], valueClass: Class[V] ): JavaPairRDD[K, V] = { - implicit val kcm = ClassManifest.fromClass(keyClass) - implicit val vcm = ClassManifest.fromClass(valueClass) + implicit val kcm: ClassTag[K] = ClassTag(keyClass) + implicit val vcm: ClassTag[V] = ClassTag(valueClass) new JavaPairRDD(sc.hadoopFile(path, inputFormatClass, keyClass, valueClass)) } @@ -240,8 +249,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork kClass: Class[K], vClass: Class[V], conf: Configuration): JavaPairRDD[K, V] = { - implicit val kcm = ClassManifest.fromClass(kClass) - implicit val vcm = ClassManifest.fromClass(vClass) + implicit val kcm: ClassTag[K] = ClassTag(kClass) + implicit val vcm: ClassTag[V] = ClassTag(vClass) new JavaPairRDD(sc.newAPIHadoopFile(path, fClass, kClass, vClass, conf)) } @@ -254,15 +263,15 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork fClass: Class[F], kClass: Class[K], vClass: Class[V]): JavaPairRDD[K, V] = { - implicit val kcm = ClassManifest.fromClass(kClass) - implicit val vcm = ClassManifest.fromClass(vClass) + implicit val kcm: ClassTag[K] = ClassTag(kClass) + implicit val vcm: ClassTag[V] = ClassTag(vClass) new JavaPairRDD(sc.newAPIHadoopRDD(conf, fClass, kClass, vClass)) } /** Build the union of two or more RDDs. */ override def union[T](first: JavaRDD[T], rest: java.util.List[JavaRDD[T]]): JavaRDD[T] = { val rdds: Seq[RDD[T]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.rdd) - implicit val cm: ClassManifest[T] = first.classManifest + implicit val cm: ClassTag[T] = first.classTag sc.union(rdds)(cm) } @@ -270,9 +279,9 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork override def union[K, V](first: JavaPairRDD[K, V], rest: java.util.List[JavaPairRDD[K, V]]) : JavaPairRDD[K, V] = { val rdds: Seq[RDD[(K, V)]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.rdd) - implicit val cm: ClassManifest[(K, V)] = first.classManifest - implicit val kcm: ClassManifest[K] = first.kManifest - implicit val vcm: ClassManifest[V] = first.vManifest + implicit val cm: ClassTag[(K, V)] = first.classTag + implicit val kcm: ClassTag[K] = first.kClassTag + implicit val vcm: ClassTag[V] = first.vClassTag new JavaPairRDD(sc.union(rdds)(cm))(kcm, vcm) } @@ -385,30 +394,23 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork /** * Set the directory under which RDDs are going to be checkpointed. The directory must - * be a HDFS path if running on a cluster. If the directory does not exist, it will - * be created. If the directory exists and useExisting is set to true, then the - * exisiting directory will be used. Otherwise an exception will be thrown to - * prevent accidental overriding of checkpoint files in the existing directory. - */ - def setCheckpointDir(dir: String, useExisting: Boolean) { - sc.setCheckpointDir(dir, useExisting) - } - - /** - * Set the directory under which RDDs are going to be checkpointed. The directory must - * be a HDFS path if running on a cluster. If the directory does not exist, it will - * be created. If the directory exists, an exception will be thrown to prevent accidental - * overriding of checkpoint files. + * be a HDFS path if running on a cluster. */ def setCheckpointDir(dir: String) { sc.setCheckpointDir(dir) } protected def checkpointFile[T](path: String): JavaRDD[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] new JavaRDD(sc.checkpointFile(path)) } + + /** + * Return a copy of this JavaSparkContext's configuration. The configuration ''cannot'' be + * changed at runtime. + */ + def getConf: SparkConf = sc.getConf } object JavaSparkContext { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java index c9cbce5624..2090efd3b9 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java +++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java @@ -17,7 +17,6 @@ package org.apache.spark.api.java; -import java.util.Arrays; import java.util.ArrayList; import java.util.List; diff --git a/core/src/main/scala/org/apache/spark/api/java/function/DoubleFlatMapFunction.java b/core/src/main/scala/org/apache/spark/api/java/function/DoubleFlatMapFunction.java index 4830067f7a..3e85052cd0 100644 --- a/core/src/main/scala/org/apache/spark/api/java/function/DoubleFlatMapFunction.java +++ b/core/src/main/scala/org/apache/spark/api/java/function/DoubleFlatMapFunction.java @@ -18,8 +18,6 @@ package org.apache.spark.api.java.function; -import scala.runtime.AbstractFunction1; - import java.io.Serializable; /** @@ -27,11 +25,7 @@ import java.io.Serializable; */ // DoubleFlatMapFunction does not extend FlatMapFunction because flatMap is // overloaded for both FlatMapFunction and DoubleFlatMapFunction. -public abstract class DoubleFlatMapFunction<T> extends AbstractFunction1<T, Iterable<Double>> +public abstract class DoubleFlatMapFunction<T> extends WrappedFunction1<T, Iterable<Double>> implements Serializable { - - public abstract Iterable<Double> call(T t); - - @Override - public final Iterable<Double> apply(T t) { return call(t); } + // Intentionally left blank } diff --git a/core/src/main/scala/org/apache/spark/api/java/function/DoubleFunction.java b/core/src/main/scala/org/apache/spark/api/java/function/DoubleFunction.java index db34cd190a..5e9b8c48b8 100644 --- a/core/src/main/scala/org/apache/spark/api/java/function/DoubleFunction.java +++ b/core/src/main/scala/org/apache/spark/api/java/function/DoubleFunction.java @@ -18,8 +18,6 @@ package org.apache.spark.api.java.function; -import scala.runtime.AbstractFunction1; - import java.io.Serializable; /** @@ -29,6 +27,5 @@ import java.io.Serializable; // are overloaded for both Function and DoubleFunction. public abstract class DoubleFunction<T> extends WrappedFunction1<T, Double> implements Serializable { - - public abstract Double call(T t) throws Exception; + // Intentionally left blank } diff --git a/core/src/main/scala/org/apache/spark/api/java/function/FlatMapFunction.scala b/core/src/main/scala/org/apache/spark/api/java/function/FlatMapFunction.scala index 158539a846..bdb01f7670 100644 --- a/core/src/main/scala/org/apache/spark/api/java/function/FlatMapFunction.scala +++ b/core/src/main/scala/org/apache/spark/api/java/function/FlatMapFunction.scala @@ -17,12 +17,11 @@ package org.apache.spark.api.java.function +import scala.reflect.ClassTag + /** * A function that returns zero or more output records from each input record. */ abstract class FlatMapFunction[T, R] extends Function[T, java.lang.Iterable[R]] { - @throws(classOf[Exception]) - def call(x: T) : java.lang.Iterable[R] - - def elementType() : ClassManifest[R] = ClassManifest.Any.asInstanceOf[ClassManifest[R]] + def elementType(): ClassTag[R] = ClassTag.Any.asInstanceOf[ClassTag[R]] } diff --git a/core/src/main/scala/org/apache/spark/api/java/function/FlatMapFunction2.scala b/core/src/main/scala/org/apache/spark/api/java/function/FlatMapFunction2.scala index 5ef6a814f5..aae1349c5e 100644 --- a/core/src/main/scala/org/apache/spark/api/java/function/FlatMapFunction2.scala +++ b/core/src/main/scala/org/apache/spark/api/java/function/FlatMapFunction2.scala @@ -17,12 +17,11 @@ package org.apache.spark.api.java.function +import scala.reflect.ClassTag + /** * A function that takes two inputs and returns zero or more output records. */ abstract class FlatMapFunction2[A, B, C] extends Function2[A, B, java.lang.Iterable[C]] { - @throws(classOf[Exception]) - def call(a: A, b:B) : java.lang.Iterable[C] - - def elementType() : ClassManifest[C] = ClassManifest.Any.asInstanceOf[ClassManifest[C]] + def elementType() : ClassTag[C] = ClassTag.Any.asInstanceOf[ClassTag[C]] } diff --git a/core/src/main/scala/org/apache/spark/api/java/function/Function.java b/core/src/main/scala/org/apache/spark/api/java/function/Function.java index b9070cfd83..537439ef53 100644 --- a/core/src/main/scala/org/apache/spark/api/java/function/Function.java +++ b/core/src/main/scala/org/apache/spark/api/java/function/Function.java @@ -17,9 +17,8 @@ package org.apache.spark.api.java.function; -import scala.reflect.ClassManifest; -import scala.reflect.ClassManifest$; -import scala.runtime.AbstractFunction1; +import scala.reflect.ClassTag; +import scala.reflect.ClassTag$; import java.io.Serializable; @@ -30,10 +29,8 @@ import java.io.Serializable; * when mapping RDDs of other types. */ public abstract class Function<T, R> extends WrappedFunction1<T, R> implements Serializable { - public abstract R call(T t) throws Exception; - - public ClassManifest<R> returnType() { - return (ClassManifest<R>) ClassManifest$.MODULE$.fromClass(Object.class); + public ClassTag<R> returnType() { + return ClassTag$.MODULE$.apply(Object.class); } } diff --git a/core/src/main/scala/org/apache/spark/api/java/function/Function2.java b/core/src/main/scala/org/apache/spark/api/java/function/Function2.java index d4c9154869..a2d1214fb4 100644 --- a/core/src/main/scala/org/apache/spark/api/java/function/Function2.java +++ b/core/src/main/scala/org/apache/spark/api/java/function/Function2.java @@ -17,9 +17,8 @@ package org.apache.spark.api.java.function; -import scala.reflect.ClassManifest; -import scala.reflect.ClassManifest$; -import scala.runtime.AbstractFunction2; +import scala.reflect.ClassTag; +import scala.reflect.ClassTag$; import java.io.Serializable; @@ -29,10 +28,8 @@ import java.io.Serializable; public abstract class Function2<T1, T2, R> extends WrappedFunction2<T1, T2, R> implements Serializable { - public abstract R call(T1 t1, T2 t2) throws Exception; - - public ClassManifest<R> returnType() { - return (ClassManifest<R>) ClassManifest$.MODULE$.fromClass(Object.class); + public ClassTag<R> returnType() { + return (ClassTag<R>) ClassTag$.MODULE$.apply(Object.class); } } diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithIndexRDD.scala b/core/src/main/scala/org/apache/spark/api/java/function/Function3.java index 3ed8339010..fb1deceab5 100644 --- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithIndexRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/function/Function3.java @@ -15,27 +15,22 @@ * limitations under the License. */ -package org.apache.spark.rdd +package org.apache.spark.api.java.function; -import org.apache.spark.{Partition, TaskContext} +import scala.reflect.ClassTag; +import scala.reflect.ClassTag$; +import scala.runtime.AbstractFunction2; +import java.io.Serializable; /** - * A variant of the MapPartitionsRDD that passes the partition index into the - * closure. This can be used to generate or collect partition specific - * information such as the number of tuples in a partition. + * A three-argument function that takes arguments of type T1, T2 and T3 and returns an R. */ -private[spark] -class MapPartitionsWithIndexRDD[U: ClassManifest, T: ClassManifest]( - prev: RDD[T], - f: (Int, Iterator[T]) => Iterator[U], - preservesPartitioning: Boolean - ) extends RDD[U](prev) { +public abstract class Function3<T1, T2, T3, R> extends WrappedFunction3<T1, T2, T3, R> + implements Serializable { - override def getPartitions: Array[Partition] = firstParent[T].partitions - - override val partitioner = if (preservesPartitioning) prev.partitioner else None - - override def compute(split: Partition, context: TaskContext) = - f(split.index, firstParent[T].iterator(split, context)) + public ClassTag<R> returnType() { + return (ClassTag<R>) ClassTag$.MODULE$.apply(Object.class); + } } + diff --git a/core/src/main/scala/org/apache/spark/api/java/function/PairFlatMapFunction.java b/core/src/main/scala/org/apache/spark/api/java/function/PairFlatMapFunction.java index c0e5544b7d..ca485b3cc2 100644 --- a/core/src/main/scala/org/apache/spark/api/java/function/PairFlatMapFunction.java +++ b/core/src/main/scala/org/apache/spark/api/java/function/PairFlatMapFunction.java @@ -18,9 +18,8 @@ package org.apache.spark.api.java.function; import scala.Tuple2; -import scala.reflect.ClassManifest; -import scala.reflect.ClassManifest$; -import scala.runtime.AbstractFunction1; +import scala.reflect.ClassTag; +import scala.reflect.ClassTag$; import java.io.Serializable; @@ -34,13 +33,11 @@ public abstract class PairFlatMapFunction<T, K, V> extends WrappedFunction1<T, Iterable<Tuple2<K, V>>> implements Serializable { - public abstract Iterable<Tuple2<K, V>> call(T t) throws Exception; - - public ClassManifest<K> keyType() { - return (ClassManifest<K>) ClassManifest$.MODULE$.fromClass(Object.class); + public ClassTag<K> keyType() { + return (ClassTag<K>) ClassTag$.MODULE$.apply(Object.class); } - public ClassManifest<V> valueType() { - return (ClassManifest<V>) ClassManifest$.MODULE$.fromClass(Object.class); + public ClassTag<V> valueType() { + return (ClassTag<V>) ClassTag$.MODULE$.apply(Object.class); } } diff --git a/core/src/main/scala/org/apache/spark/api/java/function/PairFunction.java b/core/src/main/scala/org/apache/spark/api/java/function/PairFunction.java index 40480fe8e8..cbe2306026 100644 --- a/core/src/main/scala/org/apache/spark/api/java/function/PairFunction.java +++ b/core/src/main/scala/org/apache/spark/api/java/function/PairFunction.java @@ -18,9 +18,8 @@ package org.apache.spark.api.java.function; import scala.Tuple2; -import scala.reflect.ClassManifest; -import scala.reflect.ClassManifest$; -import scala.runtime.AbstractFunction1; +import scala.reflect.ClassTag; +import scala.reflect.ClassTag$; import java.io.Serializable; @@ -29,17 +28,14 @@ import java.io.Serializable; */ // PairFunction does not extend Function because some UDF functions, like map, // are overloaded for both Function and PairFunction. -public abstract class PairFunction<T, K, V> - extends WrappedFunction1<T, Tuple2<K, V>> +public abstract class PairFunction<T, K, V> extends WrappedFunction1<T, Tuple2<K, V>> implements Serializable { - public abstract Tuple2<K, V> call(T t) throws Exception; - - public ClassManifest<K> keyType() { - return (ClassManifest<K>) ClassManifest$.MODULE$.fromClass(Object.class); + public ClassTag<K> keyType() { + return (ClassTag<K>) ClassTag$.MODULE$.apply(Object.class); } - public ClassManifest<V> valueType() { - return (ClassManifest<V>) ClassManifest$.MODULE$.fromClass(Object.class); + public ClassTag<V> valueType() { + return (ClassTag<V>) ClassTag$.MODULE$.apply(Object.class); } } diff --git a/core/src/main/scala/org/apache/spark/api/java/function/WrappedFunction3.scala b/core/src/main/scala/org/apache/spark/api/java/function/WrappedFunction3.scala new file mode 100644 index 0000000000..d314dbdf1d --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/java/function/WrappedFunction3.scala @@ -0,0 +1,34 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.java.function + +import scala.runtime.AbstractFunction3 + +/** + * Subclass of Function3 for ease of calling from Java. The main thing it does is re-expose the + * apply() method as call() and declare that it can throw Exception (since AbstractFunction3.apply + * isn't marked to allow that). + */ +private[spark] abstract class WrappedFunction3[T1, T2, T3, R] + extends AbstractFunction3[T1, T2, T3, R] { + @throws(classOf[Exception]) + def call(t1: T1, t2: T2, t3: T3): R + + final def apply(t1: T1, t2: T2, t3: T3): R = call(t1, t2, t3) +} + diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonPartitioner.scala b/core/src/main/scala/org/apache/spark/api/python/PythonPartitioner.scala index b090c6edf3..2be4e323be 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonPartitioner.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonPartitioner.scala @@ -17,12 +17,13 @@ package org.apache.spark.api.python -import org.apache.spark.Partitioner import java.util.Arrays + +import org.apache.spark.Partitioner import org.apache.spark.util.Utils /** - * A [[org.apache.spark.Partitioner]] that performs handling of byte arrays, for use by the Python API. + * A [[org.apache.spark.Partitioner]] that performs handling of long-valued keys, for use by the Python API. * * Stores the unique id() of the Python-side partitioning function so that it is incorporated into * equality comparisons. Correctness requires that the id is a unique identifier for the @@ -30,6 +31,7 @@ import org.apache.spark.util.Utils * function). This can be ensured by using the Python id() function and maintaining a reference * to the Python partitioning function so that its id() is not reused. */ + private[spark] class PythonPartitioner( override val numPartitions: Int, val pyPartitionFunctionId: Long) @@ -37,7 +39,9 @@ private[spark] class PythonPartitioner( override def getPartition(key: Any): Int = key match { case null => 0 - case key: Array[Byte] => Utils.nonNegativeMod(Arrays.hashCode(key), numPartitions) + // we don't trust the Python partition function to return valid partition ID's so + // let's do a modulo numPartitions in any case + case key: Long => Utils.nonNegativeMod(key.toInt, numPartitions) case _ => Utils.nonNegativeMod(key.hashCode(), numPartitions) } diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index ccd3833964..32cc70e8c9 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -22,18 +22,17 @@ import java.net._ import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections} import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD} import org.apache.spark.broadcast.Broadcast import org.apache.spark._ import org.apache.spark.rdd.RDD -import org.apache.spark.rdd.PipedRDD import org.apache.spark.util.Utils - -private[spark] class PythonRDD[T: ClassManifest]( +private[spark] class PythonRDD[T: ClassTag]( parent: RDD[T], - command: Seq[String], + command: Array[Byte], envVars: JMap[String, String], pythonIncludes: JList[String], preservePartitoning: Boolean, @@ -42,23 +41,12 @@ private[spark] class PythonRDD[T: ClassManifest]( accumulator: Accumulator[JList[Array[Byte]]]) extends RDD[Array[Byte]](parent) { - val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt - - // Similar to Runtime.exec(), if we are given a single string, split it into words - // using a standard StringTokenizer (i.e. by spaces) - def this(parent: RDD[T], command: String, envVars: JMap[String, String], - pythonIncludes: JList[String], - preservePartitoning: Boolean, pythonExec: String, - broadcastVars: JList[Broadcast[Array[Byte]]], - accumulator: Accumulator[JList[Array[Byte]]]) = - this(parent, PipedRDD.tokenize(command), envVars, pythonIncludes, preservePartitoning, pythonExec, - broadcastVars, accumulator) + val bufferSize = conf.get("spark.buffer.size", "65536").toInt override def getPartitions = parent.partitions override val partitioner = if (preservePartitoning) parent.partitioner else None - override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = { val startTime = System.currentTimeMillis val env = SparkEnv.get @@ -71,11 +59,10 @@ private[spark] class PythonRDD[T: ClassManifest]( SparkEnv.set(env) val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize) val dataOut = new DataOutputStream(stream) - val printOut = new PrintWriter(stream) // Partition index dataOut.writeInt(split.index) // sparkFilesDir - PythonRDD.writeAsPickle(SparkFiles.getRootDirectory, dataOut) + dataOut.writeUTF(SparkFiles.getRootDirectory) // Broadcast variables dataOut.writeInt(broadcastVars.length) for (broadcast <- broadcastVars) { @@ -85,21 +72,16 @@ private[spark] class PythonRDD[T: ClassManifest]( } // Python includes (*.zip and *.egg files) dataOut.writeInt(pythonIncludes.length) - for (f <- pythonIncludes) { - PythonRDD.writeAsPickle(f, dataOut) - } + pythonIncludes.foreach(dataOut.writeUTF) dataOut.flush() - // Serialized user code - for (elem <- command) { - printOut.println(elem) - } - printOut.flush() + // Serialized command: + dataOut.writeInt(command.length) + dataOut.write(command) // Data values for (elem <- parent.iterator(split, context)) { - PythonRDD.writeAsPickle(elem, dataOut) + PythonRDD.writeToStream(elem, dataOut) } dataOut.flush() - printOut.flush() worker.shutdownOutput() } catch { case e: IOException => @@ -132,7 +114,7 @@ private[spark] class PythonRDD[T: ClassManifest]( val obj = new Array[Byte](length) stream.readFully(obj) obj - case -3 => + case SpecialLengths.TIMING_DATA => // Timing data from worker val bootTime = stream.readLong() val initTime = stream.readLong() @@ -143,30 +125,30 @@ private[spark] class PythonRDD[T: ClassManifest]( val total = finishTime - startTime logInfo("Times: total = %s, boot = %s, init = %s, finish = %s".format(total, boot, init, finish)) read - case -2 => + case SpecialLengths.PYTHON_EXCEPTION_THROWN => // Signals that an exception has been thrown in python val exLength = stream.readInt() val obj = new Array[Byte](exLength) stream.readFully(obj) throw new PythonException(new String(obj)) - case -1 => + case SpecialLengths.END_OF_DATA_SECTION => // We've finished the data section of the output, but we can still - // read some accumulator updates; let's do that, breaking when we - // get a negative length record. - var len2 = stream.readInt() - while (len2 >= 0) { - val update = new Array[Byte](len2) + // read some accumulator updates: + val numAccumulatorUpdates = stream.readInt() + (1 to numAccumulatorUpdates).foreach { _ => + val updateLen = stream.readInt() + val update = new Array[Byte](updateLen) stream.readFully(update) accumulator += Collections.singletonList(update) - len2 = stream.readInt() + } - new Array[Byte](0) + Array.empty[Byte] } } catch { case eof: EOFException => { throw new SparkException("Python worker exited unexpectedly (crashed)", eof) } - case e => throw e + case e: Throwable => throw e } } @@ -187,72 +169,25 @@ private class PythonException(msg: String) extends Exception(msg) * This is used by PySpark's shuffle operations. */ private class PairwiseRDD(prev: RDD[Array[Byte]]) extends - RDD[(Array[Byte], Array[Byte])](prev) { + RDD[(Long, Array[Byte])](prev) { override def getPartitions = prev.partitions override def compute(split: Partition, context: TaskContext) = prev.iterator(split, context).grouped(2).map { - case Seq(a, b) => (a, b) + case Seq(a, b) => (Utils.deserializeLongValue(a), b) case x => throw new SparkException("PairwiseRDD: unexpected value: " + x) } - val asJavaPairRDD : JavaPairRDD[Array[Byte], Array[Byte]] = JavaPairRDD.fromRDD(this) + val asJavaPairRDD : JavaPairRDD[Long, Array[Byte]] = JavaPairRDD.fromRDD(this) } -private[spark] object PythonRDD { - - /** Strips the pickle PROTO and STOP opcodes from the start and end of a pickle */ - def stripPickle(arr: Array[Byte]) : Array[Byte] = { - arr.slice(2, arr.length - 1) - } +private object SpecialLengths { + val END_OF_DATA_SECTION = -1 + val PYTHON_EXCEPTION_THROWN = -2 + val TIMING_DATA = -3 +} - /** - * Write strings, pickled Python objects, or pairs of pickled objects to a data output stream. - * The data format is a 32-bit integer representing the pickled object's length (in bytes), - * followed by the pickled data. - * - * Pickle module: - * - * http://docs.python.org/2/library/pickle.html - * - * The pickle protocol is documented in the source of the `pickle` and `pickletools` modules: - * - * http://hg.python.org/cpython/file/2.6/Lib/pickle.py - * http://hg.python.org/cpython/file/2.6/Lib/pickletools.py - * - * @param elem the object to write - * @param dOut a data output stream - */ - def writeAsPickle(elem: Any, dOut: DataOutputStream) { - if (elem.isInstanceOf[Array[Byte]]) { - val arr = elem.asInstanceOf[Array[Byte]] - dOut.writeInt(arr.length) - dOut.write(arr) - } else if (elem.isInstanceOf[scala.Tuple2[Array[Byte], Array[Byte]]]) { - val t = elem.asInstanceOf[scala.Tuple2[Array[Byte], Array[Byte]]] - val length = t._1.length + t._2.length - 3 - 3 + 4 // stripPickle() removes 3 bytes - dOut.writeInt(length) - dOut.writeByte(Pickle.PROTO) - dOut.writeByte(Pickle.TWO) - dOut.write(PythonRDD.stripPickle(t._1)) - dOut.write(PythonRDD.stripPickle(t._2)) - dOut.writeByte(Pickle.TUPLE2) - dOut.writeByte(Pickle.STOP) - } else if (elem.isInstanceOf[String]) { - // For uniformity, strings are wrapped into Pickles. - val s = elem.asInstanceOf[String].getBytes("UTF-8") - val length = 2 + 1 + 4 + s.length + 1 - dOut.writeInt(length) - dOut.writeByte(Pickle.PROTO) - dOut.writeByte(Pickle.TWO) - dOut.write(Pickle.BINUNICODE) - dOut.writeInt(Integer.reverseBytes(s.length)) - dOut.write(s) - dOut.writeByte(Pickle.STOP) - } else { - throw new SparkException("Unexpected RDD type") - } - } +private[spark] object PythonRDD { - def readRDDFromPickleFile(sc: JavaSparkContext, filename: String, parallelism: Int) : + def readRDDFromFile(sc: JavaSparkContext, filename: String, parallelism: Int): JavaRDD[Array[Byte]] = { val file = new DataInputStream(new FileInputStream(filename)) val objs = new collection.mutable.ArrayBuffer[Array[Byte]] @@ -265,39 +200,41 @@ private[spark] object PythonRDD { } } catch { case eof: EOFException => {} - case e => throw e + case e: Throwable => throw e } JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism)) } - def writeIteratorToPickleFile[T](items: java.util.Iterator[T], filename: String) { + def writeToStream(elem: Any, dataOut: DataOutputStream) { + elem match { + case bytes: Array[Byte] => + dataOut.writeInt(bytes.length) + dataOut.write(bytes) + case pair: (Array[Byte], Array[Byte]) => + dataOut.writeInt(pair._1.length) + dataOut.write(pair._1) + dataOut.writeInt(pair._2.length) + dataOut.write(pair._2) + case str: String => + dataOut.writeUTF(str) + case other => + throw new SparkException("Unexpected element type " + other.getClass) + } + } + + def writeToFile[T](items: java.util.Iterator[T], filename: String) { import scala.collection.JavaConverters._ - writeIteratorToPickleFile(items.asScala, filename) + writeToFile(items.asScala, filename) } - def writeIteratorToPickleFile[T](items: Iterator[T], filename: String) { + def writeToFile[T](items: Iterator[T], filename: String) { val file = new DataOutputStream(new FileOutputStream(filename)) for (item <- items) { - writeAsPickle(item, file) + writeToStream(item, file) } file.close() } - def takePartition[T](rdd: RDD[T], partition: Int): Iterator[T] = { - implicit val cm : ClassManifest[T] = rdd.elementClassManifest - rdd.context.runJob(rdd, ((x: Iterator[T]) => x.toArray), Seq(partition), true).head.iterator - } -} - -private object Pickle { - val PROTO: Byte = 0x80.toByte - val TWO: Byte = 0x02.toByte - val BINUNICODE: Byte = 'X' - val STOP: Byte = '.' - val TUPLE2: Byte = 0x86.toByte - val EMPTY_LIST: Byte = ']' - val MARK: Byte = '(' - val APPENDS: Byte = 'e' } private class BytesToString extends org.apache.spark.api.java.function.Function[Array[Byte], String] { @@ -308,12 +245,12 @@ private class BytesToString extends org.apache.spark.api.java.function.Function[ * Internal class that acts as an `AccumulatorParam` for Python accumulators. Inside, it * collects a list of pickled strings that we pass to Python through a socket. */ -class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int) +private class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int) extends AccumulatorParam[JList[Array[Byte]]] { Utils.checkHost(serverHost, "Expected hostname") - val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + val bufferSize = SparkEnv.get.conf.get("spark.buffer.size", "65536").toInt override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new JArrayList diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala index 67d45723ba..f291266fcf 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala @@ -64,7 +64,7 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String startDaemon() new Socket(daemonHost, daemonPort) } - case e => throw e + case e: Throwable => throw e } } } @@ -198,7 +198,7 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String } }.start() } catch { - case e => { + case e: Throwable => { stopDaemon() throw e } diff --git a/core/src/main/scala/org/apache/spark/broadcast/BitTorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/BitTorrentBroadcast.scala deleted file mode 100644 index 93e7815ab5..0000000000 --- a/core/src/main/scala/org/apache/spark/broadcast/BitTorrentBroadcast.scala +++ /dev/null @@ -1,1058 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.broadcast - -import java.io._ -import java.net._ -import java.util.{BitSet, Comparator, Timer, TimerTask, UUID} -import java.util.concurrent.atomic.AtomicInteger - -import scala.collection.mutable.{ListBuffer, Map, Set} -import scala.math - -import org.apache.spark._ -import org.apache.spark.storage.StorageLevel -import org.apache.spark.util.Utils - -private[spark] class BitTorrentBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long) - extends Broadcast[T](id) - with Logging - with Serializable { - - def value = value_ - - def blockId: String = "broadcast_" + id - - MultiTracker.synchronized { - SparkEnv.get.blockManager.putSingle(blockId, value_, StorageLevel.MEMORY_AND_DISK, false) - } - - @transient var arrayOfBlocks: Array[BroadcastBlock] = null - @transient var hasBlocksBitVector: BitSet = null - @transient var numCopiesSent: Array[Int] = null - @transient var totalBytes = -1 - @transient var totalBlocks = -1 - @transient var hasBlocks = new AtomicInteger(0) - - // Used ONLY by driver to track how many unique blocks have been sent out - @transient var sentBlocks = new AtomicInteger(0) - - @transient var listenPortLock = new Object - @transient var guidePortLock = new Object - @transient var totalBlocksLock = new Object - - @transient var listOfSources = ListBuffer[SourceInfo]() - - @transient var serveMR: ServeMultipleRequests = null - - // Used only in driver - @transient var guideMR: GuideMultipleRequests = null - - // Used only in Workers - @transient var ttGuide: TalkToGuide = null - - @transient var hostAddress = Utils.localIpAddress - @transient var listenPort = -1 - @transient var guidePort = -1 - - @transient var stopBroadcast = false - - // Must call this after all the variables have been created/initialized - if (!isLocal) { - sendBroadcast() - } - - def sendBroadcast() { - logInfo("Local host address: " + hostAddress) - - // Create a variableInfo object and store it in valueInfos - var variableInfo = MultiTracker.blockifyObject(value_) - - // Prepare the value being broadcasted - arrayOfBlocks = variableInfo.arrayOfBlocks - totalBytes = variableInfo.totalBytes - totalBlocks = variableInfo.totalBlocks - hasBlocks.set(variableInfo.totalBlocks) - - // Guide has all the blocks - hasBlocksBitVector = new BitSet(totalBlocks) - hasBlocksBitVector.set(0, totalBlocks) - - // Guide still hasn't sent any block - numCopiesSent = new Array[Int](totalBlocks) - - guideMR = new GuideMultipleRequests - guideMR.setDaemon(true) - guideMR.start() - logInfo("GuideMultipleRequests started...") - - // Must always come AFTER guideMR is created - while (guidePort == -1) { - guidePortLock.synchronized { guidePortLock.wait() } - } - - serveMR = new ServeMultipleRequests - serveMR.setDaemon(true) - serveMR.start() - logInfo("ServeMultipleRequests started...") - - // Must always come AFTER serveMR is created - while (listenPort == -1) { - listenPortLock.synchronized { listenPortLock.wait() } - } - - // Must always come AFTER listenPort is created - val driverSource = - SourceInfo(hostAddress, listenPort, totalBlocks, totalBytes) - hasBlocksBitVector.synchronized { - driverSource.hasBlocksBitVector = hasBlocksBitVector - } - - // In the beginning, this is the only known source to Guide - listOfSources += driverSource - - // Register with the Tracker - MultiTracker.registerBroadcast(id, - SourceInfo(hostAddress, guidePort, totalBlocks, totalBytes)) - } - - private def readObject(in: ObjectInputStream) { - in.defaultReadObject() - MultiTracker.synchronized { - SparkEnv.get.blockManager.getSingle(blockId) match { - case Some(x) => - value_ = x.asInstanceOf[T] - - case None => - logInfo("Started reading broadcast variable " + id) - // Initializing everything because driver will only send null/0 values - // Only the 1st worker in a node can be here. Others will get from cache - initializeWorkerVariables() - - logInfo("Local host address: " + hostAddress) - - // Start local ServeMultipleRequests thread first - serveMR = new ServeMultipleRequests - serveMR.setDaemon(true) - serveMR.start() - logInfo("ServeMultipleRequests started...") - - val start = System.nanoTime - - val receptionSucceeded = receiveBroadcast(id) - if (receptionSucceeded) { - value_ = MultiTracker.unBlockifyObject[T](arrayOfBlocks, totalBytes, totalBlocks) - SparkEnv.get.blockManager.putSingle( - blockId, value_, StorageLevel.MEMORY_AND_DISK, false) - } else { - logError("Reading broadcast variable " + id + " failed") - } - - val time = (System.nanoTime - start) / 1e9 - logInfo("Reading broadcast variable " + id + " took " + time + " s") - } - } - } - - // Initialize variables in the worker node. Driver sends everything as 0/null - private def initializeWorkerVariables() { - arrayOfBlocks = null - hasBlocksBitVector = null - numCopiesSent = null - totalBytes = -1 - totalBlocks = -1 - hasBlocks = new AtomicInteger(0) - - listenPortLock = new Object - totalBlocksLock = new Object - - serveMR = null - ttGuide = null - - hostAddress = Utils.localIpAddress - listenPort = -1 - - listOfSources = ListBuffer[SourceInfo]() - - stopBroadcast = false - } - - private def getLocalSourceInfo: SourceInfo = { - // Wait till hostName and listenPort are OK - while (listenPort == -1) { - listenPortLock.synchronized { listenPortLock.wait() } - } - - // Wait till totalBlocks and totalBytes are OK - while (totalBlocks == -1) { - totalBlocksLock.synchronized { totalBlocksLock.wait() } - } - - var localSourceInfo = SourceInfo( - hostAddress, listenPort, totalBlocks, totalBytes) - - localSourceInfo.hasBlocks = hasBlocks.get - - hasBlocksBitVector.synchronized { - localSourceInfo.hasBlocksBitVector = hasBlocksBitVector - } - - return localSourceInfo - } - - // Add new SourceInfo to the listOfSources. Update if it exists already. - // Optimizing just by OR-ing the BitVectors was BAD for performance - private def addToListOfSources(newSourceInfo: SourceInfo) { - listOfSources.synchronized { - if (listOfSources.contains(newSourceInfo)) { - listOfSources = listOfSources - newSourceInfo - } - listOfSources += newSourceInfo - } - } - - private def addToListOfSources(newSourceInfos: ListBuffer[SourceInfo]) { - newSourceInfos.foreach { newSourceInfo => - addToListOfSources(newSourceInfo) - } - } - - class TalkToGuide(gInfo: SourceInfo) - extends Thread with Logging { - override def run() { - - // Keep exchaning information until all blocks have been received - while (hasBlocks.get < totalBlocks) { - talkOnce - Thread.sleep(MultiTracker.ranGen.nextInt( - MultiTracker.MaxKnockInterval - MultiTracker.MinKnockInterval) + - MultiTracker.MinKnockInterval) - } - - // Talk one more time to let the Guide know of reception completion - talkOnce - } - - // Connect to Guide and send this worker's information - private def talkOnce { - var clientSocketToGuide: Socket = null - var oosGuide: ObjectOutputStream = null - var oisGuide: ObjectInputStream = null - - clientSocketToGuide = new Socket(gInfo.hostAddress, gInfo.listenPort) - oosGuide = new ObjectOutputStream(clientSocketToGuide.getOutputStream) - oosGuide.flush() - oisGuide = new ObjectInputStream(clientSocketToGuide.getInputStream) - - // Send local information - oosGuide.writeObject(getLocalSourceInfo) - oosGuide.flush() - - // Receive source information from Guide - var suitableSources = - oisGuide.readObject.asInstanceOf[ListBuffer[SourceInfo]] - logDebug("Received suitableSources from Driver " + suitableSources) - - addToListOfSources(suitableSources) - - oisGuide.close() - oosGuide.close() - clientSocketToGuide.close() - } - } - - def receiveBroadcast(variableID: Long): Boolean = { - val gInfo = MultiTracker.getGuideInfo(variableID) - - if (gInfo.listenPort == SourceInfo.TxOverGoToDefault) { - return false - } - - // Wait until hostAddress and listenPort are created by the - // ServeMultipleRequests thread - while (listenPort == -1) { - listenPortLock.synchronized { listenPortLock.wait() } - } - - // Setup initial states of variables - totalBlocks = gInfo.totalBlocks - arrayOfBlocks = new Array[BroadcastBlock](totalBlocks) - hasBlocksBitVector = new BitSet(totalBlocks) - numCopiesSent = new Array[Int](totalBlocks) - totalBlocksLock.synchronized { totalBlocksLock.notifyAll() } - totalBytes = gInfo.totalBytes - - // Start ttGuide to periodically talk to the Guide - var ttGuide = new TalkToGuide(gInfo) - ttGuide.setDaemon(true) - ttGuide.start() - logInfo("TalkToGuide started...") - - // Start pController to run TalkToPeer threads - var pcController = new PeerChatterController - pcController.setDaemon(true) - pcController.start() - logInfo("PeerChatterController started...") - - // FIXME: Must fix this. This might never break if broadcast fails. - // We should be able to break and send false. Also need to kill threads - while (hasBlocks.get < totalBlocks) { - Thread.sleep(MultiTracker.MaxKnockInterval) - } - - return true - } - - class PeerChatterController - extends Thread with Logging { - private var peersNowTalking = ListBuffer[SourceInfo]() - // TODO: There is a possible bug with blocksInRequestBitVector when a - // certain bit is NOT unset upon failure resulting in an infinite loop. - private var blocksInRequestBitVector = new BitSet(totalBlocks) - - override def run() { - var threadPool = Utils.newDaemonFixedThreadPool(MultiTracker.MaxChatSlots) - - while (hasBlocks.get < totalBlocks) { - var numThreadsToCreate = 0 - listOfSources.synchronized { - numThreadsToCreate = math.min(listOfSources.size, MultiTracker.MaxChatSlots) - - threadPool.getActiveCount - } - - while (hasBlocks.get < totalBlocks && numThreadsToCreate > 0) { - var peerToTalkTo = pickPeerToTalkToRandom - - if (peerToTalkTo != null) - logDebug("Peer chosen: " + peerToTalkTo + " with " + peerToTalkTo.hasBlocksBitVector) - else - logDebug("No peer chosen...") - - if (peerToTalkTo != null) { - threadPool.execute(new TalkToPeer(peerToTalkTo)) - - // Add to peersNowTalking. Remove in the thread. We have to do this - // ASAP, otherwise pickPeerToTalkTo picks the same peer more than once - peersNowTalking.synchronized { peersNowTalking += peerToTalkTo } - } - - numThreadsToCreate = numThreadsToCreate - 1 - } - - // Sleep for a while before starting some more threads - Thread.sleep(MultiTracker.MinKnockInterval) - } - // Shutdown the thread pool - threadPool.shutdown() - } - - // Right now picking the one that has the most blocks this peer wants - // Also picking peer randomly if no one has anything interesting - private def pickPeerToTalkToRandom: SourceInfo = { - var curPeer: SourceInfo = null - var curMax = 0 - - logDebug("Picking peers to talk to...") - - // Find peers that are not connected right now - var peersNotInUse = ListBuffer[SourceInfo]() - listOfSources.synchronized { - peersNowTalking.synchronized { - peersNotInUse = listOfSources -- peersNowTalking - } - } - - // Select the peer that has the most blocks that this receiver does not - peersNotInUse.foreach { eachSource => - var tempHasBlocksBitVector: BitSet = null - hasBlocksBitVector.synchronized { - tempHasBlocksBitVector = hasBlocksBitVector.clone.asInstanceOf[BitSet] - } - tempHasBlocksBitVector.flip(0, tempHasBlocksBitVector.size) - tempHasBlocksBitVector.and(eachSource.hasBlocksBitVector) - - if (tempHasBlocksBitVector.cardinality > curMax) { - curPeer = eachSource - curMax = tempHasBlocksBitVector.cardinality - } - } - - // Always picking randomly - if (curPeer == null && peersNotInUse.size > 0) { - // Pick uniformly the i'th required peer - var i = MultiTracker.ranGen.nextInt(peersNotInUse.size) - - var peerIter = peersNotInUse.iterator - curPeer = peerIter.next - - while (i > 0) { - curPeer = peerIter.next - i = i - 1 - } - } - - return curPeer - } - - // Picking peer with the weight of rare blocks it has - private def pickPeerToTalkToRarestFirst: SourceInfo = { - // Find peers that are not connected right now - var peersNotInUse = ListBuffer[SourceInfo]() - listOfSources.synchronized { - peersNowTalking.synchronized { - peersNotInUse = listOfSources -- peersNowTalking - } - } - - // Count the number of copies of each block in the neighborhood - var numCopiesPerBlock = Array.tabulate [Int](totalBlocks)(_ => 0) - - listOfSources.synchronized { - listOfSources.foreach { eachSource => - for (i <- 0 until totalBlocks) { - numCopiesPerBlock(i) += - ( if (eachSource.hasBlocksBitVector.get(i)) 1 else 0 ) - } - } - } - - // A block is considered rare if there are at most 2 copies of that block - // This CONSTANT could be a function of the neighborhood size - var rareBlocksIndices = ListBuffer[Int]() - for (i <- 0 until totalBlocks) { - if (numCopiesPerBlock(i) > 0 && numCopiesPerBlock(i) <= 2) { - rareBlocksIndices += i - } - } - - // Find peers with rare blocks - var peersWithRareBlocks = ListBuffer[(SourceInfo, Int)]() - var totalRareBlocks = 0 - - peersNotInUse.foreach { eachPeer => - var hasRareBlocks = 0 - rareBlocksIndices.foreach { rareBlock => - if (eachPeer.hasBlocksBitVector.get(rareBlock)) { - hasRareBlocks += 1 - } - } - - if (hasRareBlocks > 0) { - peersWithRareBlocks += ((eachPeer, hasRareBlocks)) - } - totalRareBlocks += hasRareBlocks - } - - // Select a peer from peersWithRareBlocks based on weight calculated from - // unique rare blocks - var selectedPeerToTalkTo: SourceInfo = null - - if (peersWithRareBlocks.size > 0) { - // Sort the peers based on how many rare blocks they have - peersWithRareBlocks.sortBy(_._2) - - var randomNumber = MultiTracker.ranGen.nextDouble - var tempSum = 0.0 - - var i = 0 - do { - tempSum += (1.0 * peersWithRareBlocks(i)._2 / totalRareBlocks) - if (tempSum >= randomNumber) { - selectedPeerToTalkTo = peersWithRareBlocks(i)._1 - } - i += 1 - } while (i < peersWithRareBlocks.size && selectedPeerToTalkTo == null) - } - - if (selectedPeerToTalkTo == null) { - selectedPeerToTalkTo = pickPeerToTalkToRandom - } - - return selectedPeerToTalkTo - } - - class TalkToPeer(peerToTalkTo: SourceInfo) - extends Thread with Logging { - private var peerSocketToSource: Socket = null - private var oosSource: ObjectOutputStream = null - private var oisSource: ObjectInputStream = null - - override def run() { - // TODO: There is a possible bug here regarding blocksInRequestBitVector - var blockToAskFor = -1 - - // Setup the timeout mechanism - var timeOutTask = new TimerTask { - override def run() { - cleanUpConnections() - } - } - - var timeOutTimer = new Timer - timeOutTimer.schedule(timeOutTask, MultiTracker.MaxKnockInterval) - - logInfo("TalkToPeer started... => " + peerToTalkTo) - - try { - // Connect to the source - peerSocketToSource = - new Socket(peerToTalkTo.hostAddress, peerToTalkTo.listenPort) - oosSource = - new ObjectOutputStream(peerSocketToSource.getOutputStream) - oosSource.flush() - oisSource = - new ObjectInputStream(peerSocketToSource.getInputStream) - - // Receive latest SourceInfo from peerToTalkTo - var newPeerToTalkTo = oisSource.readObject.asInstanceOf[SourceInfo] - // Update listOfSources - addToListOfSources(newPeerToTalkTo) - - // Turn the timer OFF, if the sender responds before timeout - timeOutTimer.cancel() - - // Send the latest SourceInfo - oosSource.writeObject(getLocalSourceInfo) - oosSource.flush() - - var keepReceiving = true - - while (hasBlocks.get < totalBlocks && keepReceiving) { - blockToAskFor = - pickBlockRandom(newPeerToTalkTo.hasBlocksBitVector) - - // No block to request - if (blockToAskFor < 0) { - // Nothing to receive from newPeerToTalkTo - keepReceiving = false - } else { - // Let other threads know that blockToAskFor is being requested - blocksInRequestBitVector.synchronized { - blocksInRequestBitVector.set(blockToAskFor) - } - - // Start with sending the blockID - oosSource.writeObject(blockToAskFor) - oosSource.flush() - - // CHANGED: Driver might send some other block than the one - // requested to ensure fast spreading of all blocks. - val recvStartTime = System.currentTimeMillis - val bcBlock = oisSource.readObject.asInstanceOf[BroadcastBlock] - val receptionTime = (System.currentTimeMillis - recvStartTime) - - logDebug("Received block: " + bcBlock.blockID + " from " + peerToTalkTo + " in " + receptionTime + " millis.") - - if (!hasBlocksBitVector.get(bcBlock.blockID)) { - arrayOfBlocks(bcBlock.blockID) = bcBlock - - // Update the hasBlocksBitVector first - hasBlocksBitVector.synchronized { - hasBlocksBitVector.set(bcBlock.blockID) - hasBlocks.getAndIncrement - } - - // Some block(may NOT be blockToAskFor) has arrived. - // In any case, blockToAskFor is not in request any more - blocksInRequestBitVector.synchronized { - blocksInRequestBitVector.set(blockToAskFor, false) - } - - // Reset blockToAskFor to -1. Else it will be considered missing - blockToAskFor = -1 - } - - // Send the latest SourceInfo - oosSource.writeObject(getLocalSourceInfo) - oosSource.flush() - } - } - } catch { - // EOFException is expected to happen because sender can break - // connection due to timeout - case eofe: java.io.EOFException => { } - case e: Exception => { - logError("TalktoPeer had a " + e) - // FIXME: Remove 'newPeerToTalkTo' from listOfSources - // We probably should have the following in some form, but not - // really here. This exception can happen if the sender just breaks connection - // listOfSources.synchronized { - // logInfo("Exception in TalkToPeer. Removing source: " + peerToTalkTo) - // listOfSources = listOfSources - peerToTalkTo - // } - } - } finally { - // blockToAskFor != -1 => there was an exception - if (blockToAskFor != -1) { - blocksInRequestBitVector.synchronized { - blocksInRequestBitVector.set(blockToAskFor, false) - } - } - - cleanUpConnections() - } - } - - // Right now it picks a block uniformly that this peer does not have - private def pickBlockRandom(txHasBlocksBitVector: BitSet): Int = { - var needBlocksBitVector: BitSet = null - - // Blocks already present - hasBlocksBitVector.synchronized { - needBlocksBitVector = hasBlocksBitVector.clone.asInstanceOf[BitSet] - } - - // Include blocks already in transmission ONLY IF - // MultiTracker.EndGameFraction has NOT been achieved - if ((1.0 * hasBlocks.get / totalBlocks) < MultiTracker.EndGameFraction) { - blocksInRequestBitVector.synchronized { - needBlocksBitVector.or(blocksInRequestBitVector) - } - } - - // Find blocks that are neither here nor in transit - needBlocksBitVector.flip(0, needBlocksBitVector.size) - - // Blocks that should/can be requested - needBlocksBitVector.and(txHasBlocksBitVector) - - if (needBlocksBitVector.cardinality == 0) { - return -1 - } else { - // Pick uniformly the i'th required block - var i = MultiTracker.ranGen.nextInt(needBlocksBitVector.cardinality) - var pickedBlockIndex = needBlocksBitVector.nextSetBit(0) - - while (i > 0) { - pickedBlockIndex = - needBlocksBitVector.nextSetBit(pickedBlockIndex + 1) - i -= 1 - } - - return pickedBlockIndex - } - } - - // Pick the block that seems to be the rarest across sources - private def pickBlockRarestFirst(txHasBlocksBitVector: BitSet): Int = { - var needBlocksBitVector: BitSet = null - - // Blocks already present - hasBlocksBitVector.synchronized { - needBlocksBitVector = hasBlocksBitVector.clone.asInstanceOf[BitSet] - } - - // Include blocks already in transmission ONLY IF - // MultiTracker.EndGameFraction has NOT been achieved - if ((1.0 * hasBlocks.get / totalBlocks) < MultiTracker.EndGameFraction) { - blocksInRequestBitVector.synchronized { - needBlocksBitVector.or(blocksInRequestBitVector) - } - } - - // Find blocks that are neither here nor in transit - needBlocksBitVector.flip(0, needBlocksBitVector.size) - - // Blocks that should/can be requested - needBlocksBitVector.and(txHasBlocksBitVector) - - if (needBlocksBitVector.cardinality == 0) { - return -1 - } else { - // Count the number of copies for each block across all sources - var numCopiesPerBlock = Array.tabulate [Int](totalBlocks)(_ => 0) - - listOfSources.synchronized { - listOfSources.foreach { eachSource => - for (i <- 0 until totalBlocks) { - numCopiesPerBlock(i) += - ( if (eachSource.hasBlocksBitVector.get(i)) 1 else 0 ) - } - } - } - - // Find the minimum - var minVal = Integer.MAX_VALUE - for (i <- 0 until totalBlocks) { - if (numCopiesPerBlock(i) > 0 && numCopiesPerBlock(i) < minVal) { - minVal = numCopiesPerBlock(i) - } - } - - // Find the blocks with the least copies that this peer does not have - var minBlocksIndices = ListBuffer[Int]() - for (i <- 0 until totalBlocks) { - if (needBlocksBitVector.get(i) && numCopiesPerBlock(i) == minVal) { - minBlocksIndices += i - } - } - - // Now select a random index from minBlocksIndices - if (minBlocksIndices.size == 0) { - return -1 - } else { - // Pick uniformly the i'th index - var i = MultiTracker.ranGen.nextInt(minBlocksIndices.size) - return minBlocksIndices(i) - } - } - } - - private def cleanUpConnections() { - if (oisSource != null) { - oisSource.close() - } - if (oosSource != null) { - oosSource.close() - } - if (peerSocketToSource != null) { - peerSocketToSource.close() - } - - // Delete from peersNowTalking - peersNowTalking.synchronized { peersNowTalking -= peerToTalkTo } - } - } - } - - class GuideMultipleRequests - extends Thread with Logging { - // Keep track of sources that have completed reception - private var setOfCompletedSources = Set[SourceInfo]() - - override def run() { - var threadPool = Utils.newDaemonCachedThreadPool() - var serverSocket: ServerSocket = null - - serverSocket = new ServerSocket(0) - guidePort = serverSocket.getLocalPort - logInfo("GuideMultipleRequests => " + serverSocket + " " + guidePort) - - guidePortLock.synchronized { guidePortLock.notifyAll() } - - try { - while (!stopBroadcast) { - var clientSocket: Socket = null - try { - serverSocket.setSoTimeout(MultiTracker.ServerSocketTimeout) - clientSocket = serverSocket.accept() - } catch { - case e: Exception => { - // Stop broadcast if at least one worker has connected and - // everyone connected so far are done. Comparing with - // listOfSources.size - 1, because it includes the Guide itself - listOfSources.synchronized { - setOfCompletedSources.synchronized { - if (listOfSources.size > 1 && - setOfCompletedSources.size == listOfSources.size - 1) { - stopBroadcast = true - logInfo("GuideMultipleRequests Timeout. stopBroadcast == true.") - } - } - } - } - } - if (clientSocket != null) { - logDebug("Guide: Accepted new client connection:" + clientSocket) - try { - threadPool.execute(new GuideSingleRequest(clientSocket)) - } catch { - // In failure, close the socket here; else, thread will close it - case ioe: IOException => { - clientSocket.close() - } - } - } - } - - // Shutdown the thread pool - threadPool.shutdown() - - logInfo("Sending stopBroadcast notifications...") - sendStopBroadcastNotifications - - MultiTracker.unregisterBroadcast(id) - } finally { - if (serverSocket != null) { - logInfo("GuideMultipleRequests now stopping...") - serverSocket.close() - } - } - } - - private def sendStopBroadcastNotifications() { - listOfSources.synchronized { - listOfSources.foreach { sourceInfo => - - var guideSocketToSource: Socket = null - var gosSource: ObjectOutputStream = null - var gisSource: ObjectInputStream = null - - try { - // Connect to the source - guideSocketToSource = new Socket(sourceInfo.hostAddress, sourceInfo.listenPort) - gosSource = new ObjectOutputStream(guideSocketToSource.getOutputStream) - gosSource.flush() - gisSource = new ObjectInputStream(guideSocketToSource.getInputStream) - - // Throw away whatever comes in - gisSource.readObject.asInstanceOf[SourceInfo] - - // Send stopBroadcast signal. listenPort = SourceInfo.StopBroadcast - gosSource.writeObject(SourceInfo("", SourceInfo.StopBroadcast)) - gosSource.flush() - } catch { - case e: Exception => { - logError("sendStopBroadcastNotifications had a " + e) - } - } finally { - if (gisSource != null) { - gisSource.close() - } - if (gosSource != null) { - gosSource.close() - } - if (guideSocketToSource != null) { - guideSocketToSource.close() - } - } - } - } - } - - class GuideSingleRequest(val clientSocket: Socket) - extends Thread with Logging { - private val oos = new ObjectOutputStream(clientSocket.getOutputStream) - oos.flush() - private val ois = new ObjectInputStream(clientSocket.getInputStream) - - private var sourceInfo: SourceInfo = null - private var selectedSources: ListBuffer[SourceInfo] = null - - override def run() { - try { - logInfo("new GuideSingleRequest is running") - // Connecting worker is sending in its information - sourceInfo = ois.readObject.asInstanceOf[SourceInfo] - - // Select a suitable source and send it back to the worker - selectedSources = selectSuitableSources(sourceInfo) - logDebug("Sending selectedSources:" + selectedSources) - oos.writeObject(selectedSources) - oos.flush() - - // Add this source to the listOfSources - addToListOfSources(sourceInfo) - } catch { - case e: Exception => { - // Assuming exception caused by receiver failure: remove - if (listOfSources != null) { - listOfSources.synchronized { listOfSources -= sourceInfo } - } - } - } finally { - logInfo("GuideSingleRequest is closing streams and sockets") - ois.close() - oos.close() - clientSocket.close() - } - } - - // Randomly select some sources to send back - private def selectSuitableSources(skipSourceInfo: SourceInfo): ListBuffer[SourceInfo] = { - var selectedSources = ListBuffer[SourceInfo]() - - // If skipSourceInfo.hasBlocksBitVector has all bits set to 'true' - // then add skipSourceInfo to setOfCompletedSources. Return blank. - if (skipSourceInfo.hasBlocks == totalBlocks) { - setOfCompletedSources.synchronized { setOfCompletedSources += skipSourceInfo } - return selectedSources - } - - listOfSources.synchronized { - if (listOfSources.size <= MultiTracker.MaxPeersInGuideResponse) { - selectedSources = listOfSources.clone - } else { - var picksLeft = MultiTracker.MaxPeersInGuideResponse - var alreadyPicked = new BitSet(listOfSources.size) - - while (picksLeft > 0) { - var i = -1 - - do { - i = MultiTracker.ranGen.nextInt(listOfSources.size) - } while (alreadyPicked.get(i)) - - var peerIter = listOfSources.iterator - var curPeer = peerIter.next - - // Set the BitSet before i is decremented - alreadyPicked.set(i) - - while (i > 0) { - curPeer = peerIter.next - i = i - 1 - } - - selectedSources += curPeer - - picksLeft = picksLeft - 1 - } - } - } - - // Remove the receiving source (if present) - selectedSources = selectedSources - skipSourceInfo - - return selectedSources - } - } - } - - class ServeMultipleRequests - extends Thread with Logging { - // Server at most MultiTracker.MaxChatSlots peers - var threadPool = Utils.newDaemonFixedThreadPool(MultiTracker.MaxChatSlots) - - override def run() { - var serverSocket = new ServerSocket(0) - listenPort = serverSocket.getLocalPort - - logInfo("ServeMultipleRequests started with " + serverSocket) - - listenPortLock.synchronized { listenPortLock.notifyAll() } - - try { - while (!stopBroadcast) { - var clientSocket: Socket = null - try { - serverSocket.setSoTimeout(MultiTracker.ServerSocketTimeout) - clientSocket = serverSocket.accept() - } catch { - case e: Exception => { } - } - if (clientSocket != null) { - logDebug("Serve: Accepted new client connection:" + clientSocket) - try { - threadPool.execute(new ServeSingleRequest(clientSocket)) - } catch { - // In failure, close socket here; else, the thread will close it - case ioe: IOException => clientSocket.close() - } - } - } - } finally { - if (serverSocket != null) { - logInfo("ServeMultipleRequests now stopping...") - serverSocket.close() - } - } - // Shutdown the thread pool - threadPool.shutdown() - } - - class ServeSingleRequest(val clientSocket: Socket) - extends Thread with Logging { - private val oos = new ObjectOutputStream(clientSocket.getOutputStream) - oos.flush() - private val ois = new ObjectInputStream(clientSocket.getInputStream) - - logInfo("new ServeSingleRequest is running") - - override def run() { - try { - // Send latest local SourceInfo to the receiver - // In the case of receiver timeout and connection close, this will - // throw a java.net.SocketException: Broken pipe - oos.writeObject(getLocalSourceInfo) - oos.flush() - - // Receive latest SourceInfo from the receiver - var rxSourceInfo = ois.readObject.asInstanceOf[SourceInfo] - - if (rxSourceInfo.listenPort == SourceInfo.StopBroadcast) { - stopBroadcast = true - } else { - addToListOfSources(rxSourceInfo) - } - - val startTime = System.currentTimeMillis - var curTime = startTime - var keepSending = true - var numBlocksToSend = MultiTracker.MaxChatBlocks - - while (!stopBroadcast && keepSending && numBlocksToSend > 0) { - // Receive which block to send - var blockToSend = ois.readObject.asInstanceOf[Int] - - // If it is driver AND at least one copy of each block has not been - // sent out already, MODIFY blockToSend - if (MultiTracker.isDriver && sentBlocks.get < totalBlocks) { - blockToSend = sentBlocks.getAndIncrement - } - - // Send the block - sendBlock(blockToSend) - rxSourceInfo.hasBlocksBitVector.set(blockToSend) - - numBlocksToSend -= 1 - - // Receive latest SourceInfo from the receiver - rxSourceInfo = ois.readObject.asInstanceOf[SourceInfo] - logDebug("rxSourceInfo: " + rxSourceInfo + " with " + rxSourceInfo.hasBlocksBitVector) - addToListOfSources(rxSourceInfo) - - curTime = System.currentTimeMillis - // Revoke sending only if there is anyone waiting in the queue - if (curTime - startTime >= MultiTracker.MaxChatTime && - threadPool.getQueue.size > 0) { - keepSending = false - } - } - } catch { - case e: Exception => logError("ServeSingleRequest had a " + e) - } finally { - logInfo("ServeSingleRequest is closing streams and sockets") - ois.close() - oos.close() - clientSocket.close() - } - } - - private def sendBlock(blockToSend: Int) { - try { - oos.writeObject(arrayOfBlocks(blockToSend)) - oos.flush() - } catch { - case e: Exception => logError("sendBlock had a " + e) - } - logDebug("Sent block: " + blockToSend + " to " + clientSocket) - } - } - } -} - -private[spark] class BitTorrentBroadcastFactory -extends BroadcastFactory { - def initialize(isDriver: Boolean) { MultiTracker.initialize(isDriver) } - - def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = - new BitTorrentBroadcast[T](value_, isLocal, id) - - def stop() { MultiTracker.stop() } -} diff --git a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala index 43c18294c5..0fc478a419 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala @@ -31,8 +31,8 @@ abstract class Broadcast[T](private[spark] val id: Long) extends Serializable { override def toString = "Broadcast(" + id + ")" } -private[spark] -class BroadcastManager(val _isDriver: Boolean) extends Logging with Serializable { +private[spark] +class BroadcastManager(val _isDriver: Boolean, conf: SparkConf) extends Logging with Serializable { private var initialized = false private var broadcastFactory: BroadcastFactory = null @@ -43,14 +43,14 @@ class BroadcastManager(val _isDriver: Boolean) extends Logging with Serializable private def initialize() { synchronized { if (!initialized) { - val broadcastFactoryClass = System.getProperty( + val broadcastFactoryClass = conf.get( "spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") broadcastFactory = Class.forName(broadcastFactoryClass).newInstance.asInstanceOf[BroadcastFactory] // Initialize appropriate BroadcastFactory and BroadcastObject - broadcastFactory.initialize(isDriver) + broadcastFactory.initialize(isDriver, conf) initialized = true } diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala index 68bff75b90..fb161ce69d 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala @@ -17,6 +17,8 @@ package org.apache.spark.broadcast +import org.apache.spark.SparkConf + /** * An interface for all the broadcast implementations in Spark (to allow * multiple broadcast implementations). SparkContext uses a user-specified @@ -24,7 +26,7 @@ package org.apache.spark.broadcast * entire Spark job. */ private[spark] trait BroadcastFactory { - def initialize(isDriver: Boolean): Unit + def initialize(isDriver: Boolean, conf: SparkConf): Unit def newBroadcast[T](value: T, isLocal: Boolean, id: Long): Broadcast[T] def stop(): Unit } diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index 9db26ae6de..db596d5fcc 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -19,28 +19,28 @@ package org.apache.spark.broadcast import java.io.{File, FileOutputStream, ObjectInputStream, OutputStream} import java.net.URL +import java.util.concurrent.TimeUnit import it.unimi.dsi.fastutil.io.FastBufferedInputStream import it.unimi.dsi.fastutil.io.FastBufferedOutputStream -import org.apache.spark.{HttpServer, Logging, SparkEnv} +import org.apache.spark.{SparkConf, HttpServer, Logging, SparkEnv} import org.apache.spark.io.CompressionCodec -import org.apache.spark.storage.StorageLevel -import org.apache.spark.util.{Utils, MetadataCleaner, TimeStampedHashSet} - +import org.apache.spark.storage.{BroadcastBlockId, StorageLevel} +import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashSet, Utils} private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long) extends Broadcast[T](id) with Logging with Serializable { - + def value = value_ - def blockId: String = "broadcast_" + id + def blockId = BroadcastBlockId(id) HttpBroadcast.synchronized { SparkEnv.get.blockManager.putSingle(blockId, value_, StorageLevel.MEMORY_AND_DISK, false) } - if (!isLocal) { + if (!isLocal) { HttpBroadcast.write(id, value_) } @@ -64,7 +64,7 @@ private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolea } private[spark] class HttpBroadcastFactory extends BroadcastFactory { - def initialize(isDriver: Boolean) { HttpBroadcast.initialize(isDriver) } + def initialize(isDriver: Boolean, conf: SparkConf) { HttpBroadcast.initialize(isDriver, conf) } def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = new HttpBroadcast[T](value_, isLocal, id) @@ -81,47 +81,56 @@ private object HttpBroadcast extends Logging { private var serverUri: String = null private var server: HttpServer = null + // TODO: This shouldn't be a global variable so that multiple SparkContexts can coexist private val files = new TimeStampedHashSet[String] - private val cleaner = new MetadataCleaner("HttpBroadcast", cleanup) + private var cleaner: MetadataCleaner = null + + private val httpReadTimeout = TimeUnit.MILLISECONDS.convert(5, TimeUnit.MINUTES).toInt - private lazy val compressionCodec = CompressionCodec.createCodec() + private var compressionCodec: CompressionCodec = null - def initialize(isDriver: Boolean) { + def initialize(isDriver: Boolean, conf: SparkConf) { synchronized { if (!initialized) { - bufferSize = System.getProperty("spark.buffer.size", "65536").toInt - compress = System.getProperty("spark.broadcast.compress", "true").toBoolean + bufferSize = conf.get("spark.buffer.size", "65536").toInt + compress = conf.get("spark.broadcast.compress", "true").toBoolean if (isDriver) { - createServer() + createServer(conf) + conf.set("spark.httpBroadcast.uri", serverUri) } - serverUri = System.getProperty("spark.httpBroadcast.uri") + serverUri = conf.get("spark.httpBroadcast.uri") + cleaner = new MetadataCleaner(MetadataCleanerType.HTTP_BROADCAST, cleanup, conf) + compressionCodec = CompressionCodec.createCodec(conf) initialized = true } } } - + def stop() { synchronized { if (server != null) { server.stop() server = null } + if (cleaner != null) { + cleaner.cancel() + cleaner = null + } + compressionCodec = null initialized = false - cleaner.cancel() } } - private def createServer() { - broadcastDir = Utils.createTempDir(Utils.getLocalDir) + private def createServer(conf: SparkConf) { + broadcastDir = Utils.createTempDir(Utils.getLocalDir(conf)) server = new HttpServer(broadcastDir) server.start() serverUri = server.uri - System.setProperty("spark.httpBroadcast.uri", serverUri) logInfo("Broadcast server started at " + serverUri) } def write(id: Long, value: Any) { - val file = new File(broadcastDir, "broadcast-" + id) + val file = new File(broadcastDir, BroadcastBlockId(id).name) val out: OutputStream = { if (compress) { compressionCodec.compressedOutputStream(new FileOutputStream(file)) @@ -137,12 +146,15 @@ private object HttpBroadcast extends Logging { } def read[T](id: Long): T = { - val url = serverUri + "/broadcast-" + id + val url = serverUri + "/" + BroadcastBlockId(id).name val in = { + val httpConnection = new URL(url).openConnection() + httpConnection.setReadTimeout(httpReadTimeout) + val inputStream = httpConnection.getInputStream if (compress) { - compressionCodec.compressedInputStream(new URL(url).openStream()) + compressionCodec.compressedInputStream(inputStream) } else { - new FastBufferedInputStream(new URL(url).openStream(), bufferSize) + new FastBufferedInputStream(inputStream, bufferSize) } } val ser = SparkEnv.get.serializer.newInstance() diff --git a/core/src/main/scala/org/apache/spark/broadcast/MultiTracker.scala b/core/src/main/scala/org/apache/spark/broadcast/MultiTracker.scala deleted file mode 100644 index 21ec94659e..0000000000 --- a/core/src/main/scala/org/apache/spark/broadcast/MultiTracker.scala +++ /dev/null @@ -1,410 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.broadcast - -import java.io._ -import java.net._ -import java.util.Random - -import scala.collection.mutable.Map - -import org.apache.spark._ -import org.apache.spark.util.Utils - -private object MultiTracker -extends Logging { - - // Tracker Messages - val REGISTER_BROADCAST_TRACKER = 0 - val UNREGISTER_BROADCAST_TRACKER = 1 - val FIND_BROADCAST_TRACKER = 2 - - // Map to keep track of guides of ongoing broadcasts - var valueToGuideMap = Map[Long, SourceInfo]() - - // Random number generator - var ranGen = new Random - - private var initialized = false - private var _isDriver = false - - private var stopBroadcast = false - - private var trackMV: TrackMultipleValues = null - - def initialize(__isDriver: Boolean) { - synchronized { - if (!initialized) { - _isDriver = __isDriver - - if (isDriver) { - trackMV = new TrackMultipleValues - trackMV.setDaemon(true) - trackMV.start() - - // Set DriverHostAddress to the driver's IP address for the slaves to read - System.setProperty("spark.MultiTracker.DriverHostAddress", Utils.localIpAddress) - } - - initialized = true - } - } - } - - def stop() { - stopBroadcast = true - } - - // Load common parameters - private var DriverHostAddress_ = System.getProperty( - "spark.MultiTracker.DriverHostAddress", "") - private var DriverTrackerPort_ = System.getProperty( - "spark.broadcast.driverTrackerPort", "11111").toInt - private var BlockSize_ = System.getProperty( - "spark.broadcast.blockSize", "4096").toInt * 1024 - private var MaxRetryCount_ = System.getProperty( - "spark.broadcast.maxRetryCount", "2").toInt - - private var TrackerSocketTimeout_ = System.getProperty( - "spark.broadcast.trackerSocketTimeout", "50000").toInt - private var ServerSocketTimeout_ = System.getProperty( - "spark.broadcast.serverSocketTimeout", "10000").toInt - - private var MinKnockInterval_ = System.getProperty( - "spark.broadcast.minKnockInterval", "500").toInt - private var MaxKnockInterval_ = System.getProperty( - "spark.broadcast.maxKnockInterval", "999").toInt - - // Load TreeBroadcast config params - private var MaxDegree_ = System.getProperty( - "spark.broadcast.maxDegree", "2").toInt - - // Load BitTorrentBroadcast config params - private var MaxPeersInGuideResponse_ = System.getProperty( - "spark.broadcast.maxPeersInGuideResponse", "4").toInt - - private var MaxChatSlots_ = System.getProperty( - "spark.broadcast.maxChatSlots", "4").toInt - private var MaxChatTime_ = System.getProperty( - "spark.broadcast.maxChatTime", "500").toInt - private var MaxChatBlocks_ = System.getProperty( - "spark.broadcast.maxChatBlocks", "1024").toInt - - private var EndGameFraction_ = System.getProperty( - "spark.broadcast.endGameFraction", "0.95").toDouble - - def isDriver = _isDriver - - // Common config params - def DriverHostAddress = DriverHostAddress_ - def DriverTrackerPort = DriverTrackerPort_ - def BlockSize = BlockSize_ - def MaxRetryCount = MaxRetryCount_ - - def TrackerSocketTimeout = TrackerSocketTimeout_ - def ServerSocketTimeout = ServerSocketTimeout_ - - def MinKnockInterval = MinKnockInterval_ - def MaxKnockInterval = MaxKnockInterval_ - - // TreeBroadcast configs - def MaxDegree = MaxDegree_ - - // BitTorrentBroadcast configs - def MaxPeersInGuideResponse = MaxPeersInGuideResponse_ - - def MaxChatSlots = MaxChatSlots_ - def MaxChatTime = MaxChatTime_ - def MaxChatBlocks = MaxChatBlocks_ - - def EndGameFraction = EndGameFraction_ - - class TrackMultipleValues - extends Thread with Logging { - override def run() { - var threadPool = Utils.newDaemonCachedThreadPool() - var serverSocket: ServerSocket = null - - serverSocket = new ServerSocket(DriverTrackerPort) - logInfo("TrackMultipleValues started at " + serverSocket) - - try { - while (!stopBroadcast) { - var clientSocket: Socket = null - try { - serverSocket.setSoTimeout(TrackerSocketTimeout) - clientSocket = serverSocket.accept() - } catch { - case e: Exception => { - if (stopBroadcast) { - logInfo("Stopping TrackMultipleValues...") - } - } - } - - if (clientSocket != null) { - try { - threadPool.execute(new Thread { - override def run() { - val oos = new ObjectOutputStream(clientSocket.getOutputStream) - oos.flush() - val ois = new ObjectInputStream(clientSocket.getInputStream) - - try { - // First, read message type - val messageType = ois.readObject.asInstanceOf[Int] - - if (messageType == REGISTER_BROADCAST_TRACKER) { - // Receive Long - val id = ois.readObject.asInstanceOf[Long] - // Receive hostAddress and listenPort - val gInfo = ois.readObject.asInstanceOf[SourceInfo] - - // Add to the map - valueToGuideMap.synchronized { - valueToGuideMap += (id -> gInfo) - } - - logInfo ("New broadcast " + id + " registered with TrackMultipleValues. Ongoing ones: " + valueToGuideMap) - - // Send dummy ACK - oos.writeObject(-1) - oos.flush() - } else if (messageType == UNREGISTER_BROADCAST_TRACKER) { - // Receive Long - val id = ois.readObject.asInstanceOf[Long] - - // Remove from the map - valueToGuideMap.synchronized { - valueToGuideMap(id) = SourceInfo("", SourceInfo.TxOverGoToDefault) - } - - logInfo ("Broadcast " + id + " unregistered from TrackMultipleValues. Ongoing ones: " + valueToGuideMap) - - // Send dummy ACK - oos.writeObject(-1) - oos.flush() - } else if (messageType == FIND_BROADCAST_TRACKER) { - // Receive Long - val id = ois.readObject.asInstanceOf[Long] - - var gInfo = - if (valueToGuideMap.contains(id)) valueToGuideMap(id) - else SourceInfo("", SourceInfo.TxNotStartedRetry) - - logDebug("Got new request: " + clientSocket + " for " + id + " : " + gInfo.listenPort) - - // Send reply back - oos.writeObject(gInfo) - oos.flush() - } else { - throw new SparkException("Undefined messageType at TrackMultipleValues") - } - } catch { - case e: Exception => { - logError("TrackMultipleValues had a " + e) - } - } finally { - ois.close() - oos.close() - clientSocket.close() - } - } - }) - } catch { - // In failure, close socket here; else, client thread will close - case ioe: IOException => clientSocket.close() - } - } - } - } finally { - serverSocket.close() - } - // Shutdown the thread pool - threadPool.shutdown() - } - } - - def getGuideInfo(variableLong: Long): SourceInfo = { - var clientSocketToTracker: Socket = null - var oosTracker: ObjectOutputStream = null - var oisTracker: ObjectInputStream = null - - var gInfo: SourceInfo = SourceInfo("", SourceInfo.TxNotStartedRetry) - - var retriesLeft = MultiTracker.MaxRetryCount - do { - try { - // Connect to the tracker to find out GuideInfo - clientSocketToTracker = - new Socket(MultiTracker.DriverHostAddress, MultiTracker.DriverTrackerPort) - oosTracker = - new ObjectOutputStream(clientSocketToTracker.getOutputStream) - oosTracker.flush() - oisTracker = - new ObjectInputStream(clientSocketToTracker.getInputStream) - - // Send messageType/intention - oosTracker.writeObject(MultiTracker.FIND_BROADCAST_TRACKER) - oosTracker.flush() - - // Send Long and receive GuideInfo - oosTracker.writeObject(variableLong) - oosTracker.flush() - gInfo = oisTracker.readObject.asInstanceOf[SourceInfo] - } catch { - case e: Exception => logError("getGuideInfo had a " + e) - } finally { - if (oisTracker != null) { - oisTracker.close() - } - if (oosTracker != null) { - oosTracker.close() - } - if (clientSocketToTracker != null) { - clientSocketToTracker.close() - } - } - - Thread.sleep(MultiTracker.ranGen.nextInt( - MultiTracker.MaxKnockInterval - MultiTracker.MinKnockInterval) + - MultiTracker.MinKnockInterval) - - retriesLeft -= 1 - } while (retriesLeft > 0 && gInfo.listenPort == SourceInfo.TxNotStartedRetry) - - logDebug("Got this guidePort from Tracker: " + gInfo.listenPort) - return gInfo - } - - def registerBroadcast(id: Long, gInfo: SourceInfo) { - val socket = new Socket(MultiTracker.DriverHostAddress, DriverTrackerPort) - val oosST = new ObjectOutputStream(socket.getOutputStream) - oosST.flush() - val oisST = new ObjectInputStream(socket.getInputStream) - - // Send messageType/intention - oosST.writeObject(REGISTER_BROADCAST_TRACKER) - oosST.flush() - - // Send Long of this broadcast - oosST.writeObject(id) - oosST.flush() - - // Send this tracker's information - oosST.writeObject(gInfo) - oosST.flush() - - // Receive ACK and throw it away - oisST.readObject.asInstanceOf[Int] - - // Shut stuff down - oisST.close() - oosST.close() - socket.close() - } - - def unregisterBroadcast(id: Long) { - val socket = new Socket(MultiTracker.DriverHostAddress, DriverTrackerPort) - val oosST = new ObjectOutputStream(socket.getOutputStream) - oosST.flush() - val oisST = new ObjectInputStream(socket.getInputStream) - - // Send messageType/intention - oosST.writeObject(UNREGISTER_BROADCAST_TRACKER) - oosST.flush() - - // Send Long of this broadcast - oosST.writeObject(id) - oosST.flush() - - // Receive ACK and throw it away - oisST.readObject.asInstanceOf[Int] - - // Shut stuff down - oisST.close() - oosST.close() - socket.close() - } - - // Helper method to convert an object to Array[BroadcastBlock] - def blockifyObject[IN](obj: IN): VariableInfo = { - val baos = new ByteArrayOutputStream - val oos = new ObjectOutputStream(baos) - oos.writeObject(obj) - oos.close() - baos.close() - val byteArray = baos.toByteArray - val bais = new ByteArrayInputStream(byteArray) - - var blockNum = (byteArray.length / BlockSize) - if (byteArray.length % BlockSize != 0) - blockNum += 1 - - var retVal = new Array[BroadcastBlock](blockNum) - var blockID = 0 - - for (i <- 0 until (byteArray.length, BlockSize)) { - val thisBlockSize = math.min(BlockSize, byteArray.length - i) - var tempByteArray = new Array[Byte](thisBlockSize) - val hasRead = bais.read(tempByteArray, 0, thisBlockSize) - - retVal(blockID) = new BroadcastBlock(blockID, tempByteArray) - blockID += 1 - } - bais.close() - - var variableInfo = VariableInfo(retVal, blockNum, byteArray.length) - variableInfo.hasBlocks = blockNum - - return variableInfo - } - - // Helper method to convert Array[BroadcastBlock] to object - def unBlockifyObject[OUT](arrayOfBlocks: Array[BroadcastBlock], - totalBytes: Int, - totalBlocks: Int): OUT = { - - var retByteArray = new Array[Byte](totalBytes) - for (i <- 0 until totalBlocks) { - System.arraycopy(arrayOfBlocks(i).byteArray, 0, retByteArray, - i * BlockSize, arrayOfBlocks(i).byteArray.length) - } - byteArrayToObject(retByteArray) - } - - private def byteArrayToObject[OUT](bytes: Array[Byte]): OUT = { - val in = new ObjectInputStream (new ByteArrayInputStream (bytes)){ - override def resolveClass(desc: ObjectStreamClass) = - Class.forName(desc.getName, false, Thread.currentThread.getContextClassLoader) - } - val retVal = in.readObject.asInstanceOf[OUT] - in.close() - return retVal - } -} - -private[spark] case class BroadcastBlock(blockID: Int, byteArray: Array[Byte]) -extends Serializable - -private[spark] case class VariableInfo(@transient arrayOfBlocks : Array[BroadcastBlock], - totalBlocks: Int, - totalBytes: Int) -extends Serializable { - @transient var hasBlocks = 0 -} diff --git a/core/src/main/scala/org/apache/spark/broadcast/SourceInfo.scala b/core/src/main/scala/org/apache/spark/broadcast/SourceInfo.scala deleted file mode 100644 index baa1fd6da4..0000000000 --- a/core/src/main/scala/org/apache/spark/broadcast/SourceInfo.scala +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.broadcast - -import java.util.BitSet - -import org.apache.spark._ - -/** - * Used to keep and pass around information of peers involved in a broadcast - */ -private[spark] case class SourceInfo (hostAddress: String, - listenPort: Int, - totalBlocks: Int = SourceInfo.UnusedParam, - totalBytes: Int = SourceInfo.UnusedParam) -extends Comparable[SourceInfo] with Logging { - - var currentLeechers = 0 - var receptionFailed = false - - var hasBlocks = 0 - var hasBlocksBitVector: BitSet = new BitSet (totalBlocks) - - // Ascending sort based on leecher count - def compareTo (o: SourceInfo): Int = (currentLeechers - o.currentLeechers) -} - -/** - * Helper Object of SourceInfo for its constants - */ -private[spark] object SourceInfo { - // Broadcast has not started yet! Should never happen. - val TxNotStartedRetry = -1 - // Broadcast has already finished. Try default mechanism. - val TxOverGoToDefault = -3 - // Other constants - val StopBroadcast = -2 - val UnusedParam = 0 -} diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala new file mode 100644 index 0000000000..9530938278 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -0,0 +1,248 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.broadcast + +import java.io._ + +import scala.math +import scala.util.Random + +import org.apache.spark._ +import org.apache.spark.storage.{BroadcastBlockId, BroadcastHelperBlockId, StorageLevel} +import org.apache.spark.util.Utils + + +private[spark] class TorrentBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long) +extends Broadcast[T](id) with Logging with Serializable { + + def value = value_ + + def broadcastId = BroadcastBlockId(id) + + TorrentBroadcast.synchronized { + SparkEnv.get.blockManager.putSingle(broadcastId, value_, StorageLevel.MEMORY_AND_DISK, false) + } + + @transient var arrayOfBlocks: Array[TorrentBlock] = null + @transient var totalBlocks = -1 + @transient var totalBytes = -1 + @transient var hasBlocks = 0 + + if (!isLocal) { + sendBroadcast() + } + + def sendBroadcast() { + var tInfo = TorrentBroadcast.blockifyObject(value_) + + totalBlocks = tInfo.totalBlocks + totalBytes = tInfo.totalBytes + hasBlocks = tInfo.totalBlocks + + // Store meta-info + val metaId = BroadcastHelperBlockId(broadcastId, "meta") + val metaInfo = TorrentInfo(null, totalBlocks, totalBytes) + TorrentBroadcast.synchronized { + SparkEnv.get.blockManager.putSingle( + metaId, metaInfo, StorageLevel.MEMORY_AND_DISK, true) + } + + // Store individual pieces + for (i <- 0 until totalBlocks) { + val pieceId = BroadcastHelperBlockId(broadcastId, "piece" + i) + TorrentBroadcast.synchronized { + SparkEnv.get.blockManager.putSingle( + pieceId, tInfo.arrayOfBlocks(i), StorageLevel.MEMORY_AND_DISK, true) + } + } + } + + // Called by JVM when deserializing an object + private def readObject(in: ObjectInputStream) { + in.defaultReadObject() + TorrentBroadcast.synchronized { + SparkEnv.get.blockManager.getSingle(broadcastId) match { + case Some(x) => + value_ = x.asInstanceOf[T] + + case None => + val start = System.nanoTime + logInfo("Started reading broadcast variable " + id) + + // Initialize @transient variables that will receive garbage values from the master. + resetWorkerVariables() + + if (receiveBroadcast(id)) { + value_ = TorrentBroadcast.unBlockifyObject[T](arrayOfBlocks, totalBytes, totalBlocks) + + // Store the merged copy in cache so that the next worker doesn't need to rebuild it. + // This creates a tradeoff between memory usage and latency. + // Storing copy doubles the memory footprint; not storing doubles deserialization cost. + SparkEnv.get.blockManager.putSingle( + broadcastId, value_, StorageLevel.MEMORY_AND_DISK, false) + + // Remove arrayOfBlocks from memory once value_ is on local cache + resetWorkerVariables() + } else { + logError("Reading broadcast variable " + id + " failed") + } + + val time = (System.nanoTime - start) / 1e9 + logInfo("Reading broadcast variable " + id + " took " + time + " s") + } + } + } + + private def resetWorkerVariables() { + arrayOfBlocks = null + totalBytes = -1 + totalBlocks = -1 + hasBlocks = 0 + } + + def receiveBroadcast(variableID: Long): Boolean = { + // Receive meta-info + val metaId = BroadcastHelperBlockId(broadcastId, "meta") + var attemptId = 10 + while (attemptId > 0 && totalBlocks == -1) { + TorrentBroadcast.synchronized { + SparkEnv.get.blockManager.getSingle(metaId) match { + case Some(x) => + val tInfo = x.asInstanceOf[TorrentInfo] + totalBlocks = tInfo.totalBlocks + totalBytes = tInfo.totalBytes + arrayOfBlocks = new Array[TorrentBlock](totalBlocks) + hasBlocks = 0 + + case None => + Thread.sleep(500) + } + } + attemptId -= 1 + } + if (totalBlocks == -1) { + return false + } + + // Receive actual blocks + val recvOrder = new Random().shuffle(Array.iterate(0, totalBlocks)(_ + 1).toList) + for (pid <- recvOrder) { + val pieceId = BroadcastHelperBlockId(broadcastId, "piece" + pid) + TorrentBroadcast.synchronized { + SparkEnv.get.blockManager.getSingle(pieceId) match { + case Some(x) => + arrayOfBlocks(pid) = x.asInstanceOf[TorrentBlock] + hasBlocks += 1 + SparkEnv.get.blockManager.putSingle( + pieceId, arrayOfBlocks(pid), StorageLevel.MEMORY_AND_DISK, true) + + case None => + throw new SparkException("Failed to get " + pieceId + " of " + broadcastId) + } + } + } + + (hasBlocks == totalBlocks) + } + +} + +private object TorrentBroadcast +extends Logging { + + private var initialized = false + private var conf: SparkConf = null + def initialize(_isDriver: Boolean, conf: SparkConf) { + TorrentBroadcast.conf = conf //TODO: we might have to fix it in tests + synchronized { + if (!initialized) { + initialized = true + } + } + } + + def stop() { + initialized = false + } + + lazy val BLOCK_SIZE = conf.get("spark.broadcast.blockSize", "4096").toInt * 1024 + + def blockifyObject[T](obj: T): TorrentInfo = { + val byteArray = Utils.serialize[T](obj) + val bais = new ByteArrayInputStream(byteArray) + + var blockNum = (byteArray.length / BLOCK_SIZE) + if (byteArray.length % BLOCK_SIZE != 0) + blockNum += 1 + + var retVal = new Array[TorrentBlock](blockNum) + var blockID = 0 + + for (i <- 0 until (byteArray.length, BLOCK_SIZE)) { + val thisBlockSize = math.min(BLOCK_SIZE, byteArray.length - i) + var tempByteArray = new Array[Byte](thisBlockSize) + val hasRead = bais.read(tempByteArray, 0, thisBlockSize) + + retVal(blockID) = new TorrentBlock(blockID, tempByteArray) + blockID += 1 + } + bais.close() + + var tInfo = TorrentInfo(retVal, blockNum, byteArray.length) + tInfo.hasBlocks = blockNum + + return tInfo + } + + def unBlockifyObject[T](arrayOfBlocks: Array[TorrentBlock], + totalBytes: Int, + totalBlocks: Int): T = { + var retByteArray = new Array[Byte](totalBytes) + for (i <- 0 until totalBlocks) { + System.arraycopy(arrayOfBlocks(i).byteArray, 0, retByteArray, + i * BLOCK_SIZE, arrayOfBlocks(i).byteArray.length) + } + Utils.deserialize[T](retByteArray, Thread.currentThread.getContextClassLoader) + } + +} + +private[spark] case class TorrentBlock( + blockID: Int, + byteArray: Array[Byte]) + extends Serializable + +private[spark] case class TorrentInfo( + @transient arrayOfBlocks : Array[TorrentBlock], + totalBlocks: Int, + totalBytes: Int) + extends Serializable { + + @transient var hasBlocks = 0 +} + +private[spark] class TorrentBroadcastFactory + extends BroadcastFactory { + + def initialize(isDriver: Boolean, conf: SparkConf) { TorrentBroadcast.initialize(isDriver, conf) } + + def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = + new TorrentBroadcast[T](value_, isLocal, id) + + def stop() { TorrentBroadcast.stop() } +} diff --git a/core/src/main/scala/org/apache/spark/broadcast/TreeBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TreeBroadcast.scala deleted file mode 100644 index 80c97ca073..0000000000 --- a/core/src/main/scala/org/apache/spark/broadcast/TreeBroadcast.scala +++ /dev/null @@ -1,603 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.broadcast - -import java.io._ -import java.net._ -import java.util.{Comparator, Random, UUID} - -import scala.collection.mutable.{ListBuffer, Map, Set} -import scala.math - -import org.apache.spark._ -import org.apache.spark.storage.StorageLevel -import org.apache.spark.util.Utils - -private[spark] class TreeBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long) -extends Broadcast[T](id) with Logging with Serializable { - - def value = value_ - - def blockId = "broadcast_" + id - - MultiTracker.synchronized { - SparkEnv.get.blockManager.putSingle(blockId, value_, StorageLevel.MEMORY_AND_DISK, false) - } - - @transient var arrayOfBlocks: Array[BroadcastBlock] = null - @transient var totalBytes = -1 - @transient var totalBlocks = -1 - @transient var hasBlocks = 0 - - @transient var listenPortLock = new Object - @transient var guidePortLock = new Object - @transient var totalBlocksLock = new Object - @transient var hasBlocksLock = new Object - - @transient var listOfSources = ListBuffer[SourceInfo]() - - @transient var serveMR: ServeMultipleRequests = null - @transient var guideMR: GuideMultipleRequests = null - - @transient var hostAddress = Utils.localIpAddress - @transient var listenPort = -1 - @transient var guidePort = -1 - - @transient var stopBroadcast = false - - // Must call this after all the variables have been created/initialized - if (!isLocal) { - sendBroadcast() - } - - def sendBroadcast() { - logInfo("Local host address: " + hostAddress) - - // Create a variableInfo object and store it in valueInfos - var variableInfo = MultiTracker.blockifyObject(value_) - - // Prepare the value being broadcasted - arrayOfBlocks = variableInfo.arrayOfBlocks - totalBytes = variableInfo.totalBytes - totalBlocks = variableInfo.totalBlocks - hasBlocks = variableInfo.totalBlocks - - guideMR = new GuideMultipleRequests - guideMR.setDaemon(true) - guideMR.start() - logInfo("GuideMultipleRequests started...") - - // Must always come AFTER guideMR is created - while (guidePort == -1) { - guidePortLock.synchronized { guidePortLock.wait() } - } - - serveMR = new ServeMultipleRequests - serveMR.setDaemon(true) - serveMR.start() - logInfo("ServeMultipleRequests started...") - - // Must always come AFTER serveMR is created - while (listenPort == -1) { - listenPortLock.synchronized { listenPortLock.wait() } - } - - // Must always come AFTER listenPort is created - val masterSource = - SourceInfo(hostAddress, listenPort, totalBlocks, totalBytes) - listOfSources += masterSource - - // Register with the Tracker - MultiTracker.registerBroadcast(id, - SourceInfo(hostAddress, guidePort, totalBlocks, totalBytes)) - } - - private def readObject(in: ObjectInputStream) { - in.defaultReadObject() - MultiTracker.synchronized { - SparkEnv.get.blockManager.getSingle(blockId) match { - case Some(x) => - value_ = x.asInstanceOf[T] - - case None => - logInfo("Started reading broadcast variable " + id) - // Initializing everything because Driver will only send null/0 values - // Only the 1st worker in a node can be here. Others will get from cache - initializeWorkerVariables() - - logInfo("Local host address: " + hostAddress) - - serveMR = new ServeMultipleRequests - serveMR.setDaemon(true) - serveMR.start() - logInfo("ServeMultipleRequests started...") - - val start = System.nanoTime - - val receptionSucceeded = receiveBroadcast(id) - if (receptionSucceeded) { - value_ = MultiTracker.unBlockifyObject[T](arrayOfBlocks, totalBytes, totalBlocks) - SparkEnv.get.blockManager.putSingle( - blockId, value_, StorageLevel.MEMORY_AND_DISK, false) - } else { - logError("Reading broadcast variable " + id + " failed") - } - - val time = (System.nanoTime - start) / 1e9 - logInfo("Reading broadcast variable " + id + " took " + time + " s") - } - } - } - - private def initializeWorkerVariables() { - arrayOfBlocks = null - totalBytes = -1 - totalBlocks = -1 - hasBlocks = 0 - - listenPortLock = new Object - totalBlocksLock = new Object - hasBlocksLock = new Object - - serveMR = null - - hostAddress = Utils.localIpAddress - listenPort = -1 - - stopBroadcast = false - } - - def receiveBroadcast(variableID: Long): Boolean = { - val gInfo = MultiTracker.getGuideInfo(variableID) - - if (gInfo.listenPort == SourceInfo.TxOverGoToDefault) { - return false - } - - // Wait until hostAddress and listenPort are created by the - // ServeMultipleRequests thread - while (listenPort == -1) { - listenPortLock.synchronized { listenPortLock.wait() } - } - - var clientSocketToDriver: Socket = null - var oosDriver: ObjectOutputStream = null - var oisDriver: ObjectInputStream = null - - // Connect and receive broadcast from the specified source, retrying the - // specified number of times in case of failures - var retriesLeft = MultiTracker.MaxRetryCount - do { - // Connect to Driver and send this worker's Information - clientSocketToDriver = new Socket(MultiTracker.DriverHostAddress, gInfo.listenPort) - oosDriver = new ObjectOutputStream(clientSocketToDriver.getOutputStream) - oosDriver.flush() - oisDriver = new ObjectInputStream(clientSocketToDriver.getInputStream) - - logDebug("Connected to Driver's guiding object") - - // Send local source information - oosDriver.writeObject(SourceInfo(hostAddress, listenPort)) - oosDriver.flush() - - // Receive source information from Driver - var sourceInfo = oisDriver.readObject.asInstanceOf[SourceInfo] - totalBlocks = sourceInfo.totalBlocks - arrayOfBlocks = new Array[BroadcastBlock](totalBlocks) - totalBlocksLock.synchronized { totalBlocksLock.notifyAll() } - totalBytes = sourceInfo.totalBytes - - logDebug("Received SourceInfo from Driver:" + sourceInfo + " My Port: " + listenPort) - - val start = System.nanoTime - val receptionSucceeded = receiveSingleTransmission(sourceInfo) - val time = (System.nanoTime - start) / 1e9 - - // Updating some statistics in sourceInfo. Driver will be using them later - if (!receptionSucceeded) { - sourceInfo.receptionFailed = true - } - - // Send back statistics to the Driver - oosDriver.writeObject(sourceInfo) - - if (oisDriver != null) { - oisDriver.close() - } - if (oosDriver != null) { - oosDriver.close() - } - if (clientSocketToDriver != null) { - clientSocketToDriver.close() - } - - retriesLeft -= 1 - } while (retriesLeft > 0 && hasBlocks < totalBlocks) - - return (hasBlocks == totalBlocks) - } - - /** - * Tries to receive broadcast from the source and returns Boolean status. - * This might be called multiple times to retry a defined number of times. - */ - private def receiveSingleTransmission(sourceInfo: SourceInfo): Boolean = { - var clientSocketToSource: Socket = null - var oosSource: ObjectOutputStream = null - var oisSource: ObjectInputStream = null - - var receptionSucceeded = false - try { - // Connect to the source to get the object itself - clientSocketToSource = new Socket(sourceInfo.hostAddress, sourceInfo.listenPort) - oosSource = new ObjectOutputStream(clientSocketToSource.getOutputStream) - oosSource.flush() - oisSource = new ObjectInputStream(clientSocketToSource.getInputStream) - - logDebug("Inside receiveSingleTransmission") - logDebug("totalBlocks: "+ totalBlocks + " " + "hasBlocks: " + hasBlocks) - - // Send the range - oosSource.writeObject((hasBlocks, totalBlocks)) - oosSource.flush() - - for (i <- hasBlocks until totalBlocks) { - val recvStartTime = System.currentTimeMillis - val bcBlock = oisSource.readObject.asInstanceOf[BroadcastBlock] - val receptionTime = (System.currentTimeMillis - recvStartTime) - - logDebug("Received block: " + bcBlock.blockID + " from " + sourceInfo + " in " + receptionTime + " millis.") - - arrayOfBlocks(hasBlocks) = bcBlock - hasBlocks += 1 - - // Set to true if at least one block is received - receptionSucceeded = true - hasBlocksLock.synchronized { hasBlocksLock.notifyAll() } - } - } catch { - case e: Exception => logError("receiveSingleTransmission had a " + e) - } finally { - if (oisSource != null) { - oisSource.close() - } - if (oosSource != null) { - oosSource.close() - } - if (clientSocketToSource != null) { - clientSocketToSource.close() - } - } - - return receptionSucceeded - } - - class GuideMultipleRequests - extends Thread with Logging { - // Keep track of sources that have completed reception - private var setOfCompletedSources = Set[SourceInfo]() - - override def run() { - var threadPool = Utils.newDaemonCachedThreadPool() - var serverSocket: ServerSocket = null - - serverSocket = new ServerSocket(0) - guidePort = serverSocket.getLocalPort - logInfo("GuideMultipleRequests => " + serverSocket + " " + guidePort) - - guidePortLock.synchronized { guidePortLock.notifyAll() } - - try { - while (!stopBroadcast) { - var clientSocket: Socket = null - try { - serverSocket.setSoTimeout(MultiTracker.ServerSocketTimeout) - clientSocket = serverSocket.accept - } catch { - case e: Exception => { - // Stop broadcast if at least one worker has connected and - // everyone connected so far are done. Comparing with - // listOfSources.size - 1, because it includes the Guide itself - listOfSources.synchronized { - setOfCompletedSources.synchronized { - if (listOfSources.size > 1 && - setOfCompletedSources.size == listOfSources.size - 1) { - stopBroadcast = true - logInfo("GuideMultipleRequests Timeout. stopBroadcast == true.") - } - } - } - } - } - if (clientSocket != null) { - logDebug("Guide: Accepted new client connection: " + clientSocket) - try { - threadPool.execute(new GuideSingleRequest(clientSocket)) - } catch { - // In failure, close() the socket here; else, the thread will close() it - case ioe: IOException => clientSocket.close() - } - } - } - - logInfo("Sending stopBroadcast notifications...") - sendStopBroadcastNotifications - - MultiTracker.unregisterBroadcast(id) - } finally { - if (serverSocket != null) { - logInfo("GuideMultipleRequests now stopping...") - serverSocket.close() - } - } - // Shutdown the thread pool - threadPool.shutdown() - } - - private def sendStopBroadcastNotifications() { - listOfSources.synchronized { - var listIter = listOfSources.iterator - while (listIter.hasNext) { - var sourceInfo = listIter.next - - var guideSocketToSource: Socket = null - var gosSource: ObjectOutputStream = null - var gisSource: ObjectInputStream = null - - try { - // Connect to the source - guideSocketToSource = new Socket(sourceInfo.hostAddress, sourceInfo.listenPort) - gosSource = new ObjectOutputStream(guideSocketToSource.getOutputStream) - gosSource.flush() - gisSource = new ObjectInputStream(guideSocketToSource.getInputStream) - - // Send stopBroadcast signal - gosSource.writeObject((SourceInfo.StopBroadcast, SourceInfo.StopBroadcast)) - gosSource.flush() - } catch { - case e: Exception => { - logError("sendStopBroadcastNotifications had a " + e) - } - } finally { - if (gisSource != null) { - gisSource.close() - } - if (gosSource != null) { - gosSource.close() - } - if (guideSocketToSource != null) { - guideSocketToSource.close() - } - } - } - } - } - - class GuideSingleRequest(val clientSocket: Socket) - extends Thread with Logging { - private val oos = new ObjectOutputStream(clientSocket.getOutputStream) - oos.flush() - private val ois = new ObjectInputStream(clientSocket.getInputStream) - - private var selectedSourceInfo: SourceInfo = null - private var thisWorkerInfo:SourceInfo = null - - override def run() { - try { - logInfo("new GuideSingleRequest is running") - // Connecting worker is sending in its hostAddress and listenPort it will - // be listening to. Other fields are invalid (SourceInfo.UnusedParam) - var sourceInfo = ois.readObject.asInstanceOf[SourceInfo] - - listOfSources.synchronized { - // Select a suitable source and send it back to the worker - selectedSourceInfo = selectSuitableSource(sourceInfo) - logDebug("Sending selectedSourceInfo: " + selectedSourceInfo) - oos.writeObject(selectedSourceInfo) - oos.flush() - - // Add this new (if it can finish) source to the list of sources - thisWorkerInfo = SourceInfo(sourceInfo.hostAddress, - sourceInfo.listenPort, totalBlocks, totalBytes) - logDebug("Adding possible new source to listOfSources: " + thisWorkerInfo) - listOfSources += thisWorkerInfo - } - - // Wait till the whole transfer is done. Then receive and update source - // statistics in listOfSources - sourceInfo = ois.readObject.asInstanceOf[SourceInfo] - - listOfSources.synchronized { - // This should work since SourceInfo is a case class - assert(listOfSources.contains(selectedSourceInfo)) - - // Remove first - // (Currently removing a source based on just one failure notification!) - listOfSources = listOfSources - selectedSourceInfo - - // Update sourceInfo and put it back in, IF reception succeeded - if (!sourceInfo.receptionFailed) { - // Add thisWorkerInfo to sources that have completed reception - setOfCompletedSources.synchronized { - setOfCompletedSources += thisWorkerInfo - } - - // Update leecher count and put it back in - selectedSourceInfo.currentLeechers -= 1 - listOfSources += selectedSourceInfo - } - } - } catch { - case e: Exception => { - // Remove failed worker from listOfSources and update leecherCount of - // corresponding source worker - listOfSources.synchronized { - if (selectedSourceInfo != null) { - // Remove first - listOfSources = listOfSources - selectedSourceInfo - // Update leecher count and put it back in - selectedSourceInfo.currentLeechers -= 1 - listOfSources += selectedSourceInfo - } - - // Remove thisWorkerInfo - if (listOfSources != null) { - listOfSources = listOfSources - thisWorkerInfo - } - } - } - } finally { - logInfo("GuideSingleRequest is closing streams and sockets") - ois.close() - oos.close() - clientSocket.close() - } - } - - // Assuming the caller to have a synchronized block on listOfSources - // Select one with the most leechers. This will level-wise fill the tree - private def selectSuitableSource(skipSourceInfo: SourceInfo): SourceInfo = { - var maxLeechers = -1 - var selectedSource: SourceInfo = null - - listOfSources.foreach { source => - if ((source.hostAddress != skipSourceInfo.hostAddress || - source.listenPort != skipSourceInfo.listenPort) && - source.currentLeechers < MultiTracker.MaxDegree && - source.currentLeechers > maxLeechers) { - selectedSource = source - maxLeechers = source.currentLeechers - } - } - - // Update leecher count - selectedSource.currentLeechers += 1 - return selectedSource - } - } - } - - class ServeMultipleRequests - extends Thread with Logging { - - var threadPool = Utils.newDaemonCachedThreadPool() - - override def run() { - var serverSocket = new ServerSocket(0) - listenPort = serverSocket.getLocalPort - - logInfo("ServeMultipleRequests started with " + serverSocket) - - listenPortLock.synchronized { listenPortLock.notifyAll() } - - try { - while (!stopBroadcast) { - var clientSocket: Socket = null - try { - serverSocket.setSoTimeout(MultiTracker.ServerSocketTimeout) - clientSocket = serverSocket.accept - } catch { - case e: Exception => { } - } - - if (clientSocket != null) { - logDebug("Serve: Accepted new client connection: " + clientSocket) - try { - threadPool.execute(new ServeSingleRequest(clientSocket)) - } catch { - // In failure, close socket here; else, the thread will close it - case ioe: IOException => clientSocket.close() - } - } - } - } finally { - if (serverSocket != null) { - logInfo("ServeMultipleRequests now stopping...") - serverSocket.close() - } - } - // Shutdown the thread pool - threadPool.shutdown() - } - - class ServeSingleRequest(val clientSocket: Socket) - extends Thread with Logging { - private val oos = new ObjectOutputStream(clientSocket.getOutputStream) - oos.flush() - private val ois = new ObjectInputStream(clientSocket.getInputStream) - - private var sendFrom = 0 - private var sendUntil = totalBlocks - - override def run() { - try { - logInfo("new ServeSingleRequest is running") - - // Receive range to send - var rangeToSend = ois.readObject.asInstanceOf[(Int, Int)] - sendFrom = rangeToSend._1 - sendUntil = rangeToSend._2 - - // If not a valid range, stop broadcast - if (sendFrom == SourceInfo.StopBroadcast && sendUntil == SourceInfo.StopBroadcast) { - stopBroadcast = true - } else { - sendObject - } - } catch { - case e: Exception => logError("ServeSingleRequest had a " + e) - } finally { - logInfo("ServeSingleRequest is closing streams and sockets") - ois.close() - oos.close() - clientSocket.close() - } - } - - private def sendObject() { - // Wait till receiving the SourceInfo from Driver - while (totalBlocks == -1) { - totalBlocksLock.synchronized { totalBlocksLock.wait() } - } - - for (i <- sendFrom until sendUntil) { - while (i == hasBlocks) { - hasBlocksLock.synchronized { hasBlocksLock.wait() } - } - try { - oos.writeObject(arrayOfBlocks(i)) - oos.flush() - } catch { - case e: Exception => logError("sendObject had a " + e) - } - logDebug("Sent block: " + i + " to " + clientSocket) - } - } - } - } -} - -private[spark] class TreeBroadcastFactory -extends BroadcastFactory { - def initialize(isDriver: Boolean) { MultiTracker.initialize(isDriver) } - - def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = - new TreeBroadcast[T](value_, isLocal, id) - - def stop() { MultiTracker.stop() } -} diff --git a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala index 1cfff5e565..275331724a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala @@ -21,12 +21,14 @@ import scala.collection.immutable.List import org.apache.spark.deploy.ExecutorState.ExecutorState import org.apache.spark.deploy.master.{WorkerInfo, ApplicationInfo} +import org.apache.spark.deploy.master.RecoveryState.MasterState import org.apache.spark.deploy.worker.ExecutorRunner import org.apache.spark.util.Utils private[deploy] sealed trait DeployMessage extends Serializable +/** Contains messages sent between Scheduler actor nodes. */ private[deploy] object DeployMessages { // Worker to Master @@ -52,17 +54,20 @@ private[deploy] object DeployMessages { exitStatus: Option[Int]) extends DeployMessage + case class WorkerSchedulerStateResponse(id: String, executors: List[ExecutorDescription]) + case class Heartbeat(workerId: String) extends DeployMessage // Master to Worker - case class RegisteredWorker(masterWebUiUrl: String) extends DeployMessage + case class RegisteredWorker(masterUrl: String, masterWebUiUrl: String) extends DeployMessage case class RegisterWorkerFailed(message: String) extends DeployMessage - case class KillExecutor(appId: String, execId: Int) extends DeployMessage + case class KillExecutor(masterUrl: String, appId: String, execId: Int) extends DeployMessage case class LaunchExecutor( + masterUrl: String, appId: String, execId: Int, appDesc: ApplicationDescription, @@ -76,9 +81,11 @@ private[deploy] object DeployMessages { case class RegisterApplication(appDescription: ApplicationDescription) extends DeployMessage + case class MasterChangeAcknowledged(appId: String) + // Master to Client - case class RegisteredApplication(appId: String) extends DeployMessage + case class RegisteredApplication(appId: String, masterUrl: String) extends DeployMessage // TODO(matei): replace hostPort with host case class ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) { @@ -94,6 +101,10 @@ private[deploy] object DeployMessages { case object StopClient + // Master to Worker & Client + + case class MasterChanged(masterUrl: String, masterWebUiUrl: String) + // MasterWebUI To Master case object RequestMasterState @@ -101,7 +112,8 @@ private[deploy] object DeployMessages { // Master to MasterWebUI case class MasterStateResponse(host: String, port: Int, workers: Array[WorkerInfo], - activeApps: Array[ApplicationInfo], completedApps: Array[ApplicationInfo]) { + activeApps: Array[ApplicationInfo], completedApps: Array[ApplicationInfo], + status: MasterState) { Utils.checkHost(host, "Required hostname") assert (port > 0) @@ -123,12 +135,7 @@ private[deploy] object DeployMessages { assert (port > 0) } - // Actor System to Master - - case object CheckForWorkerTimeOut - - case object RequestWebUIPort - - case class WebUIPortResponse(webUIBoundPort: Int) + // Actor System to Worker + case object SendHeartbeat } diff --git a/core/src/main/scala/org/apache/spark/deploy/ExecutorDescription.scala b/core/src/main/scala/org/apache/spark/deploy/ExecutorDescription.scala new file mode 100644 index 0000000000..2abf0b69dd --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/ExecutorDescription.scala @@ -0,0 +1,34 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy + +/** + * Used to send state on-the-wire about Executors from Worker to Master. + * This state is sufficient for the Master to reconstruct its internal data structures during + * failover. + */ +private[spark] class ExecutorDescription( + val appId: String, + val execId: Int, + val cores: Int, + val state: ExecutorState.Value) + extends Serializable { + + override def toString: String = + "ExecutorState(appId=%s, execId=%d, cores=%d, state=%s)".format(appId, execId, cores, state) +} diff --git a/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala b/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala index fcfea96ad6..37dfa7fec0 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala @@ -17,8 +17,7 @@ package org.apache.spark.deploy -private[spark] object ExecutorState - extends Enumeration("LAUNCHING", "LOADING", "RUNNING", "KILLED", "FAILED", "LOST") { +private[spark] object ExecutorState extends Enumeration { val LAUNCHING, LOADING, RUNNING, KILLED, FAILED, LOST = Value diff --git a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala new file mode 100644 index 0000000000..4dfb19ed8a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala @@ -0,0 +1,420 @@ +/* + * + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + */ + +package org.apache.spark.deploy + +import java.io._ +import java.net.URL +import java.util.concurrent.TimeoutException + +import scala.concurrent.{Await, future, promise} +import scala.concurrent.duration._ +import scala.concurrent.ExecutionContext.Implicits.global +import scala.collection.mutable.ListBuffer +import scala.sys.process._ + +import net.liftweb.json.JsonParser + +import org.apache.spark.{Logging, SparkContext} +import org.apache.spark.deploy.master.RecoveryState + +/** + * This suite tests the fault tolerance of the Spark standalone scheduler, mainly the Master. + * In order to mimic a real distributed cluster more closely, Docker is used. + * Execute using + * ./spark-class org.apache.spark.deploy.FaultToleranceTest + * + * Make sure that that the environment includes the following properties in SPARK_DAEMON_JAVA_OPTS: + * - spark.deploy.recoveryMode=ZOOKEEPER + * - spark.deploy.zookeeper.url=172.17.42.1:2181 + * Note that 172.17.42.1 is the default docker ip for the host and 2181 is the default ZK port. + * + * Unfortunately, due to the Docker dependency this suite cannot be run automatically without a + * working installation of Docker. In addition to having Docker, the following are assumed: + * - Docker can run without sudo (see http://docs.docker.io/en/latest/use/basics/) + * - The docker images tagged spark-test-master and spark-test-worker are built from the + * docker/ directory. Run 'docker/spark-test/build' to generate these. + */ +private[spark] object FaultToleranceTest extends App with Logging { + val masters = ListBuffer[TestMasterInfo]() + val workers = ListBuffer[TestWorkerInfo]() + var sc: SparkContext = _ + + var numPassed = 0 + var numFailed = 0 + + val sparkHome = System.getenv("SPARK_HOME") + assertTrue(sparkHome != null, "Run with a valid SPARK_HOME") + + val containerSparkHome = "/opt/spark" + val dockerMountDir = "%s:%s".format(sparkHome, containerSparkHome) + + System.setProperty("spark.driver.host", "172.17.42.1") // default docker host ip + + def afterEach() { + if (sc != null) { + sc.stop() + sc = null + } + terminateCluster() + } + + test("sanity-basic") { + addMasters(1) + addWorkers(1) + createClient() + assertValidClusterState() + } + + test("sanity-many-masters") { + addMasters(3) + addWorkers(3) + createClient() + assertValidClusterState() + } + + test("single-master-halt") { + addMasters(3) + addWorkers(2) + createClient() + assertValidClusterState() + + killLeader() + delay(30 seconds) + assertValidClusterState() + createClient() + assertValidClusterState() + } + + test("single-master-restart") { + addMasters(1) + addWorkers(2) + createClient() + assertValidClusterState() + + killLeader() + addMasters(1) + delay(30 seconds) + assertValidClusterState() + + killLeader() + addMasters(1) + delay(30 seconds) + assertValidClusterState() + } + + test("cluster-failure") { + addMasters(2) + addWorkers(2) + createClient() + assertValidClusterState() + + terminateCluster() + addMasters(2) + addWorkers(2) + assertValidClusterState() + } + + test("all-but-standby-failure") { + addMasters(2) + addWorkers(2) + createClient() + assertValidClusterState() + + killLeader() + workers.foreach(_.kill()) + workers.clear() + delay(30 seconds) + addWorkers(2) + assertValidClusterState() + } + + test("rolling-outage") { + addMasters(1) + delay() + addMasters(1) + delay() + addMasters(1) + addWorkers(2) + createClient() + assertValidClusterState() + assertTrue(getLeader == masters.head) + + (1 to 3).foreach { _ => + killLeader() + delay(30 seconds) + assertValidClusterState() + assertTrue(getLeader == masters.head) + addMasters(1) + } + } + + def test(name: String)(fn: => Unit) { + try { + fn + numPassed += 1 + logInfo("Passed: " + name) + } catch { + case e: Exception => + numFailed += 1 + logError("FAILED: " + name, e) + } + afterEach() + } + + def addMasters(num: Int) { + (1 to num).foreach { _ => masters += SparkDocker.startMaster(dockerMountDir) } + } + + def addWorkers(num: Int) { + val masterUrls = getMasterUrls(masters) + (1 to num).foreach { _ => workers += SparkDocker.startWorker(dockerMountDir, masterUrls) } + } + + /** Creates a SparkContext, which constructs a Client to interact with our cluster. */ + def createClient() = { + if (sc != null) { sc.stop() } + // Counter-hack: Because of a hack in SparkEnv#create() that changes this + // property, we need to reset it. + System.setProperty("spark.driver.port", "0") + sc = new SparkContext(getMasterUrls(masters), "fault-tolerance", containerSparkHome) + } + + def getMasterUrls(masters: Seq[TestMasterInfo]): String = { + "spark://" + masters.map(master => master.ip + ":7077").mkString(",") + } + + def getLeader: TestMasterInfo = { + val leaders = masters.filter(_.state == RecoveryState.ALIVE) + assertTrue(leaders.size == 1) + leaders(0) + } + + def killLeader(): Unit = { + masters.foreach(_.readState()) + val leader = getLeader + masters -= leader + leader.kill() + } + + def delay(secs: Duration = 5.seconds) = Thread.sleep(secs.toMillis) + + def terminateCluster() { + masters.foreach(_.kill()) + workers.foreach(_.kill()) + masters.clear() + workers.clear() + } + + /** This includes Client retry logic, so it may take a while if the cluster is recovering. */ + def assertUsable() = { + val f = future { + try { + val res = sc.parallelize(0 until 10).collect() + assertTrue(res.toList == (0 until 10)) + true + } catch { + case e: Exception => + logError("assertUsable() had exception", e) + e.printStackTrace() + false + } + } + + // Avoid waiting indefinitely (e.g., we could register but get no executors). + assertTrue(Await.result(f, 120 seconds)) + } + + /** + * Asserts that the cluster is usable and that the expected masters and workers + * are all alive in a proper configuration (e.g., only one leader). + */ + def assertValidClusterState() = { + assertUsable() + var numAlive = 0 + var numStandby = 0 + var numLiveApps = 0 + var liveWorkerIPs: Seq[String] = List() + + def stateValid(): Boolean = { + (workers.map(_.ip) -- liveWorkerIPs).isEmpty && + numAlive == 1 && numStandby == masters.size - 1 && numLiveApps >= 1 + } + + val f = future { + try { + while (!stateValid()) { + Thread.sleep(1000) + + numAlive = 0 + numStandby = 0 + numLiveApps = 0 + + masters.foreach(_.readState()) + + for (master <- masters) { + master.state match { + case RecoveryState.ALIVE => + numAlive += 1 + liveWorkerIPs = master.liveWorkerIPs + case RecoveryState.STANDBY => + numStandby += 1 + case _ => // ignore + } + + numLiveApps += master.numLiveApps + } + } + true + } catch { + case e: Exception => + logError("assertValidClusterState() had exception", e) + false + } + } + + try { + assertTrue(Await.result(f, 120 seconds)) + } catch { + case e: TimeoutException => + logError("Master states: " + masters.map(_.state)) + logError("Num apps: " + numLiveApps) + logError("IPs expected: " + workers.map(_.ip) + " / found: " + liveWorkerIPs) + throw new RuntimeException("Failed to get into acceptable cluster state after 2 min.", e) + } + } + + def assertTrue(bool: Boolean, message: String = "") { + if (!bool) { + throw new IllegalStateException("Assertion failed: " + message) + } + } + + logInfo("Ran %s tests, %s passed and %s failed".format(numPassed+numFailed, numPassed, numFailed)) +} + +private[spark] class TestMasterInfo(val ip: String, val dockerId: DockerId, val logFile: File) + extends Logging { + + implicit val formats = net.liftweb.json.DefaultFormats + var state: RecoveryState.Value = _ + var liveWorkerIPs: List[String] = _ + var numLiveApps = 0 + + logDebug("Created master: " + this) + + def readState() { + try { + val masterStream = new InputStreamReader(new URL("http://%s:8080/json".format(ip)).openStream) + val json = JsonParser.parse(masterStream, closeAutomatically = true) + + val workers = json \ "workers" + val liveWorkers = workers.children.filter(w => (w \ "state").extract[String] == "ALIVE") + liveWorkerIPs = liveWorkers.map(w => (w \ "host").extract[String]) + + numLiveApps = (json \ "activeapps").children.size + + val status = json \\ "status" + val stateString = status.extract[String] + state = RecoveryState.values.filter(state => state.toString == stateString).head + } catch { + case e: Exception => + // ignore, no state update + logWarning("Exception", e) + } + } + + def kill() { Docker.kill(dockerId) } + + override def toString: String = + "[ip=%s, id=%s, logFile=%s, state=%s]". + format(ip, dockerId.id, logFile.getAbsolutePath, state) +} + +private[spark] class TestWorkerInfo(val ip: String, val dockerId: DockerId, val logFile: File) + extends Logging { + + implicit val formats = net.liftweb.json.DefaultFormats + + logDebug("Created worker: " + this) + + def kill() { Docker.kill(dockerId) } + + override def toString: String = + "[ip=%s, id=%s, logFile=%s]".format(ip, dockerId, logFile.getAbsolutePath) +} + +private[spark] object SparkDocker { + def startMaster(mountDir: String): TestMasterInfo = { + val cmd = Docker.makeRunCmd("spark-test-master", mountDir = mountDir) + val (ip, id, outFile) = startNode(cmd) + new TestMasterInfo(ip, id, outFile) + } + + def startWorker(mountDir: String, masters: String): TestWorkerInfo = { + val cmd = Docker.makeRunCmd("spark-test-worker", args = masters, mountDir = mountDir) + val (ip, id, outFile) = startNode(cmd) + new TestWorkerInfo(ip, id, outFile) + } + + private def startNode(dockerCmd: ProcessBuilder) : (String, DockerId, File) = { + val ipPromise = promise[String]() + val outFile = File.createTempFile("fault-tolerance-test", "") + outFile.deleteOnExit() + val outStream: FileWriter = new FileWriter(outFile) + def findIpAndLog(line: String): Unit = { + if (line.startsWith("CONTAINER_IP=")) { + val ip = line.split("=")(1) + ipPromise.success(ip) + } + + outStream.write(line + "\n") + outStream.flush() + } + + dockerCmd.run(ProcessLogger(findIpAndLog _)) + val ip = Await.result(ipPromise.future, 30 seconds) + val dockerId = Docker.getLastProcessId + (ip, dockerId, outFile) + } +} + +private[spark] class DockerId(val id: String) { + override def toString = id +} + +private[spark] object Docker extends Logging { + def makeRunCmd(imageTag: String, args: String = "", mountDir: String = ""): ProcessBuilder = { + val mountCmd = if (mountDir != "") { " -v " + mountDir } else "" + + val cmd = "docker run %s %s %s".format(mountCmd, imageTag, args) + logDebug("Run command: " + cmd) + cmd + } + + def kill(dockerId: DockerId) : Unit = { + "docker kill %s".format(dockerId.id).! + } + + def getLastProcessId: DockerId = { + var id: String = null + "docker ps -l -q".!(ProcessLogger(line => id = line)) + new DockerId(id) + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala index 87a703427c..e607b8c6f4 100644 --- a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala @@ -41,6 +41,7 @@ private[spark] object JsonProtocol { ("starttime" -> obj.startTime) ~ ("id" -> obj.id) ~ ("name" -> obj.desc.name) ~ + ("appuiurl" -> obj.appUiUrl) ~ ("cores" -> obj.desc.maxCores) ~ ("user" -> obj.desc.user) ~ ("memoryperslave" -> obj.desc.memoryPerSlave) ~ @@ -64,14 +65,15 @@ private[spark] object JsonProtocol { } def writeMasterState(obj: MasterStateResponse) = { - ("url" -> ("spark://" + obj.uri)) ~ + ("url" -> obj.uri) ~ ("workers" -> obj.workers.toList.map(writeWorkerInfo)) ~ ("cores" -> obj.workers.map(_.cores).sum) ~ ("coresused" -> obj.workers.map(_.coresUsed).sum) ~ ("memory" -> obj.workers.map(_.memory).sum) ~ ("memoryused" -> obj.workers.map(_.memoryUsed).sum) ~ ("activeapps" -> obj.activeApps.toList.map(writeApplicationInfo)) ~ - ("completedapps" -> obj.completedApps.toList.map(writeApplicationInfo)) + ("completedapps" -> obj.completedApps.toList.map(writeApplicationInfo)) ~ + ("status" -> obj.status.toString) } def writeWorkerState(obj: WorkerStateResponse) = { diff --git a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala index 10161c8204..ffc0cb0903 100644 --- a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala +++ b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala @@ -17,12 +17,12 @@ package org.apache.spark.deploy -import akka.actor.{ActorRef, Props, Actor, ActorSystem, Terminated} +import akka.actor.ActorSystem import org.apache.spark.deploy.worker.Worker import org.apache.spark.deploy.master.Master -import org.apache.spark.util.{Utils, AkkaUtils} -import org.apache.spark.{Logging} +import org.apache.spark.util.Utils +import org.apache.spark.{SparkConf, Logging} import scala.collection.mutable.ArrayBuffer @@ -34,36 +34,41 @@ import scala.collection.mutable.ArrayBuffer */ private[spark] class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: Int) extends Logging { - + private val localHostname = Utils.localHostName() private val masterActorSystems = ArrayBuffer[ActorSystem]() private val workerActorSystems = ArrayBuffer[ActorSystem]() - - def start(): String = { + + def start(): Array[String] = { logInfo("Starting a local Spark cluster with " + numWorkers + " workers.") /* Start the Master */ - val (masterSystem, masterPort, _) = Master.startSystemAndActor(localHostname, 0, 0) + val conf = new SparkConf(false) + val (masterSystem, masterPort, _) = Master.startSystemAndActor(localHostname, 0, 0, conf) masterActorSystems += masterSystem val masterUrl = "spark://" + localHostname + ":" + masterPort + val masters = Array(masterUrl) /* Start the Workers */ for (workerNum <- 1 to numWorkers) { val (workerSystem, _) = Worker.startSystemAndActor(localHostname, 0, 0, coresPerWorker, - memoryPerWorker, masterUrl, null, Some(workerNum)) + memoryPerWorker, masters, null, Some(workerNum)) workerActorSystems += workerSystem } - return masterUrl + masters } def stop() { logInfo("Shutting down local Spark cluster.") // Stop the workers before the master so they don't get upset that it disconnected + // TODO: In Akka 2.1.x, ActorSystem.awaitTermination hangs when you have remote actors! + // This is unfortunate, but for now we just comment it out. workerActorSystems.foreach(_.shutdown()) - workerActorSystems.foreach(_.awaitTermination()) - + //workerActorSystems.foreach(_.awaitTermination()) masterActorSystems.foreach(_.shutdown()) - masterActorSystems.foreach(_.awaitTermination()) + //masterActorSystems.foreach(_.awaitTermination()) + masterActorSystems.clear() + workerActorSystems.clear() } } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index 0a5f4c368f..27dc42bf7e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -16,21 +16,75 @@ */ package org.apache.spark.deploy + +import java.security.PrivilegedExceptionAction + import org.apache.hadoop.conf.Configuration import org.apache.hadoop.mapred.JobConf +import org.apache.hadoop.security.UserGroupInformation +import org.apache.spark.{SparkContext, SparkException} /** - * Contains util methods to interact with Hadoop from spark. + * Contains util methods to interact with Hadoop from Spark. */ +private[spark] class SparkHadoopUtil { + val conf = newConfiguration() + UserGroupInformation.setConfiguration(conf) + + def runAsUser(user: String)(func: () => Unit) { + // if we are already running as the user intended there is no reason to do the doAs. It + // will actually break secure HDFS access as it doesn't fill in the credentials. Also if + // the user is UNKNOWN then we shouldn't be creating a remote unknown user + // (this is actually the path spark on yarn takes) since SPARK_USER is initialized only + // in SparkContext. + val currentUser = Option(System.getProperty("user.name")). + getOrElse(SparkContext.SPARK_UNKNOWN_USER) + if (user != SparkContext.SPARK_UNKNOWN_USER && currentUser != user) { + val ugi = UserGroupInformation.createRemoteUser(user) + ugi.doAs(new PrivilegedExceptionAction[Unit] { + def run: Unit = func() + }) + } else { + func() + } + } - // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems + /** + * Return an appropriate (subclass) of Configuration. Creating config can initializes some Hadoop + * subsystems. + */ def newConfiguration(): Configuration = new Configuration() - // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster + /** + * Add any user credentials to the job conf which are necessary for running on a secure Hadoop + * cluster. + */ def addCredentials(conf: JobConf) {} def isYarnMode(): Boolean = { false } +} + +object SparkHadoopUtil { + + private val hadoop = { + val yarnMode = java.lang.Boolean.valueOf( + System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) + if (yarnMode) { + try { + Class.forName("org.apache.spark.deploy.yarn.YarnSparkHadoopUtil") + .newInstance() + .asInstanceOf[SparkHadoopUtil] + } catch { + case th: Throwable => throw new SparkException("Unable to load YARN support", th) + } + } else { + new SparkHadoopUtil + } + } + def get: SparkHadoopUtil = { + hadoop + } } diff --git a/core/src/main/scala/org/apache/spark/deploy/client/Client.scala b/core/src/main/scala/org/apache/spark/deploy/client/Client.scala index a342dd724a..9bbd635ab9 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/Client.scala @@ -19,59 +19,102 @@ package org.apache.spark.deploy.client import java.util.concurrent.TimeoutException +import scala.concurrent.Await +import scala.concurrent.duration._ + import akka.actor._ -import akka.actor.Terminated import akka.pattern.ask -import akka.util.Duration -import akka.remote.RemoteClientDisconnected -import akka.remote.RemoteClientLifeCycleEvent -import akka.remote.RemoteClientShutdown -import akka.dispatch.Await - -import org.apache.spark.Logging +import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} +import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ import org.apache.spark.deploy.master.Master - +import org.apache.spark.util.AkkaUtils /** * The main class used to talk to a Spark deploy cluster. Takes a master URL, an app description, * and a listener for cluster events, and calls back the listener when various events occur. + * + * @param masterUrls Each url should look like spark://host:port. */ private[spark] class Client( actorSystem: ActorSystem, - masterUrl: String, + masterUrls: Array[String], appDescription: ApplicationDescription, - listener: ClientListener) + listener: ClientListener, + conf: SparkConf) extends Logging { + val REGISTRATION_TIMEOUT = 20.seconds + val REGISTRATION_RETRIES = 3 + + var masterAddress: Address = null var actor: ActorRef = null var appId: String = null + var registered = false + var activeMasterUrl: String = null class ClientActor extends Actor with Logging { - var master: ActorRef = null - var masterAddress: Address = null + var master: ActorSelection = null var alreadyDisconnected = false // To avoid calling listener.disconnected() multiple times + var alreadyDead = false // To avoid calling listener.dead() multiple times override def preStart() { - logInfo("Connecting to master " + masterUrl) + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) try { - master = context.actorFor(Master.toAkkaUrl(masterUrl)) - masterAddress = master.path.address - master ! RegisterApplication(appDescription) - context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) - context.watch(master) // Doesn't work with remote actors, but useful for testing + registerWithMaster() } catch { case e: Exception => - logError("Failed to connect to master", e) + logWarning("Failed to connect to master", e) markDisconnected() context.stop(self) } } + def tryRegisterAllMasters() { + for (masterUrl <- masterUrls) { + logInfo("Connecting to master " + masterUrl + "...") + val actor = context.actorSelection(Master.toAkkaUrl(masterUrl)) + actor ! RegisterApplication(appDescription) + } + } + + def registerWithMaster() { + tryRegisterAllMasters() + + import context.dispatcher + var retries = 0 + lazy val retryTimer: Cancellable = + context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) { + retries += 1 + if (registered) { + retryTimer.cancel() + } else if (retries >= REGISTRATION_RETRIES) { + logError("All masters are unresponsive! Giving up.") + markDead() + } else { + tryRegisterAllMasters() + } + } + retryTimer // start timer + } + + def changeMaster(url: String) { + activeMasterUrl = url + master = context.actorSelection(Master.toAkkaUrl(activeMasterUrl)) + masterAddress = activeMasterUrl match { + case Master.sparkUrlRegex(host, port) => + Address("akka.tcp", Master.systemName, host, port.toInt) + case x => + throw new SparkException("Invalid spark URL: " + x) + } + } + override def receive = { - case RegisteredApplication(appId_) => + case RegisteredApplication(appId_, masterUrl) => appId = appId_ + registered = true + changeMaster(masterUrl) listener.connected(appId) case ApplicationRemoved(message) => @@ -92,23 +135,18 @@ private[spark] class Client( listener.executorRemoved(fullId, message.getOrElse(""), exitStatus) } - case Terminated(actor_) if actor_ == master => - logError("Connection to master failed; stopping client") - markDisconnected() - context.stop(self) - - case RemoteClientDisconnected(transport, address) if address == masterAddress => - logError("Connection to master failed; stopping client") - markDisconnected() - context.stop(self) + case MasterChanged(masterUrl, masterWebUiUrl) => + logInfo("Master has changed, new master is at " + masterUrl) + changeMaster(masterUrl) + alreadyDisconnected = false + sender ! MasterChangeAcknowledged(appId) - case RemoteClientShutdown(transport, address) if address == masterAddress => - logError("Connection to master failed; stopping client") + case DisassociatedEvent(_, address, _) if address == masterAddress => + logWarning(s"Connection to $address failed; waiting for master to reconnect...") markDisconnected() - context.stop(self) case StopClient => - markDisconnected() + markDead() sender ! true context.stop(self) } @@ -122,6 +160,13 @@ private[spark] class Client( alreadyDisconnected = true } } + + def markDead() { + if (!alreadyDead) { + listener.dead() + alreadyDead = true + } + } } def start() { @@ -132,7 +177,7 @@ private[spark] class Client( def stop() { if (actor != null) { try { - val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + val timeout = AkkaUtils.askTimeout(conf) val future = actor.ask(StopClient)(timeout) Await.result(future, timeout) } catch { diff --git a/core/src/main/scala/org/apache/spark/deploy/client/ClientListener.scala b/core/src/main/scala/org/apache/spark/deploy/client/ClientListener.scala index 4605368c11..be7a11bd15 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/ClientListener.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/ClientListener.scala @@ -27,8 +27,12 @@ package org.apache.spark.deploy.client private[spark] trait ClientListener { def connected(appId: String): Unit + /** Disconnection may be a temporary state, as we fail over to a new Master. */ def disconnected(): Unit + /** Dead means that we couldn't find any Masters to connect to, and have given up. */ + def dead(): Unit + def executorAdded(fullId: String, workerId: String, hostPort: String, cores: Int, memory: Int): Unit def executorRemoved(fullId: String, message: String, exitStatus: Option[Int]): Unit diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala index d5e9a0e095..ef649fd80c 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala @@ -18,7 +18,7 @@ package org.apache.spark.deploy.client import org.apache.spark.util.{Utils, AkkaUtils} -import org.apache.spark.{Logging} +import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.deploy.{Command, ApplicationDescription} private[spark] object TestClient { @@ -33,6 +33,11 @@ private[spark] object TestClient { System.exit(0) } + def dead() { + logInfo("Could not connect to master") + System.exit(0) + } + def executorAdded(id: String, workerId: String, hostPort: String, cores: Int, memory: Int) {} def executorRemoved(id: String, message: String, exitStatus: Option[Int]) {} @@ -40,11 +45,13 @@ private[spark] object TestClient { def main(args: Array[String]) { val url = args(0) - val (actorSystem, port) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0) + val (actorSystem, port) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0, + conf = new SparkConf) val desc = new ApplicationDescription( - "TestClient", 1, 512, Command("spark.deploy.client.TestExecutor", Seq(), Map()), "dummy-spark-home", "ignored") + "TestClient", 1, 512, Command("spark.deploy.client.TestExecutor", Seq(), Map()), + "dummy-spark-home", "ignored") val listener = new TestListener - val client = new Client(actorSystem, url, desc, listener) + val client = new Client(actorSystem, Array(url), desc, listener, new SparkConf) client.start() actorSystem.awaitTermination() } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala index bd5327627a..5150b7c7de 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala @@ -29,23 +29,46 @@ private[spark] class ApplicationInfo( val submitDate: Date, val driver: ActorRef, val appUiUrl: String) -{ - var state = ApplicationState.WAITING - var executors = new mutable.HashMap[Int, ExecutorInfo] - var coresGranted = 0 - var endTime = -1L - val appSource = new ApplicationSource(this) - - private var nextExecutorId = 0 - - def newExecutorId(): Int = { - val id = nextExecutorId - nextExecutorId += 1 - id + extends Serializable { + + @transient var state: ApplicationState.Value = _ + @transient var executors: mutable.HashMap[Int, ExecutorInfo] = _ + @transient var coresGranted: Int = _ + @transient var endTime: Long = _ + @transient var appSource: ApplicationSource = _ + + @transient private var nextExecutorId: Int = _ + + init() + + private def readObject(in: java.io.ObjectInputStream) : Unit = { + in.defaultReadObject() + init() + } + + private def init() { + state = ApplicationState.WAITING + executors = new mutable.HashMap[Int, ExecutorInfo] + coresGranted = 0 + endTime = -1L + appSource = new ApplicationSource(this) + nextExecutorId = 0 + } + + private def newExecutorId(useID: Option[Int] = None): Int = { + useID match { + case Some(id) => + nextExecutorId = math.max(nextExecutorId, id + 1) + id + case None => + val id = nextExecutorId + nextExecutorId += 1 + id + } } - def addExecutor(worker: WorkerInfo, cores: Int): ExecutorInfo = { - val exec = new ExecutorInfo(newExecutorId(), this, worker, cores, desc.memoryPerSlave) + def addExecutor(worker: WorkerInfo, cores: Int, useID: Option[Int] = None): ExecutorInfo = { + val exec = new ExecutorInfo(newExecutorId(useID), this, worker, cores, desc.memoryPerSlave) executors(exec.id) = exec coresGranted += cores exec diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationSource.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationSource.scala index 5a24042e14..c87b66f047 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationSource.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationSource.scala @@ -34,7 +34,7 @@ class ApplicationSource(val application: ApplicationInfo) extends Source { override def getValue: Long = application.duration }) - metricRegistry.register(MetricRegistry.name("cores", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("cores"), new Gauge[Int] { override def getValue: Int = application.coresGranted }) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationState.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationState.scala index 7e804223cf..67e6c5d66a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationState.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationState.scala @@ -17,12 +17,11 @@ package org.apache.spark.deploy.master -private[spark] object ApplicationState - extends Enumeration("WAITING", "RUNNING", "FINISHED", "FAILED") { +private[spark] object ApplicationState extends Enumeration { type ApplicationState = Value - val WAITING, RUNNING, FINISHED, FAILED = Value + val WAITING, RUNNING, FINISHED, FAILED, UNKNOWN = Value val MAX_NUM_RETRY = 10 } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ExecutorInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ExecutorInfo.scala index cf384a985e..76db61dd61 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ExecutorInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ExecutorInfo.scala @@ -17,7 +17,7 @@ package org.apache.spark.deploy.master -import org.apache.spark.deploy.ExecutorState +import org.apache.spark.deploy.{ExecutorDescription, ExecutorState} private[spark] class ExecutorInfo( val id: Int, @@ -28,5 +28,10 @@ private[spark] class ExecutorInfo( var state = ExecutorState.LAUNCHING + /** Copy all state (non-val) variables from the given on-the-wire ExecutorDescription. */ + def copyState(execDesc: ExecutorDescription) { + state = execDesc.state + } + def fullId: String = application.id + "/" + id } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala new file mode 100644 index 0000000000..043945a211 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala @@ -0,0 +1,90 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.master + +import java.io._ + +import scala.Serializable + +import akka.serialization.Serialization +import org.apache.spark.Logging + +/** + * Stores data in a single on-disk directory with one file per application and worker. + * Files are deleted when applications and workers are removed. + * + * @param dir Directory to store files. Created if non-existent (but not recursively). + * @param serialization Used to serialize our objects. + */ +private[spark] class FileSystemPersistenceEngine( + val dir: String, + val serialization: Serialization) + extends PersistenceEngine with Logging { + + new File(dir).mkdir() + + override def addApplication(app: ApplicationInfo) { + val appFile = new File(dir + File.separator + "app_" + app.id) + serializeIntoFile(appFile, app) + } + + override def removeApplication(app: ApplicationInfo) { + new File(dir + File.separator + "app_" + app.id).delete() + } + + override def addWorker(worker: WorkerInfo) { + val workerFile = new File(dir + File.separator + "worker_" + worker.id) + serializeIntoFile(workerFile, worker) + } + + override def removeWorker(worker: WorkerInfo) { + new File(dir + File.separator + "worker_" + worker.id).delete() + } + + override def readPersistedData(): (Seq[ApplicationInfo], Seq[WorkerInfo]) = { + val sortedFiles = new File(dir).listFiles().sortBy(_.getName) + val appFiles = sortedFiles.filter(_.getName.startsWith("app_")) + val apps = appFiles.map(deserializeFromFile[ApplicationInfo]) + val workerFiles = sortedFiles.filter(_.getName.startsWith("worker_")) + val workers = workerFiles.map(deserializeFromFile[WorkerInfo]) + (apps, workers) + } + + private def serializeIntoFile(file: File, value: AnyRef) { + val created = file.createNewFile() + if (!created) { throw new IllegalStateException("Could not create file: " + file) } + + val serializer = serialization.findSerializerFor(value) + val serialized = serializer.toBinary(value) + + val out = new FileOutputStream(file) + out.write(serialized) + out.close() + } + + def deserializeFromFile[T](file: File)(implicit m: Manifest[T]): T = { + val fileData = new Array[Byte](file.length().asInstanceOf[Int]) + val dis = new DataInputStream(new FileInputStream(file)) + dis.readFully(fileData) + dis.close() + + val clazz = m.runtimeClass.asInstanceOf[Class[T]] + val serializer = serialization.serializerFor(clazz) + serializer.fromBinary(fileData).asInstanceOf[T] + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala new file mode 100644 index 0000000000..f25a1ad3bf --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala @@ -0,0 +1,45 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.master + +import akka.actor.{Actor, ActorRef} + +import org.apache.spark.deploy.master.MasterMessages.ElectedLeader + +/** + * A LeaderElectionAgent keeps track of whether the current Master is the leader, meaning it + * is the only Master serving requests. + * In addition to the API provided, the LeaderElectionAgent will use of the following messages + * to inform the Master of leader changes: + * [[org.apache.spark.deploy.master.MasterMessages.ElectedLeader ElectedLeader]] + * [[org.apache.spark.deploy.master.MasterMessages.RevokedLeadership RevokedLeadership]] + */ +private[spark] trait LeaderElectionAgent extends Actor { + val masterActor: ActorRef +} + +/** Single-node implementation of LeaderElectionAgent -- we're initially and always the leader. */ +private[spark] class MonarchyLeaderAgent(val masterActor: ActorRef) extends LeaderElectionAgent { + override def preStart() { + masterActor ! ElectedLeader + } + + override def receive = { + case _ => + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index bde59905bc..7b696cfcca 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -17,34 +17,38 @@ package org.apache.spark.deploy.master -import java.util.Date import java.text.SimpleDateFormat +import java.util.Date import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} +import scala.concurrent.Await +import scala.concurrent.duration._ import akka.actor._ -import akka.actor.Terminated -import akka.dispatch.Await import akka.pattern.ask -import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientDisconnected, RemoteClientShutdown} -import akka.util.duration._ -import akka.util.Timeout +import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} +import akka.serialization.SerializationExtension -import org.apache.spark.{Logging, SparkException} +import org.apache.spark.{SparkConf, SparkContext, Logging, SparkException} import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ +import org.apache.spark.deploy.master.MasterMessages._ import org.apache.spark.deploy.master.ui.MasterWebUI import org.apache.spark.metrics.MetricsSystem -import org.apache.spark.util.{Utils, AkkaUtils} -import akka.util.{Duration, Timeout} - +import org.apache.spark.util.{AkkaUtils, Utils} private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Actor with Logging { + import context.dispatcher // to use Akka's scheduler.schedule() + + val conf = new SparkConf + val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs - val WORKER_TIMEOUT = System.getProperty("spark.worker.timeout", "60").toLong * 1000 - val RETAINED_APPLICATIONS = System.getProperty("spark.deploy.retainedApplications", "200").toInt - val REAPER_ITERATIONS = System.getProperty("spark.dead.worker.persistence", "15").toInt - + val WORKER_TIMEOUT = conf.get("spark.worker.timeout", "60").toLong * 1000 + val RETAINED_APPLICATIONS = conf.get("spark.deploy.retainedApplications", "200").toInt + val REAPER_ITERATIONS = conf.get("spark.dead.worker.persistence", "15").toInt + val RECOVERY_DIR = conf.get("spark.deploy.recoveryDirectory", "") + val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE") + var nextAppNumber = 0 val workers = new HashSet[WorkerInfo] val idToWorker = new HashMap[String, WorkerInfo] @@ -59,12 +63,10 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act val waitingApps = new ArrayBuffer[ApplicationInfo] val completedApps = new ArrayBuffer[ApplicationInfo] - var firstApp: Option[ApplicationInfo] = None - Utils.checkHost(host, "Expected hostname") - val masterMetricsSystem = MetricsSystem.createMetricsSystem("master") - val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications") + val masterMetricsSystem = MetricsSystem.createMetricsSystem("master", conf) + val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications", conf) val masterSource = new MasterSource(this) val webUi = new MasterWebUI(this, webUiPort) @@ -74,51 +76,112 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act if (envVar != null) envVar else host } + val masterUrl = "spark://" + host + ":" + port + var masterWebUiUrl: String = _ + + var state = RecoveryState.STANDBY + + var persistenceEngine: PersistenceEngine = _ + + var leaderElectionAgent: ActorRef = _ + // As a temporary workaround before better ways of configuring memory, we allow users to set // a flag that will perform round-robin scheduling across the nodes (spreading out each app // among all the nodes) instead of trying to consolidate each app onto a small # of nodes. - val spreadOutApps = System.getProperty("spark.deploy.spreadOut", "true").toBoolean + val spreadOutApps = conf.get("spark.deploy.spreadOut", "true").toBoolean override def preStart() { - logInfo("Starting Spark master at spark://" + host + ":" + port) + logInfo("Starting Spark master at " + masterUrl) // Listen for remote client disconnection events, since they don't go through Akka's watch() - context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) webUi.start() + masterWebUiUrl = "http://" + masterPublicAddress + ":" + webUi.boundPort.get context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis, self, CheckForWorkerTimeOut) masterMetricsSystem.registerSource(masterSource) masterMetricsSystem.start() applicationMetricsSystem.start() + + persistenceEngine = RECOVERY_MODE match { + case "ZOOKEEPER" => + logInfo("Persisting recovery state to ZooKeeper") + new ZooKeeperPersistenceEngine(SerializationExtension(context.system), conf) + case "FILESYSTEM" => + logInfo("Persisting recovery state to directory: " + RECOVERY_DIR) + new FileSystemPersistenceEngine(RECOVERY_DIR, SerializationExtension(context.system)) + case _ => + new BlackHolePersistenceEngine() + } + + leaderElectionAgent = RECOVERY_MODE match { + case "ZOOKEEPER" => + context.actorOf(Props(classOf[ZooKeeperLeaderElectionAgent], self, masterUrl, conf)) + case _ => + context.actorOf(Props(classOf[MonarchyLeaderAgent], self)) + } + } + + override def preRestart(reason: Throwable, message: Option[Any]) { + super.preRestart(reason, message) // calls postStop()! + logError("Master actor restarted due to exception", reason) } override def postStop() { webUi.stop() masterMetricsSystem.stop() applicationMetricsSystem.stop() + persistenceEngine.close() + context.stop(leaderElectionAgent) } override def receive = { - case RegisterWorker(id, host, workerPort, cores, memory, worker_webUiPort, publicAddress) => { + case ElectedLeader => { + val (storedApps, storedWorkers) = persistenceEngine.readPersistedData() + state = if (storedApps.isEmpty && storedWorkers.isEmpty) + RecoveryState.ALIVE + else + RecoveryState.RECOVERING + logInfo("I have been elected leader! New state: " + state) + if (state == RecoveryState.RECOVERING) { + beginRecovery(storedApps, storedWorkers) + context.system.scheduler.scheduleOnce(WORKER_TIMEOUT millis) { completeRecovery() } + } + } + + case RevokedLeadership => { + logError("Leadership has been revoked -- master shutting down.") + System.exit(0) + } + + case RegisterWorker(id, workerHost, workerPort, cores, memory, workerWebUiPort, publicAddress) => { logInfo("Registering worker %s:%d with %d cores, %s RAM".format( host, workerPort, cores, Utils.megabytesToString(memory))) - if (idToWorker.contains(id)) { + if (state == RecoveryState.STANDBY) { + // ignore, don't send response + } else if (idToWorker.contains(id)) { sender ! RegisterWorkerFailed("Duplicate worker ID") } else { - addWorker(id, host, workerPort, cores, memory, worker_webUiPort, publicAddress) - context.watch(sender) // This doesn't work with remote actors but helps for testing - sender ! RegisteredWorker("http://" + masterPublicAddress + ":" + webUi.boundPort.get) + val worker = new WorkerInfo(id, workerHost, workerPort, cores, memory, + sender, workerWebUiPort, publicAddress) + registerWorker(worker) + persistenceEngine.addWorker(worker) + sender ! RegisteredWorker(masterUrl, masterWebUiUrl) schedule() } } case RegisterApplication(description) => { - logInfo("Registering app " + description.name) - val app = addApplication(description, sender) - logInfo("Registered app " + description.name + " with ID " + app.id) - waitingApps += app - context.watch(sender) // This doesn't work with remote actors but helps for testing - sender ! RegisteredApplication(app.id) - schedule() + if (state == RecoveryState.STANDBY) { + // ignore, don't send response + } else { + logInfo("Registering app " + description.name) + val app = createApplication(description, sender) + registerApplication(app) + logInfo("Registered app " + description.name + " with ID " + app.id) + persistenceEngine.addApplication(app) + sender ! RegisteredApplication(app.id, masterUrl) + schedule() + } } case ExecutorStateChanged(appId, execId, state, message, exitStatus) => { @@ -158,27 +221,49 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act } } - case Terminated(actor) => { - // The disconnected actor could've been either a worker or an app; remove whichever of - // those we have an entry for in the corresponding actor hashmap - actorToWorker.get(actor).foreach(removeWorker) - actorToApp.get(actor).foreach(finishApplication) + case MasterChangeAcknowledged(appId) => { + idToApp.get(appId) match { + case Some(app) => + logInfo("Application has been re-registered: " + appId) + app.state = ApplicationState.WAITING + case None => + logWarning("Master change ack from unknown app: " + appId) + } + + if (canCompleteRecovery) { completeRecovery() } } - case RemoteClientDisconnected(transport, address) => { - // The disconnected client could've been either a worker or an app; remove whichever it was - addressToWorker.get(address).foreach(removeWorker) - addressToApp.get(address).foreach(finishApplication) + case WorkerSchedulerStateResponse(workerId, executors) => { + idToWorker.get(workerId) match { + case Some(worker) => + logInfo("Worker has been re-registered: " + workerId) + worker.state = WorkerState.ALIVE + + val validExecutors = executors.filter(exec => idToApp.get(exec.appId).isDefined) + for (exec <- validExecutors) { + val app = idToApp.get(exec.appId).get + val execInfo = app.addExecutor(worker, exec.cores, Some(exec.execId)) + worker.addExecutor(execInfo) + execInfo.copyState(exec) + } + case None => + logWarning("Scheduler state from unknown worker: " + workerId) + } + + if (canCompleteRecovery) { completeRecovery() } } - case RemoteClientShutdown(transport, address) => { + case DisassociatedEvent(_, address, _) => { // The disconnected client could've been either a worker or an app; remove whichever it was + logInfo(s"$address got disassociated, removing it.") addressToWorker.get(address).foreach(removeWorker) addressToApp.get(address).foreach(finishApplication) + if (state == RecoveryState.RECOVERING && canCompleteRecovery) { completeRecovery() } } case RequestMasterState => { - sender ! MasterStateResponse(host, port, workers.toArray, apps.toArray, completedApps.toArray) + sender ! MasterStateResponse(host, port, workers.toArray, apps.toArray, completedApps.toArray, + state) } case CheckForWorkerTimeOut => { @@ -190,6 +275,50 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act } } + def canCompleteRecovery = + workers.count(_.state == WorkerState.UNKNOWN) == 0 && + apps.count(_.state == ApplicationState.UNKNOWN) == 0 + + def beginRecovery(storedApps: Seq[ApplicationInfo], storedWorkers: Seq[WorkerInfo]) { + for (app <- storedApps) { + logInfo("Trying to recover app: " + app.id) + try { + registerApplication(app) + app.state = ApplicationState.UNKNOWN + app.driver ! MasterChanged(masterUrl, masterWebUiUrl) + } catch { + case e: Exception => logInfo("App " + app.id + " had exception on reconnect") + } + } + + for (worker <- storedWorkers) { + logInfo("Trying to recover worker: " + worker.id) + try { + registerWorker(worker) + worker.state = WorkerState.UNKNOWN + worker.actor ! MasterChanged(masterUrl, masterWebUiUrl) + } catch { + case e: Exception => logInfo("Worker " + worker.id + " had exception on reconnect") + } + } + } + + def completeRecovery() { + // Ensure "only-once" recovery semantics using a short synchronization period. + synchronized { + if (state != RecoveryState.RECOVERING) { return } + state = RecoveryState.COMPLETING_RECOVERY + } + + // Kill off any workers and apps that didn't respond to us. + workers.filter(_.state == WorkerState.UNKNOWN).foreach(removeWorker) + apps.filter(_.state == ApplicationState.UNKNOWN).foreach(finishApplication) + + state = RecoveryState.ALIVE + schedule() + logInfo("Recovery complete - resuming operations!") + } + /** * Can an app use the given worker? True if the worker has enough memory and we haven't already * launched an executor for the app on it (right now the standalone backend doesn't like having @@ -204,6 +333,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act * every time a new app joins or resource availability changes. */ def schedule() { + if (state != RecoveryState.ALIVE) { return } // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app // in the queue, then the second app, etc. if (spreadOutApps) { @@ -251,14 +381,13 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act def launchExecutor(worker: WorkerInfo, exec: ExecutorInfo, sparkHome: String) { logInfo("Launching executor " + exec.fullId + " on worker " + worker.id) worker.addExecutor(exec) - worker.actor ! LaunchExecutor( + worker.actor ! LaunchExecutor(masterUrl, exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory, sparkHome) exec.application.driver ! ExecutorAdded( exec.id, worker.id, worker.hostPort, exec.cores, exec.memory) } - def addWorker(id: String, host: String, port: Int, cores: Int, memory: Int, webUiPort: Int, - publicAddress: String): WorkerInfo = { + def registerWorker(worker: WorkerInfo): Unit = { // There may be one or more refs to dead workers on this same node (w/ different ID's), // remove them. workers.filter { w => @@ -266,12 +395,17 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act }.foreach { w => workers -= w } - val worker = new WorkerInfo(id, host, port, cores, memory, sender, webUiPort, publicAddress) + + val workerAddress = worker.actor.path.address + if (addressToWorker.contains(workerAddress)) { + logInfo("Attempted to re-register worker at same address: " + workerAddress) + return + } + workers += worker idToWorker(worker.id) = worker - actorToWorker(sender) = worker - addressToWorker(sender.path.address) = worker - worker + actorToWorker(worker.actor) = worker + addressToWorker(workerAddress) = worker } def removeWorker(worker: WorkerInfo) { @@ -286,25 +420,28 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act exec.id, ExecutorState.LOST, Some("worker lost"), None) exec.application.removeExecutor(exec) } + persistenceEngine.removeWorker(worker) } - def addApplication(desc: ApplicationDescription, driver: ActorRef): ApplicationInfo = { + def createApplication(desc: ApplicationDescription, driver: ActorRef): ApplicationInfo = { val now = System.currentTimeMillis() val date = new Date(now) - val app = new ApplicationInfo(now, newApplicationId(date), desc, date, driver, desc.appUiUrl) + new ApplicationInfo(now, newApplicationId(date), desc, date, driver, desc.appUiUrl) + } + + def registerApplication(app: ApplicationInfo): Unit = { + val appAddress = app.driver.path.address + if (addressToWorker.contains(appAddress)) { + logInfo("Attempted to re-register application at same address: " + appAddress) + return + } + applicationMetricsSystem.registerSource(app.appSource) apps += app idToApp(app.id) = app - actorToApp(driver) = app - addressToApp(driver.path.address) = app - if (firstApp == None) { - firstApp = Some(app) - } - val workersAlive = workers.filter(_.state == WorkerState.ALIVE).toArray - if (workersAlive.size > 0 && !workersAlive.exists(_.memoryFree >= desc.memoryPerSlave)) { - logWarning("Could not find any workers with enough memory for " + firstApp.get.id) - } - app + actorToApp(app.driver) = app + addressToApp(appAddress) = app + waitingApps += app } def finishApplication(app: ApplicationInfo) { @@ -329,13 +466,14 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act waitingApps -= app for (exec <- app.executors.values) { exec.worker.removeExecutor(exec) - exec.worker.actor ! KillExecutor(exec.application.id, exec.id) + exec.worker.actor ! KillExecutor(masterUrl, exec.application.id, exec.id) exec.state = ExecutorState.KILLED } app.markFinished(state) if (state != ApplicationState.FINISHED) { app.driver ! ApplicationRemoved(state.toString) } + persistenceEngine.removeApplication(app) schedule() } } @@ -359,41 +497,42 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act removeWorker(worker) } else { if (worker.lastHeartbeat < currentTime - ((REAPER_ITERATIONS + 1) * WORKER_TIMEOUT)) - workers -= worker // we've seen this DEAD worker in the UI, etc. for long enough; cull it + workers -= worker // we've seen this DEAD worker in the UI, etc. for long enough; cull it } } } } private[spark] object Master { - private val systemName = "sparkMaster" + val systemName = "sparkMaster" private val actorName = "Master" - private val sparkUrlRegex = "spark://([^:]+):([0-9]+)".r + val sparkUrlRegex = "spark://([^:]+):([0-9]+)".r def main(argStrings: Array[String]) { - val args = new MasterArguments(argStrings) - val (actorSystem, _, _) = startSystemAndActor(args.host, args.port, args.webUiPort) + val conf = new SparkConf + val args = new MasterArguments(argStrings, conf) + val (actorSystem, _, _) = startSystemAndActor(args.host, args.port, args.webUiPort, conf) actorSystem.awaitTermination() } - /** Returns an `akka://...` URL for the Master actor given a sparkUrl `spark://host:ip`. */ + /** Returns an `akka.tcp://...` URL for the Master actor given a sparkUrl `spark://host:ip`. */ def toAkkaUrl(sparkUrl: String): String = { sparkUrl match { case sparkUrlRegex(host, port) => - "akka://%s@%s:%s/user/%s".format(systemName, host, port, actorName) + "akka.tcp://%s@%s:%s/user/%s".format(systemName, host, port, actorName) case _ => throw new SparkException("Invalid master URL: " + sparkUrl) } } - def startSystemAndActor(host: String, port: Int, webUiPort: Int): (ActorSystem, Int, Int) = { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port) - val actor = actorSystem.actorOf(Props(new Master(host, boundPort, webUiPort)), name = actorName) - val timeoutDuration = Duration.create( - System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") - implicit val timeout = Timeout(timeoutDuration) - val respFuture = actor ? RequestWebUIPort // ask pattern - val resp = Await.result(respFuture, timeoutDuration).asInstanceOf[WebUIPortResponse] + def startSystemAndActor(host: String, port: Int, webUiPort: Int, conf: SparkConf) + : (ActorSystem, Int, Int) = + { + val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port, conf = conf) + val actor = actorSystem.actorOf(Props(classOf[Master], host, boundPort, webUiPort), actorName) + val timeout = AkkaUtils.askTimeout(conf) + val respFuture = actor.ask(RequestWebUIPort)(timeout) + val resp = Await.result(respFuture, timeout).asInstanceOf[WebUIPortResponse] (actorSystem, boundPort, resp.webUIBoundPort) } } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala index 9d89b455fb..e7f3224091 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala @@ -18,16 +18,17 @@ package org.apache.spark.deploy.master import org.apache.spark.util.{Utils, IntParam} +import org.apache.spark.SparkConf /** * Command-line parser for the master. */ -private[spark] class MasterArguments(args: Array[String]) { +private[spark] class MasterArguments(args: Array[String], conf: SparkConf) { var host = Utils.localHostName() var port = 7077 var webUiPort = 8080 - - // Check for settings in environment variables + + // Check for settings in environment variables if (System.getenv("SPARK_MASTER_HOST") != null) { host = System.getenv("SPARK_MASTER_HOST") } @@ -37,8 +38,8 @@ private[spark] class MasterArguments(args: Array[String]) { if (System.getenv("SPARK_MASTER_WEBUI_PORT") != null) { webUiPort = System.getenv("SPARK_MASTER_WEBUI_PORT").toInt } - if (System.getProperty("master.ui.port") != null) { - webUiPort = System.getProperty("master.ui.port").toInt + if (conf.contains("master.ui.port")) { + webUiPort = conf.get("master.ui.port").toInt } parse(args.toList) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala new file mode 100644 index 0000000000..74a9f8cd82 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala @@ -0,0 +1,46 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.master + +sealed trait MasterMessages extends Serializable + +/** Contains messages seen only by the Master and its associated entities. */ +private[master] object MasterMessages { + + // LeaderElectionAgent to Master + + case object ElectedLeader + + case object RevokedLeadership + + // Actor System to LeaderElectionAgent + + case object CheckLeader + + // Actor System to Master + + case object CheckForWorkerTimeOut + + case class BeginRecovery(storedApps: Seq[ApplicationInfo], storedWorkers: Seq[WorkerInfo]) + + case object CompleteRecovery + + case object RequestWebUIPort + + case class WebUIPortResponse(webUIBoundPort: Int) +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterSource.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterSource.scala index 23d1cb77da..36c1b87b7f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/MasterSource.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterSource.scala @@ -26,17 +26,17 @@ private[spark] class MasterSource(val master: Master) extends Source { val sourceName = "master" // Gauge for worker numbers in cluster - metricRegistry.register(MetricRegistry.name("workers","number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("workers"), new Gauge[Int] { override def getValue: Int = master.workers.size }) // Gauge for application numbers in cluster - metricRegistry.register(MetricRegistry.name("apps", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("apps"), new Gauge[Int] { override def getValue: Int = master.apps.size }) // Gauge for waiting application numbers in cluster - metricRegistry.register(MetricRegistry.name("waitingApps", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("waitingApps"), new Gauge[Int] { override def getValue: Int = master.waitingApps.size }) } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala new file mode 100644 index 0000000000..94b986caf2 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.master + +/** + * Allows Master to persist any state that is necessary in order to recover from a failure. + * The following semantics are required: + * - addApplication and addWorker are called before completing registration of a new app/worker. + * - removeApplication and removeWorker are called at any time. + * Given these two requirements, we will have all apps and workers persisted, but + * we might not have yet deleted apps or workers that finished (so their liveness must be verified + * during recovery). + */ +private[spark] trait PersistenceEngine { + def addApplication(app: ApplicationInfo) + + def removeApplication(app: ApplicationInfo) + + def addWorker(worker: WorkerInfo) + + def removeWorker(worker: WorkerInfo) + + /** + * Returns the persisted data sorted by their respective ids (which implies that they're + * sorted by time of creation). + */ + def readPersistedData(): (Seq[ApplicationInfo], Seq[WorkerInfo]) + + def close() {} +} + +private[spark] class BlackHolePersistenceEngine extends PersistenceEngine { + override def addApplication(app: ApplicationInfo) {} + override def removeApplication(app: ApplicationInfo) {} + override def addWorker(worker: WorkerInfo) {} + override def removeWorker(worker: WorkerInfo) {} + override def readPersistedData() = (Nil, Nil) +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryState.scala b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryState.scala new file mode 100644 index 0000000000..256a5a7c28 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryState.scala @@ -0,0 +1,24 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.master + +private[spark] object RecoveryState extends Enumeration { + type MasterState = Value + + val STANDBY, ALIVE, RECOVERING, COMPLETING_RECOVERY = Value +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/SparkZooKeeperSession.scala b/core/src/main/scala/org/apache/spark/deploy/master/SparkZooKeeperSession.scala new file mode 100644 index 0000000000..999090ad74 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/SparkZooKeeperSession.scala @@ -0,0 +1,205 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.master + +import scala.collection.JavaConversions._ + +import org.apache.zookeeper._ +import org.apache.zookeeper.Watcher.Event.KeeperState +import org.apache.zookeeper.data.Stat + +import org.apache.spark.{SparkConf, Logging} + +/** + * Provides a Scala-side interface to the standard ZooKeeper client, with the addition of retry + * logic. If the ZooKeeper session expires or otherwise dies, a new ZooKeeper session will be + * created. If ZooKeeper remains down after several retries, the given + * [[org.apache.spark.deploy.master.SparkZooKeeperWatcher SparkZooKeeperWatcher]] will be + * informed via zkDown(). + * + * Additionally, all commands sent to ZooKeeper will be retried until they either fail too many + * times or a semantic exception is thrown (e.g., "node already exists"). + */ +private[spark] class SparkZooKeeperSession(zkWatcher: SparkZooKeeperWatcher, + conf: SparkConf) extends Logging { + val ZK_URL = conf.get("spark.deploy.zookeeper.url", "") + + val ZK_ACL = ZooDefs.Ids.OPEN_ACL_UNSAFE + val ZK_TIMEOUT_MILLIS = 30000 + val RETRY_WAIT_MILLIS = 5000 + val ZK_CHECK_PERIOD_MILLIS = 10000 + val MAX_RECONNECT_ATTEMPTS = 3 + + private var zk: ZooKeeper = _ + + private val watcher = new ZooKeeperWatcher() + private var reconnectAttempts = 0 + private var closed = false + + /** Connect to ZooKeeper to start the session. Must be called before anything else. */ + def connect() { + connectToZooKeeper() + + new Thread() { + override def run() = sessionMonitorThread() + }.start() + } + + def sessionMonitorThread(): Unit = { + while (!closed) { + Thread.sleep(ZK_CHECK_PERIOD_MILLIS) + if (zk.getState != ZooKeeper.States.CONNECTED) { + reconnectAttempts += 1 + val attemptsLeft = MAX_RECONNECT_ATTEMPTS - reconnectAttempts + if (attemptsLeft <= 0) { + logError("Could not connect to ZooKeeper: system failure") + zkWatcher.zkDown() + close() + } else { + logWarning("ZooKeeper connection failed, retrying " + attemptsLeft + " more times...") + connectToZooKeeper() + } + } + } + } + + def close() { + if (!closed && zk != null) { zk.close() } + closed = true + } + + private def connectToZooKeeper() { + if (zk != null) zk.close() + zk = new ZooKeeper(ZK_URL, ZK_TIMEOUT_MILLIS, watcher) + } + + /** + * Attempts to maintain a live ZooKeeper exception despite (very) transient failures. + * Mainly useful for handling the natural ZooKeeper session expiration. + */ + private class ZooKeeperWatcher extends Watcher { + def process(event: WatchedEvent) { + if (closed) { return } + + event.getState match { + case KeeperState.SyncConnected => + reconnectAttempts = 0 + zkWatcher.zkSessionCreated() + case KeeperState.Expired => + connectToZooKeeper() + case KeeperState.Disconnected => + logWarning("ZooKeeper disconnected, will retry...") + case s => // Do nothing + } + } + } + + def create(path: String, bytes: Array[Byte], createMode: CreateMode): String = { + retry { + zk.create(path, bytes, ZK_ACL, createMode) + } + } + + def exists(path: String, watcher: Watcher = null): Stat = { + retry { + zk.exists(path, watcher) + } + } + + def getChildren(path: String, watcher: Watcher = null): List[String] = { + retry { + zk.getChildren(path, watcher).toList + } + } + + def getData(path: String): Array[Byte] = { + retry { + zk.getData(path, false, null) + } + } + + def delete(path: String, version: Int = -1): Unit = { + retry { + zk.delete(path, version) + } + } + + /** + * Creates the given directory (non-recursively) if it doesn't exist. + * All znodes are created in PERSISTENT mode with no data. + */ + def mkdir(path: String) { + if (exists(path) == null) { + try { + create(path, "".getBytes, CreateMode.PERSISTENT) + } catch { + case e: Exception => + // If the exception caused the directory not to be created, bubble it up, + // otherwise ignore it. + if (exists(path) == null) { throw e } + } + } + } + + /** + * Recursively creates all directories up to the given one. + * All znodes are created in PERSISTENT mode with no data. + */ + def mkdirRecursive(path: String) { + var fullDir = "" + for (dentry <- path.split("/").tail) { + fullDir += "/" + dentry + mkdir(fullDir) + } + } + + /** + * Retries the given function up to 3 times. The assumption is that failure is transient, + * UNLESS it is a semantic exception (i.e., trying to get data from a node that doesn't exist), + * in which case the exception will be thrown without retries. + * + * @param fn Block to execute, possibly multiple times. + */ + def retry[T](fn: => T, n: Int = MAX_RECONNECT_ATTEMPTS): T = { + try { + fn + } catch { + case e: KeeperException.NoNodeException => throw e + case e: KeeperException.NodeExistsException => throw e + case e: Exception if n > 0 => + logError("ZooKeeper exception, " + n + " more retries...", e) + Thread.sleep(RETRY_WAIT_MILLIS) + retry(fn, n-1) + } + } +} + +trait SparkZooKeeperWatcher { + /** + * Called whenever a ZK session is created -- + * this will occur when we create our first session as well as each time + * the session expires or errors out. + */ + def zkSessionCreated() + + /** + * Called if ZK appears to be completely down (i.e., not just a transient error). + * We will no longer attempt to reconnect to ZK, and the SparkZooKeeperSession is considered dead. + */ + def zkDown() +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala index 6219f11f2a..e05f587b58 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala @@ -22,28 +22,44 @@ import scala.collection.mutable import org.apache.spark.util.Utils private[spark] class WorkerInfo( - val id: String, - val host: String, - val port: Int, - val cores: Int, - val memory: Int, - val actor: ActorRef, - val webUiPort: Int, - val publicAddress: String) { + val id: String, + val host: String, + val port: Int, + val cores: Int, + val memory: Int, + val actor: ActorRef, + val webUiPort: Int, + val publicAddress: String) + extends Serializable { Utils.checkHost(host, "Expected hostname") assert (port > 0) - var executors = new mutable.HashMap[String, ExecutorInfo] // fullId => info - var state: WorkerState.Value = WorkerState.ALIVE - var coresUsed = 0 - var memoryUsed = 0 + @transient var executors: mutable.HashMap[String, ExecutorInfo] = _ // fullId => info + @transient var state: WorkerState.Value = _ + @transient var coresUsed: Int = _ + @transient var memoryUsed: Int = _ - var lastHeartbeat = System.currentTimeMillis() + @transient var lastHeartbeat: Long = _ + + init() def coresFree: Int = cores - coresUsed def memoryFree: Int = memory - memoryUsed + private def readObject(in: java.io.ObjectInputStream) : Unit = { + in.defaultReadObject() + init() + } + + private def init() { + executors = new mutable.HashMap + state = WorkerState.ALIVE + coresUsed = 0 + memoryUsed = 0 + lastHeartbeat = System.currentTimeMillis() + } + def hostPort: String = { assert (port > 0) host + ":" + port diff --git a/core/src/main/scala/org/apache/spark/deploy/master/WorkerState.scala b/core/src/main/scala/org/apache/spark/deploy/master/WorkerState.scala index b5ee6dca79..0b36ef6005 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/WorkerState.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/WorkerState.scala @@ -17,8 +17,8 @@ package org.apache.spark.deploy.master -private[spark] object WorkerState extends Enumeration("ALIVE", "DEAD", "DECOMMISSIONED") { +private[spark] object WorkerState extends Enumeration { type WorkerState = Value - val ALIVE, DEAD, DECOMMISSIONED = Value + val ALIVE, DEAD, DECOMMISSIONED, UNKNOWN = Value } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala new file mode 100644 index 0000000000..77c23fb9fb --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala @@ -0,0 +1,137 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.master + +import akka.actor.ActorRef +import org.apache.zookeeper._ +import org.apache.zookeeper.Watcher.Event.EventType + +import org.apache.spark.{SparkConf, Logging} +import org.apache.spark.deploy.master.MasterMessages._ + +private[spark] class ZooKeeperLeaderElectionAgent(val masterActor: ActorRef, + masterUrl: String, conf: SparkConf) + extends LeaderElectionAgent with SparkZooKeeperWatcher with Logging { + + val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/leader_election" + + private val watcher = new ZooKeeperWatcher() + private val zk = new SparkZooKeeperSession(this, conf) + private var status = LeadershipStatus.NOT_LEADER + private var myLeaderFile: String = _ + private var leaderUrl: String = _ + + override def preStart() { + logInfo("Starting ZooKeeper LeaderElection agent") + zk.connect() + } + + override def zkSessionCreated() { + synchronized { + zk.mkdirRecursive(WORKING_DIR) + myLeaderFile = + zk.create(WORKING_DIR + "/master_", masterUrl.getBytes, CreateMode.EPHEMERAL_SEQUENTIAL) + self ! CheckLeader + } + } + + override def preRestart(reason: scala.Throwable, message: scala.Option[scala.Any]) { + logError("LeaderElectionAgent failed, waiting " + zk.ZK_TIMEOUT_MILLIS + "...", reason) + Thread.sleep(zk.ZK_TIMEOUT_MILLIS) + super.preRestart(reason, message) + } + + override def zkDown() { + logError("ZooKeeper down! LeaderElectionAgent shutting down Master.") + System.exit(1) + } + + override def postStop() { + zk.close() + } + + override def receive = { + case CheckLeader => checkLeader() + } + + private class ZooKeeperWatcher extends Watcher { + def process(event: WatchedEvent) { + if (event.getType == EventType.NodeDeleted) { + logInfo("Leader file disappeared, a master is down!") + self ! CheckLeader + } + } + } + + /** Uses ZK leader election. Navigates several ZK potholes along the way. */ + def checkLeader() { + val masters = zk.getChildren(WORKING_DIR).toList + val leader = masters.sorted.head + val leaderFile = WORKING_DIR + "/" + leader + + // Setup a watch for the current leader. + zk.exists(leaderFile, watcher) + + try { + leaderUrl = new String(zk.getData(leaderFile)) + } catch { + // A NoNodeException may be thrown if old leader died since the start of this method call. + // This is fine -- just check again, since we're guaranteed to see the new values. + case e: KeeperException.NoNodeException => + logInfo("Leader disappeared while reading it -- finding next leader") + checkLeader() + return + } + + // Synchronization used to ensure no interleaving between the creation of a new session and the + // checking of a leader, which could cause us to delete our real leader file erroneously. + synchronized { + val isLeader = myLeaderFile == leaderFile + if (!isLeader && leaderUrl == masterUrl) { + // We found a different master file pointing to this process. + // This can happen in the following two cases: + // (1) The master process was restarted on the same node. + // (2) The ZK server died between creating the file and returning the name of the file. + // For this case, we will end up creating a second file, and MUST explicitly delete the + // first one, since our ZK session is still open. + // Note that this deletion will cause a NodeDeleted event to be fired so we check again for + // leader changes. + assert(leaderFile < myLeaderFile) + logWarning("Cleaning up old ZK master election file that points to this master.") + zk.delete(leaderFile) + } else { + updateLeadershipStatus(isLeader) + } + } + } + + def updateLeadershipStatus(isLeader: Boolean) { + if (isLeader && status == LeadershipStatus.NOT_LEADER) { + status = LeadershipStatus.LEADER + masterActor ! ElectedLeader + } else if (!isLeader && status == LeadershipStatus.LEADER) { + status = LeadershipStatus.NOT_LEADER + masterActor ! RevokedLeadership + } + } + + private object LeadershipStatus extends Enumeration { + type LeadershipStatus = Value + val LEADER, NOT_LEADER = Value + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala new file mode 100644 index 0000000000..52000d4f9c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala @@ -0,0 +1,85 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.master + +import org.apache.spark.{SparkConf, Logging} +import org.apache.zookeeper._ + +import akka.serialization.Serialization + +class ZooKeeperPersistenceEngine(serialization: Serialization, conf: SparkConf) + extends PersistenceEngine + with SparkZooKeeperWatcher + with Logging +{ + val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/master_status" + + val zk = new SparkZooKeeperSession(this, conf) + + zk.connect() + + override def zkSessionCreated() { + zk.mkdirRecursive(WORKING_DIR) + } + + override def zkDown() { + logError("PersistenceEngine disconnected from ZooKeeper -- ZK looks down.") + } + + override def addApplication(app: ApplicationInfo) { + serializeIntoFile(WORKING_DIR + "/app_" + app.id, app) + } + + override def removeApplication(app: ApplicationInfo) { + zk.delete(WORKING_DIR + "/app_" + app.id) + } + + override def addWorker(worker: WorkerInfo) { + serializeIntoFile(WORKING_DIR + "/worker_" + worker.id, worker) + } + + override def removeWorker(worker: WorkerInfo) { + zk.delete(WORKING_DIR + "/worker_" + worker.id) + } + + override def close() { + zk.close() + } + + override def readPersistedData(): (Seq[ApplicationInfo], Seq[WorkerInfo]) = { + val sortedFiles = zk.getChildren(WORKING_DIR).toList.sorted + val appFiles = sortedFiles.filter(_.startsWith("app_")) + val apps = appFiles.map(deserializeFromFile[ApplicationInfo]) + val workerFiles = sortedFiles.filter(_.startsWith("worker_")) + val workers = workerFiles.map(deserializeFromFile[WorkerInfo]) + (apps, workers) + } + + private def serializeIntoFile(path: String, value: AnyRef) { + val serializer = serialization.findSerializerFor(value) + val serialized = serializer.toBinary(value) + zk.create(path, serialized, CreateMode.PERSISTENT) + } + + def deserializeFromFile[T](filename: String)(implicit m: Manifest[T]): T = { + val fileData = zk.getData("/spark/master_status/" + filename) + val clazz = m.runtimeClass.asInstanceOf[Class[T]] + val serializer = serialization.serializerFor(clazz) + serializer.fromBinary(fileData).asInstanceOf[T] + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala index f4e574d15d..dbb0cb90f5 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala @@ -17,31 +17,28 @@ package org.apache.spark.deploy.master.ui +import scala.concurrent.Await import scala.xml.Node -import akka.dispatch.Await import akka.pattern.ask -import akka.util.duration._ - import javax.servlet.http.HttpServletRequest - import net.liftweb.json.JsonAST.JValue -import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} import org.apache.spark.deploy.JsonProtocol +import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} import org.apache.spark.deploy.master.ExecutorInfo import org.apache.spark.ui.UIUtils import org.apache.spark.util.Utils private[spark] class ApplicationPage(parent: MasterWebUI) { val master = parent.masterActorRef - implicit val timeout = parent.timeout + val timeout = parent.timeout /** Executor details for a particular application */ def renderJson(request: HttpServletRequest): JValue = { val appId = request.getParameter("appId") val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse] - val state = Await.result(stateFuture, 30 seconds) + val state = Await.result(stateFuture, timeout) val app = state.activeApps.find(_.id == appId).getOrElse({ state.completedApps.find(_.id == appId).getOrElse(null) }) @@ -52,7 +49,7 @@ private[spark] class ApplicationPage(parent: MasterWebUI) { def render(request: HttpServletRequest): Seq[Node] = { val appId = request.getParameter("appId") val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse] - val state = Await.result(stateFuture, 30 seconds) + val state = Await.result(stateFuture, timeout) val app = state.activeApps.find(_.id == appId).getOrElse({ state.completedApps.find(_.id == appId).getOrElse(null) }) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/IndexPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/IndexPage.scala index d7a57229b0..4ef762892c 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/IndexPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/IndexPage.scala @@ -17,37 +17,33 @@ package org.apache.spark.deploy.master.ui -import javax.servlet.http.HttpServletRequest - +import scala.concurrent.Await import scala.xml.Node -import akka.dispatch.Await import akka.pattern.ask -import akka.util.duration._ - +import javax.servlet.http.HttpServletRequest import net.liftweb.json.JsonAST.JValue -import org.apache.spark.deploy.DeployWebUI +import org.apache.spark.deploy.{DeployWebUI, JsonProtocol} import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} -import org.apache.spark.deploy.JsonProtocol import org.apache.spark.deploy.master.{ApplicationInfo, WorkerInfo} import org.apache.spark.ui.UIUtils import org.apache.spark.util.Utils private[spark] class IndexPage(parent: MasterWebUI) { val master = parent.masterActorRef - implicit val timeout = parent.timeout + val timeout = parent.timeout def renderJson(request: HttpServletRequest): JValue = { val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse] - val state = Await.result(stateFuture, 30 seconds) + val state = Await.result(stateFuture, timeout) JsonProtocol.writeMasterState(state) } /** Index view listing applications and executors */ def render(request: HttpServletRequest): Seq[Node] = { val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse] - val state = Await.result(stateFuture, 30 seconds) + val state = Await.result(stateFuture, timeout) val workerHeaders = Seq("Id", "Address", "State", "Cores", "Memory") val workers = state.workers.sortBy(_.id) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala index f4df729e87..ead35662fc 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala @@ -17,25 +17,21 @@ package org.apache.spark.deploy.master.ui -import akka.util.Duration - import javax.servlet.http.HttpServletRequest - import org.eclipse.jetty.server.{Handler, Server} -import org.apache.spark.{Logging} +import org.apache.spark.Logging import org.apache.spark.deploy.master.Master import org.apache.spark.ui.JettyUtils import org.apache.spark.ui.JettyUtils._ -import org.apache.spark.util.Utils +import org.apache.spark.util.{AkkaUtils, Utils} /** * Web UI server for the standalone master. */ private[spark] class MasterWebUI(val master: Master, requestedPort: Int) extends Logging { - implicit val timeout = Duration.create( - System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + val timeout = AkkaUtils.askTimeout(master.conf) val host = Utils.localHostName() val port = requestedPort diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala index d48545ecc1..d4084820f6 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala @@ -43,7 +43,8 @@ private[spark] class ExecutorRunner( val workerId: String, val host: String, val sparkHome: File, - val workDir: File) + val workDir: File, + var state: ExecutorState.Value) extends Logging { val fullId = appId + "/" + execId @@ -83,7 +84,8 @@ private[spark] class ExecutorRunner( process.destroy() process.waitFor() } - worker ! ExecutorStateChanged(appId, execId, ExecutorState.KILLED, None, None) + state = ExecutorState.KILLED + worker ! ExecutorStateChanged(appId, execId, state, None, None) Runtime.getRuntime.removeShutdownHook(shutdownHook) } } @@ -102,7 +104,7 @@ private[spark] class ExecutorRunner( // SPARK-698: do not call the run.cmd script, as process.destroy() // fails to kill a process tree on Windows Seq(runner) ++ buildJavaOpts() ++ Seq(command.mainClass) ++ - command.arguments.map(substituteVariables) + (command.arguments ++ Seq(appId)).map(substituteVariables) } /** @@ -180,9 +182,9 @@ private[spark] class ExecutorRunner( // long-lived processes only. However, in the future, we might restart the executor a few // times on the same machine. val exitCode = process.waitFor() + state = ExecutorState.FAILED val message = "Command exited with code " + exitCode - worker ! ExecutorStateChanged(appId, execId, ExecutorState.FAILED, Some(message), - Some(exitCode)) + worker ! ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode)) } catch { case interrupted: InterruptedException => logInfo("Runner thread for executor " + fullId + " interrupted") @@ -192,8 +194,9 @@ private[spark] class ExecutorRunner( if (process != null) { process.destroy() } + state = ExecutorState.FAILED val message = e.getClass + ": " + e.getMessage - worker ! ExecutorStateChanged(appId, execId, ExecutorState.FAILED, Some(message), None) + worker ! ExecutorStateChanged(appId, execId, state, Some(message), None) } } } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index 09530beb3b..fcaf4e92b1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -17,34 +17,37 @@ package org.apache.spark.deploy.worker +import java.io.File import java.text.SimpleDateFormat import java.util.Date -import java.io.File import scala.collection.mutable.HashMap +import scala.concurrent.duration._ -import akka.actor.{ActorRef, Props, Actor, ActorSystem, Terminated} -import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected} -import akka.util.duration._ - -import org.apache.spark.{Logging} -import org.apache.spark.deploy.ExecutorState +import akka.actor._ +import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} +import org.apache.spark.{Logging, SparkConf, SparkException} +import org.apache.spark.deploy.{ExecutorDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ import org.apache.spark.deploy.master.Master import org.apache.spark.deploy.worker.ui.WorkerWebUI import org.apache.spark.metrics.MetricsSystem -import org.apache.spark.util.{Utils, AkkaUtils} - +import org.apache.spark.util.{AkkaUtils, Utils} +/** + * @param masterUrls Each url should look like spark://host:port. + */ private[spark] class Worker( host: String, port: Int, webUiPort: Int, cores: Int, memory: Int, - masterUrl: String, - workDirPath: String = null) + masterUrls: Array[String], + workDirPath: String = null, + val conf: SparkConf) extends Actor with Logging { + import context.dispatcher Utils.checkHost(host, "Expected hostname") assert (port > 0) @@ -52,10 +55,21 @@ private[spark] class Worker( val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss") // For worker and executor IDs // Send a heartbeat every (heartbeat timeout) / 4 milliseconds - val HEARTBEAT_MILLIS = System.getProperty("spark.worker.timeout", "60").toLong * 1000 / 4 + val HEARTBEAT_MILLIS = conf.get("spark.worker.timeout", "60").toLong * 1000 / 4 - var master: ActorRef = null - var masterWebUiUrl : String = "" + val REGISTRATION_TIMEOUT = 20.seconds + val REGISTRATION_RETRIES = 3 + + // Index into masterUrls that we're currently trying to register with. + var masterIndex = 0 + + val masterLock: Object = new Object() + var master: ActorSelection = null + var masterAddress: Address = null + var activeMasterUrl: String = "" + var activeMasterWebUiUrl : String = "" + @volatile var registered = false + @volatile var connected = false val workerId = generateWorkerId() var sparkHome: File = null var workDir: File = null @@ -70,7 +84,7 @@ private[spark] class Worker( var coresUsed = 0 var memoryUsed = 0 - val metricsSystem = MetricsSystem.createMetricsSystem("worker") + val metricsSystem = MetricsSystem.createMetricsSystem("worker", conf) val workerSource = new WorkerSource(this) def coresFree: Int = cores - coresUsed @@ -95,52 +109,110 @@ private[spark] class Worker( } override def preStart() { + assert(!registered) logInfo("Starting Spark worker %s:%d with %d cores, %s RAM".format( host, port, cores, Utils.megabytesToString(memory))) sparkHome = new File(Option(System.getenv("SPARK_HOME")).getOrElse(".")) logInfo("Spark home: " + sparkHome) createWorkDir() webUi = new WorkerWebUI(this, workDir, Some(webUiPort)) - + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) webUi.start() - connectToMaster() + registerWithMaster() metricsSystem.registerSource(workerSource) metricsSystem.start() } - def connectToMaster() { - logInfo("Connecting to master " + masterUrl) - master = context.actorFor(Master.toAkkaUrl(masterUrl)) - master ! RegisterWorker(workerId, host, port, cores, memory, webUi.boundPort.get, publicAddress) - context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) - context.watch(master) // Doesn't work with remote actors, but useful for testing + def changeMaster(url: String, uiUrl: String) { + masterLock.synchronized { + activeMasterUrl = url + activeMasterWebUiUrl = uiUrl + master = context.actorSelection(Master.toAkkaUrl(activeMasterUrl)) + masterAddress = activeMasterUrl match { + case Master.sparkUrlRegex(_host, _port) => + Address("akka.tcp", Master.systemName, _host, _port.toInt) + case x => + throw new SparkException("Invalid spark URL: " + x) + } + connected = true + } + } + + def tryRegisterAllMasters() { + for (masterUrl <- masterUrls) { + logInfo("Connecting to master " + masterUrl + "...") + val actor = context.actorSelection(Master.toAkkaUrl(masterUrl)) + actor ! RegisterWorker(workerId, host, port, cores, memory, webUi.boundPort.get, + publicAddress) + } + } + + def registerWithMaster() { + tryRegisterAllMasters() + + var retries = 0 + lazy val retryTimer: Cancellable = + context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) { + retries += 1 + if (registered) { + retryTimer.cancel() + } else if (retries >= REGISTRATION_RETRIES) { + logError("All masters are unresponsive! Giving up.") + System.exit(1) + } else { + tryRegisterAllMasters() + } + } + retryTimer // start timer } override def receive = { - case RegisteredWorker(url) => - masterWebUiUrl = url - logInfo("Successfully registered with master") - context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis) { - master ! Heartbeat(workerId) + case RegisteredWorker(masterUrl, masterWebUiUrl) => + logInfo("Successfully registered with master " + masterUrl) + registered = true + changeMaster(masterUrl, masterWebUiUrl) + context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis, self, SendHeartbeat) + + case SendHeartbeat => + masterLock.synchronized { + if (connected) { master ! Heartbeat(workerId) } } + case MasterChanged(masterUrl, masterWebUiUrl) => + logInfo("Master has changed, new master is at " + masterUrl) + changeMaster(masterUrl, masterWebUiUrl) + + val execs = executors.values. + map(e => new ExecutorDescription(e.appId, e.execId, e.cores, e.state)) + sender ! WorkerSchedulerStateResponse(workerId, execs.toList) + case RegisterWorkerFailed(message) => - logError("Worker registration failed: " + message) - System.exit(1) - - case LaunchExecutor(appId, execId, appDesc, cores_, memory_, execSparkHome_) => - logInfo("Asked to launch executor %s/%d for %s".format(appId, execId, appDesc.name)) - val manager = new ExecutorRunner( - appId, execId, appDesc, cores_, memory_, self, workerId, host, new File(execSparkHome_), workDir) - executors(appId + "/" + execId) = manager - manager.start() - coresUsed += cores_ - memoryUsed += memory_ - master ! ExecutorStateChanged(appId, execId, ExecutorState.RUNNING, None, None) + if (!registered) { + logError("Worker registration failed: " + message) + System.exit(1) + } + + case LaunchExecutor(masterUrl, appId, execId, appDesc, cores_, memory_, execSparkHome_) => + if (masterUrl != activeMasterUrl) { + logWarning("Invalid Master (" + masterUrl + ") attempted to launch executor.") + } else { + logInfo("Asked to launch executor %s/%d for %s".format(appId, execId, appDesc.name)) + val manager = new ExecutorRunner(appId, execId, appDesc, cores_, memory_, + self, workerId, host, new File(execSparkHome_), workDir, ExecutorState.RUNNING) + executors(appId + "/" + execId) = manager + manager.start() + coresUsed += cores_ + memoryUsed += memory_ + masterLock.synchronized { + master ! ExecutorStateChanged(appId, execId, manager.state, None, None) + } + } case ExecutorStateChanged(appId, execId, state, message, exitStatus) => - master ! ExecutorStateChanged(appId, execId, state, message, exitStatus) + masterLock.synchronized { + master ! ExecutorStateChanged(appId, execId, state, message, exitStatus) + } val fullId = appId + "/" + execId if (ExecutorState.isFinished(state)) { val executor = executors(fullId) @@ -153,32 +225,34 @@ private[spark] class Worker( memoryUsed -= executor.memory } - case KillExecutor(appId, execId) => - val fullId = appId + "/" + execId - executors.get(fullId) match { - case Some(executor) => - logInfo("Asked to kill executor " + fullId) - executor.kill() - case None => - logInfo("Asked to kill unknown executor " + fullId) + case KillExecutor(masterUrl, appId, execId) => + if (masterUrl != activeMasterUrl) { + logWarning("Invalid Master (" + masterUrl + ") attempted to launch executor " + execId) + } else { + val fullId = appId + "/" + execId + executors.get(fullId) match { + case Some(executor) => + logInfo("Asked to kill executor " + fullId) + executor.kill() + case None => + logInfo("Asked to kill unknown executor " + fullId) + } } - case Terminated(_) | RemoteClientDisconnected(_, _) | RemoteClientShutdown(_, _) => + case x: DisassociatedEvent if x.remoteAddress == masterAddress => + logInfo(s"$x Disassociated !") masterDisconnected() case RequestWorkerState => { sender ! WorkerStateResponse(host, port, workerId, executors.values.toList, - finishedExecutors.values.toList, masterUrl, cores, memory, - coresUsed, memoryUsed, masterWebUiUrl) + finishedExecutors.values.toList, activeMasterUrl, cores, memory, + coresUsed, memoryUsed, activeMasterWebUiUrl) } } def masterDisconnected() { - // TODO: It would be nice to try to reconnect to the master, but just shut down for now. - // (Note that if reconnecting we would also need to assign IDs differently.) - logError("Connection to master failed! Shutting down.") - executors.values.foreach(_.kill()) - System.exit(1) + logError("Connection to master failed! Waiting for master to reconnect...") + connected = false } def generateWorkerId(): String = { @@ -193,20 +267,25 @@ private[spark] class Worker( } private[spark] object Worker { + def main(argStrings: Array[String]) { val args = new WorkerArguments(argStrings) val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort, args.cores, - args.memory, args.master, args.workDir) + args.memory, args.masters, args.workDir) actorSystem.awaitTermination() } def startSystemAndActor(host: String, port: Int, webUiPort: Int, cores: Int, memory: Int, - masterUrl: String, workDir: String, workerNumber: Option[Int] = None): (ActorSystem, Int) = { + masterUrls: Array[String], workDir: String, workerNumber: Option[Int] = None) + : (ActorSystem, Int) = + { // The LocalSparkCluster runs multiple local sparkWorkerX actor systems + val conf = new SparkConf val systemName = "sparkWorker" + workerNumber.map(_.toString).getOrElse("") - val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port) - val actor = actorSystem.actorOf(Props(new Worker(host, boundPort, webUiPort, cores, memory, - masterUrl, workDir)), name = "Worker") + val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port, + conf = conf) + actorSystem.actorOf(Props(classOf[Worker], host, boundPort, webUiPort, cores, memory, + masterUrls, workDir, conf), name = "Worker") (actorSystem, boundPort) } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala index 0ae89a864f..3ed528e6b3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala @@ -29,7 +29,7 @@ private[spark] class WorkerArguments(args: Array[String]) { var webUiPort = 8081 var cores = inferDefaultCores() var memory = inferDefaultMemory() - var master: String = null + var masters: Array[String] = null var workDir: String = null // Check for settings in environment variables @@ -86,14 +86,14 @@ private[spark] class WorkerArguments(args: Array[String]) { printUsageAndExit(0) case value :: tail => - if (master != null) { // Two positional arguments were given + if (masters != null) { // Two positional arguments were given printUsageAndExit(1) } - master = value + masters = value.stripPrefix("spark://").split(",").map("spark://" + _) parse(tail) case Nil => - if (master == null) { // No positional argument was given + if (masters == null) { // No positional argument was given printUsageAndExit(1) } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerSource.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerSource.scala index df269fd047..b7ddd8c816 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerSource.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerSource.scala @@ -25,27 +25,27 @@ private[spark] class WorkerSource(val worker: Worker) extends Source { val sourceName = "worker" val metricRegistry = new MetricRegistry() - metricRegistry.register(MetricRegistry.name("executors", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("executors"), new Gauge[Int] { override def getValue: Int = worker.executors.size }) // Gauge for cores used of this worker - metricRegistry.register(MetricRegistry.name("coresUsed", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("coresUsed"), new Gauge[Int] { override def getValue: Int = worker.coresUsed }) // Gauge for memory used of this worker - metricRegistry.register(MetricRegistry.name("memUsed", "MBytes"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("memUsed_MB"), new Gauge[Int] { override def getValue: Int = worker.memoryUsed }) // Gauge for cores free of this worker - metricRegistry.register(MetricRegistry.name("coresFree", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("coresFree"), new Gauge[Int] { override def getValue: Int = worker.coresFree }) // Gauge for memory free of this worker - metricRegistry.register(MetricRegistry.name("memFree", "MBytes"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("memFree_MB"), new Gauge[Int] { override def getValue: Int = worker.memoryFree }) } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/IndexPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/IndexPage.scala index d2d3617498..0d59048313 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/IndexPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/IndexPage.scala @@ -21,9 +21,10 @@ import javax.servlet.http.HttpServletRequest import scala.xml.Node -import akka.dispatch.Await +import scala.concurrent.duration._ +import scala.concurrent.Await + import akka.pattern.ask -import akka.util.duration._ import net.liftweb.json.JsonAST.JValue @@ -41,13 +42,13 @@ private[spark] class IndexPage(parent: WorkerWebUI) { def renderJson(request: HttpServletRequest): JValue = { val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse] - val workerState = Await.result(stateFuture, 30 seconds) + val workerState = Await.result(stateFuture, timeout) JsonProtocol.writeWorkerState(workerState) } def render(request: HttpServletRequest): Seq[Node] = { val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse] - val workerState = Await.result(stateFuture, 30 seconds) + val workerState = Await.result(stateFuture, timeout) val executorHeaders = Seq("ExecutorID", "Cores", "Memory", "Job Details", "Logs") val runningExecutorTable = diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala index 95d6007f3b..c382034c99 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala @@ -17,20 +17,16 @@ package org.apache.spark.deploy.worker.ui -import akka.util.{Duration, Timeout} - -import java.io.{FileInputStream, File} +import java.io.File import javax.servlet.http.HttpServletRequest - import org.eclipse.jetty.server.{Handler, Server} +import org.apache.spark.{Logging, SparkConf} import org.apache.spark.deploy.worker.Worker -import org.apache.spark.{Logging} -import org.apache.spark.ui.JettyUtils +import org.apache.spark.ui.{JettyUtils, UIUtils} import org.apache.spark.ui.JettyUtils._ -import org.apache.spark.ui.UIUtils -import org.apache.spark.util.Utils +import org.apache.spark.util.{AkkaUtils, Utils} /** * Web UI server for the standalone worker. @@ -38,11 +34,10 @@ import org.apache.spark.util.Utils private[spark] class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[Int] = None) extends Logging { - implicit val timeout = Timeout( - Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")) + val timeout = AkkaUtils.askTimeout(worker.conf) val host = Utils.localHostName() val port = requestedPort.getOrElse( - System.getProperty("worker.ui.port", WorkerWebUI.DEFAULT_PORT).toInt) + worker.conf.get("worker.ui.port", WorkerWebUI.DEFAULT_PORT).toInt) var server: Option[Server] = None var boundPort: Option[Int] = None @@ -105,7 +100,7 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I val logText = <node>{Utils.offsetBytes(path, startByte, endByte)}</node> - val linkToMaster = <p><a href={worker.masterWebUiUrl}>Back to Master</a></p> + val linkToMaster = <p><a href={worker.activeMasterWebUiUrl}>Back to Master</a></p> val range = <span>Bytes {startByte.toString} - {endByte.toString} of {logLength}</span> @@ -145,12 +140,12 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I <body> {linkToMaster} <div> - <div style="float:left;width:40%">{backButton}</div> + <div style="float:left; margin-right:10px">{backButton}</div> <div style="float:left;">{range}</div> - <div style="float:right;">{nextButton}</div> + <div style="float:right; margin-left:10px">{nextButton}</div> </div> <br /> - <div style="height:500px;overflow:auto;padding:5px;"> + <div style="height:500px; overflow:auto; padding:5px;"> <pre>{logText}</pre> </div> </body> diff --git a/core/src/main/scala/org/apache/spark/executor/StandaloneExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 7839023868..53a2b94a52 100644 --- a/core/src/main/scala/org/apache/spark/executor/StandaloneExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -19,16 +19,15 @@ package org.apache.spark.executor import java.nio.ByteBuffer -import akka.actor.{ActorRef, Actor, Props, Terminated} -import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected} +import akka.actor._ +import akka.remote._ -import org.apache.spark.{Logging, SparkEnv} +import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.TaskState.TaskState -import org.apache.spark.scheduler.cluster.StandaloneClusterMessages._ +import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ import org.apache.spark.util.{Utils, AkkaUtils} - -private[spark] class StandaloneExecutorBackend( +private[spark] class CoarseGrainedExecutorBackend( driverUrl: String, executorId: String, hostPort: String, @@ -40,14 +39,13 @@ private[spark] class StandaloneExecutorBackend( Utils.checkHostPort(hostPort, "Expected hostport") var executor: Executor = null - var driver: ActorRef = null + var driver: ActorSelection = null override def preStart() { logInfo("Connecting to driver: " + driverUrl) - driver = context.actorFor(driverUrl) + driver = context.actorSelection(driverUrl) driver ! RegisterExecutor(executorId, hostPort, cores) - context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) - context.watch(driver) // Doesn't work with remote actors, but useful for testing + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) } override def receive = { @@ -63,15 +61,28 @@ private[spark] class StandaloneExecutorBackend( case LaunchTask(taskDesc) => logInfo("Got assigned task " + taskDesc.taskId) if (executor == null) { - logError("Received launchTask but executor was null") + logError("Received LaunchTask command but executor was null") System.exit(1) } else { executor.launchTask(this, taskDesc.taskId, taskDesc.serializedTask) } - case Terminated(_) | RemoteClientDisconnected(_, _) | RemoteClientShutdown(_, _) => - logError("Driver terminated or disconnected! Shutting down.") + case KillTask(taskId, _) => + if (executor == null) { + logError("Received KillTask command but executor was null") + System.exit(1) + } else { + executor.killTask(taskId) + } + + case x: DisassociatedEvent => + logError(s"Driver $x disassociated! Shutting down.") System.exit(1) + + case StopExecutor => + logInfo("Driver commanded a shutdown") + context.stop(self) + context.system.shutdown() } override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) { @@ -79,27 +90,30 @@ private[spark] class StandaloneExecutorBackend( } } -private[spark] object StandaloneExecutorBackend { +private[spark] object CoarseGrainedExecutorBackend { def run(driverUrl: String, executorId: String, hostname: String, cores: Int) { // Debug code Utils.checkHost(hostname) // Create a new ActorSystem to run the backend, because we can't create a SparkEnv / Executor // before getting started with all our system properties, etc - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("sparkExecutor", hostname, 0) + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("sparkExecutor", hostname, 0, + indestructible = true, conf = new SparkConf) // set it val sparkHostPort = hostname + ":" + boundPort - System.setProperty("spark.hostPort", sparkHostPort) - val actor = actorSystem.actorOf( - Props(new StandaloneExecutorBackend(driverUrl, executorId, sparkHostPort, cores)), +// conf.set("spark.hostPort", sparkHostPort) + actorSystem.actorOf( + Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId, sparkHostPort, cores), name = "Executor") actorSystem.awaitTermination() } def main(args: Array[String]) { if (args.length < 4) { - //the reason we allow the last frameworkId argument is to make it easy to kill rogue executors - System.err.println("Usage: StandaloneExecutorBackend <driverUrl> <executorId> <hostname> <cores> [<appid>]") + //the reason we allow the last appid argument is to make it easy to kill rogue executors + System.err.println( + "Usage: CoarseGrainedExecutorBackend <driverUrl> <executorId> <hostname> <cores> " + + "[<appid>]") System.exit(1) } run(args(0), args(1), args(2), args(3).toInt) diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index ceae3b8289..3c92c205ea 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -17,7 +17,7 @@ package org.apache.spark.executor -import java.io.{File} +import java.io.File import java.lang.management.ManagementFactory import java.nio.ByteBuffer import java.util.concurrent._ @@ -25,18 +25,20 @@ import java.util.concurrent._ import scala.collection.JavaConversions._ import scala.collection.mutable.HashMap -import org.apache.spark.scheduler._ import org.apache.spark._ +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.scheduler._ +import org.apache.spark.storage.{StorageLevel, TaskResultBlockId} import org.apache.spark.util.Utils - /** - * The Mesos executor for Spark. + * Spark executor used with Mesos and the standalone scheduler. */ private[spark] class Executor( executorId: String, slaveHostname: String, - properties: Seq[(String, String)]) + properties: Seq[(String, String)], + isLocal: Boolean = false) extends Logging { // Application dependencies (added through SparkContext) that we've fetched so far on this node. @@ -46,8 +48,6 @@ private[spark] class Executor( private val EMPTY_BYTE_BUFFER = ByteBuffer.wrap(new Array[Byte](0)) - initLogging() - // No ip or host:port - just hostname Utils.checkHost(slaveHostname, "Expected executed slave to be a hostname") // must not have port specified. @@ -56,16 +56,17 @@ private[spark] class Executor( // Make sure the local hostname we report matches the cluster scheduler's name for this host Utils.setCustomHostname(slaveHostname) - // Set spark.* system properties from executor arg - for ((key, value) <- properties) { - System.setProperty(key, value) - } + // Set spark.* properties from executor arg + val conf = new SparkConf(false) + conf.setAll(properties) // If we are in yarn mode, systems can have different disk layouts so we must set it // to what Yarn on this system said was available. This will be used later when SparkEnv // created. - if (java.lang.Boolean.valueOf(System.getenv("SPARK_YARN_MODE"))) { - System.setProperty("spark.local.dir", getYarnLocalDirs()) + if (java.lang.Boolean.valueOf( + System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE")))) + { + conf.set("spark.local.dir", getYarnLocalDirs()) } // Create our ClassLoader and set it on this thread @@ -73,46 +74,79 @@ private[spark] class Executor( private val replClassLoader = addReplClassLoaderIfNeeded(urlClassLoader) Thread.currentThread.setContextClassLoader(replClassLoader) - // Make any thread terminations due to uncaught exceptions kill the entire - // executor process to avoid surprising stalls. - Thread.setDefaultUncaughtExceptionHandler( - new Thread.UncaughtExceptionHandler { - override def uncaughtException(thread: Thread, exception: Throwable) { - try { - logError("Uncaught exception in thread " + thread, exception) - - // We may have been called from a shutdown hook. If so, we must not call System.exit(). - // (If we do, we will deadlock.) - if (!Utils.inShutdown()) { - if (exception.isInstanceOf[OutOfMemoryError]) { - System.exit(ExecutorExitCode.OOM) - } else { - System.exit(ExecutorExitCode.UNCAUGHT_EXCEPTION) + if (!isLocal) { + // Setup an uncaught exception handler for non-local mode. + // Make any thread terminations due to uncaught exceptions kill the entire + // executor process to avoid surprising stalls. + Thread.setDefaultUncaughtExceptionHandler( + new Thread.UncaughtExceptionHandler { + override def uncaughtException(thread: Thread, exception: Throwable) { + try { + logError("Uncaught exception in thread " + thread, exception) + + // We may have been called from a shutdown hook. If so, we must not call System.exit(). + // (If we do, we will deadlock.) + if (!Utils.inShutdown()) { + if (exception.isInstanceOf[OutOfMemoryError]) { + System.exit(ExecutorExitCode.OOM) + } else { + System.exit(ExecutorExitCode.UNCAUGHT_EXCEPTION) + } } + } catch { + case oom: OutOfMemoryError => Runtime.getRuntime.halt(ExecutorExitCode.OOM) + case t: Throwable => Runtime.getRuntime.halt(ExecutorExitCode.UNCAUGHT_EXCEPTION_TWICE) } - } catch { - case oom: OutOfMemoryError => Runtime.getRuntime.halt(ExecutorExitCode.OOM) - case t: Throwable => Runtime.getRuntime.halt(ExecutorExitCode.UNCAUGHT_EXCEPTION_TWICE) } } - } - ) + ) + } val executorSource = new ExecutorSource(this, executorId) // Initialize Spark environment (using system properties read above) - val env = SparkEnv.createFromSystemProperties(executorId, slaveHostname, 0, false, false) - SparkEnv.set(env) - env.metricsSystem.registerSource(executorSource) + private val env = { + if (!isLocal) { + val _env = SparkEnv.create(conf, executorId, slaveHostname, 0, + isDriver = false, isLocal = false) + SparkEnv.set(_env) + _env.metricsSystem.registerSource(executorSource) + _env + } else { + SparkEnv.get + } + } - private val akkaFrameSize = env.actorSystem.settings.config.getBytes("akka.remote.netty.message-frame-size") + // Akka's message frame size. If task result is bigger than this, we use the block manager + // to send the result back. + private val akkaFrameSize = { + env.actorSystem.settings.config.getBytes("akka.remote.netty.tcp.maximum-frame-size") + } // Start worker thread pool - val threadPool = new ThreadPoolExecutor( - 1, 128, 600, TimeUnit.SECONDS, new SynchronousQueue[Runnable]) + val threadPool = Utils.newDaemonCachedThreadPool("Executor task launch worker") + + // Maintains the list of running tasks. + private val runningTasks = new ConcurrentHashMap[Long, TaskRunner] + + val sparkUser = Option(System.getenv("SPARK_USER")).getOrElse(SparkContext.SPARK_UNKNOWN_USER) def launchTask(context: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer) { - threadPool.execute(new TaskRunner(context, taskId, serializedTask)) + val tr = new TaskRunner(context, taskId, serializedTask) + runningTasks.put(taskId, tr) + threadPool.execute(tr) + } + + def killTask(taskId: Long) { + val tr = runningTasks.get(taskId) + if (tr != null) { + tr.kill() + // We remove the task also in the finally block in TaskRunner.run. + // The reason we need to remove it here is because killTask might be called before the task + // is even launched, and never reaching that finally block. ConcurrentHashMap's remove is + // idempotent. + runningTasks.remove(taskId) + } } /** Get the Yarn approved local directories. */ @@ -124,62 +158,106 @@ private[spark] class Executor( .getOrElse(Option(System.getenv("LOCAL_DIRS")) .getOrElse("")) - if (localDirs.isEmpty()) { + if (localDirs.isEmpty) { throw new Exception("Yarn Local dirs can't be empty") } - return localDirs + localDirs } - class TaskRunner(context: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer) + class TaskRunner(execBackend: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer) extends Runnable { - override def run() { + @volatile private var killed = false + @volatile private var task: Task[Any] = _ + + def kill() { + logInfo("Executor is trying to kill task " + taskId) + killed = true + if (task != null) { + task.kill() + } + } + + override def run(): Unit = SparkHadoopUtil.get.runAsUser(sparkUser) { () => val startTime = System.currentTimeMillis() SparkEnv.set(env) Thread.currentThread.setContextClassLoader(replClassLoader) val ser = SparkEnv.get.closureSerializer.newInstance() logInfo("Running task ID " + taskId) - context.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER) + execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER) var attemptedTask: Option[Task[Any]] = None var taskStart: Long = 0 - def getTotalGCTime = ManagementFactory.getGarbageCollectorMXBeans.map(g => g.getCollectionTime).sum - val startGCTime = getTotalGCTime + def gcTime = ManagementFactory.getGarbageCollectorMXBeans.map(_.getCollectionTime).sum + val startGCTime = gcTime try { SparkEnv.set(env) Accumulators.clear() val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask) updateDependencies(taskFiles, taskJars) - val task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader) + task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader) + + // If this task has been killed before we deserialized it, let's quit now. Otherwise, + // continue executing the task. + if (killed) { + logInfo("Executor killed task " + taskId) + execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled)) + return + } + attemptedTask = Some(task) - logInfo("Its epoch is " + task.epoch) + logDebug("Task " + taskId +"'s epoch is " + task.epoch) env.mapOutputTracker.updateEpoch(task.epoch) + + // Run the actual task and measure its runtime. taskStart = System.currentTimeMillis() val value = task.run(taskId.toInt) val taskFinish = System.currentTimeMillis() + + // If the task has been killed, let's fail it. + if (task.killed) { + logInfo("Executor killed task " + taskId) + execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled)) + return + } + + val resultSer = SparkEnv.get.serializer.newInstance() + val beforeSerialization = System.currentTimeMillis() + val valueBytes = resultSer.serialize(value) + val afterSerialization = System.currentTimeMillis() + for (m <- task.metrics) { - m.hostname = Utils.localHostName + m.hostname = Utils.localHostName() m.executorDeserializeTime = (taskStart - startTime).toInt m.executorRunTime = (taskFinish - taskStart).toInt - m.jvmGCTime = getTotalGCTime - startGCTime + m.jvmGCTime = gcTime - startGCTime + m.resultSerializationTime = (afterSerialization - beforeSerialization).toInt } - //TODO I'd also like to track the time it takes to serialize the task results, but that is huge headache, b/c - // we need to serialize the task metrics first. If TaskMetrics had a custom serialized format, we could - // just change the relevants bytes in the byte buffer + val accumUpdates = Accumulators.values - val result = new TaskResult(value, accumUpdates, task.metrics.getOrElse(null)) - val serializedResult = ser.serialize(result) - logInfo("Serialized size of result for " + taskId + " is " + serializedResult.limit) - if (serializedResult.limit >= (akkaFrameSize - 1024)) { - context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(TaskResultTooBigFailure())) - return + + val directResult = new DirectTaskResult(valueBytes, accumUpdates, task.metrics.getOrElse(null)) + val serializedDirectResult = ser.serialize(directResult) + logInfo("Serialized size of result for " + taskId + " is " + serializedDirectResult.limit) + val serializedResult = { + if (serializedDirectResult.limit >= akkaFrameSize - 1024) { + logInfo("Storing result for " + taskId + " in local BlockManager") + val blockId = TaskResultBlockId(taskId) + env.blockManager.putBytes( + blockId, serializedDirectResult, StorageLevel.MEMORY_AND_DISK_SER) + ser.serialize(new IndirectTaskResult[Any](blockId)) + } else { + logInfo("Sending result for " + taskId + " directly to driver") + serializedDirectResult + } } - context.statusUpdate(taskId, TaskState.FINISHED, serializedResult) + + execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult) logInfo("Finished task ID " + taskId) } catch { case ffe: FetchFailedException => { val reason = ffe.toTaskEndReason - context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) + execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) } case t: Throwable => { @@ -187,10 +265,10 @@ private[spark] class Executor( val metrics = attemptedTask.flatMap(t => t.metrics) for (m <- metrics) { m.executorRunTime = serviceTime - m.jvmGCTime = getTotalGCTime - startGCTime + m.jvmGCTime = gcTime - startGCTime } val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics) - context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) + execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) // TODO: Should we exit the whole executor here? On the one hand, the failed task may // have left some weird state around depending on when the exception was thrown, but on @@ -198,6 +276,8 @@ private[spark] class Executor( logError("Exception in task ID " + taskId, t) //System.exit(1) } + } finally { + runningTasks.remove(taskId) } } } @@ -207,7 +287,7 @@ private[spark] class Executor( * created by the interpreter to the search path */ private def createClassLoader(): ExecutorURLClassLoader = { - var loader = this.getClass.getClassLoader + val loader = this.getClass.getClassLoader // For each of the jars in the jarSet, add them to the class loader. // We assume each of the files has already been fetched. @@ -222,14 +302,14 @@ private[spark] class Executor( * new classes defined by the REPL as the user types code */ private def addReplClassLoaderIfNeeded(parent: ClassLoader): ClassLoader = { - val classUri = System.getProperty("spark.repl.class.uri") + val classUri = conf.get("spark.repl.class.uri", null) if (classUri != null) { logInfo("Using REPL class URI: " + classUri) try { val klass = Class.forName("org.apache.spark.repl.ExecutorClassLoader") .asInstanceOf[Class[_ <: ClassLoader]] val constructor = klass.getConstructor(classOf[String], classOf[ClassLoader]) - return constructor.newInstance(classUri, parent) + constructor.newInstance(classUri, parent) } catch { case _: ClassNotFoundException => logError("Could not find org.apache.spark.repl.ExecutorClassLoader on classpath!") @@ -237,7 +317,7 @@ private[spark] class Executor( null } } else { - return parent + parent } } @@ -250,12 +330,12 @@ private[spark] class Executor( // Fetch missing dependencies for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) { logInfo("Fetching " + name + " with timestamp " + timestamp) - Utils.fetchFile(name, new File(SparkFiles.getRootDirectory)) + Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf) currentFiles(name) = timestamp } for ((name, timestamp) <- newJars if currentJars.getOrElse(name, -1L) < timestamp) { logInfo("Fetching " + name + " with timestamp " + timestamp) - Utils.fetchFile(name, new File(SparkFiles.getRootDirectory)) + Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf) currentJars(name) = timestamp // Add it to our class loader val localName = name.split("/").last diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala index 18c9dc1c0a..97176e4f5b 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala @@ -20,8 +20,6 @@ package org.apache.spark.executor import com.codahale.metrics.{Gauge, MetricRegistry} import org.apache.hadoop.fs.FileSystem -import org.apache.hadoop.hdfs.DistributedFileSystem -import org.apache.hadoop.fs.LocalFileSystem import scala.collection.JavaConversions._ @@ -43,31 +41,31 @@ class ExecutorSource(val executor: Executor, executorId: String) extends Source val sourceName = "executor.%s".format(executorId) // Gauge for executor thread pool's actively executing task counts - metricRegistry.register(MetricRegistry.name("threadpool", "activeTask", "count"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("threadpool", "activeTasks"), new Gauge[Int] { override def getValue: Int = executor.threadPool.getActiveCount() }) // Gauge for executor thread pool's approximate total number of tasks that have been completed - metricRegistry.register(MetricRegistry.name("threadpool", "completeTask", "count"), new Gauge[Long] { + metricRegistry.register(MetricRegistry.name("threadpool", "completeTasks"), new Gauge[Long] { override def getValue: Long = executor.threadPool.getCompletedTaskCount() }) // Gauge for executor thread pool's current number of threads - metricRegistry.register(MetricRegistry.name("threadpool", "currentPool", "size"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("threadpool", "currentPool_size"), new Gauge[Int] { override def getValue: Int = executor.threadPool.getPoolSize() }) // Gauge got executor thread pool's largest number of threads that have ever simultaneously been in th pool - metricRegistry.register(MetricRegistry.name("threadpool", "maxPool", "size"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("threadpool", "maxPool_size"), new Gauge[Int] { override def getValue: Int = executor.threadPool.getMaximumPoolSize() }) // Gauge for file system stats of this executor for (scheme <- Array("hdfs", "file")) { - registerFileSystemStat(scheme, "bytesRead", _.getBytesRead(), 0L) - registerFileSystemStat(scheme, "bytesWritten", _.getBytesWritten(), 0L) - registerFileSystemStat(scheme, "readOps", _.getReadOps(), 0) - registerFileSystemStat(scheme, "largeReadOps", _.getLargeReadOps(), 0) - registerFileSystemStat(scheme, "writeOps", _.getWriteOps(), 0) + registerFileSystemStat(scheme, "read_bytes", _.getBytesRead(), 0L) + registerFileSystemStat(scheme, "write_bytes", _.getBytesWritten(), 0L) + registerFileSystemStat(scheme, "read_ops", _.getReadOps(), 0) + registerFileSystemStat(scheme, "largeRead_ops", _.getLargeReadOps(), 0) + registerFileSystemStat(scheme, "write_ops", _.getWriteOps(), 0) } } diff --git a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala index da62091980..b56d8c9912 100644 --- a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala @@ -18,14 +18,18 @@ package org.apache.spark.executor import java.nio.ByteBuffer -import org.apache.mesos.{Executor => MesosExecutor, MesosExecutorDriver, MesosNativeLibrary, ExecutorDriver} -import org.apache.mesos.Protos.{TaskState => MesosTaskState, TaskStatus => MesosTaskStatus, _} -import org.apache.spark.TaskState.TaskState + import com.google.protobuf.ByteString -import org.apache.spark.{Logging} + +import org.apache.mesos.{Executor => MesosExecutor, MesosExecutorDriver, MesosNativeLibrary, ExecutorDriver} +import org.apache.mesos.Protos.{TaskStatus => MesosTaskStatus, _} + +import org.apache.spark.Logging import org.apache.spark.TaskState +import org.apache.spark.TaskState.TaskState import org.apache.spark.util.Utils + private[spark] class MesosExecutorBackend extends MesosExecutor with ExecutorBackend @@ -71,7 +75,11 @@ private[spark] class MesosExecutorBackend } override def killTask(d: ExecutorDriver, t: TaskID) { - logWarning("Mesos asked us to kill task " + t.getValue + "; ignoring (not yet implemented)") + if (executor == null) { + logError("Received KillTask but executor was null") + } else { + executor.killTask(t.getValue.toLong) + } } override def reregistered(d: ExecutorDriver, p2: SlaveInfo) {} diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala index f311141148..bb1471d9ee 100644 --- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala +++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala @@ -44,6 +44,11 @@ class TaskMetrics extends Serializable { var jvmGCTime: Long = _ /** + * Amount of time spent serializing the task result + */ + var resultSerializationTime: Long = _ + + /** * If this task reads from shuffle output, metrics on getting shuffle data will be collected here */ var shuffleReadMetrics: Option[ShuffleReadMetrics] = None @@ -61,45 +66,53 @@ object TaskMetrics { class ShuffleReadMetrics extends Serializable { /** - * Time when shuffle finishs + * Absolute time when this task finished reading shuffle data */ var shuffleFinishTime: Long = _ /** - * Total number of blocks fetched in a shuffle (remote or local) + * Number of blocks fetched in this shuffle by this task (remote or local) */ var totalBlocksFetched: Int = _ /** - * Number of remote blocks fetched in a shuffle + * Number of remote blocks fetched in this shuffle by this task */ var remoteBlocksFetched: Int = _ /** - * Local blocks fetched in a shuffle + * Number of local blocks fetched in this shuffle by this task */ var localBlocksFetched: Int = _ /** - * Total time that is spent blocked waiting for shuffle to fetch data + * Time the task spent waiting for remote shuffle blocks. This only includes the time + * blocking on shuffle input data. For instance if block B is being fetched while the task is + * still not finished processing block A, it is not considered to be blocking on block B. */ var fetchWaitTime: Long = _ /** - * The total amount of time for all the shuffle fetches. This adds up time from overlapping - * shuffles, so can be longer than task time + * Total time spent fetching remote shuffle blocks. This aggregates the time spent fetching all + * input blocks. Since block fetches are both pipelined and parallelized, this can + * exceed fetchWaitTime and executorRunTime. */ var remoteFetchTime: Long = _ /** - * Total number of remote bytes read from a shuffle + * Total number of remote bytes read from the shuffle by this task */ var remoteBytesRead: Long = _ } class ShuffleWriteMetrics extends Serializable { /** - * Number of bytes written for a shuffle + * Number of bytes written for the shuffle by this task */ var shuffleBytesWritten: Long = _ + + /** + * Time the task spent blocking on writes to disk or buffer cache, in nanoseconds + */ + var shuffleWriteTime: Long = _ } diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala index 570a979b56..a1e98845f6 100644 --- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala +++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala @@ -22,6 +22,7 @@ import java.io.{InputStream, OutputStream} import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} import org.xerial.snappy.{SnappyInputStream, SnappyOutputStream} +import org.apache.spark.{SparkEnv, SparkConf} /** @@ -37,15 +38,15 @@ trait CompressionCodec { private[spark] object CompressionCodec { - - def createCodec(): CompressionCodec = { - createCodec(System.getProperty( + def createCodec(conf: SparkConf): CompressionCodec = { + createCodec(conf, conf.get( "spark.io.compression.codec", classOf[LZFCompressionCodec].getName)) } - def createCodec(codecName: String): CompressionCodec = { - Class.forName(codecName, true, Thread.currentThread.getContextClassLoader) - .newInstance().asInstanceOf[CompressionCodec] + def createCodec(conf: SparkConf, codecName: String): CompressionCodec = { + val ctor = Class.forName(codecName, true, Thread.currentThread.getContextClassLoader) + .getConstructor(classOf[SparkConf]) + ctor.newInstance(conf).asInstanceOf[CompressionCodec] } } @@ -53,7 +54,7 @@ private[spark] object CompressionCodec { /** * LZF implementation of [[org.apache.spark.io.CompressionCodec]]. */ -class LZFCompressionCodec extends CompressionCodec { +class LZFCompressionCodec(conf: SparkConf) extends CompressionCodec { override def compressedOutputStream(s: OutputStream): OutputStream = { new LZFOutputStream(s).setFinishBlockOnFlush(true) @@ -67,10 +68,10 @@ class LZFCompressionCodec extends CompressionCodec { * Snappy implementation of [[org.apache.spark.io.CompressionCodec]]. * Block size can be configured by spark.io.compression.snappy.block.size. */ -class SnappyCompressionCodec extends CompressionCodec { +class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec { override def compressedOutputStream(s: OutputStream): OutputStream = { - val blockSize = System.getProperty("spark.io.compression.snappy.block.size", "32768").toInt + val blockSize = conf.get("spark.io.compression.snappy.block.size", "32768").toInt new SnappyOutputStream(s, blockSize) } diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala index caab748d60..6f9f29969e 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala @@ -26,7 +26,6 @@ import scala.util.matching.Regex import org.apache.spark.Logging private[spark] class MetricsConfig(val configFile: Option[String]) extends Logging { - initLogging() val DEFAULT_PREFIX = "*" val INSTANCE_REGEX = "^(\\*|[a-zA-Z]+)\\.(.+)".r diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala index bec0c83be8..9930537b34 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala @@ -24,7 +24,7 @@ import java.util.concurrent.TimeUnit import scala.collection.mutable -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.spark.metrics.sink.{MetricsServlet, Sink} import org.apache.spark.metrics.source.Source @@ -62,10 +62,10 @@ import org.apache.spark.metrics.source.Source * * [options] is the specific property of this source or sink. */ -private[spark] class MetricsSystem private (val instance: String) extends Logging { - initLogging() +private[spark] class MetricsSystem private (val instance: String, + conf: SparkConf) extends Logging { - val confFile = System.getProperty("spark.metrics.conf") + val confFile = conf.get("spark.metrics.conf", null) val metricsConfig = new MetricsConfig(Option(confFile)) val sinks = new mutable.ArrayBuffer[Sink] @@ -159,5 +159,6 @@ private[spark] object MetricsSystem { } } - def createMetricsSystem(instance: String): MetricsSystem = new MetricsSystem(instance) + def createMetricsSystem(instance: String, conf: SparkConf): MetricsSystem = + new MetricsSystem(instance, conf) } diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/GraphiteSink.scala b/core/src/main/scala/org/apache/spark/metrics/sink/GraphiteSink.scala new file mode 100644 index 0000000000..cdcfec8ca7 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/metrics/sink/GraphiteSink.scala @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.metrics.sink + +import java.util.Properties +import java.util.concurrent.TimeUnit +import java.net.InetSocketAddress + +import com.codahale.metrics.MetricRegistry +import com.codahale.metrics.graphite.{GraphiteReporter, Graphite} + +import org.apache.spark.metrics.MetricsSystem + +class GraphiteSink(val property: Properties, val registry: MetricRegistry) extends Sink { + val GRAPHITE_DEFAULT_PERIOD = 10 + val GRAPHITE_DEFAULT_UNIT = "SECONDS" + val GRAPHITE_DEFAULT_PREFIX = "" + + val GRAPHITE_KEY_HOST = "host" + val GRAPHITE_KEY_PORT = "port" + val GRAPHITE_KEY_PERIOD = "period" + val GRAPHITE_KEY_UNIT = "unit" + val GRAPHITE_KEY_PREFIX = "prefix" + + def propertyToOption(prop: String) = Option(property.getProperty(prop)) + + if (!propertyToOption(GRAPHITE_KEY_HOST).isDefined) { + throw new Exception("Graphite sink requires 'host' property.") + } + + if (!propertyToOption(GRAPHITE_KEY_PORT).isDefined) { + throw new Exception("Graphite sink requires 'port' property.") + } + + val host = propertyToOption(GRAPHITE_KEY_HOST).get + val port = propertyToOption(GRAPHITE_KEY_PORT).get.toInt + + val pollPeriod = propertyToOption(GRAPHITE_KEY_PERIOD) match { + case Some(s) => s.toInt + case None => GRAPHITE_DEFAULT_PERIOD + } + + val pollUnit = propertyToOption(GRAPHITE_KEY_UNIT) match { + case Some(s) => TimeUnit.valueOf(s.toUpperCase()) + case None => TimeUnit.valueOf(GRAPHITE_DEFAULT_UNIT) + } + + val prefix = propertyToOption(GRAPHITE_KEY_PREFIX).getOrElse(GRAPHITE_DEFAULT_PREFIX) + + MetricsSystem.checkMinimalPollingPeriod(pollUnit, pollPeriod) + + val graphite: Graphite = new Graphite(new InetSocketAddress(host, port)) + + val reporter: GraphiteReporter = GraphiteReporter.forRegistry(registry) + .convertDurationsTo(TimeUnit.MILLISECONDS) + .convertRatesTo(TimeUnit.SECONDS) + .prefixedWith(prefix) + .build(graphite) + + override def start() { + reporter.start(pollPeriod, pollUnit) + } + + override def stop() { + reporter.stop() + } +} diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala index e15a839c4e..46c40d0a2a 100644 --- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala @@ -31,13 +31,13 @@ import scala.collection.mutable.SynchronizedMap import scala.collection.mutable.SynchronizedQueue import scala.collection.mutable.ArrayBuffer -import akka.dispatch.{Await, Promise, ExecutionContext, Future} -import akka.util.Duration -import akka.util.duration._ -import org.apache.spark.util.Utils +import scala.concurrent.{Await, Promise, ExecutionContext, Future} +import scala.concurrent.duration.Duration +import scala.concurrent.duration._ +import org.apache.spark.util.Utils -private[spark] class ConnectionManager(port: Int) extends Logging { +private[spark] class ConnectionManager(port: Int, conf: SparkConf) extends Logging { class MessageStatus( val message: Message, @@ -54,22 +54,22 @@ private[spark] class ConnectionManager(port: Int) extends Logging { private val selector = SelectorProvider.provider.openSelector() private val handleMessageExecutor = new ThreadPoolExecutor( - System.getProperty("spark.core.connection.handler.threads.min","20").toInt, - System.getProperty("spark.core.connection.handler.threads.max","60").toInt, - System.getProperty("spark.core.connection.handler.threads.keepalive","60").toInt, TimeUnit.SECONDS, + conf.get("spark.core.connection.handler.threads.min", "20").toInt, + conf.get("spark.core.connection.handler.threads.max", "60").toInt, + conf.get("spark.core.connection.handler.threads.keepalive", "60").toInt, TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) private val handleReadWriteExecutor = new ThreadPoolExecutor( - System.getProperty("spark.core.connection.io.threads.min","4").toInt, - System.getProperty("spark.core.connection.io.threads.max","32").toInt, - System.getProperty("spark.core.connection.io.threads.keepalive","60").toInt, TimeUnit.SECONDS, + conf.get("spark.core.connection.io.threads.min", "4").toInt, + conf.get("spark.core.connection.io.threads.max", "32").toInt, + conf.get("spark.core.connection.io.threads.keepalive", "60").toInt, TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) // Use a different, yet smaller, thread pool - infrequently used with very short lived tasks : which should be executed asap private val handleConnectExecutor = new ThreadPoolExecutor( - System.getProperty("spark.core.connection.connect.threads.min","1").toInt, - System.getProperty("spark.core.connection.connect.threads.max","8").toInt, - System.getProperty("spark.core.connection.connect.threads.keepalive","60").toInt, TimeUnit.SECONDS, + conf.get("spark.core.connection.connect.threads.min", "1").toInt, + conf.get("spark.core.connection.connect.threads.max", "8").toInt, + conf.get("spark.core.connection.connect.threads.keepalive", "60").toInt, TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) private val serverChannel = ServerSocketChannel.open() @@ -79,7 +79,8 @@ private[spark] class ConnectionManager(port: Int) extends Logging { private val keyInterestChangeRequests = new SynchronizedQueue[(SelectionKey, Int)] private val registerRequests = new SynchronizedQueue[SendingConnection] - implicit val futureExecContext = ExecutionContext.fromExecutor(Utils.newDaemonCachedThreadPool()) + implicit val futureExecContext = ExecutionContext.fromExecutor( + Utils.newDaemonCachedThreadPool("Connection manager future execution context")) private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message]= null @@ -593,7 +594,7 @@ private[spark] class ConnectionManager(port: Int) extends Logging { private[spark] object ConnectionManager { def main(args: Array[String]) { - val manager = new ConnectionManager(9999) + val manager = new ConnectionManager(9999, new SparkConf) manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { println("Received [" + msg + "] from [" + id + "]") None diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManagerTest.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManagerTest.scala index 8d9ad9604d..4f5742d29b 100644 --- a/core/src/main/scala/org/apache/spark/network/ConnectionManagerTest.scala +++ b/core/src/main/scala/org/apache/spark/network/ConnectionManagerTest.scala @@ -25,8 +25,8 @@ import scala.io.Source import java.nio.ByteBuffer import java.net.InetAddress -import akka.dispatch.Await -import akka.util.duration._ +import scala.concurrent.Await +import scala.concurrent.duration._ private[spark] object ConnectionManagerTest extends Logging{ def main(args: Array[String]) { diff --git a/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala b/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala index 781715108b..1c9d6030d6 100644 --- a/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala +++ b/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala @@ -19,19 +19,19 @@ package org.apache.spark.network import java.nio.ByteBuffer import java.net.InetAddress +import org.apache.spark.SparkConf private[spark] object ReceiverTest { - def main(args: Array[String]) { - val manager = new ConnectionManager(9999) + val manager = new ConnectionManager(9999, new SparkConf) println("Started connection manager with id = " + manager.id) - - manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { + + manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { /*println("Received [" + msg + "] from [" + id + "] at " + System.currentTimeMillis)*/ - val buffer = ByteBuffer.wrap("response".getBytes()) + val buffer = ByteBuffer.wrap("response".getBytes) Some(Message.createBufferMessage(buffer, msg.id)) }) - Thread.currentThread.join() + Thread.currentThread.join() } } diff --git a/core/src/main/scala/org/apache/spark/network/SenderTest.scala b/core/src/main/scala/org/apache/spark/network/SenderTest.scala index 777574980f..dcbd183c88 100644 --- a/core/src/main/scala/org/apache/spark/network/SenderTest.scala +++ b/core/src/main/scala/org/apache/spark/network/SenderTest.scala @@ -19,29 +19,29 @@ package org.apache.spark.network import java.nio.ByteBuffer import java.net.InetAddress +import org.apache.spark.SparkConf private[spark] object SenderTest { - def main(args: Array[String]) { - + if (args.length < 2) { println("Usage: SenderTest <target host> <target port>") System.exit(1) } - + val targetHost = args(0) val targetPort = args(1).toInt val targetConnectionManagerId = new ConnectionManagerId(targetHost, targetPort) - val manager = new ConnectionManager(0) + val manager = new ConnectionManager(0, new SparkConf) println("Started connection manager with id = " + manager.id) - manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { + manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { println("Received [" + msg + "] from [" + id + "]") None }) - - val size = 100 * 1024 * 1024 + + val size = 100 * 1024 * 1024 val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte)) buffer.flip @@ -50,7 +50,7 @@ private[spark] object SenderTest { val count = 100 (0 until count).foreach(i => { val dataMessage = Message.createBufferMessage(buffer.duplicate) - val startTime = System.currentTimeMillis + val startTime = System.currentTimeMillis /*println("Started timer at " + startTime)*/ val responseStr = manager.sendMessageReliablySync(targetConnectionManagerId, dataMessage) match { case Some(response) => diff --git a/core/src/main/scala/org/apache/spark/network/netty/FileHeader.scala b/core/src/main/scala/org/apache/spark/network/netty/FileHeader.scala index 3c29700920..1b9fa1e53a 100644 --- a/core/src/main/scala/org/apache/spark/network/netty/FileHeader.scala +++ b/core/src/main/scala/org/apache/spark/network/netty/FileHeader.scala @@ -20,17 +20,18 @@ package org.apache.spark.network.netty import io.netty.buffer._ import org.apache.spark.Logging +import org.apache.spark.storage.{TestBlockId, BlockId} private[spark] class FileHeader ( val fileLen: Int, - val blockId: String) extends Logging { + val blockId: BlockId) extends Logging { lazy val buffer = { val buf = Unpooled.buffer() buf.capacity(FileHeader.HEADER_SIZE) buf.writeInt(fileLen) - buf.writeInt(blockId.length) - blockId.foreach((x: Char) => buf.writeByte(x)) + buf.writeInt(blockId.name.length) + blockId.name.foreach((x: Char) => buf.writeByte(x)) //padding the rest of header if (FileHeader.HEADER_SIZE - buf.readableBytes > 0 ) { buf.writeZero(FileHeader.HEADER_SIZE - buf.readableBytes) @@ -57,18 +58,15 @@ private[spark] object FileHeader { for (i <- 1 to idLength) { idBuilder += buf.readByte().asInstanceOf[Char] } - val blockId = idBuilder.toString() + val blockId = BlockId(idBuilder.toString()) new FileHeader(length, blockId) } - - def main (args:Array[String]){ - - val header = new FileHeader(25,"block_0"); - val buf = header.buffer; - val newheader = FileHeader.create(buf); - System.out.println("id="+newheader.blockId+",size="+newheader.fileLen) - + def main (args:Array[String]) { + val header = new FileHeader(25, TestBlockId("my_block")) + val buf = header.buffer + val newHeader = FileHeader.create(buf) + System.out.println("id=" + newHeader.blockId + ",size=" + newHeader.fileLen) } } diff --git a/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala b/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala index 9493ccffd9..b729eb11c5 100644 --- a/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala +++ b/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala @@ -23,25 +23,26 @@ import io.netty.buffer.ByteBuf import io.netty.channel.ChannelHandlerContext import io.netty.util.CharsetUtil -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.network.ConnectionManagerId import scala.collection.JavaConverters._ +import org.apache.spark.storage.BlockId -private[spark] class ShuffleCopier extends Logging { +private[spark] class ShuffleCopier(conf: SparkConf) extends Logging { - def getBlock(host: String, port: Int, blockId: String, - resultCollectCallback: (String, Long, ByteBuf) => Unit) { + def getBlock(host: String, port: Int, blockId: BlockId, + resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) { val handler = new ShuffleCopier.ShuffleClientHandler(resultCollectCallback) - val connectTimeout = System.getProperty("spark.shuffle.netty.connect.timeout", "60000").toInt + val connectTimeout = conf.get("spark.shuffle.netty.connect.timeout", "60000").toInt val fc = new FileClient(handler, connectTimeout) try { fc.init() fc.connect(host, port) - fc.sendRequest(blockId) + fc.sendRequest(blockId.name) fc.waitForClose() fc.close() } catch { @@ -53,14 +54,14 @@ private[spark] class ShuffleCopier extends Logging { } } - def getBlock(cmId: ConnectionManagerId, blockId: String, - resultCollectCallback: (String, Long, ByteBuf) => Unit) { + def getBlock(cmId: ConnectionManagerId, blockId: BlockId, + resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) { getBlock(cmId.host, cmId.port, blockId, resultCollectCallback) } def getBlocks(cmId: ConnectionManagerId, - blocks: Seq[(String, Long)], - resultCollectCallback: (String, Long, ByteBuf) => Unit) { + blocks: Seq[(BlockId, Long)], + resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) { for ((blockId, size) <- blocks) { getBlock(cmId, blockId, resultCollectCallback) @@ -71,22 +72,22 @@ private[spark] class ShuffleCopier extends Logging { private[spark] object ShuffleCopier extends Logging { - private class ShuffleClientHandler(resultCollectCallBack: (String, Long, ByteBuf) => Unit) + private class ShuffleClientHandler(resultCollectCallBack: (BlockId, Long, ByteBuf) => Unit) extends FileClientHandler with Logging { override def handle(ctx: ChannelHandlerContext, in: ByteBuf, header: FileHeader) { - logDebug("Received Block: " + header.blockId + " (" + header.fileLen + "B)"); + logDebug("Received Block: " + header.blockId + " (" + header.fileLen + "B)") resultCollectCallBack(header.blockId, header.fileLen.toLong, in.readBytes(header.fileLen)) } - override def handleError(blockId: String) { + override def handleError(blockId: BlockId) { if (!isComplete) { resultCollectCallBack(blockId, -1, null) } } } - def echoResultCollectCallBack(blockId: String, size: Long, content: ByteBuf) { + def echoResultCollectCallBack(blockId: BlockId, size: Long, content: ByteBuf) { if (size != -1) { logInfo("File: " + blockId + " content is : \" " + content.toString(CharsetUtil.UTF_8) + "\"") } @@ -99,20 +100,20 @@ private[spark] object ShuffleCopier extends Logging { } val host = args(0) val port = args(1).toInt - val file = args(2) + val blockId = BlockId(args(2)) val threads = if (args.length > 3) args(3).toInt else 10 val copiers = Executors.newFixedThreadPool(80) - val tasks = (for (i <- Range(0, threads)) yield { + val tasks = (for (i <- Range(0, threads)) yield { Executors.callable(new Runnable() { def run() { - val copier = new ShuffleCopier() - copier.getBlock(host, port, file, echoResultCollectCallBack) + val copier = new ShuffleCopier(new SparkConf) + copier.getBlock(host, port, blockId, echoResultCollectCallBack) } }) }).asJava copiers.invokeAll(tasks) - copiers.shutdown + copiers.shutdown() System.exit(0) } } diff --git a/core/src/main/scala/org/apache/spark/network/netty/ShuffleSender.scala b/core/src/main/scala/org/apache/spark/network/netty/ShuffleSender.scala index 8afcbe190a..546d921067 100644 --- a/core/src/main/scala/org/apache/spark/network/netty/ShuffleSender.scala +++ b/core/src/main/scala/org/apache/spark/network/netty/ShuffleSender.scala @@ -21,6 +21,7 @@ import java.io.File import org.apache.spark.Logging import org.apache.spark.util.Utils +import org.apache.spark.storage.{BlockId, FileSegment} private[spark] class ShuffleSender(portIn: Int, val pResolver: PathResolver) extends Logging { @@ -53,8 +54,8 @@ private[spark] object ShuffleSender { val localDirs = args.drop(2).map(new File(_)) val pResovler = new PathResolver { - override def getAbsolutePath(blockId: String): String = { - if (!blockId.startsWith("shuffle_")) { + override def getBlockLocation(blockId: BlockId): FileSegment = { + if (!blockId.isShuffle) { throw new Exception("Block " + blockId + " is not a shuffle block") } // Figure out which local directory it hashes to, and which subdirectory in that @@ -62,8 +63,8 @@ private[spark] object ShuffleSender { val dirId = hash % localDirs.length val subDirId = (hash / localDirs.length) % subDirsPerLocalDir val subDir = new File(localDirs(dirId), "%02x".format(subDirId)) - val file = new File(subDir, blockId) - return file.getAbsolutePath + val file = new File(subDir, blockId.name) + return new FileSegment(file, 0, file.length()) } } val sender = new ShuffleSender(port, pResovler) diff --git a/core/src/main/scala/org/apache/spark/package.scala b/core/src/main/scala/org/apache/spark/package.scala index f132e2b735..70a5a8caff 100644 --- a/core/src/main/scala/org/apache/spark/package.scala +++ b/core/src/main/scala/org/apache/spark/package.scala @@ -15,6 +15,8 @@ * limitations under the License. */ +package org.apache + /** * Core Spark functionality. [[org.apache.spark.SparkContext]] serves as the main entry point to * Spark, while [[org.apache.spark.rdd.RDD]] is the data type representing a distributed collection, diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala new file mode 100644 index 0000000000..d1c74a5063 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala @@ -0,0 +1,123 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import java.util.concurrent.atomic.AtomicLong + +import scala.collection.mutable.ArrayBuffer +import scala.concurrent.ExecutionContext.Implicits.global +import scala.reflect.ClassTag + +import org.apache.spark.{ComplexFutureAction, FutureAction, Logging} + +/** + * A set of asynchronous RDD actions available through an implicit conversion. + * Import `org.apache.spark.SparkContext._` at the top of your program to use these functions. + */ +class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Logging { + + /** + * Returns a future for counting the number of elements in the RDD. + */ + def countAsync(): FutureAction[Long] = { + val totalCount = new AtomicLong + self.context.submitJob( + self, + (iter: Iterator[T]) => { + var result = 0L + while (iter.hasNext) { + result += 1L + iter.next() + } + result + }, + Range(0, self.partitions.size), + (index: Int, data: Long) => totalCount.addAndGet(data), + totalCount.get()) + } + + /** + * Returns a future for retrieving all elements of this RDD. + */ + def collectAsync(): FutureAction[Seq[T]] = { + val results = new Array[Array[T]](self.partitions.size) + self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.size), + (index, data) => results(index) = data, results.flatten.toSeq) + } + + /** + * Returns a future for retrieving the first num elements of the RDD. + */ + def takeAsync(num: Int): FutureAction[Seq[T]] = { + val f = new ComplexFutureAction[Seq[T]] + + f.run { + val results = new ArrayBuffer[T](num) + val totalParts = self.partitions.length + var partsScanned = 0 + while (results.size < num && partsScanned < totalParts) { + // The number of partitions to try in this iteration. It is ok for this number to be + // greater than totalParts because we actually cap it at totalParts in runJob. + var numPartsToTry = 1 + if (partsScanned > 0) { + // If we didn't find any rows after the first iteration, just try all partitions next. + // Otherwise, interpolate the number of partitions we need to try, but overestimate it + // by 50%. + if (results.size == 0) { + numPartsToTry = totalParts - 1 + } else { + numPartsToTry = (1.5 * num * partsScanned / results.size).toInt + } + } + numPartsToTry = math.max(0, numPartsToTry) // guard against negative num of partitions + + val left = num - results.size + val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts) + + val buf = new Array[Array[T]](p.size) + f.runJob(self, + (it: Iterator[T]) => it.take(left).toArray, + p, + (index: Int, data: Array[T]) => buf(index) = data, + Unit) + + buf.foreach(results ++= _.take(num - results.size)) + partsScanned += numPartsToTry + } + results.toSeq + } + + f + } + + /** + * Applies a function f to all elements of this RDD. + */ + def foreachAsync(f: T => Unit): FutureAction[Unit] = { + self.context.submitJob[T, Unit, Unit](self, _.foreach(f), Range(0, self.partitions.size), + (index, data) => Unit, Unit) + } + + /** + * Applies a function f to each partition of this RDD. + */ + def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = { + self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.size), + (index, data) => Unit, Unit) + } +} diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala index bca6956a18..424354ae16 100644 --- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala @@ -17,15 +17,17 @@ package org.apache.spark.rdd +import scala.reflect.ClassTag + import org.apache.spark.{SparkContext, SparkEnv, Partition, TaskContext} -import org.apache.spark.storage.BlockManager +import org.apache.spark.storage.{BlockId, BlockManager} -private[spark] class BlockRDDPartition(val blockId: String, idx: Int) extends Partition { +private[spark] class BlockRDDPartition(val blockId: BlockId, idx: Int) extends Partition { val index = idx } private[spark] -class BlockRDD[T: ClassManifest](sc: SparkContext, @transient blockIds: Array[String]) +class BlockRDD[T: ClassTag](sc: SparkContext, @transient blockIds: Array[BlockId]) extends RDD[T](sc, Nil) { @transient lazy val locations_ = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get) diff --git a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala index 9b0c882481..87b950ba43 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala @@ -18,6 +18,7 @@ package org.apache.spark.rdd import java.io.{ObjectOutputStream, IOException} +import scala.reflect.ClassTag import org.apache.spark._ @@ -43,7 +44,7 @@ class CartesianPartition( } private[spark] -class CartesianRDD[T: ClassManifest, U:ClassManifest]( +class CartesianRDD[T: ClassTag, U: ClassTag]( sc: SparkContext, var rdd1 : RDD[T], var rdd2 : RDD[U]) @@ -70,7 +71,7 @@ class CartesianRDD[T: ClassManifest, U:ClassManifest]( override def compute(split: Partition, context: TaskContext) = { val currSplit = split.asInstanceOf[CartesianPartition] for (x <- rdd1.iterator(currSplit.s1, context); - y <- rdd2.iterator(currSplit.s2, context)) yield (x, y) + y <- rdd2.iterator(currSplit.s2, context)) yield (x, y) } override def getDependencies: Seq[Dependency[_]] = List( diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala index 3311757189..6d4f46125f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala @@ -17,14 +17,13 @@ package org.apache.spark.rdd +import java.io.IOException +import scala.reflect.ClassTag import org.apache.spark._ -import org.apache.hadoop.mapred.{FileInputFormat, SequenceFileInputFormat, JobConf, Reporter} +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.deploy.SparkHadoopUtil import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.io.{NullWritable, BytesWritable} -import org.apache.hadoop.util.ReflectionUtils import org.apache.hadoop.fs.Path -import java.io.{File, IOException, EOFException} -import java.text.NumberFormat private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {} @@ -32,9 +31,11 @@ private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {} * This RDD represents a RDD checkpoint file (similar to HadoopRDD). */ private[spark] -class CheckpointRDD[T: ClassManifest](sc: SparkContext, val checkpointPath: String) +class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String) extends RDD[T](sc, Nil) { + val broadcastedConf = sc.broadcast(new SerializableWritable(sc.hadoopConfiguration)) + @transient val fs = new Path(checkpointPath).getFileSystem(sc.hadoopConfiguration) override def getPartitions: Array[Partition] = { @@ -66,7 +67,7 @@ class CheckpointRDD[T: ClassManifest](sc: SparkContext, val checkpointPath: Stri override def compute(split: Partition, context: TaskContext): Iterator[T] = { val file = new Path(checkpointPath, CheckpointRDD.splitIdToFile(split.index)) - CheckpointRDD.readFromFile(file, context) + CheckpointRDD.readFromFile(file, broadcastedConf, context) } override def checkpoint() { @@ -75,17 +76,20 @@ class CheckpointRDD[T: ClassManifest](sc: SparkContext, val checkpointPath: Stri } private[spark] object CheckpointRDD extends Logging { - def splitIdToFile(splitId: Int): String = { "part-%05d".format(splitId) } - def writeToFile[T](path: String, blockSize: Int = -1)(ctx: TaskContext, iterator: Iterator[T]) { + def writeToFile[T]( + path: String, + broadcastedConf: Broadcast[SerializableWritable[Configuration]], + blockSize: Int = -1 + )(ctx: TaskContext, iterator: Iterator[T]) { val env = SparkEnv.get val outputDir = new Path(path) - val fs = outputDir.getFileSystem(env.hadoop.newConfiguration()) + val fs = outputDir.getFileSystem(broadcastedConf.value.value) - val finalOutputName = splitIdToFile(ctx.splitId) + val finalOutputName = splitIdToFile(ctx.partitionId) val finalOutputPath = new Path(outputDir, finalOutputName) val tempOutputPath = new Path(outputDir, "." + finalOutputName + "-attempt-" + ctx.attemptId) @@ -93,7 +97,7 @@ private[spark] object CheckpointRDD extends Logging { throw new IOException("Checkpoint failed: temporary path " + tempOutputPath + " already exists") } - val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + val bufferSize = env.conf.get("spark.buffer.size", "65536").toInt val fileOutputStream = if (blockSize < 0) { fs.create(tempOutputPath, false, bufferSize) @@ -120,10 +124,14 @@ private[spark] object CheckpointRDD extends Logging { } } - def readFromFile[T](path: Path, context: TaskContext): Iterator[T] = { + def readFromFile[T]( + path: Path, + broadcastedConf: Broadcast[SerializableWritable[Configuration]], + context: TaskContext + ): Iterator[T] = { val env = SparkEnv.get - val fs = path.getFileSystem(env.hadoop.newConfiguration()) - val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + val fs = path.getFileSystem(broadcastedConf.value.value) + val bufferSize = env.conf.get("spark.buffer.size", "65536").toInt val fileInputStream = fs.open(path, bufferSize) val serializer = env.serializer.newInstance() val deserializeStream = serializer.deserializeStream(fileInputStream) @@ -145,8 +153,10 @@ private[spark] object CheckpointRDD extends Logging { val sc = new SparkContext(cluster, "CheckpointRDD Test") val rdd = sc.makeRDD(1 to 10, 10).flatMap(x => 1 to 10000) val path = new Path(hdfsPath, "temp") - val fs = path.getFileSystem(env.hadoop.newConfiguration()) - sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, 1024) _) + val conf = SparkHadoopUtil.get.newConfiguration() + val fs = path.getFileSystem(conf) + val broadcastedConf = sc.broadcast(new SerializableWritable(conf)) + sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, broadcastedConf, 1024) _) val cpRDD = new CheckpointRDD[Int](sc, path.toString) assert(cpRDD.partitions.length == rdd.partitions.length, "Number of partitions is not the same") assert(cpRDD.collect.toList == rdd.collect.toList, "Data of partitions not the same") diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala index 0187256a8e..4ba4696fef 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala @@ -18,13 +18,12 @@ package org.apache.spark.rdd import java.io.{ObjectOutputStream, IOException} -import java.util.{HashMap => JHashMap} -import scala.collection.JavaConversions import scala.collection.mutable.ArrayBuffer -import org.apache.spark.{Partition, Partitioner, SparkEnv, TaskContext} +import org.apache.spark.{InterruptibleIterator, Partition, Partitioner, SparkEnv, TaskContext} import org.apache.spark.{Dependency, OneToOneDependency, ShuffleDependency} +import org.apache.spark.util.AppendOnlyMap private[spark] sealed trait CoGroupSplitDep extends Serializable @@ -105,20 +104,17 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: val split = s.asInstanceOf[CoGroupPartition] val numRdds = split.deps.size // e.g. for `(k, a) cogroup (k, b)`, K -> Seq(ArrayBuffer as, ArrayBuffer bs) - val map = new JHashMap[K, Seq[ArrayBuffer[Any]]] + val map = new AppendOnlyMap[K, Seq[ArrayBuffer[Any]]] - def getSeq(k: K): Seq[ArrayBuffer[Any]] = { - val seq = map.get(k) - if (seq != null) { - seq - } else { - val seq = Array.fill(numRdds)(new ArrayBuffer[Any]) - map.put(k, seq) - seq - } + val update: (Boolean, Seq[ArrayBuffer[Any]]) => Seq[ArrayBuffer[Any]] = (hadVal, oldVal) => { + if (hadVal) oldVal else Array.fill(numRdds)(new ArrayBuffer[Any]) + } + + val getSeq = (k: K) => { + map.changeValue(k, update) } - val ser = SparkEnv.get.serializerManager.get(serializerClass) + val ser = SparkEnv.get.serializerManager.get(serializerClass, SparkEnv.get.conf) for ((dep, depNum) <- split.deps.zipWithIndex) dep match { case NarrowCoGroupSplitDep(rdd, _, itsSplit) => { // Read them from the parent @@ -129,12 +125,12 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: case ShuffleCoGroupSplitDep(shuffleId) => { // Read map outputs of shuffle val fetcher = SparkEnv.get.shuffleFetcher - fetcher.fetch[Product2[K, Any]](shuffleId, split.index, context.taskMetrics, ser).foreach { + fetcher.fetch[Product2[K, Any]](shuffleId, split.index, context, ser).foreach { kv => getSeq(kv._1)(depNum) += kv._2 } } } - JavaConversions.mapAsScalaMap(map).iterator + new InterruptibleIterator(context, map.iterator) } override def clearDependencies() { diff --git a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala index c5de6362a9..98da35763b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala @@ -22,6 +22,7 @@ import java.io.{ObjectOutputStream, IOException} import scala.collection.mutable import scala.Some import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag /** * Class that captures a coalesced RDD by essentially keeping track of parent partitions @@ -68,7 +69,7 @@ case class CoalescedRDDPartition( * @param maxPartitions number of desired partitions in the coalesced RDD * @param balanceSlack used to trade-off balance and locality. 1.0 is all locality, 0 is all balance */ -class CoalescedRDD[T: ClassManifest]( +class CoalescedRDD[T: ClassTag]( @transient var prev: RDD[T], maxPartitions: Int, balanceSlack: Double = 0.10) diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala index a4bec41752..688c310ee9 100644 --- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala @@ -24,6 +24,8 @@ import org.apache.spark.partial.SumEvaluator import org.apache.spark.util.StatCounter import org.apache.spark.{TaskContext, Logging} +import scala.collection.immutable.NumericRange + /** * Extra functions available on RDDs of Doubles through an implicit conversion. * Import `org.apache.spark.SparkContext._` at the top of your program to use these functions. @@ -76,4 +78,129 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { val evaluator = new SumEvaluator(self.partitions.size, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) } + + /** + * Compute a histogram of the data using bucketCount number of buckets evenly + * spaced between the minimum and maximum of the RDD. For example if the min + * value is 0 and the max is 100 and there are two buckets the resulting + * buckets will be [0, 50) [50, 100]. bucketCount must be at least 1 + * If the RDD contains infinity, NaN throws an exception + * If the elements in RDD do not vary (max == min) always returns a single bucket. + */ + def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = { + // Compute the minimum and the maxium + val (max: Double, min: Double) = self.mapPartitions { items => + Iterator(items.foldRight(Double.NegativeInfinity, + Double.PositiveInfinity)((e: Double, x: Pair[Double, Double]) => + (x._1.max(e), x._2.min(e)))) + }.reduce { (maxmin1, maxmin2) => + (maxmin1._1.max(maxmin2._1), maxmin1._2.min(maxmin2._2)) + } + if (min.isNaN || max.isNaN || max.isInfinity || min.isInfinity ) { + throw new UnsupportedOperationException( + "Histogram on either an empty RDD or RDD containing +/-infinity or NaN") + } + val increment = (max-min)/bucketCount.toDouble + val range = if (increment != 0) { + Range.Double.inclusive(min, max, increment) + } else { + List(min, min) + } + val buckets = range.toArray + (buckets, histogram(buckets, true)) + } + + /** + * Compute a histogram using the provided buckets. The buckets are all open + * to the left except for the last which is closed + * e.g. for the array + * [1, 10, 20, 50] the buckets are [1, 10) [10, 20) [20, 50] + * e.g 1<=x<10 , 10<=x<20, 20<=x<50 + * And on the input of 1 and 50 we would have a histogram of 1, 0, 0 + * + * Note: if your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched + * from an O(log n) inseration to O(1) per element. (where n = # buckets) if you set evenBuckets + * to true. + * buckets must be sorted and not contain any duplicates. + * buckets array must be at least two elements + * All NaN entries are treated the same. If you have a NaN bucket it must be + * the maximum value of the last position and all NaN entries will be counted + * in that bucket. + */ + def histogram(buckets: Array[Double], evenBuckets: Boolean = false): Array[Long] = { + if (buckets.length < 2) { + throw new IllegalArgumentException("buckets array must have at least two elements") + } + // The histogramPartition function computes the partail histogram for a given + // partition. The provided bucketFunction determines which bucket in the array + // to increment or returns None if there is no bucket. This is done so we can + // specialize for uniformly distributed buckets and save the O(log n) binary + // search cost. + def histogramPartition(bucketFunction: (Double) => Option[Int])(iter: Iterator[Double]): + Iterator[Array[Long]] = { + val counters = new Array[Long](buckets.length - 1) + while (iter.hasNext) { + bucketFunction(iter.next()) match { + case Some(x: Int) => {counters(x) += 1} + case _ => {} + } + } + Iterator(counters) + } + // Merge the counters. + def mergeCounters(a1: Array[Long], a2: Array[Long]): Array[Long] = { + a1.indices.foreach(i => a1(i) += a2(i)) + a1 + } + // Basic bucket function. This works using Java's built in Array + // binary search. Takes log(size(buckets)) + def basicBucketFunction(e: Double): Option[Int] = { + val location = java.util.Arrays.binarySearch(buckets, e) + if (location < 0) { + // If the location is less than 0 then the insertion point in the array + // to keep it sorted is -location-1 + val insertionPoint = -location-1 + // If we have to insert before the first element or after the last one + // its out of bounds. + // We do this rather than buckets.lengthCompare(insertionPoint) + // because Array[Double] fails to override it (for now). + if (insertionPoint > 0 && insertionPoint < buckets.length) { + Some(insertionPoint-1) + } else { + None + } + } else if (location < buckets.length - 1) { + // Exact match, just insert here + Some(location) + } else { + // Exact match to the last element + Some(location - 1) + } + } + // Determine the bucket function in constant time. Requires that buckets are evenly spaced + def fastBucketFunction(min: Double, increment: Double, count: Int)(e: Double): Option[Int] = { + // If our input is not a number unless the increment is also NaN then we fail fast + if (e.isNaN()) { + return None + } + val bucketNumber = (e - min)/(increment) + // We do this rather than buckets.lengthCompare(bucketNumber) + // because Array[Double] fails to override it (for now). + if (bucketNumber > count || bucketNumber < 0) { + None + } else { + Some(bucketNumber.toInt.min(count - 1)) + } + } + // Decide which bucket function to pass to histogramPartition. We decide here + // rather than having a general function so that the decission need only be made + // once rather than once per shard + val bucketFunction = if (evenBuckets) { + fastBucketFunction(buckets(0), buckets(1)-buckets(0), buckets.length-1) _ + } else { + basicBucketFunction _ + } + self.mapPartitions(histogramPartition(bucketFunction)).reduce(mergeCounters) + } + } diff --git a/core/src/main/scala/org/apache/spark/rdd/EmptyRDD.scala b/core/src/main/scala/org/apache/spark/rdd/EmptyRDD.scala index c8900d1a93..a84e5f9fd8 100644 --- a/core/src/main/scala/org/apache/spark/rdd/EmptyRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/EmptyRDD.scala @@ -17,13 +17,14 @@ package org.apache.spark.rdd -import org.apache.spark.{SparkContext, SparkEnv, Partition, TaskContext} +import scala.reflect.ClassTag +import org.apache.spark.{Partition, SparkContext, TaskContext} /** * An RDD that is empty, i.e. has no element in it. */ -class EmptyRDD[T: ClassManifest](sc: SparkContext) extends RDD[T](sc, Nil) { +class EmptyRDD[T: ClassTag](sc: SparkContext) extends RDD[T](sc, Nil) { override def getPartitions: Array[Partition] = Array.empty diff --git a/core/src/main/scala/org/apache/spark/rdd/FilteredRDD.scala b/core/src/main/scala/org/apache/spark/rdd/FilteredRDD.scala index 5312dc0b59..e74c83b90b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/FilteredRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/FilteredRDD.scala @@ -18,8 +18,9 @@ package org.apache.spark.rdd import org.apache.spark.{OneToOneDependency, Partition, TaskContext} +import scala.reflect.ClassTag -private[spark] class FilteredRDD[T: ClassManifest]( +private[spark] class FilteredRDD[T: ClassTag]( prev: RDD[T], f: T => Boolean) extends RDD[T](prev) { diff --git a/core/src/main/scala/org/apache/spark/rdd/FlatMappedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/FlatMappedRDD.scala index cbdf6d84c0..4d1878fc14 100644 --- a/core/src/main/scala/org/apache/spark/rdd/FlatMappedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/FlatMappedRDD.scala @@ -18,10 +18,11 @@ package org.apache.spark.rdd import org.apache.spark.{Partition, TaskContext} +import scala.reflect.ClassTag private[spark] -class FlatMappedRDD[U: ClassManifest, T: ClassManifest]( +class FlatMappedRDD[U: ClassTag, T: ClassTag]( prev: RDD[T], f: T => TraversableOnce[U]) extends RDD[U](prev) { diff --git a/core/src/main/scala/org/apache/spark/rdd/GlommedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/GlommedRDD.scala index 829545d7b0..1a694475f6 100644 --- a/core/src/main/scala/org/apache/spark/rdd/GlommedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/GlommedRDD.scala @@ -18,8 +18,9 @@ package org.apache.spark.rdd import org.apache.spark.{Partition, TaskContext} +import scala.reflect.ClassTag -private[spark] class GlommedRDD[T: ClassManifest](prev: RDD[T]) +private[spark] class GlommedRDD[T: ClassTag](prev: RDD[T]) extends RDD[Array[T]](prev) { override def getPartitions: Array[Partition] = firstParent[T].partitions diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala index 2cb6734e41..53f77a38f5 100644 --- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala @@ -19,6 +19,7 @@ package org.apache.spark.rdd import java.io.EOFException +import org.apache.hadoop.mapred.FileInputFormat import org.apache.hadoop.mapred.InputFormat import org.apache.hadoop.mapred.InputSplit import org.apache.hadoop.mapred.JobConf @@ -26,7 +27,9 @@ import org.apache.hadoop.mapred.RecordReader import org.apache.hadoop.mapred.Reporter import org.apache.hadoop.util.ReflectionUtils -import org.apache.spark.{Logging, Partition, SerializableWritable, SparkContext, SparkEnv, TaskContext} +import org.apache.spark._ +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.util.NextIterator import org.apache.hadoop.conf.{Configuration, Configurable} @@ -36,7 +39,7 @@ import org.apache.hadoop.conf.{Configuration, Configurable} */ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSplit) extends Partition { - + val inputSplit = new SerializableWritable[InputSplit](s) override def hashCode(): Int = (41 * (41 + rddId) + idx).toInt @@ -45,29 +48,97 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp } /** - * An RDD that reads a Hadoop dataset as specified by a JobConf (e.g. files in HDFS, the local file - * system, or S3, tables in HBase, etc). + * An RDD that provides core functionality for reading data stored in Hadoop (e.g., files in HDFS, + * sources in HBase, or S3). + * + * @param sc The SparkContext to associate the RDD with. + * @param broadcastedConf A general Hadoop Configuration, or a subclass of it. If the enclosed + * variabe references an instance of JobConf, then that JobConf will be used for the Hadoop job. + * Otherwise, a new JobConf will be created on each slave using the enclosed Configuration. + * @param initLocalJobConfFuncOpt Optional closure used to initialize any JobConf that HadoopRDD + * creates. + * @param inputFormatClass Storage format of the data to be read. + * @param keyClass Class of the key associated with the inputFormatClass. + * @param valueClass Class of the value associated with the inputFormatClass. + * @param minSplits Minimum number of Hadoop Splits (HadoopRDD partitions) to generate. */ class HadoopRDD[K, V]( sc: SparkContext, - @transient conf: JobConf, + broadcastedConf: Broadcast[SerializableWritable[Configuration]], + initLocalJobConfFuncOpt: Option[JobConf => Unit], inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], minSplits: Int) extends RDD[(K, V)](sc, Nil) with Logging { - // A Hadoop JobConf can be about 10 KB, which is pretty big, so broadcast it - private val confBroadcast = sc.broadcast(new SerializableWritable(conf)) + def this( + sc: SparkContext, + conf: JobConf, + inputFormatClass: Class[_ <: InputFormat[K, V]], + keyClass: Class[K], + valueClass: Class[V], + minSplits: Int) = { + this( + sc, + sc.broadcast(new SerializableWritable(conf)) + .asInstanceOf[Broadcast[SerializableWritable[Configuration]]], + None /* initLocalJobConfFuncOpt */, + inputFormatClass, + keyClass, + valueClass, + minSplits) + } + + protected val jobConfCacheKey = "rdd_%d_job_conf".format(id) + + protected val inputFormatCacheKey = "rdd_%d_input_format".format(id) + + // Returns a JobConf that will be used on slaves to obtain input splits for Hadoop reads. + protected def getJobConf(): JobConf = { + val conf: Configuration = broadcastedConf.value.value + if (conf.isInstanceOf[JobConf]) { + // A user-broadcasted JobConf was provided to the HadoopRDD, so always use it. + return conf.asInstanceOf[JobConf] + } else if (HadoopRDD.containsCachedMetadata(jobConfCacheKey)) { + // getJobConf() has been called previously, so there is already a local cache of the JobConf + // needed by this RDD. + return HadoopRDD.getCachedMetadata(jobConfCacheKey).asInstanceOf[JobConf] + } else { + // Create a JobConf that will be cached and used across this RDD's getJobConf() calls in the + // local process. The local cache is accessed through HadoopRDD.putCachedMetadata(). + // The caching helps minimize GC, since a JobConf can contain ~10KB of temporary objects. + val newJobConf = new JobConf(broadcastedConf.value.value) + initLocalJobConfFuncOpt.map(f => f(newJobConf)) + HadoopRDD.putCachedMetadata(jobConfCacheKey, newJobConf) + return newJobConf + } + } + + protected def getInputFormat(conf: JobConf): InputFormat[K, V] = { + if (HadoopRDD.containsCachedMetadata(inputFormatCacheKey)) { + return HadoopRDD.getCachedMetadata(inputFormatCacheKey).asInstanceOf[InputFormat[K, V]] + } + // Once an InputFormat for this RDD is created, cache it so that only one reflection call is + // done in each local process. + val newInputFormat = ReflectionUtils.newInstance(inputFormatClass.asInstanceOf[Class[_]], conf) + .asInstanceOf[InputFormat[K, V]] + if (newInputFormat.isInstanceOf[Configurable]) { + newInputFormat.asInstanceOf[Configurable].setConf(conf) + } + HadoopRDD.putCachedMetadata(inputFormatCacheKey, newInputFormat) + return newInputFormat + } override def getPartitions: Array[Partition] = { - val env = SparkEnv.get - env.hadoop.addCredentials(conf) - val inputFormat = createInputFormat(conf) + val jobConf = getJobConf() + // add the credentials here as this can be called before SparkContext initialized + SparkHadoopUtil.get.addCredentials(jobConf) + val inputFormat = getInputFormat(jobConf) if (inputFormat.isInstanceOf[Configurable]) { - inputFormat.asInstanceOf[Configurable].setConf(conf) + inputFormat.asInstanceOf[Configurable].setConf(jobConf) } - val inputSplits = inputFormat.getSplits(conf, minSplits) + val inputSplits = inputFormat.getSplits(jobConf, minSplits) val array = new Array[Partition](inputSplits.size) for (i <- 0 until inputSplits.size) { array(i) = new HadoopPartition(id, i, inputSplits(i)) @@ -75,46 +146,41 @@ class HadoopRDD[K, V]( array } - def createInputFormat(conf: JobConf): InputFormat[K, V] = { - ReflectionUtils.newInstance(inputFormatClass.asInstanceOf[Class[_]], conf) - .asInstanceOf[InputFormat[K, V]] - } - - override def compute(theSplit: Partition, context: TaskContext) = new NextIterator[(K, V)] { - val split = theSplit.asInstanceOf[HadoopPartition] - logInfo("Input split: " + split.inputSplit) - var reader: RecordReader[K, V] = null - - val conf = confBroadcast.value.value - val fmt = createInputFormat(conf) - if (fmt.isInstanceOf[Configurable]) { - fmt.asInstanceOf[Configurable].setConf(conf) - } - reader = fmt.getRecordReader(split.inputSplit.value, conf, Reporter.NULL) - - // Register an on-task-completion callback to close the input stream. - context.addOnCompleteCallback{ () => closeIfNeeded() } - - val key: K = reader.createKey() - val value: V = reader.createValue() - - override def getNext() = { - try { - finished = !reader.next(key, value) - } catch { - case eof: EOFException => - finished = true + override def compute(theSplit: Partition, context: TaskContext) = { + val iter = new NextIterator[(K, V)] { + val split = theSplit.asInstanceOf[HadoopPartition] + logInfo("Input split: " + split.inputSplit) + var reader: RecordReader[K, V] = null + + val jobConf = getJobConf() + val inputFormat = getInputFormat(jobConf) + reader = inputFormat.getRecordReader(split.inputSplit.value, jobConf, Reporter.NULL) + + // Register an on-task-completion callback to close the input stream. + context.addOnCompleteCallback{ () => closeIfNeeded() } + + val key: K = reader.createKey() + val value: V = reader.createValue() + + override def getNext() = { + try { + finished = !reader.next(key, value) + } catch { + case eof: EOFException => + finished = true + } + (key, value) } - (key, value) - } - override def close() { - try { - reader.close() - } catch { - case e: Exception => logWarning("Exception in RecordReader.close()", e) + override def close() { + try { + reader.close() + } catch { + case e: Exception => logWarning("Exception in RecordReader.close()", e) + } } } + new InterruptibleIterator[(K, V)](context, iter) } override def getPreferredLocations(split: Partition): Seq[String] = { @@ -127,5 +193,18 @@ class HadoopRDD[K, V]( // Do nothing. Hadoop RDD should not be checkpointed. } - def getConf: Configuration = confBroadcast.value.value + def getConf: Configuration = getJobConf() +} + +private[spark] object HadoopRDD { + /** + * The three methods below are helpers for accessing the local map, a property of the SparkEnv of + * the local process. + */ + def getCachedMetadata(key: String) = SparkEnv.get.hadoopJobMetadata.get(key) + + def containsCachedMetadata(key: String) = SparkEnv.get.hadoopJobMetadata.containsKey(key) + + def putCachedMetadata(key: String, value: Any) = + SparkEnv.get.hadoopJobMetadata.put(key, value) } diff --git a/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala b/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala index aca0146884..8df8718f3b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala @@ -19,6 +19,8 @@ package org.apache.spark.rdd import java.sql.{Connection, ResultSet} +import scala.reflect.ClassTag + import org.apache.spark.{Logging, Partition, SparkContext, TaskContext} import org.apache.spark.util.NextIterator @@ -45,7 +47,7 @@ private[spark] class JdbcPartition(idx: Int, val lower: Long, val upper: Long) e * This should only call getInt, getString, etc; the RDD takes care of calling next. * The default maps a ResultSet to an array of Object. */ -class JdbcRDD[T: ClassManifest]( +class JdbcRDD[T: ClassTag]( sc: SparkContext, getConnection: () => Connection, sql: String, diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala index 203179c4ea..db15baf503 100644 --- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala @@ -18,20 +18,18 @@ package org.apache.spark.rdd import org.apache.spark.{Partition, TaskContext} +import scala.reflect.ClassTag - -private[spark] -class MapPartitionsRDD[U: ClassManifest, T: ClassManifest]( +private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag]( prev: RDD[T], - f: Iterator[T] => Iterator[U], + f: (TaskContext, Int, Iterator[T]) => Iterator[U], // (TaskContext, partition index, iterator) preservesPartitioning: Boolean = false) extends RDD[U](prev) { - override val partitioner = - if (preservesPartitioning) firstParent[T].partitioner else None + override val partitioner = if (preservesPartitioning) firstParent[T].partitioner else None override def getPartitions: Array[Partition] = firstParent[T].partitions override def compute(split: Partition, context: TaskContext) = - f(firstParent[T].iterator(split, context)) + f(context, split.index, firstParent[T].iterator(split, context)) } diff --git a/core/src/main/scala/org/apache/spark/rdd/MappedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MappedRDD.scala index e8be1c4816..8d7c288593 100644 --- a/core/src/main/scala/org/apache/spark/rdd/MappedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/MappedRDD.scala @@ -17,10 +17,12 @@ package org.apache.spark.rdd +import scala.reflect.ClassTag + import org.apache.spark.{Partition, TaskContext} private[spark] -class MappedRDD[U: ClassManifest, T: ClassManifest](prev: RDD[T], f: T => U) +class MappedRDD[U: ClassTag, T: ClassTag](prev: RDD[T], f: T => U) extends RDD[U](prev) { override def getPartitions: Array[Partition] = firstParent[T].partitions diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala index 7b3a89f7e0..2662d48c84 100644 --- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala @@ -24,7 +24,7 @@ import org.apache.hadoop.conf.{Configurable, Configuration} import org.apache.hadoop.io.Writable import org.apache.hadoop.mapreduce._ -import org.apache.spark.{Dependency, Logging, Partition, SerializableWritable, SparkContext, TaskContext} +import org.apache.spark.{InterruptibleIterator, Logging, Partition, SerializableWritable, SparkContext, TaskContext} private[spark] @@ -71,49 +71,52 @@ class NewHadoopRDD[K, V]( result } - override def compute(theSplit: Partition, context: TaskContext) = new Iterator[(K, V)] { - val split = theSplit.asInstanceOf[NewHadoopPartition] - logInfo("Input split: " + split.serializableHadoopSplit) - val conf = confBroadcast.value.value - val attemptId = newTaskAttemptID(jobtrackerId, id, true, split.index, 0) - val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId) - val format = inputFormatClass.newInstance - if (format.isInstanceOf[Configurable]) { - format.asInstanceOf[Configurable].setConf(conf) - } - val reader = format.createRecordReader( - split.serializableHadoopSplit.value, hadoopAttemptContext) - reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext) - - // Register an on-task-completion callback to close the input stream. - context.addOnCompleteCallback(() => close()) - - var havePair = false - var finished = false - - override def hasNext: Boolean = { - if (!finished && !havePair) { - finished = !reader.nextKeyValue - havePair = !finished + override def compute(theSplit: Partition, context: TaskContext) = { + val iter = new Iterator[(K, V)] { + val split = theSplit.asInstanceOf[NewHadoopPartition] + logInfo("Input split: " + split.serializableHadoopSplit) + val conf = confBroadcast.value.value + val attemptId = newTaskAttemptID(jobtrackerId, id, true, split.index, 0) + val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId) + val format = inputFormatClass.newInstance + if (format.isInstanceOf[Configurable]) { + format.asInstanceOf[Configurable].setConf(conf) + } + val reader = format.createRecordReader( + split.serializableHadoopSplit.value, hadoopAttemptContext) + reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext) + + // Register an on-task-completion callback to close the input stream. + context.addOnCompleteCallback(() => close()) + + var havePair = false + var finished = false + + override def hasNext: Boolean = { + if (!finished && !havePair) { + finished = !reader.nextKeyValue + havePair = !finished + } + !finished } - !finished - } - override def next: (K, V) = { - if (!hasNext) { - throw new java.util.NoSuchElementException("End of stream") + override def next(): (K, V) = { + if (!hasNext) { + throw new java.util.NoSuchElementException("End of stream") + } + havePair = false + (reader.getCurrentKey, reader.getCurrentValue) } - havePair = false - return (reader.getCurrentKey, reader.getCurrentValue) - } - private def close() { - try { - reader.close() - } catch { - case e: Exception => logWarning("Exception in RecordReader.close()", e) + private def close() { + try { + reader.close() + } catch { + case e: Exception => logWarning("Exception in RecordReader.close()", e) + } } } + new InterruptibleIterator(context, iter) } override def getPreferredLocations(split: Partition): Seq[String] = { diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala index 697be8b997..d5691f2267 100644 --- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala @@ -17,7 +17,9 @@ package org.apache.spark.rdd -import org.apache.spark.{RangePartitioner, Logging} +import scala.reflect.ClassTag + +import org.apache.spark.{Logging, RangePartitioner} /** * Extra functions available on RDDs of (key, value) pairs where the key is sortable through @@ -25,9 +27,9 @@ import org.apache.spark.{RangePartitioner, Logging} * use these functions. They will work with any key type that has a `scala.math.Ordered` * implementation. */ -class OrderedRDDFunctions[K <% Ordered[K]: ClassManifest, - V: ClassManifest, - P <: Product2[K, V] : ClassManifest]( +class OrderedRDDFunctions[K <% Ordered[K]: ClassTag, + V: ClassTag, + P <: Product2[K, V] : ClassTag]( self: RDD[P]) extends Logging with Serializable { diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index a47c512275..04a8d05988 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -25,6 +25,7 @@ import java.util.{HashMap => JHashMap} import scala.collection.{mutable, Map} import scala.collection.mutable.ArrayBuffer import scala.collection.JavaConversions._ +import scala.reflect.{ClassTag, classTag} import org.apache.hadoop.mapred._ import org.apache.hadoop.io.compress.CompressionCodec @@ -39,18 +40,21 @@ import org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil import org.apache.hadoop.mapreduce.{Job => NewAPIHadoopJob} import org.apache.hadoop.mapreduce.{RecordWriter => NewRecordWriter} +import com.clearspring.analytics.stream.cardinality.HyperLogLog + import org.apache.spark._ import org.apache.spark.SparkContext._ import org.apache.spark.partial.{BoundedDouble, PartialResult} import org.apache.spark.Aggregator import org.apache.spark.Partitioner import org.apache.spark.Partitioner.defaultPartitioner +import org.apache.spark.util.SerializableHyperLogLog /** * Extra functions available on RDDs of (key, value) pairs through an implicit conversion. * Import `org.apache.spark.SparkContext._` at the top of your program to use these functions. */ -class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) +class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) extends Logging with SparkHadoopMapReduceUtil with Serializable { @@ -84,18 +88,24 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) } val aggregator = new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners) if (self.partitioner == Some(partitioner)) { - self.mapPartitions(aggregator.combineValuesByKey, preservesPartitioning = true) + self.mapPartitionsWithContext((context, iter) => { + new InterruptibleIterator(context, aggregator.combineValuesByKey(iter)) + }, preservesPartitioning = true) } else if (mapSideCombine) { val combined = self.mapPartitions(aggregator.combineValuesByKey, preservesPartitioning = true) val partitioned = new ShuffledRDD[K, C, (K, C)](combined, partitioner) .setSerializer(serializerClass) - partitioned.mapPartitions(aggregator.combineCombinersByKey, preservesPartitioning = true) + partitioned.mapPartitionsWithContext((context, iter) => { + new InterruptibleIterator(context, aggregator.combineCombinersByKey(iter)) + }, preservesPartitioning = true) } else { // Don't apply map-side combiner. // A sanity check to make sure mergeCombiners is not defined. assert(mergeCombiners == null) val values = new ShuffledRDD[K, V, (K, V)](self, partitioner).setSerializer(serializerClass) - values.mapPartitions(aggregator.combineValuesByKey, preservesPartitioning = true) + values.mapPartitionsWithContext((context, iter) => { + new InterruptibleIterator(context, aggregator.combineValuesByKey(iter)) + }, preservesPartitioning = true) } } @@ -201,6 +211,45 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) } /** + * Return approximate number of distinct values for each key in this RDD. + * The accuracy of approximation can be controlled through the relative standard deviation + * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in + * more accurate counts but increase the memory footprint and vise versa. Uses the provided + * Partitioner to partition the output RDD. + */ + def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = { + val createHLL = (v: V) => new SerializableHyperLogLog(new HyperLogLog(relativeSD)).add(v) + val mergeValueHLL = (hll: SerializableHyperLogLog, v: V) => hll.add(v) + val mergeHLL = (h1: SerializableHyperLogLog, h2: SerializableHyperLogLog) => h1.merge(h2) + + combineByKey(createHLL, mergeValueHLL, mergeHLL, partitioner).mapValues(_.value.cardinality()) + } + + /** + * Return approximate number of distinct values for each key in this RDD. + * The accuracy of approximation can be controlled through the relative standard deviation + * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in + * more accurate counts but increase the memory footprint and vise versa. HashPartitions the + * output RDD into numPartitions. + * + */ + def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): RDD[(K, Long)] = { + countApproxDistinctByKey(relativeSD, new HashPartitioner(numPartitions)) + } + + /** + * Return approximate number of distinct values for each key this RDD. + * The accuracy of approximation can be controlled through the relative standard deviation + * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in + * more accurate counts but increase the memory footprint and vise versa. The default value of + * relativeSD is 0.05. Hash-partitions the output RDD using the existing partitioner/parallelism + * level. + */ + def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = { + countApproxDistinctByKey(relativeSD, defaultPartitioner(self)) + } + + /** * Merge the values for each key using an associative reduce function. This will also perform * the merging locally on each mapper before sending results to a reducer, similarly to a * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions. @@ -409,7 +458,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) throw new SparkException("Default partitioner cannot partition array keys.") } val cg = new CoGroupedRDD[K](Seq(self, other), partitioner) - val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classManifest[K], Manifests.seqSeqManifest) + val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classTag[K], ClassTags.seqSeqClassTag) prfs.mapValues { case Seq(vs, ws) => (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]]) } @@ -425,7 +474,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) throw new SparkException("Default partitioner cannot partition array keys.") } val cg = new CoGroupedRDD[K](Seq(self, other1, other2), partitioner) - val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classManifest[K], Manifests.seqSeqManifest) + val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classTag[K], ClassTags.seqSeqClassTag) prfs.mapValues { case Seq(vs, w1s, w2s) => (vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W1]], w2s.asInstanceOf[Seq[W2]]) } @@ -482,15 +531,15 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ - def subtractByKey[W: ClassManifest](other: RDD[(K, W)]): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.size))) /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ - def subtractByKey[W: ClassManifest](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] = subtractByKey(other, new HashPartitioner(numPartitions)) /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ - def subtractByKey[W: ClassManifest](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = new SubtractedRDD[K, V, W](self, other, p) /** @@ -519,8 +568,8 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class * supporting the key and value types K and V in this RDD. */ - def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassManifest[F]) { - saveAsHadoopFile(path, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]]) + def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) { + saveAsHadoopFile(path, getKeyClass, getValueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } /** @@ -529,16 +578,16 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) * supplied codec. */ def saveAsHadoopFile[F <: OutputFormat[K, V]]( - path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassManifest[F]) { - saveAsHadoopFile(path, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]], codec) + path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassTag[F]) { + saveAsHadoopFile(path, getKeyClass, getValueClass, fm.runtimeClass.asInstanceOf[Class[F]], codec) } /** * Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat` * (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD. */ - def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String)(implicit fm: ClassManifest[F]) { - saveAsNewAPIHadoopFile(path, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]]) + def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) { + saveAsNewAPIHadoopFile(path, getKeyClass, getValueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } /** @@ -564,7 +613,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) // around by taking a mod. We expect that no task will be attempted 2 billion times. val attemptNumber = (context.attemptId % Int.MaxValue).toInt /* "reduce task" <split #> <attempt # = spark task #> */ - val attemptId = newTaskAttemptID(jobtrackerID, stageId, false, context.splitId, attemptNumber) + val attemptId = newTaskAttemptID(jobtrackerID, stageId, false, context.partitionId, attemptNumber) val hadoopContext = newTaskAttemptContext(wrappedConf.value, attemptId) val format = outputFormatClass.newInstance val committer = format.getOutputCommitter(hadoopContext) @@ -663,7 +712,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) // around by taking a mod. We expect that no task will be attempted 2 billion times. val attemptNumber = (context.attemptId % Int.MaxValue).toInt - writer.setup(context.stageId, context.splitId, attemptNumber) + writer.setup(context.stageId, context.partitionId, attemptNumber) writer.open() var count = 0 @@ -692,11 +741,11 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) */ def values: RDD[V] = self.map(_._2) - private[spark] def getKeyClass() = implicitly[ClassManifest[K]].erasure + private[spark] def getKeyClass() = implicitly[ClassTag[K]].runtimeClass - private[spark] def getValueClass() = implicitly[ClassManifest[V]].erasure + private[spark] def getValueClass() = implicitly[ClassTag[V]].runtimeClass } -private[spark] object Manifests { - val seqSeqManifest = classManifest[Seq[Seq[_]]] +private[spark] object ClassTags { + val seqSeqClassTag = classTag[Seq[Seq[_]]] } diff --git a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala index 6dbd4309aa..09d0a8189d 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala @@ -20,13 +20,15 @@ package org.apache.spark.rdd import scala.collection.immutable.NumericRange import scala.collection.mutable.ArrayBuffer import scala.collection.Map +import scala.reflect.ClassTag + import org.apache.spark._ import java.io._ import scala.Serializable import org.apache.spark.serializer.JavaSerializer import org.apache.spark.util.Utils -private[spark] class ParallelCollectionPartition[T: ClassManifest]( +private[spark] class ParallelCollectionPartition[T: ClassTag]( var rddId: Long, var slice: Int, var values: Seq[T]) @@ -78,7 +80,7 @@ private[spark] class ParallelCollectionPartition[T: ClassManifest]( } } -private[spark] class ParallelCollectionRDD[T: ClassManifest]( +private[spark] class ParallelCollectionRDD[T: ClassTag]( @transient sc: SparkContext, @transient data: Seq[T], numSlices: Int, @@ -94,8 +96,9 @@ private[spark] class ParallelCollectionRDD[T: ClassManifest]( slices.indices.map(i => new ParallelCollectionPartition(id, i, slices(i))).toArray } - override def compute(s: Partition, context: TaskContext) = - s.asInstanceOf[ParallelCollectionPartition[T]].iterator + override def compute(s: Partition, context: TaskContext) = { + new InterruptibleIterator(context, s.asInstanceOf[ParallelCollectionPartition[T]].iterator) + } override def getPreferredLocations(s: Partition): Seq[String] = { locationPrefs.getOrElse(s.index, Nil) @@ -108,7 +111,7 @@ private object ParallelCollectionRDD { * collections specially, encoding the slices as other Ranges to minimize memory cost. This makes * it efficient to run Spark over RDDs representing large sets of numbers. */ - def slice[T: ClassManifest](seq: Seq[T], numSlices: Int): Seq[Seq[T]] = { + def slice[T: ClassTag](seq: Seq[T], numSlices: Int): Seq[Seq[T]] = { if (numSlices < 1) { throw new IllegalArgumentException("Positive number of slices required") } diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala index 165cd412fc..ea8885b36e 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala @@ -17,6 +17,8 @@ package org.apache.spark.rdd +import scala.reflect.ClassTag + import org.apache.spark.{NarrowDependency, SparkEnv, Partition, TaskContext} @@ -33,11 +35,13 @@ class PruneDependency[T](rdd: RDD[T], @transient partitionFilterFunc: Int => Boo extends NarrowDependency[T](rdd) { @transient - val partitions: Array[Partition] = rdd.partitions.zipWithIndex - .filter(s => partitionFilterFunc(s._2)) + val partitions: Array[Partition] = rdd.partitions + .filter(s => partitionFilterFunc(s.index)).zipWithIndex .map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition } - override def getParents(partitionId: Int) = List(partitions(partitionId).index) + override def getParents(partitionId: Int) = { + List(partitions(partitionId).asInstanceOf[PartitionPruningRDDPartition].parentSplit.index) + } } @@ -47,7 +51,7 @@ class PruneDependency[T](rdd: RDD[T], @transient partitionFilterFunc: Int => Boo * and the execution DAG has a filter on the key, we can avoid launching tasks * on partitions that don't have the range covering the key. */ -class PartitionPruningRDD[T: ClassManifest]( +class PartitionPruningRDD[T: ClassTag]( @transient prev: RDD[T], @transient partitionFilterFunc: Int => Boolean) extends RDD[T](prev.context, List(new PruneDependency(prev, partitionFilterFunc))) { @@ -67,6 +71,6 @@ object PartitionPruningRDD { * when its type T is not known at compile time. */ def create[T](rdd: RDD[T], partitionFilterFunc: Int => Boolean) = { - new PartitionPruningRDD[T](rdd, partitionFilterFunc)(rdd.elementClassManifest) + new PartitionPruningRDD[T](rdd, partitionFilterFunc)(rdd.elementClassTag) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala index d5304ab0ae..1dbbe39898 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala @@ -24,6 +24,7 @@ import scala.collection.Map import scala.collection.JavaConversions._ import scala.collection.mutable.ArrayBuffer import scala.io.Source +import scala.reflect.ClassTag import org.apache.spark.{SparkEnv, Partition, TaskContext} import org.apache.spark.broadcast.Broadcast @@ -33,7 +34,7 @@ import org.apache.spark.broadcast.Broadcast * An RDD that pipes the contents of each parent partition through an external command * (printing them one per line) and returns the output as a collection of strings. */ -class PipedRDD[T: ClassManifest]( +class PipedRDD[T: ClassTag]( prev: RDD[T], command: Seq[String], envVars: Map[String, String], diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 1893627ee2..6a7b0f8a86 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -23,6 +23,9 @@ import scala.collection.Map import scala.collection.JavaConversions.mapAsScalaMap import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap +import scala.reflect.{classTag, ClassTag} + import org.apache.hadoop.io.BytesWritable import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.io.NullWritable @@ -30,6 +33,7 @@ import org.apache.hadoop.io.Text import org.apache.hadoop.mapred.TextOutputFormat import it.unimi.dsi.fastutil.objects.{Object2LongOpenHashMap => OLMap} +import com.clearspring.analytics.stream.cardinality.HyperLogLog import org.apache.spark.Partitioner._ import org.apache.spark.api.java.JavaRDD @@ -38,7 +42,7 @@ import org.apache.spark.partial.CountEvaluator import org.apache.spark.partial.GroupedCountEvaluator import org.apache.spark.partial.PartialResult import org.apache.spark.storage.StorageLevel -import org.apache.spark.util.{Utils, BoundedPriorityQueue} +import org.apache.spark.util.{Utils, BoundedPriorityQueue, SerializableHyperLogLog} import org.apache.spark.SparkContext._ import org.apache.spark._ @@ -69,7 +73,7 @@ import org.apache.spark._ * [[http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf Spark paper]] for more details * on RDD internals. */ -abstract class RDD[T: ClassManifest]( +abstract class RDD[T: ClassTag]( @transient private var sc: SparkContext, @transient private var deps: Seq[Dependency[_]] ) extends Serializable with Logging { @@ -78,6 +82,7 @@ abstract class RDD[T: ClassManifest]( def this(@transient oneParent: RDD[_]) = this(oneParent.context , List(new OneToOneDependency(oneParent))) + private[spark] def conf = sc.conf // ======================================================================= // Methods that should be implemented by subclasses of RDD // ======================================================================= @@ -101,7 +106,7 @@ abstract class RDD[T: ClassManifest]( protected def getPreferredLocations(split: Partition): Seq[String] = Nil /** Optionally overridden by subclasses to specify how they are partitioned. */ - val partitioner: Option[Partitioner] = None + @transient val partitioner: Option[Partitioner] = None // ======================================================================= // Methods and fields available on all RDDs @@ -114,7 +119,7 @@ abstract class RDD[T: ClassManifest]( val id: Int = sc.newRddId() /** A friendly name for this RDD */ - var name: String = null + @transient var name: String = null /** Assign a name to this RDD */ def setName(_name: String) = { @@ -123,7 +128,7 @@ abstract class RDD[T: ClassManifest]( } /** User-defined generator of this RDD*/ - var generator = Utils.getCallSiteInfo.firstUserClass + @transient var generator = Utils.getCallSiteInfo.firstUserClass /** Reset generator*/ def setGenerator(_generator: String) = { @@ -243,13 +248,13 @@ abstract class RDD[T: ClassManifest]( /** * Return a new RDD by applying a function to all elements of this RDD. */ - def map[U: ClassManifest](f: T => U): RDD[U] = new MappedRDD(this, sc.clean(f)) + def map[U: ClassTag](f: T => U): RDD[U] = new MappedRDD(this, sc.clean(f)) /** * Return a new RDD by first applying a function to all elements of this * RDD, and then flattening the results. */ - def flatMap[U: ClassManifest](f: T => TraversableOnce[U]): RDD[U] = + def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = new FlatMappedRDD(this, sc.clean(f)) /** @@ -266,6 +271,19 @@ abstract class RDD[T: ClassManifest]( def distinct(): RDD[T] = distinct(partitions.size) /** + * Return a new RDD that has exactly numPartitions partitions. + * + * Can increase or decrease the level of parallelism in this RDD. Internally, this uses + * a shuffle to redistribute data. + * + * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, + * which can avoid performing a shuffle. + */ + def repartition(numPartitions: Int): RDD[T] = { + coalesce(numPartitions, true) + } + + /** * Return a new RDD that is reduced into `numPartitions` partitions. * * This results in a narrow dependency, e.g. if you go from 1000 partitions @@ -361,25 +379,25 @@ abstract class RDD[T: ClassManifest]( * Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of * elements (a, b) where a is in `this` and b is in `other`. */ - def cartesian[U: ClassManifest](other: RDD[U]): RDD[(T, U)] = new CartesianRDD(sc, this, other) + def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = new CartesianRDD(sc, this, other) /** * Return an RDD of grouped items. */ - def groupBy[K: ClassManifest](f: T => K): RDD[(K, Seq[T])] = + def groupBy[K: ClassTag](f: T => K): RDD[(K, Seq[T])] = groupBy[K](f, defaultPartitioner(this)) /** * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements * mapping to that key. */ - def groupBy[K: ClassManifest](f: T => K, numPartitions: Int): RDD[(K, Seq[T])] = + def groupBy[K: ClassTag](f: T => K, numPartitions: Int): RDD[(K, Seq[T])] = groupBy(f, new HashPartitioner(numPartitions)) /** * Return an RDD of grouped items. */ - def groupBy[K: ClassManifest](f: T => K, p: Partitioner): RDD[(K, Seq[T])] = { + def groupBy[K: ClassTag](f: T => K, p: Partitioner): RDD[(K, Seq[T])] = { val cleanF = sc.clean(f) this.map(t => (cleanF(t), t)).groupByKey(p) } @@ -395,7 +413,6 @@ abstract class RDD[T: ClassManifest]( def pipe(command: String, env: Map[String, String]): RDD[String] = new PipedRDD(this, command, env) - /** * Return an RDD created by piping elements to a forked external process. * The print behavior can be customized by providing two functions. @@ -418,49 +435,64 @@ abstract class RDD[T: ClassManifest]( command: Seq[String], env: Map[String, String] = Map(), printPipeContext: (String => Unit) => Unit = null, - printRDDElement: (T, String => Unit) => Unit = null): RDD[String] = + printRDDElement: (T, String => Unit) => Unit = null): RDD[String] = { new PipedRDD(this, command, env, if (printPipeContext ne null) sc.clean(printPipeContext) else null, if (printRDDElement ne null) sc.clean(printRDDElement) else null) + } /** * Return a new RDD by applying a function to each partition of this RDD. */ - def mapPartitions[U: ClassManifest](f: Iterator[T] => Iterator[U], - preservesPartitioning: Boolean = false): RDD[U] = - new MapPartitionsRDD(this, sc.clean(f), preservesPartitioning) + def mapPartitions[U: ClassTag]( + f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(iter) + new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) + } /** * Return a new RDD by applying a function to each partition of this RDD, while tracking the index * of the original partition. */ - def mapPartitionsWithIndex[U: ClassManifest]( - f: (Int, Iterator[T]) => Iterator[U], - preservesPartitioning: Boolean = false): RDD[U] = - new MapPartitionsWithIndexRDD(this, sc.clean(f), preservesPartitioning) + def mapPartitionsWithIndex[U: ClassTag]( + f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(index, iter) + new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) + } + + /** + * Return a new RDD by applying a function to each partition of this RDD. This is a variant of + * mapPartitions that also passes the TaskContext into the closure. + */ + def mapPartitionsWithContext[U: ClassTag]( + f: (TaskContext, Iterator[T]) => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = { + val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(context, iter) + new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) + } /** * Return a new RDD by applying a function to each partition of this RDD, while tracking the index * of the original partition. */ @deprecated("use mapPartitionsWithIndex", "0.7.0") - def mapPartitionsWithSplit[U: ClassManifest]( - f: (Int, Iterator[T]) => Iterator[U], - preservesPartitioning: Boolean = false): RDD[U] = - new MapPartitionsWithIndexRDD(this, sc.clean(f), preservesPartitioning) + def mapPartitionsWithSplit[U: ClassTag]( + f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + mapPartitionsWithIndex(f, preservesPartitioning) + } /** * Maps f over this RDD, where f takes an additional parameter of type A. This * additional parameter is produced by constructA, which is called in each * partition with the index of that partition. */ - def mapWith[A: ClassManifest, U: ClassManifest](constructA: Int => A, preservesPartitioning: Boolean = false) - (f:(T, A) => U): RDD[U] = { - def iterF(index: Int, iter: Iterator[T]): Iterator[U] = { - val a = constructA(index) - iter.map(t => f(t, a)) - } - new MapPartitionsWithIndexRDD(this, sc.clean(iterF _), preservesPartitioning) + def mapWith[A: ClassTag, U: ClassTag] + (constructA: Int => A, preservesPartitioning: Boolean = false) + (f: (T, A) => U): RDD[U] = { + mapPartitionsWithIndex((index, iter) => { + val a = constructA(index) + iter.map(t => f(t, a)) + }, preservesPartitioning) } /** @@ -468,13 +500,13 @@ abstract class RDD[T: ClassManifest]( * additional parameter is produced by constructA, which is called in each * partition with the index of that partition. */ - def flatMapWith[A: ClassManifest, U: ClassManifest](constructA: Int => A, preservesPartitioning: Boolean = false) - (f:(T, A) => Seq[U]): RDD[U] = { - def iterF(index: Int, iter: Iterator[T]): Iterator[U] = { - val a = constructA(index) - iter.flatMap(t => f(t, a)) - } - new MapPartitionsWithIndexRDD(this, sc.clean(iterF _), preservesPartitioning) + def flatMapWith[A: ClassTag, U: ClassTag] + (constructA: Int => A, preservesPartitioning: Boolean = false) + (f: (T, A) => Seq[U]): RDD[U] = { + mapPartitionsWithIndex((index, iter) => { + val a = constructA(index) + iter.flatMap(t => f(t, a)) + }, preservesPartitioning) } /** @@ -482,13 +514,11 @@ abstract class RDD[T: ClassManifest]( * This additional parameter is produced by constructA, which is called in each * partition with the index of that partition. */ - def foreachWith[A: ClassManifest](constructA: Int => A) - (f:(T, A) => Unit) { - def iterF(index: Int, iter: Iterator[T]): Iterator[T] = { - val a = constructA(index) - iter.map(t => {f(t, a); t}) - } - (new MapPartitionsWithIndexRDD(this, sc.clean(iterF _), true)).foreach(_ => {}) + def foreachWith[A: ClassTag](constructA: Int => A)(f: (T, A) => Unit) { + mapPartitionsWithIndex { (index, iter) => + val a = constructA(index) + iter.map(t => {f(t, a); t}) + }.foreach(_ => {}) } /** @@ -496,13 +526,11 @@ abstract class RDD[T: ClassManifest]( * additional parameter is produced by constructA, which is called in each * partition with the index of that partition. */ - def filterWith[A: ClassManifest](constructA: Int => A) - (p:(T, A) => Boolean): RDD[T] = { - def iterF(index: Int, iter: Iterator[T]): Iterator[T] = { - val a = constructA(index) - iter.filter(t => p(t, a)) - } - new MapPartitionsWithIndexRDD(this, sc.clean(iterF _), true) + def filterWith[A: ClassTag](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = { + mapPartitionsWithIndex((index, iter) => { + val a = constructA(index) + iter.filter(t => p(t, a)) + }, preservesPartitioning = true) } /** @@ -511,7 +539,7 @@ abstract class RDD[T: ClassManifest]( * partitions* and the *same number of elements in each partition* (e.g. one was made through * a map on the other). */ - def zip[U: ClassManifest](other: RDD[U]): RDD[(T, U)] = new ZippedRDD(sc, this, other) + def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = new ZippedRDD(sc, this, other) /** * Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by @@ -519,20 +547,30 @@ abstract class RDD[T: ClassManifest]( * *same number of partitions*, but does *not* require them to have the same number * of elements in each partition. */ - def zipPartitions[B: ClassManifest, V: ClassManifest] + def zipPartitions[B: ClassTag, V: ClassTag] (rdd2: RDD[B]) (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2) + new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, false) - def zipPartitions[B: ClassManifest, C: ClassManifest, V: ClassManifest] + def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag] + (rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean) + (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = + new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, preservesPartitioning) + + def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C]) (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3) + new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, false) + + def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag] + (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean) + (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = + new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, preservesPartitioning) - def zipPartitions[B: ClassManifest, C: ClassManifest, D: ClassManifest, V: ClassManifest] + def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D]) (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4) + new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, false) // Actions (launch a job to return a value to the user program) @@ -541,16 +579,14 @@ abstract class RDD[T: ClassManifest]( * Applies a function f to all elements of this RDD. */ def foreach(f: T => Unit) { - val cleanF = sc.clean(f) - sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF)) + sc.runJob(this, (iter: Iterator[T]) => iter.foreach(f)) } /** * Applies a function f to each partition of this RDD. */ def foreachPartition(f: Iterator[T] => Unit) { - val cleanF = sc.clean(f) - sc.runJob(this, (iter: Iterator[T]) => cleanF(iter)) + sc.runJob(this, (iter: Iterator[T]) => f(iter)) } /** @@ -569,7 +605,7 @@ abstract class RDD[T: ClassManifest]( /** * Return an RDD that contains all matching values by applying `f`. */ - def collect[U: ClassManifest](f: PartialFunction[T, U]): RDD[U] = { + def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = { filter(f.isDefinedAt).map(f) } @@ -659,7 +695,7 @@ abstract class RDD[T: ClassManifest]( * allowed to modify and return their first argument instead of creating a new U to avoid memory * allocation. */ - def aggregate[U: ClassManifest](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = { + def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance()) val cleanSeqOp = sc.clean(seqOp) @@ -675,6 +711,8 @@ abstract class RDD[T: ClassManifest]( */ def count(): Long = { sc.runJob(this, (iter: Iterator[T]) => { + // Use a while loop to count the number of elements rather than iter.size because + // iter.size uses a for loop, which is slightly slower in current version of Scala. var result = 0L while (iter.hasNext) { result += 1L @@ -706,7 +744,7 @@ abstract class RDD[T: ClassManifest]( * combine step happens locally on the master, equivalent to running a single reduce task. */ def countByValue(): Map[T, Long] = { - if (elementClassManifest.erasure.isArray) { + if (elementClassTag.runtimeClass.isArray) { throw new SparkException("countByValue() does not support arrays") } // TODO: This should perhaps be distributed by default. @@ -737,7 +775,7 @@ abstract class RDD[T: ClassManifest]( timeout: Long, confidence: Double = 0.95 ): PartialResult[Map[T, BoundedDouble]] = { - if (elementClassManifest.erasure.isArray) { + if (elementClassTag.runtimeClass.isArray) { throw new SparkException("countByValueApprox() does not support arrays") } val countPartition: (TaskContext, Iterator[T]) => OLMap[T] = { (ctx, iter) => @@ -753,6 +791,19 @@ abstract class RDD[T: ClassManifest]( } /** + * Return approximate number of distinct elements in the RDD. + * + * The accuracy of approximation can be controlled through the relative standard deviation + * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in + * more accurate counts but increase the memory footprint and vise versa. The default value of + * relativeSD is 0.05. + */ + def countApproxDistinct(relativeSD: Double = 0.05): Long = { + val zeroCounter = new SerializableHyperLogLog(new HyperLogLog(relativeSD)) + aggregate(zeroCounter)(_.add(_), _.merge(_)).value.cardinality() + } + + /** * Take the first num elements of the RDD. It works by first scanning one partition, and use the * results from that partition to estimate the number of additional partitions needed to satisfy * the limit. @@ -902,14 +953,14 @@ abstract class RDD[T: ClassManifest]( private var storageLevel: StorageLevel = StorageLevel.NONE /** Record user function generating this RDD. */ - private[spark] val origin = Utils.formatSparkCallSite + @transient private[spark] val origin = Utils.formatSparkCallSite - private[spark] def elementClassManifest: ClassManifest[T] = classManifest[T] + private[spark] def elementClassTag: ClassTag[T] = classTag[T] private[spark] var checkpointData: Option[RDDCheckpointData[T]] = None /** Returns the first parent RDD */ - protected[spark] def firstParent[U: ClassManifest] = { + protected[spark] def firstParent[U: ClassTag] = { dependencies.head.rdd.asInstanceOf[RDD[U]] } @@ -917,7 +968,7 @@ abstract class RDD[T: ClassManifest]( def context = sc // Avoid handling doCheckpoint multiple times to prevent excessive recursion - private var doCheckpointCalled = false + @transient private var doCheckpointCalled = false /** * Performs the checkpointing of this RDD by saving this. It is called by the DAGScheduler @@ -971,7 +1022,7 @@ abstract class RDD[T: ClassManifest]( origin) def toJavaRDD() : JavaRDD[T] = { - new JavaRDD(this)(elementClassManifest) + new JavaRDD(this)(elementClassTag) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala index 6009a41570..642dabaad5 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala @@ -17,10 +17,12 @@ package org.apache.spark.rdd +import scala.reflect.ClassTag + import org.apache.hadoop.fs.Path import org.apache.hadoop.conf.Configuration -import org.apache.spark.{Partition, SparkException, Logging} +import org.apache.spark.{SerializableWritable, Partition, SparkException, Logging} import org.apache.spark.scheduler.{ResultTask, ShuffleMapTask} /** @@ -38,7 +40,7 @@ private[spark] object CheckpointState extends Enumeration { * manages the post-checkpoint state by providing the updated partitions, iterator and preferred locations * of the checkpointed RDD. */ -private[spark] class RDDCheckpointData[T: ClassManifest](rdd: RDD[T]) +private[spark] class RDDCheckpointData[T: ClassTag](rdd: RDD[T]) extends Logging with Serializable { import CheckpointState._ @@ -83,14 +85,21 @@ private[spark] class RDDCheckpointData[T: ClassManifest](rdd: RDD[T]) // Create the output path for the checkpoint val path = new Path(rdd.context.checkpointDir.get, "rdd-" + rdd.id) - val fs = path.getFileSystem(new Configuration()) + val fs = path.getFileSystem(rdd.context.hadoopConfiguration) if (!fs.mkdirs(path)) { throw new SparkException("Failed to create checkpoint path " + path) } // Save to file, and reload it as an RDD - rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path.toString) _) + val broadcastedConf = rdd.context.broadcast( + new SerializableWritable(rdd.context.hadoopConfiguration)) + rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path.toString, broadcastedConf) _) val newRDD = new CheckpointRDD[T](rdd.context, path.toString) + if (newRDD.partitions.size != rdd.partitions.size) { + throw new SparkException( + "Checkpoint RDD " + newRDD + "("+ newRDD.partitions.size + ") has different " + + "number of partitions than original RDD " + rdd + "(" + rdd.partitions.size + ")") + } // Change the dependencies and partitions of the RDD RDDCheckpointData.synchronized { @@ -99,8 +108,8 @@ private[spark] class RDDCheckpointData[T: ClassManifest](rdd: RDD[T]) rdd.markCheckpointed(newRDD) // Update the RDD's dependencies and partitions cpState = Checkpointed RDDCheckpointData.clearTaskCaches() - logInfo("Done checkpointing RDD " + rdd.id + ", new parent is RDD " + newRDD.id) } + logInfo("Done checkpointing RDD " + rdd.id + " to " + path + ", new parent is RDD " + newRDD.id) } // Get preferred location of a split after checkpointing diff --git a/core/src/main/scala/org/apache/spark/rdd/SampledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SampledRDD.scala index 2c5253ae30..d433670cc2 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SampledRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SampledRDD.scala @@ -17,6 +17,7 @@ package org.apache.spark.rdd +import scala.reflect.ClassTag import java.util.Random import cern.jet.random.Poisson @@ -29,9 +30,9 @@ class SampledRDDPartition(val prev: Partition, val seed: Int) extends Partition override val index: Int = prev.index } -class SampledRDD[T: ClassManifest]( +class SampledRDD[T: ClassTag]( prev: RDD[T], - withReplacement: Boolean, + withReplacement: Boolean, frac: Double, seed: Int) extends RDD[T](prev) { diff --git a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala index 5fe4676029..2d1bd5b481 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala @@ -14,9 +14,10 @@ * See the License for the specific language governing permissions and * limitations under the License. */ - package org.apache.spark.rdd +import scala.reflect.{ ClassTag, classTag} + import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapred.SequenceFileOutputFormat import org.apache.hadoop.io.compress.CompressionCodec @@ -32,15 +33,15 @@ import org.apache.spark.Logging * * Import `org.apache.spark.SparkContext._` at the top of their program to use these functions. */ -class SequenceFileRDDFunctions[K <% Writable: ClassManifest, V <% Writable : ClassManifest]( +class SequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable : ClassTag]( self: RDD[(K, V)]) extends Logging with Serializable { - private def getWritableClass[T <% Writable: ClassManifest](): Class[_ <: Writable] = { + private def getWritableClass[T <% Writable: ClassTag](): Class[_ <: Writable] = { val c = { - if (classOf[Writable].isAssignableFrom(classManifest[T].erasure)) { - classManifest[T].erasure + if (classOf[Writable].isAssignableFrom(classTag[T].runtimeClass)) { + classTag[T].runtimeClass } else { // We get the type of the Writable class by looking at the apply method which converts // from T to Writable. Since we have two apply methods we filter out the one which diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala index 9537152335..0ccb309d0d 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala @@ -17,8 +17,10 @@ package org.apache.spark.rdd -import org.apache.spark.{Dependency, Partitioner, SparkEnv, ShuffleDependency, Partition, TaskContext} +import scala.reflect.ClassTag +import org.apache.spark.{Dependency, Partition, Partitioner, ShuffleDependency, + SparkEnv, TaskContext} private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition { override val index = idx @@ -32,7 +34,7 @@ private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition { * @tparam K the key class. * @tparam V the value class. */ -class ShuffledRDD[K, V, P <: Product2[K, V] : ClassManifest]( +class ShuffledRDD[K, V, P <: Product2[K, V] : ClassTag]( @transient var prev: RDD[P], part: Partitioner) extends RDD[P](prev.context, Nil) { @@ -56,8 +58,8 @@ class ShuffledRDD[K, V, P <: Product2[K, V] : ClassManifest]( override def compute(split: Partition, context: TaskContext): Iterator[P] = { val shuffledId = dependencies.head.asInstanceOf[ShuffleDependency[K, V]].shuffleId - SparkEnv.get.shuffleFetcher.fetch[P](shuffledId, split.index, context.taskMetrics, - SparkEnv.get.serializerManager.get(serializerClass)) + SparkEnv.get.shuffleFetcher.fetch[P](shuffledId, split.index, context, + SparkEnv.get.serializerManager.get(serializerClass, SparkEnv.get.conf)) } override def clearDependencies() { diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala index 8c1a29dfff..4f90c7d3d6 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala @@ -18,8 +18,11 @@ package org.apache.spark.rdd import java.util.{HashMap => JHashMap} + import scala.collection.JavaConversions._ import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + import org.apache.spark.Partitioner import org.apache.spark.Dependency import org.apache.spark.TaskContext @@ -45,7 +48,7 @@ import org.apache.spark.OneToOneDependency * you can use `rdd1`'s partitioner/partition size and not worry about running * out of memory because of the size of `rdd2`. */ -private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassManifest]( +private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( @transient var rdd1: RDD[_ <: Product2[K, V]], @transient var rdd2: RDD[_ <: Product2[K, W]], part: Partitioner) @@ -90,7 +93,7 @@ private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassM override def compute(p: Partition, context: TaskContext): Iterator[(K, V)] = { val partition = p.asInstanceOf[CoGroupPartition] - val serializer = SparkEnv.get.serializerManager.get(serializerClass) + val serializer = SparkEnv.get.serializerManager.get(serializerClass, SparkEnv.get.conf) val map = new JHashMap[K, ArrayBuffer[V]] def getSeq(k: K): ArrayBuffer[V] = { val seq = map.get(k) @@ -108,7 +111,7 @@ private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassM } case ShuffleCoGroupSplitDep(shuffleId) => { val iter = SparkEnv.get.shuffleFetcher.fetch[Product2[K, V]](shuffleId, partition.index, - context.taskMetrics, serializer) + context, serializer) iter.foreach(op) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala index ae8a9f36a6..08a41ac558 100644 --- a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala @@ -18,10 +18,13 @@ package org.apache.spark.rdd import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + import org.apache.spark.{Dependency, RangeDependency, SparkContext, Partition, TaskContext} + import java.io.{ObjectOutputStream, IOException} -private[spark] class UnionPartition[T: ClassManifest](idx: Int, rdd: RDD[T], splitIndex: Int) +private[spark] class UnionPartition[T: ClassTag](idx: Int, rdd: RDD[T], splitIndex: Int) extends Partition { var split: Partition = rdd.partitions(splitIndex) @@ -40,7 +43,7 @@ private[spark] class UnionPartition[T: ClassManifest](idx: Int, rdd: RDD[T], spl } } -class UnionRDD[T: ClassManifest]( +class UnionRDD[T: ClassTag]( sc: SparkContext, @transient var rdds: Seq[RDD[T]]) extends RDD[T](sc, Nil) { // Nil since we implement getDependencies diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala index 31e6fd519d..83be3c6eb4 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala @@ -19,10 +19,12 @@ package org.apache.spark.rdd import org.apache.spark.{OneToOneDependency, SparkContext, Partition, TaskContext} import java.io.{ObjectOutputStream, IOException} +import scala.reflect.ClassTag private[spark] class ZippedPartitionsPartition( idx: Int, - @transient rdds: Seq[RDD[_]]) + @transient rdds: Seq[RDD[_]], + @transient val preferredLocations: Seq[String]) extends Partition { override val index: Int = idx @@ -37,33 +39,31 @@ private[spark] class ZippedPartitionsPartition( } } -abstract class ZippedPartitionsBaseRDD[V: ClassManifest]( +abstract class ZippedPartitionsBaseRDD[V: ClassTag]( sc: SparkContext, - var rdds: Seq[RDD[_]]) + var rdds: Seq[RDD[_]], + preservesPartitioning: Boolean = false) extends RDD[V](sc, rdds.map(x => new OneToOneDependency(x))) { + override val partitioner = + if (preservesPartitioning) firstParent[Any].partitioner else None + override def getPartitions: Array[Partition] = { - val sizes = rdds.map(x => x.partitions.size) - if (!sizes.forall(x => x == sizes(0))) { + val numParts = rdds.head.partitions.size + if (!rdds.forall(rdd => rdd.partitions.size == numParts)) { throw new IllegalArgumentException("Can't zip RDDs with unequal numbers of partitions") } - val array = new Array[Partition](sizes(0)) - for (i <- 0 until sizes(0)) { - array(i) = new ZippedPartitionsPartition(i, rdds) + Array.tabulate[Partition](numParts) { i => + val prefs = rdds.map(rdd => rdd.preferredLocations(rdd.partitions(i))) + // Check whether there are any hosts that match all RDDs; otherwise return the union + val exactMatchLocations = prefs.reduce((x, y) => x.intersect(y)) + val locs = if (!exactMatchLocations.isEmpty) exactMatchLocations else prefs.flatten.distinct + new ZippedPartitionsPartition(i, rdds, locs) } - array } override def getPreferredLocations(s: Partition): Seq[String] = { - val parts = s.asInstanceOf[ZippedPartitionsPartition].partitions - val prefs = rdds.zip(parts).map { case (rdd, p) => rdd.preferredLocations(p) } - // Check whether there are any hosts that match all RDDs; otherwise return the union - val exactMatchLocations = prefs.reduce((x, y) => x.intersect(y)) - if (!exactMatchLocations.isEmpty) { - exactMatchLocations - } else { - prefs.flatten.distinct - } + s.asInstanceOf[ZippedPartitionsPartition].preferredLocations } override def clearDependencies() { @@ -72,12 +72,13 @@ abstract class ZippedPartitionsBaseRDD[V: ClassManifest]( } } -class ZippedPartitionsRDD2[A: ClassManifest, B: ClassManifest, V: ClassManifest]( +class ZippedPartitionsRDD2[A: ClassTag, B: ClassTag, V: ClassTag]( sc: SparkContext, f: (Iterator[A], Iterator[B]) => Iterator[V], var rdd1: RDD[A], - var rdd2: RDD[B]) - extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2)) { + var rdd2: RDD[B], + preservesPartitioning: Boolean = false) + extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2), preservesPartitioning) { override def compute(s: Partition, context: TaskContext): Iterator[V] = { val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions @@ -92,13 +93,14 @@ class ZippedPartitionsRDD2[A: ClassManifest, B: ClassManifest, V: ClassManifest] } class ZippedPartitionsRDD3 - [A: ClassManifest, B: ClassManifest, C: ClassManifest, V: ClassManifest]( + [A: ClassTag, B: ClassTag, C: ClassTag, V: ClassTag]( sc: SparkContext, f: (Iterator[A], Iterator[B], Iterator[C]) => Iterator[V], var rdd1: RDD[A], var rdd2: RDD[B], - var rdd3: RDD[C]) - extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3)) { + var rdd3: RDD[C], + preservesPartitioning: Boolean = false) + extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3), preservesPartitioning) { override def compute(s: Partition, context: TaskContext): Iterator[V] = { val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions @@ -116,14 +118,15 @@ class ZippedPartitionsRDD3 } class ZippedPartitionsRDD4 - [A: ClassManifest, B: ClassManifest, C: ClassManifest, D:ClassManifest, V: ClassManifest]( + [A: ClassTag, B: ClassTag, C: ClassTag, D:ClassTag, V: ClassTag]( sc: SparkContext, f: (Iterator[A], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V], var rdd1: RDD[A], var rdd2: RDD[B], var rdd3: RDD[C], - var rdd4: RDD[D]) - extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3, rdd4)) { + var rdd4: RDD[D], + preservesPartitioning: Boolean = false) + extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3, rdd4), preservesPartitioning) { override def compute(s: Partition, context: TaskContext): Iterator[V] = { val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedRDD.scala index 567b67dfee..fb5b070c18 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ZippedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ZippedRDD.scala @@ -18,10 +18,12 @@ package org.apache.spark.rdd import org.apache.spark.{OneToOneDependency, SparkContext, Partition, TaskContext} + import java.io.{ObjectOutputStream, IOException} +import scala.reflect.ClassTag -private[spark] class ZippedPartition[T: ClassManifest, U: ClassManifest]( +private[spark] class ZippedPartition[T: ClassTag, U: ClassTag]( idx: Int, @transient rdd1: RDD[T], @transient rdd2: RDD[U] @@ -42,7 +44,7 @@ private[spark] class ZippedPartition[T: ClassManifest, U: ClassManifest]( } } -class ZippedRDD[T: ClassManifest, U: ClassManifest]( +class ZippedRDD[T: ClassTag, U: ClassTag]( sc: SparkContext, var rdd1: RDD[T], var rdd2: RDD[U]) diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 8a55df4af0..043e01dbfb 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -19,17 +19,20 @@ package org.apache.spark.scheduler import java.io.NotSerializableException import java.util.Properties -import java.util.concurrent.{LinkedBlockingQueue, TimeUnit} import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map} +import scala.concurrent.duration._ +import scala.reflect.ClassTag + +import akka.actor._ import org.apache.spark._ import org.apache.spark.rdd.RDD import org.apache.spark.executor.TaskMetrics import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult} -import org.apache.spark.storage.{BlockManager, BlockManagerMaster} -import org.apache.spark.util.{MetadataCleaner, TimeStampedHashMap} +import org.apache.spark.storage.{BlockId, BlockManager, BlockManagerMaster, RDDBlockId} +import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashMap} /** * The high-level scheduling layer that implements stage-oriented scheduling. It computes a DAG of @@ -41,80 +44,96 @@ import org.apache.spark.util.{MetadataCleaner, TimeStampedHashMap} * locations to run each task on, based on the current cache status, and passes these to the * low-level TaskScheduler. Furthermore, it handles failures due to shuffle output files being * lost, in which case old stages may need to be resubmitted. Failures *within* a stage that are - * not caused by shuffie file loss are handled by the TaskScheduler, which will retry each task + * not caused by shuffle file loss are handled by the TaskScheduler, which will retry each task * a small number of times before cancelling the whole stage. * * THREADING: This class runs all its logic in a single thread executing the run() method, to which - * events are submitted using a synchonized queue (eventQueue). The public API methods, such as + * events are submitted using a synchronized queue (eventQueue). The public API methods, such as * runJob, taskEnded and executorLost, post events asynchronously to this queue. All other methods * should be private. */ private[spark] class DAGScheduler( taskSched: TaskScheduler, - mapOutputTracker: MapOutputTracker, + mapOutputTracker: MapOutputTrackerMaster, blockManagerMaster: BlockManagerMaster, env: SparkEnv) - extends TaskSchedulerListener with Logging { + extends Logging { def this(taskSched: TaskScheduler) { - this(taskSched, SparkEnv.get.mapOutputTracker, SparkEnv.get.blockManager.master, SparkEnv.get) + this(taskSched, SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], + SparkEnv.get.blockManager.master, SparkEnv.get) } - taskSched.setListener(this) + taskSched.setDAGScheduler(this) // Called by TaskScheduler to report task's starting. - override def taskStarted(task: Task[_], taskInfo: TaskInfo) { - eventQueue.put(BeginEvent(task, taskInfo)) + def taskStarted(task: Task[_], taskInfo: TaskInfo) { + eventProcessActor ! BeginEvent(task, taskInfo) + } + + // Called to report that a task has completed and results are being fetched remotely. + def taskGettingResult(task: Task[_], taskInfo: TaskInfo) { + eventProcessActor ! GettingResultEvent(task, taskInfo) } // Called by TaskScheduler to report task completions or failures. - override def taskEnded( + def taskEnded( task: Task[_], reason: TaskEndReason, result: Any, accumUpdates: Map[Long, Any], taskInfo: TaskInfo, taskMetrics: TaskMetrics) { - eventQueue.put(CompletionEvent(task, reason, result, accumUpdates, taskInfo, taskMetrics)) + eventProcessActor ! CompletionEvent(task, reason, result, accumUpdates, taskInfo, taskMetrics) } // Called by TaskScheduler when an executor fails. - override def executorLost(execId: String) { - eventQueue.put(ExecutorLost(execId)) + def executorLost(execId: String) { + eventProcessActor ! ExecutorLost(execId) } // Called by TaskScheduler when a host is added - override def executorGained(execId: String, host: String) { - eventQueue.put(ExecutorGained(execId, host)) + def executorGained(execId: String, host: String) { + eventProcessActor ! ExecutorGained(execId, host) } - // Called by TaskScheduler to cancel an entire TaskSet due to repeated failures. - override def taskSetFailed(taskSet: TaskSet, reason: String) { - eventQueue.put(TaskSetFailed(taskSet, reason)) + // Called by TaskScheduler to cancel an entire TaskSet due to either repeated failures or + // cancellation of the job itself. + def taskSetFailed(taskSet: TaskSet, reason: String) { + eventProcessActor ! TaskSetFailed(taskSet, reason) } // The time, in millis, to wait for fetch failure events to stop coming in after one is detected; // this is a simplistic way to avoid resubmitting tasks in the non-fetchable map stage one by one // as more failure events come in - val RESUBMIT_TIMEOUT = 50L + val RESUBMIT_TIMEOUT = 50.milliseconds // The time, in millis, to wake up between polls of the completion queue in order to potentially // resubmit failed stages val POLL_TIMEOUT = 10L - private val eventQueue = new LinkedBlockingQueue[DAGSchedulerEvent] + // Warns the user if a stage contains a task with size greater than this value (in KB) + val TASK_SIZE_TO_WARN = 100 + + private var eventProcessActor: ActorRef = _ + + private[scheduler] val nextJobId = new AtomicInteger(0) - val nextJobId = new AtomicInteger(0) + def numTotalJobs: Int = nextJobId.get() - val nextStageId = new AtomicInteger(0) + private val nextStageId = new AtomicInteger(0) - val stageIdToStage = new TimeStampedHashMap[Int, Stage] + private[scheduler] val jobIdToStageIds = new TimeStampedHashMap[Int, HashSet[Int]] - val shuffleToMapStage = new TimeStampedHashMap[Int, Stage] + private[scheduler] val stageIdToJobIds = new TimeStampedHashMap[Int, HashSet[Int]] + + private[scheduler] val stageIdToStage = new TimeStampedHashMap[Int, Stage] + + private[scheduler] val shuffleToMapStage = new TimeStampedHashMap[Int, Stage] private[spark] val stageToInfos = new TimeStampedHashMap[Stage, StageInfo] - private val listenerBus = new SparkListenerBus() + private[spark] val listenerBus = new SparkListenerBus() // Contains the locations that each RDD's partitions are cached on private val cacheLocs = new HashMap[Int, Array[Seq[TaskLocation]]] @@ -127,27 +146,71 @@ class DAGScheduler( // stray messages to detect. val failedEpoch = new HashMap[String, Long] + // stage id to the active job val idToActiveJob = new HashMap[Int, ActiveJob] val waiting = new HashSet[Stage] // Stages we need to run whose parents aren't done val running = new HashSet[Stage] // Stages we are running right now val failed = new HashSet[Stage] // Stages that must be resubmitted due to fetch failures - val pendingTasks = new TimeStampedHashMap[Stage, HashSet[Task[_]]] // Missing tasks from each stage + // Missing tasks from each stage + val pendingTasks = new TimeStampedHashMap[Stage, HashSet[Task[_]]] var lastFetchFailureTime: Long = 0 // Used to wait a bit to avoid repeated resubmits val activeJobs = new HashSet[ActiveJob] val resultStageToJob = new HashMap[Stage, ActiveJob] - val metadataCleaner = new MetadataCleaner("DAGScheduler", this.cleanup) + val metadataCleaner = new MetadataCleaner( + MetadataCleanerType.DAG_SCHEDULER, this.cleanup, env.conf) - // Start a thread to run the DAGScheduler event loop + /** + * Starts the event processing actor. The actor has two responsibilities: + * + * 1. Waits for events like job submission, task finished, task failure etc., and calls + * [[org.apache.spark.scheduler.DAGScheduler.processEvent()]] to process them. + * 2. Schedules a periodical task to resubmit failed stages. + * + * NOTE: the actor cannot be started in the constructor, because the periodical task references + * some internal states of the enclosing [[org.apache.spark.scheduler.DAGScheduler]] object, thus + * cannot be scheduled until the [[org.apache.spark.scheduler.DAGScheduler]] is fully constructed. + */ def start() { - new Thread("DAGScheduler") { - setDaemon(true) - override def run() { - DAGScheduler.this.run() + eventProcessActor = env.actorSystem.actorOf(Props(new Actor { + /** + * A handle to the periodical task, used to cancel the task when the actor is stopped. + */ + var resubmissionTask: Cancellable = _ + + override def preStart() { + import context.dispatcher + /** + * A message is sent to the actor itself periodically to remind the actor to resubmit failed + * stages. In this way, stage resubmission can be done within the same thread context of + * other event processing logic to avoid unnecessary synchronization overhead. + */ + resubmissionTask = context.system.scheduler.schedule( + RESUBMIT_TIMEOUT, RESUBMIT_TIMEOUT, self, ResubmitFailedStages) } - }.start() + + /** + * The main event loop of the DAG scheduler. + */ + def receive = { + case event: DAGSchedulerEvent => + logDebug("Got event of type " + event.getClass.getName) + + /** + * All events are forwarded to `processEvent()`, so that the event processing logic can + * easily tested without starting a dedicated actor. Please refer to `DAGSchedulerSuite` + * for details. + */ + if (!processEvent(event)) { + submitWaitingStages() + } else { + resubmissionTask.cancel() + context.stop(self) + } + } + })) } def addSparkListener(listener: SparkListener) { @@ -156,7 +219,7 @@ class DAGScheduler( private def getCacheLocs(rdd: RDD[_]): Array[Seq[TaskLocation]] = { if (!cacheLocs.contains(rdd.id)) { - val blockIds = rdd.partitions.indices.map(index=> "rdd_%d_%d".format(rdd.id, index)).toArray + val blockIds = rdd.partitions.indices.map(index=> RDDBlockId(rdd.id, index)).toArray[BlockId] val locs = BlockManager.blockIdsToBlockManagers(blockIds, env, blockManagerMaster) cacheLocs(rdd.id) = blockIds.map { id => locs.getOrElse(id, Nil).map(bm => TaskLocation(bm.host, bm.executorId)) @@ -178,34 +241,62 @@ class DAGScheduler( shuffleToMapStage.get(shuffleDep.shuffleId) match { case Some(stage) => stage case None => - val stage = newStage(shuffleDep.rdd, Some(shuffleDep), jobId) + val stage = + newOrUsedStage(shuffleDep.rdd, shuffleDep.rdd.partitions.size, shuffleDep, jobId) shuffleToMapStage(shuffleDep.shuffleId) = stage stage } } /** - * Create a Stage for the given RDD, either as a shuffle map stage (for a ShuffleDependency) or - * as a result stage for the final RDD used directly in an action. The stage will also be - * associated with the provided jobId. + * Create a Stage -- either directly for use as a result stage, or as part of the (re)-creation + * of a shuffle map stage in newOrUsedStage. The stage will be associated with the provided + * jobId. Production of shuffle map stages should always use newOrUsedStage, not newStage + * directly. */ private def newStage( rdd: RDD[_], + numTasks: Int, shuffleDep: Option[ShuffleDependency[_,_]], jobId: Int, callSite: Option[String] = None) : Stage = { - if (shuffleDep != None) { + val id = nextStageId.getAndIncrement() + val stage = + new Stage(id, rdd, numTasks, shuffleDep, getParentStages(rdd, jobId), jobId, callSite) + stageIdToStage(id) = stage + updateJobIdStageIdMaps(jobId, stage) + stageToInfos(stage) = new StageInfo(stage) + stage + } + + /** + * Create a shuffle map Stage for the given RDD. The stage will also be associated with the + * provided jobId. If a stage for the shuffleId existed previously so that the shuffleId is + * present in the MapOutputTracker, then the number and location of available outputs are + * recovered from the MapOutputTracker + */ + private def newOrUsedStage( + rdd: RDD[_], + numTasks: Int, + shuffleDep: ShuffleDependency[_,_], + jobId: Int, + callSite: Option[String] = None) + : Stage = + { + val stage = newStage(rdd, numTasks, Some(shuffleDep), jobId, callSite) + if (mapOutputTracker.has(shuffleDep.shuffleId)) { + val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId) + val locs = MapOutputTracker.deserializeMapStatuses(serLocs) + for (i <- 0 until locs.size) stage.outputLocs(i) = List(locs(i)) + stage.numAvailableOutputs = locs.size + } else { // Kind of ugly: need to register RDDs with the cache and map output tracker here // since we can't do it in the RDD constructor because # of partitions is unknown logInfo("Registering RDD " + rdd.id + " (" + rdd.origin + ")") - mapOutputTracker.registerShuffle(shuffleDep.get.shuffleId, rdd.partitions.size) + mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.size) } - val id = nextStageId.getAndIncrement() - val stage = new Stage(id, rdd, shuffleDep, getParentStages(rdd, jobId), jobId, callSite) - stageIdToStage(id) = stage - stageToInfos(stage) = StageInfo(stage) stage } @@ -261,32 +352,129 @@ class DAGScheduler( } /** - * Returns (and does not submit) a JobSubmitted event suitable to run a given job, and a - * JobWaiter whose getResult() method will return the result of the job when it is complete. - * - * The job is assumed to have at least one partition; zero partition jobs should be handled - * without a JobSubmitted event. + * Registers the given jobId among the jobs that need the given stage and + * all of that stage's ancestors. */ - private[scheduler] def prepareJob[T, U: ClassManifest]( - finalRdd: RDD[T], + private def updateJobIdStageIdMaps(jobId: Int, stage: Stage) { + def updateJobIdStageIdMapsList(stages: List[Stage]) { + if (!stages.isEmpty) { + val s = stages.head + stageIdToJobIds.getOrElseUpdate(s.id, new HashSet[Int]()) += jobId + jobIdToStageIds.getOrElseUpdate(jobId, new HashSet[Int]()) += s.id + val parents = getParentStages(s.rdd, jobId) + val parentsWithoutThisJobId = parents.filter(p => + !stageIdToJobIds.get(p.id).exists(_.contains(jobId))) + updateJobIdStageIdMapsList(parentsWithoutThisJobId ++ stages.tail) + } + } + updateJobIdStageIdMapsList(List(stage)) + } + + /** + * Removes job and any stages that are not needed by any other job. Returns the set of ids for + * stages that were removed. The associated tasks for those stages need to be cancelled if we + * got here via job cancellation. + */ + private def removeJobAndIndependentStages(jobId: Int): Set[Int] = { + val registeredStages = jobIdToStageIds(jobId) + val independentStages = new HashSet[Int]() + if (registeredStages.isEmpty) { + logError("No stages registered for job " + jobId) + } else { + stageIdToJobIds.filterKeys(stageId => registeredStages.contains(stageId)).foreach { + case (stageId, jobSet) => + if (!jobSet.contains(jobId)) { + logError( + "Job %d not registered for stage %d even though that stage was registered for the job" + .format(jobId, stageId)) + } else { + def removeStage(stageId: Int) { + // data structures based on Stage + stageIdToStage.get(stageId).foreach { s => + if (running.contains(s)) { + logDebug("Removing running stage %d".format(stageId)) + running -= s + } + stageToInfos -= s + shuffleToMapStage.keys.filter(shuffleToMapStage(_) == s).foreach(shuffleId => + shuffleToMapStage.remove(shuffleId)) + if (pendingTasks.contains(s) && !pendingTasks(s).isEmpty) { + logDebug("Removing pending status for stage %d".format(stageId)) + } + pendingTasks -= s + if (waiting.contains(s)) { + logDebug("Removing stage %d from waiting set.".format(stageId)) + waiting -= s + } + if (failed.contains(s)) { + logDebug("Removing stage %d from failed set.".format(stageId)) + failed -= s + } + } + // data structures based on StageId + stageIdToStage -= stageId + stageIdToJobIds -= stageId + + logDebug("After removal of stage %d, remaining stages = %d" + .format(stageId, stageIdToStage.size)) + } + + jobSet -= jobId + if (jobSet.isEmpty) { // no other job needs this stage + independentStages += stageId + removeStage(stageId) + } + } + } + } + independentStages.toSet + } + + private def jobIdToStageIdsRemove(jobId: Int) { + if (!jobIdToStageIds.contains(jobId)) { + logDebug("Trying to remove unregistered job " + jobId) + } else { + removeJobAndIndependentStages(jobId) + jobIdToStageIds -= jobId + } + } + + /** + * Submit a job to the job scheduler and get a JobWaiter object back. The JobWaiter object + * can be used to block until the the job finishes executing or can be used to cancel the job. + */ + def submitJob[T, U]( + rdd: RDD[T], func: (TaskContext, Iterator[T]) => U, partitions: Seq[Int], callSite: String, allowLocal: Boolean, resultHandler: (Int, U) => Unit, - properties: Properties = null) - : (JobSubmitted, JobWaiter[U]) = + properties: Properties = null): JobWaiter[U] = { + // Check to make sure we are not launching a task on a partition that does not exist. + val maxPartitions = rdd.partitions.length + partitions.find(p => p >= maxPartitions).foreach { p => + throw new IllegalArgumentException( + "Attempting to access a non-existent partition: " + p + ". " + + "Total number of partitions: " + maxPartitions) + } + + val jobId = nextJobId.getAndIncrement() + if (partitions.size == 0) { + return new JobWaiter[U](this, jobId, 0, resultHandler) + } + assert(partitions.size > 0) - val waiter = new JobWaiter(partitions.size, resultHandler) val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] - val toSubmit = JobSubmitted(finalRdd, func2, partitions.toArray, allowLocal, callSite, waiter, - properties) - (toSubmit, waiter) + val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler) + eventProcessActor ! JobSubmitted( + jobId, rdd, func2, partitions.toArray, allowLocal, callSite, waiter, properties) + waiter } - def runJob[T, U: ClassManifest]( - finalRdd: RDD[T], + def runJob[T, U: ClassTag]( + rdd: RDD[T], func: (TaskContext, Iterator[T]) => U, partitions: Seq[Int], callSite: String, @@ -294,21 +482,7 @@ class DAGScheduler( resultHandler: (Int, U) => Unit, properties: Properties = null) { - if (partitions.size == 0) { - return - } - - // Check to make sure we are not launching a task on a partition that does not exist. - val maxPartitions = finalRdd.partitions.length - partitions.find(p => p >= maxPartitions).foreach { p => - throw new IllegalArgumentException( - "Attempting to access a non-existent partition: " + p + ". " + - "Total number of partitions: " + maxPartitions) - } - - val (toSubmit: JobSubmitted, waiter: JobWaiter[_]) = prepareJob( - finalRdd, func, partitions, callSite, allowLocal, resultHandler, properties) - eventQueue.put(toSubmit) + val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties) waiter.awaitResult() match { case JobSucceeded => {} case JobFailed(exception: Exception, _) => @@ -329,19 +503,52 @@ class DAGScheduler( val listener = new ApproximateActionListener(rdd, func, evaluator, timeout) val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] val partitions = (0 until rdd.partitions.size).toArray - eventQueue.put(JobSubmitted(rdd, func2, partitions, allowLocal = false, callSite, listener, properties)) + val jobId = nextJobId.getAndIncrement() + eventProcessActor ! JobSubmitted( + jobId, rdd, func2, partitions, allowLocal = false, callSite, listener, properties) listener.awaitResult() // Will throw an exception if the job fails } /** - * Process one event retrieved from the event queue. - * Returns true if we should stop the event loop. + * Cancel a job that is running or waiting in the queue. + */ + def cancelJob(jobId: Int) { + logInfo("Asked to cancel job " + jobId) + eventProcessActor ! JobCancelled(jobId) + } + + def cancelJobGroup(groupId: String) { + logInfo("Asked to cancel job group " + groupId) + eventProcessActor ! JobGroupCancelled(groupId) + } + + /** + * Cancel all jobs that are running or waiting in the queue. + */ + def cancelAllJobs() { + eventProcessActor ! AllJobsCancelled + } + + /** + * Process one event retrieved from the event processing actor. + * + * @param event The event to be processed. + * @return `true` if we should stop the event loop. */ private[scheduler] def processEvent(event: DAGSchedulerEvent): Boolean = { event match { - case JobSubmitted(finalRDD, func, partitions, allowLocal, callSite, listener, properties) => - val jobId = nextJobId.getAndIncrement() - val finalStage = newStage(finalRDD, None, jobId, Some(callSite)) + case JobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite, listener, properties) => + var finalStage: Stage = null + try { + // New stage creation may throw an exception if, for example, jobs are run on a HadoopRDD + // whose underlying HDFS files have been deleted. + finalStage = newStage(rdd, partitions.size, None, jobId, Some(callSite)) + } catch { + case e: Exception => + logWarning("Creating new stage failed due to exception - job: " + jobId, e) + listener.jobFailed(e) + return false + } val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties) clearCacheLocs() logInfo("Got job " + job.jobId + " (" + callSite + ") with " + partitions.length + @@ -351,31 +558,69 @@ class DAGScheduler( logInfo("Missing parents: " + getMissingParentStages(finalStage)) if (allowLocal && finalStage.parents.size == 0 && partitions.length == 1) { // Compute very short actions like first() or take() with no parent stages locally. + listenerBus.post(SparkListenerJobStart(job, Array(), properties)) runLocally(job) } else { - listenerBus.post(SparkListenerJobStart(job, properties)) idToActiveJob(jobId) = job activeJobs += job resultStageToJob(finalStage) = job + listenerBus.post(SparkListenerJobStart(job, jobIdToStageIds(jobId).toArray, properties)) submitStage(finalStage) } + case JobCancelled(jobId) => + handleJobCancellation(jobId) + + case JobGroupCancelled(groupId) => + // Cancel all jobs belonging to this job group. + // First finds all active jobs with this group id, and then kill stages for them. + val activeInGroup = activeJobs.filter(activeJob => + groupId == activeJob.properties.get(SparkContext.SPARK_JOB_GROUP_ID)) + val jobIds = activeInGroup.map(_.jobId) + jobIds.foreach { handleJobCancellation } + + case AllJobsCancelled => + // Cancel all running jobs. + running.map(_.jobId).foreach { handleJobCancellation } + activeJobs.clear() // These should already be empty by this point, + idToActiveJob.clear() // but just in case we lost track of some jobs... + case ExecutorGained(execId, host) => handleExecutorGained(execId, host) case ExecutorLost(execId) => handleExecutorLost(execId) - case begin: BeginEvent => - listenerBus.post(SparkListenerTaskStart(begin.task, begin.taskInfo)) + case BeginEvent(task, taskInfo) => + for ( + job <- idToActiveJob.get(task.stageId); + stage <- stageIdToStage.get(task.stageId); + stageInfo <- stageToInfos.get(stage) + ) { + if (taskInfo.serializedSize > TASK_SIZE_TO_WARN * 1024 && + !stageInfo.emittedTaskSizeWarning) { + stageInfo.emittedTaskSizeWarning = true + logWarning(("Stage %d (%s) contains a task of very large " + + "size (%d KB). The maximum recommended task size is %d KB.").format( + task.stageId, stageInfo.name, taskInfo.serializedSize / 1024, TASK_SIZE_TO_WARN)) + } + } + listenerBus.post(SparkListenerTaskStart(task, taskInfo)) - case completion: CompletionEvent => - listenerBus.post(SparkListenerTaskEnd( - completion.task, completion.reason, completion.taskInfo, completion.taskMetrics)) + case GettingResultEvent(task, taskInfo) => + listenerBus.post(SparkListenerTaskGettingResult(task, taskInfo)) + + case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) => + listenerBus.post(SparkListenerTaskEnd(task, reason, taskInfo, taskMetrics)) handleTaskCompletion(completion) case TaskSetFailed(taskSet, reason) => - abortStage(stageIdToStage(taskSet.stageId), reason) + stageIdToStage.get(taskSet.stageId).foreach { abortStage(_, reason) } + + case ResubmitFailedStages => + if (failed.size > 0) { + resubmitFailedStages() + } case StopDAGScheduler => // Cancel any active jobs @@ -421,42 +666,6 @@ class DAGScheduler( } } - - /** - * The main event loop of the DAG scheduler, which waits for new-job / task-finished / failure - * events and responds by launching tasks. This runs in a dedicated thread and receives events - * via the eventQueue. - */ - private def run() { - SparkEnv.set(env) - - while (true) { - val event = eventQueue.poll(POLL_TIMEOUT, TimeUnit.MILLISECONDS) - if (event != null) { - logDebug("Got event of type " + event.getClass.getName) - } - this.synchronized { // needed in case other threads makes calls into methods of this class - if (event != null) { - if (processEvent(event)) { - return - } - } - - val time = System.currentTimeMillis() // TODO: use a pluggable clock for testability - // Periodically resubmit failed stages if some map output fetches have failed and we have - // waited at least RESUBMIT_TIMEOUT. We wait for this short time because when a node fails, - // tasks on many other nodes are bound to get a fetch failure, and they won't all get it at - // the same time, so we want to make sure we've identified all the reduce tasks that depend - // on the failed node. - if (failed.size > 0 && time > lastFetchFailureTime + RESUBMIT_TIMEOUT) { - resubmitFailedStages() - } else { - submitWaitingStages() - } - } - } - } - /** * Run a job on an RDD locally, assuming it has only a single partition and no dependencies. * We run the operation in a separate thread just in case it takes a bunch of time, so that we @@ -473,6 +682,7 @@ class DAGScheduler( // Broken out for easier testing in DAGSchedulerSuite. protected def runLocallyWithinThread(job: ActiveJob) { + var jobResult: JobResult = JobSucceeded try { SparkEnv.set(env) val rdd = job.finalStage.rdd @@ -487,31 +697,59 @@ class DAGScheduler( } } catch { case e: Exception => + jobResult = JobFailed(e, Some(job.finalStage)) job.listener.jobFailed(e) + } finally { + val s = job.finalStage + stageIdToJobIds -= s.id // clean up data structures that were populated for a local job, + stageIdToStage -= s.id // but that won't get cleaned up via the normal paths through + stageToInfos -= s // completion events or stage abort + jobIdToStageIds -= job.jobId + listenerBus.post(SparkListenerJobEnd(job, jobResult)) + } + } + + /** Finds the earliest-created active job that needs the stage */ + // TODO: Probably should actually find among the active jobs that need this + // stage the one with the highest priority (highest-priority pool, earliest created). + // That should take care of at least part of the priority inversion problem with + // cross-job dependencies. + private def activeJobForStage(stage: Stage): Option[Int] = { + if (stageIdToJobIds.contains(stage.id)) { + val jobsThatUseStage: Array[Int] = stageIdToJobIds(stage.id).toArray.sorted + jobsThatUseStage.find(idToActiveJob.contains(_)) + } else { + None } } /** Submits stage, but first recursively submits any missing parents. */ private def submitStage(stage: Stage) { - logDebug("submitStage(" + stage + ")") - if (!waiting(stage) && !running(stage) && !failed(stage)) { - val missing = getMissingParentStages(stage).sortBy(_.id) - logDebug("missing: " + missing) - if (missing == Nil) { - logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents") - submitMissingTasks(stage) - running += stage - } else { - for (parent <- missing) { - submitStage(parent) + val jobId = activeJobForStage(stage) + if (jobId.isDefined) { + logDebug("submitStage(" + stage + ")") + if (!waiting(stage) && !running(stage) && !failed(stage)) { + val missing = getMissingParentStages(stage).sortBy(_.id) + logDebug("missing: " + missing) + if (missing == Nil) { + logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents") + submitMissingTasks(stage, jobId.get) + running += stage + } else { + for (parent <- missing) { + submitStage(parent) + } + waiting += stage } - waiting += stage } + } else { + abortStage(stage, "No active job for stage " + stage.id) } } + /** Called when stage's parents are available and we can now do its task. */ - private def submitMissingTasks(stage: Stage) { + private def submitMissingTasks(stage: Stage, jobId: Int) { logDebug("submitMissingTasks(" + stage + ")") // Get our pending tasks and remember them in our pendingTasks entry val myPending = pendingTasks.getOrElseUpdate(stage, new HashSet) @@ -532,7 +770,7 @@ class DAGScheduler( } } - val properties = if (idToActiveJob.contains(stage.jobId)) { + val properties = if (idToActiveJob.contains(jobId)) { idToActiveJob(stage.jobId).properties } else { //this stage will be assigned to "default" pool @@ -541,7 +779,7 @@ class DAGScheduler( // must be run listener before possible NotSerializableException // should be "StageSubmitted" first and then "JobEnded" - listenerBus.post(SparkListenerStageSubmitted(stage, tasks.size, properties)) + listenerBus.post(SparkListenerStageSubmitted(stageToInfos(stage), properties)) if (tasks.size > 0) { // Preemptively serialize a task to make sure it can be serialized. We are catching this @@ -552,7 +790,7 @@ class DAGScheduler( SparkEnv.get.closureSerializer.newInstance().serialize(tasks.head) } catch { case e: NotSerializableException => - abortStage(stage, e.toString) + abortStage(stage, "Task not serializable: " + e.toString) running -= stage return } @@ -562,9 +800,7 @@ class DAGScheduler( logDebug("New pending tasks: " + myPending) taskSched.submitTasks( new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties)) - if (!stage.submissionTime.isDefined) { - stage.submissionTime = Some(System.currentTimeMillis()) - } + stageToInfos(stage).submissionTime = Some(System.currentTimeMillis()) } else { logDebug("Stage " + stage + " is actually done; %b %d %d".format( stage.isAvailable, stage.numAvailableOutputs, stage.numPartitions)) @@ -578,16 +814,21 @@ class DAGScheduler( */ private def handleTaskCompletion(event: CompletionEvent) { val task = event.task + + if (!stageIdToStage.contains(task.stageId)) { + // Skip all the actions if the stage has been cancelled. + return + } val stage = stageIdToStage(task.stageId) def markStageAsFinished(stage: Stage) = { - val serviceTime = stage.submissionTime match { + val serviceTime = stageToInfos(stage).submissionTime match { case Some(t) => "%.03f".format((System.currentTimeMillis() - t) / 1000.0) - case _ => "Unkown" + case _ => "Unknown" } logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime)) - stage.completionTime = Some(System.currentTimeMillis) - listenerBus.post(StageCompleted(stageToInfos(stage))) + stageToInfos(stage).completionTime = Some(System.currentTimeMillis()) + listenerBus.post(SparkListenerStageCompleted(stageToInfos(stage))) running -= stage } event.reason match { @@ -611,6 +852,7 @@ class DAGScheduler( activeJobs -= job resultStageToJob -= stage markStageAsFinished(stage) + jobIdToStageIdsRemove(job.jobId) listenerBus.post(SparkListenerJobEnd(job, JobSucceeded)) } job.listener.taskSucceeded(rt.outputId, event.result) @@ -626,7 +868,7 @@ class DAGScheduler( if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) { logInfo("Ignoring possibly bogus ShuffleMapTask completion from " + execId) } else { - stage.addOutputLoc(smt.partition, status) + stage.addOutputLoc(smt.partitionId, status) } if (running.contains(stage) && pendingTasks(stage).isEmpty) { markStageAsFinished(stage) @@ -647,7 +889,7 @@ class DAGScheduler( changeEpoch = true) } clearCacheLocs() - if (stage.outputLocs.count(_ == Nil) != 0) { + if (stage.outputLocs.exists(_ == Nil)) { // Some tasks had failed; let's resubmit this stage // TODO: Lower-level scheduler should also deal with this logInfo("Resubmitting " + stage + " (" + stage.name + @@ -664,9 +906,12 @@ class DAGScheduler( } waiting --= newlyRunnable running ++= newlyRunnable - for (stage <- newlyRunnable.sortBy(_.id)) { + for { + stage <- newlyRunnable.sortBy(_.id) + jobId <- activeJobForStage(stage) + } { logInfo("Submitting " + stage + " (" + stage.rdd + "), which is now runnable") - submitMissingTasks(stage) + submitMissingTasks(stage, jobId) } } } @@ -704,6 +949,9 @@ class DAGScheduler( case ExceptionFailure(className, description, stackTrace, metrics) => // Do nothing here, left up to the TaskScheduler to decide how to handle user failures + case TaskResultLost => + // Do nothing here; the TaskScheduler handles these failures and resubmits the task. + case other => // Unrecognized failure - abort all jobs depending on this stage abortStage(stageIdToStage(task.stageId), task + " failed: " + other) @@ -747,21 +995,42 @@ class DAGScheduler( } } + private def handleJobCancellation(jobId: Int) { + if (!jobIdToStageIds.contains(jobId)) { + logDebug("Trying to cancel unregistered job " + jobId) + } else { + val independentStages = removeJobAndIndependentStages(jobId) + independentStages.foreach { taskSched.cancelTasks } + val error = new SparkException("Job %d cancelled".format(jobId)) + val job = idToActiveJob(jobId) + job.listener.jobFailed(error) + jobIdToStageIds -= jobId + activeJobs -= job + idToActiveJob -= jobId + listenerBus.post(SparkListenerJobEnd(job, JobFailed(error, Some(job.finalStage)))) + } + } + /** * Aborts all jobs depending on a particular Stage. This is called in response to a task set - * being cancelled by the TaskScheduler. Use taskSetFailed() to inject this event from outside. + * being canceled by the TaskScheduler. Use taskSetFailed() to inject this event from outside. */ private def abortStage(failedStage: Stage, reason: String) { + if (!stageIdToStage.contains(failedStage.id)) { + // Skip all the actions if the stage has been removed. + return + } val dependentStages = resultStageToJob.keys.filter(x => stageDependsOn(x, failedStage)).toSeq - failedStage.completionTime = Some(System.currentTimeMillis()) + stageToInfos(failedStage).completionTime = Some(System.currentTimeMillis()) for (resultStage <- dependentStages) { val job = resultStageToJob(resultStage) - val error = new SparkException("Job failed: " + reason) + val error = new SparkException("Job aborted: " + reason) job.listener.jobFailed(error) - listenerBus.post(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage)))) + jobIdToStageIdsRemove(job.jobId) idToActiveJob -= resultStage.jobId activeJobs -= job resultStageToJob -= resultStage + listenerBus.post(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage)))) } if (dependentStages.isEmpty) { logInfo("Ignoring failure of " + failedStage + " because all jobs depending on it are done") @@ -819,7 +1088,7 @@ class DAGScheduler( // If the RDD has narrow dependencies, pick the first partition of the first narrow dep // that has any placement preferences. Ideally we would choose based on transfer sizes, // but this will do for now. - rdd.dependencies.foreach(_ match { + rdd.dependencies.foreach { case n: NarrowDependency[_] => for (inPart <- n.getParents(partition)) { val locs = getPreferredLocs(n.rdd, inPart) @@ -827,30 +1096,29 @@ class DAGScheduler( return locs } case _ => - }) + } Nil } private def cleanup(cleanupTime: Long) { - var sizeBefore = stageIdToStage.size - stageIdToStage.clearOldValues(cleanupTime) - logInfo("stageIdToStage " + sizeBefore + " --> " + stageIdToStage.size) - - sizeBefore = shuffleToMapStage.size - shuffleToMapStage.clearOldValues(cleanupTime) - logInfo("shuffleToMapStage " + sizeBefore + " --> " + shuffleToMapStage.size) - - sizeBefore = pendingTasks.size - pendingTasks.clearOldValues(cleanupTime) - logInfo("pendingTasks " + sizeBefore + " --> " + pendingTasks.size) - - sizeBefore = stageToInfos.size - stageToInfos.clearOldValues(cleanupTime) - logInfo("stageToInfos " + sizeBefore + " --> " + stageToInfos.size) + Map( + "stageIdToStage" -> stageIdToStage, + "shuffleToMapStage" -> shuffleToMapStage, + "pendingTasks" -> pendingTasks, + "stageToInfos" -> stageToInfos, + "jobIdToStageIds" -> jobIdToStageIds, + "stageIdToJobIds" -> stageIdToJobIds). + foreach { case(s, t) => { + val sizeBefore = t.size + t.clearOldValues(cleanupTime) + logInfo("%s %d --> %d".format(s, sizeBefore, t.size)) + }} } def stop() { - eventQueue.put(StopDAGScheduler) + if (eventProcessActor != null) { + eventProcessActor ! StopDAGScheduler + } metadataCleaner.cancel() taskSched.stop() } diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala index 10ff1b4376..add1187613 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala @@ -31,9 +31,10 @@ import org.apache.spark.executor.TaskMetrics * submitted) but there is a single "logic" thread that reads these events and takes decisions. * This greatly simplifies synchronization. */ -private[spark] sealed trait DAGSchedulerEvent +private[scheduler] sealed trait DAGSchedulerEvent -private[spark] case class JobSubmitted( +private[scheduler] case class JobSubmitted( + jobId: Int, finalRDD: RDD[_], func: (TaskContext, Iterator[_]) => _, partitions: Array[Int], @@ -43,9 +44,19 @@ private[spark] case class JobSubmitted( properties: Properties = null) extends DAGSchedulerEvent -private[spark] case class BeginEvent(task: Task[_], taskInfo: TaskInfo) extends DAGSchedulerEvent +private[scheduler] case class JobCancelled(jobId: Int) extends DAGSchedulerEvent -private[spark] case class CompletionEvent( +private[scheduler] case class JobGroupCancelled(groupId: String) extends DAGSchedulerEvent + +private[scheduler] case object AllJobsCancelled extends DAGSchedulerEvent + +private[scheduler] +case class BeginEvent(task: Task[_], taskInfo: TaskInfo) extends DAGSchedulerEvent + +private[scheduler] +case class GettingResultEvent(task: Task[_], taskInfo: TaskInfo) extends DAGSchedulerEvent + +private[scheduler] case class CompletionEvent( task: Task[_], reason: TaskEndReason, result: Any, @@ -54,10 +65,13 @@ private[spark] case class CompletionEvent( taskMetrics: TaskMetrics) extends DAGSchedulerEvent -private[spark] case class ExecutorGained(execId: String, host: String) extends DAGSchedulerEvent +private[scheduler] case class ExecutorGained(execId: String, host: String) extends DAGSchedulerEvent + +private[scheduler] case class ExecutorLost(execId: String) extends DAGSchedulerEvent -private[spark] case class ExecutorLost(execId: String) extends DAGSchedulerEvent +private[scheduler] +case class TaskSetFailed(taskSet: TaskSet, reason: String) extends DAGSchedulerEvent -private[spark] case class TaskSetFailed(taskSet: TaskSet, reason: String) extends DAGSchedulerEvent +private[scheduler] case object ResubmitFailedStages extends DAGSchedulerEvent -private[spark] case object StopDAGScheduler extends DAGSchedulerEvent +private[scheduler] case object StopDAGScheduler extends DAGSchedulerEvent diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala index 446d490cc9..7b5c0e29ad 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerSource.scala @@ -27,23 +27,23 @@ private[spark] class DAGSchedulerSource(val dagScheduler: DAGScheduler, sc: Spar val metricRegistry = new MetricRegistry() val sourceName = "%s.DAGScheduler".format(sc.appName) - metricRegistry.register(MetricRegistry.name("stage", "failedStages", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("stage", "failedStages"), new Gauge[Int] { override def getValue: Int = dagScheduler.failed.size }) - metricRegistry.register(MetricRegistry.name("stage", "runningStages", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("stage", "runningStages"), new Gauge[Int] { override def getValue: Int = dagScheduler.running.size }) - metricRegistry.register(MetricRegistry.name("stage", "waitingStages", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("stage", "waitingStages"), new Gauge[Int] { override def getValue: Int = dagScheduler.waiting.size }) - metricRegistry.register(MetricRegistry.name("job", "allJobs", "number"), new Gauge[Int] { - override def getValue: Int = dagScheduler.nextJobId.get() + metricRegistry.register(MetricRegistry.name("job", "allJobs"), new Gauge[Int] { + override def getValue: Int = dagScheduler.numTotalJobs }) - metricRegistry.register(MetricRegistry.name("job", "activeJobs", "number"), new Gauge[Int] { + metricRegistry.register(MetricRegistry.name("job", "activeJobs"), new Gauge[Int] { override def getValue: Int = dagScheduler.activeJobs.size }) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorLossReason.scala b/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala index 5077b2b48b..2bc43a9186 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorLossReason.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.scheduler.cluster +package org.apache.spark.scheduler import org.apache.spark.executor.ExecutorExitCode diff --git a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala index 370ccd183c..90eb8a747f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala @@ -18,6 +18,7 @@ package org.apache.spark.scheduler import org.apache.spark.{Logging, SparkEnv} +import org.apache.spark.deploy.SparkHadoopUtil import scala.collection.immutable.Set import org.apache.hadoop.mapred.{FileInputFormat, JobConf} import org.apache.hadoop.security.UserGroupInformation @@ -31,7 +32,7 @@ import scala.collection.JavaConversions._ /** * Parses and holds information about inputFormat (and files) specified as a parameter. */ -class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Class[_], +class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Class[_], val path: String) extends Logging { var mapreduceInputFormat: Boolean = false @@ -39,7 +40,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl validate() - override def toString(): String = { + override def toString: String = { "InputFormatInfo " + super.toString + " .. inputFormatClazz " + inputFormatClazz + ", path : " + path } @@ -87,9 +88,8 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl // This method does not expect failures, since validate has already passed ... private def prefLocsFromMapreduceInputFormat(): Set[SplitInfo] = { - val env = SparkEnv.get val conf = new JobConf(configuration) - env.hadoop.addCredentials(conf) + SparkHadoopUtil.get.addCredentials(conf) FileInputFormat.setInputPaths(conf, path) val instance: org.apache.hadoop.mapreduce.InputFormat[_, _] = @@ -108,9 +108,8 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl // This method does not expect failures, since validate has already passed ... private def prefLocsFromMapredInputFormat(): Set[SplitInfo] = { - val env = SparkEnv.get val jobConf = new JobConf(configuration) - env.hadoop.addCredentials(jobConf) + SparkHadoopUtil.get.addCredentials(jobConf) FileInputFormat.setInputPaths(jobConf, path) val instance: org.apache.hadoop.mapred.InputFormat[_, _] = @@ -126,7 +125,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl } private def findPreferredLocations(): Set[SplitInfo] = { - logDebug("mapreduceInputFormat : " + mapreduceInputFormat + ", mapredInputFormat : " + mapredInputFormat + + logDebug("mapreduceInputFormat : " + mapreduceInputFormat + ", mapredInputFormat : " + mapredInputFormat + ", inputFormatClazz : " + inputFormatClazz) if (mapreduceInputFormat) { return prefLocsFromMapreduceInputFormat() @@ -144,14 +143,14 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl object InputFormatInfo { /** Computes the preferred locations based on input(s) and returned a location to block map. - Typical use of this method for allocation would follow some algo like this - (which is what we currently do in YARN branch) : + Typical use of this method for allocation would follow some algo like this: + a) For each host, count number of splits hosted on that host. b) Decrement the currently allocated containers on that host. c) Compute rack info for each host and update rack -> count map based on (b). d) Allocate nodes based on (c) - e) On the allocation result, ensure that we dont allocate "too many" jobs on a single node - (even if data locality on that is very high) : this is to prevent fragility of job if a single + e) On the allocation result, ensure that we dont allocate "too many" jobs on a single node + (even if data locality on that is very high) : this is to prevent fragility of job if a single (or small set of) hosts go down. go to (a) until required nodes are allocated. diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala index 3628b1b078..f8fa5a9f7a 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala @@ -1,292 +1,380 @@ -/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.scheduler
-
-import java.io.PrintWriter
-import java.io.File
-import java.io.FileNotFoundException
-import java.text.SimpleDateFormat
-import java.util.{Date, Properties}
-import java.util.concurrent.LinkedBlockingQueue
-
-import scala.collection.mutable.{Map, HashMap, ListBuffer}
-import scala.io.Source
-
-import org.apache.spark._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.executor.TaskMetrics
-
-// Used to record runtime information for each job, including RDD graph
-// tasks' start/stop shuffle information and information from outside
-
-class JobLogger(val logDirName: String) extends SparkListener with Logging {
- private val logDir =
- if (System.getenv("SPARK_LOG_DIR") != null)
- System.getenv("SPARK_LOG_DIR")
- else
- "/tmp/spark"
- private val jobIDToPrintWriter = new HashMap[Int, PrintWriter]
- private val stageIDToJobID = new HashMap[Int, Int]
- private val jobIDToStages = new HashMap[Int, ListBuffer[Stage]]
- private val DATE_FORMAT = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss")
- private val eventQueue = new LinkedBlockingQueue[SparkListenerEvents]
-
- createLogDir()
- def this() = this(String.valueOf(System.currentTimeMillis()))
-
- def getLogDir = logDir
- def getJobIDtoPrintWriter = jobIDToPrintWriter
- def getStageIDToJobID = stageIDToJobID
- def getJobIDToStages = jobIDToStages
- def getEventQueue = eventQueue
-
- // Create a folder for log files, the folder's name is the creation time of the jobLogger
- protected def createLogDir() {
- val dir = new File(logDir + "/" + logDirName + "/")
- if (dir.exists()) {
- return
- }
- if (dir.mkdirs() == false) {
- logError("create log directory error:" + logDir + "/" + logDirName + "/")
- }
- }
-
- // Create a log file for one job, the file name is the jobID
- protected def createLogWriter(jobID: Int) {
- try{
- val fileWriter = new PrintWriter(logDir + "/" + logDirName + "/" + jobID)
- jobIDToPrintWriter += (jobID -> fileWriter)
- } catch {
- case e: FileNotFoundException => e.printStackTrace()
- }
- }
-
- // Close log file, and clean the stage relationship in stageIDToJobID
- protected def closeLogWriter(jobID: Int) =
- jobIDToPrintWriter.get(jobID).foreach { fileWriter =>
- fileWriter.close()
- jobIDToStages.get(jobID).foreach(_.foreach{ stage =>
- stageIDToJobID -= stage.id
- })
- jobIDToPrintWriter -= jobID
- jobIDToStages -= jobID
- }
-
- // Write log information to log file, withTime parameter controls whether to recored
- // time stamp for the information
- protected def jobLogInfo(jobID: Int, info: String, withTime: Boolean = true) {
- var writeInfo = info
- if (withTime) {
- val date = new Date(System.currentTimeMillis())
- writeInfo = DATE_FORMAT.format(date) + ": " +info
- }
- jobIDToPrintWriter.get(jobID).foreach(_.println(writeInfo))
- }
-
- protected def stageLogInfo(stageID: Int, info: String, withTime: Boolean = true) =
- stageIDToJobID.get(stageID).foreach(jobID => jobLogInfo(jobID, info, withTime))
-
- protected def buildJobDep(jobID: Int, stage: Stage) {
- if (stage.jobId == jobID) {
- jobIDToStages.get(jobID) match {
- case Some(stageList) => stageList += stage
- case None => val stageList = new ListBuffer[Stage]
- stageList += stage
- jobIDToStages += (jobID -> stageList)
- }
- stageIDToJobID += (stage.id -> jobID)
- stage.parents.foreach(buildJobDep(jobID, _))
- }
- }
-
- protected def recordStageDep(jobID: Int) {
- def getRddsInStage(rdd: RDD[_]): ListBuffer[RDD[_]] = {
- var rddList = new ListBuffer[RDD[_]]
- rddList += rdd
- rdd.dependencies.foreach{ dep => dep match {
- case shufDep: ShuffleDependency[_,_] =>
- case _ => rddList ++= getRddsInStage(dep.rdd)
- }
- }
- rddList
- }
- jobIDToStages.get(jobID).foreach {_.foreach { stage =>
- var depRddDesc: String = ""
- getRddsInStage(stage.rdd).foreach { rdd =>
- depRddDesc += rdd.id + ","
- }
- var depStageDesc: String = ""
- stage.parents.foreach { stage =>
- depStageDesc += "(" + stage.id + "," + stage.shuffleDep.get.shuffleId + ")"
- }
- jobLogInfo(jobID, "STAGE_ID=" + stage.id + " RDD_DEP=(" +
- depRddDesc.substring(0, depRddDesc.length - 1) + ")" +
- " STAGE_DEP=" + depStageDesc, false)
- }
- }
- }
-
- // Generate indents and convert to String
- protected def indentString(indent: Int) = {
- val sb = new StringBuilder()
- for (i <- 1 to indent) {
- sb.append(" ")
- }
- sb.toString()
- }
-
- protected def getRddName(rdd: RDD[_]) = {
- var rddName = rdd.getClass.getName
- if (rdd.name != null) {
- rddName = rdd.name
- }
- rddName
- }
-
- protected def recordRddInStageGraph(jobID: Int, rdd: RDD[_], indent: Int) {
- val rddInfo = "RDD_ID=" + rdd.id + "(" + getRddName(rdd) + "," + rdd.generator + ")"
- jobLogInfo(jobID, indentString(indent) + rddInfo, false)
- rdd.dependencies.foreach{ dep => dep match {
- case shufDep: ShuffleDependency[_,_] =>
- val depInfo = "SHUFFLE_ID=" + shufDep.shuffleId
- jobLogInfo(jobID, indentString(indent + 1) + depInfo, false)
- case _ => recordRddInStageGraph(jobID, dep.rdd, indent + 1)
- }
- }
- }
-
- protected def recordStageDepGraph(jobID: Int, stage: Stage, indent: Int = 0) {
- var stageInfo: String = ""
- if (stage.isShuffleMap) {
- stageInfo = "STAGE_ID=" + stage.id + " MAP_STAGE SHUFFLE_ID=" +
- stage.shuffleDep.get.shuffleId
- }else{
- stageInfo = "STAGE_ID=" + stage.id + " RESULT_STAGE"
- }
- if (stage.jobId == jobID) {
- jobLogInfo(jobID, indentString(indent) + stageInfo, false)
- recordRddInStageGraph(jobID, stage.rdd, indent)
- stage.parents.foreach(recordStageDepGraph(jobID, _, indent + 2))
- } else
- jobLogInfo(jobID, indentString(indent) + stageInfo + " JOB_ID=" + stage.jobId, false)
- }
-
- // Record task metrics into job log files
- protected def recordTaskMetrics(stageID: Int, status: String,
- taskInfo: TaskInfo, taskMetrics: TaskMetrics) {
- val info = " TID=" + taskInfo.taskId + " STAGE_ID=" + stageID +
- " START_TIME=" + taskInfo.launchTime + " FINISH_TIME=" + taskInfo.finishTime +
- " EXECUTOR_ID=" + taskInfo.executorId + " HOST=" + taskMetrics.hostname
- val executorRunTime = " EXECUTOR_RUN_TIME=" + taskMetrics.executorRunTime
- val readMetrics =
- taskMetrics.shuffleReadMetrics match {
- case Some(metrics) =>
- " SHUFFLE_FINISH_TIME=" + metrics.shuffleFinishTime +
- " BLOCK_FETCHED_TOTAL=" + metrics.totalBlocksFetched +
- " BLOCK_FETCHED_LOCAL=" + metrics.localBlocksFetched +
- " BLOCK_FETCHED_REMOTE=" + metrics.remoteBlocksFetched +
- " REMOTE_FETCH_WAIT_TIME=" + metrics.fetchWaitTime +
- " REMOTE_FETCH_TIME=" + metrics.remoteFetchTime +
- " REMOTE_BYTES_READ=" + metrics.remoteBytesRead
- case None => ""
- }
- val writeMetrics =
- taskMetrics.shuffleWriteMetrics match {
- case Some(metrics) =>
- " SHUFFLE_BYTES_WRITTEN=" + metrics.shuffleBytesWritten
- case None => ""
- }
- stageLogInfo(stageID, status + info + executorRunTime + readMetrics + writeMetrics)
- }
-
- override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) {
- stageLogInfo(
- stageSubmitted.stage.id,
- "STAGE_ID=%d STATUS=SUBMITTED TASK_SIZE=%d".format(
- stageSubmitted.stage.id, stageSubmitted.taskSize))
- }
-
- override def onStageCompleted(stageCompleted: StageCompleted) {
- stageLogInfo(
- stageCompleted.stageInfo.stage.id,
- "STAGE_ID=%d STATUS=COMPLETED".format(stageCompleted.stageInfo.stage.id))
-
- }
-
- override def onTaskStart(taskStart: SparkListenerTaskStart) { }
-
- override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
- val task = taskEnd.task
- val taskInfo = taskEnd.taskInfo
- var taskStatus = ""
- task match {
- case resultTask: ResultTask[_, _] => taskStatus = "TASK_TYPE=RESULT_TASK"
- case shuffleMapTask: ShuffleMapTask => taskStatus = "TASK_TYPE=SHUFFLE_MAP_TASK"
- }
- taskEnd.reason match {
- case Success => taskStatus += " STATUS=SUCCESS"
- recordTaskMetrics(task.stageId, taskStatus, taskInfo, taskEnd.taskMetrics)
- case Resubmitted =>
- taskStatus += " STATUS=RESUBMITTED TID=" + taskInfo.taskId +
- " STAGE_ID=" + task.stageId
- stageLogInfo(task.stageId, taskStatus)
- case FetchFailed(bmAddress, shuffleId, mapId, reduceId) =>
- taskStatus += " STATUS=FETCHFAILED TID=" + taskInfo.taskId + " STAGE_ID=" +
- task.stageId + " SHUFFLE_ID=" + shuffleId + " MAP_ID=" +
- mapId + " REDUCE_ID=" + reduceId
- stageLogInfo(task.stageId, taskStatus)
- case OtherFailure(message) =>
- taskStatus += " STATUS=FAILURE TID=" + taskInfo.taskId +
- " STAGE_ID=" + task.stageId + " INFO=" + message
- stageLogInfo(task.stageId, taskStatus)
- case _ =>
- }
- }
-
- override def onJobEnd(jobEnd: SparkListenerJobEnd) {
- val job = jobEnd.job
- var info = "JOB_ID=" + job.jobId
- jobEnd.jobResult match {
- case JobSucceeded => info += " STATUS=SUCCESS"
- case JobFailed(exception, _) =>
- info += " STATUS=FAILED REASON="
- exception.getMessage.split("\\s+").foreach(info += _ + "_")
- case _ =>
- }
- jobLogInfo(job.jobId, info.substring(0, info.length - 1).toUpperCase)
- closeLogWriter(job.jobId)
- }
-
- protected def recordJobProperties(jobID: Int, properties: Properties) {
- if(properties != null) {
- val description = properties.getProperty(SparkContext.SPARK_JOB_DESCRIPTION, "")
- jobLogInfo(jobID, description, false)
- }
- }
-
- override def onJobStart(jobStart: SparkListenerJobStart) {
- val job = jobStart.job
- val properties = jobStart.properties
- createLogWriter(job.jobId)
- recordJobProperties(job.jobId, properties)
- buildJobDep(job.jobId, job.finalStage)
- recordStageDep(job.jobId)
- recordStageDepGraph(job.jobId, job.finalStage)
- jobLogInfo(job.jobId, "JOB_ID=" + job.jobId + " STATUS=STARTED")
- }
-}
+/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import java.io.{IOException, File, FileNotFoundException, PrintWriter} +import java.text.SimpleDateFormat +import java.util.{Date, Properties} +import java.util.concurrent.LinkedBlockingQueue + +import scala.collection.mutable.{HashMap, HashSet, ListBuffer} + +import org.apache.spark._ +import org.apache.spark.rdd.RDD +import org.apache.spark.executor.TaskMetrics +import org.apache.spark.storage.StorageLevel + +/** + * A logger class to record runtime information for jobs in Spark. This class outputs one log file + * for each Spark job, containing RDD graph, tasks start/stop, shuffle information. + * JobLogger is a subclass of SparkListener, use addSparkListener to add JobLogger to a SparkContext + * after the SparkContext is created. + * Note that each JobLogger only works for one SparkContext + * @param logDirName The base directory for the log files. + */ + +class JobLogger(val user: String, val logDirName: String) + extends SparkListener with Logging { + + def this() = this(System.getProperty("user.name", "<unknown>"), + String.valueOf(System.currentTimeMillis())) + + private val logDir = + if (System.getenv("SPARK_LOG_DIR") != null) + System.getenv("SPARK_LOG_DIR") + else + "/tmp/spark-%s".format(user) + + private val jobIDToPrintWriter = new HashMap[Int, PrintWriter] + private val stageIDToJobID = new HashMap[Int, Int] + private val jobIDToStages = new HashMap[Int, ListBuffer[Stage]] + private val DATE_FORMAT = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") + private val eventQueue = new LinkedBlockingQueue[SparkListenerEvents] + + createLogDir() + + // The following 5 functions are used only in testing. + private[scheduler] def getLogDir = logDir + private[scheduler] def getJobIDtoPrintWriter = jobIDToPrintWriter + private[scheduler] def getStageIDToJobID = stageIDToJobID + private[scheduler] def getJobIDToStages = jobIDToStages + private[scheduler] def getEventQueue = eventQueue + + /** Create a folder for log files, the folder's name is the creation time of jobLogger */ + protected def createLogDir() { + val dir = new File(logDir + "/" + logDirName + "/") + if (dir.exists()) { + return + } + if (dir.mkdirs() == false) { + // JobLogger should throw a exception rather than continue to construct this object. + throw new IOException("create log directory error:" + logDir + "/" + logDirName + "/") + } + } + + /** + * Create a log file for one job + * @param jobID ID of the job + * @exception FileNotFoundException Fail to create log file + */ + protected def createLogWriter(jobID: Int) { + try { + val fileWriter = new PrintWriter(logDir + "/" + logDirName + "/" + jobID) + jobIDToPrintWriter += (jobID -> fileWriter) + } catch { + case e: FileNotFoundException => e.printStackTrace() + } + } + + /** + * Close log file, and clean the stage relationship in stageIDToJobID + * @param jobID ID of the job + */ + protected def closeLogWriter(jobID: Int) { + jobIDToPrintWriter.get(jobID).foreach { fileWriter => + fileWriter.close() + jobIDToStages.get(jobID).foreach(_.foreach{ stage => + stageIDToJobID -= stage.id + }) + jobIDToPrintWriter -= jobID + jobIDToStages -= jobID + } + } + + /** + * Write info into log file + * @param jobID ID of the job + * @param info Info to be recorded + * @param withTime Controls whether to record time stamp before the info, default is true + */ + protected def jobLogInfo(jobID: Int, info: String, withTime: Boolean = true) { + var writeInfo = info + if (withTime) { + val date = new Date(System.currentTimeMillis()) + writeInfo = DATE_FORMAT.format(date) + ": " +info + } + jobIDToPrintWriter.get(jobID).foreach(_.println(writeInfo)) + } + + /** + * Write info into log file + * @param stageID ID of the stage + * @param info Info to be recorded + * @param withTime Controls whether to record time stamp before the info, default is true + */ + protected def stageLogInfo(stageID: Int, info: String, withTime: Boolean = true) { + stageIDToJobID.get(stageID).foreach(jobID => jobLogInfo(jobID, info, withTime)) + } + + /** + * Build stage dependency for a job + * @param jobID ID of the job + * @param stage Root stage of the job + */ + protected def buildJobDep(jobID: Int, stage: Stage) { + if (stage.jobId == jobID) { + jobIDToStages.get(jobID) match { + case Some(stageList) => stageList += stage + case None => val stageList = new ListBuffer[Stage] + stageList += stage + jobIDToStages += (jobID -> stageList) + } + stageIDToJobID += (stage.id -> jobID) + stage.parents.foreach(buildJobDep(jobID, _)) + } + } + + /** + * Record stage dependency and RDD dependency for a stage + * @param jobID Job ID of the stage + */ + protected def recordStageDep(jobID: Int) { + def getRddsInStage(rdd: RDD[_]): ListBuffer[RDD[_]] = { + var rddList = new ListBuffer[RDD[_]] + rddList += rdd + rdd.dependencies.foreach { + case shufDep: ShuffleDependency[_, _] => + case dep: Dependency[_] => rddList ++= getRddsInStage(dep.rdd) + } + rddList + } + jobIDToStages.get(jobID).foreach {_.foreach { stage => + var depRddDesc: String = "" + getRddsInStage(stage.rdd).foreach { rdd => + depRddDesc += rdd.id + "," + } + var depStageDesc: String = "" + stage.parents.foreach { stage => + depStageDesc += "(" + stage.id + "," + stage.shuffleDep.get.shuffleId + ")" + } + jobLogInfo(jobID, "STAGE_ID=" + stage.id + " RDD_DEP=(" + + depRddDesc.substring(0, depRddDesc.length - 1) + ")" + + " STAGE_DEP=" + depStageDesc, false) + } + } + } + + /** + * Generate indents and convert to String + * @param indent Number of indents + * @return string of indents + */ + protected def indentString(indent: Int): String = { + val sb = new StringBuilder() + for (i <- 1 to indent) { + sb.append(" ") + } + sb.toString() + } + + /** + * Get RDD's name + * @param rdd Input RDD + * @return String of RDD's name + */ + protected def getRddName(rdd: RDD[_]): String = { + var rddName = rdd.getClass.getSimpleName + if (rdd.name != null) { + rddName = rdd.name + } + rddName + } + + /** + * Record RDD dependency graph in a stage + * @param jobID Job ID of the stage + * @param rdd Root RDD of the stage + * @param indent Indent number before info + */ + protected def recordRddInStageGraph(jobID: Int, rdd: RDD[_], indent: Int) { + val rddInfo = + if (rdd.getStorageLevel != StorageLevel.NONE) { + "RDD_ID=" + rdd.id + " " + getRddName(rdd) + " CACHED" + " " + + rdd.origin + " " + rdd.generator + } else { + "RDD_ID=" + rdd.id + " " + getRddName(rdd) + " NONE" + " " + + rdd.origin + " " + rdd.generator + } + jobLogInfo(jobID, indentString(indent) + rddInfo, false) + rdd.dependencies.foreach { + case shufDep: ShuffleDependency[_, _] => + val depInfo = "SHUFFLE_ID=" + shufDep.shuffleId + jobLogInfo(jobID, indentString(indent + 1) + depInfo, false) + case dep: Dependency[_] => recordRddInStageGraph(jobID, dep.rdd, indent + 1) + } + } + + /** + * Record stage dependency graph of a job + * @param jobID Job ID of the stage + * @param stage Root stage of the job + * @param indent Indent number before info, default is 0 + */ + protected def recordStageDepGraph(jobID: Int, stage: Stage, idSet: HashSet[Int], indent: Int = 0) { + val stageInfo = if (stage.isShuffleMap) { + "STAGE_ID=" + stage.id + " MAP_STAGE SHUFFLE_ID=" + stage.shuffleDep.get.shuffleId + } else { + "STAGE_ID=" + stage.id + " RESULT_STAGE" + } + if (stage.jobId == jobID) { + jobLogInfo(jobID, indentString(indent) + stageInfo, false) + if (!idSet.contains(stage.id)) { + idSet += stage.id + recordRddInStageGraph(jobID, stage.rdd, indent) + stage.parents.foreach(recordStageDepGraph(jobID, _, idSet, indent + 2)) + } + } else { + jobLogInfo(jobID, indentString(indent) + stageInfo + " JOB_ID=" + stage.jobId, false) + } + } + + /** + * Record task metrics into job log files, including execution info and shuffle metrics + * @param stageID Stage ID of the task + * @param status Status info of the task + * @param taskInfo Task description info + * @param taskMetrics Task running metrics + */ + protected def recordTaskMetrics(stageID: Int, status: String, + taskInfo: TaskInfo, taskMetrics: TaskMetrics) { + val info = " TID=" + taskInfo.taskId + " STAGE_ID=" + stageID + + " START_TIME=" + taskInfo.launchTime + " FINISH_TIME=" + taskInfo.finishTime + + " EXECUTOR_ID=" + taskInfo.executorId + " HOST=" + taskMetrics.hostname + val executorRunTime = " EXECUTOR_RUN_TIME=" + taskMetrics.executorRunTime + val readMetrics = taskMetrics.shuffleReadMetrics match { + case Some(metrics) => + " SHUFFLE_FINISH_TIME=" + metrics.shuffleFinishTime + + " BLOCK_FETCHED_TOTAL=" + metrics.totalBlocksFetched + + " BLOCK_FETCHED_LOCAL=" + metrics.localBlocksFetched + + " BLOCK_FETCHED_REMOTE=" + metrics.remoteBlocksFetched + + " REMOTE_FETCH_WAIT_TIME=" + metrics.fetchWaitTime + + " REMOTE_FETCH_TIME=" + metrics.remoteFetchTime + + " REMOTE_BYTES_READ=" + metrics.remoteBytesRead + case None => "" + } + val writeMetrics = taskMetrics.shuffleWriteMetrics match { + case Some(metrics) => " SHUFFLE_BYTES_WRITTEN=" + metrics.shuffleBytesWritten + case None => "" + } + stageLogInfo(stageID, status + info + executorRunTime + readMetrics + writeMetrics) + } + + /** + * When stage is submitted, record stage submit info + * @param stageSubmitted Stage submitted event + */ + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) { + stageLogInfo(stageSubmitted.stage.stageId,"STAGE_ID=%d STATUS=SUBMITTED TASK_SIZE=%d".format( + stageSubmitted.stage.stageId, stageSubmitted.stage.numTasks)) + } + + /** + * When stage is completed, record stage completion status + * @param stageCompleted Stage completed event + */ + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) { + stageLogInfo(stageCompleted.stage.stageId, "STAGE_ID=%d STATUS=COMPLETED".format( + stageCompleted.stage.stageId)) + } + + override def onTaskStart(taskStart: SparkListenerTaskStart) { } + + /** + * When task ends, record task completion status and metrics + * @param taskEnd Task end event + */ + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) { + val task = taskEnd.task + val taskInfo = taskEnd.taskInfo + var taskStatus = "" + task match { + case resultTask: ResultTask[_, _] => taskStatus = "TASK_TYPE=RESULT_TASK" + case shuffleMapTask: ShuffleMapTask => taskStatus = "TASK_TYPE=SHUFFLE_MAP_TASK" + } + taskEnd.reason match { + case Success => taskStatus += " STATUS=SUCCESS" + recordTaskMetrics(task.stageId, taskStatus, taskInfo, taskEnd.taskMetrics) + case Resubmitted => + taskStatus += " STATUS=RESUBMITTED TID=" + taskInfo.taskId + + " STAGE_ID=" + task.stageId + stageLogInfo(task.stageId, taskStatus) + case FetchFailed(bmAddress, shuffleId, mapId, reduceId) => + taskStatus += " STATUS=FETCHFAILED TID=" + taskInfo.taskId + " STAGE_ID=" + + task.stageId + " SHUFFLE_ID=" + shuffleId + " MAP_ID=" + + mapId + " REDUCE_ID=" + reduceId + stageLogInfo(task.stageId, taskStatus) + case _ => + } + } + + /** + * When job ends, recording job completion status and close log file + * @param jobEnd Job end event + */ + override def onJobEnd(jobEnd: SparkListenerJobEnd) { + val job = jobEnd.job + var info = "JOB_ID=" + job.jobId + jobEnd.jobResult match { + case JobSucceeded => info += " STATUS=SUCCESS" + case JobFailed(exception, _) => + info += " STATUS=FAILED REASON=" + exception.getMessage.split("\\s+").foreach(info += _ + "_") + case _ => + } + jobLogInfo(job.jobId, info.substring(0, info.length - 1).toUpperCase) + closeLogWriter(job.jobId) + } + + /** + * Record job properties into job log file + * @param jobID ID of the job + * @param properties Properties of the job + */ + protected def recordJobProperties(jobID: Int, properties: Properties) { + if(properties != null) { + val description = properties.getProperty(SparkContext.SPARK_JOB_DESCRIPTION, "") + jobLogInfo(jobID, description, false) + } + } + + /** + * When job starts, record job property and stage graph + * @param jobStart Job start event + */ + override def onJobStart(jobStart: SparkListenerJobStart) { + val job = jobStart.job + val properties = jobStart.properties + createLogWriter(job.jobId) + recordJobProperties(job.jobId, properties) + buildJobDep(job.jobId, job.finalStage) + recordStageDep(job.jobId) + recordStageDepGraph(job.jobId, job.finalStage, new HashSet[Int]) + jobLogInfo(job.jobId, "JOB_ID=" + job.jobId + " STATUS=STARTED") + } +} + diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala index 200d881799..b026f860a8 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala @@ -17,48 +17,59 @@ package org.apache.spark.scheduler -import scala.collection.mutable.ArrayBuffer - /** * An object that waits for a DAGScheduler job to complete. As tasks finish, it passes their * results to the given handler function. */ -private[spark] class JobWaiter[T](totalTasks: Int, resultHandler: (Int, T) => Unit) +private[spark] class JobWaiter[T]( + dagScheduler: DAGScheduler, + jobId: Int, + totalTasks: Int, + resultHandler: (Int, T) => Unit) extends JobListener { private var finishedTasks = 0 - private var jobFinished = false // Is the job as a whole finished (succeeded or failed)? - private var jobResult: JobResult = null // If the job is finished, this will be its result + // Is the job as a whole finished (succeeded or failed)? + @volatile + private var _jobFinished = totalTasks == 0 - override def taskSucceeded(index: Int, result: Any) { - synchronized { - if (jobFinished) { - throw new UnsupportedOperationException("taskSucceeded() called on a finished JobWaiter") - } - resultHandler(index, result.asInstanceOf[T]) - finishedTasks += 1 - if (finishedTasks == totalTasks) { - jobFinished = true - jobResult = JobSucceeded - this.notifyAll() - } - } + def jobFinished = _jobFinished + + // If the job is finished, this will be its result. In the case of 0 task jobs (e.g. zero + // partition RDDs), we set the jobResult directly to JobSucceeded. + private var jobResult: JobResult = if (jobFinished) JobSucceeded else null + + /** + * Sends a signal to the DAGScheduler to cancel the job. The cancellation itself is handled + * asynchronously. After the low level scheduler cancels all the tasks belonging to this job, it + * will fail this job with a SparkException. + */ + def cancel() { + dagScheduler.cancelJob(jobId) } - override def jobFailed(exception: Exception) { - synchronized { - if (jobFinished) { - throw new UnsupportedOperationException("jobFailed() called on a finished JobWaiter") - } - jobFinished = true - jobResult = JobFailed(exception, None) + override def taskSucceeded(index: Int, result: Any): Unit = synchronized { + if (_jobFinished) { + throw new UnsupportedOperationException("taskSucceeded() called on a finished JobWaiter") + } + resultHandler(index, result.asInstanceOf[T]) + finishedTasks += 1 + if (finishedTasks == totalTasks) { + _jobFinished = true + jobResult = JobSucceeded this.notifyAll() } } + override def jobFailed(exception: Exception): Unit = synchronized { + _jobFinished = true + jobResult = JobFailed(exception, None) + this.notifyAll() + } + def awaitResult(): JobResult = synchronized { - while (!jobFinished) { + while (!_jobFinished) { this.wait() } return jobResult diff --git a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala index c9a66b3a75..1791242215 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala @@ -43,9 +43,12 @@ private[spark] class Pool( var runningTasks = 0 var priority = 0 - var stageId = 0 + + // A pool's stage id is used to break the tie in scheduling. + var stageId = -1 + var name = poolName - var parent:Schedulable = null + var parent: Pool = null var taskSetSchedulingAlgorithm: SchedulingAlgorithm = { schedulingMode match { @@ -101,21 +104,17 @@ private[spark] class Pool( return sortedTaskSetQueue } - override def increaseRunningTasks(taskNum: Int) { + def increaseRunningTasks(taskNum: Int) { runningTasks += taskNum if (parent != null) { parent.increaseRunningTasks(taskNum) } } - override def decreaseRunningTasks(taskNum: Int) { + def decreaseRunningTasks(taskNum: Int) { runningTasks -= taskNum if (parent != null) { parent.decreaseRunningTasks(taskNum) } } - - override def hasPendingTasks(): Boolean = { - schedulableQueue.exists(_.hasPendingTasks()) - } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala index 07e8317e3a..28f3ba53b8 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala @@ -23,7 +23,7 @@ import java.util.zip.{GZIPInputStream, GZIPOutputStream} import org.apache.spark._ import org.apache.spark.rdd.RDD import org.apache.spark.rdd.RDDCheckpointData -import org.apache.spark.util.{MetadataCleaner, TimeStampedHashMap} +import org.apache.spark.util.{MetadataCleanerType, MetadataCleaner, TimeStampedHashMap} private[spark] object ResultTask { @@ -32,23 +32,25 @@ private[spark] object ResultTask { // expensive on the master node if it needs to launch thousands of tasks. val serializedInfoCache = new TimeStampedHashMap[Int, Array[Byte]] - val metadataCleaner = new MetadataCleaner("ResultTask", serializedInfoCache.clearOldValues) + // TODO: This object shouldn't have global variables + val metadataCleaner = new MetadataCleaner( + MetadataCleanerType.RESULT_TASK, serializedInfoCache.clearOldValues, new SparkConf) def serializeInfo(stageId: Int, rdd: RDD[_], func: (TaskContext, Iterator[_]) => _): Array[Byte] = { synchronized { val old = serializedInfoCache.get(stageId).orNull if (old != null) { - return old + old } else { val out = new ByteArrayOutputStream - val ser = SparkEnv.get.closureSerializer.newInstance + val ser = SparkEnv.get.closureSerializer.newInstance() val objOut = ser.serializeStream(new GZIPOutputStream(out)) objOut.writeObject(rdd) objOut.writeObject(func) objOut.close() val bytes = out.toByteArray serializedInfoCache.put(stageId, bytes) - return bytes + bytes } } } @@ -56,11 +58,11 @@ private[spark] object ResultTask { def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], (TaskContext, Iterator[_]) => _) = { val loader = Thread.currentThread.getContextClassLoader val in = new GZIPInputStream(new ByteArrayInputStream(bytes)) - val ser = SparkEnv.get.closureSerializer.newInstance + val ser = SparkEnv.get.closureSerializer.newInstance() val objIn = ser.deserializeStream(in) val rdd = objIn.readObject().asInstanceOf[RDD[_]] val func = objIn.readObject().asInstanceOf[(TaskContext, Iterator[_]) => _] - return (rdd, func) + (rdd, func) } def clearCache() { @@ -71,29 +73,37 @@ private[spark] object ResultTask { } +/** + * A task that sends back the output to the driver application. + * + * See [[org.apache.spark.scheduler.Task]] for more information. + * + * @param stageId id of the stage this task belongs to + * @param rdd input to func + * @param func a function to apply on a partition of the RDD + * @param _partitionId index of the number in the RDD + * @param locs preferred task execution locations for locality scheduling + * @param outputId index of the task in this job (a job can launch tasks on only a subset of the + * input RDD's partitions). + */ private[spark] class ResultTask[T, U]( stageId: Int, var rdd: RDD[T], var func: (TaskContext, Iterator[T]) => U, - var partition: Int, + _partitionId: Int, @transient locs: Seq[TaskLocation], var outputId: Int) - extends Task[U](stageId) with Externalizable { + extends Task[U](stageId, _partitionId) with Externalizable { def this() = this(0, null, null, 0, null, 0) - var split = if (rdd == null) { - null - } else { - rdd.partitions(partition) - } + var split = if (rdd == null) null else rdd.partitions(partitionId) @transient private val preferredLocs: Seq[TaskLocation] = { if (locs == null) Nil else locs.toSet.toSeq } - override def run(attemptId: Long): U = { - val context = new TaskContext(stageId, partition, attemptId, runningLocally = false) + override def runTask(context: TaskContext): U = { metrics = Some(context.taskMetrics) try { func(context, rdd.iterator(split, context)) @@ -104,17 +114,17 @@ private[spark] class ResultTask[T, U]( override def preferredLocations: Seq[TaskLocation] = preferredLocs - override def toString = "ResultTask(" + stageId + ", " + partition + ")" + override def toString = "ResultTask(" + stageId + ", " + partitionId + ")" override def writeExternal(out: ObjectOutput) { RDDCheckpointData.synchronized { - split = rdd.partitions(partition) + split = rdd.partitions(partitionId) out.writeInt(stageId) val bytes = ResultTask.serializeInfo( stageId, rdd, func.asInstanceOf[(TaskContext, Iterator[_]) => _]) out.writeInt(bytes.length) out.write(bytes) - out.writeInt(partition) + out.writeInt(partitionId) out.writeInt(outputId) out.writeLong(epoch) out.writeObject(split) @@ -129,7 +139,7 @@ private[spark] class ResultTask[T, U]( val (rdd_, func_) = ResultTask.deserializeInfo(stageId, bytes) rdd = rdd_.asInstanceOf[RDD[T]] func = func_.asInstanceOf[(TaskContext, Iterator[T]) => U] - partition = in.readInt() + partitionId = in.readInt() outputId = in.readInt() epoch = in.readLong() split = in.readObject().asInstanceOf[Partition] diff --git a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala index 857adaef5a..d573e125a3 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala @@ -25,7 +25,7 @@ import scala.collection.mutable.ArrayBuffer * there are two type of Schedulable entities(Pools and TaskSetManagers) */ private[spark] trait Schedulable { - var parent: Schedulable + var parent: Pool // child queues def schedulableQueue: ArrayBuffer[Schedulable] def schedulingMode: SchedulingMode @@ -36,13 +36,10 @@ private[spark] trait Schedulable { def stageId: Int def name: String - def increaseRunningTasks(taskNum: Int): Unit - def decreaseRunningTasks(taskNum: Int): Unit def addSchedulable(schedulable: Schedulable): Unit def removeSchedulable(schedulable: Schedulable): Unit def getSchedulableByName(name: String): Schedulable def executorLost(executorId: String, host: String): Unit def checkSpeculatableTasks(): Boolean def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] - def hasPendingTasks(): Boolean } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala index 4e25086ec9..3cf995ea74 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala @@ -20,7 +20,7 @@ package org.apache.spark.scheduler import java.io.{FileInputStream, InputStream} import java.util.{NoSuchElementException, Properties} -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import scala.xml.XML @@ -30,7 +30,10 @@ import scala.xml.XML * addTaskSetManager: build the leaf nodes(TaskSetManagers) */ private[spark] trait SchedulableBuilder { + def rootPool: Pool + def buildPools() + def addTaskSetManager(manager: Schedulable, properties: Properties) } @@ -46,10 +49,10 @@ private[spark] class FIFOSchedulableBuilder(val rootPool: Pool) } } -private[spark] class FairSchedulableBuilder(val rootPool: Pool) +private[spark] class FairSchedulableBuilder(val rootPool: Pool, conf: SparkConf) extends SchedulableBuilder with Logging { - val schedulerAllocFile = Option(System.getProperty("spark.scheduler.allocation.file")) + val schedulerAllocFile = conf.getOption("spark.scheduler.allocation.file") val DEFAULT_SCHEDULER_FILE = "fairscheduler.xml" val FAIR_SCHEDULER_PROPERTIES = "spark.scheduler.pool" val DEFAULT_POOL_NAME = "default" diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala index d57eb3276f..02bdbba825 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala @@ -15,12 +15,12 @@ * limitations under the License. */ -package org.apache.spark.scheduler.cluster +package org.apache.spark.scheduler -import org.apache.spark.{SparkContext} +import org.apache.spark.SparkContext /** - * A backend interface for cluster scheduling systems that allows plugging in different ones under + * A backend interface for scheduling systems that allows plugging in different ones under * ClusterScheduler. We assume a Mesos-like model where the application gets resource offers as * machines become available and can launch tasks on them. */ @@ -30,8 +30,5 @@ private[spark] trait SchedulerBackend { def reviveOffers(): Unit def defaultParallelism(): Int - // Memory used by each executor (in megabytes) - protected val executorMemory: Int = SparkContext.executorMemoryRequested - - // TODO: Probably want to add a killTask too + def killTask(taskId: Long, executorId: String): Unit = throw new UnsupportedOperationException } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulingMode.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulingMode.scala index 0a786deb16..3832ee7ff6 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SchedulingMode.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulingMode.scala @@ -22,7 +22,7 @@ package org.apache.spark.scheduler * to order tasks amongst a Schedulable's sub-queues * "NONE" is used when the a Schedulable has no sub-queues. */ -object SchedulingMode extends Enumeration("FAIR", "FIFO", "NONE") { +object SchedulingMode extends Enumeration { type SchedulingMode = Value val FAIR,FIFO,NONE = Value diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala index d23df0dd2b..a37ead5632 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala @@ -25,7 +25,7 @@ import scala.collection.mutable.HashMap import org.apache.spark._ import org.apache.spark.executor.ShuffleWriteMetrics import org.apache.spark.storage._ -import org.apache.spark.util.{TimeStampedHashMap, MetadataCleaner} +import org.apache.spark.util.{MetadataCleanerType, TimeStampedHashMap, MetadataCleaner} import org.apache.spark.rdd.RDD import org.apache.spark.rdd.RDDCheckpointData @@ -37,7 +37,9 @@ private[spark] object ShuffleMapTask { // expensive on the master node if it needs to launch thousands of tasks. val serializedInfoCache = new TimeStampedHashMap[Int, Array[Byte]] - val metadataCleaner = new MetadataCleaner("ShuffleMapTask", serializedInfoCache.clearOldValues) + // TODO: This object shouldn't have global variables + val metadataCleaner = new MetadataCleaner( + MetadataCleanerType.SHUFFLE_MAP_TASK, serializedInfoCache.clearOldValues, new SparkConf) def serializeInfo(stageId: Int, rdd: RDD[_], dep: ShuffleDependency[_,_]): Array[Byte] = { synchronized { @@ -53,7 +55,7 @@ private[spark] object ShuffleMapTask { objOut.close() val bytes = out.toByteArray serializedInfoCache.put(stageId, bytes) - return bytes + bytes } } } @@ -66,7 +68,7 @@ private[spark] object ShuffleMapTask { val objIn = ser.deserializeStream(in) val rdd = objIn.readObject().asInstanceOf[RDD[_]] val dep = objIn.readObject().asInstanceOf[ShuffleDependency[_,_]] - return (rdd, dep) + (rdd, dep) } } @@ -75,7 +77,7 @@ private[spark] object ShuffleMapTask { val in = new GZIPInputStream(new ByteArrayInputStream(bytes)) val objIn = new ObjectInputStream(in) val set = objIn.readObject().asInstanceOf[Array[(String, Long)]].toMap - return (HashMap(set.toSeq: _*)) + HashMap(set.toSeq: _*) } def clearCache() { @@ -85,13 +87,25 @@ private[spark] object ShuffleMapTask { } } +/** + * A ShuffleMapTask divides the elements of an RDD into multiple buckets (based on a partitioner + * specified in the ShuffleDependency). + * + * See [[org.apache.spark.scheduler.Task]] for more information. + * + * @param stageId id of the stage this task belongs to + * @param rdd the final RDD in this stage + * @param dep the ShuffleDependency + * @param _partitionId index of the number in the RDD + * @param locs preferred task execution locations for locality scheduling + */ private[spark] class ShuffleMapTask( stageId: Int, var rdd: RDD[_], var dep: ShuffleDependency[_,_], - var partition: Int, + _partitionId: Int, @transient private var locs: Seq[TaskLocation]) - extends Task[MapStatus](stageId) + extends Task[MapStatus](stageId, _partitionId) with Externalizable with Logging { @@ -101,16 +115,16 @@ private[spark] class ShuffleMapTask( if (locs == null) Nil else locs.toSet.toSeq } - var split = if (rdd == null) null else rdd.partitions(partition) + var split = if (rdd == null) null else rdd.partitions(partitionId) override def writeExternal(out: ObjectOutput) { RDDCheckpointData.synchronized { - split = rdd.partitions(partition) + split = rdd.partitions(partitionId) out.writeInt(stageId) val bytes = ShuffleMapTask.serializeInfo(stageId, rdd, dep) out.writeInt(bytes.length) out.write(bytes) - out.writeInt(partition) + out.writeInt(partitionId) out.writeLong(epoch) out.writeObject(split) } @@ -124,68 +138,73 @@ private[spark] class ShuffleMapTask( val (rdd_, dep_) = ShuffleMapTask.deserializeInfo(stageId, bytes) rdd = rdd_ dep = dep_ - partition = in.readInt() + partitionId = in.readInt() epoch = in.readLong() split = in.readObject().asInstanceOf[Partition] } - override def run(attemptId: Long): MapStatus = { + override def runTask(context: TaskContext): MapStatus = { val numOutputSplits = dep.partitioner.numPartitions - - val taskContext = new TaskContext(stageId, partition, attemptId, runningLocally = false) - metrics = Some(taskContext.taskMetrics) + metrics = Some(context.taskMetrics) val blockManager = SparkEnv.get.blockManager - var shuffle: ShuffleBlocks = null - var buckets: ShuffleWriterGroup = null + val shuffleBlockManager = blockManager.shuffleBlockManager + var shuffle: ShuffleWriterGroup = null + var success = false try { // Obtain all the block writers for shuffle blocks. - val ser = SparkEnv.get.serializerManager.get(dep.serializerClass) - shuffle = blockManager.shuffleBlockManager.forShuffle(dep.shuffleId, numOutputSplits, ser) - buckets = shuffle.acquireWriters(partition) + val ser = SparkEnv.get.serializerManager.get(dep.serializerClass, SparkEnv.get.conf) + shuffle = shuffleBlockManager.forMapTask(dep.shuffleId, partitionId, numOutputSplits, ser) // Write the map output to its associated buckets. - for (elem <- rdd.iterator(split, taskContext)) { + for (elem <- rdd.iterator(split, context)) { val pair = elem.asInstanceOf[Product2[Any, Any]] val bucketId = dep.partitioner.getPartition(pair._1) - buckets.writers(bucketId).write(pair) + shuffle.writers(bucketId).write(pair) } // Commit the writes. Get the size of each bucket block (total block size). var totalBytes = 0L - val compressedSizes: Array[Byte] = buckets.writers.map { writer: BlockObjectWriter => + var totalTime = 0L + val compressedSizes: Array[Byte] = shuffle.writers.map { writer: BlockObjectWriter => writer.commit() writer.close() - val size = writer.size() + val size = writer.fileSegment().length totalBytes += size + totalTime += writer.timeWriting() MapOutputTracker.compressSize(size) } // Update shuffle metrics. val shuffleMetrics = new ShuffleWriteMetrics shuffleMetrics.shuffleBytesWritten = totalBytes + shuffleMetrics.shuffleWriteTime = totalTime metrics.get.shuffleWriteMetrics = Some(shuffleMetrics) - return new MapStatus(blockManager.blockManagerId, compressedSizes) + success = true + new MapStatus(blockManager.blockManagerId, compressedSizes) } catch { case e: Exception => // If there is an exception from running the task, revert the partial writes // and throw the exception upstream to Spark. - if (buckets != null) { - buckets.writers.foreach(_.revertPartialWrites()) + if (shuffle != null && shuffle.writers != null) { + for (writer <- shuffle.writers) { + writer.revertPartialWrites() + writer.close() + } } throw e } finally { // Release the writers back to the shuffle block manager. - if (shuffle != null && buckets != null) { - shuffle.releaseWriters(buckets) + if (shuffle != null && shuffle.writers != null) { + shuffle.releaseWriters(success) } // Execute the callbacks on task completion. - taskContext.executeOnCompleteCallbacks() + context.executeOnCompleteCallbacks() } } override def preferredLocations: Seq[TaskLocation] = preferredLocs - override def toString = "ShuffleMapTask(%d, %d)".format(stageId, partition) + override def toString = "ShuffleMapTask(%d, %d)".format(stageId, partitionId) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala index 62b521ad45..627995c826 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala @@ -24,17 +24,20 @@ import org.apache.spark.executor.TaskMetrics sealed trait SparkListenerEvents -case class SparkListenerStageSubmitted(stage: Stage, taskSize: Int, properties: Properties) +case class SparkListenerStageSubmitted(stage: StageInfo, properties: Properties) extends SparkListenerEvents -case class StageCompleted(val stageInfo: StageInfo) extends SparkListenerEvents +case class SparkListenerStageCompleted(val stage: StageInfo) extends SparkListenerEvents case class SparkListenerTaskStart(task: Task[_], taskInfo: TaskInfo) extends SparkListenerEvents +case class SparkListenerTaskGettingResult( + task: Task[_], taskInfo: TaskInfo) extends SparkListenerEvents + case class SparkListenerTaskEnd(task: Task[_], reason: TaskEndReason, taskInfo: TaskInfo, taskMetrics: TaskMetrics) extends SparkListenerEvents -case class SparkListenerJobStart(job: ActiveJob, properties: Properties = null) +case class SparkListenerJobStart(job: ActiveJob, stageIds: Array[Int], properties: Properties = null) extends SparkListenerEvents case class SparkListenerJobEnd(job: ActiveJob, jobResult: JobResult) @@ -44,7 +47,7 @@ trait SparkListener { /** * Called when a stage is completed, with information on the completed stage */ - def onStageCompleted(stageCompleted: StageCompleted) { } + def onStageCompleted(stageCompleted: SparkListenerStageCompleted) { } /** * Called when a stage is submitted @@ -54,7 +57,13 @@ trait SparkListener { /** * Called when a task starts */ - def onTaskStart(taskEnd: SparkListenerTaskStart) { } + def onTaskStart(taskStart: SparkListenerTaskStart) { } + + /** + * Called when a task begins remotely fetching its result (will not be called for tasks that do + * not need to fetch the result remotely). + */ + def onTaskGettingResult(taskGettingResult: SparkListenerTaskGettingResult) { } /** * Called when a task ends @@ -77,10 +86,10 @@ trait SparkListener { * Simple SparkListener that logs a few summary statistics when each stage completes */ class StatsReportListener extends SparkListener with Logging { - override def onStageCompleted(stageCompleted: StageCompleted) { + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) { import org.apache.spark.scheduler.StatsReportListener._ implicit val sc = stageCompleted - this.logInfo("Finished stage: " + stageCompleted.stageInfo) + this.logInfo("Finished stage: " + stageCompleted.stage) showMillisDistribution("task runtime:", (info, _) => Some(info.duration)) //shuffle write @@ -93,7 +102,7 @@ class StatsReportListener extends SparkListener with Logging { //runtime breakdown - val runtimePcts = stageCompleted.stageInfo.taskInfos.map{ + val runtimePcts = stageCompleted.stage.taskInfos.map{ case (info, metrics) => RuntimePercentage(info.duration, metrics) } showDistribution("executor (non-fetch) time pct: ", Distribution(runtimePcts.map{_.executorPct * 100}), "%2.0f %%") @@ -110,20 +119,24 @@ object StatsReportListener extends Logging { val probabilities = percentiles.map{_ / 100.0} val percentilesHeader = "\t" + percentiles.mkString("%\t") + "%" - def extractDoubleDistribution(stage:StageCompleted, getMetric: (TaskInfo,TaskMetrics) => Option[Double]): Option[Distribution] = { - Distribution(stage.stageInfo.taskInfos.flatMap{ + def extractDoubleDistribution(stage: SparkListenerStageCompleted, + getMetric: (TaskInfo,TaskMetrics) => Option[Double]) + : Option[Distribution] = { + Distribution(stage.stage.taskInfos.flatMap { case ((info,metric)) => getMetric(info, metric)}) } //is there some way to setup the types that I can get rid of this completely? - def extractLongDistribution(stage:StageCompleted, getMetric: (TaskInfo,TaskMetrics) => Option[Long]): Option[Distribution] = { + def extractLongDistribution(stage: SparkListenerStageCompleted, + getMetric: (TaskInfo,TaskMetrics) => Option[Long]) + : Option[Distribution] = { extractDoubleDistribution(stage, (info, metric) => getMetric(info,metric).map{_.toDouble}) } def showDistribution(heading: String, d: Distribution, formatNumber: Double => String) { val stats = d.statCounter - logInfo(heading + stats) val quantiles = d.getQuantiles(probabilities).map{formatNumber} + logInfo(heading + stats) logInfo(percentilesHeader) logInfo("\t" + quantiles.mkString("\t")) } @@ -138,12 +151,12 @@ object StatsReportListener extends Logging { } def showDistribution(heading:String, format: String, getMetric: (TaskInfo,TaskMetrics) => Option[Double]) - (implicit stage: StageCompleted) { + (implicit stage: SparkListenerStageCompleted) { showDistribution(heading, extractDoubleDistribution(stage, getMetric), format) } def showBytesDistribution(heading:String, getMetric: (TaskInfo,TaskMetrics) => Option[Long]) - (implicit stage: StageCompleted) { + (implicit stage: SparkListenerStageCompleted) { showBytesDistribution(heading, extractLongDistribution(stage, getMetric)) } @@ -160,12 +173,10 @@ object StatsReportListener extends Logging { } def showMillisDistribution(heading: String, getMetric: (TaskInfo, TaskMetrics) => Option[Long]) - (implicit stage: StageCompleted) { + (implicit stage: SparkListenerStageCompleted) { showMillisDistribution(heading, extractLongDistribution(stage, getMetric)) } - - val seconds = 1000L val minutes = seconds * 60 val hours = minutes * 60 @@ -189,7 +200,6 @@ object StatsReportListener extends Logging { } - case class RuntimePercentage(executorPct: Double, fetchPct: Option[Double], other: Double) object RuntimePercentage { def apply(totalTime: Long, metrics: TaskMetrics): RuntimePercentage = { diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala index a65e1ecd6d..e7defd768b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala @@ -41,7 +41,7 @@ private[spark] class SparkListenerBus() extends Logging { event match {
case stageSubmitted: SparkListenerStageSubmitted =>
sparkListeners.foreach(_.onStageSubmitted(stageSubmitted))
- case stageCompleted: StageCompleted =>
+ case stageCompleted: SparkListenerStageCompleted =>
sparkListeners.foreach(_.onStageCompleted(stageCompleted))
case jobStart: SparkListenerJobStart =>
sparkListeners.foreach(_.onJobStart(jobStart))
@@ -49,6 +49,8 @@ private[spark] class SparkListenerBus() extends Logging { sparkListeners.foreach(_.onJobEnd(jobEnd))
case taskStart: SparkListenerTaskStart =>
sparkListeners.foreach(_.onTaskStart(taskStart))
+ case taskGettingResult: SparkListenerTaskGettingResult =>
+ sparkListeners.foreach(_.onTaskGettingResult(taskGettingResult))
case taskEnd: SparkListenerTaskEnd =>
sparkListeners.foreach(_.onTaskEnd(taskEnd))
case _ =>
@@ -70,5 +72,22 @@ private[spark] class SparkListenerBus() extends Logging { queueFullErrorMessageLogged = true
}
}
-}
+ /**
+ * Waits until there are no more events in the queue, or until the specified time has elapsed.
+ * Used for testing only. Returns true if the queue has emptied and false is the specified time
+ * elapsed before the queue emptied.
+ */
+ def waitUntilEmpty(timeoutMillis: Int): Boolean = {
+ val finishTime = System.currentTimeMillis + timeoutMillis
+ while (!eventQueue.isEmpty()) {
+ if (System.currentTimeMillis > finishTime) {
+ return false
+ }
+ /* Sleep rather than using wait/notify, because this is used only for testing and wait/notify
+ * add overhead in the general case. */
+ Thread.sleep(10)
+ }
+ return true
+ }
+}
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala index aa293dc6b3..7cb3fe46e5 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala @@ -39,6 +39,7 @@ import org.apache.spark.storage.BlockManagerId private[spark] class Stage( val id: Int, val rdd: RDD[_], + val numTasks: Int, val shuffleDep: Option[ShuffleDependency[_,_]], // Output shuffle if stage is a map stage val parents: List[Stage], val jobId: Int, @@ -49,11 +50,6 @@ private[spark] class Stage( val numPartitions = rdd.partitions.size val outputLocs = Array.fill[List[MapStatus]](numPartitions)(Nil) var numAvailableOutputs = 0 - - /** When first task was submitted to scheduler. */ - var submissionTime: Option[Long] = None - var completionTime: Option[Long] = None - private var nextAttemptId = 0 def isAvailable: Boolean = { diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala index b6f11969e5..e9f2198a00 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala @@ -21,9 +21,17 @@ import scala.collection._ import org.apache.spark.executor.TaskMetrics -case class StageInfo( - val stage: Stage, +class StageInfo( + stage: Stage, val taskInfos: mutable.Buffer[(TaskInfo, TaskMetrics)] = mutable.Buffer[(TaskInfo, TaskMetrics)]() ) { - override def toString = stage.rdd.toString + val stageId = stage.id + /** When this stage was submitted from the DAGScheduler to a TaskScheduler. */ + var submissionTime: Option[Long] = None + var completionTime: Option[Long] = None + val rddName = stage.rdd.name + val name = stage.name + val numPartitions = stage.numPartitions + val numTasks = stage.numTasks + var emittedTaskSizeWarning = false } diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index 598d91752a..69b42e86ea 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -17,25 +17,74 @@ package org.apache.spark.scheduler -import org.apache.spark.serializer.SerializerInstance import java.io.{DataInputStream, DataOutputStream} import java.nio.ByteBuffer -import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream -import org.apache.spark.util.ByteBufferInputStream + import scala.collection.mutable.HashMap + +import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream + +import org.apache.spark.TaskContext import org.apache.spark.executor.TaskMetrics +import org.apache.spark.serializer.SerializerInstance +import org.apache.spark.util.ByteBufferInputStream + /** - * A task to execute on a worker node. + * A unit of execution. We have two kinds of Task's in Spark: + * - [[org.apache.spark.scheduler.ShuffleMapTask]] + * - [[org.apache.spark.scheduler.ResultTask]] + * + * A Spark job consists of one or more stages. The very last stage in a job consists of multiple + * ResultTask's, while earlier stages consist of ShuffleMapTasks. A ResultTask executes the task + * and sends the task output back to the driver application. A ShuffleMapTask executes the task + * and divides the task output to multiple buckets (based on the task's partitioner). + * + * @param stageId id of the stage this task belongs to + * @param partitionId index of the number in the RDD */ -private[spark] abstract class Task[T](val stageId: Int) extends Serializable { - def run(attemptId: Long): T +private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) extends Serializable { + + final def run(attemptId: Long): T = { + context = new TaskContext(stageId, partitionId, attemptId, runningLocally = false) + if (_killed) { + kill() + } + runTask(context) + } + + def runTask(context: TaskContext): T + def preferredLocations: Seq[TaskLocation] = Nil - var epoch: Long = -1 // Map output tracker epoch. Will be set by TaskScheduler. + // Map output tracker epoch. Will be set by TaskScheduler. + var epoch: Long = -1 var metrics: Option[TaskMetrics] = None + // Task context, to be initialized in run(). + @transient protected var context: TaskContext = _ + + // A flag to indicate whether the task is killed. This is used in case context is not yet + // initialized when kill() is invoked. + @volatile @transient private var _killed = false + + /** + * Whether the task has been killed. + */ + def killed: Boolean = _killed + + /** + * Kills a task by setting the interrupted flag to true. This relies on the upper level Spark + * code and user code to properly handle the flag. This function should be idempotent so it can + * be called multiple times. + */ + def kill() { + _killed = true + if (context != null) { + context.interrupted = true + } + } } /** diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala index 7c2a422aff..3c22edd524 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala @@ -31,9 +31,27 @@ class TaskInfo( val host: String, val taskLocality: TaskLocality.TaskLocality) { + /** + * The time when the task started remotely getting the result. Will not be set if the + * task result was sent immediately when the task finished (as opposed to sending an + * IndirectTaskResult and later fetching the result from the block manager). + */ + var gettingResultTime: Long = 0 + + /** + * The time when the task has completed successfully (including the time to remotely fetch + * results, if necessary). + */ var finishTime: Long = 0 + var failed = false + var serializedSize: Int = 0 + + def markGettingResult(time: Long = System.currentTimeMillis) { + gettingResultTime = time + } + def markSuccessful(time: Long = System.currentTimeMillis) { finishTime = time } @@ -43,6 +61,8 @@ class TaskInfo( failed = true } + def gettingResult: Boolean = gettingResultTime != 0 + def finished: Boolean = finishTime != 0 def successful: Boolean = finished && !failed @@ -52,6 +72,8 @@ class TaskInfo( def status: String = { if (running) "RUNNING" + else if (gettingResult) + "GET RESULT" else if (failed) "FAILED" else if (successful) diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskLocality.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskLocality.scala index 47b0f387aa..35de13c385 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskLocality.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskLocality.scala @@ -18,9 +18,7 @@ package org.apache.spark.scheduler -private[spark] object TaskLocality - extends Enumeration("PROCESS_LOCAL", "NODE_LOCAL", "RACK_LOCAL", "ANY") -{ +private[spark] object TaskLocality extends Enumeration { // process local is expected to be used ONLY within tasksetmanager for now. val PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY = Value diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala index 5c7e5bb977..e80cc6b0f6 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala @@ -24,23 +24,26 @@ import org.apache.spark.executor.TaskMetrics import org.apache.spark.{SparkEnv} import java.nio.ByteBuffer import org.apache.spark.util.Utils +import org.apache.spark.storage.BlockId // Task result. Also contains updates to accumulator variables. -// TODO: Use of distributed cache to return result is a hack to get around -// what seems to be a bug with messages over 60KB in libprocess; fix it +private[spark] sealed trait TaskResult[T] + +/** A reference to a DirectTaskResult that has been stored in the worker's BlockManager. */ private[spark] -class TaskResult[T](var value: T, var accumUpdates: Map[Long, Any], var metrics: TaskMetrics) - extends Externalizable -{ - def this() = this(null.asInstanceOf[T], null, null) +case class IndirectTaskResult[T](blockId: BlockId) extends TaskResult[T] with Serializable - override def writeExternal(out: ObjectOutput) { +/** A TaskResult that contains the task's return value and accumulator updates. */ +private[spark] +class DirectTaskResult[T](var valueBytes: ByteBuffer, var accumUpdates: Map[Long, Any], var metrics: TaskMetrics) + extends TaskResult[T] with Externalizable { + + def this() = this(null.asInstanceOf[ByteBuffer], null, null) - val objectSer = SparkEnv.get.serializer.newInstance() - val bb = objectSer.serialize(value) + override def writeExternal(out: ObjectOutput) { - out.writeInt(bb.remaining()) - Utils.writeByteBuffer(bb, out) + out.writeInt(valueBytes.remaining); + Utils.writeByteBuffer(valueBytes, out) out.writeInt(accumUpdates.size) for ((key, value) <- accumUpdates) { @@ -52,12 +55,10 @@ class TaskResult[T](var value: T, var accumUpdates: Map[Long, Any], var metrics: override def readExternal(in: ObjectInput) { - val objectSer = SparkEnv.get.serializer.newInstance() - val blen = in.readInt() val byteVal = new Array[Byte](blen) in.readFully(byteVal) - value = objectSer.deserialize(ByteBuffer.wrap(byteVal)) + valueBytes = ByteBuffer.wrap(byteVal) val numUpdates = in.readInt if (numUpdates == 0) { @@ -70,4 +71,9 @@ class TaskResult[T](var value: T, var accumUpdates: Map[Long, Any], var metrics: } metrics = in.readObject().asInstanceOf[TaskMetrics] } + + def value(): T = { + val resultSer = SparkEnv.get.serializer.newInstance() + return resultSer.deserialize(valueBytes) + } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala new file mode 100644 index 0000000000..e22b1e53e8 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala @@ -0,0 +1,108 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import java.nio.ByteBuffer +import java.util.concurrent.{LinkedBlockingDeque, ThreadFactory, ThreadPoolExecutor, TimeUnit} + +import org.apache.spark._ +import org.apache.spark.TaskState.TaskState +import org.apache.spark.serializer.SerializerInstance +import org.apache.spark.util.Utils + +/** + * Runs a thread pool that deserializes and remotely fetches (if necessary) task results. + */ +private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedulerImpl) + extends Logging { + + private val THREADS = sparkEnv.conf.get("spark.resultGetter.threads", "4").toInt + private val getTaskResultExecutor = Utils.newDaemonFixedThreadPool( + THREADS, "Result resolver thread") + + protected val serializer = new ThreadLocal[SerializerInstance] { + override def initialValue(): SerializerInstance = { + return sparkEnv.closureSerializer.newInstance() + } + } + + def enqueueSuccessfulTask( + taskSetManager: TaskSetManager, tid: Long, serializedData: ByteBuffer) { + getTaskResultExecutor.execute(new Runnable { + override def run() { + try { + val result = serializer.get().deserialize[TaskResult[_]](serializedData) match { + case directResult: DirectTaskResult[_] => directResult + case IndirectTaskResult(blockId) => + logDebug("Fetching indirect task result for TID %s".format(tid)) + scheduler.handleTaskGettingResult(taskSetManager, tid) + val serializedTaskResult = sparkEnv.blockManager.getRemoteBytes(blockId) + if (!serializedTaskResult.isDefined) { + /* We won't be able to get the task result if the machine that ran the task failed + * between when the task ended and when we tried to fetch the result, or if the + * block manager had to flush the result. */ + scheduler.handleFailedTask( + taskSetManager, tid, TaskState.FINISHED, Some(TaskResultLost)) + return + } + val deserializedResult = serializer.get().deserialize[DirectTaskResult[_]]( + serializedTaskResult.get) + sparkEnv.blockManager.master.removeBlock(blockId) + deserializedResult + } + result.metrics.resultSize = serializedData.limit() + scheduler.handleSuccessfulTask(taskSetManager, tid, result) + } catch { + case cnf: ClassNotFoundException => + val loader = Thread.currentThread.getContextClassLoader + taskSetManager.abort("ClassNotFound with classloader: " + loader) + case ex: Throwable => + taskSetManager.abort("Exception while deserializing and fetching task: %s".format(ex)) + } + } + }) + } + + def enqueueFailedTask(taskSetManager: TaskSetManager, tid: Long, taskState: TaskState, + serializedData: ByteBuffer) { + var reason: Option[TaskEndReason] = None + getTaskResultExecutor.execute(new Runnable { + override def run() { + try { + if (serializedData != null && serializedData.limit() > 0) { + reason = Some(serializer.get().deserialize[TaskEndReason]( + serializedData, getClass.getClassLoader)) + } + } catch { + case cnd: ClassNotFoundException => + // Log an error but keep going here -- the task failed, so not catastropic if we can't + // deserialize the reason. + val loader = Thread.currentThread.getContextClassLoader + logError( + "Could not deserialize TaskEndReason: ClassNotFound with classloader " + loader) + case ex: Throwable => {} + } + scheduler.handleFailedTask(taskSetManager, tid, taskState, reason) + } + }) + } + + def stop() { + getTaskResultExecutor.shutdownNow() + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala index 7c2a9f03d7..17b6d97e90 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala @@ -20,12 +20,12 @@ package org.apache.spark.scheduler import org.apache.spark.scheduler.SchedulingMode.SchedulingMode /** - * Low-level task scheduler interface, implemented by both ClusterScheduler and LocalScheduler. - * Each TaskScheduler schedulers task for a single SparkContext. - * These schedulers get sets of tasks submitted to them from the DAGScheduler for each stage, - * and are responsible for sending the tasks to the cluster, running them, retrying if there - * are failures, and mitigating stragglers. They return events to the DAGScheduler through - * the TaskSchedulerListener interface. + * Low-level task scheduler interface, currently implemented exclusively by the ClusterScheduler. + * This interface allows plugging in different task schedulers. Each TaskScheduler schedulers tasks + * for a single SparkContext. These schedulers get sets of tasks submitted to them from the + * DAGScheduler for each stage, and are responsible for sending the tasks to the cluster, running + * them, retrying if there are failures, and mitigating stragglers. They return events to the + * DAGScheduler. */ private[spark] trait TaskScheduler { @@ -45,8 +45,11 @@ private[spark] trait TaskScheduler { // Submit a sequence of tasks to run. def submitTasks(taskSet: TaskSet): Unit - // Set a listener for upcalls. This is guaranteed to be set before submitTasks is called. - def setListener(listener: TaskSchedulerListener): Unit + // Cancel a stage. + def cancelTasks(stageId: Int) + + // Set the DAG scheduler for upcalls. This is guaranteed to be set before submitTasks is called. + def setDAGScheduler(dagScheduler: DAGScheduler): Unit // Get the default level of parallelism to use in the cluster, as a hint for sizing jobs. def defaultParallelism(): Int diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index a6dee604b7..d94b706854 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -15,46 +15,54 @@ * limitations under the License. */ -package org.apache.spark.scheduler.cluster +package org.apache.spark.scheduler -import java.lang.{Boolean => JBoolean} +import java.nio.ByteBuffer +import java.util.concurrent.atomic.AtomicLong +import java.util.{TimerTask, Timer} import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap import scala.collection.mutable.HashSet +import scala.concurrent.duration._ import org.apache.spark._ import org.apache.spark.TaskState.TaskState -import org.apache.spark.scheduler._ import org.apache.spark.scheduler.SchedulingMode.SchedulingMode -import java.nio.ByteBuffer -import java.util.concurrent.atomic.AtomicLong -import java.util.{TimerTask, Timer} /** - * The main TaskScheduler implementation, for running tasks on a cluster. Clients should first call - * initialize() and start(), then submit task sets through the runTasks method. - * - * This class can work with multiple types of clusters by acting through a SchedulerBackend. + * Schedules tasks for multiple types of clusters by acting through a SchedulerBackend. + * It can also work with a local setup by using a LocalBackend and setting isLocal to true. * It handles common logic, like determining a scheduling order across jobs, waking up to launch * speculative tasks, etc. * + * Clients should first call initialize() and start(), then submit task sets through the + * runTasks method. + * * THREADING: SchedulerBackends and task-submitting clients can call this class from multiple * threads, so it needs locks in public API methods to maintain its state. In addition, some * SchedulerBackends sycnchronize on themselves when they want to send events here, and then * acquire a lock on us, so we need to make sure that we don't try to lock the backend while * we are holding a lock on ourselves. */ -private[spark] class ClusterScheduler(val sc: SparkContext) - extends TaskScheduler - with Logging +private[spark] class TaskSchedulerImpl( + val sc: SparkContext, + val maxTaskFailures: Int, + isLocal: Boolean = false) + extends TaskScheduler with Logging { + def this(sc: SparkContext) = this(sc, sc.conf.get("spark.task.maxFailures", "4").toInt) + + val conf = sc.conf + // How often to check for speculative tasks - val SPECULATION_INTERVAL = System.getProperty("spark.speculation.interval", "100").toLong + val SPECULATION_INTERVAL = conf.get("spark.speculation.interval", "100").toLong // Threshold above which we warn user initial TaskSet may be starved - val STARVATION_TIMEOUT = System.getProperty("spark.starvation.timeout", "15000").toLong + val STARVATION_TIMEOUT = conf.get("spark.starvation.timeout", "15000").toLong + // TaskSetManagers are not thread safe, so any access to one should be synchronized + // on this class. val activeTaskSets = new HashMap[String, TaskSetManager] val taskIdToTaskSetId = new HashMap[Long, String] @@ -65,7 +73,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext) @volatile private var hasLaunchedTask = false private val starvationTimer = new Timer(true) - // Incrementing Mesos task IDs + // Incrementing task IDs val nextTaskId = new AtomicLong(0) // Which executor IDs we have executors on @@ -77,14 +85,8 @@ private[spark] class ClusterScheduler(val sc: SparkContext) private val executorIdToHost = new HashMap[String, String] - // JAR server, if any JARs were added by the user to the SparkContext - var jarServer: HttpServer = null - - // URIs of JARs to pass to executor - var jarUris: String = "" - // Listener object to pass upcalls into - var listener: TaskSchedulerListener = null + var dagScheduler: DAGScheduler = null var backend: SchedulerBackend = null @@ -94,14 +96,17 @@ private[spark] class ClusterScheduler(val sc: SparkContext) var rootPool: Pool = null // default scheduler is FIFO val schedulingMode: SchedulingMode = SchedulingMode.withName( - System.getProperty("spark.scheduler.mode", "FIFO")) + conf.get("spark.scheduler.mode", "FIFO")) + + // This is a var so that we can reset it for testing purposes. + private[spark] var taskResultGetter = new TaskResultGetter(sc.env, this) - override def setListener(listener: TaskSchedulerListener) { - this.listener = listener + override def setDAGScheduler(dagScheduler: DAGScheduler) { + this.dagScheduler = dagScheduler } - def initialize(context: SchedulerBackend) { - backend = context + def initialize(backend: SchedulerBackend) { + this.backend = backend // temporarily set rootPool name to empty rootPool = new Pool("", schedulingMode, 0, 0) schedulableBuilder = { @@ -109,7 +114,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext) case SchedulingMode.FIFO => new FIFOSchedulableBuilder(rootPool) case SchedulingMode.FAIR => - new FairSchedulableBuilder(rootPool) + new FairSchedulableBuilder(rootPool, conf) } } schedulableBuilder.buildPools() @@ -120,22 +125,13 @@ private[spark] class ClusterScheduler(val sc: SparkContext) override def start() { backend.start() - if (System.getProperty("spark.speculation", "false").toBoolean) { - new Thread("ClusterScheduler speculation check") { - setDaemon(true) - - override def run() { - logInfo("Starting speculative execution thread") - while (true) { - try { - Thread.sleep(SPECULATION_INTERVAL) - } catch { - case e: InterruptedException => {} - } - checkSpeculatableTasks() - } - } - }.start() + if (!isLocal && conf.get("spark.speculation", "false").toBoolean) { + logInfo("Starting speculative execution thread") + import sc.env.actorSystem.dispatcher + sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, + SPECULATION_INTERVAL milliseconds) { + checkSpeculatableTasks() + } } } @@ -143,12 +139,12 @@ private[spark] class ClusterScheduler(val sc: SparkContext) val tasks = taskSet.tasks logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks") this.synchronized { - val manager = new ClusterTaskSetManager(this, taskSet) + val manager = new TaskSetManager(this, taskSet, maxTaskFailures) activeTaskSets(taskSet.id) = manager schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties) taskSetTaskIds(taskSet.id) = new HashSet[Long]() - if (!hasReceivedTask) { + if (!isLocal && !hasReceivedTask) { starvationTimer.scheduleAtFixedRate(new TimerTask() { override def run() { if (!hasLaunchedTask) { @@ -166,8 +162,33 @@ private[spark] class ClusterScheduler(val sc: SparkContext) backend.reviveOffers() } - def taskSetFinished(manager: TaskSetManager) { - this.synchronized { + override def cancelTasks(stageId: Int): Unit = synchronized { + logInfo("Cancelling stage " + stageId) + activeTaskSets.find(_._2.stageId == stageId).foreach { case (_, tsm) => + // There are two possible cases here: + // 1. The task set manager has been created and some tasks have been scheduled. + // In this case, send a kill signal to the executors to kill the task and then abort + // the stage. + // 2. The task set manager has been created but no tasks has been scheduled. In this case, + // simply abort the stage. + val taskIds = taskSetTaskIds(tsm.taskSet.id) + if (taskIds.size > 0) { + taskIds.foreach { tid => + val execId = taskIdToExecutorId(tid) + backend.killTask(tid, execId) + } + } + logInfo("Stage %d was cancelled".format(stageId)) + tsm.removeAllRunningTasks() + taskSetFinished(tsm) + } + } + + def taskSetFinished(manager: TaskSetManager): Unit = synchronized { + // Check to see if the given task set has been removed. This is possible in the case of + // multiple unrecoverable task failures (e.g. if the entire task set is killed when it has + // more than one running tasks). + if (activeTaskSets.contains(manager.taskSet.id)) { activeTaskSets -= manager.taskSet.id manager.parent.removeSchedulable(manager) logInfo("Remove TaskSet %s from pool %s".format(manager.taskSet.id, manager.parent.name)) @@ -234,9 +255,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { - var taskSetToUpdate: Option[TaskSetManager] = None var failedExecutor: Option[String] = None - var taskFailed = false synchronized { try { if (state == TaskState.LOST && taskIdToExecutorId.contains(tid)) { @@ -249,9 +268,6 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } taskIdToTaskSetId.get(tid) match { case Some(taskSetId) => - if (activeTaskSets.contains(taskSetId)) { - taskSetToUpdate = Some(activeTaskSets(taskSetId)) - } if (TaskState.isFinished(state)) { taskIdToTaskSetId.remove(tid) if (taskSetTaskIds.contains(taskSetId)) { @@ -259,8 +275,14 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } taskIdToExecutorId.remove(tid) } - if (state == TaskState.FAILED) { - taskFailed = true + activeTaskSets.get(taskSetId).foreach { taskSet => + if (state == TaskState.FINISHED) { + taskSet.removeRunningTask(tid) + taskResultGetter.enqueueSuccessfulTask(taskSet, tid, serializedData) + } else if (Set(TaskState.FAILED, TaskState.KILLED, TaskState.LOST).contains(state)) { + taskSet.removeRunningTask(tid) + taskResultGetter.enqueueFailedTask(taskSet, tid, state, serializedData) + } } case None => logInfo("Ignoring update from TID " + tid + " because its task set is gone") @@ -269,16 +291,33 @@ private[spark] class ClusterScheduler(val sc: SparkContext) case e: Exception => logError("Exception in statusUpdate", e) } } - // Update the task set and DAGScheduler without holding a lock on this, since that can deadlock - if (taskSetToUpdate != None) { - taskSetToUpdate.get.statusUpdate(tid, state, serializedData) - } + // Update the DAGScheduler without holding a lock on this, since that can deadlock if (failedExecutor != None) { - listener.executorLost(failedExecutor.get) + dagScheduler.executorLost(failedExecutor.get) backend.reviveOffers() } - if (taskFailed) { - // Also revive offers if a task had failed for some reason other than host lost + } + + def handleTaskGettingResult(taskSetManager: TaskSetManager, tid: Long) { + taskSetManager.handleTaskGettingResult(tid) + } + + def handleSuccessfulTask( + taskSetManager: TaskSetManager, + tid: Long, + taskResult: DirectTaskResult[_]) = synchronized { + taskSetManager.handleSuccessfulTask(tid, taskResult) + } + + def handleFailedTask( + taskSetManager: TaskSetManager, + tid: Long, + taskState: TaskState, + reason: Option[TaskEndReason]) = synchronized { + taskSetManager.handleFailedTask(tid, taskState, reason) + if (taskState != TaskState.KILLED) { + // Need to revive offers again now that the task set manager state has been updated to + // reflect failed tasks that need to be re-run. backend.reviveOffers() } } @@ -308,8 +347,8 @@ private[spark] class ClusterScheduler(val sc: SparkContext) if (backend != null) { backend.stop() } - if (jarServer != null) { - jarServer.stop() + if (taskResultGetter != null) { + taskResultGetter.stop() } // sleeping for an arbitrary 5 seconds : to ensure that messages are sent out. @@ -319,7 +358,6 @@ private[spark] class ClusterScheduler(val sc: SparkContext) override def defaultParallelism() = backend.defaultParallelism() - // Check for speculatable tasks in all our active jobs. def checkSpeculatableTasks() { var shouldRevive = false @@ -331,13 +369,6 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } } - // Check for pending tasks in all our active jobs. - def hasPendingTasks: Boolean = { - synchronized { - rootPool.hasPendingTasks() - } - } - def executorLost(executorId: String, reason: ExecutorLossReason) { var failedExecutor: Option[String] = None @@ -355,9 +386,9 @@ private[spark] class ClusterScheduler(val sc: SparkContext) logError("Lost an executor " + executorId + " (already removed): " + reason) } } - // Call listener.executorLost without holding the lock on this to prevent deadlock + // Call dagScheduler.executorLost without holding the lock on this to prevent deadlock if (failedExecutor != None) { - listener.executorLost(failedExecutor.get) + dagScheduler.executorLost(failedExecutor.get) backend.reviveOffers() } } @@ -376,7 +407,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } def executorGained(execId: String, host: String) { - listener.executorGained(execId, host) + dagScheduler.executorGained(execId, host) } def getExecutorsAliveOnHost(host: String): Option[Set[String]] = synchronized { @@ -396,7 +427,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } -object ClusterScheduler { +private[spark] object TaskSchedulerImpl { /** * Used to balance containers across hosts. * diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerListener.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerListener.scala deleted file mode 100644 index 593fa9fb93..0000000000 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerListener.scala +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.scheduler - -import scala.collection.mutable.Map - -import org.apache.spark.TaskEndReason -import org.apache.spark.executor.TaskMetrics - -/** - * Interface for getting events back from the TaskScheduler. - */ -private[spark] trait TaskSchedulerListener { - // A task has started. - def taskStarted(task: Task[_], taskInfo: TaskInfo) - - // A task has finished or failed. - def taskEnded(task: Task[_], reason: TaskEndReason, result: Any, accumUpdates: Map[Long, Any], - taskInfo: TaskInfo, taskMetrics: TaskMetrics): Unit - - // A node was added to the cluster. - def executorGained(execId: String, host: String): Unit - - // A node was lost from the cluster. - def executorLost(execId: String): Unit - - // The TaskScheduler wants to abort an entire task set. - def taskSetFailed(taskSet: TaskSet, reason: String): Unit -} diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala index c3ad325156..03bf760837 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala @@ -31,5 +31,9 @@ private[spark] class TaskSet( val properties: Properties) { val id: String = stageId + "." + attempt + def kill() { + tasks.foreach(_.kill()) + } + override def toString: String = "TaskSet " + id } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index f192b0b7a4..67ad99a4d7 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -17,34 +17,698 @@ package org.apache.spark.scheduler -import java.nio.ByteBuffer +import java.io.NotSerializableException +import java.util.Arrays +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap +import scala.collection.mutable.HashSet +import scala.math.max +import scala.math.min + +import org.apache.spark.{ExceptionFailure, FetchFailed, Logging, Resubmitted, SparkEnv, + Success, TaskEndReason, TaskKilled, TaskResultLost, TaskState} import org.apache.spark.TaskState.TaskState +import org.apache.spark.util.{Clock, SystemClock} + /** - * Tracks and schedules the tasks within a single TaskSet. This class keeps track of the status of - * each task and is responsible for retries on failure and locality. The main interfaces to it - * are resourceOffer, which asks the TaskSet whether it wants to run a task on one node, and - * statusUpdate, which tells it that one of its tasks changed state (e.g. finished). + * Schedules the tasks within a single TaskSet in the ClusterScheduler. This class keeps track of + * each task, retries tasks if they fail (up to a limited number of times), and + * handles locality-aware scheduling for this TaskSet via delay scheduling. The main interfaces + * to it are resourceOffer, which asks the TaskSet whether it wants to run a task on one node, + * and statusUpdate, which tells it that one of its tasks changed state (e.g. finished). + * + * THREADING: This class is designed to only be called from code with a lock on the + * TaskScheduler (e.g. its event handlers). It should not be called from other threads. * - * THREADING: This class is designed to only be called from code with a lock on the TaskScheduler - * (e.g. its event handlers). It should not be called from other threads. + * @param sched the ClusterScheduler associated with the TaskSetManager + * @param taskSet the TaskSet to manage scheduling for + * @param maxTaskFailures if any particular task fails more than this number of times, the entire + * task set will be aborted */ -private[spark] trait TaskSetManager extends Schedulable { - def schedulableQueue = null - - def schedulingMode = SchedulingMode.NONE - - def taskSet: TaskSet +private[spark] class TaskSetManager( + sched: TaskSchedulerImpl, + val taskSet: TaskSet, + val maxTaskFailures: Int, + clock: Clock = SystemClock) + extends Schedulable with Logging +{ + val conf = sched.sc.conf + + // CPUs to request per task + val CPUS_PER_TASK = conf.get("spark.task.cpus", "1").toInt + + // Quantile of tasks at which to start speculation + val SPECULATION_QUANTILE = conf.get("spark.speculation.quantile", "0.75").toDouble + val SPECULATION_MULTIPLIER = conf.get("spark.speculation.multiplier", "1.5").toDouble + + // Serializer for closures and tasks. + val env = SparkEnv.get + val ser = env.closureSerializer.newInstance() + + val tasks = taskSet.tasks + val numTasks = tasks.length + val copiesRunning = new Array[Int](numTasks) + val successful = new Array[Boolean](numTasks) + val numFailures = new Array[Int](numTasks) + val taskAttempts = Array.fill[List[TaskInfo]](numTasks)(Nil) + var tasksSuccessful = 0 + + var weight = 1 + var minShare = 0 + var priority = taskSet.priority + var stageId = taskSet.stageId + var name = "TaskSet_"+taskSet.stageId.toString + var parent: Pool = null + + var runningTasks = 0 + private val runningTasksSet = new HashSet[Long] + + // Set of pending tasks for each executor. These collections are actually + // treated as stacks, in which new tasks are added to the end of the + // ArrayBuffer and removed from the end. This makes it faster to detect + // tasks that repeatedly fail because whenever a task failed, it is put + // back at the head of the stack. They are also only cleaned up lazily; + // when a task is launched, it remains in all the pending lists except + // the one that it was launched from, but gets removed from them later. + private val pendingTasksForExecutor = new HashMap[String, ArrayBuffer[Int]] + + // Set of pending tasks for each host. Similar to pendingTasksForExecutor, + // but at host level. + private val pendingTasksForHost = new HashMap[String, ArrayBuffer[Int]] + + // Set of pending tasks for each rack -- similar to the above. + private val pendingTasksForRack = new HashMap[String, ArrayBuffer[Int]] + + // Set containing pending tasks with no locality preferences. + val pendingTasksWithNoPrefs = new ArrayBuffer[Int] + + // Set containing all pending tasks (also used as a stack, as above). + val allPendingTasks = new ArrayBuffer[Int] + + // Tasks that can be speculated. Since these will be a small fraction of total + // tasks, we'll just hold them in a HashSet. + val speculatableTasks = new HashSet[Int] + + // Task index, start and finish time for each task attempt (indexed by task ID) + val taskInfos = new HashMap[Long, TaskInfo] + + // How frequently to reprint duplicate exceptions in full, in milliseconds + val EXCEPTION_PRINT_INTERVAL = + conf.get("spark.logging.exceptionPrintInterval", "10000").toLong + + // Map of recent exceptions (identified by string representation and top stack frame) to + // duplicate count (how many times the same exception has appeared) and time the full exception + // was printed. This should ideally be an LRU map that can drop old exceptions automatically. + val recentExceptions = HashMap[String, (Int, Long)]() + + // Figure out the current map output tracker epoch and set it on all tasks + val epoch = sched.mapOutputTracker.getEpoch + logDebug("Epoch for " + taskSet + ": " + epoch) + for (t <- tasks) { + t.epoch = epoch + } + + // Add all our tasks to the pending lists. We do this in reverse order + // of task index so that tasks with low indices get launched first. + for (i <- (0 until numTasks).reverse) { + addPendingTask(i) + } + + // Figure out which locality levels we have in our TaskSet, so we can do delay scheduling + val myLocalityLevels = computeValidLocalityLevels() + val localityWaits = myLocalityLevels.map(getLocalityWait) // Time to wait at each level + + // Delay scheduling variables: we keep track of our current locality level and the time we + // last launched a task at that level, and move up a level when localityWaits[curLevel] expires. + // We then move down if we manage to launch a "more local" task. + var currentLocalityIndex = 0 // Index of our current locality level in validLocalityLevels + var lastLaunchTime = clock.getTime() // Time we last launched a task at this level + + override def schedulableQueue = null + + override def schedulingMode = SchedulingMode.NONE + + /** + * Add a task to all the pending-task lists that it should be on. If readding is set, we are + * re-adding the task so only include it in each list if it's not already there. + */ + private def addPendingTask(index: Int, readding: Boolean = false) { + // Utility method that adds `index` to a list only if readding=false or it's not already there + def addTo(list: ArrayBuffer[Int]) { + if (!readding || !list.contains(index)) { + list += index + } + } + + var hadAliveLocations = false + for (loc <- tasks(index).preferredLocations) { + for (execId <- loc.executorId) { + if (sched.isExecutorAlive(execId)) { + addTo(pendingTasksForExecutor.getOrElseUpdate(execId, new ArrayBuffer)) + hadAliveLocations = true + } + } + if (sched.hasExecutorsAliveOnHost(loc.host)) { + addTo(pendingTasksForHost.getOrElseUpdate(loc.host, new ArrayBuffer)) + for (rack <- sched.getRackForHost(loc.host)) { + addTo(pendingTasksForRack.getOrElseUpdate(rack, new ArrayBuffer)) + } + hadAliveLocations = true + } + } + + if (!hadAliveLocations) { + // Even though the task might've had preferred locations, all of those hosts or executors + // are dead; put it in the no-prefs list so we can schedule it elsewhere right away. + addTo(pendingTasksWithNoPrefs) + } + + if (!readding) { + allPendingTasks += index // No point scanning this whole list to find the old task there + } + } + + /** + * Return the pending tasks list for a given executor ID, or an empty list if + * there is no map entry for that host + */ + private def getPendingTasksForExecutor(executorId: String): ArrayBuffer[Int] = { + pendingTasksForExecutor.getOrElse(executorId, ArrayBuffer()) + } + + /** + * Return the pending tasks list for a given host, or an empty list if + * there is no map entry for that host + */ + private def getPendingTasksForHost(host: String): ArrayBuffer[Int] = { + pendingTasksForHost.getOrElse(host, ArrayBuffer()) + } + + /** + * Return the pending rack-local task list for a given rack, or an empty list if + * there is no map entry for that rack + */ + private def getPendingTasksForRack(rack: String): ArrayBuffer[Int] = { + pendingTasksForRack.getOrElse(rack, ArrayBuffer()) + } + + /** + * Dequeue a pending task from the given list and return its index. + * Return None if the list is empty. + * This method also cleans up any tasks in the list that have already + * been launched, since we want that to happen lazily. + */ + private def findTaskFromList(list: ArrayBuffer[Int]): Option[Int] = { + while (!list.isEmpty) { + val index = list.last + list.trimEnd(1) + if (copiesRunning(index) == 0 && !successful(index)) { + return Some(index) + } + } + return None + } + + /** Check whether a task is currently running an attempt on a given host */ + private def hasAttemptOnHost(taskIndex: Int, host: String): Boolean = { + !taskAttempts(taskIndex).exists(_.host == host) + } + + /** + * Return a speculative task for a given executor if any are available. The task should not have + * an attempt running on this host, in case the host is slow. In addition, the task should meet + * the given locality constraint. + */ + private def findSpeculativeTask(execId: String, host: String, locality: TaskLocality.Value) + : Option[(Int, TaskLocality.Value)] = + { + speculatableTasks.retain(index => !successful(index)) // Remove finished tasks from set + + if (!speculatableTasks.isEmpty) { + // Check for process-local or preference-less tasks; note that tasks can be process-local + // on multiple nodes when we replicate cached blocks, as in Spark Streaming + for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) { + val prefs = tasks(index).preferredLocations + val executors = prefs.flatMap(_.executorId) + if (prefs.size == 0 || executors.contains(execId)) { + speculatableTasks -= index + return Some((index, TaskLocality.PROCESS_LOCAL)) + } + } + + // Check for node-local tasks + if (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) { + for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) { + val locations = tasks(index).preferredLocations.map(_.host) + if (locations.contains(host)) { + speculatableTasks -= index + return Some((index, TaskLocality.NODE_LOCAL)) + } + } + } + // Check for rack-local tasks + if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { + for (rack <- sched.getRackForHost(host)) { + for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) { + val racks = tasks(index).preferredLocations.map(_.host).map(sched.getRackForHost) + if (racks.contains(rack)) { + speculatableTasks -= index + return Some((index, TaskLocality.RACK_LOCAL)) + } + } + } + } + + // Check for non-local tasks + if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { + for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) { + speculatableTasks -= index + return Some((index, TaskLocality.ANY)) + } + } + } + + return None + } + + /** + * Dequeue a pending task for a given node and return its index and locality level. + * Only search for tasks matching the given locality constraint. + */ + private def findTask(execId: String, host: String, locality: TaskLocality.Value) + : Option[(Int, TaskLocality.Value)] = + { + for (index <- findTaskFromList(getPendingTasksForExecutor(execId))) { + return Some((index, TaskLocality.PROCESS_LOCAL)) + } + + if (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) { + for (index <- findTaskFromList(getPendingTasksForHost(host))) { + return Some((index, TaskLocality.NODE_LOCAL)) + } + } + + if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { + for { + rack <- sched.getRackForHost(host) + index <- findTaskFromList(getPendingTasksForRack(rack)) + } { + return Some((index, TaskLocality.RACK_LOCAL)) + } + } + + // Look for no-pref tasks after rack-local tasks since they can run anywhere. + for (index <- findTaskFromList(pendingTasksWithNoPrefs)) { + return Some((index, TaskLocality.PROCESS_LOCAL)) + } + + if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { + for (index <- findTaskFromList(allPendingTasks)) { + return Some((index, TaskLocality.ANY)) + } + } + + // Finally, if all else has failed, find a speculative task + return findSpeculativeTask(execId, host, locality) + } + + /** + * Respond to an offer of a single executor from the scheduler by finding a task + */ def resourceOffer( execId: String, host: String, availableCpus: Int, maxLocality: TaskLocality.TaskLocality) - : Option[TaskDescription] + : Option[TaskDescription] = + { + if (tasksSuccessful < numTasks && availableCpus >= CPUS_PER_TASK) { + val curTime = clock.getTime() + + var allowedLocality = getAllowedLocalityLevel(curTime) + if (allowedLocality > maxLocality) { + allowedLocality = maxLocality // We're not allowed to search for farther-away tasks + } + + findTask(execId, host, allowedLocality) match { + case Some((index, taskLocality)) => { + // Found a task; do some bookkeeping and return a task description + val task = tasks(index) + val taskId = sched.newTaskId() + // Figure out whether this should count as a preferred launch + logInfo("Starting task %s:%d as TID %s on executor %s: %s (%s)".format( + taskSet.id, index, taskId, execId, host, taskLocality)) + // Do various bookkeeping + copiesRunning(index) += 1 + val info = new TaskInfo(taskId, index, curTime, execId, host, taskLocality) + taskInfos(taskId) = info + taskAttempts(index) = info :: taskAttempts(index) + // Update our locality level for delay scheduling + currentLocalityIndex = getLocalityIndex(taskLocality) + lastLaunchTime = curTime + // Serialize and return the task + val startTime = clock.getTime() + // We rely on the DAGScheduler to catch non-serializable closures and RDDs, so in here + // we assume the task can be serialized without exceptions. + val serializedTask = Task.serializeWithDependencies( + task, sched.sc.addedFiles, sched.sc.addedJars, ser) + val timeTaken = clock.getTime() - startTime + addRunningTask(taskId) + logInfo("Serialized task %s:%d as %d bytes in %d ms".format( + taskSet.id, index, serializedTask.limit, timeTaken)) + val taskName = "task %s:%d".format(taskSet.id, index) + if (taskAttempts(index).size == 1) + taskStarted(task,info) + return Some(new TaskDescription(taskId, execId, taskName, index, serializedTask)) + } + case _ => + } + } + return None + } + + /** + * Get the level we can launch tasks according to delay scheduling, based on current wait time. + */ + private def getAllowedLocalityLevel(curTime: Long): TaskLocality.TaskLocality = { + while (curTime - lastLaunchTime >= localityWaits(currentLocalityIndex) && + currentLocalityIndex < myLocalityLevels.length - 1) + { + // Jump to the next locality level, and remove our waiting time for the current one since + // we don't want to count it again on the next one + lastLaunchTime += localityWaits(currentLocalityIndex) + currentLocalityIndex += 1 + } + myLocalityLevels(currentLocalityIndex) + } + + /** + * Find the index in myLocalityLevels for a given locality. This is also designed to work with + * localities that are not in myLocalityLevels (in case we somehow get those) by returning the + * next-biggest level we have. Uses the fact that the last value in myLocalityLevels is ANY. + */ + def getLocalityIndex(locality: TaskLocality.TaskLocality): Int = { + var index = 0 + while (locality > myLocalityLevels(index)) { + index += 1 + } + index + } + + private def taskStarted(task: Task[_], info: TaskInfo) { + sched.dagScheduler.taskStarted(task, info) + } + + def handleTaskGettingResult(tid: Long) = { + val info = taskInfos(tid) + info.markGettingResult() + sched.dagScheduler.taskGettingResult(tasks(info.index), info) + } + + /** + * Marks the task as successful and notifies the DAGScheduler that a task has ended. + */ + def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]) = { + val info = taskInfos(tid) + val index = info.index + info.markSuccessful() + removeRunningTask(tid) + if (!successful(index)) { + logInfo("Finished TID %s in %d ms on %s (progress: %d/%d)".format( + tid, info.duration, info.host, tasksSuccessful, numTasks)) + sched.dagScheduler.taskEnded( + tasks(index), Success, result.value, result.accumUpdates, info, result.metrics) + + // Mark successful and stop if all the tasks have succeeded. + tasksSuccessful += 1 + successful(index) = true + if (tasksSuccessful == numTasks) { + sched.taskSetFinished(this) + } + } else { + logInfo("Ignorning task-finished event for TID " + tid + " because task " + + index + " has already completed successfully") + } + } + + /** + * Marks the task as failed, re-adds it to the list of pending tasks, and notifies the + * DAG Scheduler. + */ + def handleFailedTask(tid: Long, state: TaskState, reason: Option[TaskEndReason]) { + val info = taskInfos(tid) + if (info.failed) { + return + } + removeRunningTask(tid) + val index = info.index + info.markFailed() + var failureReason = "unknown" + if (!successful(index)) { + logWarning("Lost TID %s (task %s:%d)".format(tid, taskSet.id, index)) + copiesRunning(index) -= 1 + // Check if the problem is a map output fetch failure. In that case, this + // task will never succeed on any node, so tell the scheduler about it. + reason.foreach { + case fetchFailed: FetchFailed => + logWarning("Loss was due to fetch failure from " + fetchFailed.bmAddress) + sched.dagScheduler.taskEnded(tasks(index), fetchFailed, null, null, info, null) + successful(index) = true + tasksSuccessful += 1 + sched.taskSetFinished(this) + removeAllRunningTasks() + return + + case TaskKilled => + logWarning("Task %d was killed.".format(tid)) + sched.dagScheduler.taskEnded(tasks(index), reason.get, null, null, info, null) + return + + case ef: ExceptionFailure => + sched.dagScheduler.taskEnded( + tasks(index), ef, null, null, info, ef.metrics.getOrElse(null)) + if (ef.className == classOf[NotSerializableException].getName()) { + // If the task result wasn't rerializable, there's no point in trying to re-execute it. + logError("Task %s:%s had a not serializable result: %s; not retrying".format( + taskSet.id, index, ef.description)) + abort("Task %s:%s had a not serializable result: %s".format( + taskSet.id, index, ef.description)) + return + } + val key = ef.description + failureReason = "Exception failure: %s".format(ef.description) + val now = clock.getTime() + val (printFull, dupCount) = { + if (recentExceptions.contains(key)) { + val (dupCount, printTime) = recentExceptions(key) + if (now - printTime > EXCEPTION_PRINT_INTERVAL) { + recentExceptions(key) = (0, now) + (true, 0) + } else { + recentExceptions(key) = (dupCount + 1, printTime) + (false, dupCount + 1) + } + } else { + recentExceptions(key) = (0, now) + (true, 0) + } + } + if (printFull) { + val locs = ef.stackTrace.map(loc => "\tat %s".format(loc.toString)) + logWarning("Loss was due to %s\n%s\n%s".format( + ef.className, ef.description, locs.mkString("\n"))) + } else { + logInfo("Loss was due to %s [duplicate %d]".format(ef.description, dupCount)) + } + + case TaskResultLost => + failureReason = "Lost result for TID %s on host %s".format(tid, info.host) + logWarning(failureReason) + sched.dagScheduler.taskEnded(tasks(index), TaskResultLost, null, null, info, null) + + case _ => {} + } + // On non-fetch failures, re-enqueue the task as pending for a max number of retries + addPendingTask(index) + if (state != TaskState.KILLED) { + numFailures(index) += 1 + if (numFailures(index) >= maxTaskFailures) { + logError("Task %s:%d failed %d times; aborting job".format( + taskSet.id, index, maxTaskFailures)) + abort("Task %s:%d failed %d times (most recent failure: %s)".format( + taskSet.id, index, maxTaskFailures, failureReason)) + } + } + } else { + logInfo("Ignoring task-lost event for TID " + tid + + " because task " + index + " is already finished") + } + } + + def error(message: String) { + // Save the error message + abort("Error: " + message) + } + + def abort(message: String) { + // TODO: Kill running tasks if we were not terminated due to a Mesos error + sched.dagScheduler.taskSetFailed(taskSet, message) + removeAllRunningTasks() + sched.taskSetFinished(this) + } + + /** If the given task ID is not in the set of running tasks, adds it. + * + * Used to keep track of the number of running tasks, for enforcing scheduling policies. + */ + def addRunningTask(tid: Long) { + if (runningTasksSet.add(tid) && parent != null) { + parent.increaseRunningTasks(1) + } + runningTasks = runningTasksSet.size + } + + /** If the given task ID is in the set of running tasks, removes it. */ + def removeRunningTask(tid: Long) { + if (runningTasksSet.remove(tid) && parent != null) { + parent.decreaseRunningTasks(1) + } + runningTasks = runningTasksSet.size + } + + private[scheduler] def removeAllRunningTasks() { + val numRunningTasks = runningTasksSet.size + runningTasksSet.clear() + if (parent != null) { + parent.decreaseRunningTasks(numRunningTasks) + } + runningTasks = 0 + } + + override def getSchedulableByName(name: String): Schedulable = { + return null + } + + override def addSchedulable(schedulable: Schedulable) {} + + override def removeSchedulable(schedulable: Schedulable) {} + + override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { + var sortedTaskSetQueue = ArrayBuffer[TaskSetManager](this) + sortedTaskSetQueue += this + return sortedTaskSetQueue + } + + /** Called by TaskScheduler when an executor is lost so we can re-enqueue our tasks */ + override def executorLost(execId: String, host: String) { + logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id) + + // Re-enqueue pending tasks for this host based on the status of the cluster -- for example, a + // task that used to have locations on only this host might now go to the no-prefs list. Note + // that it's okay if we add a task to the same queue twice (if it had multiple preferred + // locations), because findTaskFromList will skip already-running tasks. + for (index <- getPendingTasksForExecutor(execId)) { + addPendingTask(index, readding=true) + } + for (index <- getPendingTasksForHost(host)) { + addPendingTask(index, readding=true) + } + + // Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage + if (tasks(0).isInstanceOf[ShuffleMapTask]) { + for ((tid, info) <- taskInfos if info.executorId == execId) { + val index = taskInfos(tid).index + if (successful(index)) { + successful(index) = false + copiesRunning(index) -= 1 + tasksSuccessful -= 1 + addPendingTask(index) + // Tell the DAGScheduler that this task was resubmitted so that it doesn't think our + // stage finishes when a total of tasks.size tasks finish. + sched.dagScheduler.taskEnded(tasks(index), Resubmitted, null, null, info, null) + } + } + } + // Also re-enqueue any tasks that were running on the node + for ((tid, info) <- taskInfos if info.running && info.executorId == execId) { + handleFailedTask(tid, TaskState.KILLED, None) + } + } + + /** + * Check for tasks to be speculated and return true if there are any. This is called periodically + * by the TaskScheduler. + * + * TODO: To make this scale to large jobs, we need to maintain a list of running tasks, so that + * we don't scan the whole task set. It might also help to make this sorted by launch time. + */ + override def checkSpeculatableTasks(): Boolean = { + // Can't speculate if we only have one task, or if all tasks have finished. + if (numTasks == 1 || tasksSuccessful == numTasks) { + return false + } + var foundTasks = false + val minFinishedForSpeculation = (SPECULATION_QUANTILE * numTasks).floor.toInt + logDebug("Checking for speculative tasks: minFinished = " + minFinishedForSpeculation) + if (tasksSuccessful >= minFinishedForSpeculation && tasksSuccessful > 0) { + val time = clock.getTime() + val durations = taskInfos.values.filter(_.successful).map(_.duration).toArray + Arrays.sort(durations) + val medianDuration = durations(min((0.5 * tasksSuccessful).round.toInt, durations.size - 1)) + val threshold = max(SPECULATION_MULTIPLIER * medianDuration, 100) + // TODO: Threshold should also look at standard deviation of task durations and have a lower + // bound based on that. + logDebug("Task length threshold for speculation: " + threshold) + for ((tid, info) <- taskInfos) { + val index = info.index + if (!successful(index) && copiesRunning(index) == 1 && info.timeRunning(time) > threshold && + !speculatableTasks.contains(index)) { + logInfo( + "Marking task %s:%d (on %s) as speculatable because it ran more than %.0f ms".format( + taskSet.id, index, info.host, threshold)) + speculatableTasks += index + foundTasks = true + } + } + } + return foundTasks + } - def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) + private def getLocalityWait(level: TaskLocality.TaskLocality): Long = { + val defaultWait = conf.get("spark.locality.wait", "3000") + level match { + case TaskLocality.PROCESS_LOCAL => + conf.get("spark.locality.wait.process", defaultWait).toLong + case TaskLocality.NODE_LOCAL => + conf.get("spark.locality.wait.node", defaultWait).toLong + case TaskLocality.RACK_LOCAL => + conf.get("spark.locality.wait.rack", defaultWait).toLong + case TaskLocality.ANY => + 0L + } + } - def error(message: String) + /** + * Compute the locality levels used in this TaskSet. Assumes that all tasks have already been + * added to queues using addPendingTask. + */ + private def computeValidLocalityLevels(): Array[TaskLocality.TaskLocality] = { + import TaskLocality.{PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY} + val levels = new ArrayBuffer[TaskLocality.TaskLocality] + if (!pendingTasksForExecutor.isEmpty && getLocalityWait(PROCESS_LOCAL) != 0) { + levels += PROCESS_LOCAL + } + if (!pendingTasksForHost.isEmpty && getLocalityWait(NODE_LOCAL) != 0) { + levels += NODE_LOCAL + } + if (!pendingTasksForRack.isEmpty && getLocalityWait(RACK_LOCAL) != 0) { + levels += RACK_LOCAL + } + levels += ANY + logDebug("Valid locality levels for " + taskSet + ": " + levels.mkString(", ")) + levels.toArray + } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/WorkerOffer.scala b/core/src/main/scala/org/apache/spark/scheduler/WorkerOffer.scala index 938f62883a..ba6bab3f91 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/WorkerOffer.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/WorkerOffer.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.scheduler.cluster +package org.apache.spark.scheduler /** * Represents free resources available on an executor. diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala deleted file mode 100644 index 411e49b021..0000000000 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala +++ /dev/null @@ -1,709 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.scheduler.cluster - -import java.nio.ByteBuffer -import java.util.{Arrays, NoSuchElementException} - -import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap -import scala.collection.mutable.HashSet -import scala.math.max -import scala.math.min -import scala.Some - -import org.apache.spark.{ExceptionFailure, FetchFailed, Logging, Resubmitted, SparkEnv, - SparkException, Success, TaskEndReason, TaskResultTooBigFailure, TaskState} -import org.apache.spark.TaskState.TaskState -import org.apache.spark.scheduler._ -import org.apache.spark.util.{SystemClock, Clock} - - -/** - * Schedules the tasks within a single TaskSet in the ClusterScheduler. This class keeps track of - * the status of each task, retries tasks if they fail (up to a limited number of times), and - * handles locality-aware scheduling for this TaskSet via delay scheduling. The main interfaces - * to it are resourceOffer, which asks the TaskSet whether it wants to run a task on one node, - * and statusUpdate, which tells it that one of its tasks changed state (e.g. finished). - * - * THREADING: This class is designed to only be called from code with a lock on the - * ClusterScheduler (e.g. its event handlers). It should not be called from other threads. - */ -private[spark] class ClusterTaskSetManager( - sched: ClusterScheduler, - val taskSet: TaskSet, - clock: Clock = SystemClock) - extends TaskSetManager - with Logging -{ - // CPUs to request per task - val CPUS_PER_TASK = System.getProperty("spark.task.cpus", "1").toInt - - // Maximum times a task is allowed to fail before failing the job - val MAX_TASK_FAILURES = System.getProperty("spark.task.maxFailures", "4").toInt - - // Quantile of tasks at which to start speculation - val SPECULATION_QUANTILE = System.getProperty("spark.speculation.quantile", "0.75").toDouble - val SPECULATION_MULTIPLIER = System.getProperty("spark.speculation.multiplier", "1.5").toDouble - - // Serializer for closures and tasks. - val env = SparkEnv.get - val ser = env.closureSerializer.newInstance() - - val tasks = taskSet.tasks - val numTasks = tasks.length - val copiesRunning = new Array[Int](numTasks) - val finished = new Array[Boolean](numTasks) - val numFailures = new Array[Int](numTasks) - val taskAttempts = Array.fill[List[TaskInfo]](numTasks)(Nil) - var tasksFinished = 0 - - var weight = 1 - var minShare = 0 - var runningTasks = 0 - var priority = taskSet.priority - var stageId = taskSet.stageId - var name = "TaskSet_"+taskSet.stageId.toString - var parent: Schedulable = null - - // Set of pending tasks for each executor. These collections are actually - // treated as stacks, in which new tasks are added to the end of the - // ArrayBuffer and removed from the end. This makes it faster to detect - // tasks that repeatedly fail because whenever a task failed, it is put - // back at the head of the stack. They are also only cleaned up lazily; - // when a task is launched, it remains in all the pending lists except - // the one that it was launched from, but gets removed from them later. - private val pendingTasksForExecutor = new HashMap[String, ArrayBuffer[Int]] - - // Set of pending tasks for each host. Similar to pendingTasksForExecutor, - // but at host level. - private val pendingTasksForHost = new HashMap[String, ArrayBuffer[Int]] - - // Set of pending tasks for each rack -- similar to the above. - private val pendingTasksForRack = new HashMap[String, ArrayBuffer[Int]] - - // Set containing pending tasks with no locality preferences. - val pendingTasksWithNoPrefs = new ArrayBuffer[Int] - - // Set containing all pending tasks (also used as a stack, as above). - val allPendingTasks = new ArrayBuffer[Int] - - // Tasks that can be speculated. Since these will be a small fraction of total - // tasks, we'll just hold them in a HashSet. - val speculatableTasks = new HashSet[Int] - - // Task index, start and finish time for each task attempt (indexed by task ID) - val taskInfos = new HashMap[Long, TaskInfo] - - // Did the TaskSet fail? - var failed = false - var causeOfFailure = "" - - // How frequently to reprint duplicate exceptions in full, in milliseconds - val EXCEPTION_PRINT_INTERVAL = - System.getProperty("spark.logging.exceptionPrintInterval", "10000").toLong - - // Map of recent exceptions (identified by string representation and top stack frame) to - // duplicate count (how many times the same exception has appeared) and time the full exception - // was printed. This should ideally be an LRU map that can drop old exceptions automatically. - val recentExceptions = HashMap[String, (Int, Long)]() - - // Figure out the current map output tracker epoch and set it on all tasks - val epoch = sched.mapOutputTracker.getEpoch - logDebug("Epoch for " + taskSet + ": " + epoch) - for (t <- tasks) { - t.epoch = epoch - } - - // Add all our tasks to the pending lists. We do this in reverse order - // of task index so that tasks with low indices get launched first. - for (i <- (0 until numTasks).reverse) { - addPendingTask(i) - } - - // Figure out which locality levels we have in our TaskSet, so we can do delay scheduling - val myLocalityLevels = computeValidLocalityLevels() - val localityWaits = myLocalityLevels.map(getLocalityWait) // Time to wait at each level - - // Delay scheduling variables: we keep track of our current locality level and the time we - // last launched a task at that level, and move up a level when localityWaits[curLevel] expires. - // We then move down if we manage to launch a "more local" task. - var currentLocalityIndex = 0 // Index of our current locality level in validLocalityLevels - var lastLaunchTime = clock.getTime() // Time we last launched a task at this level - - /** - * Add a task to all the pending-task lists that it should be on. If readding is set, we are - * re-adding the task so only include it in each list if it's not already there. - */ - private def addPendingTask(index: Int, readding: Boolean = false) { - // Utility method that adds `index` to a list only if readding=false or it's not already there - def addTo(list: ArrayBuffer[Int]) { - if (!readding || !list.contains(index)) { - list += index - } - } - - var hadAliveLocations = false - for (loc <- tasks(index).preferredLocations) { - for (execId <- loc.executorId) { - if (sched.isExecutorAlive(execId)) { - addTo(pendingTasksForExecutor.getOrElseUpdate(execId, new ArrayBuffer)) - hadAliveLocations = true - } - } - if (sched.hasExecutorsAliveOnHost(loc.host)) { - addTo(pendingTasksForHost.getOrElseUpdate(loc.host, new ArrayBuffer)) - for (rack <- sched.getRackForHost(loc.host)) { - addTo(pendingTasksForRack.getOrElseUpdate(rack, new ArrayBuffer)) - } - hadAliveLocations = true - } - } - - if (!hadAliveLocations) { - // Even though the task might've had preferred locations, all of those hosts or executors - // are dead; put it in the no-prefs list so we can schedule it elsewhere right away. - addTo(pendingTasksWithNoPrefs) - } - - if (!readding) { - allPendingTasks += index // No point scanning this whole list to find the old task there - } - } - - /** - * Return the pending tasks list for a given executor ID, or an empty list if - * there is no map entry for that host - */ - private def getPendingTasksForExecutor(executorId: String): ArrayBuffer[Int] = { - pendingTasksForExecutor.getOrElse(executorId, ArrayBuffer()) - } - - /** - * Return the pending tasks list for a given host, or an empty list if - * there is no map entry for that host - */ - private def getPendingTasksForHost(host: String): ArrayBuffer[Int] = { - pendingTasksForHost.getOrElse(host, ArrayBuffer()) - } - - /** - * Return the pending rack-local task list for a given rack, or an empty list if - * there is no map entry for that rack - */ - private def getPendingTasksForRack(rack: String): ArrayBuffer[Int] = { - pendingTasksForRack.getOrElse(rack, ArrayBuffer()) - } - - /** - * Dequeue a pending task from the given list and return its index. - * Return None if the list is empty. - * This method also cleans up any tasks in the list that have already - * been launched, since we want that to happen lazily. - */ - private def findTaskFromList(list: ArrayBuffer[Int]): Option[Int] = { - while (!list.isEmpty) { - val index = list.last - list.trimEnd(1) - if (copiesRunning(index) == 0 && !finished(index)) { - return Some(index) - } - } - return None - } - - /** Check whether a task is currently running an attempt on a given host */ - private def hasAttemptOnHost(taskIndex: Int, host: String): Boolean = { - !taskAttempts(taskIndex).exists(_.host == host) - } - - /** - * Return a speculative task for a given executor if any are available. The task should not have - * an attempt running on this host, in case the host is slow. In addition, the task should meet - * the given locality constraint. - */ - private def findSpeculativeTask(execId: String, host: String, locality: TaskLocality.Value) - : Option[(Int, TaskLocality.Value)] = - { - speculatableTasks.retain(index => !finished(index)) // Remove finished tasks from set - - if (!speculatableTasks.isEmpty) { - // Check for process-local or preference-less tasks; note that tasks can be process-local - // on multiple nodes when we replicate cached blocks, as in Spark Streaming - for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) { - val prefs = tasks(index).preferredLocations - val executors = prefs.flatMap(_.executorId) - if (prefs.size == 0 || executors.contains(execId)) { - speculatableTasks -= index - return Some((index, TaskLocality.PROCESS_LOCAL)) - } - } - - // Check for node-local tasks - if (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) { - for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) { - val locations = tasks(index).preferredLocations.map(_.host) - if (locations.contains(host)) { - speculatableTasks -= index - return Some((index, TaskLocality.NODE_LOCAL)) - } - } - } - - // Check for rack-local tasks - if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { - for (rack <- sched.getRackForHost(host)) { - for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) { - val racks = tasks(index).preferredLocations.map(_.host).map(sched.getRackForHost) - if (racks.contains(rack)) { - speculatableTasks -= index - return Some((index, TaskLocality.RACK_LOCAL)) - } - } - } - } - - // Check for non-local tasks - if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { - for (index <- speculatableTasks if !hasAttemptOnHost(index, host)) { - speculatableTasks -= index - return Some((index, TaskLocality.ANY)) - } - } - } - - return None - } - - /** - * Dequeue a pending task for a given node and return its index and locality level. - * Only search for tasks matching the given locality constraint. - */ - private def findTask(execId: String, host: String, locality: TaskLocality.Value) - : Option[(Int, TaskLocality.Value)] = - { - for (index <- findTaskFromList(getPendingTasksForExecutor(execId))) { - return Some((index, TaskLocality.PROCESS_LOCAL)) - } - - if (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) { - for (index <- findTaskFromList(getPendingTasksForHost(host))) { - return Some((index, TaskLocality.NODE_LOCAL)) - } - } - - if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { - for { - rack <- sched.getRackForHost(host) - index <- findTaskFromList(getPendingTasksForRack(rack)) - } { - return Some((index, TaskLocality.RACK_LOCAL)) - } - } - - // Look for no-pref tasks after rack-local tasks since they can run anywhere. - for (index <- findTaskFromList(pendingTasksWithNoPrefs)) { - return Some((index, TaskLocality.PROCESS_LOCAL)) - } - - if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { - for (index <- findTaskFromList(allPendingTasks)) { - return Some((index, TaskLocality.ANY)) - } - } - - // Finally, if all else has failed, find a speculative task - return findSpeculativeTask(execId, host, locality) - } - - /** - * Respond to an offer of a single executor from the scheduler by finding a task - */ - override def resourceOffer( - execId: String, - host: String, - availableCpus: Int, - maxLocality: TaskLocality.TaskLocality) - : Option[TaskDescription] = - { - if (tasksFinished < numTasks && availableCpus >= CPUS_PER_TASK) { - val curTime = clock.getTime() - - var allowedLocality = getAllowedLocalityLevel(curTime) - if (allowedLocality > maxLocality) { - allowedLocality = maxLocality // We're not allowed to search for farther-away tasks - } - - findTask(execId, host, allowedLocality) match { - case Some((index, taskLocality)) => { - // Found a task; do some bookkeeping and return a task description - val task = tasks(index) - val taskId = sched.newTaskId() - // Figure out whether this should count as a preferred launch - logInfo("Starting task %s:%d as TID %s on executor %s: %s (%s)".format( - taskSet.id, index, taskId, execId, host, taskLocality)) - // Do various bookkeeping - copiesRunning(index) += 1 - val info = new TaskInfo(taskId, index, curTime, execId, host, taskLocality) - taskInfos(taskId) = info - taskAttempts(index) = info :: taskAttempts(index) - // Update our locality level for delay scheduling - currentLocalityIndex = getLocalityIndex(taskLocality) - lastLaunchTime = curTime - // Serialize and return the task - val startTime = clock.getTime() - // We rely on the DAGScheduler to catch non-serializable closures and RDDs, so in here - // we assume the task can be serialized without exceptions. - val serializedTask = Task.serializeWithDependencies( - task, sched.sc.addedFiles, sched.sc.addedJars, ser) - val timeTaken = clock.getTime() - startTime - increaseRunningTasks(1) - logInfo("Serialized task %s:%d as %d bytes in %d ms".format( - taskSet.id, index, serializedTask.limit, timeTaken)) - val taskName = "task %s:%d".format(taskSet.id, index) - if (taskAttempts(index).size == 1) - taskStarted(task,info) - return Some(new TaskDescription(taskId, execId, taskName, index, serializedTask)) - } - case _ => - } - } - return None - } - - /** - * Get the level we can launch tasks according to delay scheduling, based on current wait time. - */ - private def getAllowedLocalityLevel(curTime: Long): TaskLocality.TaskLocality = { - while (curTime - lastLaunchTime >= localityWaits(currentLocalityIndex) && - currentLocalityIndex < myLocalityLevels.length - 1) - { - // Jump to the next locality level, and remove our waiting time for the current one since - // we don't want to count it again on the next one - lastLaunchTime += localityWaits(currentLocalityIndex) - currentLocalityIndex += 1 - } - myLocalityLevels(currentLocalityIndex) - } - - /** - * Find the index in myLocalityLevels for a given locality. This is also designed to work with - * localities that are not in myLocalityLevels (in case we somehow get those) by returning the - * next-biggest level we have. Uses the fact that the last value in myLocalityLevels is ANY. - */ - def getLocalityIndex(locality: TaskLocality.TaskLocality): Int = { - var index = 0 - while (locality > myLocalityLevels(index)) { - index += 1 - } - index - } - - /** Called by cluster scheduler when one of our tasks changes state */ - override def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { - SparkEnv.set(env) - state match { - case TaskState.FINISHED => - taskFinished(tid, state, serializedData) - case TaskState.LOST => - taskLost(tid, state, serializedData) - case TaskState.FAILED => - taskLost(tid, state, serializedData) - case TaskState.KILLED => - taskLost(tid, state, serializedData) - case _ => - } - } - - def taskStarted(task: Task[_], info: TaskInfo) { - sched.listener.taskStarted(task, info) - } - - def taskFinished(tid: Long, state: TaskState, serializedData: ByteBuffer) { - val info = taskInfos(tid) - if (info.failed) { - // We might get two task-lost messages for the same task in coarse-grained Mesos mode, - // or even from Mesos itself when acks get delayed. - return - } - val index = info.index - info.markSuccessful() - decreaseRunningTasks(1) - if (!finished(index)) { - tasksFinished += 1 - logInfo("Finished TID %s in %d ms on %s (progress: %d/%d)".format( - tid, info.duration, info.host, tasksFinished, numTasks)) - // Deserialize task result and pass it to the scheduler - try { - val result = ser.deserialize[TaskResult[_]](serializedData) - result.metrics.resultSize = serializedData.limit() - sched.listener.taskEnded( - tasks(index), Success, result.value, result.accumUpdates, info, result.metrics) - } catch { - case cnf: ClassNotFoundException => - val loader = Thread.currentThread().getContextClassLoader - throw new SparkException("ClassNotFound with classloader: " + loader, cnf) - case ex => throw ex - } - // Mark finished and stop if we've finished all the tasks - finished(index) = true - if (tasksFinished == numTasks) { - sched.taskSetFinished(this) - } - } else { - logInfo("Ignoring task-finished event for TID " + tid + - " because task " + index + " is already finished") - } - } - - def taskLost(tid: Long, state: TaskState, serializedData: ByteBuffer) { - val info = taskInfos(tid) - if (info.failed) { - // We might get two task-lost messages for the same task in coarse-grained Mesos mode, - // or even from Mesos itself when acks get delayed. - return - } - val index = info.index - info.markFailed() - decreaseRunningTasks(1) - if (!finished(index)) { - logInfo("Lost TID %s (task %s:%d)".format(tid, taskSet.id, index)) - copiesRunning(index) -= 1 - // Check if the problem is a map output fetch failure. In that case, this - // task will never succeed on any node, so tell the scheduler about it. - if (serializedData != null && serializedData.limit() > 0) { - val reason = ser.deserialize[TaskEndReason](serializedData, getClass.getClassLoader) - reason match { - case fetchFailed: FetchFailed => - logInfo("Loss was due to fetch failure from " + fetchFailed.bmAddress) - sched.listener.taskEnded(tasks(index), fetchFailed, null, null, info, null) - finished(index) = true - tasksFinished += 1 - sched.taskSetFinished(this) - decreaseRunningTasks(runningTasks) - return - - case taskResultTooBig: TaskResultTooBigFailure => - logInfo("Loss was due to task %s result exceeding Akka frame size; aborting job".format( - tid)) - abort("Task %s result exceeded Akka frame size".format(tid)) - return - - case ef: ExceptionFailure => - sched.listener.taskEnded(tasks(index), ef, null, null, info, ef.metrics.getOrElse(null)) - val key = ef.description - val now = clock.getTime() - val (printFull, dupCount) = { - if (recentExceptions.contains(key)) { - val (dupCount, printTime) = recentExceptions(key) - if (now - printTime > EXCEPTION_PRINT_INTERVAL) { - recentExceptions(key) = (0, now) - (true, 0) - } else { - recentExceptions(key) = (dupCount + 1, printTime) - (false, dupCount + 1) - } - } else { - recentExceptions(key) = (0, now) - (true, 0) - } - } - if (printFull) { - val locs = ef.stackTrace.map(loc => "\tat %s".format(loc.toString)) - logInfo("Loss was due to %s\n%s\n%s".format( - ef.className, ef.description, locs.mkString("\n"))) - } else { - logInfo("Loss was due to %s [duplicate %d]".format(ef.description, dupCount)) - } - - case _ => {} - } - } - // On non-fetch failures, re-enqueue the task as pending for a max number of retries - addPendingTask(index) - // Count failed attempts only on FAILED and LOST state (not on KILLED) - if (state == TaskState.FAILED || state == TaskState.LOST) { - numFailures(index) += 1 - if (numFailures(index) > MAX_TASK_FAILURES) { - logError("Task %s:%d failed more than %d times; aborting job".format( - taskSet.id, index, MAX_TASK_FAILURES)) - abort("Task %s:%d failed more than %d times".format(taskSet.id, index, MAX_TASK_FAILURES)) - } - } - } else { - logInfo("Ignoring task-lost event for TID " + tid + - " because task " + index + " is already finished") - } - } - - override def error(message: String) { - // Save the error message - abort("Error: " + message) - } - - def abort(message: String) { - failed = true - causeOfFailure = message - // TODO: Kill running tasks if we were not terminated due to a Mesos error - sched.listener.taskSetFailed(taskSet, message) - decreaseRunningTasks(runningTasks) - sched.taskSetFinished(this) - } - - override def increaseRunningTasks(taskNum: Int) { - runningTasks += taskNum - if (parent != null) { - parent.increaseRunningTasks(taskNum) - } - } - - override def decreaseRunningTasks(taskNum: Int) { - runningTasks -= taskNum - if (parent != null) { - parent.decreaseRunningTasks(taskNum) - } - } - - override def getSchedulableByName(name: String): Schedulable = { - return null - } - - override def addSchedulable(schedulable: Schedulable) {} - - override def removeSchedulable(schedulable: Schedulable) {} - - override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { - var sortedTaskSetQueue = ArrayBuffer[TaskSetManager](this) - sortedTaskSetQueue += this - return sortedTaskSetQueue - } - - /** Called by cluster scheduler when an executor is lost so we can re-enqueue our tasks */ - override def executorLost(execId: String, host: String) { - logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id) - - // Re-enqueue pending tasks for this host based on the status of the cluster -- for example, a - // task that used to have locations on only this host might now go to the no-prefs list. Note - // that it's okay if we add a task to the same queue twice (if it had multiple preferred - // locations), because findTaskFromList will skip already-running tasks. - for (index <- getPendingTasksForExecutor(execId)) { - addPendingTask(index, readding=true) - } - for (index <- getPendingTasksForHost(host)) { - addPendingTask(index, readding=true) - } - - // Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage - if (tasks(0).isInstanceOf[ShuffleMapTask]) { - for ((tid, info) <- taskInfos if info.executorId == execId) { - val index = taskInfos(tid).index - if (finished(index)) { - finished(index) = false - copiesRunning(index) -= 1 - tasksFinished -= 1 - addPendingTask(index) - // Tell the DAGScheduler that this task was resubmitted so that it doesn't think our - // stage finishes when a total of tasks.size tasks finish. - sched.listener.taskEnded(tasks(index), Resubmitted, null, null, info, null) - } - } - } - // Also re-enqueue any tasks that were running on the node - for ((tid, info) <- taskInfos if info.running && info.executorId == execId) { - taskLost(tid, TaskState.KILLED, null) - } - } - - /** - * Check for tasks to be speculated and return true if there are any. This is called periodically - * by the ClusterScheduler. - * - * TODO: To make this scale to large jobs, we need to maintain a list of running tasks, so that - * we don't scan the whole task set. It might also help to make this sorted by launch time. - */ - override def checkSpeculatableTasks(): Boolean = { - // Can't speculate if we only have one task, or if all tasks have finished. - if (numTasks == 1 || tasksFinished == numTasks) { - return false - } - var foundTasks = false - val minFinishedForSpeculation = (SPECULATION_QUANTILE * numTasks).floor.toInt - logDebug("Checking for speculative tasks: minFinished = " + minFinishedForSpeculation) - if (tasksFinished >= minFinishedForSpeculation) { - val time = clock.getTime() - val durations = taskInfos.values.filter(_.successful).map(_.duration).toArray - Arrays.sort(durations) - val medianDuration = durations(min((0.5 * numTasks).round.toInt, durations.size - 1)) - val threshold = max(SPECULATION_MULTIPLIER * medianDuration, 100) - // TODO: Threshold should also look at standard deviation of task durations and have a lower - // bound based on that. - logDebug("Task length threshold for speculation: " + threshold) - for ((tid, info) <- taskInfos) { - val index = info.index - if (!finished(index) && copiesRunning(index) == 1 && info.timeRunning(time) > threshold && - !speculatableTasks.contains(index)) { - logInfo( - "Marking task %s:%d (on %s) as speculatable because it ran more than %.0f ms".format( - taskSet.id, index, info.host, threshold)) - speculatableTasks += index - foundTasks = true - } - } - } - return foundTasks - } - - override def hasPendingTasks(): Boolean = { - numTasks > 0 && tasksFinished < numTasks - } - - private def getLocalityWait(level: TaskLocality.TaskLocality): Long = { - val defaultWait = System.getProperty("spark.locality.wait", "3000") - level match { - case TaskLocality.PROCESS_LOCAL => - System.getProperty("spark.locality.wait.process", defaultWait).toLong - case TaskLocality.NODE_LOCAL => - System.getProperty("spark.locality.wait.node", defaultWait).toLong - case TaskLocality.RACK_LOCAL => - System.getProperty("spark.locality.wait.rack", defaultWait).toLong - case TaskLocality.ANY => - 0L - } - } - - /** - * Compute the locality levels used in this TaskSet. Assumes that all tasks have already been - * added to queues using addPendingTask. - */ - private def computeValidLocalityLevels(): Array[TaskLocality.TaskLocality] = { - import TaskLocality.{PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY} - val levels = new ArrayBuffer[TaskLocality.TaskLocality] - if (!pendingTasksForExecutor.isEmpty && getLocalityWait(PROCESS_LOCAL) != 0) { - levels += PROCESS_LOCAL - } - if (!pendingTasksForHost.isEmpty && getLocalityWait(NODE_LOCAL) != 0) { - levels += NODE_LOCAL - } - if (!pendingTasksForRack.isEmpty && getLocalityWait(RACK_LOCAL) != 0) { - levels += RACK_LOCAL - } - levels += ANY - logDebug("Valid locality levels for " + taskSet + ": " + levels.mkString(", ")) - levels.toArray - } -} diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/StandaloneClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala index c0b836bf1a..53316dae2a 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/StandaloneClusterMessage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala @@ -24,26 +24,28 @@ import org.apache.spark.scheduler.TaskDescription import org.apache.spark.util.{Utils, SerializableBuffer} -private[spark] sealed trait StandaloneClusterMessage extends Serializable +private[spark] sealed trait CoarseGrainedClusterMessage extends Serializable -private[spark] object StandaloneClusterMessages { +private[spark] object CoarseGrainedClusterMessages { // Driver to executors - case class LaunchTask(task: TaskDescription) extends StandaloneClusterMessage + case class LaunchTask(task: TaskDescription) extends CoarseGrainedClusterMessage + + case class KillTask(taskId: Long, executor: String) extends CoarseGrainedClusterMessage case class RegisteredExecutor(sparkProperties: Seq[(String, String)]) - extends StandaloneClusterMessage + extends CoarseGrainedClusterMessage - case class RegisterExecutorFailed(message: String) extends StandaloneClusterMessage + case class RegisterExecutorFailed(message: String) extends CoarseGrainedClusterMessage // Executors to driver case class RegisterExecutor(executorId: String, hostPort: String, cores: Int) - extends StandaloneClusterMessage { + extends CoarseGrainedClusterMessage { Utils.checkHostPort(hostPort, "Expected host port") } case class StatusUpdate(executorId: String, taskId: Long, state: TaskState, - data: SerializableBuffer) extends StandaloneClusterMessage + data: SerializableBuffer) extends CoarseGrainedClusterMessage object StatusUpdate { /** Alternate factory method that takes a ByteBuffer directly for the data field */ @@ -54,10 +56,14 @@ private[spark] object StandaloneClusterMessages { } // Internal messages in driver - case object ReviveOffers extends StandaloneClusterMessage + case object ReviveOffers extends CoarseGrainedClusterMessage + + case object StopDriver extends CoarseGrainedClusterMessage + + case object StopExecutor extends CoarseGrainedClusterMessage - case object StopDriver extends StandaloneClusterMessage + case object StopExecutors extends CoarseGrainedClusterMessage - case class RemoveExecutor(executorId: String, reason: String) extends StandaloneClusterMessage + case class RemoveExecutor(executorId: String, reason: String) extends CoarseGrainedClusterMessage } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/StandaloneSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index f3aeea43d5..2f5bcafe40 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/StandaloneSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -20,45 +20,51 @@ package org.apache.spark.scheduler.cluster import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} +import scala.concurrent.Await +import scala.concurrent.duration._ import akka.actor._ -import akka.dispatch.Await import akka.pattern.ask -import akka.remote.{RemoteClientShutdown, RemoteClientDisconnected, RemoteClientLifeCycleEvent} -import akka.util.Duration -import akka.util.duration._ +import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} import org.apache.spark.{SparkException, Logging, TaskState} -import org.apache.spark.scheduler.TaskDescription -import org.apache.spark.scheduler.cluster.StandaloneClusterMessages._ -import org.apache.spark.util.Utils +import org.apache.spark.{Logging, SparkException, TaskState} +import org.apache.spark.scheduler.{TaskSchedulerImpl, SchedulerBackend, SlaveLost, TaskDescription, + WorkerOffer} +import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ +import org.apache.spark.util.{AkkaUtils, Utils} /** - * A standalone scheduler backend, which waits for standalone executors to connect to it through - * Akka. These may be executed in a variety of ways, such as Mesos tasks for the coarse-grained - * Mesos mode or standalone processes for Spark's standalone deploy mode (spark.deploy.*). + * A scheduler backend that waits for coarse grained executors to connect to it through Akka. + * This backend holds onto each executor for the duration of the Spark job rather than relinquishing + * executors whenever a task is done and asking the scheduler to launch a new executor for + * each new task. Executors may be launched in a variety of ways, such as Mesos tasks for the + * coarse-grained Mesos mode or standalone processes for Spark's standalone deploy mode + * (spark.deploy.*). */ private[spark] -class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: ActorSystem) +class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: ActorSystem) extends SchedulerBackend with Logging { // Use an atomic variable to track total number of cores in the cluster for simplicity and speed var totalCoreCount = new AtomicInteger(0) + val conf = scheduler.sc.conf + private val timeout = AkkaUtils.askTimeout(conf) class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor { private val executorActor = new HashMap[String, ActorRef] private val executorAddress = new HashMap[String, Address] private val executorHost = new HashMap[String, String] private val freeCores = new HashMap[String, Int] - private val actorToExecutorId = new HashMap[ActorRef, String] private val addressToExecutorId = new HashMap[Address, String] override def preStart() { // Listen for remote client disconnection events, since they don't go through Akka's watch() - context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) // Periodically revive offers to allow delay scheduling to work - val reviveInterval = System.getProperty("spark.scheduler.revive.interval", "1000").toLong + val reviveInterval = conf.get("spark.scheduler.revive.interval", "1000").toLong + import context.dispatcher context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers) } @@ -70,12 +76,10 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor } else { logInfo("Registered executor: " + sender + " with ID " + executorId) sender ! RegisteredExecutor(sparkProperties) - context.watch(sender) executorActor(executorId) = sender executorHost(executorId) = Utils.parseHostPort(hostPort)._1 freeCores(executorId) = cores executorAddress(executorId) = sender.path.address - actorToExecutorId(sender) = executorId addressToExecutorId(sender.path.address) = executorId totalCoreCount.addAndGet(cores) makeOffers() @@ -84,29 +88,40 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor case StatusUpdate(executorId, taskId, state, data) => scheduler.statusUpdate(taskId, state, data.value) if (TaskState.isFinished(state)) { - freeCores(executorId) += 1 - makeOffers(executorId) + if (executorActor.contains(executorId)) { + freeCores(executorId) += 1 + makeOffers(executorId) + } else { + // Ignoring the update since we don't know about the executor. + val msg = "Ignored task status update (%d state %s) from unknown executor %s with ID %s" + logWarning(msg.format(taskId, state, sender, executorId)) + } } case ReviveOffers => makeOffers() + case KillTask(taskId, executorId) => + executorActor(executorId) ! KillTask(taskId, executorId) + case StopDriver => sender ! true context.stop(self) + case StopExecutors => + logInfo("Asking each executor to shut down") + for (executor <- executorActor.values) { + executor ! StopExecutor + } + sender ! true + case RemoveExecutor(executorId, reason) => removeExecutor(executorId, reason) sender ! true - case Terminated(actor) => - actorToExecutorId.get(actor).foreach(removeExecutor(_, "Akka actor terminated")) + case DisassociatedEvent(_, address, _) => + addressToExecutorId.get(address).foreach(removeExecutor(_, "remote Akka client disassociated")) - case RemoteClientDisconnected(transport, address) => - addressToExecutorId.get(address).foreach(removeExecutor(_, "remote Akka client disconnected")) - - case RemoteClientShutdown(transport, address) => - addressToExecutorId.get(address).foreach(removeExecutor(_, "remote Akka client shutdown")) } // Make fake resource offers on all executors @@ -134,7 +149,6 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor if (executorActor.contains(executorId)) { logInfo("Executor " + executorId + " disconnected, so removing it") val numCores = freeCores(executorId) - actorToExecutorId -= executorActor(executorId) addressToExecutorId -= executorAddress(executorId) executorActor -= executorId executorHost -= executorId @@ -150,25 +164,35 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor override def start() { val properties = new ArrayBuffer[(String, String)] - val iterator = System.getProperties.entrySet.iterator - while (iterator.hasNext) { - val entry = iterator.next - val (key, value) = (entry.getKey.toString, entry.getValue.toString) + for ((key, value) <- scheduler.sc.conf.getAll) { if (key.startsWith("spark.") && !key.equals("spark.hostPort")) { properties += ((key, value)) } } + //TODO (prashant) send conf instead of properties driverActor = actorSystem.actorOf( - Props(new DriverActor(properties)), name = StandaloneSchedulerBackend.ACTOR_NAME) + Props(new DriverActor(properties)), name = CoarseGrainedSchedulerBackend.ACTOR_NAME) } - private val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + def stopExecutors() { + try { + if (driverActor != null) { + logInfo("Shutting down all executors") + val future = driverActor.ask(StopExecutors)(timeout) + Await.ready(future, timeout) + } + } catch { + case e: Exception => + throw new SparkException("Error asking standalone scheduler to shut down executors", e) + } + } override def stop() { + stopExecutors() try { if (driverActor != null) { val future = driverActor.ask(StopDriver)(timeout) - Await.result(future, timeout) + Await.ready(future, timeout) } } catch { case e: Exception => @@ -180,14 +204,20 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor driverActor ! ReviveOffers } - override def defaultParallelism() = Option(System.getProperty("spark.default.parallelism")) - .map(_.toInt).getOrElse(math.max(totalCoreCount.get(), 2)) + override def killTask(taskId: Long, executorId: String) { + driverActor ! KillTask(taskId, executorId) + } + + override def defaultParallelism(): Int = { + conf.getOption("spark.default.parallelism").map(_.toInt).getOrElse( + math.max(totalCoreCount.get(), 2)) + } // Called by subclasses when notified of a lost worker def removeExecutor(executorId: String, reason: String) { try { val future = driverActor.ask(RemoveExecutor(executorId, reason))(timeout) - Await.result(future, timeout) + Await.ready(future, timeout) } catch { case e: Exception => throw new SparkException("Error notifying standalone scheduler's driver actor", e) @@ -195,6 +225,6 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor } } -private[spark] object StandaloneSchedulerBackend { - val ACTOR_NAME = "StandaloneScheduler" +private[spark] object CoarseGrainedSchedulerBackend { + val ACTOR_NAME = "CoarseGrainedScheduler" } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala new file mode 100644 index 0000000000..b44d1e43c8 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala @@ -0,0 +1,69 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{Path, FileSystem} + +import org.apache.spark.{Logging, SparkContext} +import org.apache.spark.scheduler.TaskSchedulerImpl + +private[spark] class SimrSchedulerBackend( + scheduler: TaskSchedulerImpl, + sc: SparkContext, + driverFilePath: String) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + with Logging { + + val tmpPath = new Path(driverFilePath + "_tmp") + val filePath = new Path(driverFilePath) + + val maxCores = conf.get("spark.simr.executor.cores", "1").toInt + + override def start() { + super.start() + + val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( + sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port"), + CoarseGrainedSchedulerBackend.ACTOR_NAME) + + val conf = new Configuration() + val fs = FileSystem.get(conf) + + logInfo("Writing to HDFS file: " + driverFilePath) + logInfo("Writing Akka address: " + driverUrl) + logInfo("Writing Spark UI Address: " + sc.ui.appUIAddress) + + // Create temporary file to prevent race condition where executors get empty driverUrl file + val temp = fs.create(tmpPath, true) + temp.writeUTF(driverUrl) + temp.writeInt(maxCores) + temp.writeUTF(sc.ui.appUIAddress) + temp.close() + + // "Atomic" rename + fs.rename(tmpPath, filePath) + } + + override def stop() { + val conf = new Configuration() + val fs = FileSystem.get(conf) + fs.delete(new Path(driverFilePath), false) + super.stop() + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala index 9c49768c0c..9858717d13 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala @@ -17,18 +17,20 @@ package org.apache.spark.scheduler.cluster +import scala.collection.mutable.HashMap + import org.apache.spark.{Logging, SparkContext} import org.apache.spark.deploy.client.{Client, ClientListener} import org.apache.spark.deploy.{Command, ApplicationDescription} -import scala.collection.mutable.HashMap +import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SlaveLost, TaskSchedulerImpl} import org.apache.spark.util.Utils private[spark] class SparkDeploySchedulerBackend( - scheduler: ClusterScheduler, + scheduler: TaskSchedulerImpl, sc: SparkContext, - master: String, + masters: Array[String], appName: String) - extends StandaloneSchedulerBackend(scheduler, sc.env.actorSystem) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) with ClientListener with Logging { @@ -36,23 +38,23 @@ private[spark] class SparkDeploySchedulerBackend( var stopping = false var shutdownCallback : (SparkDeploySchedulerBackend) => Unit = _ - val maxCores = System.getProperty("spark.cores.max", Int.MaxValue.toString).toInt + val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt override def start() { super.start() // The endpoint for executors to talk to us - val driverUrl = "akka://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), System.getProperty("spark.driver.port"), - StandaloneSchedulerBackend.ACTOR_NAME) + val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( + conf.get("spark.driver.host"), conf.get("spark.driver.port"), + CoarseGrainedSchedulerBackend.ACTOR_NAME) val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}") val command = Command( - "org.apache.spark.executor.StandaloneExecutorBackend", args, sc.executorEnvs) + "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs) val sparkHome = sc.getSparkHome().getOrElse(null) - val appDesc = new ApplicationDescription(appName, maxCores, executorMemory, command, sparkHome, + val appDesc = new ApplicationDescription(appName, maxCores, sc.executorMemory, command, sparkHome, "http://" + sc.ui.appUIAddress) - client = new Client(sc.env.actorSystem, master, appDesc, this) + client = new Client(sc.env.actorSystem, masters, appDesc, this, conf) client.start() } @@ -71,8 +73,14 @@ private[spark] class SparkDeploySchedulerBackend( override def disconnected() { if (!stopping) { - logError("Disconnected from Spark cluster!") - scheduler.error("Disconnected from Spark cluster") + logWarning("Disconnected from Spark cluster! Waiting for reconnection...") + } + } + + override def dead() { + if (!stopping) { + logError("Spark cluster looks dead, giving up.") + scheduler.error("Spark cluster looks down") } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index 15b3397a3a..0494ca8726 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -30,23 +30,25 @@ import org.apache.mesos._ import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _} import org.apache.spark.{SparkException, Logging, SparkContext, TaskState} -import org.apache.spark.scheduler.cluster.{ClusterScheduler, StandaloneSchedulerBackend} +import org.apache.spark.scheduler.TaskSchedulerImpl +import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend /** * A SchedulerBackend that runs tasks on Mesos, but uses "coarse-grained" tasks, where it holds * onto each Mesos node for the duration of the Spark job instead of relinquishing cores whenever * a task is done. It launches Spark tasks within the coarse-grained Mesos tasks using the - * StandaloneBackend mechanism. This class is useful for lower and more predictable latency. + * CoarseGrainedSchedulerBackend mechanism. This class is useful for lower and more predictable + * latency. * * Unfortunately this has a bit of duplication from MesosSchedulerBackend, but it seems hard to * remove this. */ private[spark] class CoarseMesosSchedulerBackend( - scheduler: ClusterScheduler, + scheduler: TaskSchedulerImpl, sc: SparkContext, master: String, appName: String) - extends StandaloneSchedulerBackend(scheduler, sc.env.actorSystem) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) with MScheduler with Logging { @@ -60,7 +62,7 @@ private[spark] class CoarseMesosSchedulerBackend( var driver: SchedulerDriver = null // Maximum number of cores to acquire (TODO: we'll need more flexible controls here) - val maxCores = System.getProperty("spark.cores.max", Int.MaxValue.toString).toInt + val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt // Cores we have acquired with each Mesos task ID val coresByTaskId = new HashMap[Int, Int] @@ -75,7 +77,7 @@ private[spark] class CoarseMesosSchedulerBackend( "Spark home is not set; set it through the spark.home system " + "property, the SPARK_HOME environment variable or the SparkContext constructor")) - val extraCoresPerSlave = System.getProperty("spark.mesos.extra.cores", "0").toInt + val extraCoresPerSlave = conf.get("spark.mesos.extra.cores", "0").toInt var nextMesosTaskId = 0 @@ -119,23 +121,23 @@ private[spark] class CoarseMesosSchedulerBackend( } val command = CommandInfo.newBuilder() .setEnvironment(environment) - val driverUrl = "akka://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), - System.getProperty("spark.driver.port"), - StandaloneSchedulerBackend.ACTOR_NAME) - val uri = System.getProperty("spark.executor.uri") + val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( + conf.get("spark.driver.host"), + conf.get("spark.driver.port"), + CoarseGrainedSchedulerBackend.ACTOR_NAME) + val uri = conf.get("spark.executor.uri", null) if (uri == null) { val runScript = new File(sparkHome, "./sbin/spark-class").getCanonicalPath command.setValue( - "\"%s\" org.apache.spark.executor.StandaloneExecutorBackend %s %s %s %d".format( + "\"%s\" org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %d".format( runScript, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores)) } else { // Grab everything to the first '.'. We'll use that and '*' to // glob the directory "correctly". val basename = uri.split('/').last.split('.').head command.setValue( - "cd %s*; ./sbin/spark-class org.apache.spark.executor.StandaloneExecutorBackend %s %s %s %d".format( - basename, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores)) + "cd %s*; ./sbin/spark-class org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %d" + .format(basename, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores)) command.addUris(CommandInfo.URI.newBuilder().setValue(uri)) } return command.build() @@ -175,11 +177,12 @@ private[spark] class CoarseMesosSchedulerBackend( val slaveId = offer.getSlaveId.toString val mem = getResource(offer.getResourcesList, "mem") val cpus = getResource(offer.getResourcesList, "cpus").toInt - if (totalCoresAcquired < maxCores && mem >= executorMemory && cpus >= 1 && + if (totalCoresAcquired < maxCores && mem >= sc.executorMemory && cpus >= 1 && failuresBySlaveId.getOrElse(slaveId, 0) < MAX_SLAVE_FAILURES && !slaveIdsWithExecutors.contains(slaveId)) { // Launch an executor on the slave val cpusToUse = math.min(cpus, maxCores - totalCoresAcquired) + totalCoresAcquired += cpusToUse val taskId = newMesosTaskId() taskIdToSlaveId(taskId) = slaveId slaveIdsWithExecutors += slaveId @@ -190,7 +193,7 @@ private[spark] class CoarseMesosSchedulerBackend( .setCommand(createCommand(offer, cpusToUse + extraCoresPerSlave)) .setName("Task " + taskId) .addResources(createResource("cpus", cpusToUse)) - .addResources(createResource("mem", executorMemory)) + .addResources(createResource("mem", sc.executorMemory)) .build() d.launchTasks(offer.getId, Collections.singletonList(task), filters) } else { diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index 7e9c05cd66..ae8d527352 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -30,9 +30,8 @@ import org.apache.mesos._ import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _} import org.apache.spark.{Logging, SparkException, SparkContext, TaskState} -import org.apache.spark.scheduler.TaskDescription -import org.apache.spark.scheduler.cluster.{ClusterScheduler, ExecutorExited, ExecutorLossReason} -import org.apache.spark.scheduler.cluster.{SchedulerBackend, SlaveLost, WorkerOffer} +import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SchedulerBackend, SlaveLost, + TaskDescription, TaskSchedulerImpl, WorkerOffer} import org.apache.spark.util.Utils /** @@ -41,7 +40,7 @@ import org.apache.spark.util.Utils * from multiple apps can run on different cores) and in time (a core can switch ownership). */ private[spark] class MesosSchedulerBackend( - scheduler: ClusterScheduler, + scheduler: TaskSchedulerImpl, sc: SparkContext, master: String, appName: String) @@ -101,7 +100,7 @@ private[spark] class MesosSchedulerBackend( } val command = CommandInfo.newBuilder() .setEnvironment(environment) - val uri = System.getProperty("spark.executor.uri") + val uri = sc.conf.get("spark.executor.uri", null) if (uri == null) { command.setValue(new File(sparkHome, "/sbin/spark-executor").getCanonicalPath) } else { @@ -114,7 +113,7 @@ private[spark] class MesosSchedulerBackend( val memory = Resource.newBuilder() .setName("mem") .setType(Value.Type.SCALAR) - .setScalar(Value.Scalar.newBuilder().setValue(executorMemory).build()) + .setScalar(Value.Scalar.newBuilder().setValue(sc.executorMemory).build()) .build() ExecutorInfo.newBuilder() .setExecutorId(ExecutorID.newBuilder().setValue(execId).build()) @@ -199,7 +198,7 @@ private[spark] class MesosSchedulerBackend( def enoughMemory(o: Offer) = { val mem = getResource(o.getResourcesList, "mem") val slaveId = o.getSlaveId.getValue - mem >= executorMemory || slaveIdsWithExecutors.contains(slaveId) + mem >= sc.executorMemory || slaveIdsWithExecutors.contains(slaveId) } for ((offer, index) <- offers.zipWithIndex if enoughMemory(offer)) { @@ -341,5 +340,5 @@ private[spark] class MesosSchedulerBackend( } // TODO: query Mesos for number of cores - override def defaultParallelism() = System.getProperty("spark.default.parallelism", "8").toInt + override def defaultParallelism() = sc.conf.get("spark.default.parallelism", "8").toInt } diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala new file mode 100644 index 0000000000..897d47a9ad --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -0,0 +1,109 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.local + +import java.nio.ByteBuffer + +import akka.actor.{Actor, ActorRef, Props} + +import org.apache.spark.{Logging, SparkContext, SparkEnv, TaskState} +import org.apache.spark.TaskState.TaskState +import org.apache.spark.executor.{Executor, ExecutorBackend} +import org.apache.spark.scheduler.{SchedulerBackend, TaskSchedulerImpl, WorkerOffer} + +private case class ReviveOffers() + +private case class StatusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) + +private case class KillTask(taskId: Long) + +/** + * Calls to LocalBackend are all serialized through LocalActor. Using an actor makes the calls on + * LocalBackend asynchronous, which is necessary to prevent deadlock between LocalBackend + * and the ClusterScheduler. + */ +private[spark] class LocalActor( + scheduler: TaskSchedulerImpl, + executorBackend: LocalBackend, + private val totalCores: Int) extends Actor with Logging { + + private var freeCores = totalCores + + private val localExecutorId = "localhost" + private val localExecutorHostname = "localhost" + + val executor = new Executor( + localExecutorId, localExecutorHostname, scheduler.conf.getAll, isLocal = true) + + def receive = { + case ReviveOffers => + reviveOffers() + + case StatusUpdate(taskId, state, serializedData) => + scheduler.statusUpdate(taskId, state, serializedData) + if (TaskState.isFinished(state)) { + freeCores += 1 + reviveOffers() + } + + case KillTask(taskId) => + executor.killTask(taskId) + } + + def reviveOffers() { + val offers = Seq(new WorkerOffer(localExecutorId, localExecutorHostname, freeCores)) + for (task <- scheduler.resourceOffers(offers).flatten) { + freeCores -= 1 + executor.launchTask(executorBackend, task.taskId, task.serializedTask) + } + } +} + +/** + * LocalBackend is used when running a local version of Spark where the executor, backend, and + * master all run in the same JVM. It sits behind a ClusterScheduler and handles launching tasks + * on a single Executor (created by the LocalBackend) running locally. + */ +private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores: Int) + extends SchedulerBackend with ExecutorBackend { + + var localActor: ActorRef = null + + override def start() { + localActor = SparkEnv.get.actorSystem.actorOf( + Props(new LocalActor(scheduler, this, totalCores)), + "LocalBackendActor") + } + + override def stop() { + } + + override def reviveOffers() { + localActor ! ReviveOffers + } + + override def defaultParallelism() = totalCores + + override def killTask(taskId: Long, executorId: String) { + localActor ! KillTask(taskId) + } + + override def statusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) { + localActor ! StatusUpdate(taskId, state, serializedData) + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalScheduler.scala deleted file mode 100644 index e29438f4ed..0000000000 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalScheduler.scala +++ /dev/null @@ -1,272 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.scheduler.local - -import java.io.File -import java.lang.management.ManagementFactory -import java.util.concurrent.atomic.AtomicInteger -import java.nio.ByteBuffer - -import scala.collection.JavaConversions._ -import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap -import scala.collection.mutable.HashSet - -import org.apache.spark._ -import org.apache.spark.TaskState.TaskState -import org.apache.spark.executor.ExecutorURLClassLoader -import org.apache.spark.scheduler._ -import org.apache.spark.scheduler.SchedulingMode.SchedulingMode -import akka.actor._ -import org.apache.spark.util.Utils - -/** - * A FIFO or Fair TaskScheduler implementation that runs tasks locally in a thread pool. Optionally - * the scheduler also allows each task to fail up to maxFailures times, which is useful for - * testing fault recovery. - */ - -private[spark] -case class LocalReviveOffers() - -private[spark] -case class LocalStatusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) - -private[spark] -class LocalActor(localScheduler: LocalScheduler, var freeCores: Int) extends Actor with Logging { - - def receive = { - case LocalReviveOffers => - launchTask(localScheduler.resourceOffer(freeCores)) - case LocalStatusUpdate(taskId, state, serializeData) => - freeCores += 1 - localScheduler.statusUpdate(taskId, state, serializeData) - launchTask(localScheduler.resourceOffer(freeCores)) - } - - def launchTask(tasks : Seq[TaskDescription]) { - for (task <- tasks) { - freeCores -= 1 - localScheduler.threadPool.submit(new Runnable { - def run() { - localScheduler.runTask(task.taskId, task.serializedTask) - } - }) - } - } -} - -private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: SparkContext) - extends TaskScheduler - with Logging { - - var attemptId = new AtomicInteger(0) - var threadPool = Utils.newDaemonFixedThreadPool(threads) - val env = SparkEnv.get - var listener: TaskSchedulerListener = null - - // Application dependencies (added through SparkContext) that we've fetched so far on this node. - // Each map holds the master's timestamp for the version of that file or JAR we got. - val currentFiles: HashMap[String, Long] = new HashMap[String, Long]() - val currentJars: HashMap[String, Long] = new HashMap[String, Long]() - - val classLoader = new ExecutorURLClassLoader(Array(), Thread.currentThread.getContextClassLoader) - - var schedulableBuilder: SchedulableBuilder = null - var rootPool: Pool = null - val schedulingMode: SchedulingMode = SchedulingMode.withName( - System.getProperty("spark.scheduler.mode", "FIFO")) - val activeTaskSets = new HashMap[String, TaskSetManager] - val taskIdToTaskSetId = new HashMap[Long, String] - val taskSetTaskIds = new HashMap[String, HashSet[Long]] - - var localActor: ActorRef = null - - override def start() { - // temporarily set rootPool name to empty - rootPool = new Pool("", schedulingMode, 0, 0) - schedulableBuilder = { - schedulingMode match { - case SchedulingMode.FIFO => - new FIFOSchedulableBuilder(rootPool) - case SchedulingMode.FAIR => - new FairSchedulableBuilder(rootPool) - } - } - schedulableBuilder.buildPools() - - localActor = env.actorSystem.actorOf(Props(new LocalActor(this, threads)), "Test") - } - - override def setListener(listener: TaskSchedulerListener) { - this.listener = listener - } - - override def submitTasks(taskSet: TaskSet) { - synchronized { - val manager = new LocalTaskSetManager(this, taskSet) - schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties) - activeTaskSets(taskSet.id) = manager - taskSetTaskIds(taskSet.id) = new HashSet[Long]() - localActor ! LocalReviveOffers - } - } - - def resourceOffer(freeCores: Int): Seq[TaskDescription] = { - synchronized { - var freeCpuCores = freeCores - val tasks = new ArrayBuffer[TaskDescription](freeCores) - val sortedTaskSetQueue = rootPool.getSortedTaskSetQueue() - for (manager <- sortedTaskSetQueue) { - logDebug("parentName:%s,name:%s,runningTasks:%s".format( - manager.parent.name, manager.name, manager.runningTasks)) - } - - var launchTask = false - for (manager <- sortedTaskSetQueue) { - do { - launchTask = false - manager.resourceOffer(null, null, freeCpuCores, null) match { - case Some(task) => - tasks += task - taskIdToTaskSetId(task.taskId) = manager.taskSet.id - taskSetTaskIds(manager.taskSet.id) += task.taskId - freeCpuCores -= 1 - launchTask = true - case None => {} - } - } while(launchTask) - } - return tasks - } - } - - def taskSetFinished(manager: TaskSetManager) { - synchronized { - activeTaskSets -= manager.taskSet.id - manager.parent.removeSchedulable(manager) - logInfo("Remove TaskSet %s from pool %s".format(manager.taskSet.id, manager.parent.name)) - taskIdToTaskSetId --= taskSetTaskIds(manager.taskSet.id) - taskSetTaskIds -= manager.taskSet.id - } - } - - def runTask(taskId: Long, bytes: ByteBuffer) { - logInfo("Running " + taskId) - val info = new TaskInfo(taskId, 0, System.currentTimeMillis(), "local", "local:1", TaskLocality.NODE_LOCAL) - // Set the Spark execution environment for the worker thread - SparkEnv.set(env) - val ser = SparkEnv.get.closureSerializer.newInstance() - val objectSer = SparkEnv.get.serializer.newInstance() - var attemptedTask: Option[Task[_]] = None - val start = System.currentTimeMillis() - var taskStart: Long = 0 - def getTotalGCTime = ManagementFactory.getGarbageCollectorMXBeans.map(g => g.getCollectionTime).sum - val startGCTime = getTotalGCTime - - try { - Accumulators.clear() - Thread.currentThread().setContextClassLoader(classLoader) - - // Serialize and deserialize the task so that accumulators are changed to thread-local ones; - // this adds a bit of unnecessary overhead but matches how the Mesos Executor works. - val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(bytes) - updateDependencies(taskFiles, taskJars) // Download any files added with addFile - val deserializedTask = ser.deserialize[Task[_]]( - taskBytes, Thread.currentThread.getContextClassLoader) - attemptedTask = Some(deserializedTask) - val deserTime = System.currentTimeMillis() - start - taskStart = System.currentTimeMillis() - - // Run it - val result: Any = deserializedTask.run(taskId) - - // Serialize and deserialize the result to emulate what the Mesos - // executor does. This is useful to catch serialization errors early - // on in development (so when users move their local Spark programs - // to the cluster, they don't get surprised by serialization errors). - val serResult = objectSer.serialize(result) - deserializedTask.metrics.get.resultSize = serResult.limit() - val resultToReturn = objectSer.deserialize[Any](serResult) - val accumUpdates = ser.deserialize[collection.mutable.Map[Long, Any]]( - ser.serialize(Accumulators.values)) - val serviceTime = System.currentTimeMillis() - taskStart - logInfo("Finished " + taskId) - deserializedTask.metrics.get.executorRunTime = serviceTime.toInt - deserializedTask.metrics.get.jvmGCTime = getTotalGCTime - startGCTime - deserializedTask.metrics.get.executorDeserializeTime = deserTime.toInt - val taskResult = new TaskResult(result, accumUpdates, deserializedTask.metrics.getOrElse(null)) - val serializedResult = ser.serialize(taskResult) - localActor ! LocalStatusUpdate(taskId, TaskState.FINISHED, serializedResult) - } catch { - case t: Throwable => { - val serviceTime = System.currentTimeMillis() - taskStart - val metrics = attemptedTask.flatMap(t => t.metrics) - for (m <- metrics) { - m.executorRunTime = serviceTime.toInt - m.jvmGCTime = getTotalGCTime - startGCTime - } - val failure = new ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics) - localActor ! LocalStatusUpdate(taskId, TaskState.FAILED, ser.serialize(failure)) - } - } - } - - /** - * Download any missing dependencies if we receive a new set of files and JARs from the - * SparkContext. Also adds any new JARs we fetched to the class loader. - */ - private def updateDependencies(newFiles: HashMap[String, Long], newJars: HashMap[String, Long]) { - synchronized { - // Fetch missing dependencies - for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) { - logInfo("Fetching " + name + " with timestamp " + timestamp) - Utils.fetchFile(name, new File(SparkFiles.getRootDirectory)) - currentFiles(name) = timestamp - } - - for ((name, timestamp) <- newJars if currentJars.getOrElse(name, -1L) < timestamp) { - logInfo("Fetching " + name + " with timestamp " + timestamp) - Utils.fetchFile(name, new File(SparkFiles.getRootDirectory)) - currentJars(name) = timestamp - // Add it to our class loader - val localName = name.split("/").last - val url = new File(SparkFiles.getRootDirectory, localName).toURI.toURL - if (!classLoader.getURLs.contains(url)) { - logInfo("Adding " + url + " to class loader") - classLoader.addURL(url) - } - } - } - } - - def statusUpdate(taskId :Long, state: TaskState, serializedData: ByteBuffer) { - synchronized { - val taskSetId = taskIdToTaskSetId(taskId) - val taskSetManager = activeTaskSets(taskSetId) - taskSetTaskIds(taskSetId) -= taskId - taskSetManager.statusUpdate(taskId, state, serializedData) - } - } - - override def stop() { - threadPool.shutdownNow() - } - - override def defaultParallelism() = threads -} diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalTaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalTaskSetManager.scala deleted file mode 100644 index a2fda4c124..0000000000 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalTaskSetManager.scala +++ /dev/null @@ -1,194 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.scheduler.local - -import java.nio.ByteBuffer -import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap - -import org.apache.spark.{ExceptionFailure, Logging, SparkEnv, Success, TaskState} -import org.apache.spark.TaskState.TaskState -import org.apache.spark.scheduler.{Schedulable, Task, TaskDescription, TaskInfo, TaskLocality, - TaskResult, TaskSet, TaskSetManager} - - -private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: TaskSet) - extends TaskSetManager with Logging { - - var parent: Schedulable = null - var weight: Int = 1 - var minShare: Int = 0 - var runningTasks: Int = 0 - var priority: Int = taskSet.priority - var stageId: Int = taskSet.stageId - var name: String = "TaskSet_" + taskSet.stageId.toString - - var failCount = new Array[Int](taskSet.tasks.size) - val taskInfos = new HashMap[Long, TaskInfo] - val numTasks = taskSet.tasks.size - var numFinished = 0 - val env = SparkEnv.get - val ser = env.closureSerializer.newInstance() - val copiesRunning = new Array[Int](numTasks) - val finished = new Array[Boolean](numTasks) - val numFailures = new Array[Int](numTasks) - val MAX_TASK_FAILURES = sched.maxFailures - - override def increaseRunningTasks(taskNum: Int): Unit = { - runningTasks += taskNum - if (parent != null) { - parent.increaseRunningTasks(taskNum) - } - } - - override def decreaseRunningTasks(taskNum: Int): Unit = { - runningTasks -= taskNum - if (parent != null) { - parent.decreaseRunningTasks(taskNum) - } - } - - override def addSchedulable(schedulable: Schedulable): Unit = { - // nothing - } - - override def removeSchedulable(schedulable: Schedulable): Unit = { - // nothing - } - - override def getSchedulableByName(name: String): Schedulable = { - return null - } - - override def executorLost(executorId: String, host: String): Unit = { - // nothing - } - - override def checkSpeculatableTasks() = true - - override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { - var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager] - sortedTaskSetQueue += this - return sortedTaskSetQueue - } - - override def hasPendingTasks() = true - - def findTask(): Option[Int] = { - for (i <- 0 to numTasks-1) { - if (copiesRunning(i) == 0 && !finished(i)) { - return Some(i) - } - } - return None - } - - override def resourceOffer( - execId: String, - host: String, - availableCpus: Int, - maxLocality: TaskLocality.TaskLocality) - : Option[TaskDescription] = - { - SparkEnv.set(sched.env) - logDebug("availableCpus:%d, numFinished:%d, numTasks:%d".format( - availableCpus.toInt, numFinished, numTasks)) - if (availableCpus > 0 && numFinished < numTasks) { - findTask() match { - case Some(index) => - val taskId = sched.attemptId.getAndIncrement() - val task = taskSet.tasks(index) - val info = new TaskInfo(taskId, index, System.currentTimeMillis(), "local", "local:1", - TaskLocality.NODE_LOCAL) - taskInfos(taskId) = info - // We rely on the DAGScheduler to catch non-serializable closures and RDDs, so in here - // we assume the task can be serialized without exceptions. - val bytes = Task.serializeWithDependencies( - task, sched.sc.addedFiles, sched.sc.addedJars, ser) - logInfo("Size of task " + taskId + " is " + bytes.limit + " bytes") - val taskName = "task %s:%d".format(taskSet.id, index) - copiesRunning(index) += 1 - increaseRunningTasks(1) - taskStarted(task, info) - return Some(new TaskDescription(taskId, null, taskName, index, bytes)) - case None => {} - } - } - return None - } - - override def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { - SparkEnv.set(env) - state match { - case TaskState.FINISHED => - taskEnded(tid, state, serializedData) - case TaskState.FAILED => - taskFailed(tid, state, serializedData) - case _ => {} - } - } - - def taskStarted(task: Task[_], info: TaskInfo) { - sched.listener.taskStarted(task, info) - } - - def taskEnded(tid: Long, state: TaskState, serializedData: ByteBuffer) { - val info = taskInfos(tid) - val index = info.index - val task = taskSet.tasks(index) - info.markSuccessful() - val result = ser.deserialize[TaskResult[_]](serializedData, getClass.getClassLoader) - result.metrics.resultSize = serializedData.limit() - sched.listener.taskEnded(task, Success, result.value, result.accumUpdates, info, result.metrics) - numFinished += 1 - decreaseRunningTasks(1) - finished(index) = true - if (numFinished == numTasks) { - sched.taskSetFinished(this) - } - } - - def taskFailed(tid: Long, state: TaskState, serializedData: ByteBuffer) { - val info = taskInfos(tid) - val index = info.index - val task = taskSet.tasks(index) - info.markFailed() - decreaseRunningTasks(1) - val reason: ExceptionFailure = ser.deserialize[ExceptionFailure]( - serializedData, getClass.getClassLoader) - sched.listener.taskEnded(task, reason, null, null, info, reason.metrics.getOrElse(null)) - if (!finished(index)) { - copiesRunning(index) -= 1 - numFailures(index) += 1 - val locs = reason.stackTrace.map(loc => "\tat %s".format(loc.toString)) - logInfo("Loss was due to %s\n%s\n%s".format( - reason.className, reason.description, locs.mkString("\n"))) - if (numFailures(index) > MAX_TASK_FAILURES) { - val errorMessage = "Task %s:%d failed more than %d times; aborting job %s".format( - taskSet.id, index, 4, reason.description) - decreaseRunningTasks(runningTasks) - sched.listener.taskSetFailed(taskSet, errorMessage) - // need to delete failed Taskset from schedule queue - sched.taskSetFinished(this) - } - } - } - - override def error(message: String) { - } -} diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala index 4de81617b1..5d3d43623d 100644 --- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala @@ -21,6 +21,7 @@ import java.io._ import java.nio.ByteBuffer import org.apache.spark.util.ByteBufferInputStream +import org.apache.spark.SparkConf private[spark] class JavaSerializationStream(out: OutputStream) extends SerializationStream { val objOut = new ObjectOutputStream(out) @@ -77,6 +78,6 @@ private[spark] class JavaSerializerInstance extends SerializerInstance { /** * A Spark serializer that uses Java's built-in serialization. */ -class JavaSerializer extends Serializer { +class JavaSerializer(conf: SparkConf) extends Serializer { def newInstance(): SerializerInstance = new JavaSerializerInstance } diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index 24ef204aa1..a24a3b04b8 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -23,38 +23,34 @@ import java.io.{EOFException, InputStream, OutputStream} import com.esotericsoftware.kryo.serializers.{JavaSerializer => KryoJavaSerializer} import com.esotericsoftware.kryo.{KryoException, Kryo} import com.esotericsoftware.kryo.io.{Input => KryoInput, Output => KryoOutput} -import com.twitter.chill.ScalaKryoInstantiator - -import org.apache.spark.{SerializableWritable, Logging} -import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock, StorageLevel} +import com.twitter.chill.{EmptyScalaKryoInstantiator, AllScalaRegistrar} +import org.apache.spark._ import org.apache.spark.broadcast.HttpBroadcast +import org.apache.spark.scheduler.MapStatus +import org.apache.spark.storage._ +import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock} /** - * A Spark serializer that uses the [[http://code.google.com/p/kryo/wiki/V1Documentation Kryo 1.x library]]. + * A Spark serializer that uses the [[https://code.google.com/p/kryo/ Kryo serialization library]]. */ -class KryoSerializer extends org.apache.spark.serializer.Serializer with Logging { - private val bufferSize = System.getProperty("spark.kryoserializer.buffer.mb", "2").toInt * 1024 * 1024 +class KryoSerializer(conf: SparkConf) extends org.apache.spark.serializer.Serializer with Logging { + private val bufferSize = { + conf.get("spark.kryoserializer.buffer.mb", "2").toInt * 1024 * 1024 + } def newKryoOutput() = new KryoOutput(bufferSize) - def newKryoInput() = new KryoInput(bufferSize) - def newKryo(): Kryo = { - val instantiator = new ScalaKryoInstantiator + val instantiator = new EmptyScalaKryoInstantiator val kryo = instantiator.newKryo() val classLoader = Thread.currentThread.getContextClassLoader - // Register some commonly used classes - val toRegister: Seq[AnyRef] = Seq( - ByteBuffer.allocate(1), - StorageLevel.MEMORY_ONLY, - PutBlock("1", ByteBuffer.allocate(1), StorageLevel.MEMORY_ONLY), - GotBlock("1", ByteBuffer.allocate(1)), - GetBlock("1") - ) + // Allow disabling Kryo reference tracking if user knows their object graphs don't have loops. + // Do this before we invoke the user registrator so the user registrator can override this. + kryo.setReferences(conf.get("spark.kryo.referenceTracking", "true").toBoolean) - for (obj <- toRegister) kryo.register(obj.getClass) + for (cls <- KryoSerializer.toRegister) kryo.register(cls) // Allow sending SerializableWritable kryo.register(classOf[SerializableWritable[_]], new KryoJavaSerializer()) @@ -62,20 +58,20 @@ class KryoSerializer extends org.apache.spark.serializer.Serializer with Logging // Allow the user to register their own classes by setting spark.kryo.registrator try { - Option(System.getProperty("spark.kryo.registrator")).foreach { regCls => + for (regCls <- conf.getOption("spark.kryo.registrator")) { logDebug("Running user registrator: " + regCls) val reg = Class.forName(regCls, true, classLoader).newInstance().asInstanceOf[KryoRegistrator] reg.registerClasses(kryo) } } catch { - case _: Exception => println("Failed to register spark.kryo.registrator") + case e: Exception => logError("Failed to run spark.kryo.registrator", e) } - kryo.setClassLoader(classLoader) - - // Allow disabling Kryo reference tracking if user knows their object graphs don't have loops - kryo.setReferences(System.getProperty("spark.kryo.referenceTracking", "true").toBoolean) + // Register Chill's classes; we do this after our ranges and the user's own classes to let + // our code override the generic serialziers in Chill for things like Seq + new AllScalaRegistrar().apply(kryo) + kryo.setClassLoader(classLoader) kryo } @@ -118,8 +114,10 @@ class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends Deser private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends SerializerInstance { val kryo = ks.newKryo() - val output = ks.newKryoOutput() - val input = ks.newKryoInput() + + // Make these lazy vals to avoid creating a buffer unless we use them + lazy val output = ks.newKryoOutput() + lazy val input = new KryoInput() def serialize[T](t: T): ByteBuffer = { output.clear() @@ -157,3 +155,21 @@ private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends Serializ trait KryoRegistrator { def registerClasses(kryo: Kryo) } + +private[serializer] object KryoSerializer { + // Commonly used classes. + private val toRegister: Seq[Class[_]] = Seq( + ByteBuffer.allocate(1).getClass, + classOf[StorageLevel], + classOf[PutBlock], + classOf[GotBlock], + classOf[GetBlock], + classOf[MapStatus], + classOf[BlockManagerId], + classOf[Array[Byte]], + (1 to 10).getClass, + (1 until 10).getClass, + (1L to 10L).getClass, + (1L until 10L).getClass + ) +} diff --git a/core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala b/core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala index 2955986fec..22465272f3 100644 --- a/core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala +++ b/core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala @@ -18,6 +18,7 @@ package org.apache.spark.serializer import java.util.concurrent.ConcurrentHashMap +import org.apache.spark.SparkConf /** @@ -32,12 +33,12 @@ private[spark] class SerializerManager { def default = _default - def setDefault(clsName: String): Serializer = { - _default = get(clsName) + def setDefault(clsName: String, conf: SparkConf): Serializer = { + _default = get(clsName, conf) _default } - def get(clsName: String): Serializer = { + def get(clsName: String, conf: SparkConf): Serializer = { if (clsName == null) { default } else { @@ -51,8 +52,9 @@ private[spark] class SerializerManager { serializer = serializers.get(clsName) if (serializer == null) { val clsLoader = Thread.currentThread.getContextClassLoader - serializer = - Class.forName(clsName, true, clsLoader).newInstance().asInstanceOf[Serializer] + val cls = Class.forName(clsName, true, clsLoader) + val constructor = cls.getConstructor(classOf[SparkConf]) + serializer = constructor.newInstance(conf).asInstanceOf[Serializer] serializers.put(clsName, serializer) } serializer diff --git a/core/src/main/scala/org/apache/spark/storage/BlockException.scala b/core/src/main/scala/org/apache/spark/storage/BlockException.scala index 290dbce4f5..0d0a2dadc7 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockException.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockException.scala @@ -18,5 +18,5 @@ package org.apache.spark.storage private[spark] -case class BlockException(blockId: String, message: String) extends Exception(message) +case class BlockException(blockId: BlockId, message: String) extends Exception(message) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala index 3aeda3879d..47478631a1 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala @@ -47,7 +47,7 @@ import org.apache.spark.util.Utils */ private[storage] -trait BlockFetcherIterator extends Iterator[(String, Option[Iterator[Any]])] +trait BlockFetcherIterator extends Iterator[(BlockId, Option[Iterator[Any]])] with Logging with BlockFetchTracker { def initialize() } @@ -57,20 +57,20 @@ private[storage] object BlockFetcherIterator { // A request to fetch one or more blocks, complete with their sizes - class FetchRequest(val address: BlockManagerId, val blocks: Seq[(String, Long)]) { + class FetchRequest(val address: BlockManagerId, val blocks: Seq[(BlockId, Long)]) { val size = blocks.map(_._2).sum } // A result of a fetch. Includes the block ID, size in bytes, and a function to deserialize // the block (since we want all deserializaton to happen in the calling thread); can also // represent a fetch failure if size == -1. - class FetchResult(val blockId: String, val size: Long, val deserialize: () => Iterator[Any]) { + class FetchResult(val blockId: BlockId, val size: Long, val deserialize: () => Iterator[Any]) { def failed: Boolean = size == -1 } class BasicBlockFetcherIterator( private val blockManager: BlockManager, - val blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])], + val blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])], serializer: Serializer) extends BlockFetcherIterator { @@ -92,12 +92,12 @@ object BlockFetcherIterator { // This represents the number of local blocks, also counting zero-sized blocks private var numLocal = 0 // BlockIds for local blocks that need to be fetched. Excludes zero-sized blocks - protected val localBlocksToFetch = new ArrayBuffer[String]() + protected val localBlocksToFetch = new ArrayBuffer[BlockId]() // This represents the number of remote blocks, also counting zero-sized blocks private var numRemote = 0 // BlockIds for remote blocks that need to be fetched. Excludes zero-sized blocks - protected val remoteBlocksToFetch = new HashSet[String]() + protected val remoteBlocksToFetch = new HashSet[BlockId]() // A queue to hold our results. protected val results = new LinkedBlockingQueue[FetchResult] @@ -167,7 +167,7 @@ object BlockFetcherIterator { logInfo("maxBytesInFlight: " + maxBytesInFlight + ", minRequest: " + minRequestSize) val iterator = blockInfos.iterator var curRequestSize = 0L - var curBlocks = new ArrayBuffer[(String, Long)] + var curBlocks = new ArrayBuffer[(BlockId, Long)] while (iterator.hasNext) { val (blockId, size) = iterator.next() // Skip empty blocks @@ -183,7 +183,7 @@ object BlockFetcherIterator { // Add this FetchRequest remoteRequests += new FetchRequest(address, curBlocks) curRequestSize = 0 - curBlocks = new ArrayBuffer[(String, Long)] + curBlocks = new ArrayBuffer[(BlockId, Long)] } } // Add in the final request @@ -241,7 +241,7 @@ object BlockFetcherIterator { override def hasNext: Boolean = resultsGotten < _numBlocksToFetch - override def next(): (String, Option[Iterator[Any]]) = { + override def next(): (BlockId, Option[Iterator[Any]]) = { resultsGotten += 1 val startFetchWait = System.currentTimeMillis() val result = results.take() @@ -267,7 +267,7 @@ object BlockFetcherIterator { class NettyBlockFetcherIterator( blockManager: BlockManager, - blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])], + blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])], serializer: Serializer) extends BasicBlockFetcherIterator(blockManager, blocksByAddress, serializer) { @@ -303,7 +303,7 @@ object BlockFetcherIterator { override protected def sendRequest(req: FetchRequest) { - def putResult(blockId: String, blockSize: Long, blockData: ByteBuf) { + def putResult(blockId: BlockId, blockSize: Long, blockData: ByteBuf) { val fetchResult = new FetchResult(blockId, blockSize, () => dataDeserialize(blockId, blockData.nioBuffer, serializer)) results.put(fetchResult) @@ -312,7 +312,7 @@ object BlockFetcherIterator { logDebug("Sending request for %d blocks (%s) from %s".format( req.blocks.size, Utils.bytesToString(req.size), req.address.host)) val cmId = new ConnectionManagerId(req.address.host, req.address.nettyPort) - val cpier = new ShuffleCopier + val cpier = new ShuffleCopier(blockManager.conf) cpier.getBlocks(cmId, req.blocks, putResult) logDebug("Sent request for remote blocks " + req.blocks + " from " + req.address.host ) } @@ -327,7 +327,7 @@ object BlockFetcherIterator { fetchRequestsSync.put(request) } - copiers = startCopiers(System.getProperty("spark.shuffle.copier.threads", "6").toInt) + copiers = startCopiers(conf.get("spark.shuffle.copier.threads", "6").toInt) logInfo("Started " + fetchRequestsSync.size + " remote gets in " + Utils.getUsedTimeMs(startTime)) @@ -337,7 +337,7 @@ object BlockFetcherIterator { logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime) + " ms") } - override def next(): (String, Option[Iterator[Any]]) = { + override def next(): (BlockId, Option[Iterator[Any]]) = { resultsGotten += 1 val result = results.take() // If all the results has been retrieved, copiers will exit automatically diff --git a/core/src/main/scala/org/apache/spark/storage/BlockId.scala b/core/src/main/scala/org/apache/spark/storage/BlockId.scala new file mode 100644 index 0000000000..7156d855d8 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/BlockId.scala @@ -0,0 +1,103 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +/** + * Identifies a particular Block of data, usually associated with a single file. + * A Block can be uniquely identified by its filename, but each type of Block has a different + * set of keys which produce its unique name. + * + * If your BlockId should be serializable, be sure to add it to the BlockId.fromString() method. + */ +private[spark] sealed abstract class BlockId { + /** A globally unique identifier for this Block. Can be used for ser/de. */ + def name: String + + // convenience methods + def asRDDId = if (isRDD) Some(asInstanceOf[RDDBlockId]) else None + def isRDD = isInstanceOf[RDDBlockId] + def isShuffle = isInstanceOf[ShuffleBlockId] + def isBroadcast = isInstanceOf[BroadcastBlockId] || isInstanceOf[BroadcastHelperBlockId] + + override def toString = name + override def hashCode = name.hashCode + override def equals(other: Any): Boolean = other match { + case o: BlockId => getClass == o.getClass && name.equals(o.name) + case _ => false + } +} + +private[spark] case class RDDBlockId(rddId: Int, splitIndex: Int) extends BlockId { + def name = "rdd_" + rddId + "_" + splitIndex +} + +private[spark] +case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId { + def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId +} + +private[spark] case class BroadcastBlockId(broadcastId: Long) extends BlockId { + def name = "broadcast_" + broadcastId +} + +private[spark] case class BroadcastHelperBlockId(broadcastId: BroadcastBlockId, hType: String) extends BlockId { + def name = broadcastId.name + "_" + hType +} + +private[spark] case class TaskResultBlockId(taskId: Long) extends BlockId { + def name = "taskresult_" + taskId +} + +private[spark] case class StreamBlockId(streamId: Int, uniqueId: Long) extends BlockId { + def name = "input-" + streamId + "-" + uniqueId +} + +// Intended only for testing purposes +private[spark] case class TestBlockId(id: String) extends BlockId { + def name = "test_" + id +} + +private[spark] object BlockId { + val RDD = "rdd_([0-9]+)_([0-9]+)".r + val SHUFFLE = "shuffle_([0-9]+)_([0-9]+)_([0-9]+)".r + val BROADCAST = "broadcast_([0-9]+)".r + val BROADCAST_HELPER = "broadcast_([0-9]+)_([A-Za-z0-9]+)".r + val TASKRESULT = "taskresult_([0-9]+)".r + val STREAM = "input-([0-9]+)-([0-9]+)".r + val TEST = "test_(.*)".r + + /** Converts a BlockId "name" String back into a BlockId. */ + def apply(id: String) = id match { + case RDD(rddId, splitIndex) => + RDDBlockId(rddId.toInt, splitIndex.toInt) + case SHUFFLE(shuffleId, mapId, reduceId) => + ShuffleBlockId(shuffleId.toInt, mapId.toInt, reduceId.toInt) + case BROADCAST(broadcastId) => + BroadcastBlockId(broadcastId.toLong) + case BROADCAST_HELPER(broadcastId, hType) => + BroadcastHelperBlockId(BroadcastBlockId(broadcastId.toLong), hType) + case TASKRESULT(taskId) => + TaskResultBlockId(taskId.toLong) + case STREAM(streamId, uniqueId) => + StreamBlockId(streamId.toInt, uniqueId.toLong) + case TEST(value) => + TestBlockId(value) + case _ => + throw new IllegalStateException("Unrecognized BlockId: " + id) + } +} diff --git a/core/src/main/scala/org/apache/spark/storage/BlockInfo.scala b/core/src/main/scala/org/apache/spark/storage/BlockInfo.scala new file mode 100644 index 0000000000..c8f397609a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/BlockInfo.scala @@ -0,0 +1,81 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.util.concurrent.ConcurrentHashMap + +private[storage] class BlockInfo(val level: StorageLevel, val tellMaster: Boolean) { + // To save space, 'pending' and 'failed' are encoded as special sizes: + @volatile var size: Long = BlockInfo.BLOCK_PENDING + private def pending: Boolean = size == BlockInfo.BLOCK_PENDING + private def failed: Boolean = size == BlockInfo.BLOCK_FAILED + private def initThread: Thread = BlockInfo.blockInfoInitThreads.get(this) + + setInitThread() + + private def setInitThread() { + // Set current thread as init thread - waitForReady will not block this thread + // (in case there is non trivial initialization which ends up calling waitForReady as part of + // initialization itself) + BlockInfo.blockInfoInitThreads.put(this, Thread.currentThread()) + } + + /** + * Wait for this BlockInfo to be marked as ready (i.e. block is finished writing). + * Return true if the block is available, false otherwise. + */ + def waitForReady(): Boolean = { + if (pending && initThread != Thread.currentThread()) { + synchronized { + while (pending) this.wait() + } + } + !failed + } + + /** Mark this BlockInfo as ready (i.e. block is finished writing) */ + def markReady(sizeInBytes: Long) { + require (sizeInBytes >= 0, "sizeInBytes was negative: " + sizeInBytes) + assert (pending) + size = sizeInBytes + BlockInfo.blockInfoInitThreads.remove(this) + synchronized { + this.notifyAll() + } + } + + /** Mark this BlockInfo as ready but failed */ + def markFailure() { + assert (pending) + size = BlockInfo.BLOCK_FAILED + BlockInfo.blockInfoInitThreads.remove(this) + synchronized { + this.notifyAll() + } + } +} + +private object BlockInfo { + // initThread is logically a BlockInfo field, but we store it here because + // it's only needed while this block is in the 'pending' state and we want + // to minimize BlockInfo's memory footprint. + private val blockInfoInitThreads = new ConcurrentHashMap[BlockInfo, Thread] + + private val BLOCK_PENDING: Long = -1L + private val BLOCK_FAILED: Long = -2L +} diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 60fdc5f2ee..6d2cda97b0 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -17,19 +17,20 @@ package org.apache.spark.storage -import java.io.{InputStream, OutputStream} +import java.io.{File, InputStream, OutputStream} import java.nio.{ByteBuffer, MappedByteBuffer} -import scala.collection.mutable.{HashMap, ArrayBuffer, HashSet} +import scala.collection.mutable.{HashMap, ArrayBuffer} +import scala.util.Random import akka.actor.{ActorSystem, Cancellable, Props} -import akka.dispatch.{Await, Future} -import akka.util.Duration -import akka.util.duration._ +import scala.concurrent.{Await, Future} +import scala.concurrent.duration.Duration +import scala.concurrent.duration._ -import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream +import it.unimi.dsi.fastutil.io.{FastBufferedOutputStream, FastByteArrayOutputStream} -import org.apache.spark.{Logging, SparkEnv, SparkException} +import org.apache.spark.{SparkConf, Logging, SparkEnv, SparkException} import org.apache.spark.io.CompressionCodec import org.apache.spark.network._ import org.apache.spark.serializer.Serializer @@ -37,86 +38,32 @@ import org.apache.spark.util._ import sun.nio.ch.DirectBuffer - private[spark] class BlockManager( executorId: String, actorSystem: ActorSystem, val master: BlockManagerMaster, val defaultSerializer: Serializer, - maxMemory: Long) + maxMemory: Long, + val conf: SparkConf) extends Logging { - private class BlockInfo(val level: StorageLevel, val tellMaster: Boolean) { - @volatile var pending: Boolean = true - @volatile var size: Long = -1L - @volatile var initThread: Thread = null - @volatile var failed = false - - setInitThread() - - private def setInitThread() { - // Set current thread as init thread - waitForReady will not block this thread - // (in case there is non trivial initialization which ends up calling waitForReady as part of - // initialization itself) - this.initThread = Thread.currentThread() - } - - /** - * Wait for this BlockInfo to be marked as ready (i.e. block is finished writing). - * Return true if the block is available, false otherwise. - */ - def waitForReady(): Boolean = { - if (initThread != Thread.currentThread() && pending) { - synchronized { - while (pending) this.wait() - } - } - !failed - } - - /** Mark this BlockInfo as ready (i.e. block is finished writing) */ - def markReady(sizeInBytes: Long) { - assert (pending) - size = sizeInBytes - initThread = null - failed = false - initThread = null - pending = false - synchronized { - this.notifyAll() - } - } - - /** Mark this BlockInfo as ready but failed */ - def markFailure() { - assert (pending) - size = 0 - initThread = null - failed = true - initThread = null - pending = false - synchronized { - this.notifyAll() - } - } - } - val shuffleBlockManager = new ShuffleBlockManager(this) + val diskBlockManager = new DiskBlockManager(shuffleBlockManager, + conf.get("spark.local.dir", System.getProperty("java.io.tmpdir"))) - private val blockInfo = new TimeStampedHashMap[String, BlockInfo] + private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo] private[storage] val memoryStore: BlockStore = new MemoryStore(this, maxMemory) - private[storage] val diskStore: DiskStore = - new DiskStore(this, System.getProperty("spark.local.dir", System.getProperty("java.io.tmpdir"))) + private[storage] val diskStore = new DiskStore(this, diskBlockManager) // If we use Netty for shuffle, start a new Netty-based shuffle sender service. private val nettyPort: Int = { - val useNetty = System.getProperty("spark.shuffle.use.netty", "false").toBoolean - val nettyPortConfig = System.getProperty("spark.shuffle.sender.port", "0").toInt - if (useNetty) diskStore.startShuffleBlockSender(nettyPortConfig) else 0 + val useNetty = conf.get("spark.shuffle.use.netty", "false").toBoolean + val nettyPortConfig = conf.get("spark.shuffle.sender.port", "0").toInt + if (useNetty) diskBlockManager.startShuffleBlockSender(nettyPortConfig) else 0 } - val connectionManager = new ConnectionManager(0) + val connectionManager = new ConnectionManager(0, conf) implicit val futureExecContext = connectionManager.futureExecContext val blockManagerId = BlockManagerId( @@ -125,18 +72,18 @@ private[spark] class BlockManager( // Max megabytes of data to keep in flight per reducer (to avoid over-allocating memory // for receiving shuffle outputs) val maxBytesInFlight = - System.getProperty("spark.reducer.maxMbInFlight", "48").toLong * 1024 * 1024 + conf.get("spark.reducer.maxMbInFlight", "48").toLong * 1024 * 1024 // Whether to compress broadcast variables that are stored - val compressBroadcast = System.getProperty("spark.broadcast.compress", "true").toBoolean + val compressBroadcast = conf.get("spark.broadcast.compress", "true").toBoolean // Whether to compress shuffle output that are stored - val compressShuffle = System.getProperty("spark.shuffle.compress", "true").toBoolean + val compressShuffle = conf.get("spark.shuffle.compress", "true").toBoolean // Whether to compress RDD partitions that are stored serialized - val compressRdds = System.getProperty("spark.rdd.compress", "false").toBoolean + val compressRdds = conf.get("spark.rdd.compress", "false").toBoolean - val heartBeatFrequency = BlockManager.getHeartBeatFrequencyFromSystemProperties + val heartBeatFrequency = BlockManager.getHeartBeatFrequency(conf) - val hostPort = Utils.localHostPort() + val hostPort = Utils.localHostPort(conf) val slaveActor = actorSystem.actorOf(Props(new BlockManagerSlaveActor(this)), name = "BlockManagerActor" + BlockManager.ID_GENERATOR.next) @@ -154,7 +101,11 @@ private[spark] class BlockManager( var heartBeatTask: Cancellable = null - val metadataCleaner = new MetadataCleaner("BlockManager", this.dropOldBlocks) + private val metadataCleaner = new MetadataCleaner( + MetadataCleanerType.BLOCK_MANAGER, this.dropOldNonBroadcastBlocks, conf) + private val broadcastCleaner = new MetadataCleaner( + MetadataCleanerType.BROADCAST_VARS, this.dropOldBroadcastBlocks, conf) + initialize() // The compression codec to use. Note that the "lazy" val is necessary because we want to delay @@ -162,14 +113,14 @@ private[spark] class BlockManager( // program could be using a user-defined codec in a third party jar, which is loaded in // Executor.updateDependencies. When the BlockManager is initialized, user level jars hasn't been // loaded yet. - private lazy val compressionCodec: CompressionCodec = CompressionCodec.createCodec() + private lazy val compressionCodec: CompressionCodec = CompressionCodec.createCodec(conf) /** * Construct a BlockManager with a memory limit set based on system properties. */ def this(execId: String, actorSystem: ActorSystem, master: BlockManagerMaster, - serializer: Serializer) = { - this(execId, actorSystem, master, serializer, BlockManager.getMaxMemoryFromSystemProperties) + serializer: Serializer, conf: SparkConf) = { + this(execId, actorSystem, master, serializer, BlockManager.getMaxMemory(conf), conf) } /** @@ -179,7 +130,7 @@ private[spark] class BlockManager( private def initialize() { master.registerBlockManager(blockManagerId, maxMemory, slaveActor) BlockManagerWorker.startBlockManagerWorker(this) - if (!BlockManager.getDisableHeartBeatsForTesting) { + if (!BlockManager.getDisableHeartBeatsForTesting(conf)) { heartBeatTask = actorSystem.scheduler.schedule(0.seconds, heartBeatFrequency.milliseconds) { heartBeat() } @@ -248,7 +199,7 @@ private[spark] class BlockManager( /** * Get storage level of local block. If no info exists for the block, then returns null. */ - def getLevel(blockId: String): StorageLevel = blockInfo.get(blockId).map(_.level).orNull + def getLevel(blockId: BlockId): StorageLevel = blockInfo.get(blockId).map(_.level).orNull /** * Tell the master about the current storage status of a block. This will send a block update @@ -258,7 +209,7 @@ private[spark] class BlockManager( * droppedMemorySize exists to account for when block is dropped from memory to disk (so it is still valid). * This ensures that update in master will compensate for the increase in memory on slave. */ - def reportBlockStatus(blockId: String, info: BlockInfo, droppedMemorySize: Long = 0L) { + def reportBlockStatus(blockId: BlockId, info: BlockInfo, droppedMemorySize: Long = 0L) { val needReregister = !tryToReportBlockStatus(blockId, info, droppedMemorySize) if (needReregister) { logInfo("Got told to reregister updating block " + blockId) @@ -269,11 +220,11 @@ private[spark] class BlockManager( } /** - * Actually send a UpdateBlockInfo message. Returns the mater's response, + * Actually send a UpdateBlockInfo message. Returns the master's response, * which will be true if the block was successfully recorded and false if * the slave needs to re-register. */ - private def tryToReportBlockStatus(blockId: String, info: BlockInfo, droppedMemorySize: Long = 0L): Boolean = { + private def tryToReportBlockStatus(blockId: BlockId, info: BlockInfo, droppedMemorySize: Long = 0L): Boolean = { val (curLevel, inMemSize, onDiskSize, tellMaster) = info.synchronized { info.level match { case null => @@ -298,7 +249,7 @@ private[spark] class BlockManager( /** * Get locations of an array of blocks. */ - def getLocationBlockIds(blockIds: Array[String]): Array[Seq[BlockManagerId]] = { + def getLocationBlockIds(blockIds: Array[BlockId]): Array[Seq[BlockManagerId]] = { val startTimeMs = System.currentTimeMillis val locations = master.getLocations(blockIds).toArray logDebug("Got multiple block location in " + Utils.getUsedTimeMs(startTimeMs)) @@ -310,7 +261,7 @@ private[spark] class BlockManager( * shuffle blocks. It is safe to do so without a lock on block info since disk store * never deletes (recent) items. */ - def getLocalFromDisk(blockId: String, serializer: Serializer): Option[Iterator[Any]] = { + def getLocalFromDisk(blockId: BlockId, serializer: Serializer): Option[Iterator[Any]] = { diskStore.getValues(blockId, serializer).orElse( sys.error("Block " + blockId + " not found on disk, though it should be")) } @@ -318,94 +269,19 @@ private[spark] class BlockManager( /** * Get block from local block manager. */ - def getLocal(blockId: String): Option[Iterator[Any]] = { + def getLocal(blockId: BlockId): Option[Iterator[Any]] = { logDebug("Getting local block " + blockId) - val info = blockInfo.get(blockId).orNull - if (info != null) { - info.synchronized { - - // In the another thread is writing the block, wait for it to become ready. - if (!info.waitForReady()) { - // If we get here, the block write failed. - logWarning("Block " + blockId + " was marked as failure.") - return None - } - - val level = info.level - logDebug("Level for block " + blockId + " is " + level) - - // Look for the block in memory - if (level.useMemory) { - logDebug("Getting block " + blockId + " from memory") - memoryStore.getValues(blockId) match { - case Some(iterator) => - return Some(iterator) - case None => - logDebug("Block " + blockId + " not found in memory") - } - } - - // Look for block on disk, potentially loading it back into memory if required - if (level.useDisk) { - logDebug("Getting block " + blockId + " from disk") - if (level.useMemory && level.deserialized) { - diskStore.getValues(blockId) match { - case Some(iterator) => - // Put the block back in memory before returning it - // TODO: Consider creating a putValues that also takes in a iterator ? - val elements = new ArrayBuffer[Any] - elements ++= iterator - memoryStore.putValues(blockId, elements, level, true).data match { - case Left(iterator2) => - return Some(iterator2) - case _ => - throw new Exception("Memory store did not return back an iterator") - } - case None => - throw new Exception("Block " + blockId + " not found on disk, though it should be") - } - } else if (level.useMemory && !level.deserialized) { - // Read it as a byte buffer into memory first, then return it - diskStore.getBytes(blockId) match { - case Some(bytes) => - // Put a copy of the block back in memory before returning it. Note that we can't - // put the ByteBuffer returned by the disk store as that's a memory-mapped file. - // The use of rewind assumes this. - assert (0 == bytes.position()) - val copyForMemory = ByteBuffer.allocate(bytes.limit) - copyForMemory.put(bytes) - memoryStore.putBytes(blockId, copyForMemory, level) - bytes.rewind() - return Some(dataDeserialize(blockId, bytes)) - case None => - throw new Exception("Block " + blockId + " not found on disk, though it should be") - } - } else { - diskStore.getValues(blockId) match { - case Some(iterator) => - return Some(iterator) - case None => - throw new Exception("Block " + blockId + " not found on disk, though it should be") - } - } - } - } - } else { - logDebug("Block " + blockId + " not registered locally") - } - return None + doGetLocal(blockId, asValues = true).asInstanceOf[Option[Iterator[Any]]] } /** * Get block from the local block manager as serialized bytes. */ - def getLocalBytes(blockId: String): Option[ByteBuffer] = { - // TODO: This whole thing is very similar to getLocal; we need to refactor it somehow + def getLocalBytes(blockId: BlockId): Option[ByteBuffer] = { logDebug("Getting local block " + blockId + " as bytes") - // As an optimization for map output fetches, if the block is for a shuffle, return it // without acquiring a lock; the disk store never deletes (recent) items so this should work - if (ShuffleBlockManager.isShuffle(blockId)) { + if (blockId.isShuffle) { return diskStore.getBytes(blockId) match { case Some(bytes) => Some(bytes) @@ -413,12 +289,15 @@ private[spark] class BlockManager( throw new Exception("Block " + blockId + " not found on disk, though it should be") } } + doGetLocal(blockId, asValues = false).asInstanceOf[Option[ByteBuffer]] + } + private def doGetLocal(blockId: BlockId, asValues: Boolean): Option[Any] = { val info = blockInfo.get(blockId).orNull if (info != null) { info.synchronized { - // In the another thread is writing the block, wait for it to become ready. + // If another thread is writing the block, wait for it to become ready. if (!info.waitForReady()) { // If we get here, the block write failed. logWarning("Block " + blockId + " was marked as failure.") @@ -431,62 +310,104 @@ private[spark] class BlockManager( // Look for the block in memory if (level.useMemory) { logDebug("Getting block " + blockId + " from memory") - memoryStore.getBytes(blockId) match { - case Some(bytes) => - return Some(bytes) + val result = if (asValues) { + memoryStore.getValues(blockId) + } else { + memoryStore.getBytes(blockId) + } + result match { + case Some(values) => + return Some(values) case None => logDebug("Block " + blockId + " not found in memory") } } - // Look for block on disk + // Look for block on disk, potentially storing it back into memory if required: if (level.useDisk) { - // Read it as a byte buffer into memory first, then return it - diskStore.getBytes(blockId) match { - case Some(bytes) => - assert (0 == bytes.position()) - if (level.useMemory) { - if (level.deserialized) { - memoryStore.putBytes(blockId, bytes, level) - } else { - // The memory store will hang onto the ByteBuffer, so give it a copy instead of - // the memory-mapped file buffer we got from the disk store - val copyForMemory = ByteBuffer.allocate(bytes.limit) - copyForMemory.put(bytes) - memoryStore.putBytes(blockId, copyForMemory, level) - } - } - bytes.rewind() - return Some(bytes) + logDebug("Getting block " + blockId + " from disk") + val bytes: ByteBuffer = diskStore.getBytes(blockId) match { + case Some(bytes) => bytes case None => throw new Exception("Block " + blockId + " not found on disk, though it should be") } + assert (0 == bytes.position()) + + if (!level.useMemory) { + // If the block shouldn't be stored in memory, we can just return it: + if (asValues) { + return Some(dataDeserialize(blockId, bytes)) + } else { + return Some(bytes) + } + } else { + // Otherwise, we also have to store something in the memory store: + if (!level.deserialized || !asValues) { + // We'll store the bytes in memory if the block's storage level includes + // "memory serialized", or if it should be cached as objects in memory + // but we only requested its serialized bytes: + val copyForMemory = ByteBuffer.allocate(bytes.limit) + copyForMemory.put(bytes) + memoryStore.putBytes(blockId, copyForMemory, level) + bytes.rewind() + } + if (!asValues) { + return Some(bytes) + } else { + val values = dataDeserialize(blockId, bytes) + if (level.deserialized) { + // Cache the values before returning them: + // TODO: Consider creating a putValues that also takes in a iterator? + val valuesBuffer = new ArrayBuffer[Any] + valuesBuffer ++= values + memoryStore.putValues(blockId, valuesBuffer, level, true).data match { + case Left(values2) => + return Some(values2) + case _ => + throw new Exception("Memory store did not return back an iterator") + } + } else { + return Some(values) + } + } + } } } } else { logDebug("Block " + blockId + " not registered locally") } - return None + None } /** * Get block from remote block managers. */ - def getRemote(blockId: String): Option[Iterator[Any]] = { - if (blockId == null) { - throw new IllegalArgumentException("Block Id is null") - } + def getRemote(blockId: BlockId): Option[Iterator[Any]] = { logDebug("Getting remote block " + blockId) - // Get locations of block - val locations = master.getLocations(blockId) + doGetRemote(blockId, asValues = true).asInstanceOf[Option[Iterator[Any]]] + } - // Get block from remote locations + /** + * Get block from remote block managers as serialized bytes. + */ + def getRemoteBytes(blockId: BlockId): Option[ByteBuffer] = { + logDebug("Getting remote block " + blockId + " as bytes") + doGetRemote(blockId, asValues = false).asInstanceOf[Option[ByteBuffer]] + } + + private def doGetRemote(blockId: BlockId, asValues: Boolean): Option[Any] = { + require(blockId != null, "BlockId is null") + val locations = Random.shuffle(master.getLocations(blockId)) for (loc <- locations) { logDebug("Getting remote block " + blockId + " from " + loc) val data = BlockManagerWorker.syncGetBlock( - GetBlock(blockId), ConnectionManagerId(loc.host, loc.port)) + GetBlock(blockId), ConnectionManagerId(loc.host, loc.port)) if (data != null) { - return Some(dataDeserialize(blockId, data)) + if (asValues) { + return Some(dataDeserialize(blockId, data)) + } else { + return Some(data) + } } logDebug("The value of block " + blockId + " is null") } @@ -497,8 +418,18 @@ private[spark] class BlockManager( /** * Get a block from the block manager (either local or remote). */ - def get(blockId: String): Option[Iterator[Any]] = { - getLocal(blockId).orElse(getRemote(blockId)) + def get(blockId: BlockId): Option[Iterator[Any]] = { + val local = getLocal(blockId) + if (local.isDefined) { + logInfo("Found block %s locally".format(blockId)) + return local + } + val remote = getRemote(blockId) + if (remote.isDefined) { + logInfo("Found block %s remotely".format(blockId)) + return remote + } + None } /** @@ -508,11 +439,11 @@ private[spark] class BlockManager( * so that we can control the maxMegabytesInFlight for the fetch. */ def getMultiple( - blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])], serializer: Serializer) + blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])], serializer: Serializer) : BlockFetcherIterator = { val iter = - if (System.getProperty("spark.shuffle.use.netty", "false").toBoolean) { + if (conf.get("spark.shuffle.use.netty", "false").toBoolean) { new BlockFetcherIterator.NettyBlockFetcherIterator(this, blocksByAddress, serializer) } else { new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer) @@ -522,7 +453,7 @@ private[spark] class BlockManager( iter } - def put(blockId: String, values: Iterator[Any], level: StorageLevel, tellMaster: Boolean) + def put(blockId: BlockId, values: Iterator[Any], level: StorageLevel, tellMaster: Boolean) : Long = { val elements = new ArrayBuffer[Any] elements ++= values @@ -531,35 +462,39 @@ private[spark] class BlockManager( /** * A short circuited method to get a block writer that can write data directly to disk. + * The Block will be appended to the File specified by filename. * This is currently used for writing shuffle files out. Callers should handle error * cases. */ - def getDiskBlockWriter(blockId: String, serializer: Serializer, bufferSize: Int) + def getDiskWriter(blockId: BlockId, file: File, serializer: Serializer, bufferSize: Int) : BlockObjectWriter = { - val writer = diskStore.getBlockWriter(blockId, serializer, bufferSize) - writer.registerCloseEventHandler(() => { - val myInfo = new BlockInfo(StorageLevel.DISK_ONLY, false) - blockInfo.put(blockId, myInfo) - myInfo.markReady(writer.size()) - }) - writer + val compressStream: OutputStream => OutputStream = wrapForCompression(blockId, _) + val syncWrites = conf.get("spark.shuffle.sync", "false").toBoolean + new DiskBlockObjectWriter(blockId, file, serializer, bufferSize, compressStream, syncWrites) } /** * Put a new block of values to the block manager. Returns its (estimated) size in bytes. */ - def put(blockId: String, values: ArrayBuffer[Any], level: StorageLevel, - tellMaster: Boolean = true) : Long = { + def put(blockId: BlockId, values: ArrayBuffer[Any], level: StorageLevel, + tellMaster: Boolean = true) : Long = { + require(values != null, "Values is null") + doPut(blockId, Left(values), level, tellMaster) + } + + /** + * Put a new block of serialized bytes to the block manager. + */ + def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel, + tellMaster: Boolean = true) { + require(bytes != null, "Bytes is null") + doPut(blockId, Right(bytes), level, tellMaster) + } - if (blockId == null) { - throw new IllegalArgumentException("Block Id is null") - } - if (values == null) { - throw new IllegalArgumentException("Values is null") - } - if (level == null || !level.isValid) { - throw new IllegalArgumentException("Storage level is null or invalid") - } + private def doPut(blockId: BlockId, data: Either[ArrayBuffer[Any], ByteBuffer], + level: StorageLevel, tellMaster: Boolean = true): Long = { + require(blockId != null, "BlockId is null") + require(level != null && level.isValid, "StorageLevel is null or invalid") // Remember the block's storage level so that we can correctly drop it to disk if it needs // to be dropped right after it got put into memory. Note, however, that other threads will @@ -575,7 +510,8 @@ private[spark] class BlockManager( return oldBlockOpt.get.size } - // TODO: So the block info exists - but previous attempt to load it (?) failed. What do we do now ? Retry on it ? + // TODO: So the block info exists - but previous attempt to load it (?) failed. + // What do we do now ? Retry on it ? oldBlockOpt.get } else { tinfo @@ -584,10 +520,10 @@ private[spark] class BlockManager( val startTimeMs = System.currentTimeMillis - // If we need to replicate the data, we'll want access to the values, but because our - // put will read the whole iterator, there will be no values left. For the case where - // the put serializes data, we'll remember the bytes, above; but for the case where it - // doesn't, such as deserialized storage, let's rely on the put returning an Iterator. + // If we're storing values and we need to replicate the data, we'll want access to the values, + // but because our put will read the whole iterator, there will be no values left. For the + // case where the put serializes data, we'll remember the bytes, above; but for the case where + // it doesn't, such as deserialized storage, let's rely on the put returning an Iterator. var valuesAfterPut: Iterator[Any] = null // Ditto for the bytes after the put @@ -596,30 +532,51 @@ private[spark] class BlockManager( // Size of the block in bytes (to return to caller) var size = 0L + // If we're storing bytes, then initiate the replication before storing them locally. + // This is faster as data is already serialized and ready to send. + val replicationFuture = if (data.isRight && level.replication > 1) { + val bufferView = data.right.get.duplicate() // Doesn't copy the bytes, just creates a wrapper + Future { + replicate(blockId, bufferView, level) + } + } else { + null + } + myInfo.synchronized { logTrace("Put for block " + blockId + " took " + Utils.getUsedTimeMs(startTimeMs) + " to get into synchronized block") var marked = false try { - if (level.useMemory) { - // Save it just to memory first, even if it also has useDisk set to true; we will later - // drop it to disk if the memory store can't hold it. - val res = memoryStore.putValues(blockId, values, level, true) - size = res.size - res.data match { - case Right(newBytes) => bytesAfterPut = newBytes - case Left(newIterator) => valuesAfterPut = newIterator + data match { + case Left(values) => { + if (level.useMemory) { + // Save it just to memory first, even if it also has useDisk set to true; we will + // drop it to disk later if the memory store can't hold it. + val res = memoryStore.putValues(blockId, values, level, true) + size = res.size + res.data match { + case Right(newBytes) => bytesAfterPut = newBytes + case Left(newIterator) => valuesAfterPut = newIterator + } + } else { + // Save directly to disk. + // Don't get back the bytes unless we replicate them. + val askForBytes = level.replication > 1 + val res = diskStore.putValues(blockId, values, level, askForBytes) + size = res.size + res.data match { + case Right(newBytes) => bytesAfterPut = newBytes + case _ => + } + } } - } else { - // Save directly to disk. - // Don't get back the bytes unless we replicate them. - val askForBytes = level.replication > 1 - val res = diskStore.putValues(blockId, values, level, askForBytes) - size = res.size - res.data match { - case Right(newBytes) => bytesAfterPut = newBytes - case _ => + case Right(bytes) => { + bytes.rewind() + // Store it only in memory at first, even if useDisk is also set to true + (if (level.useMemory) memoryStore else diskStore).putBytes(blockId, bytes, level) + size = bytes.limit } } @@ -644,132 +601,46 @@ private[spark] class BlockManager( } logDebug("Put block " + blockId + " locally took " + Utils.getUsedTimeMs(startTimeMs)) - // Replicate block if required + // Either we're storing bytes and we asynchronously started replication, or we're storing + // values and need to serialize and replicate them now: if (level.replication > 1) { - val remoteStartTime = System.currentTimeMillis - // Serialize the block if not already done - if (bytesAfterPut == null) { - if (valuesAfterPut == null) { - throw new SparkException( - "Underlying put returned neither an Iterator nor bytes! This shouldn't happen.") - } - bytesAfterPut = dataSerialize(blockId, valuesAfterPut) - } - replicate(blockId, bytesAfterPut, level) - logDebug("Put block " + blockId + " remotely took " + Utils.getUsedTimeMs(remoteStartTime)) - } - BlockManager.dispose(bytesAfterPut) - - return size - } - - - /** - * Put a new block of serialized bytes to the block manager. - */ - def putBytes( - blockId: String, bytes: ByteBuffer, level: StorageLevel, tellMaster: Boolean = true) { - - if (blockId == null) { - throw new IllegalArgumentException("Block Id is null") - } - if (bytes == null) { - throw new IllegalArgumentException("Bytes is null") - } - if (level == null || !level.isValid) { - throw new IllegalArgumentException("Storage level is null or invalid") - } - - // Remember the block's storage level so that we can correctly drop it to disk if it needs - // to be dropped right after it got put into memory. Note, however, that other threads will - // not be able to get() this block until we call markReady on its BlockInfo. - val myInfo = { - val tinfo = new BlockInfo(level, tellMaster) - // Do atomically ! - val oldBlockOpt = blockInfo.putIfAbsent(blockId, tinfo) - - if (oldBlockOpt.isDefined) { - if (oldBlockOpt.get.waitForReady()) { - logWarning("Block " + blockId + " already exists on this machine; not re-adding it") - return - } - - // TODO: So the block info exists - but previous attempt to load it (?) failed. What do we do now ? Retry on it ? - oldBlockOpt.get - } else { - tinfo - } - } - - val startTimeMs = System.currentTimeMillis - - // Initiate the replication before storing it locally. This is faster as - // data is already serialized and ready for sending - val replicationFuture = if (level.replication > 1) { - val bufferView = bytes.duplicate() // Doesn't copy the bytes, just creates a wrapper - Future { - replicate(blockId, bufferView, level) - } - } else { - null - } - - myInfo.synchronized { - logDebug("PutBytes for block " + blockId + " took " + Utils.getUsedTimeMs(startTimeMs) - + " to get into synchronized block") - - var marked = false - try { - if (level.useMemory) { - // Store it only in memory at first, even if useDisk is also set to true - bytes.rewind() - memoryStore.putBytes(blockId, bytes, level) - } else { - bytes.rewind() - diskStore.putBytes(blockId, bytes, level) - } - - // assert (0 == bytes.position(), "" + bytes) - - // Now that the block is in either the memory or disk store, let other threads read it, - // and tell the master about it. - marked = true - myInfo.markReady(bytes.limit) - if (tellMaster) { - reportBlockStatus(blockId, myInfo) - } - } finally { - // If we failed at putting the block to memory/disk, notify other possible readers - // that it has failed, and then remove it from the block info map. - if (! marked) { - // Note that the remove must happen before markFailure otherwise another thread - // could've inserted a new BlockInfo before we remove it. - blockInfo.remove(blockId) - myInfo.markFailure() - logWarning("Putting block " + blockId + " failed") + data match { + case Right(bytes) => Await.ready(replicationFuture, Duration.Inf) + case Left(values) => { + val remoteStartTime = System.currentTimeMillis + // Serialize the block if not already done + if (bytesAfterPut == null) { + if (valuesAfterPut == null) { + throw new SparkException( + "Underlying put returned neither an Iterator nor bytes! This shouldn't happen.") + } + bytesAfterPut = dataSerialize(blockId, valuesAfterPut) + } + replicate(blockId, bytesAfterPut, level) + logDebug("Put block " + blockId + " remotely took " + + Utils.getUsedTimeMs(remoteStartTime)) } } } - // If replication had started, then wait for it to finish - if (level.replication > 1) { - Await.ready(replicationFuture, Duration.Inf) - } + BlockManager.dispose(bytesAfterPut) if (level.replication > 1) { - logDebug("PutBytes for block " + blockId + " with replication took " + + logDebug("Put for block " + blockId + " with replication took " + Utils.getUsedTimeMs(startTimeMs)) } else { - logDebug("PutBytes for block " + blockId + " without replication took " + + logDebug("Put for block " + blockId + " without replication took " + Utils.getUsedTimeMs(startTimeMs)) } + + size } /** * Replicate block to another node. */ var cachedPeers: Seq[BlockManagerId] = null - private def replicate(blockId: String, data: ByteBuffer, level: StorageLevel) { + private def replicate(blockId: BlockId, data: ByteBuffer, level: StorageLevel) { val tLevel = StorageLevel(level.useDisk, level.useMemory, level.deserialized, 1) if (cachedPeers == null) { cachedPeers = master.getPeers(blockManagerId, level.replication - 1) @@ -792,14 +663,14 @@ private[spark] class BlockManager( /** * Read a block consisting of a single object. */ - def getSingle(blockId: String): Option[Any] = { + def getSingle(blockId: BlockId): Option[Any] = { get(blockId).map(_.next()) } /** * Write a block consisting of a single object. */ - def putSingle(blockId: String, value: Any, level: StorageLevel, tellMaster: Boolean = true) { + def putSingle(blockId: BlockId, value: Any, level: StorageLevel, tellMaster: Boolean = true) { put(blockId, Iterator(value), level, tellMaster) } @@ -807,7 +678,7 @@ private[spark] class BlockManager( * Drop a block from memory, possibly putting it on disk if applicable. Called when the memory * store reaches its limit and needs to free up space. */ - def dropFromMemory(blockId: String, data: Either[ArrayBuffer[Any], ByteBuffer]) { + def dropFromMemory(blockId: BlockId, data: Either[ArrayBuffer[Any], ByteBuffer]) { logInfo("Dropping block " + blockId + " from memory") val info = blockInfo.get(blockId).orNull if (info != null) { @@ -856,16 +727,15 @@ private[spark] class BlockManager( // TODO: Instead of doing a linear scan on the blockInfo map, create another map that maps // from RDD.id to blocks. logInfo("Removing RDD " + rddId) - val rddPrefix = "rdd_" + rddId + "_" - val blocksToRemove = blockInfo.filter(_._1.startsWith(rddPrefix)).map(_._1) - blocksToRemove.foreach(blockId => removeBlock(blockId, false)) + val blocksToRemove = blockInfo.keys.flatMap(_.asRDDId).filter(_.rddId == rddId) + blocksToRemove.foreach(blockId => removeBlock(blockId, tellMaster = false)) blocksToRemove.size } /** * Remove a block from both memory and disk. */ - def removeBlock(blockId: String, tellMaster: Boolean = true) { + def removeBlock(blockId: BlockId, tellMaster: Boolean = true) { logInfo("Removing block " + blockId) val info = blockInfo.get(blockId).orNull if (info != null) info.synchronized { @@ -886,13 +756,22 @@ private[spark] class BlockManager( } } - def dropOldBlocks(cleanupTime: Long) { - logInfo("Dropping blocks older than " + cleanupTime) + private def dropOldNonBroadcastBlocks(cleanupTime: Long) { + logInfo("Dropping non broadcast blocks older than " + cleanupTime) + dropOldBlocks(cleanupTime, !_.isBroadcast) + } + + private def dropOldBroadcastBlocks(cleanupTime: Long) { + logInfo("Dropping broadcast blocks older than " + cleanupTime) + dropOldBlocks(cleanupTime, _.isBroadcast) + } + + private def dropOldBlocks(cleanupTime: Long, shouldDrop: (BlockId => Boolean)) { val iterator = blockInfo.internalMap.entrySet().iterator() while (iterator.hasNext) { val entry = iterator.next() val (id, info, time) = (entry.getKey, entry.getValue._1, entry.getValue._2) - if (time < cleanupTime) { + if (time < cleanupTime && shouldDrop(id)) { info.synchronized { val level = info.level if (level.useMemory) { @@ -909,39 +788,45 @@ private[spark] class BlockManager( } } - def shouldCompress(blockId: String): Boolean = { - if (ShuffleBlockManager.isShuffle(blockId)) { - compressShuffle - } else if (blockId.startsWith("broadcast_")) { - compressBroadcast - } else if (blockId.startsWith("rdd_")) { - compressRdds - } else { - false // Won't happen in a real cluster, but it can in tests - } + def shouldCompress(blockId: BlockId): Boolean = blockId match { + case ShuffleBlockId(_, _, _) => compressShuffle + case BroadcastBlockId(_) => compressBroadcast + case RDDBlockId(_, _) => compressRdds + case _ => false } /** * Wrap an output stream for compression if block compression is enabled for its block type */ - def wrapForCompression(blockId: String, s: OutputStream): OutputStream = { + def wrapForCompression(blockId: BlockId, s: OutputStream): OutputStream = { if (shouldCompress(blockId)) compressionCodec.compressedOutputStream(s) else s } /** * Wrap an input stream for compression if block compression is enabled for its block type */ - def wrapForCompression(blockId: String, s: InputStream): InputStream = { + def wrapForCompression(blockId: BlockId, s: InputStream): InputStream = { if (shouldCompress(blockId)) compressionCodec.compressedInputStream(s) else s } + /** Serializes into a stream. */ + def dataSerializeStream( + blockId: BlockId, + outputStream: OutputStream, + values: Iterator[Any], + serializer: Serializer = defaultSerializer) { + val byteStream = new FastBufferedOutputStream(outputStream) + val ser = serializer.newInstance() + ser.serializeStream(wrapForCompression(blockId, byteStream)).writeAll(values).close() + } + + /** Serializes into a byte buffer. */ def dataSerialize( - blockId: String, + blockId: BlockId, values: Iterator[Any], serializer: Serializer = defaultSerializer): ByteBuffer = { val byteStream = new FastByteArrayOutputStream(4096) - val ser = serializer.newInstance() - ser.serializeStream(wrapForCompression(blockId, byteStream)).writeAll(values).close() + dataSerializeStream(blockId, byteStream, values, serializer) byteStream.trim() ByteBuffer.wrap(byteStream.array) } @@ -951,7 +836,7 @@ private[spark] class BlockManager( * the iterator is reached. */ def dataDeserialize( - blockId: String, + blockId: BlockId, bytes: ByteBuffer, serializer: Serializer = defaultSerializer): Iterator[Any] = { bytes.rewind() @@ -969,25 +854,25 @@ private[spark] class BlockManager( memoryStore.clear() diskStore.clear() metadataCleaner.cancel() + broadcastCleaner.cancel() logInfo("BlockManager stopped") } } private[spark] object BlockManager extends Logging { - val ID_GENERATOR = new IdGenerator - def getMaxMemoryFromSystemProperties: Long = { - val memoryFraction = System.getProperty("spark.storage.memoryFraction", "0.66").toDouble + def getMaxMemory(conf: SparkConf): Long = { + val memoryFraction = conf.get("spark.storage.memoryFraction", "0.66").toDouble (Runtime.getRuntime.maxMemory * memoryFraction).toLong } - def getHeartBeatFrequencyFromSystemProperties: Long = - System.getProperty("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong / 4 + def getHeartBeatFrequency(conf: SparkConf): Long = + conf.get("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong / 4 - def getDisableHeartBeatsForTesting: Boolean = - System.getProperty("spark.test.disableBlockManagerHeartBeat", "false").toBoolean + def getDisableHeartBeatsForTesting(conf: SparkConf): Boolean = + conf.get("spark.test.disableBlockManagerHeartBeat", "false").toBoolean /** * Attempt to clean up a ByteBuffer if it is memory-mapped. This uses an *unsafe* Sun API that @@ -1005,20 +890,20 @@ private[spark] object BlockManager extends Logging { } def blockIdsToBlockManagers( - blockIds: Array[String], + blockIds: Array[BlockId], env: SparkEnv, blockManagerMaster: BlockManagerMaster = null) - : Map[String, Seq[BlockManagerId]] = + : Map[BlockId, Seq[BlockManagerId]] = { - // env == null and blockManagerMaster != null is used in tests + // blockManagerMaster != null is used in tests assert (env != null || blockManagerMaster != null) - val blockLocations: Seq[Seq[BlockManagerId]] = if (env != null) { + val blockLocations: Seq[Seq[BlockManagerId]] = if (blockManagerMaster == null) { env.blockManager.getLocationBlockIds(blockIds) } else { blockManagerMaster.getLocations(blockIds) } - val blockManagers = new HashMap[String, Seq[BlockManagerId]] + val blockManagers = new HashMap[BlockId, Seq[BlockManagerId]] for (i <- 0 until blockIds.length) { blockManagers(blockIds(i)) = blockLocations(i) } @@ -1026,21 +911,20 @@ private[spark] object BlockManager extends Logging { } def blockIdsToExecutorIds( - blockIds: Array[String], + blockIds: Array[BlockId], env: SparkEnv, blockManagerMaster: BlockManagerMaster = null) - : Map[String, Seq[String]] = + : Map[BlockId, Seq[String]] = { blockIdsToBlockManagers(blockIds, env, blockManagerMaster).mapValues(s => s.map(_.executorId)) } def blockIdsToHosts( - blockIds: Array[String], + blockIds: Array[BlockId], env: SparkEnv, blockManagerMaster: BlockManagerMaster = null) - : Map[String, Seq[String]] = + : Map[BlockId, Seq[String]] = { blockIdsToBlockManagers(blockIds, env, blockManagerMaster).mapValues(s => s.map(_.host)) } } - diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index cf463d6ffc..b5afe8cd23 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -17,23 +17,26 @@ package org.apache.spark.storage -import akka.actor.ActorRef -import akka.dispatch.{Await, Future} +import scala.concurrent.{Await, Future} +import scala.concurrent.ExecutionContext.Implicits.global + +import akka.actor._ import akka.pattern.ask -import akka.util.Duration -import org.apache.spark.{Logging, SparkException} +import org.apache.spark.{SparkConf, Logging, SparkException} import org.apache.spark.storage.BlockManagerMessages._ +import org.apache.spark.util.AkkaUtils +private[spark] +class BlockManagerMaster(var driverActor : Either[ActorRef, ActorSelection], + conf: SparkConf) extends Logging { -private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Logging { - - val AKKA_RETRY_ATTEMPTS: Int = System.getProperty("spark.akka.num.retries", "3").toInt - val AKKA_RETRY_INTERVAL_MS: Int = System.getProperty("spark.akka.retry.wait", "3000").toInt + val AKKA_RETRY_ATTEMPTS: Int = conf.get("spark.akka.num.retries", "3").toInt + val AKKA_RETRY_INTERVAL_MS: Int = conf.get("spark.akka.retry.wait", "3000").toInt val DRIVER_AKKA_ACTOR_NAME = "BlockManagerMaster" - val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + val timeout = AkkaUtils.askTimeout(conf) /** Remove a dead executor from the driver actor. This is only called on the driver side. */ def removeExecutor(execId: String) { @@ -60,7 +63,7 @@ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Loggi def updateBlockInfo( blockManagerId: BlockManagerId, - blockId: String, + blockId: BlockId, storageLevel: StorageLevel, memSize: Long, diskSize: Long): Boolean = { @@ -71,12 +74,12 @@ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Loggi } /** Get locations of the blockId from the driver */ - def getLocations(blockId: String): Seq[BlockManagerId] = { + def getLocations(blockId: BlockId): Seq[BlockManagerId] = { askDriverWithReply[Seq[BlockManagerId]](GetLocations(blockId)) } /** Get locations of multiple blockIds from the driver */ - def getLocations(blockIds: Array[String]): Seq[Seq[BlockManagerId]] = { + def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = { askDriverWithReply[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) } @@ -94,7 +97,7 @@ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Loggi * Remove a block from the slaves that have it. This can only be used to remove * blocks that the driver knows about. */ - def removeBlock(blockId: String) { + def removeBlock(blockId: BlockId) { askDriverWithReply(RemoveBlock(blockId)) } @@ -156,7 +159,10 @@ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Loggi while (attempts < AKKA_RETRY_ATTEMPTS) { attempts += 1 try { - val future = driverActor.ask(message)(timeout) + val future = driverActor match { + case Left(a: ActorRef) => a.ask(message)(timeout) + case Right(b: ActorSelection) => b.ask(message)(timeout) + } val result = Await.result(future, timeout) if (result == null) { throw new SparkException("BlockManagerMaster returned null") diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala index c7b23ab094..58452d9657 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala @@ -21,24 +21,22 @@ import java.util.{HashMap => JHashMap} import scala.collection.mutable import scala.collection.JavaConversions._ +import scala.concurrent.Future +import scala.concurrent.duration._ import akka.actor.{Actor, ActorRef, Cancellable} -import akka.dispatch.Future import akka.pattern.ask -import akka.util.Duration -import akka.util.duration._ -import org.apache.spark.{Logging, SparkException} +import org.apache.spark.{SparkConf, Logging, SparkException} import org.apache.spark.storage.BlockManagerMessages._ -import org.apache.spark.util.Utils - +import org.apache.spark.util.{AkkaUtils, Utils} /** * BlockManagerMasterActor is an actor on the master node to track statuses of * all slaves' block managers. */ private[spark] -class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { +class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf) extends Actor with Logging { // Mapping from block manager id to the block manager's information. private val blockManagerInfo = @@ -48,23 +46,21 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { private val blockManagerIdByExecutor = new mutable.HashMap[String, BlockManagerId] // Mapping from block id to the set of block managers that have the block. - private val blockLocations = new JHashMap[String, mutable.HashSet[BlockManagerId]] - - val akkaTimeout = Duration.create( - System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + private val blockLocations = new JHashMap[BlockId, mutable.HashSet[BlockManagerId]] - initLogging() + private val akkaTimeout = AkkaUtils.askTimeout(conf) - val slaveTimeout = System.getProperty("spark.storage.blockManagerSlaveTimeoutMs", - "" + (BlockManager.getHeartBeatFrequencyFromSystemProperties * 3)).toLong + val slaveTimeout = conf.get("spark.storage.blockManagerSlaveTimeoutMs", + "" + (BlockManager.getHeartBeatFrequency(conf) * 3)).toLong - val checkTimeoutInterval = System.getProperty("spark.storage.blockManagerTimeoutIntervalMs", + val checkTimeoutInterval = conf.get("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong var timeoutCheckingTask: Cancellable = null override def preStart() { - if (!BlockManager.getDisableHeartBeatsForTesting) { + if (!BlockManager.getDisableHeartBeatsForTesting(conf)) { + import context.dispatcher timeoutCheckingTask = context.system.scheduler.schedule( 0.seconds, checkTimeoutInterval.milliseconds, self, ExpireDeadHosts) } @@ -129,10 +125,9 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { // First remove the metadata for the given RDD, and then asynchronously remove the blocks // from the slaves. - val prefix = "rdd_" + rddId + "_" // Find all blocks for the given RDD, remove the block from both blockLocations and // the blockManagerInfo that is tracking the blocks. - val blocks = blockLocations.keySet().filter(_.startsWith(prefix)) + val blocks = blockLocations.keys.flatMap(_.asRDDId).filter(_.rddId == rddId) blocks.foreach { blockId => val bms: mutable.HashSet[BlockManagerId] = blockLocations.get(blockId) bms.foreach(bm => blockManagerInfo.get(bm).foreach(_.removeBlock(blockId))) @@ -198,7 +193,7 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { // Remove a block from the slaves that have it. This can only be used to remove // blocks that the master knows about. - private def removeBlockFromWorkers(blockId: String) { + private def removeBlockFromWorkers(blockId: BlockId) { val locations = blockLocations.get(blockId) if (locations != null) { locations.foreach { blockManagerId: BlockManagerId => @@ -228,9 +223,7 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { } private def register(id: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) { - if (id.executorId == "<driver>" && !isLocal) { - // Got a register message from the master node; don't register it - } else if (!blockManagerInfo.contains(id)) { + if (!blockManagerInfo.contains(id)) { blockManagerIdByExecutor.get(id.executorId) match { case Some(manager) => // A block manager of the same executor already exists. @@ -247,7 +240,7 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { private def updateBlockInfo( blockManagerId: BlockManagerId, - blockId: String, + blockId: BlockId, storageLevel: StorageLevel, memSize: Long, diskSize: Long) { @@ -292,11 +285,11 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { sender ! true } - private def getLocations(blockId: String): Seq[BlockManagerId] = { + private def getLocations(blockId: BlockId): Seq[BlockManagerId] = { if (blockLocations.containsKey(blockId)) blockLocations.get(blockId).toSeq else Seq.empty } - private def getLocationsMultipleBlockIds(blockIds: Array[String]): Seq[Seq[BlockManagerId]] = { + private def getLocationsMultipleBlockIds(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = { blockIds.map(blockId => getLocations(blockId)) } @@ -330,7 +323,7 @@ object BlockManagerMasterActor { private var _remainingMem: Long = maxMem // Mapping from block id to its status. - private val _blocks = new JHashMap[String, BlockStatus] + private val _blocks = new JHashMap[BlockId, BlockStatus] logInfo("Registering block manager %s with %s RAM".format( blockManagerId.hostPort, Utils.bytesToString(maxMem))) @@ -339,7 +332,7 @@ object BlockManagerMasterActor { _lastSeenMs = System.currentTimeMillis() } - def updateBlockInfo(blockId: String, storageLevel: StorageLevel, memSize: Long, + def updateBlockInfo(blockId: BlockId, storageLevel: StorageLevel, memSize: Long, diskSize: Long) { updateLastSeenMs() @@ -383,7 +376,7 @@ object BlockManagerMasterActor { } } - def removeBlock(blockId: String) { + def removeBlock(blockId: BlockId) { if (_blocks.containsKey(blockId)) { _remainingMem += _blocks.get(blockId).memSize _blocks.remove(blockId) @@ -394,7 +387,7 @@ object BlockManagerMasterActor { def lastSeenMs: Long = _lastSeenMs - def blocks: JHashMap[String, BlockStatus] = _blocks + def blocks: JHashMap[BlockId, BlockStatus] = _blocks override def toString: String = "BlockManagerInfo " + timeMs + " " + _remainingMem diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala index 24333a179c..45f51da288 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala @@ -30,7 +30,7 @@ private[storage] object BlockManagerMessages { // Remove a block from the slaves that have it. This can only be used to remove // blocks that the master knows about. - case class RemoveBlock(blockId: String) extends ToBlockManagerSlave + case class RemoveBlock(blockId: BlockId) extends ToBlockManagerSlave // Remove all blocks belonging to a specific RDD. case class RemoveRdd(rddId: Int) extends ToBlockManagerSlave @@ -51,7 +51,7 @@ private[storage] object BlockManagerMessages { class UpdateBlockInfo( var blockManagerId: BlockManagerId, - var blockId: String, + var blockId: BlockId, var storageLevel: StorageLevel, var memSize: Long, var diskSize: Long) @@ -62,7 +62,7 @@ private[storage] object BlockManagerMessages { override def writeExternal(out: ObjectOutput) { blockManagerId.writeExternal(out) - out.writeUTF(blockId) + out.writeUTF(blockId.name) storageLevel.writeExternal(out) out.writeLong(memSize) out.writeLong(diskSize) @@ -70,7 +70,7 @@ private[storage] object BlockManagerMessages { override def readExternal(in: ObjectInput) { blockManagerId = BlockManagerId(in) - blockId = in.readUTF() + blockId = BlockId(in.readUTF()) storageLevel = StorageLevel(in) memSize = in.readLong() diskSize = in.readLong() @@ -79,7 +79,7 @@ private[storage] object BlockManagerMessages { object UpdateBlockInfo { def apply(blockManagerId: BlockManagerId, - blockId: String, + blockId: BlockId, storageLevel: StorageLevel, memSize: Long, diskSize: Long): UpdateBlockInfo = { @@ -87,14 +87,14 @@ private[storage] object BlockManagerMessages { } // For pattern-matching - def unapply(h: UpdateBlockInfo): Option[(BlockManagerId, String, StorageLevel, Long, Long)] = { + def unapply(h: UpdateBlockInfo): Option[(BlockManagerId, BlockId, StorageLevel, Long, Long)] = { Some((h.blockManagerId, h.blockId, h.storageLevel, h.memSize, h.diskSize)) } } - case class GetLocations(blockId: String) extends ToBlockManagerMaster + case class GetLocations(blockId: BlockId) extends ToBlockManagerMaster - case class GetLocationsMultipleBlockIds(blockIds: Array[String]) extends ToBlockManagerMaster + case class GetLocationsMultipleBlockIds(blockIds: Array[BlockId]) extends ToBlockManagerMaster case class GetPeers(blockManagerId: BlockManagerId, size: Int) extends ToBlockManagerMaster diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala index 951503019f..3a65e55733 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala @@ -26,6 +26,7 @@ import org.apache.spark.storage.BlockManagerMessages._ * An actor to take commands from the master to execute options. For example, * this is used to remove blocks from the slave's BlockManager. */ +private[storage] class BlockManagerSlaveActor(blockManager: BlockManager) extends Actor { override def receive = { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala index acc3951088..365866d1e3 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala @@ -28,7 +28,7 @@ private[spark] class BlockManagerSource(val blockManager: BlockManager, sc: Spar val metricRegistry = new MetricRegistry() val sourceName = "%s.BlockManager".format(sc.appName) - metricRegistry.register(MetricRegistry.name("memory", "maxMem", "MBytes"), new Gauge[Long] { + metricRegistry.register(MetricRegistry.name("memory", "maxMem_MB"), new Gauge[Long] { override def getValue: Long = { val storageStatusList = blockManager.master.getStorageStatus val maxMem = storageStatusList.map(_.maxMem).reduce(_ + _) @@ -36,7 +36,7 @@ private[spark] class BlockManagerSource(val blockManager: BlockManager, sc: Spar } }) - metricRegistry.register(MetricRegistry.name("memory", "remainingMem", "MBytes"), new Gauge[Long] { + metricRegistry.register(MetricRegistry.name("memory", "remainingMem_MB"), new Gauge[Long] { override def getValue: Long = { val storageStatusList = blockManager.master.getStorageStatus val remainingMem = storageStatusList.map(_.memRemaining).reduce(_ + _) @@ -44,7 +44,7 @@ private[spark] class BlockManagerSource(val blockManager: BlockManager, sc: Spar } }) - metricRegistry.register(MetricRegistry.name("memory", "memUsed", "MBytes"), new Gauge[Long] { + metricRegistry.register(MetricRegistry.name("memory", "memUsed_MB"), new Gauge[Long] { override def getValue: Long = { val storageStatusList = blockManager.master.getStorageStatus val maxMem = storageStatusList.map(_.maxMem).reduce(_ + _) @@ -53,7 +53,7 @@ private[spark] class BlockManagerSource(val blockManager: BlockManager, sc: Spar } }) - metricRegistry.register(MetricRegistry.name("disk", "diskSpaceUsed", "MBytes"), new Gauge[Long] { + metricRegistry.register(MetricRegistry.name("disk", "diskSpaceUsed_MB"), new Gauge[Long] { override def getValue: Long = { val storageStatusList = blockManager.master.getStorageStatus val diskSpaceUsed = storageStatusList diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerWorker.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerWorker.scala index 678c38203c..21f003609b 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerWorker.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerWorker.scala @@ -30,7 +30,6 @@ import org.apache.spark.util.Utils * TODO: Use event model. */ private[spark] class BlockManagerWorker(val blockManager: BlockManager) extends Logging { - initLogging() blockManager.connectionManager.onReceiveMessage(onBlockMessageReceive) @@ -77,7 +76,7 @@ private[spark] class BlockManagerWorker(val blockManager: BlockManager) extends } } - private def putBlock(id: String, bytes: ByteBuffer, level: StorageLevel) { + private def putBlock(id: BlockId, bytes: ByteBuffer, level: StorageLevel) { val startTimeMs = System.currentTimeMillis() logDebug("PutBlock " + id + " started from " + startTimeMs + " with data: " + bytes) blockManager.putBytes(id, bytes, level) @@ -85,7 +84,7 @@ private[spark] class BlockManagerWorker(val blockManager: BlockManager) extends + " with data size: " + bytes.limit) } - private def getBlock(id: String): ByteBuffer = { + private def getBlock(id: BlockId): ByteBuffer = { val startTimeMs = System.currentTimeMillis() logDebug("GetBlock " + id + " started from " + startTimeMs) val buffer = blockManager.getLocalBytes(id) match { @@ -101,8 +100,6 @@ private[spark] class BlockManagerWorker(val blockManager: BlockManager) extends private[spark] object BlockManagerWorker extends Logging { private var blockManagerWorker: BlockManagerWorker = null - initLogging() - def startBlockManagerWorker(manager: BlockManager) { blockManagerWorker = new BlockManagerWorker(manager) } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala b/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala index d8fa6a91d1..80dcb5a207 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala @@ -24,9 +24,9 @@ import scala.collection.mutable.ArrayBuffer import org.apache.spark.network._ -private[spark] case class GetBlock(id: String) -private[spark] case class GotBlock(id: String, data: ByteBuffer) -private[spark] case class PutBlock(id: String, data: ByteBuffer, level: StorageLevel) +private[spark] case class GetBlock(id: BlockId) +private[spark] case class GotBlock(id: BlockId, data: ByteBuffer) +private[spark] case class PutBlock(id: BlockId, data: ByteBuffer, level: StorageLevel) private[spark] class BlockMessage() { // Un-initialized: typ = 0 @@ -34,7 +34,7 @@ private[spark] class BlockMessage() { // GotBlock: typ = 2 // PutBlock: typ = 3 private var typ: Int = BlockMessage.TYPE_NON_INITIALIZED - private var id: String = null + private var id: BlockId = null private var data: ByteBuffer = null private var level: StorageLevel = null @@ -74,7 +74,7 @@ private[spark] class BlockMessage() { for (i <- 1 to idLength) { idBuilder += buffer.getChar() } - id = idBuilder.toString() + id = BlockId(idBuilder.toString) if (typ == BlockMessage.TYPE_PUT_BLOCK) { @@ -109,28 +109,17 @@ private[spark] class BlockMessage() { set(buffer) } - def getType: Int = { - return typ - } - - def getId: String = { - return id - } - - def getData: ByteBuffer = { - return data - } - - def getLevel: StorageLevel = { - return level - } + def getType: Int = typ + def getId: BlockId = id + def getData: ByteBuffer = data + def getLevel: StorageLevel = level def toBufferMessage: BufferMessage = { val startTime = System.currentTimeMillis val buffers = new ArrayBuffer[ByteBuffer]() - var buffer = ByteBuffer.allocate(4 + 4 + id.length() * 2) - buffer.putInt(typ).putInt(id.length()) - id.foreach((x: Char) => buffer.putChar(x)) + var buffer = ByteBuffer.allocate(4 + 4 + id.name.length * 2) + buffer.putInt(typ).putInt(id.name.length) + id.name.foreach((x: Char) => buffer.putChar(x)) buffer.flip() buffers += buffer @@ -212,7 +201,8 @@ private[spark] object BlockMessage { def main(args: Array[String]) { val B = new BlockMessage() - B.set(new PutBlock("ABC", ByteBuffer.allocate(10), StorageLevel.MEMORY_AND_DISK_SER_2)) + val blockId = TestBlockId("ABC") + B.set(new PutBlock(blockId, ByteBuffer.allocate(10), StorageLevel.MEMORY_AND_DISK_SER_2)) val bMsg = B.toBufferMessage val C = new BlockMessage() C.set(bMsg) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockMessageArray.scala b/core/src/main/scala/org/apache/spark/storage/BlockMessageArray.scala index 0aaf846b5b..a06f50a0ac 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockMessageArray.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockMessageArray.scala @@ -37,8 +37,6 @@ class BlockMessageArray(var blockMessages: Seq[BlockMessage]) extends Seq[BlockM def length = blockMessages.length - initLogging() - def set(bufferMessage: BufferMessage) { val startTime = System.currentTimeMillis val newBlockMessages = new ArrayBuffer[BlockMessage]() @@ -111,14 +109,15 @@ private[spark] object BlockMessageArray { } def main(args: Array[String]) { - val blockMessages = + val blockMessages = (0 until 10).map { i => if (i % 2 == 0) { val buffer = ByteBuffer.allocate(100) buffer.clear - BlockMessage.fromPutBlock(PutBlock(i.toString, buffer, StorageLevel.MEMORY_ONLY_SER)) + BlockMessage.fromPutBlock(PutBlock(TestBlockId(i.toString), buffer, + StorageLevel.MEMORY_ONLY_SER)) } else { - BlockMessage.fromGetBlock(GetBlock(i.toString)) + BlockMessage.fromGetBlock(GetBlock(TestBlockId(i.toString))) } } val blockMessageArray = new BlockMessageArray(blockMessages) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index 39f103297f..61e63c60d5 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -17,6 +17,13 @@ package org.apache.spark.storage +import java.io.{FileOutputStream, File, OutputStream} +import java.nio.channels.FileChannel + +import it.unimi.dsi.fastutil.io.FastBufferedOutputStream + +import org.apache.spark.Logging +import org.apache.spark.serializer.{SerializationStream, Serializer} /** * An interface for writing JVM objects to some underlying storage. This interface allows @@ -25,22 +32,14 @@ package org.apache.spark.storage * * This interface does not support concurrent writes. */ -abstract class BlockObjectWriter(val blockId: String) { - - var closeEventHandler: () => Unit = _ +abstract class BlockObjectWriter(val blockId: BlockId) { def open(): BlockObjectWriter - def close() { - closeEventHandler() - } + def close() def isOpen: Boolean - def registerCloseEventHandler(handler: () => Unit) { - closeEventHandler = handler - } - /** * Flush the partial writes and commit them as a single atomic block. Return the * number of bytes written for this commit. @@ -59,7 +58,127 @@ abstract class BlockObjectWriter(val blockId: String) { def write(value: Any) /** - * Size of the valid writes, in bytes. + * Returns the file segment of committed data that this Writer has written. + */ + def fileSegment(): FileSegment + + /** + * Cumulative time spent performing blocking writes, in ns. */ - def size(): Long + def timeWriting(): Long +} + +/** BlockObjectWriter which writes directly to a file on disk. Appends to the given file. */ +class DiskBlockObjectWriter( + blockId: BlockId, + file: File, + serializer: Serializer, + bufferSize: Int, + compressStream: OutputStream => OutputStream, + syncWrites: Boolean) + extends BlockObjectWriter(blockId) + with Logging +{ + + /** Intercepts write calls and tracks total time spent writing. Not thread safe. */ + private class TimeTrackingOutputStream(out: OutputStream) extends OutputStream { + def timeWriting = _timeWriting + private var _timeWriting = 0L + + private def callWithTiming(f: => Unit) = { + val start = System.nanoTime() + f + _timeWriting += (System.nanoTime() - start) + } + + def write(i: Int): Unit = callWithTiming(out.write(i)) + override def write(b: Array[Byte]) = callWithTiming(out.write(b)) + override def write(b: Array[Byte], off: Int, len: Int) = callWithTiming(out.write(b, off, len)) + override def close() = out.close() + override def flush() = out.flush() + } + + /** The file channel, used for repositioning / truncating the file. */ + private var channel: FileChannel = null + private var bs: OutputStream = null + private var fos: FileOutputStream = null + private var ts: TimeTrackingOutputStream = null + private var objOut: SerializationStream = null + private val initialPosition = file.length() + private var lastValidPosition = initialPosition + private var initialized = false + private var _timeWriting = 0L + + override def open(): BlockObjectWriter = { + fos = new FileOutputStream(file, true) + ts = new TimeTrackingOutputStream(fos) + channel = fos.getChannel() + lastValidPosition = initialPosition + bs = compressStream(new FastBufferedOutputStream(ts, bufferSize)) + objOut = serializer.newInstance().serializeStream(bs) + initialized = true + this + } + + override def close() { + if (initialized) { + if (syncWrites) { + // Force outstanding writes to disk and track how long it takes + objOut.flush() + val start = System.nanoTime() + fos.getFD.sync() + _timeWriting += System.nanoTime() - start + } + objOut.close() + + _timeWriting += ts.timeWriting + + channel = null + bs = null + fos = null + ts = null + objOut = null + } + } + + override def isOpen: Boolean = objOut != null + + override def commit(): Long = { + if (initialized) { + // NOTE: Flush the serializer first and then the compressed/buffered output stream + objOut.flush() + bs.flush() + val prevPos = lastValidPosition + lastValidPosition = channel.position() + lastValidPosition - prevPos + } else { + // lastValidPosition is zero if stream is uninitialized + lastValidPosition + } + } + + override def revertPartialWrites() { + if (initialized) { + // Discard current writes. We do this by flushing the outstanding writes and + // truncate the file to the last valid position. + objOut.flush() + bs.flush() + channel.truncate(lastValidPosition) + } + } + + override def write(value: Any) { + if (!initialized) { + open() + } + objOut.writeObject(value) + } + + override def fileSegment(): FileSegment = { + val bytesWritten = lastValidPosition - initialPosition + new FileSegment(file, initialPosition, bytesWritten) + } + + // Only valid if called after close() + override def timeWriting() = _timeWriting } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockStore.scala b/core/src/main/scala/org/apache/spark/storage/BlockStore.scala index fa834371f4..ea42656240 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockStore.scala @@ -27,7 +27,7 @@ import org.apache.spark.Logging */ private[spark] abstract class BlockStore(val blockManager: BlockManager) extends Logging { - def putBytes(blockId: String, bytes: ByteBuffer, level: StorageLevel) + def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel) /** * Put in a block and, possibly, also return its content as either bytes or another Iterator. @@ -36,26 +36,26 @@ abstract class BlockStore(val blockManager: BlockManager) extends Logging { * @return a PutResult that contains the size of the data, as well as the values put if * returnValues is true (if not, the result's data field can be null) */ - def putValues(blockId: String, values: ArrayBuffer[Any], level: StorageLevel, + def putValues(blockId: BlockId, values: ArrayBuffer[Any], level: StorageLevel, returnValues: Boolean) : PutResult /** * Return the size of a block in bytes. */ - def getSize(blockId: String): Long + def getSize(blockId: BlockId): Long - def getBytes(blockId: String): Option[ByteBuffer] + def getBytes(blockId: BlockId): Option[ByteBuffer] - def getValues(blockId: String): Option[Iterator[Any]] + def getValues(blockId: BlockId): Option[Iterator[Any]] /** * Remove a block, if it exists. * @param blockId the block to remove. * @return True if the block was found and removed, False otherwise. */ - def remove(blockId: String): Boolean + def remove(blockId: BlockId): Boolean - def contains(blockId: String): Boolean + def contains(blockId: BlockId): Boolean def clear() { } } diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala new file mode 100644 index 0000000000..55dcb3742c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -0,0 +1,151 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.io.File +import java.text.SimpleDateFormat +import java.util.{Date, Random} + +import org.apache.spark.Logging +import org.apache.spark.executor.ExecutorExitCode +import org.apache.spark.network.netty.{PathResolver, ShuffleSender} +import org.apache.spark.util.Utils + +/** + * Creates and maintains the logical mapping between logical blocks and physical on-disk + * locations. By default, one block is mapped to one file with a name given by its BlockId. + * However, it is also possible to have a block map to only a segment of a file, by calling + * mapBlockToFileSegment(). + * + * @param rootDirs The directories to use for storing block files. Data will be hashed among these. + */ +private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootDirs: String) + extends PathResolver with Logging { + + private val MAX_DIR_CREATION_ATTEMPTS: Int = 10 + private val subDirsPerLocalDir = shuffleManager.conf.get("spark.diskStore.subDirectories", "64").toInt + + // Create one local directory for each path mentioned in spark.local.dir; then, inside this + // directory, create multiple subdirectories that we will hash files into, in order to avoid + // having really large inodes at the top level. + private val localDirs: Array[File] = createLocalDirs() + private val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir)) + private var shuffleSender : ShuffleSender = null + + addShutdownHook() + + /** + * Returns the phyiscal file segment in which the given BlockId is located. + * If the BlockId has been mapped to a specific FileSegment, that will be returned. + * Otherwise, we assume the Block is mapped to a whole file identified by the BlockId directly. + */ + def getBlockLocation(blockId: BlockId): FileSegment = { + if (blockId.isShuffle && shuffleManager.consolidateShuffleFiles) { + shuffleManager.getBlockLocation(blockId.asInstanceOf[ShuffleBlockId]) + } else { + val file = getFile(blockId.name) + new FileSegment(file, 0, file.length()) + } + } + + def getFile(filename: String): File = { + // Figure out which local directory it hashes to, and which subdirectory in that + val hash = Utils.nonNegativeHash(filename) + val dirId = hash % localDirs.length + val subDirId = (hash / localDirs.length) % subDirsPerLocalDir + + // Create the subdirectory if it doesn't already exist + var subDir = subDirs(dirId)(subDirId) + if (subDir == null) { + subDir = subDirs(dirId).synchronized { + val old = subDirs(dirId)(subDirId) + if (old != null) { + old + } else { + val newDir = new File(localDirs(dirId), "%02x".format(subDirId)) + newDir.mkdir() + subDirs(dirId)(subDirId) = newDir + newDir + } + } + } + + new File(subDir, filename) + } + + def getFile(blockId: BlockId): File = getFile(blockId.name) + + private def createLocalDirs(): Array[File] = { + logDebug("Creating local directories at root dirs '" + rootDirs + "'") + val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss") + rootDirs.split(",").map { rootDir => + var foundLocalDir = false + var localDir: File = null + var localDirId: String = null + var tries = 0 + val rand = new Random() + while (!foundLocalDir && tries < MAX_DIR_CREATION_ATTEMPTS) { + tries += 1 + try { + localDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) + localDir = new File(rootDir, "spark-local-" + localDirId) + if (!localDir.exists) { + foundLocalDir = localDir.mkdirs() + } + } catch { + case e: Exception => + logWarning("Attempt " + tries + " to create local dir " + localDir + " failed", e) + } + } + if (!foundLocalDir) { + logError("Failed " + MAX_DIR_CREATION_ATTEMPTS + + " attempts to create local dir in " + rootDir) + System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR) + } + logInfo("Created local directory at " + localDir) + localDir + } + } + + private def addShutdownHook() { + localDirs.foreach(localDir => Utils.registerShutdownDeleteDir(localDir)) + Runtime.getRuntime.addShutdownHook(new Thread("delete Spark local dirs") { + override def run() { + logDebug("Shutdown hook called") + localDirs.foreach { localDir => + try { + if (!Utils.hasRootAsShutdownDeleteDir(localDir)) Utils.deleteRecursively(localDir) + } catch { + case t: Throwable => + logError("Exception while deleting local spark dir: " + localDir, t) + } + } + + if (shuffleSender != null) { + shuffleSender.stop() + } + } + }) + } + + private[storage] def startShuffleBlockSender(port: Int): Int = { + shuffleSender = new ShuffleSender(port, this) + logInfo("Created ShuffleSender binding to port : " + shuffleSender.port) + shuffleSender.port + } +} diff --git a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala index 63447baf8c..5a1e7b4444 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala @@ -17,153 +17,46 @@ package org.apache.spark.storage -import java.io.{File, FileOutputStream, OutputStream, RandomAccessFile} +import java.io.{FileOutputStream, RandomAccessFile} import java.nio.ByteBuffer -import java.nio.channels.FileChannel import java.nio.channels.FileChannel.MapMode -import java.util.{Random, Date} -import java.text.SimpleDateFormat import scala.collection.mutable.ArrayBuffer -import it.unimi.dsi.fastutil.io.FastBufferedOutputStream - -import org.apache.spark.executor.ExecutorExitCode -import org.apache.spark.serializer.{Serializer, SerializationStream} import org.apache.spark.Logging -import org.apache.spark.network.netty.ShuffleSender -import org.apache.spark.network.netty.PathResolver +import org.apache.spark.serializer.Serializer import org.apache.spark.util.Utils /** * Stores BlockManager blocks on disk. */ -private class DiskStore(blockManager: BlockManager, rootDirs: String) +private class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManager) extends BlockStore(blockManager) with Logging { - class DiskBlockObjectWriter(blockId: String, serializer: Serializer, bufferSize: Int) - extends BlockObjectWriter(blockId) { - - private val f: File = createFile(blockId /*, allowAppendExisting */) - - // The file channel, used for repositioning / truncating the file. - private var channel: FileChannel = null - private var bs: OutputStream = null - private var objOut: SerializationStream = null - private var lastValidPosition = 0L - private var initialized = false - - override def open(): DiskBlockObjectWriter = { - val fos = new FileOutputStream(f, true) - channel = fos.getChannel() - bs = blockManager.wrapForCompression(blockId, new FastBufferedOutputStream(fos, bufferSize)) - objOut = serializer.newInstance().serializeStream(bs) - initialized = true - this - } - - override def close() { - if (initialized) { - objOut.close() - channel = null - bs = null - objOut = null - } - // Invoke the close callback handler. - super.close() - } - - override def isOpen: Boolean = objOut != null - - // Flush the partial writes, and set valid length to be the length of the entire file. - // Return the number of bytes written for this commit. - override def commit(): Long = { - if (initialized) { - // NOTE: Flush the serializer first and then the compressed/buffered output stream - objOut.flush() - bs.flush() - val prevPos = lastValidPosition - lastValidPosition = channel.position() - lastValidPosition - prevPos - } else { - // lastValidPosition is zero if stream is uninitialized - lastValidPosition - } - } - - override def revertPartialWrites() { - if (initialized) { - // Discard current writes. We do this by flushing the outstanding writes and - // truncate the file to the last valid position. - objOut.flush() - bs.flush() - channel.truncate(lastValidPosition) - } - } - - override def write(value: Any) { - if (!initialized) { - open() - } - objOut.writeObject(value) - } - - override def size(): Long = lastValidPosition - } - - private val MAX_DIR_CREATION_ATTEMPTS: Int = 10 - private val subDirsPerLocalDir = System.getProperty("spark.diskStore.subDirectories", "64").toInt - - private var shuffleSender : ShuffleSender = null - // Create one local directory for each path mentioned in spark.local.dir; then, inside this - // directory, create multiple subdirectories that we will hash files into, in order to avoid - // having really large inodes at the top level. - private val localDirs: Array[File] = createLocalDirs() - private val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir)) - - addShutdownHook() - - def getBlockWriter(blockId: String, serializer: Serializer, bufferSize: Int) - : BlockObjectWriter = { - new DiskBlockObjectWriter(blockId, serializer, bufferSize) + override def getSize(blockId: BlockId): Long = { + diskManager.getBlockLocation(blockId).length } - override def getSize(blockId: String): Long = { - getFile(blockId).length() - } - - override def putBytes(blockId: String, _bytes: ByteBuffer, level: StorageLevel) { + override def putBytes(blockId: BlockId, _bytes: ByteBuffer, level: StorageLevel) { // So that we do not modify the input offsets ! // duplicate does not copy buffer, so inexpensive val bytes = _bytes.duplicate() logDebug("Attempting to put block " + blockId) val startTime = System.currentTimeMillis - val file = createFile(blockId) - val channel = new RandomAccessFile(file, "rw").getChannel() + val file = diskManager.getFile(blockId) + val channel = new FileOutputStream(file).getChannel() while (bytes.remaining > 0) { channel.write(bytes) } channel.close() val finishTime = System.currentTimeMillis logDebug("Block %s stored as %s file on disk in %d ms".format( - blockId, Utils.bytesToString(bytes.limit), (finishTime - startTime))) - } - - private def getFileBytes(file: File): ByteBuffer = { - val length = file.length() - val channel = new RandomAccessFile(file, "r").getChannel() - val buffer = try { - channel.map(MapMode.READ_ONLY, 0, length) - } finally { - channel.close() - } - - buffer + file.getName, Utils.bytesToString(bytes.limit), (finishTime - startTime))) } override def putValues( - blockId: String, + blockId: BlockId, values: ArrayBuffer[Any], level: StorageLevel, returnValues: Boolean) @@ -171,159 +64,62 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) logDebug("Attempting to write values for block " + blockId) val startTime = System.currentTimeMillis - val file = createFile(blockId) - val fileOut = blockManager.wrapForCompression(blockId, - new FastBufferedOutputStream(new FileOutputStream(file))) - val objOut = blockManager.defaultSerializer.newInstance().serializeStream(fileOut) - objOut.writeAll(values.iterator) - objOut.close() - val length = file.length() + val file = diskManager.getFile(blockId) + val outputStream = new FileOutputStream(file) + blockManager.dataSerializeStream(blockId, outputStream, values.iterator) + val length = file.length val timeTaken = System.currentTimeMillis - startTime logDebug("Block %s stored as %s file on disk in %d ms".format( - blockId, Utils.bytesToString(length), timeTaken)) + file.getName, Utils.bytesToString(length), timeTaken)) if (returnValues) { // Return a byte buffer for the contents of the file - val buffer = getFileBytes(file) + val buffer = getBytes(blockId).get PutResult(length, Right(buffer)) } else { PutResult(length, null) } } - override def getBytes(blockId: String): Option[ByteBuffer] = { - val file = getFile(blockId) - val bytes = getFileBytes(file) - Some(bytes) + override def getBytes(blockId: BlockId): Option[ByteBuffer] = { + val segment = diskManager.getBlockLocation(blockId) + val channel = new RandomAccessFile(segment.file, "r").getChannel() + val buffer = try { + channel.map(MapMode.READ_ONLY, segment.offset, segment.length) + } finally { + channel.close() + } + Some(buffer) } - override def getValues(blockId: String): Option[Iterator[Any]] = { - getBytes(blockId).map(bytes => blockManager.dataDeserialize(blockId, bytes)) + override def getValues(blockId: BlockId): Option[Iterator[Any]] = { + getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer)) } /** * A version of getValues that allows a custom serializer. This is used as part of the * shuffle short-circuit code. */ - def getValues(blockId: String, serializer: Serializer): Option[Iterator[Any]] = { + def getValues(blockId: BlockId, serializer: Serializer): Option[Iterator[Any]] = { getBytes(blockId).map(bytes => blockManager.dataDeserialize(blockId, bytes, serializer)) } - override def remove(blockId: String): Boolean = { - val file = getFile(blockId) - if (file.exists()) { + override def remove(blockId: BlockId): Boolean = { + val fileSegment = diskManager.getBlockLocation(blockId) + val file = fileSegment.file + if (file.exists() && file.length() == fileSegment.length) { file.delete() } else { - false - } - } - - override def contains(blockId: String): Boolean = { - getFile(blockId).exists() - } - - private def createFile(blockId: String, allowAppendExisting: Boolean = false): File = { - val file = getFile(blockId) - if (!allowAppendExisting && file.exists()) { - // NOTE(shivaram): Delete the file if it exists. This might happen if a ShuffleMap task - // was rescheduled on the same machine as the old task. - logWarning("File for block " + blockId + " already exists on disk: " + file + ". Deleting") - file.delete() - } - file - } - - private def getFile(blockId: String): File = { - logDebug("Getting file for block " + blockId) - - // Figure out which local directory it hashes to, and which subdirectory in that - val hash = Utils.nonNegativeHash(blockId) - val dirId = hash % localDirs.length - val subDirId = (hash / localDirs.length) % subDirsPerLocalDir - - // Create the subdirectory if it doesn't already exist - var subDir = subDirs(dirId)(subDirId) - if (subDir == null) { - subDir = subDirs(dirId).synchronized { - val old = subDirs(dirId)(subDirId) - if (old != null) { - old - } else { - val newDir = new File(localDirs(dirId), "%02x".format(subDirId)) - newDir.mkdir() - subDirs(dirId)(subDirId) = newDir - newDir - } - } - } - - new File(subDir, blockId) - } - - private def createLocalDirs(): Array[File] = { - logDebug("Creating local directories at root dirs '" + rootDirs + "'") - val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss") - rootDirs.split(",").map { rootDir => - var foundLocalDir = false - var localDir: File = null - var localDirId: String = null - var tries = 0 - val rand = new Random() - while (!foundLocalDir && tries < MAX_DIR_CREATION_ATTEMPTS) { - tries += 1 - try { - localDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) - localDir = new File(rootDir, "spark-local-" + localDirId) - if (!localDir.exists) { - foundLocalDir = localDir.mkdirs() - } - } catch { - case e: Exception => - logWarning("Attempt " + tries + " to create local dir " + localDir + " failed", e) - } + if (fileSegment.length < file.length()) { + logWarning("Could not delete block associated with only a part of a file: " + blockId) } - if (!foundLocalDir) { - logError("Failed " + MAX_DIR_CREATION_ATTEMPTS + - " attempts to create local dir in " + rootDir) - System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR) - } - logInfo("Created local directory at " + localDir) - localDir + false } } - private def addShutdownHook() { - localDirs.foreach(localDir => Utils.registerShutdownDeleteDir(localDir)) - Runtime.getRuntime.addShutdownHook(new Thread("delete Spark local dirs") { - override def run() { - logDebug("Shutdown hook called") - localDirs.foreach { localDir => - try { - if (!Utils.hasRootAsShutdownDeleteDir(localDir)) Utils.deleteRecursively(localDir) - } catch { - case t: Throwable => - logError("Exception while deleting local spark dir: " + localDir, t) - } - } - if (shuffleSender != null) { - shuffleSender.stop - } - } - }) - } - - private[storage] def startShuffleBlockSender(port: Int): Int = { - val pResolver = new PathResolver { - override def getAbsolutePath(blockId: String): String = { - if (!blockId.startsWith("shuffle_")) { - return null - } - DiskStore.this.getFile(blockId).getAbsolutePath() - } - } - shuffleSender = new ShuffleSender(port, pResolver) - logInfo("Created ShuffleSender binding to port : "+ shuffleSender.port) - shuffleSender.port + override def contains(blockId: BlockId): Boolean = { + val file = diskManager.getBlockLocation(blockId).file + file.exists() } } diff --git a/core/src/main/scala/org/apache/spark/storage/FileSegment.scala b/core/src/main/scala/org/apache/spark/storage/FileSegment.scala new file mode 100644 index 0000000000..555486830a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/FileSegment.scala @@ -0,0 +1,28 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.io.File + +/** + * References a particular segment of a file (potentially the entire file), + * based off an offset and a length. + */ +private[spark] class FileSegment(val file: File, val offset: Long, val length : Long) { + override def toString = "(name=%s, offset=%d, length=%d)".format(file.getName, offset, length) +} diff --git a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala index 77a39c71ed..05f676c6e2 100644 --- a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala @@ -32,7 +32,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) case class Entry(value: Any, size: Long, deserialized: Boolean) - private val entries = new LinkedHashMap[String, Entry](32, 0.75f, true) + private val entries = new LinkedHashMap[BlockId, Entry](32, 0.75f, true) @volatile private var currentMemory = 0L // Object used to ensure that only one thread is putting blocks and if necessary, dropping // blocks from the memory store. @@ -42,13 +42,13 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) def freeMemory: Long = maxMemory - currentMemory - override def getSize(blockId: String): Long = { + override def getSize(blockId: BlockId): Long = { entries.synchronized { entries.get(blockId).size } } - override def putBytes(blockId: String, _bytes: ByteBuffer, level: StorageLevel) { + override def putBytes(blockId: BlockId, _bytes: ByteBuffer, level: StorageLevel) { // Work on a duplicate - since the original input might be used elsewhere. val bytes = _bytes.duplicate() bytes.rewind() @@ -64,7 +64,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) } override def putValues( - blockId: String, + blockId: BlockId, values: ArrayBuffer[Any], level: StorageLevel, returnValues: Boolean) @@ -81,7 +81,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) } } - override def getBytes(blockId: String): Option[ByteBuffer] = { + override def getBytes(blockId: BlockId): Option[ByteBuffer] = { val entry = entries.synchronized { entries.get(blockId) } @@ -94,7 +94,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) } } - override def getValues(blockId: String): Option[Iterator[Any]] = { + override def getValues(blockId: BlockId): Option[Iterator[Any]] = { val entry = entries.synchronized { entries.get(blockId) } @@ -108,7 +108,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) } } - override def remove(blockId: String): Boolean = { + override def remove(blockId: BlockId): Boolean = { entries.synchronized { val entry = entries.remove(blockId) if (entry != null) { @@ -131,14 +131,10 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) } /** - * Return the RDD ID that a given block ID is from, or null if it is not an RDD block. + * Return the RDD ID that a given block ID is from, or None if it is not an RDD block. */ - private def getRddId(blockId: String): String = { - if (blockId.startsWith("rdd_")) { - blockId.split('_')(1) - } else { - null - } + private def getRddId(blockId: BlockId): Option[Int] = { + blockId.asRDDId.map(_.rddId) } /** @@ -151,7 +147,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) * blocks to free memory for one block, another thread may use up the freed space for * another block. */ - private def tryToPut(blockId: String, value: Any, size: Long, deserialized: Boolean): Boolean = { + private def tryToPut(blockId: BlockId, value: Any, size: Long, deserialized: Boolean): Boolean = { // TODO: Its possible to optimize the locking by locking entries only when selecting blocks // to be dropped. Once the to-be-dropped blocks have been selected, and lock on entries has been // released, it must be ensured that those to-be-dropped blocks are not double counted for @@ -195,7 +191,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) * Assumes that a lock is held by the caller to ensure only one thread is dropping blocks. * Otherwise, the freed space may fill up before the caller puts in their new value. */ - private def ensureFreeSpace(blockIdToAdd: String, space: Long): Boolean = { + private def ensureFreeSpace(blockIdToAdd: BlockId, space: Long): Boolean = { logInfo("ensureFreeSpace(%d) called with curMem=%d, maxMem=%d".format( space, currentMemory, maxMemory)) @@ -207,7 +203,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) if (maxMemory - currentMemory < space) { val rddToAdd = getRddId(blockIdToAdd) - val selectedBlocks = new ArrayBuffer[String]() + val selectedBlocks = new ArrayBuffer[BlockId]() var selectedMemory = 0L // This is synchronized to ensure that the set of entries is not changed @@ -218,7 +214,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) while (maxMemory - (currentMemory - selectedMemory) < space && iterator.hasNext) { val pair = iterator.next() val blockId = pair.getKey - if (rddToAdd != null && rddToAdd == getRddId(blockId)) { + if (rddToAdd != None && rddToAdd == getRddId(blockId)) { logInfo("Will not store " + blockIdToAdd + " as it would require dropping another " + "block from the same RDD") return false @@ -252,7 +248,7 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) return true } - override def contains(blockId: String): Boolean = { + override def contains(blockId: BlockId): Boolean = { entries.synchronized { entries.containsKey(blockId) } } } diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala index 9da11efb57..39dc7bb19a 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala @@ -17,51 +17,220 @@ package org.apache.spark.storage -import org.apache.spark.serializer.Serializer +import java.io.File +import java.util.concurrent.ConcurrentLinkedQueue +import java.util.concurrent.atomic.AtomicInteger +import scala.collection.JavaConversions._ -private[spark] -class ShuffleWriterGroup(val id: Int, val writers: Array[BlockObjectWriter]) +import org.apache.spark.serializer.Serializer +import org.apache.spark.util.{MetadataCleanerType, MetadataCleaner, TimeStampedHashMap} +import org.apache.spark.util.collection.{PrimitiveKeyOpenHashMap, PrimitiveVector} +import org.apache.spark.storage.ShuffleBlockManager.ShuffleFileGroup +/** A group of writers for a ShuffleMapTask, one writer per reducer. */ +private[spark] trait ShuffleWriterGroup { + val writers: Array[BlockObjectWriter] -private[spark] -trait ShuffleBlocks { - def acquireWriters(mapId: Int): ShuffleWriterGroup - def releaseWriters(group: ShuffleWriterGroup) + /** @param success Indicates all writes were successful. If false, no blocks will be recorded. */ + def releaseWriters(success: Boolean) } - +/** + * Manages assigning disk-based block writers to shuffle tasks. Each shuffle task gets one file + * per reducer (this set of files is called a ShuffleFileGroup). + * + * As an optimization to reduce the number of physical shuffle files produced, multiple shuffle + * blocks are aggregated into the same file. There is one "combined shuffle file" per reducer + * per concurrently executing shuffle task. As soon as a task finishes writing to its shuffle + * files, it releases them for another task. + * Regarding the implementation of this feature, shuffle files are identified by a 3-tuple: + * - shuffleId: The unique id given to the entire shuffle stage. + * - bucketId: The id of the output partition (i.e., reducer id) + * - fileId: The unique id identifying a group of "combined shuffle files." Only one task at a + * time owns a particular fileId, and this id is returned to a pool when the task finishes. + * Each shuffle file is then mapped to a FileSegment, which is a 3-tuple (file, offset, length) + * that specifies where in a given file the actual block data is located. + * + * Shuffle file metadata is stored in a space-efficient manner. Rather than simply mapping + * ShuffleBlockIds directly to FileSegments, each ShuffleFileGroup maintains a list of offsets for + * each block stored in each file. In order to find the location of a shuffle block, we search the + * files within a ShuffleFileGroups associated with the block's reducer. + */ private[spark] class ShuffleBlockManager(blockManager: BlockManager) { + def conf = blockManager.conf + + // Turning off shuffle file consolidation causes all shuffle Blocks to get their own file. + // TODO: Remove this once the shuffle file consolidation feature is stable. + val consolidateShuffleFiles = + conf.get("spark.shuffle.consolidateFiles", "false").toBoolean + + private val bufferSize = conf.get("spark.shuffle.file.buffer.kb", "100").toInt * 1024 + + /** + * Contains all the state related to a particular shuffle. This includes a pool of unused + * ShuffleFileGroups, as well as all ShuffleFileGroups that have been created for the shuffle. + */ + private class ShuffleState(val numBuckets: Int) { + val nextFileId = new AtomicInteger(0) + val unusedFileGroups = new ConcurrentLinkedQueue[ShuffleFileGroup]() + val allFileGroups = new ConcurrentLinkedQueue[ShuffleFileGroup]() + + /** + * The mapIds of all map tasks completed on this Executor for this shuffle. + * NB: This is only populated if consolidateShuffleFiles is FALSE. We don't need it otherwise. + */ + val completedMapTasks = new ConcurrentLinkedQueue[Int]() + } + + type ShuffleId = Int + private val shuffleStates = new TimeStampedHashMap[ShuffleId, ShuffleState] + + private val metadataCleaner = + new MetadataCleaner(MetadataCleanerType.SHUFFLE_BLOCK_MANAGER, this.cleanup, conf) + + def forMapTask(shuffleId: Int, mapId: Int, numBuckets: Int, serializer: Serializer) = { + new ShuffleWriterGroup { + shuffleStates.putIfAbsent(shuffleId, new ShuffleState(numBuckets)) + private val shuffleState = shuffleStates(shuffleId) + private var fileGroup: ShuffleFileGroup = null + + val writers: Array[BlockObjectWriter] = if (consolidateShuffleFiles) { + fileGroup = getUnusedFileGroup() + Array.tabulate[BlockObjectWriter](numBuckets) { bucketId => + val blockId = ShuffleBlockId(shuffleId, mapId, bucketId) + blockManager.getDiskWriter(blockId, fileGroup(bucketId), serializer, bufferSize) + } + } else { + Array.tabulate[BlockObjectWriter](numBuckets) { bucketId => + val blockId = ShuffleBlockId(shuffleId, mapId, bucketId) + val blockFile = blockManager.diskBlockManager.getFile(blockId) + blockManager.getDiskWriter(blockId, blockFile, serializer, bufferSize) + } + } + + override def releaseWriters(success: Boolean) { + if (consolidateShuffleFiles) { + if (success) { + val offsets = writers.map(_.fileSegment().offset) + fileGroup.recordMapOutput(mapId, offsets) + } + recycleFileGroup(fileGroup) + } else { + shuffleState.completedMapTasks.add(mapId) + } + } - def forShuffle(shuffleId: Int, numBuckets: Int, serializer: Serializer): ShuffleBlocks = { - new ShuffleBlocks { - // Get a group of writers for a map task. - override def acquireWriters(mapId: Int): ShuffleWriterGroup = { - val bufferSize = System.getProperty("spark.shuffle.file.buffer.kb", "100").toInt * 1024 - val writers = Array.tabulate[BlockObjectWriter](numBuckets) { bucketId => - val blockId = ShuffleBlockManager.blockId(shuffleId, bucketId, mapId) - blockManager.getDiskBlockWriter(blockId, serializer, bufferSize) + private def getUnusedFileGroup(): ShuffleFileGroup = { + val fileGroup = shuffleState.unusedFileGroups.poll() + if (fileGroup != null) fileGroup else newFileGroup() + } + + private def newFileGroup(): ShuffleFileGroup = { + val fileId = shuffleState.nextFileId.getAndIncrement() + val files = Array.tabulate[File](numBuckets) { bucketId => + val filename = physicalFileName(shuffleId, bucketId, fileId) + blockManager.diskBlockManager.getFile(filename) } - new ShuffleWriterGroup(mapId, writers) + val fileGroup = new ShuffleFileGroup(fileId, shuffleId, files) + shuffleState.allFileGroups.add(fileGroup) + fileGroup } - override def releaseWriters(group: ShuffleWriterGroup) = { - // Nothing really to release here. + private def recycleFileGroup(group: ShuffleFileGroup) { + shuffleState.unusedFileGroups.add(group) } } } -} + /** + * Returns the physical file segment in which the given BlockId is located. + * This function should only be called if shuffle file consolidation is enabled, as it is + * an error condition if we don't find the expected block. + */ + def getBlockLocation(id: ShuffleBlockId): FileSegment = { + // Search all file groups associated with this shuffle. + val shuffleState = shuffleStates(id.shuffleId) + for (fileGroup <- shuffleState.allFileGroups) { + val segment = fileGroup.getFileSegmentFor(id.mapId, id.reduceId) + if (segment.isDefined) { return segment.get } + } + throw new IllegalStateException("Failed to find shuffle block: " + id) + } + + private def physicalFileName(shuffleId: Int, bucketId: Int, fileId: Int) = { + "merged_shuffle_%d_%d_%d".format(shuffleId, bucketId, fileId) + } + + private def cleanup(cleanupTime: Long) { + shuffleStates.clearOldValues(cleanupTime, (shuffleId, state) => { + if (consolidateShuffleFiles) { + for (fileGroup <- state.allFileGroups; file <- fileGroup.files) { + file.delete() + } + } else { + for (mapId <- state.completedMapTasks; reduceId <- 0 until state.numBuckets) { + val blockId = new ShuffleBlockId(shuffleId, mapId, reduceId) + blockManager.diskBlockManager.getFile(blockId).delete() + } + } + }) + } +} private[spark] object ShuffleBlockManager { + /** + * A group of shuffle files, one per reducer. + * A particular mapper will be assigned a single ShuffleFileGroup to write its output to. + */ + private class ShuffleFileGroup(val shuffleId: Int, val fileId: Int, val files: Array[File]) { + /** + * Stores the absolute index of each mapId in the files of this group. For instance, + * if mapId 5 is the first block in each file, mapIdToIndex(5) = 0. + */ + private val mapIdToIndex = new PrimitiveKeyOpenHashMap[Int, Int]() - // Returns the block id for a given shuffle block. - def blockId(shuffleId: Int, bucketId: Int, groupId: Int): String = { - "shuffle_" + shuffleId + "_" + groupId + "_" + bucketId - } + /** + * Stores consecutive offsets of blocks into each reducer file, ordered by position in the file. + * This ordering allows us to compute block lengths by examining the following block offset. + * Note: mapIdToIndex(mapId) returns the index of the mapper into the vector for every + * reducer. + */ + private val blockOffsetsByReducer = Array.fill[PrimitiveVector[Long]](files.length) { + new PrimitiveVector[Long]() + } - // Returns true if the block is a shuffle block. - def isShuffle(blockId: String): Boolean = blockId.startsWith("shuffle_") + def numBlocks = mapIdToIndex.size + + def apply(bucketId: Int) = files(bucketId) + + def recordMapOutput(mapId: Int, offsets: Array[Long]) { + mapIdToIndex(mapId) = numBlocks + for (i <- 0 until offsets.length) { + blockOffsetsByReducer(i) += offsets(i) + } + } + + /** Returns the FileSegment associated with the given map task, or None if no entry exists. */ + def getFileSegmentFor(mapId: Int, reducerId: Int): Option[FileSegment] = { + val file = files(reducerId) + val blockOffsets = blockOffsetsByReducer(reducerId) + val index = mapIdToIndex.getOrElse(mapId, -1) + if (index >= 0) { + val offset = blockOffsets(index) + val length = + if (index + 1 < numBlocks) { + blockOffsets(index + 1) - offset + } else { + file.length() - offset + } + assert(length >= 0) + Some(new FileSegment(file, offset, length)) + } else { + None + } + } + } } diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala index 632ff047d1..b5596dffd3 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala @@ -101,7 +101,7 @@ class StorageLevel private( var result = "" result += (if (useDisk) "Disk " else "") result += (if (useMemory) "Memory " else "") - result += (if (deserialized) "Deserialized " else "Serialized") + result += (if (deserialized) "Deserialized " else "Serialized ") result += "%sx Replicated".format(replication) result } diff --git a/core/src/main/scala/org/apache/spark/storage/StoragePerfTester.scala b/core/src/main/scala/org/apache/spark/storage/StoragePerfTester.scala new file mode 100644 index 0000000000..40734aab49 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/StoragePerfTester.scala @@ -0,0 +1,103 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.util.concurrent.atomic.AtomicLong +import java.util.concurrent.{CountDownLatch, Executors} + +import org.apache.spark.serializer.KryoSerializer +import org.apache.spark.SparkContext +import org.apache.spark.util.Utils + +/** + * Utility for micro-benchmarking shuffle write performance. + * + * Writes simulated shuffle output from several threads and records the observed throughput. + */ +object StoragePerfTester { + def main(args: Array[String]) = { + /** Total amount of data to generate. Distributed evenly amongst maps and reduce splits. */ + val dataSizeMb = Utils.memoryStringToMb(sys.env.getOrElse("OUTPUT_DATA", "1g")) + + /** Number of map tasks. All tasks execute concurrently. */ + val numMaps = sys.env.get("NUM_MAPS").map(_.toInt).getOrElse(8) + + /** Number of reduce splits for each map task. */ + val numOutputSplits = sys.env.get("NUM_REDUCERS").map(_.toInt).getOrElse(500) + + val recordLength = 1000 // ~1KB records + val totalRecords = dataSizeMb * 1000 + val recordsPerMap = totalRecords / numMaps + + val writeData = "1" * recordLength + val executor = Executors.newFixedThreadPool(numMaps) + + System.setProperty("spark.shuffle.compress", "false") + System.setProperty("spark.shuffle.sync", "true") + + // This is only used to instantiate a BlockManager. All thread scheduling is done manually. + val sc = new SparkContext("local[4]", "Write Tester") + val blockManager = sc.env.blockManager + + def writeOutputBytes(mapId: Int, total: AtomicLong) = { + val shuffle = blockManager.shuffleBlockManager.forMapTask(1, mapId, numOutputSplits, + new KryoSerializer(sc.conf)) + val writers = shuffle.writers + for (i <- 1 to recordsPerMap) { + writers(i % numOutputSplits).write(writeData) + } + writers.map {w => + w.commit() + total.addAndGet(w.fileSegment().length) + w.close() + } + + shuffle.releaseWriters(true) + } + + val start = System.currentTimeMillis() + val latch = new CountDownLatch(numMaps) + val totalBytes = new AtomicLong() + for (task <- 1 to numMaps) { + executor.submit(new Runnable() { + override def run() = { + try { + writeOutputBytes(task, totalBytes) + latch.countDown() + } catch { + case e: Exception => + println("Exception in child thread: " + e + " " + e.getMessage) + System.exit(1) + } + } + }) + } + latch.await() + val end = System.currentTimeMillis() + val time = (end - start) / 1000.0 + val bytesPerSecond = totalBytes.get() / time + val bytesPerFile = (totalBytes.get() / (numOutputSplits * numMaps.toDouble)).toLong + + System.err.println("files_total\t\t%s".format(numMaps * numOutputSplits)) + System.err.println("bytes_per_file\t\t%s".format(Utils.bytesToString(bytesPerFile))) + System.err.println("agg_throughput\t\t%s/s".format(Utils.bytesToString(bytesPerSecond.toLong))) + + executor.shutdown() + sc.stop() + } +} diff --git a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala index 2bb7715696..1720007e4e 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala @@ -23,20 +23,24 @@ import org.apache.spark.util.Utils private[spark] case class StorageStatus(blockManagerId: BlockManagerId, maxMem: Long, - blocks: Map[String, BlockStatus]) { + blocks: Map[BlockId, BlockStatus]) { - def memUsed(blockPrefix: String = "") = { - blocks.filterKeys(_.startsWith(blockPrefix)).values.map(_.memSize). - reduceOption(_+_).getOrElse(0l) - } + def memUsed() = blocks.values.map(_.memSize).reduceOption(_+_).getOrElse(0L) - def diskUsed(blockPrefix: String = "") = { - blocks.filterKeys(_.startsWith(blockPrefix)).values.map(_.diskSize). - reduceOption(_+_).getOrElse(0l) - } + def memUsedByRDD(rddId: Int) = + rddBlocks.filterKeys(_.rddId == rddId).values.map(_.memSize).reduceOption(_+_).getOrElse(0L) + + def diskUsed() = blocks.values.map(_.diskSize).reduceOption(_+_).getOrElse(0L) + + def diskUsedByRDD(rddId: Int) = + rddBlocks.filterKeys(_.rddId == rddId).values.map(_.diskSize).reduceOption(_+_).getOrElse(0L) def memRemaining : Long = maxMem - memUsed() + def rddBlocks = blocks.flatMap { + case (rdd: RDDBlockId, status) => Some(rdd, status) + case _ => None + } } case class RDDInfo(id: Int, name: String, storageLevel: StorageLevel, @@ -60,7 +64,7 @@ object StorageUtils { /* Returns RDD-level information, compiled from a list of StorageStatus objects */ def rddInfoFromStorageStatus(storageStatusList: Seq[StorageStatus], sc: SparkContext) : Array[RDDInfo] = { - rddInfoFromBlockStatusList(storageStatusList.flatMap(_.blocks).toMap, sc) + rddInfoFromBlockStatusList(storageStatusList.flatMap(_.rddBlocks).toMap[RDDBlockId, BlockStatus], sc) } /* Returns a map of blocks to their locations, compiled from a list of StorageStatus objects */ @@ -71,26 +75,21 @@ object StorageUtils { } /* Given a list of BlockStatus objets, returns information for each RDD */ - def rddInfoFromBlockStatusList(infos: Map[String, BlockStatus], + def rddInfoFromBlockStatusList(infos: Map[RDDBlockId, BlockStatus], sc: SparkContext) : Array[RDDInfo] = { // Group by rddId, ignore the partition name - val groupedRddBlocks = infos.filterKeys(_.startsWith("rdd_")).groupBy { case(k, v) => - k.substring(0,k.lastIndexOf('_')) - }.mapValues(_.values.toArray) + val groupedRddBlocks = infos.groupBy { case(k, v) => k.rddId }.mapValues(_.values.toArray) // For each RDD, generate an RDDInfo object - val rddInfos = groupedRddBlocks.map { case (rddKey, rddBlocks) => + val rddInfos = groupedRddBlocks.map { case (rddId, rddBlocks) => // Add up memory and disk sizes val memSize = rddBlocks.map(_.memSize).reduce(_ + _) val diskSize = rddBlocks.map(_.diskSize).reduce(_ + _) - // Find the id of the RDD, e.g. rdd_1 => 1 - val rddId = rddKey.split("_").last.toInt - // Get the friendly name and storage level for the RDD, if available sc.persistentRdds.get(rddId).map { r => - val rddName = Option(r.name).getOrElse(rddKey) + val rddName = Option(r.name).getOrElse(rddId.toString) val rddStorageLevel = r.getStorageLevel RDDInfo(rddId, rddName, rddStorageLevel, rddBlocks.length, r.partitions.size, memSize, diskSize) } @@ -101,16 +100,14 @@ object StorageUtils { rddInfos } - /* Removes all BlockStatus object that are not part of a block prefix */ - def filterStorageStatusByPrefix(storageStatusList: Array[StorageStatus], - prefix: String) : Array[StorageStatus] = { + /* Filters storage status by a given RDD id. */ + def filterStorageStatusByRDD(storageStatusList: Array[StorageStatus], rddId: Int) + : Array[StorageStatus] = { storageStatusList.map { status => - val newBlocks = status.blocks.filterKeys(_.startsWith(prefix)) + val newBlocks = status.rddBlocks.filterKeys(_.rddId == rddId).toMap[BlockId, BlockStatus] //val newRemainingMem = status.maxMem - newBlocks.values.map(_.memSize).reduce(_ + _) StorageStatus(status.blockManagerId, status.maxMem, newBlocks) } - } - } diff --git a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala index f2ae8dd97d..dca98c6c05 100644 --- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala +++ b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala @@ -22,6 +22,7 @@ import akka.actor._ import java.util.concurrent.ArrayBlockingQueue import util.Random import org.apache.spark.serializer.KryoSerializer +import org.apache.spark.{SparkConf, SparkContext} /** * This class tests the BlockManager and MemoryStore for thread safety and @@ -36,11 +37,11 @@ private[spark] object ThreadingTest { val numBlocksPerProducer = 20000 private[spark] class ProducerThread(manager: BlockManager, id: Int) extends Thread { - val queue = new ArrayBlockingQueue[(String, Seq[Int])](100) + val queue = new ArrayBlockingQueue[(BlockId, Seq[Int])](100) override def run() { for (i <- 1 to numBlocksPerProducer) { - val blockId = "b-" + id + "-" + i + val blockId = TestBlockId("b-" + id + "-" + i) val blockSize = Random.nextInt(1000) val block = (1 to blockSize).map(_ => Random.nextInt()) val level = randomLevel() @@ -64,7 +65,7 @@ private[spark] object ThreadingTest { private[spark] class ConsumerThread( manager: BlockManager, - queue: ArrayBlockingQueue[(String, Seq[Int])] + queue: ArrayBlockingQueue[(BlockId, Seq[Int])] ) extends Thread { var numBlockConsumed = 0 @@ -91,11 +92,12 @@ private[spark] object ThreadingTest { def main(args: Array[String]) { System.setProperty("spark.kryoserializer.buffer.mb", "1") val actorSystem = ActorSystem("test") - val serializer = new KryoSerializer + val conf = new SparkConf() + val serializer = new KryoSerializer(conf) val blockManagerMaster = new BlockManagerMaster( - actorSystem.actorOf(Props(new BlockManagerMasterActor(true)))) + Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf)))), conf) val blockManager = new BlockManager( - "<driver>", actorSystem, blockManagerMaster, serializer, 1024 * 1024) + "<driver>", actorSystem, blockManagerMaster, serializer, 1024 * 1024, conf) val producers = (1 to numProducers).map(i => new ProducerThread(blockManager, i)) val consumers = producers.map(p => new ConsumerThread(blockManager, p.queue)) producers.foreach(_.start) diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index f1d86c0221..50dfdbdf5a 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -1,4 +1,4 @@ -/* +/* * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. @@ -32,7 +32,7 @@ import org.apache.spark.util.Utils /** Top level user interface for Spark */ private[spark] class SparkUI(sc: SparkContext) extends Logging { val host = Option(System.getenv("SPARK_PUBLIC_DNS")).getOrElse(Utils.localHostName()) - val port = Option(System.getProperty("spark.ui.port")).getOrElse(SparkUI.DEFAULT_PORT).toInt + val port = sc.conf.get("spark.ui.port", SparkUI.DEFAULT_PORT).toInt var boundPort: Option[Int] = None var server: Option[Server] = None diff --git a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala index b4128b71e7..f207627e52 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala @@ -19,7 +19,7 @@ package org.apache.spark.ui import scala.util.Random -import org.apache.spark.SparkContext +import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.SparkContext._ import org.apache.spark.scheduler.SchedulingMode @@ -30,22 +30,23 @@ import org.apache.spark.scheduler.SchedulingMode * Usage: ./run spark.ui.UIWorkloadGenerator [master] */ private[spark] object UIWorkloadGenerator { + val NUM_PARTITIONS = 100 val INTER_JOB_WAIT_MS = 5000 def main(args: Array[String]) { if (args.length < 2) { - println("usage: ./sbin/spark-class spark.ui.UIWorkloadGenerator [master] [FIFO|FAIR]") + println("usage: ./sbin/spark-class org.apache.spark.ui.UIWorkloadGenerator [master] [FIFO|FAIR]") System.exit(1) } - val master = args(0) - val schedulingMode = SchedulingMode.withName(args(1)) - val appName = "Spark UI Tester" + val conf = new SparkConf().setMaster(args(0)).setAppName("Spark UI tester") + + val schedulingMode = SchedulingMode.withName(args(1)) if (schedulingMode == SchedulingMode.FAIR) { - System.setProperty("spark.scheduler.mode", "FAIR") + conf.set("spark.scheduler.mode", "FAIR") } - val sc = new SparkContext(master, appName) + val sc = new SparkContext(conf) def setProperties(s: String) = { if(schedulingMode == SchedulingMode.FAIR) { @@ -55,11 +56,11 @@ private[spark] object UIWorkloadGenerator { } val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS) - def nextFloat() = (new Random()).nextFloat() + def nextFloat() = new Random().nextFloat() val jobs = Seq[(String, () => Long)]( ("Count", baseData.count), - ("Cache and Count", baseData.map(x => x).cache.count), + ("Cache and Count", baseData.map(x => x).cache().count), ("Single Shuffle", baseData.map(x => (x % 10, x)).reduceByKey(_ + _).count), ("Entirely failed phase", baseData.map(x => throw new Exception).count), ("Partially failed phase", { diff --git a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentUI.scala b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentUI.scala index c5bf2acc9e..88f41be8d3 100644 --- a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentUI.scala @@ -48,12 +48,15 @@ private[spark] class EnvironmentUI(sc: SparkContext) { def jvmTable = UIUtils.listingTable(Seq("Name", "Value"), jvmRow, jvmInformation, fixedWidth = true) - val properties = System.getProperties.iterator.toSeq - val classPathProperty = properties.find { case (k, v) => - k.contains("java.class.path") + val sparkProperties = sc.conf.getAll.sorted + + val systemProperties = System.getProperties.iterator.toSeq + val classPathProperty = systemProperties.find { case (k, v) => + k == "java.class.path" }.getOrElse(("", "")) - val sparkProperties = properties.filter(_._1.startsWith("spark")).sorted - val otherProperties = properties.diff(sparkProperties :+ classPathProperty).sorted + val otherProperties = systemProperties.filter { case (k, v) => + k != "java.class.path" && !k.startsWith("spark.") + }.sorted val propertyHeaders = Seq("Name", "Value") def propertyRow(kv: (String, String)) = <tr><td>{kv._1}</td><td>{kv._2}</td></tr> @@ -63,7 +66,7 @@ private[spark] class EnvironmentUI(sc: SparkContext) { UIUtils.listingTable(propertyHeaders, propertyRow, otherProperties, fixedWidth = true) val classPathEntries = classPathProperty._2 - .split(System.getProperty("path.separator", ":")) + .split(sc.conf.get("path.separator", ":")) .filterNot(e => e.isEmpty) .map(e => (e, "System Classpath")) val addedJars = sc.addedJars.iterator.toSeq.map{case (path, time) => (path, "Added By User")} diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsUI.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsUI.scala index 42e9be6e19..a31a7e1d58 100644 --- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsUI.scala @@ -56,7 +56,8 @@ private[spark] class ExecutorsUI(val sc: SparkContext) { val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize)).fold(0L)(_+_) val execHead = Seq("Executor ID", "Address", "RDD blocks", "Memory used", "Disk used", - "Active tasks", "Failed tasks", "Complete tasks", "Total tasks") + "Active tasks", "Failed tasks", "Complete tasks", "Total tasks", "Task Time", "Shuffle Read", + "Shuffle Write") def execRow(kv: Seq[String]) = { <tr> @@ -73,10 +74,13 @@ private[spark] class ExecutorsUI(val sc: SparkContext) { <td>{kv(7)}</td> <td>{kv(8)}</td> <td>{kv(9)}</td> + <td>{Utils.msDurationToString(kv(10).toLong)}</td> + <td>{Utils.bytesToString(kv(11).toLong)}</td> + <td>{Utils.bytesToString(kv(12).toLong)}</td> </tr> } - val execInfo = for (b <- 0 until storageStatusList.size) yield getExecInfo(b) + val execInfo = for (statusId <- 0 until storageStatusList.size) yield getExecInfo(statusId) val execTable = UIUtils.listingTable(execHead, execRow, execInfo) val content = @@ -99,17 +103,21 @@ private[spark] class ExecutorsUI(val sc: SparkContext) { UIUtils.headerSparkPage(content, sc, "Executors (" + execInfo.size + ")", Executors) } - def getExecInfo(a: Int): Seq[String] = { - val execId = sc.getExecutorStorageStatus(a).blockManagerId.executorId - val hostPort = sc.getExecutorStorageStatus(a).blockManagerId.hostPort - val rddBlocks = sc.getExecutorStorageStatus(a).blocks.size.toString - val memUsed = sc.getExecutorStorageStatus(a).memUsed().toString - val maxMem = sc.getExecutorStorageStatus(a).maxMem.toString - val diskUsed = sc.getExecutorStorageStatus(a).diskUsed().toString - val activeTasks = listener.executorToTasksActive.get(a.toString).map(l => l.size).getOrElse(0) - val failedTasks = listener.executorToTasksFailed.getOrElse(a.toString, 0) - val completedTasks = listener.executorToTasksComplete.getOrElse(a.toString, 0) + def getExecInfo(statusId: Int): Seq[String] = { + val status = sc.getExecutorStorageStatus(statusId) + val execId = status.blockManagerId.executorId + val hostPort = status.blockManagerId.hostPort + val rddBlocks = status.blocks.size.toString + val memUsed = status.memUsed().toString + val maxMem = status.maxMem.toString + val diskUsed = status.diskUsed().toString + val activeTasks = listener.executorToTasksActive.getOrElse(execId, HashSet.empty[Long]).size + val failedTasks = listener.executorToTasksFailed.getOrElse(execId, 0) + val completedTasks = listener.executorToTasksComplete.getOrElse(execId, 0) val totalTasks = activeTasks + failedTasks + completedTasks + val totalDuration = listener.executorToDuration.getOrElse(execId, 0) + val totalShuffleRead = listener.executorToShuffleRead.getOrElse(execId, 0) + val totalShuffleWrite = listener.executorToShuffleWrite.getOrElse(execId, 0) Seq( execId, @@ -121,7 +129,10 @@ private[spark] class ExecutorsUI(val sc: SparkContext) { activeTasks.toString, failedTasks.toString, completedTasks.toString, - totalTasks.toString + totalTasks.toString, + totalDuration.toString, + totalShuffleRead.toString, + totalShuffleWrite.toString ) } @@ -129,6 +140,9 @@ private[spark] class ExecutorsUI(val sc: SparkContext) { val executorToTasksActive = HashMap[String, HashSet[TaskInfo]]() val executorToTasksComplete = HashMap[String, Int]() val executorToTasksFailed = HashMap[String, Int]() + val executorToDuration = HashMap[String, Long]() + val executorToShuffleRead = HashMap[String, Long]() + val executorToShuffleWrite = HashMap[String, Long]() override def onTaskStart(taskStart: SparkListenerTaskStart) { val eid = taskStart.taskInfo.executorId @@ -139,6 +153,9 @@ private[spark] class ExecutorsUI(val sc: SparkContext) { override def onTaskEnd(taskEnd: SparkListenerTaskEnd) { val eid = taskEnd.taskInfo.executorId val activeTasks = executorToTasksActive.getOrElseUpdate(eid, new HashSet[TaskInfo]()) + val newDuration = executorToDuration.getOrElse(eid, 0L) + taskEnd.taskInfo.duration + executorToDuration.put(eid, newDuration) + activeTasks -= taskEnd.taskInfo val (failureInfo, metrics): (Option[ExceptionFailure], Option[TaskMetrics]) = taskEnd.reason match { @@ -149,6 +166,17 @@ private[spark] class ExecutorsUI(val sc: SparkContext) { executorToTasksComplete(eid) = executorToTasksComplete.getOrElse(eid, 0) + 1 (None, Option(taskEnd.taskMetrics)) } + + // update shuffle read/write + if (null != taskEnd.taskMetrics) { + taskEnd.taskMetrics.shuffleReadMetrics.foreach(shuffleRead => + executorToShuffleRead.put(eid, executorToShuffleRead.getOrElse(eid, 0L) + + shuffleRead.remoteBytesRead)) + + taskEnd.taskMetrics.shuffleWriteMetrics.foreach(shuffleWrite => + executorToShuffleWrite.put(eid, executorToShuffleWrite.getOrElse(eid, 0L) + + shuffleWrite.shuffleBytesWritten)) + } } } } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala new file mode 100644 index 0000000000..3c53e88380 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorSummary.scala @@ -0,0 +1,27 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ui.jobs + +/** class for reporting aggregated metrics for each executors in stageUI */ +private[spark] class ExecutorSummary { + var taskTime : Long = 0 + var failedTasks : Int = 0 + var succeededTasks : Int = 0 + var shuffleRead : Long = 0 + var shuffleWrite : Long = 0 +} diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala new file mode 100644 index 0000000000..0dd876480a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala @@ -0,0 +1,90 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ui.jobs + +import scala.xml.Node + +import org.apache.spark.scheduler.SchedulingMode +import org.apache.spark.util.Utils +import scala.collection.mutable + +/** Page showing executor summary */ +private[spark] class ExecutorTable(val parent: JobProgressUI, val stageId: Int) { + + val listener = parent.listener + val dateFmt = parent.dateFmt + val isFairScheduler = listener.sc.getSchedulingMode == SchedulingMode.FAIR + + def toNodeSeq(): Seq[Node] = { + listener.synchronized { + executorTable() + } + } + + /** Special table which merges two header cells. */ + private def executorTable[T](): Seq[Node] = { + <table class="table table-bordered table-striped table-condensed sortable"> + <thead> + <th>Executor ID</th> + <th>Address</th> + <th>Task Time</th> + <th>Total Tasks</th> + <th>Failed Tasks</th> + <th>Succeeded Tasks</th> + <th>Shuffle Read</th> + <th>Shuffle Write</th> + </thead> + <tbody> + {createExecutorTable()} + </tbody> + </table> + } + + private def createExecutorTable() : Seq[Node] = { + // make a executor-id -> address map + val executorIdToAddress = mutable.HashMap[String, String]() + val storageStatusList = parent.sc.getExecutorStorageStatus + for (statusId <- 0 until storageStatusList.size) { + val blockManagerId = parent.sc.getExecutorStorageStatus(statusId).blockManagerId + val address = blockManagerId.hostPort + val executorId = blockManagerId.executorId + executorIdToAddress.put(executorId, address) + } + + val executorIdToSummary = listener.stageIdToExecutorSummaries.get(stageId) + executorIdToSummary match { + case Some(x) => { + x.toSeq.sortBy(_._1).map{ + case (k,v) => { + <tr> + <td>{k}</td> + <td>{executorIdToAddress.getOrElse(k, "CANNOT FIND ADDRESS")}</td> + <td>{parent.formatDuration(v.taskTime)}</td> + <td>{v.failedTasks + v.succeededTasks}</td> + <td>{v.failedTasks}</td> + <td>{v.succeededTasks}</td> + <td>{Utils.bytesToString(v.shuffleRead)}</td> + <td>{Utils.bytesToString(v.shuffleWrite)}</td> + </tr> + } + } + } + case _ => { Seq[Node]() } + } + } +} diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/IndexPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/IndexPage.scala index b39c0e9769..ca5a28625b 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/IndexPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/IndexPage.scala @@ -38,7 +38,7 @@ private[spark] class IndexPage(parent: JobProgressUI) { val now = System.currentTimeMillis() var activeTime = 0L - for (tasks <- listener.stageToTasksActive.values; t <- tasks) { + for (tasks <- listener.stageIdToTasksActive.values; t <- tasks) { activeTime += t.timeRunning(now) } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala index eb3b4e8522..b7b87250b9 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala @@ -33,55 +33,56 @@ import org.apache.spark.scheduler._ */ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkListener { // How many stages to remember - val RETAINED_STAGES = System.getProperty("spark.ui.retained_stages", "1000").toInt + val RETAINED_STAGES = sc.conf.get("spark.ui.retained_stages", "1000").toInt val DEFAULT_POOL_NAME = "default" - val stageToPool = new HashMap[Stage, String]() - val stageToDescription = new HashMap[Stage, String]() - val poolToActiveStages = new HashMap[String, HashSet[Stage]]() + val stageIdToPool = new HashMap[Int, String]() + val stageIdToDescription = new HashMap[Int, String]() + val poolToActiveStages = new HashMap[String, HashSet[StageInfo]]() - val activeStages = HashSet[Stage]() - val completedStages = ListBuffer[Stage]() - val failedStages = ListBuffer[Stage]() + val activeStages = HashSet[StageInfo]() + val completedStages = ListBuffer[StageInfo]() + val failedStages = ListBuffer[StageInfo]() // Total metrics reflect metrics only for completed tasks var totalTime = 0L var totalShuffleRead = 0L var totalShuffleWrite = 0L - val stageToTime = HashMap[Int, Long]() - val stageToShuffleRead = HashMap[Int, Long]() - val stageToShuffleWrite = HashMap[Int, Long]() - val stageToTasksActive = HashMap[Int, HashSet[TaskInfo]]() - val stageToTasksComplete = HashMap[Int, Int]() - val stageToTasksFailed = HashMap[Int, Int]() - val stageToTaskInfos = + val stageIdToTime = HashMap[Int, Long]() + val stageIdToShuffleRead = HashMap[Int, Long]() + val stageIdToShuffleWrite = HashMap[Int, Long]() + val stageIdToTasksActive = HashMap[Int, HashSet[TaskInfo]]() + val stageIdToTasksComplete = HashMap[Int, Int]() + val stageIdToTasksFailed = HashMap[Int, Int]() + val stageIdToTaskInfos = HashMap[Int, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]]() + val stageIdToExecutorSummaries = HashMap[Int, HashMap[String, ExecutorSummary]]() override def onJobStart(jobStart: SparkListenerJobStart) {} - override def onStageCompleted(stageCompleted: StageCompleted) = synchronized { - val stage = stageCompleted.stageInfo.stage - poolToActiveStages(stageToPool(stage)) -= stage + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized { + val stage = stageCompleted.stage + poolToActiveStages(stageIdToPool(stage.stageId)) -= stage activeStages -= stage completedStages += stage trimIfNecessary(completedStages) } /** If stages is too large, remove and garbage collect old stages */ - def trimIfNecessary(stages: ListBuffer[Stage]) = synchronized { + def trimIfNecessary(stages: ListBuffer[StageInfo]) = synchronized { if (stages.size > RETAINED_STAGES) { val toRemove = RETAINED_STAGES / 10 stages.takeRight(toRemove).foreach( s => { - stageToTaskInfos.remove(s.id) - stageToTime.remove(s.id) - stageToShuffleRead.remove(s.id) - stageToShuffleWrite.remove(s.id) - stageToTasksActive.remove(s.id) - stageToTasksComplete.remove(s.id) - stageToTasksFailed.remove(s.id) - stageToPool.remove(s) - if (stageToDescription.contains(s)) {stageToDescription.remove(s)} + stageIdToTaskInfos.remove(s.stageId) + stageIdToTime.remove(s.stageId) + stageIdToShuffleRead.remove(s.stageId) + stageIdToShuffleWrite.remove(s.stageId) + stageIdToTasksActive.remove(s.stageId) + stageIdToTasksComplete.remove(s.stageId) + stageIdToTasksFailed.remove(s.stageId) + stageIdToPool.remove(s.stageId) + if (stageIdToDescription.contains(s.stageId)) {stageIdToDescription.remove(s.stageId)} }) stages.trimEnd(toRemove) } @@ -95,63 +96,99 @@ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkList val poolName = Option(stageSubmitted.properties).map { p => p.getProperty("spark.scheduler.pool", DEFAULT_POOL_NAME) }.getOrElse(DEFAULT_POOL_NAME) - stageToPool(stage) = poolName + stageIdToPool(stage.stageId) = poolName val description = Option(stageSubmitted.properties).flatMap { p => Option(p.getProperty(SparkContext.SPARK_JOB_DESCRIPTION)) } - description.map(d => stageToDescription(stage) = d) + description.map(d => stageIdToDescription(stage.stageId) = d) - val stages = poolToActiveStages.getOrElseUpdate(poolName, new HashSet[Stage]()) + val stages = poolToActiveStages.getOrElseUpdate(poolName, new HashSet[StageInfo]()) stages += stage } - + override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized { val sid = taskStart.task.stageId - val tasksActive = stageToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]()) + val tasksActive = stageIdToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]()) tasksActive += taskStart.taskInfo - val taskList = stageToTaskInfos.getOrElse( + val taskList = stageIdToTaskInfos.getOrElse( sid, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) taskList += ((taskStart.taskInfo, None, None)) - stageToTaskInfos(sid) = taskList + stageIdToTaskInfos(sid) = taskList + } + + override def onTaskGettingResult(taskGettingResult: SparkListenerTaskGettingResult) + = synchronized { + // Do nothing: because we don't do a deep copy of the TaskInfo, the TaskInfo in + // stageToTaskInfos already has the updated status. } - + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { val sid = taskEnd.task.stageId - val tasksActive = stageToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]()) + + // create executor summary map if necessary + val executorSummaryMap = stageIdToExecutorSummaries.getOrElseUpdate(key = sid, + op = new HashMap[String, ExecutorSummary]()) + executorSummaryMap.getOrElseUpdate(key = taskEnd.taskInfo.executorId, + op = new ExecutorSummary()) + + val executorSummary = executorSummaryMap.get(taskEnd.taskInfo.executorId) + executorSummary match { + case Some(y) => { + // first update failed-task, succeed-task + taskEnd.reason match { + case Success => + y.succeededTasks += 1 + case _ => + y.failedTasks += 1 + } + + // update duration + y.taskTime += taskEnd.taskInfo.duration + + Option(taskEnd.taskMetrics).foreach { taskMetrics => + taskMetrics.shuffleReadMetrics.foreach { y.shuffleRead += _.remoteBytesRead } + taskMetrics.shuffleWriteMetrics.foreach { y.shuffleWrite += _.shuffleBytesWritten } + } + } + case _ => {} + } + + val tasksActive = stageIdToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]()) tasksActive -= taskEnd.taskInfo + val (failureInfo, metrics): (Option[ExceptionFailure], Option[TaskMetrics]) = taskEnd.reason match { case e: ExceptionFailure => - stageToTasksFailed(sid) = stageToTasksFailed.getOrElse(sid, 0) + 1 + stageIdToTasksFailed(sid) = stageIdToTasksFailed.getOrElse(sid, 0) + 1 (Some(e), e.metrics) case _ => - stageToTasksComplete(sid) = stageToTasksComplete.getOrElse(sid, 0) + 1 + stageIdToTasksComplete(sid) = stageIdToTasksComplete.getOrElse(sid, 0) + 1 (None, Option(taskEnd.taskMetrics)) } - stageToTime.getOrElseUpdate(sid, 0L) + stageIdToTime.getOrElseUpdate(sid, 0L) val time = metrics.map(m => m.executorRunTime).getOrElse(0) - stageToTime(sid) += time + stageIdToTime(sid) += time totalTime += time - stageToShuffleRead.getOrElseUpdate(sid, 0L) + stageIdToShuffleRead.getOrElseUpdate(sid, 0L) val shuffleRead = metrics.flatMap(m => m.shuffleReadMetrics).map(s => s.remoteBytesRead).getOrElse(0L) - stageToShuffleRead(sid) += shuffleRead + stageIdToShuffleRead(sid) += shuffleRead totalShuffleRead += shuffleRead - stageToShuffleWrite.getOrElseUpdate(sid, 0L) + stageIdToShuffleWrite.getOrElseUpdate(sid, 0L) val shuffleWrite = metrics.flatMap(m => m.shuffleWriteMetrics).map(s => s.shuffleBytesWritten).getOrElse(0L) - stageToShuffleWrite(sid) += shuffleWrite + stageIdToShuffleWrite(sid) += shuffleWrite totalShuffleWrite += shuffleWrite - val taskList = stageToTaskInfos.getOrElse( + val taskList = stageIdToTaskInfos.getOrElse( sid, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) taskList -= ((taskEnd.taskInfo, None, None)) taskList += ((taskEnd.taskInfo, metrics, failureInfo)) - stageToTaskInfos(sid) = taskList + stageIdToTaskInfos(sid) = taskList } override def onJobEnd(jobEnd: SparkListenerJobEnd) = synchronized { @@ -159,10 +196,15 @@ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkList case end: SparkListenerJobEnd => end.jobResult match { case JobFailed(ex, Some(stage)) => - activeStages -= stage - poolToActiveStages(stageToPool(stage)) -= stage - failedStages += stage - trimIfNecessary(failedStages) + /* If two jobs share a stage we could get this failure message twice. So we first + * check whether we've already retired this stage. */ + val stageInfo = activeStages.filter(s => s.stageId == stage.id).headOption + stageInfo.foreach {s => + activeStages -= s + poolToActiveStages(stageIdToPool(stage.id)) -= s + failedStages += s + trimIfNecessary(failedStages) + } case _ => } case _ => diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressUI.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressUI.scala index e7eab374ad..c1ee2f3d00 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressUI.scala @@ -17,7 +17,7 @@ package org.apache.spark.ui.jobs -import akka.util.Duration +import scala.concurrent.duration._ import java.text.SimpleDateFormat diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala index 06810d8dbc..cfeeccda41 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala @@ -21,13 +21,13 @@ import scala.collection.mutable.HashMap import scala.collection.mutable.HashSet import scala.xml.Node -import org.apache.spark.scheduler.{Schedulable, Stage} +import org.apache.spark.scheduler.{Schedulable, StageInfo} import org.apache.spark.ui.UIUtils /** Table showing list of pools */ private[spark] class PoolTable(pools: Seq[Schedulable], listener: JobProgressListener) { - var poolToActiveStages: HashMap[String, HashSet[Stage]] = listener.poolToActiveStages + var poolToActiveStages: HashMap[String, HashSet[StageInfo]] = listener.poolToActiveStages def toNodeSeq(): Seq[Node] = { listener.synchronized { @@ -35,7 +35,7 @@ private[spark] class PoolTable(pools: Seq[Schedulable], listener: JobProgressLis } } - private def poolTable(makeRow: (Schedulable, HashMap[String, HashSet[Stage]]) => Seq[Node], + private def poolTable(makeRow: (Schedulable, HashMap[String, HashSet[StageInfo]]) => Seq[Node], rows: Seq[Schedulable] ): Seq[Node] = { <table class="table table-bordered table-striped table-condensed sortable table-fixed"> @@ -53,7 +53,7 @@ private[spark] class PoolTable(pools: Seq[Schedulable], listener: JobProgressLis </table> } - private def poolRow(p: Schedulable, poolToActiveStages: HashMap[String, HashSet[Stage]]) + private def poolRow(p: Schedulable, poolToActiveStages: HashMap[String, HashSet[StageInfo]]) : Seq[Node] = { val activeStages = poolToActiveStages.get(p.name) match { case Some(stages) => stages.size diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala index 163a3746ea..8dcfeacb60 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala @@ -40,7 +40,7 @@ private[spark] class StagePage(parent: JobProgressUI) { val stageId = request.getParameter("id").toInt val now = System.currentTimeMillis() - if (!listener.stageToTaskInfos.contains(stageId)) { + if (!listener.stageIdToTaskInfos.contains(stageId)) { val content = <div> <h4>Summary Metrics</h4> No tasks have started yet @@ -49,23 +49,25 @@ private[spark] class StagePage(parent: JobProgressUI) { return headerSparkPage(content, parent.sc, "Details for Stage %s".format(stageId), Stages) } - val tasks = listener.stageToTaskInfos(stageId).toSeq.sortBy(_._1.launchTime) + val tasks = listener.stageIdToTaskInfos(stageId).toSeq.sortBy(_._1.launchTime) val numCompleted = tasks.count(_._1.finished) - val shuffleReadBytes = listener.stageToShuffleRead.getOrElse(stageId, 0L) + val shuffleReadBytes = listener.stageIdToShuffleRead.getOrElse(stageId, 0L) val hasShuffleRead = shuffleReadBytes > 0 - val shuffleWriteBytes = listener.stageToShuffleWrite.getOrElse(stageId, 0L) + val shuffleWriteBytes = listener.stageIdToShuffleWrite.getOrElse(stageId, 0L) val hasShuffleWrite = shuffleWriteBytes > 0 var activeTime = 0L - listener.stageToTasksActive(stageId).foreach(activeTime += _.timeRunning(now)) + listener.stageIdToTasksActive(stageId).foreach(activeTime += _.timeRunning(now)) + + val finishedTasks = listener.stageIdToTaskInfos(stageId).filter(_._1.finished) val summary = <div> <ul class="unstyled"> <li> - <strong>CPU time: </strong> - {parent.formatDuration(listener.stageToTime.getOrElse(stageId, 0L) + activeTime)} + <strong>Total task time across all tasks: </strong> + {parent.formatDuration(listener.stageIdToTime.getOrElse(stageId, 0L) + activeTime)} </li> {if (hasShuffleRead) <li> @@ -83,10 +85,10 @@ private[spark] class StagePage(parent: JobProgressUI) { </div> val taskHeaders: Seq[String] = - Seq("Task ID", "Status", "Locality Level", "Executor", "Launch Time", "Duration") ++ - Seq("GC Time") ++ + Seq("Task Index", "Task ID", "Status", "Locality Level", "Executor", "Launch Time") ++ + Seq("Duration", "GC Time", "Result Ser Time") ++ {if (hasShuffleRead) Seq("Shuffle Read") else Nil} ++ - {if (hasShuffleWrite) Seq("Shuffle Write") else Nil} ++ + {if (hasShuffleWrite) Seq("Write Time", "Shuffle Write") else Nil} ++ Seq("Errors") val taskTable = listingTable(taskHeaders, taskRow(hasShuffleRead, hasShuffleWrite), tasks) @@ -99,11 +101,43 @@ private[spark] class StagePage(parent: JobProgressUI) { None } else { + val serializationTimes = validTasks.map{case (info, metrics, exception) => + metrics.get.resultSerializationTime.toDouble} + val serializationQuantiles = "Result serialization time" +: Distribution(serializationTimes).get.getQuantiles().map( + ms => parent.formatDuration(ms.toLong)) + val serviceTimes = validTasks.map{case (info, metrics, exception) => metrics.get.executorRunTime.toDouble} val serviceQuantiles = "Duration" +: Distribution(serviceTimes).get.getQuantiles().map( ms => parent.formatDuration(ms.toLong)) + val gettingResultTimes = validTasks.map{case (info, metrics, exception) => + if (info.gettingResultTime > 0) { + (info.finishTime - info.gettingResultTime).toDouble + } else { + 0.0 + } + } + val gettingResultQuantiles = ("Time spent fetching task results" +: + Distribution(gettingResultTimes).get.getQuantiles().map( + millis => parent.formatDuration(millis.toLong))) + // The scheduler delay includes the network delay to send the task to the worker + // machine and to send back the result (but not the time to fetch the task result, + // if it needed to be fetched from the block manager on the worker). + val schedulerDelays = validTasks.map{case (info, metrics, exception) => + val totalExecutionTime = { + if (info.gettingResultTime > 0) { + (info.gettingResultTime - info.launchTime).toDouble + } else { + (info.finishTime - info.launchTime).toDouble + } + } + totalExecutionTime - metrics.get.executorRunTime + } + val schedulerDelayQuantiles = ("Scheduler delay" +: + Distribution(schedulerDelays).get.getQuantiles().map( + millis => parent.formatDuration(millis.toLong))) + def getQuantileCols(data: Seq[Double]) = Distribution(data).get.getQuantiles().map(d => Utils.bytesToString(d.toLong)) @@ -119,7 +153,11 @@ private[spark] class StagePage(parent: JobProgressUI) { } val shuffleWriteQuantiles = "Shuffle Write" +: getQuantileCols(shuffleWriteSizes) - val listings: Seq[Seq[String]] = Seq(serviceQuantiles, + val listings: Seq[Seq[String]] = Seq( + serializationQuantiles, + serviceQuantiles, + gettingResultQuantiles, + schedulerDelayQuantiles, if (hasShuffleRead) shuffleReadQuantiles else Nil, if (hasShuffleWrite) shuffleWriteQuantiles else Nil) @@ -128,12 +166,13 @@ private[spark] class StagePage(parent: JobProgressUI) { def quantileRow(data: Seq[String]): Seq[Node] = <tr> {data.map(d => <td>{d}</td>)} </tr> Some(listingTable(quantileHeaders, quantileRow, listings, fixedWidth = true)) } - + val executorTable = new ExecutorTable(parent, stageId) val content = summary ++ <h4>Summary Metrics for {numCompleted} Completed Tasks</h4> ++ <div>{summaryTable.getOrElse("No tasks have reported metrics yet.")}</div> ++ - <h4>Tasks</h4> ++ taskTable; + <h4>Aggregated Metrics by Executors</h4> ++ executorTable.toNodeSeq() ++ + <h4>Tasks</h4> ++ taskTable headerSparkPage(content, parent.sc, "Details for Stage %d".format(stageId), Stages) } @@ -151,8 +190,23 @@ private[spark] class StagePage(parent: JobProgressUI) { val formatDuration = if (info.status == "RUNNING") parent.formatDuration(duration) else metrics.map(m => parent.formatDuration(m.executorRunTime)).getOrElse("") val gcTime = metrics.map(m => m.jvmGCTime).getOrElse(0L) + val serializationTime = metrics.map(m => m.resultSerializationTime).getOrElse(0L) + + val maybeShuffleRead = metrics.flatMap{m => m.shuffleReadMetrics}.map{s => s.remoteBytesRead} + val shuffleReadSortable = maybeShuffleRead.map(_.toString).getOrElse("") + val shuffleReadReadable = maybeShuffleRead.map{Utils.bytesToString(_)}.getOrElse("") + + val maybeShuffleWrite = metrics.flatMap{m => m.shuffleWriteMetrics}.map{s => s.shuffleBytesWritten} + val shuffleWriteSortable = maybeShuffleWrite.map(_.toString).getOrElse("") + val shuffleWriteReadable = maybeShuffleWrite.map{Utils.bytesToString(_)}.getOrElse("") + + val maybeWriteTime = metrics.flatMap{m => m.shuffleWriteMetrics}.map{s => s.shuffleWriteTime} + val writeTimeSortable = maybeWriteTime.map(_.toString).getOrElse("") + val writeTimeReadable = maybeWriteTime.map{ t => t / (1000 * 1000)}.map{ ms => + if (ms == 0) "" else parent.formatDuration(ms)}.getOrElse("") <tr> + <td>{info.index}</td> <td>{info.taskId}</td> <td>{info.status}</td> <td>{info.taskLocality}</td> @@ -164,13 +218,21 @@ private[spark] class StagePage(parent: JobProgressUI) { <td sorttable_customkey={gcTime.toString}> {if (gcTime > 0) parent.formatDuration(gcTime) else ""} </td> + <td sorttable_customkey={serializationTime.toString}> + {if (serializationTime > 0) parent.formatDuration(serializationTime) else ""} + </td> {if (shuffleRead) { - <td>{metrics.flatMap{m => m.shuffleReadMetrics}.map{s => - Utils.bytesToString(s.remoteBytesRead)}.getOrElse("")}</td> + <td sorttable_customkey={shuffleReadSortable}> + {shuffleReadReadable} + </td> }} {if (shuffleWrite) { - <td>{metrics.flatMap{m => m.shuffleWriteMetrics}.map{s => - Utils.bytesToString(s.shuffleBytesWritten)}.getOrElse("")}</td> + <td sorttable_customkey={writeTimeSortable}> + {writeTimeReadable} + </td> + <td sorttable_customkey={shuffleWriteSortable}> + {shuffleWriteReadable} + </td> }} <td>{exception.map(e => <span> diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala index 07db8622da..463d85dfd5 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala @@ -22,13 +22,13 @@ import java.util.Date import scala.xml.Node import scala.collection.mutable.HashSet -import org.apache.spark.scheduler.{SchedulingMode, Stage, TaskInfo} +import org.apache.spark.scheduler.{SchedulingMode, StageInfo, TaskInfo} import org.apache.spark.ui.UIUtils import org.apache.spark.util.Utils /** Page showing list of all ongoing and recently finished stages */ -private[spark] class StageTable(val stages: Seq[Stage], val parent: JobProgressUI) { +private[spark] class StageTable(val stages: Seq[StageInfo], val parent: JobProgressUI) { val listener = parent.listener val dateFmt = parent.dateFmt @@ -48,7 +48,7 @@ private[spark] class StageTable(val stages: Seq[Stage], val parent: JobProgressU {if (isFairScheduler) {<th>Pool Name</th>} else {}} <th>Description</th> <th>Submitted</th> - <th>Duration</th> + <th>Task Time</th> <th>Tasks: Succeeded/Total</th> <th>Shuffle Read</th> <th>Shuffle Write</th> @@ -73,40 +73,43 @@ private[spark] class StageTable(val stages: Seq[Stage], val parent: JobProgressU } - private def stageRow(s: Stage): Seq[Node] = { + private def stageRow(s: StageInfo): Seq[Node] = { val submissionTime = s.submissionTime match { case Some(t) => dateFmt.format(new Date(t)) case None => "Unknown" } - val shuffleRead = listener.stageToShuffleRead.getOrElse(s.id, 0L) match { + val shuffleReadSortable = listener.stageIdToShuffleRead.getOrElse(s.stageId, 0L) + val shuffleRead = shuffleReadSortable match { case 0 => "" case b => Utils.bytesToString(b) } - val shuffleWrite = listener.stageToShuffleWrite.getOrElse(s.id, 0L) match { + + val shuffleWriteSortable = listener.stageIdToShuffleWrite.getOrElse(s.stageId, 0L) + val shuffleWrite = shuffleWriteSortable match { case 0 => "" case b => Utils.bytesToString(b) } - val startedTasks = listener.stageToTasksActive.getOrElse(s.id, HashSet[TaskInfo]()).size - val completedTasks = listener.stageToTasksComplete.getOrElse(s.id, 0) - val failedTasks = listener.stageToTasksFailed.getOrElse(s.id, 0) match { + val startedTasks = listener.stageIdToTasksActive.getOrElse(s.stageId, HashSet[TaskInfo]()).size + val completedTasks = listener.stageIdToTasksComplete.getOrElse(s.stageId, 0) + val failedTasks = listener.stageIdToTasksFailed.getOrElse(s.stageId, 0) match { case f if f > 0 => "(%s failed)".format(f) case _ => "" } - val totalTasks = s.numPartitions + val totalTasks = s.numTasks - val poolName = listener.stageToPool.get(s) + val poolName = listener.stageIdToPool.get(s.stageId) val nameLink = - <a href={"%s/stages/stage?id=%s".format(UIUtils.prependBaseUri(),s.id)}>{s.name}</a> - val description = listener.stageToDescription.get(s) + <a href={"%s/stages/stage?id=%s".format(UIUtils.prependBaseUri(),s.stageId)}>{s.name}</a> + val description = listener.stageIdToDescription.get(s.stageId) .map(d => <div><em>{d}</em></div><div>{nameLink}</div>).getOrElse(nameLink) val finishTime = s.completionTime.getOrElse(System.currentTimeMillis()) val duration = s.submissionTime.map(t => finishTime - t) <tr> - <td>{s.id}</td> + <td>{s.stageId}</td> {if (isFairScheduler) { <td><a href={"%s/stages/pool?poolname=%s".format(UIUtils.prependBaseUri(),poolName.get)}> {poolName.get}</a></td>} @@ -119,8 +122,8 @@ private[spark] class StageTable(val stages: Seq[Stage], val parent: JobProgressU <td class="progress-cell"> {makeProgressBar(startedTasks, completedTasks, failedTasks, totalTasks)} </td> - <td>{shuffleRead}</td> - <td>{shuffleWrite}</td> + <td sorttable_customekey={shuffleReadSortable.toString}>{shuffleRead}</td> + <td sorttable_customekey={shuffleWriteSortable.toString}>{shuffleWrite}</td> </tr> } } diff --git a/core/src/main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala b/core/src/main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala index 1d633d374a..39f422dd6b 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala @@ -17,7 +17,7 @@ package org.apache.spark.ui.storage -import akka.util.Duration +import scala.concurrent.duration._ import javax.servlet.http.HttpServletRequest @@ -28,9 +28,6 @@ import org.apache.spark.ui.JettyUtils._ /** Web UI showing storage status of all RDD's in the given SparkContext. */ private[spark] class BlockManagerUI(val sc: SparkContext) extends Logging { - implicit val timeout = Duration.create( - System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") - val indexPage = new IndexPage(this) val rddPage = new RDDPage(this) diff --git a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala index 43c1257677..b83cd54f3c 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala @@ -21,7 +21,7 @@ import javax.servlet.http.HttpServletRequest import scala.xml.Node -import org.apache.spark.storage.{StorageStatus, StorageUtils} +import org.apache.spark.storage.{BlockId, StorageStatus, StorageUtils} import org.apache.spark.storage.BlockManagerMasterActor.BlockStatus import org.apache.spark.ui.UIUtils._ import org.apache.spark.ui.Page._ @@ -33,21 +33,20 @@ private[spark] class RDDPage(parent: BlockManagerUI) { val sc = parent.sc def render(request: HttpServletRequest): Seq[Node] = { - val id = request.getParameter("id") - val prefix = "rdd_" + id.toString + val id = request.getParameter("id").toInt val storageStatusList = sc.getExecutorStorageStatus - val filteredStorageStatusList = StorageUtils. - filterStorageStatusByPrefix(storageStatusList, prefix) + val filteredStorageStatusList = StorageUtils.filterStorageStatusByRDD(storageStatusList, id) val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head val workerHeaders = Seq("Host", "Memory Usage", "Disk Usage") - val workers = filteredStorageStatusList.map((prefix, _)) + val workers = filteredStorageStatusList.map((id, _)) val workerTable = listingTable(workerHeaders, workerRow, workers) val blockHeaders = Seq("Block Name", "Storage Level", "Size in Memory", "Size on Disk", "Executors") - val blockStatuses = filteredStorageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1) + val blockStatuses = filteredStorageStatusList.flatMap(_.blocks).toArray. + sortWith(_._1.name < _._1.name) val blockLocations = StorageUtils.blockLocationsFromStorageStatus(filteredStorageStatusList) val blocks = blockStatuses.map { case(id, status) => (id, status, blockLocations.get(id).getOrElse(Seq("UNKNOWN"))) @@ -99,7 +98,7 @@ private[spark] class RDDPage(parent: BlockManagerUI) { headerSparkPage(content, parent.sc, "RDD Storage Info for " + rddInfo.name, Storage) } - def blockRow(row: (String, BlockStatus, Seq[String])): Seq[Node] = { + def blockRow(row: (BlockId, BlockStatus, Seq[String])): Seq[Node] = { val (id, block, locations) = row <tr> <td>{id}</td> @@ -118,15 +117,15 @@ private[spark] class RDDPage(parent: BlockManagerUI) { </tr> } - def workerRow(worker: (String, StorageStatus)): Seq[Node] = { - val (prefix, status) = worker + def workerRow(worker: (Int, StorageStatus)): Seq[Node] = { + val (rddId, status) = worker <tr> <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td> <td> - {Utils.bytesToString(status.memUsed(prefix))} + {Utils.bytesToString(status.memUsedByRDD(rddId))} ({Utils.bytesToString(status.memRemaining)} Remaining) </td> - <td>{Utils.bytesToString(status.diskUsed(prefix))}</td> + <td>{Utils.bytesToString(status.diskUsedByRDD(rddId))}</td> </tr> } } diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala index d4c5065c3f..362cea5e3e 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -17,11 +17,11 @@ package org.apache.spark.util -import akka.actor.{ActorSystem, ExtendedActorSystem} -import com.typesafe.config.ConfigFactory -import akka.util.duration._ -import akka.remote.RemoteActorRefProvider +import scala.concurrent.duration.{Duration, FiniteDuration} +import akka.actor.{ActorSystem, ExtendedActorSystem, IndestructibleActorSystem} +import com.typesafe.config.ConfigFactory +import org.apache.spark.SparkConf /** * Various utility classes for working with Akka. @@ -34,39 +34,61 @@ private[spark] object AkkaUtils { * * Note: the `name` parameter is important, as even if a client sends a message to right * host + port, if the system name is incorrect, Akka will drop the message. + * + * If indestructible is set to true, the Actor System will continue running in the event + * of a fatal exception. This is used by [[org.apache.spark.executor.Executor]]. */ - def createActorSystem(name: String, host: String, port: Int): (ActorSystem, Int) = { - val akkaThreads = System.getProperty("spark.akka.threads", "4").toInt - val akkaBatchSize = System.getProperty("spark.akka.batchSize", "15").toInt - val akkaTimeout = System.getProperty("spark.akka.timeout", "60").toInt - val akkaFrameSize = System.getProperty("spark.akka.frameSize", "10").toInt - val lifecycleEvents = if (System.getProperty("spark.akka.logLifecycleEvents", "false").toBoolean) "on" else "off" - // 10 seconds is the default akka timeout, but in a cluster, we need higher by default. - val akkaWriteTimeout = System.getProperty("spark.akka.writeTimeout", "30").toInt - - val akkaConf = ConfigFactory.parseString(""" - akka.daemonic = on - akka.event-handlers = ["akka.event.slf4j.Slf4jEventHandler"] - akka.stdout-loglevel = "ERROR" - akka.actor.provider = "akka.remote.RemoteActorRefProvider" - akka.remote.transport = "akka.remote.netty.NettyRemoteTransport" - akka.remote.netty.hostname = "%s" - akka.remote.netty.port = %d - akka.remote.netty.connection-timeout = %ds - akka.remote.netty.message-frame-size = %d MiB - akka.remote.netty.execution-pool-size = %d - akka.actor.default-dispatcher.throughput = %d - akka.remote.log-remote-lifecycle-events = %s - akka.remote.netty.write-timeout = %ds - """.format(host, port, akkaTimeout, akkaFrameSize, akkaThreads, akkaBatchSize, - lifecycleEvents, akkaWriteTimeout)) + def createActorSystem(name: String, host: String, port: Int, indestructible: Boolean = false, + conf: SparkConf): (ActorSystem, Int) = { + + val akkaThreads = conf.get("spark.akka.threads", "4").toInt + val akkaBatchSize = conf.get("spark.akka.batchSize", "15").toInt + + val akkaTimeout = conf.get("spark.akka.timeout", "100").toInt - val actorSystem = ActorSystem(name, akkaConf) + val akkaFrameSize = conf.get("spark.akka.frameSize", "10").toInt + val lifecycleEvents = + if (conf.get("spark.akka.logLifecycleEvents", "false").toBoolean) "on" else "off" + + val akkaHeartBeatPauses = conf.get("spark.akka.heartbeat.pauses", "600").toInt + val akkaFailureDetector = + conf.get("spark.akka.failure-detector.threshold", "300.0").toDouble + val akkaHeartBeatInterval = conf.get("spark.akka.heartbeat.interval", "1000").toInt + + val akkaConf = ConfigFactory.parseString( + s""" + |akka.daemonic = on + |akka.loggers = [""akka.event.slf4j.Slf4jLogger""] + |akka.stdout-loglevel = "ERROR" + |akka.jvm-exit-on-fatal-error = off + |akka.remote.transport-failure-detector.heartbeat-interval = $akkaHeartBeatInterval s + |akka.remote.transport-failure-detector.acceptable-heartbeat-pause = $akkaHeartBeatPauses s + |akka.remote.transport-failure-detector.threshold = $akkaFailureDetector + |akka.actor.provider = "akka.remote.RemoteActorRefProvider" + |akka.remote.netty.tcp.transport-class = "akka.remote.transport.netty.NettyTransport" + |akka.remote.netty.tcp.hostname = "$host" + |akka.remote.netty.tcp.port = $port + |akka.remote.netty.tcp.tcp-nodelay = on + |akka.remote.netty.tcp.connection-timeout = $akkaTimeout s + |akka.remote.netty.tcp.maximum-frame-size = ${akkaFrameSize}MiB + |akka.remote.netty.tcp.execution-pool-size = $akkaThreads + |akka.actor.default-dispatcher.throughput = $akkaBatchSize + |akka.remote.log-remote-lifecycle-events = $lifecycleEvents + """.stripMargin) + + val actorSystem = if (indestructible) { + IndestructibleActorSystem(name, akkaConf) + } else { + ActorSystem(name, akkaConf) + } - // Figure out the port number we bound to, in case port was passed as 0. This is a bit of a - // hack because Akka doesn't let you figure out the port through the public API yet. val provider = actorSystem.asInstanceOf[ExtendedActorSystem].provider - val boundPort = provider.asInstanceOf[RemoteActorRefProvider].transport.address.port.get - return (actorSystem, boundPort) + val boundPort = provider.getDefaultAddress.port.get + (actorSystem, boundPort) + } + + /** Returns the default Spark timeout to use for Akka ask operations. */ + def askTimeout(conf: SparkConf): FiniteDuration = { + Duration.create(conf.get("spark.akka.askTimeout", "30").toLong, "seconds") } } diff --git a/core/src/main/scala/org/apache/spark/util/AppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/AppendOnlyMap.scala new file mode 100644 index 0000000000..8bb4ee3bfa --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/AppendOnlyMap.scala @@ -0,0 +1,237 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +/** + * A simple open hash table optimized for the append-only use case, where keys + * are never removed, but the value for each key may be changed. + * + * This implementation uses quadratic probing with a power-of-2 hash table + * size, which is guaranteed to explore all spaces for each key (see + * http://en.wikipedia.org/wiki/Quadratic_probing). + * + * TODO: Cache the hash values of each key? java.util.HashMap does that. + */ +private[spark] +class AppendOnlyMap[K, V](initialCapacity: Int = 64) extends Iterable[(K, V)] with Serializable { + require(initialCapacity <= (1 << 29), "Can't make capacity bigger than 2^29 elements") + require(initialCapacity >= 1, "Invalid initial capacity") + + private var capacity = nextPowerOf2(initialCapacity) + private var mask = capacity - 1 + private var curSize = 0 + private var growThreshold = LOAD_FACTOR * capacity + + // Holds keys and values in the same array for memory locality; specifically, the order of + // elements is key0, value0, key1, value1, key2, value2, etc. + private var data = new Array[AnyRef](2 * capacity) + + // Treat the null key differently so we can use nulls in "data" to represent empty items. + private var haveNullValue = false + private var nullValue: V = null.asInstanceOf[V] + + private val LOAD_FACTOR = 0.7 + + /** Get the value for a given key */ + def apply(key: K): V = { + val k = key.asInstanceOf[AnyRef] + if (k.eq(null)) { + return nullValue + } + var pos = rehash(k.hashCode) & mask + var i = 1 + while (true) { + val curKey = data(2 * pos) + if (k.eq(curKey) || k.equals(curKey)) { + return data(2 * pos + 1).asInstanceOf[V] + } else if (curKey.eq(null)) { + return null.asInstanceOf[V] + } else { + val delta = i + pos = (pos + delta) & mask + i += 1 + } + } + return null.asInstanceOf[V] + } + + /** Set the value for a key */ + def update(key: K, value: V): Unit = { + val k = key.asInstanceOf[AnyRef] + if (k.eq(null)) { + if (!haveNullValue) { + incrementSize() + } + nullValue = value + haveNullValue = true + return + } + var pos = rehash(key.hashCode) & mask + var i = 1 + while (true) { + val curKey = data(2 * pos) + if (curKey.eq(null)) { + data(2 * pos) = k + data(2 * pos + 1) = value.asInstanceOf[AnyRef] + incrementSize() // Since we added a new key + return + } else if (k.eq(curKey) || k.equals(curKey)) { + data(2 * pos + 1) = value.asInstanceOf[AnyRef] + return + } else { + val delta = i + pos = (pos + delta) & mask + i += 1 + } + } + } + + /** + * Set the value for key to updateFunc(hadValue, oldValue), where oldValue will be the old value + * for key, if any, or null otherwise. Returns the newly updated value. + */ + def changeValue(key: K, updateFunc: (Boolean, V) => V): V = { + val k = key.asInstanceOf[AnyRef] + if (k.eq(null)) { + if (!haveNullValue) { + incrementSize() + } + nullValue = updateFunc(haveNullValue, nullValue) + haveNullValue = true + return nullValue + } + var pos = rehash(k.hashCode) & mask + var i = 1 + while (true) { + val curKey = data(2 * pos) + if (k.eq(curKey) || k.equals(curKey)) { + val newValue = updateFunc(true, data(2 * pos + 1).asInstanceOf[V]) + data(2 * pos + 1) = newValue.asInstanceOf[AnyRef] + return newValue + } else if (curKey.eq(null)) { + val newValue = updateFunc(false, null.asInstanceOf[V]) + data(2 * pos) = k + data(2 * pos + 1) = newValue.asInstanceOf[AnyRef] + incrementSize() + return newValue + } else { + val delta = i + pos = (pos + delta) & mask + i += 1 + } + } + null.asInstanceOf[V] // Never reached but needed to keep compiler happy + } + + /** Iterator method from Iterable */ + override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { + var pos = -1 + + /** Get the next value we should return from next(), or null if we're finished iterating */ + def nextValue(): (K, V) = { + if (pos == -1) { // Treat position -1 as looking at the null value + if (haveNullValue) { + return (null.asInstanceOf[K], nullValue) + } + pos += 1 + } + while (pos < capacity) { + if (!data(2 * pos).eq(null)) { + return (data(2 * pos).asInstanceOf[K], data(2 * pos + 1).asInstanceOf[V]) + } + pos += 1 + } + null + } + + override def hasNext: Boolean = nextValue() != null + + override def next(): (K, V) = { + val value = nextValue() + if (value == null) { + throw new NoSuchElementException("End of iterator") + } + pos += 1 + value + } + } + + override def size: Int = curSize + + /** Increase table size by 1, rehashing if necessary */ + private def incrementSize() { + curSize += 1 + if (curSize > growThreshold) { + growTable() + } + } + + /** + * Re-hash a value to deal better with hash functions that don't differ in the lower bits. + * We use the Murmur Hash 3 finalization step that's also used in fastutil. + */ + private def rehash(h: Int): Int = { + it.unimi.dsi.fastutil.HashCommon.murmurHash3(h) + } + + /** Double the table's size and re-hash everything */ + private def growTable() { + val newCapacity = capacity * 2 + if (newCapacity >= (1 << 30)) { + // We can't make the table this big because we want an array of 2x + // that size for our data, but array sizes are at most Int.MaxValue + throw new Exception("Can't make capacity bigger than 2^29 elements") + } + val newData = new Array[AnyRef](2 * newCapacity) + val newMask = newCapacity - 1 + // Insert all our old values into the new array. Note that because our old keys are + // unique, there's no need to check for equality here when we insert. + var oldPos = 0 + while (oldPos < capacity) { + if (!data(2 * oldPos).eq(null)) { + val key = data(2 * oldPos) + val value = data(2 * oldPos + 1) + var newPos = rehash(key.hashCode) & newMask + var i = 1 + var keepGoing = true + while (keepGoing) { + val curKey = newData(2 * newPos) + if (curKey.eq(null)) { + newData(2 * newPos) = key + newData(2 * newPos + 1) = value + keepGoing = false + } else { + val delta = i + newPos = (newPos + delta) & newMask + i += 1 + } + } + } + oldPos += 1 + } + data = newData + capacity = newCapacity + mask = newMask + growThreshold = LOAD_FACTOR * newCapacity + } + + private def nextPowerOf2(n: Int): Int = { + val highBit = Integer.highestOneBit(n) + if (highBit == n) n else highBit << 1 + } +} diff --git a/core/src/main/scala/org/apache/spark/util/BoundedPriorityQueue.scala b/core/src/main/scala/org/apache/spark/util/BoundedPriorityQueue.scala index 0b51c23f7b..a38329df03 100644 --- a/core/src/main/scala/org/apache/spark/util/BoundedPriorityQueue.scala +++ b/core/src/main/scala/org/apache/spark/util/BoundedPriorityQueue.scala @@ -34,6 +34,8 @@ class BoundedPriorityQueue[A](maxSize: Int)(implicit ord: Ordering[A]) override def iterator: Iterator[A] = underlying.iterator.asScala + override def size: Int = underlying.size + override def ++=(xs: TraversableOnce[A]): this.type = { xs.foreach { this += _ } this diff --git a/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala b/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala new file mode 100644 index 0000000000..bf71882ef7 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala @@ -0,0 +1,68 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// Must be in akka.actor package as ActorSystemImpl is protected[akka]. +package akka.actor + +import scala.util.control.{ControlThrowable, NonFatal} + +import com.typesafe.config.Config + +/** + * An [[akka.actor.ActorSystem]] which refuses to shut down in the event of a fatal exception. + * This is necessary as Spark Executors are allowed to recover from fatal exceptions + * (see [[org.apache.spark.executor.Executor]]). + */ +object IndestructibleActorSystem { + def apply(name: String, config: Config): ActorSystem = + apply(name, config, ActorSystem.findClassLoader()) + + def apply(name: String, config: Config, classLoader: ClassLoader): ActorSystem = + new IndestructibleActorSystemImpl(name, config, classLoader).start() +} + +private[akka] class IndestructibleActorSystemImpl( + override val name: String, + applicationConfig: Config, + classLoader: ClassLoader) + extends ActorSystemImpl(name, applicationConfig, classLoader) { + + protected override def uncaughtExceptionHandler: Thread.UncaughtExceptionHandler = { + val fallbackHandler = super.uncaughtExceptionHandler + + new Thread.UncaughtExceptionHandler() { + def uncaughtException(thread: Thread, cause: Throwable): Unit = { + if (isFatalError(cause) && !settings.JvmExitOnFatalError) { + log.error(cause, "Uncaught fatal error from thread [{}] not shutting down " + + "ActorSystem [{}] tolerating and continuing.... ", thread.getName, name) + //shutdown() //TODO make it configurable + } else { + fallbackHandler.uncaughtException(thread, cause) + } + } + } + } + + def isFatalError(e: Throwable): Boolean = { + e match { + case NonFatal(_) | _: InterruptedException | _: NotImplementedError | _: ControlThrowable => + false + case _ => + true + } + } +} diff --git a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala index a430a75451..aa7f52cafb 100644 --- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala @@ -17,19 +17,26 @@ package org.apache.spark.util -import java.util.concurrent.{TimeUnit, ScheduledFuture, Executors} import java.util.{TimerTask, Timer} -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, SparkContext, Logging} /** * Runs a timer task to periodically clean up metadata (e.g. old files or hashtable entries) */ -class MetadataCleaner(name: String, cleanupFunc: (Long) => Unit) extends Logging { - private val delaySeconds = MetadataCleaner.getDelaySeconds +class MetadataCleaner( + cleanerType: MetadataCleanerType.MetadataCleanerType, + cleanupFunc: (Long) => Unit, + conf: SparkConf) + extends Logging +{ + val name = cleanerType.toString + + private val delaySeconds = MetadataCleaner.getDelaySeconds(conf, cleanerType) private val periodSeconds = math.max(10, delaySeconds / 10) private val timer = new Timer(name + " cleanup timer", true) + private val task = new TimerTask { override def run() { try { @@ -53,9 +60,43 @@ class MetadataCleaner(name: String, cleanupFunc: (Long) => Unit) extends Logging } } +object MetadataCleanerType extends Enumeration { + + val MAP_OUTPUT_TRACKER, SPARK_CONTEXT, HTTP_BROADCAST, DAG_SCHEDULER, RESULT_TASK, + SHUFFLE_MAP_TASK, BLOCK_MANAGER, SHUFFLE_BLOCK_MANAGER, BROADCAST_VARS = Value + + type MetadataCleanerType = Value + def systemProperty(which: MetadataCleanerType.MetadataCleanerType) = "spark.cleaner.ttl." + which.toString +} + +// TODO: This mutates a Conf to set properties right now, which is kind of ugly when used in the +// initialization of StreamingContext. It's okay for users trying to configure stuff themselves. object MetadataCleaner { - def getDelaySeconds = System.getProperty("spark.cleaner.ttl", "-1").toInt - def setDelaySeconds(delay: Int) { System.setProperty("spark.cleaner.ttl", delay.toString) } + def getDelaySeconds(conf: SparkConf) = { + conf.get("spark.cleaner.ttl", "3500").toInt + } + + def getDelaySeconds(conf: SparkConf, cleanerType: MetadataCleanerType.MetadataCleanerType): Int = + { + conf.get(MetadataCleanerType.systemProperty(cleanerType), getDelaySeconds(conf).toString) + .toInt + } + + def setDelaySeconds(conf: SparkConf, cleanerType: MetadataCleanerType.MetadataCleanerType, + delay: Int) + { + conf.set(MetadataCleanerType.systemProperty(cleanerType), delay.toString) + } + + def setDelaySeconds(conf: SparkConf, delay: Int, resetAll: Boolean = true) { + // override for all ? + conf.set("spark.cleaner.ttl", delay.toString) + if (resetAll) { + for (cleanerType <- MetadataCleanerType.values) { + System.clearProperty(MetadataCleanerType.systemProperty(cleanerType)) + } + } + } } diff --git a/core/src/main/scala/org/apache/spark/util/SerializableHyperLogLog.scala b/core/src/main/scala/org/apache/spark/util/SerializableHyperLogLog.scala new file mode 100644 index 0000000000..8b4e7c104c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/SerializableHyperLogLog.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import java.io.{Externalizable, ObjectOutput, ObjectInput} +import com.clearspring.analytics.stream.cardinality.{ICardinality, HyperLogLog} + +/** + * A wrapper around [[com.clearspring.analytics.stream.cardinality.HyperLogLog]] that is serializable. + */ +private[spark] +class SerializableHyperLogLog(var value: ICardinality) extends Externalizable { + + def this() = this(null) // For deserialization + + def merge(other: SerializableHyperLogLog) = new SerializableHyperLogLog(value.merge(other.value)) + + def add[T](elem: T) = { + this.value.offer(elem) + this + } + + def readExternal(in: ObjectInput) { + val byteLength = in.readInt() + val bytes = new Array[Byte](byteLength) + in.readFully(bytes) + value = HyperLogLog.Builder.build(bytes) + } + + def writeExternal(out: ObjectOutput) { + val bytes = value.getBytes() + out.writeInt(bytes.length) + out.write(bytes) + } +} diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala index a25b37a2a9..bddb3bb735 100644 --- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala +++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala @@ -30,10 +30,10 @@ import java.lang.management.ManagementFactory import scala.collection.mutable.ArrayBuffer import it.unimi.dsi.fastutil.ints.IntOpenHashSet -import org.apache.spark.Logging +import org.apache.spark.{SparkEnv, SparkConf, SparkContext, Logging} /** - * Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in + * Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in * memory-aware caches. * * Based on the following JavaWorld article: @@ -89,9 +89,11 @@ private[spark] object SizeEstimator extends Logging { classInfos.put(classOf[Object], new ClassInfo(objectSize, Nil)) } - private def getIsCompressedOops : Boolean = { + private def getIsCompressedOops: Boolean = { + // This is only used by tests to override the detection of compressed oops. The test + // actually uses a system property instead of a SparkConf, so we'll stick with that. if (System.getProperty("spark.test.useCompressedOops") != null) { - return System.getProperty("spark.test.useCompressedOops").toBoolean + return System.getProperty("spark.test.useCompressedOops").toBoolean } try { @@ -103,7 +105,7 @@ private[spark] object SizeEstimator extends Logging { val getVMMethod = hotSpotMBeanClass.getDeclaredMethod("getVMOption", Class.forName("java.lang.String")) - val bean = ManagementFactory.newPlatformMXBeanProxy(server, + val bean = ManagementFactory.newPlatformMXBeanProxy(server, hotSpotMBeanName, hotSpotMBeanClass) // TODO: We could use reflection on the VMOption returned ? return getVMMethod.invoke(bean, "UseCompressedOops").toString.contains("true") @@ -251,7 +253,7 @@ private[spark] object SizeEstimator extends Logging { if (info != null) { return info } - + val parent = getClassInfo(cls.getSuperclass) var shellSize = parent.shellSize var pointerFields = parent.pointerFields diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala index 277de2f8a6..181ae2fd45 100644 --- a/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala @@ -85,7 +85,7 @@ class TimeStampedHashMap[A, B] extends Map[A, B]() with Logging { } override def filter(p: ((A, B)) => Boolean): Map[A, B] = { - JavaConversions.asScalaConcurrentMap(internalMap).map(kv => (kv._1, kv._2._1)).filter(p) + JavaConversions.mapAsScalaConcurrentMap(internalMap).map(kv => (kv._1, kv._2._1)).filter(p) } override def empty: Map[A, B] = new TimeStampedHashMap[A, B]() @@ -104,19 +104,28 @@ class TimeStampedHashMap[A, B] extends Map[A, B]() with Logging { def toMap: immutable.Map[A, B] = iterator.toMap /** - * Removes old key-value pairs that have timestamp earlier than `threshTime` + * Removes old key-value pairs that have timestamp earlier than `threshTime`, + * calling the supplied function on each such entry before removing. */ - def clearOldValues(threshTime: Long) { + def clearOldValues(threshTime: Long, f: (A, B) => Unit) { val iterator = internalMap.entrySet().iterator() - while(iterator.hasNext) { + while (iterator.hasNext) { val entry = iterator.next() if (entry.getValue._2 < threshTime) { + f(entry.getKey, entry.getValue._1) logDebug("Removing key " + entry.getKey) iterator.remove() } } } + /** + * Removes old key-value pairs that have timestamp earlier than `threshTime` + */ + def clearOldValues(threshTime: Long) { + clearOldValues(threshTime, (_, _) => ()) + } + private def currentTime: Long = System.currentTimeMillis() } diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 886f071503..5f1253100b 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -18,15 +18,15 @@ package org.apache.spark.util import java.io._ -import java.net.{InetAddress, URL, URI, NetworkInterface, Inet4Address, ServerSocket} +import java.net.{InetAddress, URL, URI, NetworkInterface, Inet4Address} import java.util.{Locale, Random, UUID} -import java.util.concurrent.{ConcurrentHashMap, Executors, ThreadFactory, ThreadPoolExecutor} -import java.util.regex.Pattern +import java.util.concurrent.{ConcurrentHashMap, Executors, ThreadPoolExecutor} -import scala.collection.Map -import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.collection.JavaConversions._ +import scala.collection.Map +import scala.collection.mutable.ArrayBuffer import scala.io.Source +import scala.reflect.ClassTag import com.google.common.io.Files import com.google.common.util.concurrent.ThreadFactoryBuilder @@ -36,14 +36,13 @@ import org.apache.hadoop.fs.{Path, FileSystem, FileUtil} import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance} import org.apache.spark.deploy.SparkHadoopUtil import java.nio.ByteBuffer -import org.apache.spark.{SparkEnv, SparkException, Logging} +import org.apache.spark.{SparkConf, SparkContext, SparkException, Logging} /** * Various utility methods used by Spark. */ private[spark] object Utils extends Logging { - /** Serialize an object using Java serialization */ def serialize[T](o: T): Array[Byte] = { val bos = new ByteArrayOutputStream() @@ -70,6 +69,19 @@ private[spark] object Utils extends Logging { return ois.readObject.asInstanceOf[T] } + /** Deserialize a Long value (used for {@link org.apache.spark.api.python.PythonPartitioner}) */ + def deserializeLongValue(bytes: Array[Byte]) : Long = { + // Note: we assume that we are given a Long value encoded in network (big-endian) byte order + var result = bytes(7) & 0xFFL + result = result + ((bytes(6) & 0xFFL) << 8) + result = result + ((bytes(5) & 0xFFL) << 16) + result = result + ((bytes(4) & 0xFFL) << 24) + result = result + ((bytes(3) & 0xFFL) << 32) + result = result + ((bytes(2) & 0xFFL) << 40) + result = result + ((bytes(1) & 0xFFL) << 48) + result + ((bytes(0) & 0xFFL) << 56) + } + /** Serialize via nested stream using specific serializer */ def serializeViaNestedStream(os: OutputStream, ser: SerializerInstance)(f: SerializationStream => Unit) = { val osWrapper = ser.serializeStream(new OutputStream { @@ -135,7 +147,7 @@ private[spark] object Utils extends Logging { return buf } - private val shutdownDeletePaths = new collection.mutable.HashSet[String]() + private val shutdownDeletePaths = new scala.collection.mutable.HashSet[String]() // Register the path to be deleted via shutdown hook def registerShutdownDeleteDir(file: File) { @@ -226,9 +238,9 @@ private[spark] object Utils extends Logging { * Throws SparkException if the target file already exists and has different contents than * the requested file. */ - def fetchFile(url: String, targetDir: File) { + def fetchFile(url: String, targetDir: File, conf: SparkConf) { val filename = url.split("/").last - val tempDir = getLocalDir + val tempDir = getLocalDir(conf) val tempFile = File.createTempFile("fetchFileTemp", null, new File(tempDir)) val targetFile = new File(targetDir, filename) val uri = new URI(url) @@ -267,9 +279,8 @@ private[spark] object Utils extends Logging { } case _ => // Use the Hadoop filesystem library, which supports file://, hdfs://, s3://, and others - val env = SparkEnv.get val uri = new URI(url) - val conf = env.hadoop.newConfiguration() + val conf = SparkHadoopUtil.get.newConfiguration() val fs = FileSystem.get(uri, conf) val in = fs.open(new Path(uri)) val out = new FileOutputStream(tempFile) @@ -299,8 +310,8 @@ private[spark] object Utils extends Logging { * return a single directory, even though the spark.local.dir property might be a list of * multiple paths. */ - def getLocalDir: String = { - System.getProperty("spark.local.dir", System.getProperty("java.io.tmpdir")).split(',')(0) + def getLocalDir(conf: SparkConf): String = { + conf.get("spark.local.dir", System.getProperty("java.io.tmpdir")).split(',')(0) } /** @@ -308,7 +319,7 @@ private[spark] object Utils extends Logging { * result in a new collection. Unlike scala.util.Random.shuffle, this method * uses a local random number generator, avoiding inter-thread contention. */ - def randomize[T: ClassManifest](seq: TraversableOnce[T]): Seq[T] = { + def randomize[T: ClassTag](seq: TraversableOnce[T]): Seq[T] = { randomizeInPlace(seq.toArray) } @@ -385,13 +396,12 @@ private[spark] object Utils extends Logging { InetAddress.getByName(address).getHostName } - def localHostPort(): String = { - val retval = System.getProperty("spark.hostPort", null) + def localHostPort(conf: SparkConf): String = { + val retval = conf.get("spark.hostPort", null) if (retval == null) { logErrorWithStack("spark.hostPort not set but invoking localHostPort") return localHostName() } - retval } @@ -403,9 +413,12 @@ private[spark] object Utils extends Logging { assert(hostPort.indexOf(':') != -1, message) } - // Used by DEBUG code : remove when all testing done def logErrorWithStack(msg: String) { - try { throw new Exception } catch { case ex: Exception => { logError(msg, ex) } } + try { + throw new Exception + } catch { + case ex: Exception => logError(msg, ex) + } } // Typically, this will be of order of number of nodes in cluster @@ -434,14 +447,17 @@ private[spark] object Utils extends Logging { hostPortParseResults.get(hostPort) } - private[spark] val daemonThreadFactory: ThreadFactory = - new ThreadFactoryBuilder().setDaemon(true).build() + private val daemonThreadFactoryBuilder: ThreadFactoryBuilder = + new ThreadFactoryBuilder().setDaemon(true) /** - * Wrapper over newCachedThreadPool. + * Wrapper over newCachedThreadPool. Thread names are formatted as prefix-ID, where ID is a + * unique, sequentially assigned integer. */ - def newDaemonCachedThreadPool(): ThreadPoolExecutor = - Executors.newCachedThreadPool(daemonThreadFactory).asInstanceOf[ThreadPoolExecutor] + def newDaemonCachedThreadPool(prefix: String): ThreadPoolExecutor = { + val threadFactory = daemonThreadFactoryBuilder.setNameFormat(prefix + "-%d").build() + Executors.newCachedThreadPool(threadFactory).asInstanceOf[ThreadPoolExecutor] + } /** * Return the string to tell how long has passed in seconds. The passing parameter should be in @@ -452,10 +468,13 @@ private[spark] object Utils extends Logging { } /** - * Wrapper over newFixedThreadPool. + * Wrapper over newFixedThreadPool. Thread names are formatted as prefix-ID, where ID is a + * unique, sequentially assigned integer. */ - def newDaemonFixedThreadPool(nThreads: Int): ThreadPoolExecutor = - Executors.newFixedThreadPool(nThreads, daemonThreadFactory).asInstanceOf[ThreadPoolExecutor] + def newDaemonFixedThreadPool(nThreads: Int, prefix: String): ThreadPoolExecutor = { + val threadFactory = daemonThreadFactoryBuilder.setNameFormat(prefix + "-%d").build() + Executors.newFixedThreadPool(nThreads, threadFactory).asInstanceOf[ThreadPoolExecutor] + } private def listFilesSafely(file: File): Seq[File] = { val files = file.listFiles() @@ -800,4 +819,34 @@ private[spark] object Utils extends Logging { // Nothing else to guard against ? hashAbs } + + /** Returns a copy of the system properties that is thread-safe to iterator over. */ + def getSystemProperties(): Map[String, String] = { + return System.getProperties().clone() + .asInstanceOf[java.util.Properties].toMap[String, String] + } + + /** + * Method executed for repeating a task for side effects. + * Unlike a for comprehension, it permits JVM JIT optimization + */ + def times(numIters: Int)(f: => Unit): Unit = { + var i = 0 + while (i < numIters) { + f + i += 1 + } + } + + /** + * Timing method based on iterations that permit JVM JIT optimization. + * @param numIters number of iterations + * @param f function to be executed + */ + def timeIt(numIters: Int)(f: => Unit): Long = { + val start = System.currentTimeMillis + times(numIters)(f) + System.currentTimeMillis - start + } + } diff --git a/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala b/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala new file mode 100644 index 0000000000..e9907e6c85 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala @@ -0,0 +1,94 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import java.util.{Random => JavaRandom} +import org.apache.spark.util.Utils.timeIt + +/** + * This class implements a XORShift random number generator algorithm + * Source: + * Marsaglia, G. (2003). Xorshift RNGs. Journal of Statistical Software, Vol. 8, Issue 14. + * @see <a href="http://www.jstatsoft.org/v08/i14/paper">Paper</a> + * This implementation is approximately 3.5 times faster than + * {@link java.util.Random java.util.Random}, partly because of the algorithm, but also due + * to renouncing thread safety. JDK's implementation uses an AtomicLong seed, this class + * uses a regular Long. We can forgo thread safety since we use a new instance of the RNG + * for each thread. + */ +private[spark] class XORShiftRandom(init: Long) extends JavaRandom(init) { + + def this() = this(System.nanoTime) + + private var seed = init + + // we need to just override next - this will be called by nextInt, nextDouble, + // nextGaussian, nextLong, etc. + override protected def next(bits: Int): Int = { + var nextSeed = seed ^ (seed << 21) + nextSeed ^= (nextSeed >>> 35) + nextSeed ^= (nextSeed << 4) + seed = nextSeed + (nextSeed & ((1L << bits) -1)).asInstanceOf[Int] + } +} + +/** Contains benchmark method and main method to run benchmark of the RNG */ +private[spark] object XORShiftRandom { + + /** + * Main method for running benchmark + * @param args takes one argument - the number of random numbers to generate + */ + def main(args: Array[String]): Unit = { + if (args.length != 1) { + println("Benchmark of XORShiftRandom vis-a-vis java.util.Random") + println("Usage: XORShiftRandom number_of_random_numbers_to_generate") + System.exit(1) + } + println(benchmark(args(0).toInt)) + } + + /** + * @param numIters Number of random numbers to generate while running the benchmark + * @return Map of execution times for {@link java.util.Random java.util.Random} + * and XORShift + */ + def benchmark(numIters: Int) = { + + val seed = 1L + val million = 1e6.toInt + val javaRand = new JavaRandom(seed) + val xorRand = new XORShiftRandom(seed) + + // this is just to warm up the JIT - we're not timing anything + timeIt(1e6.toInt) { + javaRand.nextInt() + xorRand.nextInt() + } + + val iters = timeIt(numIters)(_) + + /* Return results as a map instead of just printing to screen + in case the user wants to do something with them */ + Map("javaTime" -> iters {javaRand.nextInt()}, + "xorTime" -> iters {xorRand.nextInt()}) + + } + +}
\ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala new file mode 100644 index 0000000000..a1a452315d --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala @@ -0,0 +1,103 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + + +/** + * A simple, fixed-size bit set implementation. This implementation is fast because it avoids + * safety/bound checking. + */ +class BitSet(numBits: Int) { + + private[this] val words = new Array[Long](bit2words(numBits)) + private[this] val numWords = words.length + + /** + * Sets the bit at the specified index to true. + * @param index the bit index + */ + def set(index: Int) { + val bitmask = 1L << (index & 0x3f) // mod 64 and shift + words(index >> 6) |= bitmask // div by 64 and mask + } + + /** + * Return the value of the bit with the specified index. The value is true if the bit with + * the index is currently set in this BitSet; otherwise, the result is false. + * + * @param index the bit index + * @return the value of the bit with the specified index + */ + def get(index: Int): Boolean = { + val bitmask = 1L << (index & 0x3f) // mod 64 and shift + (words(index >> 6) & bitmask) != 0 // div by 64 and mask + } + + /** Return the number of bits set to true in this BitSet. */ + def cardinality(): Int = { + var sum = 0 + var i = 0 + while (i < numWords) { + sum += java.lang.Long.bitCount(words(i)) + i += 1 + } + sum + } + + /** + * Returns the index of the first bit that is set to true that occurs on or after the + * specified starting index. If no such bit exists then -1 is returned. + * + * To iterate over the true bits in a BitSet, use the following loop: + * + * for (int i = bs.nextSetBit(0); i >= 0; i = bs.nextSetBit(i+1)) { + * // operate on index i here + * } + * + * @param fromIndex the index to start checking from (inclusive) + * @return the index of the next set bit, or -1 if there is no such bit + */ + def nextSetBit(fromIndex: Int): Int = { + var wordIndex = fromIndex >> 6 + if (wordIndex >= numWords) { + return -1 + } + + // Try to find the next set bit in the current word + val subIndex = fromIndex & 0x3f + var word = words(wordIndex) >> subIndex + if (word != 0) { + return (wordIndex << 6) + subIndex + java.lang.Long.numberOfTrailingZeros(word) + } + + // Find the next set bit in the rest of the words + wordIndex += 1 + while (wordIndex < numWords) { + word = words(wordIndex) + if (word != 0) { + return (wordIndex << 6) + java.lang.Long.numberOfTrailingZeros(word) + } + wordIndex += 1 + } + + -1 + } + + /** Return the number of longs it would take to hold numBits. */ + private def bit2words(numBits: Int) = ((numBits - 1) >> 6) + 1 +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala new file mode 100644 index 0000000000..c26f23d500 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala @@ -0,0 +1,153 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import scala.reflect.ClassTag + +/** + * A fast hash map implementation for nullable keys. This hash map supports insertions and updates, + * but not deletions. This map is about 5X faster than java.util.HashMap, while using much less + * space overhead. + * + * Under the hood, it uses our OpenHashSet implementation. + */ +private[spark] +class OpenHashMap[K >: Null : ClassTag, @specialized(Long, Int, Double) V: ClassTag]( + initialCapacity: Int) + extends Iterable[(K, V)] + with Serializable { + + def this() = this(64) + + protected var _keySet = new OpenHashSet[K](initialCapacity) + + // Init in constructor (instead of in declaration) to work around a Scala compiler specialization + // bug that would generate two arrays (one for Object and one for specialized T). + private var _values: Array[V] = _ + _values = new Array[V](_keySet.capacity) + + @transient private var _oldValues: Array[V] = null + + // Treat the null key differently so we can use nulls in "data" to represent empty items. + private var haveNullValue = false + private var nullValue: V = null.asInstanceOf[V] + + override def size: Int = if (haveNullValue) _keySet.size + 1 else _keySet.size + + /** Get the value for a given key */ + def apply(k: K): V = { + if (k == null) { + nullValue + } else { + val pos = _keySet.getPos(k) + if (pos < 0) { + null.asInstanceOf[V] + } else { + _values(pos) + } + } + } + + /** Set the value for a key */ + def update(k: K, v: V) { + if (k == null) { + haveNullValue = true + nullValue = v + } else { + val pos = _keySet.addWithoutResize(k) & OpenHashSet.POSITION_MASK + _values(pos) = v + _keySet.rehashIfNeeded(k, grow, move) + _oldValues = null + } + } + + /** + * If the key doesn't exist yet in the hash map, set its value to defaultValue; otherwise, + * set its value to mergeValue(oldValue). + * + * @return the newly updated value. + */ + def changeValue(k: K, defaultValue: => V, mergeValue: (V) => V): V = { + if (k == null) { + if (haveNullValue) { + nullValue = mergeValue(nullValue) + } else { + haveNullValue = true + nullValue = defaultValue + } + nullValue + } else { + val pos = _keySet.addWithoutResize(k) + if ((pos & OpenHashSet.NONEXISTENCE_MASK) != 0) { + val newValue = defaultValue + _values(pos & OpenHashSet.POSITION_MASK) = newValue + _keySet.rehashIfNeeded(k, grow, move) + newValue + } else { + _values(pos) = mergeValue(_values(pos)) + _values(pos) + } + } + } + + override def iterator = new Iterator[(K, V)] { + var pos = -1 + var nextPair: (K, V) = computeNextPair() + + /** Get the next value we should return from next(), or null if we're finished iterating */ + def computeNextPair(): (K, V) = { + if (pos == -1) { // Treat position -1 as looking at the null value + if (haveNullValue) { + pos += 1 + return (null.asInstanceOf[K], nullValue) + } + pos += 1 + } + pos = _keySet.nextPos(pos) + if (pos >= 0) { + val ret = (_keySet.getValue(pos), _values(pos)) + pos += 1 + ret + } else { + null + } + } + + def hasNext = nextPair != null + + def next() = { + val pair = nextPair + nextPair = computeNextPair() + pair + } + } + + // The following member variables are declared as protected instead of private for the + // specialization to work (specialized class extends the non-specialized one and needs access + // to the "private" variables). + // They also should have been val's. We use var's because there is a Scala compiler bug that + // would throw illegal access error at runtime if they are declared as val's. + protected var grow = (newCapacity: Int) => { + _oldValues = _values + _values = new Array[V](newCapacity) + } + + protected var move = (oldPos: Int, newPos: Int) => { + _values(newPos) = _oldValues(oldPos) + } +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala new file mode 100644 index 0000000000..87e009a4de --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala @@ -0,0 +1,279 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import scala.reflect._ + +/** + * A simple, fast hash set optimized for non-null insertion-only use case, where keys are never + * removed. + * + * The underlying implementation uses Scala compiler's specialization to generate optimized + * storage for two primitive types (Long and Int). It is much faster than Java's standard HashSet + * while incurring much less memory overhead. This can serve as building blocks for higher level + * data structures such as an optimized HashMap. + * + * This OpenHashSet is designed to serve as building blocks for higher level data structures + * such as an optimized hash map. Compared with standard hash set implementations, this class + * provides its various callbacks interfaces (e.g. allocateFunc, moveFunc) and interfaces to + * retrieve the position of a key in the underlying array. + * + * It uses quadratic probing with a power-of-2 hash table size, which is guaranteed + * to explore all spaces for each key (see http://en.wikipedia.org/wiki/Quadratic_probing). + */ +private[spark] +class OpenHashSet[@specialized(Long, Int) T: ClassTag]( + initialCapacity: Int, + loadFactor: Double) + extends Serializable { + + require(initialCapacity <= (1 << 29), "Can't make capacity bigger than 2^29 elements") + require(initialCapacity >= 1, "Invalid initial capacity") + require(loadFactor < 1.0, "Load factor must be less than 1.0") + require(loadFactor > 0.0, "Load factor must be greater than 0.0") + + import OpenHashSet._ + + def this(initialCapacity: Int) = this(initialCapacity, 0.7) + + def this() = this(64) + + // The following member variables are declared as protected instead of private for the + // specialization to work (specialized class extends the non-specialized one and needs access + // to the "private" variables). + + protected val hasher: Hasher[T] = { + // It would've been more natural to write the following using pattern matching. But Scala 2.9.x + // compiler has a bug when specialization is used together with this pattern matching, and + // throws: + // scala.tools.nsc.symtab.Types$TypeError: type mismatch; + // found : scala.reflect.AnyValManifest[Long] + // required: scala.reflect.ClassTag[Int] + // at scala.tools.nsc.typechecker.Contexts$Context.error(Contexts.scala:298) + // at scala.tools.nsc.typechecker.Infer$Inferencer.error(Infer.scala:207) + // ... + val mt = classTag[T] + if (mt == ClassTag.Long) { + (new LongHasher).asInstanceOf[Hasher[T]] + } else if (mt == ClassTag.Int) { + (new IntHasher).asInstanceOf[Hasher[T]] + } else { + new Hasher[T] + } + } + + protected var _capacity = nextPowerOf2(initialCapacity) + protected var _mask = _capacity - 1 + protected var _size = 0 + protected var _growThreshold = (loadFactor * _capacity).toInt + + protected var _bitset = new BitSet(_capacity) + + // Init of the array in constructor (instead of in declaration) to work around a Scala compiler + // specialization bug that would generate two arrays (one for Object and one for specialized T). + protected var _data: Array[T] = _ + _data = new Array[T](_capacity) + + /** Number of elements in the set. */ + def size: Int = _size + + /** The capacity of the set (i.e. size of the underlying array). */ + def capacity: Int = _capacity + + /** Return true if this set contains the specified element. */ + def contains(k: T): Boolean = getPos(k) != INVALID_POS + + /** + * Add an element to the set. If the set is over capacity after the insertion, grow the set + * and rehash all elements. + */ + def add(k: T) { + addWithoutResize(k) + rehashIfNeeded(k, grow, move) + } + + /** + * Add an element to the set. This one differs from add in that it doesn't trigger rehashing. + * The caller is responsible for calling rehashIfNeeded. + * + * Use (retval & POSITION_MASK) to get the actual position, and + * (retval & EXISTENCE_MASK) != 0 for prior existence. + * + * @return The position where the key is placed, plus the highest order bit is set if the key + * exists previously. + */ + def addWithoutResize(k: T): Int = { + var pos = hashcode(hasher.hash(k)) & _mask + var i = 1 + while (true) { + if (!_bitset.get(pos)) { + // This is a new key. + _data(pos) = k + _bitset.set(pos) + _size += 1 + return pos | NONEXISTENCE_MASK + } else if (_data(pos) == k) { + // Found an existing key. + return pos + } else { + val delta = i + pos = (pos + delta) & _mask + i += 1 + } + } + // Never reached here + assert(INVALID_POS != INVALID_POS) + INVALID_POS + } + + /** + * Rehash the set if it is overloaded. + * @param k A parameter unused in the function, but to force the Scala compiler to specialize + * this method. + * @param allocateFunc Callback invoked when we are allocating a new, larger array. + * @param moveFunc Callback invoked when we move the key from one position (in the old data array) + * to a new position (in the new data array). + */ + def rehashIfNeeded(k: T, allocateFunc: (Int) => Unit, moveFunc: (Int, Int) => Unit) { + if (_size > _growThreshold) { + rehash(k, allocateFunc, moveFunc) + } + } + + /** + * Return the position of the element in the underlying array, or INVALID_POS if it is not found. + */ + def getPos(k: T): Int = { + var pos = hashcode(hasher.hash(k)) & _mask + var i = 1 + while (true) { + if (!_bitset.get(pos)) { + return INVALID_POS + } else if (k == _data(pos)) { + return pos + } else { + val delta = i + pos = (pos + delta) & _mask + i += 1 + } + } + // Never reached here + INVALID_POS + } + + /** Return the value at the specified position. */ + def getValue(pos: Int): T = _data(pos) + + /** + * Return the next position with an element stored, starting from the given position inclusively. + */ + def nextPos(fromPos: Int): Int = _bitset.nextSetBit(fromPos) + + /** + * Double the table's size and re-hash everything. We are not really using k, but it is declared + * so Scala compiler can specialize this method (which leads to calling the specialized version + * of putInto). + * + * @param k A parameter unused in the function, but to force the Scala compiler to specialize + * this method. + * @param allocateFunc Callback invoked when we are allocating a new, larger array. + * @param moveFunc Callback invoked when we move the key from one position (in the old data array) + * to a new position (in the new data array). + */ + private def rehash(k: T, allocateFunc: (Int) => Unit, moveFunc: (Int, Int) => Unit) { + val newCapacity = _capacity * 2 + allocateFunc(newCapacity) + val newBitset = new BitSet(newCapacity) + val newData = new Array[T](newCapacity) + val newMask = newCapacity - 1 + + var oldPos = 0 + while (oldPos < capacity) { + if (_bitset.get(oldPos)) { + val key = _data(oldPos) + var newPos = hashcode(hasher.hash(key)) & newMask + var i = 1 + var keepGoing = true + // No need to check for equality here when we insert so this has one less if branch than + // the similar code path in addWithoutResize. + while (keepGoing) { + if (!newBitset.get(newPos)) { + // Inserting the key at newPos + newData(newPos) = key + newBitset.set(newPos) + moveFunc(oldPos, newPos) + keepGoing = false + } else { + val delta = i + newPos = (newPos + delta) & newMask + i += 1 + } + } + } + oldPos += 1 + } + + _bitset = newBitset + _data = newData + _capacity = newCapacity + _mask = newMask + _growThreshold = (loadFactor * newCapacity).toInt + } + + /** + * Re-hash a value to deal better with hash functions that don't differ in the lower bits. + * We use the Murmur Hash 3 finalization step that's also used in fastutil. + */ + private def hashcode(h: Int): Int = it.unimi.dsi.fastutil.HashCommon.murmurHash3(h) + + private def nextPowerOf2(n: Int): Int = { + val highBit = Integer.highestOneBit(n) + if (highBit == n) n else highBit << 1 + } +} + + +private[spark] +object OpenHashSet { + + val INVALID_POS = -1 + val NONEXISTENCE_MASK = 0x80000000 + val POSITION_MASK = 0xEFFFFFF + + /** + * A set of specialized hash function implementation to avoid boxing hash code computation + * in the specialized implementation of OpenHashSet. + */ + sealed class Hasher[@specialized(Long, Int) T] { + def hash(o: T): Int = o.hashCode() + } + + class LongHasher extends Hasher[Long] { + override def hash(o: Long): Int = (o ^ (o >>> 32)).toInt + } + + class IntHasher extends Hasher[Int] { + override def hash(o: Int): Int = o + } + + private def grow1(newSize: Int) {} + private def move1(oldPos: Int, newPos: Int) { } + + private val grow = grow1 _ + private val move = move1 _ +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala new file mode 100644 index 0000000000..2e1ef06cbc --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala @@ -0,0 +1,128 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import scala.reflect._ + +/** + * A fast hash map implementation for primitive, non-null keys. This hash map supports + * insertions and updates, but not deletions. This map is about an order of magnitude + * faster than java.util.HashMap, while using much less space overhead. + * + * Under the hood, it uses our OpenHashSet implementation. + */ +private[spark] +class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, + @specialized(Long, Int, Double) V: ClassTag]( + initialCapacity: Int) + extends Iterable[(K, V)] + with Serializable { + + def this() = this(64) + + require(classTag[K] == classTag[Long] || classTag[K] == classTag[Int]) + + // Init in constructor (instead of in declaration) to work around a Scala compiler specialization + // bug that would generate two arrays (one for Object and one for specialized T). + protected var _keySet: OpenHashSet[K] = _ + private var _values: Array[V] = _ + _keySet = new OpenHashSet[K](initialCapacity) + _values = new Array[V](_keySet.capacity) + + private var _oldValues: Array[V] = null + + override def size = _keySet.size + + /** Get the value for a given key */ + def apply(k: K): V = { + val pos = _keySet.getPos(k) + _values(pos) + } + + /** Get the value for a given key, or returns elseValue if it doesn't exist. */ + def getOrElse(k: K, elseValue: V): V = { + val pos = _keySet.getPos(k) + if (pos >= 0) _values(pos) else elseValue + } + + /** Set the value for a key */ + def update(k: K, v: V) { + val pos = _keySet.addWithoutResize(k) & OpenHashSet.POSITION_MASK + _values(pos) = v + _keySet.rehashIfNeeded(k, grow, move) + _oldValues = null + } + + /** + * If the key doesn't exist yet in the hash map, set its value to defaultValue; otherwise, + * set its value to mergeValue(oldValue). + * + * @return the newly updated value. + */ + def changeValue(k: K, defaultValue: => V, mergeValue: (V) => V): V = { + val pos = _keySet.addWithoutResize(k) + if ((pos & OpenHashSet.NONEXISTENCE_MASK) != 0) { + val newValue = defaultValue + _values(pos & OpenHashSet.POSITION_MASK) = newValue + _keySet.rehashIfNeeded(k, grow, move) + newValue + } else { + _values(pos) = mergeValue(_values(pos)) + _values(pos) + } + } + + override def iterator = new Iterator[(K, V)] { + var pos = 0 + var nextPair: (K, V) = computeNextPair() + + /** Get the next value we should return from next(), or null if we're finished iterating */ + def computeNextPair(): (K, V) = { + pos = _keySet.nextPos(pos) + if (pos >= 0) { + val ret = (_keySet.getValue(pos), _values(pos)) + pos += 1 + ret + } else { + null + } + } + + def hasNext = nextPair != null + + def next() = { + val pair = nextPair + nextPair = computeNextPair() + pair + } + } + + // The following member variables are declared as protected instead of private for the + // specialization to work (specialized class extends the unspecialized one and needs access + // to the "private" variables). + // They also should have been val's. We use var's because there is a Scala compiler bug that + // would throw illegal access error at runtime if they are declared as val's. + protected var grow = (newCapacity: Int) => { + _oldValues = _values + _values = new Array[V](newCapacity) + } + + protected var move = (oldPos: Int, newPos: Int) => { + _values(newPos) = _oldValues(oldPos) + } +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala new file mode 100644 index 0000000000..b84eb65c62 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala @@ -0,0 +1,69 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import scala.reflect.ClassTag + +/** + * An append-only, non-threadsafe, array-backed vector that is optimized for primitive types. + */ +private[spark] +class PrimitiveVector[@specialized(Long, Int, Double) V: ClassTag](initialSize: Int = 64) { + private var _numElements = 0 + private var _array: Array[V] = _ + + // NB: This must be separate from the declaration, otherwise the specialized parent class + // will get its own array with the same initial size. + _array = new Array[V](initialSize) + + def apply(index: Int): V = { + require(index < _numElements) + _array(index) + } + + def +=(value: V) { + if (_numElements == _array.length) { + resize(_array.length * 2) + } + _array(_numElements) = value + _numElements += 1 + } + + def capacity: Int = _array.length + + def length: Int = _numElements + + def size: Int = _numElements + + /** Gets the underlying array backing this vector. */ + def array: Array[V] = _array + + /** Trims this vector so that the capacity is equal to the size. */ + def trim(): PrimitiveVector[V] = resize(size) + + /** Resizes the array, dropping elements if the total length decreases. */ + def resize(newLength: Int): PrimitiveVector[V] = { + val newArray = new Array[V](newLength) + _array.copyToArray(newArray) + _array = newArray + if (newLength < _numElements) { + _numElements = newLength + } + this + } +} diff --git a/core/src/test/resources/spark.conf b/core/src/test/resources/spark.conf new file mode 100644 index 0000000000..aa4e751235 --- /dev/null +++ b/core/src/test/resources/spark.conf @@ -0,0 +1,8 @@ +# A simple spark.conf file used only in our unit tests + +spark.test.intTestProperty = 1 + +spark.test { + stringTestProperty = "hi" + listTestProperty = ["a", "b"] +} diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala index 4434f3b87c..c443c5266e 100644 --- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala @@ -27,6 +27,21 @@ import org.apache.spark.SparkContext._ class AccumulatorSuite extends FunSuite with ShouldMatchers with LocalSparkContext { + + implicit def setAccum[A] = new AccumulableParam[mutable.Set[A], A] { + def addInPlace(t1: mutable.Set[A], t2: mutable.Set[A]) : mutable.Set[A] = { + t1 ++= t2 + t1 + } + def addAccumulator(t1: mutable.Set[A], t2: A) : mutable.Set[A] = { + t1 += t2 + t1 + } + def zero(t: mutable.Set[A]) : mutable.Set[A] = { + new mutable.HashSet[A]() + } + } + test ("basic accumulation"){ sc = new SparkContext("local", "test") val acc : Accumulator[Int] = sc.accumulator(0) @@ -51,7 +66,6 @@ class AccumulatorSuite extends FunSuite with ShouldMatchers with LocalSparkConte } test ("add value to collection accumulators") { - import SetAccum._ val maxI = 1000 for (nThreads <- List(1, 10)) { //test single & multi-threaded sc = new SparkContext("local[" + nThreads + "]", "test") @@ -68,22 +82,7 @@ class AccumulatorSuite extends FunSuite with ShouldMatchers with LocalSparkConte } } - implicit object SetAccum extends AccumulableParam[mutable.Set[Any], Any] { - def addInPlace(t1: mutable.Set[Any], t2: mutable.Set[Any]) : mutable.Set[Any] = { - t1 ++= t2 - t1 - } - def addAccumulator(t1: mutable.Set[Any], t2: Any) : mutable.Set[Any] = { - t1 += t2 - t1 - } - def zero(t: mutable.Set[Any]) : mutable.Set[Any] = { - new mutable.HashSet[Any]() - } - } - test ("value not readable in tasks") { - import SetAccum._ val maxI = 1000 for (nThreads <- List(1, 10)) { //test single & multi-threaded sc = new SparkContext("local[" + nThreads + "]", "test") @@ -125,7 +124,6 @@ class AccumulatorSuite extends FunSuite with ShouldMatchers with LocalSparkConte } test ("localValue readable in tasks") { - import SetAccum._ val maxI = 1000 for (nThreads <- List(1, 10)) { //test single & multi-threaded sc = new SparkContext("local[" + nThreads + "]", "test") diff --git a/core/src/test/scala/org/apache/spark/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/BroadcastSuite.scala index b3a53d928b..e022accee6 100644 --- a/core/src/test/scala/org/apache/spark/BroadcastSuite.scala +++ b/core/src/test/scala/org/apache/spark/BroadcastSuite.scala @@ -20,8 +20,42 @@ package org.apache.spark import org.scalatest.FunSuite class BroadcastSuite extends FunSuite with LocalSparkContext { - - test("basic broadcast") { + + override def afterEach() { + super.afterEach() + System.clearProperty("spark.broadcast.factory") + } + + test("Using HttpBroadcast locally") { + System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") + sc = new SparkContext("local", "test") + val list = List(1, 2, 3, 4) + val listBroadcast = sc.broadcast(list) + val results = sc.parallelize(1 to 2).map(x => (x, listBroadcast.value.sum)) + assert(results.collect.toSet === Set((1, 10), (2, 10))) + } + + test("Accessing HttpBroadcast variables from multiple threads") { + System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") + sc = new SparkContext("local[10]", "test") + val list = List(1, 2, 3, 4) + val listBroadcast = sc.broadcast(list) + val results = sc.parallelize(1 to 10).map(x => (x, listBroadcast.value.sum)) + assert(results.collect.toSet === (1 to 10).map(x => (x, 10)).toSet) + } + + test("Accessing HttpBroadcast variables in a local cluster") { + System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") + val numSlaves = 4 + sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test") + val list = List(1, 2, 3, 4) + val listBroadcast = sc.broadcast(list) + val results = sc.parallelize(1 to numSlaves).map(x => (x, listBroadcast.value.sum)) + assert(results.collect.toSet === (1 to numSlaves).map(x => (x, 10)).toSet) + } + + test("Using TorrentBroadcast locally") { + System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.TorrentBroadcastFactory") sc = new SparkContext("local", "test") val list = List(1, 2, 3, 4) val listBroadcast = sc.broadcast(list) @@ -29,11 +63,23 @@ class BroadcastSuite extends FunSuite with LocalSparkContext { assert(results.collect.toSet === Set((1, 10), (2, 10))) } - test("broadcast variables accessed in multiple threads") { + test("Accessing TorrentBroadcast variables from multiple threads") { + System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.TorrentBroadcastFactory") sc = new SparkContext("local[10]", "test") val list = List(1, 2, 3, 4) val listBroadcast = sc.broadcast(list) val results = sc.parallelize(1 to 10).map(x => (x, listBroadcast.value.sum)) assert(results.collect.toSet === (1 to 10).map(x => (x, 10)).toSet) } + + test("Accessing TorrentBroadcast variables in a local cluster") { + System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.TorrentBroadcastFactory") + val numSlaves = 4 + sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test") + val list = List(1, 2, 3, 4) + val listBroadcast = sc.broadcast(list) + val results = sc.parallelize(1 to numSlaves).map(x => (x, listBroadcast.value.sum)) + assert(results.collect.toSet === (1 to numSlaves).map(x => (x, 10)).toSet) + } + } diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala index 3a7171c488..ea936e815b 100644 --- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala @@ -23,7 +23,7 @@ import org.scalatest.{BeforeAndAfter, FunSuite} import org.scalatest.mock.EasyMockSugar import org.apache.spark.rdd.RDD -import org.apache.spark.storage.{BlockManager, StorageLevel} +import org.apache.spark.storage.{BlockManager, RDDBlockId, StorageLevel} // TODO: Test the CacheManager's thread-safety aspects class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar { @@ -52,13 +52,14 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar test("get uncached rdd") { expecting { - blockManager.get("rdd_0_0").andReturn(None) - blockManager.put("rdd_0_0", ArrayBuffer[Any](1, 2, 3, 4), StorageLevel.MEMORY_ONLY, true). - andReturn(0) + blockManager.get(RDDBlockId(0, 0)).andReturn(None) + blockManager.put(RDDBlockId(0, 0), ArrayBuffer[Any](1, 2, 3, 4), StorageLevel.MEMORY_ONLY, + true).andReturn(0) } whenExecuting(blockManager) { - val context = new TaskContext(0, 0, 0, runningLocally = false, null) + val context = new TaskContext(0, 0, 0, interrupted = false, runningLocally = false, + taskMetrics = null) val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) assert(value.toList === List(1, 2, 3, 4)) } @@ -66,11 +67,12 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar test("get cached rdd") { expecting { - blockManager.get("rdd_0_0").andReturn(Some(ArrayBuffer(5, 6, 7).iterator)) + blockManager.get(RDDBlockId(0, 0)).andReturn(Some(ArrayBuffer(5, 6, 7).iterator)) } whenExecuting(blockManager) { - val context = new TaskContext(0, 0, 0, runningLocally = false, null) + val context = new TaskContext(0, 0, 0, interrupted = false, runningLocally = false, + taskMetrics = null) val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) assert(value.toList === List(5, 6, 7)) } @@ -79,11 +81,12 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar test("get uncached local rdd") { expecting { // Local computation should not persist the resulting value, so don't expect a put(). - blockManager.get("rdd_0_0").andReturn(None) + blockManager.get(RDDBlockId(0, 0)).andReturn(None) } whenExecuting(blockManager) { - val context = new TaskContext(0, 0, 0, runningLocally = true, null) + val context = new TaskContext(0, 0, 0, runningLocally = true, interrupted = false, + taskMetrics = null) val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) assert(value.toList === List(1, 2, 3, 4)) } diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala index d9103aebb7..70bfb81661 100644 --- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala +++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala @@ -17,16 +17,15 @@ package org.apache.spark +import scala.reflect.ClassTag import org.scalatest.FunSuite import java.io.File import org.apache.spark.rdd._ import org.apache.spark.SparkContext._ -import storage.StorageLevel +import org.apache.spark.storage.{BlockId, StorageLevel, TestBlockId} import org.apache.spark.util.Utils class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { - initLogging() - var checkpointDir: File = _ val partitioner = new HashPartitioner(2) @@ -62,8 +61,6 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { testCheckpointing(_.sample(false, 0.5, 0)) testCheckpointing(_.glom()) testCheckpointing(_.mapPartitions(_.map(_.toString))) - testCheckpointing(r => new MapPartitionsWithIndexRDD(r, - (i: Int, iter: Iterator[Int]) => iter.map(_.toString), false )) testCheckpointing(_.map(x => (x % 2, 1)).reduceByKey(_ + _).mapValues(_.toString)) testCheckpointing(_.map(x => (x % 2, 1)).reduceByKey(_ + _).flatMapValues(x => 1 to x)) testCheckpointing(_.pipe(Seq("cat"))) @@ -83,7 +80,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { } test("BlockRDD") { - val blockId = "id" + val blockId = TestBlockId("id") val blockManager = SparkEnv.get.blockManager blockManager.putSingle(blockId, "test", StorageLevel.MEMORY_ONLY) val blockRDD = new BlockRDD[String](sc, Array(blockId)) @@ -191,7 +188,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { } test("CheckpointRDD with zero partitions") { - val rdd = new BlockRDD[Int](sc, Array[String]()) + val rdd = new BlockRDD[Int](sc, Array[BlockId]()) assert(rdd.partitions.size === 0) assert(rdd.isCheckpointed === false) rdd.checkpoint() @@ -207,7 +204,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { * not, but this is not done by default as usually the partitions do not refer to any RDD and * therefore never store the lineage. */ - def testCheckpointing[U: ClassManifest]( + def testCheckpointing[U: ClassTag]( op: (RDD[Int]) => RDD[U], testRDDSize: Boolean = true, testRDDPartitionSize: Boolean = false @@ -276,7 +273,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { * RDDs partitions. So even if the parent RDD is checkpointed and its partitions changed, * this RDD will remember the partitions and therefore potentially the whole lineage. */ - def testParentCheckpointing[U: ClassManifest]( + def testParentCheckpointing[U: ClassTag]( op: (RDD[Int]) => RDD[U], testRDDSize: Boolean, testRDDPartitionSize: Boolean diff --git a/core/src/test/scala/org/apache/spark/DistributedSuite.scala b/core/src/test/scala/org/apache/spark/DistributedSuite.scala index 7a856d4081..d9cb7fead5 100644 --- a/core/src/test/scala/org/apache/spark/DistributedSuite.scala +++ b/core/src/test/scala/org/apache/spark/DistributedSuite.scala @@ -18,24 +18,14 @@ package org.apache.spark import network.ConnectionManagerId -import org.scalatest.FunSuite import org.scalatest.BeforeAndAfter import org.scalatest.concurrent.Timeouts._ +import org.scalatest.FunSuite import org.scalatest.matchers.ShouldMatchers -import org.scalatest.prop.Checkers import org.scalatest.time.{Span, Millis} -import org.scalacheck.Arbitrary._ -import org.scalacheck.Gen -import org.scalacheck.Prop._ -import org.eclipse.jetty.server.{Server, Request, Handler} - -import com.google.common.io.Files - -import scala.collection.mutable.ArrayBuffer import SparkContext._ -import storage.{GetBlock, BlockManagerWorker, StorageLevel} -import ui.JettyUtils +import org.apache.spark.storage.{BlockManagerWorker, GetBlock, RDDBlockId, StorageLevel} class NotSerializableClass @@ -132,7 +122,7 @@ class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter sc.parallelize(1 to 10, 10).foreach(x => println(x / 0)) } assert(thrown.getClass === classOf[SparkException]) - assert(thrown.getMessage.contains("more than 4 times")) + assert(thrown.getMessage.contains("failed 4 times")) } test("caching") { @@ -193,7 +183,7 @@ class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter // Get all the locations of the first partition and try to fetch the partitions // from those locations. - val blockIds = data.partitions.indices.map(index => "rdd_%d_%d".format(data.id, index)).toArray + val blockIds = data.partitions.indices.map(index => RDDBlockId(data.id, index)).toArray val blockId = blockIds(0) val blockManager = SparkEnv.get.blockManager blockManager.master.getLocations(blockId).foreach(id => { @@ -313,25 +303,13 @@ class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter Thread.sleep(200) } } catch { - case _ => { Thread.sleep(10) } + case _: Throwable => { Thread.sleep(10) } // Do nothing. We might see exceptions because block manager // is racing this thread to remove entries from the driver. } } } - test("job should fail if TaskResult exceeds Akka frame size") { - // We must use local-cluster mode since results are returned differently - // when running under LocalScheduler: - sc = new SparkContext("local-cluster[1,1,512]", "test") - val akkaFrameSize = - sc.env.actorSystem.settings.config.getBytes("akka.remote.netty.message-frame-size").toInt - val rdd = sc.parallelize(Seq(1)).map{x => new Array[Byte](akkaFrameSize)} - val exception = intercept[SparkException] { - rdd.reduce((x, y) => x) - } - exception.getMessage should endWith("result exceeded Akka frame size") - } } object DistributedSuite { diff --git a/core/src/test/scala/org/apache/spark/DriverSuite.scala b/core/src/test/scala/org/apache/spark/DriverSuite.scala index 5e5ece0033..c37fd9ab62 100644 --- a/core/src/test/scala/org/apache/spark/DriverSuite.scala +++ b/core/src/test/scala/org/apache/spark/DriverSuite.scala @@ -34,7 +34,7 @@ class DriverSuite extends FunSuite with Timeouts { // Regression test for SPARK-530: "Spark driver process doesn't exit after finishing" val masters = Table(("master"), ("local"), ("local-cluster[2,1,512]")) forAll(masters) { (master: String) => - failAfter(30 seconds) { + failAfter(60 seconds) { Utils.execute(Seq("./sbin/spark-class", "org.apache.spark.DriverWithoutCleanup", master), new File(System.getenv("SPARK_HOME"))) } diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala index af448fcb37..befdc1589f 100644 --- a/core/src/test/scala/org/apache/spark/FailureSuite.scala +++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala @@ -42,7 +42,7 @@ class FailureSuite extends FunSuite with LocalSparkContext { // Run a 3-task map job in which task 1 deterministically fails once, and check // whether the job completes successfully and we ran 4 tasks in total. test("failure in a single-stage job") { - sc = new SparkContext("local[1,1]", "test") + sc = new SparkContext("local[1,2]", "test") val results = sc.makeRDD(1 to 3, 3).map { x => FailureSuiteState.synchronized { FailureSuiteState.tasksRun += 1 @@ -62,7 +62,7 @@ class FailureSuite extends FunSuite with LocalSparkContext { // Run a map-reduce job in which a reduce task deterministically fails once. test("failure in a two-stage job") { - sc = new SparkContext("local[1,1]", "test") + sc = new SparkContext("local[1,2]", "test") val results = sc.makeRDD(1 to 3).map(x => (x, x)).groupByKey(3).map { case (k, v) => FailureSuiteState.synchronized { diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala index 35d1d41af1..c210dd5c3b 100644 --- a/core/src/test/scala/org/apache/spark/FileServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala @@ -120,4 +120,20 @@ class FileServerSuite extends FunSuite with LocalSparkContext { }.collect() assert(result.toSet === Set((1,2), (2,7), (3,121))) } + + test ("Dynamically adding JARS on a standalone cluster using local: URL") { + sc = new SparkContext("local-cluster[1,1,512]", "test") + val sampleJarFile = getClass.getClassLoader.getResource("uncommons-maths-1.2.2.jar").getFile() + sc.addJar(sampleJarFile.replace("file", "local")) + val testData = Array((1,1), (1,1), (2,1), (3,5), (2,3), (3,0)) + val result = sc.parallelize(testData).reduceByKey { (x,y) => + val fac = Thread.currentThread.getContextClassLoader() + .loadClass("org.uncommons.maths.Maths") + .getDeclaredMethod("factorial", classOf[Int]) + val a = fac.invoke(null, x.asInstanceOf[java.lang.Integer]).asInstanceOf[Long].toInt + val b = fac.invoke(null, y.asInstanceOf[java.lang.Integer]).asInstanceOf[Long].toInt + a + b + }.collect() + assert(result.toSet === Set((1,2), (2,7), (3,121))) + } } diff --git a/core/src/test/scala/org/apache/spark/JavaAPISuite.java b/core/src/test/scala/org/apache/spark/JavaAPISuite.java index 591c1d498d..23ec6c3b31 100644 --- a/core/src/test/scala/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/scala/org/apache/spark/JavaAPISuite.java @@ -365,6 +365,20 @@ public class JavaAPISuite implements Serializable { } @Test + public void javaDoubleRDDHistoGram() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + // Test using generated buckets + Tuple2<double[], long[]> results = rdd.histogram(2); + double[] expected_buckets = {1.0, 2.5, 4.0}; + long[] expected_counts = {2, 2}; + Assert.assertArrayEquals(expected_buckets, results._1, 0.1); + Assert.assertArrayEquals(expected_counts, results._2); + // Test with provided buckets + long[] histogram = rdd.histogram(expected_buckets); + Assert.assertArrayEquals(expected_counts, histogram); + } + + @Test public void map() { JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); JavaDoubleRDD doubles = rdd.map(new DoubleFunction<Integer>() { @@ -473,6 +487,27 @@ public class JavaAPISuite implements Serializable { } @Test + public void repartition() { + // Shrinking number of partitions + JavaRDD<Integer> in1 = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8), 2); + JavaRDD<Integer> repartitioned1 = in1.repartition(4); + List<List<Integer>> result1 = repartitioned1.glom().collect(); + Assert.assertEquals(4, result1.size()); + for (List<Integer> l: result1) { + Assert.assertTrue(l.size() > 0); + } + + // Growing number of partitions + JavaRDD<Integer> in2 = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8), 4); + JavaRDD<Integer> repartitioned2 = in2.repartition(2); + List<List<Integer>> result2 = repartitioned2.glom().collect(); + Assert.assertEquals(2, result2.size()); + for (List<Integer> l: result2) { + Assert.assertTrue(l.size() > 0); + } + } + + @Test public void persist() { JavaDoubleRDD doubleRDD = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0)); doubleRDD = doubleRDD.persist(StorageLevel.DISK_ONLY()); @@ -495,7 +530,7 @@ public class JavaAPISuite implements Serializable { @Test public void iterator() { JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2); - TaskContext context = new TaskContext(0, 0, 0, false, null); + TaskContext context = new TaskContext(0, 0, 0, false, false, null); Assert.assertEquals(1, rdd.iterator(rdd.splits().get(0), context).next().intValue()); } @@ -816,7 +851,7 @@ public class JavaAPISuite implements Serializable { public void checkpointAndComputation() { File tempDir = Files.createTempDir(); JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); - sc.setCheckpointDir(tempDir.getAbsolutePath(), true); + sc.setCheckpointDir(tempDir.getAbsolutePath()); Assert.assertEquals(false, rdd.isCheckpointed()); rdd.checkpoint(); rdd.count(); // Forces the DAG to cause a checkpoint @@ -828,7 +863,7 @@ public class JavaAPISuite implements Serializable { public void checkpointAndRestore() { File tempDir = Files.createTempDir(); JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); - sc.setCheckpointDir(tempDir.getAbsolutePath(), true); + sc.setCheckpointDir(tempDir.getAbsolutePath()); Assert.assertEquals(false, rdd.isCheckpointed()); rdd.checkpoint(); rdd.count(); // Forces the DAG to cause a checkpoint @@ -862,4 +897,69 @@ public class JavaAPISuite implements Serializable { new Tuple2<Integer, Integer>(0, 4)), rdd3.collect()); } + + @Test + public void collectPartitions() { + JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6, 7), 3); + + JavaPairRDD<Integer, Integer> rdd2 = rdd1.map(new PairFunction<Integer, Integer, Integer>() { + @Override + public Tuple2<Integer, Integer> call(Integer i) throws Exception { + return new Tuple2<Integer, Integer>(i, i % 2); + } + }); + + List[] parts = rdd1.collectPartitions(new int[] {0}); + Assert.assertEquals(Arrays.asList(1, 2), parts[0]); + + parts = rdd1.collectPartitions(new int[] {1, 2}); + Assert.assertEquals(Arrays.asList(3, 4), parts[0]); + Assert.assertEquals(Arrays.asList(5, 6, 7), parts[1]); + + Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(1, 1), + new Tuple2<Integer, Integer>(2, 0)), + rdd2.collectPartitions(new int[] {0})[0]); + + parts = rdd2.collectPartitions(new int[] {1, 2}); + Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(3, 1), + new Tuple2<Integer, Integer>(4, 0)), + parts[0]); + Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(5, 1), + new Tuple2<Integer, Integer>(6, 0), + new Tuple2<Integer, Integer>(7, 1)), + parts[1]); + } + + @Test + public void countApproxDistinct() { + List<Integer> arrayData = new ArrayList<Integer>(); + int size = 100; + for (int i = 0; i < 100000; i++) { + arrayData.add(i % size); + } + JavaRDD<Integer> simpleRdd = sc.parallelize(arrayData, 10); + Assert.assertTrue(Math.abs((simpleRdd.countApproxDistinct(0.2) - size) / (size * 1.0)) < 0.2); + Assert.assertTrue(Math.abs((simpleRdd.countApproxDistinct(0.05) - size) / (size * 1.0)) <= 0.05); + Assert.assertTrue(Math.abs((simpleRdd.countApproxDistinct(0.01) - size) / (size * 1.0)) <= 0.01); + } + + @Test + public void countApproxDistinctByKey() { + double relativeSD = 0.001; + + List<Tuple2<Integer, Integer>> arrayData = new ArrayList<Tuple2<Integer, Integer>>(); + for (int i = 10; i < 100; i++) + for (int j = 0; j < i; j++) + arrayData.add(new Tuple2<Integer, Integer>(i, j)); + + JavaPairRDD<Integer, Integer> pairRdd = sc.parallelizePairs(arrayData); + List<Tuple2<Integer, Object>> res = pairRdd.countApproxDistinctByKey(relativeSD).collect(); + for (Tuple2<Integer, Object> resItem : res) { + double count = (double)resItem._1(); + Long resCount = (Long)resItem._2(); + Double error = Math.abs((resCount - count) / count); + Assert.assertTrue(error < relativeSD); + } + + } } diff --git a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala new file mode 100644 index 0000000000..1121e06e2e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala @@ -0,0 +1,209 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark + +import java.util.concurrent.Semaphore + +import scala.concurrent.Await +import scala.concurrent.duration.Duration +import scala.concurrent.future +import scala.concurrent.ExecutionContext.Implicits.global + +import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.matchers.ShouldMatchers + +import org.apache.spark.SparkContext._ +import org.apache.spark.scheduler.{SparkListenerTaskStart, SparkListener} + + +/** + * Test suite for cancelling running jobs. We run the cancellation tasks for single job action + * (e.g. count) as well as multi-job action (e.g. take). We test the local and cluster schedulers + * in both FIFO and fair scheduling modes. + */ +class JobCancellationSuite extends FunSuite with ShouldMatchers with BeforeAndAfter + with LocalSparkContext { + + override def afterEach() { + super.afterEach() + resetSparkContext() + System.clearProperty("spark.scheduler.mode") + } + + test("local mode, FIFO scheduler") { + System.setProperty("spark.scheduler.mode", "FIFO") + sc = new SparkContext("local[2]", "test") + testCount() + testTake() + // Make sure we can still launch tasks. + assert(sc.parallelize(1 to 10, 2).count === 10) + } + + test("local mode, fair scheduler") { + System.setProperty("spark.scheduler.mode", "FAIR") + val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() + System.setProperty("spark.scheduler.allocation.file", xmlPath) + sc = new SparkContext("local[2]", "test") + testCount() + testTake() + // Make sure we can still launch tasks. + assert(sc.parallelize(1 to 10, 2).count === 10) + } + + test("cluster mode, FIFO scheduler") { + System.setProperty("spark.scheduler.mode", "FIFO") + sc = new SparkContext("local-cluster[2,1,512]", "test") + testCount() + testTake() + // Make sure we can still launch tasks. + assert(sc.parallelize(1 to 10, 2).count === 10) + } + + test("cluster mode, fair scheduler") { + System.setProperty("spark.scheduler.mode", "FAIR") + val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() + System.setProperty("spark.scheduler.allocation.file", xmlPath) + sc = new SparkContext("local-cluster[2,1,512]", "test") + testCount() + testTake() + // Make sure we can still launch tasks. + assert(sc.parallelize(1 to 10, 2).count === 10) + } + + test("job group") { + sc = new SparkContext("local[2]", "test") + + // Add a listener to release the semaphore once any tasks are launched. + val sem = new Semaphore(0) + sc.dagScheduler.addSparkListener(new SparkListener { + override def onTaskStart(taskStart: SparkListenerTaskStart) { + sem.release() + } + }) + + // jobA is the one to be cancelled. + val jobA = future { + sc.setJobGroup("jobA", "this is a job to be cancelled") + sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.count() + } + + sc.clearJobGroup() + val jobB = sc.parallelize(1 to 100, 2).countAsync() + + // Block until both tasks of job A have started and cancel job A. + sem.acquire(2) + sc.cancelJobGroup("jobA") + val e = intercept[SparkException] { Await.result(jobA, Duration.Inf) } + assert(e.getMessage contains "cancel") + + // Once A is cancelled, job B should finish fairly quickly. + assert(jobB.get() === 100) + } +/* + test("two jobs sharing the same stage") { + // sem1: make sure cancel is issued after some tasks are launched + // sem2: make sure the first stage is not finished until cancel is issued + val sem1 = new Semaphore(0) + val sem2 = new Semaphore(0) + + sc = new SparkContext("local[2]", "test") + sc.dagScheduler.addSparkListener(new SparkListener { + override def onTaskStart(taskStart: SparkListenerTaskStart) { + sem1.release() + } + }) + + // Create two actions that would share the some stages. + val rdd = sc.parallelize(1 to 10, 2).map { i => + sem2.acquire() + (i, i) + }.reduceByKey(_+_) + val f1 = rdd.collectAsync() + val f2 = rdd.countAsync() + + // Kill one of the action. + future { + sem1.acquire() + f1.cancel() + sem2.release(10) + } + + // Expect both to fail now. + // TODO: update this test when we change Spark so cancelling f1 wouldn't affect f2. + intercept[SparkException] { f1.get() } + intercept[SparkException] { f2.get() } + } + */ + def testCount() { + // Cancel before launching any tasks + { + val f = sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.countAsync() + future { f.cancel() } + val e = intercept[SparkException] { f.get() } + assert(e.getMessage.contains("cancelled") || e.getMessage.contains("killed")) + } + + // Cancel after some tasks have been launched + { + // Add a listener to release the semaphore once any tasks are launched. + val sem = new Semaphore(0) + sc.dagScheduler.addSparkListener(new SparkListener { + override def onTaskStart(taskStart: SparkListenerTaskStart) { + sem.release() + } + }) + + val f = sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.countAsync() + future { + // Wait until some tasks were launched before we cancel the job. + sem.acquire() + f.cancel() + } + val e = intercept[SparkException] { f.get() } + assert(e.getMessage.contains("cancelled") || e.getMessage.contains("killed")) + } + } + + def testTake() { + // Cancel before launching any tasks + { + val f = sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.takeAsync(5000) + future { f.cancel() } + val e = intercept[SparkException] { f.get() } + assert(e.getMessage.contains("cancelled") || e.getMessage.contains("killed")) + } + + // Cancel after some tasks have been launched + { + // Add a listener to release the semaphore once any tasks are launched. + val sem = new Semaphore(0) + sc.dagScheduler.addSparkListener(new SparkListener { + override def onTaskStart(taskStart: SparkListenerTaskStart) { + sem.release() + } + }) + val f = sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.takeAsync(5000) + future { + sem.acquire() + f.cancel() + } + val e = intercept[SparkException] { f.get() } + assert(e.getMessage.contains("cancelled") || e.getMessage.contains("killed")) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala index 459e257d79..8dd5786da6 100644 --- a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala +++ b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala @@ -30,7 +30,7 @@ trait LocalSparkContext extends BeforeAndAfterEach with BeforeAndAfterAll { self @transient var sc: SparkContext = _ override def beforeAll() { - InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory()); + InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory()) super.beforeAll() } diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala index 6013320eaa..10b8b441fd 100644 --- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala @@ -25,7 +25,7 @@ import org.apache.spark.storage.BlockManagerId import org.apache.spark.util.AkkaUtils class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { - + private val conf = new SparkConf test("compressSize") { assert(MapOutputTracker.compressSize(0L) === 0) assert(MapOutputTracker.compressSize(1L) === 1) @@ -48,15 +48,15 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { test("master start and stop") { val actorSystem = ActorSystem("test") - val tracker = new MapOutputTracker() - tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerActor(tracker))) + val tracker = new MapOutputTrackerMaster(conf) + tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) tracker.stop() } test("master register and fetch") { val actorSystem = ActorSystem("test") - val tracker = new MapOutputTracker() - tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerActor(tracker))) + val tracker = new MapOutputTrackerMaster(conf) + tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) tracker.registerShuffle(10, 2) val compressedSize1000 = MapOutputTracker.compressSize(1000L) val compressedSize10000 = MapOutputTracker.compressSize(10000L) @@ -74,19 +74,17 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { test("master register and unregister and fetch") { val actorSystem = ActorSystem("test") - val tracker = new MapOutputTracker() - tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerActor(tracker))) + val tracker = new MapOutputTrackerMaster(conf) + tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) tracker.registerShuffle(10, 2) val compressedSize1000 = MapOutputTracker.compressSize(1000L) val compressedSize10000 = MapOutputTracker.compressSize(10000L) - val size1000 = MapOutputTracker.decompressSize(compressedSize1000) - val size10000 = MapOutputTracker.decompressSize(compressedSize10000) tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000, 0), Array(compressedSize1000, compressedSize1000, compressedSize1000))) tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0), Array(compressedSize10000, compressedSize1000, compressedSize1000))) - // As if we had two simulatenous fetch failures + // As if we had two simultaneous fetch failures tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0)) tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0)) @@ -98,18 +96,18 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { test("remote fetch") { val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0) + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, conf = conf) System.setProperty("spark.driver.port", boundPort.toString) // Will be cleared by LocalSparkContext System.setProperty("spark.hostPort", hostname + ":" + boundPort) - val masterTracker = new MapOutputTracker() - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerActor(masterTracker)), "MapOutputTracker") + val masterTracker = new MapOutputTrackerMaster(conf) + masterTracker.trackerActor = Left(actorSystem.actorOf( + Props(new MapOutputTrackerMasterActor(masterTracker)), "MapOutputTracker")) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0) - val slaveTracker = new MapOutputTracker() - slaveTracker.trackerActor = slaveSystem.actorFor( - "akka://spark@localhost:" + boundPort + "/user/MapOutputTracker") + val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = conf) + val slaveTracker = new MapOutputTracker(conf) + slaveTracker.trackerActor = Right(slaveSystem.actorSelection( + "akka.tcp://spark@localhost:" + boundPort + "/user/MapOutputTracker")) masterTracker.registerShuffle(10, 1) masterTracker.incrementEpoch() diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala index 7d938917f2..1374d01774 100644 --- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala +++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala @@ -142,11 +142,11 @@ class PartitioningSuite extends FunSuite with SharedSparkContext { .filter(_ >= 0.0) // Run the partitions, including the consecutive empty ones, through StatCounter - val stats: StatCounter = rdd.stats(); - assert(abs(6.0 - stats.sum) < 0.01); - assert(abs(6.0/2 - rdd.mean) < 0.01); - assert(abs(1.0 - rdd.variance) < 0.01); - assert(abs(1.0 - rdd.stdev) < 0.01); + val stats: StatCounter = rdd.stats() + assert(abs(6.0 - stats.sum) < 0.01) + assert(abs(6.0/2 - rdd.mean) < 0.01) + assert(abs(1.0 - rdd.variance) < 0.01) + assert(abs(1.0 - rdd.stdev) < 0.01) // Add other tests here for classes that should be able to handle empty partitions correctly } diff --git a/core/src/test/scala/org/apache/spark/SharedSparkContext.scala b/core/src/test/scala/org/apache/spark/SharedSparkContext.scala index 288aa14eeb..c650ef4ed5 100644 --- a/core/src/test/scala/org/apache/spark/SharedSparkContext.scala +++ b/core/src/test/scala/org/apache/spark/SharedSparkContext.scala @@ -27,8 +27,10 @@ trait SharedSparkContext extends BeforeAndAfterAll { self: Suite => def sc: SparkContext = _sc + var conf = new SparkConf(false) + override def beforeAll() { - _sc = new SparkContext("local", "test") + _sc = new SparkContext("local", "test", conf) super.beforeAll() } diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala new file mode 100644 index 0000000000..ef5936dd2f --- /dev/null +++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala @@ -0,0 +1,110 @@ +package org.apache.spark + +import org.scalatest.FunSuite + +class SparkConfSuite extends FunSuite with LocalSparkContext { + // This test uses the spark.conf in core/src/test/resources, which has a few test properties + test("loading from spark.conf") { + val conf = new SparkConf() + assert(conf.get("spark.test.intTestProperty") === "1") + assert(conf.get("spark.test.stringTestProperty") === "hi") + // NOTE: we don't use list properties yet, but when we do, we'll have to deal with this syntax + assert(conf.get("spark.test.listTestProperty") === "[a, b]") + } + + // This test uses the spark.conf in core/src/test/resources, which has a few test properties + test("system properties override spark.conf") { + try { + System.setProperty("spark.test.intTestProperty", "2") + val conf = new SparkConf() + assert(conf.get("spark.test.intTestProperty") === "2") + assert(conf.get("spark.test.stringTestProperty") === "hi") + } finally { + System.clearProperty("spark.test.intTestProperty") + } + } + + test("initializing without loading defaults") { + try { + System.setProperty("spark.test.intTestProperty", "2") + val conf = new SparkConf(false) + assert(!conf.contains("spark.test.intTestProperty")) + assert(!conf.contains("spark.test.stringTestProperty")) + } finally { + System.clearProperty("spark.test.intTestProperty") + } + } + + test("named set methods") { + val conf = new SparkConf(false) + + conf.setMaster("local[3]") + conf.setAppName("My app") + conf.setSparkHome("/path") + conf.setJars(Seq("a.jar", "b.jar")) + conf.setExecutorEnv("VAR1", "value1") + conf.setExecutorEnv(Seq(("VAR2", "value2"), ("VAR3", "value3"))) + + assert(conf.get("spark.master") === "local[3]") + assert(conf.get("spark.app.name") === "My app") + assert(conf.get("spark.home") === "/path") + assert(conf.get("spark.jars") === "a.jar,b.jar") + assert(conf.get("spark.executorEnv.VAR1") === "value1") + assert(conf.get("spark.executorEnv.VAR2") === "value2") + assert(conf.get("spark.executorEnv.VAR3") === "value3") + + // Test the Java-friendly versions of these too + conf.setJars(Array("c.jar", "d.jar")) + conf.setExecutorEnv(Array(("VAR4", "value4"), ("VAR5", "value5"))) + assert(conf.get("spark.jars") === "c.jar,d.jar") + assert(conf.get("spark.executorEnv.VAR4") === "value4") + assert(conf.get("spark.executorEnv.VAR5") === "value5") + } + + test("basic get and set") { + val conf = new SparkConf(false) + assert(conf.getAll.toSet === Set()) + conf.set("k1", "v1") + conf.setAll(Seq(("k2", "v2"), ("k3", "v3"))) + assert(conf.getAll.toSet === Set(("k1", "v1"), ("k2", "v2"), ("k3", "v3"))) + conf.set("k1", "v4") + conf.setAll(Seq(("k2", "v5"), ("k3", "v6"))) + assert(conf.getAll.toSet === Set(("k1", "v4"), ("k2", "v5"), ("k3", "v6"))) + assert(conf.contains("k1"), "conf did not contain k1") + assert(!conf.contains("k4"), "conf contained k4") + assert(conf.get("k1") === "v4") + intercept[Exception] { conf.get("k4") } + assert(conf.get("k4", "not found") === "not found") + assert(conf.getOption("k1") === Some("v4")) + assert(conf.getOption("k4") === None) + } + + test("creating SparkContext without master and app name") { + val conf = new SparkConf(false) + intercept[SparkException] { sc = new SparkContext(conf) } + } + + test("creating SparkContext without master") { + val conf = new SparkConf(false).setAppName("My app") + intercept[SparkException] { sc = new SparkContext(conf) } + } + + test("creating SparkContext without app name") { + val conf = new SparkConf(false).setMaster("local") + intercept[SparkException] { sc = new SparkContext(conf) } + } + + test("creating SparkContext with both master and app name") { + val conf = new SparkConf(false).setMaster("local").setAppName("My app") + sc = new SparkContext(conf) + assert(sc.master === "local") + assert(sc.appName === "My app") + } + + test("SparkContext property overriding") { + val conf = new SparkConf(false).setMaster("local").setAppName("My app") + sc = new SparkContext("local[2]", "My other app", conf) + assert(sc.master === "local[2]") + assert(sc.appName === "My other app") + } +} diff --git a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala new file mode 100644 index 0000000000..f28d5c7b13 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala @@ -0,0 +1,132 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark + +import org.scalatest.{FunSuite, PrivateMethodTester} + +import org.apache.spark.scheduler.{TaskSchedulerImpl, TaskScheduler} +import org.apache.spark.scheduler.cluster.{SimrSchedulerBackend, SparkDeploySchedulerBackend} +import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend} +import org.apache.spark.scheduler.local.LocalBackend + +class SparkContextSchedulerCreationSuite + extends FunSuite with PrivateMethodTester with LocalSparkContext with Logging { + + def createTaskScheduler(master: String): TaskSchedulerImpl = { + // Create local SparkContext to setup a SparkEnv. We don't actually want to start() the + // real schedulers, so we don't want to create a full SparkContext with the desired scheduler. + sc = new SparkContext("local", "test") + val createTaskSchedulerMethod = PrivateMethod[TaskScheduler]('createTaskScheduler) + val sched = SparkContext invokePrivate createTaskSchedulerMethod(sc, master, "test") + sched.asInstanceOf[TaskSchedulerImpl] + } + + test("bad-master") { + val e = intercept[SparkException] { + createTaskScheduler("localhost:1234") + } + assert(e.getMessage.contains("Could not parse Master URL")) + } + + test("local") { + val sched = createTaskScheduler("local") + sched.backend match { + case s: LocalBackend => assert(s.totalCores === 1) + case _ => fail() + } + } + + test("local-n") { + val sched = createTaskScheduler("local[5]") + assert(sched.maxTaskFailures === 1) + sched.backend match { + case s: LocalBackend => assert(s.totalCores === 5) + case _ => fail() + } + } + + test("local-n-failures") { + val sched = createTaskScheduler("local[4, 2]") + assert(sched.maxTaskFailures === 2) + sched.backend match { + case s: LocalBackend => assert(s.totalCores === 4) + case _ => fail() + } + } + + test("simr") { + createTaskScheduler("simr://uri").backend match { + case s: SimrSchedulerBackend => // OK + case _ => fail() + } + } + + test("local-cluster") { + createTaskScheduler("local-cluster[3, 14, 512]").backend match { + case s: SparkDeploySchedulerBackend => // OK + case _ => fail() + } + } + + def testYarn(master: String, expectedClassName: String) { + try { + val sched = createTaskScheduler(master) + assert(sched.getClass === Class.forName(expectedClassName)) + } catch { + case e: SparkException => + assert(e.getMessage.contains("YARN mode not available")) + logWarning("YARN not available, could not test actual YARN scheduler creation") + case e: Throwable => fail(e) + } + } + + test("yarn-standalone") { + testYarn("yarn-standalone", "org.apache.spark.scheduler.cluster.YarnClusterScheduler") + } + + test("yarn-client") { + testYarn("yarn-client", "org.apache.spark.scheduler.cluster.YarnClientClusterScheduler") + } + + def testMesos(master: String, expectedClass: Class[_]) { + try { + val sched = createTaskScheduler(master) + assert(sched.backend.getClass === expectedClass) + } catch { + case e: UnsatisfiedLinkError => + assert(e.getMessage.contains("no mesos in")) + logWarning("Mesos not available, could not test actual Mesos scheduler creation") + case e: Throwable => fail(e) + } + } + + test("mesos fine-grained") { + System.setProperty("spark.mesos.coarse", "false") + testMesos("mesos://localhost:1234", classOf[MesosSchedulerBackend]) + } + + test("mesos coarse-grained") { + System.setProperty("spark.mesos.coarse", "true") + testMesos("mesos://localhost:1234", classOf[CoarseMesosSchedulerBackend]) + } + + test("mesos with zookeeper") { + System.setProperty("spark.mesos.coarse", "false") + testMesos("zk://localhost:1234,localhost:2345", classOf[MesosSchedulerBackend]) + } +} diff --git a/core/src/test/scala/org/apache/spark/UnpersistSuite.scala b/core/src/test/scala/org/apache/spark/UnpersistSuite.scala index 46a2da1724..768ca3850e 100644 --- a/core/src/test/scala/org/apache/spark/UnpersistSuite.scala +++ b/core/src/test/scala/org/apache/spark/UnpersistSuite.scala @@ -37,7 +37,7 @@ class UnpersistSuite extends FunSuite with LocalSparkContext { Thread.sleep(200) } } catch { - case _ => { Thread.sleep(10) } + case _: Throwable => { Thread.sleep(10) } // Do nothing. We might see exceptions because block manager // is racing this thread to remove entries from the driver. } diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala index 05f8545c7b..0b38e239f9 100644 --- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala @@ -25,7 +25,7 @@ import net.liftweb.json.JsonAST.JValue import org.scalatest.FunSuite import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse} -import org.apache.spark.deploy.master.{ApplicationInfo, WorkerInfo} +import org.apache.spark.deploy.master.{ApplicationInfo, RecoveryState, WorkerInfo} import org.apache.spark.deploy.worker.ExecutorRunner class JsonProtocolSuite extends FunSuite { @@ -53,7 +53,8 @@ class JsonProtocolSuite extends FunSuite { val workers = Array[WorkerInfo](createWorkerInfo(), createWorkerInfo()) val activeApps = Array[ApplicationInfo](createAppInfo()) val completedApps = Array[ApplicationInfo]() - val stateResponse = new MasterStateResponse("host", 8080, workers, activeApps, completedApps) + val stateResponse = new MasterStateResponse("host", 8080, workers, activeApps, completedApps, + RecoveryState.ALIVE) val output = JsonProtocol.writeMasterState(stateResponse) assertValidJson(output) } @@ -79,7 +80,7 @@ class JsonProtocolSuite extends FunSuite { } def createExecutorRunner() : ExecutorRunner = { new ExecutorRunner("appId", 123, createAppDesc(), 4, 1234, null, "workerId", "host", - new File("sparkHome"), new File("workDir")) + new File("sparkHome"), new File("workDir"), ExecutorState.RUNNING) } def assertValidJson(json: JValue) { diff --git a/core/src/test/scala/org/apache/spark/PartitionPruningRDDSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala index 21f16ef2c6..4cb4ddc9cd 100644 --- a/core/src/test/scala/org/apache/spark/PartitionPruningRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala @@ -15,31 +15,22 @@ * limitations under the License. */ -package org.apache.spark +package org.apache.spark.deploy.worker +import java.io.File import org.scalatest.FunSuite -import org.apache.spark.SparkContext._ -import org.apache.spark.rdd.{RDD, PartitionPruningRDD} +import org.apache.spark.deploy.{ExecutorState, Command, ApplicationDescription} +class ExecutorRunnerTest extends FunSuite { + test("command includes appId") { + def f(s:String) = new File(s) + val sparkHome = sys.env("SPARK_HOME") + val appDesc = new ApplicationDescription("app name", 8, 500, Command("foo", Seq(),Map()), + sparkHome, "appUiUrl") + val appId = "12345-worker321-9876" + val er = new ExecutorRunner(appId, 1, appDesc, 8, 500, null, "blah", "worker321", f(sparkHome), + f("ooga"), ExecutorState.RUNNING) -class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext { - - test("Pruned Partitions inherit locality prefs correctly") { - class TestPartition(i: Int) extends Partition { - def index = i - } - val rdd = new RDD[Int](sc, Nil) { - override protected def getPartitions = { - Array[Partition]( - new TestPartition(1), - new TestPartition(2), - new TestPartition(3)) - } - def compute(split: Partition, context: TaskContext) = {Iterator()} - } - val prunedRDD = PartitionPruningRDD.create(rdd, {x => if (x==2) true else false}) - val p = prunedRDD.partitions(0) - assert(p.index == 2) - assert(prunedRDD.partitions.length == 1) + assert(er.buildCommandSeq().last === appId) } } diff --git a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala index ab81bfbe55..8d7546085f 100644 --- a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala +++ b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala @@ -20,9 +20,11 @@ package org.apache.spark.io import java.io.{ByteArrayInputStream, ByteArrayOutputStream} import org.scalatest.FunSuite +import org.apache.spark.SparkConf class CompressionCodecSuite extends FunSuite { + val conf = new SparkConf(false) def testCodec(codec: CompressionCodec) { // Write 1000 integers to the output stream, compressed. @@ -43,19 +45,19 @@ class CompressionCodecSuite extends FunSuite { } test("default compression codec") { - val codec = CompressionCodec.createCodec() + val codec = CompressionCodec.createCodec(conf) assert(codec.getClass === classOf[LZFCompressionCodec]) testCodec(codec) } test("lzf compression codec") { - val codec = CompressionCodec.createCodec(classOf[LZFCompressionCodec].getName) + val codec = CompressionCodec.createCodec(conf, classOf[LZFCompressionCodec].getName) assert(codec.getClass === classOf[LZFCompressionCodec]) testCodec(codec) } test("snappy compression codec") { - val codec = CompressionCodec.createCodec(classOf[SnappyCompressionCodec].getName) + val codec = CompressionCodec.createCodec(conf, classOf[SnappyCompressionCodec].getName) assert(codec.getClass === classOf[SnappyCompressionCodec]) testCodec(codec) } diff --git a/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala b/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala index 7181333adf..71a2c6c498 100644 --- a/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala +++ b/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala @@ -19,17 +19,19 @@ package org.apache.spark.metrics import org.scalatest.{BeforeAndAfter, FunSuite} import org.apache.spark.deploy.master.MasterSource +import org.apache.spark.SparkConf class MetricsSystemSuite extends FunSuite with BeforeAndAfter { var filePath: String = _ + var conf: SparkConf = null before { filePath = getClass.getClassLoader.getResource("test_metrics_system.properties").getFile() - System.setProperty("spark.metrics.conf", filePath) + conf = new SparkConf(false).set("spark.metrics.conf", filePath) } test("MetricsSystem with default config") { - val metricsSystem = MetricsSystem.createMetricsSystem("default") + val metricsSystem = MetricsSystem.createMetricsSystem("default", conf) val sources = metricsSystem.sources val sinks = metricsSystem.sinks @@ -39,7 +41,7 @@ class MetricsSystemSuite extends FunSuite with BeforeAndAfter { } test("MetricsSystem with sources add") { - val metricsSystem = MetricsSystem.createMetricsSystem("test") + val metricsSystem = MetricsSystem.createMetricsSystem("test", conf) val sources = metricsSystem.sources val sinks = metricsSystem.sinks diff --git a/core/src/test/scala/org/apache/spark/rdd/AsyncRDDActionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/AsyncRDDActionsSuite.scala new file mode 100644 index 0000000000..0d4c10db8e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rdd/AsyncRDDActionsSuite.scala @@ -0,0 +1,202 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import java.util.concurrent.Semaphore + +import scala.concurrent.{Await, TimeoutException} +import scala.concurrent.duration.Duration +import scala.concurrent.ExecutionContext.Implicits.global + +import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.concurrent.Timeouts +import org.scalatest.time.SpanSugar._ + +import org.apache.spark.SparkContext._ +import org.apache.spark.{SparkContext, SparkException, LocalSparkContext} + + +class AsyncRDDActionsSuite extends FunSuite with BeforeAndAfterAll with Timeouts { + + @transient private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local[2]", "test") + } + + override def afterAll() { + LocalSparkContext.stop(sc) + sc = null + } + + lazy val zeroPartRdd = new EmptyRDD[Int](sc) + + test("countAsync") { + assert(zeroPartRdd.countAsync().get() === 0) + assert(sc.parallelize(1 to 10000, 5).countAsync().get() === 10000) + } + + test("collectAsync") { + assert(zeroPartRdd.collectAsync().get() === Seq.empty) + + val collected = sc.parallelize(1 to 1000, 3).collectAsync().get() + assert(collected === (1 to 1000)) + } + + test("foreachAsync") { + zeroPartRdd.foreachAsync(i => Unit).get() + + val accum = sc.accumulator(0) + sc.parallelize(1 to 1000, 3).foreachAsync { i => + accum += 1 + }.get() + assert(accum.value === 1000) + } + + test("foreachPartitionAsync") { + zeroPartRdd.foreachPartitionAsync(iter => Unit).get() + + val accum = sc.accumulator(0) + sc.parallelize(1 to 1000, 9).foreachPartitionAsync { iter => + accum += 1 + }.get() + assert(accum.value === 9) + } + + test("takeAsync") { + def testTake(rdd: RDD[Int], input: Seq[Int], num: Int) { + val expected = input.take(num) + val saw = rdd.takeAsync(num).get() + assert(saw == expected, "incorrect result for rdd with %d partitions (expected %s, saw %s)" + .format(rdd.partitions.size, expected, saw)) + } + val input = Range(1, 1000) + + var rdd = sc.parallelize(input, 1) + for (num <- Seq(0, 1, 999, 1000)) { + testTake(rdd, input, num) + } + + rdd = sc.parallelize(input, 2) + for (num <- Seq(0, 1, 3, 500, 501, 999, 1000)) { + testTake(rdd, input, num) + } + + rdd = sc.parallelize(input, 100) + for (num <- Seq(0, 1, 500, 501, 999, 1000)) { + testTake(rdd, input, num) + } + + rdd = sc.parallelize(input, 1000) + for (num <- Seq(0, 1, 3, 999, 1000)) { + testTake(rdd, input, num) + } + } + + /** + * Make sure onComplete, onSuccess, and onFailure are invoked correctly in the case + * of a successful job execution. + */ + test("async success handling") { + val f = sc.parallelize(1 to 10, 2).countAsync() + + // Use a semaphore to make sure onSuccess and onComplete's success path will be called. + // If not, the test will hang. + val sem = new Semaphore(0) + + f.onComplete { + case scala.util.Success(res) => + sem.release() + case scala.util.Failure(e) => + info("Should not have reached this code path (onComplete matching Failure)") + throw new Exception("Task should succeed") + } + f.onSuccess { case a: Any => + sem.release() + } + f.onFailure { case t => + info("Should not have reached this code path (onFailure)") + throw new Exception("Task should succeed") + } + assert(f.get() === 10) + + failAfter(10 seconds) { + sem.acquire(2) + } + } + + /** + * Make sure onComplete, onSuccess, and onFailure are invoked correctly in the case + * of a failed job execution. + */ + test("async failure handling") { + val f = sc.parallelize(1 to 10, 2).map { i => + throw new Exception("intentional"); i + }.countAsync() + + // Use a semaphore to make sure onFailure and onComplete's failure path will be called. + // If not, the test will hang. + val sem = new Semaphore(0) + + f.onComplete { + case scala.util.Success(res) => + info("Should not have reached this code path (onComplete matching Success)") + throw new Exception("Task should fail") + case scala.util.Failure(e) => + sem.release() + } + f.onSuccess { case a: Any => + info("Should not have reached this code path (onSuccess)") + throw new Exception("Task should fail") + } + f.onFailure { case t => + sem.release() + } + intercept[SparkException] { + f.get() + } + + failAfter(10 seconds) { + sem.acquire(2) + } + } + + /** + * Awaiting FutureAction results + */ + test("FutureAction result, infinite wait") { + val f = sc.parallelize(1 to 100, 4) + .countAsync() + assert(Await.result(f, Duration.Inf) === 100) + } + + test("FutureAction result, finite wait") { + val f = sc.parallelize(1 to 100, 4) + .countAsync() + assert(Await.result(f, Duration(30, "seconds")) === 100) + } + + test("FutureAction result, timeout") { + val f = sc.parallelize(1 to 100, 4) + .mapPartitions(itr => { Thread.sleep(20); itr }) + .countAsync() + intercept[TimeoutException] { + Await.result(f, Duration(20, "milliseconds")) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala new file mode 100644 index 0000000000..7f50a5a47c --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala @@ -0,0 +1,271 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import scala.math.abs +import scala.collection.mutable.ArrayBuffer + +import org.scalatest.FunSuite + +import org.apache.spark.SparkContext._ +import org.apache.spark.rdd._ +import org.apache.spark._ + +class DoubleRDDSuite extends FunSuite with SharedSparkContext { + // Verify tests on the histogram functionality. We test with both evenly + // and non-evenly spaced buckets as the bucket lookup function changes. + test("WorksOnEmpty") { + // Make sure that it works on an empty input + val rdd: RDD[Double] = sc.parallelize(Seq()) + val buckets = Array(0.0, 10.0) + val histogramResults = rdd.histogram(buckets) + val histogramResults2 = rdd.histogram(buckets, true) + val expectedHistogramResults = Array(0) + assert(histogramResults === expectedHistogramResults) + assert(histogramResults2 === expectedHistogramResults) + } + + test("WorksWithOutOfRangeWithOneBucket") { + // Verify that if all of the elements are out of range the counts are zero + val rdd = sc.parallelize(Seq(10.01, -0.01)) + val buckets = Array(0.0, 10.0) + val histogramResults = rdd.histogram(buckets) + val histogramResults2 = rdd.histogram(buckets, true) + val expectedHistogramResults = Array(0) + assert(histogramResults === expectedHistogramResults) + assert(histogramResults2 === expectedHistogramResults) + } + + test("WorksInRangeWithOneBucket") { + // Verify the basic case of one bucket and all elements in that bucket works + val rdd = sc.parallelize(Seq(1, 2, 3, 4)) + val buckets = Array(0.0, 10.0) + val histogramResults = rdd.histogram(buckets) + val histogramResults2 = rdd.histogram(buckets, true) + val expectedHistogramResults = Array(4) + assert(histogramResults === expectedHistogramResults) + assert(histogramResults2 === expectedHistogramResults) + } + + test("WorksInRangeWithOneBucketExactMatch") { + // Verify the basic case of one bucket and all elements in that bucket works + val rdd = sc.parallelize(Seq(1, 2, 3, 4)) + val buckets = Array(1.0, 4.0) + val histogramResults = rdd.histogram(buckets) + val histogramResults2 = rdd.histogram(buckets, true) + val expectedHistogramResults = Array(4) + assert(histogramResults === expectedHistogramResults) + assert(histogramResults2 === expectedHistogramResults) + } + + test("WorksWithOutOfRangeWithTwoBuckets") { + // Verify that out of range works with two buckets + val rdd = sc.parallelize(Seq(10.01, -0.01)) + val buckets = Array(0.0, 5.0, 10.0) + val histogramResults = rdd.histogram(buckets) + val histogramResults2 = rdd.histogram(buckets, true) + val expectedHistogramResults = Array(0, 0) + assert(histogramResults === expectedHistogramResults) + assert(histogramResults2 === expectedHistogramResults) + } + + test("WorksWithOutOfRangeWithTwoUnEvenBuckets") { + // Verify that out of range works with two un even buckets + val rdd = sc.parallelize(Seq(10.01, -0.01)) + val buckets = Array(0.0, 4.0, 10.0) + val histogramResults = rdd.histogram(buckets) + val expectedHistogramResults = Array(0, 0) + assert(histogramResults === expectedHistogramResults) + } + + test("WorksInRangeWithTwoBuckets") { + // Make sure that it works with two equally spaced buckets and elements in each + val rdd = sc.parallelize(Seq(1, 2, 3, 5, 6)) + val buckets = Array(0.0, 5.0, 10.0) + val histogramResults = rdd.histogram(buckets) + val histogramResults2 = rdd.histogram(buckets, true) + val expectedHistogramResults = Array(3, 2) + assert(histogramResults === expectedHistogramResults) + assert(histogramResults2 === expectedHistogramResults) + } + + test("WorksInRangeWithTwoBucketsAndNaN") { + // Make sure that it works with two equally spaced buckets and elements in each + val rdd = sc.parallelize(Seq(1, 2, 3, 5, 6, Double.NaN)) + val buckets = Array(0.0, 5.0, 10.0) + val histogramResults = rdd.histogram(buckets) + val histogramResults2 = rdd.histogram(buckets, true) + val expectedHistogramResults = Array(3, 2) + assert(histogramResults === expectedHistogramResults) + assert(histogramResults2 === expectedHistogramResults) + } + + test("WorksInRangeWithTwoUnevenBuckets") { + // Make sure that it works with two unequally spaced buckets and elements in each + val rdd = sc.parallelize(Seq(1, 2, 3, 5, 6)) + val buckets = Array(0.0, 5.0, 11.0) + val histogramResults = rdd.histogram(buckets) + val expectedHistogramResults = Array(3, 2) + assert(histogramResults === expectedHistogramResults) + } + + test("WorksMixedRangeWithTwoUnevenBuckets") { + // Make sure that it works with two unequally spaced buckets and elements in each + val rdd = sc.parallelize(Seq(-0.01, 0.0, 1, 2, 3, 5, 6, 11.0, 11.01)) + val buckets = Array(0.0, 5.0, 11.0) + val histogramResults = rdd.histogram(buckets) + val expectedHistogramResults = Array(4, 3) + assert(histogramResults === expectedHistogramResults) + } + + test("WorksMixedRangeWithFourUnevenBuckets") { + // Make sure that it works with two unequally spaced buckets and elements in each + val rdd = sc.parallelize(Seq(-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, + 200.0, 200.1)) + val buckets = Array(0.0, 5.0, 11.0, 12.0, 200.0) + val histogramResults = rdd.histogram(buckets) + val expectedHistogramResults = Array(4, 2, 1, 3) + assert(histogramResults === expectedHistogramResults) + } + + test("WorksMixedRangeWithUnevenBucketsAndNaN") { + // Make sure that it works with two unequally spaced buckets and elements in each + val rdd = sc.parallelize(Seq(-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, + 200.0, 200.1, Double.NaN)) + val buckets = Array(0.0, 5.0, 11.0, 12.0, 200.0) + val histogramResults = rdd.histogram(buckets) + val expectedHistogramResults = Array(4, 2, 1, 3) + assert(histogramResults === expectedHistogramResults) + } + // Make sure this works with a NaN end bucket + test("WorksMixedRangeWithUnevenBucketsAndNaNAndNaNRange") { + // Make sure that it works with two unequally spaced buckets and elements in each + val rdd = sc.parallelize(Seq(-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, + 200.0, 200.1, Double.NaN)) + val buckets = Array(0.0, 5.0, 11.0, 12.0, 200.0, Double.NaN) + val histogramResults = rdd.histogram(buckets) + val expectedHistogramResults = Array(4, 2, 1, 2, 3) + assert(histogramResults === expectedHistogramResults) + } + // Make sure this works with a NaN end bucket and an inifity + test("WorksMixedRangeWithUnevenBucketsAndNaNAndNaNRangeAndInfity") { + // Make sure that it works with two unequally spaced buckets and elements in each + val rdd = sc.parallelize(Seq(-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, + 200.0, 200.1, 1.0/0.0, -1.0/0.0, Double.NaN)) + val buckets = Array(0.0, 5.0, 11.0, 12.0, 200.0, Double.NaN) + val histogramResults = rdd.histogram(buckets) + val expectedHistogramResults = Array(4, 2, 1, 2, 4) + assert(histogramResults === expectedHistogramResults) + } + + test("WorksWithOutOfRangeWithInfiniteBuckets") { + // Verify that out of range works with two buckets + val rdd = sc.parallelize(Seq(10.01, -0.01, Double.NaN)) + val buckets = Array(-1.0/0.0 , 0.0, 1.0/0.0) + val histogramResults = rdd.histogram(buckets) + val expectedHistogramResults = Array(1, 1) + assert(histogramResults === expectedHistogramResults) + } + // Test the failure mode with an invalid bucket array + test("ThrowsExceptionOnInvalidBucketArray") { + val rdd = sc.parallelize(Seq(1.0)) + // Empty array + intercept[IllegalArgumentException] { + val buckets = Array.empty[Double] + val result = rdd.histogram(buckets) + } + // Single element array + intercept[IllegalArgumentException] { + val buckets = Array(1.0) + val result = rdd.histogram(buckets) + } + } + + // Test automatic histogram function + test("WorksWithoutBucketsBasic") { + // Verify the basic case of one bucket and all elements in that bucket works + val rdd = sc.parallelize(Seq(1, 2, 3, 4)) + val (histogramBuckets, histogramResults) = rdd.histogram(1) + val expectedHistogramResults = Array(4) + val expectedHistogramBuckets = Array(1.0, 4.0) + assert(histogramResults === expectedHistogramResults) + assert(histogramBuckets === expectedHistogramBuckets) + } + // Test automatic histogram function with a single element + test("WorksWithoutBucketsBasicSingleElement") { + // Verify the basic case of one bucket and all elements in that bucket works + val rdd = sc.parallelize(Seq(1)) + val (histogramBuckets, histogramResults) = rdd.histogram(1) + val expectedHistogramResults = Array(1) + val expectedHistogramBuckets = Array(1.0, 1.0) + assert(histogramResults === expectedHistogramResults) + assert(histogramBuckets === expectedHistogramBuckets) + } + // Test automatic histogram function with a single element + test("WorksWithoutBucketsBasicNoRange") { + // Verify the basic case of one bucket and all elements in that bucket works + val rdd = sc.parallelize(Seq(1, 1, 1, 1)) + val (histogramBuckets, histogramResults) = rdd.histogram(1) + val expectedHistogramResults = Array(4) + val expectedHistogramBuckets = Array(1.0, 1.0) + assert(histogramResults === expectedHistogramResults) + assert(histogramBuckets === expectedHistogramBuckets) + } + + test("WorksWithoutBucketsBasicTwo") { + // Verify the basic case of one bucket and all elements in that bucket works + val rdd = sc.parallelize(Seq(1, 2, 3, 4)) + val (histogramBuckets, histogramResults) = rdd.histogram(2) + val expectedHistogramResults = Array(2, 2) + val expectedHistogramBuckets = Array(1.0, 2.5, 4.0) + assert(histogramResults === expectedHistogramResults) + assert(histogramBuckets === expectedHistogramBuckets) + } + + test("WorksWithoutBucketsWithMoreRequestedThanElements") { + // Verify the basic case of one bucket and all elements in that bucket works + val rdd = sc.parallelize(Seq(1, 2)) + val (histogramBuckets, histogramResults) = rdd.histogram(10) + val expectedHistogramResults = + Array(1, 0, 0, 0, 0, 0, 0, 0, 0, 1) + val expectedHistogramBuckets = + Array(1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0) + assert(histogramResults === expectedHistogramResults) + assert(histogramBuckets === expectedHistogramBuckets) + } + + // Test the failure mode with an invalid RDD + test("ThrowsExceptionOnInvalidRDDs") { + // infinity + intercept[UnsupportedOperationException] { + val rdd = sc.parallelize(Seq(1, 1.0/0.0)) + val result = rdd.histogram(1) + } + // NaN + intercept[UnsupportedOperationException] { + val rdd = sc.parallelize(Seq(1, Double.NaN)) + val result = rdd.histogram(1) + } + // Empty + intercept[UnsupportedOperationException] { + val rdd: RDD[Double] = sc.parallelize(Seq()) + val result = rdd.histogram(1) + } + } + +} diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala index 31f97fc139..5da538a1dd 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.rdd import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashSet +import scala.util.Random import org.scalatest.FunSuite @@ -106,7 +107,40 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { } } visit(sums) - assert(deps.size === 2) // ShuffledRDD, ParallelCollection + assert(deps.size === 2) // ShuffledRDD, ParallelCollection. + } + + test("countApproxDistinctByKey") { + def error(est: Long, size: Long) = math.abs(est - size) / size.toDouble + + /* Since HyperLogLog unique counting is approximate, and the relative standard deviation is + * only a statistical bound, the tests can fail for large values of relativeSD. We will be using + * relatively tight error bounds to check correctness of functionality rather than checking + * whether the approximation conforms with the requested bound. + */ + val relativeSD = 0.001 + + // For each value i, there are i tuples with first element equal to i. + // Therefore, the expected count for key i would be i. + val stacked = (1 to 100).flatMap(i => (1 to i).map(j => (i, j))) + val rdd1 = sc.parallelize(stacked) + val counted1 = rdd1.countApproxDistinctByKey(relativeSD).collect() + counted1.foreach{ + case(k, count) => assert(error(count, k) < relativeSD) + } + + val rnd = new Random() + + // The expected count for key num would be num + val randStacked = (1 to 100).flatMap { i => + val num = rnd.nextInt % 500 + (1 to num).map(j => (num, j)) + } + val rdd2 = sc.parallelize(randStacked) + val counted2 = rdd2.countApproxDistinctByKey(relativeSD, 4).collect() + counted2.foreach{ + case(k, count) => assert(error(count, k) < relativeSD) + } } test("join") { diff --git a/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala new file mode 100644 index 0000000000..53a7b7c44d --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala @@ -0,0 +1,86 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import org.scalatest.FunSuite +import org.apache.spark.{TaskContext, Partition, SharedSparkContext} + + +class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext { + + + test("Pruned Partitions inherit locality prefs correctly") { + + val rdd = new RDD[Int](sc, Nil) { + override protected def getPartitions = { + Array[Partition]( + new TestPartition(0, 1), + new TestPartition(1, 1), + new TestPartition(2, 1)) + } + + def compute(split: Partition, context: TaskContext) = { + Iterator() + } + } + val prunedRDD = PartitionPruningRDD.create(rdd, { + x => if (x == 2) true else false + }) + assert(prunedRDD.partitions.length == 1) + val p = prunedRDD.partitions(0) + assert(p.index == 0) + assert(p.asInstanceOf[PartitionPruningRDDPartition].parentSplit.index == 2) + } + + + test("Pruned Partitions can be unioned ") { + + val rdd = new RDD[Int](sc, Nil) { + override protected def getPartitions = { + Array[Partition]( + new TestPartition(0, 4), + new TestPartition(1, 5), + new TestPartition(2, 6)) + } + + def compute(split: Partition, context: TaskContext) = { + List(split.asInstanceOf[TestPartition].testValue).iterator + } + } + val prunedRDD1 = PartitionPruningRDD.create(rdd, { + x => if (x == 0) true else false + }) + + val prunedRDD2 = PartitionPruningRDD.create(rdd, { + x => if (x == 2) true else false + }) + + val merged = prunedRDD1 ++ prunedRDD2 + assert(merged.count() == 2) + val take = merged.take(2) + assert(take.apply(0) == 4) + assert(take.apply(1) == 6) + } +} + +class TestPartition(i: Int, value: Int) extends Partition with Serializable { + def index = i + + def testValue = this.value + +} diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala index 6d1bc5e296..1383359f85 100644 --- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala @@ -63,6 +63,19 @@ class RDDSuite extends FunSuite with SharedSparkContext { } } + test("countApproxDistinct") { + + def error(est: Long, size: Long) = math.abs(est - size) / size.toDouble + + val size = 100 + val uniformDistro = for (i <- 1 to 100000) yield i % size + val simpleRdd = sc.makeRDD(uniformDistro) + assert(error(simpleRdd.countApproxDistinct(0.2), size) < 0.2) + assert(error(simpleRdd.countApproxDistinct(0.05), size) < 0.05) + assert(error(simpleRdd.countApproxDistinct(0.01), size) < 0.01) + assert(error(simpleRdd.countApproxDistinct(0.001), size) < 0.001) + } + test("SparkContext.union") { val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) assert(sc.union(nums).collect().toList === List(1, 2, 3, 4)) @@ -139,6 +152,26 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(rdd.union(emptyKv).collect().size === 2) } + test("repartitioned RDDs") { + val data = sc.parallelize(1 to 1000, 10) + + // Coalesce partitions + val repartitioned1 = data.repartition(2) + assert(repartitioned1.partitions.size == 2) + val partitions1 = repartitioned1.glom().collect() + assert(partitions1(0).length > 0) + assert(partitions1(1).length > 0) + assert(repartitioned1.collect().toSet === (1 to 1000).toSet) + + // Split partitions + val repartitioned2 = data.repartition(20) + assert(repartitioned2.partitions.size == 20) + val partitions2 = repartitioned2.glom().collect() + assert(partitions2(0).length > 0) + assert(partitions2(19).length > 0) + assert(repartitioned2.collect().toSet === (1 to 1000).toSet) + } + test("coalesced RDDs") { val data = sc.parallelize(1 to 10, 10) @@ -224,8 +257,8 @@ class RDDSuite extends FunSuite with SharedSparkContext { // test that you get over 90% locality in each group val minLocality = coalesced2.partitions .map(part => part.asInstanceOf[CoalescedRDDPartition].localFraction) - .foldLeft(1.)((perc, loc) => math.min(perc,loc)) - assert(minLocality >= 0.90, "Expected 90% locality but got " + (minLocality*100.).toInt + "%") + .foldLeft(1.0)((perc, loc) => math.min(perc,loc)) + assert(minLocality >= 0.90, "Expected 90% locality but got " + (minLocality*100.0).toInt + "%") // test that the groups are load balanced with 100 +/- 20 elements in each val maxImbalance = coalesced2.partitions @@ -237,9 +270,9 @@ class RDDSuite extends FunSuite with SharedSparkContext { val coalesced3 = data3.coalesce(numMachines*2) val minLocality2 = coalesced3.partitions .map(part => part.asInstanceOf[CoalescedRDDPartition].localFraction) - .foldLeft(1.)((perc, loc) => math.min(perc,loc)) + .foldLeft(1.0)((perc, loc) => math.min(perc,loc)) assert(minLocality2 >= 0.90, "Expected 90% locality for derived RDD but got " + - (minLocality2*100.).toInt + "%") + (minLocality2*100.0).toInt + "%") } test("zipped RDDs") { diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ClusterSchedulerSuite.scala index 1b50ce06b3..7bf2020fe3 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/ClusterSchedulerSuite.scala @@ -15,14 +15,12 @@ * limitations under the License. */ -package org.apache.spark.scheduler.cluster +package org.apache.spark.scheduler import org.scalatest.FunSuite import org.scalatest.BeforeAndAfter import org.apache.spark._ -import org.apache.spark.scheduler._ -import org.apache.spark.scheduler.cluster._ import scala.collection.mutable.ArrayBuffer import java.util.Properties @@ -31,9 +29,9 @@ class FakeTaskSetManager( initPriority: Int, initStageId: Int, initNumTasks: Int, - clusterScheduler: ClusterScheduler, + clusterScheduler: TaskSchedulerImpl, taskSet: TaskSet) - extends ClusterTaskSetManager(clusterScheduler, taskSet) { + extends TaskSetManager(clusterScheduler, taskSet, 0) { parent = null weight = 1 @@ -43,16 +41,16 @@ class FakeTaskSetManager( stageId = initStageId name = "TaskSet_"+stageId override val numTasks = initNumTasks - tasksFinished = 0 + tasksSuccessful = 0 - override def increaseRunningTasks(taskNum: Int) { + def increaseRunningTasks(taskNum: Int) { runningTasks += taskNum if (parent != null) { parent.increaseRunningTasks(taskNum) } } - override def decreaseRunningTasks(taskNum: Int) { + def decreaseRunningTasks(taskNum: Int) { runningTasks -= taskNum if (parent != null) { parent.decreaseRunningTasks(taskNum) @@ -79,7 +77,7 @@ class FakeTaskSetManager( maxLocality: TaskLocality.TaskLocality) : Option[TaskDescription] = { - if (tasksFinished + runningTasks < numTasks) { + if (tasksSuccessful + runningTasks < numTasks) { increaseRunningTasks(1) return Some(new TaskDescription(0, execId, "task 0:0", 0, null)) } @@ -92,8 +90,8 @@ class FakeTaskSetManager( def taskFinished() { decreaseRunningTasks(1) - tasksFinished +=1 - if (tasksFinished == numTasks) { + tasksSuccessful +=1 + if (tasksSuccessful == numTasks) { parent.removeSchedulable(this) } } @@ -106,7 +104,7 @@ class FakeTaskSetManager( class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging { - def createDummyTaskSetManager(priority: Int, stage: Int, numTasks: Int, cs: ClusterScheduler, taskSet: TaskSet): FakeTaskSetManager = { + def createDummyTaskSetManager(priority: Int, stage: Int, numTasks: Int, cs: TaskSchedulerImpl, taskSet: TaskSet): FakeTaskSetManager = { new FakeTaskSetManager(priority, stage, numTasks, cs , taskSet) } @@ -114,7 +112,8 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging val taskSetQueue = rootPool.getSortedTaskSetQueue() /* Just for Test*/ for (manager <- taskSetQueue) { - logInfo("parentName:%s, parent running tasks:%d, name:%s,runningTasks:%d".format(manager.parent.name, manager.parent.runningTasks, manager.name, manager.runningTasks)) + logInfo("parentName:%s, parent running tasks:%d, name:%s,runningTasks:%d".format( + manager.parent.name, manager.parent.runningTasks, manager.name, manager.runningTasks)) } for (taskSet <- taskSetQueue) { taskSet.resourceOffer("execId_1", "hostname_1", 1, TaskLocality.ANY) match { @@ -132,7 +131,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging test("FIFO Scheduler Test") { sc = new SparkContext("local", "ClusterSchedulerSuite") - val clusterScheduler = new ClusterScheduler(sc) + val clusterScheduler = new TaskSchedulerImpl(sc) var tasks = ArrayBuffer[Task[_]]() val task = new FakeTask(0) tasks += task @@ -159,7 +158,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging test("Fair Scheduler Test") { sc = new SparkContext("local", "ClusterSchedulerSuite") - val clusterScheduler = new ClusterScheduler(sc) + val clusterScheduler = new TaskSchedulerImpl(sc) var tasks = ArrayBuffer[Task[_]]() val task = new FakeTask(0) tasks += task @@ -168,7 +167,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() System.setProperty("spark.scheduler.allocation.file", xmlPath) val rootPool = new Pool("", SchedulingMode.FAIR, 0, 0) - val schedulableBuilder = new FairSchedulableBuilder(rootPool) + val schedulableBuilder = new FairSchedulableBuilder(rootPool, sc.conf) schedulableBuilder.buildPools() assert(rootPool.getSchedulableByName("default") != null) @@ -216,7 +215,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging test("Nested Pool Test") { sc = new SparkContext("local", "ClusterSchedulerSuite") - val clusterScheduler = new ClusterScheduler(sc) + val clusterScheduler = new TaskSchedulerImpl(sc) var tasks = ArrayBuffer[Task[_]]() val task = new FakeTask(0) tasks += task diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index 9ed591e494..2aa259daf3 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -17,24 +17,14 @@ package org.apache.spark.scheduler -import scala.collection.mutable.{Map, HashMap} +import scala.Tuple2 +import scala.collection.mutable.{HashMap, Map} -import org.scalatest.FunSuite -import org.scalatest.BeforeAndAfter - -import org.apache.spark.LocalSparkContext -import org.apache.spark.MapOutputTracker +import org.apache.spark._ import org.apache.spark.rdd.RDD -import org.apache.spark.SparkContext -import org.apache.spark.Partition -import org.apache.spark.TaskContext -import org.apache.spark.{Dependency, ShuffleDependency, OneToOneDependency} -import org.apache.spark.{FetchFailed, Success, TaskEndReason} -import org.apache.spark.storage.{BlockManagerId, BlockManagerMaster} - -import org.apache.spark.scheduler.Pool -import org.apache.spark.scheduler.SchedulingMode import org.apache.spark.scheduler.SchedulingMode.SchedulingMode +import org.apache.spark.storage.{BlockId, BlockManagerId, BlockManagerMaster} +import org.scalatest.{BeforeAndAfter, FunSuite} /** * Tests for DAGScheduler. These tests directly call the event processing functions in DAGScheduler @@ -49,7 +39,7 @@ import org.apache.spark.scheduler.SchedulingMode.SchedulingMode * and capturing the resulting TaskSets from the mock TaskScheduler. */ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { - + val conf = new SparkConf /** Set of TaskSets the DAGScheduler has requested executed. */ val taskSets = scala.collection.mutable.Buffer[TaskSet]() val taskScheduler = new TaskScheduler() { @@ -62,11 +52,12 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont taskSet.tasks.foreach(_.epoch = mapOutputTracker.getEpoch) taskSets += taskSet } - override def setListener(listener: TaskSchedulerListener) = {} + override def cancelTasks(stageId: Int) {} + override def setDAGScheduler(dagScheduler: DAGScheduler) = {} override def defaultParallelism() = 2 } - var mapOutputTracker: MapOutputTracker = null + var mapOutputTracker: MapOutputTrackerMaster = null var scheduler: DAGScheduler = null /** @@ -76,16 +67,11 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont */ val cacheLocations = new HashMap[(Int, Int), Seq[BlockManagerId]] // stub out BlockManagerMaster.getLocations to use our cacheLocations - val blockManagerMaster = new BlockManagerMaster(null) { - override def getLocations(blockIds: Array[String]): Seq[Seq[BlockManagerId]] = { - blockIds.map { name => - val pieces = name.split("_") - if (pieces(0) == "rdd") { - val key = pieces(1).toInt -> pieces(2).toInt - cacheLocations.getOrElse(key, Seq()) - } else { - Seq() - } + val blockManagerMaster = new BlockManagerMaster(null, conf) { + override def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = { + blockIds.map { + _.asRDDId.map(id => (id.rddId -> id.splitIndex)).flatMap(key => cacheLocations.get(key)). + getOrElse(Seq()) }.toSeq } override def removeExecutor(execId: String) { @@ -106,8 +92,8 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont taskSets.clear() cacheLocations.clear() results.clear() - mapOutputTracker = new MapOutputTracker() - scheduler = new DAGScheduler(taskScheduler, mapOutputTracker, blockManagerMaster, null) { + mapOutputTracker = new MapOutputTrackerMaster(conf) + scheduler = new DAGScheduler(taskScheduler, mapOutputTracker, blockManagerMaster, sc.env) { override def runLocally(job: ActiveJob) { // don't bother with the thread while unit testing runLocallyWithinThread(job) @@ -188,7 +174,8 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont func: (TaskContext, Iterator[_]) => _ = jobComputeFunc, allowLocal: Boolean = false, listener: JobListener = listener) { - runEvent(JobSubmitted(rdd, func, partitions, allowLocal, null, listener)) + val jobId = scheduler.nextJobId.getAndIncrement() + runEvent(JobSubmitted(jobId, rdd, func, partitions, allowLocal, null, listener)) } /** Sends TaskSetFailed to the scheduler. */ @@ -212,6 +199,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont submit(rdd, Array(0)) complete(taskSets(0), List((Success, 42))) assert(results === Map(0 -> 42)) + assertDataStructuresEmpty } test("local job") { @@ -222,8 +210,10 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont override def getPreferredLocations(split: Partition) = Nil override def toString = "DAGSchedulerSuite Local RDD" } - runEvent(JobSubmitted(rdd, jobComputeFunc, Array(0), true, null, listener)) + val jobId = scheduler.nextJobId.getAndIncrement() + runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, null, listener)) assert(results === Map(0 -> 42)) + assertDataStructuresEmpty } test("run trivial job w/ dependency") { @@ -232,6 +222,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont submit(finalRdd, Array(0)) complete(taskSets(0), Seq((Success, 42))) assert(results === Map(0 -> 42)) + assertDataStructuresEmpty } test("cache location preferences w/ dependency") { @@ -244,12 +235,14 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont assertLocations(taskSet, Seq(Seq("hostA", "hostB"))) complete(taskSet, Seq((Success, 42))) assert(results === Map(0 -> 42)) + assertDataStructuresEmpty } test("trivial job failure") { submit(makeRdd(1, Nil), Array(0)) failed(taskSets(0), "some failure") - assert(failure.getMessage === "Job failed: some failure") + assert(failure.getMessage === "Job aborted: some failure") + assertDataStructuresEmpty } test("run trivial shuffle") { @@ -265,6 +258,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont Array(makeBlockManagerId("hostA"), makeBlockManagerId("hostB"))) complete(taskSets(1), Seq((Success, 42))) assert(results === Map(0 -> 42)) + assertDataStructuresEmpty } test("run trivial shuffle with fetch failure") { @@ -290,6 +284,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) === Array("hostA", "hostB")) complete(taskSets(3), Seq((Success, 43))) assert(results === Map(0 -> 42, 1 -> 43)) + assertDataStructuresEmpty } test("ignore late map task completions") { @@ -318,6 +313,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont Array(makeBlockManagerId("hostB"), makeBlockManagerId("hostA"))) complete(taskSets(1), Seq((Success, 42), (Success, 43))) assert(results === Map(0 -> 42, 1 -> 43)) + assertDataStructuresEmpty } test("run trivial shuffle with out-of-band failure and retry") { @@ -334,15 +330,16 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont complete(taskSets(0), Seq( (Success, makeMapStatus("hostA", 1)), (Success, makeMapStatus("hostB", 1)))) - // have hostC complete the resubmitted task - complete(taskSets(1), Seq((Success, makeMapStatus("hostC", 1)))) - assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) === - Array(makeBlockManagerId("hostC"), makeBlockManagerId("hostB"))) - complete(taskSets(2), Seq((Success, 42))) - assert(results === Map(0 -> 42)) - } - - test("recursive shuffle failures") { + // have hostC complete the resubmitted task + complete(taskSets(1), Seq((Success, makeMapStatus("hostC", 1)))) + assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) === + Array(makeBlockManagerId("hostC"), makeBlockManagerId("hostB"))) + complete(taskSets(2), Seq((Success, 42))) + assert(results === Map(0 -> 42)) + assertDataStructuresEmpty + } + + test("recursive shuffle failures") { val shuffleOneRdd = makeRdd(2, Nil) val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null) val shuffleTwoRdd = makeRdd(2, List(shuffleDepOne)) @@ -368,6 +365,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont complete(taskSets(4), Seq((Success, makeMapStatus("hostA", 1)))) complete(taskSets(5), Seq((Success, 42))) assert(results === Map(0 -> 42)) + assertDataStructuresEmpty } test("cached post-shuffle") { @@ -399,6 +397,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont complete(taskSets(3), Seq((Success, makeMapStatus("hostD", 1)))) complete(taskSets(4), Seq((Success, 42))) assert(results === Map(0 -> 42)) + assertDataStructuresEmpty } /** @@ -418,4 +417,18 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont private def makeBlockManagerId(host: String): BlockManagerId = BlockManagerId("exec-" + host, host, 12345, 0) + private def assertDataStructuresEmpty = { + assert(scheduler.pendingTasks.isEmpty) + assert(scheduler.activeJobs.isEmpty) + assert(scheduler.failed.isEmpty) + assert(scheduler.idToActiveJob.isEmpty) + assert(scheduler.jobIdToStageIds.isEmpty) + assert(scheduler.stageIdToJobIds.isEmpty) + assert(scheduler.stageIdToStage.isEmpty) + assert(scheduler.stageToInfos.isEmpty) + assert(scheduler.resultStageToJob.isEmpty) + assert(scheduler.running.isEmpty) + assert(scheduler.shuffleToMapStage.isEmpty) + assert(scheduler.waiting.isEmpty) + } } diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/FakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala index 2f12aaed18..0b90c4e74c 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/cluster/FakeTask.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala @@ -15,12 +15,12 @@ * limitations under the License. */ -package org.apache.spark.scheduler.cluster +package org.apache.spark.scheduler -import org.apache.spark.scheduler.{TaskLocation, Task} +import org.apache.spark.TaskContext -class FakeTask(stageId: Int, prefLocs: Seq[TaskLocation] = Nil) extends Task[Int](stageId) { - override def run(attemptId: Long): Int = 0 +class FakeTask(stageId: Int, prefLocs: Seq[TaskLocation] = Nil) extends Task[Int](stageId, 0) { + override def runTask(context: TaskContext): Int = 0 override def preferredLocations: Seq[TaskLocation] = prefLocs } diff --git a/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala index cece60dda7..5cc48ee00a 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala @@ -31,6 +31,7 @@ import org.apache.spark.rdd.RDD class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers { + val WAIT_TIMEOUT_MILLIS = 10000 test("inner method") { sc = new SparkContext("local", "joblogger") @@ -58,11 +59,14 @@ class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers val parentRdd = makeRdd(4, Nil) val shuffleDep = new ShuffleDependency(parentRdd, null) val rootRdd = makeRdd(4, List(shuffleDep)) - val shuffleMapStage = new Stage(1, parentRdd, Some(shuffleDep), Nil, jobID, None) - val rootStage = new Stage(0, rootRdd, None, List(shuffleMapStage), jobID, None) - - joblogger.onStageSubmitted(SparkListenerStageSubmitted(rootStage, 4, null)) - joblogger.getRddNameTest(parentRdd) should be (parentRdd.getClass.getName) + val shuffleMapStage = + new Stage(1, parentRdd, parentRdd.partitions.size, Some(shuffleDep), Nil, jobID, None) + val rootStage = + new Stage(0, rootRdd, rootRdd.partitions.size, None, List(shuffleMapStage), jobID, None) + val rootStageInfo = new StageInfo(rootStage) + + joblogger.onStageSubmitted(SparkListenerStageSubmitted(rootStageInfo, null)) + joblogger.getRddNameTest(parentRdd) should be (parentRdd.getClass.getSimpleName) parentRdd.setName("MyRDD") joblogger.getRddNameTest(parentRdd) should be ("MyRDD") joblogger.createLogWriterTest(jobID) @@ -88,8 +92,12 @@ class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers sc.addSparkListener(joblogger) val rdd = sc.parallelize(1 to 1e2.toInt, 4).map{ i => (i % 12, 2 * i) } rdd.reduceByKey(_+_).collect() + + assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + + val user = System.getProperty("user.name", SparkContext.SPARK_UNKNOWN_USER) - joblogger.getLogDir should be ("/tmp/spark") + joblogger.getLogDir should be ("/tmp/spark-%s".format(user)) joblogger.getJobIDtoPrintWriter.size should be (1) joblogger.getStageIDToJobID.size should be (2) joblogger.getStageIDToJobID.get(0) should be (Some(0)) @@ -109,13 +117,15 @@ class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = onTaskEndCount += 1 override def onJobEnd(jobEnd: SparkListenerJobEnd) = onJobEndCount += 1 override def onJobStart(jobStart: SparkListenerJobStart) = onJobStartCount += 1 - override def onStageCompleted(stageCompleted: StageCompleted) = onStageCompletedCount += 1 + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = onStageCompletedCount += 1 override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = onStageSubmittedCount += 1 } sc.addSparkListener(joblogger) val rdd = sc.parallelize(1 to 1e2.toInt, 4).map{ i => (i % 12, 2 * i) } rdd.reduceByKey(_+_).collect() - + + assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + joblogger.onJobStartCount should be (1) joblogger.onJobEndCount should be (1) joblogger.onTaskEndCount should be (8) diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala index aac7c207cb..1a16e438c4 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala @@ -17,20 +17,63 @@ package org.apache.spark.scheduler -import org.scalatest.FunSuite -import org.apache.spark.{SparkContext, LocalSparkContext} -import scala.collection.mutable +import scala.collection.mutable.{Buffer, HashSet} + +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} import org.scalatest.matchers.ShouldMatchers + +import org.apache.spark.{LocalSparkContext, SparkContext} import org.apache.spark.SparkContext._ -/** - * - */ +class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatchers + with BeforeAndAfter with BeforeAndAfterAll { + /** Length of time to wait while draining listener events. */ + val WAIT_TIMEOUT_MILLIS = 10000 + + before { + sc = new SparkContext("local", "SparkListenerSuite") + } + + override def afterAll { + System.clearProperty("spark.akka.frameSize") + } + + test("basic creation of StageInfo") { + val listener = new SaveStageInfo + sc.addSparkListener(listener) + val rdd1 = sc.parallelize(1 to 100, 4) + val rdd2 = rdd1.map(x => x.toString) + rdd2.setName("Target RDD") + rdd2.count + + assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + + listener.stageInfos.size should be {1} + val first = listener.stageInfos.head + first.rddName should be {"Target RDD"} + first.numTasks should be {4} + first.numPartitions should be {4} + first.submissionTime should be ('defined) + first.completionTime should be ('defined) + first.taskInfos.length should be {4} + } + + test("StageInfo with fewer tasks than partitions") { + val listener = new SaveStageInfo + sc.addSparkListener(listener) + val rdd1 = sc.parallelize(1 to 100, 4) + val rdd2 = rdd1.map(x => x.toString) + sc.runJob(rdd2, (items: Iterator[String]) => items.size, Seq(0, 1), true) + + assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) -class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatchers { + listener.stageInfos.size should be {1} + val first = listener.stageInfos.head + first.numTasks should be {2} + first.numPartitions should be {4} + } test("local metrics") { - sc = new SparkContext("local[4]", "test") val listener = new SaveStageInfo sc.addSparkListener(listener) sc.addSparkListener(new StatsReportListener) @@ -41,8 +84,9 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc i } - val d = sc.parallelize(1 to 1e4.toInt, 64).map{i => w(i)} - d.count + val d = sc.parallelize(0 to 1e4.toInt, 64).map{i => w(i)} + d.count() + assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) listener.stageInfos.size should be (1) val d2 = d.map{i => w(i) -> i * 2}.setName("shuffle input 1") @@ -52,25 +96,33 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc val d4 = d2.cogroup(d3, 64).map{case(k,(v1,v2)) => w(k) -> (v1.size, v2.size)} d4.setName("A Cogroup") - d4.collectAsMap + d4.collectAsMap() + assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) listener.stageInfos.size should be (4) - listener.stageInfos.foreach {stageInfo => - //small test, so some tasks might take less than 1 millisecond, but average should be greater than 1 ms + listener.stageInfos.foreach { stageInfo => + /* small test, so some tasks might take less than 1 millisecond, but average should be greater + * than 0 ms. */ checkNonZeroAvg(stageInfo.taskInfos.map{_._1.duration}, stageInfo + " duration") - checkNonZeroAvg(stageInfo.taskInfos.map{_._2.executorRunTime.toLong}, stageInfo + " executorRunTime") - checkNonZeroAvg(stageInfo.taskInfos.map{_._2.executorDeserializeTime.toLong}, stageInfo + " executorDeserializeTime") - if (stageInfo.stage.rdd.name == d4.name) { - checkNonZeroAvg(stageInfo.taskInfos.map{_._2.shuffleReadMetrics.get.fetchWaitTime}, stageInfo + " fetchWaitTime") + checkNonZeroAvg( + stageInfo.taskInfos.map{_._2.executorRunTime.toLong}, + stageInfo + " executorRunTime") + checkNonZeroAvg( + stageInfo.taskInfos.map{_._2.executorDeserializeTime.toLong}, + stageInfo + " executorDeserializeTime") + if (stageInfo.rddName == d4.name) { + checkNonZeroAvg( + stageInfo.taskInfos.map{_._2.shuffleReadMetrics.get.fetchWaitTime}, + stageInfo + " fetchWaitTime") } - stageInfo.taskInfos.foreach{case (taskInfo, taskMetrics) => + stageInfo.taskInfos.foreach { case (taskInfo, taskMetrics) => taskMetrics.resultSize should be > (0l) - if (isStage(stageInfo, Set(d2.name, d3.name), Set(d4.name))) { + if (stageInfo.rddName == d2.name || stageInfo.rddName == d3.name) { taskMetrics.shuffleWriteMetrics should be ('defined) taskMetrics.shuffleWriteMetrics.get.shuffleBytesWritten should be > (0l) } - if (stageInfo.stage.rdd.name == d4.name) { + if (stageInfo.rddName == d4.name) { taskMetrics.shuffleReadMetrics should be ('defined) val sm = taskMetrics.shuffleReadMetrics.get sm.totalBlocksFetched should be > (0) @@ -83,20 +135,65 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc } } - def checkNonZeroAvg(m: Traversable[Long], msg: String) { - assert(m.sum / m.size.toDouble > 0.0, msg) + test("onTaskGettingResult() called when result fetched remotely") { + val listener = new SaveTaskEvents + sc.addSparkListener(listener) + + // Make a task whose result is larger than the akka frame size + System.setProperty("spark.akka.frameSize", "1") + val akkaFrameSize = + sc.env.actorSystem.settings.config.getBytes("akka.remote.netty.tcp.maximum-frame-size").toInt + val result = sc.parallelize(Seq(1), 1).map(x => 1.to(akkaFrameSize).toArray).reduce((x,y) => x) + assert(result === 1.to(akkaFrameSize).toArray) + + assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + val TASK_INDEX = 0 + assert(listener.startedTasks.contains(TASK_INDEX)) + assert(listener.startedGettingResultTasks.contains(TASK_INDEX)) + assert(listener.endedTasks.contains(TASK_INDEX)) } - def isStage(stageInfo: StageInfo, rddNames: Set[String], excludedNames: Set[String]) = { - val names = Set(stageInfo.stage.rdd.name) ++ stageInfo.stage.rdd.dependencies.map{_.rdd.name} - !names.intersect(rddNames).isEmpty && names.intersect(excludedNames).isEmpty + test("onTaskGettingResult() not called when result sent directly") { + val listener = new SaveTaskEvents + sc.addSparkListener(listener) + + // Make a task whose result is larger than the akka frame size + val result = sc.parallelize(Seq(1), 1).map(x => 2 * x).reduce((x, y) => x) + assert(result === 2) + + assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + val TASK_INDEX = 0 + assert(listener.startedTasks.contains(TASK_INDEX)) + assert(listener.startedGettingResultTasks.isEmpty == true) + assert(listener.endedTasks.contains(TASK_INDEX)) + } + + def checkNonZeroAvg(m: Traversable[Long], msg: String) { + assert(m.sum / m.size.toDouble > 0.0, msg) } class SaveStageInfo extends SparkListener { - val stageInfos = mutable.Buffer[StageInfo]() - override def onStageCompleted(stage: StageCompleted) { - stageInfos += stage.stageInfo + val stageInfos = Buffer[StageInfo]() + override def onStageCompleted(stage: SparkListenerStageCompleted) { + stageInfos += stage.stage } } + class SaveTaskEvents extends SparkListener { + val startedTasks = new HashSet[Int]() + val startedGettingResultTasks = new HashSet[Int]() + val endedTasks = new HashSet[Int]() + + override def onTaskStart(taskStart: SparkListenerTaskStart) { + startedTasks += taskStart.taskInfo.index + } + + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) { + endedTasks += taskEnd.taskInfo.index + } + + override def onTaskGettingResult(taskGettingResult: SparkListenerTaskGettingResult) { + startedGettingResultTasks += taskGettingResult.taskInfo.index + } + } } diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala new file mode 100644 index 0000000000..4b52d9651e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala @@ -0,0 +1,112 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import java.nio.ByteBuffer + +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} + +import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkEnv} +import org.apache.spark.storage.TaskResultBlockId + +/** + * Removes the TaskResult from the BlockManager before delegating to a normal TaskResultGetter. + * + * Used to test the case where a BlockManager evicts the task result (or dies) before the + * TaskResult is retrieved. + */ +class ResultDeletingTaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedulerImpl) + extends TaskResultGetter(sparkEnv, scheduler) { + var removedResult = false + + override def enqueueSuccessfulTask( + taskSetManager: TaskSetManager, tid: Long, serializedData: ByteBuffer) { + if (!removedResult) { + // Only remove the result once, since we'd like to test the case where the task eventually + // succeeds. + serializer.get().deserialize[TaskResult[_]](serializedData) match { + case IndirectTaskResult(blockId) => + sparkEnv.blockManager.master.removeBlock(blockId) + case directResult: DirectTaskResult[_] => + taskSetManager.abort("Internal error: expect only indirect results") + } + serializedData.rewind() + removedResult = true + } + super.enqueueSuccessfulTask(taskSetManager, tid, serializedData) + } +} + +/** + * Tests related to handling task results (both direct and indirect). + */ +class TaskResultGetterSuite extends FunSuite with BeforeAndAfter with BeforeAndAfterAll + with LocalSparkContext { + + override def beforeAll { + // Set the Akka frame size to be as small as possible (it must be an integer, so 1 is as small + // as we can make it) so the tests don't take too long. + System.setProperty("spark.akka.frameSize", "1") + } + + override def afterAll { + System.clearProperty("spark.akka.frameSize") + } + + test("handling results smaller than Akka frame size") { + sc = new SparkContext("local", "test") + val result = sc.parallelize(Seq(1), 1).map(x => 2 * x).reduce((x, y) => x) + assert(result === 2) + } + + test("handling results larger than Akka frame size") { + sc = new SparkContext("local", "test") + val akkaFrameSize = + sc.env.actorSystem.settings.config.getBytes("akka.remote.netty.tcp.maximum-frame-size").toInt + val result = sc.parallelize(Seq(1), 1).map(x => 1.to(akkaFrameSize).toArray).reduce((x, y) => x) + assert(result === 1.to(akkaFrameSize).toArray) + + val RESULT_BLOCK_ID = TaskResultBlockId(0) + assert(sc.env.blockManager.master.getLocations(RESULT_BLOCK_ID).size === 0, + "Expect result to be removed from the block manager.") + } + + test("task retried if result missing from block manager") { + // Set the maximum number of task failures to > 0, so that the task set isn't aborted + // after the result is missing. + sc = new SparkContext("local[1,2]", "test") + // If this test hangs, it's probably because no resource offers were made after the task + // failed. + val scheduler: TaskSchedulerImpl = sc.taskScheduler match { + case clusterScheduler: TaskSchedulerImpl => + clusterScheduler + case _ => + assert(false, "Expect local cluster to use ClusterScheduler") + throw new ClassCastException + } + scheduler.taskResultGetter = new ResultDeletingTaskResultGetter(sc.env, scheduler) + val akkaFrameSize = + sc.env.actorSystem.settings.config.getBytes("akka.remote.netty.tcp.maximum-frame-size").toInt + val result = sc.parallelize(Seq(1), 1).map(x => 1.to(akkaFrameSize).toArray).reduce((x, y) => x) + assert(result === 1.to(akkaFrameSize).toArray) + + // Make sure two tasks were run (one failed one, and a second retried one). + assert(scheduler.nextTaskId.get() === 2) + } +} + diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala index ff70a2cdf0..1eec6726f4 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.scheduler.cluster +package org.apache.spark.scheduler import scala.collection.mutable.ArrayBuffer import scala.collection.mutable @@ -23,48 +23,51 @@ import scala.collection.mutable import org.scalatest.FunSuite import org.apache.spark._ -import org.apache.spark.scheduler._ import org.apache.spark.executor.TaskMetrics import java.nio.ByteBuffer import org.apache.spark.util.{Utils, FakeClock} +class FakeDAGScheduler(taskScheduler: FakeClusterScheduler) extends DAGScheduler(taskScheduler) { + override def taskStarted(task: Task[_], taskInfo: TaskInfo) { + taskScheduler.startedTasks += taskInfo.index + } + + override def taskEnded( + task: Task[_], + reason: TaskEndReason, + result: Any, + accumUpdates: mutable.Map[Long, Any], + taskInfo: TaskInfo, + taskMetrics: TaskMetrics) { + taskScheduler.endedTasks(taskInfo.index) = reason + } + + override def executorGained(execId: String, host: String) {} + + override def executorLost(execId: String) {} + + override def taskSetFailed(taskSet: TaskSet, reason: String) { + taskScheduler.taskSetsFailed += taskSet.id + } +} + /** * A mock ClusterScheduler implementation that just remembers information about tasks started and * feedback received from the TaskSetManagers. Note that it's important to initialize this with * a list of "live" executors and their hostnames for isExecutorAlive and hasExecutorsAliveOnHost - * to work, and these are required for locality in ClusterTaskSetManager. + * to work, and these are required for locality in TaskSetManager. */ class FakeClusterScheduler(sc: SparkContext, liveExecutors: (String, String)* /* execId, host */) - extends ClusterScheduler(sc) + extends TaskSchedulerImpl(sc) { val startedTasks = new ArrayBuffer[Long] val endedTasks = new mutable.HashMap[Long, TaskEndReason] val finishedManagers = new ArrayBuffer[TaskSetManager] + val taskSetsFailed = new ArrayBuffer[String] val executors = new mutable.HashMap[String, String] ++ liveExecutors - listener = new TaskSchedulerListener { - def taskStarted(task: Task[_], taskInfo: TaskInfo) { - startedTasks += taskInfo.index - } - - def taskEnded( - task: Task[_], - reason: TaskEndReason, - result: Any, - accumUpdates: mutable.Map[Long, Any], - taskInfo: TaskInfo, - taskMetrics: TaskMetrics) - { - endedTasks(taskInfo.index) = reason - } - - def executorGained(execId: String, host: String) {} - - def executorLost(execId: String) {} - - def taskSetFailed(taskSet: TaskSet, reason: String) {} - } + dagScheduler = new FakeDAGScheduler(this) def removeExecutor(execId: String): Unit = executors -= execId @@ -75,16 +78,19 @@ class FakeClusterScheduler(sc: SparkContext, liveExecutors: (String, String)* /* override def hasExecutorsAliveOnHost(host: String): Boolean = executors.values.exists(_ == host) } -class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { +class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { import TaskLocality.{ANY, PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL} - val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong + private val conf = new SparkConf + + val LOCALITY_WAIT = conf.get("spark.locality.wait", "3000").toLong + val MAX_TASK_FAILURES = 4 test("TaskSet with no preferences") { sc = new SparkContext("local", "test") val sched = new FakeClusterScheduler(sc, ("exec1", "host1")) val taskSet = createTaskSet(1) - val manager = new ClusterTaskSetManager(sched, taskSet) + val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES) // Offer a host with no CPUs assert(manager.resourceOffer("exec1", "host1", 0, ANY) === None) @@ -101,7 +107,7 @@ class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Lo assert(manager.resourceOffer("exec1", "host1", 2, PROCESS_LOCAL) === None) // Tell it the task has finished - manager.statusUpdate(0, TaskState.FINISHED, createTaskResult(0)) + manager.handleSuccessfulTask(0, createTaskResult(0)) assert(sched.endedTasks(0) === Success) assert(sched.finishedManagers.contains(manager)) } @@ -110,7 +116,7 @@ class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Lo sc = new SparkContext("local", "test") val sched = new FakeClusterScheduler(sc, ("exec1", "host1")) val taskSet = createTaskSet(3) - val manager = new ClusterTaskSetManager(sched, taskSet) + val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES) // First three offers should all find tasks for (i <- 0 until 3) { @@ -125,14 +131,14 @@ class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Lo assert(manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL) === None) // Finish the first two tasks - manager.statusUpdate(0, TaskState.FINISHED, createTaskResult(0)) - manager.statusUpdate(1, TaskState.FINISHED, createTaskResult(1)) + manager.handleSuccessfulTask(0, createTaskResult(0)) + manager.handleSuccessfulTask(1, createTaskResult(1)) assert(sched.endedTasks(0) === Success) assert(sched.endedTasks(1) === Success) assert(!sched.finishedManagers.contains(manager)) // Finish the last task - manager.statusUpdate(2, TaskState.FINISHED, createTaskResult(2)) + manager.handleSuccessfulTask(2, createTaskResult(2)) assert(sched.endedTasks(2) === Success) assert(sched.finishedManagers.contains(manager)) } @@ -147,7 +153,7 @@ class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Lo Seq() // Last task has no locality prefs ) val clock = new FakeClock - val manager = new ClusterTaskSetManager(sched, taskSet, clock) + val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // First offer host1, exec1: first task should be chosen assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0) @@ -193,7 +199,7 @@ class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Lo Seq(TaskLocation("host2")) ) val clock = new FakeClock - val manager = new ClusterTaskSetManager(sched, taskSet, clock) + val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // First offer host1: first task should be chosen assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0) @@ -230,7 +236,7 @@ class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Lo Seq(TaskLocation("host3")) ) val clock = new FakeClock - val manager = new ClusterTaskSetManager(sched, taskSet, clock) + val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) // First offer host1: first task should be chosen assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0) @@ -253,6 +259,47 @@ class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Lo assert(manager.resourceOffer("exec2", "host2", 1, ANY) === None) } + test("task result lost") { + sc = new SparkContext("local", "test") + val sched = new FakeClusterScheduler(sc, ("exec1", "host1")) + val taskSet = createTaskSet(1) + val clock = new FakeClock + val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) + + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0) + + // Tell it the task has finished but the result was lost. + manager.handleFailedTask(0, TaskState.FINISHED, Some(TaskResultLost)) + assert(sched.endedTasks(0) === TaskResultLost) + + // Re-offer the host -- now we should get task 0 again. + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0) + } + + test("repeated failures lead to task set abortion") { + sc = new SparkContext("local", "test") + val sched = new FakeClusterScheduler(sc, ("exec1", "host1")) + val taskSet = createTaskSet(1) + val clock = new FakeClock + val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES, clock) + + // Fail the task MAX_TASK_FAILURES times, and check that the task set is aborted + // after the last failure. + (1 to manager.maxTaskFailures).foreach { index => + val offerResult = manager.resourceOffer("exec1", "host1", 1, ANY) + assert(offerResult != None, + "Expect resource offer on iteration %s to return a task".format(index)) + assert(offerResult.get.index === 0) + manager.handleFailedTask(offerResult.get.taskId, TaskState.FINISHED, Some(TaskResultLost)) + if (index < MAX_TASK_FAILURES) { + assert(!sched.taskSetsFailed.contains(taskSet.id)) + } else { + assert(sched.taskSetsFailed.contains(taskSet.id)) + } + } + } + + /** * Utility method to create a TaskSet, potentially setting a particular sequence of preferred * locations for each task (given as varargs) if this sequence is not empty. @@ -267,7 +314,8 @@ class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Lo new TaskSet(tasks, 0, 0, 0, null) } - def createTaskResult(id: Int): ByteBuffer = { - ByteBuffer.wrap(Utils.serialize(new TaskResult[Int](id, mutable.Map.empty, new TaskMetrics))) + def createTaskResult(id: Int): DirectTaskResult[Int] = { + val valueSer = SparkEnv.get.serializer.newInstance() + new DirectTaskResult[Int](valueSer.serialize(id), mutable.Map.empty, new TaskMetrics) } } diff --git a/core/src/test/scala/org/apache/spark/scheduler/local/LocalSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/local/LocalSchedulerSuite.scala deleted file mode 100644 index af76c843e8..0000000000 --- a/core/src/test/scala/org/apache/spark/scheduler/local/LocalSchedulerSuite.scala +++ /dev/null @@ -1,223 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.scheduler.local - -import org.scalatest.FunSuite -import org.scalatest.BeforeAndAfter - -import org.apache.spark._ -import org.apache.spark.scheduler._ -import org.apache.spark.scheduler.cluster._ -import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.{ConcurrentMap, HashMap} -import java.util.concurrent.Semaphore -import java.util.concurrent.CountDownLatch -import java.util.Properties - -class Lock() { - var finished = false - def jobWait() = { - synchronized { - while(!finished) { - this.wait() - } - } - } - - def jobFinished() = { - synchronized { - finished = true - this.notifyAll() - } - } -} - -object TaskThreadInfo { - val threadToLock = HashMap[Int, Lock]() - val threadToRunning = HashMap[Int, Boolean]() - val threadToStarted = HashMap[Int, CountDownLatch]() -} - -/* - * 1. each thread contains one job. - * 2. each job contains one stage. - * 3. each stage only contains one task. - * 4. each task(launched) must be lanched orderly(using threadToStarted) to make sure - * it will get cpu core resource, and will wait to finished after user manually - * release "Lock" and then cluster will contain another free cpu cores. - * 5. each task(pending) must use "sleep" to make sure it has been added to taskSetManager queue, - * thus it will be scheduled later when cluster has free cpu cores. - */ -class LocalSchedulerSuite extends FunSuite with LocalSparkContext { - - def createThread(threadIndex: Int, poolName: String, sc: SparkContext, sem: Semaphore) { - - TaskThreadInfo.threadToRunning(threadIndex) = false - val nums = sc.parallelize(threadIndex to threadIndex, 1) - TaskThreadInfo.threadToLock(threadIndex) = new Lock() - TaskThreadInfo.threadToStarted(threadIndex) = new CountDownLatch(1) - new Thread { - if (poolName != null) { - sc.setLocalProperty("spark.scheduler.pool", poolName) - } - override def run() { - val ans = nums.map(number => { - TaskThreadInfo.threadToRunning(number) = true - TaskThreadInfo.threadToStarted(number).countDown() - TaskThreadInfo.threadToLock(number).jobWait() - TaskThreadInfo.threadToRunning(number) = false - number - }).collect() - assert(ans.toList === List(threadIndex)) - sem.release() - } - }.start() - } - - test("Local FIFO scheduler end-to-end test") { - System.setProperty("spark.scheduler.mode", "FIFO") - sc = new SparkContext("local[4]", "test") - val sem = new Semaphore(0) - - createThread(1,null,sc,sem) - TaskThreadInfo.threadToStarted(1).await() - createThread(2,null,sc,sem) - TaskThreadInfo.threadToStarted(2).await() - createThread(3,null,sc,sem) - TaskThreadInfo.threadToStarted(3).await() - createThread(4,null,sc,sem) - TaskThreadInfo.threadToStarted(4).await() - // thread 5 and 6 (stage pending)must meet following two points - // 1. stages (taskSetManager) of jobs in thread 5 and 6 should be add to taskSetManager - // queue before executing TaskThreadInfo.threadToLock(1).jobFinished() - // 2. priority of stage in thread 5 should be prior to priority of stage in thread 6 - // So I just use "sleep" 1s here for each thread. - // TODO: any better solution? - createThread(5,null,sc,sem) - Thread.sleep(1000) - createThread(6,null,sc,sem) - Thread.sleep(1000) - - assert(TaskThreadInfo.threadToRunning(1) === true) - assert(TaskThreadInfo.threadToRunning(2) === true) - assert(TaskThreadInfo.threadToRunning(3) === true) - assert(TaskThreadInfo.threadToRunning(4) === true) - assert(TaskThreadInfo.threadToRunning(5) === false) - assert(TaskThreadInfo.threadToRunning(6) === false) - - TaskThreadInfo.threadToLock(1).jobFinished() - TaskThreadInfo.threadToStarted(5).await() - - assert(TaskThreadInfo.threadToRunning(1) === false) - assert(TaskThreadInfo.threadToRunning(2) === true) - assert(TaskThreadInfo.threadToRunning(3) === true) - assert(TaskThreadInfo.threadToRunning(4) === true) - assert(TaskThreadInfo.threadToRunning(5) === true) - assert(TaskThreadInfo.threadToRunning(6) === false) - - TaskThreadInfo.threadToLock(3).jobFinished() - TaskThreadInfo.threadToStarted(6).await() - - assert(TaskThreadInfo.threadToRunning(1) === false) - assert(TaskThreadInfo.threadToRunning(2) === true) - assert(TaskThreadInfo.threadToRunning(3) === false) - assert(TaskThreadInfo.threadToRunning(4) === true) - assert(TaskThreadInfo.threadToRunning(5) === true) - assert(TaskThreadInfo.threadToRunning(6) === true) - - TaskThreadInfo.threadToLock(2).jobFinished() - TaskThreadInfo.threadToLock(4).jobFinished() - TaskThreadInfo.threadToLock(5).jobFinished() - TaskThreadInfo.threadToLock(6).jobFinished() - sem.acquire(6) - } - - test("Local fair scheduler end-to-end test") { - sc = new SparkContext("local[8]", "LocalSchedulerSuite") - val sem = new Semaphore(0) - System.setProperty("spark.scheduler.mode", "FAIR") - val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() - System.setProperty("spark.scheduler.allocation.file", xmlPath) - - createThread(10,"1",sc,sem) - TaskThreadInfo.threadToStarted(10).await() - createThread(20,"2",sc,sem) - TaskThreadInfo.threadToStarted(20).await() - createThread(30,"3",sc,sem) - TaskThreadInfo.threadToStarted(30).await() - - assert(TaskThreadInfo.threadToRunning(10) === true) - assert(TaskThreadInfo.threadToRunning(20) === true) - assert(TaskThreadInfo.threadToRunning(30) === true) - - createThread(11,"1",sc,sem) - TaskThreadInfo.threadToStarted(11).await() - createThread(21,"2",sc,sem) - TaskThreadInfo.threadToStarted(21).await() - createThread(31,"3",sc,sem) - TaskThreadInfo.threadToStarted(31).await() - - assert(TaskThreadInfo.threadToRunning(11) === true) - assert(TaskThreadInfo.threadToRunning(21) === true) - assert(TaskThreadInfo.threadToRunning(31) === true) - - createThread(12,"1",sc,sem) - TaskThreadInfo.threadToStarted(12).await() - createThread(22,"2",sc,sem) - TaskThreadInfo.threadToStarted(22).await() - createThread(32,"3",sc,sem) - - assert(TaskThreadInfo.threadToRunning(12) === true) - assert(TaskThreadInfo.threadToRunning(22) === true) - assert(TaskThreadInfo.threadToRunning(32) === false) - - TaskThreadInfo.threadToLock(10).jobFinished() - TaskThreadInfo.threadToStarted(32).await() - - assert(TaskThreadInfo.threadToRunning(32) === true) - - //1. Similar with above scenario, sleep 1s for stage of 23 and 33 to be added to taskSetManager - // queue so that cluster will assign free cpu core to stage 23 after stage 11 finished. - //2. priority of 23 and 33 will be meaningless as using fair scheduler here. - createThread(23,"2",sc,sem) - createThread(33,"3",sc,sem) - Thread.sleep(1000) - - TaskThreadInfo.threadToLock(11).jobFinished() - TaskThreadInfo.threadToStarted(23).await() - - assert(TaskThreadInfo.threadToRunning(23) === true) - assert(TaskThreadInfo.threadToRunning(33) === false) - - TaskThreadInfo.threadToLock(12).jobFinished() - TaskThreadInfo.threadToStarted(33).await() - - assert(TaskThreadInfo.threadToRunning(33) === true) - - TaskThreadInfo.threadToLock(20).jobFinished() - TaskThreadInfo.threadToLock(21).jobFinished() - TaskThreadInfo.threadToLock(22).jobFinished() - TaskThreadInfo.threadToLock(23).jobFinished() - TaskThreadInfo.threadToLock(30).jobFinished() - TaskThreadInfo.threadToLock(31).jobFinished() - TaskThreadInfo.threadToLock(32).jobFinished() - TaskThreadInfo.threadToLock(33).jobFinished() - - sem.acquire(11) - } -} diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala index 0164dda0ba..3898583275 100644 --- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala @@ -22,12 +22,15 @@ import scala.collection.mutable import com.esotericsoftware.kryo.Kryo import org.scalatest.FunSuite -import org.apache.spark.SharedSparkContext +import org.apache.spark.{SparkConf, SharedSparkContext} import org.apache.spark.serializer.KryoTest._ class KryoSerializerSuite extends FunSuite with SharedSparkContext { + conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") + conf.set("spark.kryo.registrator", classOf[MyRegistrator].getName) + test("basic types") { - val ser = (new KryoSerializer).newInstance() + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) } @@ -57,7 +60,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { } test("pairs") { - val ser = (new KryoSerializer).newInstance() + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) } @@ -81,7 +84,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { } test("Scala data structures") { - val ser = (new KryoSerializer).newInstance() + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) } @@ -103,10 +106,29 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { check(List(mutable.HashMap("one" -> 1, "two" -> 2),mutable.HashMap(1->"one",2->"two",3->"three"))) } - test("custom registrator") { - System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName) + test("ranges") { + val ser = new KryoSerializer(conf).newInstance() + def check[T](t: T) { + assert(ser.deserialize[T](ser.serialize(t)) === t) + // Check that very long ranges don't get written one element at a time + assert(ser.serialize(t).limit < 100) + } + check(1 to 1000000) + check(1 to 1000000 by 2) + check(1 until 1000000) + check(1 until 1000000 by 2) + check(1L to 1000000L) + check(1L to 1000000L by 2L) + check(1L until 1000000L) + check(1L until 1000000L by 2L) + check(1.0 to 1000000.0 by 1.0) + check(1.0 to 1000000.0 by 2.0) + check(1.0 until 1000000.0 by 1.0) + check(1.0 until 1000000.0 by 2.0) + } - val ser = (new KryoSerializer).newInstance() + test("custom registrator") { + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) } @@ -151,6 +173,10 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { assert (sc.parallelize( Array((1, 11), (2, 22), (3, 33)) ).collect().head === (1, 11)) } + test("kryo with SerializableHyperLogLog") { + assert(sc.parallelize( Array(1, 2, 3, 2, 3, 3, 2, 3, 1) ).countApproxDistinct(0.01) === 3) + } + test("kryo with reduce") { val control = 1 :: 2 :: Nil val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)) @@ -165,18 +191,6 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { .fold(new ClassWithoutNoArgConstructor(10))((t1, t2) => new ClassWithoutNoArgConstructor(t1.x + t2.x)).x assert(10 + control.sum === result) } - - override def beforeAll() { - System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer") - System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName) - super.beforeAll() - } - - override def afterAll() { - super.afterAll() - System.clearProperty("spark.kryo.registrator") - System.clearProperty("spark.serializer") - } } object KryoTest { diff --git a/core/src/test/scala/org/apache/spark/storage/BlockIdSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockIdSuite.scala new file mode 100644 index 0000000000..b647e8a672 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/storage/BlockIdSuite.scala @@ -0,0 +1,114 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import org.scalatest.FunSuite + +class BlockIdSuite extends FunSuite { + def assertSame(id1: BlockId, id2: BlockId) { + assert(id1.name === id2.name) + assert(id1.hashCode === id2.hashCode) + assert(id1 === id2) + } + + def assertDifferent(id1: BlockId, id2: BlockId) { + assert(id1.name != id2.name) + assert(id1.hashCode != id2.hashCode) + assert(id1 != id2) + } + + test("test-bad-deserialization") { + try { + // Try to deserialize an invalid block id. + BlockId("myblock") + fail() + } catch { + case e: IllegalStateException => // OK + case _: Throwable => fail() + } + } + + test("rdd") { + val id = RDDBlockId(1, 2) + assertSame(id, RDDBlockId(1, 2)) + assertDifferent(id, RDDBlockId(1, 1)) + assert(id.name === "rdd_1_2") + assert(id.asRDDId.get.rddId === 1) + assert(id.asRDDId.get.splitIndex === 2) + assert(id.isRDD) + assertSame(id, BlockId(id.toString)) + } + + test("shuffle") { + val id = ShuffleBlockId(1, 2, 3) + assertSame(id, ShuffleBlockId(1, 2, 3)) + assertDifferent(id, ShuffleBlockId(3, 2, 3)) + assert(id.name === "shuffle_1_2_3") + assert(id.asRDDId === None) + assert(id.shuffleId === 1) + assert(id.mapId === 2) + assert(id.reduceId === 3) + assert(id.isShuffle) + assertSame(id, BlockId(id.toString)) + } + + test("broadcast") { + val id = BroadcastBlockId(42) + assertSame(id, BroadcastBlockId(42)) + assertDifferent(id, BroadcastBlockId(123)) + assert(id.name === "broadcast_42") + assert(id.asRDDId === None) + assert(id.broadcastId === 42) + assert(id.isBroadcast) + assertSame(id, BlockId(id.toString)) + } + + test("taskresult") { + val id = TaskResultBlockId(60) + assertSame(id, TaskResultBlockId(60)) + assertDifferent(id, TaskResultBlockId(61)) + assert(id.name === "taskresult_60") + assert(id.asRDDId === None) + assert(id.taskId === 60) + assert(!id.isRDD) + assertSame(id, BlockId(id.toString)) + } + + test("stream") { + val id = StreamBlockId(1, 100) + assertSame(id, StreamBlockId(1, 100)) + assertDifferent(id, StreamBlockId(2, 101)) + assert(id.name === "input-1-100") + assert(id.asRDDId === None) + assert(id.streamId === 1) + assert(id.uniqueId === 100) + assert(!id.isBroadcast) + assertSame(id, BlockId(id.toString)) + } + + test("test") { + val id = TestBlockId("abc") + assertSame(id, TestBlockId("abc")) + assertDifferent(id, TestBlockId("ab")) + assert(id.name === "test_abc") + assert(id.asRDDId === None) + assert(id.id === "abc") + assert(!id.isShuffle) + assertSame(id, BlockId(id.toString)) + } +} diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index 038a9acb85..a0fc3445be 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -31,9 +31,10 @@ import org.scalatest.time.SpanSugar._ import org.apache.spark.util.{SizeEstimator, Utils, AkkaUtils, ByteBufferInputStream} import org.apache.spark.serializer.{JavaSerializer, KryoSerializer} - +import org.apache.spark.{SparkConf, SparkContext} class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodTester { + private val conf = new SparkConf(false) var store: BlockManager = null var store2: BlockManager = null var actorSystem: ActorSystem = null @@ -43,26 +44,31 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT var oldHeartBeat: String = null // Reuse a serializer across tests to avoid creating a new thread-local buffer on each test - System.setProperty("spark.kryoserializer.buffer.mb", "1") - val serializer = new KryoSerializer + conf.set("spark.kryoserializer.buffer.mb", "1") + val serializer = new KryoSerializer(conf) + + // Implicitly convert strings to BlockIds for test clarity. + implicit def StringToBlockId(value: String): BlockId = new TestBlockId(value) + def rdd(rddId: Int, splitId: Int) = RDDBlockId(rddId, splitId) before { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("test", "localhost", 0) + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("test", "localhost", 0, conf = conf) this.actorSystem = actorSystem - System.setProperty("spark.driver.port", boundPort.toString) - System.setProperty("spark.hostPort", "localhost:" + boundPort) + conf.set("spark.driver.port", boundPort.toString) + conf.set("spark.hostPort", "localhost:" + boundPort) master = new BlockManagerMaster( - actorSystem.actorOf(Props(new BlockManagerMasterActor(true)))) + Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf)))), conf) // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case - oldArch = System.setProperty("os.arch", "amd64") - oldOops = System.setProperty("spark.test.useCompressedOops", "true") - oldHeartBeat = System.setProperty("spark.storage.disableBlockManagerHeartBeat", "true") + System.setProperty("os.arch", "amd64") + conf.set("os.arch", "amd64") + conf.set("spark.test.useCompressedOops", "true") + conf.set("spark.storage.disableBlockManagerHeartBeat", "true") val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() // Set some value ... - System.setProperty("spark.hostPort", Utils.localHostName() + ":" + 1111) + conf.set("spark.hostPort", Utils.localHostName() + ":" + 1111) } after { @@ -83,13 +89,13 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT master = null if (oldArch != null) { - System.setProperty("os.arch", oldArch) + conf.set("os.arch", oldArch) } else { System.clearProperty("os.arch") } if (oldOops != null) { - System.setProperty("spark.test.useCompressedOops", oldOops) + conf.set("spark.test.useCompressedOops", oldOops) } else { System.clearProperty("spark.test.useCompressedOops") } @@ -130,7 +136,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("master + 1 manager interaction") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -160,8 +166,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("master + 2 managers interaction") { - store = new BlockManager("exec1", actorSystem, master, serializer, 2000) - store2 = new BlockManager("exec2", actorSystem, master, new KryoSerializer, 2000) + store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf) + store2 = new BlockManager("exec2", actorSystem, master, new KryoSerializer(conf), 2000, conf) val peers = master.getPeers(store.blockManagerId, 1) assert(peers.size === 1, "master did not return the other manager as a peer") @@ -176,7 +182,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("removing block") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -224,41 +230,41 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("removing rdd") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) // Putting a1, a2 and a3 in memory. - store.putSingle("rdd_0_0", a1, StorageLevel.MEMORY_ONLY) - store.putSingle("rdd_0_1", a2, StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 0), a1, StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 1), a2, StorageLevel.MEMORY_ONLY) store.putSingle("nonrddblock", a3, StorageLevel.MEMORY_ONLY) master.removeRdd(0, blocking = false) eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { - store.getSingle("rdd_0_0") should be (None) - master.getLocations("rdd_0_0") should have size 0 + store.getSingle(rdd(0, 0)) should be (None) + master.getLocations(rdd(0, 0)) should have size 0 } eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { - store.getSingle("rdd_0_1") should be (None) - master.getLocations("rdd_0_1") should have size 0 + store.getSingle(rdd(0, 1)) should be (None) + master.getLocations(rdd(0, 1)) should have size 0 } eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { store.getSingle("nonrddblock") should not be (None) master.getLocations("nonrddblock") should have size (1) } - store.putSingle("rdd_0_0", a1, StorageLevel.MEMORY_ONLY) - store.putSingle("rdd_0_1", a2, StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 0), a1, StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 1), a2, StorageLevel.MEMORY_ONLY) master.removeRdd(0, blocking = true) - store.getSingle("rdd_0_0") should be (None) - master.getLocations("rdd_0_0") should have size 0 - store.getSingle("rdd_0_1") should be (None) - master.getLocations("rdd_0_1") should have size 0 + store.getSingle(rdd(0, 0)) should be (None) + master.getLocations(rdd(0, 0)) should have size 0 + store.getSingle(rdd(0, 1)) should be (None) + master.getLocations(rdd(0, 1)) should have size 0 } test("reregistration on heart beat") { val heartBeat = PrivateMethod[Unit]('heartBeat) - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) @@ -274,7 +280,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("reregistration on block update") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) @@ -293,7 +299,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("reregistration doesn't dead lock") { val heartBeat = PrivateMethod[Unit]('heartBeat) - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = List(new Array[Byte](400)) @@ -330,7 +336,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU storage") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -349,7 +355,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU storage with serialization") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -368,49 +374,49 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU for partitions of same RDD") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) - store.putSingle("rdd_0_1", a1, StorageLevel.MEMORY_ONLY) - store.putSingle("rdd_0_2", a2, StorageLevel.MEMORY_ONLY) - store.putSingle("rdd_0_3", a3, StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 1), a1, StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 2), a2, StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 3), a3, StorageLevel.MEMORY_ONLY) // Even though we accessed rdd_0_3 last, it should not have replaced partitions 1 and 2 // from the same RDD - assert(store.getSingle("rdd_0_3") === None, "rdd_0_3 was in store") - assert(store.getSingle("rdd_0_2") != None, "rdd_0_2 was not in store") - assert(store.getSingle("rdd_0_1") != None, "rdd_0_1 was not in store") + assert(store.getSingle(rdd(0, 3)) === None, "rdd_0_3 was in store") + assert(store.getSingle(rdd(0, 2)) != None, "rdd_0_2 was not in store") + assert(store.getSingle(rdd(0, 1)) != None, "rdd_0_1 was not in store") // Check that rdd_0_3 doesn't replace them even after further accesses - assert(store.getSingle("rdd_0_3") === None, "rdd_0_3 was in store") - assert(store.getSingle("rdd_0_3") === None, "rdd_0_3 was in store") - assert(store.getSingle("rdd_0_3") === None, "rdd_0_3 was in store") + assert(store.getSingle(rdd(0, 3)) === None, "rdd_0_3 was in store") + assert(store.getSingle(rdd(0, 3)) === None, "rdd_0_3 was in store") + assert(store.getSingle(rdd(0, 3)) === None, "rdd_0_3 was in store") } test("in-memory LRU for partitions of multiple RDDs") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) - store.putSingle("rdd_0_1", new Array[Byte](400), StorageLevel.MEMORY_ONLY) - store.putSingle("rdd_0_2", new Array[Byte](400), StorageLevel.MEMORY_ONLY) - store.putSingle("rdd_1_1", new Array[Byte](400), StorageLevel.MEMORY_ONLY) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) + store.putSingle(rdd(0, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 2), new Array[Byte](400), StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(1, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY) // At this point rdd_1_1 should've replaced rdd_0_1 - assert(store.memoryStore.contains("rdd_1_1"), "rdd_1_1 was not in store") - assert(!store.memoryStore.contains("rdd_0_1"), "rdd_0_1 was in store") - assert(store.memoryStore.contains("rdd_0_2"), "rdd_0_2 was not in store") + assert(store.memoryStore.contains(rdd(1, 1)), "rdd_1_1 was not in store") + assert(!store.memoryStore.contains(rdd(0, 1)), "rdd_0_1 was in store") + assert(store.memoryStore.contains(rdd(0, 2)), "rdd_0_2 was not in store") // Do a get() on rdd_0_2 so that it is the most recently used item - assert(store.getSingle("rdd_0_2") != None, "rdd_0_2 was not in store") + assert(store.getSingle(rdd(0, 2)) != None, "rdd_0_2 was not in store") // Put in more partitions from RDD 0; they should replace rdd_1_1 - store.putSingle("rdd_0_3", new Array[Byte](400), StorageLevel.MEMORY_ONLY) - store.putSingle("rdd_0_4", new Array[Byte](400), StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 3), new Array[Byte](400), StorageLevel.MEMORY_ONLY) + store.putSingle(rdd(0, 4), new Array[Byte](400), StorageLevel.MEMORY_ONLY) // Now rdd_1_1 should be dropped to add rdd_0_3, but then rdd_0_2 should *not* be dropped // when we try to add rdd_0_4. - assert(!store.memoryStore.contains("rdd_1_1"), "rdd_1_1 was in store") - assert(!store.memoryStore.contains("rdd_0_1"), "rdd_0_1 was in store") - assert(!store.memoryStore.contains("rdd_0_4"), "rdd_0_4 was in store") - assert(store.memoryStore.contains("rdd_0_2"), "rdd_0_2 was not in store") - assert(store.memoryStore.contains("rdd_0_3"), "rdd_0_3 was not in store") + assert(!store.memoryStore.contains(rdd(1, 1)), "rdd_1_1 was in store") + assert(!store.memoryStore.contains(rdd(0, 1)), "rdd_0_1 was in store") + assert(!store.memoryStore.contains(rdd(0, 4)), "rdd_0_4 was in store") + assert(store.memoryStore.contains(rdd(0, 2)), "rdd_0_2 was not in store") + assert(store.memoryStore.contains(rdd(0, 3)), "rdd_0_3 was not in store") } test("on-disk storage") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -423,7 +429,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -438,7 +444,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with getLocalBytes") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -453,7 +459,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with serialization") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -468,7 +474,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with serialization and getLocalBytes") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -483,7 +489,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("LRU with mixed storage levels") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -508,7 +514,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU with streams") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val list1 = List(new Array[Byte](200), new Array[Byte](200)) val list2 = List(new Array[Byte](200), new Array[Byte](200)) val list3 = List(new Array[Byte](200), new Array[Byte](200)) @@ -532,7 +538,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("LRU with mixed storage levels and streams") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val list1 = List(new Array[Byte](200), new Array[Byte](200)) val list2 = List(new Array[Byte](200), new Array[Byte](200)) val list3 = List(new Array[Byte](200), new Array[Byte](200)) @@ -578,7 +584,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("overly large block") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 500) + store = new BlockManager("<driver>", actorSystem, master, serializer, 500, conf) store.putSingle("a1", new Array[Byte](1000), StorageLevel.MEMORY_ONLY) assert(store.getSingle("a1") === None, "a1 was in store") store.putSingle("a2", new Array[Byte](1000), StorageLevel.MEMORY_AND_DISK) @@ -588,50 +594,53 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("block compression") { try { - System.setProperty("spark.shuffle.compress", "true") - store = new BlockManager("exec1", actorSystem, master, serializer, 2000) - store.putSingle("shuffle_0_0_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) - assert(store.memoryStore.getSize("shuffle_0_0_0") <= 100, "shuffle_0_0_0 was not compressed") + conf.set("spark.shuffle.compress", "true") + store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf) + store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) <= 100, + "shuffle_0_0_0 was not compressed") store.stop() store = null - System.setProperty("spark.shuffle.compress", "false") - store = new BlockManager("exec2", actorSystem, master, serializer, 2000) - store.putSingle("shuffle_0_0_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) - assert(store.memoryStore.getSize("shuffle_0_0_0") >= 1000, "shuffle_0_0_0 was compressed") + conf.set("spark.shuffle.compress", "false") + store = new BlockManager("exec2", actorSystem, master, serializer, 2000, conf) + store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) >= 1000, + "shuffle_0_0_0 was compressed") store.stop() store = null - System.setProperty("spark.broadcast.compress", "true") - store = new BlockManager("exec3", actorSystem, master, serializer, 2000) - store.putSingle("broadcast_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) - assert(store.memoryStore.getSize("broadcast_0") <= 100, "broadcast_0 was not compressed") + conf.set("spark.broadcast.compress", "true") + store = new BlockManager("exec3", actorSystem, master, serializer, 2000, conf) + store.putSingle(BroadcastBlockId(0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize(BroadcastBlockId(0)) <= 100, + "broadcast_0 was not compressed") store.stop() store = null - System.setProperty("spark.broadcast.compress", "false") - store = new BlockManager("exec4", actorSystem, master, serializer, 2000) - store.putSingle("broadcast_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) - assert(store.memoryStore.getSize("broadcast_0") >= 1000, "broadcast_0 was compressed") + conf.set("spark.broadcast.compress", "false") + store = new BlockManager("exec4", actorSystem, master, serializer, 2000, conf) + store.putSingle(BroadcastBlockId(0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize(BroadcastBlockId(0)) >= 1000, "broadcast_0 was compressed") store.stop() store = null - System.setProperty("spark.rdd.compress", "true") - store = new BlockManager("exec5", actorSystem, master, serializer, 2000) - store.putSingle("rdd_0_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) - assert(store.memoryStore.getSize("rdd_0_0") <= 100, "rdd_0_0 was not compressed") + conf.set("spark.rdd.compress", "true") + store = new BlockManager("exec5", actorSystem, master, serializer, 2000, conf) + store.putSingle(rdd(0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize(rdd(0, 0)) <= 100, "rdd_0_0 was not compressed") store.stop() store = null - System.setProperty("spark.rdd.compress", "false") - store = new BlockManager("exec6", actorSystem, master, serializer, 2000) - store.putSingle("rdd_0_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) - assert(store.memoryStore.getSize("rdd_0_0") >= 1000, "rdd_0_0 was compressed") + conf.set("spark.rdd.compress", "false") + store = new BlockManager("exec6", actorSystem, master, serializer, 2000, conf) + store.putSingle(rdd(0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize(rdd(0, 0)) >= 1000, "rdd_0_0 was compressed") store.stop() store = null // Check that any other block types are also kept uncompressed - store = new BlockManager("exec7", actorSystem, master, serializer, 2000) + store = new BlockManager("exec7", actorSystem, master, serializer, 2000, conf) store.putSingle("other_block", new Array[Byte](1000), StorageLevel.MEMORY_ONLY) assert(store.memoryStore.getSize("other_block") >= 1000, "other_block was compressed") store.stop() @@ -645,7 +654,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("block store put failure") { // Use Java serializer so we can create an unserializable error. - store = new BlockManager("<driver>", actorSystem, master, new JavaSerializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, new JavaSerializer(conf), 1200, conf) // The put should fail since a1 is not serializable. class UnserializableClass diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala new file mode 100644 index 0000000000..af4b31d53c --- /dev/null +++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala @@ -0,0 +1,109 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.io.{File, FileWriter} + +import scala.collection.mutable + +import com.google.common.io.Files +import org.apache.spark.SparkConf +import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite} +import scala.util.Try +import akka.actor.{Props, ActorSelection, ActorSystem} + +class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach { + private val testConf = new SparkConf(false) + val rootDir0 = Files.createTempDir() + rootDir0.deleteOnExit() + val rootDir1 = Files.createTempDir() + rootDir1.deleteOnExit() + val rootDirs = rootDir0.getName + "," + rootDir1.getName + println("Created root dirs: " + rootDirs) + + // This suite focuses primarily on consolidation features, + // so we coerce consolidation if not already enabled. + testConf.set("spark.shuffle.consolidateFiles", "true") + + val shuffleBlockManager = new ShuffleBlockManager(null) { + override def conf = testConf.clone + var idToSegmentMap = mutable.Map[ShuffleBlockId, FileSegment]() + override def getBlockLocation(id: ShuffleBlockId) = idToSegmentMap(id) + } + + var diskBlockManager: DiskBlockManager = _ + + override def beforeEach() { + diskBlockManager = new DiskBlockManager(shuffleBlockManager, rootDirs) + shuffleBlockManager.idToSegmentMap.clear() + } + + test("basic block creation") { + val blockId = new TestBlockId("test") + assertSegmentEquals(blockId, blockId.name, 0, 0) + + val newFile = diskBlockManager.getFile(blockId) + writeToFile(newFile, 10) + assertSegmentEquals(blockId, blockId.name, 0, 10) + + newFile.delete() + } + + test("block appending") { + val blockId = new TestBlockId("test") + val newFile = diskBlockManager.getFile(blockId) + writeToFile(newFile, 15) + assertSegmentEquals(blockId, blockId.name, 0, 15) + val newFile2 = diskBlockManager.getFile(blockId) + assert(newFile === newFile2) + writeToFile(newFile2, 12) + assertSegmentEquals(blockId, blockId.name, 0, 27) + newFile.delete() + } + + test("block remapping") { + val filename = "test" + val blockId0 = new ShuffleBlockId(1, 2, 3) + val newFile = diskBlockManager.getFile(filename) + writeToFile(newFile, 15) + shuffleBlockManager.idToSegmentMap(blockId0) = new FileSegment(newFile, 0, 15) + assertSegmentEquals(blockId0, filename, 0, 15) + + val blockId1 = new ShuffleBlockId(1, 2, 4) + val newFile2 = diskBlockManager.getFile(filename) + writeToFile(newFile2, 12) + shuffleBlockManager.idToSegmentMap(blockId1) = new FileSegment(newFile, 15, 12) + assertSegmentEquals(blockId1, filename, 15, 12) + + assert(newFile === newFile2) + newFile.delete() + } + + def assertSegmentEquals(blockId: BlockId, filename: String, offset: Int, length: Int) { + val segment = diskBlockManager.getBlockLocation(blockId) + assert(segment.file.getName === filename) + assert(segment.offset === offset) + assert(segment.length === length) + } + + def writeToFile(file: File, numBytes: Int) { + val writer = new FileWriter(file, true) + for (i <- 0 until numBytes) writer.write(i) + writer.close() + } +} diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala index 07c9f2382b..3764f4d1a0 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala @@ -26,10 +26,14 @@ class UISuite extends FunSuite { test("jetty port increases under contention") { val startPort = 4040 val server = new Server(startPort) - server.start() + + Try { server.start() } match { + case Success(s) => + case Failure(e) => + // Either case server port is busy hence setup for test complete + } val (jettyServer1, boundPort1) = JettyUtils.startJettyServer("localhost", startPort, Seq()) val (jettyServer2, boundPort2) = JettyUtils.startJettyServer("localhost", startPort, Seq()) - // Allow some wiggle room in case ports on the machine are under contention assert(boundPort1 > startPort && boundPort1 < startPort + 10) assert(boundPort2 > boundPort1 && boundPort2 < boundPort1 + 10) diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala new file mode 100644 index 0000000000..67a57a0e7f --- /dev/null +++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala @@ -0,0 +1,73 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ui.jobs + +import org.scalatest.FunSuite +import org.apache.spark.scheduler._ +import org.apache.spark.{LocalSparkContext, SparkContext, Success} +import org.apache.spark.scheduler.SparkListenerTaskStart +import org.apache.spark.executor.{ShuffleReadMetrics, TaskMetrics} + +class JobProgressListenerSuite extends FunSuite with LocalSparkContext { + test("test executor id to summary") { + val sc = new SparkContext("local", "test") + val listener = new JobProgressListener(sc) + val taskMetrics = new TaskMetrics() + val shuffleReadMetrics = new ShuffleReadMetrics() + + // nothing in it + assert(listener.stageIdToExecutorSummaries.size == 0) + + // finish this task, should get updated shuffleRead + shuffleReadMetrics.remoteBytesRead = 1000 + taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics) + var taskInfo = new TaskInfo(1234L, 0, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL) + taskInfo.finishTime = 1 + listener.onTaskEnd(new SparkListenerTaskEnd( + new ShuffleMapTask(0, null, null, 0, null), Success, taskInfo, taskMetrics)) + assert(listener.stageIdToExecutorSummaries.getOrElse(0, fail()).getOrElse("exe-1", fail()) + .shuffleRead == 1000) + + // finish a task with unknown executor-id, nothing should happen + taskInfo = new TaskInfo(1234L, 0, 1000L, "exe-unknown", "host1", TaskLocality.NODE_LOCAL) + taskInfo.finishTime = 1 + listener.onTaskEnd(new SparkListenerTaskEnd( + new ShuffleMapTask(0, null, null, 0, null), Success, taskInfo, taskMetrics)) + assert(listener.stageIdToExecutorSummaries.size == 1) + + // finish this task, should get updated duration + shuffleReadMetrics.remoteBytesRead = 1000 + taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics) + taskInfo = new TaskInfo(1235L, 0, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL) + taskInfo.finishTime = 1 + listener.onTaskEnd(new SparkListenerTaskEnd( + new ShuffleMapTask(0, null, null, 0, null), Success, taskInfo, taskMetrics)) + assert(listener.stageIdToExecutorSummaries.getOrElse(0, fail()).getOrElse("exe-1", fail()) + .shuffleRead == 2000) + + // finish this task, should get updated duration + shuffleReadMetrics.remoteBytesRead = 1000 + taskMetrics.shuffleReadMetrics = Some(shuffleReadMetrics) + taskInfo = new TaskInfo(1236L, 0, 0L, "exe-2", "host1", TaskLocality.NODE_LOCAL) + taskInfo.finishTime = 1 + listener.onTaskEnd(new SparkListenerTaskEnd( + new ShuffleMapTask(0, null, null, 0, null), Success, taskInfo, taskMetrics)) + assert(listener.stageIdToExecutorSummaries.getOrElse(0, fail()).getOrElse("exe-2", fail()) + .shuffleRead == 1000) + } +} diff --git a/core/src/test/scala/org/apache/spark/util/AppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/AppendOnlyMapSuite.scala new file mode 100644 index 0000000000..7177919a58 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/AppendOnlyMapSuite.scala @@ -0,0 +1,154 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import scala.collection.mutable.HashSet + +import org.scalatest.FunSuite + +class AppendOnlyMapSuite extends FunSuite { + test("initialization") { + val goodMap1 = new AppendOnlyMap[Int, Int](1) + assert(goodMap1.size === 0) + val goodMap2 = new AppendOnlyMap[Int, Int](255) + assert(goodMap2.size === 0) + val goodMap3 = new AppendOnlyMap[Int, Int](256) + assert(goodMap3.size === 0) + intercept[IllegalArgumentException] { + new AppendOnlyMap[Int, Int](1 << 30) // Invalid map size: bigger than 2^29 + } + intercept[IllegalArgumentException] { + new AppendOnlyMap[Int, Int](-1) + } + intercept[IllegalArgumentException] { + new AppendOnlyMap[Int, Int](0) + } + } + + test("object keys and values") { + val map = new AppendOnlyMap[String, String]() + for (i <- 1 to 100) { + map("" + i) = "" + i + } + assert(map.size === 100) + for (i <- 1 to 100) { + assert(map("" + i) === "" + i) + } + assert(map("0") === null) + assert(map("101") === null) + assert(map(null) === null) + val set = new HashSet[(String, String)] + for ((k, v) <- map) { // Test the foreach method + set += ((k, v)) + } + assert(set === (1 to 100).map(_.toString).map(x => (x, x)).toSet) + } + + test("primitive keys and values") { + val map = new AppendOnlyMap[Int, Int]() + for (i <- 1 to 100) { + map(i) = i + } + assert(map.size === 100) + for (i <- 1 to 100) { + assert(map(i) === i) + } + assert(map(0) === null) + assert(map(101) === null) + val set = new HashSet[(Int, Int)] + for ((k, v) <- map) { // Test the foreach method + set += ((k, v)) + } + assert(set === (1 to 100).map(x => (x, x)).toSet) + } + + test("null keys") { + val map = new AppendOnlyMap[String, String]() + for (i <- 1 to 100) { + map("" + i) = "" + i + } + assert(map.size === 100) + assert(map(null) === null) + map(null) = "hello" + assert(map.size === 101) + assert(map(null) === "hello") + } + + test("null values") { + val map = new AppendOnlyMap[String, String]() + for (i <- 1 to 100) { + map("" + i) = null + } + assert(map.size === 100) + assert(map("1") === null) + assert(map(null) === null) + assert(map.size === 100) + map(null) = null + assert(map.size === 101) + assert(map(null) === null) + } + + test("changeValue") { + val map = new AppendOnlyMap[String, String]() + for (i <- 1 to 100) { + map("" + i) = "" + i + } + assert(map.size === 100) + for (i <- 1 to 100) { + val res = map.changeValue("" + i, (hadValue, oldValue) => { + assert(hadValue === true) + assert(oldValue === "" + i) + oldValue + "!" + }) + assert(res === i + "!") + } + // Iterate from 101 to 400 to make sure the map grows a couple of times, because we had a + // bug where changeValue would return the wrong result when the map grew on that insert + for (i <- 101 to 400) { + val res = map.changeValue("" + i, (hadValue, oldValue) => { + assert(hadValue === false) + i + "!" + }) + assert(res === i + "!") + } + assert(map.size === 400) + assert(map(null) === null) + map.changeValue(null, (hadValue, oldValue) => { + assert(hadValue === false) + "null!" + }) + assert(map.size === 401) + map.changeValue(null, (hadValue, oldValue) => { + assert(hadValue === true) + assert(oldValue === "null!") + "null!!" + }) + assert(map.size === 401) + } + + test("inserting in capacity-1 map") { + val map = new AppendOnlyMap[String, String](1) + for (i <- 1 to 100) { + map("" + i) = "" + i + } + assert(map.size === 100) + for (i <- 1 to 100) { + assert(map("" + i) === "" + i) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala index 4e40dcbdee..11ebdc352b 100644 --- a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala @@ -20,6 +20,7 @@ package org.apache.spark.util import org.scalatest.FunSuite import org.scalatest.BeforeAndAfterAll import org.scalatest.PrivateMethodTester +import org.apache.spark.SparkContext class DummyClass1 {} @@ -63,54 +64,53 @@ class SizeEstimatorSuite } test("simple classes") { - assert(SizeEstimator.estimate(new DummyClass1) === 16) - assert(SizeEstimator.estimate(new DummyClass2) === 16) - assert(SizeEstimator.estimate(new DummyClass3) === 24) - assert(SizeEstimator.estimate(new DummyClass4(null)) === 24) - assert(SizeEstimator.estimate(new DummyClass4(new DummyClass3)) === 48) + expectResult(16)(SizeEstimator.estimate(new DummyClass1)) + expectResult(16)(SizeEstimator.estimate(new DummyClass2)) + expectResult(24)(SizeEstimator.estimate(new DummyClass3)) + expectResult(24)(SizeEstimator.estimate(new DummyClass4(null))) + expectResult(48)(SizeEstimator.estimate(new DummyClass4(new DummyClass3))) } // NOTE: The String class definition varies across JDK versions (1.6 vs. 1.7) and vendors // (Sun vs IBM). Use a DummyString class to make tests deterministic. test("strings") { - assert(SizeEstimator.estimate(DummyString("")) === 40) - assert(SizeEstimator.estimate(DummyString("a")) === 48) - assert(SizeEstimator.estimate(DummyString("ab")) === 48) - assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 56) + expectResult(40)(SizeEstimator.estimate(DummyString(""))) + expectResult(48)(SizeEstimator.estimate(DummyString("a"))) + expectResult(48)(SizeEstimator.estimate(DummyString("ab"))) + expectResult(56)(SizeEstimator.estimate(DummyString("abcdefgh"))) } test("primitive arrays") { - assert(SizeEstimator.estimate(new Array[Byte](10)) === 32) - assert(SizeEstimator.estimate(new Array[Char](10)) === 40) - assert(SizeEstimator.estimate(new Array[Short](10)) === 40) - assert(SizeEstimator.estimate(new Array[Int](10)) === 56) - assert(SizeEstimator.estimate(new Array[Long](10)) === 96) - assert(SizeEstimator.estimate(new Array[Float](10)) === 56) - assert(SizeEstimator.estimate(new Array[Double](10)) === 96) - assert(SizeEstimator.estimate(new Array[Int](1000)) === 4016) - assert(SizeEstimator.estimate(new Array[Long](1000)) === 8016) + expectResult(32)(SizeEstimator.estimate(new Array[Byte](10))) + expectResult(40)(SizeEstimator.estimate(new Array[Char](10))) + expectResult(40)(SizeEstimator.estimate(new Array[Short](10))) + expectResult(56)(SizeEstimator.estimate(new Array[Int](10))) + expectResult(96)(SizeEstimator.estimate(new Array[Long](10))) + expectResult(56)(SizeEstimator.estimate(new Array[Float](10))) + expectResult(96)(SizeEstimator.estimate(new Array[Double](10))) + expectResult(4016)(SizeEstimator.estimate(new Array[Int](1000))) + expectResult(8016)(SizeEstimator.estimate(new Array[Long](1000))) } test("object arrays") { // Arrays containing nulls should just have one pointer per element - assert(SizeEstimator.estimate(new Array[String](10)) === 56) - assert(SizeEstimator.estimate(new Array[AnyRef](10)) === 56) - + expectResult(56)(SizeEstimator.estimate(new Array[String](10))) + expectResult(56)(SizeEstimator.estimate(new Array[AnyRef](10))) // For object arrays with non-null elements, each object should take one pointer plus // however many bytes that class takes. (Note that Array.fill calls the code in its // second parameter separately for each object, so we get distinct objects.) - assert(SizeEstimator.estimate(Array.fill(10)(new DummyClass1)) === 216) - assert(SizeEstimator.estimate(Array.fill(10)(new DummyClass2)) === 216) - assert(SizeEstimator.estimate(Array.fill(10)(new DummyClass3)) === 296) - assert(SizeEstimator.estimate(Array(new DummyClass1, new DummyClass2)) === 56) + expectResult(216)(SizeEstimator.estimate(Array.fill(10)(new DummyClass1))) + expectResult(216)(SizeEstimator.estimate(Array.fill(10)(new DummyClass2))) + expectResult(296)(SizeEstimator.estimate(Array.fill(10)(new DummyClass3))) + expectResult(56)(SizeEstimator.estimate(Array(new DummyClass1, new DummyClass2))) // Past size 100, our samples 100 elements, but we should still get the right size. - assert(SizeEstimator.estimate(Array.fill(1000)(new DummyClass3)) === 28016) + expectResult(28016)(SizeEstimator.estimate(Array.fill(1000)(new DummyClass3))) // If an array contains the *same* element many times, we should only count it once. val d1 = new DummyClass1 - assert(SizeEstimator.estimate(Array.fill(10)(d1)) === 72) // 10 pointers plus 8-byte object - assert(SizeEstimator.estimate(Array.fill(100)(d1)) === 432) // 100 pointers plus 8-byte object + expectResult(72)(SizeEstimator.estimate(Array.fill(10)(d1))) // 10 pointers plus 8-byte object + expectResult(432)(SizeEstimator.estimate(Array.fill(100)(d1))) // 100 pointers plus 8-byte object // Same thing with huge array containing the same element many times. Note that this won't // return exactly 4032 because it can't tell that *all* the elements will equal the first @@ -128,11 +128,10 @@ class SizeEstimatorSuite val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() - assert(SizeEstimator.estimate(DummyString("")) === 40) - assert(SizeEstimator.estimate(DummyString("a")) === 48) - assert(SizeEstimator.estimate(DummyString("ab")) === 48) - assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 56) - + expectResult(40)(SizeEstimator.estimate(DummyString(""))) + expectResult(48)(SizeEstimator.estimate(DummyString("a"))) + expectResult(48)(SizeEstimator.estimate(DummyString("ab"))) + expectResult(56)(SizeEstimator.estimate(DummyString("abcdefgh"))) resetOrClear("os.arch", arch) } @@ -141,14 +140,13 @@ class SizeEstimatorSuite test("64-bit arch with no compressed oops") { val arch = System.setProperty("os.arch", "amd64") val oops = System.setProperty("spark.test.useCompressedOops", "false") - val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() - assert(SizeEstimator.estimate(DummyString("")) === 56) - assert(SizeEstimator.estimate(DummyString("a")) === 64) - assert(SizeEstimator.estimate(DummyString("ab")) === 64) - assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 72) + expectResult(56)(SizeEstimator.estimate(DummyString(""))) + expectResult(64)(SizeEstimator.estimate(DummyString("a"))) + expectResult(64)(SizeEstimator.estimate(DummyString("ab"))) + expectResult(72)(SizeEstimator.estimate(DummyString("abcdefgh"))) resetOrClear("os.arch", arch) resetOrClear("spark.test.useCompressedOops", oops) diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index e2859caf58..4684c8c972 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -20,6 +20,7 @@ package org.apache.spark.util import com.google.common.base.Charsets import com.google.common.io.Files import java.io.{ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream, File} +import java.nio.{ByteBuffer, ByteOrder} import org.scalatest.FunSuite import org.apache.commons.io.FileUtils import scala.util.Random @@ -135,5 +136,15 @@ class UtilsSuite extends FunSuite { FileUtils.deleteDirectory(tmpDir2) } + + test("deserialize long value") { + val testval : Long = 9730889947L + val bbuf = ByteBuffer.allocate(8) + assert(bbuf.hasArray) + bbuf.order(ByteOrder.BIG_ENDIAN) + bbuf.putLong(testval) + assert(bbuf.array.length === 8) + assert(Utils.deserializeLongValue(bbuf.array) === testval) + } } diff --git a/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala b/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala new file mode 100644 index 0000000000..b78367b6ca --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala @@ -0,0 +1,76 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import java.util.Random +import org.scalatest.FlatSpec +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers +import org.apache.spark.util.Utils.times + +class XORShiftRandomSuite extends FunSuite with ShouldMatchers { + + def fixture = new { + val seed = 1L + val xorRand = new XORShiftRandom(seed) + val hundMil = 1e8.toInt + } + + /* + * This test is based on a chi-squared test for randomness. The values are hard-coded + * so as not to create Spark's dependency on apache.commons.math3 just to call one + * method for calculating the exact p-value for a given number of random numbers + * and bins. In case one would want to move to a full-fledged test based on + * apache.commons.math3, the relevant class is here: + * org.apache.commons.math3.stat.inference.ChiSquareTest + */ + test ("XORShift generates valid random numbers") { + + val f = fixture + + val numBins = 10 + // create 10 bins + val bins = Array.fill(numBins)(0) + + // populate bins based on modulus of the random number + times(f.hundMil) {bins(math.abs(f.xorRand.nextInt) % 10) += 1} + + /* since the seed is deterministic, until the algorithm is changed, we know the result will be + * exactly this: Array(10004908, 9993136, 9994600, 10000744, 10000091, 10002474, 10002272, + * 10000790, 10002286, 9998699), so the test will never fail at the prespecified (5%) + * significance level. However, should the RNG implementation change, the test should still + * pass at the same significance level. The chi-squared test done in R gave the following + * results: + * > chisq.test(c(10004908, 9993136, 9994600, 10000744, 10000091, 10002474, 10002272, + * 10000790, 10002286, 9998699)) + * Chi-squared test for given probabilities + * data: c(10004908, 9993136, 9994600, 10000744, 10000091, 10002474, 10002272, 10000790, + * 10002286, 9998699) + * X-squared = 11.975, df = 9, p-value = 0.2147 + * Note that the p-value was ~0.22. The test will fail if alpha < 0.05, which for 100 million + * random numbers + * and 10 bins will happen at X-squared of ~16.9196. So, the test will fail if X-squared + * is greater than or equal to that number. + */ + val binSize = f.hundMil/numBins + val xSquared = bins.map(x => math.pow((binSize - x), 2)/binSize).sum + xSquared should be < (16.9196) + + } + +}
\ No newline at end of file diff --git a/core/src/test/scala/org/apache/spark/util/collection/BitSetSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/BitSetSuite.scala new file mode 100644 index 0000000000..0f1ab3d20e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/BitSetSuite.scala @@ -0,0 +1,73 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import org.scalatest.FunSuite + + +class BitSetSuite extends FunSuite { + + test("basic set and get") { + val setBits = Seq(0, 9, 1, 10, 90, 96) + val bitset = new BitSet(100) + + for (i <- 0 until 100) { + assert(!bitset.get(i)) + } + + setBits.foreach(i => bitset.set(i)) + + for (i <- 0 until 100) { + if (setBits.contains(i)) { + assert(bitset.get(i)) + } else { + assert(!bitset.get(i)) + } + } + assert(bitset.cardinality() === setBits.size) + } + + test("100% full bit set") { + val bitset = new BitSet(10000) + for (i <- 0 until 10000) { + assert(!bitset.get(i)) + bitset.set(i) + } + for (i <- 0 until 10000) { + assert(bitset.get(i)) + } + assert(bitset.cardinality() === 10000) + } + + test("nextSetBit") { + val setBits = Seq(0, 9, 1, 10, 90, 96) + val bitset = new BitSet(100) + setBits.foreach(i => bitset.set(i)) + + assert(bitset.nextSetBit(0) === 0) + assert(bitset.nextSetBit(1) === 1) + assert(bitset.nextSetBit(2) === 9) + assert(bitset.nextSetBit(9) === 9) + assert(bitset.nextSetBit(10) === 10) + assert(bitset.nextSetBit(11) === 90) + assert(bitset.nextSetBit(80) === 90) + assert(bitset.nextSetBit(91) === 96) + assert(bitset.nextSetBit(96) === 96) + assert(bitset.nextSetBit(97) === -1) + } +} diff --git a/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala new file mode 100644 index 0000000000..e9b62ea70d --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala @@ -0,0 +1,177 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import scala.collection.mutable.HashSet +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers +import org.apache.spark.util.SizeEstimator + +class OpenHashMapSuite extends FunSuite with ShouldMatchers { + + test("size for specialized, primitive value (int)") { + val capacity = 1024 + val map = new OpenHashMap[String, Int](capacity) + val actualSize = SizeEstimator.estimate(map) + // 64 bit for pointers, 32 bit for ints, and 1 bit for the bitset. + val expectedSize = capacity * (64 + 32 + 1) / 8 + // Make sure we are not allocating a significant amount of memory beyond our expected. + actualSize should be <= (expectedSize * 1.1).toLong + } + + test("initialization") { + val goodMap1 = new OpenHashMap[String, Int](1) + assert(goodMap1.size === 0) + val goodMap2 = new OpenHashMap[String, Int](255) + assert(goodMap2.size === 0) + val goodMap3 = new OpenHashMap[String, String](256) + assert(goodMap3.size === 0) + intercept[IllegalArgumentException] { + new OpenHashMap[String, Int](1 << 30) // Invalid map size: bigger than 2^29 + } + intercept[IllegalArgumentException] { + new OpenHashMap[String, Int](-1) + } + intercept[IllegalArgumentException] { + new OpenHashMap[String, String](0) + } + } + + test("primitive value") { + val map = new OpenHashMap[String, Int] + + for (i <- 1 to 1000) { + map(i.toString) = i + assert(map(i.toString) === i) + } + + assert(map.size === 1000) + assert(map(null) === 0) + + map(null) = -1 + assert(map.size === 1001) + assert(map(null) === -1) + + for (i <- 1 to 1000) { + assert(map(i.toString) === i) + } + + // Test iterator + val set = new HashSet[(String, Int)] + for ((k, v) <- map) { + set.add((k, v)) + } + val expected = (1 to 1000).map(x => (x.toString, x)) :+ (null.asInstanceOf[String], -1) + assert(set === expected.toSet) + } + + test("non-primitive value") { + val map = new OpenHashMap[String, String] + + for (i <- 1 to 1000) { + map(i.toString) = i.toString + assert(map(i.toString) === i.toString) + } + + assert(map.size === 1000) + assert(map(null) === null) + + map(null) = "-1" + assert(map.size === 1001) + assert(map(null) === "-1") + + for (i <- 1 to 1000) { + assert(map(i.toString) === i.toString) + } + + // Test iterator + val set = new HashSet[(String, String)] + for ((k, v) <- map) { + set.add((k, v)) + } + val expected = (1 to 1000).map(_.toString).map(x => (x, x)) :+ (null.asInstanceOf[String], "-1") + assert(set === expected.toSet) + } + + test("null keys") { + val map = new OpenHashMap[String, String]() + for (i <- 1 to 100) { + map(i.toString) = i.toString + } + assert(map.size === 100) + assert(map(null) === null) + map(null) = "hello" + assert(map.size === 101) + assert(map(null) === "hello") + } + + test("null values") { + val map = new OpenHashMap[String, String]() + for (i <- 1 to 100) { + map(i.toString) = null + } + assert(map.size === 100) + assert(map("1") === null) + assert(map(null) === null) + assert(map.size === 100) + map(null) = null + assert(map.size === 101) + assert(map(null) === null) + } + + test("changeValue") { + val map = new OpenHashMap[String, String]() + for (i <- 1 to 100) { + map(i.toString) = i.toString + } + assert(map.size === 100) + for (i <- 1 to 100) { + val res = map.changeValue(i.toString, { assert(false); "" }, v => { + assert(v === i.toString) + v + "!" + }) + assert(res === i + "!") + } + // Iterate from 101 to 400 to make sure the map grows a couple of times, because we had a + // bug where changeValue would return the wrong result when the map grew on that insert + for (i <- 101 to 400) { + val res = map.changeValue(i.toString, { i + "!" }, v => { assert(false); v }) + assert(res === i + "!") + } + assert(map.size === 400) + assert(map(null) === null) + map.changeValue(null, { "null!" }, v => { assert(false); v }) + assert(map.size === 401) + map.changeValue(null, { assert(false); "" }, v => { + assert(v === "null!") + "null!!" + }) + assert(map.size === 401) + } + + test("inserting in capacity-1 map") { + val map = new OpenHashMap[String, String](1) + for (i <- 1 to 100) { + map(i.toString) = i.toString + } + assert(map.size === 100) + for (i <- 1 to 100) { + assert(map(i.toString) === i.toString) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/util/collection/OpenHashSetSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/OpenHashSetSuite.scala new file mode 100644 index 0000000000..1b24f8f287 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/OpenHashSetSuite.scala @@ -0,0 +1,180 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers + +import org.apache.spark.util.SizeEstimator + + +class OpenHashSetSuite extends FunSuite with ShouldMatchers { + + test("size for specialized, primitive int") { + val loadFactor = 0.7 + val set = new OpenHashSet[Int](64, loadFactor) + for (i <- 0 until 1024) { + set.add(i) + } + assert(set.size === 1024) + assert(set.capacity > 1024) + val actualSize = SizeEstimator.estimate(set) + // 32 bits for the ints + 1 bit for the bitset + val expectedSize = set.capacity * (32 + 1) / 8 + // Make sure we are not allocating a significant amount of memory beyond our expected. + actualSize should be <= (expectedSize * 1.1).toLong + } + + test("primitive int") { + val set = new OpenHashSet[Int] + assert(set.size === 0) + assert(!set.contains(10)) + assert(!set.contains(50)) + assert(!set.contains(999)) + assert(!set.contains(10000)) + + set.add(10) + assert(set.contains(10)) + assert(!set.contains(50)) + assert(!set.contains(999)) + assert(!set.contains(10000)) + + set.add(50) + assert(set.size === 2) + assert(set.contains(10)) + assert(set.contains(50)) + assert(!set.contains(999)) + assert(!set.contains(10000)) + + set.add(999) + assert(set.size === 3) + assert(set.contains(10)) + assert(set.contains(50)) + assert(set.contains(999)) + assert(!set.contains(10000)) + + set.add(50) + assert(set.size === 3) + assert(set.contains(10)) + assert(set.contains(50)) + assert(set.contains(999)) + assert(!set.contains(10000)) + } + + test("primitive long") { + val set = new OpenHashSet[Long] + assert(set.size === 0) + assert(!set.contains(10L)) + assert(!set.contains(50L)) + assert(!set.contains(999L)) + assert(!set.contains(10000L)) + + set.add(10L) + assert(set.size === 1) + assert(set.contains(10L)) + assert(!set.contains(50L)) + assert(!set.contains(999L)) + assert(!set.contains(10000L)) + + set.add(50L) + assert(set.size === 2) + assert(set.contains(10L)) + assert(set.contains(50L)) + assert(!set.contains(999L)) + assert(!set.contains(10000L)) + + set.add(999L) + assert(set.size === 3) + assert(set.contains(10L)) + assert(set.contains(50L)) + assert(set.contains(999L)) + assert(!set.contains(10000L)) + + set.add(50L) + assert(set.size === 3) + assert(set.contains(10L)) + assert(set.contains(50L)) + assert(set.contains(999L)) + assert(!set.contains(10000L)) + } + + test("non-primitive") { + val set = new OpenHashSet[String] + assert(set.size === 0) + assert(!set.contains(10.toString)) + assert(!set.contains(50.toString)) + assert(!set.contains(999.toString)) + assert(!set.contains(10000.toString)) + + set.add(10.toString) + assert(set.size === 1) + assert(set.contains(10.toString)) + assert(!set.contains(50.toString)) + assert(!set.contains(999.toString)) + assert(!set.contains(10000.toString)) + + set.add(50.toString) + assert(set.size === 2) + assert(set.contains(10.toString)) + assert(set.contains(50.toString)) + assert(!set.contains(999.toString)) + assert(!set.contains(10000.toString)) + + set.add(999.toString) + assert(set.size === 3) + assert(set.contains(10.toString)) + assert(set.contains(50.toString)) + assert(set.contains(999.toString)) + assert(!set.contains(10000.toString)) + + set.add(50.toString) + assert(set.size === 3) + assert(set.contains(10.toString)) + assert(set.contains(50.toString)) + assert(set.contains(999.toString)) + assert(!set.contains(10000.toString)) + } + + test("non-primitive set growth") { + val set = new OpenHashSet[String] + for (i <- 1 to 1000) { + set.add(i.toString) + } + assert(set.size === 1000) + assert(set.capacity > 1000) + for (i <- 1 to 100) { + set.add(i.toString) + } + assert(set.size === 1000) + assert(set.capacity > 1000) + } + + test("primitive set growth") { + val set = new OpenHashSet[Long] + for (i <- 1 to 1000) { + set.add(i.toLong) + } + assert(set.size === 1000) + assert(set.capacity > 1000) + for (i <- 1 to 100) { + set.add(i.toLong) + } + assert(set.size === 1000) + assert(set.capacity > 1000) + } +} diff --git a/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala new file mode 100644 index 0000000000..3b60decee9 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import scala.collection.mutable.HashSet +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers +import org.apache.spark.util.SizeEstimator + +class PrimitiveKeyOpenHashMapSuite extends FunSuite with ShouldMatchers { + + test("size for specialized, primitive key, value (int, int)") { + val capacity = 1024 + val map = new PrimitiveKeyOpenHashMap[Int, Int](capacity) + val actualSize = SizeEstimator.estimate(map) + // 32 bit for keys, 32 bit for values, and 1 bit for the bitset. + val expectedSize = capacity * (32 + 32 + 1) / 8 + // Make sure we are not allocating a significant amount of memory beyond our expected. + actualSize should be <= (expectedSize * 1.1).toLong + } + + test("initialization") { + val goodMap1 = new PrimitiveKeyOpenHashMap[Int, Int](1) + assert(goodMap1.size === 0) + val goodMap2 = new PrimitiveKeyOpenHashMap[Int, Int](255) + assert(goodMap2.size === 0) + val goodMap3 = new PrimitiveKeyOpenHashMap[Int, Int](256) + assert(goodMap3.size === 0) + intercept[IllegalArgumentException] { + new PrimitiveKeyOpenHashMap[Int, Int](1 << 30) // Invalid map size: bigger than 2^29 + } + intercept[IllegalArgumentException] { + new PrimitiveKeyOpenHashMap[Int, Int](-1) + } + intercept[IllegalArgumentException] { + new PrimitiveKeyOpenHashMap[Int, Int](0) + } + } + + test("basic operations") { + val longBase = 1000000L + val map = new PrimitiveKeyOpenHashMap[Long, Int] + + for (i <- 1 to 1000) { + map(i + longBase) = i + assert(map(i + longBase) === i) + } + + assert(map.size === 1000) + + for (i <- 1 to 1000) { + assert(map(i + longBase) === i) + } + + // Test iterator + val set = new HashSet[(Long, Int)] + for ((k, v) <- map) { + set.add((k, v)) + } + assert(set === (1 to 1000).map(x => (x + longBase, x)).toSet) + } + + test("null values") { + val map = new PrimitiveKeyOpenHashMap[Long, String]() + for (i <- 1 to 100) { + map(i.toLong) = null + } + assert(map.size === 100) + assert(map(1.toLong) === null) + } + + test("changeValue") { + val map = new PrimitiveKeyOpenHashMap[Long, String]() + for (i <- 1 to 100) { + map(i.toLong) = i.toString + } + assert(map.size === 100) + for (i <- 1 to 100) { + val res = map.changeValue(i.toLong, { assert(false); "" }, v => { + assert(v === i.toString) + v + "!" + }) + assert(res === i + "!") + } + // Iterate from 101 to 400 to make sure the map grows a couple of times, because we had a + // bug where changeValue would return the wrong result when the map grew on that insert + for (i <- 101 to 400) { + val res = map.changeValue(i.toLong, { i + "!" }, v => { assert(false); v }) + assert(res === i + "!") + } + assert(map.size === 400) + } + + test("inserting in capacity-1 map") { + val map = new PrimitiveKeyOpenHashMap[Long, String](1) + for (i <- 1 to 100) { + map(i.toLong) = i.toString + } + assert(map.size === 100) + for (i <- 1 to 100) { + assert(map(i.toLong) === i.toString) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/util/collection/PrimitiveVectorSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveVectorSuite.scala new file mode 100644 index 0000000000..970dade628 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveVectorSuite.scala @@ -0,0 +1,117 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import org.scalatest.FunSuite + +import org.apache.spark.util.SizeEstimator + +class PrimitiveVectorSuite extends FunSuite { + + test("primitive value") { + val vector = new PrimitiveVector[Int] + + for (i <- 0 until 1000) { + vector += i + assert(vector(i) === i) + } + + assert(vector.size === 1000) + assert(vector.size == vector.length) + intercept[IllegalArgumentException] { + vector(1000) + } + + for (i <- 0 until 1000) { + assert(vector(i) == i) + } + } + + test("non-primitive value") { + val vector = new PrimitiveVector[String] + + for (i <- 0 until 1000) { + vector += i.toString + assert(vector(i) === i.toString) + } + + assert(vector.size === 1000) + assert(vector.size == vector.length) + intercept[IllegalArgumentException] { + vector(1000) + } + + for (i <- 0 until 1000) { + assert(vector(i) == i.toString) + } + } + + test("ideal growth") { + val vector = new PrimitiveVector[Long](initialSize = 1) + vector += 1 + for (i <- 1 until 1024) { + vector += i + assert(vector.size === i + 1) + assert(vector.capacity === Integer.highestOneBit(i) * 2) + } + assert(vector.capacity === 1024) + vector += 1024 + assert(vector.capacity === 2048) + } + + test("ideal size") { + val vector = new PrimitiveVector[Long](8192) + for (i <- 0 until 8192) { + vector += i + } + assert(vector.size === 8192) + assert(vector.capacity === 8192) + val actualSize = SizeEstimator.estimate(vector) + val expectedSize = 8192 * 8 + // Make sure we are not allocating a significant amount of memory beyond our expected. + // Due to specialization wonkiness, we need to ensure we don't have 2 copies of the array. + assert(actualSize < expectedSize * 1.1) + } + + test("resizing") { + val vector = new PrimitiveVector[Long] + for (i <- 0 until 4097) { + vector += i + } + assert(vector.size === 4097) + assert(vector.capacity === 8192) + vector.trim() + assert(vector.size === 4097) + assert(vector.capacity === 4097) + vector.resize(5000) + assert(vector.size === 4097) + assert(vector.capacity === 5000) + vector.resize(4000) + assert(vector.size === 4000) + assert(vector.capacity === 4000) + vector.resize(5000) + assert(vector.size === 4000) + assert(vector.capacity === 5000) + for (i <- 0 until 4000) { + assert(vector(i) == i) + } + intercept[IllegalArgumentException] { + vector(4000) + } + } +} diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 0000000000..bf59e77d11 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,5 @@ +Spark docker files +=========== + +Drawn from Matt Massie's docker files (https://github.com/massie/dockerfiles), +as well as some updates from Andre Schumacher (https://github.com/AndreSchumacher/docker).
\ No newline at end of file diff --git a/docker/build b/docker/build new file mode 100755 index 0000000000..253a2fc8dd --- /dev/null +++ b/docker/build @@ -0,0 +1,22 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +docker images > /dev/null || { echo Please install docker in non-sudo mode. ; exit; } + +./spark-test/build
\ No newline at end of file diff --git a/docker/spark-test/README.md b/docker/spark-test/README.md new file mode 100644 index 0000000000..ec0baf6e6d --- /dev/null +++ b/docker/spark-test/README.md @@ -0,0 +1,11 @@ +Spark Docker files usable for testing and development purposes. + +These images are intended to be run like so: + + docker run -v $SPARK_HOME:/opt/spark spark-test-master + docker run -v $SPARK_HOME:/opt/spark spark-test-worker spark://<master_ip>:7077 + +Using this configuration, the containers will have their Spark directories +mounted to your actual `SPARK_HOME`, allowing you to modify and recompile +your Spark source and have them immediately usable in the docker images +(without rebuilding them). diff --git a/docker/spark-test/base/Dockerfile b/docker/spark-test/base/Dockerfile new file mode 100644 index 0000000000..60962776dd --- /dev/null +++ b/docker/spark-test/base/Dockerfile @@ -0,0 +1,38 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +FROM ubuntu:precise + +RUN echo "deb http://archive.ubuntu.com/ubuntu precise main universe" > /etc/apt/sources.list + +# Upgrade package index +RUN apt-get update + +# install a few other useful packages plus Open Jdk 7 +RUN apt-get install -y less openjdk-7-jre-headless net-tools vim-tiny sudo openssh-server + +ENV SCALA_VERSION 2.9.3 +ENV SPARK_VERSION 0.8.1 +ENV CDH_VERSION cdh4 +ENV SCALA_HOME /opt/scala-$SCALA_VERSION +ENV SPARK_HOME /opt/spark +ENV PATH $SPARK_HOME:$SCALA_HOME/bin:$PATH + +# Install Scala +ADD http://www.scala-lang.org/files/archive/scala-$SCALA_VERSION.tgz / +RUN (cd / && gunzip < scala-$SCALA_VERSION.tgz)|(cd /opt && tar -xvf -) +RUN rm /scala-$SCALA_VERSION.tgz diff --git a/docker/spark-test/build b/docker/spark-test/build new file mode 100755 index 0000000000..6f9e197433 --- /dev/null +++ b/docker/spark-test/build @@ -0,0 +1,22 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +docker build -t spark-test-base spark-test/base/ +docker build -t spark-test-master spark-test/master/ +docker build -t spark-test-worker spark-test/worker/ diff --git a/docker/spark-test/master/Dockerfile b/docker/spark-test/master/Dockerfile new file mode 100644 index 0000000000..f729534ab6 --- /dev/null +++ b/docker/spark-test/master/Dockerfile @@ -0,0 +1,21 @@ +# Spark Master +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +FROM spark-test-base +ADD default_cmd /root/ +CMD ["/root/default_cmd"] diff --git a/docker/spark-test/master/default_cmd b/docker/spark-test/master/default_cmd new file mode 100755 index 0000000000..a5b1303c2e --- /dev/null +++ b/docker/spark-test/master/default_cmd @@ -0,0 +1,22 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +IP=$(ip -o -4 addr list eth0 | perl -n -e 'if (m{inet\s([\d\.]+)\/\d+\s}xms) { print $1 }') +echo "CONTAINER_IP=$IP" +/opt/spark/spark-class org.apache.spark.deploy.master.Master -i $IP diff --git a/docker/spark-test/worker/Dockerfile b/docker/spark-test/worker/Dockerfile new file mode 100644 index 0000000000..890febe7b6 --- /dev/null +++ b/docker/spark-test/worker/Dockerfile @@ -0,0 +1,22 @@ +# Spark Worker +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +FROM spark-test-base +ENV SPARK_WORKER_PORT 8888 +ADD default_cmd /root/ +ENTRYPOINT ["/root/default_cmd"] diff --git a/docker/spark-test/worker/default_cmd b/docker/spark-test/worker/default_cmd new file mode 100755 index 0000000000..ab6336f70c --- /dev/null +++ b/docker/spark-test/worker/default_cmd @@ -0,0 +1,22 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +IP=$(ip -o -4 addr list eth0 | perl -n -e 'if (m{inet\s([\d\.]+)\/\d+\s}xms) { print $1 }') +echo "CONTAINER_IP=$IP" +/opt/spark/spark-class org.apache.spark.deploy.worker.Worker $1 diff --git a/docs/_config.yml b/docs/_config.yml index 48ecb8d0c9..11d18f0ac2 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -4,7 +4,7 @@ markdown: kramdown # These allow the documentation to be updated with nerw releases # of Spark, Scala, and Mesos. SPARK_VERSION: 0.9.0-incubating-SNAPSHOT -SPARK_VERSION_SHORT: 0.9.0-SNAPSHOT -SCALA_VERSION: 2.9.3 +SPARK_VERSION_SHORT: 0.9.0 +SCALA_VERSION: 2.10 MESOS_VERSION: 0.13.0 SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index 238ad26de0..ad7969d012 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -6,7 +6,7 @@ <head> <meta charset="utf-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> - <title>{{ page.title }} - Spark {{site.SPARK_VERSION}} Documentation</title> + <title>{{ page.title }} - Spark {{site.SPARK_VERSION_SHORT}} Documentation</title> <meta name="description" content=""> <link rel="stylesheet" href="css/bootstrap.min.css"> @@ -74,12 +74,12 @@ <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a> <ul class="dropdown-menu"> - <li><a href="api/core/index.html">Spark Core for Java/Scala</a></li> + <li><a href="api/core/index.html#org.apache.spark.package">Spark Core for Java/Scala</a></li> <li><a href="api/pyspark/index.html">Spark Core for Python</a></li> <li class="divider"></li> - <li><a href="api/streaming/index.html">Spark Streaming</a></li> - <li><a href="api/mllib/index.html">MLlib (Machine Learning)</a></li> - <li><a href="api/bagel/index.html">Bagel (Pregel on Spark)</a></li> + <li><a href="api/streaming/index.html#org.apache.spark.streaming.package">Spark Streaming</a></li> + <li><a href="api/mllib/index.html#org.apache.spark.mllib.package">MLlib (Machine Learning)</a></li> + <li><a href="api/bagel/index.html#org.apache.spark.bagel.package">Bagel (Pregel on Spark)</a></li> </ul> </li> @@ -109,7 +109,7 @@ </ul> </li> </ul> - <!--<p class="navbar-text pull-right"><span class="version-text">v{{site.SPARK_VERSION}}</span></p>--> + <!--<p class="navbar-text pull-right"><span class="version-text">v{{site.SPARK_VERSION_SHORT}}</span></p>--> </div> </div> </div> diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb index c574ea7f5c..431de909cb 100644 --- a/docs/_plugins/copy_api_dirs.rb +++ b/docs/_plugins/copy_api_dirs.rb @@ -35,7 +35,7 @@ if not (ENV['SKIP_API'] == '1' or ENV['SKIP_SCALADOC'] == '1') # Copy over the scaladoc from each project into the docs directory. # This directory will be copied over to _site when `jekyll` command is run. projects.each do |project_name| - source = "../" + project_name + "/target/scala-2.9.3/api" + source = "../" + project_name + "/target/scala-2.10/api" dest = "api/" + project_name puts "echo making directory " + dest diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md index 140190a38c..de001e6c52 100644 --- a/docs/bagel-programming-guide.md +++ b/docs/bagel-programming-guide.md @@ -106,7 +106,7 @@ _Example_ ## Operations -Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/apache/incubator-spark/blob/master/bagel/src/main/scala/spark/bagel/Bagel.scala) for details. +Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/apache/incubator-spark/blob/master/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala) for details. ### Actions diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md index 19c01e179f..c709001632 100644 --- a/docs/building-with-maven.md +++ b/docs/building-with-maven.md @@ -45,6 +45,12 @@ For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with # Cloudera CDH 4.2.0 with MapReduce v2 $ mvn -Phadoop2-yarn -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-chd4.2.0 -DskipTests clean package +Hadoop versions 2.2.x and newer can be built by setting the ```new-yarn``` and the ```yarn.version``` as follows: + + # Apache Hadoop 2.2.X and newer + $ mvn -Dyarn.version=2.2.0 -Dhadoop.version=2.2.0 -Pnew-yarn + +The build process handles Hadoop 2.2.x as a special case that uses the directory ```new-yarn```, which supports the new YARN API. Furthermore, for this version, the build depends on artifacts published by the spark-project to enable Akka 2.0.5 to work with protobuf 2.5. ## Spark Tests in Maven ## diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index f679cad713..e16703292c 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -13,7 +13,7 @@ object in your main program (called the _driver program_). Specifically, to run on a cluster, the SparkContext can connect to several types of _cluster managers_ (either Spark's own standalone cluster manager or Mesos/YARN), which allocate resources across applications. Once connected, Spark acquires *executors* on nodes in the cluster, which are -worker processes that run computations and store data for your application. +worker processes that run computations and store data for your application. Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to the executors. Finally, SparkContext sends *tasks* for the executors to run. @@ -45,7 +45,7 @@ The system currently supports three cluster managers: easy to set up a cluster. * [Apache Mesos](running-on-mesos.html) -- a general cluster manager that can also run Hadoop MapReduce and service applications. -* [Hadoop YARN](running-on-yarn.html) -- the resource manager in Hadoop 2.0. +* [Hadoop YARN](running-on-yarn.html) -- the resource manager in Hadoop 2. In addition, Spark's [EC2 launch scripts](ec2-scripts.html) make it easy to launch a standalone cluster on Amazon EC2. @@ -57,6 +57,18 @@ which takes a list of JAR files (Java/Scala) or .egg and .zip libraries (Python) worker nodes. You can also dynamically add new files to be sent to executors with `SparkContext.addJar` and `addFile`. +## URIs for addJar / addFile + +- **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and every executor + pulls the file from the driver HTTP server +- **hdfs:**, **http:**, **https:**, **ftp:** - these pull down files and JARs from the URI as expected +- **local:** - a URI starting with local:/ is expected to exist as a local file on each worker node. This + means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker, + or shared via NFS, GlusterFS, etc. + +Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes. +Over time this can use up a significant amount of space and will need to be cleaned up. + # Monitoring Each driver program has a web UI, typically on port 4040, that displays information about running diff --git a/docs/configuration.md b/docs/configuration.md index 7940d41a27..567aba07f0 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -3,26 +3,37 @@ layout: global title: Spark Configuration --- -Spark provides three main locations to configure the system: +Spark provides three locations to configure the system: -* [Java system properties](#system-properties), which control internal configuration parameters and can be set - either programmatically (by calling `System.setProperty` *before* creating a `SparkContext`) or through - JVM arguments. -* [Environment variables](#environment-variables) for configuring per-machine settings such as the IP address, - which can be set in the `conf/spark-env.sh` script. -* [Logging configuration](#configuring-logging), which is done through `log4j.properties`. +* [Spark properties](#spark-properties) control most application parameters and can be set by passing + a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java + system properties. +* [Environment variables](#environment-variables) can be used to set per-machine settings, such as + the IP address, through the `conf/spark-env.sh` script on each node. +* [Logging](#configuring-logging) can be configured through `log4j.properties`. -# System Properties +# Spark Properties -To set a system property for configuring Spark, you need to either pass it with a -D flag to the JVM (for example `java -Dspark.cores.max=5 MyProgram`) or call `System.setProperty` in your code *before* creating your Spark context, as follows: +Spark properties control most application settings and are configured separately for each application. +The preferred way to set them is by passing a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) +class to your SparkContext constructor. +Alternatively, Spark will also load them from Java system properties (for compatibility with old versions +of Spark) and from a [`spark.conf` file](#configuration-files) on your classpath. + +SparkConf lets you configure most of the common properties to initialize a cluster (e.g., master URL and +application name), as well as arbitrary key-value pairs through the `set()` method. For example, we could +initialize an application as follows: {% highlight scala %} -System.setProperty("spark.cores.max", "5") -val sc = new SparkContext(...) +val conf = new SparkConf() + .setMaster("local") + .setAppName("My application") + .set("spark.executor.memory", "1g") +val sc = new SparkContext(conf) {% endhighlight %} -Most of the configurable system properties control internal settings that have reasonable default values. However, +Most of the properties control internal settings that have reasonable default values. However, there are at least five properties that you will commonly want to control: <table class="table"> @@ -149,7 +160,7 @@ Apart from these, the following properties are also available, and may be useful <td>spark.io.compression.codec</td> <td>org.apache.spark.io.<br />LZFCompressionCodec</td> <td> - The compression codec class to use for various compressions. By default, Spark provides two + The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides two codecs: <code>org.apache.spark.io.LZFCompressionCodec</code> and <code>org.apache.spark.io.SnappyCompressionCodec</code>. </td> </tr> @@ -275,12 +286,33 @@ Apart from these, the following properties are also available, and may be useful </tr> <tr> <td>spark.akka.timeout</td> - <td>20</td> + <td>100</td> <td> Communication timeout between Spark nodes, in seconds. </td> </tr> <tr> + <td>spark.akka.heartbeat.pauses</td> + <td>600</td> + <td> + This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` if you need to. + </td> +</tr> +<tr> + <td>spark.akka.failure-detector.threshold</td> + <td>300.0</td> + <td> + This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). This maps to akka's `akka.remote.transport-failure-detector.threshold`. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to. + </td> +</tr> +<tr> + <td>spark.akka.heartbeat.interval</td> + <td>1000</td> + <td> + This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure detector can be, a sensistive failure detector can help evict rogue executors really quick. However this is usually not the case as gc pauses and network lags are expected in a real spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes leading to flooding the network with those. + </td> +</tr> +<tr> <td>spark.driver.host</td> <td>(local hostname)</td> <td> @@ -319,14 +351,85 @@ Apart from these, the following properties are also available, and may be useful Should be greater than or equal to 1. Number of allowed retries = this value - 1. </td> </tr> - +<tr> + <td>spark.broadcast.blockSize</td> + <td>4096</td> + <td> + Size of each piece of a block in kilobytes for <code>TorrentBroadcastFactory</code>. + Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, <code>BlockManager</code> might take a performance hit. + </td> +</tr> +<tr> + <td>spark.shuffle.consolidateFiles</td> + <td>false</td> + <td> + If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations. + </td> +</tr> +<tr> + <td>spark.speculation</td> + <td>false</td> + <td> + If set to "true", performs speculative execution of tasks. This means if one or more tasks are running slowly in a stage, they will be re-launched. + </td> +</tr> +<tr> + <td>spark.speculation.interval</td> + <td>100</td> + <td> + How often Spark will check for tasks to speculate, in milliseconds. + </td> +</tr> +<tr> + <td>spark.speculation.quantile</td> + <td>0.75</td> + <td> + Percentage of tasks which must be complete before speculation is enabled for a particular stage. + </td> +</tr> +<tr> + <td>spark.speculation.multiplier</td> + <td>1.5</td> + <td> + How many times slower a task is than the median to be considered for speculation. + </td> +</tr> </table> +## Viewing Spark Properties + +The application web UI at `http://<driver>:4040` lists Spark properties in the "Environment" tab. +This is a useful place to check to make sure that your properties have been set correctly. + +## Configuration Files + +You can also configure Spark properties through a `spark.conf` file on your Java classpath. +Because these properties are usually application-specific, we recommend putting this fine *only* on your +application's classpath, and not in a global Spark classpath. + +The `spark.conf` file uses Typesafe Config's [HOCON format](https://github.com/typesafehub/config#json-superset), +which is a superset of Java properties files and JSON. For example, the following is a simple config file: + +{% highlight awk %} +# Comments are allowed +spark.executor.memory = 512m +spark.serializer = org.apache.spark.serializer.KryoSerializer +{% endhighlight %} + +The format also allows hierarchical nesting, as follows: + +{% highlight awk %} +spark.akka { + threads = 8 + timeout = 200 +} +{% endhighlight %} + # Environment Variables -Certain Spark settings can also be configured through environment variables, which are read from the `conf/spark-env.sh` +Certain Spark settings can be configured through environment variables, which are read from the `conf/spark-env.sh` script in the directory where Spark is installed (or `conf/spark-env.cmd` on Windows). These variables are meant to be for machine-specific settings, such -as library search paths. While Java system properties can also be set here, for application settings, we recommend setting +as library search paths. While Spark properties can also be set there through `SPARK_JAVA_OPTS`, for per-application settings, we recommend setting these properties within the application instead of in `spark-env.sh` so that different applications can use different settings. @@ -343,7 +446,8 @@ The following variables can be set in `spark-env.sh`: Note that applications can also add dependencies for themselves through `SparkContext.addJar` -- we recommend doing that when possible. * `SPARK_JAVA_OPTS`, to add JVM options. This includes Java options like garbage collector settings and any system - properties that you'd like to pass with `-D` (e.g., `-Dspark.local.dir=/disk1,/disk2`). + properties that you'd like to pass with `-D`. One use case is to set some Spark properties differently on this + machine, e.g., `-Dspark.local.dir=/disk1,/disk2`. * Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each machine and maximum memory. diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css index ccb529eaba..119bb5dba7 100644 --- a/docs/css/bootstrap.min.css +++ b/docs/css/bootstrap.min.css @@ -6,4 +6,4 @@ * http://www.apache.org/licenses/LICENSE-2.0 * * Designed and built with all the love in the world @twitter by @mdo and @fat. - */article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto 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index 1e5575d657..156a727026 100644 --- a/docs/ec2-scripts.md +++ b/docs/ec2-scripts.md @@ -98,7 +98,7 @@ permissions on your private key file, you can run `launch` with the `bin/hadoop` script in that directory. Note that the data in this HDFS goes away when you stop and restart a machine. - There is also a *persistent HDFS* instance in - `/root/presistent-hdfs` that will keep data across cluster restarts. + `/root/persistent-hdfs` that will keep data across cluster restarts. Typically each node has relatively little space of persistent data (about 3 GB), but you can use the `--ebs-vol-size` option to `spark-ec2` to attach a persistent EBS volume to each node for diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md index f706625fe9..de6a2b0a43 100644 --- a/docs/hadoop-third-party-distributions.md +++ b/docs/hadoop-third-party-distributions.md @@ -10,7 +10,7 @@ with these distributions: # Compile-time Hadoop Version When compiling Spark, you'll need to -[set the SPARK_HADOOP_VERSION flag](http://localhost:4000/index.html#a-note-about-hadoop-versions): +[set the SPARK_HADOOP_VERSION flag](index.html#a-note-about-hadoop-versions): SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly @@ -25,8 +25,8 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors. <h3>CDH Releases</h3> <table class="table" style="width:350px; margin-right: 20px;"> <tr><th>Release</th><th>Version code</th></tr> - <tr><td>CDH 4.X.X (YARN mode)</td><td>2.0.0-chd4.X.X</td></tr> - <tr><td>CDH 4.X.X</td><td>2.0.0-mr1-chd4.X.X</td></tr> + <tr><td>CDH 4.X.X (YARN mode)</td><td>2.0.0-cdh4.X.X</td></tr> + <tr><td>CDH 4.X.X</td><td>2.0.0-mr1-cdh4.X.X</td></tr> <tr><td>CDH 3u6</td><td>0.20.2-cdh3u6</td></tr> <tr><td>CDH 3u5</td><td>0.20.2-cdh3u5</td></tr> <tr><td>CDH 3u4</td><td>0.20.2-cdh3u4</td></tr> @@ -40,6 +40,7 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors. <tr><td>HDP 1.2</td><td>1.1.2</td></tr> <tr><td>HDP 1.1</td><td>1.0.3</td></tr> <tr><td>HDP 1.0</td><td>1.0.3</td></tr> + <tr><td>HDP 2.0</td><td>2.2.0</td></tr> </table> </td> </tr> diff --git a/docs/index.md b/docs/index.md index bd386a8a8f..d3ac696d1e 100644 --- a/docs/index.md +++ b/docs/index.md @@ -56,14 +56,16 @@ Hadoop, you must build Spark against the same version that your cluster uses. By default, Spark links to Hadoop 1.0.4. You can change this by setting the `SPARK_HADOOP_VERSION` variable when compiling: - SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly + SPARK_HADOOP_VERSION=2.2.0 sbt/sbt assembly -In addition, if you wish to run Spark on [YARN](running-on-yarn.md), set +In addition, if you wish to run Spark on [YARN](running-on-yarn.html), set `SPARK_YARN` to `true`: SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly -(Note that on Windows, you need to set the environment variables on separate lines, e.g., `set SPARK_HADOOP_VERSION=1.2.1`.) +Note that on Windows, you need to set the environment variables on separate lines, e.g., `set SPARK_HADOOP_VERSION=1.2.1`. + +For this version of Spark (0.8.1) Hadoop 2.2.x (or newer) users will have to build Spark and publish it locally. See [Launching Spark on YARN](running-on-yarn.html). This is needed because Hadoop 2.2 has non backwards compatible API changes. # Where to Go from Here diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md index d304c5497b..5951155fe3 100644 --- a/docs/job-scheduling.md +++ b/docs/job-scheduling.md @@ -32,12 +32,12 @@ Resource allocation can be configured as follows, based on the cluster type: * **Standalone mode:** By default, applications submitted to the standalone mode cluster will run in FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit - the number of nodes an application uses by setting the `spark.cores.max` system property in it. This + the number of nodes an application uses by setting the `spark.cores.max` configuration property in it. This will allow multiple users/applications to run concurrently. For example, you might launch a long-running server that uses 10 cores, and allow users to launch shells that use 20 cores each. Finally, in addition to controlling cores, each application's `spark.executor.memory` setting controls its memory use. -* **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` system property to `true`, +* **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`, and optionally set `spark.cores.max` to limit each application's resource share as in the standalone mode. You should also set `spark.executor.memory` to control the executor memory. * **YARN:** The `--num-workers` option to the Spark YARN client controls how many workers it will allocate @@ -78,11 +78,13 @@ of cluster resources. This means that short jobs submitted while a long job is r resources right away and still get good response times, without waiting for the long job to finish. This mode is best for multi-user settings. -To enable the fair scheduler, simply set the `spark.scheduler.mode` to `FAIR` before creating +To enable the fair scheduler, simply set the `spark.scheduler.mode` property to `FAIR` when configuring a SparkContext: {% highlight scala %} -System.setProperty("spark.scheduler.mode", "FAIR") +val conf = new SparkConf().setMaster(...).setAppName(...) +conf.set("spark.scheduler.mode", "FAIR") +val sc = new SparkContext(conf) {% endhighlight %} ## Fair Scheduler Pools @@ -91,15 +93,15 @@ The fair scheduler also supports grouping jobs into _pools_, and setting differe (e.g. weight) for each pool. This can be useful to create a "high-priority" pool for more important jobs, for example, or to group the jobs of each user together and give _users_ equal shares regardless of how many concurrent jobs they have instead of giving _jobs_ equal shares. This approach is modeled after the -[Hadoop Fair Scheduler](http://hadoop.apache.org/docs/stable/fair_scheduler.html). +[Hadoop Fair Scheduler](http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/FairScheduler.html). Without any intervention, newly submitted jobs go into a _default pool_, but jobs' pools can be set by adding the `spark.scheduler.pool` "local property" to the SparkContext in the thread that's submitting them. This is done as follows: {% highlight scala %} -// Assuming context is your SparkContext variable -context.setLocalProperty("spark.scheduler.pool", "pool1") +// Assuming sc is your SparkContext variable +sc.setLocalProperty("spark.scheduler.pool", "pool1") {% endhighlight %} After setting this local property, _all_ jobs submitted within this thread (by calls in this thread @@ -108,7 +110,7 @@ it easy to have a thread run multiple jobs on behalf of the same user. If you'd pool that a thread is associated with, simply call: {% highlight scala %} -context.setLocalProperty("spark.scheduler.pool", null) +sc.setLocalProperty("spark.scheduler.pool", null) {% endhighlight %} ## Default Behavior of Pools @@ -138,10 +140,11 @@ properties: of the cluster. By default, each pool's `minShare` is 0. The pool properties can be set by creating an XML file, similar to `conf/fairscheduler.xml.template`, -and setting the `spark.scheduler.allocation.file` property: +and setting a `spark.scheduler.allocation.file` property in your +[SparkConf](configuration.html#spark-properties). {% highlight scala %} -System.setProperty("spark.scheduler.allocation.file", "/path/to/file") +conf.set("spark.scheduler.allocation.file", "/path/to/file") {% endhighlight %} The format of the XML file is simply a `<pool>` element for each pool, with different elements diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index f991d86c8d..c1ff9c417c 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -144,10 +144,9 @@ Available algorithms for clustering: # Collaborative Filtering -[Collaborative -filtering](http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering) +[Collaborative filtering](http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering) is commonly used for recommender systems. These techniques aim to fill in the -missing entries of a user-product association matrix. MLlib currently supports +missing entries of a user-item association matrix. MLlib currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. In particular, we implement the [alternating least squares @@ -158,7 +157,24 @@ following parameters: * *numBlocks* is the number of blacks used to parallelize computation (set to -1 to auto-configure). * *rank* is the number of latent factors in our model. * *iterations* is the number of iterations to run. -* *lambda* specifies the regularization parameter in ALS. +* *lambda* specifies the regularization parameter in ALS. +* *implicitPrefs* specifies whether to use the *explicit feedback* ALS variant or one adapted for *implicit feedback* data +* *alpha* is a parameter applicable to the implicit feedback variant of ALS that governs the *baseline* confidence in preference observations + +## Explicit vs Implicit Feedback + +The standard approach to matrix factorization based collaborative filtering treats +the entries in the user-item matrix as *explicit* preferences given by the user to the item. + +It is common in many real-world use cases to only have access to *implicit feedback* +(e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with +such data is taken from +[Collaborative Filtering for Implicit Feedback Datasets](http://research.yahoo.com/pub/2433). +Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as +a combination of binary preferences and *confidence values*. The ratings are then related +to the level of confidence in observed user preferences, rather than explicit ratings given to items. +The model then tries to find latent factors that can be used to predict the expected preference of a user +for an item. Available algorithms for collaborative filtering: diff --git a/docs/monitoring.md b/docs/monitoring.md index 5f456b999b..0d5eb7065e 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -32,7 +32,8 @@ Spark has a configurable metrics system based on the This allows users to report Spark metrics to a variety of sinks including HTTP, JMX, and CSV files. The metrics system is configured via a configuration file that Spark expects to be present at `$SPARK_HOME/conf/metrics.conf`. A custom file location can be specified via the -`spark.metrics.conf` Java system property. Spark's metrics are decoupled into different +`spark.metrics.conf` [configuration property](configuration.html#spark-properties). +Spark's metrics are decoupled into different _instances_ corresponding to Spark components. Within each instance, you can configure a set of sinks to which metrics are reported. The following instances are currently supported: @@ -50,6 +51,7 @@ Each instance can report to zero or more _sinks_. Sinks are contained in the * `GangliaSink`: Sends metrics to a Ganglia node or multicast group. * `JmxSink`: Registers metrics for viewing in a JXM console. * `MetricsServlet`: Adds a servlet within the existing Spark UI to serve metrics data as JSON data. +* `GraphiteSink`: Sends metrics to a Graphite node. The syntax of the metrics configuration file is defined in an example configuration file, `$SPARK_HOME/conf/metrics.conf.template`. diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index f67a1cc49c..96f93e24fe 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -16,7 +16,7 @@ This guide will show how to use the Spark features described there in Python. There are a few key differences between the Python and Scala APIs: * Python is dynamically typed, so RDDs can hold objects of multiple types. -* PySpark does not yet support a few API calls, such as `lookup`, `sort`, and non-text input files, though these will be added in future releases. +* PySpark does not yet support a few API calls, such as `lookup` and non-text input files, though these will be added in future releases. In PySpark, RDDs support the same methods as their Scala counterparts but take Python functions and return Python collection types. Short functions can be passed to RDD methods using Python's [`lambda`](http://www.diveintopython.net/power_of_introspection/lambda_functions.html) syntax: @@ -131,6 +131,18 @@ sc = SparkContext("local", "App Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg Files listed here will be added to the `PYTHONPATH` and shipped to remote worker machines. Code dependencies can be added to an existing SparkContext using its `addPyFile()` method. +You can set [configuration properties](configuration.html#spark-properties) by passing a +[SparkConf](api/pyspark/pyspark.conf.SparkConf-class.html) object to SparkContext: + +{% highlight python %} +from pyspark import SparkConf, SparkContext +conf = (SparkConf() + .setMaster("local") + .setAppName("My app") + .set("spark.executor.memory", "1g")) +sc = SparkContext(conf = conf) +{% endhighlight %} + # API Docs [API documentation](api/pyspark/index.html) for PySpark is available as Epydoc. diff --git a/docs/quick-start.md b/docs/quick-start.md index 8f782db5b8..bdf127d404 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -124,7 +124,7 @@ object SimpleApp { } {% endhighlight %} -This program simply counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the proogram. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. +This program just counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the proogram. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds a repository that Spark depends on: @@ -193,7 +193,7 @@ public class SimpleApp { } {% endhighlight %} -This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail. +This program just counts the number of lines containing 'a' and the number containing 'b' in a text file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail. To build the program, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version. @@ -224,11 +224,11 @@ To build the program, we also write a Maven `pom.xml` file that lists Spark as a If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS: {% highlight xml %} - <dependency> - <groupId>org.apache.hadoop</groupId> - <artifactId>hadoop-client</artifactId> - <version>...</version> - </dependency> +<dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <version>...</version> +</dependency> {% endhighlight %} We lay out these files according to the canonical Maven directory structure: @@ -270,7 +270,7 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs) {% endhighlight %} -This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. +This program just counts the number of lines containing 'a' and the number containing 'b' in a text file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala and Java examples, we use a SparkContext to create RDDs. We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference. @@ -301,14 +301,38 @@ assembly jar (or "uber" jar) containing your code and its dependencies. Both have assembly plugins. When creating assembly jars, list Spark itself as a `provided` dependency; it need not be bundled since it is already present on the slaves. Once you have an assembled jar, -add it to the SparkContext as shown here. It is also possible to submit -your dependent jars one-by-one when creating a SparkContext. +add it to the SparkContext as shown here. It is also possible to add +your dependent jars one-by-one using the `addJar` method of `SparkContext`. + +For Python, you can use the `pyFiles` argument of SparkContext +or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed. ### Setting Configuration Options -Spark includes several configuration options which influence the behavior -of your application. These should be set as -[JVM system properties](configuration.html#system-properties) in your -program. The options will be captured and shipped to all slave nodes. +Spark includes several [configuration options](configuration.html#spark-properties) +that influence the behavior of your application. +These should be set by building a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) +object and passing it to the SparkContext constructor. +For example, in Java and Scala, you can do: + +{% highlight scala %} +import org.apache.spark.{SparkConf, SparkContext} +val conf = new SparkConf() + .setMaster("local") + .setAppName("My application") + .set("spark.executor.memory", "1g") +val sc = new SparkContext(conf) +{% endhighlight %} + +Or in Python: + +{% highlight scala %} +from pyspark import SparkConf, SparkContext +conf = SparkConf() +conf.setMaster("local") +conf.setAppName("My application") +conf.set("spark.executor.memory", "1g")) +sc = SparkContext(conf = conf) +{% endhighlight %} ### Accessing Hadoop Filesystems diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 322ff585f1..68259f0cb8 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -15,15 +15,16 @@ Spark can run on clusters managed by [Apache Mesos](http://mesos.apache.org/). F * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is usually `<prefix>/lib/libmesos.so` (where the prefix is `/usr/local` by default, see above). Also, on Mac OS X, the library is called `libmesos.dylib` instead of `libmesos.so`. * `export SPARK_EXECUTOR_URI=<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`. * `export MASTER=mesos://HOST:PORT` where HOST:PORT is the host and port (default: 5050) of your Mesos master (or `zk://...` if using Mesos with ZooKeeper). -8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the first parameter. In addition, you'll need to set the `spark.executor.uri` property. For example: +8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the master URL. In addition, you'll need to set the `spark.executor.uri` property. For example: {% highlight scala %} -System.setProperty("spark.executor.uri", "<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>") -val sc = new SparkContext("mesos://HOST:5050", "App Name", ...) +val conf = new SparkConf() + .setMaster("mesos://HOST:5050") + .setAppName("My app") + .set("spark.executor.uri", "<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>") +val sc = new SparkContext(conf) {% endhighlight %} -If you want to run Spark on Amazon EC2, you can use the Spark [EC2 launch scripts](ec2-scripts.html), which provide an easy way to launch a cluster with Mesos, Spark, and HDFS pre-configured. This will get you a cluster in about five minutes without any configuration on your part. - # Mesos Run Modes Spark can run over Mesos in two modes: "fine-grained" and "coarse-grained". In fine-grained mode, which is the default, @@ -34,17 +35,15 @@ launch only *one* long-running Spark task on each Mesos machine, and dynamically it. The benefit is much lower startup overhead, but at the cost of reserving the Mesos resources for the complete duration of the application. -To run in coarse-grained mode, set the `spark.mesos.coarse` system property to true *before* creating your SparkContext: +To run in coarse-grained mode, set the `spark.mesos.coarse` property in your [SparkConf](configuration.html#spark-properties): {% highlight scala %} -System.setProperty("spark.mesos.coarse", "true") -val sc = new SparkContext("mesos://HOST:5050", "App Name", ...) +conf.set("spark.mesos.coarse", "true") {% endhighlight %} In addition, for coarse-grained mode, you can control the maximum number of resources Spark will acquire. By default, it will acquire *all* cores in the cluster (that get offered by Mesos), which only makes sense if you run just one -application at a time. You can cap the maximum number of cores using `System.setProperty("spark.cores.max", "10")` (for example). -Again, this must be done *before* initializing a SparkContext. +application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example). # Running Alongside Hadoop diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 767eb5cdac..00cad99881 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -17,10 +17,12 @@ This can be built by setting the Hadoop version and `SPARK_YARN` environment var The assembled JAR will be something like this: `./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`. +The build process now also supports new YARN versions (2.2.x). See below. # Preparations - Building a YARN-enabled assembly (see above). +- The assembled jar can be installed into HDFS or used locally. - Your application code must be packaged into a separate JAR file. If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different. @@ -30,16 +32,26 @@ If you want to test out the YARN deployment mode, you can use the current Spark Most of the configs are the same for Spark on YARN as other deploys. See the Configuration page for more information on those. These are configs that are specific to SPARK on YARN. Environment variables: + * `SPARK_YARN_USER_ENV`, to add environment variables to the Spark processes launched on YARN. This can be a comma separated list of environment variables, e.g. `SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar"`. System Properties: -* 'spark.yarn.applicationMaster.waitTries', property to set the number of times the ApplicationMaster waits for the the spark master and then also the number of tries it waits for the Spark Context to be intialized. Default is 10. + +* `spark.yarn.applicationMaster.waitTries`, property to set the number of times the ApplicationMaster waits for the the spark master and then also the number of tries it waits for the Spark Context to be intialized. Default is 10. +* `spark.yarn.submit.file.replication`, the HDFS replication level for the files uploaded into HDFS for the application. These include things like the spark jar, the app jar, and any distributed cache files/archives. +* `spark.yarn.preserve.staging.files`, set to true to preserve the staged files(spark jar, app jar, distributed cache files) at the end of the job rather then delete them. +* `spark.yarn.scheduler.heartbeat.interval-ms`, the interval in ms in which the Spark application master heartbeats into the YARN ResourceManager. Default is 5 seconds. +* `spark.yarn.max.worker.failures`, the maximum number of worker failures before failing the application. Default is the number of workers requested times 2 with minimum of 3. # Launching Spark on YARN Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the hadoop cluster. This would be used to connect to the cluster, write to the dfs and submit jobs to the resource manager. +There are two scheduler mode that can be used to launch spark application on YARN. + +## Launch spark application by YARN Client with yarn-standalone mode. + The command to launch the YARN Client is as follows: SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./sbin/spark-class org.apache.spark.deploy.yarn.Client \ @@ -47,10 +59,15 @@ The command to launch the YARN Client is as follows: --class <APP_MAIN_CLASS> \ --args <APP_MAIN_ARGUMENTS> \ --num-workers <NUMBER_OF_WORKER_MACHINES> \ + --master-class <ApplicationMaster_CLASS> --master-memory <MEMORY_FOR_MASTER> \ --worker-memory <MEMORY_PER_WORKER> \ --worker-cores <CORES_PER_WORKER> \ - --queue <queue_name> + --name <application_name> \ + --queue <queue_name> \ + --addJars <any_local_files_used_in_SparkContext.addJar> \ + --files <files_for_distributed_cache> \ + --archives <archives_for_distributed_cache> For example: @@ -76,10 +93,37 @@ For example: $ cat $YARN_APP_LOGS_DIR/$YARN_APP_ID/container*_000001/stdout Pi is roughly 3.13794 -The above starts a YARN Client programs which periodically polls the Application Master for status updates and displays them in the console. The client will exit once your application has finished running. +The above starts a YARN Client programs which start the default Application Master. Then SparkPi will be run as a child thread of Application Master, YARN Client will periodically polls the Application Master for status updates and displays them in the console. The client will exit once your application has finished running. + +With this mode, your application is actually run on the remote machine where the Application Master is run upon. Thus application that involve local interaction will not work well, e.g. spark-shell. + +## Launch spark application with yarn-client mode. + +With yarn-client mode, the application will be launched locally. Just like running application or spark-shell on Local / Mesos / Standalone mode. The launch method is also the similar with them, just make sure that when you need to specify a master url, use "yarn-client" instead. And you also need to export the env value for SPARK_JAR and SPARK_YARN_APP_JAR + +In order to tune worker core/number/memory etc. You need to export SPARK_WORKER_CORES, SPARK_WORKER_MEMORY, SPARK_WORKER_INSTANCES e.g. by ./conf/spark-env.sh + +For example: + + SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \ + SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ + ./run-example org.apache.spark.examples.SparkPi yarn-client + + + SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \ + SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ + MASTER=yarn-client ./spark-shell + +# Building Spark for Hadoop/YARN 2.2.x + +Hadoop 2.2.x users must build Spark and publish it locally. The SBT build process handles Hadoop 2.2.x as a special case. This version of Hadoop has new YARN API changes and depends on a Protobuf version (2.5) that is not compatible with the Akka version (2.0.5) that Spark uses. Therefore, if the Hadoop version (e.g. set through ```SPARK_HADOOP_VERSION```) starts with 2.2.0 or higher then the build process will depend on Akka artifacts distributed by the Spark project compatible with Protobuf 2.5. Furthermore, the build process then uses the directory ```new-yarn``` (instead of ```yarn```), which supports the new YARN API. The build process should seamlessly work out of the box. + +See [Building Spark with Maven](building-with-maven.html) for instructions on how to build Spark using the Maven process. # Important Notes -- When your application instantiates a Spark context it must use a special "yarn-standalone" master url. This starts the scheduler without forcing it to connect to a cluster. A good way to handle this is to pass "yarn-standalone" as an argument to your program, as shown in the example above. - We do not requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed. - The local directories used for spark will be the local directories configured for YARN (Hadoop Yarn config yarn.nodemanager.local-dirs). If the user specifies spark.local.dir, it will be ignored. +- The --files and --archives options support specifying file names with the # similar to Hadoop. For example you can specify: --files localtest.txt#appSees.txt and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name appSees.txt and your application should use the name as appSees.txt to reference it when running on YARN. +- The --addJars option allows the SparkContext.addJar function to work if you are using it with local files. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files. +- YARN 2.2.x users cannot simply depend on the Spark packages without building Spark, as the published Spark artifacts are compiled to work with the pre 2.2 API. Those users must build Spark and publish it locally. diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index 03647a2ad2..1db255ca53 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -49,6 +49,9 @@ This is done through the following constructor: new SparkContext(master, appName, [sparkHome], [jars]) {% endhighlight %} +or through `new SparkContext(conf)`, which takes a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) +object for more advanced configuration. + The `master` parameter is a string specifying a [Spark or Mesos cluster URL](#master-urls) to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later. In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `spark-shell` on four cores, use @@ -94,7 +97,6 @@ If you want to run your application on a cluster, you will need to specify the t If you run `spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it. This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed. - # Resilient Distributed Datasets (RDDs) Spark revolves around the concept of a _resilient distributed dataset_ (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are currently two types of RDDs: *parallelized collections*, which take an existing Scala collection and run functions on it in parallel, and *Hadoop datasets*, which run functions on each record of a file in Hadoop distributed file system or any other storage system supported by Hadoop. Both types of RDDs can be operated on through the same methods. @@ -142,7 +144,7 @@ All transformations in Spark are <i>lazy</i>, in that they do not compute their By default, each transformed RDD is recomputed each time you run an action on it. However, you may also *persist* an RDD in memory using the `persist` (or `cache`) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting datasets on disk, or replicated across the cluster. The next section in this document describes these options. -The following tables list the transformations and actions currently supported (see also the [RDD API doc](api/core/index.html#org.apache.spark.RDD) for details): +The following tables list the transformations and actions currently supported (see also the [RDD API doc](api/core/index.html#org.apache.spark.rdd.RDD) for details): ### Transformations @@ -211,7 +213,7 @@ The following tables list the transformations and actions currently supported (s </tr> </table> -A complete list of transformations is available in the [RDD API doc](api/core/index.html#org.apache.spark.RDD). +A complete list of transformations is available in the [RDD API doc](api/core/index.html#org.apache.spark.rdd.RDD). ### Actions @@ -259,7 +261,7 @@ A complete list of transformations is available in the [RDD API doc](api/core/in </tr> </table> -A complete list of actions is available in the [RDD API doc](api/core/index.html#org.apache.spark.RDD). +A complete list of actions is available in the [RDD API doc](api/core/index.html#org.apache.spark.rdd.RDD). ## RDD Persistence @@ -363,7 +365,7 @@ res2: Int = 10 # Where to Go from Here -You can see some [example Spark programs](http://www.spark-project.org/examples.html) on the Spark website. +You can see some [example Spark programs](http://spark.incubator.apache.org/examples.html) on the Spark website. In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `run-example` script included in Spark; for example: ./run-example org.apache.spark.examples.SparkPi diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index b3f9160673..c2ce23081a 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -3,6 +3,9 @@ layout: global title: Spark Standalone Mode --- +* This will become a table of contents (this text will be scraped). +{:toc} + In addition to running on the Mesos or YARN cluster managers, Spark also provides a simple standalone deploy mode. You can launch a standalone cluster either manually, by starting a master and workers by hand, or use our provided [launch scripts](#cluster-launch-scripts). It is also possible to run these daemons on a single machine for testing. # Installing Spark Standalone to a Cluster @@ -48,11 +51,11 @@ Finally, the following configuration options can be passed to the master and wor </tr> <tr> <td><code>-c CORES</code>, <code>--cores CORES</code></td> - <td>Total CPU cores to allow Spark applicatons to use on the machine (default: all available); only on worker</td> + <td>Total CPU cores to allow Spark applications to use on the machine (default: all available); only on worker</td> </tr> <tr> <td><code>-m MEM</code>, <code>--memory MEM</code></td> - <td>Total amount of memory to allow Spark applicatons to use on the machine, in a format like 1000M or 2G (default: your machine's total RAM minus 1 GB); only on worker</td> + <td>Total amount of memory to allow Spark applications to use on the machine, in a format like 1000M or 2G (default: your machine's total RAM minus 1 GB); only on worker</td> </tr> <tr> <td><code>-d DIR</code>, <code>--work-dir DIR</code></td> @@ -151,11 +154,18 @@ You can also pass an option `-c <numCores>` to control the number of cores that The standalone cluster mode currently only supports a simple FIFO scheduler across applications. However, to allow multiple concurrent users, you can control the maximum number of resources each -application will acquire. +application will use. By default, it will acquire *all* cores in the cluster, which only makes sense if you just run one -application at a time. You can cap the number of cores using -`System.setProperty("spark.cores.max", "10")` (for example). -This value must be set *before* initializing your SparkContext. +application at a time. You can cap the number of cores by setting `spark.cores.max` in your +[SparkConf](configuration.html#spark-properties). For example: + +{% highlight scala %} +val conf = new SparkConf() + .setMaster(...) + .setAppName(...) + .set("spark.cores.max", "10") +val sc = new SparkContext(conf) +{% endhighlight %} # Monitoring and Logging @@ -169,3 +179,75 @@ In addition, detailed log output for each job is also written to the work direct You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on). + +# High Availability + +By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below. + +## Standby Masters with ZooKeeper + +**Overview** + +Utilizing ZooKeeper to provide leader election and some state storage, you can launch multiple Masters in your cluster connected to the same ZooKeeper instance. One will be elected "leader" and the others will remain in standby mode. If the current leader dies, another Master will be elected, recover the old Master's state, and then resume scheduling. The entire recovery process (from the time the the first leader goes down) should take between 1 and 2 minutes. Note that this delay only affects scheduling _new_ applications -- applications that were already running during Master failover are unaffected. + +Learn more about getting started with ZooKeeper [here](http://zookeeper.apache.org/doc/trunk/zookeeperStarted.html). + +**Configuration** + +In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration: + +<table class="table"> + <tr><th style="width:21%">System property</th><th>Meaning</th></tr> + <tr> + <td><code>spark.deploy.recoveryMode</code></td> + <td>Set to ZOOKEEPER to enable standby Master recovery mode (default: NONE).</td> + </tr> + <tr> + <td><code>spark.deploy.zookeeper.url</code></td> + <td>The ZooKeeper cluster url (e.g., 192.168.1.100:2181,192.168.1.101:2181).</td> + </tr> + <tr> + <td><code>spark.deploy.zookeeper.dir</code></td> + <td>The directory in ZooKeeper to store recovery state (default: /spark).</td> + </tr> +</table> + +Possible gotcha: If you have multiple Masters in your cluster but fail to correctly configure the Masters to use ZooKeeper, the Masters will fail to discover each other and think they're all leaders. This will not lead to a healthy cluster state (as all Masters will schedule independently). + +**Details** + +After you have a ZooKeeper cluster set up, enabling high availability is straightforward. Simply start multiple Master processes on different nodes with the same ZooKeeper configuration (ZooKeeper URL and directory). Masters can be added and removed at any time. + +In order to schedule new applications or add Workers to the cluster, they need to know the IP address of the current leader. This can be accomplished by simply passing in a list of Masters where you used to pass in a single one. For example, you might start your SparkContext pointing to ``spark://host1:port1,host2:port2``. This would cause your SparkContext to try registering with both Masters -- if ``host1`` goes down, this configuration would still be correct as we'd find the new leader, ``host2``. + +There's an important distinction to be made between "registering with a Master" and normal operation. When starting up, an application or Worker needs to be able to find and register with the current lead Master. Once it successfully registers, though, it is "in the system" (i.e., stored in ZooKeeper). If failover occurs, the new leader will contact all previously registered applications and Workers to inform them of the change in leadership, so they need not even have known of the existence of the new Master at startup. + +Due to this property, new Masters can be created at any time, and the only thing you need to worry about is that _new_ applications and Workers can find it to register with in case it becomes the leader. Once registered, you're taken care of. + +## Single-Node Recovery with Local File System + +**Overview** + +ZooKeeper is the best way to go for production-level high availability, but if you just want to be able to restart the Master if it goes down, FILESYSTEM mode can take care of it. When applications and Workers register, they have enough state written to the provided directory so that they can be recovered upon a restart of the Master process. + +**Configuration** + +In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration: + +<table class="table"> + <tr><th style="width:21%">System property</th><th>Meaning</th></tr> + <tr> + <td><code>spark.deploy.recoveryMode</code></td> + <td>Set to FILESYSTEM to enable single-node recovery mode (default: NONE).</td> + </tr> + <tr> + <td><code>spark.deploy.recoveryDirectory</code></td> + <td>The directory in which Spark will store recovery state, accessible from the Master's perspective.</td> + </tr> +</table> + +**Details** + +* This solution can be used in tandem with a process monitor/manager like [monit](http://mmonit.com/monit/), or just to enable manual recovery via restart. +* While filesystem recovery seems straightforwardly better than not doing any recovery at all, this mode may be suboptimal for certain development or experimental purposes. In particular, killing a master via stop-master.sh does not clean up its recovery state, so whenever you start a new Master, it will enter recovery mode. This could increase the startup time by up to 1 minute if it needs to wait for all previously-registered Workers/clients to timeout. +* While it's not officially supported, you could mount an NFS directory as the recovery directory. If the original Master node dies completely, you could then start a Master on a different node, which would correctly recover all previously registered Workers/applications (equivalent to ZooKeeper recovery). Future applications will have to be able to find the new Master, however, in order to register. diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index c7df172024..6f97db6a3e 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -73,6 +73,10 @@ DStreams support many of the transformations available on normal Spark RDD's: Iterator[T] => Iterator[U] when running on an DStream of type T. </td> </tr> <tr> + <td> <b>repartition</b>(<i>numPartitions</i>) </td> + <td> Changes the level of parallelism in this DStream by creating more or fewer partitions. </td> +</tr> +<tr> <td> <b>union</b>(<i>otherStream</i>) </td> <td> Return a new DStream that contains the union of the elements in the source DStream and the argument DStream. </td> </tr> @@ -122,12 +126,12 @@ Spark Streaming features windowed computations, which allow you to apply transfo <table class="table"> <tr><th style="width:30%">Transformation</th><th>Meaning</th></tr> <tr> - <td> <b>window</b>(<i>windowDuration</i>, </i>slideDuration</i>) </td> + <td> <b>window</b>(<i>windowDuration</i>, <i>slideDuration</i>) </td> <td> Return a new DStream which is computed based on windowed batches of the source DStream. <i>windowDuration</i> is the width of the window and <i>slideTime</i> is the frequency during which the window is calculated. Both times must be multiples of the batch interval. </td> </tr> <tr> - <td> <b>countByWindow</b>(<i>windowDuration</i>, </i>slideDuration</i>) </td> + <td> <b>countByWindow</b>(<i>windowDuration</i>, <i>slideDuration</i>) </td> <td> Return a sliding count of elements in the stream. <i>windowDuration</i> and <i>slideDuration</i> are exactly as defined in <code>window()</code>. </td> </tr> @@ -161,7 +165,6 @@ Spark Streaming features windowed computations, which allow you to apply transfo <i>windowDuration</i> and <i>slideDuration</i> are exactly as defined in <code>window()</code>. </td> </tr> - </table> A complete list of DStream operations is available in the API documentation of [DStream](api/streaming/index.html#org.apache.spark.streaming.DStream) and [PairDStreamFunctions](api/streaming/index.html#org.apache.spark.streaming.PairDStreamFunctions). @@ -211,7 +214,7 @@ ssc.stop() {% endhighlight %} # Example -A simple example to start off is the [NetworkWordCount](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala). This example counts the words received from a network server every second. Given below is the relevant sections of the source code. You can find the full source code in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/NetworkWordCount.scala` . +A simple example to start off is the [NetworkWordCount](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala). This example counts the words received from a network server every second. Given below is the relevant sections of the source code. You can find the full source code in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/NetworkWordCount.scala` . {% highlight scala %} import org.apache.spark.streaming.{Seconds, StreamingContext} @@ -280,7 +283,7 @@ Time: 1357008430000 ms </td> </table> -You can find more examples in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/`. They can be run in the similar manner using `./run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files. +You can find more examples in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/`. They can be run in the similar manner using `./run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files. # DStream Persistence Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, using `persist()` method on a DStream would automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple operations on the same data). For window-based operations like `reduceByWindow` and `reduceByKeyAndWindow` and state-based operations like `updateStateByKey`, this is implicitly true. Hence, DStreams generated by window-based operations are automatically persisted in memory, without the developer calling `persist()`. @@ -323,7 +326,7 @@ Getting the best performance of a Spark Streaming application on a cluster requi There are a number of optimizations that can be done in Spark to minimize the processing time of each batch. These have been discussed in detail in [Tuning Guide](tuning.html). This section highlights some of the most important ones. ### Level of Parallelism -Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of parallelism as an argument (see the [`PairDStreamFunctions`](api/streaming/index.html#org.apache.spark.PairDStreamFunctions) documentation), or set the system property `spark.default.parallelism` to change the default. +Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of parallelism as an argument (see the [`PairDStreamFunctions`](api/streaming/index.html#org.apache.spark.PairDStreamFunctions) documentation), or set the [config property](configuration.html#spark-properties) `spark.default.parallelism` to change the default. ### Data Serialization The overhead of data serialization can be significant, especially when sub-second batch sizes are to be achieved. There are two aspects to it. @@ -346,7 +349,7 @@ For a Spark Streaming application running on a cluster to be stable, the process A good approach to figure out the right batch size for your application is to test it with a conservative batch size (say, 5-10 seconds) and a low data rate. To verify whether the system is able to keep up with data rate, you can check the value of the end-to-end delay experienced by each processed batch (in the Spark master logs, find the line having the phrase "Total delay"). If the delay is maintained to be less than the batch size, then system is stable. Otherwise, if the delay is continuously increasing, it means that the system is unable to keep up and it therefore unstable. Once you have an idea of a stable configuration, you can try increasing the data rate and/or reducing the batch size. Note that momentary increase in the delay due to temporary data rate increases maybe fine as long as the delay reduces back to a low value (i.e., less than batch size). ## 24/7 Operation -By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.). But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic cleanup of it metadata. This can be enabled by setting the Java system property `spark.cleaner.ttl` to the number of seconds you want any metadata to persist. For example, setting `spark.cleaner.ttl` to 600 would cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes. Note, that this property needs to be set before the SparkContext is created. +By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.). But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic cleanup of it metadata. This can be enabled by setting the [config property](configuration.html#spark-properties) `spark.cleaner.ttl` to the number of seconds you want any metadata to persist. For example, setting `spark.cleaner.ttl` to 600 would cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes. Note, that this property needs to be set before the SparkContext is created. This value is closely tied with any window operation that is being used. Any window operation would require the input data to be persisted in memory for at least the duration of the window. Hence it is necessary to set the delay to at least the value of the largest window operation used in the Spark Streaming application. If this delay is set too low, the application will throw an exception saying so. @@ -480,7 +483,7 @@ Similar to [Spark's Java API](java-programming-guide.html), we also provide a Ja 1. Functions for transformations must be implemented as subclasses of [Function](api/core/index.html#org.apache.spark.api.java.function.Function) and [Function2](api/core/index.html#org.apache.spark.api.java.function.Function2) 1. Unlike the Scala API, the Java API handles DStreams for key-value pairs using a separate [JavaPairDStream](api/streaming/index.html#org.apache.spark.streaming.api.java.JavaPairDStream) class(similar to [JavaRDD and JavaPairRDD](java-programming-guide.html#rdd-classes). DStream functions like `map` and `filter` are implemented separately by JavaDStreams and JavaPairDStream to return DStreams of appropriate types. -Spark's [Java Programming Guide](java-programming-guide.html) gives more ideas about using the Java API. To extends the ideas presented for the RDDs to DStreams, we present parts of the Java version of the same NetworkWordCount example presented above. The full source code is given at `<spark repo>/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java` +Spark's [Java Programming Guide](java-programming-guide.html) gives more ideas about using the Java API. To extends the ideas presented for the RDDs to DStreams, we present parts of the Java version of the same NetworkWordCount example presented above. The full source code is given at `<spark repo>/examples/src/main/java/org/apache/spark/streaming/examples/JavaNetworkWordCount.java` The streaming context and the socket stream from input source is started by using a `JavaStreamingContext`, that has the same parameters and provides the same input streams as its Scala counterpart. @@ -524,5 +527,5 @@ JavaPairDStream<String, Integer> wordCounts = words.map( # Where to Go from Here * API docs - [Scala](api/streaming/index.html#org.apache.spark.streaming.package) and [Java](api/streaming/index.html#org.apache.spark.streaming.api.java.package) -* More examples - [Scala](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/spark/streaming/examples) and [Java](https://github.com/apache/incubator-spark/tree/master/examples/src/main/java/spark/streaming/examples) +* More examples - [Scala](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/org/apache/spark/streaming/examples) and [Java](https://github.com/apache/incubator-spark/tree/master/examples/src/main/java/org/apache/spark/streaming/examples) * [Paper describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf) diff --git a/docs/tuning.md b/docs/tuning.md index 28d88a2659..bbb870085c 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -38,13 +38,15 @@ in your operations) and performance. It provides two serialization libraries: `Serializable` types and requires you to *register* the classes you'll use in the program in advance for best performance. -You can switch to using Kryo by calling `System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer")` -*before* creating your SparkContext. The only reason it is not the default is because of the custom +You can switch to using Kryo by initializing your job with a [SparkConf](configuration.html#spark-properties) +and calling `conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")`. +This setting configures the serializer used for not only shuffling data between worker +nodes but also when serializing RDDs to disk. The only reason Kryo is not the default is because of the custom registration requirement, but we recommend trying it in any network-intensive application. Finally, to register your classes with Kryo, create a public class that extends [`org.apache.spark.serializer.KryoRegistrator`](api/core/index.html#org.apache.spark.serializer.KryoRegistrator) and set the -`spark.kryo.registrator` system property to point to it, as follows: +`spark.kryo.registrator` config property to point to it, as follows: {% highlight scala %} import com.esotericsoftware.kryo.Kryo @@ -57,17 +59,17 @@ class MyRegistrator extends KryoRegistrator { } } -// Make sure to set these properties *before* creating a SparkContext! -System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer") -System.setProperty("spark.kryo.registrator", "mypackage.MyRegistrator") -val sc = new SparkContext(...) +val conf = new SparkConf().setMaster(...).setAppName(...) +conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") +conf.set("spark.kryo.registrator", "mypackage.MyRegistrator") +val sc = new SparkContext(conf) {% endhighlight %} The [Kryo documentation](http://code.google.com/p/kryo/) describes more advanced registration options, such as adding custom serialization code. If your objects are large, you may also need to increase the `spark.kryoserializer.buffer.mb` -system property. The default is 32, but this value needs to be large enough to hold the *largest* +config property. The default is 2, but this value needs to be large enough to hold the *largest* object you will serialize. Finally, if you don't register your classes, Kryo will still work, but it will have to store the @@ -164,7 +166,7 @@ cache RDDs. This means that 33% of memory is available for any objects created d In case your tasks slow down and you find that your JVM is garbage-collecting frequently or running out of memory, lowering this value will help reduce the memory consumption. To change this to say 50%, you can call -`System.setProperty("spark.storage.memoryFraction", "0.5")`. Combined with the use of serialized caching, +`conf.set("spark.storage.memoryFraction", "0.5")` on your SparkConf. Combined with the use of serialized caching, using a smaller cache should be sufficient to mitigate most of the garbage collection problems. In case you are interested in further tuning the Java GC, continue reading below. @@ -175,7 +177,7 @@ To further tune garbage collection, we first need to understand some basic infor * Java Heap space is divided in to two regions Young and Old. The Young generation is meant to hold short-lived objects while the Old generation is intended for objects with longer lifetimes. -* The Young generation is further divided into three regions [Eden, Survivor1, Survivor2]. +* The Young generation is further divided into three regions \[Eden, Survivor1, Survivor2\]. * A simplified description of the garbage collection procedure: When Eden is full, a minor GC is run on Eden and objects that are alive from Eden and Survivor1 are copied to Survivor2. The Survivor regions are swapped. If an object is old @@ -218,7 +220,7 @@ enough. Spark automatically sets the number of "map" tasks to run on each file a distributed "reduce" operations, such as `groupByKey` and `reduceByKey`, it uses the largest parent RDD's number of partitions. You can pass the level of parallelism as a second argument (see the [`spark.PairRDDFunctions`](api/core/index.html#org.apache.spark.rdd.PairRDDFunctions) documentation), -or set the system property `spark.default.parallelism` to change the default. +or set the config property `spark.default.parallelism` to change the default. In general, we recommend 2-3 tasks per CPU core in your cluster. ## Memory Usage of Reduce Tasks diff --git a/ec2/README b/ec2/README index 0add81312c..433da37b4c 100644 --- a/ec2/README +++ b/ec2/README @@ -1,4 +1,4 @@ This folder contains a script, spark-ec2, for launching Spark clusters on Amazon EC2. Usage instructions are available online at: -http://spark-project.org/docs/latest/ec2-scripts.html +http://spark.incubator.apache.org/docs/latest/ec2-scripts.html diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 1190ed47f6..a2b0e7e7f4 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -70,14 +70,14 @@ def parse_args(): "slaves across multiple (an additional $0.01/Gb for bandwidth" + "between zones applies)") parser.add_option("-a", "--ami", help="Amazon Machine Image ID to use") - parser.add_option("-v", "--spark-version", default="0.7.3", + parser.add_option("-v", "--spark-version", default="0.8.0", help="Version of Spark to use: 'X.Y.Z' or a specific git hash") - parser.add_option("--spark-git-repo", - default="https://github.com/mesos/spark", + parser.add_option("--spark-git-repo", + default="https://github.com/apache/incubator-spark", help="Github repo from which to checkout supplied commit hash") parser.add_option("--hadoop-major-version", default="1", help="Major version of Hadoop (default: 1)") - parser.add_option("-D", metavar="[ADDRESS:]PORT", dest="proxy_port", + parser.add_option("-D", metavar="[ADDRESS:]PORT", dest="proxy_port", help="Use SSH dynamic port forwarding to create a SOCKS proxy at " + "the given local address (for use with login)") parser.add_option("--resume", action="store_true", default=False, @@ -101,6 +101,8 @@ def parse_args(): help="The SSH user you want to connect as (default: root)") parser.add_option("--delete-groups", action="store_true", default=False, help="When destroying a cluster, delete the security groups that were created") + parser.add_option("--use-existing-master", action="store_true", default=False, + help="Launch fresh slaves, but use an existing stopped master if possible") (opts, args) = parser.parse_args() if len(args) != 2: @@ -155,7 +157,7 @@ def is_active(instance): # Return correct versions of Spark and Shark, given the supplied Spark version def get_spark_shark_version(opts): - spark_shark_map = {"0.7.3": "0.7.0"} + spark_shark_map = {"0.7.3": "0.7.1", "0.8.0": "0.8.0"} version = opts.spark_version.replace("v", "") if version not in spark_shark_map: print >> stderr, "Don't know about Spark version: %s" % version @@ -191,7 +193,7 @@ def get_spark_ami(opts): instance_type = "pvm" print >> stderr,\ "Don't recognize %s, assuming type is pvm" % opts.instance_type - + ami_path = "%s/%s/%s" % (AMI_PREFIX, opts.region, instance_type) try: ami = urllib2.urlopen(ami_path).read().strip() @@ -215,6 +217,7 @@ def launch_cluster(conn, opts, cluster_name): master_group.authorize(src_group=slave_group) master_group.authorize('tcp', 22, 22, '0.0.0.0/0') master_group.authorize('tcp', 8080, 8081, '0.0.0.0/0') + master_group.authorize('tcp', 19999, 19999, '0.0.0.0/0') master_group.authorize('tcp', 50030, 50030, '0.0.0.0/0') master_group.authorize('tcp', 50070, 50070, '0.0.0.0/0') master_group.authorize('tcp', 60070, 60070, '0.0.0.0/0') @@ -232,9 +235,9 @@ def launch_cluster(conn, opts, cluster_name): slave_group.authorize('tcp', 60075, 60075, '0.0.0.0/0') # Check if instances are already running in our groups - active_nodes = get_existing_cluster(conn, opts, cluster_name, - die_on_error=False) - if any(active_nodes): + existing_masters, existing_slaves = get_existing_cluster(conn, opts, cluster_name, + die_on_error=False) + if existing_slaves or (existing_masters and not opts.use_existing_master): print >> stderr, ("ERROR: There are already instances running in " + "group %s or %s" % (master_group.name, slave_group.name)) sys.exit(1) @@ -335,21 +338,28 @@ def launch_cluster(conn, opts, cluster_name): zone, slave_res.id) i += 1 - # Launch masters - master_type = opts.master_instance_type - if master_type == "": - master_type = opts.instance_type - if opts.zone == 'all': - opts.zone = random.choice(conn.get_all_zones()).name - master_res = image.run(key_name = opts.key_pair, - security_groups = [master_group], - instance_type = master_type, - placement = opts.zone, - min_count = 1, - max_count = 1, - block_device_map = block_map) - master_nodes = master_res.instances - print "Launched master in %s, regid = %s" % (zone, master_res.id) + # Launch or resume masters + if existing_masters: + print "Starting master..." + for inst in existing_masters: + if inst.state not in ["shutting-down", "terminated"]: + inst.start() + master_nodes = existing_masters + else: + master_type = opts.master_instance_type + if master_type == "": + master_type = opts.instance_type + if opts.zone == 'all': + opts.zone = random.choice(conn.get_all_zones()).name + master_res = image.run(key_name = opts.key_pair, + security_groups = [master_group], + instance_type = master_type, + placement = opts.zone, + min_count = 1, + max_count = 1, + block_device_map = block_map) + master_nodes = master_res.instances + print "Launched master in %s, regid = %s" % (zone, master_res.id) # Return all the instances return (master_nodes, slave_nodes) @@ -403,8 +413,8 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): print slave.public_dns_name ssh_write(slave.public_dns_name, opts, ['tar', 'x'], dot_ssh_tar) - modules = ['spark', 'shark', 'ephemeral-hdfs', 'persistent-hdfs', - 'mapreduce', 'spark-standalone'] + modules = ['spark', 'shark', 'ephemeral-hdfs', 'persistent-hdfs', + 'mapreduce', 'spark-standalone', 'tachyon'] if opts.hadoop_major_version == "1": modules = filter(lambda x: x != "mapreduce", modules) @@ -579,7 +589,7 @@ def ssh(host, opts, command): while True: try: return subprocess.check_call( - ssh_command(opts) + ['-t', '%s@%s' % (opts.user, host), stringify_command(command)]) + ssh_command(opts) + ['-t', '-t', '%s@%s' % (opts.user, host), stringify_command(command)]) except subprocess.CalledProcessError as e: if (tries > 2): # If this was an ssh failure, provide the user with hints. @@ -668,12 +678,12 @@ def real_main(): print "Terminating slaves..." for inst in slave_nodes: inst.terminate() - + # Delete security groups as well if opts.delete_groups: print "Deleting security groups (this will take some time)..." group_names = [cluster_name + "-master", cluster_name + "-slaves"] - + attempt = 1; while attempt <= 3: print "Attempt %d" % attempt @@ -720,7 +730,7 @@ def real_main(): if opts.proxy_port != None: proxy_opt = ['-D', opts.proxy_port] subprocess.check_call( - ssh_command(opts) + proxy_opt + ['-t', "%s@%s" % (opts.user, master)]) + ssh_command(opts) + proxy_opt + ['-t', '-t', "%s@%s" % (opts.user, master)]) elif action == "get-master": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) @@ -731,6 +741,7 @@ def real_main(): cluster_name + "?\nDATA ON EPHEMERAL DISKS WILL BE LOST, " + "BUT THE CLUSTER WILL KEEP USING SPACE ON\n" + "AMAZON EBS IF IT IS EBS-BACKED!!\n" + + "All data on spot-instance slaves will be lost.\n" + "Stop cluster " + cluster_name + " (y/N): ") if response == "y": (master_nodes, slave_nodes) = get_existing_cluster( @@ -742,7 +753,10 @@ def real_main(): print "Stopping slaves..." for inst in slave_nodes: if inst.state not in ["shutting-down", "terminated"]: - inst.stop() + if inst.spot_instance_request_id: + inst.terminate() + else: + inst.stop() elif action == "start": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) diff --git a/examples/pom.xml b/examples/pom.xml index b9cc6f5e0a..7a7032c319 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -26,33 +26,48 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-examples</artifactId> + <artifactId>spark-examples_2.10</artifactId> <packaging>jar</packaging> <name>Spark Project Examples</name> <url>http://spark.incubator.apache.org/</url> + <repositories> + <repository> + <id>apache-repo</id> + <name>Apache Repository</name> + <url>https://repository.apache.org/content/repositories/releases</url> + <releases> + <enabled>true</enabled> + </releases> + <snapshots> + <enabled>false</enabled> + </snapshots> + </repository> + </repositories> + + <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-streaming</artifactId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> <version>${project.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-mllib</artifactId> + <artifactId>spark-mllib_${scala.binary.version}</artifactId> <version>${project.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-bagel</artifactId> + <artifactId>spark-bagel_${scala.binary.version}</artifactId> <version>${project.version}</version> <scope>provided</scope> </dependency> @@ -72,28 +87,43 @@ </exclusions> </dependency> <dependency> + <groupId>com.sksamuel.kafka</groupId> + <artifactId>kafka_${scala.binary.version}</artifactId> + <version>0.8.0-beta1</version> + <exclusions> + <exclusion> + <groupId>com.sun.jmx</groupId> + <artifactId>jmxri</artifactId> + </exclusion> + <exclusion> + <groupId>com.sun.jdmk</groupId> + <artifactId>jmxtools</artifactId> + </exclusion> + </exclusions> + </dependency> + <dependency> <groupId>org.eclipse.jetty</groupId> <artifactId>jetty-server</artifactId> </dependency> <dependency> <groupId>com.twitter</groupId> - <artifactId>algebird-core_2.9.2</artifactId> + <artifactId>algebird-core_${scala.binary.version}</artifactId> <version>0.1.11</version> </dependency> <dependency> <groupId>org.scalatest</groupId> - <artifactId>scalatest_${scala.version}</artifactId> + <artifactId>scalatest_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> <dependency> <groupId>org.scalacheck</groupId> - <artifactId>scalacheck_${scala.version}</artifactId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> <dependency> <groupId>org.apache.cassandra</groupId> <artifactId>cassandra-all</artifactId> - <version>1.2.5</version> + <version>1.2.6</version> <exclusions> <exclusion> <groupId>com.google.guava</groupId> @@ -123,13 +153,21 @@ <groupId>org.apache.cassandra.deps</groupId> <artifactId>avro</artifactId> </exclusion> + <exclusion> + <groupId>org.sonatype.sisu.inject</groupId> + <artifactId>*</artifactId> + </exclusion> + <exclusion> + <groupId>org.xerial.snappy</groupId> + <artifactId>*</artifactId> + </exclusion> </exclusions> </dependency> </dependencies> <build> - <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> - <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> @@ -161,7 +199,7 @@ </goals> <configuration> <transformers> - <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/> + <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" /> <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"> <resource>reference.conf</resource> </transformer> diff --git a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java index 152f029213..407cd7ccfa 100644 --- a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java +++ b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java @@ -123,7 +123,7 @@ public class JavaLogQuery { }); List<Tuple2<Tuple3<String, String, String>, Stats>> output = counts.collect(); - for (Tuple2 t : output) { + for (Tuple2<?,?> t : output) { System.out.println(t._1 + "\t" + t._2); } System.exit(0); diff --git a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java index c5603a639b..89aed8f279 100644 --- a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java +++ b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java @@ -21,7 +21,6 @@ import scala.Tuple2; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.api.java.function.FlatMapFunction; import org.apache.spark.api.java.function.Function; import org.apache.spark.api.java.function.Function2; import org.apache.spark.api.java.function.PairFlatMapFunction; @@ -106,7 +105,7 @@ public class JavaPageRank { // Collects all URL ranks and dump them to console. List<Tuple2<String, Double>> output = ranks.collect(); - for (Tuple2 tuple : output) { + for (Tuple2<?,?> tuple : output) { System.out.println(tuple._1 + " has rank: " + tuple._2 + "."); } diff --git a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java index 07d32ad659..bd6383e13d 100644 --- a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java +++ b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java @@ -58,7 +58,7 @@ public class JavaWordCount { }); List<Tuple2<String, Integer>> output = counts.collect(); - for (Tuple2 tuple : output) { + for (Tuple2<?,?> tuple : output) { System.out.println(tuple._1 + ": " + tuple._2); } System.exit(0); diff --git a/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java b/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java index 628cb892b6..45a0d237da 100644 --- a/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java +++ b/examples/src/main/java/org/apache/spark/mllib/examples/JavaALS.java @@ -25,7 +25,6 @@ import org.apache.spark.mllib.recommendation.ALS; import org.apache.spark.mllib.recommendation.MatrixFactorizationModel; import org.apache.spark.mllib.recommendation.Rating; -import java.io.Serializable; import java.util.Arrays; import java.util.StringTokenizer; diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java new file mode 100644 index 0000000000..22994fb2ec --- /dev/null +++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java @@ -0,0 +1,98 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.examples; + +import java.util.Map; +import java.util.HashMap; + +import com.google.common.collect.Lists; +import org.apache.spark.api.java.function.FlatMapFunction; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.api.java.function.Function2; +import org.apache.spark.api.java.function.PairFunction; +import org.apache.spark.streaming.Duration; +import org.apache.spark.streaming.api.java.JavaDStream; +import org.apache.spark.streaming.api.java.JavaPairDStream; +import org.apache.spark.streaming.api.java.JavaStreamingContext; +import scala.Tuple2; + +/** + * Consumes messages from one or more topics in Kafka and does wordcount. + * Usage: JavaKafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads> + * <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1. + * <zkQuorum> is a list of one or more zookeeper servers that make quorum + * <group> is the name of kafka consumer group + * <topics> is a list of one or more kafka topics to consume from + * <numThreads> is the number of threads the kafka consumer should use + * + * Example: + * `./run-example org.apache.spark.streaming.examples.JavaKafkaWordCount local[2] zoo01,zoo02, + * zoo03 my-consumer-group topic1,topic2 1` + */ + +public class JavaKafkaWordCount { + public static void main(String[] args) { + if (args.length < 5) { + System.err.println("Usage: KafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>"); + System.exit(1); + } + + // Create the context with a 1 second batch size + JavaStreamingContext ssc = new JavaStreamingContext(args[0], "KafkaWordCount", + new Duration(2000), System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR")); + + int numThreads = Integer.parseInt(args[4]); + Map<String, Integer> topicMap = new HashMap<String, Integer>(); + String[] topics = args[3].split(","); + for (String topic: topics) { + topicMap.put(topic, numThreads); + } + + JavaPairDStream<String, String> messages = ssc.kafkaStream(args[1], args[2], topicMap); + + JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() { + @Override + public String call(Tuple2<String, String> tuple2) throws Exception { + return tuple2._2(); + } + }); + + JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() { + @Override + public Iterable<String> call(String x) { + return Lists.newArrayList(x.split(" ")); + } + }); + + JavaPairDStream<String, Integer> wordCounts = words.map( + new PairFunction<String, String, Integer>() { + @Override + public Tuple2<String, Integer> call(String s) throws Exception { + return new Tuple2<String, Integer>(s, 1); + } + }).reduceByKey(new Function2<Integer, Integer, Integer>() { + @Override + public Integer call(Integer i1, Integer i2) throws Exception { + return i1 + i2; + } + }); + + wordCounts.print(); + ssc.start(); + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala index 868ff81f67..a119980992 100644 --- a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala @@ -22,27 +22,36 @@ import org.apache.spark.SparkContext object BroadcastTest { def main(args: Array[String]) { if (args.length == 0) { - System.err.println("Usage: BroadcastTest <master> [<slices>] [numElem]") + System.err.println("Usage: BroadcastTest <master> [slices] [numElem] [broadcastAlgo] [blockSize]") System.exit(1) } + val bcName = if (args.length > 3) args(3) else "Http" + val blockSize = if (args.length > 4) args(4) else "4096" + + System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast." + bcName + "BroadcastFactory") + System.setProperty("spark.broadcast.blockSize", blockSize) + val sc = new SparkContext(args(0), "Broadcast Test", System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) + val slices = if (args.length > 1) args(1).toInt else 2 val num = if (args.length > 2) args(2).toInt else 1000000 - var arr1 = new Array[Int](num) + val arr1 = new Array[Int](num) for (i <- 0 until arr1.length) { arr1(i) = i } - for (i <- 0 until 2) { + for (i <- 0 until 3) { println("Iteration " + i) println("===========") + val startTime = System.nanoTime val barr1 = sc.broadcast(arr1) - sc.parallelize(1 to 10, slices).foreach { - i => println(barr1.value.size) - } + val observedSizes = sc.parallelize(1 to 10, slices).map(_ => barr1.value.size) + // Collect the small RDD so we can print the observed sizes locally. + observedSizes.collect().foreach(i => println(i)) + println("Iteration %d took %.0f milliseconds".format(i, (System.nanoTime - startTime) / 1E6)) } System.exit(0) diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala index 4af45b2b4a..83db8b9e26 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala @@ -120,7 +120,7 @@ object LocalALS { System.exit(1) } } - printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS); + printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS) val R = generateR() diff --git a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala index f79f0142b8..e1afc29f9a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala @@ -18,35 +18,38 @@ package org.apache.spark.examples import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD object MultiBroadcastTest { def main(args: Array[String]) { if (args.length == 0) { - System.err.println("Usage: BroadcastTest <master> [<slices>] [numElem]") + System.err.println("Usage: MultiBroadcastTest <master> [<slices>] [numElem]") System.exit(1) } - val sc = new SparkContext(args(0), "Broadcast Test", + val sc = new SparkContext(args(0), "Multi-Broadcast Test", System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) val slices = if (args.length > 1) args(1).toInt else 2 val num = if (args.length > 2) args(2).toInt else 1000000 - var arr1 = new Array[Int](num) + val arr1 = new Array[Int](num) for (i <- 0 until arr1.length) { arr1(i) = i } - var arr2 = new Array[Int](num) + val arr2 = new Array[Int](num) for (i <- 0 until arr2.length) { arr2(i) = i } val barr1 = sc.broadcast(arr1) val barr2 = sc.broadcast(arr2) - sc.parallelize(1 to 10, slices).foreach { - i => println(barr1.value.size + barr2.value.size) + val observedSizes: RDD[(Int, Int)] = sc.parallelize(1 to 10, slices).map { _ => + (barr1.value.size, barr2.value.size) } + // Collect the small RDD so we can print the observed sizes locally. + observedSizes.collect().foreach(i => println(i)) System.exit(0) } diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala index 646682878f..86dd9ca1b3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala @@ -21,6 +21,7 @@ import java.util.Random import scala.math.exp import org.apache.spark.util.Vector import org.apache.spark._ +import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.scheduler.InputFormatInfo /** @@ -51,7 +52,7 @@ object SparkHdfsLR { System.exit(1) } val inputPath = args(1) - val conf = SparkEnv.get.hadoop.newConfiguration() + val conf = SparkHadoopUtil.get.newConfiguration() val sc = new SparkContext(args(0), "SparkHdfsLR", System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")), Map(), InputFormatInfo.computePreferredLocations( diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala index f7bf75b4e5..bc2db39c12 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala @@ -21,8 +21,6 @@ import java.util.Random import org.apache.spark.SparkContext import org.apache.spark.util.Vector import org.apache.spark.SparkContext._ -import scala.collection.mutable.HashMap -import scala.collection.mutable.HashSet /** * K-means clustering. diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala index 5a2bc9b0d0..a689e5a360 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala @@ -38,6 +38,6 @@ object SparkPi { if (x*x + y*y < 1) 1 else 0 }.reduce(_ + _) println("Pi is roughly " + 4.0 * count / n) - System.exit(0) + spark.stop() } } diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala index 5a7a9d1bd8..8543ce0e32 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala @@ -65,7 +65,7 @@ object SparkTC { oldCount = nextCount // Perform the join, obtaining an RDD of (y, (z, x)) pairs, // then project the result to obtain the new (x, z) paths. - tc = tc.union(tc.join(edges).map(x => (x._2._2, x._2._1))).distinct().cache(); + tc = tc.union(tc.join(edges).map(x => (x._2._2, x._2._1))).distinct().cache() nextCount = tc.count() } while (nextCount != oldCount) diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala index 72b5c7b88e..4c0de46964 100644 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala +++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala @@ -36,16 +36,18 @@ object WikipediaPageRank { System.err.println("Usage: WikipediaPageRank <inputFile> <threshold> <numPartitions> <host> <usePartitioner>") System.exit(-1) } - - System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer") - System.setProperty("spark.kryo.registrator", classOf[PRKryoRegistrator].getName) + val sparkConf = new SparkConf() + sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") + sparkConf.set("spark.kryo.registrator", classOf[PRKryoRegistrator].getName) val inputFile = args(0) val threshold = args(1).toDouble val numPartitions = args(2).toInt val host = args(3) val usePartitioner = args(4).toBoolean - val sc = new SparkContext(host, "WikipediaPageRank") + + sparkConf.setMaster(host).setAppName("WikipediaPageRank") + val sc = new SparkContext(sparkConf) // Parse the Wikipedia page data into a graph val input = sc.textFile(inputFile) diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala index ddf6855325..2cf273a702 100644 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala +++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala @@ -34,15 +34,19 @@ object WikipediaPageRankStandalone { System.err.println("Usage: WikipediaPageRankStandalone <inputFile> <threshold> <numIterations> <host> <usePartitioner>") System.exit(-1) } + val sparkConf = new SparkConf() + sparkConf.set("spark.serializer", "spark.bagel.examples.WPRSerializer") - System.setProperty("spark.serializer", "spark.bagel.examples.WPRSerializer") val inputFile = args(0) val threshold = args(1).toDouble val numIterations = args(2).toInt val host = args(3) val usePartitioner = args(4).toBoolean - val sc = new SparkContext(host, "WikipediaPageRankStandalone") + + sparkConf.setMaster(host).setAppName("WikipediaPageRankStandalone") + + val sc = new SparkContext(sparkConf) val input = sc.textFile(inputFile) val partitioner = new HashPartitioner(sc.defaultParallelism) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala index cd3423a07b..2402409e6e 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala @@ -19,12 +19,14 @@ package org.apache.spark.streaming.examples import scala.collection.mutable.LinkedList import scala.util.Random +import scala.reflect.ClassTag import akka.actor.Actor import akka.actor.ActorRef import akka.actor.Props import akka.actor.actorRef2Scala +import org.apache.spark.SparkConf import org.apache.spark.streaming.Seconds import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.StreamingContext.toPairDStreamFunctions @@ -82,10 +84,10 @@ class FeederActor extends Actor { * * @see [[org.apache.spark.streaming.examples.FeederActor]] */ -class SampleActorReceiver[T: ClassManifest](urlOfPublisher: String) +class SampleActorReceiver[T: ClassTag](urlOfPublisher: String) extends Actor with Receiver { - lazy private val remotePublisher = context.actorFor(urlOfPublisher) + lazy private val remotePublisher = context.actorSelection(urlOfPublisher) override def preStart = remotePublisher ! SubscribeReceiver(context.self) @@ -115,12 +117,12 @@ object FeederActor { val Seq(host, port) = args.toSeq - val actorSystem = AkkaUtils.createActorSystem("test", host, port.toInt)._1 + val actorSystem = AkkaUtils.createActorSystem("test", host, port.toInt, conf = new SparkConf)._1 val feeder = actorSystem.actorOf(Props[FeederActor], "FeederActor") println("Feeder started as:" + feeder) - actorSystem.awaitTermination(); + actorSystem.awaitTermination() } } @@ -164,7 +166,7 @@ object ActorWordCount { */ val lines = ssc.actorStream[String]( - Props(new SampleActorReceiver[String]("akka://test@%s:%s/user/FeederActor".format( + Props(new SampleActorReceiver[String]("akka.tcp://test@%s:%s/user/FeederActor".format( host, port.toInt))), "SampleReceiver") //compute wordcount diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala index 12f939d5a7..570ba4c81a 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala @@ -18,13 +18,11 @@ package org.apache.spark.streaming.examples import java.util.Properties -import kafka.message.Message -import kafka.producer.SyncProducerConfig + import kafka.producer._ -import org.apache.spark.SparkContext + import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext._ -import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.util.RawTextHelper._ /** @@ -54,9 +52,10 @@ object KafkaWordCount { ssc.checkpoint("checkpoint") val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap - val lines = ssc.kafkaStream(zkQuorum, group, topicpMap) + val lines = ssc.kafkaStream(zkQuorum, group, topicpMap).map(_._2) val words = lines.flatMap(_.split(" ")) - val wordCounts = words.map(x => (x, 1l)).reduceByKeyAndWindow(add _, subtract _, Minutes(10), Seconds(2), 2) + val wordCounts = words.map(x => (x, 1l)) + .reduceByKeyAndWindow(add _, subtract _, Minutes(10), Seconds(2), 2) wordCounts.print() ssc.start() @@ -68,15 +67,16 @@ object KafkaWordCountProducer { def main(args: Array[String]) { if (args.length < 2) { - System.err.println("Usage: KafkaWordCountProducer <zkQuorum> <topic> <messagesPerSec> <wordsPerMessage>") + System.err.println("Usage: KafkaWordCountProducer <metadataBrokerList> <topic> " + + "<messagesPerSec> <wordsPerMessage>") System.exit(1) } - val Array(zkQuorum, topic, messagesPerSec, wordsPerMessage) = args + val Array(brokers, topic, messagesPerSec, wordsPerMessage) = args // Zookeper connection properties val props = new Properties() - props.put("zk.connect", zkQuorum) + props.put("metadata.broker.list", brokers) props.put("serializer.class", "kafka.serializer.StringEncoder") val config = new ProducerConfig(props) @@ -85,11 +85,13 @@ object KafkaWordCountProducer { // Send some messages while(true) { val messages = (1 to messagesPerSec.toInt).map { messageNum => - (1 to wordsPerMessage.toInt).map(x => scala.util.Random.nextInt(10).toString).mkString(" ") + val str = (1 to wordsPerMessage.toInt).map(x => scala.util.Random.nextInt(10).toString) + .mkString(" ") + + new KeyedMessage[String, String](topic, str) }.toArray - println(messages.mkString(",")) - val data = new ProducerData[String, String](topic, messages) - producer.send(data) + + producer.send(messages: _*) Thread.sleep(100) } } diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala new file mode 100644 index 0000000000..ff332a0282 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala @@ -0,0 +1,107 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.examples + +import org.apache.spark.streaming.{ Seconds, StreamingContext } +import org.apache.spark.streaming.StreamingContext._ +import org.apache.spark.streaming.dstream.MQTTReceiver +import org.apache.spark.storage.StorageLevel + +import org.eclipse.paho.client.mqttv3.MqttClient +import org.eclipse.paho.client.mqttv3.MqttClientPersistence +import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence +import org.eclipse.paho.client.mqttv3.MqttException +import org.eclipse.paho.client.mqttv3.MqttMessage +import org.eclipse.paho.client.mqttv3.MqttTopic + +/** + * A simple Mqtt publisher for demonstration purposes, repeatedly publishes + * Space separated String Message "hello mqtt demo for spark streaming" + */ +object MQTTPublisher { + + var client: MqttClient = _ + + def main(args: Array[String]) { + if (args.length < 2) { + System.err.println("Usage: MQTTPublisher <MqttBrokerUrl> <topic>") + System.exit(1) + } + + val Seq(brokerUrl, topic) = args.toSeq + + try { + var peristance:MqttClientPersistence =new MqttDefaultFilePersistence("/tmp") + client = new MqttClient(brokerUrl, MqttClient.generateClientId(), peristance) + } catch { + case e: MqttException => println("Exception Caught: " + e) + } + + client.connect() + + val msgtopic: MqttTopic = client.getTopic(topic) + val msg: String = "hello mqtt demo for spark streaming" + + while (true) { + val message: MqttMessage = new MqttMessage(String.valueOf(msg).getBytes()) + msgtopic.publish(message) + println("Published data. topic: " + msgtopic.getName() + " Message: " + message) + } + client.disconnect() + } +} + +/** + * A sample wordcount with MqttStream stream + * + * To work with Mqtt, Mqtt Message broker/server required. + * Mosquitto (http://mosquitto.org/) is an open source Mqtt Broker + * In ubuntu mosquitto can be installed using the command `$ sudo apt-get install mosquitto` + * Eclipse paho project provides Java library for Mqtt Client http://www.eclipse.org/paho/ + * Example Java code for Mqtt Publisher and Subscriber can be found here https://bitbucket.org/mkjinesh/mqttclient + * Usage: MQTTWordCount <master> <MqttbrokerUrl> <topic> + * In local mode, <master> should be 'local[n]' with n > 1 + * <MqttbrokerUrl> and <topic> describe where Mqtt publisher is running. + * + * To run this example locally, you may run publisher as + * `$ ./run-example org.apache.spark.streaming.examples.MQTTPublisher tcp://localhost:1883 foo` + * and run the example as + * `$ ./run-example org.apache.spark.streaming.examples.MQTTWordCount local[2] tcp://localhost:1883 foo` + */ +object MQTTWordCount { + + def main(args: Array[String]) { + if (args.length < 3) { + System.err.println( + "Usage: MQTTWordCount <master> <MqttbrokerUrl> <topic>" + + " In local mode, <master> should be 'local[n]' with n > 1") + System.exit(1) + } + + val Seq(master, brokerUrl, topic) = args.toSeq + + val ssc = new StreamingContext(master, "MqttWordCount", Seconds(2), System.getenv("SPARK_HOME"), + Seq(System.getenv("SPARK_EXAMPLES_JAR"))) + val lines = ssc.mqttStream(brokerUrl, topic, StorageLevel.MEMORY_ONLY) + + val words = lines.flatMap(x => x.toString.split(" ")) + val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) + wordCounts.print() + ssc.start() + } +} diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala index c8743b9e25..e83ce78aa5 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala @@ -23,6 +23,7 @@ import akka.zeromq._ import org.apache.spark.streaming.{ Seconds, StreamingContext } import org.apache.spark.streaming.StreamingContext._ import akka.zeromq.Subscribe +import akka.util.ByteString /** * A simple publisher for demonstration purposes, repeatedly publishes random Messages @@ -40,10 +41,11 @@ object SimpleZeroMQPublisher { val acs: ActorSystem = ActorSystem() val pubSocket = ZeroMQExtension(acs).newSocket(SocketType.Pub, Bind(url)) - val messages: Array[String] = Array("words ", "may ", "count ") + implicit def stringToByteString(x: String) = ByteString(x) + val messages: List[ByteString] = List("words ", "may ", "count ") while (true) { Thread.sleep(1000) - pubSocket ! ZMQMessage(Frame(topic) :: messages.map(x => Frame(x.getBytes)).toList) + pubSocket ! ZMQMessage(ByteString(topic) :: messages) } acs.awaitTermination() } @@ -78,7 +80,7 @@ object ZeroMQWordCount { val ssc = new StreamingContext(master, "ZeroMQWordCount", Seconds(2), System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - def bytesToStringIterator(x: Seq[Seq[Byte]]) = (x.map(x => new String(x.toArray))).iterator + def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator //For this stream, a zeroMQ publisher should be running. val lines = ssc.zeroMQStream(url, Subscribe(topic), bytesToStringIterator) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala index 884d6d6f34..de70c50473 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala @@ -17,17 +17,19 @@ package org.apache.spark.streaming.examples.clickstream -import java.net.{InetAddress,ServerSocket,Socket,SocketException} -import java.io.{InputStreamReader, BufferedReader, PrintWriter} +import java.net.ServerSocket +import java.io.PrintWriter import util.Random /** Represents a page view on a website with associated dimension data.*/ -class PageView(val url : String, val status : Int, val zipCode : Int, val userID : Int) { +class PageView(val url : String, val status : Int, val zipCode : Int, val userID : Int) + extends Serializable { override def toString() : String = { "%s\t%s\t%s\t%s\n".format(url, status, zipCode, userID) } } -object PageView { + +object PageView extends Serializable { def fromString(in : String) : PageView = { val parts = in.split("\t") new PageView(parts(0), parts(1).toInt, parts(2).toInt, parts(3).toInt) @@ -39,6 +41,9 @@ object PageView { * This should be used in tandem with PageViewStream.scala. Example: * $ ./run-example spark.streaming.examples.clickstream.PageViewGenerator 44444 10 * $ ./run-example spark.streaming.examples.clickstream.PageViewStream errorRatePerZipCode localhost 44444 + * + * When running this, you may want to set the root logging level to ERROR in + * conf/log4j.properties to reduce the verbosity of the output. * */ object PageViewGenerator { val pages = Map("http://foo.com/" -> .7, diff --git a/make-distribution.sh b/make-distribution.sh index 13843bb6c2..82638a23a0 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -95,7 +95,7 @@ cp $FWDIR/assembly/target/scala*/*assembly*hadoop*.jar "$DISTDIR/jars/" # Copy other things mkdir "$DISTDIR"/conf -cp "$FWDIR/conf/*.template" "$DISTDIR"/conf +cp "$FWDIR"/conf/*.template "$DISTDIR"/conf cp -r "$FWDIR/bin" "$DISTDIR" cp -r "$FWDIR/python" "$DISTDIR" cp -r "$FWDIR/sbin" "$DISTDIR" diff --git a/mllib/pom.xml b/mllib/pom.xml index 4ef4f0ae4e..dda3900afe 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -26,7 +26,7 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-mllib</artifactId> + <artifactId>spark-mllib_2.10</artifactId> <packaging>jar</packaging> <name>Spark Project ML Library</name> <url>http://spark.incubator.apache.org/</url> @@ -34,7 +34,7 @@ <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> @@ -48,12 +48,12 @@ </dependency> <dependency> <groupId>org.scalatest</groupId> - <artifactId>scalatest_${scala.version}</artifactId> + <artifactId>scalatest_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> <dependency> <groupId>org.scalacheck</groupId> - <artifactId>scalacheck_${scala.version}</artifactId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> <dependency> @@ -63,8 +63,8 @@ </dependency> </dependencies> <build> - <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> - <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> <plugins> <plugin> <groupId>org.scalatest</groupId> diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala new file mode 100644 index 0000000000..8247c1ebc5 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -0,0 +1,232 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.api.python +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.mllib.regression._ +import org.apache.spark.mllib.classification._ +import org.apache.spark.mllib.clustering._ +import org.apache.spark.mllib.recommendation._ +import org.apache.spark.rdd.RDD +import java.nio.ByteBuffer +import java.nio.ByteOrder +import java.nio.DoubleBuffer + +/** + * The Java stubs necessary for the Python mllib bindings. + */ +class PythonMLLibAPI extends Serializable { + private def deserializeDoubleVector(bytes: Array[Byte]): Array[Double] = { + val packetLength = bytes.length + if (packetLength < 16) { + throw new IllegalArgumentException("Byte array too short.") + } + val bb = ByteBuffer.wrap(bytes) + bb.order(ByteOrder.nativeOrder()) + val magic = bb.getLong() + if (magic != 1) { + throw new IllegalArgumentException("Magic " + magic + " is wrong.") + } + val length = bb.getLong() + if (packetLength != 16 + 8 * length) { + throw new IllegalArgumentException("Length " + length + " is wrong.") + } + val db = bb.asDoubleBuffer() + val ans = new Array[Double](length.toInt) + db.get(ans) + return ans + } + + private def serializeDoubleVector(doubles: Array[Double]): Array[Byte] = { + val len = doubles.length + val bytes = new Array[Byte](16 + 8 * len) + val bb = ByteBuffer.wrap(bytes) + bb.order(ByteOrder.nativeOrder()) + bb.putLong(1) + bb.putLong(len) + val db = bb.asDoubleBuffer() + db.put(doubles) + return bytes + } + + private def deserializeDoubleMatrix(bytes: Array[Byte]): Array[Array[Double]] = { + val packetLength = bytes.length + if (packetLength < 24) { + throw new IllegalArgumentException("Byte array too short.") + } + val bb = ByteBuffer.wrap(bytes) + bb.order(ByteOrder.nativeOrder()) + val magic = bb.getLong() + if (magic != 2) { + throw new IllegalArgumentException("Magic " + magic + " is wrong.") + } + val rows = bb.getLong() + val cols = bb.getLong() + if (packetLength != 24 + 8 * rows * cols) { + throw new IllegalArgumentException("Size " + rows + "x" + cols + " is wrong.") + } + val db = bb.asDoubleBuffer() + val ans = new Array[Array[Double]](rows.toInt) + var i = 0 + for (i <- 0 until rows.toInt) { + ans(i) = new Array[Double](cols.toInt) + db.get(ans(i)) + } + return ans + } + + private def serializeDoubleMatrix(doubles: Array[Array[Double]]): Array[Byte] = { + val rows = doubles.length + var cols = 0 + if (rows > 0) { + cols = doubles(0).length + } + val bytes = new Array[Byte](24 + 8 * rows * cols) + val bb = ByteBuffer.wrap(bytes) + bb.order(ByteOrder.nativeOrder()) + bb.putLong(2) + bb.putLong(rows) + bb.putLong(cols) + val db = bb.asDoubleBuffer() + var i = 0 + for (i <- 0 until rows) { + db.put(doubles(i)) + } + return bytes + } + + private def trainRegressionModel(trainFunc: (RDD[LabeledPoint], Array[Double]) => GeneralizedLinearModel, + dataBytesJRDD: JavaRDD[Array[Byte]], initialWeightsBA: Array[Byte]): + java.util.LinkedList[java.lang.Object] = { + val data = dataBytesJRDD.rdd.map(xBytes => { + val x = deserializeDoubleVector(xBytes) + LabeledPoint(x(0), x.slice(1, x.length)) + }) + val initialWeights = deserializeDoubleVector(initialWeightsBA) + val model = trainFunc(data, initialWeights) + val ret = new java.util.LinkedList[java.lang.Object]() + ret.add(serializeDoubleVector(model.weights)) + ret.add(model.intercept: java.lang.Double) + return ret + } + + /** + * Java stub for Python mllib LinearRegressionWithSGD.train() + */ + def trainLinearRegressionModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], + numIterations: Int, stepSize: Double, miniBatchFraction: Double, + initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { + return trainRegressionModel((data, initialWeights) => + LinearRegressionWithSGD.train(data, numIterations, stepSize, + miniBatchFraction, initialWeights), + dataBytesJRDD, initialWeightsBA) + } + + /** + * Java stub for Python mllib LassoWithSGD.train() + */ + def trainLassoModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, + stepSize: Double, regParam: Double, miniBatchFraction: Double, + initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { + return trainRegressionModel((data, initialWeights) => + LassoWithSGD.train(data, numIterations, stepSize, regParam, + miniBatchFraction, initialWeights), + dataBytesJRDD, initialWeightsBA) + } + + /** + * Java stub for Python mllib RidgeRegressionWithSGD.train() + */ + def trainRidgeModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, + stepSize: Double, regParam: Double, miniBatchFraction: Double, + initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { + return trainRegressionModel((data, initialWeights) => + RidgeRegressionWithSGD.train(data, numIterations, stepSize, regParam, + miniBatchFraction, initialWeights), + dataBytesJRDD, initialWeightsBA) + } + + /** + * Java stub for Python mllib SVMWithSGD.train() + */ + def trainSVMModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, + stepSize: Double, regParam: Double, miniBatchFraction: Double, + initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { + return trainRegressionModel((data, initialWeights) => + SVMWithSGD.train(data, numIterations, stepSize, regParam, + miniBatchFraction, initialWeights), + dataBytesJRDD, initialWeightsBA) + } + + /** + * Java stub for Python mllib LogisticRegressionWithSGD.train() + */ + def trainLogisticRegressionModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], + numIterations: Int, stepSize: Double, miniBatchFraction: Double, + initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { + return trainRegressionModel((data, initialWeights) => + LogisticRegressionWithSGD.train(data, numIterations, stepSize, + miniBatchFraction, initialWeights), + dataBytesJRDD, initialWeightsBA) + } + + /** + * Java stub for Python mllib KMeans.train() + */ + def trainKMeansModel(dataBytesJRDD: JavaRDD[Array[Byte]], k: Int, + maxIterations: Int, runs: Int, initializationMode: String): + java.util.List[java.lang.Object] = { + val data = dataBytesJRDD.rdd.map(xBytes => deserializeDoubleVector(xBytes)) + val model = KMeans.train(data, k, maxIterations, runs, initializationMode) + val ret = new java.util.LinkedList[java.lang.Object]() + ret.add(serializeDoubleMatrix(model.clusterCenters)) + return ret + } + + private def unpackRating(ratingBytes: Array[Byte]): Rating = { + val bb = ByteBuffer.wrap(ratingBytes) + bb.order(ByteOrder.nativeOrder()) + val user = bb.getInt() + val product = bb.getInt() + val rating = bb.getDouble() + return new Rating(user, product, rating) + } + + /** + * Java stub for Python mllib ALS.train(). This stub returns a handle + * to the Java object instead of the content of the Java object. Extra care + * needs to be taken in the Python code to ensure it gets freed on exit; see + * the Py4J documentation. + */ + def trainALSModel(ratingsBytesJRDD: JavaRDD[Array[Byte]], rank: Int, + iterations: Int, lambda: Double, blocks: Int): MatrixFactorizationModel = { + val ratings = ratingsBytesJRDD.rdd.map(unpackRating) + return ALS.train(ratings, rank, iterations, lambda, blocks) + } + + /** + * Java stub for Python mllib ALS.trainImplicit(). This stub returns a + * handle to the Java object instead of the content of the Java object. + * Extra care needs to be taken in the Python code to ensure it gets freed on + * exit; see the Py4J documentation. + */ + def trainImplicitALSModel(ratingsBytesJRDD: JavaRDD[Array[Byte]], rank: Int, + iterations: Int, lambda: Double, blocks: Int, alpha: Double): MatrixFactorizationModel = { + val ratings = ratingsBytesJRDD.rdd.map(unpackRating) + return ALS.trainImplicit(ratings, rank, iterations, lambda, blocks, alpha) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala index edbf77dbcc..0dee9399a8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala @@ -18,15 +18,16 @@ package org.apache.spark.mllib.clustering import scala.collection.mutable.ArrayBuffer -import scala.util.Random + +import org.jblas.DoubleMatrix import org.apache.spark.SparkContext import org.apache.spark.SparkContext._ import org.apache.spark.rdd.RDD import org.apache.spark.Logging import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.util.XORShiftRandom -import org.jblas.DoubleMatrix /** @@ -195,7 +196,7 @@ class KMeans private ( */ private def initRandom(data: RDD[Array[Double]]): Array[ClusterCenters] = { // Sample all the cluster centers in one pass to avoid repeated scans - val sample = data.takeSample(true, runs * k, new Random().nextInt()).toSeq + val sample = data.takeSample(true, runs * k, new XORShiftRandom().nextInt()).toSeq Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k).toArray) } @@ -210,7 +211,7 @@ class KMeans private ( */ private def initKMeansParallel(data: RDD[Array[Double]]): Array[ClusterCenters] = { // Initialize each run's center to a random point - val seed = new Random().nextInt() + val seed = new XORShiftRandom().nextInt() val sample = data.takeSample(true, runs, seed).toSeq val centers = Array.tabulate(runs)(r => ArrayBuffer(sample(r))) @@ -222,7 +223,7 @@ class KMeans private ( for (r <- 0 until runs) yield (r, KMeans.pointCost(centerArrays(r), point)) }.reduceByKey(_ + _).collectAsMap() val chosen = data.mapPartitionsWithIndex { (index, points) => - val rand = new Random(seed ^ (step << 16) ^ index) + val rand = new XORShiftRandom(seed ^ (step << 16) ^ index) for { p <- points r <- 0 until runs diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index be002d02bc..8b27ecf82c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -21,7 +21,8 @@ import scala.collection.mutable.{ArrayBuffer, BitSet} import scala.util.Random import scala.util.Sorting -import org.apache.spark.{HashPartitioner, Partitioner, SparkContext} +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.{Logging, HashPartitioner, Partitioner, SparkContext} import org.apache.spark.storage.StorageLevel import org.apache.spark.rdd.RDD import org.apache.spark.serializer.KryoRegistrator @@ -61,6 +62,12 @@ case class Rating(val user: Int, val product: Int, val rating: Double) /** * Alternating Least Squares matrix factorization. * + * ALS attempts to estimate the ratings matrix `R` as the product of two lower-rank matrices, + * `X` and `Y`, i.e. `Xt * Y = R`. Typically these approximations are called 'factor' matrices. + * The general approach is iterative. During each iteration, one of the factor matrices is held + * constant, while the other is solved for using least squares. The newly-solved factor matrix is + * then held constant while solving for the other factor matrix. + * * This is a blocked implementation of the ALS factorization algorithm that groups the two sets * of factors (referred to as "users" and "products") into blocks and reduces communication by only * sending one copy of each user vector to each product block on each iteration, and only for the @@ -70,11 +77,21 @@ case class Rating(val user: Int, val product: Int, val rating: Double) * vectors it receives from each user block it will depend on). This allows us to send only an * array of feature vectors between each user block and product block, and have the product block * find the users' ratings and update the products based on these messages. + * + * For implicit preference data, the algorithm used is based on + * "Collaborative Filtering for Implicit Feedback Datasets", available at + * [[http://research.yahoo.com/pub/2433]], adapted for the blocked approach used here. + * + * Essentially instead of finding the low-rank approximations to the rating matrix `R`, + * this finds the approximations for a preference matrix `P` where the elements of `P` are 1 if r > 0 + * and 0 if r = 0. The ratings then act as 'confidence' values related to strength of indicated user + * preferences rather than explicit ratings given to items. */ -class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var lambda: Double) - extends Serializable +class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var lambda: Double, + var implicitPrefs: Boolean, var alpha: Double) + extends Serializable with Logging { - def this() = this(-1, 10, 10, 0.01) + def this() = this(-1, 10, 10, 0.01, false, 1.0) /** * Set the number of blocks to parallelize the computation into; pass -1 for an auto-configured @@ -103,6 +120,16 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l this } + def setImplicitPrefs(implicitPrefs: Boolean): ALS = { + this.implicitPrefs = implicitPrefs + this + } + + def setAlpha(alpha: Double): ALS = { + this.alpha = alpha + this + } + /** * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples. * Returns a MatrixFactorizationModel with feature vectors for each user and product. @@ -147,19 +174,24 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l } } - for (iter <- 0 until iterations) { + for (iter <- 1 to iterations) { // perform ALS update - products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda) - users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda) + logInfo("Re-computing I given U (Iteration %d/%d)".format(iter, iterations)) + // YtY / XtX is an Option[DoubleMatrix] and is only required for the implicit feedback model + val YtY = computeYtY(users) + val YtYb = ratings.context.broadcast(YtY) + products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda, + alpha, YtYb) + logInfo("Re-computing U given I (Iteration %d/%d)".format(iter, iterations)) + val XtX = computeYtY(products) + val XtXb = ratings.context.broadcast(XtX) + users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda, + alpha, XtXb) } // Flatten and cache the two final RDDs to un-block them - val usersOut = users.join(userOutLinks).flatMap { case (b, (factors, outLinkBlock)) => - for (i <- 0 until factors.length) yield (outLinkBlock.elementIds(i), factors(i)) - } - val productsOut = products.join(productOutLinks).flatMap { case (b, (factors, outLinkBlock)) => - for (i <- 0 until factors.length) yield (outLinkBlock.elementIds(i), factors(i)) - } + val usersOut = unblockFactors(users, userOutLinks) + val productsOut = unblockFactors(products, productOutLinks) usersOut.persist() productsOut.persist() @@ -168,6 +200,40 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l } /** + * Computes the (`rank x rank`) matrix `YtY`, where `Y` is the (`nui x rank`) matrix of factors + * for each user (or product), in a distributed fashion. Here `reduceByKeyLocally` is used as + * the driver program requires `YtY` to broadcast it to the slaves + * @param factors the (block-distributed) user or product factor vectors + * @return Option[YtY] - whose value is only used in the implicit preference model + */ + def computeYtY(factors: RDD[(Int, Array[Array[Double]])]) = { + if (implicitPrefs) { + Option( + factors.flatMapValues{ case factorArray => + factorArray.map{ vector => + val x = new DoubleMatrix(vector) + x.mmul(x.transpose()) + } + }.reduceByKeyLocally((a, b) => a.addi(b)) + .values + .reduce((a, b) => a.addi(b)) + ) + } else { + None + } + } + + /** + * Flatten out blocked user or product factors into an RDD of (id, factor vector) pairs + */ + def unblockFactors(blockedFactors: RDD[(Int, Array[Array[Double]])], + outLinks: RDD[(Int, OutLinkBlock)]) = { + blockedFactors.join(outLinks).flatMap{ case (b, (factors, outLinkBlock)) => + for (i <- 0 until factors.length) yield (outLinkBlock.elementIds(i), factors(i)) + } + } + + /** * Make the out-links table for a block of the users (or products) dataset given the list of * (user, product, rating) values for the users in that block (or the opposite for products). */ @@ -251,7 +317,9 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l userInLinks: RDD[(Int, InLinkBlock)], partitioner: Partitioner, rank: Int, - lambda: Double) + lambda: Double, + alpha: Double, + YtY: Broadcast[Option[DoubleMatrix]]) : RDD[(Int, Array[Array[Double]])] = { val numBlocks = products.partitions.size @@ -265,7 +333,9 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l toSend.zipWithIndex.map{ case (buf, idx) => (idx, (bid, buf.toArray)) } }.groupByKey(partitioner) .join(userInLinks) - .mapValues{ case (messages, inLinkBlock) => updateBlock(messages, inLinkBlock, rank, lambda) } + .mapValues{ case (messages, inLinkBlock) => + updateBlock(messages, inLinkBlock, rank, lambda, alpha, YtY) + } } /** @@ -273,7 +343,7 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l * it received from each product and its InLinkBlock. */ def updateBlock(messages: Seq[(Int, Array[Array[Double]])], inLinkBlock: InLinkBlock, - rank: Int, lambda: Double) + rank: Int, lambda: Double, alpha: Double, YtY: Broadcast[Option[DoubleMatrix]]) : Array[Array[Double]] = { // Sort the incoming block factor messages by block ID and make them an array @@ -298,8 +368,14 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l fillXtX(x, tempXtX) val (us, rs) = inLinkBlock.ratingsForBlock(productBlock)(p) for (i <- 0 until us.length) { - userXtX(us(i)).addi(tempXtX) - SimpleBlas.axpy(rs(i), x, userXy(us(i))) + implicitPrefs match { + case false => + userXtX(us(i)).addi(tempXtX) + SimpleBlas.axpy(rs(i), x, userXy(us(i))) + case true => + userXtX(us(i)).addi(tempXtX.mul(alpha * rs(i))) + SimpleBlas.axpy(1 + alpha * rs(i), x, userXy(us(i))) + } } } } @@ -311,7 +387,10 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l // Add regularization (0 until rank).foreach(i => fullXtX.data(i*rank + i) += lambda) // Solve the resulting matrix, which is symmetric and positive-definite - Solve.solvePositive(fullXtX, userXy(index)).data + implicitPrefs match { + case false => Solve.solvePositive(fullXtX, userXy(index)).data + case true => Solve.solvePositive(fullXtX.add(YtY.value.get), userXy(index)).data + } } } @@ -381,7 +460,7 @@ object ALS { blocks: Int) : MatrixFactorizationModel = { - new ALS(blocks, rank, iterations, lambda).run(ratings) + new ALS(blocks, rank, iterations, lambda, false, 1.0).run(ratings) } /** @@ -419,6 +498,68 @@ object ALS { train(ratings, rank, iterations, 0.01, -1) } + /** + * Train a matrix factorization model given an RDD of 'implicit preferences' given by users + * to some products, in the form of (userID, productID, preference) pairs. We approximate the + * ratings matrix as the product of two lower-rank matrices of a given rank (number of features). + * To solve for these features, we run a given number of iterations of ALS. This is done using + * a level of parallelism given by `blocks`. + * + * @param ratings RDD of (userID, productID, rating) pairs + * @param rank number of features to use + * @param iterations number of iterations of ALS (recommended: 10-20) + * @param lambda regularization factor (recommended: 0.01) + * @param blocks level of parallelism to split computation into + * @param alpha confidence parameter (only applies when immplicitPrefs = true) + */ + def trainImplicit( + ratings: RDD[Rating], + rank: Int, + iterations: Int, + lambda: Double, + blocks: Int, + alpha: Double) + : MatrixFactorizationModel = + { + new ALS(blocks, rank, iterations, lambda, true, alpha).run(ratings) + } + + /** + * Train a matrix factorization model given an RDD of 'implicit preferences' given by users to + * some products, in the form of (userID, productID, preference) pairs. We approximate the + * ratings matrix as the product of two lower-rank matrices of a given rank (number of features). + * To solve for these features, we run a given number of iterations of ALS. The level of + * parallelism is determined automatically based on the number of partitions in `ratings`. + * + * @param ratings RDD of (userID, productID, rating) pairs + * @param rank number of features to use + * @param iterations number of iterations of ALS (recommended: 10-20) + * @param lambda regularization factor (recommended: 0.01) + */ + def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double) + : MatrixFactorizationModel = + { + trainImplicit(ratings, rank, iterations, lambda, -1, alpha) + } + + /** + * Train a matrix factorization model given an RDD of 'implicit preferences' ratings given by + * users to some products, in the form of (userID, productID, rating) pairs. We approximate the + * ratings matrix as the product of two lower-rank matrices of a given rank (number of features). + * To solve for these features, we run a given number of iterations of ALS. The level of + * parallelism is determined automatically based on the number of partitions in `ratings`. + * Model parameters `alpha` and `lambda` are set to reasonable default values + * + * @param ratings RDD of (userID, productID, rating) pairs + * @param rank number of features to use + * @param iterations number of iterations of ALS (recommended: 10-20) + */ + def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int) + : MatrixFactorizationModel = + { + trainImplicit(ratings, rank, iterations, 0.01, -1, 1.0) + } + private class ALSRegistrator extends KryoRegistrator { override def registerClasses(kryo: Kryo) { kryo.register(classOf[Rating]) @@ -426,29 +567,36 @@ object ALS { } def main(args: Array[String]) { - if (args.length != 5 && args.length != 6) { - println("Usage: ALS <master> <ratings_file> <rank> <iterations> <output_dir> [<blocks>]") + if (args.length < 5 || args.length > 9) { + println("Usage: ALS <master> <ratings_file> <rank> <iterations> <output_dir> " + + "[<lambda>] [<implicitPrefs>] [<alpha>] [<blocks>]") System.exit(1) } val (master, ratingsFile, rank, iters, outputDir) = (args(0), args(1), args(2).toInt, args(3).toInt, args(4)) - val blocks = if (args.length == 6) args(5).toInt else -1 - System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer") - System.setProperty("spark.kryo.registrator", classOf[ALSRegistrator].getName) - System.setProperty("spark.kryo.referenceTracking", "false") - System.setProperty("spark.kryoserializer.buffer.mb", "8") - System.setProperty("spark.locality.wait", "10000") + val lambda = if (args.length >= 6) args(5).toDouble else 0.01 + val implicitPrefs = if (args.length >= 7) args(6).toBoolean else false + val alpha = if (args.length >= 8) args(7).toDouble else 1 + val blocks = if (args.length == 9) args(8).toInt else -1 val sc = new SparkContext(master, "ALS") + sc.conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") + sc.conf.set("spark.kryo.registrator", classOf[ALSRegistrator].getName) + sc.conf.set("spark.kryo.referenceTracking", "false") + sc.conf.set("spark.kryoserializer.buffer.mb", "8") + sc.conf.set("spark.locality.wait", "10000") + val ratings = sc.textFile(ratingsFile).map { line => val fields = line.split(',') Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble) } - val model = ALS.train(ratings, rank, iters, 0.01, blocks) + val model = new ALS(rank = rank, iterations = iters, lambda = lambda, + numBlocks = blocks, implicitPrefs = implicitPrefs, alpha = alpha).run(ratings) + model.userFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") } .saveAsTextFile(outputDir + "/userFeatures") model.productFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") } .saveAsTextFile(outputDir + "/productFeatures") println("Final user/product features written to " + outputDir) - System.exit(0) + sc.stop() } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala index 5aec867257..d5f3f6b8db 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala @@ -83,7 +83,7 @@ object MFDataGenerator{ scala.math.round(.99 * m * n)).toInt val rand = new Random() val mn = m * n - val shuffled = rand.shuffle(1 to mn toIterable) + val shuffled = rand.shuffle(1 to mn toList) val omega = shuffled.slice(0, sampSize) val ordered = omega.sortWith(_ < _).toArray diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java index 32d3934ac1..33b99f4bd3 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java @@ -77,7 +77,7 @@ public class JavaKMeansSuite implements Serializable { @Test public void runKMeansUsingStaticMethods() { - List<double[]> points = new ArrayList(); + List<double[]> points = new ArrayList<double[]>(); points.add(new double[]{1.0, 2.0, 6.0}); points.add(new double[]{1.0, 3.0, 0.0}); points.add(new double[]{1.0, 4.0, 6.0}); @@ -94,7 +94,7 @@ public class JavaKMeansSuite implements Serializable { @Test public void runKMeansUsingConstructor() { - List<double[]> points = new ArrayList(); + List<double[]> points = new ArrayList<double[]>(); points.add(new double[]{1.0, 2.0, 6.0}); points.add(new double[]{1.0, 3.0, 0.0}); points.add(new double[]{1.0, 4.0, 6.0}); diff --git a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java index 3323f6cee2..b40f552e0d 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java @@ -19,8 +19,7 @@ package org.apache.spark.mllib.recommendation; import java.io.Serializable; import java.util.List; - -import scala.Tuple2; +import java.lang.Math; import org.junit.After; import org.junit.Assert; @@ -48,7 +47,7 @@ public class JavaALSSuite implements Serializable { } void validatePrediction(MatrixFactorizationModel model, int users, int products, int features, - DoubleMatrix trueRatings, double matchThreshold) { + DoubleMatrix trueRatings, double matchThreshold, boolean implicitPrefs, DoubleMatrix truePrefs) { DoubleMatrix predictedU = new DoubleMatrix(users, features); List<scala.Tuple2<Object, double[]>> userFeatures = model.userFeatures().toJavaRDD().collect(); for (int i = 0; i < features; ++i) { @@ -68,12 +67,32 @@ public class JavaALSSuite implements Serializable { DoubleMatrix predictedRatings = predictedU.mmul(predictedP.transpose()); - for (int u = 0; u < users; ++u) { - for (int p = 0; p < products; ++p) { - double prediction = predictedRatings.get(u, p); - double correct = trueRatings.get(u, p); - Assert.assertTrue(Math.abs(prediction - correct) < matchThreshold); + if (!implicitPrefs) { + for (int u = 0; u < users; ++u) { + for (int p = 0; p < products; ++p) { + double prediction = predictedRatings.get(u, p); + double correct = trueRatings.get(u, p); + Assert.assertTrue(String.format("Prediction=%2.4f not below match threshold of %2.2f", + prediction, matchThreshold), Math.abs(prediction - correct) < matchThreshold); + } + } + } else { + // For implicit prefs we use the confidence-weighted RMSE to test (ref Mahout's implicit ALS tests) + double sqErr = 0.0; + double denom = 0.0; + for (int u = 0; u < users; ++u) { + for (int p = 0; p < products; ++p) { + double prediction = predictedRatings.get(u, p); + double truePref = truePrefs.get(u, p); + double confidence = 1.0 + /* alpha = */ 1.0 * trueRatings.get(u, p); + double err = confidence * (truePref - prediction) * (truePref - prediction); + sqErr += err; + denom += 1.0; + } } + double rmse = Math.sqrt(sqErr / denom); + Assert.assertTrue(String.format("Confidence-weighted RMSE=%2.4f above threshold of %2.2f", + rmse, matchThreshold), Math.abs(rmse) < matchThreshold); } } @@ -81,30 +100,62 @@ public class JavaALSSuite implements Serializable { public void runALSUsingStaticMethods() { int features = 1; int iterations = 15; - int users = 10; - int products = 10; - scala.Tuple2<List<Rating>, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( - users, products, features, 0.7); + int users = 50; + int products = 100; + scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( + users, products, features, 0.7, false); JavaRDD<Rating> data = sc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations); - validatePrediction(model, users, products, features, testData._2(), 0.3); + validatePrediction(model, users, products, features, testData._2(), 0.3, false, testData._3()); } @Test public void runALSUsingConstructor() { int features = 2; int iterations = 15; - int users = 20; - int products = 30; - scala.Tuple2<List<Rating>, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( - users, products, features, 0.7); + int users = 100; + int products = 200; + scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( + users, products, features, 0.7, false); JavaRDD<Rating> data = sc.parallelize(testData._1()); MatrixFactorizationModel model = new ALS().setRank(features) .setIterations(iterations) .run(data.rdd()); - validatePrediction(model, users, products, features, testData._2(), 0.3); + validatePrediction(model, users, products, features, testData._2(), 0.3, false, testData._3()); + } + + @Test + public void runImplicitALSUsingStaticMethods() { + int features = 1; + int iterations = 15; + int users = 80; + int products = 160; + scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( + users, products, features, 0.7, true); + + JavaRDD<Rating> data = sc.parallelize(testData._1()); + MatrixFactorizationModel model = ALS.trainImplicit(data.rdd(), features, iterations); + validatePrediction(model, users, products, features, testData._2(), 0.4, true, testData._3()); + } + + @Test + public void runImplicitALSUsingConstructor() { + int features = 2; + int iterations = 15; + int users = 100; + int products = 200; + scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( + users, products, features, 0.7, true); + + JavaRDD<Rating> data = sc.parallelize(testData._1()); + + MatrixFactorizationModel model = new ALS().setRank(features) + .setIterations(iterations) + .setImplicitPrefs(true) + .run(data.rdd()); + validatePrediction(model, users, products, features, testData._2(), 0.4, true, testData._3()); } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala index 347ef238f4..fafc5ec5f2 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala @@ -34,16 +34,19 @@ object ALSSuite { users: Int, products: Int, features: Int, - samplingRate: Double): (java.util.List[Rating], DoubleMatrix) = { - val (sampledRatings, trueRatings) = generateRatings(users, products, features, samplingRate) - (seqAsJavaList(sampledRatings), trueRatings) + samplingRate: Double, + implicitPrefs: Boolean): (java.util.List[Rating], DoubleMatrix, DoubleMatrix) = { + val (sampledRatings, trueRatings, truePrefs) = + generateRatings(users, products, features, samplingRate, implicitPrefs) + (seqAsJavaList(sampledRatings), trueRatings, truePrefs) } def generateRatings( users: Int, products: Int, features: Int, - samplingRate: Double): (Seq[Rating], DoubleMatrix) = { + samplingRate: Double, + implicitPrefs: Boolean = false): (Seq[Rating], DoubleMatrix, DoubleMatrix) = { val rand = new Random(42) // Create a random matrix with uniform values from -1 to 1 @@ -52,14 +55,20 @@ object ALSSuite { val userMatrix = randomMatrix(users, features) val productMatrix = randomMatrix(features, products) - val trueRatings = userMatrix.mmul(productMatrix) + val (trueRatings, truePrefs) = implicitPrefs match { + case true => + val raw = new DoubleMatrix(users, products, Array.fill(users * products)(rand.nextInt(10).toDouble): _*) + val prefs = new DoubleMatrix(users, products, raw.data.map(v => if (v > 0) 1.0 else 0.0): _*) + (raw, prefs) + case false => (userMatrix.mmul(productMatrix), null) + } val sampledRatings = { for (u <- 0 until users; p <- 0 until products if rand.nextDouble() < samplingRate) yield Rating(u, p, trueRatings.get(u, p)) } - (sampledRatings, trueRatings) + (sampledRatings, trueRatings, truePrefs) } } @@ -78,11 +87,19 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll { } test("rank-1 matrices") { - testALS(10, 20, 1, 15, 0.7, 0.3) + testALS(50, 100, 1, 15, 0.7, 0.3) } test("rank-2 matrices") { - testALS(20, 30, 2, 15, 0.7, 0.3) + testALS(100, 200, 2, 15, 0.7, 0.3) + } + + test("rank-1 matrices implicit") { + testALS(80, 160, 1, 15, 0.7, 0.4, true) + } + + test("rank-2 matrices implicit") { + testALS(100, 200, 2, 15, 0.7, 0.4, true) } /** @@ -96,11 +113,14 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll { * @param matchThreshold max difference allowed to consider a predicted rating correct */ def testALS(users: Int, products: Int, features: Int, iterations: Int, - samplingRate: Double, matchThreshold: Double) + samplingRate: Double, matchThreshold: Double, implicitPrefs: Boolean = false) { - val (sampledRatings, trueRatings) = ALSSuite.generateRatings(users, products, - features, samplingRate) - val model = ALS.train(sc.parallelize(sampledRatings), features, iterations) + val (sampledRatings, trueRatings, truePrefs) = ALSSuite.generateRatings(users, products, + features, samplingRate, implicitPrefs) + val model = implicitPrefs match { + case false => ALS.train(sc.parallelize(sampledRatings), features, iterations) + case true => ALS.trainImplicit(sc.parallelize(sampledRatings), features, iterations) + } val predictedU = new DoubleMatrix(users, features) for ((u, vec) <- model.userFeatures.collect(); i <- 0 until features) { @@ -112,12 +132,31 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll { } val predictedRatings = predictedU.mmul(predictedP.transpose) - for (u <- 0 until users; p <- 0 until products) { - val prediction = predictedRatings.get(u, p) - val correct = trueRatings.get(u, p) - if (math.abs(prediction - correct) > matchThreshold) { - fail("Model failed to predict (%d, %d): %f vs %f\ncorr: %s\npred: %s\nU: %s\n P: %s".format( - u, p, correct, prediction, trueRatings, predictedRatings, predictedU, predictedP)) + if (!implicitPrefs) { + for (u <- 0 until users; p <- 0 until products) { + val prediction = predictedRatings.get(u, p) + val correct = trueRatings.get(u, p) + if (math.abs(prediction - correct) > matchThreshold) { + fail("Model failed to predict (%d, %d): %f vs %f\ncorr: %s\npred: %s\nU: %s\n P: %s".format( + u, p, correct, prediction, trueRatings, predictedRatings, predictedU, predictedP)) + } + } + } else { + // For implicit prefs we use the confidence-weighted RMSE to test (ref Mahout's tests) + var sqErr = 0.0 + var denom = 0.0 + for (u <- 0 until users; p <- 0 until products) { + val prediction = predictedRatings.get(u, p) + val truePref = truePrefs.get(u, p) + val confidence = 1 + 1.0 * trueRatings.get(u, p) + val err = confidence * (truePref - prediction) * (truePref - prediction) + sqErr += err + denom += 1 + } + val rmse = math.sqrt(sqErr / denom) + if (math.abs(rmse) > matchThreshold) { + fail("Model failed to predict RMSE: %f\ncorr: %s\npred: %s\nU: %s\n P: %s".format( + rmse, truePrefs, predictedRatings, predictedU, predictedP)) } } } diff --git a/new-yarn/pom.xml b/new-yarn/pom.xml new file mode 100644 index 0000000000..4cd28f34e3 --- /dev/null +++ b/new-yarn/pom.xml @@ -0,0 +1,161 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- + ~ Licensed to the Apache Software Foundation (ASF) under one or more + ~ contributor license agreements. See the NOTICE file distributed with + ~ this work for additional information regarding copyright ownership. + ~ The ASF licenses this file to You under the Apache License, Version 2.0 + ~ (the "License"); you may not use this file except in compliance with + ~ the License. You may obtain a copy of the License at + ~ + ~ http://www.apache.org/licenses/LICENSE-2.0 + ~ + ~ Unless required by applicable law or agreed to in writing, software + ~ distributed under the License is distributed on an "AS IS" BASIS, + ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + ~ See the License for the specific language governing permissions and + ~ limitations under the License. + --> +<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>org.apache.spark</groupId> + <artifactId>spark-parent</artifactId> + <version>0.9.0-incubating-SNAPSHOT</version> + <relativePath>../pom.xml</relativePath> + </parent> + + <groupId>org.apache.spark</groupId> + <artifactId>spark-yarn_2.10</artifactId> + <packaging>jar</packaging> + <name>Spark Project YARN Support</name> + <url>http://spark.incubator.apache.org/</url> + + <dependencies> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-core_2.10</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-client</artifactId> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <version>${yarn.version}</version> + </dependency> + <dependency> + <groupId>org.apache.avro</groupId> + <artifactId>avro</artifactId> + </dependency> + <dependency> + <groupId>org.apache.avro</groupId> + <artifactId>avro-ipc</artifactId> + </dependency> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_2.10</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.mockito</groupId> + <artifactId>mockito-all</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + + <build> + <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-shade-plugin</artifactId> + <configuration> + <shadedArtifactAttached>false</shadedArtifactAttached> + <outputFile>${project.build.directory}/${project.artifactId}-${project.version}-shaded.jar</outputFile> + <artifactSet> + <includes> + <include>*:*</include> + </includes> + </artifactSet> + <filters> + <filter> + <artifact>*:*</artifact> + <excludes> + <exclude>META-INF/*.SF</exclude> + <exclude>META-INF/*.DSA</exclude> + <exclude>META-INF/*.RSA</exclude> + </excludes> + </filter> + </filters> + </configuration> + <executions> + <execution> + <phase>package</phase> + <goals> + <goal>shade</goal> + </goals> + <configuration> + <transformers> + <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" /> + <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"> + <resource>reference.conf</resource> + </transformer> + </transformers> + </configuration> + </execution> + </executions> + </plugin> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-antrun-plugin</artifactId> + <executions> + <execution> + <phase>test</phase> + <goals> + <goal>run</goal> + </goals> + <configuration> + <exportAntProperties>true</exportAntProperties> + <tasks> + <property name="spark.classpath" refid="maven.test.classpath" /> + <property environment="env" /> + <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry."> + <condition> + <not> + <or> + <isset property="env.SCALA_HOME" /> + <isset property="env.SCALA_LIBRARY_PATH" /> + </or> + </not> + </condition> + </fail> + </tasks> + </configuration> + </execution> + </executions> + </plugin> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + <configuration> + <environmentVariables> + <SPARK_HOME>${basedir}/..</SPARK_HOME> + <SPARK_TESTING>1</SPARK_TESTING> + <SPARK_CLASSPATH>${spark.classpath}</SPARK_CLASSPATH> + </environmentVariables> + </configuration> + </plugin> + </plugins> + </build> +</project> diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala new file mode 100644 index 0000000000..91e35e2d34 --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -0,0 +1,446 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.io.IOException +import java.net.Socket +import java.util.concurrent.CopyOnWriteArrayList +import java.util.concurrent.atomic.{AtomicInteger, AtomicReference} + +import scala.collection.JavaConversions._ + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.net.NetUtils +import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.util.ShutdownHookManager +import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.protocolrecords._ +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.client.api.AMRMClient +import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.ipc.YarnRPC +import org.apache.hadoop.yarn.util.{ConverterUtils, Records} + +import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.util.Utils + + +class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) extends Logging { + + def this(args: ApplicationMasterArguments) = this(args, new Configuration()) + + private var rpc: YarnRPC = YarnRPC.create(conf) + private val yarnConf: YarnConfiguration = new YarnConfiguration(conf) + private var appAttemptId: ApplicationAttemptId = _ + private var userThread: Thread = _ + private val fs = FileSystem.get(yarnConf) + + private var yarnAllocator: YarnAllocationHandler = _ + private var isFinished: Boolean = false + private var uiAddress: String = _ + private val maxAppAttempts: Int = conf.getInt( + YarnConfiguration.RM_AM_MAX_ATTEMPTS, YarnConfiguration.DEFAULT_RM_AM_MAX_ATTEMPTS) + private var isLastAMRetry: Boolean = true + private var amClient: AMRMClient[ContainerRequest] = _ + + // Default to numWorkers * 2, with minimum of 3 + private val maxNumWorkerFailures = conf.getOrElse("spark.yarn.max.worker.failures", + math.max(args.numWorkers * 2, 3).toString()).toInt + + def run() { + // Setup the directories so things go to YARN approved directories rather + // than user specified and /tmp. + conf.set("spark.local.dir", getLocalDirs()) + + // Use priority 30 as it's higher then HDFS. It's same priority as MapReduce is using. + ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30) + + appAttemptId = getApplicationAttemptId() + isLastAMRetry = appAttemptId.getAttemptId() >= maxAppAttempts + amClient = AMRMClient.createAMRMClient() + amClient.init(yarnConf) + amClient.start() + + // Workaround until hadoop moves to something which has + // https://issues.apache.org/jira/browse/HADOOP-8406 - fixed in (2.0.2-alpha but no 0.23 line) + // org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(conf) + + ApplicationMaster.register(this) + + // Start the user's JAR + userThread = startUserClass() + + // This a bit hacky, but we need to wait until the spark.driver.port property has + // been set by the Thread executing the user class. + waitForSparkMaster() + + waitForSparkContextInitialized() + + // Do this after Spark master is up and SparkContext is created so that we can register UI Url. + val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster() + + // Allocate all containers + allocateWorkers() + + // Wait for the user class to Finish + userThread.join() + + System.exit(0) + } + + /** Get the Yarn approved local directories. */ + private def getLocalDirs(): String = { + // Hadoop 0.23 and 2.x have different Environment variable names for the + // local dirs, so lets check both. We assume one of the 2 is set. + // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X + val localDirs = Option(System.getenv("YARN_LOCAL_DIRS")) + .getOrElse(Option(System.getenv("LOCAL_DIRS")) + .getOrElse("")) + + if (localDirs.isEmpty()) { + throw new Exception("Yarn Local dirs can't be empty") + } + localDirs + } + + private def getApplicationAttemptId(): ApplicationAttemptId = { + val envs = System.getenv() + val containerIdString = envs.get(ApplicationConstants.Environment.CONTAINER_ID.name()) + val containerId = ConverterUtils.toContainerId(containerIdString) + val appAttemptId = containerId.getApplicationAttemptId() + logInfo("ApplicationAttemptId: " + appAttemptId) + appAttemptId + } + + private def registerApplicationMaster(): RegisterApplicationMasterResponse = { + logInfo("Registering the ApplicationMaster") + amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress) + } + + private def waitForSparkMaster() { + logInfo("Waiting for Spark driver to be reachable.") + var driverUp = false + var tries = 0 + val numTries = conf.getOrElse("spark.yarn.applicationMaster.waitTries", "10").toInt + while (!driverUp && tries < numTries) { + val driverHost = conf.get("spark.driver.host") + val driverPort = conf.get("spark.driver.port") + try { + val socket = new Socket(driverHost, driverPort.toInt) + socket.close() + logInfo("Driver now available: %s:%s".format(driverHost, driverPort)) + driverUp = true + } catch { + case e: Exception => { + logWarning("Failed to connect to driver at %s:%s, retrying ...". + format(driverHost, driverPort)) + Thread.sleep(100) + tries = tries + 1 + } + } + } + } + + private def startUserClass(): Thread = { + logInfo("Starting the user JAR in a separate Thread") + val mainMethod = Class.forName( + args.userClass, + false /* initialize */, + Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]]) + val t = new Thread { + override def run() { + var successed = false + try { + // Copy + var mainArgs: Array[String] = new Array[String](args.userArgs.size) + args.userArgs.copyToArray(mainArgs, 0, args.userArgs.size) + mainMethod.invoke(null, mainArgs) + // some job script has "System.exit(0)" at the end, for example SparkPi, SparkLR + // userThread will stop here unless it has uncaught exception thrown out + // It need shutdown hook to set SUCCEEDED + successed = true + } finally { + logDebug("finishing main") + isLastAMRetry = true + if (successed) { + ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) + } else { + ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.FAILED) + } + } + } + } + t.start() + t + } + + // This need to happen before allocateWorkers() + private def waitForSparkContextInitialized() { + logInfo("Waiting for Spark context initialization") + try { + var sparkContext: SparkContext = null + ApplicationMaster.sparkContextRef.synchronized { + var numTries = 0 + val waitTime = 10000L + val maxNumTries = conf.getOrElse("spark.yarn.ApplicationMaster.waitTries", "10").toInt + while (ApplicationMaster.sparkContextRef.get() == null && numTries < maxNumTries) { + logInfo("Waiting for Spark context initialization ... " + numTries) + numTries = numTries + 1 + ApplicationMaster.sparkContextRef.wait(waitTime) + } + sparkContext = ApplicationMaster.sparkContextRef.get() + assert(sparkContext != null || numTries >= maxNumTries) + + if (sparkContext != null) { + uiAddress = sparkContext.ui.appUIAddress + this.yarnAllocator = YarnAllocationHandler.newAllocator( + yarnConf, + amClient, + appAttemptId, + args, + sparkContext.preferredNodeLocationData) + } else { + logWarning("Unable to retrieve SparkContext inspite of waiting for %d, maxNumTries = %d". + format(numTries * waitTime, maxNumTries)) + this.yarnAllocator = YarnAllocationHandler.newAllocator( + yarnConf, + amClient, + appAttemptId, + args) + } + } + } finally { + // In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT : + // so that the loop (in ApplicationMaster.sparkContextInitialized) breaks. + ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT) + } + } + + private def allocateWorkers() { + try { + logInfo("Allocating " + args.numWorkers + " workers.") + // Wait until all containers have finished + // TODO: This is a bit ugly. Can we make it nicer? + // TODO: Handle container failure + yarnAllocator.addResourceRequests(args.numWorkers) + // Exits the loop if the user thread exits. + while (yarnAllocator.getNumWorkersRunning < args.numWorkers && userThread.isAlive) { + if (yarnAllocator.getNumWorkersFailed >= maxNumWorkerFailures) { + finishApplicationMaster(FinalApplicationStatus.FAILED, + "max number of worker failures reached") + } + yarnAllocator.allocateResources() + ApplicationMaster.incrementAllocatorLoop(1) + Thread.sleep(100) + } + } finally { + // In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT, + // so that the loop in ApplicationMaster#sparkContextInitialized() breaks. + ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT) + } + logInfo("All workers have launched.") + + // Launch a progress reporter thread, else the app will get killed after expiration + // (def: 10mins) timeout. + if (userThread.isAlive) { + // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses. + val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000) + + // we want to be reasonably responsive without causing too many requests to RM. + val schedulerInterval = + conf.getOrElse("spark.yarn.scheduler.heartbeat.interval-ms", "5000").toLong + + // must be <= timeoutInterval / 2. + val interval = math.min(timeoutInterval / 2, schedulerInterval) + + launchReporterThread(interval) + } + } + + private def launchReporterThread(_sleepTime: Long): Thread = { + val sleepTime = if (_sleepTime <= 0 ) 0 else _sleepTime + + val t = new Thread { + override def run() { + while (userThread.isAlive) { + if (yarnAllocator.getNumWorkersFailed >= maxNumWorkerFailures) { + finishApplicationMaster(FinalApplicationStatus.FAILED, + "max number of worker failures reached") + } + val missingWorkerCount = args.numWorkers - yarnAllocator.getNumWorkersRunning - + yarnAllocator.getNumPendingAllocate + if (missingWorkerCount > 0) { + logInfo("Allocating %d containers to make up for (potentially) lost containers". + format(missingWorkerCount)) + yarnAllocator.addResourceRequests(missingWorkerCount) + } + sendProgress() + Thread.sleep(sleepTime) + } + } + } + // Setting to daemon status, though this is usually not a good idea. + t.setDaemon(true) + t.start() + logInfo("Started progress reporter thread - sleep time : " + sleepTime) + t + } + + private def sendProgress() { + logDebug("Sending progress") + // Simulated with an allocate request with no nodes requested. + yarnAllocator.allocateResources() + } + + /* + def printContainers(containers: List[Container]) = { + for (container <- containers) { + logInfo("Launching shell command on a new container." + + ", containerId=" + container.getId() + + ", containerNode=" + container.getNodeId().getHost() + + ":" + container.getNodeId().getPort() + + ", containerNodeURI=" + container.getNodeHttpAddress() + + ", containerState" + container.getState() + + ", containerResourceMemory" + + container.getResource().getMemory()) + } + } + */ + + def finishApplicationMaster(status: FinalApplicationStatus, diagnostics: String = "") { + synchronized { + if (isFinished) { + return + } + isFinished = true + } + + logInfo("finishApplicationMaster with " + status) + // Set tracking URL to empty since we don't have a history server. + amClient.unregisterApplicationMaster(status, "" /* appMessage */, "" /* appTrackingUrl */) + } + + /** + * Clean up the staging directory. + */ + private def cleanupStagingDir() { + var stagingDirPath: Path = null + try { + val preserveFiles = conf.getOrElse("spark.yarn.preserve.staging.files", "false").toBoolean + if (!preserveFiles) { + stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR")) + if (stagingDirPath == null) { + logError("Staging directory is null") + return + } + logInfo("Deleting staging directory " + stagingDirPath) + fs.delete(stagingDirPath, true) + } + } catch { + case ioe: IOException => + logError("Failed to cleanup staging dir " + stagingDirPath, ioe) + } + } + + // The shutdown hook that runs when a signal is received AND during normal close of the JVM. + class AppMasterShutdownHook(appMaster: ApplicationMaster) extends Runnable { + + def run() { + logInfo("AppMaster received a signal.") + // we need to clean up staging dir before HDFS is shut down + // make sure we don't delete it until this is the last AM + if (appMaster.isLastAMRetry) appMaster.cleanupStagingDir() + } + } +} + +object ApplicationMaster { + // Number of times to wait for the allocator loop to complete. + // Each loop iteration waits for 100ms, so maximum of 3 seconds. + // This is to ensure that we have reasonable number of containers before we start + // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be + // optimal as more containers are available. Might need to handle this better. + private val ALLOCATOR_LOOP_WAIT_COUNT = 30 + + private val applicationMasters = new CopyOnWriteArrayList[ApplicationMaster]() + + val sparkContextRef: AtomicReference[SparkContext] = + new AtomicReference[SparkContext](null /* initialValue */) + + val yarnAllocatorLoop: AtomicInteger = new AtomicInteger(0) + + def incrementAllocatorLoop(by: Int) { + val count = yarnAllocatorLoop.getAndAdd(by) + if (count >= ALLOCATOR_LOOP_WAIT_COUNT) { + yarnAllocatorLoop.synchronized { + // to wake threads off wait ... + yarnAllocatorLoop.notifyAll() + } + } + } + + def register(master: ApplicationMaster) { + applicationMasters.add(master) + } + + // TODO(harvey): See whether this should be discarded - it isn't used anywhere atm... + def sparkContextInitialized(sc: SparkContext): Boolean = { + var modified = false + sparkContextRef.synchronized { + modified = sparkContextRef.compareAndSet(null, sc) + sparkContextRef.notifyAll() + } + + // Add a shutdown hook - as a best case effort in case users do not call sc.stop or do + // System.exit. + // Should not really have to do this, but it helps YARN to evict resources earlier. + // Not to mention, prevent the Client from declaring failure even though we exited properly. + // Note that this will unfortunately not properly clean up the staging files because it gets + // called too late, after the filesystem is already shutdown. + if (modified) { + Runtime.getRuntime().addShutdownHook(new Thread with Logging { + // This is not only logs, but also ensures that log system is initialized for this instance + // when we are actually 'run'-ing. + logInfo("Adding shutdown hook for context " + sc) + override def run() { + logInfo("Invoking sc stop from shutdown hook") + sc.stop() + // Best case ... + for (master <- applicationMasters) { + master.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) + } + } + } ) + } + + // Wait for initialization to complete and atleast 'some' nodes can get allocated. + yarnAllocatorLoop.synchronized { + while (yarnAllocatorLoop.get() <= ALLOCATOR_LOOP_WAIT_COUNT) { + yarnAllocatorLoop.wait(1000L) + } + } + modified + } + + def main(argStrings: Array[String]) { + val args = new ApplicationMasterArguments(argStrings) + new ApplicationMaster(args).run() + } +} diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala new file mode 100644 index 0000000000..f76a5ddd39 --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala @@ -0,0 +1,94 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import org.apache.spark.util.IntParam +import collection.mutable.ArrayBuffer + +class ApplicationMasterArguments(val args: Array[String]) { + var userJar: String = null + var userClass: String = null + var userArgs: Seq[String] = Seq[String]() + var workerMemory = 1024 + var workerCores = 1 + var numWorkers = 2 + + parseArgs(args.toList) + + private def parseArgs(inputArgs: List[String]): Unit = { + val userArgsBuffer = new ArrayBuffer[String]() + + var args = inputArgs + + while (! args.isEmpty) { + + args match { + case ("--jar") :: value :: tail => + userJar = value + args = tail + + case ("--class") :: value :: tail => + userClass = value + args = tail + + case ("--args") :: value :: tail => + userArgsBuffer += value + args = tail + + case ("--num-workers") :: IntParam(value) :: tail => + numWorkers = value + args = tail + + case ("--worker-memory") :: IntParam(value) :: tail => + workerMemory = value + args = tail + + case ("--worker-cores") :: IntParam(value) :: tail => + workerCores = value + args = tail + + case Nil => + if (userJar == null || userClass == null) { + printUsageAndExit(1) + } + + case _ => + printUsageAndExit(1, args) + } + } + + userArgs = userArgsBuffer.readOnly + } + + def printUsageAndExit(exitCode: Int, unknownParam: Any = null) { + if (unknownParam != null) { + System.err.println("Unknown/unsupported param " + unknownParam) + } + System.err.println( + "Usage: org.apache.spark.deploy.yarn.ApplicationMaster [options] \n" + + "Options:\n" + + " --jar JAR_PATH Path to your application's JAR file (required)\n" + + " --class CLASS_NAME Name of your application's main class (required)\n" + + " --args ARGS Arguments to be passed to your application's main class.\n" + + " Mutliple invocations are possible, each will be passed in order.\n" + + " --num-workers NUM Number of workers to start (Default: 2)\n" + + " --worker-cores NUM Number of cores for the workers (Default: 1)\n" + + " --worker-memory MEM Memory per Worker (e.g. 1000M, 2G) (Default: 1G)\n") + System.exit(exitCode) + } +} diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala new file mode 100644 index 0000000000..1bba6a5ae4 --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -0,0 +1,521 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.net.{InetAddress, UnknownHostException, URI} +import java.nio.ByteBuffer + +import scala.collection.JavaConversions._ +import scala.collection.mutable.HashMap +import scala.collection.mutable.Map + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileContext, FileStatus, FileSystem, Path, FileUtil} +import org.apache.hadoop.fs.permission.FsPermission; +import org.apache.hadoop.io.DataOutputBuffer +import org.apache.hadoop.mapred.Master +import org.apache.hadoop.net.NetUtils +import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.ApplicationConstants.Environment +import org.apache.hadoop.yarn.api.protocolrecords._ +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.client.api.impl.YarnClientImpl +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.ipc.YarnRPC +import org.apache.hadoop.yarn.util.{Apps, Records} + +import org.apache.spark.Logging +import org.apache.spark.util.Utils +import org.apache.spark.deploy.SparkHadoopUtil + + +/** + * The entry point (starting in Client#main() and Client#run()) for launching Spark on YARN. The + * Client submits an application to the global ResourceManager to launch Spark's ApplicationMaster, + * which will launch a Spark master process and negotiate resources throughout its duration. + */ +class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl with Logging { + + var rpc: YarnRPC = YarnRPC.create(conf) + val yarnConf: YarnConfiguration = new YarnConfiguration(conf) + val credentials = UserGroupInformation.getCurrentUser().getCredentials() + private val SPARK_STAGING: String = ".sparkStaging" + private val distCacheMgr = new ClientDistributedCacheManager() + + // Staging directory is private! -> rwx-------- + val STAGING_DIR_PERMISSION: FsPermission = FsPermission.createImmutable(0700: Short) + // App files are world-wide readable and owner writable -> rw-r--r-- + val APP_FILE_PERMISSION: FsPermission = FsPermission.createImmutable(0644: Short) + + def this(args: ClientArguments) = this(new Configuration(), args) + + def runApp(): ApplicationId = { + validateArgs() + // Initialize and start the client service. + init(yarnConf) + start() + + // Log details about this YARN cluster (e.g, the number of slave machines/NodeManagers). + logClusterResourceDetails() + + // Prepare to submit a request to the ResourcManager (specifically its ApplicationsManager (ASM) + // interface). + + // Get a new client application. + val newApp = super.createApplication() + val newAppResponse = newApp.getNewApplicationResponse() + val appId = newAppResponse.getApplicationId() + + verifyClusterResources(newAppResponse) + + // Set up resource and environment variables. + val appStagingDir = getAppStagingDir(appId) + val localResources = prepareLocalResources(appStagingDir) + val launchEnv = setupLaunchEnv(localResources, appStagingDir) + val amContainer = createContainerLaunchContext(newAppResponse, localResources, launchEnv) + + // Set up an application submission context. + val appContext = newApp.getApplicationSubmissionContext() + appContext.setApplicationName(args.appName) + appContext.setQueue(args.amQueue) + appContext.setAMContainerSpec(amContainer) + + // Memory for the ApplicationMaster. + val memoryResource = Records.newRecord(classOf[Resource]).asInstanceOf[Resource] + memoryResource.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + appContext.setResource(memoryResource) + + // Finally, submit and monitor the application. + submitApp(appContext) + appId + } + + def run() { + val appId = runApp() + monitorApplication(appId) + System.exit(0) + } + + // TODO(harvey): This could just go in ClientArguments. + def validateArgs() = { + Map( + (System.getenv("SPARK_JAR") == null) -> "Error: You must set SPARK_JAR environment variable!", + (args.userJar == null) -> "Error: You must specify a user jar!", + (args.userClass == null) -> "Error: You must specify a user class!", + (args.numWorkers <= 0) -> "Error: You must specify atleast 1 worker!", + (args.amMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: AM memory size must be" + + "greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD), + (args.workerMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: Worker memory size" + + "must be greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD.toString) + ).foreach { case(cond, errStr) => + if (cond) { + logError(errStr) + args.printUsageAndExit(1) + } + } + } + + def getAppStagingDir(appId: ApplicationId): String = { + SPARK_STAGING + Path.SEPARATOR + appId.toString() + Path.SEPARATOR + } + + def logClusterResourceDetails() { + val clusterMetrics: YarnClusterMetrics = super.getYarnClusterMetrics + logInfo("Got Cluster metric info from ApplicationsManager (ASM), number of NodeManagers: " + + clusterMetrics.getNumNodeManagers) + + val queueInfo: QueueInfo = super.getQueueInfo(args.amQueue) + logInfo("""Queue info ... queueName: %s, queueCurrentCapacity: %s, queueMaxCapacity: %s, + queueApplicationCount = %s, queueChildQueueCount = %s""".format( + queueInfo.getQueueName, + queueInfo.getCurrentCapacity, + queueInfo.getMaximumCapacity, + queueInfo.getApplications.size, + queueInfo.getChildQueues.size)) + } + + def verifyClusterResources(app: GetNewApplicationResponse) = { + val maxMem = app.getMaximumResourceCapability().getMemory() + logInfo("Max mem capabililty of a single resource in this cluster " + maxMem) + + // If we have requested more then the clusters max for a single resource then exit. + if (args.workerMemory > maxMem) { + logError("Required worker memory (%d MB), is above the max threshold (%d MB) of this cluster.". + format(args.workerMemory, maxMem)) + System.exit(1) + } + val amMem = args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD + if (amMem > maxMem) { + logError("Required AM memory (%d) is above the max threshold (%d) of this cluster". + format(args.amMemory, maxMem)) + System.exit(1) + } + + // We could add checks to make sure the entire cluster has enough resources but that involves + // getting all the node reports and computing ourselves. + } + + /** See if two file systems are the same or not. */ + private def compareFs(srcFs: FileSystem, destFs: FileSystem): Boolean = { + val srcUri = srcFs.getUri() + val dstUri = destFs.getUri() + if (srcUri.getScheme() == null) { + return false + } + if (!srcUri.getScheme().equals(dstUri.getScheme())) { + return false + } + var srcHost = srcUri.getHost() + var dstHost = dstUri.getHost() + if ((srcHost != null) && (dstHost != null)) { + try { + srcHost = InetAddress.getByName(srcHost).getCanonicalHostName() + dstHost = InetAddress.getByName(dstHost).getCanonicalHostName() + } catch { + case e: UnknownHostException => + return false + } + if (!srcHost.equals(dstHost)) { + return false + } + } else if (srcHost == null && dstHost != null) { + return false + } else if (srcHost != null && dstHost == null) { + return false + } + //check for ports + if (srcUri.getPort() != dstUri.getPort()) { + return false + } + return true + } + + /** Copy the file into HDFS if needed. */ + private def copyRemoteFile( + dstDir: Path, + originalPath: Path, + replication: Short, + setPerms: Boolean = false): Path = { + val fs = FileSystem.get(conf) + val remoteFs = originalPath.getFileSystem(conf) + var newPath = originalPath + if (! compareFs(remoteFs, fs)) { + newPath = new Path(dstDir, originalPath.getName()) + logInfo("Uploading " + originalPath + " to " + newPath) + FileUtil.copy(remoteFs, originalPath, fs, newPath, false, conf) + fs.setReplication(newPath, replication) + if (setPerms) fs.setPermission(newPath, new FsPermission(APP_FILE_PERMISSION)) + } + // Resolve any symlinks in the URI path so using a "current" symlink to point to a specific + // version shows the specific version in the distributed cache configuration + val qualPath = fs.makeQualified(newPath) + val fc = FileContext.getFileContext(qualPath.toUri(), conf) + val destPath = fc.resolvePath(qualPath) + destPath + } + + def prepareLocalResources(appStagingDir: String): HashMap[String, LocalResource] = { + logInfo("Preparing Local resources") + // Upload Spark and the application JAR to the remote file system if necessary. Add them as + // local resources to the application master. + val fs = FileSystem.get(conf) + + val delegTokenRenewer = Master.getMasterPrincipal(conf) + if (UserGroupInformation.isSecurityEnabled()) { + if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { + logError("Can't get Master Kerberos principal for use as renewer") + System.exit(1) + } + } + val dst = new Path(fs.getHomeDirectory(), appStagingDir) + val replication = conf.getOrElse("spark.yarn.submit.file.replication", "3").toShort + + if (UserGroupInformation.isSecurityEnabled()) { + val dstFs = dst.getFileSystem(conf) + dstFs.addDelegationTokens(delegTokenRenewer, credentials) + } + + val localResources = HashMap[String, LocalResource]() + FileSystem.mkdirs(fs, dst, new FsPermission(STAGING_DIR_PERMISSION)) + + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + + Map( + Client.SPARK_JAR -> System.getenv("SPARK_JAR"), Client.APP_JAR -> args.userJar, + Client.LOG4J_PROP -> System.getenv("SPARK_LOG4J_CONF") + ).foreach { case(destName, _localPath) => + val localPath: String = if (_localPath != null) _localPath.trim() else "" + if (! localPath.isEmpty()) { + var localURI = new URI(localPath) + // If not specified assume these are in the local filesystem to keep behavior like Hadoop + if (localURI.getScheme() == null) { + localURI = new URI(FileSystem.getLocal(conf).makeQualified(new Path(localPath)).toString) + } + val setPermissions = if (destName.equals(Client.APP_JAR)) true else false + val destPath = copyRemoteFile(dst, new Path(localURI), replication, setPermissions) + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + destName, statCache) + } + } + + // Handle jars local to the ApplicationMaster. + if ((args.addJars != null) && (!args.addJars.isEmpty())){ + args.addJars.split(',').foreach { case file: String => + val localURI = new URI(file.trim()) + val localPath = new Path(localURI) + val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) + val destPath = copyRemoteFile(dst, localPath, replication) + // Only add the resource to the Spark ApplicationMaster. + val appMasterOnly = true + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + linkname, statCache, appMasterOnly) + } + } + + // Handle any distributed cache files + if ((args.files != null) && (!args.files.isEmpty())){ + args.files.split(',').foreach { case file: String => + val localURI = new URI(file.trim()) + val localPath = new Path(localURI) + val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) + val destPath = copyRemoteFile(dst, localPath, replication) + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + linkname, statCache) + } + } + + // Handle any distributed cache archives + if ((args.archives != null) && (!args.archives.isEmpty())) { + args.archives.split(',').foreach { case file:String => + val localURI = new URI(file.trim()) + val localPath = new Path(localURI) + val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) + val destPath = copyRemoteFile(dst, localPath, replication) + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, + linkname, statCache) + } + } + + UserGroupInformation.getCurrentUser().addCredentials(credentials) + localResources + } + + def setupLaunchEnv( + localResources: HashMap[String, LocalResource], + stagingDir: String): HashMap[String, String] = { + logInfo("Setting up the launch environment") + val log4jConfLocalRes = localResources.getOrElse(Client.LOG4J_PROP, null) + + val env = new HashMap[String, String]() + + Client.populateClasspath(yarnConf, log4jConfLocalRes != null, env) + env("SPARK_YARN_MODE") = "true" + env("SPARK_YARN_STAGING_DIR") = stagingDir + + // Set the environment variables to be passed on to the Workers. + distCacheMgr.setDistFilesEnv(env) + distCacheMgr.setDistArchivesEnv(env) + + // Allow users to specify some environment variables. + Apps.setEnvFromInputString(env, System.getenv("SPARK_YARN_USER_ENV")) + + // Add each SPARK_* key to the environment. + System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k,v) => env(k) = v } + + env + } + + def userArgsToString(clientArgs: ClientArguments): String = { + val prefix = " --args " + val args = clientArgs.userArgs + val retval = new StringBuilder() + for (arg <- args){ + retval.append(prefix).append(" '").append(arg).append("' ") + } + retval.toString + } + + def createContainerLaunchContext( + newApp: GetNewApplicationResponse, + localResources: HashMap[String, LocalResource], + env: HashMap[String, String]): ContainerLaunchContext = { + logInfo("Setting up container launch context") + val amContainer = Records.newRecord(classOf[ContainerLaunchContext]) + amContainer.setLocalResources(localResources) + amContainer.setEnvironment(env) + + // TODO: Need a replacement for the following code to fix -Xmx? + // val minResMemory: Int = newApp.getMinimumResourceCapability().getMemory() + // var amMemory = ((args.amMemory / minResMemory) * minResMemory) + + // ((if ((args.amMemory % minResMemory) == 0) 0 else minResMemory) - + // YarnAllocationHandler.MEMORY_OVERHEAD) + + // Extra options for the JVM + var JAVA_OPTS = "" + + // Add Xmx for AM memory + JAVA_OPTS += "-Xmx" + args.amMemory + "m" + + val tmpDir = new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR) + JAVA_OPTS += " -Djava.io.tmpdir=" + tmpDir + + // TODO: Remove once cpuset version is pushed out. + // The context is, default gc for server class machines ends up using all cores to do gc - + // hence if there are multiple containers in same node, Spark GC affects all other containers' + // performance (which can be that of other Spark containers) + // Instead of using this, rely on cpusets by YARN to enforce "proper" Spark behavior in + // multi-tenant environments. Not sure how default Java GC behaves if it is limited to subset + // of cores on a node. + val useConcurrentAndIncrementalGC = env.isDefinedAt("SPARK_USE_CONC_INCR_GC") && + java.lang.Boolean.parseBoolean(env("SPARK_USE_CONC_INCR_GC")) + if (useConcurrentAndIncrementalGC) { + // In our expts, using (default) throughput collector has severe perf ramifications in + // multi-tenant machines + JAVA_OPTS += " -XX:+UseConcMarkSweepGC " + JAVA_OPTS += " -XX:+CMSIncrementalMode " + JAVA_OPTS += " -XX:+CMSIncrementalPacing " + JAVA_OPTS += " -XX:CMSIncrementalDutyCycleMin=0 " + JAVA_OPTS += " -XX:CMSIncrementalDutyCycle=10 " + } + + if (env.isDefinedAt("SPARK_JAVA_OPTS")) { + JAVA_OPTS += " " + env("SPARK_JAVA_OPTS") + } + + // Command for the ApplicationMaster + var javaCommand = "java" + val javaHome = System.getenv("JAVA_HOME") + if ((javaHome != null && !javaHome.isEmpty()) || env.isDefinedAt("JAVA_HOME")) { + javaCommand = Environment.JAVA_HOME.$() + "/bin/java" + } + + val commands = List[String]( + javaCommand + + " -server " + + JAVA_OPTS + + " " + args.amClass + + " --class " + args.userClass + + " --jar " + args.userJar + + userArgsToString(args) + + " --worker-memory " + args.workerMemory + + " --worker-cores " + args.workerCores + + " --num-workers " + args.numWorkers + + " 1> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout" + + " 2> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr") + + logInfo("Command for starting the Spark ApplicationMaster: " + commands(0)) + amContainer.setCommands(commands) + + // Setup security tokens. + val dob = new DataOutputBuffer() + credentials.writeTokenStorageToStream(dob) + amContainer.setTokens(ByteBuffer.wrap(dob.getData())) + + amContainer + } + + def submitApp(appContext: ApplicationSubmissionContext) = { + // Submit the application to the applications manager. + logInfo("Submitting application to ASM") + super.submitApplication(appContext) + } + + def monitorApplication(appId: ApplicationId): Boolean = { + val interval = new SparkConf().getOrElse("spark.yarn.report.interval", "1000").toLong + + while (true) { + Thread.sleep(interval) + val report = super.getApplicationReport(appId) + + logInfo("Application report from ASM: \n" + + "\t application identifier: " + appId.toString() + "\n" + + "\t appId: " + appId.getId() + "\n" + + "\t clientToAMToken: " + report.getClientToAMToken() + "\n" + + "\t appDiagnostics: " + report.getDiagnostics() + "\n" + + "\t appMasterHost: " + report.getHost() + "\n" + + "\t appQueue: " + report.getQueue() + "\n" + + "\t appMasterRpcPort: " + report.getRpcPort() + "\n" + + "\t appStartTime: " + report.getStartTime() + "\n" + + "\t yarnAppState: " + report.getYarnApplicationState() + "\n" + + "\t distributedFinalState: " + report.getFinalApplicationStatus() + "\n" + + "\t appTrackingUrl: " + report.getTrackingUrl() + "\n" + + "\t appUser: " + report.getUser() + ) + + val state = report.getYarnApplicationState() + val dsStatus = report.getFinalApplicationStatus() + if (state == YarnApplicationState.FINISHED || + state == YarnApplicationState.FAILED || + state == YarnApplicationState.KILLED) { + return true + } + } + true + } +} + +object Client { + val SPARK_JAR: String = "spark.jar" + val APP_JAR: String = "app.jar" + val LOG4J_PROP: String = "log4j.properties" + + def main(argStrings: Array[String]) { + // Set an env variable indicating we are running in YARN mode. + // Note: anything env variable with SPARK_ prefix gets propagated to all (remote) processes - + // see Client#setupLaunchEnv(). + System.setProperty("SPARK_YARN_MODE", "true") + + val args = new ClientArguments(argStrings) + + (new Client(args)).run() + } + + // Based on code from org.apache.hadoop.mapreduce.v2.util.MRApps + def populateHadoopClasspath(conf: Configuration, env: HashMap[String, String]) { + for (c <- conf.getStrings(YarnConfiguration.YARN_APPLICATION_CLASSPATH)) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, c.trim) + } + } + + def populateClasspath(conf: Configuration, addLog4j: Boolean, env: HashMap[String, String]) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$()) + // If log4j present, ensure ours overrides all others + if (addLog4j) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + LOG4J_PROP) + } + // Normally the users app.jar is last in case conflicts with spark jars + val userClasspathFirst = conf.getOrElse("spark.yarn.user.classpath.first", "false") + .toBoolean + if (userClasspathFirst) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + APP_JAR) + } + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + SPARK_JAR) + Client.populateHadoopClasspath(conf, env) + + if (!userClasspathFirst) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + APP_JAR) + } + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + "*") + } +} diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala new file mode 100644 index 0000000000..1a9bb97b3e --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -0,0 +1,149 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import scala.collection.mutable.{ArrayBuffer, HashMap} + +import org.apache.spark.scheduler.{InputFormatInfo, SplitInfo} +import org.apache.spark.util.IntParam +import org.apache.spark.util.MemoryParam + + +// TODO: Add code and support for ensuring that yarn resource 'tasks' are location aware ! +class ClientArguments(val args: Array[String]) { + var addJars: String = null + var files: String = null + var archives: String = null + var userJar: String = null + var userClass: String = null + var userArgs: Seq[String] = Seq[String]() + var workerMemory = 1024 // MB + var workerCores = 1 + var numWorkers = 2 + var amQueue = conf.getOrElse("QUEUE", "default") + var amMemory: Int = 512 // MB + var amClass: String = "org.apache.spark.deploy.yarn.ApplicationMaster" + var appName: String = "Spark" + // TODO + var inputFormatInfo: List[InputFormatInfo] = null + // TODO(harvey) + var priority = 0 + + parseArgs(args.toList) + + private def parseArgs(inputArgs: List[String]): Unit = { + val userArgsBuffer: ArrayBuffer[String] = new ArrayBuffer[String]() + val inputFormatMap: HashMap[String, InputFormatInfo] = new HashMap[String, InputFormatInfo]() + + var args = inputArgs + + while (!args.isEmpty) { + args match { + case ("--jar") :: value :: tail => + userJar = value + args = tail + + case ("--class") :: value :: tail => + userClass = value + args = tail + + case ("--args") :: value :: tail => + userArgsBuffer += value + args = tail + + case ("--master-class") :: value :: tail => + amClass = value + args = tail + + case ("--master-memory") :: MemoryParam(value) :: tail => + amMemory = value + args = tail + + case ("--num-workers") :: IntParam(value) :: tail => + numWorkers = value + args = tail + + case ("--worker-memory") :: MemoryParam(value) :: tail => + workerMemory = value + args = tail + + case ("--worker-cores") :: IntParam(value) :: tail => + workerCores = value + args = tail + + case ("--queue") :: value :: tail => + amQueue = value + args = tail + + case ("--name") :: value :: tail => + appName = value + args = tail + + case ("--addJars") :: value :: tail => + addJars = value + args = tail + + case ("--files") :: value :: tail => + files = value + args = tail + + case ("--archives") :: value :: tail => + archives = value + args = tail + + case Nil => + if (userJar == null || userClass == null) { + printUsageAndExit(1) + } + + case _ => + printUsageAndExit(1, args) + } + } + + userArgs = userArgsBuffer.readOnly + inputFormatInfo = inputFormatMap.values.toList + } + + + def printUsageAndExit(exitCode: Int, unknownParam: Any = null) { + if (unknownParam != null) { + System.err.println("Unknown/unsupported param " + unknownParam) + } + System.err.println( + "Usage: org.apache.spark.deploy.yarn.Client [options] \n" + + "Options:\n" + + " --jar JAR_PATH Path to your application's JAR file (required)\n" + + " --class CLASS_NAME Name of your application's main class (required)\n" + + " --args ARGS Arguments to be passed to your application's main class.\n" + + " Mutliple invocations are possible, each will be passed in order.\n" + + " --num-workers NUM Number of workers to start (Default: 2)\n" + + " --worker-cores NUM Number of cores for the workers (Default: 1). This is unsused right now.\n" + + " --master-class CLASS_NAME Class Name for Master (Default: spark.deploy.yarn.ApplicationMaster)\n" + + " --master-memory MEM Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)\n" + + " --worker-memory MEM Memory per Worker (e.g. 1000M, 2G) (Default: 1G)\n" + + " --name NAME The name of your application (Default: Spark)\n" + + " --queue QUEUE The hadoop queue to use for allocation requests (Default: 'default')\n" + + " --addJars jars Comma separated list of local jars that want SparkContext.addJar to work with.\n" + + " --files files Comma separated list of files to be distributed with the job.\n" + + " --archives archives Comma separated list of archives to be distributed with the job." + ) + System.exit(exitCode) + } + +} diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala new file mode 100644 index 0000000000..5f159b073f --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala @@ -0,0 +1,228 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.net.URI + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.FileStatus +import org.apache.hadoop.fs.FileSystem +import org.apache.hadoop.fs.Path +import org.apache.hadoop.fs.permission.FsAction +import org.apache.hadoop.yarn.api.records.LocalResource +import org.apache.hadoop.yarn.api.records.LocalResourceVisibility +import org.apache.hadoop.yarn.api.records.LocalResourceType +import org.apache.hadoop.yarn.util.{Records, ConverterUtils} + +import org.apache.spark.Logging + +import scala.collection.mutable.HashMap +import scala.collection.mutable.LinkedHashMap +import scala.collection.mutable.Map + + +/** Client side methods to setup the Hadoop distributed cache */ +class ClientDistributedCacheManager() extends Logging { + private val distCacheFiles: Map[String, Tuple3[String, String, String]] = + LinkedHashMap[String, Tuple3[String, String, String]]() + private val distCacheArchives: Map[String, Tuple3[String, String, String]] = + LinkedHashMap[String, Tuple3[String, String, String]]() + + + /** + * Add a resource to the list of distributed cache resources. This list can + * be sent to the ApplicationMaster and possibly the workers so that it can + * be downloaded into the Hadoop distributed cache for use by this application. + * Adds the LocalResource to the localResources HashMap passed in and saves + * the stats of the resources to they can be sent to the workers and verified. + * + * @param fs FileSystem + * @param conf Configuration + * @param destPath path to the resource + * @param localResources localResource hashMap to insert the resource into + * @param resourceType LocalResourceType + * @param link link presented in the distributed cache to the destination + * @param statCache cache to store the file/directory stats + * @param appMasterOnly Whether to only add the resource to the app master + */ + def addResource( + fs: FileSystem, + conf: Configuration, + destPath: Path, + localResources: HashMap[String, LocalResource], + resourceType: LocalResourceType, + link: String, + statCache: Map[URI, FileStatus], + appMasterOnly: Boolean = false) = { + val destStatus = fs.getFileStatus(destPath) + val amJarRsrc = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] + amJarRsrc.setType(resourceType) + val visibility = getVisibility(conf, destPath.toUri(), statCache) + amJarRsrc.setVisibility(visibility) + amJarRsrc.setResource(ConverterUtils.getYarnUrlFromPath(destPath)) + amJarRsrc.setTimestamp(destStatus.getModificationTime()) + amJarRsrc.setSize(destStatus.getLen()) + if (link == null || link.isEmpty()) throw new Exception("You must specify a valid link name") + localResources(link) = amJarRsrc + + if (appMasterOnly == false) { + val uri = destPath.toUri() + val pathURI = new URI(uri.getScheme(), uri.getAuthority(), uri.getPath(), null, link) + if (resourceType == LocalResourceType.FILE) { + distCacheFiles(pathURI.toString()) = (destStatus.getLen().toString(), + destStatus.getModificationTime().toString(), visibility.name()) + } else { + distCacheArchives(pathURI.toString()) = (destStatus.getLen().toString(), + destStatus.getModificationTime().toString(), visibility.name()) + } + } + } + + /** + * Adds the necessary cache file env variables to the env passed in + * @param env + */ + def setDistFilesEnv(env: Map[String, String]) = { + val (keys, tupleValues) = distCacheFiles.unzip + val (sizes, timeStamps, visibilities) = tupleValues.unzip3 + + if (keys.size > 0) { + env("SPARK_YARN_CACHE_FILES") = keys.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_TIME_STAMPS") = + timeStamps.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_FILE_SIZES") = + sizes.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_VISIBILITIES") = + visibilities.reduceLeft[String] { (acc,n) => acc + "," + n } + } + } + + /** + * Adds the necessary cache archive env variables to the env passed in + * @param env + */ + def setDistArchivesEnv(env: Map[String, String]) = { + val (keys, tupleValues) = distCacheArchives.unzip + val (sizes, timeStamps, visibilities) = tupleValues.unzip3 + + if (keys.size > 0) { + env("SPARK_YARN_CACHE_ARCHIVES") = keys.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") = + timeStamps.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") = + sizes.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") = + visibilities.reduceLeft[String] { (acc,n) => acc + "," + n } + } + } + + /** + * Returns the local resource visibility depending on the cache file permissions + * @param conf + * @param uri + * @param statCache + * @return LocalResourceVisibility + */ + def getVisibility(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): + LocalResourceVisibility = { + if (isPublic(conf, uri, statCache)) { + return LocalResourceVisibility.PUBLIC + } + return LocalResourceVisibility.PRIVATE + } + + /** + * Returns a boolean to denote whether a cache file is visible to all(public) + * or not + * @param conf + * @param uri + * @param statCache + * @return true if the path in the uri is visible to all, false otherwise + */ + def isPublic(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): Boolean = { + val fs = FileSystem.get(uri, conf) + val current = new Path(uri.getPath()) + //the leaf level file should be readable by others + if (!checkPermissionOfOther(fs, current, FsAction.READ, statCache)) { + return false + } + return ancestorsHaveExecutePermissions(fs, current.getParent(), statCache) + } + + /** + * Returns true if all ancestors of the specified path have the 'execute' + * permission set for all users (i.e. that other users can traverse + * the directory heirarchy to the given path) + * @param fs + * @param path + * @param statCache + * @return true if all ancestors have the 'execute' permission set for all users + */ + def ancestorsHaveExecutePermissions(fs: FileSystem, path: Path, + statCache: Map[URI, FileStatus]): Boolean = { + var current = path + while (current != null) { + //the subdirs in the path should have execute permissions for others + if (!checkPermissionOfOther(fs, current, FsAction.EXECUTE, statCache)) { + return false + } + current = current.getParent() + } + return true + } + + /** + * Checks for a given path whether the Other permissions on it + * imply the permission in the passed FsAction + * @param fs + * @param path + * @param action + * @param statCache + * @return true if the path in the uri is visible to all, false otherwise + */ + def checkPermissionOfOther(fs: FileSystem, path: Path, + action: FsAction, statCache: Map[URI, FileStatus]): Boolean = { + val status = getFileStatus(fs, path.toUri(), statCache) + val perms = status.getPermission() + val otherAction = perms.getOtherAction() + if (otherAction.implies(action)) { + return true + } + return false + } + + /** + * Checks to see if the given uri exists in the cache, if it does it + * returns the existing FileStatus, otherwise it stats the uri, stores + * it in the cache, and returns the FileStatus. + * @param fs + * @param uri + * @param statCache + * @return FileStatus + */ + def getFileStatus(fs: FileSystem, uri: URI, statCache: Map[URI, FileStatus]): FileStatus = { + val stat = statCache.get(uri) match { + case Some(existstat) => existstat + case None => + val newStat = fs.getFileStatus(new Path(uri)) + statCache.put(uri, newStat) + newStat + } + return stat + } +} diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala new file mode 100644 index 0000000000..f7d73f0d83 --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala @@ -0,0 +1,222 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.net.Socket +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.net.NetUtils +import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.api.protocolrecords._ +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.util.{ConverterUtils, Records} +import akka.actor._ +import akka.remote._ +import akka.actor.Terminated +import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.util.{Utils, AkkaUtils} +import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend +import org.apache.spark.scheduler.SplitInfo +import org.apache.hadoop.yarn.client.api.AMRMClient +import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest + +class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) extends Logging { + + def this(args: ApplicationMasterArguments) = this(args, new Configuration()) + + private var appAttemptId: ApplicationAttemptId = _ + private var reporterThread: Thread = _ + private val yarnConf: YarnConfiguration = new YarnConfiguration(conf) + + private var yarnAllocator: YarnAllocationHandler = _ + private var driverClosed:Boolean = false + + private var amClient: AMRMClient[ContainerRequest] = _ + + val actorSystem : ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0)._1 + var actor: ActorRef = _ + + // This actor just working as a monitor to watch on Driver Actor. + class MonitorActor(driverUrl: String) extends Actor { + + var driver: ActorSelection = null + + override def preStart() { + logInfo("Listen to driver: " + driverUrl) + driver = context.actorSelection(driverUrl) + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) + } + + override def receive = { + case x: DisassociatedEvent => + logInfo("Driver terminated or disconnected! Shutting down.") + driverClosed = true + } + } + + def run() { + + amClient = AMRMClient.createAMRMClient() + amClient.init(yarnConf) + amClient.start() + + appAttemptId = getApplicationAttemptId() + registerApplicationMaster() + + waitForSparkMaster() + + // Allocate all containers + allocateWorkers() + + // Launch a progress reporter thread, else app will get killed after expiration (def: 10mins) timeout + // ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapse. + + val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000) + // must be <= timeoutInterval/ 2. + // On other hand, also ensure that we are reasonably responsive without causing too many requests to RM. + // so atleast 1 minute or timeoutInterval / 10 - whichever is higher. + val interval = math.min(timeoutInterval / 2, math.max(timeoutInterval/ 10, 60000L)) + reporterThread = launchReporterThread(interval) + + // Wait for the reporter thread to Finish. + reporterThread.join() + + finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) + actorSystem.shutdown() + + logInfo("Exited") + System.exit(0) + } + + private def getApplicationAttemptId(): ApplicationAttemptId = { + val envs = System.getenv() + val containerIdString = envs.get(ApplicationConstants.Environment.CONTAINER_ID.name()) + val containerId = ConverterUtils.toContainerId(containerIdString) + val appAttemptId = containerId.getApplicationAttemptId() + logInfo("ApplicationAttemptId: " + appAttemptId) + appAttemptId + } + + private def registerApplicationMaster(): RegisterApplicationMasterResponse = { + logInfo("Registering the ApplicationMaster") + // TODO:(Raymond) Find out Spark UI address and fill in here? + amClient.registerApplicationMaster(Utils.localHostName(), 0, "") + } + + private def waitForSparkMaster() { + logInfo("Waiting for Spark driver to be reachable.") + var driverUp = false + val hostport = args.userArgs(0) + val (driverHost, driverPort) = Utils.parseHostPort(hostport) + while(!driverUp) { + try { + val socket = new Socket(driverHost, driverPort) + socket.close() + logInfo("Driver now available: %s:%s".format(driverHost, driverPort)) + driverUp = true + } catch { + case e: Exception => + logError("Failed to connect to driver at %s:%s, retrying ...". + format(driverHost, driverPort)) + Thread.sleep(100) + } + } + conf.set("spark.driver.host", driverHost) + conf.set("spark.driver.port", driverPort.toString) + + val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( + driverHost, driverPort.toString, CoarseGrainedSchedulerBackend.ACTOR_NAME) + + actor = actorSystem.actorOf(Props(new MonitorActor(driverUrl)), name = "YarnAM") + } + + + private def allocateWorkers() { + + // Fixme: should get preferredNodeLocationData from SparkContext, just fake a empty one for now. + val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] = + scala.collection.immutable.Map() + + yarnAllocator = YarnAllocationHandler.newAllocator( + yarnConf, + amClient, + appAttemptId, + args, + preferredNodeLocationData) + + logInfo("Allocating " + args.numWorkers + " workers.") + // Wait until all containers have finished + // TODO: This is a bit ugly. Can we make it nicer? + // TODO: Handle container failure + + yarnAllocator.addResourceRequests(args.numWorkers) + while(yarnAllocator.getNumWorkersRunning < args.numWorkers) { + yarnAllocator.allocateResources() + Thread.sleep(100) + } + + logInfo("All workers have launched.") + + } + + // TODO: We might want to extend this to allocate more containers in case they die ! + private def launchReporterThread(_sleepTime: Long): Thread = { + val sleepTime = if (_sleepTime <= 0 ) 0 else _sleepTime + + val t = new Thread { + override def run() { + while (!driverClosed) { + val missingWorkerCount = args.numWorkers - yarnAllocator.getNumWorkersRunning - + yarnAllocator.getNumPendingAllocate + if (missingWorkerCount > 0) { + logInfo("Allocating %d containers to make up for (potentially) lost containers". + format(missingWorkerCount)) + yarnAllocator.addResourceRequests(missingWorkerCount) + } + sendProgress() + Thread.sleep(sleepTime) + } + } + } + // setting to daemon status, though this is usually not a good idea. + t.setDaemon(true) + t.start() + logInfo("Started progress reporter thread - sleep time : " + sleepTime) + t + } + + private def sendProgress() { + logDebug("Sending progress") + // simulated with an allocate request with no nodes requested ... + yarnAllocator.allocateResources() + } + + def finishApplicationMaster(status: FinalApplicationStatus) { + logInfo("finish ApplicationMaster with " + status) + amClient.unregisterApplicationMaster(status, "" /* appMessage */, "" /* appTrackingUrl */) + } + +} + + +object WorkerLauncher { + def main(argStrings: Array[String]) { + val args = new ApplicationMasterArguments(argStrings) + new WorkerLauncher(args).run() + } +} diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerRunnable.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerRunnable.scala new file mode 100644 index 0000000000..9f5523c4b9 --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerRunnable.scala @@ -0,0 +1,209 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.net.URI +import java.nio.ByteBuffer +import java.security.PrivilegedExceptionAction + +import scala.collection.JavaConversions._ +import scala.collection.mutable.HashMap + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path +import org.apache.hadoop.io.DataOutputBuffer +import org.apache.hadoop.net.NetUtils +import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.ApplicationConstants.Environment +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.api.records.impl.pb.ProtoUtils +import org.apache.hadoop.yarn.api.protocolrecords._ +import org.apache.hadoop.yarn.client.api.NMClient +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.ipc.YarnRPC +import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records} + +import org.apache.spark.Logging + + +class WorkerRunnable( + container: Container, + conf: Configuration, + masterAddress: String, + slaveId: String, + hostname: String, + workerMemory: Int, + workerCores: Int) + extends Runnable with Logging { + + var rpc: YarnRPC = YarnRPC.create(conf) + var nmClient: NMClient = _ + val yarnConf: YarnConfiguration = new YarnConfiguration(conf) + + def run = { + logInfo("Starting Worker Container") + nmClient = NMClient.createNMClient() + nmClient.init(yarnConf) + nmClient.start() + startContainer + } + + def startContainer = { + logInfo("Setting up ContainerLaunchContext") + + val ctx = Records.newRecord(classOf[ContainerLaunchContext]) + .asInstanceOf[ContainerLaunchContext] + + val localResources = prepareLocalResources + ctx.setLocalResources(localResources) + + val env = prepareEnvironment + ctx.setEnvironment(env) + + // Extra options for the JVM + var JAVA_OPTS = "" + // Set the JVM memory + val workerMemoryString = workerMemory + "m" + JAVA_OPTS += "-Xms" + workerMemoryString + " -Xmx" + workerMemoryString + " " + if (env.isDefinedAt("SPARK_JAVA_OPTS")) { + JAVA_OPTS += env("SPARK_JAVA_OPTS") + " " + } + + JAVA_OPTS += " -Djava.io.tmpdir=" + + new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR) + " " + + // Commenting it out for now - so that people can refer to the properties if required. Remove + // it once cpuset version is pushed out. + // The context is, default gc for server class machines end up using all cores to do gc - hence + // if there are multiple containers in same node, spark gc effects all other containers + // performance (which can also be other spark containers) + // Instead of using this, rely on cpusets by YARN to enforce spark behaves 'properly' in + // multi-tenant environments. Not sure how default java gc behaves if it is limited to subset + // of cores on a node. +/* + else { + // If no java_opts specified, default to using -XX:+CMSIncrementalMode + // It might be possible that other modes/config is being done in SPARK_JAVA_OPTS, so we dont + // want to mess with it. + // In our expts, using (default) throughput collector has severe perf ramnifications in + // multi-tennent machines + // The options are based on + // http://www.oracle.com/technetwork/java/gc-tuning-5-138395.html#0.0.0.%20When%20to%20Use%20the%20Concurrent%20Low%20Pause%20Collector|outline + JAVA_OPTS += " -XX:+UseConcMarkSweepGC " + JAVA_OPTS += " -XX:+CMSIncrementalMode " + JAVA_OPTS += " -XX:+CMSIncrementalPacing " + JAVA_OPTS += " -XX:CMSIncrementalDutyCycleMin=0 " + JAVA_OPTS += " -XX:CMSIncrementalDutyCycle=10 " + } +*/ + + val credentials = UserGroupInformation.getCurrentUser().getCredentials() + val dob = new DataOutputBuffer() + credentials.writeTokenStorageToStream(dob) + ctx.setTokens(ByteBuffer.wrap(dob.getData())) + + var javaCommand = "java" + val javaHome = System.getenv("JAVA_HOME") + if ((javaHome != null && !javaHome.isEmpty()) || env.isDefinedAt("JAVA_HOME")) { + javaCommand = Environment.JAVA_HOME.$() + "/bin/java" + } + + val commands = List[String](javaCommand + + " -server " + + // Kill if OOM is raised - leverage yarn's failure handling to cause rescheduling. + // Not killing the task leaves various aspects of the worker and (to some extent) the jvm in + // an inconsistent state. + // TODO: If the OOM is not recoverable by rescheduling it on different node, then do + // 'something' to fail job ... akin to blacklisting trackers in mapred ? + " -XX:OnOutOfMemoryError='kill %p' " + + JAVA_OPTS + + " org.apache.spark.executor.CoarseGrainedExecutorBackend " + + masterAddress + " " + + slaveId + " " + + hostname + " " + + workerCores + + " 1> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout" + + " 2> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr") + logInfo("Setting up worker with commands: " + commands) + ctx.setCommands(commands) + + // Send the start request to the ContainerManager + nmClient.startContainer(container, ctx) + } + + private def setupDistributedCache( + file: String, + rtype: LocalResourceType, + localResources: HashMap[String, LocalResource], + timestamp: String, + size: String, + vis: String) = { + val uri = new URI(file) + val amJarRsrc = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] + amJarRsrc.setType(rtype) + amJarRsrc.setVisibility(LocalResourceVisibility.valueOf(vis)) + amJarRsrc.setResource(ConverterUtils.getYarnUrlFromURI(uri)) + amJarRsrc.setTimestamp(timestamp.toLong) + amJarRsrc.setSize(size.toLong) + localResources(uri.getFragment()) = amJarRsrc + } + + def prepareLocalResources: HashMap[String, LocalResource] = { + logInfo("Preparing Local resources") + val localResources = HashMap[String, LocalResource]() + + if (System.getenv("SPARK_YARN_CACHE_FILES") != null) { + val timeStamps = System.getenv("SPARK_YARN_CACHE_FILES_TIME_STAMPS").split(',') + val fileSizes = System.getenv("SPARK_YARN_CACHE_FILES_FILE_SIZES").split(',') + val distFiles = System.getenv("SPARK_YARN_CACHE_FILES").split(',') + val visibilities = System.getenv("SPARK_YARN_CACHE_FILES_VISIBILITIES").split(',') + for( i <- 0 to distFiles.length - 1) { + setupDistributedCache(distFiles(i), LocalResourceType.FILE, localResources, timeStamps(i), + fileSizes(i), visibilities(i)) + } + } + + if (System.getenv("SPARK_YARN_CACHE_ARCHIVES") != null) { + val timeStamps = System.getenv("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS").split(',') + val fileSizes = System.getenv("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES").split(',') + val distArchives = System.getenv("SPARK_YARN_CACHE_ARCHIVES").split(',') + val visibilities = System.getenv("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES").split(',') + for( i <- 0 to distArchives.length - 1) { + setupDistributedCache(distArchives(i), LocalResourceType.ARCHIVE, localResources, + timeStamps(i), fileSizes(i), visibilities(i)) + } + } + + logInfo("Prepared Local resources " + localResources) + localResources + } + + def prepareEnvironment: HashMap[String, String] = { + val env = new HashMap[String, String]() + + Client.populateClasspath(yarnConf, System.getenv("SPARK_YARN_LOG4J_PATH") != null, env) + + // Allow users to specify some environment variables + Apps.setEnvFromInputString(env, System.getenv("SPARK_YARN_USER_ENV")) + + System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k,v) => env(k) = v } + env + } + +} diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala new file mode 100644 index 0000000000..abc3447746 --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala @@ -0,0 +1,687 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.lang.{Boolean => JBoolean} +import java.util.{Collections, Set => JSet} +import java.util.concurrent.{CopyOnWriteArrayList, ConcurrentHashMap} +import java.util.concurrent.atomic.AtomicInteger + +import scala.collection +import scala.collection.JavaConversions._ +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} + +import org.apache.spark.Logging +import org.apache.spark.scheduler.{SplitInfo,TaskSchedulerImpl} +import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend +import org.apache.spark.util.Utils + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.yarn.api.ApplicationMasterProtocol +import org.apache.hadoop.yarn.api.records.ApplicationAttemptId +import org.apache.hadoop.yarn.api.records.{Container, ContainerId, ContainerStatus} +import org.apache.hadoop.yarn.api.records.{Priority, Resource, ResourceRequest} +import org.apache.hadoop.yarn.api.protocolrecords.{AllocateRequest, AllocateResponse} +import org.apache.hadoop.yarn.client.api.AMRMClient +import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest +import org.apache.hadoop.yarn.util.{RackResolver, Records} + + +object AllocationType extends Enumeration ("HOST", "RACK", "ANY") { + type AllocationType = Value + val HOST, RACK, ANY = Value +} + +// TODO: +// Too many params. +// Needs to be mt-safe +// Need to refactor this to make it 'cleaner' ... right now, all computation is reactive - should +// make it more proactive and decoupled. + +// Note that right now, we assume all node asks as uniform in terms of capabilities and priority +// Refer to http://developer.yahoo.com/blogs/hadoop/posts/2011/03/mapreduce-nextgen-scheduler/ for +// more info on how we are requesting for containers. +private[yarn] class YarnAllocationHandler( + val conf: Configuration, + val amClient: AMRMClient[ContainerRequest], + val appAttemptId: ApplicationAttemptId, + val maxWorkers: Int, + val workerMemory: Int, + val workerCores: Int, + val preferredHostToCount: Map[String, Int], + val preferredRackToCount: Map[String, Int]) + extends Logging { + // These three are locked on allocatedHostToContainersMap. Complementary data structures + // allocatedHostToContainersMap : containers which are running : host, Set<containerid> + // allocatedContainerToHostMap: container to host mapping. + private val allocatedHostToContainersMap = + new HashMap[String, collection.mutable.Set[ContainerId]]() + + private val allocatedContainerToHostMap = new HashMap[ContainerId, String]() + + // allocatedRackCount is populated ONLY if allocation happens (or decremented if this is an + // allocated node) + // As with the two data structures above, tightly coupled with them, and to be locked on + // allocatedHostToContainersMap + private val allocatedRackCount = new HashMap[String, Int]() + + // Containers which have been released. + private val releasedContainerList = new CopyOnWriteArrayList[ContainerId]() + // Containers to be released in next request to RM + private val pendingReleaseContainers = new ConcurrentHashMap[ContainerId, Boolean] + + // Number of container requests that have been sent to, but not yet allocated by the + // ApplicationMaster. + private val numPendingAllocate = new AtomicInteger() + private val numWorkersRunning = new AtomicInteger() + // Used to generate a unique id per worker + private val workerIdCounter = new AtomicInteger() + private val lastResponseId = new AtomicInteger() + private val numWorkersFailed = new AtomicInteger() + + def getNumPendingAllocate: Int = numPendingAllocate.intValue + + def getNumWorkersRunning: Int = numWorkersRunning.intValue + + def getNumWorkersFailed: Int = numWorkersFailed.intValue + + def isResourceConstraintSatisfied(container: Container): Boolean = { + container.getResource.getMemory >= (workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + } + + def releaseContainer(container: Container) { + val containerId = container.getId + pendingReleaseContainers.put(containerId, true) + amClient.releaseAssignedContainer(containerId) + } + + def allocateResources() { + // We have already set the container request. Poll the ResourceManager for a response. + // This doubles as a heartbeat if there are no pending container requests. + val progressIndicator = 0.1f + val allocateResponse = amClient.allocate(progressIndicator) + + val allocatedContainers = allocateResponse.getAllocatedContainers() + if (allocatedContainers.size > 0) { + var numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * allocatedContainers.size) + + if (numPendingAllocateNow < 0) { + numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * numPendingAllocateNow) + } + + logDebug(""" + Allocated containers: %d + Current worker count: %d + Containers released: %s + Containers to-be-released: %s + Cluster resources: %s + """.format( + allocatedContainers.size, + numWorkersRunning.get(), + releasedContainerList, + pendingReleaseContainers, + allocateResponse.getAvailableResources)) + + val hostToContainers = new HashMap[String, ArrayBuffer[Container]]() + + for (container <- allocatedContainers) { + if (isResourceConstraintSatisfied(container)) { + // Add the accepted `container` to the host's list of already accepted, + // allocated containers + val host = container.getNodeId.getHost + val containersForHost = hostToContainers.getOrElseUpdate(host, + new ArrayBuffer[Container]()) + containersForHost += container + } else { + // Release container, since it doesn't satisfy resource constraints. + releaseContainer(container) + } + } + + // Find the appropriate containers to use. + // TODO: Cleanup this group-by... + val dataLocalContainers = new HashMap[String, ArrayBuffer[Container]]() + val rackLocalContainers = new HashMap[String, ArrayBuffer[Container]]() + val offRackContainers = new HashMap[String, ArrayBuffer[Container]]() + + for (candidateHost <- hostToContainers.keySet) { + val maxExpectedHostCount = preferredHostToCount.getOrElse(candidateHost, 0) + val requiredHostCount = maxExpectedHostCount - allocatedContainersOnHost(candidateHost) + + val remainingContainersOpt = hostToContainers.get(candidateHost) + assert(remainingContainersOpt.isDefined) + var remainingContainers = remainingContainersOpt.get + + if (requiredHostCount >= remainingContainers.size) { + // Since we have <= required containers, add all remaining containers to + // `dataLocalContainers`. + dataLocalContainers.put(candidateHost, remainingContainers) + // There are no more free containers remaining. + remainingContainers = null + } else if (requiredHostCount > 0) { + // Container list has more containers than we need for data locality. + // Split the list into two: one based on the data local container count, + // (`remainingContainers.size` - `requiredHostCount`), and the other to hold remaining + // containers. + val (dataLocal, remaining) = remainingContainers.splitAt( + remainingContainers.size - requiredHostCount) + dataLocalContainers.put(candidateHost, dataLocal) + + // Invariant: remainingContainers == remaining + + // YARN has a nasty habit of allocating a ton of containers on a host - discourage this. + // Add each container in `remaining` to list of containers to release. If we have an + // insufficient number of containers, then the next allocation cycle will reallocate + // (but won't treat it as data local). + // TODO(harvey): Rephrase this comment some more. + for (container <- remaining) releaseContainer(container) + remainingContainers = null + } + + // For rack local containers + if (remainingContainers != null) { + val rack = YarnAllocationHandler.lookupRack(conf, candidateHost) + if (rack != null) { + val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0) + val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) - + rackLocalContainers.getOrElse(rack, List()).size + + if (requiredRackCount >= remainingContainers.size) { + // Add all remaining containers to to `dataLocalContainers`. + dataLocalContainers.put(rack, remainingContainers) + remainingContainers = null + } else if (requiredRackCount > 0) { + // Container list has more containers that we need for data locality. + // Split the list into two: one based on the data local container count, + // (`remainingContainers.size` - `requiredHostCount`), and the other to hold remaining + // containers. + val (rackLocal, remaining) = remainingContainers.splitAt( + remainingContainers.size - requiredRackCount) + val existingRackLocal = rackLocalContainers.getOrElseUpdate(rack, + new ArrayBuffer[Container]()) + + existingRackLocal ++= rackLocal + + remainingContainers = remaining + } + } + } + + if (remainingContainers != null) { + // Not all containers have been consumed - add them to the list of off-rack containers. + offRackContainers.put(candidateHost, remainingContainers) + } + } + + // Now that we have split the containers into various groups, go through them in order: + // first host-local, then rack-local, and finally off-rack. + // Note that the list we create below tries to ensure that not all containers end up within + // a host if there is a sufficiently large number of hosts/containers. + val allocatedContainersToProcess = new ArrayBuffer[Container](allocatedContainers.size) + allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(dataLocalContainers) + allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(rackLocalContainers) + allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(offRackContainers) + + // Run each of the allocated containers. + for (container <- allocatedContainersToProcess) { + val numWorkersRunningNow = numWorkersRunning.incrementAndGet() + val workerHostname = container.getNodeId.getHost + val containerId = container.getId + + val workerMemoryOverhead = (workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + assert(container.getResource.getMemory >= workerMemoryOverhead) + + if (numWorkersRunningNow > maxWorkers) { + logInfo("""Ignoring container %s at host %s, since we already have the required number of + containers for it.""".format(containerId, workerHostname)) + releaseContainer(container) + numWorkersRunning.decrementAndGet() + } else { + val workerId = workerIdCounter.incrementAndGet().toString + val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( + conf.get("spark.driver.host"), + conf.get("spark.driver.port"), + CoarseGrainedSchedulerBackend.ACTOR_NAME) + + logInfo("Launching container %s for on host %s".format(containerId, workerHostname)) + + // To be safe, remove the container from `pendingReleaseContainers`. + pendingReleaseContainers.remove(containerId) + + val rack = YarnAllocationHandler.lookupRack(conf, workerHostname) + allocatedHostToContainersMap.synchronized { + val containerSet = allocatedHostToContainersMap.getOrElseUpdate(workerHostname, + new HashSet[ContainerId]()) + + containerSet += containerId + allocatedContainerToHostMap.put(containerId, workerHostname) + + if (rack != null) { + allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1) + } + } + logInfo("Launching WorkerRunnable. driverUrl: %s, workerHostname: %s".format(driverUrl, workerHostname)) + val workerRunnable = new WorkerRunnable( + container, + conf, + driverUrl, + workerId, + workerHostname, + workerMemory, + workerCores) + new Thread(workerRunnable).start() + } + } + logDebug(""" + Finished allocating %s containers (from %s originally). + Current number of workers running: %d, + releasedContainerList: %s, + pendingReleaseContainers: %s + """.format( + allocatedContainersToProcess, + allocatedContainers, + numWorkersRunning.get(), + releasedContainerList, + pendingReleaseContainers)) + } + + val completedContainers = allocateResponse.getCompletedContainersStatuses() + if (completedContainers.size > 0) { + logDebug("Completed %d containers".format(completedContainers.size)) + + for (completedContainer <- completedContainers) { + val containerId = completedContainer.getContainerId + + if (pendingReleaseContainers.containsKey(containerId)) { + // YarnAllocationHandler already marked the container for release, so remove it from + // `pendingReleaseContainers`. + pendingReleaseContainers.remove(containerId) + } else { + // Decrement the number of workers running. The next iteration of the ApplicationMaster's + // reporting thread will take care of allocating. + numWorkersRunning.decrementAndGet() + logInfo("Completed container %s (state: %s, exit status: %s)".format( + containerId, + completedContainer.getState, + completedContainer.getExitStatus())) + // Hadoop 2.2.X added a ContainerExitStatus we should switch to use + // there are some exit status' we shouldn't necessarily count against us, but for + // now I think its ok as none of the containers are expected to exit + if (completedContainer.getExitStatus() != 0) { + logInfo("Container marked as failed: " + containerId) + numWorkersFailed.incrementAndGet() + } + } + + allocatedHostToContainersMap.synchronized { + if (allocatedContainerToHostMap.containsKey(containerId)) { + val hostOpt = allocatedContainerToHostMap.get(containerId) + assert(hostOpt.isDefined) + val host = hostOpt.get + + val containerSetOpt = allocatedHostToContainersMap.get(host) + assert(containerSetOpt.isDefined) + val containerSet = containerSetOpt.get + + containerSet.remove(containerId) + if (containerSet.isEmpty) { + allocatedHostToContainersMap.remove(host) + } else { + allocatedHostToContainersMap.update(host, containerSet) + } + + allocatedContainerToHostMap.remove(containerId) + + // TODO: Move this part outside the synchronized block? + val rack = YarnAllocationHandler.lookupRack(conf, host) + if (rack != null) { + val rackCount = allocatedRackCount.getOrElse(rack, 0) - 1 + if (rackCount > 0) { + allocatedRackCount.put(rack, rackCount) + } else { + allocatedRackCount.remove(rack) + } + } + } + } + } + logDebug(""" + Finished processing %d completed containers. + Current number of workers running: %d, + releasedContainerList: %s, + pendingReleaseContainers: %s + """.format( + completedContainers.size, + numWorkersRunning.get(), + releasedContainerList, + pendingReleaseContainers)) + } + } + + def createRackResourceRequests( + hostContainers: ArrayBuffer[ContainerRequest] + ): ArrayBuffer[ContainerRequest] = { + // Generate modified racks and new set of hosts under it before issuing requests. + val rackToCounts = new HashMap[String, Int]() + + for (container <- hostContainers) { + val candidateHost = container.getNodes.last + assert(YarnAllocationHandler.ANY_HOST != candidateHost) + + val rack = YarnAllocationHandler.lookupRack(conf, candidateHost) + if (rack != null) { + var count = rackToCounts.getOrElse(rack, 0) + count += 1 + rackToCounts.put(rack, count) + } + } + + val requestedContainers = new ArrayBuffer[ContainerRequest](rackToCounts.size) + for ((rack, count) <- rackToCounts) { + requestedContainers ++= createResourceRequests( + AllocationType.RACK, + rack, + count, + YarnAllocationHandler.PRIORITY) + } + + requestedContainers + } + + def allocatedContainersOnHost(host: String): Int = { + var retval = 0 + allocatedHostToContainersMap.synchronized { + retval = allocatedHostToContainersMap.getOrElse(host, Set()).size + } + retval + } + + def allocatedContainersOnRack(rack: String): Int = { + var retval = 0 + allocatedHostToContainersMap.synchronized { + retval = allocatedRackCount.getOrElse(rack, 0) + } + retval + } + + def addResourceRequests(numWorkers: Int) { + val containerRequests: List[ContainerRequest] = + if (numWorkers <= 0 || preferredHostToCount.isEmpty) { + logDebug("numWorkers: " + numWorkers + ", host preferences: " + + preferredHostToCount.isEmpty) + createResourceRequests( + AllocationType.ANY, + resource = null, + numWorkers, + YarnAllocationHandler.PRIORITY).toList + } else { + // Request for all hosts in preferred nodes and for numWorkers - + // candidates.size, request by default allocation policy. + val hostContainerRequests = new ArrayBuffer[ContainerRequest](preferredHostToCount.size) + for ((candidateHost, candidateCount) <- preferredHostToCount) { + val requiredCount = candidateCount - allocatedContainersOnHost(candidateHost) + + if (requiredCount > 0) { + hostContainerRequests ++= createResourceRequests( + AllocationType.HOST, + candidateHost, + requiredCount, + YarnAllocationHandler.PRIORITY) + } + } + val rackContainerRequests: List[ContainerRequest] = createRackResourceRequests( + hostContainerRequests).toList + + val anyContainerRequests = createResourceRequests( + AllocationType.ANY, + resource = null, + numWorkers, + YarnAllocationHandler.PRIORITY) + + val containerRequestBuffer = new ArrayBuffer[ContainerRequest]( + hostContainerRequests.size + rackContainerRequests.size() + anyContainerRequests.size) + + containerRequestBuffer ++= hostContainerRequests + containerRequestBuffer ++= rackContainerRequests + containerRequestBuffer ++= anyContainerRequests + containerRequestBuffer.toList + } + + for (request <- containerRequests) { + amClient.addContainerRequest(request) + } + + if (numWorkers > 0) { + numPendingAllocate.addAndGet(numWorkers) + logInfo("Will Allocate %d worker containers, each with %d memory".format( + numWorkers, + (workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD))) + } else { + logDebug("Empty allocation request ...") + } + + for (request <- containerRequests) { + val nodes = request.getNodes + var hostStr = if (nodes == null || nodes.isEmpty) { + "Any" + } else { + nodes.last + } + logInfo("Container request (host: %s, priority: %s, capability: %s".format( + hostStr, + request.getPriority().getPriority, + request.getCapability)) + } + } + + private def createResourceRequests( + requestType: AllocationType.AllocationType, + resource: String, + numWorkers: Int, + priority: Int + ): ArrayBuffer[ContainerRequest] = { + + // If hostname is specified, then we need at least two requests - node local and rack local. + // There must be a third request, which is ANY. That will be specially handled. + requestType match { + case AllocationType.HOST => { + assert(YarnAllocationHandler.ANY_HOST != resource) + val hostname = resource + val nodeLocal = constructContainerRequests( + Array(hostname), + racks = null, + numWorkers, + priority) + + // Add `hostname` to the global (singleton) host->rack mapping in YarnAllocationHandler. + YarnAllocationHandler.populateRackInfo(conf, hostname) + nodeLocal + } + case AllocationType.RACK => { + val rack = resource + constructContainerRequests(hosts = null, Array(rack), numWorkers, priority) + } + case AllocationType.ANY => constructContainerRequests( + hosts = null, racks = null, numWorkers, priority) + case _ => throw new IllegalArgumentException( + "Unexpected/unsupported request type: " + requestType) + } + } + + private def constructContainerRequests( + hosts: Array[String], + racks: Array[String], + numWorkers: Int, + priority: Int + ): ArrayBuffer[ContainerRequest] = { + + val memoryResource = Records.newRecord(classOf[Resource]) + memoryResource.setMemory(workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + + val prioritySetting = Records.newRecord(classOf[Priority]) + prioritySetting.setPriority(priority) + + val requests = new ArrayBuffer[ContainerRequest]() + for (i <- 0 until numWorkers) { + requests += new ContainerRequest(memoryResource, hosts, racks, prioritySetting) + } + requests + } +} + +object YarnAllocationHandler { + + val ANY_HOST = "*" + // All requests are issued with same priority : we do not (yet) have any distinction between + // request types (like map/reduce in hadoop for example) + val PRIORITY = 1 + + // Additional memory overhead - in mb. + val MEMORY_OVERHEAD = 384 + + // Host to rack map - saved from allocation requests. We are expecting this not to change. + // Note that it is possible for this to change : and ResurceManager will indicate that to us via + // update response to allocate. But we are punting on handling that for now. + private val hostToRack = new ConcurrentHashMap[String, String]() + private val rackToHostSet = new ConcurrentHashMap[String, JSet[String]]() + + + def newAllocator( + conf: Configuration, + amClient: AMRMClient[ContainerRequest], + appAttemptId: ApplicationAttemptId, + args: ApplicationMasterArguments + ): YarnAllocationHandler = { + new YarnAllocationHandler( + conf, + amClient, + appAttemptId, + args.numWorkers, + args.workerMemory, + args.workerCores, + Map[String, Int](), + Map[String, Int]()) + } + + def newAllocator( + conf: Configuration, + amClient: AMRMClient[ContainerRequest], + appAttemptId: ApplicationAttemptId, + args: ApplicationMasterArguments, + map: collection.Map[String, + collection.Set[SplitInfo]] + ): YarnAllocationHandler = { + val (hostToSplitCount, rackToSplitCount) = generateNodeToWeight(conf, map) + new YarnAllocationHandler( + conf, + amClient, + appAttemptId, + args.numWorkers, + args.workerMemory, + args.workerCores, + hostToSplitCount, + rackToSplitCount) + } + + def newAllocator( + conf: Configuration, + amClient: AMRMClient[ContainerRequest], + appAttemptId: ApplicationAttemptId, + maxWorkers: Int, + workerMemory: Int, + workerCores: Int, + map: collection.Map[String, collection.Set[SplitInfo]] + ): YarnAllocationHandler = { + val (hostToCount, rackToCount) = generateNodeToWeight(conf, map) + new YarnAllocationHandler( + conf, + amClient, + appAttemptId, + maxWorkers, + workerMemory, + workerCores, + hostToCount, + rackToCount) + } + + // A simple method to copy the split info map. + private def generateNodeToWeight( + conf: Configuration, + input: collection.Map[String, collection.Set[SplitInfo]] + ): (Map[String, Int], Map[String, Int]) = { + + if (input == null) { + return (Map[String, Int](), Map[String, Int]()) + } + + val hostToCount = new HashMap[String, Int] + val rackToCount = new HashMap[String, Int] + + for ((host, splits) <- input) { + val hostCount = hostToCount.getOrElse(host, 0) + hostToCount.put(host, hostCount + splits.size) + + val rack = lookupRack(conf, host) + if (rack != null){ + val rackCount = rackToCount.getOrElse(host, 0) + rackToCount.put(host, rackCount + splits.size) + } + } + + (hostToCount.toMap, rackToCount.toMap) + } + + def lookupRack(conf: Configuration, host: String): String = { + if (!hostToRack.contains(host)) { + populateRackInfo(conf, host) + } + hostToRack.get(host) + } + + def fetchCachedHostsForRack(rack: String): Option[Set[String]] = { + Option(rackToHostSet.get(rack)).map { set => + val convertedSet: collection.mutable.Set[String] = set + // TODO: Better way to get a Set[String] from JSet. + convertedSet.toSet + } + } + + def populateRackInfo(conf: Configuration, hostname: String) { + Utils.checkHost(hostname) + + if (!hostToRack.containsKey(hostname)) { + // If there are repeated failures to resolve, all to an ignore list. + val rackInfo = RackResolver.resolve(conf, hostname) + if (rackInfo != null && rackInfo.getNetworkLocation != null) { + val rack = rackInfo.getNetworkLocation + hostToRack.put(hostname, rack) + if (! rackToHostSet.containsKey(rack)) { + rackToHostSet.putIfAbsent(rack, + Collections.newSetFromMap(new ConcurrentHashMap[String, JBoolean]())) + } + rackToHostSet.get(rack).add(hostname) + + // TODO(harvey): Figure out what this comment means... + // Since RackResolver caches, we are disabling this for now ... + } /* else { + // right ? Else we will keep calling rack resolver in case we cant resolve rack info ... + hostToRack.put(hostname, null) + } */ + } + } +} diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala new file mode 100644 index 0000000000..2ba2366ead --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -0,0 +1,43 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.hadoop.mapred.JobConf +import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.conf.Configuration + +/** + * Contains util methods to interact with Hadoop from spark. + */ +class YarnSparkHadoopUtil extends SparkHadoopUtil { + + // Note that all params which start with SPARK are propagated all the way through, so if in yarn mode, this MUST be set to true. + override def isYarnMode(): Boolean = { true } + + // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems + // Always create a new config, dont reuse yarnConf. + override def newConfiguration(): Configuration = new YarnConfiguration(new Configuration()) + + // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster + override def addCredentials(conf: JobConf) { + val jobCreds = conf.getCredentials() + jobCreds.mergeAll(UserGroupInformation.getCurrentUser().getCredentials()) + } +} diff --git a/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala b/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala new file mode 100644 index 0000000000..522e0a9ad7 --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala @@ -0,0 +1,48 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster + +import org.apache.spark._ +import org.apache.hadoop.conf.Configuration +import org.apache.spark.deploy.yarn.YarnAllocationHandler +import org.apache.spark.scheduler.TaskSchedulerImpl +import org.apache.spark.util.Utils + +/** + * + * This scheduler launch worker through Yarn - by call into Client to launch WorkerLauncher as AM. + */ +private[spark] class YarnClientClusterScheduler(sc: SparkContext, conf: Configuration) extends TaskSchedulerImpl(sc) { + + def this(sc: SparkContext) = this(sc, new Configuration()) + + // By default, rack is unknown + override def getRackForHost(hostPort: String): Option[String] = { + val host = Utils.parseHostPort(hostPort)._1 + val retval = YarnAllocationHandler.lookupRack(conf, host) + if (retval != null) Some(retval) else None + } + + override def postStartHook() { + + // The yarn application is running, but the worker might not yet ready + // Wait for a few seconds for the slaves to bootstrap and register with master - best case attempt + Thread.sleep(2000L) + logInfo("YarnClientClusterScheduler.postStartHook done") + } +} diff --git a/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala new file mode 100644 index 0000000000..4b69f5078b --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster + +import org.apache.hadoop.yarn.api.records.{ApplicationId, YarnApplicationState} +import org.apache.spark.{SparkException, Logging, SparkContext} +import org.apache.spark.deploy.yarn.{Client, ClientArguments} +import org.apache.spark.scheduler.TaskSchedulerImpl + +private[spark] class YarnClientSchedulerBackend( + scheduler: TaskSchedulerImpl, + sc: SparkContext) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + with Logging { + + var client: Client = null + var appId: ApplicationId = null + + override def start() { + super.start() + + val defalutWorkerCores = "2" + val defalutWorkerMemory = "512m" + val defaultWorkerNumber = "1" + + val userJar = System.getenv("SPARK_YARN_APP_JAR") + var workerCores = System.getenv("SPARK_WORKER_CORES") + var workerMemory = System.getenv("SPARK_WORKER_MEMORY") + var workerNumber = System.getenv("SPARK_WORKER_INSTANCES") + + if (userJar == null) + throw new SparkException("env SPARK_YARN_APP_JAR is not set") + + if (workerCores == null) + workerCores = defalutWorkerCores + if (workerMemory == null) + workerMemory = defalutWorkerMemory + if (workerNumber == null) + workerNumber = defaultWorkerNumber + + val driverHost = conf.get("spark.driver.host") + val driverPort = conf.get("spark.driver.port") + val hostport = driverHost + ":" + driverPort + + val argsArray = Array[String]( + "--class", "notused", + "--jar", userJar, + "--args", hostport, + "--worker-memory", workerMemory, + "--worker-cores", workerCores, + "--num-workers", workerNumber, + "--master-class", "org.apache.spark.deploy.yarn.WorkerLauncher" + ) + + val args = new ClientArguments(argsArray) + client = new Client(args) + appId = client.runApp() + waitForApp() + } + + def waitForApp() { + + // TODO : need a better way to find out whether the workers are ready or not + // maybe by resource usage report? + while(true) { + val report = client.getApplicationReport(appId) + + logInfo("Application report from ASM: \n" + + "\t appMasterRpcPort: " + report.getRpcPort() + "\n" + + "\t appStartTime: " + report.getStartTime() + "\n" + + "\t yarnAppState: " + report.getYarnApplicationState() + "\n" + ) + + // Ready to go, or already gone. + val state = report.getYarnApplicationState() + if (state == YarnApplicationState.RUNNING) { + return + } else if (state == YarnApplicationState.FINISHED || + state == YarnApplicationState.FAILED || + state == YarnApplicationState.KILLED) { + throw new SparkException("Yarn application already ended," + + "might be killed or not able to launch application master.") + } + + Thread.sleep(1000) + } + } + + override def stop() { + super.stop() + client.stop() + logInfo("Stoped") + } + +} diff --git a/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala b/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala new file mode 100644 index 0000000000..a4638cc863 --- /dev/null +++ b/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster + +import org.apache.spark._ +import org.apache.spark.deploy.yarn.{ApplicationMaster, YarnAllocationHandler} +import org.apache.spark.scheduler.TaskSchedulerImpl +import org.apache.spark.util.Utils +import org.apache.hadoop.conf.Configuration + +/** + * + * This is a simple extension to ClusterScheduler - to ensure that appropriate initialization of ApplicationMaster, etc is done + */ +private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration) extends TaskSchedulerImpl(sc) { + + logInfo("Created YarnClusterScheduler") + + def this(sc: SparkContext) = this(sc, new Configuration()) + + // Nothing else for now ... initialize application master : which needs sparkContext to determine how to allocate + // Note that only the first creation of SparkContext influences (and ideally, there must be only one SparkContext, right ?) + // Subsequent creations are ignored - since nodes are already allocated by then. + + + // By default, rack is unknown + override def getRackForHost(hostPort: String): Option[String] = { + val host = Utils.parseHostPort(hostPort)._1 + val retval = YarnAllocationHandler.lookupRack(conf, host) + if (retval != null) Some(retval) else None + } + + override def postStartHook() { + val sparkContextInitialized = ApplicationMaster.sparkContextInitialized(sc) + if (sparkContextInitialized){ + // Wait for a few seconds for the slaves to bootstrap and register with master - best case attempt + Thread.sleep(3000L) + } + logInfo("YarnClusterScheduler.postStartHook done") + } +} diff --git a/new-yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala b/new-yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala new file mode 100644 index 0000000000..2941356bc5 --- /dev/null +++ b/new-yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala @@ -0,0 +1,220 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.net.URI + +import org.scalatest.FunSuite +import org.scalatest.mock.MockitoSugar +import org.mockito.Mockito.when + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.FileStatus +import org.apache.hadoop.fs.FileSystem +import org.apache.hadoop.fs.Path +import org.apache.hadoop.fs.permission.FsAction +import org.apache.hadoop.yarn.api.records.LocalResource +import org.apache.hadoop.yarn.api.records.LocalResourceVisibility +import org.apache.hadoop.yarn.api.records.LocalResourceType +import org.apache.hadoop.yarn.util.{Records, ConverterUtils} + +import scala.collection.mutable.HashMap +import scala.collection.mutable.Map + + +class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { + + class MockClientDistributedCacheManager extends ClientDistributedCacheManager { + override def getVisibility(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): + LocalResourceVisibility = { + return LocalResourceVisibility.PRIVATE + } + } + + test("test getFileStatus empty") { + val distMgr = new ClientDistributedCacheManager() + val fs = mock[FileSystem] + val uri = new URI("/tmp/testing") + when(fs.getFileStatus(new Path(uri))).thenReturn(new FileStatus()) + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + val stat = distMgr.getFileStatus(fs, uri, statCache) + assert(stat.getPath() === null) + } + + test("test getFileStatus cached") { + val distMgr = new ClientDistributedCacheManager() + val fs = mock[FileSystem] + val uri = new URI("/tmp/testing") + val realFileStatus = new FileStatus(10, false, 1, 1024, 10, 10, null, "testOwner", + null, new Path("/tmp/testing")) + when(fs.getFileStatus(new Path(uri))).thenReturn(new FileStatus()) + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus](uri -> realFileStatus) + val stat = distMgr.getFileStatus(fs, uri, statCache) + assert(stat.getPath().toString() === "/tmp/testing") + } + + test("test addResource") { + val distMgr = new MockClientDistributedCacheManager() + val fs = mock[FileSystem] + val conf = new Configuration() + val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") + val localResources = HashMap[String, LocalResource]() + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + when(fs.getFileStatus(destPath)).thenReturn(new FileStatus()) + + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, "link", + statCache, false) + val resource = localResources("link") + assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) + assert(ConverterUtils.getPathFromYarnURL(resource.getResource()) === destPath) + assert(resource.getTimestamp() === 0) + assert(resource.getSize() === 0) + assert(resource.getType() === LocalResourceType.FILE) + + val env = new HashMap[String, String]() + distMgr.setDistFilesEnv(env) + assert(env("SPARK_YARN_CACHE_FILES") === "file:/foo.invalid.com:8080/tmp/testing#link") + assert(env("SPARK_YARN_CACHE_FILES_TIME_STAMPS") === "0") + assert(env("SPARK_YARN_CACHE_FILES_FILE_SIZES") === "0") + assert(env("SPARK_YARN_CACHE_FILES_VISIBILITIES") === LocalResourceVisibility.PRIVATE.name()) + + distMgr.setDistArchivesEnv(env) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") === None) + + //add another one and verify both there and order correct + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + null, new Path("/tmp/testing2")) + val destPath2 = new Path("file:///foo.invalid.com:8080/tmp/testing2") + when(fs.getFileStatus(destPath2)).thenReturn(realFileStatus) + distMgr.addResource(fs, conf, destPath2, localResources, LocalResourceType.FILE, "link2", + statCache, false) + val resource2 = localResources("link2") + assert(resource2.getVisibility() === LocalResourceVisibility.PRIVATE) + assert(ConverterUtils.getPathFromYarnURL(resource2.getResource()) === destPath2) + assert(resource2.getTimestamp() === 10) + assert(resource2.getSize() === 20) + assert(resource2.getType() === LocalResourceType.FILE) + + val env2 = new HashMap[String, String]() + distMgr.setDistFilesEnv(env2) + val timestamps = env2("SPARK_YARN_CACHE_FILES_TIME_STAMPS").split(',') + val files = env2("SPARK_YARN_CACHE_FILES").split(',') + val sizes = env2("SPARK_YARN_CACHE_FILES_FILE_SIZES").split(',') + val visibilities = env2("SPARK_YARN_CACHE_FILES_VISIBILITIES") .split(',') + assert(files(0) === "file:/foo.invalid.com:8080/tmp/testing#link") + assert(timestamps(0) === "0") + assert(sizes(0) === "0") + assert(visibilities(0) === LocalResourceVisibility.PRIVATE.name()) + + assert(files(1) === "file:/foo.invalid.com:8080/tmp/testing2#link2") + assert(timestamps(1) === "10") + assert(sizes(1) === "20") + assert(visibilities(1) === LocalResourceVisibility.PRIVATE.name()) + } + + test("test addResource link null") { + val distMgr = new MockClientDistributedCacheManager() + val fs = mock[FileSystem] + val conf = new Configuration() + val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") + val localResources = HashMap[String, LocalResource]() + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + when(fs.getFileStatus(destPath)).thenReturn(new FileStatus()) + + intercept[Exception] { + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, null, + statCache, false) + } + assert(localResources.get("link") === None) + assert(localResources.size === 0) + } + + test("test addResource appmaster only") { + val distMgr = new MockClientDistributedCacheManager() + val fs = mock[FileSystem] + val conf = new Configuration() + val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") + val localResources = HashMap[String, LocalResource]() + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + null, new Path("/tmp/testing")) + when(fs.getFileStatus(destPath)).thenReturn(realFileStatus) + + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, "link", + statCache, true) + val resource = localResources("link") + assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) + assert(ConverterUtils.getPathFromYarnURL(resource.getResource()) === destPath) + assert(resource.getTimestamp() === 10) + assert(resource.getSize() === 20) + assert(resource.getType() === LocalResourceType.ARCHIVE) + + val env = new HashMap[String, String]() + distMgr.setDistFilesEnv(env) + assert(env.get("SPARK_YARN_CACHE_FILES") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_TIME_STAMPS") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_FILE_SIZES") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_VISIBILITIES") === None) + + distMgr.setDistArchivesEnv(env) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") === None) + } + + test("test addResource archive") { + val distMgr = new MockClientDistributedCacheManager() + val fs = mock[FileSystem] + val conf = new Configuration() + val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") + val localResources = HashMap[String, LocalResource]() + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + null, new Path("/tmp/testing")) + when(fs.getFileStatus(destPath)).thenReturn(realFileStatus) + + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, "link", + statCache, false) + val resource = localResources("link") + assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) + assert(ConverterUtils.getPathFromYarnURL(resource.getResource()) === destPath) + assert(resource.getTimestamp() === 10) + assert(resource.getSize() === 20) + assert(resource.getType() === LocalResourceType.ARCHIVE) + + val env = new HashMap[String, String]() + + distMgr.setDistArchivesEnv(env) + assert(env("SPARK_YARN_CACHE_ARCHIVES") === "file:/foo.invalid.com:8080/tmp/testing#link") + assert(env("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") === "10") + assert(env("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") === "20") + assert(env("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") === LocalResourceVisibility.PRIVATE.name()) + + distMgr.setDistFilesEnv(env) + assert(env.get("SPARK_YARN_CACHE_FILES") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_TIME_STAMPS") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_FILE_SIZES") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_VISIBILITIES") === None) + } + + +} @@ -21,7 +21,7 @@ <parent> <groupId>org.apache</groupId> <artifactId>apache</artifactId> - <version>11</version> + <version>13</version> </parent> <groupId>org.apache.spark</groupId> <artifactId>spark-parent</artifactId> @@ -40,6 +40,7 @@ <connection>scm:git:git@github.com:apache/incubator-spark.git</connection> <developerConnection>scm:git:https://git-wip-us.apache.org/repos/asf/incubator-spark.git</developerConnection> <url>scm:git:git@github.com:apache/incubator-spark.git</url> + <tag>HEAD</tag> </scm> <developers> <developer> @@ -60,6 +61,29 @@ <maven>3.0.0</maven> </prerequisites> + <mailingLists> + <mailingList> + <name>Dev Mailing List</name> + <post>dev@spark.incubator.apache.org</post> + <subscribe>dev-subscribe@spark.incubator.apache.org</subscribe> + <unsubscribe>dev-unsubscribe@spark.incubator.apache.org</unsubscribe> + </mailingList> + + <mailingList> + <name>User Mailing List</name> + <post>user@spark.incubator.apache.org</post> + <subscribe>user-subscribe@spark.incubator.apache.org</subscribe> + <unsubscribe>user-unsubscribe@spark.incubator.apache.org</unsubscribe> + </mailingList> + + <mailingList> + <name>Commits Mailing List</name> + <post>commits@spark.incubator.apache.org</post> + <subscribe>commits-subscribe@spark.incubator.apache.org</subscribe> + <unsubscribe>commits-unsubscribe@spark.incubator.apache.org</unsubscribe> + </mailingList> + </mailingLists> + <modules> <module>core</module> <module>bagel</module> @@ -75,13 +99,17 @@ <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> - <java.version>1.5</java.version> - <scala.version>2.9.3</scala.version> + <java.version>1.6</java.version> + + <scala.version>2.10.3</scala.version> + <scala.binary.version>2.10</scala.binary.version> <mesos.version>0.13.0</mesos.version> - <akka.version>2.0.5</akka.version> + <akka.group>org.spark-project.akka</akka.group> + <akka.version>2.2.3-shaded-protobuf</akka.version> <slf4j.version>1.7.2</slf4j.version> <log4j.version>1.2.17</log4j.version> <hadoop.version>1.0.4</hadoop.version> + <protobuf.version>2.4.1</protobuf.version> <yarn.version>0.23.7</yarn.version> <hbase.version>0.94.6</hbase.version> @@ -90,10 +118,10 @@ </properties> <repositories> - <repository> - <id>jboss-repo</id> - <name>JBoss Repository</name> - <url>http://repository.jboss.org/nexus/content/repositories/releases/</url> + <repository> + <id>maven-repo</id> <!-- This should be at top, it makes maven try the central repo first and then others and hence faster dep resolution --> + <name>Maven Repository</name> + <url>http://repo.maven.apache.org/maven2</url> <releases> <enabled>true</enabled> </releases> @@ -102,9 +130,9 @@ </snapshots> </repository> <repository> - <id>cloudera-repo</id> - <name>Cloudera Repository</name> - <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url> + <id>jboss-repo</id> + <name>JBoss Repository</name> + <url>http://repository.jboss.org/nexus/content/repositories/releases</url> <releases> <enabled>true</enabled> </releases> @@ -113,9 +141,9 @@ </snapshots> </repository> <repository> - <id>akka-repo</id> - <name>Akka Repository</name> - <url>http://repo.akka.io/releases/</url> + <id>mqtt-repo</id> + <name>MQTT Repository</name> + <url>https://repo.eclipse.org/content/repositories/paho-releases</url> <releases> <enabled>true</enabled> </releases> @@ -124,41 +152,6 @@ </snapshots> </repository> </repositories> - <pluginRepositories> - <pluginRepository> - <id>oss-sonatype-releases</id> - <name>OSS Sonatype</name> - <url>https://oss.sonatype.org/content/repositories/releases</url> - <releases> - <enabled>true</enabled> - </releases> - <snapshots> - <enabled>false</enabled> - </snapshots> - </pluginRepository> - <pluginRepository> - <id>oss-sonatype-snapshots</id> - <name>OSS Sonatype</name> - <url>https://oss.sonatype.org/content/repositories/snapshots</url> - <releases> - <enabled>false</enabled> - </releases> - <snapshots> - <enabled>true</enabled> - </snapshots> - </pluginRepository> - <pluginRepository> - <id>oss-sonatype</id> - <name>OSS Sonatype</name> - <url>https://oss.sonatype.org/content/groups/public</url> - <releases> - <enabled>true</enabled> - </releases> - <snapshots> - <enabled>true</enabled> - </snapshots> - </pluginRepository> - </pluginRepositories> <dependencyManagement> <dependencies> @@ -208,13 +201,23 @@ <version>4.0</version> </dependency> <dependency> + <groupId>com.clearspring.analytics</groupId> + <artifactId>stream</artifactId> + <version>2.4.0</version> + </dependency> + <!-- In theory we need not directly depend on protobuf since Spark does not directly + use it. However, when building with Hadoop/YARN 2.2 Maven doesn't correctly bump + the protobuf version up from the one Mesos gives. For now we include this variable + to explicitly bump the version when building with YARN. It would be nice to figure + out why Maven can't resolve this correctly (like SBT does). --> + <dependency> <groupId>com.google.protobuf</groupId> <artifactId>protobuf-java</artifactId> - <version>2.4.1</version> + <version>${protobuf.version}</version> </dependency> <dependency> <groupId>com.twitter</groupId> - <artifactId>chill_2.9.3</artifactId> + <artifactId>chill_${scala.binary.version}</artifactId> <version>0.3.1</version> </dependency> <dependency> @@ -223,19 +226,48 @@ <version>0.3.1</version> </dependency> <dependency> - <groupId>com.typesafe.akka</groupId> - <artifactId>akka-actor</artifactId> + <groupId>${akka.group}</groupId> + <artifactId>akka-actor_${scala.binary.version}</artifactId> <version>${akka.version}</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> </dependency> <dependency> - <groupId>com.typesafe.akka</groupId> - <artifactId>akka-remote</artifactId> + <groupId>${akka.group}</groupId> + <artifactId>akka-remote_${scala.binary.version}</artifactId> <version>${akka.version}</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> + </dependency> + <dependency> + <groupId>${akka.group}</groupId> + <artifactId>akka-slf4j_${scala.binary.version}</artifactId> + <version>${akka.version}</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> </dependency> <dependency> - <groupId>com.typesafe.akka</groupId> - <artifactId>akka-slf4j</artifactId> + <groupId>${akka.group}</groupId> + <artifactId>akka-zeromq_${scala.binary.version}</artifactId> <version>${akka.version}</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> </dependency> <dependency> <groupId>it.unimi.dsi</groupId> @@ -248,11 +280,6 @@ <version>1.2.0</version> </dependency> <dependency> - <groupId>com.github.scala-incubator.io</groupId> - <artifactId>scala-io-file_2.9.2</artifactId> - <version>0.4.1</version> - </dependency> - <dependency> <groupId>org.apache.mesos</groupId> <artifactId>mesos</artifactId> <version>${mesos.version}</version> @@ -260,7 +287,7 @@ <dependency> <groupId>io.netty</groupId> <artifactId>netty-all</artifactId> - <version>4.0.0.Beta2</version> + <version>4.0.13.Final</version> </dependency> <dependency> <groupId>org.apache.derby</groupId> @@ -270,8 +297,14 @@ </dependency> <dependency> <groupId>net.liftweb</groupId> - <artifactId>lift-json_2.9.2</artifactId> - <version>2.5</version> + <artifactId>lift-json_${scala.binary.version}</artifactId> + <version>2.5.1</version> + <exclusions> + <exclusion> + <groupId>org.scala-lang</groupId> + <artifactId>scalap</artifactId> + </exclusion> + </exclusions> </dependency> <dependency> <groupId>com.codahale.metrics</groupId> @@ -294,6 +327,11 @@ <version>3.0.0</version> </dependency> <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-graphite</artifactId> + <version>3.0.0</version> + </dependency> + <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-compiler</artifactId> <version>${scala.version}</version> @@ -309,32 +347,36 @@ <version>${scala.version}</version> </dependency> <dependency> - <groupId>org.scala-lang</groupId> - <artifactId>scalap</artifactId> - <version>${scala.version}</version> - </dependency> - - <dependency> <groupId>log4j</groupId> <artifactId>log4j</artifactId> <version>${log4j.version}</version> </dependency> - <dependency> <groupId>org.scalatest</groupId> - <artifactId>scalatest_${scala.version}</artifactId> + <artifactId>scalatest_${scala.binary.version}</artifactId> <version>1.9.1</version> <scope>test</scope> </dependency> <dependency> + <groupId>commons-io</groupId> + <artifactId>commons-io</artifactId> + <version>2.4</version> + </dependency> + <dependency> <groupId>org.easymock</groupId> <artifactId>easymock</artifactId> <version>3.1</version> <scope>test</scope> </dependency> + <dependency> + <groupId>org.mockito</groupId> + <artifactId>mockito-all</artifactId> + <version>1.8.5</version> + <scope>test</scope> + </dependency> <dependency> <groupId>org.scalacheck</groupId> - <artifactId>scalacheck_${scala.version}</artifactId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> <version>1.10.0</version> <scope>test</scope> </dependency> @@ -345,6 +387,17 @@ <scope>test</scope> </dependency> <dependency> + <groupId>org.apache.zookeeper</groupId> + <artifactId>zookeeper</artifactId> + <version>3.4.5</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> + </dependency> + <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>${hadoop.version}</version> @@ -359,19 +412,11 @@ </exclusion> <exclusion> <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-core-asl</artifactId> + <artifactId>*</artifactId> </exclusion> <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-mapper-asl</artifactId> - </exclusion> - <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-jaxrs</artifactId> - </exclusion> - <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-xc</artifactId> + <groupId>org.sonatype.sisu.inject</groupId> + <artifactId>*</artifactId> </exclusion> </exclusions> </dependency> @@ -395,19 +440,11 @@ </exclusion> <exclusion> <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-core-asl</artifactId> + <artifactId>*</artifactId> </exclusion> <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-mapper-asl</artifactId> - </exclusion> - <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-jaxrs</artifactId> - </exclusion> - <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-xc</artifactId> + <groupId>org.sonatype.sisu.inject</groupId> + <artifactId>*</artifactId> </exclusion> </exclusions> </dependency> @@ -426,22 +463,15 @@ </exclusion> <exclusion> <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-core-asl</artifactId> + <artifactId>*</artifactId> </exclusion> <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-mapper-asl</artifactId> - </exclusion> - <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-jaxrs</artifactId> - </exclusion> - <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-xc</artifactId> + <groupId>org.sonatype.sisu.inject</groupId> + <artifactId>*</artifactId> </exclusion> </exclusions> </dependency> + <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-yarn-client</artifactId> @@ -457,19 +487,11 @@ </exclusion> <exclusion> <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-core-asl</artifactId> - </exclusion> - <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-mapper-asl</artifactId> - </exclusion> - <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-jaxrs</artifactId> + <artifactId>*</artifactId> </exclusion> <exclusion> - <groupId>org.codehaus.jackson</groupId> - <artifactId>jackson-xc</artifactId> + <groupId>org.sonatype.sisu.inject</groupId> + <artifactId>*</artifactId> </exclusion> </exclusions> </dependency> @@ -488,6 +510,10 @@ <groupId>org.jboss.netty</groupId> <artifactId>netty</artifactId> </exclusion> + <exclusion> + <groupId>io.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> </exclusions> </dependency> </dependencies> @@ -603,7 +629,7 @@ <junitxml>.</junitxml> <filereports>${project.build.directory}/SparkTestSuite.txt</filereports> <argLine>-Xms64m -Xmx3g</argLine> - <stderr/> + <stderr /> </configuration> <executions> <execution> @@ -701,6 +727,7 @@ <hadoop.major.version>2</hadoop.major.version> <!-- 0.23.* is same as 2.0.* - except hardened to run production jobs --> <hadoop.version>0.23.7</hadoop.version> + <protobuf.version>2.5.0</protobuf.version> <!--<hadoop.version>2.0.5-alpha</hadoop.version> --> </properties> @@ -712,7 +739,7 @@ <repository> <id>maven-root</id> <name>Maven root repository</name> - <url>http://repo1.maven.org/maven2/</url> + <url>http://repo1.maven.org/maven2</url> <releases> <enabled>true</enabled> </releases> @@ -727,6 +754,39 @@ </dependencies> </dependencyManagement> </profile> + + <profile> + <id>new-yarn</id> + <properties> + <hadoop.major.version>2</hadoop.major.version> + <hadoop.version>2.2.0</hadoop.version> + <protobuf.version>2.5.0</protobuf.version> + </properties> + + <modules> + <module>new-yarn</module> + </modules> + + <repositories> + <repository> + <id>maven-root</id> + <name>Maven root repository</name> + <url>http://repo1.maven.org/maven2</url> + <releases> + <enabled>true</enabled> + </releases> + <snapshots> + <enabled>false</enabled> + </snapshots> + </repository> + </repositories> + + <dependencyManagement> + <dependencies> + </dependencies> + </dependencyManagement> + </profile> + <profile> <id>repl-bin</id> <activation> diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index aef246d8a9..2eef2dfa5e 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -28,14 +28,19 @@ object SparkBuild extends Build { // "2.0.0-mr1-cdh4.2.0" for Cloudera Hadoop. Note that these variables can be set // through the environment variables SPARK_HADOOP_VERSION and SPARK_YARN. val DEFAULT_HADOOP_VERSION = "1.0.4" + + // Whether the Hadoop version to build against is 2.2.x, or a variant of it. This can be set + // through the SPARK_IS_NEW_HADOOP environment variable. + val DEFAULT_IS_NEW_HADOOP = false + val DEFAULT_YARN = false // HBase version; set as appropriate. val HBASE_VERSION = "0.94.6" // Target JVM version - val SCALAC_JVM_VERSION = "jvm-1.5" - val JAVAC_JVM_VERSION = "1.5" + val SCALAC_JVM_VERSION = "jvm-1.6" + val JAVAC_JVM_VERSION = "1.6" lazy val root = Project("root", file("."), settings = rootSettings) aggregate(allProjects: _*) @@ -55,32 +60,47 @@ object SparkBuild extends Build { lazy val mllib = Project("mllib", file("mllib"), settings = mllibSettings) dependsOn(core) - lazy val yarn = Project("yarn", file("yarn"), settings = yarnSettings) dependsOn(core) - lazy val assemblyProj = Project("assembly", file("assembly"), settings = assemblyProjSettings) .dependsOn(core, bagel, mllib, repl, streaming) dependsOn(maybeYarn: _*) + lazy val assembleDeps = TaskKey[Unit]("assemble-deps", "Build assembly of dependencies and packages Spark projects") + // A configuration to set an alternative publishLocalConfiguration lazy val MavenCompile = config("m2r") extend(Compile) lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy") // Allows build configuration to be set through environment variables lazy val hadoopVersion = scala.util.Properties.envOrElse("SPARK_HADOOP_VERSION", DEFAULT_HADOOP_VERSION) + lazy val isNewHadoop = scala.util.Properties.envOrNone("SPARK_IS_NEW_HADOOP") match { + case None => { + val isNewHadoopVersion = "2.[2-9]+".r.findFirstIn(hadoopVersion).isDefined + (isNewHadoopVersion|| DEFAULT_IS_NEW_HADOOP) + } + case Some(v) => v.toBoolean + } + lazy val isYarnEnabled = scala.util.Properties.envOrNone("SPARK_YARN") match { case None => DEFAULT_YARN case Some(v) => v.toBoolean } // Conditionally include the yarn sub-project - lazy val maybeYarn = if(isYarnEnabled) Seq[ClasspathDependency](yarn) else Seq[ClasspathDependency]() - lazy val maybeYarnRef = if(isYarnEnabled) Seq[ProjectReference](yarn) else Seq[ProjectReference]() - lazy val allProjects = Seq[ProjectReference]( - core, repl, examples, bagel, streaming, mllib, tools, assemblyProj) ++ maybeYarnRef + lazy val yarn = Project("yarn", file(if (isNewHadoop) "new-yarn" else "yarn"), settings = yarnSettings) dependsOn(core) + + //lazy val yarn = Project("yarn", file("yarn"), settings = yarnSettings) dependsOn(core) + + lazy val maybeYarn = if (isYarnEnabled) Seq[ClasspathDependency](yarn) else Seq[ClasspathDependency]() + lazy val maybeYarnRef = if (isYarnEnabled) Seq[ProjectReference](yarn) else Seq[ProjectReference]() + + // Everything except assembly, tools and examples belong to packageProjects + lazy val packageProjects = Seq[ProjectReference](core, repl, bagel, streaming, mllib) ++ maybeYarnRef + + lazy val allProjects = packageProjects ++ Seq[ProjectReference](examples, tools, assemblyProj) def sharedSettings = Defaults.defaultSettings ++ Seq( - organization := "org.apache.spark", - version := "0.9.0-incubating-SNAPSHOT", - scalaVersion := "2.9.3", + organization := "org.apache.spark", + version := "0.9.0-incubating-SNAPSHOT", + scalaVersion := "2.10.3", scalacOptions := Seq("-Xmax-classfile-name", "120", "-unchecked", "-deprecation", "-target:" + SCALAC_JVM_VERSION), javacOptions := Seq("-target", JAVAC_JVM_VERSION, "-source", JAVAC_JVM_VERSION), @@ -94,13 +114,16 @@ object SparkBuild extends Build { fork := true, javaOptions += "-Xmx3g", + // Show full stack trace and duration in test cases. + testOptions in Test += Tests.Argument("-oDF"), + // Only allow one test at a time, even across projects, since they run in the same JVM concurrentRestrictions in Global += Tags.limit(Tags.Test, 1), - // Shared between both core and streaming. - resolvers ++= Seq("Akka Repository" at "http://repo.akka.io/releases/"), + // also check the local Maven repository ~/.m2 + resolvers ++= Seq(Resolver.file("Local Maven Repo", file(Path.userHome + "/.m2/repository"))), - // For Sonatype publishing + // For Sonatype publishing resolvers ++= Seq("sonatype-snapshots" at "https://oss.sonatype.org/content/repositories/snapshots", "sonatype-staging" at "https://oss.sonatype.org/service/local/staging/deploy/maven2/"), @@ -153,13 +176,21 @@ object SparkBuild extends Build { */ + libraryDependencies ++= Seq( - "org.eclipse.jetty" % "jetty-server" % "7.6.8.v20121106", - "org.scalatest" %% "scalatest" % "1.9.1" % "test", - "org.scalacheck" %% "scalacheck" % "1.10.0" % "test", - "com.novocode" % "junit-interface" % "0.9" % "test", - "org.easymock" % "easymock" % "3.1" % "test" + "io.netty" % "netty-all" % "4.0.13.Final", + "org.eclipse.jetty" % "jetty-server" % "7.6.8.v20121106", + /** Workaround for SPARK-959. Dependency used by org.eclipse.jetty. Fixed in ivy 2.3.0. */ + "org.eclipse.jetty.orbit" % "javax.servlet" % "2.5.0.v201103041518" artifacts Artifact("javax.servlet", "jar", "jar"), + "org.scalatest" %% "scalatest" % "1.9.1" % "test", + "org.scalacheck" %% "scalacheck" % "1.10.0" % "test", + "com.novocode" % "junit-interface" % "0.9" % "test", + "org.easymock" % "easymock" % "3.1" % "test", + "org.mockito" % "mockito-all" % "1.8.5" % "test", + "commons-io" % "commons-io" % "2.4" % "test" ), + + parallelExecution := true, /* Workaround for issue #206 (fixed after SBT 0.11.0) */ watchTransitiveSources <<= Defaults.inDependencies[Task[Seq[File]]](watchSources.task, const(std.TaskExtra.constant(Nil)), aggregate = true, includeRoot = true) apply { _.join.map(_.flatten) }, @@ -175,6 +206,7 @@ object SparkBuild extends Build { val slf4jVersion = "1.7.2" + val excludeCglib = ExclusionRule(organization = "org.sonatype.sisu.inject") val excludeJackson = ExclusionRule(organization = "org.codehaus.jackson") val excludeNetty = ExclusionRule(organization = "org.jboss.netty") val excludeAsm = ExclusionRule(organization = "asm") @@ -183,60 +215,63 @@ object SparkBuild extends Build { def coreSettings = sharedSettings ++ Seq( name := "spark-core", resolvers ++= Seq( - "JBoss Repository" at "http://repository.jboss.org/nexus/content/repositories/releases/", - "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/" + "JBoss Repository" at "http://repository.jboss.org/nexus/content/repositories/releases/", + "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/" ), libraryDependencies ++= Seq( - "com.google.guava" % "guava" % "14.0.1", - "com.google.code.findbugs" % "jsr305" % "1.3.9", - "log4j" % "log4j" % "1.2.17", - "org.slf4j" % "slf4j-api" % slf4jVersion, - "org.slf4j" % "slf4j-log4j12" % slf4jVersion, - "commons-daemon" % "commons-daemon" % "1.0.10", // workaround for bug HADOOP-9407 - "com.ning" % "compress-lzf" % "0.8.4", - "org.xerial.snappy" % "snappy-java" % "1.0.5", - "org.ow2.asm" % "asm" % "4.0", - "com.google.protobuf" % "protobuf-java" % "2.4.1", - "com.typesafe.akka" % "akka-actor" % "2.0.5" excludeAll(excludeNetty), - "com.typesafe.akka" % "akka-remote" % "2.0.5" excludeAll(excludeNetty), - "com.typesafe.akka" % "akka-slf4j" % "2.0.5" excludeAll(excludeNetty), - "it.unimi.dsi" % "fastutil" % "6.4.4", - "colt" % "colt" % "1.2.0", - "net.liftweb" % "lift-json_2.9.2" % "2.5", - "org.apache.mesos" % "mesos" % "0.13.0", - "io.netty" % "netty-all" % "4.0.0.Beta2", - "org.apache.derby" % "derby" % "10.4.2.0" % "test", - "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm), - "net.java.dev.jets3t" % "jets3t" % "0.7.1", - "org.apache.avro" % "avro" % "1.7.4", - "org.apache.avro" % "avro-ipc" % "1.7.4" excludeAll(excludeNetty), - "com.codahale.metrics" % "metrics-core" % "3.0.0", - "com.codahale.metrics" % "metrics-jvm" % "3.0.0", - "com.codahale.metrics" % "metrics-json" % "3.0.0", - "com.codahale.metrics" % "metrics-ganglia" % "3.0.0", - "com.twitter" % "chill_2.9.3" % "0.3.1", - "com.twitter" % "chill-java" % "0.3.1" - ) + "com.google.guava" % "guava" % "14.0.1", + "com.google.code.findbugs" % "jsr305" % "1.3.9", + "log4j" % "log4j" % "1.2.17", + "org.slf4j" % "slf4j-api" % slf4jVersion, + "org.slf4j" % "slf4j-log4j12" % slf4jVersion, + "commons-daemon" % "commons-daemon" % "1.0.10", // workaround for bug HADOOP-9407 + "com.ning" % "compress-lzf" % "0.8.4", + "org.xerial.snappy" % "snappy-java" % "1.0.5", + "org.ow2.asm" % "asm" % "4.0", + "org.spark-project.akka" %% "akka-remote" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty), + "org.spark-project.akka" %% "akka-slf4j" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty), + "net.liftweb" %% "lift-json" % "2.5.1" excludeAll(excludeNetty), + "it.unimi.dsi" % "fastutil" % "6.4.4", + "colt" % "colt" % "1.2.0", + "org.apache.mesos" % "mesos" % "0.13.0", + "net.java.dev.jets3t" % "jets3t" % "0.7.1", + "org.apache.derby" % "derby" % "10.4.2.0" % "test", + "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), + "org.apache.avro" % "avro" % "1.7.4", + "org.apache.avro" % "avro-ipc" % "1.7.4" excludeAll(excludeNetty), + "org.apache.zookeeper" % "zookeeper" % "3.4.5" excludeAll(excludeNetty), + "com.codahale.metrics" % "metrics-core" % "3.0.0", + "com.codahale.metrics" % "metrics-jvm" % "3.0.0", + "com.codahale.metrics" % "metrics-json" % "3.0.0", + "com.codahale.metrics" % "metrics-ganglia" % "3.0.0", + "com.codahale.metrics" % "metrics-graphite" % "3.0.0", + "com.twitter" %% "chill" % "0.3.1", + "com.twitter" % "chill-java" % "0.3.1", + "com.typesafe" % "config" % "1.0.2", + "com.clearspring.analytics" % "stream" % "2.5.1" + ) ) def rootSettings = sharedSettings ++ Seq( publish := {} ) - def replSettings = sharedSettings ++ Seq( + def replSettings = sharedSettings ++ Seq( name := "spark-repl", - libraryDependencies <+= scalaVersion("org.scala-lang" % "scala-compiler" % _) + libraryDependencies <+= scalaVersion(v => "org.scala-lang" % "scala-compiler" % v ), + libraryDependencies <+= scalaVersion(v => "org.scala-lang" % "jline" % v ), + libraryDependencies <+= scalaVersion(v => "org.scala-lang" % "scala-reflect" % v ) ) + def examplesSettings = sharedSettings ++ Seq( name := "spark-examples", libraryDependencies ++= Seq( - "com.twitter" % "algebird-core_2.9.2" % "0.1.11", - + "com.twitter" %% "algebird-core" % "0.1.11", + "org.apache.hbase" % "hbase" % "0.94.6" excludeAll(excludeNetty, excludeAsm), "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeNetty, excludeAsm), - - "org.apache.cassandra" % "cassandra-all" % "1.2.5" + "org.apache.cassandra" % "cassandra-all" % "1.2.6" exclude("com.google.guava", "guava") exclude("com.googlecode.concurrentlinkedhashmap", "concurrentlinkedhashmap-lru") exclude("com.ning","compress-lzf") @@ -245,12 +280,13 @@ object SparkBuild extends Build { exclude("log4j","log4j") exclude("org.apache.cassandra.deps", "avro") excludeAll(excludeSnappy) + excludeAll(excludeCglib) ) ) ++ assemblySettings ++ extraAssemblySettings def toolsSettings = sharedSettings ++ Seq( name := "spark-tools" - ) + ) ++ assemblySettings ++ extraAssemblySettings def bagelSettings = sharedSettings ++ Seq( name := "spark-bagel" @@ -266,13 +302,21 @@ object SparkBuild extends Build { def streamingSettings = sharedSettings ++ Seq( name := "spark-streaming", resolvers ++= Seq( - "Akka Repository" at "http://repo.akka.io/releases/" + "Eclipse Repository" at "https://repo.eclipse.org/content/repositories/paho-releases/", + "Apache repo" at "https://repository.apache.org/content/repositories/releases" ), + libraryDependencies ++= Seq( - "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty, excludeSnappy), - "com.github.sgroschupf" % "zkclient" % "0.1" excludeAll(excludeNetty), - "org.twitter4j" % "twitter4j-stream" % "3.0.3" excludeAll(excludeNetty), - "com.typesafe.akka" % "akka-zeromq" % "2.0.5" excludeAll(excludeNetty) + "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty, excludeSnappy), + "com.sksamuel.kafka" %% "kafka" % "0.8.0-beta1" + exclude("com.sun.jdmk", "jmxtools") + exclude("com.sun.jmx", "jmxri") + exclude("net.sf.jopt-simple", "jopt-simple") + excludeAll(excludeNetty), + "org.eclipse.paho" % "mqtt-client" % "0.4.0", + "com.github.sgroschupf" % "zkclient" % "0.1" excludeAll(excludeNetty), + "org.twitter4j" % "twitter4j-stream" % "3.0.3" excludeAll(excludeNetty), + "org.spark-project.akka" %% "akka-zeromq" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty) ) ) @@ -287,16 +331,18 @@ object SparkBuild extends Build { def yarnEnabledSettings = Seq( libraryDependencies ++= Seq( // Exclude rule required for all ? - "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm), - "org.apache.hadoop" % "hadoop-yarn-api" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm), - "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm), - "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm) + "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), + "org.apache.hadoop" % "hadoop-yarn-api" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), + "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), + "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib) ) ) def assemblyProjSettings = sharedSettings ++ Seq( name := "spark-assembly", - jarName in assembly <<= version map { v => "spark-assembly-" + v + "-hadoop" + hadoopVersion + ".jar" } + assembleDeps in Compile <<= (packageProjects.map(packageBin in Compile in _) ++ Seq(packageDependency in Compile)).dependOn, + jarName in assembly <<= version map { v => "spark-assembly-" + v + "-hadoop" + hadoopVersion + ".jar" }, + jarName in packageDependency <<= version map { v => "spark-assembly-" + v + "-hadoop" + hadoopVersion + "-deps.jar" } ) ++ assemblySettings ++ extraAssemblySettings def extraAssemblySettings() = Seq( @@ -304,7 +350,8 @@ object SparkBuild extends Build { mergeStrategy in assembly := { case m if m.toLowerCase.endsWith("manifest.mf") => MergeStrategy.discard case m if m.toLowerCase.matches("meta-inf.*\\.sf$") => MergeStrategy.discard - case "META-INF/services/org.apache.hadoop.fs.FileSystem" => MergeStrategy.concat + case "log4j.properties" => MergeStrategy.discard + case m if m.toLowerCase.startsWith("meta-inf/services/") => MergeStrategy.filterDistinctLines case "reference.conf" => MergeStrategy.concat case _ => MergeStrategy.first } diff --git a/project/plugins.sbt b/project/plugins.sbt index cfcd85082a..4ba0e4280a 100644 --- a/project/plugins.sbt +++ b/project/plugins.sbt @@ -4,7 +4,7 @@ resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/release resolvers += "Spray Repository" at "http://repo.spray.cc/" -addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.9.1") +addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.9.2") addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.2.0") diff --git a/python/epydoc.conf b/python/epydoc.conf index 1d0d002d36..95a6af0974 100644 --- a/python/epydoc.conf +++ b/python/epydoc.conf @@ -32,6 +32,6 @@ target: docs/ private: no -exclude: pyspark.cloudpickle pyspark.worker pyspark.join pyspark.serializers +exclude: pyspark.cloudpickle pyspark.worker pyspark.join pyspark.java_gateway pyspark.examples pyspark.shell pyspark.test - pyspark.rddsampler pyspark.daemon + pyspark.rddsampler pyspark.daemon pyspark.mllib._common diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py index 1f35f6f939..2b2c3a061a 100644 --- a/python/pyspark/__init__.py +++ b/python/pyspark/__init__.py @@ -20,28 +20,34 @@ PySpark is the Python API for Spark. Public classes: - - L{SparkContext<pyspark.context.SparkContext>} - Main entry point for Spark functionality. - - L{RDD<pyspark.rdd.RDD>} - A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. - - L{Broadcast<pyspark.broadcast.Broadcast>} - A broadcast variable that gets reused across tasks. - - L{Accumulator<pyspark.accumulators.Accumulator>} - An "add-only" shared variable that tasks can only add values to. - - L{SparkFiles<pyspark.files.SparkFiles>} - Access files shipped with jobs. - - L{StorageLevel<pyspark.storagelevel.StorageLevel>} - Finer-grained cache persistence levels. + - L{SparkContext<pyspark.context.SparkContext>} + Main entry point for Spark functionality. + - L{RDD<pyspark.rdd.RDD>} + A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. + - L{Broadcast<pyspark.broadcast.Broadcast>} + A broadcast variable that gets reused across tasks. + - L{Accumulator<pyspark.accumulators.Accumulator>} + An "add-only" shared variable that tasks can only add values to. + - L{SparkConf<pyspark.conf.SparkConf>} + For configuring Spark. + - L{SparkFiles<pyspark.files.SparkFiles>} + Access files shipped with jobs. + - L{StorageLevel<pyspark.storagelevel.StorageLevel>} + Finer-grained cache persistence levels. """ + + + import sys import os sys.path.insert(0, os.path.join(os.environ["SPARK_HOME"], "python/lib/py4j0.7.egg")) +from pyspark.conf import SparkConf from pyspark.context import SparkContext from pyspark.rdd import RDD from pyspark.files import SparkFiles from pyspark.storagelevel import StorageLevel -__all__ = ["SparkContext", "RDD", "SparkFiles", "StorageLevel"] +__all__ = ["SparkConf", "SparkContext", "RDD", "SparkFiles", "StorageLevel"] diff --git a/python/pyspark/accumulators.py b/python/pyspark/accumulators.py index d367f91967..2204e9c9ca 100644 --- a/python/pyspark/accumulators.py +++ b/python/pyspark/accumulators.py @@ -42,6 +42,13 @@ >>> a.value 13 +>>> b = sc.accumulator(0) +>>> def g(x): +... b.add(x) +>>> rdd.foreach(g) +>>> b.value +6 + >>> from pyspark.accumulators import AccumulatorParam >>> class VectorAccumulatorParam(AccumulatorParam): ... def zero(self, value): @@ -83,8 +90,10 @@ import struct import SocketServer import threading from pyspark.cloudpickle import CloudPickler -from pyspark.serializers import read_int, read_with_length, load_pickle +from pyspark.serializers import read_int, PickleSerializer + +pickleSer = PickleSerializer() # Holds accumulators registered on the current machine, keyed by ID. This is then used to send # the local accumulator updates back to the driver program at the end of a task. @@ -139,9 +148,13 @@ class Accumulator(object): raise Exception("Accumulator.value cannot be accessed inside tasks") self._value = value + def add(self, term): + """Adds a term to this accumulator's value""" + self._value = self.accum_param.addInPlace(self._value, term) + def __iadd__(self, term): """The += operator; adds a term to this accumulator's value""" - self._value = self.accum_param.addInPlace(self._value, term) + self.add(term) return self def __str__(self): @@ -200,7 +213,7 @@ class _UpdateRequestHandler(SocketServer.StreamRequestHandler): from pyspark.accumulators import _accumulatorRegistry num_updates = read_int(self.rfile) for _ in range(num_updates): - (aid, update) = load_pickle(read_with_length(self.rfile)) + (aid, update) = pickleSer._read_with_length(self.rfile) _accumulatorRegistry[aid] += update # Write a byte in acknowledgement self.wfile.write(struct.pack("!b", 1)) diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py index dfdaba274f..43f40f8783 100644 --- a/python/pyspark/broadcast.py +++ b/python/pyspark/broadcast.py @@ -45,7 +45,18 @@ def _from_id(bid): class Broadcast(object): + """ + A broadcast variable created with + L{SparkContext.broadcast()<pyspark.context.SparkContext.broadcast>}. + Access its value through C{.value}. + """ + def __init__(self, bid, value, java_broadcast=None, pickle_registry=None): + """ + Should not be called directly by users -- use + L{SparkContext.broadcast()<pyspark.context.SparkContext.broadcast>} + instead. + """ self.value = value self.bid = bid self._jbroadcast = java_broadcast diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py new file mode 100644 index 0000000000..d72aed6a30 --- /dev/null +++ b/python/pyspark/conf.py @@ -0,0 +1,171 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +>>> from pyspark.conf import SparkConf +>>> from pyspark.context import SparkContext +>>> conf = SparkConf() +>>> conf.setMaster("local").setAppName("My app") +<pyspark.conf.SparkConf object at ...> +>>> conf.get("spark.master") +u'local' +>>> conf.get("spark.app.name") +u'My app' +>>> sc = SparkContext(conf=conf) +>>> sc.master +u'local' +>>> sc.appName +u'My app' +>>> sc.sparkHome == None +True + +>>> conf = SparkConf() +>>> conf.setSparkHome("/path") +<pyspark.conf.SparkConf object at ...> +>>> conf.get("spark.home") +u'/path' +>>> conf.setExecutorEnv("VAR1", "value1") +<pyspark.conf.SparkConf object at ...> +>>> conf.setExecutorEnv(pairs = [("VAR3", "value3"), ("VAR4", "value4")]) +<pyspark.conf.SparkConf object at ...> +>>> conf.get("spark.executorEnv.VAR1") +u'value1' +>>> print conf.toDebugString() +spark.executorEnv.VAR1=value1 +spark.executorEnv.VAR3=value3 +spark.executorEnv.VAR4=value4 +spark.home=/path +>>> sorted(conf.getAll(), key=lambda p: p[0]) +[(u'spark.executorEnv.VAR1', u'value1'), (u'spark.executorEnv.VAR3', u'value3'), (u'spark.executorEnv.VAR4', u'value4'), (u'spark.home', u'/path')] +""" + + +class SparkConf(object): + """ + Configuration for a Spark application. Used to set various Spark + parameters as key-value pairs. + + Most of the time, you would create a SparkConf object with + C{SparkConf()}, which will load values from C{spark.*} Java system + properties and any C{spark.conf} on your Spark classpath. In this + case, system properties take priority over C{spark.conf}, and any + parameters you set directly on the C{SparkConf} object take priority + over both of those. + + For unit tests, you can also call C{SparkConf(false)} to skip + loading external settings and get the same configuration no matter + what is on the classpath. + + All setter methods in this class support chaining. For example, + you can write C{conf.setMaster("local").setAppName("My app")}. + + Note that once a SparkConf object is passed to Spark, it is cloned + and can no longer be modified by the user. + """ + + def __init__(self, loadDefaults=True, _jvm=None): + """ + Create a new Spark configuration. + + @param loadDefaults: whether to load values from Java system + properties and classpath (True by default) + @param _jvm: internal parameter used to pass a handle to the + Java VM; does not need to be set by users + """ + from pyspark.context import SparkContext + SparkContext._ensure_initialized() + _jvm = _jvm or SparkContext._jvm + self._jconf = _jvm.SparkConf(loadDefaults) + + def set(self, key, value): + """Set a configuration property.""" + self._jconf.set(key, unicode(value)) + return self + + def setMaster(self, value): + """Set master URL to connect to.""" + self._jconf.setMaster(value) + return self + + def setAppName(self, value): + """Set application name.""" + self._jconf.setAppName(value) + return self + + def setSparkHome(self, value): + """Set path where Spark is installed on worker nodes.""" + self._jconf.setSparkHome(value) + return self + + def setExecutorEnv(self, key=None, value=None, pairs=None): + """Set an environment variable to be passed to executors.""" + if (key != None and pairs != None) or (key == None and pairs == None): + raise Exception("Either pass one key-value pair or a list of pairs") + elif key != None: + self._jconf.setExecutorEnv(key, value) + elif pairs != None: + for (k, v) in pairs: + self._jconf.setExecutorEnv(k, v) + return self + + def setAll(self, pairs): + """ + Set multiple parameters, passed as a list of key-value pairs. + + @param pairs: list of key-value pairs to set + """ + for (k, v) in pairs: + self._jconf.set(k, v) + return self + + def get(self, key, defaultValue=None): + """Get the configured value for some key, or return a default otherwise.""" + if defaultValue == None: # Py4J doesn't call the right get() if we pass None + if not self._jconf.contains(key): + return None + return self._jconf.get(key) + else: + return self._jconf.get(key, defaultValue) + + def getAll(self): + """Get all values as a list of key-value pairs.""" + pairs = [] + for elem in self._jconf.getAll(): + pairs.append((elem._1(), elem._2())) + return pairs + + def contains(self, key): + """Does this configuration contain a given key?""" + return self._jconf.contains(key) + + def toDebugString(self): + """ + Returns a printable version of the configuration, as a list of + key=value pairs, one per line. + """ + return self._jconf.toDebugString() + + +def _test(): + import doctest + (failure_count, test_count) = doctest.testmod(optionflags=doctest.ELLIPSIS) + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 597110321a..f955aad7a4 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -24,9 +24,10 @@ from tempfile import NamedTemporaryFile from pyspark import accumulators from pyspark.accumulators import Accumulator from pyspark.broadcast import Broadcast +from pyspark.conf import SparkConf from pyspark.files import SparkFiles from pyspark.java_gateway import launch_gateway -from pyspark.serializers import dump_pickle, write_with_length, batched +from pyspark.serializers import PickleSerializer, BatchedSerializer, MUTF8Deserializer from pyspark.storagelevel import StorageLevel from pyspark.rdd import RDD @@ -42,21 +43,22 @@ class SparkContext(object): _gateway = None _jvm = None - _writeIteratorToPickleFile = None - _takePartition = None + _writeToFile = None _next_accum_id = 0 _active_spark_context = None _lock = Lock() _python_includes = None # zip and egg files that need to be added to PYTHONPATH - def __init__(self, master, jobName, sparkHome=None, pyFiles=None, - environment=None, batchSize=1024): + + def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None, + environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None): """ - Create a new SparkContext. + Create a new SparkContext. At least the master and app name should be set, + either through the named parameters here or through C{conf}. @param master: Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). - @param jobName: A name for your job, to display on the cluster web UI + @param appName: A name for your job, to display on the cluster web UI. @param sparkHome: Location where Spark is installed on cluster nodes. @param pyFiles: Collection of .zip or .py files to send to the cluster and add to PYTHONPATH. These can be paths on the local file @@ -66,29 +68,59 @@ class SparkContext(object): @param batchSize: The number of Python objects represented as a single Java object. Set 1 to disable batching or -1 to use an unlimited batch size. + @param serializer: The serializer for RDDs. + @param conf: A L{SparkConf} object setting Spark properties. + + + >>> from pyspark.context import SparkContext + >>> sc = SparkContext('local', 'test') + + >>> sc2 = SparkContext('local', 'test2') # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + ValueError:... """ - with SparkContext._lock: - if SparkContext._active_spark_context: - raise ValueError("Cannot run multiple SparkContexts at once") - else: - SparkContext._active_spark_context = self - if not SparkContext._gateway: - SparkContext._gateway = launch_gateway() - SparkContext._jvm = SparkContext._gateway.jvm - SparkContext._writeIteratorToPickleFile = \ - SparkContext._jvm.PythonRDD.writeIteratorToPickleFile - SparkContext._takePartition = \ - SparkContext._jvm.PythonRDD.takePartition - self.master = master - self.jobName = jobName - self.sparkHome = sparkHome or None # None becomes null in Py4J + SparkContext._ensure_initialized(self) + self.environment = environment or {} - self.batchSize = batchSize # -1 represents a unlimited batch size + self._conf = conf or SparkConf(_jvm=self._jvm) + self._batchSize = batchSize # -1 represents an unlimited batch size + self._unbatched_serializer = serializer + if batchSize == 1: + self.serializer = self._unbatched_serializer + else: + self.serializer = BatchedSerializer(self._unbatched_serializer, + batchSize) + + # Set any parameters passed directly to us on the conf + if master: + self._conf.setMaster(master) + if appName: + self._conf.setAppName(appName) + if sparkHome: + self._conf.setSparkHome(sparkHome) + if environment: + for key, value in environment.iteritems(): + self._conf.setExecutorEnv(key, value) + + # Check that we have at least the required parameters + if not self._conf.contains("spark.master"): + raise Exception("A master URL must be set in your configuration") + if not self._conf.contains("spark.app.name"): + raise Exception("An application name must be set in your configuration") + + # Read back our properties from the conf in case we loaded some of them from + # the classpath or an external config file + self.master = self._conf.get("spark.master") + self.appName = self._conf.get("spark.app.name") + self.sparkHome = self._conf.get("spark.home", None) + for (k, v) in self._conf.getAll(): + if k.startswith("spark.executorEnv."): + varName = k[len("spark.executorEnv."):] + self.environment[varName] = v # Create the Java SparkContext through Py4J - empty_string_array = self._gateway.new_array(self._jvm.String, 0) - self._jsc = self._jvm.JavaSparkContext(master, jobName, sparkHome, - empty_string_array) + self._jsc = self._jvm.JavaSparkContext(self._conf._jconf) # Create a single Accumulator in Java that we'll send all our updates through; # they will be passed back to us through a TCP server @@ -99,6 +131,7 @@ class SparkContext(object): self._jvm.PythonAccumulatorParam(host, port)) self.pythonExec = os.environ.get("PYSPARK_PYTHON", 'python') + # Broadcast's __reduce__ method stores Broadcast instances here. # This allows other code to determine which Broadcast instances have # been pickled, so it can determine which Java broadcast objects to @@ -115,10 +148,33 @@ class SparkContext(object): self.addPyFile(path) # Create a temporary directory inside spark.local.dir: - local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir() + local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf()) self._temp_dir = \ self._jvm.org.apache.spark.util.Utils.createTempDir(local_dir).getAbsolutePath() + @classmethod + def _ensure_initialized(cls, instance=None): + with SparkContext._lock: + if not SparkContext._gateway: + SparkContext._gateway = launch_gateway() + SparkContext._jvm = SparkContext._gateway.jvm + SparkContext._writeToFile = SparkContext._jvm.PythonRDD.writeToFile + + if instance: + if SparkContext._active_spark_context and SparkContext._active_spark_context != instance: + raise ValueError("Cannot run multiple SparkContexts at once") + else: + SparkContext._active_spark_context = instance + + @classmethod + def setSystemProperty(cls, key, value): + """ + Set a Java system property, such as spark.executor.memory. This must + must be invoked before instantiating SparkContext. + """ + SparkContext._ensure_initialized() + SparkContext._jvm.java.lang.System.setProperty(key, value) + @property def defaultParallelism(self): """ @@ -158,15 +214,17 @@ class SparkContext(object): # Make sure we distribute data evenly if it's smaller than self.batchSize if "__len__" not in dir(c): c = list(c) # Make it a list so we can compute its length - batchSize = min(len(c) // numSlices, self.batchSize) + batchSize = min(len(c) // numSlices, self._batchSize) if batchSize > 1: - c = batched(c, batchSize) - for x in c: - write_with_length(dump_pickle(x), tempFile) + serializer = BatchedSerializer(self._unbatched_serializer, + batchSize) + else: + serializer = self._unbatched_serializer + serializer.dump_stream(c, tempFile) tempFile.close() - readRDDFromPickleFile = self._jvm.PythonRDD.readRDDFromPickleFile - jrdd = readRDDFromPickleFile(self._jsc, tempFile.name, numSlices) - return RDD(jrdd, self) + readRDDFromFile = self._jvm.PythonRDD.readRDDFromFile + jrdd = readRDDFromFile(self._jsc, tempFile.name, numSlices) + return RDD(jrdd, self, serializer) def textFile(self, name, minSplits=None): """ @@ -175,29 +233,50 @@ class SparkContext(object): RDD of Strings. """ minSplits = minSplits or min(self.defaultParallelism, 2) - jrdd = self._jsc.textFile(name, minSplits) - return RDD(jrdd, self) + return RDD(self._jsc.textFile(name, minSplits), self, + MUTF8Deserializer()) - def _checkpointFile(self, name): + def _checkpointFile(self, name, input_deserializer): jrdd = self._jsc.checkpointFile(name) - return RDD(jrdd, self) + return RDD(jrdd, self, input_deserializer) def union(self, rdds): """ Build the union of a list of RDDs. + + This supports unions() of RDDs with different serialized formats, + although this forces them to be reserialized using the default + serializer: + + >>> path = os.path.join(tempdir, "union-text.txt") + >>> with open(path, "w") as testFile: + ... testFile.write("Hello") + >>> textFile = sc.textFile(path) + >>> textFile.collect() + [u'Hello'] + >>> parallelized = sc.parallelize(["World!"]) + >>> sorted(sc.union([textFile, parallelized]).collect()) + [u'Hello', 'World!'] """ + first_jrdd_deserializer = rdds[0]._jrdd_deserializer + if any(x._jrdd_deserializer != first_jrdd_deserializer for x in rdds): + rdds = [x._reserialize() for x in rdds] first = rdds[0]._jrdd rest = [x._jrdd for x in rdds[1:]] - rest = ListConverter().convert(rest, self.gateway._gateway_client) - return RDD(self._jsc.union(first, rest), self) + rest = ListConverter().convert(rest, self._gateway._gateway_client) + return RDD(self._jsc.union(first, rest), self, + rdds[0]._jrdd_deserializer) def broadcast(self, value): """ - Broadcast a read-only variable to the cluster, returning a C{Broadcast} + Broadcast a read-only variable to the cluster, returning a + L{Broadcast<pyspark.broadcast.Broadcast>} object for reading it in distributed functions. The variable will be sent to each cluster only once. """ - jbroadcast = self._jsc.broadcast(bytearray(dump_pickle(value))) + pickleSer = PickleSerializer() + pickled = pickleSer.dumps(value) + jbroadcast = self._jsc.broadcast(bytearray(pickled)) return Broadcast(jbroadcast.id(), value, jbroadcast, self._pickled_broadcast_vars) @@ -209,7 +288,7 @@ class SparkContext(object): and floating-point numbers if you do not provide one. For other types, a custom AccumulatorParam can be used. """ - if accum_param == None: + if accum_param is None: if isinstance(value, int): accum_param = accumulators.INT_ACCUMULATOR_PARAM elif isinstance(value, float): @@ -268,17 +347,12 @@ class SparkContext(object): self._python_includes.append(filename) sys.path.append(os.path.join(SparkFiles.getRootDirectory(), filename)) # for tests in local mode - def setCheckpointDir(self, dirName, useExisting=False): + def setCheckpointDir(self, dirName): """ Set the directory under which RDDs are going to be checkpointed. The directory must be a HDFS path if running on a cluster. - - If the directory does not exist, it will be created. If the directory - exists and C{useExisting} is set to true, then the exisiting directory - will be used. Otherwise an exception will be thrown to prevent - accidental overriding of checkpoint files in the existing directory. """ - self._jsc.sc().setCheckpointDir(dirName, useExisting) + self._jsc.sc().setCheckpointDir(dirName) def _getJavaStorageLevel(self, storageLevel): """ diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index b872ae61d5..7243ee6861 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -60,7 +60,9 @@ def launch_gateway(): # Connect to the gateway gateway = JavaGateway(GatewayClient(port=port), auto_convert=False) # Import the classes used by PySpark + java_import(gateway.jvm, "org.apache.spark.SparkConf") java_import(gateway.jvm, "org.apache.spark.api.java.*") java_import(gateway.jvm, "org.apache.spark.api.python.*") + java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*") java_import(gateway.jvm, "scala.Tuple2") return gateway diff --git a/python/pyspark/mllib/__init__.py b/python/pyspark/mllib/__init__.py new file mode 100644 index 0000000000..b1a5df109b --- /dev/null +++ b/python/pyspark/mllib/__init__.py @@ -0,0 +1,20 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +Python bindings for MLlib. +""" diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py new file mode 100644 index 0000000000..e74ba0fabc --- /dev/null +++ b/python/pyspark/mllib/_common.py @@ -0,0 +1,227 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from numpy import ndarray, copyto, float64, int64, int32, ones, array_equal, array, dot, shape +from pyspark import SparkContext + +# Double vector format: +# +# [8-byte 1] [8-byte length] [length*8 bytes of data] +# +# Double matrix format: +# +# [8-byte 2] [8-byte rows] [8-byte cols] [rows*cols*8 bytes of data] +# +# This is all in machine-endian. That means that the Java interpreter and the +# Python interpreter must agree on what endian the machine is. + +def _deserialize_byte_array(shape, ba, offset): + """Wrapper around ndarray aliasing hack. + + >>> x = array([1.0, 2.0, 3.0, 4.0, 5.0]) + >>> array_equal(x, _deserialize_byte_array(x.shape, x.data, 0)) + True + >>> x = array([1.0, 2.0, 3.0, 4.0]).reshape(2,2) + >>> array_equal(x, _deserialize_byte_array(x.shape, x.data, 0)) + True + """ + ar = ndarray(shape=shape, buffer=ba, offset=offset, dtype="float64", + order='C') + return ar.copy() + +def _serialize_double_vector(v): + """Serialize a double vector into a mutually understood format.""" + if type(v) != ndarray: + raise TypeError("_serialize_double_vector called on a %s; " + "wanted ndarray" % type(v)) + if v.dtype != float64: + raise TypeError("_serialize_double_vector called on an ndarray of %s; " + "wanted ndarray of float64" % v.dtype) + if v.ndim != 1: + raise TypeError("_serialize_double_vector called on a %ddarray; " + "wanted a 1darray" % v.ndim) + length = v.shape[0] + ba = bytearray(16 + 8*length) + header = ndarray(shape=[2], buffer=ba, dtype="int64") + header[0] = 1 + header[1] = length + copyto(ndarray(shape=[length], buffer=ba, offset=16, + dtype="float64"), v) + return ba + +def _deserialize_double_vector(ba): + """Deserialize a double vector from a mutually understood format. + + >>> x = array([1.0, 2.0, 3.0, 4.0, -1.0, 0.0, -0.0]) + >>> array_equal(x, _deserialize_double_vector(_serialize_double_vector(x))) + True + """ + if type(ba) != bytearray: + raise TypeError("_deserialize_double_vector called on a %s; " + "wanted bytearray" % type(ba)) + if len(ba) < 16: + raise TypeError("_deserialize_double_vector called on a %d-byte array, " + "which is too short" % len(ba)) + if (len(ba) & 7) != 0: + raise TypeError("_deserialize_double_vector called on a %d-byte array, " + "which is not a multiple of 8" % len(ba)) + header = ndarray(shape=[2], buffer=ba, dtype="int64") + if header[0] != 1: + raise TypeError("_deserialize_double_vector called on bytearray " + "with wrong magic") + length = header[1] + if len(ba) != 8*length + 16: + raise TypeError("_deserialize_double_vector called on bytearray " + "with wrong length") + return _deserialize_byte_array([length], ba, 16) + +def _serialize_double_matrix(m): + """Serialize a double matrix into a mutually understood format.""" + if (type(m) == ndarray and m.dtype == float64 and m.ndim == 2): + rows = m.shape[0] + cols = m.shape[1] + ba = bytearray(24 + 8 * rows * cols) + header = ndarray(shape=[3], buffer=ba, dtype="int64") + header[0] = 2 + header[1] = rows + header[2] = cols + copyto(ndarray(shape=[rows, cols], buffer=ba, offset=24, + dtype="float64", order='C'), m) + return ba + else: + raise TypeError("_serialize_double_matrix called on a " + "non-double-matrix") + +def _deserialize_double_matrix(ba): + """Deserialize a double matrix from a mutually understood format.""" + if type(ba) != bytearray: + raise TypeError("_deserialize_double_matrix called on a %s; " + "wanted bytearray" % type(ba)) + if len(ba) < 24: + raise TypeError("_deserialize_double_matrix called on a %d-byte array, " + "which is too short" % len(ba)) + if (len(ba) & 7) != 0: + raise TypeError("_deserialize_double_matrix called on a %d-byte array, " + "which is not a multiple of 8" % len(ba)) + header = ndarray(shape=[3], buffer=ba, dtype="int64") + if (header[0] != 2): + raise TypeError("_deserialize_double_matrix called on bytearray " + "with wrong magic") + rows = header[1] + cols = header[2] + if (len(ba) != 8*rows*cols + 24): + raise TypeError("_deserialize_double_matrix called on bytearray " + "with wrong length") + return _deserialize_byte_array([rows, cols], ba, 24) + +def _linear_predictor_typecheck(x, coeffs): + """Check that x is a one-dimensional vector of the right shape. + This is a temporary hackaround until I actually implement bulk predict.""" + if type(x) == ndarray: + if x.ndim == 1: + if x.shape == coeffs.shape: + pass + else: + raise RuntimeError("Got array of %d elements; wanted %d" + % (shape(x)[0], shape(coeffs)[0])) + else: + raise RuntimeError("Bulk predict not yet supported.") + elif (type(x) == RDD): + raise RuntimeError("Bulk predict not yet supported.") + else: + raise TypeError("Argument of type " + type(x).__name__ + " unsupported") + +def _get_unmangled_rdd(data, serializer): + dataBytes = data.map(serializer) + dataBytes._bypass_serializer = True + dataBytes.cache() + return dataBytes + +# Map a pickled Python RDD of numpy double vectors to a Java RDD of +# _serialized_double_vectors +def _get_unmangled_double_vector_rdd(data): + return _get_unmangled_rdd(data, _serialize_double_vector) + +class LinearModel(object): + """Something that has a vector of coefficients and an intercept.""" + def __init__(self, coeff, intercept): + self._coeff = coeff + self._intercept = intercept + +class LinearRegressionModelBase(LinearModel): + """A linear regression model. + + >>> lrmb = LinearRegressionModelBase(array([1.0, 2.0]), 0.1) + >>> abs(lrmb.predict(array([-1.03, 7.777])) - 14.624) < 1e-6 + True + """ + def predict(self, x): + """Predict the value of the dependent variable given a vector x""" + """containing values for the independent variables.""" + _linear_predictor_typecheck(x, self._coeff) + return dot(self._coeff, x) + self._intercept + +# If we weren't given initial weights, take a zero vector of the appropriate +# length. +def _get_initial_weights(initial_weights, data): + if initial_weights is None: + initial_weights = data.first() + if type(initial_weights) != ndarray: + raise TypeError("At least one data element has type " + + type(initial_weights).__name__ + " which is not ndarray") + if initial_weights.ndim != 1: + raise TypeError("At least one data element has " + + initial_weights.ndim + " dimensions, which is not 1") + initial_weights = ones([initial_weights.shape[0] - 1]) + return initial_weights + +# train_func should take two parameters, namely data and initial_weights, and +# return the result of a call to the appropriate JVM stub. +# _regression_train_wrapper is responsible for setup and error checking. +def _regression_train_wrapper(sc, train_func, klass, data, initial_weights): + initial_weights = _get_initial_weights(initial_weights, data) + dataBytes = _get_unmangled_double_vector_rdd(data) + ans = train_func(dataBytes, _serialize_double_vector(initial_weights)) + if len(ans) != 2: + raise RuntimeError("JVM call result had unexpected length") + elif type(ans[0]) != bytearray: + raise RuntimeError("JVM call result had first element of type " + + type(ans[0]).__name__ + " which is not bytearray") + elif type(ans[1]) != float: + raise RuntimeError("JVM call result had second element of type " + + type(ans[0]).__name__ + " which is not float") + return klass(_deserialize_double_vector(ans[0]), ans[1]) + +def _serialize_rating(r): + ba = bytearray(16) + intpart = ndarray(shape=[2], buffer=ba, dtype=int32) + doublepart = ndarray(shape=[1], buffer=ba, dtype=float64, offset=8) + intpart[0], intpart[1], doublepart[0] = r + return ba + +def _test(): + import doctest + globs = globals().copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) + (failure_count, test_count) = doctest.testmod(globs=globs, + optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py new file mode 100644 index 0000000000..70de332d34 --- /dev/null +++ b/python/pyspark/mllib/classification.py @@ -0,0 +1,86 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from numpy import array, dot, shape +from pyspark import SparkContext +from pyspark.mllib._common import \ + _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \ + _serialize_double_matrix, _deserialize_double_matrix, \ + _serialize_double_vector, _deserialize_double_vector, \ + _get_initial_weights, _serialize_rating, _regression_train_wrapper, \ + LinearModel, _linear_predictor_typecheck +from math import exp, log + +class LogisticRegressionModel(LinearModel): + """A linear binary classification model derived from logistic regression. + + >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2) + >>> lrm = LogisticRegressionWithSGD.train(sc, sc.parallelize(data)) + >>> lrm.predict(array([1.0])) != None + True + """ + def predict(self, x): + _linear_predictor_typecheck(x, self._coeff) + margin = dot(x, self._coeff) + self._intercept + prob = 1/(1 + exp(-margin)) + return 1 if prob > 0.5 else 0 + +class LogisticRegressionWithSGD(object): + @classmethod + def train(cls, sc, data, iterations=100, step=1.0, + mini_batch_fraction=1.0, initial_weights=None): + """Train a logistic regression model on the given data.""" + return _regression_train_wrapper(sc, lambda d, i: + sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd, + iterations, step, mini_batch_fraction, i), + LogisticRegressionModel, data, initial_weights) + +class SVMModel(LinearModel): + """A support vector machine. + + >>> data = array([0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0]).reshape(4,2) + >>> svm = SVMWithSGD.train(sc, sc.parallelize(data)) + >>> svm.predict(array([1.0])) != None + True + """ + def predict(self, x): + _linear_predictor_typecheck(x, self._coeff) + margin = dot(x, self._coeff) + self._intercept + return 1 if margin >= 0 else 0 + +class SVMWithSGD(object): + @classmethod + def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0, + mini_batch_fraction=1.0, initial_weights=None): + """Train a support vector machine on the given data.""" + return _regression_train_wrapper(sc, lambda d, i: + sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd, + iterations, step, reg_param, mini_batch_fraction, i), + SVMModel, data, initial_weights) + +def _test(): + import doctest + globs = globals().copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) + (failure_count, test_count) = doctest.testmod(globs=globs, + optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py new file mode 100644 index 0000000000..8cf20e591a --- /dev/null +++ b/python/pyspark/mllib/clustering.py @@ -0,0 +1,79 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from numpy import array, dot +from math import sqrt +from pyspark import SparkContext +from pyspark.mllib._common import \ + _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \ + _serialize_double_matrix, _deserialize_double_matrix, \ + _serialize_double_vector, _deserialize_double_vector, \ + _get_initial_weights, _serialize_rating, _regression_train_wrapper + +class KMeansModel(object): + """A clustering model derived from the k-means method. + + >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2) + >>> clusters = KMeans.train(sc, sc.parallelize(data), 2, maxIterations=10, runs=30, initialization_mode="random") + >>> clusters.predict(array([0.0, 0.0])) == clusters.predict(array([1.0, 1.0])) + True + >>> clusters.predict(array([8.0, 9.0])) == clusters.predict(array([9.0, 8.0])) + True + >>> clusters = KMeans.train(sc, sc.parallelize(data), 2) + """ + def __init__(self, centers_): + self.centers = centers_ + + def predict(self, x): + """Find the cluster to which x belongs in this model.""" + best = 0 + best_distance = 1e75 + for i in range(0, self.centers.shape[0]): + diff = x - self.centers[i] + distance = sqrt(dot(diff, diff)) + if distance < best_distance: + best = i + best_distance = distance + return best + +class KMeans(object): + @classmethod + def train(cls, sc, data, k, maxIterations=100, runs=1, + initialization_mode="k-means||"): + """Train a k-means clustering model.""" + dataBytes = _get_unmangled_double_vector_rdd(data) + ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(dataBytes._jrdd, + k, maxIterations, runs, initialization_mode) + if len(ans) != 1: + raise RuntimeError("JVM call result had unexpected length") + elif type(ans[0]) != bytearray: + raise RuntimeError("JVM call result had first element of type " + + type(ans[0]) + " which is not bytearray") + return KMeansModel(_deserialize_double_matrix(ans[0])) + +def _test(): + import doctest + globs = globals().copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) + (failure_count, test_count) = doctest.testmod(globs=globs, + optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py new file mode 100644 index 0000000000..14d06cba21 --- /dev/null +++ b/python/pyspark/mllib/recommendation.py @@ -0,0 +1,74 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark import SparkContext +from pyspark.mllib._common import \ + _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \ + _serialize_double_matrix, _deserialize_double_matrix, \ + _serialize_double_vector, _deserialize_double_vector, \ + _get_initial_weights, _serialize_rating, _regression_train_wrapper + +class MatrixFactorizationModel(object): + """A matrix factorisation model trained by regularized alternating + least-squares. + + >>> r1 = (1, 1, 1.0) + >>> r2 = (1, 2, 2.0) + >>> r3 = (2, 1, 2.0) + >>> ratings = sc.parallelize([r1, r2, r3]) + >>> model = ALS.trainImplicit(sc, ratings, 1) + >>> model.predict(2,2) is not None + True + """ + + def __init__(self, sc, java_model): + self._context = sc + self._java_model = java_model + + def __del__(self): + self._context._gateway.detach(self._java_model) + + def predict(self, user, product): + return self._java_model.predict(user, product) + +class ALS(object): + @classmethod + def train(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1): + ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) + mod = sc._jvm.PythonMLLibAPI().trainALSModel(ratingBytes._jrdd, + rank, iterations, lambda_, blocks) + return MatrixFactorizationModel(sc, mod) + + @classmethod + def trainImplicit(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01): + ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) + mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(ratingBytes._jrdd, + rank, iterations, lambda_, blocks, alpha) + return MatrixFactorizationModel(sc, mod) + +def _test(): + import doctest + globs = globals().copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) + (failure_count, test_count) = doctest.testmod(globs=globs, + optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py new file mode 100644 index 0000000000..a3a68b29e0 --- /dev/null +++ b/python/pyspark/mllib/regression.py @@ -0,0 +1,110 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from numpy import array, dot +from pyspark import SparkContext +from pyspark.mllib._common import \ + _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \ + _serialize_double_matrix, _deserialize_double_matrix, \ + _serialize_double_vector, _deserialize_double_vector, \ + _get_initial_weights, _serialize_rating, _regression_train_wrapper, \ + _linear_predictor_typecheck + +class LinearModel(object): + """Something that has a vector of coefficients and an intercept.""" + def __init__(self, coeff, intercept): + self._coeff = coeff + self._intercept = intercept + +class LinearRegressionModelBase(LinearModel): + """A linear regression model. + + >>> lrmb = LinearRegressionModelBase(array([1.0, 2.0]), 0.1) + >>> abs(lrmb.predict(array([-1.03, 7.777])) - 14.624) < 1e-6 + True + """ + def predict(self, x): + """Predict the value of the dependent variable given a vector x""" + """containing values for the independent variables.""" + _linear_predictor_typecheck(x, self._coeff) + return dot(self._coeff, x) + self._intercept + +class LinearRegressionModel(LinearRegressionModelBase): + """A linear regression model derived from a least-squares fit. + + >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2) + >>> lrm = LinearRegressionWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0])) + """ + +class LinearRegressionWithSGD(object): + @classmethod + def train(cls, sc, data, iterations=100, step=1.0, + mini_batch_fraction=1.0, initial_weights=None): + """Train a linear regression model on the given data.""" + return _regression_train_wrapper(sc, lambda d, i: + sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD( + d._jrdd, iterations, step, mini_batch_fraction, i), + LinearRegressionModel, data, initial_weights) + +class LassoModel(LinearRegressionModelBase): + """A linear regression model derived from a least-squares fit with an + l_1 penalty term. + + >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2) + >>> lrm = LassoWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0])) + """ + +class LassoWithSGD(object): + @classmethod + def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0, + mini_batch_fraction=1.0, initial_weights=None): + """Train a Lasso regression model on the given data.""" + return _regression_train_wrapper(sc, lambda d, i: + sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd, + iterations, step, reg_param, mini_batch_fraction, i), + LassoModel, data, initial_weights) + +class RidgeRegressionModel(LinearRegressionModelBase): + """A linear regression model derived from a least-squares fit with an + l_2 penalty term. + + >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2) + >>> lrm = RidgeRegressionWithSGD.train(sc, sc.parallelize(data), initial_weights=array([1.0])) + """ + +class RidgeRegressionWithSGD(object): + @classmethod + def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0, + mini_batch_fraction=1.0, initial_weights=None): + """Train a ridge regression model on the given data.""" + return _regression_train_wrapper(sc, lambda d, i: + sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd, + iterations, step, reg_param, mini_batch_fraction, i), + RidgeRegressionModel, data, initial_weights) + +def _test(): + import doctest + globs = globals().copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) + (failure_count, test_count) = doctest.testmod(globs=globs, + optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 58e1849cad..f87923e6fa 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -18,7 +18,7 @@ from base64 import standard_b64encode as b64enc import copy from collections import defaultdict -from itertools import chain, ifilter, imap, product +from itertools import chain, ifilter, imap import operator import os import sys @@ -27,9 +27,8 @@ from subprocess import Popen, PIPE from tempfile import NamedTemporaryFile from threading import Thread -from pyspark import cloudpickle -from pyspark.serializers import batched, Batch, dump_pickle, load_pickle, \ - read_from_pickle_file +from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \ + BatchedSerializer, CloudPickleSerializer, pack_long from pyspark.join import python_join, python_left_outer_join, \ python_right_outer_join, python_cogroup from pyspark.statcounter import StatCounter @@ -48,12 +47,15 @@ class RDD(object): operated on in parallel. """ - def __init__(self, jrdd, ctx): + def __init__(self, jrdd, ctx, jrdd_deserializer): self._jrdd = jrdd self.is_cached = False self.is_checkpointed = False self.ctx = ctx - self._partitionFunc = None + self._jrdd_deserializer = jrdd_deserializer + + def __repr__(self): + return self._jrdd.toString() @property def context(self): @@ -117,8 +119,6 @@ class RDD(object): else: return None - # TODO persist(self, storageLevel) - def map(self, f, preservesPartitioning=False): """ Return a new RDD containing the distinct elements in this RDD. @@ -227,7 +227,7 @@ class RDD(object): total = num samples = self.sample(withReplacement, fraction, seed).collect() - + # If the first sample didn't turn out large enough, keep trying to take samples; # this shouldn't happen often because we use a big multiplier for their initial size. # See: scala/spark/RDD.scala @@ -249,7 +249,23 @@ class RDD(object): >>> rdd.union(rdd).collect() [1, 1, 2, 3, 1, 1, 2, 3] """ - return RDD(self._jrdd.union(other._jrdd), self.ctx) + if self._jrdd_deserializer == other._jrdd_deserializer: + rdd = RDD(self._jrdd.union(other._jrdd), self.ctx, + self._jrdd_deserializer) + return rdd + else: + # These RDDs contain data in different serialized formats, so we + # must normalize them to the default serializer. + self_copy = self._reserialize() + other_copy = other._reserialize() + return RDD(self_copy._jrdd.union(other_copy._jrdd), self.ctx, + self.ctx.serializer) + + def _reserialize(self): + if self._jrdd_deserializer == self.ctx.serializer: + return self + else: + return self.map(lambda x: x, preservesPartitioning=True) def __add__(self, other): """ @@ -263,7 +279,55 @@ class RDD(object): raise TypeError return self.union(other) - # TODO: sort + def sortByKey(self, ascending=True, numPartitions=None, keyfunc = lambda x: x): + """ + Sorts this RDD, which is assumed to consist of (key, value) pairs. + + >>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)] + >>> sc.parallelize(tmp).sortByKey(True, 2).collect() + [('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)] + >>> tmp2 = [('Mary', 1), ('had', 2), ('a', 3), ('little', 4), ('lamb', 5)] + >>> tmp2.extend([('whose', 6), ('fleece', 7), ('was', 8), ('white', 9)]) + >>> sc.parallelize(tmp2).sortByKey(True, 3, keyfunc=lambda k: k.lower()).collect() + [('a', 3), ('fleece', 7), ('had', 2), ('lamb', 5), ('little', 4), ('Mary', 1), ('was', 8), ('white', 9), ('whose', 6)] + """ + if numPartitions is None: + numPartitions = self.ctx.defaultParallelism + + bounds = list() + + # first compute the boundary of each part via sampling: we want to partition + # the key-space into bins such that the bins have roughly the same + # number of (key, value) pairs falling into them + if numPartitions > 1: + rddSize = self.count() + maxSampleSize = numPartitions * 20.0 # constant from Spark's RangePartitioner + fraction = min(maxSampleSize / max(rddSize, 1), 1.0) + + samples = self.sample(False, fraction, 1).map(lambda (k, v): k).collect() + samples = sorted(samples, reverse=(not ascending), key=keyfunc) + + # we have numPartitions many parts but one of the them has + # an implicit boundary + for i in range(0, numPartitions - 1): + index = (len(samples) - 1) * (i + 1) / numPartitions + bounds.append(samples[index]) + + def rangePartitionFunc(k): + p = 0 + while p < len(bounds) and keyfunc(k) > bounds[p]: + p += 1 + if ascending: + return p + else: + return numPartitions-1-p + + def mapFunc(iterator): + yield sorted(iterator, reverse=(not ascending), key=lambda (k, v): keyfunc(k)) + + return (self.partitionBy(numPartitions, partitionFunc=rangePartitionFunc) + .mapPartitions(mapFunc,preservesPartitioning=True) + .flatMap(lambda x: x, preservesPartitioning=True)) def glom(self): """ @@ -288,17 +352,9 @@ class RDD(object): [(1, 1), (1, 2), (2, 1), (2, 2)] """ # Due to batching, we can't use the Java cartesian method. - java_cartesian = RDD(self._jrdd.cartesian(other._jrdd), self.ctx) - def unpack_batches(pair): - (x, y) = pair - if type(x) == Batch or type(y) == Batch: - xs = x.items if type(x) == Batch else [x] - ys = y.items if type(y) == Batch else [y] - for pair in product(xs, ys): - yield pair - else: - yield pair - return java_cartesian.flatMap(unpack_batches) + deserializer = CartesianDeserializer(self._jrdd_deserializer, + other._jrdd_deserializer) + return RDD(self._jrdd.cartesian(other._jrdd), self.ctx, deserializer) def groupBy(self, f, numPartitions=None): """ @@ -345,8 +401,8 @@ class RDD(object): """ Return a list that contains all of the elements in this RDD. """ - picklesInJava = self._jrdd.collect().iterator() - return list(self._collect_iterator_through_file(picklesInJava)) + bytesInJava = self._jrdd.collect().iterator() + return list(self._collect_iterator_through_file(bytesInJava)) def _collect_iterator_through_file(self, iterator): # Transferring lots of data through Py4J can be slow because @@ -354,10 +410,10 @@ class RDD(object): # file and read it back. tempFile = NamedTemporaryFile(delete=False, dir=self.ctx._temp_dir) tempFile.close() - self.ctx._writeIteratorToPickleFile(iterator, tempFile.name) + self.ctx._writeToFile(iterator, tempFile.name) # Read the data into Python and deserialize it: with open(tempFile.name, 'rb') as tempFile: - for item in read_from_pickle_file(tempFile): + for item in self._jrdd_deserializer.load_stream(tempFile): yield item os.unlink(tempFile.name) @@ -425,7 +481,7 @@ class RDD(object): 3 """ return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() - + def stats(self): """ Return a L{StatCounter} object that captures the mean, variance @@ -462,7 +518,7 @@ class RDD(object): 0.816... """ return self.stats().stdev() - + def sampleStdev(self): """ Compute the sample standard deviation of this RDD's elements (which corrects for bias in @@ -523,9 +579,14 @@ class RDD(object): # Take only up to num elements from each partition we try mapped = self.mapPartitions(takeUpToNum) items = [] + # TODO(shivaram): Similar to the scala implementation, update the take + # method to scan multiple splits based on an estimate of how many elements + # we have per-split. for partition in range(mapped._jrdd.splits().size()): - iterator = self.ctx._takePartition(mapped._jrdd.rdd(), partition) - items.extend(self._collect_iterator_through_file(iterator)) + partitionsToTake = self.ctx._gateway.new_array(self.ctx._jvm.int, 1) + partitionsToTake[0] = partition + iterator = mapped._jrdd.collectPartitions(partitionsToTake)[0].iterator() + items.extend(mapped._collect_iterator_through_file(iterator)) if len(items) >= num: break return items[:num] @@ -552,7 +613,10 @@ class RDD(object): '0\\n1\\n2\\n3\\n4\\n5\\n6\\n7\\n8\\n9\\n' """ def func(split, iterator): - return (str(x).encode("utf-8") for x in iterator) + for x in iterator: + if not isinstance(x, basestring): + x = unicode(x) + yield x.encode("utf-8") keyed = PipelinedRDD(self, func) keyed._bypass_serializer = True keyed._jrdd.map(self.ctx._jvm.BytesToString()).saveAsTextFile(path) @@ -689,20 +753,23 @@ class RDD(object): # Transferring O(n) objects to Java is too expensive. Instead, we'll # form the hash buckets in Python, transferring O(numPartitions) objects # to Java. Each object is a (splitNumber, [objects]) pair. + outputSerializer = self.ctx._unbatched_serializer def add_shuffle_key(split, iterator): + buckets = defaultdict(list) + for (k, v) in iterator: buckets[partitionFunc(k) % numPartitions].append((k, v)) for (split, items) in buckets.iteritems(): - yield str(split) - yield dump_pickle(Batch(items)) + yield pack_long(split) + yield outputSerializer.dumps(items) keyed = PipelinedRDD(self, add_shuffle_key) keyed._bypass_serializer = True pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD() partitioner = self.ctx._jvm.PythonPartitioner(numPartitions, id(partitionFunc)) jrdd = pairRDD.partitionBy(partitioner).values() - rdd = RDD(jrdd, self.ctx) + rdd = RDD(jrdd, self.ctx, BatchedSerializer(outputSerializer)) # This is required so that id(partitionFunc) remains unique, even if # partitionFunc is a lambda: rdd._partitionFunc = partitionFunc @@ -739,7 +806,8 @@ class RDD(object): numPartitions = self.ctx.defaultParallelism def combineLocally(iterator): combiners = {} - for (k, v) in iterator: + for x in iterator: + (k, v) = x if k not in combiners: combiners[k] = createCombiner(v) else: @@ -830,9 +898,9 @@ class RDD(object): >>> y = sc.parallelize([("a", 3), ("c", None)]) >>> sorted(x.subtractByKey(y).collect()) [('b', 4), ('b', 5)] - """ - filter_func = lambda tpl: len(tpl[1][0]) > 0 and len(tpl[1][1]) == 0 - map_func = lambda tpl: [(tpl[0], val) for val in tpl[1][0]] + """ + filter_func = lambda (key, vals): len(vals[0]) > 0 and len(vals[1]) == 0 + map_func = lambda (key, vals): [(key, val) for val in vals[0]] return self.cogroup(other, numPartitions).filter(filter_func).flatMap(map_func) def subtract(self, other, numPartitions=None): @@ -881,50 +949,52 @@ class PipelinedRDD(RDD): 20 """ def __init__(self, prev, func, preservesPartitioning=False): - if isinstance(prev, PipelinedRDD) and prev._is_pipelinable(): + if not isinstance(prev, PipelinedRDD) or not prev._is_pipelinable(): + # This transformation is the first in its stage: + self.func = func + self.preservesPartitioning = preservesPartitioning + self._prev_jrdd = prev._jrdd + self._prev_jrdd_deserializer = prev._jrdd_deserializer + else: prev_func = prev.func def pipeline_func(split, iterator): return func(split, prev_func(split, iterator)) self.func = pipeline_func self.preservesPartitioning = \ prev.preservesPartitioning and preservesPartitioning - self._prev_jrdd = prev._prev_jrdd - else: - self.func = func - self.preservesPartitioning = preservesPartitioning - self._prev_jrdd = prev._jrdd + self._prev_jrdd = prev._prev_jrdd # maintain the pipeline + self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer self.is_cached = False self.is_checkpointed = False self.ctx = prev.ctx self.prev = prev self._jrdd_val = None + self._jrdd_deserializer = self.ctx.serializer self._bypass_serializer = False @property def _jrdd(self): if self._jrdd_val: return self._jrdd_val - func = self.func - if not self._bypass_serializer and self.ctx.batchSize != 1: - oldfunc = self.func - batchSize = self.ctx.batchSize - def batched_func(split, iterator): - return batched(oldfunc(split, iterator), batchSize) - func = batched_func - cmds = [func, self._bypass_serializer] - pipe_command = ' '.join(b64enc(cloudpickle.dumps(f)) for f in cmds) + if self._bypass_serializer: + serializer = NoOpSerializer() + else: + serializer = self.ctx.serializer + command = (self.func, self._prev_jrdd_deserializer, serializer) + pickled_command = CloudPickleSerializer().dumps(command) broadcast_vars = ListConverter().convert( [x._jbroadcast for x in self.ctx._pickled_broadcast_vars], self.ctx._gateway._gateway_client) self.ctx._pickled_broadcast_vars.clear() - class_manifest = self._prev_jrdd.classManifest() + class_tag = self._prev_jrdd.classTag() env = MapConverter().convert(self.ctx.environment, self.ctx._gateway._gateway_client) includes = ListConverter().convert(self.ctx._python_includes, self.ctx._gateway._gateway_client) python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(), - pipe_command, env, includes, self.preservesPartitioning, self.ctx.pythonExec, - broadcast_vars, self.ctx._javaAccumulator, class_manifest) + bytearray(pickled_command), env, includes, self.preservesPartitioning, + self.ctx.pythonExec, broadcast_vars, self.ctx._javaAccumulator, + class_tag) self._jrdd_val = python_rdd.asJavaRDD() return self._jrdd_val diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index fecacd1241..2a500ab919 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -15,45 +15,269 @@ # limitations under the License. # -import struct +""" +PySpark supports custom serializers for transferring data; this can improve +performance. + +By default, PySpark uses L{PickleSerializer} to serialize objects using Python's +C{cPickle} serializer, which can serialize nearly any Python object. +Other serializers, like L{MarshalSerializer}, support fewer datatypes but can be +faster. + +The serializer is chosen when creating L{SparkContext}: + +>>> from pyspark.context import SparkContext +>>> from pyspark.serializers import MarshalSerializer +>>> sc = SparkContext('local', 'test', serializer=MarshalSerializer()) +>>> sc.parallelize(list(range(1000))).map(lambda x: 2 * x).take(10) +[0, 2, 4, 6, 8, 10, 12, 14, 16, 18] +>>> sc.stop() + +By default, PySpark serialize objects in batches; the batch size can be +controlled through SparkContext's C{batchSize} parameter +(the default size is 1024 objects): + +>>> sc = SparkContext('local', 'test', batchSize=2) +>>> rdd = sc.parallelize(range(16), 4).map(lambda x: x) + +Behind the scenes, this creates a JavaRDD with four partitions, each of +which contains two batches of two objects: + +>>> rdd.glom().collect() +[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]] +>>> rdd._jrdd.count() +8L +>>> sc.stop() + +A batch size of -1 uses an unlimited batch size, and a size of 1 disables +batching: + +>>> sc = SparkContext('local', 'test', batchSize=1) +>>> rdd = sc.parallelize(range(16), 4).map(lambda x: x) +>>> rdd.glom().collect() +[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]] +>>> rdd._jrdd.count() +16L +""" + import cPickle +from itertools import chain, izip, product +import marshal +import struct +from pyspark import cloudpickle + + +__all__ = ["PickleSerializer", "MarshalSerializer"] + + +class SpecialLengths(object): + END_OF_DATA_SECTION = -1 + PYTHON_EXCEPTION_THROWN = -2 + TIMING_DATA = -3 + + +class Serializer(object): + + def dump_stream(self, iterator, stream): + """ + Serialize an iterator of objects to the output stream. + """ + raise NotImplementedError + + def load_stream(self, stream): + """ + Return an iterator of deserialized objects from the input stream. + """ + raise NotImplementedError + + + def _load_stream_without_unbatching(self, stream): + return self.load_stream(stream) + + # Note: our notion of "equality" is that output generated by + # equal serializers can be deserialized using the same serializer. + + # This default implementation handles the simple cases; + # subclasses should override __eq__ as appropriate. + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not self.__eq__(other) + + +class FramedSerializer(Serializer): + """ + Serializer that writes objects as a stream of (length, data) pairs, + where C{length} is a 32-bit integer and data is C{length} bytes. + """ + + def dump_stream(self, iterator, stream): + for obj in iterator: + self._write_with_length(obj, stream) + + def load_stream(self, stream): + while True: + try: + yield self._read_with_length(stream) + except EOFError: + return + + def _write_with_length(self, obj, stream): + serialized = self.dumps(obj) + write_int(len(serialized), stream) + stream.write(serialized) + + def _read_with_length(self, stream): + length = read_int(stream) + obj = stream.read(length) + if obj == "": + raise EOFError + return self.loads(obj) + + def dumps(self, obj): + """ + Serialize an object into a byte array. + When batching is used, this will be called with an array of objects. + """ + raise NotImplementedError + + def loads(self, obj): + """ + Deserialize an object from a byte array. + """ + raise NotImplementedError + + +class BatchedSerializer(Serializer): + """ + Serializes a stream of objects in batches by calling its wrapped + Serializer with streams of objects. + """ + + UNLIMITED_BATCH_SIZE = -1 + + def __init__(self, serializer, batchSize=UNLIMITED_BATCH_SIZE): + self.serializer = serializer + self.batchSize = batchSize + + def _batched(self, iterator): + if self.batchSize == self.UNLIMITED_BATCH_SIZE: + yield list(iterator) + else: + items = [] + count = 0 + for item in iterator: + items.append(item) + count += 1 + if count == self.batchSize: + yield items + items = [] + count = 0 + if items: + yield items + + def dump_stream(self, iterator, stream): + self.serializer.dump_stream(self._batched(iterator), stream) + + def load_stream(self, stream): + return chain.from_iterable(self._load_stream_without_unbatching(stream)) + + def _load_stream_without_unbatching(self, stream): + return self.serializer.load_stream(stream) + + def __eq__(self, other): + return isinstance(other, BatchedSerializer) and \ + other.serializer == self.serializer + + def __str__(self): + return "BatchedSerializer<%s>" % str(self.serializer) + + +class CartesianDeserializer(FramedSerializer): + """ + Deserializes the JavaRDD cartesian() of two PythonRDDs. + """ + + def __init__(self, key_ser, val_ser): + self.key_ser = key_ser + self.val_ser = val_ser + + def load_stream(self, stream): + key_stream = self.key_ser._load_stream_without_unbatching(stream) + val_stream = self.val_ser._load_stream_without_unbatching(stream) + key_is_batched = isinstance(self.key_ser, BatchedSerializer) + val_is_batched = isinstance(self.val_ser, BatchedSerializer) + for (keys, vals) in izip(key_stream, val_stream): + keys = keys if key_is_batched else [keys] + vals = vals if val_is_batched else [vals] + for pair in product(keys, vals): + yield pair + + def __eq__(self, other): + return isinstance(other, CartesianDeserializer) and \ + self.key_ser == other.key_ser and self.val_ser == other.val_ser + + def __str__(self): + return "CartesianDeserializer<%s, %s>" % \ + (str(self.key_ser), str(self.val_ser)) + + +class NoOpSerializer(FramedSerializer): + + def loads(self, obj): return obj + def dumps(self, obj): return obj -class Batch(object): +class PickleSerializer(FramedSerializer): """ - Used to store multiple RDD entries as a single Java object. + Serializes objects using Python's cPickle serializer: - This relieves us from having to explicitly track whether an RDD - is stored as batches of objects and avoids problems when processing - the union() of batched and unbatched RDDs (e.g. the union() of textFile() - with another RDD). + http://docs.python.org/2/library/pickle.html + + This serializer supports nearly any Python object, but may + not be as fast as more specialized serializers. + """ + + def dumps(self, obj): return cPickle.dumps(obj, 2) + loads = cPickle.loads + +class CloudPickleSerializer(PickleSerializer): + + def dumps(self, obj): return cloudpickle.dumps(obj, 2) + + +class MarshalSerializer(FramedSerializer): """ - def __init__(self, items): - self.items = items + Serializes objects using Python's Marshal serializer: + http://docs.python.org/2/library/marshal.html -def batched(iterator, batchSize): - if batchSize == -1: # unlimited batch size - yield Batch(list(iterator)) - else: - items = [] - count = 0 - for item in iterator: - items.append(item) - count += 1 - if count == batchSize: - yield Batch(items) - items = [] - count = 0 - if items: - yield Batch(items) + This serializer is faster than PickleSerializer but supports fewer datatypes. + """ + dumps = marshal.dumps + loads = marshal.loads -def dump_pickle(obj): - return cPickle.dumps(obj, 2) +class MUTF8Deserializer(Serializer): + """ + Deserializes streams written by Java's DataOutputStream.writeUTF(). + """ + + def loads(self, stream): + length = struct.unpack('>H', stream.read(2))[0] + return stream.read(length).decode('utf8') -load_pickle = cPickle.loads + def load_stream(self, stream): + while True: + try: + yield self.loads(stream) + except struct.error: + return + except EOFError: + return def read_long(stream): @@ -67,6 +291,10 @@ def write_long(value, stream): stream.write(struct.pack("!q", value)) +def pack_long(value): + return struct.pack("!q", value) + + def read_int(stream): length = stream.read(4) if length == "": @@ -81,24 +309,3 @@ def write_int(value, stream): def write_with_length(obj, stream): write_int(len(obj), stream) stream.write(obj) - - -def read_with_length(stream): - length = read_int(stream) - obj = stream.read(length) - if obj == "": - raise EOFError - return obj - - -def read_from_pickle_file(stream): - try: - while True: - obj = load_pickle(read_with_length(stream)) - if type(obj) == Batch: # We don't care about inheritance - for item in obj.items: - yield item - else: - yield obj - except EOFError: - return diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index a475959090..ef07eb437b 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -42,7 +42,7 @@ print "Using Python version %s (%s, %s)" % ( platform.python_version(), platform.python_build()[0], platform.python_build()[1]) -print "Spark context avaiable as sc." +print "Spark context available as sc." if add_files != None: print "Adding files: [%s]" % ", ".join(add_files) diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index 29d6a128f6..7acb6eaf10 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -19,6 +19,8 @@ Unit tests for PySpark; additional tests are implemented as doctests in individual modules. """ +from fileinput import input +from glob import glob import os import shutil import sys @@ -71,8 +73,8 @@ class TestCheckpoint(PySparkTestCase): time.sleep(1) # 1 second self.assertTrue(flatMappedRDD.isCheckpointed()) self.assertEqual(flatMappedRDD.collect(), result) - self.assertEqual(self.checkpointDir.name, - os.path.dirname(flatMappedRDD.getCheckpointFile())) + self.assertEqual("file:" + self.checkpointDir.name, + os.path.dirname(os.path.dirname(flatMappedRDD.getCheckpointFile()))) def test_checkpoint_and_restore(self): parCollection = self.sc.parallelize([1, 2, 3, 4]) @@ -86,7 +88,8 @@ class TestCheckpoint(PySparkTestCase): time.sleep(1) # 1 second self.assertTrue(flatMappedRDD.getCheckpointFile() is not None) - recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile()) + recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile(), + flatMappedRDD._jrdd_deserializer) self.assertEquals([1, 2, 3, 4], recovered.collect()) @@ -137,6 +140,19 @@ class TestAddFile(PySparkTestCase): self.assertEqual("Hello World from inside a package!", UserClass().hello()) +class TestRDDFunctions(PySparkTestCase): + + def test_save_as_textfile_with_unicode(self): + # Regression test for SPARK-970 + x = u"\u00A1Hola, mundo!" + data = self.sc.parallelize([x]) + tempFile = NamedTemporaryFile(delete=True) + tempFile.close() + data.saveAsTextFile(tempFile.name) + raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*"))) + self.assertEqual(x, unicode(raw_contents.strip(), "utf-8")) + + class TestIO(PySparkTestCase): def test_stdout_redirection(self): diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py index d63c2aaef7..f2b3f3c142 100644 --- a/python/pyspark/worker.py +++ b/python/pyspark/worker.py @@ -23,23 +23,22 @@ import sys import time import socket import traceback -from base64 import standard_b64decode # CloudPickler needs to be imported so that depicklers are registered using the # copy_reg module. from pyspark.accumulators import _accumulatorRegistry from pyspark.broadcast import Broadcast, _broadcastRegistry from pyspark.cloudpickle import CloudPickler from pyspark.files import SparkFiles -from pyspark.serializers import write_with_length, read_with_length, write_int, \ - read_long, write_long, read_int, dump_pickle, load_pickle, read_from_pickle_file +from pyspark.serializers import write_with_length, write_int, read_long, \ + write_long, read_int, SpecialLengths, MUTF8Deserializer, PickleSerializer -def load_obj(infile): - return load_pickle(standard_b64decode(infile.readline().strip())) +pickleSer = PickleSerializer() +mutf8_deserializer = MUTF8Deserializer() def report_times(outfile, boot, init, finish): - write_int(-3, outfile) + write_int(SpecialLengths.TIMING_DATA, outfile) write_long(1000 * boot, outfile) write_long(1000 * init, outfile) write_long(1000 * finish, outfile) @@ -52,7 +51,7 @@ def main(infile, outfile): return # fetch name of workdir - spark_files_dir = load_pickle(read_with_length(infile)) + spark_files_dir = mutf8_deserializer.loads(infile) SparkFiles._root_directory = spark_files_dir SparkFiles._is_running_on_worker = True @@ -60,38 +59,33 @@ def main(infile, outfile): num_broadcast_variables = read_int(infile) for _ in range(num_broadcast_variables): bid = read_long(infile) - value = read_with_length(infile) - _broadcastRegistry[bid] = Broadcast(bid, load_pickle(value)) + value = pickleSer._read_with_length(infile) + _broadcastRegistry[bid] = Broadcast(bid, value) # fetch names of includes (*.zip and *.egg files) and construct PYTHONPATH sys.path.append(spark_files_dir) # *.py files that were added will be copied here num_python_includes = read_int(infile) for _ in range(num_python_includes): - sys.path.append(os.path.join(spark_files_dir, load_pickle(read_with_length(infile)))) + filename = mutf8_deserializer.loads(infile) + sys.path.append(os.path.join(spark_files_dir, filename)) - # now load function - func = load_obj(infile) - bypassSerializer = load_obj(infile) - if bypassSerializer: - dumps = lambda x: x - else: - dumps = dump_pickle + command = pickleSer._read_with_length(infile) + (func, deserializer, serializer) = command init_time = time.time() - iterator = read_from_pickle_file(infile) try: - for obj in func(split_index, iterator): - write_with_length(dumps(obj), outfile) + iterator = deserializer.load_stream(infile) + serializer.dump_stream(func(split_index, iterator), outfile) except Exception as e: - write_int(-2, outfile) + write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile) write_with_length(traceback.format_exc(), outfile) sys.exit(-1) finish_time = time.time() report_times(outfile, boot_time, init_time, finish_time) # Mark the beginning of the accumulators section of the output - write_int(-1, outfile) - for aid, accum in _accumulatorRegistry.items(): - write_with_length(dump_pickle((aid, accum._value)), outfile) - write_int(-1, outfile) + write_int(SpecialLengths.END_OF_DATA_SECTION, outfile) + write_int(len(_accumulatorRegistry), outfile) + for (aid, accum) in _accumulatorRegistry.items(): + pickleSer._write_with_length((aid, accum._value), outfile) if __name__ == '__main__': diff --git a/python/run-tests b/python/run-tests index 8a08ae3df9..feba97cee0 100755 --- a/python/run-tests +++ b/python/run-tests @@ -29,14 +29,16 @@ FAILED=0 rm -f unit-tests.log function run_test() { - $FWDIR/bin/pyspark $1 2>&1 | tee -a unit-tests.log + SPARK_TESTING=0 $FWDIR/bin/pyspark $1 2>&1 | tee -a unit-tests.log FAILED=$((PIPESTATUS[0]||$FAILED)) } run_test "pyspark/rdd.py" run_test "pyspark/context.py" +run_test "pyspark/conf.py" run_test "-m doctest pyspark/broadcast.py" run_test "-m doctest pyspark/accumulators.py" +run_test "-m doctest pyspark/serializers.py" run_test "pyspark/tests.py" if [[ $FAILED != 0 ]]; then diff --git a/python/test_support/userlibrary.py b/python/test_support/userlibrary.py index 5bb6f5009f..8e4a6292bc 100755 --- a/python/test_support/userlibrary.py +++ b/python/test_support/userlibrary.py @@ -1,3 +1,20 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + """ Used to test shipping of code depenencies with SparkContext.addPyFile(). """ diff --git a/repl-bin/pom.xml b/repl-bin/pom.xml index 05aadc7bdf..869dbdb9b0 100644 --- a/repl-bin/pom.xml +++ b/repl-bin/pom.xml @@ -26,7 +26,7 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-repl-bin</artifactId> + <artifactId>spark-repl-bin_2.10</artifactId> <packaging>pom</packaging> <name>Spark Project REPL binary packaging</name> <url>http://spark.incubator.apache.org/</url> @@ -40,18 +40,18 @@ <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-bagel</artifactId> + <artifactId>spark-bagel_${scala.binary.version}</artifactId> <version>${project.version}</version> <scope>runtime</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-repl</artifactId> + <artifactId>spark-repl_${scala.binary.version}</artifactId> <version>${project.version}</version> <scope>runtime</scope> </dependency> @@ -89,7 +89,7 @@ </goals> <configuration> <transformers> - <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/> + <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" /> <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"> <resource>reference.conf</resource> </transformer> diff --git a/repl-bin/src/deb/bin/run b/repl-bin/src/deb/bin/run index d34f18906b..3a6f22f41f 100755 --- a/repl-bin/src/deb/bin/run +++ b/repl-bin/src/deb/bin/run @@ -17,7 +17,7 @@ # limitations under the License. # -SCALA_VERSION=2.9.3 +SCALA_VERSION=2.10 # Figure out where the Scala framework is installed FWDIR="$(cd `dirname $0`; pwd)" diff --git a/repl-bin/src/deb/bin/spark-executor b/repl-bin/src/deb/bin/spark-executor index bcfae22677..052d76fb8d 100755 --- a/repl-bin/src/deb/bin/spark-executor +++ b/repl-bin/src/deb/bin/spark-executor @@ -19,4 +19,4 @@ FWDIR="$(cd `dirname $0`; pwd)" echo "Running spark-executor with framework dir = $FWDIR" -exec $FWDIR/run spark.executor.MesosExecutorBackend +exec $FWDIR/run org.apache.spark.executor.MesosExecutorBackend diff --git a/repl-bin/src/deb/bin/spark-shell b/repl-bin/src/deb/bin/spark-shell index ec7e33e1e3..118349d7c3 100755 --- a/repl-bin/src/deb/bin/spark-shell +++ b/repl-bin/src/deb/bin/spark-shell @@ -18,4 +18,4 @@ # FWDIR="$(cd `dirname $0`; pwd)" -exec $FWDIR/run spark.repl.Main "$@" +exec $FWDIR/run org.apache.spark.repl.Main "$@" diff --git a/repl/lib/scala-jline.jar b/repl/lib/scala-jline.jar Binary files differdeleted file mode 100644 index 2f18c95cdd..0000000000 --- a/repl/lib/scala-jline.jar +++ /dev/null diff --git a/repl/pom.xml b/repl/pom.xml index f71184f865..2dfe7ac900 100644 --- a/repl/pom.xml +++ b/repl/pom.xml @@ -26,7 +26,7 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-repl</artifactId> + <artifactId>spark-repl_2.10</artifactId> <packaging>jar</packaging> <name>Spark Project REPL</name> <url>http://spark.incubator.apache.org/</url> @@ -39,18 +39,18 @@ <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-bagel</artifactId> + <artifactId>spark-bagel_${scala.binary.version}</artifactId> <version>${project.version}</version> <scope>runtime</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-mllib</artifactId> + <artifactId>spark-mllib_${scala.binary.version}</artifactId> <version>${project.version}</version> <scope>runtime</scope> </dependency> @@ -61,10 +61,12 @@ <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-compiler</artifactId> + <version>${scala.version}</version> </dependency> <dependency> <groupId>org.scala-lang</groupId> <artifactId>jline</artifactId> + <version>${scala.version}</version> </dependency> <dependency> <groupId>org.slf4j</groupId> @@ -76,18 +78,18 @@ </dependency> <dependency> <groupId>org.scalatest</groupId> - <artifactId>scalatest_${scala.version}</artifactId> + <artifactId>scalatest_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> <dependency> <groupId>org.scalacheck</groupId> - <artifactId>scalacheck_${scala.version}</artifactId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> </dependencies> <build> - <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> - <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> @@ -101,14 +103,14 @@ <configuration> <exportAntProperties>true</exportAntProperties> <tasks> - <property name="spark.classpath" refid="maven.test.classpath"/> - <property environment="env"/> + <property name="spark.classpath" refid="maven.test.classpath" /> + <property environment="env" /> <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry."> <condition> <not> <or> - <isset property="env.SCALA_HOME"/> - <isset property="env.SCALA_LIBRARY_PATH"/> + <isset property="env.SCALA_HOME" /> + <isset property="env.SCALA_LIBRARY_PATH" /> </or> </not> </condition> diff --git a/repl/src/main/scala/org/apache/spark/repl/Main.scala b/repl/src/main/scala/org/apache/spark/repl/Main.scala index 17e149f8ab..14b448d076 100644 --- a/repl/src/main/scala/org/apache/spark/repl/Main.scala +++ b/repl/src/main/scala/org/apache/spark/repl/Main.scala @@ -20,12 +20,12 @@ package org.apache.spark.repl import scala.collection.mutable.Set object Main { - private var _interp: SparkILoop = null - + private var _interp: SparkILoop = _ + def interp = _interp - + def interp_=(i: SparkILoop) { _interp = i } - + def main(args: Array[String]) { _interp = new SparkILoop _interp.process(args) diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala b/repl/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala new file mode 100644 index 0000000000..b2e1df173e --- /dev/null +++ b/repl/src/main/scala/org/apache/spark/repl/SparkExprTyper.scala @@ -0,0 +1,109 @@ +/* NSC -- new Scala compiler + * Copyright 2005-2013 LAMP/EPFL + * @author Paul Phillips + */ + +package org.apache.spark.repl + +import scala.tools.nsc._ +import scala.tools.nsc.interpreter._ + +import scala.reflect.internal.util.BatchSourceFile +import scala.tools.nsc.ast.parser.Tokens.EOF + +import org.apache.spark.Logging + +trait SparkExprTyper extends Logging { + val repl: SparkIMain + + import repl._ + import global.{ reporter => _, Import => _, _ } + import definitions._ + import syntaxAnalyzer.{ UnitParser, UnitScanner, token2name } + import naming.freshInternalVarName + + object codeParser extends { val global: repl.global.type = repl.global } with CodeHandlers[Tree] { + def applyRule[T](code: String, rule: UnitParser => T): T = { + reporter.reset() + val scanner = newUnitParser(code) + val result = rule(scanner) + + if (!reporter.hasErrors) + scanner.accept(EOF) + + result + } + + def defns(code: String) = stmts(code) collect { case x: DefTree => x } + def expr(code: String) = applyRule(code, _.expr()) + def stmts(code: String) = applyRule(code, _.templateStats()) + def stmt(code: String) = stmts(code).last // guaranteed nonempty + } + + /** Parse a line into a sequence of trees. Returns None if the input is incomplete. */ + def parse(line: String): Option[List[Tree]] = debugging(s"""parse("$line")""") { + var isIncomplete = false + reporter.withIncompleteHandler((_, _) => isIncomplete = true) { + val trees = codeParser.stmts(line) + if (reporter.hasErrors) Some(Nil) + else if (isIncomplete) None + else Some(trees) + } + } + // def parsesAsExpr(line: String) = { + // import codeParser._ + // (opt expr line).isDefined + // } + + def symbolOfLine(code: String): Symbol = { + def asExpr(): Symbol = { + val name = freshInternalVarName() + // Typing it with a lazy val would give us the right type, but runs + // into compiler bugs with things like existentials, so we compile it + // behind a def and strip the NullaryMethodType which wraps the expr. + val line = "def " + name + " = {\n" + code + "\n}" + + interpretSynthetic(line) match { + case IR.Success => + val sym0 = symbolOfTerm(name) + // drop NullaryMethodType + val sym = sym0.cloneSymbol setInfo afterTyper(sym0.info.finalResultType) + if (sym.info.typeSymbol eq UnitClass) NoSymbol + else sym + case _ => NoSymbol + } + } + def asDefn(): Symbol = { + val old = repl.definedSymbolList.toSet + + interpretSynthetic(code) match { + case IR.Success => + repl.definedSymbolList filterNot old match { + case Nil => NoSymbol + case sym :: Nil => sym + case syms => NoSymbol.newOverloaded(NoPrefix, syms) + } + case _ => NoSymbol + } + } + beQuietDuring(asExpr()) orElse beQuietDuring(asDefn()) + } + + private var typeOfExpressionDepth = 0 + def typeOfExpression(expr: String, silent: Boolean = true): Type = { + if (typeOfExpressionDepth > 2) { + logDebug("Terminating typeOfExpression recursion for expression: " + expr) + return NoType + } + typeOfExpressionDepth += 1 + // Don't presently have a good way to suppress undesirable success output + // while letting errors through, so it is first trying it silently: if there + // is an error, and errors are desired, then it re-evaluates non-silently + // to induce the error message. + try beSilentDuring(symbolOfLine(expr).tpe) match { + case NoType if !silent => symbolOfLine(expr).tpe // generate error + case tpe => tpe + } + finally typeOfExpressionDepth -= 1 + } +} diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 36f54a22cf..f108c70f21 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -1,28 +1,41 @@ /* NSC -- new Scala compiler - * Copyright 2005-2011 LAMP/EPFL + * Copyright 2005-2013 LAMP/EPFL * @author Alexander Spoon */ package org.apache.spark.repl + import scala.tools.nsc._ import scala.tools.nsc.interpreter._ +import scala.tools.nsc.interpreter.{ Results => IR } import Predef.{ println => _, _ } -import java.io.{ BufferedReader, FileReader, PrintWriter } +import java.io.{ BufferedReader, FileReader } +import java.util.concurrent.locks.ReentrantLock import scala.sys.process.Process -import session._ -import scala.tools.nsc.interpreter.{ Results => IR } -import scala.tools.util.{ SignalManager, Signallable, Javap } +import scala.tools.nsc.interpreter.session._ +import scala.util.Properties.{ jdkHome, javaVersion } +import scala.tools.util.{ Javap } import scala.annotation.tailrec -import scala.util.control.Exception.{ ignoring } import scala.collection.mutable.ListBuffer import scala.concurrent.ops -import util.{ ClassPath, Exceptional, stringFromWriter, stringFromStream } -import interpreter._ -import io.{ File, Sources } +import scala.tools.nsc.util.{ ClassPath, Exceptional, stringFromWriter, stringFromStream } +import scala.tools.nsc.interpreter._ +import scala.tools.nsc.io.{ File, Directory } +import scala.reflect.NameTransformer._ +import scala.tools.nsc.util.ScalaClassLoader +import scala.tools.nsc.util.ScalaClassLoader._ +import scala.tools.util._ +import scala.language.{implicitConversions, existentials} +import scala.reflect.{ClassTag, classTag} +import scala.tools.reflect.StdRuntimeTags._ + +import java.lang.{Class => jClass} +import scala.reflect.api.{Mirror, TypeCreator, Universe => ApiUniverse} import org.apache.spark.Logging +import org.apache.spark.SparkConf import org.apache.spark.SparkContext /** The Scala interactive shell. It provides a read-eval-print loop @@ -37,45 +50,86 @@ import org.apache.spark.SparkContext * @author Lex Spoon * @version 1.2 */ -class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: Option[String]) +class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, + val master: Option[String]) extends AnyRef with LoopCommands + with SparkILoopInit with Logging { - def this(in0: BufferedReader, out: PrintWriter, master: String) = this(Some(in0), out, Some(master)) - def this(in0: BufferedReader, out: PrintWriter) = this(Some(in0), out, None) - def this() = this(None, new PrintWriter(Console.out, true), None) - + def this(in0: BufferedReader, out: JPrintWriter, master: String) = this(Some(in0), out, Some(master)) + def this(in0: BufferedReader, out: JPrintWriter) = this(Some(in0), out, None) + def this() = this(None, new JPrintWriter(Console.out, true), None) + var in: InteractiveReader = _ // the input stream from which commands come var settings: Settings = _ var intp: SparkIMain = _ - /* - lazy val power = { - val g = intp.global - Power[g.type](this, g) - } - */ - - // TODO - // object opt extends AestheticSettings - // - @deprecated("Use `intp` instead.", "2.9.0") - def interpreter = intp - - @deprecated("Use `intp` instead.", "2.9.0") - def interpreter_= (i: SparkIMain): Unit = intp = i - + @deprecated("Use `intp` instead.", "2.9.0") def interpreter = intp + @deprecated("Use `intp` instead.", "2.9.0") def interpreter_= (i: SparkIMain): Unit = intp = i + + /** Having inherited the difficult "var-ness" of the repl instance, + * I'm trying to work around it by moving operations into a class from + * which it will appear a stable prefix. + */ + private def onIntp[T](f: SparkIMain => T): T = f(intp) + + class IMainOps[T <: SparkIMain](val intp: T) { + import intp._ + import global._ + + def printAfterTyper(msg: => String) = + intp.reporter printMessage afterTyper(msg) + + /** Strip NullaryMethodType artifacts. */ + private def replInfo(sym: Symbol) = { + sym.info match { + case NullaryMethodType(restpe) if sym.isAccessor => restpe + case info => info + } + } + def echoTypeStructure(sym: Symbol) = + printAfterTyper("" + deconstruct.show(replInfo(sym))) + + def echoTypeSignature(sym: Symbol, verbose: Boolean) = { + if (verbose) SparkILoop.this.echo("// Type signature") + printAfterTyper("" + replInfo(sym)) + + if (verbose) { + SparkILoop.this.echo("\n// Internal Type structure") + echoTypeStructure(sym) + } + } + } + implicit def stabilizeIMain(intp: SparkIMain) = new IMainOps[intp.type](intp) + + /** TODO - + * -n normalize + * -l label with case class parameter names + * -c complete - leave nothing out + */ + private def typeCommandInternal(expr: String, verbose: Boolean): Result = { + onIntp { intp => + val sym = intp.symbolOfLine(expr) + if (sym.exists) intp.echoTypeSignature(sym, verbose) + else "" + } + } + + var sparkContext: SparkContext = _ + + override def echoCommandMessage(msg: String) { + intp.reporter printMessage msg + } + + // def isAsync = !settings.Yreplsync.value + def isAsync = false + // lazy val power = new Power(intp, new StdReplVals(this))(tagOfStdReplVals, classTag[StdReplVals]) def history = in.history /** The context class loader at the time this object was created */ protected val originalClassLoader = Thread.currentThread.getContextClassLoader - // Install a signal handler so we can be prodded. - private val signallable = - /*if (isReplDebug) Signallable("Dump repl state.")(dumpCommand()) - else*/ null - // classpath entries added via :cp var addedClasspath: String = "" @@ -87,74 +141,49 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: /** Record a command for replay should the user request a :replay */ def addReplay(cmd: String) = replayCommandStack ::= cmd - - /** Try to install sigint handler: ignore failure. Signal handler - * will interrupt current line execution if any is in progress. - * - * Attempting to protect the repl from accidental exit, we only honor - * a single ctrl-C if the current buffer is empty: otherwise we look - * for a second one within a short time. - */ - private def installSigIntHandler() { - def onExit() { - Console.println("") // avoiding "shell prompt in middle of line" syndrome - sys.exit(1) - } - ignoring(classOf[Exception]) { - SignalManager("INT") = { - if (intp == null) - onExit() - else if (intp.lineManager.running) - intp.lineManager.cancel() - else if (in.currentLine != "") { - // non-empty buffer, so make them hit ctrl-C a second time - SignalManager("INT") = onExit() - io.timer(5)(installSigIntHandler()) // and restore original handler if they don't - } - else onExit() - } - } + + def savingReplayStack[T](body: => T): T = { + val saved = replayCommandStack + try body + finally replayCommandStack = saved + } + def savingReader[T](body: => T): T = { + val saved = in + try body + finally in = saved } + + def sparkCleanUp(){ + echo("Stopping spark context.") + intp.beQuietDuring { + command("sc.stop()") + } + } /** Close the interpreter and set the var to null. */ def closeInterpreter() { if (intp ne null) { - intp.close + sparkCleanUp() + intp.close() intp = null - Thread.currentThread.setContextClassLoader(originalClassLoader) } } - + class SparkILoopInterpreter extends SparkIMain(settings, out) { + outer => + override lazy val formatting = new Formatting { def prompt = SparkILoop.this.prompt } - override protected def createLineManager() = new Line.Manager { - override def onRunaway(line: Line[_]): Unit = { - val template = """ - |// She's gone rogue, captain! Have to take her out! - |// Calling Thread.stop on runaway %s with offending code: - |// scala> %s""".stripMargin - - echo(template.format(line.thread, line.code)) - // XXX no way to suppress the deprecation warning - line.thread.stop() - in.redrawLine() - } - } - override protected def parentClassLoader = { - SparkHelper.explicitParentLoader(settings).getOrElse( classOf[SparkILoop].getClassLoader ) - } + override protected def parentClassLoader = SparkHelper.explicitParentLoader(settings).getOrElse(classOf[SparkILoop].getClassLoader) } /** Create a new interpreter. */ def createInterpreter() { if (addedClasspath != "") settings.classpath append addedClasspath - + intp = new SparkILoopInterpreter - intp.setContextClassLoader() - installSigIntHandler() } /** print a friendly help message */ @@ -168,10 +197,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: private def helpSummary() = { val usageWidth = commands map (_.usageMsg.length) max val formatStr = "%-" + usageWidth + "s %s %s" - + echo("All commands can be abbreviated, e.g. :he instead of :help.") echo("Those marked with a * have more detailed help, e.g. :help imports.\n") - + commands foreach { cmd => val star = if (cmd.hasLongHelp) "*" else " " echo(formatStr.format(cmd.usageMsg, star, cmd.help)) @@ -182,7 +211,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: case Nil => echo(cmd + ": no such command. Type :help for help.") case xs => echo(cmd + " is ambiguous: did you mean " + xs.map(":" + _.name).mkString(" or ") + "?") } - Result(true, None) + Result(true, None) } private def matchingCommands(cmd: String) = commands filter (_.name startsWith cmd) private def uniqueCommand(cmd: String): Option[LoopCommand] = { @@ -193,31 +222,16 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: case xs => xs find (_.name == cmd) } } - - /** Print a welcome message */ - def printWelcome() { - echo("""Welcome to - ____ __ - / __/__ ___ _____/ /__ - _\ \/ _ \/ _ `/ __/ '_/ - /___/ .__/\_,_/_/ /_/\_\ version 0.9.0-SNAPSHOT - /_/ -""") - import Properties._ - val welcomeMsg = "Using Scala %s (%s, Java %s)".format( - versionString, javaVmName, javaVersion) - echo(welcomeMsg) - } - + /** Show the history */ lazy val historyCommand = new LoopCommand("history", "show the history (optional num is commands to show)") { override def usage = "[num]" def defaultLines = 20 - + def apply(line: String): Result = { if (history eq NoHistory) return "No history available." - + val xs = words(line) val current = history.index val count = try xs.head.toInt catch { case _: Exception => defaultLines } @@ -229,32 +243,38 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: } } - private def echo(msg: String) = { + // When you know you are most likely breaking into the middle + // of a line being typed. This softens the blow. + protected def echoAndRefresh(msg: String) = { + echo("\n" + msg) + in.redrawLine() + } + protected def echo(msg: String) = { out println msg out.flush() } - private def echoNoNL(msg: String) = { + protected def echoNoNL(msg: String) = { out print msg out.flush() } - + /** Search the history */ def searchHistory(_cmdline: String) { val cmdline = _cmdline.toLowerCase val offset = history.index - history.size + 1 - + for ((line, index) <- history.asStrings.zipWithIndex ; if line.toLowerCase contains cmdline) echo("%d %s".format(index + offset, line)) } - + private var currentPrompt = Properties.shellPromptString def setPrompt(prompt: String) = currentPrompt = prompt /** Prompt to print when awaiting input */ def prompt = currentPrompt - + import LoopCommand.{ cmd, nullary } - /** Standard commands **/ + /** Standard commands */ lazy val standardCommands = List( cmd("cp", "<path>", "add a jar or directory to the classpath", addClasspath), cmd("help", "[command]", "print this summary or command-specific help", helpCommand), @@ -263,53 +283,30 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: cmd("imports", "[name name ...]", "show import history, identifying sources of names", importsCommand), cmd("implicits", "[-v]", "show the implicits in scope", implicitsCommand), cmd("javap", "<path|class>", "disassemble a file or class name", javapCommand), - nullary("keybindings", "show how ctrl-[A-Z] and other keys are bound", keybindingsCommand), cmd("load", "<path>", "load and interpret a Scala file", loadCommand), nullary("paste", "enter paste mode: all input up to ctrl-D compiled together", pasteCommand), - //nullary("power", "enable power user mode", powerCmd), - nullary("quit", "exit the interpreter", () => Result(false, None)), +// nullary("power", "enable power user mode", powerCmd), + nullary("quit", "exit the repl", () => Result(false, None)), nullary("replay", "reset execution and replay all previous commands", replay), + nullary("reset", "reset the repl to its initial state, forgetting all session entries", resetCommand), shCommand, nullary("silent", "disable/enable automatic printing of results", verbosity), - cmd("type", "<expr>", "display the type of an expression without evaluating it", typeCommand) + cmd("type", "[-v] <expr>", "display the type of an expression without evaluating it", typeCommand), + nullary("warnings", "show the suppressed warnings from the most recent line which had any", warningsCommand) ) - + /** Power user commands */ lazy val powerCommands: List[LoopCommand] = List( - //nullary("dump", "displays a view of the interpreter's internal state", dumpCommand), - //cmd("phase", "<phase>", "set the implicit phase for power commands", phaseCommand), - cmd("wrap", "<method>", "name of method to wrap around each repl line", wrapCommand) withLongHelp (""" - |:wrap - |:wrap clear - |:wrap <method> - | - |Installs a wrapper around each line entered into the repl. - |Currently it must be the simple name of an existing method - |with the specific signature shown in the following example. - | - |def timed[T](body: => T): T = { - | val start = System.nanoTime - | try body - | finally println((System.nanoTime - start) + " nanos elapsed.") - |} - |:wrap timed - | - |If given no argument, :wrap names the wrapper installed. - |An argument of clear will remove the wrapper if any is active. - |Note that wrappers do not compose (a new one replaces the old - |one) and also that the :phase command uses the same machinery, - |so setting :wrap will clear any :phase setting. - """.stripMargin.trim) + // cmd("phase", "<phase>", "set the implicit phase for power commands", phaseCommand) ) - - /* - private def dumpCommand(): Result = { - echo("" + power) - history.asStrings takeRight 30 foreach echo - in.redrawLine() - } - */ - + + // private def dumpCommand(): Result = { + // echo("" + power) + // history.asStrings takeRight 30 foreach echo + // in.redrawLine() + // } + // private def valsCommand(): Result = power.valsDescription + private val typeTransforms = List( "scala.collection.immutable." -> "immutable.", "scala.collection.mutable." -> "mutable.", @@ -317,7 +314,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: "java.lang." -> "jl.", "scala.runtime." -> "runtime." ) - + private def importsCommand(line: String): Result = { val tokens = words(line) val handlers = intp.languageWildcardHandlers ++ intp.importHandlers @@ -333,7 +330,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: val implicitMsg = if (imps.isEmpty) "" else imps.size + " are implicit" val foundMsg = if (found.isEmpty) "" else found.mkString(" // imports: ", ", ", "") val statsMsg = List(typeMsg, termMsg, implicitMsg) filterNot (_ == "") mkString ("(", ", ", ")") - + intp.reporter.printMessage("%2d) %-30s %s%s".format( idx + 1, handler.importString, @@ -342,12 +339,11 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: )) } } - - private def implicitsCommand(line: String): Result = { - val intp = SparkILoop.this.intp + + private def implicitsCommand(line: String): Result = onIntp { intp => import intp._ - import global.Symbol - + import global._ + def p(x: Any) = intp.reporter.printMessage("" + x) // If an argument is given, only show a source with that @@ -360,17 +356,17 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: else (args exists (source.name.toString contains _)) } } - + if (filtered.isEmpty) return "No implicits have been imported other than those in Predef." - + filtered foreach { case (source, syms) => p("/* " + syms.size + " implicit members imported from " + source.fullName + " */") - + // This groups the members by where the symbol is defined val byOwner = syms groupBy (_.owner) - val sortedOwners = byOwner.toList sortBy { case (owner, _) => intp.afterTyper(source.info.baseClasses indexOf owner) } + val sortedOwners = byOwner.toList sortBy { case (owner, _) => afterTyper(source.info.baseClasses indexOf owner) } sortedOwners foreach { case (owner, members) => @@ -388,10 +384,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: xss map (xs => xs sortBy (_.name.toString)) } - - val ownerMessage = if (owner == source) " defined in " else " inherited from " + + val ownerMessage = if (owner == source) " defined in " else " inherited from " p(" /* " + members.size + ownerMessage + owner.fullName + " */") - + memberGroups foreach { group => group foreach (s => p(" " + intp.symbolDefString(s))) p("") @@ -400,158 +396,182 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: p("") } } - - protected def newJavap() = new Javap(intp.classLoader, new SparkIMain.ReplStrippingWriter(intp)) { + + private def findToolsJar() = { + val jdkPath = Directory(jdkHome) + val jar = jdkPath / "lib" / "tools.jar" toFile; + + if (jar isFile) + Some(jar) + else if (jdkPath.isDirectory) + jdkPath.deepFiles find (_.name == "tools.jar") + else None + } + private def addToolsJarToLoader() = { + val cl = findToolsJar match { + case Some(tools) => ScalaClassLoader.fromURLs(Seq(tools.toURL), intp.classLoader) + case _ => intp.classLoader + } + if (Javap.isAvailable(cl)) { + logDebug(":javap available.") + cl + } + else { + logDebug(":javap unavailable: no tools.jar at " + jdkHome) + intp.classLoader + } + } + + protected def newJavap() = new JavapClass(addToolsJarToLoader(), new SparkIMain.ReplStrippingWriter(intp)) { override def tryClass(path: String): Array[Byte] = { - // Look for Foo first, then Foo$, but if Foo$ is given explicitly, - // we have to drop the $ to find object Foo, then tack it back onto - // the end of the flattened name. - def className = intp flatName path - def moduleName = (intp flatName path.stripSuffix("$")) + "$" + val hd :: rest = path split '.' toList; + // If there are dots in the name, the first segment is the + // key to finding it. + if (rest.nonEmpty) { + intp optFlatName hd match { + case Some(flat) => + val clazz = flat :: rest mkString NAME_JOIN_STRING + val bytes = super.tryClass(clazz) + if (bytes.nonEmpty) bytes + else super.tryClass(clazz + MODULE_SUFFIX_STRING) + case _ => super.tryClass(path) + } + } + else { + // Look for Foo first, then Foo$, but if Foo$ is given explicitly, + // we have to drop the $ to find object Foo, then tack it back onto + // the end of the flattened name. + def className = intp flatName path + def moduleName = (intp flatName path.stripSuffix(MODULE_SUFFIX_STRING)) + MODULE_SUFFIX_STRING - val bytes = super.tryClass(className) - if (bytes.nonEmpty) bytes - else super.tryClass(moduleName) + val bytes = super.tryClass(className) + if (bytes.nonEmpty) bytes + else super.tryClass(moduleName) + } } } + // private lazy val javap = substituteAndLog[Javap]("javap", NoJavap)(newJavap()) private lazy val javap = try newJavap() catch { case _: Exception => null } - - private def typeCommand(line: String): Result = { - intp.typeOfExpression(line) match { - case Some(tp) => tp.toString - case _ => "Failed to determine type." + + // Still todo: modules. + private def typeCommand(line0: String): Result = { + line0.trim match { + case "" => ":type [-v] <expression>" + case s if s startsWith "-v " => typeCommandInternal(s stripPrefix "-v " trim, true) + case s => typeCommandInternal(s, false) } } - + + private def warningsCommand(): Result = { + if (intp.lastWarnings.isEmpty) + "Can't find any cached warnings." + else + intp.lastWarnings foreach { case (pos, msg) => intp.reporter.warning(pos, msg) } + } + private def javapCommand(line: String): Result = { if (javap == null) - return ":javap unavailable on this platform." - if (line == "") - return ":javap [-lcsvp] [path1 path2 ...]" - - javap(words(line)) foreach { res => - if (res.isError) return "Failed: " + res.value - else res.show() - } - } - private def keybindingsCommand(): Result = { - if (in.keyBindings.isEmpty) "Key bindings unavailable." - else { - echo("Reading jline properties for default key bindings.") - echo("Accuracy not guaranteed: treat this as a guideline only.\n") - in.keyBindings foreach (x => echo ("" + x)) - } + ":javap unavailable, no tools.jar at %s. Set JDK_HOME.".format(jdkHome) + else if (javaVersion startsWith "1.7") + ":javap not yet working with java 1.7" + else if (line == "") + ":javap [-lcsvp] [path1 path2 ...]" + else + javap(words(line)) foreach { res => + if (res.isError) return "Failed: " + res.value + else res.show() + } } + private def wrapCommand(line: String): Result = { def failMsg = "Argument to :wrap must be the name of a method with signature [T](=> T): T" - val intp = SparkILoop.this.intp - val g: intp.global.type = intp.global - import g._ - - words(line) match { - case Nil => - intp.executionWrapper match { - case "" => "No execution wrapper is set." - case s => "Current execution wrapper: " + s - } - case "clear" :: Nil => - intp.executionWrapper match { - case "" => "No execution wrapper is set." - case s => intp.clearExecutionWrapper() ; "Cleared execution wrapper." - } - case wrapper :: Nil => - intp.typeOfExpression(wrapper) match { - case Some(PolyType(List(targ), MethodType(List(arg), restpe))) => - intp setExecutionWrapper intp.pathToTerm(wrapper) - "Set wrapper to '" + wrapper + "'" - case Some(x) => - failMsg + "\nFound: " + x - case _ => - failMsg + "\nFound: <unknown>" - } - case _ => failMsg - } - } + onIntp { intp => + import intp._ + import global._ - private def pathToPhaseWrapper = intp.pathToTerm("$r") + ".phased.atCurrent" - /* - private def phaseCommand(name: String): Result = { - // This line crashes us in TreeGen: - // - // if (intp.power.phased set name) "..." - // - // Exception in thread "main" java.lang.AssertionError: assertion failed: ._7.type - // at scala.Predef$.assert(Predef.scala:99) - // at scala.tools.nsc.ast.TreeGen.mkAttributedQualifier(TreeGen.scala:69) - // at scala.tools.nsc.ast.TreeGen.mkAttributedQualifier(TreeGen.scala:44) - // at scala.tools.nsc.ast.TreeGen.mkAttributedRef(TreeGen.scala:101) - // at scala.tools.nsc.ast.TreeGen.mkAttributedStableRef(TreeGen.scala:143) - // - // But it works like so, type annotated. - val phased: Phased = power.phased - import phased.NoPhaseName - - if (name == "clear") { - phased.set(NoPhaseName) - intp.clearExecutionWrapper() - "Cleared active phase." - } - else if (name == "") phased.get match { - case NoPhaseName => "Usage: :phase <expr> (e.g. typer, erasure.next, erasure+3)" - case ph => "Active phase is '%s'. (To clear, :phase clear)".format(phased.get) - } - else { - val what = phased.parse(name) - if (what.isEmpty || !phased.set(what)) - "'" + name + "' does not appear to represent a valid phase." - else { - intp.setExecutionWrapper(pathToPhaseWrapper) - val activeMessage = - if (what.toString.length == name.length) "" + what - else "%s (%s)".format(what, name) - - "Active phase is now: " + activeMessage + words(line) match { + case Nil => + intp.executionWrapper match { + case "" => "No execution wrapper is set." + case s => "Current execution wrapper: " + s + } + case "clear" :: Nil => + intp.executionWrapper match { + case "" => "No execution wrapper is set." + case s => intp.clearExecutionWrapper() ; "Cleared execution wrapper." + } + case wrapper :: Nil => + intp.typeOfExpression(wrapper) match { + case PolyType(List(targ), MethodType(List(arg), restpe)) => + intp setExecutionWrapper intp.pathToTerm(wrapper) + "Set wrapper to '" + wrapper + "'" + case tp => + failMsg + "\nFound: <unknown>" + } + case _ => failMsg } } } - */ - + + private def pathToPhaseWrapper = intp.pathToTerm("$r") + ".phased.atCurrent" + // private def phaseCommand(name: String): Result = { + // val phased: Phased = power.phased + // import phased.NoPhaseName + + // if (name == "clear") { + // phased.set(NoPhaseName) + // intp.clearExecutionWrapper() + // "Cleared active phase." + // } + // else if (name == "") phased.get match { + // case NoPhaseName => "Usage: :phase <expr> (e.g. typer, erasure.next, erasure+3)" + // case ph => "Active phase is '%s'. (To clear, :phase clear)".format(phased.get) + // } + // else { + // val what = phased.parse(name) + // if (what.isEmpty || !phased.set(what)) + // "'" + name + "' does not appear to represent a valid phase." + // else { + // intp.setExecutionWrapper(pathToPhaseWrapper) + // val activeMessage = + // if (what.toString.length == name.length) "" + what + // else "%s (%s)".format(what, name) + + // "Active phase is now: " + activeMessage + // } + // } + // } + /** Available commands */ - def commands: List[LoopCommand] = standardCommands /* ++ ( + def commands: List[LoopCommand] = standardCommands /*++ ( if (isReplPower) powerCommands else Nil )*/ - + val replayQuestionMessage = - """|The repl compiler has crashed spectacularly. Shall I replay your - |session? I can re-run all lines except the last one. + """|That entry seems to have slain the compiler. Shall I replay + |your session? I can re-run each line except the last one. |[y/n] """.trim.stripMargin - private val crashRecovery: PartialFunction[Throwable, Unit] = { + private val crashRecovery: PartialFunction[Throwable, Boolean] = { case ex: Throwable => - if (settings.YrichExes.value) { - val sources = implicitly[Sources] - echo("\n" + ex.getMessage) - echo( - if (isReplDebug) "[searching " + sources.path + " for exception contexts...]" - else "[searching for exception contexts...]" - ) - echo(Exceptional(ex).force().context()) - } - else { - echo(util.stackTraceString(ex)) - } + echo(intp.global.throwableAsString(ex)) + ex match { case _: NoSuchMethodError | _: NoClassDefFoundError => - echo("Unrecoverable error.") + echo("\nUnrecoverable error.") throw ex case _ => - def fn(): Boolean = in.readYesOrNo(replayQuestionMessage, { echo("\nYou must enter y or n.") ; fn() }) + def fn(): Boolean = + try in.readYesOrNo(replayQuestionMessage, { echo("\nYou must enter y or n.") ; fn() }) + catch { case _: RuntimeException => false } + if (fn()) replay() else echo("\nAbandoning crashed session.") } + true } /** The main read-eval-print loop for the repl. It calls @@ -564,66 +584,89 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: in readLine prompt } // return false if repl should exit - def processLine(line: String): Boolean = + def processLine(line: String): Boolean = { + if (isAsync) { + if (!awaitInitialized()) return false + runThunks() + } if (line eq null) false // assume null means EOF else command(line) match { case Result(false, _) => false case Result(_, Some(finalLine)) => addReplay(finalLine) ; true case _ => true } - - while (true) { - try if (!processLine(readOneLine)) return - catch crashRecovery } + def innerLoop() { + if ( try processLine(readOneLine()) catch crashRecovery ) + innerLoop() + } + innerLoop() } /** interpret all lines from a specified file */ - def interpretAllFrom(file: File) { - val oldIn = in - val oldReplay = replayCommandStack - - try file applyReader { reader => - in = SimpleReader(reader, out, false) - echo("Loading " + file + "...") - loop() - } - finally { - in = oldIn - replayCommandStack = oldReplay + def interpretAllFrom(file: File) { + savingReader { + savingReplayStack { + file applyReader { reader => + in = SimpleReader(reader, out, false) + echo("Loading " + file + "...") + loop() + } + } } } - /** create a new interpreter and replay all commands so far */ + /** create a new interpreter and replay the given commands */ def replay() { - closeInterpreter() - createInterpreter() - for (cmd <- replayCommands) { + reset() + if (replayCommandStack.isEmpty) + echo("Nothing to replay.") + else for (cmd <- replayCommands) { echo("Replaying: " + cmd) // flush because maybe cmd will have its own output command(cmd) echo("") } } - + def resetCommand() { + echo("Resetting repl state.") + if (replayCommandStack.nonEmpty) { + echo("Forgetting this session history:\n") + replayCommands foreach echo + echo("") + replayCommandStack = Nil + } + if (intp.namedDefinedTerms.nonEmpty) + echo("Forgetting all expression results and named terms: " + intp.namedDefinedTerms.mkString(", ")) + if (intp.definedTypes.nonEmpty) + echo("Forgetting defined types: " + intp.definedTypes.mkString(", ")) + + reset() + } + + def reset() { + intp.reset() + // unleashAndSetPhase() + } + /** fork a shell and run a command */ lazy val shCommand = new LoopCommand("sh", "run a shell command (result is implicitly => List[String])") { override def usage = "<command line>" def apply(line: String): Result = line match { case "" => showUsage() - case _ => + case _ => val toRun = classOf[ProcessResult].getName + "(" + string2codeQuoted(line) + ")" intp interpret toRun () } } - + def withFile(filename: String)(action: File => Unit) { val f = File(filename) - + if (f.exists) action(f) else echo("That file does not exist") } - + def loadCommand(arg: String) = { var shouldReplay: Option[String] = None withFile(arg)(f => { @@ -633,6 +676,20 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: Result(true, shouldReplay) } + def addAllClasspath(args: Seq[String]): Unit = { + var added = false + var totalClasspath = "" + for (arg <- args) { + val f = File(arg).normalize + if (f.exists) { + added = true + addedClasspath = ClassPath.join(addedClasspath, f.path) + totalClasspath = ClassPath.join(settings.classpath.value, addedClasspath) + } + } + if (added) replay() + } + def addClasspath(arg: String): Unit = { val f = File(arg).normalize if (f.exists) { @@ -643,23 +700,36 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: } else echo("The path '" + f + "' doesn't seem to exist.") } - + def powerCmd(): Result = { if (isReplPower) "Already in power mode." - else enablePowerMode() + else enablePowerMode(false) } - def enablePowerMode() = { - //replProps.power setValue true - //power.unleash() - //echo(power.banner) + + def enablePowerMode(isDuringInit: Boolean) = { + // replProps.power setValue true + // unleashAndSetPhase() + // asyncEcho(isDuringInit, power.banner) + } + // private def unleashAndSetPhase() { +// if (isReplPower) { +// // power.unleash() +// // Set the phase to "typer" +// intp beSilentDuring phaseCommand("typer") +// } +// } + + def asyncEcho(async: Boolean, msg: => String) { + if (async) asyncMessage(msg) + else echo(msg) } - + def verbosity() = { - val old = intp.printResults - intp.printResults = !old - echo("Switched " + (if (old) "off" else "on") + " result printing.") + // val old = intp.printResults + // intp.printResults = !old + // echo("Switched " + (if (old) "off" else "on") + " result printing.") } - + /** Run one command submitted by the user. Two values are returned: * (1) whether to keep running, (2) the line to record for replay, * if any. */ @@ -674,11 +744,11 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: else if (intp.global == null) Result(false, None) // Notice failure to create compiler else Result(true, interpretStartingWith(line)) } - + private def readWhile(cond: String => Boolean) = { Iterator continually in.readLine("") takeWhile (x => x != null && cond(x)) } - + def pasteCommand(): Result = { echo("// Entering paste mode (ctrl-D to finish)\n") val code = readWhile(_ => true) mkString "\n" @@ -686,23 +756,19 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: intp interpret code () } - + private object paste extends Pasted { val ContinueString = " | " val PromptString = "scala> " - + def interpret(line: String): Unit = { echo(line.trim) intp interpret line echo("") } - + def transcript(start: String) = { - // Printing this message doesn't work very well because it's buried in the - // transcript they just pasted. Todo: a short timer goes off when - // lines stop coming which tells them to hit ctrl-D. - // - // echo("// Detected repl transcript paste: ctrl-D to finish.") + echo("\n// Detected repl transcript paste: ctrl-D to finish.\n") apply(Iterator(start) ++ readWhile(_.trim != PromptString.trim)) } } @@ -717,7 +783,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: def interpretStartingWith(code: String): Option[String] = { // signal completion non-completion input has been received in.completion.resetVerbosity() - + def reallyInterpret = { val reallyResult = intp.interpret(code) (reallyResult, reallyResult match { @@ -727,7 +793,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: if (in.interactive && code.endsWith("\n\n")) { echo("You typed two blank lines. Starting a new command.") None - } + } else in.readLine(ContinueString) match { case null => // we know compilation is going to fail since we're at EOF and the @@ -741,10 +807,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: } }) } - + /** Here we place ourselves between the user and the interpreter and examine * the input they are ostensibly submitting. We intervene in several cases: - * + * * 1) If the line starts with "scala> " it is assumed to be an interpreter paste. * 2) If the line starts with "." (but not ".." or "./") it is treated as an invocation * on the previous result. @@ -759,28 +825,12 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: else if (Completion.looksLikeInvocation(code) && intp.mostRecentVar != "") { interpretStartingWith(intp.mostRecentVar + code) } - else { - def runCompletion = in.completion execute code map (intp bindValue _) - /** Due to my accidentally letting file completion execution sneak ahead - * of actual parsing this now operates in such a way that the scala - * interpretation always wins. However to avoid losing useful file - * completion I let it fail and then check the others. So if you - * type /tmp it will echo a failure and then give you a Directory object. - * It's not pretty: maybe I'll implement the silence bits I need to avoid - * echoing the failure. - */ - if (intp isParseable code) { - val (code, result) = reallyInterpret - //if (power != null && code == IR.Error) - // runCompletion - - result - } - else runCompletion match { - case Some(_) => None // completion hit: avoid the latent error - case _ => reallyInterpret._2 // trigger the latent error - } + else if (code.trim startsWith "//") { + // line comment, do nothing + None } + else + reallyInterpret._2 } // runs :load `file` on any files passed via -i @@ -794,7 +844,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: } case _ => } - + /** Tries to create a JLineReader, falling back to SimpleReader: * unless settings or properties are such that it should start * with SimpleReader. @@ -802,7 +852,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: def chooseReader(settings: Settings): InteractiveReader = { if (settings.Xnojline.value || Properties.isEmacsShell) SimpleReader() - else try SparkJLineReader( + else try new SparkJLineReader( if (settings.noCompletion.value) NoCompletion else new SparkJLineCompletion(intp) ) @@ -813,107 +863,110 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: } } - def initializeSpark() { - intp.beQuietDuring { - command(""" - org.apache.spark.repl.Main.interp.out.println("Creating SparkContext..."); - org.apache.spark.repl.Main.interp.out.flush(); - @transient val sc = org.apache.spark.repl.Main.interp.createSparkContext(); - org.apache.spark.repl.Main.interp.out.println("Spark context available as sc."); - org.apache.spark.repl.Main.interp.out.flush(); - """) - command("import org.apache.spark.SparkContext._") - } - echo("Type in expressions to have them evaluated.") - echo("Type :help for more information.") - } - - var sparkContext: SparkContext = null - - def createSparkContext(): SparkContext = { - val uri = System.getenv("SPARK_EXECUTOR_URI") - if (uri != null) { - System.setProperty("spark.executor.uri", uri) - } - val master = this.master match { - case Some(m) => m - case None => { - val prop = System.getenv("MASTER") - if (prop != null) prop else "local" - } - } - val jars = Option(System.getenv("ADD_JARS")).map(_.split(',')) - .getOrElse(new Array[String](0)) - .map(new java.io.File(_).getAbsolutePath) - sparkContext = new SparkContext(master, "Spark shell", System.getenv("SPARK_HOME"), jars) - sparkContext - } - - def process(settings: Settings): Boolean = { - // Ensure logging is initialized before any Spark threads try to use logs - // (because SLF4J initialization is not thread safe) - initLogging() - - printWelcome() - echo("Initializing interpreter...") - - // Add JARS specified in Spark's ADD_JARS variable to classpath - val jars = Option(System.getenv("ADD_JARS")).map(_.split(',')).getOrElse(new Array[String](0)) - jars.foreach(settings.classpath.append(_)) + val u: scala.reflect.runtime.universe.type = scala.reflect.runtime.universe + val m = u.runtimeMirror(getClass.getClassLoader) + private def tagOfStaticClass[T: ClassTag]: u.TypeTag[T] = + u.TypeTag[T]( + m, + new TypeCreator { + def apply[U <: ApiUniverse with Singleton](m: Mirror[U]): U # Type = + m.staticClass(classTag[T].runtimeClass.getName).toTypeConstructor.asInstanceOf[U # Type] + }) + def process(settings: Settings): Boolean = savingContextLoader { this.settings = settings createInterpreter() - + // sets in to some kind of reader depending on environmental cues in = in0 match { case Some(reader) => SimpleReader(reader, out, true) - case None => chooseReader(settings) + case None => + // some post-initialization + chooseReader(settings) match { + case x: SparkJLineReader => addThunk(x.consoleReader.postInit) ; x + case x => x + } } + lazy val tagOfSparkIMain = tagOfStaticClass[org.apache.spark.repl.SparkIMain] + // Bind intp somewhere out of the regular namespace where + // we can get at it in generated code. + addThunk(intp.quietBind(NamedParam[SparkIMain]("$intp", intp)(tagOfSparkIMain, classTag[SparkIMain]))) + addThunk({ + import scala.tools.nsc.io._ + import Properties.userHome + import scala.compat.Platform.EOL + val autorun = replProps.replAutorunCode.option flatMap (f => io.File(f).safeSlurp()) + if (autorun.isDefined) intp.quietRun(autorun.get) + }) + + addThunk(printWelcome()) + addThunk(initializeSpark()) loadFiles(settings) // it is broken on startup; go ahead and exit if (intp.reporter.hasErrors) return false - - try { - // this is about the illusion of snappiness. We call initialize() - // which spins off a separate thread, then print the prompt and try - // our best to look ready. Ideally the user will spend a - // couple seconds saying "wow, it starts so fast!" and by the time - // they type a command the compiler is ready to roll. - intp.initialize() - initializeSpark() - if (isReplPower) { - echo("Starting in power mode, one moment...\n") - enablePowerMode() - } - loop() + + // This is about the illusion of snappiness. We call initialize() + // which spins off a separate thread, then print the prompt and try + // our best to look ready. The interlocking lazy vals tend to + // inter-deadlock, so we break the cycle with a single asynchronous + // message to an actor. + if (isAsync) { + intp initialize initializedCallback() + createAsyncListener() // listens for signal to run postInitialization + } + else { + intp.initializeSynchronous() + postInitialization() } + // printWelcome() + + try loop() + catch AbstractOrMissingHandler() finally closeInterpreter() + true } + def createSparkContext(): SparkContext = { + val execUri = System.getenv("SPARK_EXECUTOR_URI") + val master = this.master match { + case Some(m) => m + case None => { + val prop = System.getenv("MASTER") + if (prop != null) prop else "local" + } + } + val jars = SparkILoop.getAddedJars.map(new java.io.File(_).getAbsolutePath) + val conf = new SparkConf() + .setMaster(master) + .setAppName("Spark shell") + .setSparkHome(System.getenv("SPARK_HOME")) + .setJars(jars) + .set("spark.repl.class.uri", intp.classServer.uri) + if (execUri != null) { + conf.set("spark.executor.uri", execUri) + } + sparkContext = new SparkContext(conf) + echo("Created spark context..") + sparkContext + } + /** process command-line arguments and do as they request */ def process(args: Array[String]): Boolean = { - val command = new CommandLine(args.toList, msg => echo("scala: " + msg)) + val command = new CommandLine(args.toList, echo) def neededHelp(): String = (if (command.settings.help.value) command.usageMsg + "\n" else "") + (if (command.settings.Xhelp.value) command.xusageMsg + "\n" else "") - + // if they asked for no help and command is valid, we call the real main neededHelp() match { case "" => command.ok && process(command.settings) case help => echoNoNL(help) ; true } } - - @deprecated("Use `process` instead", "2.9.0") - def main(args: Array[String]): Unit = { - if (isReplDebug) - System.out.println(new java.util.Date) - - process(args) - } + @deprecated("Use `process` instead", "2.9.0") def main(settings: Settings): Unit = process(settings) } @@ -922,15 +975,17 @@ object SparkILoop { implicit def loopToInterpreter(repl: SparkILoop): SparkIMain = repl.intp private def echo(msg: String) = Console println msg + def getAddedJars: Array[String] = Option(System.getenv("ADD_JARS")).map(_.split(',')).getOrElse(new Array[String](0)) + // Designed primarily for use by test code: take a String with a // bunch of code, and prints out a transcript of what it would look // like if you'd just typed it into the repl. def runForTranscript(code: String, settings: Settings): String = { import java.io.{ BufferedReader, StringReader, OutputStreamWriter } - + stringFromStream { ostream => Console.withOut(ostream) { - val output = new PrintWriter(new OutputStreamWriter(ostream), true) { + val output = new JPrintWriter(new OutputStreamWriter(ostream), true) { override def write(str: String) = { // completely skip continuation lines if (str forall (ch => ch.isWhitespace || ch == '|')) () @@ -949,26 +1004,29 @@ object SparkILoop { } } val repl = new SparkILoop(input, output) + if (settings.classpath.isDefault) settings.classpath.value = sys.props("java.class.path") + getAddedJars.foreach(settings.classpath.append(_)) + repl process settings } } } - + /** Creates an interpreter loop with default settings and feeds * the given code to it as input. */ def run(code: String, sets: Settings = new Settings): String = { import java.io.{ BufferedReader, StringReader, OutputStreamWriter } - + stringFromStream { ostream => Console.withOut(ostream) { val input = new BufferedReader(new StringReader(code)) - val output = new PrintWriter(new OutputStreamWriter(ostream), true) - val repl = new SparkILoop(input, output) - + val output = new JPrintWriter(new OutputStreamWriter(ostream), true) + val repl = new ILoop(input, output) + if (sets.classpath.isDefault) sets.classpath.value = sys.props("java.class.path") @@ -977,32 +1035,4 @@ object SparkILoop { } } def run(lines: List[String]): String = run(lines map (_ + "\n") mkString) - - // provide the enclosing type T - // in order to set up the interpreter's classpath and parent class loader properly - def breakIf[T: Manifest](assertion: => Boolean, args: NamedParam*): Unit = - if (assertion) break[T](args.toList) - - // start a repl, binding supplied args - def break[T: Manifest](args: List[NamedParam]): Unit = { - val msg = if (args.isEmpty) "" else " Binding " + args.size + " value%s.".format( - if (args.size == 1) "" else "s" - ) - echo("Debug repl starting." + msg) - val repl = new SparkILoop { - override def prompt = "\ndebug> " - } - repl.settings = new Settings(echo) - repl.settings.embeddedDefaults[T] - repl.createInterpreter() - repl.in = SparkJLineReader(repl) - - // rebind exit so people don't accidentally call sys.exit by way of predef - repl.quietRun("""def exit = println("Type :quit to resume program execution.")""") - args foreach (p => repl.bind(p.name, p.tpe, p.value)) - repl.loop() - - echo("\nDebug repl exiting.") - repl.closeInterpreter() - } } diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala new file mode 100644 index 0000000000..21b1ba305d --- /dev/null +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala @@ -0,0 +1,143 @@ +/* NSC -- new Scala compiler + * Copyright 2005-2013 LAMP/EPFL + * @author Paul Phillips + */ + +package org.apache.spark.repl + +import scala.tools.nsc._ +import scala.tools.nsc.interpreter._ + +import scala.reflect.internal.util.Position +import scala.util.control.Exception.ignoring +import scala.tools.nsc.util.stackTraceString + +/** + * Machinery for the asynchronous initialization of the repl. + */ +trait SparkILoopInit { + self: SparkILoop => + + /** Print a welcome message */ + def printWelcome() { + echo("""Welcome to + ____ __ + / __/__ ___ _____/ /__ + _\ \/ _ \/ _ `/ __/ '_/ + /___/ .__/\_,_/_/ /_/\_\ version 0.9.0-SNAPSHOT + /_/ +""") + import Properties._ + val welcomeMsg = "Using Scala %s (%s, Java %s)".format( + versionString, javaVmName, javaVersion) + echo(welcomeMsg) + echo("Type in expressions to have them evaluated.") + echo("Type :help for more information.") + } + + protected def asyncMessage(msg: String) { + if (isReplInfo || isReplPower) + echoAndRefresh(msg) + } + + private val initLock = new java.util.concurrent.locks.ReentrantLock() + private val initCompilerCondition = initLock.newCondition() // signal the compiler is initialized + private val initLoopCondition = initLock.newCondition() // signal the whole repl is initialized + private val initStart = System.nanoTime + + private def withLock[T](body: => T): T = { + initLock.lock() + try body + finally initLock.unlock() + } + // a condition used to ensure serial access to the compiler. + @volatile private var initIsComplete = false + @volatile private var initError: String = null + private def elapsed() = "%.3f".format((System.nanoTime - initStart).toDouble / 1000000000L) + + // the method to be called when the interpreter is initialized. + // Very important this method does nothing synchronous (i.e. do + // not try to use the interpreter) because until it returns, the + // repl's lazy val `global` is still locked. + protected def initializedCallback() = withLock(initCompilerCondition.signal()) + + // Spins off a thread which awaits a single message once the interpreter + // has been initialized. + protected def createAsyncListener() = { + io.spawn { + withLock(initCompilerCondition.await()) + asyncMessage("[info] compiler init time: " + elapsed() + " s.") + postInitialization() + } + } + + // called from main repl loop + protected def awaitInitialized(): Boolean = { + if (!initIsComplete) + withLock { while (!initIsComplete) initLoopCondition.await() } + if (initError != null) { + println(""" + |Failed to initialize the REPL due to an unexpected error. + |This is a bug, please, report it along with the error diagnostics printed below. + |%s.""".stripMargin.format(initError) + ) + false + } else true + } + // private def warningsThunks = List( + // () => intp.bind("lastWarnings", "" + typeTag[List[(Position, String)]], intp.lastWarnings _), + // ) + + protected def postInitThunks = List[Option[() => Unit]]( + Some(intp.setContextClassLoader _), + if (isReplPower) Some(() => enablePowerMode(true)) else None + ).flatten + // ++ ( + // warningsThunks + // ) + // called once after init condition is signalled + protected def postInitialization() { + try { + postInitThunks foreach (f => addThunk(f())) + runThunks() + } catch { + case ex: Throwable => + initError = stackTraceString(ex) + throw ex + } finally { + initIsComplete = true + + if (isAsync) { + asyncMessage("[info] total init time: " + elapsed() + " s.") + withLock(initLoopCondition.signal()) + } + } + } + + def initializeSpark() { + intp.beQuietDuring { + command(""" + @transient val sc = org.apache.spark.repl.Main.interp.createSparkContext(); + """) + command("import org.apache.spark.SparkContext._") + } + echo("Spark context available as sc.") + } + + // code to be executed only after the interpreter is initialized + // and the lazy val `global` can be accessed without risk of deadlock. + private var pendingThunks: List[() => Unit] = Nil + protected def addThunk(body: => Unit) = synchronized { + pendingThunks :+= (() => body) + } + protected def runThunks(): Unit = synchronized { + if (pendingThunks.nonEmpty) + logDebug("Clearing " + pendingThunks.size + " thunks.") + + while (pendingThunks.nonEmpty) { + val thunk = pendingThunks.head + pendingThunks = pendingThunks.tail + thunk() + } + } +} diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala index e6e35c9b5d..59fdb0b37a 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala @@ -1,5 +1,5 @@ /* NSC -- new Scala compiler - * Copyright 2005-2011 LAMP/EPFL + * Copyright 2005-2013 LAMP/EPFL * @author Martin Odersky */ @@ -9,304 +9,330 @@ import scala.tools.nsc._ import scala.tools.nsc.interpreter._ import Predef.{ println => _, _ } -import java.io.{ PrintWriter } -import java.lang.reflect +import util.stringFromWriter +import scala.reflect.internal.util._ import java.net.URL -import util.{ Set => _, _ } -import io.{ AbstractFile, PlainFile, VirtualDirectory } -import reporters.{ ConsoleReporter, Reporter } -import symtab.{ Flags, Names } -import scala.tools.nsc.interpreter.{ Results => IR } +import scala.sys.BooleanProp +import io.{AbstractFile, PlainFile, VirtualDirectory} + +import reporters._ +import symtab.Flags +import scala.reflect.internal.Names import scala.tools.util.PathResolver -import scala.tools.nsc.util.{ ScalaClassLoader, Exceptional } +import scala.tools.nsc.util.ScalaClassLoader import ScalaClassLoader.URLClassLoader -import Exceptional.unwrap +import scala.tools.nsc.util.Exceptional.unwrap import scala.collection.{ mutable, immutable } -import scala.PartialFunction.{ cond, condOpt } import scala.util.control.Exception.{ ultimately } -import scala.reflect.NameTransformer import SparkIMain._ - -import org.apache.spark.HttpServer +import java.util.concurrent.Future +import typechecker.Analyzer +import scala.language.implicitConversions +import scala.reflect.runtime.{ universe => ru } +import scala.reflect.{ ClassTag, classTag } +import scala.tools.reflect.StdRuntimeTags._ +import scala.util.control.ControlThrowable +import util.stackTraceString + +import org.apache.spark.{HttpServer, SparkConf, Logging} import org.apache.spark.util.Utils -import org.apache.spark.SparkEnv - -/** An interpreter for Scala code. - * - * The main public entry points are compile(), interpret(), and bind(). - * The compile() method loads a complete Scala file. The interpret() method - * executes one line of Scala code at the request of the user. The bind() - * method binds an object to a variable that can then be used by later - * interpreted code. - * - * The overall approach is based on compiling the requested code and then - * using a Java classloader and Java reflection to run the code - * and access its results. - * - * In more detail, a single compiler instance is used - * to accumulate all successfully compiled or interpreted Scala code. To - * "interpret" a line of code, the compiler generates a fresh object that - * includes the line of code and which has public member(s) to export - * all variables defined by that code. To extract the result of an - * interpreted line to show the user, a second "result object" is created - * which imports the variables exported by the above object and then - * exports a single member named "$export". To accomodate user expressions - * that read from variables or methods defined in previous statements, "import" - * statements are used. - * - * This interpreter shares the strengths and weaknesses of using the - * full compiler-to-Java. The main strength is that interpreted code - * behaves exactly as does compiled code, including running at full speed. - * The main weakness is that redefining classes and methods is not handled - * properly, because rebinding at the Java level is technically difficult. - * - * @author Moez A. Abdel-Gawad - * @author Lex Spoon - */ -class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends SparkImports { - imain => - - /** construct an interpreter that reports to Console */ - def this(settings: Settings) = this(settings, new NewLinePrintWriter(new ConsoleWriter, true)) - def this() = this(new Settings()) - - /** whether to print out result lines */ - var printResults: Boolean = true - - /** whether to print errors */ - var totalSilence: Boolean = false - - private val RESULT_OBJECT_PREFIX = "RequestResult$" - - lazy val formatting: Formatting = new Formatting { - val prompt = Properties.shellPromptString - } - import formatting._ - - val SPARK_DEBUG_REPL: Boolean = (System.getenv("SPARK_DEBUG_REPL") == "1") - - /** Local directory to save .class files too */ - val outputDir = { - val tmp = System.getProperty("java.io.tmpdir") - val rootDir = System.getProperty("spark.repl.classdir", tmp) - Utils.createTempDir(rootDir) - } - if (SPARK_DEBUG_REPL) { - echo("Output directory: " + outputDir) - } - /** Scala compiler virtual directory for outputDir */ - val virtualDirectory = new PlainFile(outputDir) +// /** directory to save .class files to */ +// private class ReplVirtualDirectory(out: JPrintWriter) extends VirtualDirectory("((memory))", None) { +// private def pp(root: AbstractFile, indentLevel: Int) { +// val spaces = " " * indentLevel +// out.println(spaces + root.name) +// if (root.isDirectory) +// root.toList sortBy (_.name) foreach (x => pp(x, indentLevel + 1)) +// } +// // print the contents hierarchically +// def show() = pp(this, 0) +// } + + /** An interpreter for Scala code. + * + * The main public entry points are compile(), interpret(), and bind(). + * The compile() method loads a complete Scala file. The interpret() method + * executes one line of Scala code at the request of the user. The bind() + * method binds an object to a variable that can then be used by later + * interpreted code. + * + * The overall approach is based on compiling the requested code and then + * using a Java classloader and Java reflection to run the code + * and access its results. + * + * In more detail, a single compiler instance is used + * to accumulate all successfully compiled or interpreted Scala code. To + * "interpret" a line of code, the compiler generates a fresh object that + * includes the line of code and which has public member(s) to export + * all variables defined by that code. To extract the result of an + * interpreted line to show the user, a second "result object" is created + * which imports the variables exported by the above object and then + * exports members called "$eval" and "$print". To accomodate user expressions + * that read from variables or methods defined in previous statements, "import" + * statements are used. + * + * This interpreter shares the strengths and weaknesses of using the + * full compiler-to-Java. The main strength is that interpreted code + * behaves exactly as does compiled code, including running at full speed. + * The main weakness is that redefining classes and methods is not handled + * properly, because rebinding at the Java level is technically difficult. + * + * @author Moez A. Abdel-Gawad + * @author Lex Spoon + */ + class SparkIMain(initialSettings: Settings, val out: JPrintWriter) extends SparkImports with Logging { + imain => - /** Jetty server that will serve our classes to worker nodes */ - val classServer = new HttpServer(outputDir) + val SPARK_DEBUG_REPL: Boolean = (System.getenv("SPARK_DEBUG_REPL") == "1") - // Start the classServer and store its URI in a spark system property - // (which will be passed to executors so that they can connect to it) - classServer.start() - System.setProperty("spark.repl.class.uri", classServer.uri) - if (SPARK_DEBUG_REPL) { - echo("Class server started, URI = " + classServer.uri) - } + /** Local directory to save .class files too */ + val outputDir = { + val tmp = System.getProperty("java.io.tmpdir") + val rootDir = new SparkConf().get("spark.repl.classdir", tmp) + Utils.createTempDir(rootDir) + } + if (SPARK_DEBUG_REPL) { + echo("Output directory: " + outputDir) + } - /* - // directory to save .class files to - val virtualDirectory = new VirtualDirectory("(memory)", None) { - private def pp(root: io.AbstractFile, indentLevel: Int) { - val spaces = " " * indentLevel - out.println(spaces + root.name) - if (root.isDirectory) - root.toList sortBy (_.name) foreach (x => pp(x, indentLevel + 1)) - } - // print the contents hierarchically - def show() = pp(this, 0) - } - */ - - /** reporter */ - lazy val reporter: ConsoleReporter = new SparkIMain.ReplReporter(this) - import reporter.{ printMessage, withoutTruncating } - - // not sure if we have some motivation to print directly to console - private def echo(msg: String) { Console println msg } - - // protected def defaultImports: List[String] = List("_root_.scala.sys.exit") - - /** We're going to go to some trouble to initialize the compiler asynchronously. - * It's critical that nothing call into it until it's been initialized or we will - * run into unrecoverable issues, but the perceived repl startup time goes - * through the roof if we wait for it. So we initialize it with a future and - * use a lazy val to ensure that any attempt to use the compiler object waits - * on the future. - */ - private val _compiler: Global = newCompiler(settings, reporter) - private var _initializeComplete = false - def isInitializeComplete = _initializeComplete - - private def _initialize(): Boolean = { - val source = """ - |class $repl_$init { - | List(1) map (_ + 1) - |} - |""".stripMargin - - val result = try { - new _compiler.Run() compileSources List(new BatchSourceFile("<init>", source)) - if (isReplDebug || settings.debug.value) { - // Can't use printMessage here, it deadlocks - Console.println("Repl compiler initialized.") + val virtualDirectory = new PlainFile(outputDir) // "directory" for classfiles + val classServer = new HttpServer(outputDir) /** Jetty server that will serve our classes to worker nodes */ + private var currentSettings: Settings = initialSettings + var printResults = true // whether to print result lines + var totalSilence = false // whether to print anything + private var _initializeComplete = false // compiler is initialized + private var _isInitialized: Future[Boolean] = null // set up initialization future + private var bindExceptions = true // whether to bind the lastException variable + private var _executionWrapper = "" // code to be wrapped around all lines + + + // Start the classServer and store its URI in a spark system property + // (which will be passed to executors so that they can connect to it) + classServer.start() + if (SPARK_DEBUG_REPL) { + echo("Class server started, URI = " + classServer.uri) } - // addImports(defaultImports: _*) - true - } - catch { - case x: AbstractMethodError => - printMessage(""" - |Failed to initialize compiler: abstract method error. - |This is most often remedied by a full clean and recompile. - |""".stripMargin - ) - x.printStackTrace() - false - case x: MissingRequirementError => printMessage(""" - |Failed to initialize compiler: %s not found. - |** Note that as of 2.8 scala does not assume use of the java classpath. - |** For the old behavior pass -usejavacp to scala, or if using a Settings - |** object programatically, settings.usejavacp.value = true.""".stripMargin.format(x.req) + + /** We're going to go to some trouble to initialize the compiler asynchronously. + * It's critical that nothing call into it until it's been initialized or we will + * run into unrecoverable issues, but the perceived repl startup time goes + * through the roof if we wait for it. So we initialize it with a future and + * use a lazy val to ensure that any attempt to use the compiler object waits + * on the future. + */ + private var _classLoader: AbstractFileClassLoader = null // active classloader + private val _compiler: Global = newCompiler(settings, reporter) // our private compiler + + private val nextReqId = { + var counter = 0 + () => { counter += 1 ; counter } + } + + def compilerClasspath: Seq[URL] = ( + if (isInitializeComplete) global.classPath.asURLs + else new PathResolver(settings).result.asURLs // the compiler's classpath ) - false + def settings = currentSettings + def mostRecentLine = prevRequestList match { + case Nil => "" + case req :: _ => req.originalLine + } + // Run the code body with the given boolean settings flipped to true. + def withoutWarnings[T](body: => T): T = beQuietDuring { + val saved = settings.nowarn.value + if (!saved) + settings.nowarn.value = true + + try body + finally if (!saved) settings.nowarn.value = false } - - try result - finally _initializeComplete = result - } - - // set up initialization future - private var _isInitialized: () => Boolean = null - def initialize() = synchronized { - if (_isInitialized == null) - _isInitialized = scala.concurrent.ops future _initialize() - } - /** the public, go through the future compiler */ - lazy val global: Global = { - initialize() + /** construct an interpreter that reports to Console */ + def this(settings: Settings) = this(settings, new NewLinePrintWriter(new ConsoleWriter, true)) + def this() = this(new Settings()) - // blocks until it is ; false means catastrophic failure - if (_isInitialized()) _compiler - else null - } - @deprecated("Use `global` for access to the compiler instance.", "2.9.0") - lazy val compiler: global.type = global - - import global._ - - object naming extends { - val global: imain.global.type = imain.global - } with Naming { - // make sure we don't overwrite their unwisely named res3 etc. - override def freshUserVarName(): String = { - val name = super.freshUserVarName() - if (definedNameMap contains name) freshUserVarName() - else name + lazy val repllog: Logger = new Logger { + val out: JPrintWriter = imain.out + val isInfo: Boolean = BooleanProp keyExists "scala.repl.info" + val isDebug: Boolean = BooleanProp keyExists "scala.repl.debug" + val isTrace: Boolean = BooleanProp keyExists "scala.repl.trace" } - } - import naming._ - - // object dossiers extends { - // val intp: imain.type = imain - // } with Dossiers { } - // import dossiers._ - - lazy val memberHandlers = new { - val intp: imain.type = imain - } with SparkMemberHandlers - import memberHandlers._ - - def atPickler[T](op: => T): T = atPhase(currentRun.picklerPhase)(op) - def afterTyper[T](op: => T): T = atPhase(currentRun.typerPhase.next)(op) - - /** Temporarily be quiet */ - def beQuietDuring[T](operation: => T): T = { - val wasPrinting = printResults - ultimately(printResults = wasPrinting) { - if (isReplDebug) echo(">> beQuietDuring") - else printResults = false - - operation + lazy val formatting: Formatting = new Formatting { + val prompt = Properties.shellPromptString } - } - def beSilentDuring[T](operation: => T): T = { - val saved = totalSilence - totalSilence = true - try operation - finally totalSilence = saved - } - - def quietRun[T](code: String) = beQuietDuring(interpret(code)) - - /** whether to bind the lastException variable */ - private var bindLastException = true - - /** A string representing code to be wrapped around all lines. */ - private var _executionWrapper: String = "" - def executionWrapper = _executionWrapper - def setExecutionWrapper(code: String) = _executionWrapper = code - def clearExecutionWrapper() = _executionWrapper = "" - - /** Temporarily stop binding lastException */ - def withoutBindingLastException[T](operation: => T): T = { - val wasBinding = bindLastException - ultimately(bindLastException = wasBinding) { - bindLastException = false - operation + lazy val reporter: ConsoleReporter = new SparkIMain.ReplReporter(this) + + import formatting._ + import reporter.{ printMessage, withoutTruncating } + + // This exists mostly because using the reporter too early leads to deadlock. + private def echo(msg: String) { Console println msg } + private def _initSources = List(new BatchSourceFile("<init>", "class $repl_$init { }")) + private def _initialize() = { + try { + // todo. if this crashes, REPL will hang + new _compiler.Run() compileSources _initSources + _initializeComplete = true + true + } + catch AbstractOrMissingHandler() } - } - - protected def createLineManager(): Line.Manager = new Line.Manager - lazy val lineManager = createLineManager() - - /** interpreter settings */ - lazy val isettings = new SparkISettings(this) - - /** Instantiate a compiler. Subclasses can override this to - * change the compiler class used by this interpreter. */ - protected def newCompiler(settings: Settings, reporter: Reporter) = { - settings.outputDirs setSingleOutput virtualDirectory - settings.exposeEmptyPackage.value = true - new Global(settings, reporter) - } - - /** the compiler's classpath, as URL's */ - lazy val compilerClasspath: List[URL] = new PathResolver(settings) asURLs + private def tquoted(s: String) = "\"\"\"" + s + "\"\"\"" + + // argument is a thunk to execute after init is done + def initialize(postInitSignal: => Unit) { + synchronized { + if (_isInitialized == null) { + _isInitialized = io.spawn { + try _initialize() + finally postInitSignal + } + } + } + } + def initializeSynchronous(): Unit = { + if (!isInitializeComplete) { + _initialize() + assert(global != null, global) + } + } + def isInitializeComplete = _initializeComplete + + /** the public, go through the future compiler */ + lazy val global: Global = { + if (isInitializeComplete) _compiler + else { + // If init hasn't been called yet you're on your own. + if (_isInitialized == null) { + logWarning("Warning: compiler accessed before init set up. Assuming no postInit code.") + initialize(()) + } + // // blocks until it is ; false means catastrophic failure + if (_isInitialized.get()) _compiler + else null + } + } + @deprecated("Use `global` for access to the compiler instance.", "2.9.0") + lazy val compiler: global.type = global + + import global._ + import definitions.{ScalaPackage, JavaLangPackage, termMember, typeMember} + import rootMirror.{RootClass, getClassIfDefined, getModuleIfDefined, getRequiredModule, getRequiredClass} + + implicit class ReplTypeOps(tp: Type) { + def orElse(other: => Type): Type = if (tp ne NoType) tp else other + def andAlso(fn: Type => Type): Type = if (tp eq NoType) tp else fn(tp) + } + + // TODO: If we try to make naming a lazy val, we run into big time + // scalac unhappiness with what look like cycles. It has not been easy to + // reduce, but name resolution clearly takes different paths. + object naming extends { + val global: imain.global.type = imain.global + } with Naming { + // make sure we don't overwrite their unwisely named res3 etc. + def freshUserTermName(): TermName = { + val name = newTermName(freshUserVarName()) + if (definedNameMap contains name) freshUserTermName() + else name + } + def isUserTermName(name: Name) = isUserVarName("" + name) + def isInternalTermName(name: Name) = isInternalVarName("" + name) + } + import naming._ + + object deconstruct extends { + val global: imain.global.type = imain.global + } with StructuredTypeStrings + + lazy val memberHandlers = new { + val intp: imain.type = imain + } with SparkMemberHandlers + import memberHandlers._ + + /** Temporarily be quiet */ + def beQuietDuring[T](body: => T): T = { + val saved = printResults + printResults = false + try body + finally printResults = saved + } + def beSilentDuring[T](operation: => T): T = { + val saved = totalSilence + totalSilence = true + try operation + finally totalSilence = saved + } + + def quietRun[T](code: String) = beQuietDuring(interpret(code)) + + + private def logAndDiscard[T](label: String, alt: => T): PartialFunction[Throwable, T] = { + case t: ControlThrowable => throw t + case t: Throwable => + logDebug(label + ": " + unwrap(t)) + logDebug(stackTraceString(unwrap(t))) + alt + } + /** takes AnyRef because it may be binding a Throwable or an Exceptional */ + + private def withLastExceptionLock[T](body: => T, alt: => T): T = { + assert(bindExceptions, "withLastExceptionLock called incorrectly.") + bindExceptions = false + + try beQuietDuring(body) + catch logAndDiscard("withLastExceptionLock", alt) + finally bindExceptions = true + } + + def executionWrapper = _executionWrapper + def setExecutionWrapper(code: String) = _executionWrapper = code + def clearExecutionWrapper() = _executionWrapper = "" + + /** interpreter settings */ + lazy val isettings = new SparkISettings(this) + + /** Instantiate a compiler. Overridable. */ + protected def newCompiler(settings: Settings, reporter: Reporter): ReplGlobal = { + settings.outputDirs setSingleOutput virtualDirectory + settings.exposeEmptyPackage.value = true + new Global(settings, reporter) with ReplGlobal { + override def toString: String = "<global>" + } + } + + /** Parent classloader. Overridable. */ + protected def parentClassLoader: ClassLoader = + SparkHelper.explicitParentLoader(settings).getOrElse( this.getClass.getClassLoader() ) - /* A single class loader is used for all commands interpreted by this Interpreter. + /* A single class loader is used for all commands interpreted by this Interpreter. It would also be possible to create a new class loader for each command to interpret. The advantages of the current approach are: - - Expressions are only evaluated one time. This is especially - significant for I/O, e.g. "val x = Console.readLine" - - The main disadvantage is: - - - Objects, classes, and methods cannot be rebound. Instead, definitions - shadow the old ones, and old code objects refer to the old - definitions. - */ - private var _classLoader: AbstractFileClassLoader = null - def resetClassLoader() = _classLoader = makeClassLoader() - def classLoader: AbstractFileClassLoader = { - if (_classLoader == null) - resetClassLoader() - - _classLoader - } - private def makeClassLoader(): AbstractFileClassLoader = { - val parent = - if (parentClassLoader == null) ScalaClassLoader fromURLs compilerClasspath - else new URLClassLoader(compilerClasspath, parentClassLoader) + - Expressions are only evaluated one time. This is especially + significant for I/O, e.g. "val x = Console.readLine" + + The main disadvantage is: - new AbstractFileClassLoader(virtualDirectory, parent) { + - Objects, classes, and methods cannot be rebound. Instead, definitions + shadow the old ones, and old code objects refer to the old + definitions. + */ + def resetClassLoader() = { + logDebug("Setting new classloader: was " + _classLoader) + _classLoader = null + ensureClassLoader() + } + final def ensureClassLoader() { + if (_classLoader == null) + _classLoader = makeClassLoader() + } + def classLoader: AbstractFileClassLoader = { + ensureClassLoader() + _classLoader + } + private class TranslatingClassLoader(parent: ClassLoader) extends AbstractFileClassLoader(virtualDirectory, parent) { /** Overridden here to try translating a simple name to the generated * class name if the original attempt fails. This method is used by * getResourceAsStream as well as findClass. @@ -314,223 +340,300 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends override protected def findAbstractFile(name: String): AbstractFile = { super.findAbstractFile(name) match { // deadlocks on startup if we try to translate names too early - case null if isInitializeComplete => generatedName(name) map (x => super.findAbstractFile(x)) orNull - case file => file + case null if isInitializeComplete => + generatedName(name) map (x => super.findAbstractFile(x)) orNull + case file => + file } } } - } - private def loadByName(s: String): JClass = - (classLoader tryToInitializeClass s) getOrElse sys.error("Failed to load expected class: '" + s + "'") - - protected def parentClassLoader: ClassLoader = - SparkHelper.explicitParentLoader(settings).getOrElse( this.getClass.getClassLoader() ) - - def getInterpreterClassLoader() = classLoader - - // Set the current Java "context" class loader to this interpreter's class loader - def setContextClassLoader() = classLoader.setAsContext() - - /** Given a simple repl-defined name, returns the real name of - * the class representing it, e.g. for "Bippy" it may return - * - * $line19.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$Bippy - */ - def generatedName(simpleName: String): Option[String] = { - if (simpleName endsWith "$") optFlatName(simpleName.init) map (_ + "$") - else optFlatName(simpleName) - } - def flatName(id: String) = optFlatName(id) getOrElse id - def optFlatName(id: String) = requestForIdent(id) map (_ fullFlatName id) - - def allDefinedNames = definedNameMap.keys.toList sortBy (_.toString) - def pathToType(id: String): String = pathToName(newTypeName(id)) - def pathToTerm(id: String): String = pathToName(newTermName(id)) - def pathToName(name: Name): String = { - if (definedNameMap contains name) - definedNameMap(name) fullPath name - else name.toString - } + private def makeClassLoader(): AbstractFileClassLoader = + new TranslatingClassLoader(parentClassLoader match { + case null => ScalaClassLoader fromURLs compilerClasspath + case p => new URLClassLoader(compilerClasspath, p) + }) + + def getInterpreterClassLoader() = classLoader + + // Set the current Java "context" class loader to this interpreter's class loader + def setContextClassLoader() = classLoader.setAsContext() + + /** Given a simple repl-defined name, returns the real name of + * the class representing it, e.g. for "Bippy" it may return + * {{{ + * $line19.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$Bippy + * }}} + */ + def generatedName(simpleName: String): Option[String] = { + if (simpleName endsWith nme.MODULE_SUFFIX_STRING) optFlatName(simpleName.init) map (_ + nme.MODULE_SUFFIX_STRING) + else optFlatName(simpleName) + } + def flatName(id: String) = optFlatName(id) getOrElse id + def optFlatName(id: String) = requestForIdent(id) map (_ fullFlatName id) + + def allDefinedNames = definedNameMap.keys.toList.sorted + def pathToType(id: String): String = pathToName(newTypeName(id)) + def pathToTerm(id: String): String = pathToName(newTermName(id)) + def pathToName(name: Name): String = { + if (definedNameMap contains name) + definedNameMap(name) fullPath name + else name.toString + } - /** Most recent tree handled which wasn't wholly synthetic. */ - private def mostRecentlyHandledTree: Option[Tree] = { - prevRequests.reverse foreach { req => - req.handlers.reverse foreach { - case x: MemberDefHandler if x.definesValue && !isInternalVarName(x.name.toString) => return Some(x.member) - case _ => () + /** Most recent tree handled which wasn't wholly synthetic. */ + private def mostRecentlyHandledTree: Option[Tree] = { + prevRequests.reverse foreach { req => + req.handlers.reverse foreach { + case x: MemberDefHandler if x.definesValue && !isInternalTermName(x.name) => return Some(x.member) + case _ => () + } } + None } - None - } - - /** Stubs for work in progress. */ - def handleTypeRedefinition(name: TypeName, old: Request, req: Request) = { - for (t1 <- old.simpleNameOfType(name) ; t2 <- req.simpleNameOfType(name)) { - DBG("Redefining type '%s'\n %s -> %s".format(name, t1, t2)) + + /** Stubs for work in progress. */ + def handleTypeRedefinition(name: TypeName, old: Request, req: Request) = { + for (t1 <- old.simpleNameOfType(name) ; t2 <- req.simpleNameOfType(name)) { + logDebug("Redefining type '%s'\n %s -> %s".format(name, t1, t2)) + } } - } - def handleTermRedefinition(name: TermName, old: Request, req: Request) = { - for (t1 <- old.compilerTypeOf get name ; t2 <- req.compilerTypeOf get name) { - // Printing the types here has a tendency to cause assertion errors, like - // assertion failed: fatal: <refinement> has owner value x, but a class owner is required - // so DBG is by-name now to keep it in the family. (It also traps the assertion error, - // but we don't want to unnecessarily risk hosing the compiler's internal state.) - DBG("Redefining term '%s'\n %s -> %s".format(name, t1, t2)) + def handleTermRedefinition(name: TermName, old: Request, req: Request) = { + for (t1 <- old.compilerTypeOf get name ; t2 <- req.compilerTypeOf get name) { + // Printing the types here has a tendency to cause assertion errors, like + // assertion failed: fatal: <refinement> has owner value x, but a class owner is required + // so DBG is by-name now to keep it in the family. (It also traps the assertion error, + // but we don't want to unnecessarily risk hosing the compiler's internal state.) + logDebug("Redefining term '%s'\n %s -> %s".format(name, t1, t2)) + } } - } - def recordRequest(req: Request) { - if (req == null || referencedNameMap == null) - return - - prevRequests += req - req.referencedNames foreach (x => referencedNameMap(x) = req) - - // warning about serially defining companions. It'd be easy - // enough to just redefine them together but that may not always - // be what people want so I'm waiting until I can do it better. - if (!settings.nowarnings.value) { + + def recordRequest(req: Request) { + if (req == null || referencedNameMap == null) + return + + prevRequests += req + req.referencedNames foreach (x => referencedNameMap(x) = req) + + // warning about serially defining companions. It'd be easy + // enough to just redefine them together but that may not always + // be what people want so I'm waiting until I can do it better. for { name <- req.definedNames filterNot (x => req.definedNames contains x.companionName) oldReq <- definedNameMap get name.companionName newSym <- req.definedSymbols get name oldSym <- oldReq.definedSymbols get name.companionName + if Seq(oldSym, newSym).permutations exists { case Seq(s1, s2) => s1.isClass && s2.isModule } } { - printMessage("warning: previously defined %s is not a companion to %s.".format(oldSym, newSym)) - printMessage("Companions must be defined together; you may wish to use :paste mode for this.") + afterTyper(replwarn(s"warning: previously defined $oldSym is not a companion to $newSym.")) + replwarn("Companions must be defined together; you may wish to use :paste mode for this.") } - } - - // Updating the defined name map - req.definedNames foreach { name => - if (definedNameMap contains name) { - if (name.isTypeName) handleTypeRedefinition(name.toTypeName, definedNameMap(name), req) - else handleTermRedefinition(name.toTermName, definedNameMap(name), req) + + // Updating the defined name map + req.definedNames foreach { name => + if (definedNameMap contains name) { + if (name.isTypeName) handleTypeRedefinition(name.toTypeName, definedNameMap(name), req) + else handleTermRedefinition(name.toTermName, definedNameMap(name), req) + } + definedNameMap(name) = req } - definedNameMap(name) = req } - } - /** Parse a line into a sequence of trees. Returns None if the input is incomplete. */ - def parse(line: String): Option[List[Tree]] = { - var justNeedsMore = false - reporter.withIncompleteHandler((pos,msg) => {justNeedsMore = true}) { - // simple parse: just parse it, nothing else - def simpleParse(code: String): List[Tree] = { - reporter.reset() - val unit = new CompilationUnit(new BatchSourceFile("<console>", code)) - val scanner = new syntaxAnalyzer.UnitParser(unit) - - scanner.templateStatSeq(false)._2 - } - val trees = simpleParse(line) - - if (reporter.hasErrors) Some(Nil) // the result did not parse, so stop - else if (justNeedsMore) None - else Some(trees) + def replwarn(msg: => String) { + if (!settings.nowarnings.value) + printMessage(msg) } - } - - def isParseable(line: String): Boolean = { - beSilentDuring { - parse(line) match { - case Some(xs) => xs.nonEmpty // parses as-is - case None => true // incomplete + + def isParseable(line: String): Boolean = { + beSilentDuring { + try parse(line) match { + case Some(xs) => xs.nonEmpty // parses as-is + case None => true // incomplete + } + catch { case x: Exception => // crashed the compiler + replwarn("Exception in isParseable(\"" + line + "\"): " + x) + false + } } } + + def compileSourcesKeepingRun(sources: SourceFile*) = { + val run = new Run() + reporter.reset() + run compileSources sources.toList + (!reporter.hasErrors, run) + } + + /** Compile an nsc SourceFile. Returns true if there are + * no compilation errors, or false otherwise. + */ + def compileSources(sources: SourceFile*): Boolean = + compileSourcesKeepingRun(sources: _*)._1 + + /** Compile a string. Returns true if there are no + * compilation errors, or false otherwise. + */ + def compileString(code: String): Boolean = + compileSources(new BatchSourceFile("<script>", code)) + + /** Build a request from the user. `trees` is `line` after being parsed. + */ + private def buildRequest(line: String, trees: List[Tree]): Request = { + executingRequest = new Request(line, trees) + executingRequest + } + + // rewriting "5 // foo" to "val x = { 5 // foo }" creates broken code because + // the close brace is commented out. Strip single-line comments. + // ... but for error message output reasons this is not used, and rather than + // enclosing in braces it is constructed like "val x =\n5 // foo". + private def removeComments(line: String): String = { + showCodeIfDebugging(line) // as we're about to lose our // show + line.lines map (s => s indexOf "//" match { + case -1 => s + case idx => s take idx + }) mkString "\n" } - /** Compile an nsc SourceFile. Returns true if there are - * no compilation errors, or false otherwise. - */ - def compileSources(sources: SourceFile*): Boolean = { - reporter.reset() - new Run() compileSources sources.toList - !reporter.hasErrors + private def safePos(t: Tree, alt: Int): Int = + try t.pos.startOrPoint + catch { case _: UnsupportedOperationException => alt } + + // Given an expression like 10 * 10 * 10 we receive the parent tree positioned + // at a '*'. So look at each subtree and find the earliest of all positions. + private def earliestPosition(tree: Tree): Int = { + var pos = Int.MaxValue + tree foreach { t => + pos = math.min(pos, safePos(t, Int.MaxValue)) + } + pos } - /** Compile a string. Returns true if there are no - * compilation errors, or false otherwise. - */ - def compileString(code: String): Boolean = - compileSources(new BatchSourceFile("<script>", code)) - /** Build a request from the user. `trees` is `line` after being parsed. - */ - private def buildRequest(line: String, trees: List[Tree]): Request = new Request(line, trees) - private def requestFromLine(line: String, synthetic: Boolean): Either[IR.Result, Request] = { - val trees = parse(indentCode(line)) match { + val content = indentCode(line) + val trees = parse(content) match { case None => return Left(IR.Incomplete) case Some(Nil) => return Left(IR.Error) // parse error or empty input case Some(trees) => trees } - - // use synthetic vars to avoid filling up the resXX slots - def varName = if (synthetic) freshInternalVarName() else freshUserVarName() - - // Treat a single bare expression specially. This is necessary due to it being hard to - // modify code at a textual level, and it being hard to submit an AST to the compiler. - if (trees.size == 1) trees.head match { - case _:Assign => // we don't want to include assignments - case _:TermTree | _:Ident | _:Select => // ... but do want these as valdefs. - requestFromLine("val %s =\n%s".format(varName, line), synthetic) match { + logDebug( + trees map (t => { + // [Eugene to Paul] previously it just said `t map ...` + // because there was an implicit conversion from Tree to a list of Trees + // however Martin and I have removed the conversion + // (it was conflicting with the new reflection API), + // so I had to rewrite this a bit + val subs = t collect { case sub => sub } + subs map (t0 => + " " + safePos(t0, -1) + ": " + t0.shortClass + "\n" + ) mkString "" + }) mkString "\n" + ) + // If the last tree is a bare expression, pinpoint where it begins using the + // AST node position and snap the line off there. Rewrite the code embodied + // by the last tree as a ValDef instead, so we can access the value. + trees.last match { + case _:Assign => // we don't want to include assignments + case _:TermTree | _:Ident | _:Select => // ... but do want other unnamed terms. + val varName = if (synthetic) freshInternalVarName() else freshUserVarName() + val rewrittenLine = ( + // In theory this would come out the same without the 1-specific test, but + // it's a cushion against any more sneaky parse-tree position vs. code mismatches: + // this way such issues will only arise on multiple-statement repl input lines, + // which most people don't use. + if (trees.size == 1) "val " + varName + " =\n" + content + else { + // The position of the last tree + val lastpos0 = earliestPosition(trees.last) + // Oh boy, the parser throws away parens so "(2+2)" is mispositioned, + // with increasingly hard to decipher positions as we move on to "() => 5", + // (x: Int) => x + 1, and more. So I abandon attempts to finesse and just + // look for semicolons and newlines, which I'm sure is also buggy. + val (raw1, raw2) = content splitAt lastpos0 + logDebug("[raw] " + raw1 + " <---> " + raw2) + + val adjustment = (raw1.reverse takeWhile (ch => (ch != ';') && (ch != '\n'))).size + val lastpos = lastpos0 - adjustment + + // the source code split at the laboriously determined position. + val (l1, l2) = content splitAt lastpos + logDebug("[adj] " + l1 + " <---> " + l2) + + val prefix = if (l1.trim == "") "" else l1 + ";\n" + // Note to self: val source needs to have this precise structure so that + // error messages print the user-submitted part without the "val res0 = " part. + val combined = prefix + "val " + varName + " =\n" + l2 + + logDebug(List( + " line" -> line, + " content" -> content, + " was" -> l2, + "combined" -> combined) map { + case (label, s) => label + ": '" + s + "'" + } mkString "\n" + ) + combined + } + ) + // Rewriting "foo ; bar ; 123" + // to "foo ; bar ; val resXX = 123" + requestFromLine(rewrittenLine, synthetic) match { case Right(req) => return Right(req withOriginalLine line) case x => return x } - case _ => + case _ => } - - // figure out what kind of request Right(buildRequest(line, trees)) } - /** - * Interpret one line of input. All feedback, including parse errors - * and evaluation results, are printed via the supplied compiler's - * reporter. Values defined are available for future interpreted - * strings. - * - * - * The return value is whether the line was interpreter successfully, - * e.g. that there were no parse errors. - * + // normalize non-public types so we don't see protected aliases like Self + def normalizeNonPublic(tp: Type) = tp match { + case TypeRef(_, sym, _) if sym.isAliasType && !sym.isPublic => tp.dealias + case _ => tp + } + + /** + * Interpret one line of input. All feedback, including parse errors + * and evaluation results, are printed via the supplied compiler's + * reporter. Values defined are available for future interpreted strings. * - * @param line ... - * @return ... + * The return value is whether the line was interpreter successfully, + * e.g. that there were no parse errors. */ def interpret(line: String): IR.Result = interpret(line, false) + def interpretSynthetic(line: String): IR.Result = interpret(line, true) def interpret(line: String, synthetic: Boolean): IR.Result = { def loadAndRunReq(req: Request) = { + classLoader.setAsContext() val (result, succeeded) = req.loadAndRun + /** To our displeasure, ConsoleReporter offers only printMessage, * which tacks a newline on the end. Since that breaks all the * output checking, we have to take one off to balance. */ - def show() = { - if (result == "") () - else printMessage(result stripSuffix "\n") - } - if (succeeded) { - if (printResults) - show() + if (printResults && result != "") + printMessage(result stripSuffix "\n") + else if (isReplDebug) // show quiet-mode activity + printMessage(result.trim.lines map ("[quiet] " + _) mkString "\n") + // Book-keeping. Have to record synthetic requests too, // as they may have been issued for information, e.g. :type recordRequest(req) IR.Success } - else { - // don't truncate stack traces - withoutTruncating(show()) - IR.Error - } + else { + // don't truncate stack traces + withoutTruncating(printMessage(result)) + IR.Error + } } - + if (global == null) IR.Error else requestFromLine(line, synthetic) match { case Left(result) => result - case Right(req) => + case Right(req) => // null indicates a disallowed statement type; otherwise compile and // fail if false (implying e.g. a type error) if (req == null || !req.compile) IR.Error @@ -546,23 +649,39 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends * @param value the object value to bind to it * @return an indication of whether the binding succeeded */ - def bind(name: String, boundType: String, value: Any): IR.Result = { + def bind(name: String, boundType: String, value: Any, modifiers: List[String] = Nil): IR.Result = { val bindRep = new ReadEvalPrint() val run = bindRep.compile(""" - |object %s { - | var value: %s = _ - | def set(x: Any) = value = x.asInstanceOf[%s] - |} - """.stripMargin.format(bindRep.evalName, boundType, boundType) - ) - bindRep.callOpt("set", value) match { - case Some(_) => interpret("val %s = %s.value".format(name, bindRep.evalPath)) - case _ => DBG("Set failed in bind(%s, %s, %s)".format(name, boundType, value)) ; IR.Error + |object %s { + | var value: %s = _ + | def set(x: Any) = value = x.asInstanceOf[%s] + |} + """.stripMargin.format(bindRep.evalName, boundType, boundType) + ) + bindRep.callEither("set", value) match { + case Left(ex) => + logDebug("Set failed in bind(%s, %s, %s)".format(name, boundType, value)) + logDebug(util.stackTraceString(ex)) + IR.Error + + case Right(_) => + val line = "%sval %s = %s.value".format(modifiers map (_ + " ") mkString, name, bindRep.evalPath) + logDebug("Interpreting: " + line) + interpret(line) } } + def directBind(name: String, boundType: String, value: Any): IR.Result = { + val result = bind(name, boundType, value) + if (result == IR.Success) + directlyBoundNames += newTermName(name) + result + } + def directBind(p: NamedParam): IR.Result = directBind(p.name, p.tpe, p.value) + def directBind[T: ru.TypeTag : ClassTag](name: String, value: T): IR.Result = directBind((name, value)) + def rebind(p: NamedParam): IR.Result = { val name = p.name - val oldType = typeOfTerm(name) getOrElse { return IR.Error } + val oldType = typeOfTerm(name) orElse { return IR.Error } val newType = p.tpe val tempName = freshInternalVarName() @@ -570,23 +689,27 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends quietRun("val %s = %s.asInstanceOf[%s]".format(name, tempName, newType)) } def quietImport(ids: String*): IR.Result = beQuietDuring(addImports(ids: _*)) - def addImports(ids: String*): IR.Result = + def addImports(ids: String*): IR.Result = if (ids.isEmpty) IR.Success else interpret("import " + ids.mkString(", ")) - def quietBind(p: NamedParam): IR.Result = beQuietDuring(bind(p)) - def bind(p: NamedParam): IR.Result = bind(p.name, p.tpe, p.value) - def bind[T: Manifest](name: String, value: T): IR.Result = bind((name, value)) - def bindValue(x: Any): IR.Result = bind(freshUserVarName(), TypeStrings.fromValue(x), x) + def quietBind(p: NamedParam): IR.Result = beQuietDuring(bind(p)) + def bind(p: NamedParam): IR.Result = bind(p.name, p.tpe, p.value) + def bind[T: ru.TypeTag : ClassTag](name: String, value: T): IR.Result = bind((name, value)) + def bindSyntheticValue(x: Any): IR.Result = bindValue(freshInternalVarName(), x) + def bindValue(x: Any): IR.Result = bindValue(freshUserVarName(), x) + def bindValue(name: String, x: Any): IR.Result = bind(name, TypeStrings.fromValue(x), x) /** Reset this interpreter, forgetting all user-specified requests. */ def reset() { - //virtualDirectory.clear() - virtualDirectory.delete() - virtualDirectory.create() + clearExecutionWrapper() resetClassLoader() resetAllCreators() prevRequests.clear() + referencedNameMap.clear() + definedNameMap.clear() + virtualDirectory.delete() + virtualDirectory.create() } /** This instance is no longer needed, so release any resources @@ -596,9 +719,9 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends reporter.flush() classServer.stop() } - + /** Here is where we: - * + * * 1) Read some source code, and put it in the "read" object. * 2) Evaluate the read object, and put the result in the "eval" object. * 3) Create a String for human consumption, and put it in the "print" object. @@ -608,115 +731,172 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends class ReadEvalPrint(lineId: Int) { def this() = this(freshLineId()) - val packageName = "$line" + lineId - val readName = "$read" - val evalName = "$eval" - val printName = "$print" - val valueMethod = "$result" // no-args method giving result - + private var lastRun: Run = _ + private var evalCaught: Option[Throwable] = None + private var conditionalWarnings: List[ConditionalWarning] = Nil + + val packageName = sessionNames.line + lineId + val readName = sessionNames.read + val evalName = sessionNames.eval + val printName = sessionNames.print + val resultName = sessionNames.result + + def bindError(t: Throwable) = { + if (!bindExceptions) // avoid looping if already binding + throw t + + val unwrapped = unwrap(t) + withLastExceptionLock[String]({ + directBind[Throwable]("lastException", unwrapped)(tagOfThrowable, classTag[Throwable]) + util.stackTraceString(unwrapped) + }, util.stackTraceString(unwrapped)) + } + // TODO: split it out into a package object and a regular // object and we can do that much less wrapping. def packageDecl = "package " + packageName - + def pathTo(name: String) = packageName + "." + name def packaged(code: String) = packageDecl + "\n\n" + code def readPath = pathTo(readName) def evalPath = pathTo(evalName) def printPath = pathTo(printName) - - def call(name: String, args: Any*): AnyRef = - evalMethod(name).invoke(evalClass, args.map(_.asInstanceOf[AnyRef]): _*) - + + def call(name: String, args: Any*): AnyRef = { + val m = evalMethod(name) + logDebug("Invoking: " + m) + if (args.nonEmpty) + logDebug(" with args: " + args.mkString(", ")) + + m.invoke(evalClass, args.map(_.asInstanceOf[AnyRef]): _*) + } + + def callEither(name: String, args: Any*): Either[Throwable, AnyRef] = + try Right(call(name, args: _*)) + catch { case ex: Throwable => Left(ex) } + def callOpt(name: String, args: Any*): Option[AnyRef] = try Some(call(name, args: _*)) - catch { case ex: Exception => - quietBind("lastException", ex) - None - } - - lazy val evalClass = loadByName(evalPath) - lazy val evalValue = callOpt(valueMethod) + catch { case ex: Throwable => bindError(ex) ; None } - def compile(source: String): Boolean = compileAndSaveRun("<console>", source) - def lineAfterTyper[T](op: => T): T = { - assert(lastRun != null, "Internal error: trying to use atPhase, but Run is null." + this) - atPhase(lastRun.typerPhase.next)(op) + class EvalException(msg: String, cause: Throwable) extends RuntimeException(msg, cause) { } + + private def evalError(path: String, ex: Throwable) = + throw new EvalException("Failed to load '" + path + "': " + ex.getMessage, ex) + + private def load(path: String): Class[_] = { + try Class.forName(path, true, classLoader) + catch { case ex: Throwable => evalError(path, unwrap(ex)) } + } + + lazy val evalClass = load(evalPath) + lazy val evalValue = callEither(resultName) match { + case Left(ex) => evalCaught = Some(ex) ; None + case Right(result) => Some(result) } - + + def compile(source: String): Boolean = compileAndSaveRun("<console>", source) + /** The innermost object inside the wrapper, found by - * following accessPath into the outer one. - */ + * following accessPath into the outer one. + */ def resolvePathToSymbol(accessPath: String): Symbol = { - //val readRoot = definitions.getModule(readPath) // the outermost wrapper - // MATEI: changed this to getClass because the root object is no longer a module (Scala singleton object) - val readRoot = definitions.getClass(readPath) // the outermost wrapper - (accessPath split '.').foldLeft(readRoot) { (sym, name) => - if (name == "") sym else - lineAfterTyper(sym.info member newTermName(name)) + // val readRoot = getRequiredModule(readPath) // the outermost wrapper + // MATEI: Changed this to getClass because the root object is no longer a module (Scala singleton object) + + val readRoot = rootMirror.getClassByName(newTypeName(readPath)) // the outermost wrapper + (accessPath split '.').foldLeft(readRoot: Symbol) { + case (sym, "") => sym + case (sym, name) => afterTyper(termMember(sym, name)) } } - - // def compileAndTypeExpr(expr: String): Option[Typer] = { - // class TyperRun extends Run { - // override def stopPhase(name: String) = name == "superaccessors" - // } - // } - private var lastRun: Run = _ - private def evalMethod(name: String) = { - val methods = evalClass.getMethods filter (_.getName == name) - assert(methods.size == 1, "Internal error - eval object method " + name + " is overloaded: " + methods) - methods.head + /** We get a bunch of repeated warnings for reasons I haven't + * entirely figured out yet. For now, squash. + */ + private def updateRecentWarnings(run: Run) { + def loop(xs: List[(Position, String)]): List[(Position, String)] = xs match { + case Nil => Nil + case ((pos, msg)) :: rest => + val filtered = rest filter { case (pos0, msg0) => + (msg != msg0) || (pos.lineContent.trim != pos0.lineContent.trim) || { + // same messages and same line content after whitespace removal + // but we want to let through multiple warnings on the same line + // from the same run. The untrimmed line will be the same since + // there's no whitespace indenting blowing it. + (pos.lineContent == pos0.lineContent) + } + } + ((pos, msg)) :: loop(filtered) + } + //PRASHANT: This leads to a NoSuchMethodError for _.warnings. Yet to figure out its purpose. + // val warnings = loop(run.allConditionalWarnings flatMap (_.warnings)) + // if (warnings.nonEmpty) + // mostRecentWarnings = warnings + } + private def evalMethod(name: String) = evalClass.getMethods filter (_.getName == name) match { + case Array(method) => method + case xs => sys.error("Internal error: eval object " + evalClass + ", " + xs.mkString("\n", "\n", "")) } private def compileAndSaveRun(label: String, code: String) = { showCodeIfDebugging(code) - reporter.reset() - lastRun = new Run() - lastRun.compileSources(List(new BatchSourceFile(label, packaged(code)))) - !reporter.hasErrors + val (success, run) = compileSourcesKeepingRun(new BatchSourceFile(label, packaged(code))) + updateRecentWarnings(run) + lastRun = run + success } } /** One line of code submitted by the user for interpretation */ - // private + // private class Request(val line: String, val trees: List[Tree]) { - val lineRep = new ReadEvalPrint() - import lineRep.lineAfterTyper - + val reqId = nextReqId() + val lineRep = new ReadEvalPrint() + private var _originalLine: String = null def withOriginalLine(s: String): this.type = { _originalLine = s ; this } def originalLine = if (_originalLine == null) line else _originalLine /** handlers for each tree in this request */ val handlers: List[MemberHandler] = trees map (memberHandlers chooseHandler _) + def defHandlers = handlers collect { case x: MemberDefHandler => x } /** all (public) names defined by these statements */ val definedNames = handlers flatMap (_.definedNames) /** list of names used by this expression */ val referencedNames: List[Name] = handlers flatMap (_.referencedNames) - + /** def and val names */ def termNames = handlers flatMap (_.definesTerm) def typeNames = handlers flatMap (_.definesType) + def definedOrImported = handlers flatMap (_.definedOrImported) + def definedSymbolList = defHandlers flatMap (_.definedSymbols) + + def definedTypeSymbol(name: String) = definedSymbols(newTypeName(name)) + def definedTermSymbol(name: String) = definedSymbols(newTermName(name)) /** Code to import bound names from previous lines - accessPath is code to - * append to objectName to access anything bound by request. - */ - val ComputedImports(importsPreamble, importsTrailer, accessPath) = + * append to objectName to access anything bound by request. + */ + val SparkComputedImports(importsPreamble, importsTrailer, accessPath) = importsCode(referencedNames.toSet) /** Code to access a variable with the specified name */ - def fullPath(vname: String) = ( - //lineRep.readPath + accessPath + ".`%s`".format(vname) + def fullPath(vname: String) = { + // lineRep.readPath + accessPath + ".`%s`".format(vname) lineRep.readPath + ".INSTANCE" + accessPath + ".`%s`".format(vname) - ) - /** Same as fullpath, but after it has been flattened, so: - * $line5.$iw.$iw.$iw.Bippy // fullPath - * $line5.$iw$$iw$$iw$Bippy // fullFlatName - */ - def fullFlatName(name: String) = - lineRep.readPath + accessPath.replace('.', '$') + "$" + name + } + /** Same as fullpath, but after it has been flattened, so: + * $line5.$iw.$iw.$iw.Bippy // fullPath + * $line5.$iw$$iw$$iw$Bippy // fullFlatName + */ + def fullFlatName(name: String) = + // lineRep.readPath + accessPath.replace('.', '$') + nme.NAME_JOIN_STRING + name + lineRep.readPath + ".INSTANCE" + accessPath.replace('.', '$') + nme.NAME_JOIN_STRING + name + + /** The unmangled symbol name, but supplemented with line info. */ + def disambiguated(name: Name): String = name + " (in " + lineRep + ")" /** Code to access a variable with the specified name */ def fullPath(vname: Name): String = fullPath(vname.toString) @@ -726,52 +906,66 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends /** generate the source code for the object that computes this request */ private object ObjectSourceCode extends CodeAssembler[MemberHandler] { + def path = pathToTerm("$intp") + def envLines = { + if (!isReplPower) Nil // power mode only for now + // $intp is not bound; punt, but include the line. + else if (path == "$intp") List( + "def $line = " + tquoted(originalLine), + "def $trees = Nil" + ) + else List( + "def $line = " + tquoted(originalLine), + "def $req = %s.requestForReqId(%s).orNull".format(path, reqId), + "def $trees = if ($req eq null) Nil else $req.trees".format(lineRep.readName, path, reqId) + ) + } + val preamble = """ |class %s extends Serializable { - | %s%s - """.stripMargin.format(lineRep.readName, importsPreamble, indentCode(toCompute)) + | %s%s%s + """.stripMargin.format(lineRep.readName, envLines.map(" " + _ + ";\n").mkString, importsPreamble, indentCode(toCompute)) val postamble = importsTrailer + "\n}" + "\n" + "object " + lineRep.readName + " {\n" + " val INSTANCE = new " + lineRep.readName + "();\n" + "}\n" val generate = (m: MemberHandler) => m extraCodeToEvaluate Request.this + /* val preamble = """ - |object %s { - | %s%s - """.stripMargin.format(lineRep.readName, importsPreamble, indentCode(toCompute)) + |object %s extends Serializable { + |%s%s%s + """.stripMargin.format(lineRep.readName, envLines.map(" " + _ + ";\n").mkString, importsPreamble, indentCode(toCompute)) val postamble = importsTrailer + "\n}" val generate = (m: MemberHandler) => m extraCodeToEvaluate Request.this */ + } - + private object ResultObjectSourceCode extends CodeAssembler[MemberHandler] { /** We only want to generate this code when the result * is a value which can be referred to as-is. - */ + */ val evalResult = if (!handlers.last.definesValue) "" else handlers.last.definesTerm match { case Some(vname) if typeOf contains vname => - """ - |lazy val $result = { - | $export - | %s - |}""".stripMargin.format(fullPath(vname)) + "lazy val %s = %s".format(lineRep.resultName, fullPath(vname)) case _ => "" } + // first line evaluates object to make sure constructor is run // initial "" so later code can uniformly be: + etc val preamble = """ |object %s { | %s - | val $export: String = %s { + | val %s: String = %s { | %s | ("" """.stripMargin.format( - lineRep.evalName, evalResult, executionWrapper, lineRep.readName + ".INSTANCE" + accessPath + lineRep.evalName, evalResult, lineRep.printName, + executionWrapper, lineRep.readName + ".INSTANCE" + accessPath ) - val postamble = """ | ) | } @@ -785,7 +979,7 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends def getEval: Option[AnyRef] = { // ensure it has been compiled compile - // try to load it and call the value method + // try to load it and call the value method lineRep.evalValue filterNot (_ == null) } @@ -797,114 +991,54 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends // compile the object containing the user's code lineRep.compile(ObjectSourceCode(handlers)) && { - // extract and remember types + // extract and remember types typeOf typesOfDefinedTerms + // Assign symbols to the original trees + // TODO - just use the new trees. + defHandlers foreach { dh => + val name = dh.member.name + definedSymbols get name foreach { sym => + dh.member setSymbol sym + logDebug("Set symbol of " + name + " to " + sym.defString) + } + } + // compile the result-extraction object - lineRep compile ResultObjectSourceCode(handlers) + withoutWarnings(lineRep compile ResultObjectSourceCode(handlers)) } } lazy val resultSymbol = lineRep.resolvePathToSymbol(accessPath) - def applyToResultMember[T](name: Name, f: Symbol => T) = lineAfterTyper(f(resultSymbol.info.nonPrivateDecl(name))) + def applyToResultMember[T](name: Name, f: Symbol => T) = afterTyper(f(resultSymbol.info.nonPrivateDecl(name))) /* typeOf lookup with encoding */ - def lookupTypeOf(name: Name) = { - typeOf.getOrElse(name, typeOf(global.encode(name.toString))) - } - def simpleNameOfType(name: TypeName) = { - (compilerTypeOf get name) map (_.typeSymbol.simpleName) - } - - private def typeMap[T](f: Type => T): Map[Name, T] = { - def toType(name: Name): T = { - // the types are all =>T; remove the => - val tp1 = lineAfterTyper(resultSymbol.info.nonPrivateDecl(name).tpe match { - case NullaryMethodType(tp) => tp - case tp => tp - }) - // normalize non-public types so we don't see protected aliases like Self - lineAfterTyper(tp1 match { - case TypeRef(_, sym, _) if !sym.isPublic => f(tp1.normalize) - case tp => f(tp) - }) - } - termNames ++ typeNames map (x => x -> toType(x)) toMap - } + def lookupTypeOf(name: Name) = typeOf.getOrElse(name, typeOf(global.encode(name.toString))) + def simpleNameOfType(name: TypeName) = (compilerTypeOf get name) map (_.typeSymbol.simpleName) + + private def typeMap[T](f: Type => T) = + mapFrom[Name, Name, T](termNames ++ typeNames)(x => f(cleanMemberDecl(resultSymbol, x))) + /** Types of variables defined by this request. */ - lazy val compilerTypeOf = typeMap[Type](x => x) + lazy val compilerTypeOf = typeMap[Type](x => x) withDefaultValue NoType /** String representations of same. */ - lazy val typeOf = typeMap[String](_.toString) - + lazy val typeOf = typeMap[String](tp => afterTyper(tp.toString)) + // lazy val definedTypes: Map[Name, Type] = { // typeNames map (x => x -> afterTyper(resultSymbol.info.nonPrivateDecl(x).tpe)) toMap // } - lazy val definedSymbols: Map[Name, Symbol] = ( + lazy val definedSymbols = ( termNames.map(x => x -> applyToResultMember(x, x => x)) ++ - typeNames.map(x => x -> compilerTypeOf.get(x).map(_.typeSymbol).getOrElse(NoSymbol)) - ).toMap - - lazy val typesOfDefinedTerms: Map[Name, Type] = - termNames map (x => x -> applyToResultMember(x, _.tpe)) toMap - - private def bindExceptionally(t: Throwable) = { - val ex: Exceptional = - if (isettings.showInternalStackTraces) Exceptional(t) - else new Exceptional(t) { - override def spanFn(frame: JavaStackFrame) = !(frame.className startsWith lineRep.evalPath) - override def contextPrelude = super.contextPrelude + "/* The repl internal portion of the stack trace is elided. */\n" - } - - quietBind("lastException", ex) - ex.contextHead + "\n(access lastException for the full trace)" - } - private def bindUnexceptionally(t: Throwable) = { - quietBind("lastException", t) - stackTraceString(t) - } + typeNames.map(x => x -> compilerTypeOf(x).typeSymbolDirect) + ).toMap[Name, Symbol] withDefaultValue NoSymbol + + lazy val typesOfDefinedTerms = mapFrom[Name, Name, Type](termNames)(x => applyToResultMember(x, _.tpe)) /** load and run the code using reflection */ def loadAndRun: (String, Boolean) = { - import interpreter.Line._ - - def handleException(t: Throwable) = { - /** We turn off the binding to accomodate ticket #2817 */ - withoutBindingLastException { - val message = - if (opt.richExes) bindExceptionally(unwrap(t)) - else bindUnexceptionally(unwrap(t)) - - (message, false) - } - } - - try { - val execution = lineManager.set(originalLine) { - // MATEI: set the right SparkEnv for our SparkContext, because - // this execution will happen in a separate thread - val sc = org.apache.spark.repl.Main.interp.sparkContext - if (sc != null && sc.env != null) - SparkEnv.set(sc.env) - // Execute the line - lineRep call "$export" - } - execution.await() - - execution.state match { - case Done => ("" + execution.get(), true) - case Threw => - val ex = execution.caught() - if (isReplDebug) - ex.printStackTrace() - - if (bindLastException) handleException(ex) - else throw ex - case Cancelled => ("Execution interrupted by signal.\n", false) - case Running => ("Execution still running! Seems impossible.", false) - } - } - finally lineManager.clear() + try { ("" + (lineRep call sessionNames.print), true) } + catch { case ex: Throwable => (lineRep.bindError(ex), false) } } override def toString = "Request(line=%s, %s trees)".format(line, trees.size) @@ -922,136 +1056,157 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends case ModuleDef(_, name, _) => name case _ => naming.mostRecentVar }) - - private def requestForName(name: Name): Option[Request] = { + + private var mostRecentWarnings: List[(global.Position, String)] = Nil + def lastWarnings = mostRecentWarnings + + def treesForRequestId(id: Int): List[Tree] = + requestForReqId(id).toList flatMap (_.trees) + + def requestForReqId(id: Int): Option[Request] = + if (executingRequest != null && executingRequest.reqId == id) Some(executingRequest) + else prevRequests find (_.reqId == id) + + def requestForName(name: Name): Option[Request] = { assert(definedNameMap != null, "definedNameMap is null") definedNameMap get name } - private def requestForIdent(line: String): Option[Request] = + def requestForIdent(line: String): Option[Request] = requestForName(newTermName(line)) orElse requestForName(newTypeName(line)) - - def safeClass(name: String): Option[Symbol] = { - try Some(definitions.getClass(newTypeName(name))) - catch { case _: MissingRequirementError => None } - } - def safeModule(name: String): Option[Symbol] = { - try Some(definitions.getModule(newTermName(name))) - catch { case _: MissingRequirementError => None } - } + + def requestHistoryForName(name: Name): List[Request] = + prevRequests.toList.reverse filter (_.definedNames contains name) + def definitionForName(name: Name): Option[MemberHandler] = requestForName(name) flatMap { req => req.handlers find (_.definedNames contains name) } - + def valueOfTerm(id: String): Option[AnyRef] = - requestForIdent(id) flatMap (_.getEval) + requestForName(newTermName(id)) flatMap (_.getEval) def classOfTerm(id: String): Option[JClass] = - valueOfTerm(id) map (_.getClass) + valueOfTerm(id) map (_.getClass) - def typeOfTerm(id: String): Option[Type] = newTermName(id) match { - case nme.ROOTPKG => Some(definitions.RootClass.tpe) - case name => requestForName(name) flatMap (_.compilerTypeOf get name) + def typeOfTerm(id: String): Type = newTermName(id) match { + case nme.ROOTPKG => RootClass.tpe + case name => requestForName(name).fold(NoType: Type)(_ compilerTypeOf name) } + + def symbolOfType(id: String): Symbol = + requestForName(newTypeName(id)).fold(NoSymbol: Symbol)(_ definedTypeSymbol id) + def symbolOfTerm(id: String): Symbol = - requestForIdent(id) flatMap (_.definedSymbols get newTermName(id)) getOrElse NoSymbol + requestForIdent(newTermName(id)).fold(NoSymbol: Symbol)(_ definedTermSymbol id) def runtimeClassAndTypeOfTerm(id: String): Option[(JClass, Type)] = { - for { - clazz <- classOfTerm(id) - tpe <- runtimeTypeOfTerm(id) - nonAnon <- new RichClass(clazz).supers.find(c => !(new RichClass(c).isScalaAnonymous)) - } yield { - (nonAnon, tpe) - } - } - - def runtimeTypeOfTerm(id: String): Option[Type] = { - for { - tpe <- typeOfTerm(id) - clazz <- classOfTerm(id) - val staticSym = tpe.typeSymbol - runtimeSym <- safeClass(clazz.getName) - if runtimeSym != staticSym - if runtimeSym isSubClass staticSym - } yield { - runtimeSym.info + classOfTerm(id) flatMap { clazz => + new RichClass(clazz).supers find(c => !(new RichClass(c).isScalaAnonymous)) map { nonAnon => + (nonAnon, runtimeTypeOfTerm(id)) + } } } - - // XXX literals. - // 1) Identifiers defined in the repl. - // 2) A path loadable via getModule. - // 3) Try interpreting it as an expression. - private var typeOfExpressionDepth = 0 - def typeOfExpression(expr: String): Option[Type] = { - DBG("typeOfExpression(" + expr + ")") - if (typeOfExpressionDepth > 2) { - DBG("Terminating typeOfExpression recursion for expression: " + expr) - return None - } - def asQualifiedImport = { - val name = expr.takeWhile(_ != '.') - importedTermNamed(name) flatMap { sym => - typeOfExpression(sym.fullName + expr.drop(name.length)) - } + def runtimeTypeOfTerm(id: String): Type = { + typeOfTerm(id) andAlso { tpe => + val clazz = classOfTerm(id) getOrElse { return NoType } + val staticSym = tpe.typeSymbol + val runtimeSym = getClassIfDefined(clazz.getName) + + if ((runtimeSym != NoSymbol) && (runtimeSym != staticSym) && (runtimeSym isSubClass staticSym)) + runtimeSym.info + else NoType } - def asModule = safeModule(expr) map (_.tpe) - def asExpr = beSilentDuring { - val lhs = freshInternalVarName() - val line = "lazy val " + lhs + " = { " + expr + " } " - - interpret(line, true) match { - case IR.Success => typeOfExpression(lhs) - case _ => None + } + def cleanMemberDecl(owner: Symbol, member: Name): Type = afterTyper { + normalizeNonPublic { + owner.info.nonPrivateDecl(member).tpe match { + case NullaryMethodType(tp) => tp + case tp => tp } } - - typeOfExpressionDepth += 1 - try typeOfTerm(expr) orElse asModule orElse asExpr orElse asQualifiedImport - finally typeOfExpressionDepth -= 1 } - // def compileAndTypeExpr(expr: String): Option[Typer] = { - // class TyperRun extends Run { - // override def stopPhase(name: String) = name == "superaccessors" - // } - // } - + + object exprTyper extends { + val repl: SparkIMain.this.type = imain + } with SparkExprTyper { } + + def parse(line: String): Option[List[Tree]] = exprTyper.parse(line) + + def symbolOfLine(code: String): Symbol = + exprTyper.symbolOfLine(code) + + def typeOfExpression(expr: String, silent: Boolean = true): Type = + exprTyper.typeOfExpression(expr, silent) + protected def onlyTerms(xs: List[Name]) = xs collect { case x: TermName => x } protected def onlyTypes(xs: List[Name]) = xs collect { case x: TypeName => x } - - def definedTerms = onlyTerms(allDefinedNames) filterNot (x => isInternalVarName(x.toString)) - def definedTypes = onlyTypes(allDefinedNames) - def definedSymbols = prevRequests.toSet flatMap ((x: Request) => x.definedSymbols.values) - + + def definedTerms = onlyTerms(allDefinedNames) filterNot isInternalTermName + def definedTypes = onlyTypes(allDefinedNames) + def definedSymbols = prevRequestList.flatMap(_.definedSymbols.values).toSet[Symbol] + def definedSymbolList = prevRequestList flatMap (_.definedSymbolList) filterNot (s => isInternalTermName(s.name)) + + // Terms with user-given names (i.e. not res0 and not synthetic) + def namedDefinedTerms = definedTerms filterNot (x => isUserVarName("" + x) || directlyBoundNames(x)) + + private def findName(name: Name) = definedSymbols find (_.name == name) getOrElse NoSymbol + + /** Translate a repl-defined identifier into a Symbol. + */ + def apply(name: String): Symbol = + types(name) orElse terms(name) + + def types(name: String): Symbol = { + val tpname = newTypeName(name) + findName(tpname) orElse getClassIfDefined(tpname) + } + def terms(name: String): Symbol = { + val termname = newTypeName(name) + findName(termname) orElse getModuleIfDefined(termname) + } + // [Eugene to Paul] possibly you could make use of TypeTags here + def types[T: ClassTag] : Symbol = types(classTag[T].runtimeClass.getName) + def terms[T: ClassTag] : Symbol = terms(classTag[T].runtimeClass.getName) + def apply[T: ClassTag] : Symbol = apply(classTag[T].runtimeClass.getName) + + def classSymbols = allDefSymbols collect { case x: ClassSymbol => x } + def methodSymbols = allDefSymbols collect { case x: MethodSymbol => x } + /** the previous requests this interpreter has processed */ - private lazy val prevRequests = mutable.ListBuffer[Request]() - private lazy val referencedNameMap = mutable.Map[Name, Request]() - private lazy val definedNameMap = mutable.Map[Name, Request]() - protected def prevRequestList = prevRequests.toList - private def allHandlers = prevRequestList flatMap (_.handlers) - def allSeenTypes = prevRequestList flatMap (_.typeOf.values.toList) distinct - def allImplicits = allHandlers filter (_.definesImplicit) flatMap (_.definedNames) - def importHandlers = allHandlers collect { case x: ImportHandler => x } - + private var executingRequest: Request = _ + private val prevRequests = mutable.ListBuffer[Request]() + private val referencedNameMap = mutable.Map[Name, Request]() + private val definedNameMap = mutable.Map[Name, Request]() + private val directlyBoundNames = mutable.Set[Name]() + + def allHandlers = prevRequestList flatMap (_.handlers) + def allDefHandlers = allHandlers collect { case x: MemberDefHandler => x } + def allDefSymbols = allDefHandlers map (_.symbol) filter (_ ne NoSymbol) + + def lastRequest = if (prevRequests.isEmpty) null else prevRequests.last + def prevRequestList = prevRequests.toList + def allSeenTypes = prevRequestList flatMap (_.typeOf.values.toList) distinct + def allImplicits = allHandlers filter (_.definesImplicit) flatMap (_.definedNames) + def importHandlers = allHandlers collect { case x: ImportHandler => x } + def visibleTermNames: List[Name] = definedTerms ++ importedTerms distinct /** Another entry point for tab-completion, ids in scope */ def unqualifiedIds = visibleTermNames map (_.toString) filterNot (_ contains "$") sorted - + /** Parse the ScalaSig to find type aliases */ def aliasForType(path: String) = ByteCode.aliasForType(path) - + def withoutUnwrapping(op: => Unit): Unit = { val saved = isettings.unwrapStrings isettings.unwrapStrings = false try op finally isettings.unwrapStrings = saved } - + def symbolDefString(sym: Symbol) = { TypeStrings.quieter( afterTyper(sym.defString), @@ -1059,38 +1214,41 @@ class SparkIMain(val settings: Settings, protected val out: PrintWriter) extends sym.owner.fullName + "." ) } - + def showCodeIfDebugging(code: String) { /** Secret bookcase entrance for repl debuggers: end the line * with "// show" and see what's going on. */ - if (SPARK_DEBUG_REPL || code.lines.exists(_.trim endsWith "// show")) { - echo(code) - parse(code) foreach (ts => ts foreach (t => withoutUnwrapping(DBG(asCompactString(t))))) + def isShow = code.lines exists (_.trim endsWith "// show") + def isShowRaw = code.lines exists (_.trim endsWith "// raw") + + // old style + beSilentDuring(parse(code)) foreach { ts => + ts foreach { t => + withoutUnwrapping(logDebug(asCompactString(t))) + } } } + // debugging def debugging[T](msg: String)(res: T) = { - DBG(msg + " " + res) + logDebug(msg + " " + res) res } - def DBG(s: => String) = if (isReplDebug) { - //try repldbg(s) - //catch { case x: AssertionError => repldbg("Assertion error printing debug string:\n " + x) } - } } /** Utility methods for the Interpreter. */ object SparkIMain { // The two name forms this is catching are the two sides of this assignment: // - // $line3.$read.$iw.$iw.Bippy = + // $line3.$read.$iw.$iw.Bippy = // $line3.$read$$iw$$iw$Bippy@4a6a00ca private def removeLineWrapper(s: String) = s.replaceAll("""\$line\d+[./]\$(read|eval|print)[$.]""", "") private def removeIWPackages(s: String) = s.replaceAll("""\$(iw|iwC|read|eval|print)[$.]""", "") private def removeSparkVals(s: String) = s.replaceAll("""\$VAL[0-9]+[$.]""", "") + def stripString(s: String) = removeSparkVals(removeIWPackages(removeLineWrapper(s))) - + trait CodeAssembler[T] { def preamble: String def generate: T => String @@ -1102,7 +1260,7 @@ object SparkIMain { code println postamble } } - + trait StrippingWriter { def isStripping: Boolean def stripImpl(str: String): String @@ -1112,17 +1270,17 @@ object SparkIMain { def maxStringLength: Int def isTruncating: Boolean def truncate(str: String): String = { - if (isTruncating && str.length > maxStringLength) + if (isTruncating && (maxStringLength != 0 && str.length > maxStringLength)) (str take maxStringLength - 3) + "..." else str } } - abstract class StrippingTruncatingWriter(out: PrintWriter) - extends PrintWriter(out) + abstract class StrippingTruncatingWriter(out: JPrintWriter) + extends JPrintWriter(out) with StrippingWriter with TruncatingWriter { self => - + def clean(str: String): String = truncate(strip(str)) override def write(str: String) = super.write(clean(str)) } @@ -1132,18 +1290,7 @@ object SparkIMain { def isStripping = isettings.unwrapStrings def isTruncating = reporter.truncationOK - def stripImpl(str: String): String = { - val cleaned = stripString(str) - var ctrlChars = 0 - cleaned map { ch => - if (ch.isControl && !ch.isWhitespace) { - ctrlChars += 1 - if (ctrlChars > 5) return "[line elided for control chars: possibly a scala signature]" - else '?' - } - else ch - } - } + def stripImpl(str: String): String = naming.unmangle(str) } class ReplReporter(intp: SparkIMain) extends ConsoleReporter(intp.settings, null, new ReplStrippingWriter(intp)) { @@ -1156,5 +1303,55 @@ object SparkIMain { } else Console.println(msg) } - } + } +} + +class SparkISettings(intp: SparkIMain) extends Logging { + /** A list of paths where :load should look */ + var loadPath = List(".") + + /** Set this to true to see repl machinery under -Yrich-exceptions. + */ + var showInternalStackTraces = false + + /** The maximum length of toString to use when printing the result + * of an evaluation. 0 means no maximum. If a printout requires + * more than this number of characters, then the printout is + * truncated. + */ + var maxPrintString = 800 + + /** The maximum number of completion candidates to print for tab + * completion without requiring confirmation. + */ + var maxAutoprintCompletion = 250 + + /** String unwrapping can be disabled if it is causing issues. + * Settings this to false means you will see Strings like "$iw.$iw.". + */ + var unwrapStrings = true + + def deprecation_=(x: Boolean) = { + val old = intp.settings.deprecation.value + intp.settings.deprecation.value = x + if (!old && x) logDebug("Enabled -deprecation output.") + else if (old && !x) logDebug("Disabled -deprecation output.") + } + + def deprecation: Boolean = intp.settings.deprecation.value + + def allSettings = Map( + "maxPrintString" -> maxPrintString, + "maxAutoprintCompletion" -> maxAutoprintCompletion, + "unwrapStrings" -> unwrapStrings, + "deprecation" -> deprecation + ) + + private def allSettingsString = + allSettings.toList sortBy (_._1) map { case (k, v) => " " + k + " = " + v + "\n" } mkString + + override def toString = """ + | SparkISettings { + | %s + | }""".stripMargin.format(allSettingsString) } diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkISettings.scala b/repl/src/main/scala/org/apache/spark/repl/SparkISettings.scala deleted file mode 100644 index 605b7b259b..0000000000 --- a/repl/src/main/scala/org/apache/spark/repl/SparkISettings.scala +++ /dev/null @@ -1,63 +0,0 @@ -/* NSC -- new Scala compiler - * Copyright 2005-2011 LAMP/EPFL - * @author Alexander Spoon - */ - -package org.apache.spark.repl - -import scala.tools.nsc._ -import scala.tools.nsc.interpreter._ - -/** Settings for the interpreter - * - * @version 1.0 - * @author Lex Spoon, 2007/3/24 - **/ -class SparkISettings(intp: SparkIMain) { - /** A list of paths where :load should look */ - var loadPath = List(".") - - /** Set this to true to see repl machinery under -Yrich-exceptions. - */ - var showInternalStackTraces = false - - /** The maximum length of toString to use when printing the result - * of an evaluation. 0 means no maximum. If a printout requires - * more than this number of characters, then the printout is - * truncated. - */ - var maxPrintString = 800 - - /** The maximum number of completion candidates to print for tab - * completion without requiring confirmation. - */ - var maxAutoprintCompletion = 250 - - /** String unwrapping can be disabled if it is causing issues. - * Settings this to false means you will see Strings like "$iw.$iw.". - */ - var unwrapStrings = true - - def deprecation_=(x: Boolean) = { - val old = intp.settings.deprecation.value - intp.settings.deprecation.value = x - if (!old && x) println("Enabled -deprecation output.") - else if (old && !x) println("Disabled -deprecation output.") - } - def deprecation: Boolean = intp.settings.deprecation.value - - def allSettings = Map( - "maxPrintString" -> maxPrintString, - "maxAutoprintCompletion" -> maxAutoprintCompletion, - "unwrapStrings" -> unwrapStrings, - "deprecation" -> deprecation - ) - - private def allSettingsString = - allSettings.toList sortBy (_._1) map { case (k, v) => " " + k + " = " + v + "\n" } mkString - - override def toString = """ - | SparkISettings { - | %s - | }""".stripMargin.format(allSettingsString) -} diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala b/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala index 41a1731d60..64084209e8 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkImports.scala @@ -1,5 +1,5 @@ /* NSC -- new Scala compiler - * Copyright 2005-2011 LAMP/EPFL + * Copyright 2005-2013 LAMP/EPFL * @author Paul Phillips */ @@ -12,30 +12,34 @@ import scala.collection.{ mutable, immutable } trait SparkImports { self: SparkIMain => - + import global._ import definitions.{ ScalaPackage, JavaLangPackage, PredefModule } import memberHandlers._ - + + def isNoImports = settings.noimports.value + def isNoPredef = settings.nopredef.value + /** Synthetic import handlers for the language defined imports. */ private def makeWildcardImportHandler(sym: Symbol): ImportHandler = { val hd :: tl = sym.fullName.split('.').toList map newTermName val tree = Import( tl.foldLeft(Ident(hd): Tree)((x, y) => Select(x, y)), - List(ImportSelector(nme.WILDCARD, -1, null, -1)) + ImportSelector.wildList ) tree setSymbol sym new ImportHandler(tree) } - + /** Symbols whose contents are language-defined to be imported. */ def languageWildcardSyms: List[Symbol] = List(JavaLangPackage, ScalaPackage, PredefModule) def languageWildcards: List[Type] = languageWildcardSyms map (_.tpe) def languageWildcardHandlers = languageWildcardSyms map makeWildcardImportHandler - - def importedTerms = onlyTerms(importHandlers flatMap (_.importedNames)) - def importedTypes = onlyTypes(importHandlers flatMap (_.importedNames)) - + + def allImportedNames = importHandlers flatMap (_.importedNames) + def importedTerms = onlyTerms(allImportedNames) + def importedTypes = onlyTypes(allImportedNames) + /** Types which have been wildcard imported, such as: * val x = "abc" ; import x._ // type java.lang.String * import java.lang.String._ // object java.lang.String @@ -48,30 +52,28 @@ trait SparkImports { * into the compiler scopes. */ def sessionWildcards: List[Type] = { - importHandlers flatMap { - case x if x.importsWildcard => x.targetType - case _ => None - } distinct + importHandlers filter (_.importsWildcard) map (_.targetType) distinct } def wildcardTypes = languageWildcards ++ sessionWildcards - + def languageSymbols = languageWildcardSyms flatMap membersAtPickler def sessionImportedSymbols = importHandlers flatMap (_.importedSymbols) def importedSymbols = languageSymbols ++ sessionImportedSymbols def importedTermSymbols = importedSymbols collect { case x: TermSymbol => x } def importedTypeSymbols = importedSymbols collect { case x: TypeSymbol => x } def implicitSymbols = importedSymbols filter (_.isImplicit) - - def importedTermNamed(name: String) = importedTermSymbols find (_.name.toString == name) + + def importedTermNamed(name: String): Symbol = + importedTermSymbols find (_.name.toString == name) getOrElse NoSymbol /** Tuples of (source, imported symbols) in the order they were imported. */ def importedSymbolsBySource: List[(Symbol, List[Symbol])] = { val lang = languageWildcardSyms map (sym => (sym, membersAtPickler(sym))) - val session = importHandlers filter (_.targetType.isDefined) map { mh => - (mh.targetType.get.typeSymbol, mh.importedSymbols) + val session = importHandlers filter (_.targetType != NoType) map { mh => + (mh.targetType.typeSymbol, mh.importedSymbols) } - + lang ++ session } def implicitSymbolsBySource: List[(Symbol, List[Symbol])] = { @@ -79,7 +81,7 @@ trait SparkImports { case (k, vs) => (k, vs filter (_.isImplicit)) } filterNot (_._2.isEmpty) } - + /** Compute imports that allow definitions from previous * requests to be visible in a new request. Returns * three pieces of related code: @@ -90,7 +92,7 @@ trait SparkImports { * 2. A code fragment that should go after the code * of the new request. * - * 3. An access path which can be traverested to access + * 3. An access path which can be traversed to access * any bindings inside code wrapped by #1 and #2 . * * The argument is a set of Names that need to be imported. @@ -103,27 +105,27 @@ trait SparkImports { * (3) It imports multiple same-named implicits, but only the * last one imported is actually usable. */ - case class ComputedImports(prepend: String, append: String, access: String) - protected def importsCode(wanted: Set[Name]): ComputedImports = { - /** Narrow down the list of requests from which imports + case class SparkComputedImports(prepend: String, append: String, access: String) + + protected def importsCode(wanted: Set[Name]): SparkComputedImports = { + /** Narrow down the list of requests from which imports * should be taken. Removes requests which cannot contribute * useful imports for the specified set of wanted names. */ case class ReqAndHandler(req: Request, handler: MemberHandler) { } - - def reqsToUse: List[ReqAndHandler] = { + + def reqsToUse: List[ReqAndHandler] = { /** Loop through a list of MemberHandlers and select which ones to keep. * 'wanted' is the set of names that need to be imported. */ def select(reqs: List[ReqAndHandler], wanted: Set[Name]): List[ReqAndHandler] = { - val isWanted = wanted contains _ // Single symbol imports might be implicits! See bug #1752. Rather than // try to finesse this, we will mimic all imports for now. def keepHandler(handler: MemberHandler) = handler match { case _: ImportHandler => true - case x => x.definesImplicit || (x.definedNames exists isWanted) + case x => x.definesImplicit || (x.definedNames exists wanted) } - + reqs match { case Nil => Nil case rh :: rest if !keepHandler(rh.handler) => select(rest, wanted) @@ -133,7 +135,7 @@ trait SparkImports { rh :: select(rest, newWanted) } } - + /** Flatten the handlers out and pair each with the original request */ select(allReqAndHandlers reverseMap { case (r, h) => ReqAndHandler(r, h) }, wanted).reverse } @@ -147,8 +149,13 @@ trait SparkImports { code append "class %sC extends Serializable {\n".format(impname) trailingBraces append "}\nval " + impname + " = new " + impname + "C;\n" accessPath append ("." + impname) - + currentImps.clear + // code append "object %s {\n".format(impname) + // trailingBraces append "}\n" + // accessPath append ("." + impname) + + // currentImps.clear } addWrapper() @@ -159,36 +166,33 @@ trait SparkImports { // If the user entered an import, then just use it; add an import wrapping // level if the import might conflict with some other import case x: ImportHandler => - if (x.importsWildcard || (currentImps exists (x.importedNames contains _))) + if (x.importsWildcard || currentImps.exists(x.importedNames contains _)) addWrapper() - + code append (x.member + "\n") - + // give wildcard imports a import wrapper all to their own if (x.importsWildcard) addWrapper() else currentImps ++= x.importedNames // For other requests, import each defined name. // import them explicitly instead of with _, so that - // ambiguity errors will not be generated. Also, quote - // the name of the variable, so that we don't need to - // handle quoting keywords separately. + // ambiguity errors will not be generated. Also, quote + // the name of the variable, so that we don't need to + // handle quoting keywords separately. case x => for (imv <- x.definedNames) { - // MATEI: Changed this check because it was messing up for case classes - // (trying to import them twice within the same wrapper), but that is more likely - // due to a miscomputation of names that makes the code think they're unique. - // Need to evaluate whether having so many wrappers is a bad thing. - /*if (currentImps contains imv)*/ - val imvName = imv.toString - if (currentImps exists (_.toString == imvName)) addWrapper() - + if (currentImps contains imv) addWrapper() val objName = req.lineRep.readPath val valName = "$VAL" + newValId(); - code.append("val " + valName + " = " + objName + ".INSTANCE;\n") - code.append("import " + valName + req.accessPath + ".`" + imv + "`;\n") - - //code append ("import %s\n" format (req fullPath imv)) + + if(!code.toString.endsWith(".`" + imv + "`;\n")) { // Which means already imported + code.append("val " + valName + " = " + objName + ".INSTANCE;\n") + code.append("import " + valName + req.accessPath + ".`" + imv + "`;\n") + } + // code.append("val " + valName + " = " + objName + ".INSTANCE;\n") + // code.append("import " + valName + req.accessPath + ".`" + imv + "`;\n") + // code append ("import " + (req fullPath imv) + "\n") currentImps += imv } } @@ -196,14 +200,14 @@ trait SparkImports { // add one extra wrapper, to prevent warnings in the common case of // redefining the value bound in the last interpreter request. addWrapper() - ComputedImports(code.toString, trailingBraces.toString, accessPath.toString) + SparkComputedImports(code.toString, trailingBraces.toString, accessPath.toString) } - + private def allReqAndHandlers = prevRequestList flatMap (req => req.handlers map (req -> _)) private def membersAtPickler(sym: Symbol): List[Symbol] = - atPickler(sym.info.nonPrivateMembers) + beforePickler(sym.info.nonPrivateMembers.toList) private var curValId = 0 diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkJLineCompletion.scala b/repl/src/main/scala/org/apache/spark/repl/SparkJLineCompletion.scala index fdc172d753..8865f82bc0 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkJLineCompletion.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkJLineCompletion.scala @@ -1,5 +1,5 @@ /* NSC -- new Scala compiler - * Copyright 2005-2011 LAMP/EPFL + * Copyright 2005-2013 LAMP/EPFL * @author Paul Phillips */ @@ -11,30 +11,31 @@ import scala.tools.nsc.interpreter._ import scala.tools.jline._ import scala.tools.jline.console.completer._ import Completion._ -import collection.mutable.ListBuffer +import scala.collection.mutable.ListBuffer +import org.apache.spark.Logging // REPL completor - queries supplied interpreter for valid // completions based on current contents of buffer. -class SparkJLineCompletion(val intp: SparkIMain) extends Completion with CompletionOutput { +class SparkJLineCompletion(val intp: SparkIMain) extends Completion with CompletionOutput with Logging { val global: intp.global.type = intp.global import global._ - import definitions.{ PredefModule, RootClass, AnyClass, AnyRefClass, ScalaPackage, JavaLangPackage } + import definitions.{ PredefModule, AnyClass, AnyRefClass, ScalaPackage, JavaLangPackage } + import rootMirror.{ RootClass, getModuleIfDefined } type ExecResult = Any - import intp.{ DBG, debugging, afterTyper } - + import intp.{ debugging } + // verbosity goes up with consecutive tabs private var verbosity: Int = 0 def resetVerbosity() = verbosity = 0 - - def getType(name: String, isModule: Boolean) = { - val f = if (isModule) definitions.getModule(_: Name) else definitions.getClass(_: Name) - try Some(f(name).tpe) - catch { case _: MissingRequirementError => None } - } - - def typeOf(name: String) = getType(name, false) - def moduleOf(name: String) = getType(name, true) - + + def getSymbol(name: String, isModule: Boolean) = ( + if (isModule) getModuleIfDefined(name) + else getModuleIfDefined(name) + ) + def getType(name: String, isModule: Boolean) = getSymbol(name, isModule).tpe + def typeOf(name: String) = getType(name, false) + def moduleOf(name: String) = getType(name, true) + trait CompilerCompletion { def tp: Type def effectiveTp = tp match { @@ -48,16 +49,16 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet private def anyMembers = AnyClass.tpe.nonPrivateMembers def anyRefMethodsToShow = Set("isInstanceOf", "asInstanceOf", "toString") - def tos(sym: Symbol) = sym.name.decode.toString - def memberNamed(s: String) = members find (x => tos(x) == s) - def hasMethod(s: String) = methods exists (x => tos(x) == s) + def tos(sym: Symbol): String = sym.decodedName + def memberNamed(s: String) = afterTyper(effectiveTp member newTermName(s)) + def hasMethod(s: String) = memberNamed(s).isMethod // XXX we'd like to say "filterNot (_.isDeprecated)" but this causes the // compiler to crash for reasons not yet known. - def members = afterTyper((effectiveTp.nonPrivateMembers ++ anyMembers) filter (_.isPublic)) - def methods = members filter (_.isMethod) - def packages = members filter (_.isPackage) - def aliases = members filter (_.isAliasType) + def members = afterTyper((effectiveTp.nonPrivateMembers.toList ++ anyMembers) filter (_.isPublic)) + def methods = members.toList filter (_.isMethod) + def packages = members.toList filter (_.isPackage) + def aliases = members.toList filter (_.isAliasType) def memberNames = members map tos def methodNames = methods map tos @@ -65,12 +66,19 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet def aliasNames = aliases map tos } + object NoTypeCompletion extends TypeMemberCompletion(NoType) { + override def memberNamed(s: String) = NoSymbol + override def members = Nil + override def follow(s: String) = None + override def alternativesFor(id: String) = Nil + } + object TypeMemberCompletion { def apply(tp: Type, runtimeType: Type, param: NamedParam): TypeMemberCompletion = { new TypeMemberCompletion(tp) { var upgraded = false lazy val upgrade = { - intp rebind param + intp rebind param intp.reporter.printMessage("\nRebinding stable value %s from %s to %s".format(param.name, tp, param.tpe)) upgraded = true new TypeMemberCompletion(runtimeType) @@ -92,7 +100,8 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet } } def apply(tp: Type): TypeMemberCompletion = { - if (tp.typeSymbol.isPackageClass) new PackageCompletion(tp) + if (tp eq NoType) NoTypeCompletion + else if (tp.typeSymbol.isPackageClass) new PackageCompletion(tp) else new TypeMemberCompletion(tp) } def imported(tp: Type) = new ImportCompletion(tp) @@ -103,9 +112,9 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet def excludeEndsWith: List[String] = Nil def excludeStartsWith: List[String] = List("<") // <byname>, <repeated>, etc. def excludeNames: List[String] = (anyref.methodNames filterNot anyRefMethodsToShow) :+ "_root_" - + def methodSignatureString(sym: Symbol) = { - SparkIMain stripString afterTyper(new MethodSymbolOutput(sym).methodString()) + IMain stripString afterTyper(new MethodSymbolOutput(sym).methodString()) } def exclude(name: String): Boolean = ( @@ -118,10 +127,10 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet def completions(verbosity: Int) = debugging(tp + " completions ==> ")(filtered(memberNames)) - + override def follow(s: String): Option[CompletionAware] = - debugging(tp + " -> '" + s + "' ==> ")(memberNamed(s) map (x => TypeMemberCompletion(x.tpe))) - + debugging(tp + " -> '" + s + "' ==> ")(Some(TypeMemberCompletion(memberNamed(s).tpe)) filterNot (_ eq NoTypeCompletion)) + override def alternativesFor(id: String): List[String] = debugging(id + " alternatives ==> ") { val alts = members filter (x => x.isMethod && tos(x) == id) map methodSignatureString @@ -131,7 +140,7 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet override def toString = "%s (%d members)".format(tp, members.size) } - + class PackageCompletion(tp: Type) extends TypeMemberCompletion(tp) { override def excludeNames = anyref.methodNames } @@ -142,43 +151,44 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet case _ => memberNames } } - + class ImportCompletion(tp: Type) extends TypeMemberCompletion(tp) { override def completions(verbosity: Int) = verbosity match { case 0 => filtered(members filterNot (_.isSetter) map tos) case _ => super.completions(verbosity) } } - + // not for completion but for excluding object anyref extends TypeMemberCompletion(AnyRefClass.tpe) { } - + // the unqualified vals/defs/etc visible in the repl object ids extends CompletionAware { override def completions(verbosity: Int) = intp.unqualifiedIds ++ List("classOf") //, "_root_") // now we use the compiler for everything. - override def follow(id: String) = { - if (completions(0) contains id) { - intp typeOfExpression id map { tpe => - def default = TypeMemberCompletion(tpe) - - // only rebinding vals in power mode for now. - if (!isReplPower) default - else intp runtimeClassAndTypeOfTerm id match { - case Some((clazz, runtimeType)) => - val sym = intp.symbolOfTerm(id) - if (sym.isStable) { - val param = new NamedParam.Untyped(id, intp valueOfTerm id getOrElse null) - TypeMemberCompletion(tpe, runtimeType, param) - } - else default - case _ => - default + override def follow(id: String): Option[CompletionAware] = { + if (!completions(0).contains(id)) + return None + + val tpe = intp typeOfExpression id + if (tpe == NoType) + return None + + def default = Some(TypeMemberCompletion(tpe)) + + // only rebinding vals in power mode for now. + if (!isReplPower) default + else intp runtimeClassAndTypeOfTerm id match { + case Some((clazz, runtimeType)) => + val sym = intp.symbolOfTerm(id) + if (sym.isStable) { + val param = new NamedParam.Untyped(id, intp valueOfTerm id getOrElse null) + Some(TypeMemberCompletion(tpe, runtimeType, param)) } - } + else default + case _ => + default } - else - None } override def toString = "<repl ids> (%s)".format(completions(0).size) } @@ -187,17 +197,10 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet private def imported = intp.sessionWildcards map TypeMemberCompletion.imported // literal Ints, Strings, etc. - object literals extends CompletionAware { - def simpleParse(code: String): Tree = { - val unit = new CompilationUnit(new util.BatchSourceFile("<console>", code)) - val scanner = new syntaxAnalyzer.UnitParser(unit) - val tss = scanner.templateStatSeq(false)._2 - - if (tss.size == 1) tss.head else EmptyTree - } - + object literals extends CompletionAware { + def simpleParse(code: String): Tree = newUnitParser(code).templateStats().last def completions(verbosity: Int) = Nil - + override def follow(id: String) = simpleParse(id) match { case x: Literal => Some(new LiteralCompletion(x)) case _ => None @@ -210,18 +213,18 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet override def follow(id: String) = id match { case "_root_" => Some(this) case _ => super.follow(id) - } + } } // members of Predef object predef extends TypeMemberCompletion(PredefModule.tpe) { override def excludeEndsWith = super.excludeEndsWith ++ List("Wrapper", "ArrayOps") override def excludeStartsWith = super.excludeStartsWith ++ List("wrap") override def excludeNames = anyref.methodNames - + override def exclude(name: String) = super.exclude(name) || ( (name contains "2") ) - + override def completions(verbosity: Int) = verbosity match { case 0 => Nil case _ => super.completions(verbosity) @@ -234,7 +237,7 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet override def exclude(name: String) = super.exclude(name) || ( skipArity(name) ) - + override def completions(verbosity: Int) = verbosity match { case 0 => filtered(packageNames ++ aliasNames) case _ => super.completions(verbosity) @@ -244,7 +247,7 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet object javalang extends PackageCompletion(JavaLangPackage.tpe) { override lazy val excludeEndsWith = super.excludeEndsWith ++ List("Exception", "Error") override lazy val excludeStartsWith = super.excludeStartsWith ++ List("CharacterData") - + override def completions(verbosity: Int) = verbosity match { case 0 => filtered(packageNames) case _ => super.completions(verbosity) @@ -256,7 +259,7 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet lazy val topLevelBase: List[CompletionAware] = List(ids, rootClass, predef, scalalang, javalang, literals) def topLevel = topLevelBase ++ imported def topLevelThreshold = 50 - + // the first tier of top level objects (doesn't include file completion) def topLevelFor(parsed: Parsed): List[String] = { val buf = new ListBuffer[String] @@ -280,19 +283,6 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet if (parsed.isEmpty) xs map ("." + _) else xs } - // chasing down results which won't parse - def execute(line: String): Option[ExecResult] = { - val parsed = Parsed(line) - def noDotOrSlash = line forall (ch => ch != '.' && ch != '/') - - if (noDotOrSlash) None // we defer all unqualified ids to the repl. - else { - (ids executionFor parsed) orElse - (rootClass executionFor parsed) orElse - (FileCompletion executionFor line) - } - } - // generic interface for querying (e.g. interpreter loop, testing) def completions(buf: String): List[String] = topLevelFor(Parsed.dotted(buf + ".", buf.length + 1)) @@ -327,11 +317,11 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet // This is jline's entry point for completion. override def complete(buf: String, cursor: Int): Candidates = { verbosity = if (isConsecutiveTabs(buf, cursor)) verbosity + 1 else 0 - DBG("\ncomplete(%s, %d) last = (%s, %d), verbosity: %s".format(buf, cursor, lastBuf, lastCursor, verbosity)) + logDebug("\ncomplete(%s, %d) last = (%s, %d), verbosity: %s".format(buf, cursor, lastBuf, lastCursor, verbosity)) // we don't try lower priority completions unless higher ones return no results. def tryCompletion(p: Parsed, completionFunction: Parsed => List[String]): Option[Candidates] = { - val winners = completionFunction(p) + val winners = completionFunction(p) if (winners.isEmpty) return None val newCursor = @@ -340,39 +330,45 @@ class SparkJLineCompletion(val intp: SparkIMain) extends Completion with Complet val advance = commonPrefix(winners) lastCursor = p.position + advance.length lastBuf = (buf take p.position) + advance - DBG("tryCompletion(%s, _) lastBuf = %s, lastCursor = %s, p.position = %s".format( + logDebug("tryCompletion(%s, _) lastBuf = %s, lastCursor = %s, p.position = %s".format( p, lastBuf, lastCursor, p.position)) p.position } - + Some(Candidates(newCursor, winners)) } - + def mkDotted = Parsed.dotted(buf, cursor) withVerbosity verbosity def mkUndelimited = Parsed.undelimited(buf, cursor) withVerbosity verbosity // a single dot is special cased to completion on the previous result def lastResultCompletion = - if (!looksLikeInvocation(buf)) None + if (!looksLikeInvocation(buf)) None else tryCompletion(Parsed.dotted(buf drop 1, cursor), lastResultFor) - def regularCompletion = tryCompletion(mkDotted, topLevelFor) - def fileCompletion = - if (!looksLikePath(buf)) None - else tryCompletion(mkUndelimited, FileCompletion completionsFor _.buffer) - - /** This is the kickoff point for all manner of theoretically possible compiler - * unhappiness - fault may be here or elsewhere, but we don't want to crash the - * repl regardless. Hopefully catching Exception is enough, but because the - * compiler still throws some Errors it may not be. + def tryAll = ( + lastResultCompletion + orElse tryCompletion(mkDotted, topLevelFor) + getOrElse Candidates(cursor, Nil) + ) + + /** + * This is the kickoff point for all manner of theoretically + * possible compiler unhappiness. The fault may be here or + * elsewhere, but we don't want to crash the repl regardless. + * The compiler makes it impossible to avoid catching Throwable + * with its unfortunate tendency to throw java.lang.Errors and + * AssertionErrors as the hats drop. We take two swings at it + * because there are some spots which like to throw an assertion + * once, then work after that. Yeah, what can I say. */ - try { - (lastResultCompletion orElse regularCompletion orElse fileCompletion) getOrElse Candidates(cursor, Nil) - } - catch { - case ex: Exception => - DBG("Error: complete(%s, %s) provoked %s".format(buf, cursor, ex)) - Candidates(cursor, List(" ", "<completion error: " + ex.getMessage + ">")) + try tryAll + catch { case ex: Throwable => + logWarning("Error: complete(%s, %s) provoked".format(buf, cursor) + ex) + Candidates(cursor, + if (isReplDebug) List("<error:" + ex + ">") + else Nil + ) } } } diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkJLineReader.scala b/repl/src/main/scala/org/apache/spark/repl/SparkJLineReader.scala index d9e1de105c..60a4d7841e 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkJLineReader.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkJLineReader.scala @@ -1,5 +1,5 @@ /* NSC -- new Scala compiler - * Copyright 2005-2011 LAMP/EPFL + * Copyright 2005-2013 LAMP/EPFL * @author Stepan Koltsov */ @@ -15,28 +15,33 @@ import scala.collection.JavaConverters._ import Completion._ import io.Streamable.slurp -/** Reads from the console using JLine */ -class SparkJLineReader(val completion: Completion) extends InteractiveReader { +/** + * Reads from the console using JLine. + */ +class SparkJLineReader(_completion: => Completion) extends InteractiveReader { val interactive = true + val consoleReader = new JLineConsoleReader() + + lazy val completion = _completion lazy val history: JLineHistory = JLineHistory() - lazy val keyBindings = - try KeyBinding parse slurp(term.getDefaultBindings) - catch { case _: Exception => Nil } private def term = consoleReader.getTerminal() def reset() = term.reset() def init() = term.init() - + def scalaToJline(tc: ScalaCompleter): Completer = new Completer { def complete(_buf: String, cursor: Int, candidates: JList[CharSequence]): Int = { - val buf = if (_buf == null) "" else _buf + val buf = if (_buf == null) "" else _buf val Candidates(newCursor, newCandidates) = tc.complete(buf, cursor) newCandidates foreach (candidates add _) newCursor } } - + class JLineConsoleReader extends ConsoleReader with ConsoleReaderHelper { + if ((history: History) ne NoHistory) + this setHistory history + // working around protected/trait/java insufficiencies. def goBack(num: Int): Unit = back(num) def readOneKey(prompt: String) = { @@ -46,34 +51,28 @@ class SparkJLineReader(val completion: Completion) extends InteractiveReader { } def eraseLine() = consoleReader.resetPromptLine("", "", 0) def redrawLineAndFlush(): Unit = { flush() ; drawLine() ; flush() } - - this setBellEnabled false - if (history ne NoHistory) - this setHistory history - - if (completion ne NoCompletion) { - val argCompletor: ArgumentCompleter = - new ArgumentCompleter(new JLineDelimiter, scalaToJline(completion.completer())) - argCompletor setStrict false - - this addCompleter argCompletor - this setAutoprintThreshold 400 // max completion candidates without warning + // override def readLine(prompt: String): String + + // A hook for running code after the repl is done initializing. + lazy val postInit: Unit = { + this setBellEnabled false + + if (completion ne NoCompletion) { + val argCompletor: ArgumentCompleter = + new ArgumentCompleter(new JLineDelimiter, scalaToJline(completion.completer())) + argCompletor setStrict false + + this addCompleter argCompletor + this setAutoprintThreshold 400 // max completion candidates without warning + } } } - - val consoleReader: JLineConsoleReader = new JLineConsoleReader() - def currentLine: String = consoleReader.getCursorBuffer.buffer.toString + def currentLine = consoleReader.getCursorBuffer.buffer.toString def redrawLine() = consoleReader.redrawLineAndFlush() - def eraseLine() = { - while (consoleReader.delete()) { } - // consoleReader.eraseLine() - } + def eraseLine() = consoleReader.eraseLine() + // Alternate implementation, not sure if/when I need this. + // def eraseLine() = while (consoleReader.delete()) { } def readOneLine(prompt: String) = consoleReader readLine prompt def readOneKey(prompt: String) = consoleReader readOneKey prompt } - -object SparkJLineReader { - def apply(intp: SparkIMain): SparkJLineReader = apply(new SparkJLineCompletion(intp)) - def apply(comp: Completion): SparkJLineReader = new SparkJLineReader(comp) -} diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkMemberHandlers.scala b/repl/src/main/scala/org/apache/spark/repl/SparkMemberHandlers.scala index a3409bf665..382f8360a7 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkMemberHandlers.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkMemberHandlers.scala @@ -1,5 +1,5 @@ /* NSC -- new Scala compiler - * Copyright 2005-2011 LAMP/EPFL + * Copyright 2005-2013 LAMP/EPFL * @author Martin Odersky */ @@ -10,13 +10,14 @@ import scala.tools.nsc.interpreter._ import scala.collection.{ mutable, immutable } import scala.PartialFunction.cond -import scala.reflect.NameTransformer -import util.Chars +import scala.reflect.internal.Chars +import scala.reflect.internal.Flags._ +import scala.language.implicitConversions trait SparkMemberHandlers { val intp: SparkIMain - import intp.{ Request, global, naming, atPickler } + import intp.{ Request, global, naming } import global._ import naming._ @@ -29,7 +30,7 @@ trait SparkMemberHandlers { front + (xs map string2codeQuoted mkString " + ") } private implicit def name2string(name: Name) = name.toString - + /** A traverser that finds all mentioned identifiers, i.e. things * that need to be imported. It might return extra names. */ @@ -54,26 +55,28 @@ trait SparkMemberHandlers { } def chooseHandler(member: Tree): MemberHandler = member match { - case member: DefDef => new DefHandler(member) - case member: ValDef => new ValHandler(member) - case member@Assign(Ident(_), _) => new AssignHandler(member) - case member: ModuleDef => new ModuleHandler(member) - case member: ClassDef => new ClassHandler(member) - case member: TypeDef => new TypeAliasHandler(member) - case member: Import => new ImportHandler(member) - case DocDef(_, documented) => chooseHandler(documented) - case member => new GenericHandler(member) + case member: DefDef => new DefHandler(member) + case member: ValDef => new ValHandler(member) + case member: Assign => new AssignHandler(member) + case member: ModuleDef => new ModuleHandler(member) + case member: ClassDef => new ClassHandler(member) + case member: TypeDef => new TypeAliasHandler(member) + case member: Import => new ImportHandler(member) + case DocDef(_, documented) => chooseHandler(documented) + case member => new GenericHandler(member) } - + sealed abstract class MemberDefHandler(override val member: MemberDef) extends MemberHandler(member) { + def symbol = if (member.symbol eq null) NoSymbol else member.symbol def name: Name = member.name def mods: Modifiers = member.mods def keyword = member.keyword - def prettyName = NameTransformer.decode(name) - + def prettyName = name.decode + override def definesImplicit = member.mods.isImplicit override def definesTerm: Option[TermName] = Some(name.toTermName) filter (_ => name.isTermName) override def definesType: Option[TypeName] = Some(name.toTypeName) filter (_ => name.isTypeName) + override def definedSymbols = if (symbol eq NoSymbol) Nil else List(symbol) } /** Class to handle one member among all the members included @@ -82,11 +85,8 @@ trait SparkMemberHandlers { sealed abstract class MemberHandler(val member: Tree) { def definesImplicit = false def definesValue = false - def isLegalTopLevel = member match { - case _: ModuleDef | _: ClassDef | _: Import => true - case _ => false - } - + def isLegalTopLevel = false + def definesTerm = Option.empty[TermName] def definesType = Option.empty[TypeName] @@ -94,6 +94,7 @@ trait SparkMemberHandlers { def importedNames = List[Name]() def definedNames = definesTerm.toList ++ definesType.toList def definedOrImported = definedNames ++ importedNames + def definedSymbols = List[Symbol]() def extraCodeToEvaluate(req: Request): String = "" def resultExtractionCode(req: Request): String = "" @@ -103,11 +104,11 @@ trait SparkMemberHandlers { } class GenericHandler(member: Tree) extends MemberHandler(member) - + class ValHandler(member: ValDef) extends MemberDefHandler(member) { - val maxStringElements = 1000 // no need to mkString billions of elements + val maxStringElements = 1000 // no need to mkString billions of elements override def definesValue = true - + override def resultExtractionCode(req: Request): String = { val isInternal = isUserVarName(name) && req.lookupTypeOf(name) == "Unit" if (!mods.isPublic || isInternal) "" @@ -116,22 +117,27 @@ trait SparkMemberHandlers { val resultString = if (mods.isLazy) codegenln(false, "<lazy>") else any2stringOf(req fullPath name, maxStringElements) - - """ + "%s: %s = " + %s""".format(prettyName, string2code(req typeOf name), resultString) + + val vidString = + if (replProps.vids) """" + " @ " + "%%8x".format(System.identityHashCode(%s)) + " """.trim.format(req fullPath name) + else "" + + """ + "%s%s: %s = " + %s""".format(string2code(prettyName), vidString, string2code(req typeOf name), resultString) } } } class DefHandler(member: DefDef) extends MemberDefHandler(member) { private def vparamss = member.vparamss - // true if 0-arity - override def definesValue = vparamss.isEmpty || vparamss.head.isEmpty + private def isMacro = member.symbol hasFlag MACRO + // true if not a macro and 0-arity + override def definesValue = !isMacro && flattensToEmpty(vparamss) override def resultExtractionCode(req: Request) = if (mods.isPublic) codegenln(name, ": ", req.typeOf(name)) else "" } class AssignHandler(member: Assign) extends MemberHandler(member) { - val lhs = member.lhs.asInstanceOf[Ident] // an unfortunate limitation + val Assign(lhs, rhs) = member val name = newTermName(freshInternalVarName()) override def definesTerm = Some(name) @@ -142,15 +148,15 @@ trait SparkMemberHandlers { /** Print out lhs instead of the generated varName */ override def resultExtractionCode(req: Request) = { val lhsType = string2code(req lookupTypeOf name) - val res = string2code(req fullPath name) - - """ + "%s: %s = " + %s + "\n" """.format(lhs, lhsType, res) + "\n" + val res = string2code(req fullPath name) + """ + "%s: %s = " + %s + "\n" """.format(string2code(lhs.toString), lhsType, res) + "\n" } } class ModuleHandler(module: ModuleDef) extends MemberDefHandler(module) { override def definesTerm = Some(name) override def definesValue = true + override def isLegalTopLevel = true override def resultExtractionCode(req: Request) = codegenln("defined module ", name) } @@ -158,7 +164,8 @@ trait SparkMemberHandlers { class ClassHandler(member: ClassDef) extends MemberDefHandler(member) { override def definesType = Some(name.toTypeName) override def definesTerm = Some(name.toTermName) filter (_ => mods.isCase) - + override def isLegalTopLevel = true + override def resultExtractionCode(req: Request) = codegenln("defined %s %s".format(keyword, name)) } @@ -173,26 +180,42 @@ trait SparkMemberHandlers { class ImportHandler(imp: Import) extends MemberHandler(imp) { val Import(expr, selectors) = imp - def targetType = intp.typeOfExpression("" + expr) - + def targetType: Type = intp.typeOfExpression("" + expr) + override def isLegalTopLevel = true + + def createImportForName(name: Name): String = { + selectors foreach { + case sel @ ImportSelector(old, _, `name`, _) => return "import %s.{ %s }".format(expr, sel) + case _ => () + } + "import %s.%s".format(expr, name) + } + // TODO: Need to track these specially to honor Predef masking attempts, + // because they must be the leading imports in the code generated for each + // line. We can use the same machinery as Contexts now, anyway. + def isPredefImport = isReferenceToPredef(expr) + // wildcard imports, e.g. import foo._ private def selectorWild = selectors filter (_.name == nme.USCOREkw) // renamed imports, e.g. import foo.{ bar => baz } private def selectorRenames = selectors map (_.rename) filterNot (_ == null) - + /** Whether this import includes a wildcard import */ val importsWildcard = selectorWild.nonEmpty - + + /** Whether anything imported is implicit .*/ + def importsImplicit = implicitSymbols.nonEmpty + def implicitSymbols = importedSymbols filter (_.isImplicit) def importedSymbols = individualSymbols ++ wildcardSymbols - + lazy val individualSymbols: List[Symbol] = - atPickler(targetType.toList flatMap (tp => individualNames map (tp nonPrivateMember _))) + beforePickler(individualNames map (targetType nonPrivateMember _)) lazy val wildcardSymbols: List[Symbol] = - if (importsWildcard) atPickler(targetType.toList flatMap (_.nonPrivateMembers)) + if (importsWildcard) beforePickler(targetType.nonPrivateMembers.toList) else Nil - + /** Complete list of names imported by a wildcard */ lazy val wildcardNames: List[Name] = wildcardSymbols map (_.name) lazy val individualNames: List[Name] = selectorRenames filterNot (_ == nme.USCOREkw) flatMap (_.bothNames) @@ -200,7 +223,7 @@ trait SparkMemberHandlers { /** The names imported by this statement */ override lazy val importedNames: List[Name] = wildcardNames ++ individualNames lazy val importsSymbolNamed: Set[String] = importedNames map (_.toString) toSet - + def importString = imp.toString override def resultExtractionCode(req: Request) = codegenln(importString) + "\n" } diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala index 8f9b632c0e..daaa2a0305 100644 --- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala +++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala @@ -1,32 +1,17 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - package org.apache.spark.repl import java.io._ import java.net.URLClassLoader import scala.collection.mutable.ArrayBuffer -import scala.collection.JavaConversions._ -import org.scalatest.FunSuite import com.google.common.io.Files +import org.scalatest.FunSuite +import org.apache.spark.SparkContext + class ReplSuite extends FunSuite { + def runInterpreter(master: String, input: String): String = { val in = new BufferedReader(new StringReader(input + "\n")) val out = new StringWriter() @@ -56,104 +41,140 @@ class ReplSuite extends FunSuite { def assertContains(message: String, output: String) { assert(output.contains(message), - "Interpreter output did not contain '" + message + "':\n" + output) + "Interpreter output did not contain '" + message + "':\n" + output) } def assertDoesNotContain(message: String, output: String) { assert(!output.contains(message), - "Interpreter output contained '" + message + "':\n" + output) + "Interpreter output contained '" + message + "':\n" + output) + } + + test("propagation of local properties") { + // A mock ILoop that doesn't install the SIGINT handler. + class ILoop(out: PrintWriter) extends SparkILoop(None, out, None) { + settings = new scala.tools.nsc.Settings + settings.usejavacp.value = true + org.apache.spark.repl.Main.interp = this + override def createInterpreter() { + intp = new SparkILoopInterpreter + intp.setContextClassLoader() + } + } + + val out = new StringWriter() + val interp = new ILoop(new PrintWriter(out)) + interp.sparkContext = new SparkContext("local", "repl-test") + interp.createInterpreter() + interp.intp.initialize() + interp.sparkContext.setLocalProperty("someKey", "someValue") + + // Make sure the value we set in the caller to interpret is propagated in the thread that + // interprets the command. + interp.interpret("org.apache.spark.repl.Main.interp.sparkContext.getLocalProperty(\"someKey\")") + assert(out.toString.contains("someValue")) + + interp.sparkContext.stop() + System.clearProperty("spark.driver.port") + System.clearProperty("spark.hostPort") } - test ("simple foreach with accumulator") { - val output = runInterpreter("local", """ - val accum = sc.accumulator(0) - sc.parallelize(1 to 10).foreach(x => accum += x) - accum.value - """) + test("simple foreach with accumulator") { + val output = runInterpreter("local", + """ + |val accum = sc.accumulator(0) + |sc.parallelize(1 to 10).foreach(x => accum += x) + |accum.value + """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res1: Int = 55", output) } - test ("external vars") { - val output = runInterpreter("local", """ - var v = 7 - sc.parallelize(1 to 10).map(x => v).collect.reduceLeft(_+_) - v = 10 - sc.parallelize(1 to 10).map(x => v).collect.reduceLeft(_+_) - """) + test("external vars") { + val output = runInterpreter("local", + """ + |var v = 7 + |sc.parallelize(1 to 10).map(x => v).collect.reduceLeft(_+_) + |v = 10 + |sc.parallelize(1 to 10).map(x => v).collect.reduceLeft(_+_) + """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res0: Int = 70", output) assertContains("res1: Int = 100", output) } - test ("external classes") { - val output = runInterpreter("local", """ - class C { - def foo = 5 - } - sc.parallelize(1 to 10).map(x => (new C).foo).collect.reduceLeft(_+_) - """) + test("external classes") { + val output = runInterpreter("local", + """ + |class C { + |def foo = 5 + |} + |sc.parallelize(1 to 10).map(x => (new C).foo).collect.reduceLeft(_+_) + """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res0: Int = 50", output) } - test ("external functions") { - val output = runInterpreter("local", """ - def double(x: Int) = x + x - sc.parallelize(1 to 10).map(x => double(x)).collect.reduceLeft(_+_) - """) + test("external functions") { + val output = runInterpreter("local", + """ + |def double(x: Int) = x + x + |sc.parallelize(1 to 10).map(x => double(x)).collect.reduceLeft(_+_) + """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res0: Int = 110", output) } - test ("external functions that access vars") { - val output = runInterpreter("local", """ - var v = 7 - def getV() = v - sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) - v = 10 - sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) - """) + test("external functions that access vars") { + val output = runInterpreter("local", + """ + |var v = 7 + |def getV() = v + |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |v = 10 + |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res0: Int = 70", output) assertContains("res1: Int = 100", output) } - test ("broadcast vars") { + test("broadcast vars") { // Test that the value that a broadcast var had when it was created is used, // even if that variable is then modified in the driver program // TODO: This doesn't actually work for arrays when we run in local mode! - val output = runInterpreter("local", """ - var array = new Array[Int](5) - val broadcastArray = sc.broadcast(array) - sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect - array(0) = 5 - sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect - """) + val output = runInterpreter("local", + """ + |var array = new Array[Int](5) + |val broadcastArray = sc.broadcast(array) + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |array(0) = 5 + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res0: Array[Int] = Array(0, 0, 0, 0, 0)", output) assertContains("res2: Array[Int] = Array(5, 0, 0, 0, 0)", output) } - test ("interacting with files") { + test("interacting with files") { val tempDir = Files.createTempDir() val out = new FileWriter(tempDir + "/input") out.write("Hello world!\n") out.write("What's up?\n") out.write("Goodbye\n") out.close() - val output = runInterpreter("local", """ - var file = sc.textFile("%s/input").cache() - file.count() - file.count() - file.count() - """.format(tempDir.getAbsolutePath)) + val output = runInterpreter("local", + """ + |var file = sc.textFile("%s/input").cache() + |file.count() + |file.count() + |file.count() + """.stripMargin.format(tempDir.getAbsolutePath)) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res0: Long = 3", output) @@ -161,19 +182,20 @@ class ReplSuite extends FunSuite { assertContains("res2: Long = 3", output) } - test ("local-cluster mode") { - val output = runInterpreter("local-cluster[1,1,512]", """ - var v = 7 - def getV() = v - sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) - v = 10 - sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) - var array = new Array[Int](5) - val broadcastArray = sc.broadcast(array) - sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect - array(0) = 5 - sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect - """) + test("local-cluster mode") { + val output = runInterpreter("local-cluster[1,1,512]", + """ + |var v = 7 + |def getV() = v + |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |v = 10 + |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |var array = new Array[Int](5) + |val broadcastArray = sc.broadcast(array) + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |array(0) = 5 + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res0: Int = 70", output) @@ -183,19 +205,20 @@ class ReplSuite extends FunSuite { } if (System.getenv("MESOS_NATIVE_LIBRARY") != null) { - test ("running on Mesos") { - val output = runInterpreter("localquiet", """ - var v = 7 - def getV() = v - sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) - v = 10 - sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) - var array = new Array[Int](5) - val broadcastArray = sc.broadcast(array) - sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect - array(0) = 5 - sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect - """) + test("running on Mesos") { + val output = runInterpreter("localquiet", + """ + |var v = 7 + |def getV() = v + |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |v = 10 + |sc.parallelize(1 to 10).map(x => getV()).collect.reduceLeft(_+_) + |var array = new Array[Int](5) + |val broadcastArray = sc.broadcast(array) + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + |array(0) = 5 + |sc.parallelize(0 to 4).map(x => broadcastArray.value(x)).collect + """.stripMargin) assertDoesNotContain("error:", output) assertDoesNotContain("Exception", output) assertContains("res0: Int = 70", output) diff --git a/sbin/compute-classpath.cmd b/sbin/compute-classpath.cmd index e0b8a8ef5f..4f60bff19c 100644 --- a/sbin/compute-classpath.cmd +++ b/sbin/compute-classpath.cmd @@ -20,7 +20,7 @@ rem rem This script computes Spark's classpath and prints it to stdout; it's used by both the "run" rem script and the ExecutorRunner in standalone cluster mode. -set SCALA_VERSION=2.9.3 +set SCALA_VERSION=2.10 rem Figure out where the Spark framework is installed set FWDIR=%~dp0..\ diff --git a/sbin/compute-classpath.sh b/sbin/compute-classpath.sh index cfe5fe7bef..0c82310421 100755 --- a/sbin/compute-classpath.sh +++ b/sbin/compute-classpath.sh @@ -20,7 +20,7 @@ # This script computes Spark's classpath and prints it to stdout; it's used by both the "run" # script and the ExecutorRunner in standalone cluster mode. -SCALA_VERSION=2.9.3 +SCALA_VERSION=2.10 # Figure out where Spark is installed FWDIR="$(cd `dirname $0`/..; pwd)" @@ -31,13 +31,27 @@ if [ -e "$FWDIR/conf/spark-env.sh" ] ; then fi # Build up classpath -CLASSPATH="$FWDIR/conf" -if [ -f "$FWDIR/RELEASE" ]; then - ASSEMBLY_JAR=`ls "$FWDIR"/jars/spark-assembly*.jar` +CLASSPATH="$SPARK_CLASSPATH:$FWDIR/conf" + +# First check if we have a dependencies jar. If so, include binary classes with the deps jar +if [ -f "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*-deps.jar ]; then + CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$FWDIR/repl/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$FWDIR/mllib/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$FWDIR/bagel/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$FWDIR/streaming/target/scala-$SCALA_VERSION/classes" + + DEPS_ASSEMBLY_JAR=`ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*-deps.jar` + CLASSPATH="$CLASSPATH:$DEPS_ASSEMBLY_JAR" else - ASSEMBLY_JAR=`ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*.jar` + # Else use spark-assembly jar from either RELEASE or assembly directory + if [ -f "$FWDIR/RELEASE" ]; then + ASSEMBLY_JAR=`ls "$FWDIR"/jars/spark-assembly*.jar` + else + ASSEMBLY_JAR=`ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*.jar` + fi + CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR" fi -CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR" # Add test classes if we're running from SBT or Maven with SPARK_TESTING set to 1 if [[ $SPARK_TESTING == 1 ]]; then diff --git a/sbin/slaves.sh b/sbin/slaves.sh index 68408bcad8..a5bc2183d8 100755 --- a/sbin/slaves.sh +++ b/sbin/slaves.sh @@ -28,7 +28,7 @@ # SPARK_SSH_OPTS Options passed to ssh when running remote commands. ## -usage="Usage: slaves.sh [--config confdir] command..." +usage="Usage: slaves.sh [--config <conf-dir>] command..." # if no args specified, show usage if [ $# -le 0 ]; then @@ -46,6 +46,23 @@ sbin=`cd "$sbin"; pwd` # spark-env.sh. Save it here. HOSTLIST=$SPARK_SLAVES +# Check if --config is passed as an argument. It is an optional parameter. +# Exit if the argument is not a directory. +if [ "$1" == "--config" ] +then + shift + conf_dir=$1 + if [ ! -d "$conf_dir" ] + then + echo "ERROR : $conf_dir is not a directory" + echo $usage + exit 1 + else + export SPARK_CONF_DIR=$conf_dir + fi + shift +fi + if [ -f "${SPARK_CONF_DIR}/spark-env.sh" ]; then . "${SPARK_CONF_DIR}/spark-env.sh" fi diff --git a/sbin/spark-class b/sbin/spark-class index 3bdc29e543..4e440d8729 100755 --- a/sbin/spark-class +++ b/sbin/spark-class @@ -17,7 +17,12 @@ # limitations under the License. # -SCALA_VERSION=2.9.3 +cygwin=false +case "`uname`" in + CYGWIN*) cygwin=true;; +esac + +SCALA_VERSION=2.10 # Figure out where the Scala framework is installed FWDIR="$(cd `dirname $0`/..; pwd)" @@ -55,7 +60,7 @@ case "$1" in 'org.apache.spark.deploy.worker.Worker') OUR_JAVA_OPTS="$OUR_JAVA_OPTS $SPARK_WORKER_OPTS" ;; - 'org.apache.spark.executor.StandaloneExecutorBackend') + 'org.apache.spark.executor.CoarseGrainedExecutorBackend') OUR_JAVA_OPTS="$OUR_JAVA_OPTS $SPARK_EXECUTOR_OPTS" ;; 'org.apache.spark.executor.MesosExecutorBackend') @@ -95,16 +100,46 @@ export JAVA_OPTS if [ ! -f "$FWDIR/RELEASE" ]; then # Exit if the user hasn't compiled Spark - ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*.jar >& /dev/null - if [[ $? != 0 ]]; then - echo "Failed to find Spark assembly in $FWDIR/assembly/target" >&2 - echo "You need to build Spark with sbt/sbt assembly before running this program" >&2 + num_jars=$(ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/ | grep "spark-assembly.*hadoop.*.jar" | wc -l) + jars_list=$(ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/ | grep "spark-assembly.*hadoop.*.jar") + if [ "$num_jars" -eq "0" ]; then + echo "Failed to find Spark assembly in $FWDIR/assembly/target/scala-$SCALA_VERSION/" >&2 + echo "You need to build Spark with 'sbt/sbt assembly' before running this program." >&2 + exit 1 + fi + if [ "$num_jars" -gt "1" ]; then + echo "Found multiple Spark assembly jars in $FWDIR/assembly/target/scala-$SCALA_VERSION:" >&2 + echo "$jars_list" + echo "Please remove all but one jar." exit 1 fi fi +TOOLS_DIR="$FWDIR"/tools +SPARK_TOOLS_JAR="" +if [ -e "$TOOLS_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar ]; then + # Use the JAR from the SBT build + export SPARK_TOOLS_JAR=`ls "$TOOLS_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar` +fi +if [ -e "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar ]; then + # Use the JAR from the Maven build + # TODO: this also needs to become an assembly! + export SPARK_TOOLS_JAR=`ls "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar` +fi + # Compute classpath using external script CLASSPATH=`$FWDIR/sbin/compute-classpath.sh` + +if [ "$1" == "org.apache.spark.tools.JavaAPICompletenessChecker" ]; then + CLASSPATH="$CLASSPATH:$SPARK_TOOLS_JAR" +fi + +if $cygwin; then + CLASSPATH=`cygpath -wp $CLASSPATH` + if [ "$1" == "org.apache.spark.tools.JavaAPICompletenessChecker" ]; then + export SPARK_TOOLS_JAR=`cygpath -w $SPARK_TOOLS_JAR` + fi +fi export CLASSPATH if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then @@ -115,3 +150,5 @@ if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then fi exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" + + diff --git a/sbin/spark-class2.cmd b/sbin/spark-class2.cmd index 5e00bd39ea..460e661476 100644 --- a/sbin/spark-class2.cmd +++ b/sbin/spark-class2.cmd @@ -17,7 +17,7 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem -set SCALA_VERSION=2.9.3 +set SCALA_VERSION=2.10 rem Figure out where the Spark framework is installed set FWDIR=%~dp0..\ @@ -65,10 +65,17 @@ if "%FOUND_JAR%"=="0" ( ) :skip_build_test +set TOOLS_DIR=%FWDIR%tools +set SPARK_TOOLS_JAR= +for %%d in ("%TOOLS_DIR%\target\scala-%SCALA_VERSION%\spark-tools*assembly*.jar") do ( + set SPARK_TOOLS_JAR=%%d +) + rem Compute classpath using external script set DONT_PRINT_CLASSPATH=1 call "%FWDIR%sbin\compute-classpath.cmd" set DONT_PRINT_CLASSPATH=0 +set CLASSPATH=%CLASSPATH%;%SPARK_TOOLS_JAR% rem Figure out where java is. set RUNNER=java diff --git a/sbin/spark-daemon.sh b/sbin/spark-daemon.sh index ae82349cc6..ca6b893b9b 100755 --- a/sbin/spark-daemon.sh +++ b/sbin/spark-daemon.sh @@ -29,7 +29,7 @@ # SPARK_NICENESS The scheduling priority for daemons. Defaults to 0. ## -usage="Usage: spark-daemon.sh [--config <conf-dir>] [--hosts hostlistfile] (start|stop) <spark-command> <spark-instance-number> <args...>" +usage="Usage: spark-daemon.sh [--config <conf-dir>] (start|stop) <spark-command> <spark-instance-number> <args...>" # if no args specified, show usage if [ $# -le 1 ]; then @@ -43,6 +43,25 @@ sbin=`cd "$sbin"; pwd` . "$sbin/spark-config.sh" # get arguments + +# Check if --config is passed as an argument. It is an optional parameter. +# Exit if the argument is not a directory. + +if [ "$1" == "--config" ] +then + shift + conf_dir=$1 + if [ ! -d "$conf_dir" ] + then + echo "ERROR : $conf_dir is not a directory" + echo $usage + exit 1 + else + export SPARK_CONF_DIR=$conf_dir + fi + shift +fi + startStop=$1 shift command=$1 diff --git a/sbin/spark-daemons.sh b/sbin/spark-daemons.sh index d91254b690..5d9f2bb51c 100755 --- a/sbin/spark-daemons.sh +++ b/sbin/spark-daemons.sh @@ -19,7 +19,7 @@ # Run a Spark command on all slave hosts. -usage="Usage: spark-daemons.sh [--config confdir] [--hosts hostlistfile] [start|stop] command instance-number args..." +usage="Usage: spark-daemons.sh [--config <conf-dir>] [start|stop] command instance-number args..." # if no args specified, show usage if [ $# -le 1 ]; then diff --git a/sbin/stop-slaves.sh b/sbin/stop-slaves.sh index 63802e6df5..c6b0b6ab66 100755 --- a/sbin/stop-slaves.sh +++ b/sbin/stop-slaves.sh @@ -17,10 +17,8 @@ # limitations under the License. # -# Starts the master on the machine this script is executed on. - -sbin=`dirname "$0"` -sbin=`cd "$sbin"; pwd` +bin=`dirname "$0"` +bin=`cd "$sbin"; pwd` . "$sbin/spark-config.sh" @@ -17,12 +17,27 @@ # limitations under the License. # -EXTRA_ARGS="" +cygwin=false +case "`uname`" in + CYGWIN*) cygwin=true;; +esac + +EXTRA_ARGS="-Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=256m" if [ "$MESOS_HOME" != "" ]; then - EXTRA_ARGS="-Djava.library.path=$MESOS_HOME/lib/java" + EXTRA_ARGS="$EXTRA_ARGS -Djava.library.path=$MESOS_HOME/lib/java" fi export SPARK_HOME=$(cd "$(dirname $0)/.." 2>&1 >/dev/null ; pwd) export SPARK_TESTING=1 # To put test classes on classpath -java -Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=256m $EXTRA_ARGS $SBT_OPTS -jar "$SPARK_HOME"/sbt/sbt-launch-*.jar "$@" +SBT_JAR="$SPARK_HOME"/sbt/sbt-launch-*.jar +if $cygwin; then + SBT_JAR=`cygpath -w $SBT_JAR` + export SPARK_HOME=`cygpath -w $SPARK_HOME` + EXTRA_ARGS="$EXTRA_ARGS -Djline.terminal=jline.UnixTerminal -Dsbt.cygwin=true" + stty -icanon min 1 -echo > /dev/null 2>&1 + java $EXTRA_ARGS $SBT_OPTS -jar $SBT_JAR "$@" + stty icanon echo > /dev/null 2>&1 +else + java $EXTRA_ARGS $SBT_OPTS -jar $SBT_JAR "$@" +fi
\ No newline at end of file diff --git a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar b/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar Binary files differdeleted file mode 100644 index 65f79925a4..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar +++ /dev/null diff --git a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar.md5 b/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar.md5 deleted file mode 100644 index 29f45f4adb..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar.md5 +++ /dev/null @@ -1 +0,0 @@ -18876b8bc2e4cef28b6d191aa49d963f
\ No newline at end of file diff --git a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar.sha1 b/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar.sha1 deleted file mode 100644 index e3bd62bac0..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.jar.sha1 +++ /dev/null @@ -1 +0,0 @@ -06b27270ffa52250a2c08703b397c99127b72060
\ No newline at end of file diff --git a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom b/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom deleted file mode 100644 index 082d35726a..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom +++ /dev/null @@ -1,9 +0,0 @@ -<?xml version="1.0" encoding="UTF-8"?> -<project xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd" xmlns="http://maven.apache.org/POM/4.0.0" - xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> - <modelVersion>4.0.0</modelVersion> - <groupId>org.apache.kafka</groupId> - <artifactId>kafka</artifactId> - <version>0.7.2-spark</version> - <description>POM was created from install:install-file</description> -</project> diff --git a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom.md5 b/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom.md5 deleted file mode 100644 index 92c4132b5b..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom.md5 +++ /dev/null @@ -1 +0,0 @@ -7bc4322266e6032bdf9ef6eebdd8097d
\ No newline at end of file diff --git a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom.sha1 b/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom.sha1 deleted file mode 100644 index 8a1d8a097a..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/0.7.2-spark/kafka-0.7.2-spark.pom.sha1 +++ /dev/null @@ -1 +0,0 @@ -d0f79e8eff0db43ca7bcf7dce2c8cd2972685c9d
\ No newline at end of file diff --git a/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml b/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml deleted file mode 100644 index 720cd51c2f..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml +++ /dev/null @@ -1,12 +0,0 @@ -<?xml version="1.0" encoding="UTF-8"?> -<metadata> - <groupId>org.apache.kafka</groupId> - <artifactId>kafka</artifactId> - <versioning> - <release>0.7.2-spark</release> - <versions> - <version>0.7.2-spark</version> - </versions> - <lastUpdated>20130121015225</lastUpdated> - </versioning> -</metadata> diff --git a/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml.md5 b/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml.md5 deleted file mode 100644 index a4ce5dc9e8..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml.md5 +++ /dev/null @@ -1 +0,0 @@ -e2b9c7c5f6370dd1d21a0aae5e8dcd77
\ No newline at end of file diff --git a/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml.sha1 b/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml.sha1 deleted file mode 100644 index b869eaf2a6..0000000000 --- a/streaming/lib/org/apache/kafka/kafka/maven-metadata-local.xml.sha1 +++ /dev/null @@ -1 +0,0 @@ -2a4341da936b6c07a09383d17ffb185ac558ee91
\ No newline at end of file diff --git a/streaming/pom.xml b/streaming/pom.xml index b260a72abb..e3b6fee9b2 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -26,23 +26,29 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-streaming</artifactId> + <artifactId>spark-streaming_2.10</artifactId> <packaging>jar</packaging> <name>Spark Project Streaming</name> <url>http://spark.incubator.apache.org/</url> <repositories> - <!-- A repository in the local filesystem for the Kafka JAR, which we modified for Scala 2.9 --> <repository> - <id>lib</id> - <url>file://${project.basedir}/lib</url> + <id>apache-repo</id> + <name>Apache Repository</name> + <url>https://repository.apache.org/content/repositories/releases</url> + <releases> + <enabled>true</enabled> + </releases> + <snapshots> + <enabled>false</enabled> + </snapshots> </repository> </repositories> <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> @@ -55,9 +61,23 @@ <version>1.9.11</version> </dependency> <dependency> - <groupId>org.apache.kafka</groupId> - <artifactId>kafka</artifactId> - <version>0.7.2-spark</version> <!-- Comes from our in-project repository --> + <groupId>com.sksamuel.kafka</groupId> + <artifactId>kafka_${scala.binary.version}</artifactId> + <version>0.8.0-beta1</version> + <exclusions> + <exclusion> + <groupId>com.sun.jmx</groupId> + <artifactId>jmxri</artifactId> + </exclusion> + <exclusion> + <groupId>com.sun.jdmk</groupId> + <artifactId>jmxtools</artifactId> + </exclusion> + <exclusion> + <groupId>net.sf.jopt-simple</groupId> + <artifactId>jopt-simple</artifactId> + </exclusion> + </exclusions> </dependency> <dependency> <groupId>org.apache.flume</groupId> @@ -68,35 +88,39 @@ <groupId>org.jboss.netty</groupId> <artifactId>netty</artifactId> </exclusion> + <exclusion> + <groupId>org.xerial.snappy</groupId> + <artifactId>*</artifactId> + </exclusion> </exclusions> </dependency> <dependency> - <groupId>com.github.sgroschupf</groupId> - <artifactId>zkclient</artifactId> - <version>0.1</version> - </dependency> - <dependency> <groupId>org.twitter4j</groupId> <artifactId>twitter4j-stream</artifactId> <version>3.0.3</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> </dependency> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> </dependency> <dependency> - <groupId>com.typesafe.akka</groupId> - <artifactId>akka-zeromq</artifactId> - <version>2.0.3</version> + <groupId>${akka.group}</groupId> + <artifactId>akka-zeromq_${scala.binary.version}</artifactId> </dependency> <dependency> <groupId>org.scalatest</groupId> - <artifactId>scalatest_${scala.version}</artifactId> + <artifactId>scalatest_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> <dependency> <groupId>org.scalacheck</groupId> - <artifactId>scalacheck_${scala.version}</artifactId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> <dependency> @@ -109,10 +133,19 @@ <artifactId>slf4j-log4j12</artifactId> <scope>test</scope> </dependency> + <dependency> + <groupId>commons-io</groupId> + <artifactId>commons-io</artifactId> + </dependency> + <dependency> + <groupId>org.eclipse.paho</groupId> + <artifactId>mqtt-client</artifactId> + <version>0.4.0</version> + </dependency> </dependencies> <build> - <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> - <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> <plugins> <plugin> <groupId>org.scalatest</groupId> diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index 2d8f072624..ca0115f90e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -21,11 +21,13 @@ import java.io._ import java.util.concurrent.Executors import java.util.concurrent.RejectedExecutionException -import org.apache.hadoop.fs.Path +import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.conf.Configuration -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.spark.io.CompressionCodec +import org.apache.spark.util.MetadataCleaner +import org.apache.spark.deploy.SparkHadoopUtil private[streaming] @@ -33,13 +35,14 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) extends Logging with Serializable { val master = ssc.sc.master val framework = ssc.sc.appName - val sparkHome = ssc.sc.sparkHome + val sparkHome = ssc.sc.getSparkHome.getOrElse(null) val jars = ssc.sc.jars - val environment = ssc.sc.environment val graph = ssc.graph val checkpointDir = ssc.checkpointDir val checkpointDuration = ssc.checkpointDuration - val pendingTimes = ssc.scheduler.jobManager.getPendingTimes() + val pendingTimes = ssc.scheduler.getPendingTimes() + val delaySeconds = MetadataCleaner.getDelaySeconds(ssc.conf) + val sparkConf = ssc.conf def validate() { assert(master != null, "Checkpoint.master is null") @@ -52,36 +55,36 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) /** - * Convenience class to speed up the writing of graph checkpoint to file + * Convenience class to handle the writing of graph checkpoint to file */ private[streaming] -class CheckpointWriter(checkpointDir: String) extends Logging { +class CheckpointWriter(conf: SparkConf, checkpointDir: String, hadoopConf: Configuration) + extends Logging +{ val file = new Path(checkpointDir, "graph") - // The file to which we actually write - and then "move" to file. - private val writeFile = new Path(file.getParent, file.getName + ".next") - private val bakFile = new Path(file.getParent, file.getName + ".bk") - - private var stopped = false - - val conf = new Configuration() - var fs = file.getFileSystem(conf) - val maxAttempts = 3 + val MAX_ATTEMPTS = 3 val executor = Executors.newFixedThreadPool(1) + val compressionCodec = CompressionCodec.createCodec(conf) + // The file to which we actually write - and then "move" to file + val writeFile = new Path(file.getParent, file.getName + ".next") + // The file to which existing checkpoint is backed up (i.e. "moved") + val bakFile = new Path(file.getParent, file.getName + ".bk") - private val compressionCodec = CompressionCodec.createCodec() + private var stopped = false + private var fs_ : FileSystem = _ - // Removed code which validates whether there is only one CheckpointWriter per path 'file' since + // Removed code which validates whether there is only one CheckpointWriter per path 'file' since // I did not notice any errors - reintroduce it ? - class CheckpointWriteHandler(checkpointTime: Time, bytes: Array[Byte]) extends Runnable { def run() { var attempts = 0 val startTime = System.currentTimeMillis() - while (attempts < maxAttempts) { + while (attempts < MAX_ATTEMPTS && !stopped) { attempts += 1 try { logDebug("Saving checkpoint for time " + checkpointTime + " to file '" + file + "'") - // This is inherently thread unsafe .. so alleviating it by writing to '.new' and then doing moves : which should be pretty fast. + // This is inherently thread unsafe, so alleviating it by writing to '.new' and + // then moving it to the final file val fos = fs.create(writeFile) fos.write(bytes) fos.close() @@ -92,13 +95,14 @@ class CheckpointWriter(checkpointDir: String) extends Logging { fs.delete(file, false) fs.rename(writeFile, file) - val finishTime = System.currentTimeMillis(); + val finishTime = System.currentTimeMillis() logInfo("Checkpoint for time " + checkpointTime + " saved to file '" + file + "', took " + bytes.length + " bytes and " + (finishTime - startTime) + " milliseconds") return } catch { case ioe: IOException => logWarning("Error writing checkpoint to file in " + attempts + " attempts", ioe) + reset() } } logError("Could not write checkpoint for time " + checkpointTime + " to file '" + file + "'") @@ -122,14 +126,26 @@ class CheckpointWriter(checkpointDir: String) extends Logging { def stop() { synchronized { - if (stopped) return ; + if (stopped) { + return + } stopped = true } executor.shutdown() val startTime = System.currentTimeMillis() val terminated = executor.awaitTermination(10, java.util.concurrent.TimeUnit.SECONDS) val endTime = System.currentTimeMillis() - logInfo("CheckpointWriter executor terminated ? " + terminated + ", waited for " + (endTime - startTime) + " ms.") + logInfo("CheckpointWriter executor terminated ? " + terminated + + ", waited for " + (endTime - startTime) + " ms.") + } + + private def fs = synchronized { + if (fs_ == null) fs_ = file.getFileSystem(hadoopConf) + fs_ + } + + private def reset() = synchronized { + fs_ = null } } @@ -137,11 +153,12 @@ class CheckpointWriter(checkpointDir: String) extends Logging { private[streaming] object CheckpointReader extends Logging { - def read(path: String): Checkpoint = { + def read(conf: SparkConf, path: String): Checkpoint = { val fs = new Path(path).getFileSystem(new Configuration()) - val attempts = Seq(new Path(path, "graph"), new Path(path, "graph.bk"), new Path(path), new Path(path + ".bk")) + val attempts = Seq(new Path(path, "graph"), new Path(path, "graph.bk"), + new Path(path), new Path(path + ".bk")) - val compressionCodec = CompressionCodec.createCodec() + val compressionCodec = CompressionCodec.createCodec(conf) attempts.foreach(file => { if (fs.exists(file)) { @@ -154,7 +171,8 @@ object CheckpointReader extends Logging { // loader to find and load classes. This is a well know Java issue and has popped up // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627) val zis = compressionCodec.compressedInputStream(fis) - val ois = new ObjectInputStreamWithLoader(zis, Thread.currentThread().getContextClassLoader) + val ois = new ObjectInputStreamWithLoader(zis, + Thread.currentThread().getContextClassLoader) val cp = ois.readObject.asInstanceOf[Checkpoint] ois.close() fs.close() diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala index 80da6bd30b..00671ba520 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala @@ -17,27 +17,23 @@ package org.apache.spark.streaming -import org.apache.spark.streaming.dstream._ import StreamingContext._ -import org.apache.spark.util.MetadataCleaner - -//import Time._ - +import org.apache.spark.streaming.dstream._ +import org.apache.spark.streaming.scheduler.Job import org.apache.spark.Logging import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel +import org.apache.spark.util.MetadataCleaner -import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap +import scala.reflect.ClassTag import java.io.{ObjectInputStream, IOException, ObjectOutputStream} -import org.apache.hadoop.fs.{FileSystem, Path} -import org.apache.hadoop.conf.Configuration /** * A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous - * sequence of RDDs (of the same type) representing a continuous stream of data (see [[org.apache.spark.RDD]] + * sequence of RDDs (of the same type) representing a continuous stream of data (see [[org.apache.spark.rdd.RDD]] * for more details on RDDs). DStreams can either be created from live data (such as, data from * HDFS, Kafka or Flume) or it can be generated by transformation existing DStreams using operations * such as `map`, `window` and `reduceByKeyAndWindow`. While a Spark Streaming program is running, each @@ -56,12 +52,10 @@ import org.apache.hadoop.conf.Configuration * - A function that is used to generate an RDD after each time interval */ -abstract class DStream[T: ClassManifest] ( +abstract class DStream[T: ClassTag] ( @transient protected[streaming] var ssc: StreamingContext ) extends Serializable with Logging { - initLogging() - // ======================================================================= // Methods that should be implemented by subclasses of DStream // ======================================================================= @@ -82,7 +76,7 @@ abstract class DStream[T: ClassManifest] ( // RDDs generated, marked as protected[streaming] so that testsuites can access it @transient protected[streaming] var generatedRDDs = new HashMap[Time, RDD[T]] () - + // Time zero for the DStream protected[streaming] var zeroTime: Time = null @@ -212,7 +206,7 @@ abstract class DStream[T: ClassManifest] ( checkpointDuration + "). Please set it to higher than " + checkpointDuration + "." ) - val metadataCleanerDelay = MetadataCleaner.getDelaySeconds + val metadataCleanerDelay = MetadataCleaner.getDelaySeconds(ssc.conf) logInfo("metadataCleanupDelay = " + metadataCleanerDelay) assert( metadataCleanerDelay < 0 || rememberDuration.milliseconds < metadataCleanerDelay * 1000, @@ -274,16 +268,16 @@ abstract class DStream[T: ClassManifest] ( /** * Retrieve a precomputed RDD of this DStream, or computes the RDD. This is an internal * method that should not be called directly. - */ + */ protected[streaming] def getOrCompute(time: Time): Option[RDD[T]] = { // If this DStream was not initialized (i.e., zeroTime not set), then do it // If RDD was already generated, then retrieve it from HashMap generatedRDDs.get(time) match { - - // If an RDD was already generated and is being reused, then + + // If an RDD was already generated and is being reused, then // probably all RDDs in this DStream will be reused and hence should be cached case Some(oldRDD) => Some(oldRDD) - + // if RDD was not generated, and if the time is valid // (based on sliding time of this DStream), then generate the RDD case None => { @@ -300,7 +294,7 @@ abstract class DStream[T: ClassManifest] ( } generatedRDDs.put(time, newRDD) Some(newRDD) - case None => + case None => None } } else { @@ -344,7 +338,7 @@ abstract class DStream[T: ClassManifest] ( dependencies.foreach(_.clearOldMetadata(time)) } - /* Adds metadata to the Stream while it is running. + /* Adds metadata to the Stream while it is running. * This methd should be overwritten by sublcasses of InputDStream. */ protected[streaming] def addMetadata(metadata: Any) { @@ -416,7 +410,7 @@ abstract class DStream[T: ClassManifest] ( // ======================================================================= /** Return a new DStream by applying a function to all elements of this DStream. */ - def map[U: ClassManifest](mapFunc: T => U): DStream[U] = { + def map[U: ClassTag](mapFunc: T => U): DStream[U] = { new MappedDStream(this, context.sparkContext.clean(mapFunc)) } @@ -424,7 +418,7 @@ abstract class DStream[T: ClassManifest] ( * Return a new DStream by applying a function to all elements of this DStream, * and then flattening the results */ - def flatMap[U: ClassManifest](flatMapFunc: T => Traversable[U]): DStream[U] = { + def flatMap[U: ClassTag](flatMapFunc: T => Traversable[U]): DStream[U] = { new FlatMappedDStream(this, context.sparkContext.clean(flatMapFunc)) } @@ -438,12 +432,19 @@ abstract class DStream[T: ClassManifest] ( */ def glom(): DStream[Array[T]] = new GlommedDStream(this) + + /** + * Return a new DStream with an increased or decreased level of parallelism. Each RDD in the + * returned DStream has exactly numPartitions partitions. + */ + def repartition(numPartitions: Int): DStream[T] = this.transform(_.repartition(numPartitions)) + /** * Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs * of this DStream. Applying mapPartitions() to an RDD applies a function to each partition * of the RDD. */ - def mapPartitions[U: ClassManifest]( + def mapPartitions[U: ClassTag]( mapPartFunc: Iterator[T] => Iterator[U], preservePartitioning: Boolean = false ): DStream[U] = { @@ -479,7 +480,7 @@ abstract class DStream[T: ClassManifest] ( /** * Apply a function to each RDD in this DStream. This is an output operator, so - * this DStream will be registered as an output stream and therefore materialized. + * 'this' DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: RDD[T] => Unit) { this.foreach((r: RDD[T], t: Time) => foreachFunc(r)) @@ -487,28 +488,60 @@ abstract class DStream[T: ClassManifest] ( /** * Apply a function to each RDD in this DStream. This is an output operator, so - * this DStream will be registered as an output stream and therefore materialized. + * 'this' DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: (RDD[T], Time) => Unit) { - val newStream = new ForEachDStream(this, context.sparkContext.clean(foreachFunc)) - ssc.registerOutputStream(newStream) - newStream + ssc.registerOutputStream(new ForEachDStream(this, context.sparkContext.clean(foreachFunc))) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream. + */ + def transform[U: ClassTag](transformFunc: RDD[T] => RDD[U]): DStream[U] = { + transform((r: RDD[T], t: Time) => context.sparkContext.clean(transformFunc(r))) } /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ - def transform[U: ClassManifest](transformFunc: RDD[T] => RDD[U]): DStream[U] = { - transform((r: RDD[T], t: Time) => transformFunc(r)) + def transform[U: ClassTag](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U] = { + //new TransformedDStream(this, context.sparkContext.clean(transformFunc)) + val cleanedF = context.sparkContext.clean(transformFunc) + val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + assert(rdds.length == 1) + cleanedF(rdds.head.asInstanceOf[RDD[T]], time) + } + new TransformedDStream[U](Seq(this), realTransformFunc) } /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream and 'other' DStream. */ - def transform[U: ClassManifest](transformFunc: (RDD[T], Time) => RDD[U]): DStream[U] = { - new TransformedDStream(this, context.sparkContext.clean(transformFunc)) + def transformWith[U: ClassTag, V: ClassTag]( + other: DStream[U], transformFunc: (RDD[T], RDD[U]) => RDD[V] + ): DStream[V] = { + val cleanedF = ssc.sparkContext.clean(transformFunc) + transformWith(other, (rdd1: RDD[T], rdd2: RDD[U], time: Time) => cleanedF(rdd1, rdd2)) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[U: ClassTag, V: ClassTag]( + other: DStream[U], transformFunc: (RDD[T], RDD[U], Time) => RDD[V] + ): DStream[V] = { + val cleanedF = ssc.sparkContext.clean(transformFunc) + val realTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + assert(rdds.length == 2) + val rdd1 = rdds(0).asInstanceOf[RDD[T]] + val rdd2 = rdds(1).asInstanceOf[RDD[U]] + cleanedF(rdd1, rdd2, time) + } + new TransformedDStream[V](Seq(this, other), realTransformFunc) } /** diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStreamCheckpointData.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStreamCheckpointData.scala index 58a0da2870..3fd5d52403 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStreamCheckpointData.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStreamCheckpointData.scala @@ -20,13 +20,16 @@ package org.apache.spark.streaming import org.apache.hadoop.fs.Path import org.apache.hadoop.fs.FileSystem import org.apache.hadoop.conf.Configuration + import collection.mutable.HashMap import org.apache.spark.Logging +import scala.collection.mutable.HashMap +import scala.reflect.ClassTag private[streaming] -class DStreamCheckpointData[T: ClassManifest] (dstream: DStream[T]) +class DStreamCheckpointData[T: ClassTag] (dstream: DStream[T]) extends Serializable with Logging { protected val data = new HashMap[Time, AnyRef]() @@ -107,4 +110,3 @@ class DStreamCheckpointData[T: ClassManifest] (dstream: DStream[T]) "[\n" + checkpointFiles.size + " checkpoint files \n" + checkpointFiles.mkString("\n") + "\n]" } } - diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala index b9a58fded6..a09b891956 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala @@ -21,9 +21,9 @@ import dstream.InputDStream import java.io.{ObjectInputStream, IOException, ObjectOutputStream} import collection.mutable.ArrayBuffer import org.apache.spark.Logging +import org.apache.spark.streaming.scheduler.Job final private[streaming] class DStreamGraph extends Serializable with Logging { - initLogging() private val inputStreams = new ArrayBuffer[InputDStream[_]]() private val outputStreams = new ArrayBuffer[DStream[_]]() diff --git a/streaming/src/main/scala/org/apache/spark/streaming/JobManager.scala b/streaming/src/main/scala/org/apache/spark/streaming/JobManager.scala deleted file mode 100644 index 5233129506..0000000000 --- a/streaming/src/main/scala/org/apache/spark/streaming/JobManager.scala +++ /dev/null @@ -1,88 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.streaming - -import org.apache.spark.Logging -import org.apache.spark.SparkEnv -import java.util.concurrent.Executors -import collection.mutable.HashMap -import collection.mutable.ArrayBuffer - - -private[streaming] -class JobManager(ssc: StreamingContext, numThreads: Int = 1) extends Logging { - - class JobHandler(ssc: StreamingContext, job: Job) extends Runnable { - def run() { - SparkEnv.set(ssc.env) - try { - val timeTaken = job.run() - logInfo("Total delay: %.5f s for job %s of time %s (execution: %.5f s)".format( - (System.currentTimeMillis() - job.time.milliseconds) / 1000.0, job.id, job.time.milliseconds, timeTaken / 1000.0)) - } catch { - case e: Exception => - logError("Running " + job + " failed", e) - } - clearJob(job) - } - } - - initLogging() - - val jobExecutor = Executors.newFixedThreadPool(numThreads) - val jobs = new HashMap[Time, ArrayBuffer[Job]] - - def runJob(job: Job) { - jobs.synchronized { - jobs.getOrElseUpdate(job.time, new ArrayBuffer[Job]) += job - } - jobExecutor.execute(new JobHandler(ssc, job)) - logInfo("Added " + job + " to queue") - } - - def stop() { - jobExecutor.shutdown() - } - - private def clearJob(job: Job) { - var timeCleared = false - val time = job.time - jobs.synchronized { - val jobsOfTime = jobs.get(time) - if (jobsOfTime.isDefined) { - jobsOfTime.get -= job - if (jobsOfTime.get.isEmpty) { - jobs -= time - timeCleared = true - } - } else { - throw new Exception("Job finished for time " + job.time + - " but time does not exist in jobs") - } - } - if (timeCleared) { - ssc.scheduler.clearOldMetadata(time) - } - } - - def getPendingTimes(): Array[Time] = { - jobs.synchronized { - jobs.keySet.toArray - } - } -} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala b/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala index 757bc98981..80af96c060 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala @@ -18,16 +18,15 @@ package org.apache.spark.streaming import org.apache.spark.streaming.StreamingContext._ -import org.apache.spark.streaming.dstream.{ReducedWindowedDStream, StateDStream} -import org.apache.spark.streaming.dstream.{CoGroupedDStream, ShuffledDStream} -import org.apache.spark.streaming.dstream.{MapValuedDStream, FlatMapValuedDStream} +import org.apache.spark.streaming.dstream._ import org.apache.spark.{Partitioner, HashPartitioner} import org.apache.spark.SparkContext._ -import org.apache.spark.rdd.{Manifests, RDD, PairRDDFunctions} +import org.apache.spark.rdd.{ClassTags, RDD, PairRDDFunctions} import org.apache.spark.storage.StorageLevel import scala.collection.mutable.ArrayBuffer +import scala.reflect.{ClassTag, classTag} import org.apache.hadoop.mapred.{JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} @@ -35,7 +34,7 @@ import org.apache.hadoop.mapred.OutputFormat import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.conf.Configuration -class PairDStreamFunctions[K: ClassManifest, V: ClassManifest](self: DStream[(K,V)]) +class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) extends Serializable { private[streaming] def ssc = self.ssc @@ -105,7 +104,7 @@ extends Serializable { * combineByKey for RDDs. Please refer to combineByKey in * [[org.apache.spark.rdd.PairRDDFunctions]] for more information. */ - def combineByKey[C: ClassManifest]( + def combineByKey[C: ClassTag]( createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiner: (C, C) => C, @@ -205,7 +204,7 @@ extends Serializable { * DStream's batching interval */ def reduceByKeyAndWindow( - reduceFunc: (V, V) => V, + reduceFunc: (V, V) => V, windowDuration: Duration, slideDuration: Duration ): DStream[(K, V)] = { @@ -336,7 +335,7 @@ extends Serializable { * corresponding state key-value pair will be eliminated. * @tparam S State type */ - def updateStateByKey[S: ClassManifest]( + def updateStateByKey[S: ClassTag]( updateFunc: (Seq[V], Option[S]) => Option[S] ): DStream[(K, S)] = { updateStateByKey(updateFunc, defaultPartitioner()) @@ -351,7 +350,7 @@ extends Serializable { * @param numPartitions Number of partitions of each RDD in the new DStream. * @tparam S State type */ - def updateStateByKey[S: ClassManifest]( + def updateStateByKey[S: ClassTag]( updateFunc: (Seq[V], Option[S]) => Option[S], numPartitions: Int ): DStream[(K, S)] = { @@ -359,7 +358,7 @@ extends Serializable { } /** - * Create a new "state" DStream where the state for each key is updated by applying + * Return a new "state" DStream where the state for each key is updated by applying * the given function on the previous state of the key and the new values of the key. * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. * @param updateFunc State update function. If `this` function returns None, then @@ -367,7 +366,7 @@ extends Serializable { * @param partitioner Partitioner for controlling the partitioning of each RDD in the new DStream. * @tparam S State type */ - def updateStateByKey[S: ClassManifest]( + def updateStateByKey[S: ClassTag]( updateFunc: (Seq[V], Option[S]) => Option[S], partitioner: Partitioner ): DStream[(K, S)] = { @@ -390,7 +389,7 @@ extends Serializable { * @param rememberPartitioner Whether to remember the paritioner object in the generated RDDs. * @tparam S State type */ - def updateStateByKey[S: ClassManifest]( + def updateStateByKey[S: ClassTag]( updateFunc: (Iterator[(K, Seq[V], Option[S])]) => Iterator[(K, S)], partitioner: Partitioner, rememberPartitioner: Boolean @@ -398,89 +397,171 @@ extends Serializable { new StateDStream(self, ssc.sc.clean(updateFunc), partitioner, rememberPartitioner) } - - def mapValues[U: ClassManifest](mapValuesFunc: V => U): DStream[(K, U)] = { + /** + * Return a new DStream by applying a map function to the value of each key-value pairs in + * 'this' DStream without changing the key. + */ + def mapValues[U: ClassTag](mapValuesFunc: V => U): DStream[(K, U)] = { new MapValuedDStream[K, V, U](self, mapValuesFunc) } - def flatMapValues[U: ClassManifest]( + /** + * Return a new DStream by applying a flatmap function to the value of each key-value pairs in + * 'this' DStream without changing the key. + */ + def flatMapValues[U: ClassTag]( flatMapValuesFunc: V => TraversableOnce[U] ): DStream[(K, U)] = { new FlatMapValuedDStream[K, V, U](self, flatMapValuesFunc) } /** - * Cogroup `this` DStream with `other` DStream. For each key k in corresponding RDDs of `this` - * or `other` DStreams, the generated RDD will contains a tuple with the list of values for that - * key in both RDDs. HashPartitioner is used to partition each generated RDD into default number + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with Spark's default number * of partitions. */ - def cogroup[W: ClassManifest](other: DStream[(K, W)]): DStream[(K, (Seq[V], Seq[W]))] = { + def cogroup[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (Seq[V], Seq[W]))] = { cogroup(other, defaultPartitioner()) } /** - * Cogroup `this` DStream with `other` DStream using a partitioner. For each key k in corresponding RDDs of `this` - * or `other` DStreams, the generated RDD will contains a tuple with the list of values for that - * key in both RDDs. Partitioner is used to partition each generated RDD. + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. */ - def cogroup[W: ClassManifest]( + def cogroup[W: ClassTag](other: DStream[(K, W)], numPartitions: Int): DStream[(K, (Seq[V], Seq[W]))] = { + cogroup(other, defaultPartitioner(numPartitions)) + } + + /** + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * The supplied [[org.apache.spark.Partitioner]] is used to partition the generated RDDs. + */ + def cogroup[W: ClassTag]( other: DStream[(K, W)], partitioner: Partitioner ): DStream[(K, (Seq[V], Seq[W]))] = { - - val cgd = new CoGroupedDStream[K]( - Seq(self.asInstanceOf[DStream[(K, _)]], other.asInstanceOf[DStream[(K, _)]]), - partitioner - ) - val pdfs = new PairDStreamFunctions[K, Seq[Seq[_]]](cgd)( - classManifest[K], - Manifests.seqSeqManifest + self.transformWith( + other, + (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.cogroup(rdd2, partitioner) ) - pdfs.mapValues { - case Seq(vs, ws) => - (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]]) - } } /** - * Join `this` DStream with `other` DStream. HashPartitioner is used - * to partition each generated RDD into default number of partitions. + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with Spark's default number of partitions. */ - def join[W: ClassManifest](other: DStream[(K, W)]): DStream[(K, (V, W))] = { + def join[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (V, W))] = { join[W](other, defaultPartitioner()) } /** - * Join `this` DStream with `other` DStream, that is, each RDD of the new DStream will - * be generated by joining RDDs from `this` and other DStream. Uses the given - * Partitioner to partition each generated RDD. + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. + */ + def join[W: ClassTag](other: DStream[(K, W)], numPartitions: Int): DStream[(K, (V, W))] = { + join[W](other, defaultPartitioner(numPartitions)) + } + + /** + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. */ - def join[W: ClassManifest]( + def join[W: ClassTag]( other: DStream[(K, W)], partitioner: Partitioner ): DStream[(K, (V, W))] = { - this.cogroup(other, partitioner) - .flatMapValues{ - case (vs, ws) => - for (v <- vs.iterator; w <- ws.iterator) yield (v, w) - } + self.transformWith( + other, + (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.join(rdd2, partitioner) + ) } /** - * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is generated - * based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix" + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default + * number of partitions. + */ + def leftOuterJoin[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (V, Option[W]))] = { + leftOuterJoin[W](other, defaultPartitioner()) + } + + /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with `numPartitions` + * partitions. + */ + def leftOuterJoin[W: ClassTag]( + other: DStream[(K, W)], + numPartitions: Int + ): DStream[(K, (V, Option[W]))] = { + leftOuterJoin[W](other, defaultPartitioner(numPartitions)) + } + + /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control + * the partitioning of each RDD. + */ + def leftOuterJoin[W: ClassTag]( + other: DStream[(K, W)], + partitioner: Partitioner + ): DStream[(K, (V, Option[W]))] = { + self.transformWith( + other, + (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.leftOuterJoin(rdd2, partitioner) + ) + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default + * number of partitions. + */ + def rightOuterJoin[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (Option[V], W))] = { + rightOuterJoin[W](other, defaultPartitioner()) + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with `numPartitions` + * partitions. + */ + def rightOuterJoin[W: ClassTag]( + other: DStream[(K, W)], + numPartitions: Int + ): DStream[(K, (Option[V], W))] = { + rightOuterJoin[W](other, defaultPartitioner(numPartitions)) + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control + * the partitioning of each RDD. + */ + def rightOuterJoin[W: ClassTag]( + other: DStream[(K, W)], + partitioner: Partitioner + ): DStream[(K, (Option[V], W))] = { + self.transformWith( + other, + (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.rightOuterJoin(rdd2, partitioner) + ) + } + + /** + * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval + * is generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix" */ def saveAsHadoopFiles[F <: OutputFormat[K, V]]( prefix: String, suffix: String - )(implicit fm: ClassManifest[F]) { - saveAsHadoopFiles(prefix, suffix, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]]) + )(implicit fm: ClassTag[F]) { + saveAsHadoopFiles(prefix, suffix, getKeyClass, getValueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } /** - * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is generated - * based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix" + * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval + * is generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix" */ def saveAsHadoopFiles( prefix: String, @@ -504,8 +585,8 @@ extends Serializable { def saveAsNewAPIHadoopFiles[F <: NewOutputFormat[K, V]]( prefix: String, suffix: String - )(implicit fm: ClassManifest[F]) { - saveAsNewAPIHadoopFiles(prefix, suffix, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]]) + )(implicit fm: ClassTag[F]) { + saveAsNewAPIHadoopFiles(prefix, suffix, getKeyClass, getValueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } /** @@ -527,9 +608,7 @@ extends Serializable { self.foreach(saveFunc) } - private def getKeyClass() = implicitly[ClassManifest[K]].erasure + private def getKeyClass() = implicitly[ClassTag[K]].runtimeClass - private def getValueClass() = implicitly[ClassManifest[V]].erasure + private def getValueClass() = implicitly[ClassTag[V]].runtimeClass } - - diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index 878725c705..304986f187 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -20,34 +20,36 @@ package org.apache.spark.streaming import akka.actor.Props import akka.actor.SupervisorStrategy import akka.zeromq.Subscribe +import akka.util.ByteString import org.apache.spark.streaming.dstream._ +import org.apache.spark.streaming.scheduler.StreamingListener import org.apache.spark._ import org.apache.spark.rdd.RDD -import org.apache.spark.streaming.receivers.ActorReceiver import org.apache.spark.streaming.receivers.ReceiverSupervisorStrategy import org.apache.spark.streaming.receivers.ZeroMQReceiver import org.apache.spark.storage.StorageLevel import org.apache.spark.util.MetadataCleaner import org.apache.spark.streaming.receivers.ActorReceiver +import org.apache.spark.streaming.scheduler.{JobScheduler, NetworkInputTracker} import scala.collection.mutable.Queue import scala.collection.Map +import scala.reflect.ClassTag import java.io.InputStream import java.util.concurrent.atomic.AtomicInteger -import java.util.UUID import org.apache.hadoop.io.LongWritable import org.apache.hadoop.io.Text import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import org.apache.hadoop.mapreduce.lib.input.TextInputFormat import org.apache.hadoop.fs.Path + import twitter4j.Status import twitter4j.auth.Authorization - /** * A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic * information (such as, cluster URL and job name) to internally create a SparkContext, it provides @@ -61,18 +63,27 @@ class StreamingContext private ( /** * Create a StreamingContext using an existing SparkContext. - * @param sparkContext Existing SparkContext - * @param batchDuration The time interval at which streaming data will be divided into batches + * @param sparkContext existing SparkContext + * @param batchDuration the time interval at which streaming data will be divided into batches */ def this(sparkContext: SparkContext, batchDuration: Duration) = { this(sparkContext, null, batchDuration) } /** + * Create a StreamingContext by providing the configuration necessary for a new SparkContext. + * @param conf a [[org.apache.spark.SparkConf]] object specifying Spark parameters + * @param batchDuration the time interval at which streaming data will be divided into batches + */ + def this(conf: SparkConf, batchDuration: Duration) = { + this(StreamingContext.createNewSparkContext(conf), null, batchDuration) + } + + /** * Create a StreamingContext by providing the details necessary for creating a new SparkContext. - * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). - * @param appName A name for your job, to display on the cluster web UI - * @param batchDuration The time interval at which streaming data will be divided into batches + * @param master cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). + * @param appName a name for your job, to display on the cluster web UI + * @param batchDuration the time interval at which streaming data will be divided into batches */ def this( master: String, @@ -85,22 +96,25 @@ class StreamingContext private ( null, batchDuration) } - /** * Re-create a StreamingContext from a checkpoint file. * @param path Path either to the directory that was specified as the checkpoint directory, or * to the checkpoint file 'graph' or 'graph.bk'. */ - def this(path: String) = this(null, CheckpointReader.read(path), null) - - initLogging() + def this(path: String) = this(null, CheckpointReader.read(new SparkConf(), path), null) if (sc_ == null && cp_ == null) { throw new Exception("Spark Streaming cannot be initialized with " + "both SparkContext and checkpoint as null") } - if (MetadataCleaner.getDelaySeconds < 0) { + private val conf_ = Option(sc_).map(_.conf).getOrElse(cp_.sparkConf) + + if(cp_ != null && cp_.delaySeconds >= 0 && MetadataCleaner.getDelaySeconds(conf_) < 0) { + MetadataCleaner.setDelaySeconds(conf_, cp_.delaySeconds) + } + + if (MetadataCleaner.getDelaySeconds(conf_) < 0) { throw new SparkException("Spark Streaming cannot be used without setting spark.cleaner.ttl; " + "set this property before creating a SparkContext (use SPARK_JAVA_OPTS for the shell)") } @@ -109,12 +123,14 @@ class StreamingContext private ( protected[streaming] val sc: SparkContext = { if (isCheckpointPresent) { - new SparkContext(cp_.master, cp_.framework, cp_.sparkHome, cp_.jars, cp_.environment) + new SparkContext(cp_.sparkConf) } else { sc_ } } + protected[streaming] val conf = sc.conf + protected[streaming] val env = SparkEnv.get protected[streaming] val graph: DStreamGraph = { @@ -135,16 +151,17 @@ class StreamingContext private ( protected[streaming] var checkpointDir: String = { if (isCheckpointPresent) { - sc.setCheckpointDir(StreamingContext.getSparkCheckpointDir(cp_.checkpointDir), true) + sc.setCheckpointDir(cp_.checkpointDir) cp_.checkpointDir } else { null } } - protected[streaming] var checkpointDuration: Duration = if (isCheckpointPresent) cp_.checkpointDuration else null - protected[streaming] var receiverJobThread: Thread = null - protected[streaming] var scheduler: Scheduler = null + protected[streaming] val checkpointDuration: Duration = { + if (isCheckpointPresent) cp_.checkpointDuration else graph.batchDuration + } + protected[streaming] val scheduler = new JobScheduler(this) /** * Return the associated Spark context @@ -169,8 +186,12 @@ class StreamingContext private ( */ def checkpoint(directory: String) { if (directory != null) { - sc.setCheckpointDir(StreamingContext.getSparkCheckpointDir(directory)) - checkpointDir = directory + val path = new Path(directory) + val fs = path.getFileSystem(sparkContext.hadoopConfiguration) + fs.mkdirs(path) + val fullPath = fs.getFileStatus(path).getPath().toString + sc.setCheckpointDir(fullPath) + checkpointDir = fullPath } else { checkpointDir = null } @@ -187,7 +208,7 @@ class StreamingContext private ( * Find more details at: http://spark-project.org/docs/latest/streaming-custom-receivers.html * @param receiver Custom implementation of NetworkReceiver */ - def networkStream[T: ClassManifest]( + def networkStream[T: ClassTag]( receiver: NetworkReceiver[T]): DStream[T] = { val inputStream = new PluggableInputDStream[T](this, receiver) @@ -207,7 +228,7 @@ class StreamingContext private ( * to ensure the type safety, i.e parametrized type of data received and actorStream * should be same. */ - def actorStream[T: ClassManifest]( + def actorStream[T: ClassTag]( props: Props, name: String, storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2, @@ -227,14 +248,14 @@ class StreamingContext private ( * and sub sequence refer to its payload. * @param storageLevel RDD storage level. Defaults to memory-only. */ - def zeroMQStream[T: ClassManifest]( + def zeroMQStream[T: ClassTag]( publisherUrl:String, subscribe: Subscribe, - bytesToObjects: Seq[Seq[Byte]] ⇒ Iterator[T], + bytesToObjects: Seq[ByteString] ⇒ Iterator[T], storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2, supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy ): DStream[T] = { - actorStream(Props(new ZeroMQReceiver(publisherUrl,subscribe,bytesToObjects)), + actorStream(Props(new ZeroMQReceiver(publisherUrl, subscribe, bytesToObjects)), "ZeroMQReceiver", storageLevel, supervisorStrategy) } @@ -252,10 +273,14 @@ class StreamingContext private ( groupId: String, topics: Map[String, Int], storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2 - ): DStream[String] = { + ): DStream[(String, String)] = { val kafkaParams = Map[String, String]( - "zk.connect" -> zkQuorum, "groupid" -> groupId, "zk.connectiontimeout.ms" -> "10000") - kafkaStream[String, kafka.serializer.StringDecoder](kafkaParams, topics, storageLevel) + "zookeeper.connect" -> zkQuorum, "group.id" -> groupId, + "zookeeper.connection.timeout.ms" -> "10000") + kafkaStream[String, String, kafka.serializer.StringDecoder, kafka.serializer.StringDecoder]( + kafkaParams, + topics, + storageLevel) } /** @@ -266,12 +291,16 @@ class StreamingContext private ( * in its own thread. * @param storageLevel Storage level to use for storing the received objects */ - def kafkaStream[T: ClassManifest, D <: kafka.serializer.Decoder[_]: Manifest]( + def kafkaStream[ + K: ClassTag, + V: ClassTag, + U <: kafka.serializer.Decoder[_]: Manifest, + T <: kafka.serializer.Decoder[_]: Manifest]( kafkaParams: Map[String, String], topics: Map[String, Int], storageLevel: StorageLevel - ): DStream[T] = { - val inputStream = new KafkaInputDStream[T, D](this, kafkaParams, topics, storageLevel) + ): DStream[(K, V)] = { + val inputStream = new KafkaInputDStream[K, V, U, T](this, kafkaParams, topics, storageLevel) registerInputStream(inputStream) inputStream } @@ -303,7 +332,7 @@ class StreamingContext private ( * @param storageLevel Storage level to use for storing the received objects * @tparam T Type of the objects received (after converting bytes to objects) */ - def socketStream[T: ClassManifest]( + def socketStream[T: ClassTag]( hostname: String, port: Int, converter: (InputStream) => Iterator[T], @@ -325,7 +354,7 @@ class StreamingContext private ( port: Int, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 ): DStream[SparkFlumeEvent] = { - val inputStream = new FlumeInputDStream(this, hostname, port, storageLevel) + val inputStream = new FlumeInputDStream[SparkFlumeEvent](this, hostname, port, storageLevel) registerInputStream(inputStream) inputStream } @@ -340,7 +369,7 @@ class StreamingContext private ( * @param storageLevel Storage level to use for storing the received objects * @tparam T Type of the objects in the received blocks */ - def rawSocketStream[T: ClassManifest]( + def rawSocketStream[T: ClassTag]( hostname: String, port: Int, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 @@ -353,16 +382,17 @@ class StreamingContext private ( /** * Create a input stream that monitors a Hadoop-compatible filesystem * for new files and reads them using the given key-value types and input format. - * File names starting with . are ignored. + * Files must be written to the monitored directory by "moving" them from another + * location within the same file system. File names starting with . are ignored. * @param directory HDFS directory to monitor for new file * @tparam K Key type for reading HDFS file * @tparam V Value type for reading HDFS file * @tparam F Input format for reading HDFS file */ def fileStream[ - K: ClassManifest, - V: ClassManifest, - F <: NewInputFormat[K, V]: ClassManifest + K: ClassTag, + V: ClassTag, + F <: NewInputFormat[K, V]: ClassTag ] (directory: String): DStream[(K, V)] = { val inputStream = new FileInputDStream[K, V, F](this, directory) registerInputStream(inputStream) @@ -372,6 +402,8 @@ class StreamingContext private ( /** * Create a input stream that monitors a Hadoop-compatible filesystem * for new files and reads them using the given key-value types and input format. + * Files must be written to the monitored directory by "moving" them from another + * location within the same file system. * @param directory HDFS directory to monitor for new file * @param filter Function to filter paths to process * @param newFilesOnly Should process only new files and ignore existing files in the directory @@ -380,9 +412,9 @@ class StreamingContext private ( * @tparam F Input format for reading HDFS file */ def fileStream[ - K: ClassManifest, - V: ClassManifest, - F <: NewInputFormat[K, V]: ClassManifest + K: ClassTag, + V: ClassTag, + F <: NewInputFormat[K, V]: ClassTag ] (directory: String, filter: Path => Boolean, newFilesOnly: Boolean): DStream[(K, V)] = { val inputStream = new FileInputDStream[K, V, F](this, directory, filter, newFilesOnly) registerInputStream(inputStream) @@ -392,7 +424,9 @@ class StreamingContext private ( /** * Create a input stream that monitors a Hadoop-compatible filesystem * for new files and reads them as text files (using key as LongWritable, value - * as Text and input format as TextInputFormat). File names starting with . are ignored. + * as Text and input format as TextInputFormat). Files must be written to the + * monitored directory by "moving" them from another location within the same + * file system. File names starting with . are ignored. * @param directory HDFS directory to monitor for new file */ def textFileStream(directory: String): DStream[String] = { @@ -424,7 +458,7 @@ class StreamingContext private ( * @param oneAtATime Whether only one RDD should be consumed from the queue in every interval * @tparam T Type of objects in the RDD */ - def queueStream[T: ClassManifest]( + def queueStream[T: ClassTag]( queue: Queue[RDD[T]], oneAtATime: Boolean = true ): DStream[T] = { @@ -440,7 +474,7 @@ class StreamingContext private ( * Set as null if no RDD should be returned when empty * @tparam T Type of objects in the RDD */ - def queueStream[T: ClassManifest]( + def queueStream[T: ClassTag]( queue: Queue[RDD[T]], oneAtATime: Boolean, defaultRDD: RDD[T] @@ -450,14 +484,40 @@ class StreamingContext private ( inputStream } +/** + * Create an input stream that receives messages pushed by a mqtt publisher. + * @param brokerUrl Url of remote mqtt publisher + * @param topic topic name to subscribe to + * @param storageLevel RDD storage level. Defaults to memory-only. + */ + + def mqttStream( + brokerUrl: String, + topic: String, + storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2): DStream[String] = { + val inputStream = new MQTTInputDStream[String](this, brokerUrl, topic, storageLevel) + registerInputStream(inputStream) + inputStream + } /** - * Create a unified DStream from multiple DStreams of the same type and same interval + * Create a unified DStream from multiple DStreams of the same type and same slide duration. */ - def union[T: ClassManifest](streams: Seq[DStream[T]]): DStream[T] = { + def union[T: ClassTag](streams: Seq[DStream[T]]): DStream[T] = { new UnionDStream[T](streams.toArray) } /** + * Create a new DStream in which each RDD is generated by applying a function on RDDs of + * the DStreams. + */ + def transform[T: ClassTag]( + dstreams: Seq[DStream[_]], + transformFunc: (Seq[RDD[_]], Time) => RDD[T] + ): DStream[T] = { + new TransformedDStream[T](dstreams, sparkContext.clean(transformFunc)) + } + + /** * Register an input stream that will be started (InputDStream.start() called) to get the * input data. */ @@ -472,6 +532,13 @@ class StreamingContext private ( graph.addOutputStream(outputStream) } + /** Add a [[org.apache.spark.streaming.scheduler.StreamingListener]] object for + * receiving system events related to streaming. + */ + def addStreamingListener(streamingListener: StreamingListener) { + scheduler.listenerBus.addListener(streamingListener) + } + protected def validate() { assert(graph != null, "Graph is null") graph.validate() @@ -487,27 +554,22 @@ class StreamingContext private ( * Start the execution of the streams. */ def start() { - if (checkpointDir != null && checkpointDuration == null && graph != null) { - checkpointDuration = graph.batchDuration - } - validate() + // Get the network input streams val networkInputStreams = graph.getInputStreams().filter(s => s match { case n: NetworkInputDStream[_] => true case _ => false }).map(_.asInstanceOf[NetworkInputDStream[_]]).toArray + // Start the network input tracker (must start before receivers) if (networkInputStreams.length > 0) { - // Start the network input tracker (must start before receivers) networkInputTracker = new NetworkInputTracker(this, networkInputStreams) networkInputTracker.start() } - Thread.sleep(1000) // Start the scheduler - scheduler = new Scheduler(this) scheduler.start() } @@ -518,7 +580,6 @@ class StreamingContext private ( try { if (scheduler != null) scheduler.stop() if (networkInputTracker != null) networkInputTracker.stop() - if (receiverJobThread != null) receiverJobThread.interrupt() sc.stop() logInfo("StreamingContext stopped successfully") } catch { @@ -530,22 +591,34 @@ class StreamingContext private ( object StreamingContext { - implicit def toPairDStreamFunctions[K: ClassManifest, V: ClassManifest](stream: DStream[(K,V)]) = { + implicit def toPairDStreamFunctions[K: ClassTag, V: ClassTag](stream: DStream[(K,V)]) = { new PairDStreamFunctions[K, V](stream) } + protected[streaming] def createNewSparkContext(conf: SparkConf): SparkContext = { + // Set the default cleaner delay to an hour if not already set. + // This should be sufficient for even 1 second batch intervals. + val sc = new SparkContext(conf) + if (MetadataCleaner.getDelaySeconds(sc.conf) < 0) { + MetadataCleaner.setDelaySeconds(sc.conf, 3600) + } + sc + } + protected[streaming] def createNewSparkContext( master: String, appName: String, sparkHome: String, jars: Seq[String], - environment: Map[String, String]): SparkContext = { + environment: Map[String, String]): SparkContext = + { + val sc = new SparkContext(master, appName, sparkHome, jars, environment) // Set the default cleaner delay to an hour if not already set. - // This should be sufficient for even 1 second interval. - if (MetadataCleaner.getDelaySeconds < 0) { - MetadataCleaner.setDelaySeconds(3600) + // This should be sufficient for even 1 second batch intervals. + if (MetadataCleaner.getDelaySeconds(sc.conf) < 0) { + MetadataCleaner.setDelaySeconds(sc.conf, 3600) } - new SparkContext(master, appName, sparkHome, jars, environment) + sc } protected[streaming] def rddToFileName[T](prefix: String, suffix: String, time: Time): String = { @@ -557,8 +630,4 @@ object StreamingContext { prefix + "-" + time.milliseconds + "." + suffix } } - - protected[streaming] def getSparkCheckpointDir(sscCheckpointDir: String): String = { - new Path(sscCheckpointDir, UUID.randomUUID.toString).toString - } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStream.scala index d1932b6b05..d29033df32 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStream.scala @@ -23,9 +23,11 @@ import org.apache.spark.api.java.JavaRDD import org.apache.spark.storage.StorageLevel import org.apache.spark.rdd.RDD +import scala.reflect.ClassTag + /** * A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous - * sequence of RDDs (of the same type) representing a continuous stream of data (see [[org.apache.spark.RDD]] + * sequence of RDDs (of the same type) representing a continuous stream of data (see [[org.apache.spark.rdd.RDD]] * for more details on RDDs). DStreams can either be created from live data (such as, data from * HDFS, Kafka or Flume) or it can be generated by transformation existing DStreams using operations * such as `map`, `window` and `reduceByKeyAndWindow`. While a Spark Streaming program is running, each @@ -41,7 +43,7 @@ import org.apache.spark.rdd.RDD * - A time interval at which the DStream generates an RDD * - A function that is used to generate an RDD after each time interval */ -class JavaDStream[T](val dstream: DStream[T])(implicit val classManifest: ClassManifest[T]) +class JavaDStream[T](val dstream: DStream[T])(implicit val classTag: ClassTag[T]) extends JavaDStreamLike[T, JavaDStream[T], JavaRDD[T]] { override def wrapRDD(rdd: RDD[T]): JavaRDD[T] = JavaRDD.fromRDD(rdd) @@ -94,9 +96,15 @@ class JavaDStream[T](val dstream: DStream[T])(implicit val classManifest: ClassM */ def union(that: JavaDStream[T]): JavaDStream[T] = dstream.union(that.dstream) + + /** + * Return a new DStream with an increased or decreased level of parallelism. Each RDD in the + * returned DStream has exactly numPartitions partitions. + */ + def repartition(numPartitions: Int): JavaDStream[T] = dstream.repartition(numPartitions) } object JavaDStream { - implicit def fromDStream[T: ClassManifest](dstream: DStream[T]): JavaDStream[T] = + implicit def fromDStream[T: ClassTag](dstream: DStream[T]): JavaDStream[T] = new JavaDStream[T](dstream) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala index 459695b7ca..64f38ce1c0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala @@ -21,17 +21,19 @@ import java.util.{List => JList} import java.lang.{Long => JLong} import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import org.apache.spark.streaming._ import org.apache.spark.api.java.{JavaPairRDD, JavaRDDLike, JavaRDD} -import org.apache.spark.api.java.function.{Function2 => JFunction2, Function => JFunction, _} +import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} +import org.apache.spark.api.java.function.{Function3 => JFunction3, _} import java.util import org.apache.spark.rdd.RDD import JavaDStream._ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T, R]] extends Serializable { - implicit val classManifest: ClassManifest[T] + implicit val classTag: ClassTag[T] def dstream: DStream[T] @@ -120,10 +122,12 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T * this DStream. Applying glom() to an RDD coalesces all elements within each partition into * an array. */ - def glom(): JavaDStream[JList[T]] = + def glom(): JavaDStream[JList[T]] = { new JavaDStream(dstream.glom().map(x => new java.util.ArrayList[T](x.toSeq))) + } + - /** Return the StreamingContext associated with this DStream */ + /** Return the [[org.apache.spark.streaming.StreamingContext]] associated with this DStream */ def context(): StreamingContext = dstream.context() /** Return a new DStream by applying a function to all elements of this DStream. */ @@ -133,7 +137,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** Return a new DStream by applying a function to all elements of this DStream. */ def map[K2, V2](f: PairFunction[T, K2, V2]): JavaPairDStream[K2, V2] = { - def cm = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K2, V2]]] + def cm = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[Tuple2[K2, V2]]] new JavaPairDStream(dstream.map(f)(cm))(f.keyType(), f.valueType()) } @@ -154,7 +158,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T def flatMap[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairDStream[K2, V2] = { import scala.collection.JavaConverters._ def fn = (x: T) => f.apply(x).asScala - def cm = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K2, V2]]] + def cm = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[Tuple2[K2, V2]]] new JavaPairDStream(dstream.flatMap(fn)(cm))(f.keyType(), f.valueType()) } @@ -238,7 +242,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Apply a function to each RDD in this DStream. This is an output operator, so - * this DStream will be registered as an output stream and therefore materialized. + * 'this' DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: JFunction[R, Void]) { dstream.foreach(rdd => foreachFunc.call(wrapRDD(rdd))) @@ -246,7 +250,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Apply a function to each RDD in this DStream. This is an output operator, so - * this DStream will be registered as an output stream and therefore materialized. + * 'this' DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: JFunction2[R, Time, Void]) { dstream.foreach((rdd, time) => foreachFunc.call(wrapRDD(rdd), time)) @@ -254,11 +258,11 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[U](transformFunc: JFunction[R, JavaRDD[U]]): JavaDStream[U] = { - implicit val cm: ClassManifest[U] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] + implicit val cm: ClassTag[U] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] def scalaTransform (in: RDD[T]): RDD[U] = transformFunc.call(wrapRDD(in)).rdd dstream.transform(scalaTransform(_)) @@ -266,11 +270,11 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[U](transformFunc: JFunction2[R, Time, JavaRDD[U]]): JavaDStream[U] = { - implicit val cm: ClassManifest[U] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] + implicit val cm: ClassTag[U] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] def scalaTransform (in: RDD[T], time: Time): RDD[U] = transformFunc.call(wrapRDD(in), time).rdd dstream.transform(scalaTransform(_, _)) @@ -278,14 +282,14 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[K2, V2](transformFunc: JFunction[R, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2] = { - implicit val cmk: ClassManifest[K2] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K2]] - implicit val cmv: ClassManifest[V2] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V2]] + implicit val cmk: ClassTag[K2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K2]] + implicit val cmv: ClassTag[V2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V2]] def scalaTransform (in: RDD[T]): RDD[(K2, V2)] = transformFunc.call(wrapRDD(in)).rdd dstream.transform(scalaTransform(_)) @@ -293,20 +297,96 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T /** * Return a new DStream in which each RDD is generated by applying a function - * on each RDD of this DStream. + * on each RDD of 'this' DStream. */ def transform[K2, V2](transformFunc: JFunction2[R, Time, JavaPairRDD[K2, V2]]): JavaPairDStream[K2, V2] = { - implicit val cmk: ClassManifest[K2] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K2]] - implicit val cmv: ClassManifest[V2] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V2]] + implicit val cmk: ClassTag[K2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K2]] + implicit val cmv: ClassTag[V2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V2]] def scalaTransform (in: RDD[T], time: Time): RDD[(K2, V2)] = transformFunc.call(wrapRDD(in), time).rdd dstream.transform(scalaTransform(_, _)) } /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[U, W]( + other: JavaDStream[U], + transformFunc: JFunction3[R, JavaRDD[U], Time, JavaRDD[W]] + ): JavaDStream[W] = { + implicit val cmu: ClassTag[U] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] + implicit val cmv: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + def scalaTransform (inThis: RDD[T], inThat: RDD[U], time: Time): RDD[W] = + transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + dstream.transformWith[U, W](other.dstream, scalaTransform(_, _, _)) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[U, K2, V2]( + other: JavaDStream[U], + transformFunc: JFunction3[R, JavaRDD[U], Time, JavaPairRDD[K2, V2]] + ): JavaPairDStream[K2, V2] = { + implicit val cmu: ClassTag[U] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] + implicit val cmk2: ClassTag[K2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K2]] + implicit val cmv2: ClassTag[V2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V2]] + def scalaTransform (inThis: RDD[T], inThat: RDD[U], time: Time): RDD[(K2, V2)] = + transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + dstream.transformWith[U, (K2, V2)](other.dstream, scalaTransform(_, _, _)) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[K2, V2, W]( + other: JavaPairDStream[K2, V2], + transformFunc: JFunction3[R, JavaPairRDD[K2, V2], Time, JavaRDD[W]] + ): JavaDStream[W] = { + implicit val cmk2: ClassTag[K2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K2]] + implicit val cmv2: ClassTag[V2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V2]] + implicit val cmw: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[W] = + transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + dstream.transformWith[(K2, V2), W](other.dstream, scalaTransform(_, _, _)) + } + + /** + * Return a new DStream in which each RDD is generated by applying a function + * on each RDD of 'this' DStream and 'other' DStream. + */ + def transformWith[K2, V2, K3, V3]( + other: JavaPairDStream[K2, V2], + transformFunc: JFunction3[R, JavaPairRDD[K2, V2], Time, JavaPairRDD[K3, V3]] + ): JavaPairDStream[K3, V3] = { + implicit val cmk2: ClassTag[K2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K2]] + implicit val cmv2: ClassTag[V2] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V2]] + implicit val cmk3: ClassTag[K3] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K3]] + implicit val cmv3: ClassTag[V3] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V3]] + def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[(K3, V3)] = + transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + dstream.transformWith[(K2, V2), (K3, V3)](other.dstream, scalaTransform(_, _, _)) + } + + /** * Enable periodic checkpointing of RDDs of this DStream * @param interval Time interval after which generated RDD will be checkpointed */ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala index 978fca33ad..dfd6e27c3e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala @@ -21,10 +21,11 @@ import java.util.{List => JList} import java.lang.{Long => JLong} import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext._ -import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} +import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2, Function3 => JFunction3} import org.apache.spark.Partitioner import org.apache.hadoop.mapred.{JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} @@ -36,8 +37,8 @@ import org.apache.spark.rdd.RDD import org.apache.spark.rdd.PairRDDFunctions class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( - implicit val kManifiest: ClassManifest[K], - implicit val vManifest: ClassManifest[V]) + implicit val kManifest: ClassTag[K], + implicit val vManifest: ClassTag[V]) extends JavaDStreamLike[(K, V), JavaPairDStream[K, V], JavaPairRDD[K, V]] { override def wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V] = JavaPairRDD.fromRDD(rdd) @@ -59,6 +60,12 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( /** Persist the RDDs of this DStream with the given storage level */ def persist(storageLevel: StorageLevel): JavaPairDStream[K, V] = dstream.persist(storageLevel) + /** + * Return a new DStream with an increased or decreased level of parallelism. Each RDD in the + * returned DStream has exactly numPartitions partitions. + */ + def repartition(numPartitions: Int): JavaPairDStream[K, V] = dstream.repartition(numPartitions) + /** Method that generates a RDD for the given Duration */ def compute(validTime: Time): JavaPairRDD[K, V] = { dstream.compute(validTime) match { @@ -148,7 +155,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( /** * Combine elements of each key in DStream's RDDs using custom function. This is similar to the - * combineByKey for RDDs. Please refer to combineByKey in [[PairRDDFunctions]] for more + * combineByKey for RDDs. Please refer to combineByKey in [[org.apache.spark.PairRDDFunctions]] for more * information. */ def combineByKey[C](createCombiner: JFunction[V, C], @@ -156,8 +163,8 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( mergeCombiners: JFunction2[C, C, C], partitioner: Partitioner ): JavaPairDStream[K, C] = { - implicit val cm: ClassManifest[C] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[C]] + implicit val cm: ClassTag[C] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[C]] dstream.combineByKey(createCombiner, mergeValue, mergeCombiners, partitioner) } @@ -413,7 +420,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( } /** - * Create a new "state" DStream where the state for each key is updated by applying + * Return a new "state" DStream where the state for each key is updated by applying * the given function on the previous state of the key and the new values of each key. * Hash partitioning is used to generate the RDDs with Spark's default number of partitions. * @param updateFunc State update function. If `this` function returns None, then @@ -422,13 +429,13 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( */ def updateStateByKey[S](updateFunc: JFunction2[JList[V], Optional[S], Optional[S]]) : JavaPairDStream[K, S] = { - implicit val cm: ClassManifest[S] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[S]] + implicit val cm: ClassTag[S] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[S]] dstream.updateStateByKey(convertUpdateStateFunction(updateFunc)) } /** - * Create a new "state" DStream where the state for each key is updated by applying + * Return a new "state" DStream where the state for each key is updated by applying * the given function on the previous state of the key and the new values of each key. * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. * @param updateFunc State update function. If `this` function returns None, then @@ -436,15 +443,17 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * @param numPartitions Number of partitions of each RDD in the new DStream. * @tparam S State type */ - def updateStateByKey[S: ClassManifest]( + def updateStateByKey[S]( updateFunc: JFunction2[JList[V], Optional[S], Optional[S]], numPartitions: Int) : JavaPairDStream[K, S] = { + implicit val cm: ClassTag[S] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[S]] dstream.updateStateByKey(convertUpdateStateFunction(updateFunc), numPartitions) } /** - * Create a new "state" DStream where the state for each key is updated by applying + * Return a new "state" DStream where the state for each key is updated by applying * the given function on the previous state of the key and the new values of the key. * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. * @param updateFunc State update function. If `this` function returns None, then @@ -452,75 +461,194 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * @param partitioner Partitioner for controlling the partitioning of each RDD in the new DStream. * @tparam S State type */ - def updateStateByKey[S: ClassManifest]( + def updateStateByKey[S]( updateFunc: JFunction2[JList[V], Optional[S], Optional[S]], partitioner: Partitioner ): JavaPairDStream[K, S] = { + implicit val cm: ClassTag[S] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[S]] dstream.updateStateByKey(convertUpdateStateFunction(updateFunc), partitioner) } + + /** + * Return a new DStream by applying a map function to the value of each key-value pairs in + * 'this' DStream without changing the key. + */ def mapValues[U](f: JFunction[V, U]): JavaPairDStream[K, U] = { - implicit val cm: ClassManifest[U] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] + implicit val cm: ClassTag[U] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] dstream.mapValues(f) } + /** + * Return a new DStream by applying a flatmap function to the value of each key-value pairs in + * 'this' DStream without changing the key. + */ def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairDStream[K, U] = { import scala.collection.JavaConverters._ def fn = (x: V) => f.apply(x).asScala - implicit val cm: ClassManifest[U] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]] + implicit val cm: ClassTag[U] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] dstream.flatMapValues(fn) } /** - * Cogroup `this` DStream with `other` DStream. For each key k in corresponding RDDs of `this` - * or `other` DStreams, the generated RDD will contains a tuple with the list of values for that - * key in both RDDs. HashPartitioner is used to partition each generated RDD into default number + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with Spark's default number * of partitions. */ def cogroup[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (JList[V], JList[W])] = { - implicit val cm: ClassManifest[W] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] dstream.cogroup(other.dstream).mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) } /** - * Cogroup `this` DStream with `other` DStream. For each key k in corresponding RDDs of `this` - * or `other` DStreams, the generated RDD will contains a tuple with the list of values for that - * key in both RDDs. Partitioner is used to partition each generated RDD. + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. */ - def cogroup[W](other: JavaPairDStream[K, W], partitioner: Partitioner) - : JavaPairDStream[K, (JList[V], JList[W])] = { - implicit val cm: ClassManifest[W] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + def cogroup[W]( + other: JavaPairDStream[K, W], + numPartitions: Int + ): JavaPairDStream[K, (JList[V], JList[W])] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + dstream.cogroup(other.dstream, numPartitions) + .mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) + } + + /** + * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. + */ + def cogroup[W]( + other: JavaPairDStream[K, W], + partitioner: Partitioner + ): JavaPairDStream[K, (JList[V], JList[W])] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] dstream.cogroup(other.dstream, partitioner) - .mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) + .mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) } /** - * Join `this` DStream with `other` DStream. HashPartitioner is used - * to partition each generated RDD into default number of partitions. + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with Spark's default number of partitions. */ def join[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, W)] = { - implicit val cm: ClassManifest[W] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] dstream.join(other.dstream) } /** - * Join `this` DStream with `other` DStream, that is, each RDD of the new DStream will - * be generated by joining RDDs from `this` and other DStream. Uses the given - * Partitioner to partition each generated RDD. + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. */ - def join[W](other: JavaPairDStream[K, W], partitioner: Partitioner) - : JavaPairDStream[K, (V, W)] = { - implicit val cm: ClassManifest[W] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[W]] + def join[W](other: JavaPairDStream[K, W], numPartitions: Int): JavaPairDStream[K, (V, W)] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + dstream.join(other.dstream, numPartitions) + } + + /** + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. + */ + def join[W]( + other: JavaPairDStream[K, W], + partitioner: Partitioner + ): JavaPairDStream[K, (V, W)] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] dstream.join(other.dstream, partitioner) } /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default + * number of partitions. + */ + def leftOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (V, Optional[W])] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + val joinResult = dstream.leftOuterJoin(other.dstream) + joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))} + } + + /** + * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with `numPartitions` + * partitions. + */ + def leftOuterJoin[W]( + other: JavaPairDStream[K, W], + numPartitions: Int + ): JavaPairDStream[K, (V, Optional[W])] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + val joinResult = dstream.leftOuterJoin(other.dstream, numPartitions) + joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))} + } + + /** + * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream. + * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD. + */ + def leftOuterJoin[W]( + other: JavaPairDStream[K, W], + partitioner: Partitioner + ): JavaPairDStream[K, (V, Optional[W])] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + val joinResult = dstream.leftOuterJoin(other.dstream, partitioner) + joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))} + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with Spark's default + * number of partitions. + */ + def rightOuterJoin[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (Optional[V], W)] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + val joinResult = dstream.rightOuterJoin(other.dstream) + joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)} + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. Hash partitioning is used to generate the RDDs with `numPartitions` + * partitions. + */ + def rightOuterJoin[W]( + other: JavaPairDStream[K, W], + numPartitions: Int + ): JavaPairDStream[K, (Optional[V], W)] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + val joinResult = dstream.rightOuterJoin(other.dstream, numPartitions) + joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)} + } + + /** + * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and + * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control + * the partitioning of each RDD. + */ + def rightOuterJoin[W]( + other: JavaPairDStream[K, W], + partitioner: Partitioner + ): JavaPairDStream[K, (Optional[V], W)] = { + implicit val cm: ClassTag[W] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[W]] + val joinResult = dstream.rightOuterJoin(other.dstream, partitioner) + joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)} + } + + /** * Save each RDD in `this` DStream as a Hadoop file. The file name at each batch interval is * generated based on `prefix` and `suffix`: "prefix-TIME_IN_MS.suffix". */ @@ -590,24 +718,29 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( dstream.saveAsNewAPIHadoopFiles(prefix, suffix, keyClass, valueClass, outputFormatClass, conf) } - override val classManifest: ClassManifest[(K, V)] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K, V]]] + /** Convert to a JavaDStream */ + def toJavaDStream(): JavaDStream[(K, V)] = { + new JavaDStream[(K, V)](dstream) + } + + override val classTag: ClassTag[(K, V)] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[Tuple2[K, V]]] } object JavaPairDStream { - implicit def fromPairDStream[K: ClassManifest, V: ClassManifest](dstream: DStream[(K, V)]) - :JavaPairDStream[K, V] = + implicit def fromPairDStream[K: ClassTag, V: ClassTag](dstream: DStream[(K, V)]) = { new JavaPairDStream[K, V](dstream) + } def fromJavaDStream[K, V](dstream: JavaDStream[(K, V)]): JavaPairDStream[K, V] = { - implicit val cmk: ClassManifest[K] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]] - implicit val cmv: ClassManifest[V] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V]] + implicit val cmk: ClassTag[K] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val cmv: ClassTag[V] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] new JavaPairDStream[K, V](dstream.dstream) } - def scalaToJavaLong[K: ClassManifest](dstream: JavaPairDStream[K, Long]) + def scalaToJavaLong[K: ClassTag](dstream: JavaPairDStream[K, Long]) : JavaPairDStream[K, JLong] = { StreamingContext.toPairDStreamFunctions(dstream.dstream).mapValues(new JLong(_)) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 54ba3e6025..b79173c6aa 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -17,26 +17,30 @@ package org.apache.spark.streaming.api.java -import java.lang.{Long => JLong, Integer => JInt} +import java.lang.{Integer => JInt} import java.io.InputStream -import java.util.{Map => JMap} +import java.util.{Map => JMap, List => JList} import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import twitter4j.Status import akka.actor.Props import akka.actor.SupervisorStrategy import akka.zeromq.Subscribe +import akka.util.ByteString + import twitter4j.auth.Authorization import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} -import org.apache.spark.api.java.{JavaSparkContext, JavaRDD} +import org.apache.spark.api.java.{JavaPairRDD, JavaSparkContext, JavaRDD} import org.apache.spark.streaming._ import org.apache.spark.streaming.dstream._ -import org.apache.spark.streaming.receivers.{ActorReceiver, ReceiverSupervisorStrategy} +import org.apache.spark.SparkConf +import org.apache.spark.streaming.scheduler.StreamingListener /** * A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic @@ -121,6 +125,14 @@ class JavaStreamingContext(val ssc: StreamingContext) { this(new StreamingContext(sparkContext.sc, batchDuration)) /** + * Creates a StreamingContext using an existing SparkContext. + * @param conf A Spark application configuration + * @param batchDuration The time interval at which streaming data will be divided into batches + */ + def this(conf: SparkConf, batchDuration: Duration) = + this(new StreamingContext(conf, batchDuration)) + + /** * Re-creates a StreamingContext from a checkpoint file. * @param path Path either to the directory that was specified as the checkpoint directory, or * to the checkpoint file 'graph' or 'graph.bk'. @@ -141,11 +153,12 @@ class JavaStreamingContext(val ssc: StreamingContext) { zkQuorum: String, groupId: String, topics: JMap[String, JInt]) - : JavaDStream[String] = { - implicit val cmt: ClassManifest[String] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[String]] + : JavaPairDStream[String, String] = { + implicit val cmt: ClassTag[String] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]] ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), StorageLevel.MEMORY_ONLY_SER_2) + } /** @@ -162,34 +175,43 @@ class JavaStreamingContext(val ssc: StreamingContext) { groupId: String, topics: JMap[String, JInt], storageLevel: StorageLevel) - : JavaDStream[String] = { - implicit val cmt: ClassManifest[String] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[String]] + : JavaPairDStream[String, String] = { + implicit val cmt: ClassTag[String] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]] ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel) } /** * Create an input stream that pulls messages form a Kafka Broker. - * @param typeClass Type of RDD - * @param decoderClass Type of kafka decoder + * @param keyTypeClass Key type of RDD + * @param valueTypeClass value type of RDD + * @param keyDecoderClass Type of kafka key decoder + * @param valueDecoderClass Type of kafka value decoder * @param kafkaParams Map of kafka configuration paramaters. * See: http://kafka.apache.org/configuration.html * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed * in its own thread. * @param storageLevel RDD storage level. Defaults to memory-only */ - def kafkaStream[T, D <: kafka.serializer.Decoder[_]]( - typeClass: Class[T], - decoderClass: Class[D], + def kafkaStream[K, V, U <: kafka.serializer.Decoder[_], T <: kafka.serializer.Decoder[_]]( + keyTypeClass: Class[K], + valueTypeClass: Class[V], + keyDecoderClass: Class[U], + valueDecoderClass: Class[T], kafkaParams: JMap[String, String], topics: JMap[String, JInt], storageLevel: StorageLevel) - : JavaDStream[T] = { - implicit val cmt: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - implicit val cmd: Manifest[D] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[D]] - ssc.kafkaStream[T, D]( + : JavaPairDStream[K, V] = { + implicit val keyCmt: ClassTag[K] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val valueCmt: ClassTag[V] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] + + implicit val keyCmd: Manifest[U] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[U]] + implicit val valueCmd: Manifest[T] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[T]] + + ssc.kafkaStream[K, V, U, T]( kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel) @@ -237,15 +259,17 @@ class JavaStreamingContext(val ssc: StreamingContext) { storageLevel: StorageLevel) : JavaDStream[T] = { def fn = (x: InputStream) => converter.apply(x).toIterator - implicit val cmt: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cmt: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] ssc.socketStream(hostname, port, fn, storageLevel) } /** - * Creates a input stream that monitors a Hadoop-compatible filesystem + * Create a input stream that monitors a Hadoop-compatible filesystem * for new files and reads them as text files (using key as LongWritable, value - * as Text and input format as TextInputFormat). File names starting with . are ignored. + * as Text and input format as TextInputFormat). Files must be written to the + * monitored directory by "moving" them from another location within the same + * file system. File names starting with . are ignored. * @param directory HDFS directory to monitor for new file */ def textFileStream(directory: String): JavaDStream[String] = { @@ -266,8 +290,8 @@ class JavaStreamingContext(val ssc: StreamingContext) { hostname: String, port: Int, storageLevel: StorageLevel): JavaDStream[T] = { - implicit val cmt: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cmt: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] JavaDStream.fromDStream(ssc.rawSocketStream(hostname, port, storageLevel)) } @@ -281,28 +305,29 @@ class JavaStreamingContext(val ssc: StreamingContext) { * @tparam T Type of the objects in the received blocks */ def rawSocketStream[T](hostname: String, port: Int): JavaDStream[T] = { - implicit val cmt: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cmt: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] JavaDStream.fromDStream(ssc.rawSocketStream(hostname, port)) } /** - * Creates a input stream that monitors a Hadoop-compatible filesystem + * Create a input stream that monitors a Hadoop-compatible filesystem * for new files and reads them using the given key-value types and input format. - * File names starting with . are ignored. + * Files must be written to the monitored directory by "moving" them from another + * location within the same file system. File names starting with . are ignored. * @param directory HDFS directory to monitor for new file * @tparam K Key type for reading HDFS file * @tparam V Value type for reading HDFS file * @tparam F Input format for reading HDFS file */ def fileStream[K, V, F <: NewInputFormat[K, V]](directory: String): JavaPairDStream[K, V] = { - implicit val cmk: ClassManifest[K] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[K]] - implicit val cmv: ClassManifest[V] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V]] - implicit val cmf: ClassManifest[F] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[F]] - ssc.fileStream[K, V, F](directory); + implicit val cmk: ClassTag[K] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val cmv: ClassTag[V] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] + implicit val cmf: ClassTag[F] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[F]] + ssc.fileStream[K, V, F](directory) } /** @@ -318,7 +343,7 @@ class JavaStreamingContext(val ssc: StreamingContext) { /** - * Creates a input stream from a Flume source. + * Create a input stream from a Flume source. * @param hostname Hostname of the slave machine to which the flume data will be sent * @param port Port of the slave machine to which the flume data will be sent */ @@ -396,7 +421,7 @@ class JavaStreamingContext(val ssc: StreamingContext) { def twitterStream(): JavaDStream[Status] = { ssc.twitterStream() } - + /** * Create an input stream with any arbitrary user implemented actor receiver. * @param props Props object defining creation of the actor @@ -414,8 +439,8 @@ class JavaStreamingContext(val ssc: StreamingContext) { storageLevel: StorageLevel, supervisorStrategy: SupervisorStrategy ): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] ssc.actorStream[T](props, name, storageLevel, supervisorStrategy) } @@ -435,8 +460,8 @@ class JavaStreamingContext(val ssc: StreamingContext) { name: String, storageLevel: StorageLevel ): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] ssc.actorStream[T](props, name, storageLevel) } @@ -454,8 +479,8 @@ class JavaStreamingContext(val ssc: StreamingContext) { props: Props, name: String ): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] ssc.actorStream[T](props, name) } @@ -472,12 +497,12 @@ class JavaStreamingContext(val ssc: StreamingContext) { def zeroMQStream[T]( publisherUrl:String, subscribe: Subscribe, - bytesToObjects: Seq[Seq[Byte]] ⇒ Iterator[T], + bytesToObjects: Seq[ByteString] ⇒ Iterator[T], storageLevel: StorageLevel, supervisorStrategy: SupervisorStrategy ): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] ssc.zeroMQStream[T](publisherUrl, subscribe, bytesToObjects, storageLevel, supervisorStrategy) } @@ -497,9 +522,9 @@ class JavaStreamingContext(val ssc: StreamingContext) { bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]], storageLevel: StorageLevel ): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - def fn(x: Seq[Seq[Byte]]) = bytesToObjects.apply(x.map(_.toArray).toArray).toIterator + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] + def fn(x: Seq[ByteString]) = bytesToObjects.apply(x.map(_.toArray).toArray).toIterator ssc.zeroMQStream[T](publisherUrl, subscribe, fn, storageLevel) } @@ -517,9 +542,9 @@ class JavaStreamingContext(val ssc: StreamingContext) { subscribe: Subscribe, bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]] ): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - def fn(x: Seq[Seq[Byte]]) = bytesToObjects.apply(x.map(_.toArray).toArray).toIterator + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] + def fn(x: Seq[ByteString]) = bytesToObjects.apply(x.map(_.toArray).toArray).toIterator ssc.zeroMQStream[T](publisherUrl, subscribe, fn) } @@ -539,8 +564,8 @@ class JavaStreamingContext(val ssc: StreamingContext) { * @tparam T Type of objects in the RDD */ def queueStream[T](queue: java.util.Queue[JavaRDD[T]]): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] val sQueue = new scala.collection.mutable.Queue[RDD[T]] sQueue.enqueue(queue.map(_.rdd).toSeq: _*) ssc.queueStream(sQueue) @@ -556,8 +581,8 @@ class JavaStreamingContext(val ssc: StreamingContext) { * @tparam T Type of objects in the RDD */ def queueStream[T](queue: java.util.Queue[JavaRDD[T]], oneAtATime: Boolean): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] val sQueue = new scala.collection.mutable.Queue[RDD[T]] sQueue.enqueue(queue.map(_.rdd).toSeq: _*) ssc.queueStream(sQueue, oneAtATime) @@ -577,14 +602,85 @@ class JavaStreamingContext(val ssc: StreamingContext) { queue: java.util.Queue[JavaRDD[T]], oneAtATime: Boolean, defaultRDD: JavaRDD[T]): JavaDStream[T] = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] val sQueue = new scala.collection.mutable.Queue[RDD[T]] sQueue.enqueue(queue.map(_.rdd).toSeq: _*) ssc.queueStream(sQueue, oneAtATime, defaultRDD.rdd) } /** + * Create a unified DStream from multiple DStreams of the same type and same slide duration. + */ + def union[T](first: JavaDStream[T], rest: JList[JavaDStream[T]]): JavaDStream[T] = { + val dstreams: Seq[DStream[T]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.dstream) + implicit val cm: ClassTag[T] = first.classTag + ssc.union(dstreams)(cm) + } + + /** + * Create a unified DStream from multiple DStreams of the same type and same slide duration. + */ + def union[K, V]( + first: JavaPairDStream[K, V], + rest: JList[JavaPairDStream[K, V]] + ): JavaPairDStream[K, V] = { + val dstreams: Seq[DStream[(K, V)]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.dstream) + implicit val cm: ClassTag[(K, V)] = first.classTag + implicit val kcm: ClassTag[K] = first.kManifest + implicit val vcm: ClassTag[V] = first.vManifest + new JavaPairDStream[K, V](ssc.union(dstreams)(cm))(kcm, vcm) + } + + /** + * Create a new DStream in which each RDD is generated by applying a function on RDDs of + * the DStreams. The order of the JavaRDDs in the transform function parameter will be the + * same as the order of corresponding DStreams in the list. Note that for adding a + * JavaPairDStream in the list of JavaDStreams, convert it to a JavaDStream using + * [[org.apache.spark.streaming.api.java.JavaPairDStream]].toJavaDStream(). + * In the transform function, convert the JavaRDD corresponding to that JavaDStream to + * a JavaPairRDD using [[org.apache.spark.api.java.JavaPairRDD]].fromJavaRDD(). + */ + def transform[T]( + dstreams: JList[JavaDStream[_]], + transformFunc: JFunction2[JList[JavaRDD[_]], Time, JavaRDD[T]] + ): JavaDStream[T] = { + implicit val cmt: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] + val scalaDStreams = dstreams.map(_.dstream).toSeq + val scalaTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + val jrdds = rdds.map(rdd => JavaRDD.fromRDD[AnyRef](rdd.asInstanceOf[RDD[AnyRef]])).toList + transformFunc.call(jrdds, time).rdd + } + ssc.transform(scalaDStreams, scalaTransformFunc) + } + + /** + * Create a new DStream in which each RDD is generated by applying a function on RDDs of + * the DStreams. The order of the JavaRDDs in the transform function parameter will be the + * same as the order of corresponding DStreams in the list. Note that for adding a + * JavaPairDStream in the list of JavaDStreams, convert it to a JavaDStream using + * [[org.apache.spark.streaming.api.java.JavaPairDStream]].toJavaDStream(). + * In the transform function, convert the JavaRDD corresponding to that JavaDStream to + * a JavaPairRDD using [[org.apache.spark.api.java.JavaPairRDD]].fromJavaRDD(). + */ + def transform[K, V]( + dstreams: JList[JavaDStream[_]], + transformFunc: JFunction2[JList[JavaRDD[_]], Time, JavaPairRDD[K, V]] + ): JavaPairDStream[K, V] = { + implicit val cmk: ClassTag[K] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val cmv: ClassTag[V] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] + val scalaDStreams = dstreams.map(_.dstream).toSeq + val scalaTransformFunc = (rdds: Seq[RDD[_]], time: Time) => { + val jrdds = rdds.map(rdd => JavaRDD.fromRDD[AnyRef](rdd.asInstanceOf[RDD[AnyRef]])).toList + transformFunc.call(jrdds, time).rdd + } + ssc.transform(scalaDStreams, scalaTransformFunc) + } + + /** * Sets the context to periodically checkpoint the DStream operations for master * fault-tolerance. The graph will be checkpointed every batch interval. * @param directory HDFS-compatible directory where the checkpoint data will be reliably stored @@ -604,6 +700,13 @@ class JavaStreamingContext(val ssc: StreamingContext) { ssc.remember(duration) } + /** Add a [[org.apache.spark.streaming.scheduler.StreamingListener]] object for + * receiving system events related to streaming. + */ + def addStreamingListener(streamingListener: StreamingListener) { + ssc.addStreamingListener(streamingListener) + } + /** * Starts the execution of the streams. */ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/CoGroupedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/CoGroupedDStream.scala deleted file mode 100644 index 4eddc755b9..0000000000 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/CoGroupedDStream.scala +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.streaming.dstream - -import org.apache.spark.Partitioner -import org.apache.spark.rdd.RDD -import org.apache.spark.rdd.CoGroupedRDD -import org.apache.spark.streaming.{Time, DStream, Duration} - -private[streaming] -class CoGroupedDStream[K : ClassManifest]( - parents: Seq[DStream[(K, _)]], - partitioner: Partitioner - ) extends DStream[(K, Seq[Seq[_]])](parents.head.ssc) { - - if (parents.length == 0) { - throw new IllegalArgumentException("Empty array of parents") - } - - if (parents.map(_.ssc).distinct.size > 1) { - throw new IllegalArgumentException("Array of parents have different StreamingContexts") - } - - if (parents.map(_.slideDuration).distinct.size > 1) { - throw new IllegalArgumentException("Array of parents have different slide times") - } - - override def dependencies = parents.toList - - override def slideDuration: Duration = parents.head.slideDuration - - override def compute(validTime: Time): Option[RDD[(K, Seq[Seq[_]])]] = { - val part = partitioner - val rdds = parents.flatMap(_.getOrCompute(validTime)) - if (rdds.size > 0) { - val q = new CoGroupedRDD[K](rdds, part) - Some(q) - } else { - None - } - } - -} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ConstantInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ConstantInputDStream.scala index a9a05c9981..f396c34758 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ConstantInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ConstantInputDStream.scala @@ -19,11 +19,12 @@ package org.apache.spark.streaming.dstream import org.apache.spark.rdd.RDD import org.apache.spark.streaming.{Time, StreamingContext} +import scala.reflect.ClassTag /** * An input stream that always returns the same RDD on each timestep. Useful for testing. */ -class ConstantInputDStream[T: ClassManifest](ssc_ : StreamingContext, rdd: RDD[T]) +class ConstantInputDStream[T: ClassTag](ssc_ : StreamingContext, rdd: RDD[T]) extends InputDStream[T](ssc_) { override def start() {} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala index fea0573b77..fb9eda8996 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala @@ -17,30 +17,31 @@ package org.apache.spark.streaming.dstream -import org.apache.spark.rdd.RDD -import org.apache.spark.rdd.UnionRDD -import org.apache.spark.streaming.{DStreamCheckpointData, StreamingContext, Time} - +import java.io.{ObjectInputStream, IOException} +import scala.collection.mutable.{HashSet, HashMap} +import scala.reflect.ClassTag import org.apache.hadoop.fs.{FileSystem, Path, PathFilter} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} +import org.apache.spark.SparkException +import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.UnionRDD +import org.apache.spark.streaming.{DStreamCheckpointData, StreamingContext, Time} -import scala.collection.mutable.{HashSet, HashMap} -import java.io.{ObjectInputStream, IOException} private[streaming] -class FileInputDStream[K: ClassManifest, V: ClassManifest, F <: NewInputFormat[K,V] : ClassManifest]( +class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : ClassTag]( @transient ssc_ : StreamingContext, directory: String, filter: Path => Boolean = FileInputDStream.defaultFilter, - newFilesOnly: Boolean = true) + newFilesOnly: Boolean = true) extends InputDStream[(K, V)](ssc_) { protected[streaming] override val checkpointData = new FileInputDStreamCheckpointData // Latest file mod time seen till any point of time - private val lastModTimeFiles = new HashSet[String]() - private var lastModTime = 0L + private val prevModTimeFiles = new HashSet[String]() + private var prevModTime = 0L @transient private var path_ : Path = null @transient private var fs_ : FileSystem = null @@ -48,13 +49,13 @@ class FileInputDStream[K: ClassManifest, V: ClassManifest, F <: NewInputFormat[K override def start() { if (newFilesOnly) { - lastModTime = graph.zeroTime.milliseconds + prevModTime = graph.zeroTime.milliseconds } else { - lastModTime = 0 + prevModTime = 0 } - logDebug("LastModTime initialized to " + lastModTime + ", new files only = " + newFilesOnly) + logDebug("LastModTime initialized to " + prevModTime + ", new files only = " + newFilesOnly) } - + override def stop() { } /** @@ -67,55 +68,22 @@ class FileInputDStream[K: ClassManifest, V: ClassManifest, F <: NewInputFormat[K * the previous call. */ override def compute(validTime: Time): Option[RDD[(K, V)]] = { - assert(validTime.milliseconds >= lastModTime, "Trying to get new files for really old time [" + validTime + " < " + lastModTime) - - // Create the filter for selecting new files - val newFilter = new PathFilter() { - // Latest file mod time seen in this round of fetching files and its corresponding files - var latestModTime = 0L - val latestModTimeFiles = new HashSet[String]() + assert(validTime.milliseconds >= prevModTime, + "Trying to get new files for really old time [" + validTime + " < " + prevModTime + "]") - def accept(path: Path): Boolean = { - if (!filter(path)) { // Reject file if it does not satisfy filter - logDebug("Rejected by filter " + path) - return false - } else { // Accept file only if - val modTime = fs.getFileStatus(path).getModificationTime() - logDebug("Mod time for " + path + " is " + modTime) - if (modTime < lastModTime) { - logDebug("Mod time less than last mod time") - return false // If the file was created before the last time it was called - } else if (modTime == lastModTime && lastModTimeFiles.contains(path.toString)) { - logDebug("Mod time equal to last mod time, but file considered already") - return false // If the file was created exactly as lastModTime but not reported yet - } else if (modTime > validTime.milliseconds) { - logDebug("Mod time more than valid time") - return false // If the file was created after the time this function call requires - } - if (modTime > latestModTime) { - latestModTime = modTime - latestModTimeFiles.clear() - logDebug("Latest mod time updated to " + latestModTime) - } - latestModTimeFiles += path.toString - logDebug("Accepted " + path) - return true - } - } - } - logDebug("Finding new files at time " + validTime + " for last mod time = " + lastModTime) - val newFiles = fs.listStatus(path, newFilter).map(_.getPath.toString) + // Find new files + val (newFiles, latestModTime, latestModTimeFiles) = findNewFiles(validTime.milliseconds) logInfo("New files at time " + validTime + ":\n" + newFiles.mkString("\n")) if (newFiles.length > 0) { // Update the modification time and the files processed for that modification time - if (lastModTime != newFilter.latestModTime) { - lastModTime = newFilter.latestModTime - lastModTimeFiles.clear() + if (prevModTime < latestModTime) { + prevModTime = latestModTime + prevModTimeFiles.clear() } - lastModTimeFiles ++= newFilter.latestModTimeFiles - logDebug("Last mod time updated to " + lastModTime) + prevModTimeFiles ++= latestModTimeFiles + logDebug("Last mod time updated to " + prevModTime) } - files += ((validTime, newFiles)) + files += ((validTime, newFiles.toArray)) Some(filesToRDD(newFiles)) } @@ -130,12 +98,28 @@ class FileInputDStream[K: ClassManifest, V: ClassManifest, F <: NewInputFormat[K oldFiles.map(p => (p._1, p._2.mkString(", "))).mkString("\n")) } + /** + * Find files which have modification timestamp <= current time and return a 3-tuple of + * (new files found, latest modification time among them, files with latest modification time) + */ + private def findNewFiles(currentTime: Long): (Seq[String], Long, Seq[String]) = { + logDebug("Trying to get new files for time " + currentTime) + val filter = new CustomPathFilter(currentTime) + val newFiles = fs.listStatus(path, filter).map(_.getPath.toString) + (newFiles, filter.latestModTime, filter.latestModTimeFiles.toSeq) + } + /** Generate one RDD from an array of files */ - protected[streaming] def filesToRDD(files: Seq[String]): RDD[(K, V)] = { - new UnionRDD( - context.sparkContext, - files.map(file => context.sparkContext.newAPIHadoopFile[K, V, F](file)) - ) + private def filesToRDD(files: Seq[String]): RDD[(K, V)] = { + val fileRDDs = files.map(file => context.sparkContext.newAPIHadoopFile[K, V, F](file)) + files.zip(fileRDDs).foreach { case (file, rdd) => { + if (rdd.partitions.size == 0) { + logError("File " + file + " has no data in it. Spark Streaming can only ingest " + + "files that have been \"moved\" to the directory assigned to the file stream. " + + "Refer to the streaming programming guide for more details.") + } + }} + new UnionRDD(context.sparkContext, fileRDDs) } private def path: Path = { @@ -148,6 +132,10 @@ class FileInputDStream[K: ClassManifest, V: ClassManifest, F <: NewInputFormat[K fs_ } + private def reset() { + fs_ = null + } + @throws(classOf[IOException]) private def readObject(ois: ObjectInputStream) { logDebug(this.getClass().getSimpleName + ".readObject used") @@ -189,11 +177,54 @@ class FileInputDStream[K: ClassManifest, V: ClassManifest, F <: NewInputFormat[K hadoopFiles.map(p => (p._1, p._2.mkString(", "))).mkString("\n") + "\n]" } } + + /** + * Custom PathFilter class to find new files that have modification timestamps <= current time, + * but have not been seen before (i.e. the file should not be in lastModTimeFiles) + */ + private[streaming] + class CustomPathFilter(maxModTime: Long) extends PathFilter { + // Latest file mod time seen in this round of fetching files and its corresponding files + var latestModTime = 0L + val latestModTimeFiles = new HashSet[String]() + + def accept(path: Path): Boolean = { + try { + if (!filter(path)) { // Reject file if it does not satisfy filter + logDebug("Rejected by filter " + path) + return false + } + val modTime = fs.getFileStatus(path).getModificationTime() + logDebug("Mod time for " + path + " is " + modTime) + if (modTime < prevModTime) { + logDebug("Mod time less than last mod time") + return false // If the file was created before the last time it was called + } else if (modTime == prevModTime && prevModTimeFiles.contains(path.toString)) { + logDebug("Mod time equal to last mod time, but file considered already") + return false // If the file was created exactly as lastModTime but not reported yet + } else if (modTime > maxModTime) { + logDebug("Mod time more than ") + return false // If the file is too new that considering it may give errors + } + if (modTime > latestModTime) { + latestModTime = modTime + latestModTimeFiles.clear() + logDebug("Latest mod time updated to " + latestModTime) + } + latestModTimeFiles += path.toString + logDebug("Accepted " + path) + } catch { + case fnfe: java.io.FileNotFoundException => + logWarning("Error finding new files", fnfe) + reset() + return false + } + return true + } + } } private[streaming] object FileInputDStream { def defaultFilter(path: Path): Boolean = !path.getName().startsWith(".") } - - diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala index 91ee2c1a36..db2e0a4cee 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala @@ -19,9 +19,10 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.{Duration, DStream, Time} import org.apache.spark.rdd.RDD +import scala.reflect.ClassTag private[streaming] -class FilteredDStream[T: ClassManifest]( +class FilteredDStream[T: ClassTag]( parent: DStream[T], filterFunc: T => Boolean ) extends DStream[T](parent.ssc) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala index ca7d7ca49e..244dc3ee4f 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala @@ -20,9 +20,10 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.{Duration, DStream, Time} import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext._ +import scala.reflect.ClassTag private[streaming] -class FlatMapValuedDStream[K: ClassManifest, V: ClassManifest, U: ClassManifest]( +class FlatMapValuedDStream[K: ClassTag, V: ClassTag, U: ClassTag]( parent: DStream[(K, V)], flatMapValueFunc: V => TraversableOnce[U] ) extends DStream[(K, U)](parent.ssc) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala index b37966f9a7..336c4b7a92 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala @@ -19,9 +19,10 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.{Duration, DStream, Time} import org.apache.spark.rdd.RDD +import scala.reflect.ClassTag private[streaming] -class FlatMappedDStream[T: ClassManifest, U: ClassManifest]( +class FlatMappedDStream[T: ClassTag, U: ClassTag]( parent: DStream[T], flatMapFunc: T => Traversable[U] ) extends DStream[U](parent.ssc) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlumeInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlumeInputDStream.scala index 18de772946..60d79175f1 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlumeInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlumeInputDStream.scala @@ -22,6 +22,7 @@ import java.io.{ObjectInput, ObjectOutput, Externalizable} import java.nio.ByteBuffer import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import org.apache.flume.source.avro.AvroSourceProtocol import org.apache.flume.source.avro.AvroFlumeEvent @@ -34,7 +35,7 @@ import org.apache.spark.util.Utils import org.apache.spark.storage.StorageLevel private[streaming] -class FlumeInputDStream[T: ClassManifest]( +class FlumeInputDStream[T: ClassTag]( @transient ssc_ : StreamingContext, host: String, port: Int, @@ -137,8 +138,8 @@ class FlumeReceiver( protected override def onStart() { val responder = new SpecificResponder( - classOf[AvroSourceProtocol], new FlumeEventServer(this)); - val server = new NettyServer(responder, new InetSocketAddress(host, port)); + classOf[AvroSourceProtocol], new FlumeEventServer(this)) + val server = new NettyServer(responder, new InetSocketAddress(host, port)) blockGenerator.start() server.start() logInfo("Flume receiver started") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala index e21bac4602..364abcde68 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala @@ -18,10 +18,12 @@ package org.apache.spark.streaming.dstream import org.apache.spark.rdd.RDD -import org.apache.spark.streaming.{Duration, DStream, Job, Time} +import org.apache.spark.streaming.{Duration, DStream, Time} +import org.apache.spark.streaming.scheduler.Job +import scala.reflect.ClassTag private[streaming] -class ForEachDStream[T: ClassManifest] ( +class ForEachDStream[T: ClassTag] ( parent: DStream[T], foreachFunc: (RDD[T], Time) => Unit ) extends DStream[Unit](parent.ssc) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala index 4294b07d91..23136f44fa 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala @@ -19,9 +19,10 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.{Duration, DStream, Time} import org.apache.spark.rdd.RDD +import scala.reflect.ClassTag private[streaming] -class GlommedDStream[T: ClassManifest](parent: DStream[T]) +class GlommedDStream[T: ClassTag](parent: DStream[T]) extends DStream[Array[T]](parent.ssc) { override def dependencies = List(parent) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala index 674b27118c..f01e67fe13 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala @@ -19,6 +19,8 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.{Time, Duration, StreamingContext, DStream} +import scala.reflect.ClassTag + /** * This is the abstract base class for all input streams. This class provides to methods * start() and stop() which called by the scheduler to start and stop receiving data/ @@ -30,7 +32,7 @@ import org.apache.spark.streaming.{Time, Duration, StreamingContext, DStream} * that requires running a receiver on the worker nodes, use NetworkInputDStream * as the parent class. */ -abstract class InputDStream[T: ClassManifest] (@transient ssc_ : StreamingContext) +abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext) extends DStream[T](ssc_) { var lastValidTime: Time = null diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/KafkaInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/KafkaInputDStream.scala index 51e913675d..526f5564c7 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/KafkaInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/KafkaInputDStream.scala @@ -19,52 +19,55 @@ package org.apache.spark.streaming.dstream import org.apache.spark.Logging import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.{Time, DStreamCheckpointData, StreamingContext} +import org.apache.spark.streaming.StreamingContext import java.util.Properties import java.util.concurrent.Executors import kafka.consumer._ -import kafka.message.{Message, MessageSet, MessageAndMetadata} import kafka.serializer.Decoder -import kafka.utils.{Utils, ZKGroupTopicDirs} -import kafka.utils.ZkUtils._ +import kafka.utils.VerifiableProperties import kafka.utils.ZKStringSerializer import org.I0Itec.zkclient._ import scala.collection.Map -import scala.collection.mutable.HashMap -import scala.collection.JavaConversions._ - +import scala.reflect.ClassTag /** * Input stream that pulls messages from a Kafka Broker. - * + * * @param kafkaParams Map of kafka configuration paramaters. See: http://kafka.apache.org/configuration.html * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed * in its own thread. * @param storageLevel RDD storage level. */ private[streaming] -class KafkaInputDStream[T: ClassManifest, D <: Decoder[_]: Manifest]( +class KafkaInputDStream[ + K: ClassTag, + V: ClassTag, + U <: Decoder[_]: Manifest, + T <: Decoder[_]: Manifest]( @transient ssc_ : StreamingContext, kafkaParams: Map[String, String], topics: Map[String, Int], storageLevel: StorageLevel - ) extends NetworkInputDStream[T](ssc_ ) with Logging { + ) extends NetworkInputDStream[(K, V)](ssc_) with Logging { - - def getReceiver(): NetworkReceiver[T] = { - new KafkaReceiver[T, D](kafkaParams, topics, storageLevel) - .asInstanceOf[NetworkReceiver[T]] + def getReceiver(): NetworkReceiver[(K, V)] = { + new KafkaReceiver[K, V, U, T](kafkaParams, topics, storageLevel) + .asInstanceOf[NetworkReceiver[(K, V)]] } } private[streaming] -class KafkaReceiver[T: ClassManifest, D <: Decoder[_]: Manifest]( - kafkaParams: Map[String, String], - topics: Map[String, Int], - storageLevel: StorageLevel +class KafkaReceiver[ + K: ClassTag, + V: ClassTag, + U <: Decoder[_]: Manifest, + T <: Decoder[_]: Manifest]( + kafkaParams: Map[String, String], + topics: Map[String, Int], + storageLevel: StorageLevel ) extends NetworkReceiver[Any] { // Handles pushing data into the BlockManager @@ -83,27 +86,35 @@ class KafkaReceiver[T: ClassManifest, D <: Decoder[_]: Manifest]( // In case we are using multiple Threads to handle Kafka Messages val executorPool = Executors.newFixedThreadPool(topics.values.reduce(_ + _)) - logInfo("Starting Kafka Consumer Stream with group: " + kafkaParams("groupid")) + logInfo("Starting Kafka Consumer Stream with group: " + kafkaParams("group.id")) // Kafka connection properties val props = new Properties() kafkaParams.foreach(param => props.put(param._1, param._2)) // Create the connection to the cluster - logInfo("Connecting to Zookeper: " + kafkaParams("zk.connect")) + logInfo("Connecting to Zookeper: " + kafkaParams("zookeeper.connect")) val consumerConfig = new ConsumerConfig(props) consumerConnector = Consumer.create(consumerConfig) - logInfo("Connected to " + kafkaParams("zk.connect")) + logInfo("Connected to " + kafkaParams("zookeeper.connect")) // When autooffset.reset is defined, it is our responsibility to try and whack the // consumer group zk node. - if (kafkaParams.contains("autooffset.reset")) { - tryZookeeperConsumerGroupCleanup(kafkaParams("zk.connect"), kafkaParams("groupid")) + if (kafkaParams.contains("auto.offset.reset")) { + tryZookeeperConsumerGroupCleanup(kafkaParams("zookeeper.connect"), kafkaParams("group.id")) } + val keyDecoder = manifest[U].runtimeClass.getConstructor(classOf[VerifiableProperties]) + .newInstance(consumerConfig.props) + .asInstanceOf[Decoder[K]] + val valueDecoder = manifest[T].runtimeClass.getConstructor(classOf[VerifiableProperties]) + .newInstance(consumerConfig.props) + .asInstanceOf[Decoder[V]] + // Create Threads for each Topic/Message Stream we are listening - val decoder = manifest[D].erasure.newInstance.asInstanceOf[Decoder[T]] - val topicMessageStreams = consumerConnector.createMessageStreams(topics, decoder) + val topicMessageStreams = consumerConnector.createMessageStreams( + topics, keyDecoder, valueDecoder) + // Start the messages handler for each partition topicMessageStreams.values.foreach { streams => @@ -112,11 +123,12 @@ class KafkaReceiver[T: ClassManifest, D <: Decoder[_]: Manifest]( } // Handles Kafka Messages - private class MessageHandler[T: ClassManifest](stream: KafkaStream[T]) extends Runnable { + private class MessageHandler[K: ClassTag, V: ClassTag](stream: KafkaStream[K, V]) + extends Runnable { def run() { logInfo("Starting MessageHandler.") for (msgAndMetadata <- stream) { - blockGenerator += msgAndMetadata.message + blockGenerator += (msgAndMetadata.key, msgAndMetadata.message) } } } @@ -135,7 +147,7 @@ class KafkaReceiver[T: ClassManifest, D <: Decoder[_]: Manifest]( zk.deleteRecursive(dir) zk.close() } catch { - case _ => // swallow + case _ : Throwable => // swallow } } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MQTTInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MQTTInputDStream.scala new file mode 100644 index 0000000000..ef4a737568 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MQTTInputDStream.scala @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.dstream + +import org.apache.spark.Logging +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{ Time, DStreamCheckpointData, StreamingContext } + +import java.util.Properties +import java.util.concurrent.Executors +import java.io.IOException + +import org.eclipse.paho.client.mqttv3.MqttCallback +import org.eclipse.paho.client.mqttv3.MqttClient +import org.eclipse.paho.client.mqttv3.MqttClientPersistence +import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence +import org.eclipse.paho.client.mqttv3.IMqttDeliveryToken +import org.eclipse.paho.client.mqttv3.MqttException +import org.eclipse.paho.client.mqttv3.MqttMessage +import org.eclipse.paho.client.mqttv3.MqttTopic + +import scala.collection.Map +import scala.collection.mutable.HashMap +import scala.collection.JavaConversions._ +import scala.reflect.ClassTag + +/** + * Input stream that subscribe messages from a Mqtt Broker. + * Uses eclipse paho as MqttClient http://www.eclipse.org/paho/ + * @param brokerUrl Url of remote mqtt publisher + * @param topic topic name to subscribe to + * @param storageLevel RDD storage level. + */ + +private[streaming] +class MQTTInputDStream[T: ClassTag]( + @transient ssc_ : StreamingContext, + brokerUrl: String, + topic: String, + storageLevel: StorageLevel + ) extends NetworkInputDStream[T](ssc_) with Logging { + + def getReceiver(): NetworkReceiver[T] = { + new MQTTReceiver(brokerUrl, topic, storageLevel) + .asInstanceOf[NetworkReceiver[T]] + } +} + +private[streaming] +class MQTTReceiver(brokerUrl: String, + topic: String, + storageLevel: StorageLevel + ) extends NetworkReceiver[Any] { + lazy protected val blockGenerator = new BlockGenerator(storageLevel) + + def onStop() { + blockGenerator.stop() + } + + def onStart() { + + blockGenerator.start() + + // Set up persistence for messages + var peristance: MqttClientPersistence = new MemoryPersistence() + + // Initializing Mqtt Client specifying brokerUrl, clientID and MqttClientPersistance + var client: MqttClient = new MqttClient(brokerUrl, "MQTTSub", peristance) + + // Connect to MqttBroker + client.connect() + + // Subscribe to Mqtt topic + client.subscribe(topic) + + // Callback automatically triggers as and when new message arrives on specified topic + var callback: MqttCallback = new MqttCallback() { + + // Handles Mqtt message + override def messageArrived(arg0: String, arg1: MqttMessage) { + blockGenerator += new String(arg1.getPayload()) + } + + override def deliveryComplete(arg0: IMqttDeliveryToken) { + } + + override def connectionLost(arg0: Throwable) { + logInfo("Connection lost " + arg0) + } + } + + // Set up callback for MqttClient + client.setCallback(callback) + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala index 5329601a6f..8a04060e5b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala @@ -19,9 +19,10 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.{Duration, DStream, Time} import org.apache.spark.rdd.RDD +import scala.reflect.ClassTag private[streaming] -class MapPartitionedDStream[T: ClassManifest, U: ClassManifest]( +class MapPartitionedDStream[T: ClassTag, U: ClassTag]( parent: DStream[T], mapPartFunc: Iterator[T] => Iterator[U], preservePartitioning: Boolean diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala index 8290df90a2..0ce364fd46 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala @@ -20,9 +20,10 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.{Duration, DStream, Time} import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext._ +import scala.reflect.ClassTag private[streaming] -class MapValuedDStream[K: ClassManifest, V: ClassManifest, U: ClassManifest]( +class MapValuedDStream[K: ClassTag, V: ClassTag, U: ClassTag]( parent: DStream[(K, V)], mapValueFunc: V => U ) extends DStream[(K, U)](parent.ssc) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala index b1682afea3..c0b7491d09 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala @@ -19,9 +19,10 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.{Duration, DStream, Time} import org.apache.spark.rdd.RDD +import scala.reflect.ClassTag private[streaming] -class MappedDStream[T: ClassManifest, U: ClassManifest] ( +class MappedDStream[T: ClassTag, U: ClassTag] ( parent: DStream[T], mapFunc: T => U ) extends DStream[U](parent.ssc) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala index 31f9891560..27d474c0a0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala @@ -21,17 +21,19 @@ import java.util.concurrent.ArrayBlockingQueue import java.nio.ByteBuffer import scala.collection.mutable.ArrayBuffer +import scala.concurrent.Await +import scala.concurrent.duration._ +import scala.reflect.ClassTag import akka.actor.{Props, Actor} import akka.pattern.ask -import akka.dispatch.Await -import akka.util.duration._ import org.apache.spark.streaming.util.{RecurringTimer, SystemClock} import org.apache.spark.streaming._ import org.apache.spark.{Logging, SparkEnv} import org.apache.spark.rdd.{RDD, BlockRDD} -import org.apache.spark.storage.StorageLevel +import org.apache.spark.storage.{BlockId, StorageLevel, StreamBlockId} +import org.apache.spark.streaming.scheduler.{DeregisterReceiver, AddBlocks, RegisterReceiver} /** * Abstract class for defining any InputDStream that has to start a receiver on worker @@ -42,7 +44,7 @@ import org.apache.spark.storage.StorageLevel * @param ssc_ Streaming context that will execute this input stream * @tparam T Class type of the object of this stream */ -abstract class NetworkInputDStream[T: ClassManifest](@transient ssc_ : StreamingContext) +abstract class NetworkInputDStream[T: ClassTag](@transient ssc_ : StreamingContext) extends InputDStream[T](ssc_) { // This is an unique identifier that is used to match the network receiver with the @@ -69,7 +71,7 @@ abstract class NetworkInputDStream[T: ClassManifest](@transient ssc_ : Streaming val blockIds = ssc.networkInputTracker.getBlockIds(id, validTime) Some(new BlockRDD[T](ssc.sc, blockIds)) } else { - Some(new BlockRDD[T](ssc.sc, Array[String]())) + Some(new BlockRDD[T](ssc.sc, Array[BlockId]())) } } } @@ -77,16 +79,14 @@ abstract class NetworkInputDStream[T: ClassManifest](@transient ssc_ : Streaming private[streaming] sealed trait NetworkReceiverMessage private[streaming] case class StopReceiver(msg: String) extends NetworkReceiverMessage -private[streaming] case class ReportBlock(blockId: String, metadata: Any) extends NetworkReceiverMessage +private[streaming] case class ReportBlock(blockId: BlockId, metadata: Any) extends NetworkReceiverMessage private[streaming] case class ReportError(msg: String) extends NetworkReceiverMessage /** * Abstract class of a receiver that can be run on worker nodes to receive external data. See * [[org.apache.spark.streaming.dstream.NetworkInputDStream]] for an explanation. */ -abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Logging { - - initLogging() +abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging { lazy protected val env = SparkEnv.get @@ -158,7 +158,7 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log /** * Pushes a block (as an ArrayBuffer filled with data) into the block manager. */ - def pushBlock(blockId: String, arrayBuffer: ArrayBuffer[T], metadata: Any, level: StorageLevel) { + def pushBlock(blockId: BlockId, arrayBuffer: ArrayBuffer[T], metadata: Any, level: StorageLevel) { env.blockManager.put(blockId, arrayBuffer.asInstanceOf[ArrayBuffer[Any]], level) actor ! ReportBlock(blockId, metadata) } @@ -166,7 +166,7 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log /** * Pushes a block (as bytes) into the block manager. */ - def pushBlock(blockId: String, bytes: ByteBuffer, metadata: Any, level: StorageLevel) { + def pushBlock(blockId: BlockId, bytes: ByteBuffer, metadata: Any, level: StorageLevel) { env.blockManager.putBytes(blockId, bytes, level) actor ! ReportBlock(blockId, metadata) } @@ -174,10 +174,10 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log /** A helper actor that communicates with the NetworkInputTracker */ private class NetworkReceiverActor extends Actor { logInfo("Attempting to register with tracker") - val ip = System.getProperty("spark.driver.host", "localhost") - val port = System.getProperty("spark.driver.port", "7077").toInt - val url = "akka://spark@%s:%s/user/NetworkInputTracker".format(ip, port) - val tracker = env.actorSystem.actorFor(url) + val ip = env.conf.get("spark.driver.host", "localhost") + val port = env.conf.get("spark.driver.port", "7077").toInt + val url = "akka.tcp://spark@%s:%s/user/NetworkInputTracker".format(ip, port) + val tracker = env.actorSystem.actorSelection(url) val timeout = 5.seconds override def preStart() { @@ -209,10 +209,10 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log class BlockGenerator(storageLevel: StorageLevel) extends Serializable with Logging { - case class Block(id: String, buffer: ArrayBuffer[T], metadata: Any = null) + case class Block(id: BlockId, buffer: ArrayBuffer[T], metadata: Any = null) val clock = new SystemClock() - val blockInterval = System.getProperty("spark.streaming.blockInterval", "200").toLong + val blockInterval = env.conf.get("spark.streaming.blockInterval", "200").toLong val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer) val blockStorageLevel = storageLevel val blocksForPushing = new ArrayBlockingQueue[Block](1000) @@ -232,16 +232,16 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log logInfo("Data handler stopped") } - def += (obj: T) { + def += (obj: T): Unit = synchronized { currentBuffer += obj } - private def updateCurrentBuffer(time: Long) { + private def updateCurrentBuffer(time: Long): Unit = synchronized { try { val newBlockBuffer = currentBuffer currentBuffer = new ArrayBuffer[T] if (newBlockBuffer.size > 0) { - val blockId = "input-" + NetworkReceiver.this.streamId + "-" + (time - blockInterval) + val blockId = StreamBlockId(NetworkReceiver.this.streamId, time - blockInterval) val newBlock = new Block(blockId, newBlockBuffer) blocksForPushing.add(newBlock) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala index 15782f5c11..6f9477020a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala @@ -18,9 +18,10 @@ package org.apache.spark.streaming.dstream import org.apache.spark.streaming.StreamingContext +import scala.reflect.ClassTag private[streaming] -class PluggableInputDStream[T: ClassManifest]( +class PluggableInputDStream[T: ClassTag]( @transient ssc_ : StreamingContext, receiver: NetworkReceiver[T]) extends NetworkInputDStream[T](ssc_) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala index 7d9f3521b1..97325f8ea3 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala @@ -19,13 +19,13 @@ package org.apache.spark.streaming.dstream import org.apache.spark.rdd.RDD import org.apache.spark.rdd.UnionRDD - import scala.collection.mutable.Queue import scala.collection.mutable.ArrayBuffer import org.apache.spark.streaming.{Time, StreamingContext} +import scala.reflect.ClassTag private[streaming] -class QueueInputDStream[T: ClassManifest]( +class QueueInputDStream[T: ClassTag]( @transient ssc: StreamingContext, val queue: Queue[RDD[T]], oneAtATime: Boolean, diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala index c91f12ecd7..dea0f26f90 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala @@ -18,9 +18,11 @@ package org.apache.spark.streaming.dstream import org.apache.spark.Logging -import org.apache.spark.storage.StorageLevel +import org.apache.spark.storage.{StorageLevel, StreamBlockId} import org.apache.spark.streaming.StreamingContext +import scala.reflect.ClassTag + import java.net.InetSocketAddress import java.nio.ByteBuffer import java.nio.channels.{ReadableByteChannel, SocketChannel} @@ -35,7 +37,7 @@ import java.util.concurrent.ArrayBlockingQueue * in the format that the system is configured with. */ private[streaming] -class RawInputDStream[T: ClassManifest]( +class RawInputDStream[T: ClassTag]( @transient ssc_ : StreamingContext, host: String, port: Int, @@ -71,7 +73,7 @@ class RawNetworkReceiver(host: String, port: Int, storageLevel: StorageLevel) var nextBlockNumber = 0 while (true) { val buffer = queue.take() - val blockId = "input-" + streamId + "-" + nextBlockNumber + val blockId = StreamBlockId(streamId, nextBlockNumber) nextBlockNumber += 1 pushBlock(blockId, buffer, null, storageLevel) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala index b88a4db959..db56345ca8 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala @@ -28,8 +28,11 @@ import org.apache.spark.storage.StorageLevel import scala.collection.mutable.ArrayBuffer import org.apache.spark.streaming.{Duration, Interval, Time, DStream} +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + private[streaming] -class ReducedWindowedDStream[K: ClassManifest, V: ClassManifest]( +class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( parent: DStream[(K, V)], reduceFunc: (V, V) => V, invReduceFunc: (V, V) => V, @@ -49,7 +52,7 @@ class ReducedWindowedDStream[K: ClassManifest, V: ClassManifest]( "must be multiple of the slide duration of parent DStream (" + parent.slideDuration + ")" ) - // Reduce each batch of data using reduceByKey which will be further reduced by window + // Reduce each batch of data using reduceByKey which will be further reduced by window // by ReducedWindowedDStream val reducedStream = parent.reduceByKey(reduceFunc, partitioner) @@ -170,5 +173,3 @@ class ReducedWindowedDStream[K: ClassManifest, V: ClassManifest]( } } } - - diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala index a95e66d761..e6e0022097 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala @@ -21,9 +21,10 @@ import org.apache.spark.Partitioner import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext._ import org.apache.spark.streaming.{Duration, DStream, Time} +import scala.reflect.ClassTag private[streaming] -class ShuffledDStream[K: ClassManifest, V: ClassManifest, C: ClassManifest]( +class ShuffledDStream[K: ClassTag, V: ClassTag, C: ClassTag]( parent: DStream[(K,V)], createCombiner: V => C, mergeValue: (C, V) => C, diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala index e2539c7396..2cdd13f205 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala @@ -21,11 +21,13 @@ import org.apache.spark.streaming.StreamingContext import org.apache.spark.storage.StorageLevel import org.apache.spark.util.NextIterator +import scala.reflect.ClassTag + import java.io._ import java.net.Socket private[streaming] -class SocketInputDStream[T: ClassManifest]( +class SocketInputDStream[T: ClassTag]( @transient ssc_ : StreamingContext, host: String, port: Int, @@ -39,7 +41,7 @@ class SocketInputDStream[T: ClassManifest]( } private[streaming] -class SocketReceiver[T: ClassManifest]( +class SocketReceiver[T: ClassTag]( host: String, port: Int, bytesToObjects: InputStream => Iterator[T], diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala index 362a6bf4cc..e0ff3ccba4 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala @@ -23,8 +23,10 @@ import org.apache.spark.SparkContext._ import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Duration, Time, DStream} +import scala.reflect.ClassTag + private[streaming] -class StateDStream[K: ClassManifest, V: ClassManifest, S: ClassManifest]( +class StateDStream[K: ClassTag, V: ClassTag, S: ClassTag]( parent: DStream[(K, V)], updateFunc: (Iterator[(K, Seq[V], Option[S])]) => Iterator[(K, S)], partitioner: Partitioner, diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala index 60485adef9..aeea060df7 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala @@ -19,18 +19,25 @@ package org.apache.spark.streaming.dstream import org.apache.spark.rdd.RDD import org.apache.spark.streaming.{Duration, DStream, Time} +import scala.reflect.ClassTag private[streaming] -class TransformedDStream[T: ClassManifest, U: ClassManifest] ( - parent: DStream[T], - transformFunc: (RDD[T], Time) => RDD[U] - ) extends DStream[U](parent.ssc) { +class TransformedDStream[U: ClassTag] ( + parents: Seq[DStream[_]], + transformFunc: (Seq[RDD[_]], Time) => RDD[U] + ) extends DStream[U](parents.head.ssc) { - override def dependencies = List(parent) + require(parents.length > 0, "List of DStreams to transform is empty") + require(parents.map(_.ssc).distinct.size == 1, "Some of the DStreams have different contexts") + require(parents.map(_.slideDuration).distinct.size == 1, + "Some of the DStreams have different slide durations") - override def slideDuration: Duration = parent.slideDuration + override def dependencies = parents.toList + + override def slideDuration: Duration = parents.head.slideDuration override def compute(validTime: Time): Option[RDD[U]] = { - parent.getOrCompute(validTime).map(transformFunc(_, validTime)) + val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq + Some(transformFunc(parentRDDs, validTime)) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala index c696bb70a8..0d84ec84f2 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala @@ -22,8 +22,11 @@ import org.apache.spark.rdd.RDD import collection.mutable.ArrayBuffer import org.apache.spark.rdd.UnionRDD +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag + private[streaming] -class UnionDStream[T: ClassManifest](parents: Array[DStream[T]]) +class UnionDStream[T: ClassTag](parents: Array[DStream[T]]) extends DStream[T](parents.head.ssc) { if (parents.length == 0) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala index 3c57294269..73d959331a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala @@ -22,8 +22,10 @@ import org.apache.spark.rdd.UnionRDD import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Duration, Interval, Time, DStream} +import scala.reflect.ClassTag + private[streaming] -class WindowedDStream[T: ClassManifest]( +class WindowedDStream[T: ClassTag]( parent: DStream[T], _windowDuration: Duration, _slideDuration: Duration) @@ -52,6 +54,3 @@ class WindowedDStream[T: ClassManifest]( Some(new UnionRDD(ssc.sc, parent.slice(currentWindow))) } } - - - diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ActorReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receivers/ActorReceiver.scala index 4b5d8c467e..fdf5371a89 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ActorReceiver.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receivers/ActorReceiver.scala @@ -20,20 +20,21 @@ package org.apache.spark.streaming.receivers import akka.actor.{ Actor, PoisonPill, Props, SupervisorStrategy } import akka.actor.{ actorRef2Scala, ActorRef } import akka.actor.{ PossiblyHarmful, OneForOneStrategy } +import akka.actor.SupervisorStrategy._ -import org.apache.spark.storage.StorageLevel +import scala.concurrent.duration._ +import scala.reflect.ClassTag + +import org.apache.spark.storage.{StorageLevel, StreamBlockId} import org.apache.spark.streaming.dstream.NetworkReceiver import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.ArrayBuffer -/** A helper with set of defaults for supervisor strategy **/ +/** A helper with set of defaults for supervisor strategy */ object ReceiverSupervisorStrategy { - import akka.util.duration._ - import akka.actor.SupervisorStrategy._ - val defaultStrategy = OneForOneStrategy(maxNrOfRetries = 10, withinTimeRange = 15 millis) { case _: RuntimeException ⇒ Restart @@ -48,10 +49,10 @@ object ReceiverSupervisorStrategy { * Find more details at: http://spark-project.org/docs/latest/streaming-custom-receivers.html * * @example {{{ - * class MyActor extends Actor with Receiver{ - * def receive { - * case anything :String ⇒ pushBlock(anything) - * } + * class MyActor extends Actor with Receiver{ + * def receive { + * case anything :String => pushBlock(anything) + * } * } * //Can be plugged in actorStream as follows * ssc.actorStream[String](Props(new MyActor),"MyActorReceiver") @@ -65,11 +66,11 @@ object ReceiverSupervisorStrategy { * */ trait Receiver { self: Actor ⇒ - def pushBlock[T: ClassManifest](iter: Iterator[T]) { + def pushBlock[T: ClassTag](iter: Iterator[T]) { context.parent ! Data(iter) } - def pushBlock[T: ClassManifest](data: T) { + def pushBlock[T: ClassTag](data: T) { context.parent ! Data(data) } @@ -83,8 +84,8 @@ case class Statistics(numberOfMsgs: Int, numberOfHiccups: Int, otherInfo: String) -/** Case class to receive data sent by child actors **/ -private[streaming] case class Data[T: ClassManifest](data: T) +/** Case class to receive data sent by child actors */ +private[streaming] case class Data[T: ClassTag](data: T) /** * Provides Actors as receivers for receiving stream. @@ -95,19 +96,19 @@ private[streaming] case class Data[T: ClassManifest](data: T) * his own Actor to run as receiver for Spark Streaming input source. * * This starts a supervisor actor which starts workers and also provides - * [http://doc.akka.io/docs/akka/2.0.5/scala/fault-tolerance.html fault-tolerance]. - * + * [http://doc.akka.io/docs/akka/2.0.5/scala/fault-tolerance.html fault-tolerance]. + * * Here's a way to start more supervisor/workers as its children. * * @example {{{ - * context.parent ! Props(new Supervisor) + * context.parent ! Props(new Supervisor) * }}} OR {{{ * context.parent ! Props(new Worker,"Worker") * }}} * * */ -private[streaming] class ActorReceiver[T: ClassManifest]( +private[streaming] class ActorReceiver[T: ClassTag]( props: Props, name: String, storageLevel: StorageLevel, @@ -120,7 +121,7 @@ private[streaming] class ActorReceiver[T: ClassManifest]( protected lazy val supervisor = env.actorSystem.actorOf(Props(new Supervisor), "Supervisor" + streamId) - private class Supervisor extends Actor { + class Supervisor extends Actor { override val supervisorStrategy = receiverSupervisorStrategy val worker = context.actorOf(props, name) @@ -159,7 +160,7 @@ private[streaming] class ActorReceiver[T: ClassManifest]( protected def pushBlock(iter: Iterator[T]) { val buffer = new ArrayBuffer[T] buffer ++= iter - pushBlock("block-" + streamId + "-" + System.nanoTime(), buffer, null, storageLevel) + pushBlock(StreamBlockId(streamId, System.nanoTime()), buffer, null, storageLevel) } protected def onStart() = { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ZeroMQReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receivers/ZeroMQReceiver.scala index 043bb8c8bf..f164d516b0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ZeroMQReceiver.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receivers/ZeroMQReceiver.scala @@ -17,7 +17,10 @@ package org.apache.spark.streaming.receivers +import scala.reflect.ClassTag + import akka.actor.Actor +import akka.util.ByteString import akka.zeromq._ import org.apache.spark.Logging @@ -25,12 +28,12 @@ import org.apache.spark.Logging /** * A receiver to subscribe to ZeroMQ stream. */ -private[streaming] class ZeroMQReceiver[T: ClassManifest](publisherUrl: String, +private[streaming] class ZeroMQReceiver[T: ClassTag](publisherUrl: String, subscribe: Subscribe, - bytesToObjects: Seq[Seq[Byte]] ⇒ Iterator[T]) + bytesToObjects: Seq[ByteString] ⇒ Iterator[T]) extends Actor with Receiver with Logging { - override def preStart() = context.system.newSocket(SocketType.Sub, Listener(self), + override def preStart() = ZeroMQExtension(context.system).newSocket(SocketType.Sub, Listener(self), Connect(publisherUrl), subscribe) def receive: Receive = { @@ -38,10 +41,10 @@ private[streaming] class ZeroMQReceiver[T: ClassManifest](publisherUrl: String, case Connecting ⇒ logInfo("connecting ...") case m: ZMQMessage ⇒ - logDebug("Received message for:" + m.firstFrameAsString) + logDebug("Received message for:" + m.frame(0)) //We ignore first frame for processing as it is the topic - val bytes = m.frames.tail.map(_.payload) + val bytes = m.frames.tail pushBlock(bytesToObjects(bytes)) case Closed ⇒ logInfo("received closed ") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala new file mode 100644 index 0000000000..4e8d07fe92 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala @@ -0,0 +1,55 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import org.apache.spark.streaming.Time + +/** + * Class having information on completed batches. + * @param batchTime Time of the batch + * @param submissionTime Clock time of when jobs of this batch was submitted to + * the streaming scheduler queue + * @param processingStartTime Clock time of when the first job of this batch started processing + * @param processingEndTime Clock time of when the last job of this batch finished processing + */ +case class BatchInfo( + batchTime: Time, + submissionTime: Long, + processingStartTime: Option[Long], + processingEndTime: Option[Long] + ) { + + /** + * Time taken for the first job of this batch to start processing from the time this batch + * was submitted to the streaming scheduler. Essentially, it is + * `processingStartTime` - `submissionTime`. + */ + def schedulingDelay = processingStartTime.map(_ - submissionTime) + + /** + * Time taken for the all jobs of this batch to finish processing from the time they started + * processing. Essentially, it is `processingEndTime` - `processingStartTime`. + */ + def processingDelay = processingEndTime.zip(processingStartTime).map(x => x._1 - x._2).headOption + + /** + * Time taken for all the jobs of this batch to finish processing from the time they + * were submitted. Essentially, it is `processingDelay` + `schedulingDelay`. + */ + def totalDelay = schedulingDelay.zip(processingDelay).map(x => x._1 + x._2).headOption +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Job.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala index 2128b7c7a6..7341bfbc99 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Job.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala @@ -15,13 +15,17 @@ * limitations under the License. */ -package org.apache.spark.streaming +package org.apache.spark.streaming.scheduler -import java.util.concurrent.atomic.AtomicLong +import org.apache.spark.streaming.Time +/** + * Class representing a Spark computation. It may contain multiple Spark jobs. + */ private[streaming] class Job(val time: Time, func: () => _) { - val id = Job.getNewId() + var id: String = _ + def run(): Long = { val startTime = System.currentTimeMillis func() @@ -29,13 +33,9 @@ class Job(val time: Time, func: () => _) { (stopTime - startTime) } - override def toString = "streaming job " + id + " @ " + time -} - -private[streaming] -object Job { - val id = new AtomicLong(0) - - def getNewId() = id.getAndIncrement() -} + def setId(number: Int) { + id = "streaming job " + time + "." + number + } + override def toString = id +}
\ No newline at end of file diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Scheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index ed892e33e6..5f8be93a98 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Scheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala @@ -15,65 +15,96 @@ * limitations under the License. */ -package org.apache.spark.streaming +package org.apache.spark.streaming.scheduler -import util.{ManualClock, RecurringTimer, Clock} +import akka.actor.{Props, Actor} import org.apache.spark.SparkEnv import org.apache.spark.Logging - +import org.apache.spark.streaming.{Checkpoint, Time, CheckpointWriter} +import org.apache.spark.streaming.util.{ManualClock, RecurringTimer, Clock} + +/** Event classes for JobGenerator */ +private[scheduler] sealed trait JobGeneratorEvent +private[scheduler] case class GenerateJobs(time: Time) extends JobGeneratorEvent +private[scheduler] case class ClearOldMetadata(time: Time) extends JobGeneratorEvent +private[scheduler] case class DoCheckpoint(time: Time) extends JobGeneratorEvent + +/** + * This class generates jobs from DStreams as well as drives checkpointing and cleaning + * up DStream metadata. + */ private[streaming] -class Scheduler(ssc: StreamingContext) extends Logging { - - initLogging() +class JobGenerator(jobScheduler: JobScheduler) extends Logging { - val concurrentJobs = System.getProperty("spark.streaming.concurrentJobs", "1").toInt - val jobManager = new JobManager(ssc, concurrentJobs) - val checkpointWriter = if (ssc.checkpointDuration != null && ssc.checkpointDir != null) { - new CheckpointWriter(ssc.checkpointDir) + val ssc = jobScheduler.ssc + val graph = ssc.graph + val eventProcessorActor = ssc.env.actorSystem.actorOf(Props(new Actor { + def receive = { + case event: JobGeneratorEvent => + logDebug("Got event of type " + event.getClass.getName) + processEvent(event) + } + })) + val clock = { + val clockClass = ssc.sc.conf.get( + "spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") + Class.forName(clockClass).newInstance().asInstanceOf[Clock] + } + val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds, + longTime => eventProcessorActor ! GenerateJobs(new Time(longTime))) + lazy val checkpointWriter = if (ssc.checkpointDuration != null && ssc.checkpointDir != null) { + new CheckpointWriter(ssc.conf, ssc.checkpointDir, ssc.sparkContext.hadoopConfiguration) } else { null } - val clockClass = System.getProperty( - "spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") - val clock = Class.forName(clockClass).newInstance().asInstanceOf[Clock] - val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds, - longTime => generateJobs(new Time(longTime))) - val graph = ssc.graph - var latestTime: Time = null - def start() = synchronized { if (ssc.isCheckpointPresent) { restart() } else { startFirstTime() } - logInfo("Scheduler started") } - - def stop() = synchronized { + + def stop() { timer.stop() - jobManager.stop() if (checkpointWriter != null) checkpointWriter.stop() ssc.graph.stop() - logInfo("Scheduler stopped") + logInfo("JobGenerator stopped") } + /** + * On batch completion, clear old metadata and checkpoint computation. + */ + private[scheduler] def onBatchCompletion(time: Time) { + eventProcessorActor ! ClearOldMetadata(time) + } + + /** Processes all events */ + private def processEvent(event: JobGeneratorEvent) { + event match { + case GenerateJobs(time) => generateJobs(time) + case ClearOldMetadata(time) => clearOldMetadata(time) + case DoCheckpoint(time) => doCheckpoint(time) + } + } + + /** Starts the generator for the first time */ private def startFirstTime() { val startTime = new Time(timer.getStartTime()) graph.start(startTime - graph.batchDuration) timer.start(startTime.milliseconds) - logInfo("Scheduler's timer started at " + startTime) + logInfo("JobGenerator started at " + startTime) } + /** Restarts the generator based on the information in checkpoint */ private def restart() { - // If manual clock is being used for testing, then // either set the manual clock to the last checkpointed time, // or if the property is defined set it to that time if (clock.isInstanceOf[ManualClock]) { val lastTime = ssc.initialCheckpoint.checkpointTime.milliseconds - val jumpTime = System.getProperty("spark.streaming.manualClock.jump", "0").toLong + val jumpTime = ssc.sc.conf.get("spark.streaming.manualClock.jump", "0").toLong clock.asInstanceOf[ManualClock].setTime(lastTime + jumpTime) } @@ -93,35 +124,31 @@ class Scheduler(ssc: StreamingContext) extends Logging { val timesToReschedule = (pendingTimes ++ downTimes).distinct.sorted(Time.ordering) logInfo("Batches to reschedule: " + timesToReschedule.mkString(", ")) timesToReschedule.foreach(time => - graph.generateJobs(time).foreach(jobManager.runJob) + jobScheduler.runJobs(time, graph.generateJobs(time)) ) // Restart the timer timer.start(restartTime.milliseconds) - logInfo("Scheduler's timer restarted at " + restartTime) + logInfo("JobGenerator restarted at " + restartTime) } /** Generate jobs and perform checkpoint for the given `time`. */ - def generateJobs(time: Time) { + private def generateJobs(time: Time) { SparkEnv.set(ssc.env) logInfo("\n-----------------------------------------------------\n") - graph.generateJobs(time).foreach(jobManager.runJob) - latestTime = time - doCheckpoint(time) + jobScheduler.runJobs(time, graph.generateJobs(time)) + eventProcessorActor ! DoCheckpoint(time) } - /** - * Clear old metadata assuming jobs of `time` have finished processing. - * And also perform checkpoint. - */ - def clearOldMetadata(time: Time) { + /** Clear DStream metadata for the given `time`. */ + private def clearOldMetadata(time: Time) { ssc.graph.clearOldMetadata(time) - doCheckpoint(time) + eventProcessorActor ! DoCheckpoint(time) } /** Perform checkpoint for the give `time`. */ - def doCheckpoint(time: Time) = synchronized { - if (ssc.checkpointDuration != null && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) { + private def doCheckpoint(time: Time) = synchronized { + if (checkpointWriter != null && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) { logInfo("Checkpointing graph for time " + time) ssc.graph.updateCheckpointData(time) checkpointWriter.write(new Checkpoint(ssc, time)) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala new file mode 100644 index 0000000000..9304fc1a93 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -0,0 +1,106 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import org.apache.spark.Logging +import org.apache.spark.SparkEnv +import java.util.concurrent.{TimeUnit, ConcurrentHashMap, Executors} +import scala.collection.mutable.HashSet +import org.apache.spark.streaming._ + +/** + * This class schedules jobs to be run on Spark. It uses the JobGenerator to generate + * the jobs and runs them using a thread pool. Number of threads + */ +private[streaming] +class JobScheduler(val ssc: StreamingContext) extends Logging { + + val jobSets = new ConcurrentHashMap[Time, JobSet] + val numConcurrentJobs = ssc.conf.get("spark.streaming.concurrentJobs", "1").toInt + val executor = Executors.newFixedThreadPool(numConcurrentJobs) + val generator = new JobGenerator(this) + val listenerBus = new StreamingListenerBus() + + def clock = generator.clock + + def start() { + generator.start() + } + + def stop() { + generator.stop() + executor.shutdown() + if (!executor.awaitTermination(5, TimeUnit.SECONDS)) { + executor.shutdownNow() + } + } + + def runJobs(time: Time, jobs: Seq[Job]) { + if (jobs.isEmpty) { + logInfo("No jobs added for time " + time) + } else { + val jobSet = new JobSet(time, jobs) + jobSets.put(time, jobSet) + jobSet.jobs.foreach(job => executor.execute(new JobHandler(job))) + logInfo("Added jobs for time " + time) + } + } + + def getPendingTimes(): Array[Time] = { + jobSets.keySet.toArray(new Array[Time](0)) + } + + private def beforeJobStart(job: Job) { + val jobSet = jobSets.get(job.time) + if (!jobSet.hasStarted) { + listenerBus.post(StreamingListenerBatchStarted(jobSet.toBatchInfo())) + } + jobSet.beforeJobStart(job) + logInfo("Starting job " + job.id + " from job set of time " + jobSet.time) + SparkEnv.set(generator.ssc.env) + } + + private def afterJobEnd(job: Job) { + val jobSet = jobSets.get(job.time) + jobSet.afterJobStop(job) + logInfo("Finished job " + job.id + " from job set of time " + jobSet.time) + if (jobSet.hasCompleted) { + jobSets.remove(jobSet.time) + generator.onBatchCompletion(jobSet.time) + logInfo("Total delay: %.3f s for time %s (execution: %.3f s)".format( + jobSet.totalDelay / 1000.0, jobSet.time.toString, + jobSet.processingDelay / 1000.0 + )) + listenerBus.post(StreamingListenerBatchCompleted(jobSet.toBatchInfo())) + } + } + + private[streaming] + class JobHandler(job: Job) extends Runnable { + def run() { + beforeJobStart(job) + try { + job.run() + } catch { + case e: Exception => + logError("Running " + job + " failed", e) + } + afterJobEnd(job) + } + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala new file mode 100644 index 0000000000..57268674ea --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala @@ -0,0 +1,68 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import scala.collection.mutable.HashSet +import org.apache.spark.streaming.Time + +/** Class representing a set of Jobs + * belong to the same batch. + */ +private[streaming] +case class JobSet(time: Time, jobs: Seq[Job]) { + + private val incompleteJobs = new HashSet[Job]() + var submissionTime = System.currentTimeMillis() // when this jobset was submitted + var processingStartTime = -1L // when the first job of this jobset started processing + var processingEndTime = -1L // when the last job of this jobset finished processing + + jobs.zipWithIndex.foreach { case (job, i) => job.setId(i) } + incompleteJobs ++= jobs + + def beforeJobStart(job: Job) { + if (processingStartTime < 0) processingStartTime = System.currentTimeMillis() + } + + def afterJobStop(job: Job) { + incompleteJobs -= job + if (hasCompleted) processingEndTime = System.currentTimeMillis() + } + + def hasStarted() = (processingStartTime > 0) + + def hasCompleted() = incompleteJobs.isEmpty + + // Time taken to process all the jobs from the time they started processing + // (i.e. not including the time they wait in the streaming scheduler queue) + def processingDelay = processingEndTime - processingStartTime + + // Time taken to process all the jobs from the time they were submitted + // (i.e. including the time they wait in the streaming scheduler queue) + def totalDelay = { + processingEndTime - time.milliseconds + } + + def toBatchInfo(): BatchInfo = { + new BatchInfo( + time, + submissionTime, + if (processingStartTime >= 0 ) Some(processingStartTime) else None, + if (processingEndTime >= 0 ) Some(processingEndTime) else None + ) + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/NetworkInputTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala index aae79a4e6f..abff55d77c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/NetworkInputTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.streaming +package org.apache.spark.streaming.scheduler import org.apache.spark.streaming.dstream.{NetworkInputDStream, NetworkReceiver} import org.apache.spark.streaming.dstream.{StopReceiver, ReportBlock, ReportError} @@ -25,15 +25,17 @@ import org.apache.spark.SparkContext._ import scala.collection.mutable.HashMap import scala.collection.mutable.Queue +import scala.concurrent.duration._ import akka.actor._ import akka.pattern.ask -import akka.util.duration._ import akka.dispatch._ +import org.apache.spark.storage.BlockId +import org.apache.spark.streaming.{Time, StreamingContext} private[streaming] sealed trait NetworkInputTrackerMessage private[streaming] case class RegisterReceiver(streamId: Int, receiverActor: ActorRef) extends NetworkInputTrackerMessage -private[streaming] case class AddBlocks(streamId: Int, blockIds: Seq[String], metadata: Any) extends NetworkInputTrackerMessage +private[streaming] case class AddBlocks(streamId: Int, blockIds: Seq[BlockId], metadata: Any) extends NetworkInputTrackerMessage private[streaming] case class DeregisterReceiver(streamId: Int, msg: String) extends NetworkInputTrackerMessage /** @@ -48,7 +50,7 @@ class NetworkInputTracker( val networkInputStreamMap = Map(networkInputStreams.map(x => (x.id, x)): _*) val receiverExecutor = new ReceiverExecutor() val receiverInfo = new HashMap[Int, ActorRef] - val receivedBlockIds = new HashMap[Int, Queue[String]] + val receivedBlockIds = new HashMap[Int, Queue[BlockId]] val timeout = 5000.milliseconds var currentTime: Time = null @@ -67,9 +69,9 @@ class NetworkInputTracker( } /** Return all the blocks received from a receiver. */ - def getBlockIds(receiverId: Int, time: Time): Array[String] = synchronized { + def getBlockIds(receiverId: Int, time: Time): Array[BlockId] = synchronized { val queue = receivedBlockIds.synchronized { - receivedBlockIds.getOrElse(receiverId, new Queue[String]()) + receivedBlockIds.getOrElse(receiverId, new Queue[BlockId]()) } val result = queue.synchronized { queue.dequeueAll(x => true) @@ -92,7 +94,7 @@ class NetworkInputTracker( case AddBlocks(streamId, blockIds, metadata) => { val tmp = receivedBlockIds.synchronized { if (!receivedBlockIds.contains(streamId)) { - receivedBlockIds += ((streamId, new Queue[String])) + receivedBlockIds += ((streamId, new Queue[BlockId])) } receivedBlockIds(streamId) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListener.scala new file mode 100644 index 0000000000..36225e190c --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListener.scala @@ -0,0 +1,75 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import scala.collection.mutable.Queue +import org.apache.spark.util.Distribution + +/** Base trait for events related to StreamingListener */ +sealed trait StreamingListenerEvent + +case class StreamingListenerBatchCompleted(batchInfo: BatchInfo) extends StreamingListenerEvent + +case class StreamingListenerBatchStarted(batchInfo: BatchInfo) extends StreamingListenerEvent + + +/** + * A listener interface for receiving information about an ongoing streaming + * computation. + */ +trait StreamingListener { + /** + * Called when processing of a batch has completed + */ + def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) { } + + /** + * Called when processing of a batch has started + */ + def onBatchStarted(batchStarted: StreamingListenerBatchStarted) { } +} + + +/** + * A simple StreamingListener that logs summary statistics across Spark Streaming batches + * @param numBatchInfos Number of last batches to consider for generating statistics (default: 10) + */ +class StatsReportListener(numBatchInfos: Int = 10) extends StreamingListener { + // Queue containing latest completed batches + val batchInfos = new Queue[BatchInfo]() + + override def onBatchCompleted(batchStarted: StreamingListenerBatchCompleted) { + batchInfos.enqueue(batchStarted.batchInfo) + if (batchInfos.size > numBatchInfos) batchInfos.dequeue() + printStats() + } + + def printStats() { + showMillisDistribution("Total delay: ", _.totalDelay) + showMillisDistribution("Processing time: ", _.processingDelay) + } + + def showMillisDistribution(heading: String, getMetric: BatchInfo => Option[Long]) { + org.apache.spark.scheduler.StatsReportListener.showMillisDistribution( + heading, extractDistribution(getMetric)) + } + + def extractDistribution(getMetric: BatchInfo => Option[Long]): Option[Distribution] = { + Distribution(batchInfos.flatMap(getMetric(_)).map(_.toDouble)) + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListenerBus.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListenerBus.scala new file mode 100644 index 0000000000..110a20f282 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/StreamingListenerBus.scala @@ -0,0 +1,81 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import org.apache.spark.Logging +import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer} +import java.util.concurrent.LinkedBlockingQueue + +/** Asynchronously passes StreamingListenerEvents to registered StreamingListeners. */ +private[spark] class StreamingListenerBus() extends Logging { + private val listeners = new ArrayBuffer[StreamingListener]() with SynchronizedBuffer[StreamingListener] + + /* Cap the capacity of the SparkListenerEvent queue so we get an explicit error (rather than + * an OOM exception) if it's perpetually being added to more quickly than it's being drained. */ + private val EVENT_QUEUE_CAPACITY = 10000 + private val eventQueue = new LinkedBlockingQueue[StreamingListenerEvent](EVENT_QUEUE_CAPACITY) + private var queueFullErrorMessageLogged = false + + new Thread("StreamingListenerBus") { + setDaemon(true) + override def run() { + while (true) { + val event = eventQueue.take + event match { + case batchStarted: StreamingListenerBatchStarted => + listeners.foreach(_.onBatchStarted(batchStarted)) + case batchCompleted: StreamingListenerBatchCompleted => + listeners.foreach(_.onBatchCompleted(batchCompleted)) + case _ => + } + } + } + }.start() + + def addListener(listener: StreamingListener) { + listeners += listener + } + + def post(event: StreamingListenerEvent) { + val eventAdded = eventQueue.offer(event) + if (!eventAdded && !queueFullErrorMessageLogged) { + logError("Dropping SparkListenerEvent because no remaining room in event queue. " + + "This likely means one of the SparkListeners is too slow and cannot keep up with the " + + "rate at which tasks are being started by the scheduler.") + queueFullErrorMessageLogged = true + } + } + + /** + * Waits until there are no more events in the queue, or until the specified time has elapsed. + * Used for testing only. Returns true if the queue has emptied and false is the specified time + * elapsed before the queue emptied. + */ + def waitUntilEmpty(timeoutMillis: Int): Boolean = { + val finishTime = System.currentTimeMillis + timeoutMillis + while (!eventQueue.isEmpty()) { + if (System.currentTimeMillis > finishTime) { + return false + } + /* Sleep rather than using wait/notify, because this is used only for testing and wait/notify + * add overhead in the general case. */ + Thread.sleep(10) + } + return true + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala index 6977957126..1559f7a9f7 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala @@ -25,6 +25,7 @@ import StreamingContext._ import scala.util.Random import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer} +import scala.reflect.ClassTag import java.io.{File, ObjectInputStream, IOException} import java.util.UUID @@ -38,7 +39,6 @@ import org.apache.hadoop.conf.Configuration private[streaming] object MasterFailureTest extends Logging { - initLogging() @volatile var killed = false @volatile var killCount = 0 @@ -120,7 +120,7 @@ object MasterFailureTest extends Logging { * Tests stream operation with multiple master failures, and verifies whether the * final set of output values is as expected or not. */ - def testOperation[T: ClassManifest]( + def testOperation[T: ClassTag]( directory: String, batchDuration: Duration, input: Seq[String], @@ -158,7 +158,7 @@ object MasterFailureTest extends Logging { * and batch duration. Returns the streaming context and the directory to which * files should be written for testing. */ - private def setupStreams[T: ClassManifest]( + private def setupStreams[T: ClassTag]( directory: String, batchDuration: Duration, operation: DStream[String] => DStream[T] @@ -192,7 +192,7 @@ object MasterFailureTest extends Logging { * Repeatedly starts and kills the streaming context until timed out or * the last expected output is generated. Finally, return */ - private def runStreams[T: ClassManifest]( + private def runStreams[T: ClassTag]( ssc_ : StreamingContext, lastExpectedOutput: T, maxTimeToRun: Long @@ -274,7 +274,7 @@ object MasterFailureTest extends Logging { * duplicate batch outputs of values from the `output`. As a result, the * expected output should not have consecutive batches with the same values as output. */ - private def verifyOutput[T: ClassManifest](output: Seq[T], expectedOutput: Seq[T]) { + private def verifyOutput[T: ClassTag](output: Seq[T], expectedOutput: Seq[T]) { // Verify whether expected outputs do not consecutive batches with same output for (i <- 0 until expectedOutput.size - 1) { assert(expectedOutput(i) != expectedOutput(i+1), @@ -305,7 +305,7 @@ object MasterFailureTest extends Logging { * ArrayBuffer. This buffer is wiped clean on being restored from checkpoint. */ private[streaming] -class TestOutputStream[T: ClassManifest]( +class TestOutputStream[T: ClassTag]( parent: DStream[T], val output: ArrayBuffer[Seq[T]] = new ArrayBuffer[Seq[T]] with SynchronizedBuffer[Seq[T]] ) extends ForEachDStream[T]( @@ -330,7 +330,6 @@ class TestOutputStream[T: ClassManifest]( */ private[streaming] class KillingThread(ssc: StreamingContext, maxKillWaitTime: Long) extends Thread with Logging { - initLogging() override def run() { try { @@ -365,7 +364,6 @@ class KillingThread(ssc: StreamingContext, maxKillWaitTime: Long) extends Thread private[streaming] class FileGeneratingThread(input: Seq[String], testDir: Path, interval: Long) extends Thread with Logging { - initLogging() override def run() { val localTestDir = Files.createTempDir() @@ -380,24 +378,24 @@ class FileGeneratingThread(input: Seq[String], testDir: Path, interval: Long) val tempHadoopFile = new Path(testDir, ".tmp_" + (i+1).toString) FileUtils.writeStringToFile(localFile, input(i).toString + "\n") var tries = 0 - var done = false - while (!done && tries < maxTries) { - tries += 1 - try { - // fs.copyFromLocalFile(new Path(localFile.toString), hadoopFile) - fs.copyFromLocalFile(new Path(localFile.toString), tempHadoopFile) - fs.rename(tempHadoopFile, hadoopFile) - done = true - } catch { - case ioe: IOException => { - fs = testDir.getFileSystem(new Configuration()) - logWarning("Attempt " + tries + " at generating file " + hadoopFile + " failed.", ioe) - } - } + var done = false + while (!done && tries < maxTries) { + tries += 1 + try { + // fs.copyFromLocalFile(new Path(localFile.toString), hadoopFile) + fs.copyFromLocalFile(new Path(localFile.toString), tempHadoopFile) + fs.rename(tempHadoopFile, hadoopFile) + done = true + } catch { + case ioe: IOException => { + fs = testDir.getFileSystem(new Configuration()) + logWarning("Attempt " + tries + " at generating file " + hadoopFile + " failed.", ioe) + } + } } - if (!done) + if (!done) logError("Could not generate file " + hadoopFile) - else + else logInfo("Generated file " + hadoopFile + " at " + System.currentTimeMillis) Thread.sleep(interval) localFile.delete() @@ -411,5 +409,3 @@ class FileGeneratingThread(input: Seq[String], testDir: Path, interval: Long) } } } - - diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala index fc8655a083..6585d494a6 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala @@ -20,7 +20,7 @@ package org.apache.spark.streaming.util import java.nio.ByteBuffer import org.apache.spark.util.{RateLimitedOutputStream, IntParam} import java.net.ServerSocket -import org.apache.spark.{Logging} +import org.apache.spark.{SparkConf, Logging} import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream import scala.io.Source import java.io.IOException @@ -42,7 +42,7 @@ object RawTextSender extends Logging { // Repeat the input data multiple times to fill in a buffer val lines = Source.fromFile(file).getLines().toArray val bufferStream = new FastByteArrayOutputStream(blockSize + 1000) - val ser = new KryoSerializer().newInstance() + val ser = new KryoSerializer(new SparkConf()).newInstance() val serStream = ser.serializeStream(bufferStream) var i = 0 while (bufferStream.position < blockSize) { diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index c0d729ff87..d53d433693 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -21,27 +21,32 @@ import com.google.common.base.Optional; import com.google.common.collect.Lists; import com.google.common.collect.Maps; import com.google.common.io.Files; + import kafka.serializer.StringDecoder; + import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; +import org.apache.spark.SparkConf; +import org.apache.spark.streaming.api.java.JavaDStreamLike; import org.junit.After; import org.junit.Assert; import org.junit.Before; import org.junit.Test; + import scala.Tuple2; +import twitter4j.Status; + import org.apache.spark.HashPartitioner; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaRDDLike; -import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.*; import org.apache.spark.storage.StorageLevel; import org.apache.spark.streaming.api.java.JavaDStream; import org.apache.spark.streaming.api.java.JavaPairDStream; import org.apache.spark.streaming.api.java.JavaStreamingContext; +import org.apache.spark.streaming.dstream.SparkFlumeEvent; import org.apache.spark.streaming.JavaTestUtils; import org.apache.spark.streaming.JavaCheckpointTestUtils; -import org.apache.spark.streaming.InputStreamsSuite; import java.io.*; import java.util.*; @@ -50,7 +55,6 @@ import akka.actor.Props; import akka.zeromq.Subscribe; - // The test suite itself is Serializable so that anonymous Function implementations can be // serialized, as an alternative to converting these anonymous classes to static inner classes; // see http://stackoverflow.com/questions/758570/. @@ -59,8 +63,11 @@ public class JavaAPISuite implements Serializable { @Before public void setUp() { - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); - ssc = new JavaStreamingContext("local[2]", "test", new Duration(1000)); + SparkConf conf = new SparkConf() + .setMaster("local[2]") + .setAppName("test") + .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + ssc = new JavaStreamingContext(conf, new Duration(1000)); ssc.checkpoint("checkpoint"); } @@ -85,8 +92,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList(3L), Arrays.asList(1L)); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream count = stream.count(); + JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<Long> count = stream.count(); JavaTestUtils.attachTestOutputStream(count); List<List<Long>> result = JavaTestUtils.runStreams(ssc, 3, 3); assertOrderInvariantEquals(expected, result); @@ -98,12 +105,12 @@ public class JavaAPISuite implements Serializable { Arrays.asList("hello", "world"), Arrays.asList("goodnight", "moon")); - List<List<Integer>> expected = Arrays.asList( + List<List<Integer>> expected = Arrays.asList( Arrays.asList(5,5), Arrays.asList(9,4)); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream letterCount = stream.map(new Function<String, Integer>() { + JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<Integer> letterCount = stream.map(new Function<String, Integer>() { @Override public Integer call(String s) throws Exception { return s.length(); @@ -128,8 +135,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList(7,8,9,4,5,6), Arrays.asList(7,8,9)); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream windowed = stream.window(new Duration(2000)); + JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<Integer> windowed = stream.window(new Duration(2000)); JavaTestUtils.attachTestOutputStream(windowed); List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 4, 4); @@ -152,8 +159,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList(7,8,9,10,11,12,13,14,15,16,17,18), Arrays.asList(13,14,15,16,17,18)); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream windowed = stream.window(new Duration(4000), new Duration(2000)); + JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<Integer> windowed = stream.window(new Duration(4000), new Duration(2000)); JavaTestUtils.attachTestOutputStream(windowed); List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 8, 4); @@ -170,8 +177,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList("giants"), Arrays.asList("yankees")); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream filtered = stream.filter(new Function<String, Boolean>() { + JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<String> filtered = stream.filter(new Function<String, Boolean>() { @Override public Boolean call(String s) throws Exception { return s.contains("a"); @@ -184,6 +191,39 @@ public class JavaAPISuite implements Serializable { } @Test + public void testRepartitionMorePartitions() { + List<List<Integer>> inputData = Arrays.asList( + Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), + Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)); + JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 2); + JavaDStream repartitioned = stream.repartition(4); + JavaTestUtils.attachTestOutputStream(repartitioned); + List<List<List<Integer>>> result = JavaTestUtils.runStreamsWithPartitions(ssc, 2, 2); + Assert.assertEquals(2, result.size()); + for (List<List<Integer>> rdd : result) { + Assert.assertEquals(4, rdd.size()); + Assert.assertEquals( + 10, rdd.get(0).size() + rdd.get(1).size() + rdd.get(2).size() + rdd.get(3).size()); + } + } + + @Test + public void testRepartitionFewerPartitions() { + List<List<Integer>> inputData = Arrays.asList( + Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), + Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)); + JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 4); + JavaDStream repartitioned = stream.repartition(2); + JavaTestUtils.attachTestOutputStream(repartitioned); + List<List<List<Integer>>> result = JavaTestUtils.runStreamsWithPartitions(ssc, 2, 2); + Assert.assertEquals(2, result.size()); + for (List<List<Integer>> rdd : result) { + Assert.assertEquals(2, rdd.size()); + Assert.assertEquals(10, rdd.get(0).size() + rdd.get(1).size()); + } + } + + @Test public void testGlom() { List<List<String>> inputData = Arrays.asList( Arrays.asList("giants", "dodgers"), @@ -193,8 +233,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList(Arrays.asList("giants", "dodgers")), Arrays.asList(Arrays.asList("yankees", "red socks"))); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream glommed = stream.glom(); + JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<List<String>> glommed = stream.glom(); JavaTestUtils.attachTestOutputStream(glommed); List<List<List<String>>> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -211,8 +251,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList("GIANTSDODGERS"), Arrays.asList("YANKEESRED SOCKS")); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream mapped = stream.mapPartitions(new FlatMapFunction<Iterator<String>, String>() { + JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<String> mapped = stream.mapPartitions(new FlatMapFunction<Iterator<String>, String>() { @Override public Iterable<String> call(Iterator<String> in) { String out = ""; @@ -223,7 +263,7 @@ public class JavaAPISuite implements Serializable { } }); JavaTestUtils.attachTestOutputStream(mapped); - List<List<List<String>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + List<List<String>> result = JavaTestUtils.runStreams(ssc, 2, 2); Assert.assertEquals(expected, result); } @@ -254,8 +294,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList(15), Arrays.asList(24)); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream reduced = stream.reduce(new IntegerSum()); + JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<Integer> reduced = stream.reduce(new IntegerSum()); JavaTestUtils.attachTestOutputStream(reduced); List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -275,8 +315,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList(39), Arrays.asList(24)); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream reducedWindowed = stream.reduceByWindow(new IntegerSum(), + JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<Integer> reducedWindowed = stream.reduceByWindow(new IntegerSum(), new IntegerDifference(), new Duration(2000), new Duration(1000)); JavaTestUtils.attachTestOutputStream(reducedWindowed); List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 4, 4); @@ -292,8 +332,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList(7,8,9)); JavaSparkContext jsc = new JavaSparkContext(ssc.ssc().sc()); - JavaRDD<Integer> rdd1 = ssc.sc().parallelize(Arrays.asList(1,2,3)); - JavaRDD<Integer> rdd2 = ssc.sc().parallelize(Arrays.asList(4,5,6)); + JavaRDD<Integer> rdd1 = ssc.sc().parallelize(Arrays.asList(1, 2, 3)); + JavaRDD<Integer> rdd2 = ssc.sc().parallelize(Arrays.asList(4, 5, 6)); JavaRDD<Integer> rdd3 = ssc.sc().parallelize(Arrays.asList(7,8,9)); LinkedList<JavaRDD<Integer>> rdds = Lists.newLinkedList(); @@ -320,17 +360,19 @@ public class JavaAPISuite implements Serializable { Arrays.asList(9,10,11)); JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream<Integer> transformed = - stream.transform(new Function<JavaRDD<Integer>, JavaRDD<Integer>>() { - @Override - public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception { - return in.map(new Function<Integer, Integer>() { - @Override - public Integer call(Integer i) throws Exception { - return i + 2; - } - }); - }}); + JavaDStream<Integer> transformed = stream.transform( + new Function<JavaRDD<Integer>, JavaRDD<Integer>>() { + @Override + public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception { + return in.map(new Function<Integer, Integer>() { + @Override + public Integer call(Integer i) throws Exception { + return i + 2; + } + }); + } + }); + JavaTestUtils.attachTestOutputStream(transformed); List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -338,6 +380,316 @@ public class JavaAPISuite implements Serializable { } @Test + public void testVariousTransform() { + // tests whether all variations of transform can be called from Java + + List<List<Integer>> inputData = Arrays.asList(Arrays.asList(1)); + JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + + List<List<Tuple2<String, Integer>>> pairInputData = + Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1))); + JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream( + JavaTestUtils.attachTestInputStream(ssc, pairInputData, 1)); + + JavaDStream<Integer> transformed1 = stream.transform( + new Function<JavaRDD<Integer>, JavaRDD<Integer>>() { + @Override + public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception { + return null; + } + } + ); + + JavaDStream<Integer> transformed2 = stream.transform( + new Function2<JavaRDD<Integer>, Time, JavaRDD<Integer>>() { + @Override public JavaRDD<Integer> call(JavaRDD<Integer> in, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<String, Integer> transformed3 = stream.transform( + new Function<JavaRDD<Integer>, JavaPairRDD<String, Integer>>() { + @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in) throws Exception { + return null; + } + } + ); + + JavaPairDStream<String, Integer> transformed4 = stream.transform( + new Function2<JavaRDD<Integer>, Time, JavaPairRDD<String, Integer>>() { + @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in, Time time) throws Exception { + return null; + } + } + ); + + JavaDStream<Integer> pairTransformed1 = pairStream.transform( + new Function<JavaPairRDD<String, Integer>, JavaRDD<Integer>>() { + @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in) throws Exception { + return null; + } + } + ); + + JavaDStream<Integer> pairTransformed2 = pairStream.transform( + new Function2<JavaPairRDD<String, Integer>, Time, JavaRDD<Integer>>() { + @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<String, String> pairTransformed3 = pairStream.transform( + new Function<JavaPairRDD<String, Integer>, JavaPairRDD<String, String>>() { + @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in) throws Exception { + return null; + } + } + ); + + JavaPairDStream<String, String> pairTransformed4 = pairStream.transform( + new Function2<JavaPairRDD<String, Integer>, Time, JavaPairRDD<String, String>>() { + @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in, Time time) throws Exception { + return null; + } + } + ); + + } + + @Test + public void testTransformWith() { + List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList( + Arrays.asList( + new Tuple2<String, String>("california", "dodgers"), + new Tuple2<String, String>("new york", "yankees")), + Arrays.asList( + new Tuple2<String, String>("california", "sharks"), + new Tuple2<String, String>("new york", "rangers"))); + + List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList( + Arrays.asList( + new Tuple2<String, String>("california", "giants"), + new Tuple2<String, String>("new york", "mets")), + Arrays.asList( + new Tuple2<String, String>("california", "ducks"), + new Tuple2<String, String>("new york", "islanders"))); + + + List<List<Tuple2<String, Tuple2<String, String>>>> expected = Arrays.asList( + Arrays.asList( + new Tuple2<String, Tuple2<String, String>>("california", + new Tuple2<String, String>("dodgers", "giants")), + new Tuple2<String, Tuple2<String, String>>("new york", + new Tuple2<String, String>("yankees", "mets"))), + Arrays.asList( + new Tuple2<String, Tuple2<String, String>>("california", + new Tuple2<String, String>("sharks", "ducks")), + new Tuple2<String, Tuple2<String, String>>("new york", + new Tuple2<String, String>("rangers", "islanders")))); + + JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream( + ssc, stringStringKVStream1, 1); + JavaPairDStream<String, String> pairStream1 = JavaPairDStream.fromJavaDStream(stream1); + + JavaDStream<Tuple2<String, String>> stream2 = JavaTestUtils.attachTestInputStream( + ssc, stringStringKVStream2, 1); + JavaPairDStream<String, String> pairStream2 = JavaPairDStream.fromJavaDStream(stream2); + + JavaPairDStream<String, Tuple2<String, String>> joined = pairStream1.transformWith( + pairStream2, + new Function3< + JavaPairRDD<String, String>, + JavaPairRDD<String, String>, + Time, + JavaPairRDD<String, Tuple2<String, String>> + >() { + @Override + public JavaPairRDD<String, Tuple2<String, String>> call( + JavaPairRDD<String, String> rdd1, + JavaPairRDD<String, String> rdd2, + Time time + ) throws Exception { + return rdd1.join(rdd2); + } + } + ); + + JavaTestUtils.attachTestOutputStream(joined); + List<List<Tuple2<String, Tuple2<String, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + + Assert.assertEquals(expected, result); + } + + + @Test + public void testVariousTransformWith() { + // tests whether all variations of transformWith can be called from Java + + List<List<Integer>> inputData1 = Arrays.asList(Arrays.asList(1)); + List<List<String>> inputData2 = Arrays.asList(Arrays.asList("x")); + JavaDStream<Integer> stream1 = JavaTestUtils.attachTestInputStream(ssc, inputData1, 1); + JavaDStream<String> stream2 = JavaTestUtils.attachTestInputStream(ssc, inputData2, 1); + + List<List<Tuple2<String, Integer>>> pairInputData1 = + Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1))); + List<List<Tuple2<Double, Character>>> pairInputData2 = + Arrays.asList(Arrays.asList(new Tuple2<Double, Character>(1.0, 'x'))); + JavaPairDStream<String, Integer> pairStream1 = JavaPairDStream.fromJavaDStream( + JavaTestUtils.attachTestInputStream(ssc, pairInputData1, 1)); + JavaPairDStream<Double, Character> pairStream2 = JavaPairDStream.fromJavaDStream( + JavaTestUtils.attachTestInputStream(ssc, pairInputData2, 1)); + + JavaDStream<Double> transformed1 = stream1.transformWith( + stream2, + new Function3<JavaRDD<Integer>, JavaRDD<String>, Time, JavaRDD<Double>>() { + @Override + public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaDStream<Double> transformed2 = stream1.transformWith( + pairStream1, + new Function3<JavaRDD<Integer>, JavaPairRDD<String, Integer>, Time, JavaRDD<Double>>() { + @Override + public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<Double, Double> transformed3 = stream1.transformWith( + stream2, + new Function3<JavaRDD<Integer>, JavaRDD<String>, Time, JavaPairRDD<Double, Double>>() { + @Override + public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<Double, Double> transformed4 = stream1.transformWith( + pairStream1, + new Function3<JavaRDD<Integer>, JavaPairRDD<String, Integer>, Time, JavaPairRDD<Double, Double>>() { + @Override + public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaDStream<Double> pairTransformed1 = pairStream1.transformWith( + stream2, + new Function3<JavaPairRDD<String, Integer>, JavaRDD<String>, Time, JavaRDD<Double>>() { + @Override + public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaDStream<Double> pairTransformed2_ = pairStream1.transformWith( + pairStream1, + new Function3<JavaPairRDD<String, Integer>, JavaPairRDD<String, Integer>, Time, JavaRDD<Double>>() { + @Override + public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<Double, Double> pairTransformed3 = pairStream1.transformWith( + stream2, + new Function3<JavaPairRDD<String, Integer>, JavaRDD<String>, Time, JavaPairRDD<Double, Double>>() { + @Override + public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception { + return null; + } + } + ); + + JavaPairDStream<Double, Double> pairTransformed4 = pairStream1.transformWith( + pairStream2, + new Function3<JavaPairRDD<String, Integer>, JavaPairRDD<Double, Character>, Time, JavaPairRDD<Double, Double>>() { + @Override + public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<Double, Character> rdd2, Time time) throws Exception { + return null; + } + } + ); + } + + @Test + public void testStreamingContextTransform(){ + List<List<Integer>> stream1input = Arrays.asList( + Arrays.asList(1), + Arrays.asList(2) + ); + + List<List<Integer>> stream2input = Arrays.asList( + Arrays.asList(3), + Arrays.asList(4) + ); + + List<List<Tuple2<Integer, String>>> pairStream1input = Arrays.asList( + Arrays.asList(new Tuple2<Integer, String>(1, "x")), + Arrays.asList(new Tuple2<Integer, String>(2, "y")) + ); + + List<List<Tuple2<Integer, Tuple2<Integer, String>>>> expected = Arrays.asList( + Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(1, new Tuple2<Integer, String>(1, "x"))), + Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(2, new Tuple2<Integer, String>(2, "y"))) + ); + + JavaDStream<Integer> stream1 = JavaTestUtils.attachTestInputStream(ssc, stream1input, 1); + JavaDStream<Integer> stream2 = JavaTestUtils.attachTestInputStream(ssc, stream2input, 1); + JavaPairDStream<Integer, String> pairStream1 = JavaPairDStream.fromJavaDStream( + JavaTestUtils.attachTestInputStream(ssc, pairStream1input, 1)); + + List<JavaDStream<?>> listOfDStreams1 = Arrays.<JavaDStream<?>>asList(stream1, stream2); + + // This is just to test whether this transform to JavaStream compiles + JavaDStream<Long> transformed1 = ssc.transform( + listOfDStreams1, + new Function2<List<JavaRDD<?>>, Time, JavaRDD<Long>>() { + public JavaRDD<Long> call(List<JavaRDD<?>> listOfRDDs, Time time) { + assert(listOfRDDs.size() == 2); + return null; + } + } + ); + + List<JavaDStream<?>> listOfDStreams2 = + Arrays.<JavaDStream<?>>asList(stream1, stream2, pairStream1.toJavaDStream()); + + JavaPairDStream<Integer, Tuple2<Integer, String>> transformed2 = ssc.transform( + listOfDStreams2, + new Function2<List<JavaRDD<?>>, Time, JavaPairRDD<Integer, Tuple2<Integer, String>>>() { + public JavaPairRDD<Integer, Tuple2<Integer, String>> call(List<JavaRDD<?>> listOfRDDs, Time time) { + assert(listOfRDDs.size() == 3); + JavaRDD<Integer> rdd1 = (JavaRDD<Integer>)listOfRDDs.get(0); + JavaRDD<Integer> rdd2 = (JavaRDD<Integer>)listOfRDDs.get(1); + JavaRDD<Tuple2<Integer, String>> rdd3 = (JavaRDD<Tuple2<Integer, String>>)listOfRDDs.get(2); + JavaPairRDD<Integer, String> prdd3 = JavaPairRDD.fromJavaRDD(rdd3); + PairFunction<Integer, Integer, Integer> mapToTuple = new PairFunction<Integer, Integer, Integer>() { + @Override + public Tuple2<Integer, Integer> call(Integer i) throws Exception { + return new Tuple2<Integer, Integer>(i, i); + } + }; + return rdd1.union(rdd2).map(mapToTuple).join(prdd3); + } + } + ); + JavaTestUtils.attachTestOutputStream(transformed2); + List<List<Tuple2<Integer, Tuple2<Integer, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + Assert.assertEquals(expected, result); + } + + @Test public void testFlatMap() { List<List<String>> inputData = Arrays.asList( Arrays.asList("go", "giants"), @@ -349,8 +701,8 @@ public class JavaAPISuite implements Serializable { Arrays.asList("b", "o", "o", "d","o","d","g","e","r","s"), Arrays.asList("a","t","h","l","e","t","i","c","s")); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream flatMapped = stream.flatMap(new FlatMapFunction<String, String>() { + JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<String> flatMapped = stream.flatMap(new FlatMapFunction<String, String>() { @Override public Iterable<String> call(String x) { return Lists.newArrayList(x.split("(?!^)")); @@ -396,8 +748,8 @@ public class JavaAPISuite implements Serializable { new Tuple2<Integer, String>(9, "c"), new Tuple2<Integer, String>(9, "s"))); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaPairDStream flatMapped = stream.flatMap(new PairFlatMapFunction<String, Integer, String>() { + JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaPairDStream<Integer,String> flatMapped = stream.flatMap(new PairFlatMapFunction<String, Integer, String>() { @Override public Iterable<Tuple2<Integer, String>> call(String in) throws Exception { List<Tuple2<Integer, String>> out = Lists.newArrayList(); @@ -430,10 +782,10 @@ public class JavaAPISuite implements Serializable { Arrays.asList(2,2,5,5), Arrays.asList(3,3,6,6)); - JavaDStream stream1 = JavaTestUtils.attachTestInputStream(ssc, inputData1, 2); - JavaDStream stream2 = JavaTestUtils.attachTestInputStream(ssc, inputData2, 2); + JavaDStream<Integer> stream1 = JavaTestUtils.attachTestInputStream(ssc, inputData1, 2); + JavaDStream<Integer> stream2 = JavaTestUtils.attachTestInputStream(ssc, inputData2, 2); - JavaDStream unioned = stream1.union(stream2); + JavaDStream<Integer> unioned = stream1.union(stream2); JavaTestUtils.attachTestOutputStream(unioned); List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -444,7 +796,7 @@ public class JavaAPISuite implements Serializable { * Performs an order-invariant comparison of lists representing two RDD streams. This allows * us to account for ordering variation within individual RDD's which occurs during windowing. */ - public static <T extends Comparable> void assertOrderInvariantEquals( + public static <T extends Comparable<T>> void assertOrderInvariantEquals( List<List<T>> expected, List<List<T>> actual) { for (List<T> list: expected) { Collections.sort(list); @@ -467,11 +819,11 @@ public class JavaAPISuite implements Serializable { Arrays.asList(new Tuple2<String, Integer>("giants", 6)), Arrays.asList(new Tuple2<String, Integer>("yankees", 7))); - JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream<String, Integer> pairStream = stream.map( new PairFunction<String, String, Integer>() { @Override - public Tuple2 call(String in) throws Exception { + public Tuple2<String, Integer> call(String in) throws Exception { return new Tuple2<String, Integer>(in, in.length()); } }); @@ -1099,7 +1451,7 @@ public class JavaAPISuite implements Serializable { JavaPairDStream<String, Tuple2<List<String>, List<String>>> grouped = pairStream1.cogroup(pairStream2); JavaTestUtils.attachTestOutputStream(grouped); - List<List<Tuple2<String, String>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + List<List<Tuple2<String, Tuple2<List<String>, List<String>>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); Assert.assertEquals(expected, result); } @@ -1142,7 +1494,38 @@ public class JavaAPISuite implements Serializable { JavaPairDStream<String, Tuple2<String, String>> joined = pairStream1.join(pairStream2); JavaTestUtils.attachTestOutputStream(joined); - List<List<Tuple2<String, Long>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + List<List<Tuple2<String, Tuple2<String, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + + Assert.assertEquals(expected, result); + } + + @Test + public void testLeftOuterJoin() { + List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList( + Arrays.asList(new Tuple2<String, String>("california", "dodgers"), + new Tuple2<String, String>("new york", "yankees")), + Arrays.asList(new Tuple2<String, String>("california", "sharks") )); + + List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList( + Arrays.asList(new Tuple2<String, String>("california", "giants") ), + Arrays.asList(new Tuple2<String, String>("new york", "islanders") ) + + ); + + List<List<Long>> expected = Arrays.asList(Arrays.asList(2L), Arrays.asList(1L)); + + JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream( + ssc, stringStringKVStream1, 1); + JavaPairDStream<String, String> pairStream1 = JavaPairDStream.fromJavaDStream(stream1); + + JavaDStream<Tuple2<String, String>> stream2 = JavaTestUtils.attachTestInputStream( + ssc, stringStringKVStream2, 1); + JavaPairDStream<String, String> pairStream2 = JavaPairDStream.fromJavaDStream(stream2); + + JavaPairDStream<String, Tuple2<String, Optional<String>>> joined = pairStream1.leftOuterJoin(pairStream2); + JavaDStream<Long> counted = joined.count(); + JavaTestUtils.attachTestOutputStream(counted); + List<List<Long>> result = JavaTestUtils.runStreams(ssc, 2, 2); Assert.assertEquals(expected, result); } @@ -1163,8 +1546,8 @@ public class JavaAPISuite implements Serializable { File tempDir = Files.createTempDir(); ssc.checkpoint(tempDir.getAbsolutePath()); - JavaDStream stream = JavaCheckpointTestUtils.attachTestInputStream(ssc, inputData, 1); - JavaDStream letterCount = stream.map(new Function<String, Integer>() { + JavaDStream<String> stream = JavaCheckpointTestUtils.attachTestInputStream(ssc, inputData, 1); + JavaDStream<Integer> letterCount = stream.map(new Function<String, Integer>() { @Override public Integer call(String s) throws Exception { return s.length(); @@ -1220,20 +1603,26 @@ public class JavaAPISuite implements Serializable { @Test public void testKafkaStream() { HashMap<String, Integer> topics = Maps.newHashMap(); - JavaDStream test1 = ssc.kafkaStream("localhost:12345", "group", topics); - JavaDStream test2 = ssc.kafkaStream("localhost:12345", "group", topics, + JavaPairDStream<String, String> test1 = ssc.kafkaStream("localhost:12345", "group", topics); + JavaPairDStream<String, String> test2 = ssc.kafkaStream("localhost:12345", "group", topics, StorageLevel.MEMORY_AND_DISK()); HashMap<String, String> kafkaParams = Maps.newHashMap(); - kafkaParams.put("zk.connect","localhost:12345"); - kafkaParams.put("groupid","consumer-group"); - JavaDStream test3 = ssc.kafkaStream(String.class, StringDecoder.class, kafkaParams, topics, + kafkaParams.put("zookeeper.connect","localhost:12345"); + kafkaParams.put("group.id","consumer-group"); + JavaPairDStream<String, String> test3 = ssc.kafkaStream( + String.class, + String.class, + StringDecoder.class, + StringDecoder.class, + kafkaParams, + topics, StorageLevel.MEMORY_AND_DISK()); } @Test public void testSocketTextStream() { - JavaDStream test = ssc.socketTextStream("localhost", 12345); + JavaDStream<String> test = ssc.socketTextStream("localhost", 12345); } @Test @@ -1253,7 +1642,7 @@ public class JavaAPISuite implements Serializable { } } - JavaDStream test = ssc.socketStream( + JavaDStream<String> test = ssc.socketStream( "localhost", 12345, new Converter(), @@ -1262,39 +1651,39 @@ public class JavaAPISuite implements Serializable { @Test public void testTextFileStream() { - JavaDStream test = ssc.textFileStream("/tmp/foo"); + JavaDStream<String> test = ssc.textFileStream("/tmp/foo"); } @Test public void testRawSocketStream() { - JavaDStream test = ssc.rawSocketStream("localhost", 12345); + JavaDStream<String> test = ssc.rawSocketStream("localhost", 12345); } @Test public void testFlumeStream() { - JavaDStream test = ssc.flumeStream("localhost", 12345, StorageLevel.MEMORY_ONLY()); + JavaDStream<SparkFlumeEvent> test = ssc.flumeStream("localhost", 12345, StorageLevel.MEMORY_ONLY()); } @Test public void testFileStream() { JavaPairDStream<String, String> foo = - ssc.<String, String, SequenceFileInputFormat>fileStream("/tmp/foo"); + ssc.<String, String, SequenceFileInputFormat<String,String>>fileStream("/tmp/foo"); } @Test public void testTwitterStream() { String[] filters = new String[] { "good", "bad", "ugly" }; - JavaDStream test = ssc.twitterStream(filters, StorageLevel.MEMORY_ONLY()); + JavaDStream<Status> test = ssc.twitterStream(filters, StorageLevel.MEMORY_ONLY()); } @Test public void testActorStream() { - JavaDStream test = ssc.actorStream((Props)null, "TestActor", StorageLevel.MEMORY_ONLY()); + JavaDStream<String> test = ssc.actorStream((Props)null, "TestActor", StorageLevel.MEMORY_ONLY()); } @Test public void testZeroMQStream() { - JavaDStream test = ssc.zeroMQStream("url", (Subscribe) null, new Function<byte[][], Iterable<String>>() { + JavaDStream<String> test = ssc.zeroMQStream("url", (Subscribe) null, new Function<byte[][], Iterable<String>>() { @Override public Iterable<String> call(byte[][] b) throws Exception { return null; diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala index 8a6604904d..42ab9590d6 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala @@ -17,7 +17,9 @@ package org.apache.spark.streaming -import collection.mutable.{SynchronizedBuffer, ArrayBuffer} +import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer} +import scala.reflect.ClassTag + import java.util.{List => JList} import org.apache.spark.streaming.api.java.{JavaPairDStream, JavaDStreamLike, JavaDStream, JavaStreamingContext} import org.apache.spark.streaming._ @@ -31,15 +33,15 @@ trait JavaTestBase extends TestSuiteBase { /** * Create a [[org.apache.spark.streaming.TestInputStream]] and attach it to the supplied context. * The stream will be derived from the supplied lists of Java objects. - **/ + */ def attachTestInputStream[T]( - ssc: JavaStreamingContext, - data: JList[JList[T]], - numPartitions: Int) = { + ssc: JavaStreamingContext, + data: JList[JList[T]], + numPartitions: Int) = { val seqData = data.map(Seq(_:_*)) - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] val dstream = new TestInputStream[T](ssc.ssc, seqData, numPartitions) ssc.ssc.registerInputStream(dstream) new JavaDStream[T](dstream) @@ -50,12 +52,11 @@ trait JavaTestBase extends TestSuiteBase { * [[org.apache.spark.streaming.TestOutputStream]]. **/ def attachTestOutputStream[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T, R]]( - dstream: JavaDStreamLike[T, This, R]) = + dstream: JavaDStreamLike[T, This, R]) = { - implicit val cm: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - val ostream = new TestOutputStream(dstream.dstream, - new ArrayBuffer[Seq[T]] with SynchronizedBuffer[Seq[T]]) + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] + val ostream = new TestOutputStreamWithPartitions(dstream.dstream) dstream.dstream.ssc.registerOutputStream(ostream) } @@ -63,16 +64,39 @@ trait JavaTestBase extends TestSuiteBase { * Process all registered streams for a numBatches batches, failing if * numExpectedOutput RDD's are not generated. Generated RDD's are collected * and returned, represented as a list for each batch interval. + * + * Returns a list of items for each RDD. */ def runStreams[V]( - ssc: JavaStreamingContext, numBatches: Int, numExpectedOutput: Int): JList[JList[V]] = { - implicit val cm: ClassManifest[V] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[V]] + ssc: JavaStreamingContext, numBatches: Int, numExpectedOutput: Int): JList[JList[V]] = { + implicit val cm: ClassTag[V] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] val res = runStreams[V](ssc.ssc, numBatches, numExpectedOutput) val out = new ArrayList[JList[V]]() res.map(entry => out.append(new ArrayList[V](entry))) out } + + /** + * Process all registered streams for a numBatches batches, failing if + * numExpectedOutput RDD's are not generated. Generated RDD's are collected + * and returned, represented as a list for each batch interval. + * + * Returns a sequence of RDD's. Each RDD is represented as several sequences of items, each + * representing one partition. + */ + def runStreamsWithPartitions[V](ssc: JavaStreamingContext, numBatches: Int, + numExpectedOutput: Int): JList[JList[JList[V]]] = { + implicit val cm: ClassTag[V] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] + val res = runStreamsWithPartitions[V](ssc.ssc, numBatches, numExpectedOutput) + val out = new ArrayList[JList[JList[V]]]() + res.map{entry => + val lists = entry.map(new ArrayList[V](_)) + out.append(new ArrayList[JList[V]](lists)) + } + out + } } object JavaTestUtils extends JavaTestBase { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index 11586f72b6..ee6b433d1f 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -18,23 +18,14 @@ package org.apache.spark.streaming import org.apache.spark.streaming.StreamingContext._ -import scala.runtime.RichInt -import util.ManualClock - -class BasicOperationsSuite extends TestSuiteBase { - override def framework() = "BasicOperationsSuite" - - before { - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") - } +import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext._ - after { - // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown - System.clearProperty("spark.driver.port") - System.clearProperty("spark.hostPort") - } +import util.ManualClock +import org.apache.spark.{SparkContext, SparkConf} +class BasicOperationsSuite extends TestSuiteBase { test("map") { val input = Seq(1 to 4, 5 to 8, 9 to 12) testOperation( @@ -82,6 +73,44 @@ class BasicOperationsSuite extends TestSuiteBase { testOperation(input, operation, output, true) } + test("repartition (more partitions)") { + val input = Seq(1 to 100, 101 to 200, 201 to 300) + val operation = (r: DStream[Int]) => r.repartition(5) + val ssc = setupStreams(input, operation, 2) + val output = runStreamsWithPartitions(ssc, 3, 3) + assert(output.size === 3) + val first = output(0) + val second = output(1) + val third = output(2) + + assert(first.size === 5) + assert(second.size === 5) + assert(third.size === 5) + + assert(first.flatten.toSet === (1 to 100).toSet) + assert(second.flatten.toSet === (101 to 200).toSet) + assert(third.flatten.toSet === (201 to 300).toSet) + } + + test("repartition (fewer partitions)") { + val input = Seq(1 to 100, 101 to 200, 201 to 300) + val operation = (r: DStream[Int]) => r.repartition(2) + val ssc = setupStreams(input, operation, 5) + val output = runStreamsWithPartitions(ssc, 3, 3) + assert(output.size === 3) + val first = output(0) + val second = output(1) + val third = output(2) + + assert(first.size === 2) + assert(second.size === 2) + assert(third.size === 2) + + assert(first.flatten.toSet === (1 to 100).toSet) + assert(second.flatten.toSet === (101 to 200).toSet) + assert(third.flatten.toSet === (201 to 300).toSet) + } + test("groupByKey") { testOperation( Seq( Seq("a", "a", "b"), Seq("", ""), Seq() ), @@ -143,6 +172,72 @@ class BasicOperationsSuite extends TestSuiteBase { ) } + test("union") { + val input = Seq(1 to 4, 101 to 104, 201 to 204) + val output = Seq(1 to 8, 101 to 108, 201 to 208) + testOperation( + input, + (s: DStream[Int]) => s.union(s.map(_ + 4)) , + output + ) + } + + test("StreamingContext.union") { + val input = Seq(1 to 4, 101 to 104, 201 to 204) + val output = Seq(1 to 12, 101 to 112, 201 to 212) + // union over 3 DStreams + testOperation( + input, + (s: DStream[Int]) => s.context.union(Seq(s, s.map(_ + 4), s.map(_ + 8))), + output + ) + } + + test("transform") { + val input = Seq(1 to 4, 5 to 8, 9 to 12) + testOperation( + input, + (r: DStream[Int]) => r.transform(rdd => rdd.map(_.toString)), // RDD.map in transform + input.map(_.map(_.toString)) + ) + } + + test("transformWith") { + val inputData1 = Seq( Seq("a", "b"), Seq("a", ""), Seq(""), Seq() ) + val inputData2 = Seq( Seq("a", "b"), Seq("b", ""), Seq(), Seq("") ) + val outputData = Seq( + Seq( ("a", (1, "x")), ("b", (1, "x")) ), + Seq( ("", (1, "x")) ), + Seq( ), + Seq( ) + ) + val operation = (s1: DStream[String], s2: DStream[String]) => { + val t1 = s1.map(x => (x, 1)) + val t2 = s2.map(x => (x, "x")) + t1.transformWith( // RDD.join in transform + t2, + (rdd1: RDD[(String, Int)], rdd2: RDD[(String, String)]) => rdd1.join(rdd2) + ) + } + testOperation(inputData1, inputData2, operation, outputData, true) + } + + test("StreamingContext.transform") { + val input = Seq(1 to 4, 101 to 104, 201 to 204) + val output = Seq(1 to 12, 101 to 112, 201 to 212) + + // transform over 3 DStreams by doing union of the 3 RDDs + val operation = (s: DStream[Int]) => { + s.context.transform( + Seq(s, s.map(_ + 4), s.map(_ + 8)), // 3 DStreams + (rdds: Seq[RDD[_]], time: Time) => + rdds.head.context.union(rdds.map(_.asInstanceOf[RDD[Int]])) // union of RDDs + ) + } + + testOperation(input, operation, output) + } + test("cogroup") { val inputData1 = Seq( Seq("a", "a", "b"), Seq("a", ""), Seq(""), Seq() ) val inputData2 = Seq( Seq("a", "a", "b"), Seq("b", ""), Seq(), Seq() ) @@ -168,7 +263,37 @@ class BasicOperationsSuite extends TestSuiteBase { Seq( ) ) val operation = (s1: DStream[String], s2: DStream[String]) => { - s1.map(x => (x,1)).join(s2.map(x => (x,"x"))) + s1.map(x => (x, 1)).join(s2.map(x => (x, "x"))) + } + testOperation(inputData1, inputData2, operation, outputData, true) + } + + test("leftOuterJoin") { + val inputData1 = Seq( Seq("a", "b"), Seq("a", ""), Seq(""), Seq() ) + val inputData2 = Seq( Seq("a", "b"), Seq("b", ""), Seq(), Seq("") ) + val outputData = Seq( + Seq( ("a", (1, Some("x"))), ("b", (1, Some("x"))) ), + Seq( ("", (1, Some("x"))), ("a", (1, None)) ), + Seq( ("", (1, None)) ), + Seq( ) + ) + val operation = (s1: DStream[String], s2: DStream[String]) => { + s1.map(x => (x, 1)).leftOuterJoin(s2.map(x => (x, "x"))) + } + testOperation(inputData1, inputData2, operation, outputData, true) + } + + test("rightOuterJoin") { + val inputData1 = Seq( Seq("a", "b"), Seq("a", ""), Seq(""), Seq() ) + val inputData2 = Seq( Seq("a", "b"), Seq("b", ""), Seq(), Seq("") ) + val outputData = Seq( + Seq( ("a", (Some(1), "x")), ("b", (Some(1), "x")) ), + Seq( ("", (Some(1), "x")), ("b", (None, "x")) ), + Seq( ), + Seq( ("", (None, "x")) ) + ) + val operation = (s1: DStream[String], s2: DStream[String]) => { + s1.map(x => (x, 1)).rightOuterJoin(s2.map(x => (x, "x"))) } testOperation(inputData1, inputData2, operation, outputData, true) } @@ -250,7 +375,11 @@ class BasicOperationsSuite extends TestSuiteBase { } test("slice") { - val ssc = new StreamingContext("local[2]", "BasicOperationSuite", Seconds(1)) + val conf2 = new SparkConf() + .setMaster("local[2]") + .setAppName("BasicOperationsSuite") + .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + val ssc = new StreamingContext(new SparkContext(conf2), Seconds(1)) val input = Seq(Seq(1), Seq(2), Seq(3), Seq(4)) val stream = new TestInputStream[Int](ssc, input, 2) ssc.registerInputStream(stream) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index a327de80b3..8dc80ac2ed 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -17,56 +17,50 @@ package org.apache.spark.streaming -import dstream.FileInputDStream -import org.apache.spark.streaming.StreamingContext._ import java.io.File -import runtime.RichInt -import org.scalatest.BeforeAndAfter + +import scala.collection.mutable.ArrayBuffer +import scala.reflect.ClassTag import org.apache.commons.io.FileUtils -import collection.mutable.{SynchronizedBuffer, ArrayBuffer} -import util.{Clock, ManualClock} -import scala.util.Random import com.google.common.io.Files - +import org.apache.hadoop.fs.{Path, FileSystem} +import org.apache.hadoop.conf.Configuration +import org.apache.spark.streaming.StreamingContext._ +import org.apache.spark.streaming.dstream.FileInputDStream +import org.apache.spark.streaming.util.ManualClock /** * This test suites tests the checkpointing functionality of DStreams - * the checkpointing of a DStream's RDDs as well as the checkpointing of * the whole DStream graph. */ -class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { +class CheckpointSuite extends TestSuiteBase { - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + var ssc: StreamingContext = null - before { + override def batchDuration = Milliseconds(500) + + override def actuallyWait = true // to allow checkpoints to be written + + override def beforeFunction() { + super.beforeFunction() FileUtils.deleteDirectory(new File(checkpointDir)) } - after { + override def afterFunction() { + super.afterFunction() if (ssc != null) ssc.stop() FileUtils.deleteDirectory(new File(checkpointDir)) - - // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown - System.clearProperty("spark.driver.port") - System.clearProperty("spark.hostPort") } - var ssc: StreamingContext = null - - override def framework = "CheckpointSuite" - - override def batchDuration = Milliseconds(500) - - override def actuallyWait = true - test("basic rdd checkpoints + dstream graph checkpoint recovery") { assert(batchDuration === Milliseconds(500), "batchDuration for this test must be 1 second") - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") val stateStreamCheckpointInterval = Seconds(1) - + val fs = FileSystem.getLocal(new Configuration()) // this ensure checkpointing occurs at least once val firstNumBatches = (stateStreamCheckpointInterval / batchDuration).toLong * 2 val secondNumBatches = firstNumBatches @@ -74,13 +68,13 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { // Setup the streams val input = (1 to 10).map(_ => Seq("a")).toSeq val operation = (st: DStream[String]) => { - val updateFunc = (values: Seq[Int], state: Option[RichInt]) => { - Some(new RichInt(values.foldLeft(0)(_ + _) + state.map(_.self).getOrElse(0))) + val updateFunc = (values: Seq[Int], state: Option[Int]) => { + Some((values.foldLeft(0)(_ + _) + state.getOrElse(0))) } st.map(x => (x, 1)) - .updateStateByKey[RichInt](updateFunc) + .updateStateByKey(updateFunc) .checkpoint(stateStreamCheckpointInterval) - .map(t => (t._1, t._2.self)) + .map(t => (t._1, t._2)) } var ssc = setupStreams(input, operation) var stateStream = ssc.graph.getOutputStreams().head.dependencies.head.dependencies.head @@ -90,11 +84,12 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { ssc.start() advanceTimeWithRealDelay(ssc, firstNumBatches) logInfo("Checkpoint data of state stream = \n" + stateStream.checkpointData) - assert(!stateStream.checkpointData.checkpointFiles.isEmpty, "No checkpointed RDDs in state stream before first failure") + assert(!stateStream.checkpointData.checkpointFiles.isEmpty, + "No checkpointed RDDs in state stream before first failure") stateStream.checkpointData.checkpointFiles.foreach { - case (time, data) => { - val file = new File(data.toString) - assert(file.exists(), "Checkpoint file '" + file +"' for time " + time + " for state stream before first failure does not exist") + case (time, file) => { + assert(fs.exists(new Path(file)), "Checkpoint file '" + file +"' for time " + time + + " for state stream before first failure does not exist") } } @@ -102,7 +97,8 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { // and check whether the earlier checkpoint files are deleted val checkpointFiles = stateStream.checkpointData.checkpointFiles.map(x => new File(x._2)) advanceTimeWithRealDelay(ssc, secondNumBatches) - checkpointFiles.foreach(file => assert(!file.exists, "Checkpoint file '" + file + "' was not deleted")) + checkpointFiles.foreach(file => + assert(!file.exists, "Checkpoint file '" + file + "' was not deleted")) ssc.stop() // Restart stream computation using the checkpoint file and check whether @@ -110,19 +106,20 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { ssc = new StreamingContext(checkpointDir) stateStream = ssc.graph.getOutputStreams().head.dependencies.head.dependencies.head logInfo("Restored data of state stream = \n[" + stateStream.generatedRDDs.mkString("\n") + "]") - assert(!stateStream.generatedRDDs.isEmpty, "No restored RDDs in state stream after recovery from first failure") + assert(!stateStream.generatedRDDs.isEmpty, + "No restored RDDs in state stream after recovery from first failure") // Run one batch to generate a new checkpoint file and check whether some RDD // is present in the checkpoint data or not ssc.start() advanceTimeWithRealDelay(ssc, 1) - assert(!stateStream.checkpointData.checkpointFiles.isEmpty, "No checkpointed RDDs in state stream before second failure") + assert(!stateStream.checkpointData.checkpointFiles.isEmpty, + "No checkpointed RDDs in state stream before second failure") stateStream.checkpointData.checkpointFiles.foreach { - case (time, data) => { - val file = new File(data.toString) - assert(file.exists(), - "Checkpoint file '" + file +"' for time " + time + " for state stream before seconds failure does not exist") + case (time, file) => { + assert(fs.exists(new Path(file)), "Checkpoint file '" + file +"' for time " + time + + " for state stream before seconds failure does not exist") } } ssc.stop() @@ -132,10 +129,12 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { ssc = new StreamingContext(checkpointDir) stateStream = ssc.graph.getOutputStreams().head.dependencies.head.dependencies.head logInfo("Restored data of state stream = \n[" + stateStream.generatedRDDs.mkString("\n") + "]") - assert(!stateStream.generatedRDDs.isEmpty, "No restored RDDs in state stream after recovery from second failure") + assert(!stateStream.generatedRDDs.isEmpty, + "No restored RDDs in state stream after recovery from second failure") - // Adjust manual clock time as if it is being restarted after a delay - System.setProperty("spark.streaming.manualClock.jump", (batchDuration.milliseconds * 7).toString) + // Adjust manual clock time as if it is being restarted after a delay; this is a hack because + // we modify the conf object, but it works for this one property + ssc.conf.set("spark.streaming.manualClock.jump", (batchDuration.milliseconds * 7).toString) ssc.start() advanceTimeWithRealDelay(ssc, 4) ssc.stop() @@ -143,6 +142,7 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { ssc = null } + // This tests whether the systm can recover from a master failure with simple // non-stateful operations. This assumes as reliable, replayable input // source - TestInputDStream. @@ -180,26 +180,23 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { val input = (1 to 10).map(_ => Seq("a")).toSeq val output = (1 to 10).map(x => Seq(("a", x))).toSeq val operation = (st: DStream[String]) => { - val updateFunc = (values: Seq[Int], state: Option[RichInt]) => { - Some(new RichInt(values.foldLeft(0)(_ + _) + state.map(_.self).getOrElse(0))) + val updateFunc = (values: Seq[Int], state: Option[Int]) => { + Some((values.foldLeft(0)(_ + _) + state.getOrElse(0))) } st.map(x => (x, 1)) - .updateStateByKey[RichInt](updateFunc) + .updateStateByKey(updateFunc) .checkpoint(batchDuration * 2) - .map(t => (t._1, t._2.self)) + .map(t => (t._1, t._2)) } testCheckpointedOperation(input, operation, output, 7) } + // This tests whether file input stream remembers what files were seen before // the master failure and uses them again to process a large window operation. // It also tests whether batches, whose processing was incomplete due to the // failure, are re-processed or not. test("recovery with file input stream") { - // Disable manual clock as FileInputDStream does not work with manual clock - val clockProperty = System.getProperty("spark.streaming.clock") - System.clearProperty("spark.streaming.clock") - // Set up the streaming context and input streams val testDir = Files.createTempDir() var ssc = new StreamingContext(master, framework, Seconds(1)) @@ -296,10 +293,6 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { ) // To ensure that all the inputs were received correctly assert(expectedOutput.last === output.last) - - // Enable manual clock back again for other tests - if (clockProperty != null) - System.setProperty("spark.streaming.clock", clockProperty) } @@ -312,7 +305,7 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { * NOTE: This takes into consideration that the last batch processed before * master failure will be re-processed after restart/recovery. */ - def testCheckpointedOperation[U: ClassManifest, V: ClassManifest]( + def testCheckpointedOperation[U: ClassTag, V: ClassTag]( input: Seq[Seq[U]], operation: DStream[U] => DStream[V], expectedOutput: Seq[Seq[V]], @@ -356,7 +349,7 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { * Advances the manual clock on the streaming scheduler by given number of batches. * It also waits for the expected amount of time for each batch. */ - def advanceTimeWithRealDelay[V: ClassManifest](ssc: StreamingContext, numBatches: Long): Seq[Seq[V]] = { + def advanceTimeWithRealDelay[V: ClassTag](ssc: StreamingContext, numBatches: Long): Seq[Seq[V]] = { val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] logInfo("Manual clock before advancing = " + clock.time) for (i <- 1 to numBatches.toInt) { @@ -366,7 +359,7 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { logInfo("Manual clock after advancing = " + clock.time) Thread.sleep(batchDuration.milliseconds) - val outputStream = ssc.graph.getOutputStreams.head.asInstanceOf[TestOutputStream[V]] - outputStream.output + val outputStream = ssc.graph.getOutputStreams.head.asInstanceOf[TestOutputStreamWithPartitions[V]] + outputStream.output.map(_.flatten) } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala index 6337c5359c..da9b04de1a 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala @@ -32,17 +32,22 @@ import collection.mutable.ArrayBuffer * This testsuite tests master failures at random times while the stream is running using * the real clock. */ -class FailureSuite extends FunSuite with BeforeAndAfter with Logging { +class FailureSuite extends TestSuiteBase with Logging { var directory = "FailureSuite" val numBatches = 30 - val batchDuration = Milliseconds(1000) - before { + override def batchDuration = Milliseconds(1000) + + override def useManualClock = false + + override def beforeFunction() { + super.beforeFunction() FileUtils.deleteDirectory(new File(directory)) } - after { + override def afterFunction() { + super.afterFunction() FileUtils.deleteDirectory(new File(directory)) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala index 42e3e51e3f..5185954521 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -23,15 +23,15 @@ import akka.actor.IOManager import akka.actor.Props import akka.util.ByteString -import dstream.SparkFlumeEvent +import org.apache.spark.streaming.dstream.{FileInputDStream, NetworkReceiver, SparkFlumeEvent} import java.net.{InetSocketAddress, SocketException, Socket, ServerSocket} import java.io.{File, BufferedWriter, OutputStreamWriter} -import java.util.concurrent.{TimeUnit, ArrayBlockingQueue} +import java.util.concurrent.{Executors, TimeUnit, ArrayBlockingQueue} import collection.mutable.{SynchronizedBuffer, ArrayBuffer} import util.ManualClock import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.receivers.Receiver -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, Logging} import scala.util.Random import org.apache.commons.io.FileUtils import org.scalatest.BeforeAndAfter @@ -44,33 +44,21 @@ import java.nio.ByteBuffer import collection.JavaConversions._ import java.nio.charset.Charset import com.google.common.io.Files +import java.util.concurrent.atomic.AtomicInteger class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val testPort = 9999 - override def checkpointDir = "checkpoint" - - before { - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") - } - - after { - // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown - System.clearProperty("spark.driver.port") - System.clearProperty("spark.hostPort") - } - - test("socket input stream") { // Start the server val testServer = new TestServer() testServer.start() // Set up the streaming context and input streams - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(conf), batchDuration) val networkStream = ssc.socketTextStream("localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) - val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String ]] + val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(networkStream, outputBuffer) def output = outputBuffer.flatMap(x => x) ssc.registerOutputStream(outputStream) @@ -113,7 +101,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { test("flume input stream") { // Set up the streaming context and input streams - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(conf), batchDuration) val flumeStream = ssc.flumeStream("localhost", testPort, StorageLevel.MEMORY_AND_DISK) val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]] with SynchronizedBuffer[Seq[SparkFlumeEvent]] @@ -124,9 +112,9 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] val input = Seq(1, 2, 3, 4, 5) Thread.sleep(1000) - val transceiver = new NettyTransceiver(new InetSocketAddress("localhost", testPort)); + val transceiver = new NettyTransceiver(new InetSocketAddress("localhost", testPort)) val client = SpecificRequestor.getClient( - classOf[AvroSourceProtocol], transceiver); + classOf[AvroSourceProtocol], transceiver) for (i <- 0 until input.size) { val event = new AvroFlumeEvent @@ -162,11 +150,11 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { test("file input stream") { // Disable manual clock as FileInputDStream does not work with manual clock - System.clearProperty("spark.streaming.clock") + conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") // Set up the streaming context and input streams val testDir = Files.createTempDir() - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(conf), batchDuration) val fileStream = ssc.textFileStream(testDir.toString) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] def output = outputBuffer.flatMap(x => x) @@ -207,7 +195,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { FileUtils.deleteDirectory(testDir) // Enable manual clock back again for other tests - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") } @@ -218,7 +206,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { testServer.start() // Set up the streaming context and input streams - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(conf), batchDuration) val networkStream = ssc.actorStream[String](Props(new TestActor(port)), "TestActor", StorageLevel.MEMORY_AND_DISK) //Had to pass the local value of port to prevent from closing over entire scope val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] @@ -262,19 +250,62 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { } test("kafka input stream") { - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(conf), batchDuration) val topics = Map("my-topic" -> 1) val test1 = ssc.kafkaStream("localhost:12345", "group", topics) val test2 = ssc.kafkaStream("localhost:12345", "group", topics, StorageLevel.MEMORY_AND_DISK) // Test specifying decoder - val kafkaParams = Map("zk.connect"->"localhost:12345","groupid"->"consumer-group") - val test3 = ssc.kafkaStream[String, kafka.serializer.StringDecoder](kafkaParams, topics, StorageLevel.MEMORY_AND_DISK) + val kafkaParams = Map("zookeeper.connect"->"localhost:12345","group.id"->"consumer-group") + val test3 = ssc.kafkaStream[ + String, + String, + kafka.serializer.StringDecoder, + kafka.serializer.StringDecoder](kafkaParams, topics, StorageLevel.MEMORY_AND_DISK) + } + + test("multi-thread receiver") { + // set up the test receiver + val numThreads = 10 + val numRecordsPerThread = 1000 + val numTotalRecords = numThreads * numRecordsPerThread + val testReceiver = new MultiThreadTestReceiver(numThreads, numRecordsPerThread) + MultiThreadTestReceiver.haveAllThreadsFinished = false + + // set up the network stream using the test receiver + val ssc = new StreamingContext(new SparkContext(conf), batchDuration) + val networkStream = ssc.networkStream[Int](testReceiver) + val countStream = networkStream.count + val outputBuffer = new ArrayBuffer[Seq[Long]] with SynchronizedBuffer[Seq[Long]] + val outputStream = new TestOutputStream(countStream, outputBuffer) + def output = outputBuffer.flatMap(x => x) + ssc.registerOutputStream(outputStream) + ssc.start() + + // Let the data from the receiver be received + val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] + val startTime = System.currentTimeMillis() + while((!MultiThreadTestReceiver.haveAllThreadsFinished || output.sum < numTotalRecords) && + System.currentTimeMillis() - startTime < 5000) { + Thread.sleep(100) + clock.addToTime(batchDuration.milliseconds) + } + Thread.sleep(1000) + logInfo("Stopping context") + ssc.stop() + + // Verify whether data received was as expected + logInfo("--------------------------------") + logInfo("output.size = " + outputBuffer.size) + logInfo("output") + outputBuffer.foreach(x => logInfo("[" + x.mkString(",") + "]")) + logInfo("--------------------------------") + assert(output.sum === numTotalRecords) } } -/** This is server to test the network input stream */ +/** This is a server to test the network input stream */ class TestServer() extends Logging { val queue = new ArrayBlockingQueue[String](100) @@ -336,6 +367,7 @@ object TestServer { } } +/** This is an actor for testing actor input stream */ class TestActor(port: Int) extends Actor with Receiver { def bytesToString(byteString: ByteString) = byteString.utf8String @@ -347,3 +379,36 @@ class TestActor(port: Int) extends Actor with Receiver { pushBlock(bytesToString(bytes)) } } + +/** This is a receiver to test multiple threads inserting data using block generator */ +class MultiThreadTestReceiver(numThreads: Int, numRecordsPerThread: Int) + extends NetworkReceiver[Int] { + lazy val executorPool = Executors.newFixedThreadPool(numThreads) + lazy val blockGenerator = new BlockGenerator(StorageLevel.MEMORY_ONLY) + lazy val finishCount = new AtomicInteger(0) + + protected def onStart() { + blockGenerator.start() + (1 to numThreads).map(threadId => { + val runnable = new Runnable { + def run() { + (1 to numRecordsPerThread).foreach(i => + blockGenerator += (threadId * numRecordsPerThread + i) ) + if (finishCount.incrementAndGet == numThreads) { + MultiThreadTestReceiver.haveAllThreadsFinished = true + } + logInfo("Finished thread " + threadId) + } + } + executorPool.submit(runnable) + }) + } + + protected def onStop() { + executorPool.shutdown() + } +} + +object MultiThreadTestReceiver { + var haveAllThreadsFinished = false +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala new file mode 100644 index 0000000000..fa64142096 --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -0,0 +1,71 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming + +import org.apache.spark.streaming.scheduler._ +import scala.collection.mutable.ArrayBuffer +import org.scalatest.matchers.ShouldMatchers + +class StreamingListenerSuite extends TestSuiteBase with ShouldMatchers { + + val input = (1 to 4).map(Seq(_)).toSeq + val operation = (d: DStream[Int]) => d.map(x => x) + + // To make sure that the processing start and end times in collected + // information are different for successive batches + override def batchDuration = Milliseconds(100) + override def actuallyWait = true + + test("basic BatchInfo generation") { + val ssc = setupStreams(input, operation) + val collector = new BatchInfoCollector + ssc.addStreamingListener(collector) + runStreams(ssc, input.size, input.size) + val batchInfos = collector.batchInfos + batchInfos should have size 4 + + batchInfos.foreach(info => { + info.schedulingDelay should not be None + info.processingDelay should not be None + info.totalDelay should not be None + info.schedulingDelay.get should be >= 0L + info.processingDelay.get should be >= 0L + info.totalDelay.get should be >= 0L + }) + + isInIncreasingOrder(batchInfos.map(_.submissionTime)) should be (true) + isInIncreasingOrder(batchInfos.map(_.processingStartTime.get)) should be (true) + isInIncreasingOrder(batchInfos.map(_.processingEndTime.get)) should be (true) + } + + /** Check if a sequence of numbers is in increasing order */ + def isInIncreasingOrder(seq: Seq[Long]): Boolean = { + for(i <- 1 until seq.size) { + if (seq(i - 1) > seq(i)) return false + } + true + } + + /** Listener that collects information on processed batches */ + class BatchInfoCollector extends StreamingListener { + val batchInfos = new ArrayBuffer[BatchInfo] + override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) { + batchInfos += batchCompleted.batchInfo + } + } +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala index 37dd9c4cc6..33464bc3a1 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -20,14 +20,15 @@ package org.apache.spark.streaming import org.apache.spark.streaming.dstream.{InputDStream, ForEachDStream} import org.apache.spark.streaming.util.ManualClock -import collection.mutable.ArrayBuffer -import collection.mutable.SynchronizedBuffer +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.SynchronizedBuffer +import scala.reflect.ClassTag import java.io.{ObjectInputStream, IOException} import org.scalatest.{BeforeAndAfter, FunSuite} -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.rdd.RDD /** @@ -35,7 +36,7 @@ import org.apache.spark.rdd.RDD * replayable, reliable message queue like Kafka. It requires a sequence as input, and * returns the i_th element at the i_th batch unde manual clock. */ -class TestInputStream[T: ClassManifest](ssc_ : StreamingContext, input: Seq[Seq[T]], numPartitions: Int) +class TestInputStream[T: ClassTag](ssc_ : StreamingContext, input: Seq[Seq[T]], numPartitions: Int) extends InputDStream[T](ssc_) { def start() {} @@ -60,8 +61,11 @@ class TestInputStream[T: ClassManifest](ssc_ : StreamingContext, input: Seq[Seq[ /** * This is a output stream just for the testsuites. All the output is collected into a * ArrayBuffer. This buffer is wiped clean on being restored from checkpoint. + * + * The buffer contains a sequence of RDD's, each containing a sequence of items */ -class TestOutputStream[T: ClassManifest](parent: DStream[T], val output: ArrayBuffer[Seq[T]]) +class TestOutputStream[T: ClassTag](parent: DStream[T], + val output: ArrayBuffer[Seq[T]] = ArrayBuffer[Seq[T]]()) extends ForEachDStream[T](parent, (rdd: RDD[T], t: Time) => { val collected = rdd.collect() output += collected @@ -76,13 +80,37 @@ class TestOutputStream[T: ClassManifest](parent: DStream[T], val output: ArrayBu } /** + * This is a output stream just for the testsuites. All the output is collected into a + * ArrayBuffer. This buffer is wiped clean on being restored from checkpoint. + * + * The buffer contains a sequence of RDD's, each containing a sequence of partitions, each + * containing a sequence of items. + */ +class TestOutputStreamWithPartitions[T: ClassTag](parent: DStream[T], + val output: ArrayBuffer[Seq[Seq[T]]] = ArrayBuffer[Seq[Seq[T]]]()) + extends ForEachDStream[T](parent, (rdd: RDD[T], t: Time) => { + val collected = rdd.glom().collect().map(_.toSeq) + output += collected + }) { + + // This is to clear the output buffer every it is read from a checkpoint + @throws(classOf[IOException]) + private def readObject(ois: ObjectInputStream) { + ois.defaultReadObject() + output.clear() + } + + def toTestOutputStream = new TestOutputStream[T](this.parent, this.output.map(_.flatten)) +} + +/** * This is the base trait for Spark Streaming testsuites. This provides basic functionality * to run user-defined set of input on user-defined stream operations, and verify the output. */ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { // Name of the framework for Spark context - def framework = "TestSuiteBase" + def framework = this.getClass.getSimpleName // Master for Spark context def master = "local[2]" @@ -99,28 +127,66 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { // Maximum time to wait before the test times out def maxWaitTimeMillis = 10000 + // Whether to use manual clock or not + def useManualClock = true + // Whether to actually wait in real time before changing manual clock def actuallyWait = false + //// A SparkConf to use in tests. Can be modified before calling setupStreams to configure things. + val conf = new SparkConf() + .setMaster(master) + .setAppName(framework) + .set("spark.cleaner.ttl", "3600") + + // Default before function for any streaming test suite. Override this + // if you want to add your stuff to "before" (i.e., don't call before { } ) + def beforeFunction() { + //if (useManualClock) { + // System.setProperty( + // "spark.streaming.clock", + // "org.apache.spark.streaming.util.ManualClock" + // ) + //} else { + // System.clearProperty("spark.streaming.clock") + //} + if (useManualClock) { + conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + } + } + + // Default after function for any streaming test suite. Override this + // if you want to add your stuff to "after" (i.e., don't call after { } ) + def afterFunction() { + // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown + System.clearProperty("spark.driver.port") + System.clearProperty("spark.hostPort") + } + + before(beforeFunction) + after(afterFunction) + /** * Set up required DStreams to test the DStream operation using the two sequences * of input collections. */ - def setupStreams[U: ClassManifest, V: ClassManifest]( + def setupStreams[U: ClassTag, V: ClassTag]( input: Seq[Seq[U]], - operation: DStream[U] => DStream[V] + operation: DStream[U] => DStream[V], + numPartitions: Int = numInputPartitions ): StreamingContext = { - + val sc = new SparkContext(conf) // Create StreamingContext - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(sc, batchDuration) if (checkpointDir != null) { ssc.checkpoint(checkpointDir) } // Setup the stream computation - val inputStream = new TestInputStream(ssc, input, numInputPartitions) + val inputStream = new TestInputStream(ssc, input, numPartitions) val operatedStream = operation(inputStream) - val outputStream = new TestOutputStream(operatedStream, new ArrayBuffer[Seq[V]] with SynchronizedBuffer[Seq[V]]) + val outputStream = new TestOutputStreamWithPartitions(operatedStream, + new ArrayBuffer[Seq[Seq[V]]] with SynchronizedBuffer[Seq[Seq[V]]]) ssc.registerInputStream(inputStream) ssc.registerOutputStream(outputStream) ssc @@ -130,14 +196,14 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { * Set up required DStreams to test the binary operation using the sequence * of input collections. */ - def setupStreams[U: ClassManifest, V: ClassManifest, W: ClassManifest]( + def setupStreams[U: ClassTag, V: ClassTag, W: ClassTag]( input1: Seq[Seq[U]], input2: Seq[Seq[V]], operation: (DStream[U], DStream[V]) => DStream[W] ): StreamingContext = { - + val sc = new SparkContext(conf) // Create StreamingContext - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(sc, batchDuration) if (checkpointDir != null) { ssc.checkpoint(checkpointDir) } @@ -146,7 +212,8 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { val inputStream1 = new TestInputStream(ssc, input1, numInputPartitions) val inputStream2 = new TestInputStream(ssc, input2, numInputPartitions) val operatedStream = operation(inputStream1, inputStream2) - val outputStream = new TestOutputStream(operatedStream, new ArrayBuffer[Seq[W]] with SynchronizedBuffer[Seq[W]]) + val outputStream = new TestOutputStreamWithPartitions(operatedStream, + new ArrayBuffer[Seq[Seq[W]]] with SynchronizedBuffer[Seq[Seq[W]]]) ssc.registerInputStream(inputStream1) ssc.registerInputStream(inputStream2) ssc.registerOutputStream(outputStream) @@ -157,18 +224,37 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { * Runs the streams set up in `ssc` on manual clock for `numBatches` batches and * returns the collected output. It will wait until `numExpectedOutput` number of * output data has been collected or timeout (set by `maxWaitTimeMillis`) is reached. + * + * Returns a sequence of items for each RDD. */ - def runStreams[V: ClassManifest]( + def runStreams[V: ClassTag]( ssc: StreamingContext, numBatches: Int, numExpectedOutput: Int ): Seq[Seq[V]] = { + // Flatten each RDD into a single Seq + runStreamsWithPartitions(ssc, numBatches, numExpectedOutput).map(_.flatten.toSeq) + } + + /** + * Runs the streams set up in `ssc` on manual clock for `numBatches` batches and + * returns the collected output. It will wait until `numExpectedOutput` number of + * output data has been collected or timeout (set by `maxWaitTimeMillis`) is reached. + * + * Returns a sequence of RDD's. Each RDD is represented as several sequences of items, each + * representing one partition. + */ + def runStreamsWithPartitions[V: ClassTag]( + ssc: StreamingContext, + numBatches: Int, + numExpectedOutput: Int + ): Seq[Seq[Seq[V]]] = { assert(numBatches > 0, "Number of batches to run stream computation is zero") assert(numExpectedOutput > 0, "Number of expected outputs after " + numBatches + " is zero") logInfo("numBatches = " + numBatches + ", numExpectedOutput = " + numExpectedOutput) // Get the output buffer - val outputStream = ssc.graph.getOutputStreams.head.asInstanceOf[TestOutputStream[V]] + val outputStream = ssc.graph.getOutputStreams.head.asInstanceOf[TestOutputStreamWithPartitions[V]] val output = outputStream.output try { @@ -202,7 +288,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { Thread.sleep(500) // Give some time for the forgetting old RDDs to complete } catch { - case e: Exception => e.printStackTrace(); throw e; + case e: Exception => {e.printStackTrace(); throw e} } finally { ssc.stop() } @@ -214,7 +300,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { * is same as the expected output values, by comparing the output * collections either as lists (order matters) or sets (order does not matter) */ - def verifyOutput[V: ClassManifest]( + def verifyOutput[V: ClassTag]( output: Seq[Seq[V]], expectedOutput: Seq[Seq[V]], useSet: Boolean @@ -244,7 +330,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { * Test unary DStream operation with a list of inputs, with number of * batches to run same as the number of expected output values */ - def testOperation[U: ClassManifest, V: ClassManifest]( + def testOperation[U: ClassTag, V: ClassTag]( input: Seq[Seq[U]], operation: DStream[U] => DStream[V], expectedOutput: Seq[Seq[V]], @@ -262,7 +348,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { * @param useSet Compare the output values with the expected output values * as sets (order matters) or as lists (order does not matter) */ - def testOperation[U: ClassManifest, V: ClassManifest]( + def testOperation[U: ClassTag, V: ClassTag]( input: Seq[Seq[U]], operation: DStream[U] => DStream[V], expectedOutput: Seq[Seq[V]], @@ -279,7 +365,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { * Test binary DStream operation with two lists of inputs, with number of * batches to run same as the number of expected output values */ - def testOperation[U: ClassManifest, V: ClassManifest, W: ClassManifest]( + def testOperation[U: ClassTag, V: ClassTag, W: ClassTag]( input1: Seq[Seq[U]], input2: Seq[Seq[V]], operation: (DStream[U], DStream[V]) => DStream[W], @@ -299,7 +385,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { * @param useSet Compare the output values with the expected output values * as sets (order matters) or as lists (order does not matter) */ - def testOperation[U: ClassManifest, V: ClassManifest, W: ClassManifest]( + def testOperation[U: ClassTag, V: ClassTag, W: ClassTag]( input1: Seq[Seq[U]], input2: Seq[Seq[V]], operation: (DStream[U], DStream[V]) => DStream[W], diff --git a/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala index f50e05c0d8..c92c34d49b 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala @@ -18,23 +18,12 @@ package org.apache.spark.streaming import org.apache.spark.streaming.StreamingContext._ -import collection.mutable.ArrayBuffer class WindowOperationsSuite extends TestSuiteBase { - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + override def maxWaitTimeMillis = 20000 // large window tests can sometimes take longer - override def framework = "WindowOperationsSuite" - - override def maxWaitTimeMillis = 20000 - - override def batchDuration = Seconds(1) - - after { - // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown - System.clearProperty("spark.driver.port") - System.clearProperty("spark.hostPort") - } + override def batchDuration = Seconds(1) // making sure its visible in this class val largerSlideInput = Seq( Seq(("a", 1)), diff --git a/tools/pom.xml b/tools/pom.xml index 29f0014128..28f5ef14b1 100644 --- a/tools/pom.xml +++ b/tools/pom.xml @@ -25,7 +25,7 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-tools</artifactId> + <artifactId>spark-tools_2.10</artifactId> <packaging>jar</packaging> <name>Spark Project Tools</name> <url>http://spark.incubator.apache.org/</url> @@ -33,24 +33,24 @@ <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-streaming</artifactId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> <groupId>org.scalatest</groupId> - <artifactId>scalatest_${scala.version}</artifactId> + <artifactId>scalatest_${scala.binary.version}</artifactId> <scope>test</scope> </dependency> </dependencies> <build> - <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> - <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> diff --git a/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala b/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala index f824c472ae..f670f65bf5 100644 --- a/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala +++ b/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala @@ -199,7 +199,7 @@ object JavaAPICompletenessChecker { private def toJavaMethod(method: SparkMethod): SparkMethod = { val params = method.parameters - .filterNot(_.name == "scala.reflect.ClassManifest") + .filterNot(_.name == "scala.reflect.ClassTag") .map(toJavaType(_, isReturnType = false)) SparkMethod(method.name, toJavaType(method.returnType, isReturnType = true), params) } @@ -212,7 +212,7 @@ object JavaAPICompletenessChecker { // internal Spark components. val excludedNames = Seq( "org.apache.spark.rdd.RDD.origin", - "org.apache.spark.rdd.RDD.elementClassManifest", + "org.apache.spark.rdd.RDD.elementClassTag", "org.apache.spark.rdd.RDD.checkpointData", "org.apache.spark.rdd.RDD.partitioner", "org.apache.spark.rdd.RDD.partitions", diff --git a/yarn/pom.xml b/yarn/pom.xml index 427fcdf545..bc64a190fd 100644 --- a/yarn/pom.xml +++ b/yarn/pom.xml @@ -25,7 +25,7 @@ </parent> <groupId>org.apache.spark</groupId> - <artifactId>spark-yarn</artifactId> + <artifactId>spark-yarn_2.10</artifactId> <packaging>jar</packaging> <name>Spark Project YARN Support</name> <url>http://spark.incubator.apache.org/</url> @@ -33,7 +33,7 @@ <dependencies> <dependency> <groupId>org.apache.spark</groupId> - <artifactId>spark-core</artifactId> + <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${project.version}</version> </dependency> <dependency> @@ -61,11 +61,21 @@ <groupId>org.apache.avro</groupId> <artifactId>avro-ipc</artifactId> </dependency> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.mockito</groupId> + <artifactId>mockito-all</artifactId> + <scope>test</scope> + </dependency> </dependencies> <build> - <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> - <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> @@ -97,7 +107,7 @@ </goals> <configuration> <transformers> - <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/> + <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" /> <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"> <resource>reference.conf</resource> </transformer> @@ -106,6 +116,46 @@ </execution> </executions> </plugin> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-antrun-plugin</artifactId> + <executions> + <execution> + <phase>test</phase> + <goals> + <goal>run</goal> + </goals> + <configuration> + <exportAntProperties>true</exportAntProperties> + <tasks> + <property name="spark.classpath" refid="maven.test.classpath" /> + <property environment="env" /> + <fail message="Please set the SCALA_HOME (or SCALA_LIBRARY_PATH if scala is on the path) environment variables and retry."> + <condition> + <not> + <or> + <isset property="env.SCALA_HOME" /> + <isset property="env.SCALA_LIBRARY_PATH" /> + </or> + </not> + </condition> + </fail> + </tasks> + </configuration> + </execution> + </executions> + </plugin> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + <configuration> + <environmentVariables> + <SPARK_HOME>${basedir}/..</SPARK_HOME> + <SPARK_TESTING>1</SPARK_TESTING> + <SPARK_CLASSPATH>${spark.classpath}</SPARK_CLASSPATH> + </environmentVariables> + </configuration> + </plugin> </plugins> </build> </project> diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 858b58d338..dc9228180f 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -17,51 +17,67 @@ package org.apache.spark.deploy.yarn +import java.io.IOException import java.net.Socket import java.util.concurrent.CopyOnWriteArrayList import java.util.concurrent.atomic.{AtomicInteger, AtomicReference} + +import scala.collection.JavaConversions._ + import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.net.NetUtils +import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.util.ShutdownHookManager import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.api.protocolrecords._ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC import org.apache.hadoop.yarn.util.{ConverterUtils, Records} -import scala.collection.JavaConversions._ + import org.apache.spark.{SparkContext, Logging} import org.apache.spark.util.Utils -import org.apache.hadoop.security.UserGroupInformation -import java.security.PrivilegedExceptionAction + class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) extends Logging { def this(args: ApplicationMasterArguments) = this(args, new Configuration()) - + private var rpc: YarnRPC = YarnRPC.create(conf) - private var resourceManager: AMRMProtocol = null - private var appAttemptId: ApplicationAttemptId = null - private var userThread: Thread = null + private var resourceManager: AMRMProtocol = _ + private var appAttemptId: ApplicationAttemptId = _ + private var userThread: Thread = _ private val yarnConf: YarnConfiguration = new YarnConfiguration(conf) + private val fs = FileSystem.get(yarnConf) + + private var yarnAllocator: YarnAllocationHandler = _ + private var isFinished: Boolean = false + private var uiAddress: String = _ + private val maxAppAttempts: Int = conf.getInt(YarnConfiguration.RM_AM_MAX_RETRIES, + YarnConfiguration.DEFAULT_RM_AM_MAX_RETRIES) + private var isLastAMRetry: Boolean = true + // default to numWorkers * 2, with minimum of 3 + private val maxNumWorkerFailures = conf.getOrElse("spark.yarn.max.worker.failures", + math.max(args.numWorkers * 2, 3).toString()).toInt - private var yarnAllocator: YarnAllocationHandler = null - private var isFinished:Boolean = false - private var uiAddress: String = "" + def run() { + // Setup the directories so things go to yarn approved directories rather + // then user specified and /tmp. + conf.set("spark.local.dir", getLocalDirs()) + // Use priority 30 as its higher then HDFS. Its same priority as MapReduce is using. + ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30) - def run() { - // setup the directories so things go to yarn approved directories rather - // then user specified and /tmp - System.setProperty("spark.local.dir", getLocalDirs()) - appAttemptId = getApplicationAttemptId() + isLastAMRetry = appAttemptId.getAttemptId() >= maxAppAttempts resourceManager = registerWithResourceManager() // Workaround until hadoop moves to something which has // https://issues.apache.org/jira/browse/HADOOP-8406 - fixed in (2.0.2-alpha but no 0.23 line) - // ignore result + // ignore result. // This does not, unfortunately, always work reliably ... but alleviates the bug a lot of times - // Hence args.workerCores = numCore disabled above. Any better option ? + // Hence args.workerCores = numCore disabled above. Any better option? // Compute number of threads for akka //val minimumMemory = appMasterResponse.getMinimumResourceCapability().getMemory() @@ -76,24 +92,24 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e // } //} // org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(conf) - + ApplicationMaster.register(this) // Start the user's JAR userThread = startUserClass() - + // This a bit hacky, but we need to wait until the spark.driver.port property has // been set by the Thread executing the user class. waitForSparkMaster() waitForSparkContextInitialized() - // do this after spark master is up and SparkContext is created so that we can register UI Url + // Do this after spark master is up and SparkContext is created so that we can register UI Url val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster() - + // Allocate all containers allocateWorkers() - - // Wait for the user class to Finish + + // Wait for the user class to Finish userThread.join() System.exit(0) @@ -106,83 +122,90 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X val localDirs = Option(System.getenv("YARN_LOCAL_DIRS")) .getOrElse(Option(System.getenv("LOCAL_DIRS")) - .getOrElse("")) + .getOrElse("")) if (localDirs.isEmpty()) { throw new Exception("Yarn Local dirs can't be empty") } - return localDirs + localDirs } - + private def getApplicationAttemptId(): ApplicationAttemptId = { val envs = System.getenv() val containerIdString = envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV) val containerId = ConverterUtils.toContainerId(containerIdString) val appAttemptId = containerId.getApplicationAttemptId() logInfo("ApplicationAttemptId: " + appAttemptId) - return appAttemptId + appAttemptId } - + private def registerWithResourceManager(): AMRMProtocol = { val rmAddress = NetUtils.createSocketAddr(yarnConf.get( YarnConfiguration.RM_SCHEDULER_ADDRESS, YarnConfiguration.DEFAULT_RM_SCHEDULER_ADDRESS)) logInfo("Connecting to ResourceManager at " + rmAddress) - return rpc.getProxy(classOf[AMRMProtocol], rmAddress, conf).asInstanceOf[AMRMProtocol] + rpc.getProxy(classOf[AMRMProtocol], rmAddress, conf).asInstanceOf[AMRMProtocol] } - + private def registerApplicationMaster(): RegisterApplicationMasterResponse = { logInfo("Registering the ApplicationMaster") val appMasterRequest = Records.newRecord(classOf[RegisterApplicationMasterRequest]) .asInstanceOf[RegisterApplicationMasterRequest] appMasterRequest.setApplicationAttemptId(appAttemptId) - // Setting this to master host,port - so that the ApplicationReport at client has some sensible info. + // Setting this to master host,port - so that the ApplicationReport at client has some + // sensible info. // Users can then monitor stderr/stdout on that node if required. appMasterRequest.setHost(Utils.localHostName()) appMasterRequest.setRpcPort(0) appMasterRequest.setTrackingUrl(uiAddress) - return resourceManager.registerApplicationMaster(appMasterRequest) + resourceManager.registerApplicationMaster(appMasterRequest) } - + private def waitForSparkMaster() { logInfo("Waiting for spark driver to be reachable.") var driverUp = false var tries = 0 - val numTries = System.getProperty("spark.yarn.applicationMaster.waitTries", "10").toInt + val numTries = conf.getOrElse("spark.yarn.applicationMaster.waitTries", "10").toInt while(!driverUp && tries < numTries) { - val driverHost = System.getProperty("spark.driver.host") - val driverPort = System.getProperty("spark.driver.port") + val driverHost = conf.get("spark.driver.host") + val driverPort = conf.get("spark.driver.port") try { val socket = new Socket(driverHost, driverPort.toInt) socket.close() - logInfo("Driver now available: " + driverHost + ":" + driverPort) + logInfo("Driver now available: %s:%s".format(driverHost, driverPort)) driverUp = true } catch { - case e: Exception => - logWarning("Failed to connect to driver at " + driverHost + ":" + driverPort + ", retrying") - Thread.sleep(100) - tries = tries + 1 + case e: Exception => { + logWarning("Failed to connect to driver at %s:%s, retrying ...". + format(driverHost, driverPort)) + Thread.sleep(100) + tries = tries + 1 + } } } } private def startUserClass(): Thread = { logInfo("Starting the user JAR in a separate Thread") - val mainMethod = Class.forName(args.userClass, false, Thread.currentThread.getContextClassLoader) - .getMethod("main", classOf[Array[String]]) + val mainMethod = Class.forName( + args.userClass, + false /* initialize */, + Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]]) val t = new Thread { override def run() { var successed = false try { // Copy - var mainArgs: Array[String] = new Array[String](args.userArgs.size()) - args.userArgs.copyToArray(mainArgs, 0, args.userArgs.size()) + var mainArgs: Array[String] = new Array[String](args.userArgs.size) + args.userArgs.copyToArray(mainArgs, 0, args.userArgs.size) mainMethod.invoke(null, mainArgs) // some job script has "System.exit(0)" at the end, for example SparkPi, SparkLR // userThread will stop here unless it has uncaught exception thrown out // It need shutdown hook to set SUCCEEDED successed = true } finally { + logDebug("finishing main") + isLastAMRetry = true if (successed) { ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) } else { @@ -192,7 +215,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } } t.start() - return t + t } // this need to happen before allocateWorkers @@ -203,7 +226,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e ApplicationMaster.sparkContextRef.synchronized { var count = 0 val waitTime = 10000L - val numTries = System.getProperty("spark.yarn.ApplicationMaster.waitTries", "10").toInt + val numTries = conf.getOrElse("spark.yarn.ApplicationMaster.waitTries", "10").toInt while (ApplicationMaster.sparkContextRef.get() == null && count < numTries) { logInfo("Waiting for spark context initialization ... " + count) count = count + 1 @@ -214,12 +237,20 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e if (null != sparkContext) { uiAddress = sparkContext.ui.appUIAddress - this.yarnAllocator = YarnAllocationHandler.newAllocator(yarnConf, resourceManager, appAttemptId, args, - sparkContext.preferredNodeLocationData) + this.yarnAllocator = YarnAllocationHandler.newAllocator( + yarnConf, + resourceManager, + appAttemptId, + args, + sparkContext.preferredNodeLocationData) } else { - logWarning("Unable to retrieve sparkContext inspite of waiting for " + count * waitTime + - ", numTries = " + numTries) - this.yarnAllocator = YarnAllocationHandler.newAllocator(yarnConf, resourceManager, appAttemptId, args) + logWarning("Unable to retrieve sparkContext inspite of waiting for %d, numTries = %d". + format(count * waitTime, numTries)) + this.yarnAllocator = YarnAllocationHandler.newAllocator( + yarnConf, + resourceManager, + appAttemptId, + args) } } } finally { @@ -229,52 +260,63 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } } - - private def allocateWorkers() { try { logInfo("Allocating " + args.numWorkers + " workers.") // Wait until all containers have finished // TODO: This is a bit ugly. Can we make it nicer? // TODO: Handle container failure - while(yarnAllocator.getNumWorkersRunning < args.numWorkers && - // If user thread exists, then quit ! - userThread.isAlive) { - this.yarnAllocator.allocateContainers(math.max(args.numWorkers - yarnAllocator.getNumWorkersRunning, 0)) - ApplicationMaster.incrementAllocatorLoop(1) - Thread.sleep(100) + // Exists the loop if the user thread exits. + while (yarnAllocator.getNumWorkersRunning < args.numWorkers && userThread.isAlive) { + if (yarnAllocator.getNumWorkersFailed >= maxNumWorkerFailures) { + finishApplicationMaster(FinalApplicationStatus.FAILED, + "max number of worker failures reached") + } + yarnAllocator.allocateContainers( + math.max(args.numWorkers - yarnAllocator.getNumWorkersRunning, 0)) + ApplicationMaster.incrementAllocatorLoop(1) + Thread.sleep(100) } } finally { - // in case of exceptions, etc - ensure that count is atleast ALLOCATOR_LOOP_WAIT_COUNT : - // so that the loop (in ApplicationMaster.sparkContextInitialized) breaks + // In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT, + // so that the loop in ApplicationMaster#sparkContextInitialized() breaks. ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT) } logInfo("All workers have launched.") - // Launch a progress reporter thread, else app will get killed after expiration (def: 10mins) timeout + // Launch a progress reporter thread, else the app will get killed after expiration + // (def: 10mins) timeout. + // TODO(harvey): Verify the timeout if (userThread.isAlive) { - // ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapse. - + // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses. val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000) - // must be <= timeoutInterval/ 2. - // On other hand, also ensure that we are reasonably responsive without causing too many requests to RM. - // so atleast 1 minute or timeoutInterval / 10 - whichever is higher. - val interval = math.min(timeoutInterval / 2, math.max(timeoutInterval/ 10, 60000L)) + + // we want to be reasonably responsive without causing too many requests to RM. + val schedulerInterval = + conf.getOrElse("spark.yarn.scheduler.heartbeat.interval-ms", "5000").toLong + + // must be <= timeoutInterval / 2. + val interval = math.min(timeoutInterval / 2, schedulerInterval) + launchReporterThread(interval) } } - // TODO: We might want to extend this to allocate more containers in case they die ! private def launchReporterThread(_sleepTime: Long): Thread = { val sleepTime = if (_sleepTime <= 0 ) 0 else _sleepTime val t = new Thread { override def run() { while (userThread.isAlive) { + if (yarnAllocator.getNumWorkersFailed >= maxNumWorkerFailures) { + finishApplicationMaster(FinalApplicationStatus.FAILED, + "max number of worker failures reached") + } val missingWorkerCount = args.numWorkers - yarnAllocator.getNumWorkersRunning if (missingWorkerCount > 0) { - logInfo("Allocating " + missingWorkerCount + " containers to make up for (potentially ?) lost containers") + logInfo("Allocating %d containers to make up for (potentially) lost containers". + format(missingWorkerCount)) yarnAllocator.allocateContainers(missingWorkerCount) } else sendProgress() @@ -282,16 +324,16 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } } } - // setting to daemon status, though this is usually not a good idea. + // Setting to daemon status, though this is usually not a good idea. t.setDaemon(true) t.start() logInfo("Started progress reporter thread - sleep time : " + sleepTime) - return t + t } private def sendProgress() { logDebug("Sending progress") - // simulated with an allocate request with no nodes requested ... + // Simulated with an allocate request with no nodes requested ... yarnAllocator.allocateContainers(0) } @@ -300,18 +342,17 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e for (container <- containers) { logInfo("Launching shell command on a new container." + ", containerId=" + container.getId() - + ", containerNode=" + container.getNodeId().getHost() + + ", containerNode=" + container.getNodeId().getHost() + ":" + container.getNodeId().getPort() + ", containerNodeURI=" + container.getNodeHttpAddress() + ", containerState" + container.getState() - + ", containerResourceMemory" + + ", containerResourceMemory" + container.getResource().getMemory()) } } */ - def finishApplicationMaster(status: FinalApplicationStatus) { - + def finishApplicationMaster(status: FinalApplicationStatus, diagnostics: String = "") { synchronized { if (isFinished) { return @@ -324,20 +365,52 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e .asInstanceOf[FinishApplicationMasterRequest] finishReq.setAppAttemptId(appAttemptId) finishReq.setFinishApplicationStatus(status) - // set tracking url to empty since we don't have a history server + finishReq.setDiagnostics(diagnostics) + // Set tracking url to empty since we don't have a history server. finishReq.setTrackingUrl("") resourceManager.finishApplicationMaster(finishReq) + } + /** + * Clean up the staging directory. + */ + private def cleanupStagingDir() { + var stagingDirPath: Path = null + try { + val preserveFiles = conf.getOrElse("spark.yarn.preserve.staging.files", "false").toBoolean + if (!preserveFiles) { + stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR")) + if (stagingDirPath == null) { + logError("Staging directory is null") + return + } + logInfo("Deleting staging directory " + stagingDirPath) + fs.delete(stagingDirPath, true) + } + } catch { + case ioe: IOException => + logError("Failed to cleanup staging dir " + stagingDirPath, ioe) + } + } + + // The shutdown hook that runs when a signal is received AND during normal close of the JVM. + class AppMasterShutdownHook(appMaster: ApplicationMaster) extends Runnable { + + def run() { + logInfo("AppMaster received a signal.") + // we need to clean up staging dir before HDFS is shut down + // make sure we don't delete it until this is the last AM + if (appMaster.isLastAMRetry) appMaster.cleanupStagingDir() + } } - } object ApplicationMaster { - // number of times to wait for the allocator loop to complete. - // each loop iteration waits for 100ms, so maximum of 3 seconds. + // Number of times to wait for the allocator loop to complete. + // Each loop iteration waits for 100ms, so maximum of 3 seconds. // This is to ensure that we have reasonable number of containers before we start - // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be optimal as more - // containers are available. Might need to handle this better. + // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be + // optimal as more containers are available. Might need to handle this better. private val ALLOCATOR_LOOP_WAIT_COUNT = 30 def incrementAllocatorLoop(by: Int) { val count = yarnAllocatorLoop.getAndAdd(by) @@ -355,7 +428,8 @@ object ApplicationMaster { applicationMasters.add(master) } - val sparkContextRef: AtomicReference[SparkContext] = new AtomicReference[SparkContext](null) + val sparkContextRef: AtomicReference[SparkContext] = + new AtomicReference[SparkContext](null /* initialValue */) val yarnAllocatorLoop: AtomicInteger = new AtomicInteger(0) def sparkContextInitialized(sc: SparkContext): Boolean = { @@ -365,25 +439,29 @@ object ApplicationMaster { sparkContextRef.notifyAll() } - // Add a shutdown hook - as a best case effort in case users do not call sc.stop or do System.exit - // Should not really have to do this, but it helps yarn to evict resources earlier. - // not to mention, prevent Client declaring failure even though we exit'ed properly. + // Add a shutdown hook - as a best case effort in case users do not call sc.stop or do + // System.exit. + // Should not really have to do this, but it helps YARN to evict resources earlier. + // Not to mention, prevent the Client from declaring failure even though we exited properly. + // Note that this will unfortunately not properly clean up the staging files because it gets + // called too late, after the filesystem is already shutdown. if (modified) { - Runtime.getRuntime().addShutdownHook(new Thread with Logging { - // This is not just to log, but also to ensure that log system is initialized for this instance when we actually are 'run' + Runtime.getRuntime().addShutdownHook(new Thread with Logging { + // This is not only logs, but also ensures that log system is initialized for this instance + // when we are actually 'run'-ing. logInfo("Adding shutdown hook for context " + sc) - override def run() { - logInfo("Invoking sc stop from shutdown hook") - sc.stop() - // best case ... + override def run() { + logInfo("Invoking sc stop from shutdown hook") + sc.stop() + // Best case ... for (master <- applicationMasters) { master.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) } - } + } } ) } - // Wait for initialization to complete and atleast 'some' nodes can get allocated + // Wait for initialization to complete and atleast 'some' nodes can get allocated. yarnAllocatorLoop.synchronized { while (yarnAllocatorLoop.get() <= ALLOCATOR_LOOP_WAIT_COUNT) { yarnAllocatorLoop.wait(1000L) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 3362010106..595a7ee8c3 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -17,37 +17,54 @@ package org.apache.spark.deploy.yarn -import java.net.{InetSocketAddress, URI} +import java.net.{InetAddress, UnknownHostException, URI} import java.nio.ByteBuffer + +import scala.collection.JavaConversions._ +import scala.collection.mutable.HashMap +import scala.collection.mutable.Map + import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.fs.{FileContext, FileStatus, FileSystem, Path, FileUtil} +import org.apache.hadoop.fs.permission.FsPermission; +import org.apache.hadoop.io.DataOutputBuffer import org.apache.hadoop.mapred.Master import org.apache.hadoop.net.NetUtils -import org.apache.hadoop.io.DataOutputBuffer import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.api._ -import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.api.protocolrecords._ +import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.client.YarnClientImpl import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC -import scala.collection.mutable.HashMap -import scala.collection.JavaConversions._ -import org.apache.spark.Logging +import org.apache.hadoop.yarn.util.{Apps, Records} + +import org.apache.spark.Logging import org.apache.spark.util.Utils -import org.apache.hadoop.yarn.util.{Apps, Records, ConverterUtils} -import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.spark.deploy.SparkHadoopUtil + class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl with Logging { - + def this(args: ClientArguments) = this(new Configuration(), args) - + var rpc: YarnRPC = YarnRPC.create(conf) val yarnConf: YarnConfiguration = new YarnConfiguration(conf) - val credentials = UserGroupInformation.getCurrentUser().getCredentials(); - - def run() { + val credentials = UserGroupInformation.getCurrentUser().getCredentials() + private val SPARK_STAGING: String = ".sparkStaging" + private val distCacheMgr = new ClientDistributedCacheManager() + + // Staging directory is private! -> rwx-------- + val STAGING_DIR_PERMISSION: FsPermission = FsPermission.createImmutable(0700:Short) + + // App files are world-wide readable and owner writable -> rw-r--r-- + val APP_FILE_PERMISSION: FsPermission = FsPermission.createImmutable(0644:Short) + + // for client user who want to monitor app status by itself. + def runApp() = { + validateArgs() + init(yarnConf) start() logClusterResourceDetails() @@ -57,8 +74,9 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl verifyClusterResources(newApp) val appContext = createApplicationSubmissionContext(appId) - val localResources = prepareLocalResources(appId, "spark") - val env = setupLaunchEnv(localResources) + val appStagingDir = getAppStagingDir(appId) + val localResources = prepareLocalResources(appStagingDir) + val env = setupLaunchEnv(localResources, appStagingDir) val amContainer = createContainerLaunchContext(newApp, localResources, env) appContext.setQueue(args.amQueue) @@ -66,30 +84,59 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl appContext.setUser(UserGroupInformation.getCurrentUser().getShortUserName()) submitApp(appContext) - + appId + } + + def run() { + val appId = runApp() monitorApplication(appId) System.exit(0) } - + + def validateArgs() = { + Map( + (System.getenv("SPARK_JAR") == null) -> "Error: You must set SPARK_JAR environment variable!", + (args.userJar == null) -> "Error: You must specify a user jar!", + (args.userClass == null) -> "Error: You must specify a user class!", + (args.numWorkers <= 0) -> "Error: You must specify atleast 1 worker!", + (args.amMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: AM memory size must be " + + "greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD), + (args.workerMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: Worker memory size " + + "must be greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD) + ).foreach { case(cond, errStr) => + if (cond) { + logError(errStr) + args.printUsageAndExit(1) + } + } + } + + def getAppStagingDir(appId: ApplicationId): String = { + SPARK_STAGING + Path.SEPARATOR + appId.toString() + Path.SEPARATOR + } def logClusterResourceDetails() { val clusterMetrics: YarnClusterMetrics = super.getYarnClusterMetrics - logInfo("Got Cluster metric info from ASM, numNodeManagers=" + clusterMetrics.getNumNodeManagers) + logInfo("Got Cluster metric info from ASM, numNodeManagers = " + + clusterMetrics.getNumNodeManagers) val queueInfo: QueueInfo = super.getQueueInfo(args.amQueue) - logInfo("Queue info .. queueName=" + queueInfo.getQueueName + ", queueCurrentCapacity=" + queueInfo.getCurrentCapacity + - ", queueMaxCapacity=" + queueInfo.getMaximumCapacity + ", queueApplicationCount=" + queueInfo.getApplications.size + - ", queueChildQueueCount=" + queueInfo.getChildQueues.size) + logInfo("""Queue info ... queueName = %s, queueCurrentCapacity = %s, queueMaxCapacity = %s, + queueApplicationCount = %s, queueChildQueueCount = %s""".format( + queueInfo.getQueueName, + queueInfo.getCurrentCapacity, + queueInfo.getMaximumCapacity, + queueInfo.getApplications.size, + queueInfo.getChildQueues.size)) } - - def verifyClusterResources(app: GetNewApplicationResponse) = { + def verifyClusterResources(app: GetNewApplicationResponse) = { val maxMem = app.getMaximumResourceCapability().getMemory() logInfo("Max mem capabililty of a single resource in this cluster " + maxMem) - - // if we have requested more then the clusters max for a single resource then exit. + + // If we have requested more then the clusters max for a single resource then exit. if (args.workerMemory > maxMem) { - logError("the worker size is to large to run on this cluster " + args.workerMemory); + logError("the worker size is to large to run on this cluster " + args.workerMemory) System.exit(1) } val amMem = args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD @@ -98,100 +145,181 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl System.exit(1) } - // We could add checks to make sure the entire cluster has enough resources but that involves getting - // all the node reports and computing ourselves + // We could add checks to make sure the entire cluster has enough resources but that involves + // getting all the node reports and computing ourselves } - + def createApplicationSubmissionContext(appId: ApplicationId): ApplicationSubmissionContext = { logInfo("Setting up application submission context for ASM") val appContext = Records.newRecord(classOf[ApplicationSubmissionContext]) appContext.setApplicationId(appId) - appContext.setApplicationName("Spark") + appContext.setApplicationName(args.appName) return appContext } - - def prepareLocalResources(appId: ApplicationId, appName: String): HashMap[String, LocalResource] = { + + /** See if two file systems are the same or not. */ + private def compareFs(srcFs: FileSystem, destFs: FileSystem): Boolean = { + val srcUri = srcFs.getUri() + val dstUri = destFs.getUri() + if (srcUri.getScheme() == null) { + return false + } + if (!srcUri.getScheme().equals(dstUri.getScheme())) { + return false + } + var srcHost = srcUri.getHost() + var dstHost = dstUri.getHost() + if ((srcHost != null) && (dstHost != null)) { + try { + srcHost = InetAddress.getByName(srcHost).getCanonicalHostName() + dstHost = InetAddress.getByName(dstHost).getCanonicalHostName() + } catch { + case e: UnknownHostException => + return false + } + if (!srcHost.equals(dstHost)) { + return false + } + } else if (srcHost == null && dstHost != null) { + return false + } else if (srcHost != null && dstHost == null) { + return false + } + //check for ports + if (srcUri.getPort() != dstUri.getPort()) { + return false + } + return true + } + + /** Copy the file into HDFS if needed. */ + private def copyRemoteFile( + dstDir: Path, + originalPath: Path, + replication: Short, + setPerms: Boolean = false): Path = { + val fs = FileSystem.get(conf) + val remoteFs = originalPath.getFileSystem(conf) + var newPath = originalPath + if (! compareFs(remoteFs, fs)) { + newPath = new Path(dstDir, originalPath.getName()) + logInfo("Uploading " + originalPath + " to " + newPath) + FileUtil.copy(remoteFs, originalPath, fs, newPath, false, conf) + fs.setReplication(newPath, replication) + if (setPerms) fs.setPermission(newPath, new FsPermission(APP_FILE_PERMISSION)) + } + // Resolve any symlinks in the URI path so using a "current" symlink to point to a specific + // version shows the specific version in the distributed cache configuration + val qualPath = fs.makeQualified(newPath) + val fc = FileContext.getFileContext(qualPath.toUri(), conf) + val destPath = fc.resolvePath(qualPath) + destPath + } + + def prepareLocalResources(appStagingDir: String): HashMap[String, LocalResource] = { logInfo("Preparing Local resources") - val locaResources = HashMap[String, LocalResource]() - // Upload Spark and the application JAR to the remote file system - // Add them as local resources to the AM + // Upload Spark and the application JAR to the remote file system if necessary. Add them as + // local resources to the AM. val fs = FileSystem.get(conf) - val delegTokenRenewer = Master.getMasterPrincipal(conf); + val delegTokenRenewer = Master.getMasterPrincipal(conf) if (UserGroupInformation.isSecurityEnabled()) { if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { logError("Can't get Master Kerberos principal for use as renewer") System.exit(1) } } + val dst = new Path(fs.getHomeDirectory(), appStagingDir) + val replication = conf.getOrElse("spark.yarn.submit.file.replication", "3").toShort - Map("spark.jar" -> System.getenv("SPARK_JAR"), "app.jar" -> args.userJar, "log4j.properties" -> System.getenv("SPARK_LOG4J_CONF")) + if (UserGroupInformation.isSecurityEnabled()) { + val dstFs = dst.getFileSystem(conf) + dstFs.addDelegationTokens(delegTokenRenewer, credentials) + } + val localResources = HashMap[String, LocalResource]() + FileSystem.mkdirs(fs, dst, new FsPermission(STAGING_DIR_PERMISSION)) + + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + + Map(Client.SPARK_JAR -> System.getenv("SPARK_JAR"), Client.APP_JAR -> args.userJar, + Client.LOG4J_PROP -> System.getenv("SPARK_LOG4J_CONF")) .foreach { case(destName, _localPath) => val localPath: String = if (_localPath != null) _localPath.trim() else "" if (! localPath.isEmpty()) { - val src = new Path(localPath) - val pathSuffix = appName + "/" + appId.getId() + destName - val dst = new Path(fs.getHomeDirectory(), pathSuffix) - logInfo("Uploading " + src + " to " + dst) - fs.copyFromLocalFile(false, true, src, dst) - val destStatus = fs.getFileStatus(dst) - - // get tokens for anything we upload to hdfs - if (UserGroupInformation.isSecurityEnabled()) { - fs.addDelegationTokens(delegTokenRenewer, credentials); + var localURI = new URI(localPath) + // if not specified assume these are in the local filesystem to keep behavior like Hadoop + if (localURI.getScheme() == null) { + localURI = new URI(FileSystem.getLocal(conf).makeQualified(new Path(localPath)).toString) } + val setPermissions = if (destName.equals(Client.APP_JAR)) true else false + val destPath = copyRemoteFile(dst, new Path(localURI), replication, setPermissions) + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + destName, statCache) + } + } + + // handle any add jars + if ((args.addJars != null) && (!args.addJars.isEmpty())){ + args.addJars.split(',').foreach { case file: String => + val localURI = new URI(file.trim()) + val localPath = new Path(localURI) + val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) + val destPath = copyRemoteFile(dst, localPath, replication) + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + linkname, statCache, true) + } + } + + // handle any distributed cache files + if ((args.files != null) && (!args.files.isEmpty())){ + args.files.split(',').foreach { case file: String => + val localURI = new URI(file.trim()) + val localPath = new Path(localURI) + val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) + val destPath = copyRemoteFile(dst, localPath, replication) + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + linkname, statCache) + } + } - val amJarRsrc = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] - amJarRsrc.setType(LocalResourceType.FILE) - amJarRsrc.setVisibility(LocalResourceVisibility.APPLICATION) - amJarRsrc.setResource(ConverterUtils.getYarnUrlFromPath(dst)) - amJarRsrc.setTimestamp(destStatus.getModificationTime()) - amJarRsrc.setSize(destStatus.getLen()) - locaResources(destName) = amJarRsrc + // handle any distributed cache archives + if ((args.archives != null) && (!args.archives.isEmpty())) { + args.archives.split(',').foreach { case file:String => + val localURI = new URI(file.trim()) + val localPath = new Path(localURI) + val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) + val destPath = copyRemoteFile(dst, localPath, replication) + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, + linkname, statCache) } } - UserGroupInformation.getCurrentUser().addCredentials(credentials); - return locaResources + + UserGroupInformation.getCurrentUser().addCredentials(credentials) + return localResources } - - def setupLaunchEnv(localResources: HashMap[String, LocalResource]): HashMap[String, String] = { + + def setupLaunchEnv( + localResources: HashMap[String, LocalResource], + stagingDir: String): HashMap[String, String] = { logInfo("Setting up the launch environment") - val log4jConfLocalRes = localResources.getOrElse("log4j.properties", null) + val log4jConfLocalRes = localResources.getOrElse(Client.LOG4J_PROP, null) val env = new HashMap[String, String]() - // If log4j present, ensure ours overrides all others - if (log4jConfLocalRes != null) Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./") - - Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./*") - Apps.addToEnvironment(env, Environment.CLASSPATH.name, "$CLASSPATH") - Client.populateHadoopClasspath(yarnConf, env) + Client.populateClasspath(yarnConf, log4jConfLocalRes != null, env) env("SPARK_YARN_MODE") = "true" - env("SPARK_YARN_JAR_PATH") = - localResources("spark.jar").getResource().getScheme.toString() + "://" + - localResources("spark.jar").getResource().getFile().toString() - env("SPARK_YARN_JAR_TIMESTAMP") = localResources("spark.jar").getTimestamp().toString() - env("SPARK_YARN_JAR_SIZE") = localResources("spark.jar").getSize().toString() - - env("SPARK_YARN_USERJAR_PATH") = - localResources("app.jar").getResource().getScheme.toString() + "://" + - localResources("app.jar").getResource().getFile().toString() - env("SPARK_YARN_USERJAR_TIMESTAMP") = localResources("app.jar").getTimestamp().toString() - env("SPARK_YARN_USERJAR_SIZE") = localResources("app.jar").getSize().toString() - - if (log4jConfLocalRes != null) { - env("SPARK_YARN_LOG4J_PATH") = - log4jConfLocalRes.getResource().getScheme.toString() + "://" + log4jConfLocalRes.getResource().getFile().toString() - env("SPARK_YARN_LOG4J_TIMESTAMP") = log4jConfLocalRes.getTimestamp().toString() - env("SPARK_YARN_LOG4J_SIZE") = log4jConfLocalRes.getSize().toString() - } + env("SPARK_YARN_STAGING_DIR") = stagingDir + + // Set the environment variables to be passed on to the Workers. + distCacheMgr.setDistFilesEnv(env) + distCacheMgr.setDistArchivesEnv(env) - // allow users to specify some environment variables + // Allow users to specify some environment variables. Apps.setEnvFromInputString(env, System.getenv("SPARK_YARN_USER_ENV")) - // Add each SPARK-* key to the environment + // Add each SPARK-* key to the environment. System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k,v) => env(k) = v } - return env + env } def userArgsToString(clientArgs: ClientArguments): String = { @@ -201,13 +329,13 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl for (arg <- args){ retval.append(prefix).append(" '").append(arg).append("' ") } - retval.toString } - def createContainerLaunchContext(newApp: GetNewApplicationResponse, - localResources: HashMap[String, LocalResource], - env: HashMap[String, String]): ContainerLaunchContext = { + def createContainerLaunchContext( + newApp: GetNewApplicationResponse, + localResources: HashMap[String, LocalResource], + env: HashMap[String, String]): ContainerLaunchContext = { logInfo("Setting up container launch context") val amContainer = Records.newRecord(classOf[ContainerLaunchContext]) amContainer.setLocalResources(localResources) @@ -215,8 +343,10 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val minResMemory: Int = newApp.getMinimumResourceCapability().getMemory() + // TODO(harvey): This can probably be a val. var amMemory = ((args.amMemory / minResMemory) * minResMemory) + - (if (0 != (args.amMemory % minResMemory)) minResMemory else 0) - YarnAllocationHandler.MEMORY_OVERHEAD + ((if ((args.amMemory % minResMemory) == 0) 0 else minResMemory) - + YarnAllocationHandler.MEMORY_OVERHEAD) // Extra options for the JVM var JAVA_OPTS = "" @@ -224,17 +354,21 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl // Add Xmx for am memory JAVA_OPTS += "-Xmx" + amMemory + "m " - JAVA_OPTS += " -Djava.io.tmpdir=" + + JAVA_OPTS += " -Djava.io.tmpdir=" + new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR) + " " - - // Commenting it out for now - so that people can refer to the properties if required. Remove it once cpuset version is pushed out. - // The context is, default gc for server class machines end up using all cores to do gc - hence if there are multiple containers in same - // node, spark gc effects all other containers performance (which can also be other spark containers) - // Instead of using this, rely on cpusets by YARN to enforce spark behaves 'properly' in multi-tenant environments. Not sure how default java gc behaves if it is - // limited to subset of cores on a node. - if (env.isDefinedAt("SPARK_USE_CONC_INCR_GC") && java.lang.Boolean.parseBoolean(env("SPARK_USE_CONC_INCR_GC"))) { - // In our expts, using (default) throughput collector has severe perf ramnifications in multi-tenant machines + // Commenting it out for now - so that people can refer to the properties if required. Remove + // it once cpuset version is pushed out. The context is, default gc for server class machines + // end up using all cores to do gc - hence if there are multiple containers in same node, + // spark gc effects all other containers performance (which can also be other spark containers) + // Instead of using this, rely on cpusets by YARN to enforce spark behaves 'properly' in + // multi-tenant environments. Not sure how default java gc behaves if it is limited to subset + // of cores on a node. + val useConcurrentAndIncrementalGC = env.isDefinedAt("SPARK_USE_CONC_INCR_GC") && + java.lang.Boolean.parseBoolean(env("SPARK_USE_CONC_INCR_GC")) + if (useConcurrentAndIncrementalGC) { + // In our expts, using (default) throughput collector has severe perf ramnifications in + // multi-tenant machines JAVA_OPTS += " -XX:+UseConcMarkSweepGC " JAVA_OPTS += " -XX:+CMSIncrementalMode " JAVA_OPTS += " -XX:+CMSIncrementalPacing " @@ -247,17 +381,17 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } // Command for the ApplicationMaster - var javaCommand = "java"; + var javaCommand = "java" val javaHome = System.getenv("JAVA_HOME") if ((javaHome != null && !javaHome.isEmpty()) || env.isDefinedAt("JAVA_HOME")) { javaCommand = Environment.JAVA_HOME.$() + "/bin/java" } - val commands = List[String](javaCommand + + val commands = List[String](javaCommand + " -server " + JAVA_OPTS + - " org.apache.spark.deploy.yarn.ApplicationMaster" + - " --class " + args.userClass + + " " + args.amClass + + " --class " + args.userClass + " --jar " + args.userJar + userArgsToString(args) + " --worker-memory " + args.workerMemory + @@ -267,29 +401,31 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl " 2> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr") logInfo("Command for the ApplicationMaster: " + commands(0)) amContainer.setCommands(commands) - + val capability = Records.newRecord(classOf[Resource]).asInstanceOf[Resource] - // Memory for the ApplicationMaster + // Memory for the ApplicationMaster. capability.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD) amContainer.setResource(capability) - // Setup security tokens + // Setup security tokens. val dob = new DataOutputBuffer() credentials.writeTokenStorageToStream(dob) amContainer.setContainerTokens(ByteBuffer.wrap(dob.getData())) - return amContainer + amContainer } - + def submitApp(appContext: ApplicationSubmissionContext) = { - // Submit the application to the applications manager + // Submit the application to the applications manager. logInfo("Submitting application to ASM") super.submitApplication(appContext) } - - def monitorApplication(appId: ApplicationId): Boolean = { - while(true) { - Thread.sleep(1000) + + def monitorApplication(appId: ApplicationId): Boolean = { + val interval = new SparkConf().getOrElse("spark.yarn.report.interval", "1000").toLong + + while (true) { + Thread.sleep(interval) val report = super.getApplicationReport(appId) logInfo("Application report from ASM: \n" + @@ -306,26 +442,31 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl "\t appTrackingUrl: " + report.getTrackingUrl() + "\n" + "\t appUser: " + report.getUser() ) - + val state = report.getYarnApplicationState() val dsStatus = report.getFinalApplicationStatus() - if (state == YarnApplicationState.FINISHED || + if (state == YarnApplicationState.FINISHED || state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) { - return true + return true } } - return true + true } } object Client { + val SPARK_JAR: String = "spark.jar" + val APP_JAR: String = "app.jar" + val LOG4J_PROP: String = "log4j.properties" + def main(argStrings: Array[String]) { // Set an env variable indicating we are running in YARN mode. // Note that anything with SPARK prefix gets propagated to all (remote) processes System.setProperty("SPARK_YARN_MODE", "true") val args = new ClientArguments(argStrings) + new Client(args).run } @@ -335,4 +476,30 @@ object Client { Apps.addToEnvironment(env, Environment.CLASSPATH.name, c.trim) } } + + def populateClasspath(conf: Configuration, addLog4j: Boolean, env: HashMap[String, String]) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$()) + // If log4j present, ensure ours overrides all others + if (addLog4j) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + LOG4J_PROP) + } + // Normally the users app.jar is last in case conflicts with spark jars + val userClasspathFirst = conf.getOrElse("spark.yarn.user.classpath.first", "false") + .toBoolean + if (userClasspathFirst) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + APP_JAR) + } + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + SPARK_JAR) + Client.populateHadoopClasspath(conf, env) + + if (!userClasspathFirst) { + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + APP_JAR) + } + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Path.SEPARATOR + "*") + } } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index cd651904d2..e9e46a193b 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -24,14 +24,19 @@ import org.apache.spark.scheduler.{InputFormatInfo, SplitInfo} // TODO: Add code and support for ensuring that yarn resource 'asks' are location aware ! class ClientArguments(val args: Array[String]) { + var addJars: String = null + var files: String = null + var archives: String = null var userJar: String = null var userClass: String = null var userArgs: Seq[String] = Seq[String]() var workerMemory = 1024 var workerCores = 1 var numWorkers = 2 - var amQueue = System.getProperty("QUEUE", "default") + var amQueue = conf.getOrElse("QUEUE", "default") var amMemory: Int = 512 + var amClass: String = "org.apache.spark.deploy.yarn.ApplicationMaster" + var appName: String = "Spark" // TODO var inputFormatInfo: List[InputFormatInfo] = null @@ -58,18 +63,22 @@ class ClientArguments(val args: Array[String]) { userArgsBuffer += value args = tail - case ("--master-memory") :: MemoryParam(value) :: tail => - amMemory = value + case ("--master-class") :: value :: tail => + amClass = value args = tail - case ("--num-workers") :: IntParam(value) :: tail => - numWorkers = value + case ("--master-memory") :: MemoryParam(value) :: tail => + amMemory = value args = tail case ("--worker-memory") :: MemoryParam(value) :: tail => workerMemory = value args = tail + case ("--num-workers") :: IntParam(value) :: tail => + numWorkers = value + args = tail + case ("--worker-cores") :: IntParam(value) :: tail => workerCores = value args = tail @@ -78,6 +87,22 @@ class ClientArguments(val args: Array[String]) { amQueue = value args = tail + case ("--name") :: value :: tail => + appName = value + args = tail + + case ("--addJars") :: value :: tail => + addJars = value + args = tail + + case ("--files") :: value :: tail => + files = value + args = tail + + case ("--archives") :: value :: tail => + archives = value + args = tail + case Nil => if (userJar == null || userClass == null) { printUsageAndExit(1) @@ -92,7 +117,7 @@ class ClientArguments(val args: Array[String]) { inputFormatInfo = inputFormatMap.values.toList } - + def printUsageAndExit(exitCode: Int, unknownParam: Any = null) { if (unknownParam != null) { System.err.println("Unknown/unsupported param " + unknownParam) @@ -100,17 +125,22 @@ class ClientArguments(val args: Array[String]) { System.err.println( "Usage: org.apache.spark.deploy.yarn.Client [options] \n" + "Options:\n" + - " --jar JAR_PATH Path to your application's JAR file (required)\n" + - " --class CLASS_NAME Name of your application's main class (required)\n" + - " --args ARGS Arguments to be passed to your application's main class.\n" + - " Mutliple invocations are possible, each will be passed in order.\n" + - " --num-workers NUM Number of workers to start (Default: 2)\n" + - " --worker-cores NUM Number of cores for the workers (Default: 1). This is unsused right now.\n" + - " --master-memory MEM Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)\n" + - " --worker-memory MEM Memory per Worker (e.g. 1000M, 2G) (Default: 1G)\n" + - " --queue QUEUE The hadoop queue to use for allocation requests (Default: 'default')" + " --jar JAR_PATH Path to your application's JAR file (required)\n" + + " --class CLASS_NAME Name of your application's main class (required)\n" + + " --args ARGS Arguments to be passed to your application's main class.\n" + + " Mutliple invocations are possible, each will be passed in order.\n" + + " --num-workers NUM Number of workers to start (Default: 2)\n" + + " --worker-cores NUM Number of cores for the workers (Default: 1). This is unsused right now.\n" + + " --master-class CLASS_NAME Class Name for Master (Default: spark.deploy.yarn.ApplicationMaster)\n" + + " --master-memory MEM Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)\n" + + " --worker-memory MEM Memory per Worker (e.g. 1000M, 2G) (Default: 1G)\n" + + " --name NAME The name of your application (Default: Spark)\n" + + " --queue QUEUE The hadoop queue to use for allocation requests (Default: 'default')\n" + + " --addJars jars Comma separated list of local jars that want SparkContext.addJar to work with.\n" + + " --files files Comma separated list of files to be distributed with the job.\n" + + " --archives archives Comma separated list of archives to be distributed with the job." ) System.exit(exitCode) } - + } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala new file mode 100644 index 0000000000..5f159b073f --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManager.scala @@ -0,0 +1,228 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.net.URI + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.FileStatus +import org.apache.hadoop.fs.FileSystem +import org.apache.hadoop.fs.Path +import org.apache.hadoop.fs.permission.FsAction +import org.apache.hadoop.yarn.api.records.LocalResource +import org.apache.hadoop.yarn.api.records.LocalResourceVisibility +import org.apache.hadoop.yarn.api.records.LocalResourceType +import org.apache.hadoop.yarn.util.{Records, ConverterUtils} + +import org.apache.spark.Logging + +import scala.collection.mutable.HashMap +import scala.collection.mutable.LinkedHashMap +import scala.collection.mutable.Map + + +/** Client side methods to setup the Hadoop distributed cache */ +class ClientDistributedCacheManager() extends Logging { + private val distCacheFiles: Map[String, Tuple3[String, String, String]] = + LinkedHashMap[String, Tuple3[String, String, String]]() + private val distCacheArchives: Map[String, Tuple3[String, String, String]] = + LinkedHashMap[String, Tuple3[String, String, String]]() + + + /** + * Add a resource to the list of distributed cache resources. This list can + * be sent to the ApplicationMaster and possibly the workers so that it can + * be downloaded into the Hadoop distributed cache for use by this application. + * Adds the LocalResource to the localResources HashMap passed in and saves + * the stats of the resources to they can be sent to the workers and verified. + * + * @param fs FileSystem + * @param conf Configuration + * @param destPath path to the resource + * @param localResources localResource hashMap to insert the resource into + * @param resourceType LocalResourceType + * @param link link presented in the distributed cache to the destination + * @param statCache cache to store the file/directory stats + * @param appMasterOnly Whether to only add the resource to the app master + */ + def addResource( + fs: FileSystem, + conf: Configuration, + destPath: Path, + localResources: HashMap[String, LocalResource], + resourceType: LocalResourceType, + link: String, + statCache: Map[URI, FileStatus], + appMasterOnly: Boolean = false) = { + val destStatus = fs.getFileStatus(destPath) + val amJarRsrc = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] + amJarRsrc.setType(resourceType) + val visibility = getVisibility(conf, destPath.toUri(), statCache) + amJarRsrc.setVisibility(visibility) + amJarRsrc.setResource(ConverterUtils.getYarnUrlFromPath(destPath)) + amJarRsrc.setTimestamp(destStatus.getModificationTime()) + amJarRsrc.setSize(destStatus.getLen()) + if (link == null || link.isEmpty()) throw new Exception("You must specify a valid link name") + localResources(link) = amJarRsrc + + if (appMasterOnly == false) { + val uri = destPath.toUri() + val pathURI = new URI(uri.getScheme(), uri.getAuthority(), uri.getPath(), null, link) + if (resourceType == LocalResourceType.FILE) { + distCacheFiles(pathURI.toString()) = (destStatus.getLen().toString(), + destStatus.getModificationTime().toString(), visibility.name()) + } else { + distCacheArchives(pathURI.toString()) = (destStatus.getLen().toString(), + destStatus.getModificationTime().toString(), visibility.name()) + } + } + } + + /** + * Adds the necessary cache file env variables to the env passed in + * @param env + */ + def setDistFilesEnv(env: Map[String, String]) = { + val (keys, tupleValues) = distCacheFiles.unzip + val (sizes, timeStamps, visibilities) = tupleValues.unzip3 + + if (keys.size > 0) { + env("SPARK_YARN_CACHE_FILES") = keys.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_TIME_STAMPS") = + timeStamps.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_FILE_SIZES") = + sizes.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_FILES_VISIBILITIES") = + visibilities.reduceLeft[String] { (acc,n) => acc + "," + n } + } + } + + /** + * Adds the necessary cache archive env variables to the env passed in + * @param env + */ + def setDistArchivesEnv(env: Map[String, String]) = { + val (keys, tupleValues) = distCacheArchives.unzip + val (sizes, timeStamps, visibilities) = tupleValues.unzip3 + + if (keys.size > 0) { + env("SPARK_YARN_CACHE_ARCHIVES") = keys.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") = + timeStamps.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") = + sizes.reduceLeft[String] { (acc,n) => acc + "," + n } + env("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") = + visibilities.reduceLeft[String] { (acc,n) => acc + "," + n } + } + } + + /** + * Returns the local resource visibility depending on the cache file permissions + * @param conf + * @param uri + * @param statCache + * @return LocalResourceVisibility + */ + def getVisibility(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): + LocalResourceVisibility = { + if (isPublic(conf, uri, statCache)) { + return LocalResourceVisibility.PUBLIC + } + return LocalResourceVisibility.PRIVATE + } + + /** + * Returns a boolean to denote whether a cache file is visible to all(public) + * or not + * @param conf + * @param uri + * @param statCache + * @return true if the path in the uri is visible to all, false otherwise + */ + def isPublic(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): Boolean = { + val fs = FileSystem.get(uri, conf) + val current = new Path(uri.getPath()) + //the leaf level file should be readable by others + if (!checkPermissionOfOther(fs, current, FsAction.READ, statCache)) { + return false + } + return ancestorsHaveExecutePermissions(fs, current.getParent(), statCache) + } + + /** + * Returns true if all ancestors of the specified path have the 'execute' + * permission set for all users (i.e. that other users can traverse + * the directory heirarchy to the given path) + * @param fs + * @param path + * @param statCache + * @return true if all ancestors have the 'execute' permission set for all users + */ + def ancestorsHaveExecutePermissions(fs: FileSystem, path: Path, + statCache: Map[URI, FileStatus]): Boolean = { + var current = path + while (current != null) { + //the subdirs in the path should have execute permissions for others + if (!checkPermissionOfOther(fs, current, FsAction.EXECUTE, statCache)) { + return false + } + current = current.getParent() + } + return true + } + + /** + * Checks for a given path whether the Other permissions on it + * imply the permission in the passed FsAction + * @param fs + * @param path + * @param action + * @param statCache + * @return true if the path in the uri is visible to all, false otherwise + */ + def checkPermissionOfOther(fs: FileSystem, path: Path, + action: FsAction, statCache: Map[URI, FileStatus]): Boolean = { + val status = getFileStatus(fs, path.toUri(), statCache) + val perms = status.getPermission() + val otherAction = perms.getOtherAction() + if (otherAction.implies(action)) { + return true + } + return false + } + + /** + * Checks to see if the given uri exists in the cache, if it does it + * returns the existing FileStatus, otherwise it stats the uri, stores + * it in the cache, and returns the FileStatus. + * @param fs + * @param uri + * @param statCache + * @return FileStatus + */ + def getFileStatus(fs: FileSystem, uri: URI, statCache: Map[URI, FileStatus]): FileStatus = { + val stat = statCache.get(uri) match { + case Some(existstat) => existstat + case None => + val newStat = fs.getFileStatus(new Path(uri)) + statCache.put(uri, newStat) + newStat + } + return stat + } +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala new file mode 100644 index 0000000000..c1e79cbe66 --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala @@ -0,0 +1,243 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.net.Socket +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.net.NetUtils +import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.records._ +import org.apache.hadoop.yarn.api.protocolrecords._ +import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.ipc.YarnRPC +import org.apache.hadoop.yarn.util.{ConverterUtils, Records} +import akka.actor._ +import akka.remote._ +import akka.actor.Terminated +import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.util.{Utils, AkkaUtils} +import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend +import org.apache.spark.scheduler.SplitInfo + +class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) extends Logging { + + def this(args: ApplicationMasterArguments) = this(args, new Configuration()) + + private val rpc: YarnRPC = YarnRPC.create(conf) + private var resourceManager: AMRMProtocol = null + private var appAttemptId: ApplicationAttemptId = null + private var reporterThread: Thread = null + private val yarnConf: YarnConfiguration = new YarnConfiguration(conf) + + private var yarnAllocator: YarnAllocationHandler = null + private var driverClosed:Boolean = false + + val actorSystem : ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0)._1 + var actor: ActorRef = null + + // This actor just working as a monitor to watch on Driver Actor. + class MonitorActor(driverUrl: String) extends Actor { + + var driver: ActorSelection = null + + override def preStart() { + logInfo("Listen to driver: " + driverUrl) + driver = context.actorSelection(driverUrl) + driver ! "hello" + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) + } + + override def receive = { + case x: DisassociatedEvent => + logInfo(s"Driver terminated or disconnected! Shutting down. $x") + driverClosed = true + } + } + + def run() { + + appAttemptId = getApplicationAttemptId() + resourceManager = registerWithResourceManager() + val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster() + + // Compute number of threads for akka + val minimumMemory = appMasterResponse.getMinimumResourceCapability().getMemory() + + if (minimumMemory > 0) { + val mem = args.workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD + val numCore = (mem / minimumMemory) + (if (0 != (mem % minimumMemory)) 1 else 0) + + if (numCore > 0) { + // do not override - hits https://issues.apache.org/jira/browse/HADOOP-8406 + // TODO: Uncomment when hadoop is on a version which has this fixed. + // args.workerCores = numCore + } + } + + waitForSparkMaster() + + // Allocate all containers + allocateWorkers() + + // Launch a progress reporter thread, else app will get killed after expiration (def: 10mins) timeout + // ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapse. + + val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000) + // must be <= timeoutInterval/ 2. + // On other hand, also ensure that we are reasonably responsive without causing too many requests to RM. + // so atleast 1 minute or timeoutInterval / 10 - whichever is higher. + val interval = math.min(timeoutInterval / 2, math.max(timeoutInterval/ 10, 60000L)) + reporterThread = launchReporterThread(interval) + + // Wait for the reporter thread to Finish. + reporterThread.join() + + finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) + actorSystem.shutdown() + + logInfo("Exited") + System.exit(0) + } + + private def getApplicationAttemptId(): ApplicationAttemptId = { + val envs = System.getenv() + val containerIdString = envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV) + val containerId = ConverterUtils.toContainerId(containerIdString) + val appAttemptId = containerId.getApplicationAttemptId() + logInfo("ApplicationAttemptId: " + appAttemptId) + return appAttemptId + } + + private def registerWithResourceManager(): AMRMProtocol = { + val rmAddress = NetUtils.createSocketAddr(yarnConf.get( + YarnConfiguration.RM_SCHEDULER_ADDRESS, + YarnConfiguration.DEFAULT_RM_SCHEDULER_ADDRESS)) + logInfo("Connecting to ResourceManager at " + rmAddress) + return rpc.getProxy(classOf[AMRMProtocol], rmAddress, conf).asInstanceOf[AMRMProtocol] + } + + private def registerApplicationMaster(): RegisterApplicationMasterResponse = { + logInfo("Registering the ApplicationMaster") + val appMasterRequest = Records.newRecord(classOf[RegisterApplicationMasterRequest]) + .asInstanceOf[RegisterApplicationMasterRequest] + appMasterRequest.setApplicationAttemptId(appAttemptId) + // Setting this to master host,port - so that the ApplicationReport at client has some sensible info. + // Users can then monitor stderr/stdout on that node if required. + appMasterRequest.setHost(Utils.localHostName()) + appMasterRequest.setRpcPort(0) + // What do we provide here ? Might make sense to expose something sensible later ? + appMasterRequest.setTrackingUrl("") + return resourceManager.registerApplicationMaster(appMasterRequest) + } + + private def waitForSparkMaster() { + logInfo("Waiting for spark driver to be reachable.") + var driverUp = false + val hostport = args.userArgs(0) + val (driverHost, driverPort) = Utils.parseHostPort(hostport) + while(!driverUp) { + try { + val socket = new Socket(driverHost, driverPort) + socket.close() + logInfo("Master now available: " + driverHost + ":" + driverPort) + driverUp = true + } catch { + case e: Exception => + logError("Failed to connect to driver at " + driverHost + ":" + driverPort) + Thread.sleep(100) + } + } + conf.set("spark.driver.host", driverHost) + conf.set("spark.driver.port", driverPort.toString) + + val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( + driverHost, driverPort.toString, CoarseGrainedSchedulerBackend.ACTOR_NAME) + + actor = actorSystem.actorOf(Props(new MonitorActor(driverUrl)), name = "YarnAM") + } + + + private def allocateWorkers() { + + // Fixme: should get preferredNodeLocationData from SparkContext, just fake a empty one for now. + val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] = scala.collection.immutable.Map() + + yarnAllocator = YarnAllocationHandler.newAllocator(yarnConf, resourceManager, appAttemptId, args, preferredNodeLocationData) + + logInfo("Allocating " + args.numWorkers + " workers.") + // Wait until all containers have finished + // TODO: This is a bit ugly. Can we make it nicer? + // TODO: Handle container failure + while(yarnAllocator.getNumWorkersRunning < args.numWorkers) { + yarnAllocator.allocateContainers(math.max(args.numWorkers - yarnAllocator.getNumWorkersRunning, 0)) + Thread.sleep(100) + } + + logInfo("All workers have launched.") + + } + + // TODO: We might want to extend this to allocate more containers in case they die ! + private def launchReporterThread(_sleepTime: Long): Thread = { + val sleepTime = if (_sleepTime <= 0 ) 0 else _sleepTime + + val t = new Thread { + override def run() { + while (!driverClosed) { + val missingWorkerCount = args.numWorkers - yarnAllocator.getNumWorkersRunning + if (missingWorkerCount > 0) { + logInfo("Allocating " + missingWorkerCount + " containers to make up for (potentially ?) lost containers") + yarnAllocator.allocateContainers(missingWorkerCount) + } + else sendProgress() + Thread.sleep(sleepTime) + } + } + } + // setting to daemon status, though this is usually not a good idea. + t.setDaemon(true) + t.start() + logInfo("Started progress reporter thread - sleep time : " + sleepTime) + return t + } + + private def sendProgress() { + logDebug("Sending progress") + // simulated with an allocate request with no nodes requested ... + yarnAllocator.allocateContainers(0) + } + + def finishApplicationMaster(status: FinalApplicationStatus) { + + logInfo("finish ApplicationMaster with " + status) + val finishReq = Records.newRecord(classOf[FinishApplicationMasterRequest]) + .asInstanceOf[FinishApplicationMasterRequest] + finishReq.setAppAttemptId(appAttemptId) + finishReq.setFinishApplicationStatus(status) + resourceManager.finishApplicationMaster(finishReq) + } + +} + + +object WorkerLauncher { + def main(argStrings: Array[String]) { + val args = new ApplicationMasterArguments(argStrings) + new WorkerLauncher(args).run() + } +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerRunnable.scala index a60e8a3007..6a90cc51cf 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerRunnable.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerRunnable.scala @@ -21,53 +21,59 @@ import java.net.URI import java.nio.ByteBuffer import java.security.PrivilegedExceptionAction +import scala.collection.JavaConversions._ +import scala.collection.mutable.HashMap + import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.fs.Path import org.apache.hadoop.io.DataOutputBuffer import org.apache.hadoop.net.NetUtils import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.api._ +import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.api.protocolrecords._ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records, ProtoUtils} -import org.apache.hadoop.yarn.api.ApplicationConstants.Environment - -import scala.collection.JavaConversions._ -import scala.collection.mutable.HashMap import org.apache.spark.Logging -import org.apache.spark.util.Utils -class WorkerRunnable(container: Container, conf: Configuration, masterAddress: String, - slaveId: String, hostname: String, workerMemory: Int, workerCores: Int) - extends Runnable with Logging { - + +class WorkerRunnable( + container: Container, + conf: Configuration, + masterAddress: String, + slaveId: String, + hostname: String, + workerMemory: Int, + workerCores: Int) + extends Runnable with Logging { + var rpc: YarnRPC = YarnRPC.create(conf) var cm: ContainerManager = null val yarnConf: YarnConfiguration = new YarnConfiguration(conf) - + def run = { logInfo("Starting Worker Container") cm = connectToCM startContainer } - + def startContainer = { logInfo("Setting up ContainerLaunchContext") - + val ctx = Records.newRecord(classOf[ContainerLaunchContext]) .asInstanceOf[ContainerLaunchContext] - + ctx.setContainerId(container.getId()) ctx.setResource(container.getResource()) val localResources = prepareLocalResources ctx.setLocalResources(localResources) - + val env = prepareEnvironment ctx.setEnvironment(env) - + // Extra options for the JVM var JAVA_OPTS = "" // Set the JVM memory @@ -80,17 +86,21 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S JAVA_OPTS += " -Djava.io.tmpdir=" + new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR) + " " - - // Commenting it out for now - so that people can refer to the properties if required. Remove it once cpuset version is pushed out. - // The context is, default gc for server class machines end up using all cores to do gc - hence if there are multiple containers in same - // node, spark gc effects all other containers performance (which can also be other spark containers) - // Instead of using this, rely on cpusets by YARN to enforce spark behaves 'properly' in multi-tenant environments. Not sure how default java gc behaves if it is - // limited to subset of cores on a node. + // Commenting it out for now - so that people can refer to the properties if required. Remove + // it once cpuset version is pushed out. + // The context is, default gc for server class machines end up using all cores to do gc - hence + // if there are multiple containers in same node, spark gc effects all other containers + // performance (which can also be other spark containers) + // Instead of using this, rely on cpusets by YARN to enforce spark behaves 'properly' in + // multi-tenant environments. Not sure how default java gc behaves if it is limited to subset + // of cores on a node. /* else { // If no java_opts specified, default to using -XX:+CMSIncrementalMode - // It might be possible that other modes/config is being done in SPARK_JAVA_OPTS, so we dont want to mess with it. - // In our expts, using (default) throughput collector has severe perf ramnifications in multi-tennent machines + // It might be possible that other modes/config is being done in SPARK_JAVA_OPTS, so we dont + // want to mess with it. + // In our expts, using (default) throughput collector has severe perf ramnifications in + // multi-tennent machines // The options are based on // http://www.oracle.com/technetwork/java/gc-tuning-5-138395.html#0.0.0.%20When%20to%20Use%20the%20Concurrent%20Low%20Pause%20Collector|outline JAVA_OPTS += " -XX:+UseConcMarkSweepGC " @@ -108,7 +118,7 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S credentials.writeTokenStorageToStream(dob) ctx.setContainerTokens(ByteBuffer.wrap(dob.getData())) - var javaCommand = "java"; + var javaCommand = "java" val javaHome = System.getenv("JAVA_HOME") if ((javaHome != null && !javaHome.isEmpty()) || env.isDefinedAt("JAVA_HOME")) { javaCommand = Environment.JAVA_HOME.$() + "/bin/java" @@ -117,11 +127,13 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S val commands = List[String](javaCommand + " -server " + // Kill if OOM is raised - leverage yarn's failure handling to cause rescheduling. - // Not killing the task leaves various aspects of the worker and (to some extent) the jvm in an inconsistent state. - // TODO: If the OOM is not recoverable by rescheduling it on different node, then do 'something' to fail job ... akin to blacklisting trackers in mapred ? + // Not killing the task leaves various aspects of the worker and (to some extent) the jvm in + // an inconsistent state. + // TODO: If the OOM is not recoverable by rescheduling it on different node, then do + // 'something' to fail job ... akin to blacklisting trackers in mapred ? " -XX:OnOutOfMemoryError='kill %p' " + JAVA_OPTS + - " org.apache.spark.executor.StandaloneExecutorBackend " + + " org.apache.spark.executor.CoarseGrainedExecutorBackend " + masterAddress + " " + slaveId + " " + hostname + " " + @@ -130,85 +142,82 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S " 2> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr") logInfo("Setting up worker with commands: " + commands) ctx.setCommands(commands) - + // Send the start request to the ContainerManager val startReq = Records.newRecord(classOf[StartContainerRequest]) .asInstanceOf[StartContainerRequest] startReq.setContainerLaunchContext(ctx) cm.startContainer(startReq) } - - + + private def setupDistributedCache( + file: String, + rtype: LocalResourceType, + localResources: HashMap[String, LocalResource], + timestamp: String, + size: String, + vis: String) = { + val uri = new URI(file) + val amJarRsrc = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] + amJarRsrc.setType(rtype) + amJarRsrc.setVisibility(LocalResourceVisibility.valueOf(vis)) + amJarRsrc.setResource(ConverterUtils.getYarnUrlFromURI(uri)) + amJarRsrc.setTimestamp(timestamp.toLong) + amJarRsrc.setSize(size.toLong) + localResources(uri.getFragment()) = amJarRsrc + } + def prepareLocalResources: HashMap[String, LocalResource] = { logInfo("Preparing Local resources") - val locaResources = HashMap[String, LocalResource]() - - // Spark JAR - val sparkJarResource = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] - sparkJarResource.setType(LocalResourceType.FILE) - sparkJarResource.setVisibility(LocalResourceVisibility.APPLICATION) - sparkJarResource.setResource(ConverterUtils.getYarnUrlFromURI( - new URI(System.getenv("SPARK_YARN_JAR_PATH")))) - sparkJarResource.setTimestamp(System.getenv("SPARK_YARN_JAR_TIMESTAMP").toLong) - sparkJarResource.setSize(System.getenv("SPARK_YARN_JAR_SIZE").toLong) - locaResources("spark.jar") = sparkJarResource - // User JAR - val userJarResource = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] - userJarResource.setType(LocalResourceType.FILE) - userJarResource.setVisibility(LocalResourceVisibility.APPLICATION) - userJarResource.setResource(ConverterUtils.getYarnUrlFromURI( - new URI(System.getenv("SPARK_YARN_USERJAR_PATH")))) - userJarResource.setTimestamp(System.getenv("SPARK_YARN_USERJAR_TIMESTAMP").toLong) - userJarResource.setSize(System.getenv("SPARK_YARN_USERJAR_SIZE").toLong) - locaResources("app.jar") = userJarResource - - // Log4j conf - if available - if (System.getenv("SPARK_YARN_LOG4J_PATH") != null) { - val log4jConfResource = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] - log4jConfResource.setType(LocalResourceType.FILE) - log4jConfResource.setVisibility(LocalResourceVisibility.APPLICATION) - log4jConfResource.setResource(ConverterUtils.getYarnUrlFromURI( - new URI(System.getenv("SPARK_YARN_LOG4J_PATH")))) - log4jConfResource.setTimestamp(System.getenv("SPARK_YARN_LOG4J_TIMESTAMP").toLong) - log4jConfResource.setSize(System.getenv("SPARK_YARN_LOG4J_SIZE").toLong) - locaResources("log4j.properties") = log4jConfResource + val localResources = HashMap[String, LocalResource]() + + if (System.getenv("SPARK_YARN_CACHE_FILES") != null) { + val timeStamps = System.getenv("SPARK_YARN_CACHE_FILES_TIME_STAMPS").split(',') + val fileSizes = System.getenv("SPARK_YARN_CACHE_FILES_FILE_SIZES").split(',') + val distFiles = System.getenv("SPARK_YARN_CACHE_FILES").split(',') + val visibilities = System.getenv("SPARK_YARN_CACHE_FILES_VISIBILITIES").split(',') + for( i <- 0 to distFiles.length - 1) { + setupDistributedCache(distFiles(i), LocalResourceType.FILE, localResources, timeStamps(i), + fileSizes(i), visibilities(i)) + } } - - logInfo("Prepared Local resources " + locaResources) - return locaResources + if (System.getenv("SPARK_YARN_CACHE_ARCHIVES") != null) { + val timeStamps = System.getenv("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS").split(',') + val fileSizes = System.getenv("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES").split(',') + val distArchives = System.getenv("SPARK_YARN_CACHE_ARCHIVES").split(',') + val visibilities = System.getenv("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES").split(',') + for( i <- 0 to distArchives.length - 1) { + setupDistributedCache(distArchives(i), LocalResourceType.ARCHIVE, localResources, + timeStamps(i), fileSizes(i), visibilities(i)) + } + } + + logInfo("Prepared Local resources " + localResources) + return localResources } - + def prepareEnvironment: HashMap[String, String] = { val env = new HashMap[String, String]() - // If log4j present, ensure ours overrides all others - if (System.getenv("SPARK_YARN_LOG4J_PATH") != null) { - // Which is correct ? - Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./log4j.properties") - Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./") - } + Client.populateClasspath(yarnConf, System.getenv("SPARK_YARN_LOG4J_PATH") != null, env) - Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./*") - Apps.addToEnvironment(env, Environment.CLASSPATH.name, "$CLASSPATH") - Client.populateHadoopClasspath(yarnConf, env) - - // allow users to specify some environment variables + // Allow users to specify some environment variables Apps.setEnvFromInputString(env, System.getenv("SPARK_YARN_USER_ENV")) System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k,v) => env(k) = v } return env } - + def connectToCM: ContainerManager = { val cmHostPortStr = container.getNodeId().getHost() + ":" + container.getNodeId().getPort() val cmAddress = NetUtils.createSocketAddr(cmHostPortStr) logInfo("Connecting to ContainerManager at " + cmHostPortStr) - // use doAs and remoteUser here so we can add the container token and not - // pollute the current users credentials with all of the individual container tokens - val user = UserGroupInformation.createRemoteUser(container.getId().toString()); - val containerToken = container.getContainerToken(); + // Use doAs and remoteUser here so we can add the container token and not pollute the current + // users credentials with all of the individual container tokens + val user = UserGroupInformation.createRemoteUser(container.getId().toString()) + val containerToken = container.getContainerToken() if (containerToken != null) { user.addToken(ProtoUtils.convertFromProtoFormat(containerToken, cmAddress)) } @@ -219,8 +228,8 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S return rpc.getProxy(classOf[ContainerManager], cmAddress, conf).asInstanceOf[ContainerManager] } - }); - return proxy; + }) + proxy } - + } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala index 6d6ef149cc..5966a0f757 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala @@ -17,87 +17,112 @@ package org.apache.spark.deploy.yarn +import java.lang.{Boolean => JBoolean} +import java.util.{Collections, Set => JSet} +import java.util.concurrent.{CopyOnWriteArrayList, ConcurrentHashMap} +import java.util.concurrent.atomic.AtomicInteger + +import scala.collection +import scala.collection.JavaConversions._ +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} + import org.apache.spark.Logging +import org.apache.spark.scheduler.{SplitInfo,TaskSchedulerImpl} +import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend import org.apache.spark.util.Utils -import org.apache.spark.scheduler.SplitInfo -import scala.collection -import org.apache.hadoop.yarn.api.records.{AMResponse, ApplicationAttemptId, ContainerId, Priority, Resource, ResourceRequest, ContainerStatus, Container} -import org.apache.spark.scheduler.cluster.{ClusterScheduler, StandaloneSchedulerBackend} + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.yarn.api.AMRMProtocol +import org.apache.hadoop.yarn.api.records.{AMResponse, ApplicationAttemptId} +import org.apache.hadoop.yarn.api.records.{Container, ContainerId, ContainerStatus} +import org.apache.hadoop.yarn.api.records.{Priority, Resource, ResourceRequest} import org.apache.hadoop.yarn.api.protocolrecords.{AllocateRequest, AllocateResponse} import org.apache.hadoop.yarn.util.{RackResolver, Records} -import java.util.concurrent.{CopyOnWriteArrayList, ConcurrentHashMap} -import java.util.concurrent.atomic.AtomicInteger -import org.apache.hadoop.yarn.api.AMRMProtocol -import collection.JavaConversions._ -import collection.mutable.{ArrayBuffer, HashMap, HashSet} -import org.apache.hadoop.conf.Configuration -import java.util.{Collections, Set => JSet} -import java.lang.{Boolean => JBoolean} -object AllocationType extends Enumeration ("HOST", "RACK", "ANY") { + +object AllocationType extends Enumeration { type AllocationType = Value val HOST, RACK, ANY = Value } -// too many params ? refactor it 'somehow' ? -// needs to be mt-safe -// Need to refactor this to make it 'cleaner' ... right now, all computation is reactive : should make it -// more proactive and decoupled. +// TODO: +// Too many params. +// Needs to be mt-safe +// Need to refactor this to make it 'cleaner' ... right now, all computation is reactive - should +// make it more proactive and decoupled. + // Note that right now, we assume all node asks as uniform in terms of capabilities and priority -// Refer to http://developer.yahoo.com/blogs/hadoop/posts/2011/03/mapreduce-nextgen-scheduler/ for more info -// on how we are requesting for containers. -private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceManager: AMRMProtocol, - val appAttemptId: ApplicationAttemptId, - val maxWorkers: Int, val workerMemory: Int, val workerCores: Int, - val preferredHostToCount: Map[String, Int], - val preferredRackToCount: Map[String, Int]) +// Refer to http://developer.yahoo.com/blogs/hadoop/posts/2011/03/mapreduce-nextgen-scheduler/ for +// more info on how we are requesting for containers. +private[yarn] class YarnAllocationHandler( + val conf: Configuration, + val resourceManager: AMRMProtocol, + val appAttemptId: ApplicationAttemptId, + val maxWorkers: Int, + val workerMemory: Int, + val workerCores: Int, + val preferredHostToCount: Map[String, Int], + val preferredRackToCount: Map[String, Int]) extends Logging { - - // These three are locked on allocatedHostToContainersMap. Complementary data structures // allocatedHostToContainersMap : containers which are running : host, Set<containerid> - // allocatedContainerToHostMap: container to host mapping - private val allocatedHostToContainersMap = new HashMap[String, collection.mutable.Set[ContainerId]]() + // allocatedContainerToHostMap: container to host mapping. + private val allocatedHostToContainersMap = + new HashMap[String, collection.mutable.Set[ContainerId]]() + private val allocatedContainerToHostMap = new HashMap[ContainerId, String]() - // allocatedRackCount is populated ONLY if allocation happens (or decremented if this is an allocated node) - // As with the two data structures above, tightly coupled with them, and to be locked on allocatedHostToContainersMap + + // allocatedRackCount is populated ONLY if allocation happens (or decremented if this is an + // allocated node) + // As with the two data structures above, tightly coupled with them, and to be locked on + // allocatedHostToContainersMap private val allocatedRackCount = new HashMap[String, Int]() - // containers which have been released. + // Containers which have been released. private val releasedContainerList = new CopyOnWriteArrayList[ContainerId]() - // containers to be released in next request to RM + // Containers to be released in next request to RM private val pendingReleaseContainers = new ConcurrentHashMap[ContainerId, Boolean] private val numWorkersRunning = new AtomicInteger() // Used to generate a unique id per worker private val workerIdCounter = new AtomicInteger() private val lastResponseId = new AtomicInteger() + private val numWorkersFailed = new AtomicInteger() def getNumWorkersRunning: Int = numWorkersRunning.intValue + def getNumWorkersFailed: Int = numWorkersFailed.intValue def isResourceConstraintSatisfied(container: Container): Boolean = { container.getResource.getMemory >= (workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD) } def allocateContainers(workersToRequest: Int) { - // We need to send the request only once from what I understand ... but for now, not modifying this much. + // We need to send the request only once from what I understand ... but for now, not modifying + // this much. // Keep polling the Resource Manager for containers val amResp = allocateWorkerResources(workersToRequest).getAMResponse val _allocatedContainers = amResp.getAllocatedContainers() - if (_allocatedContainers.size > 0) { - - logDebug("Allocated " + _allocatedContainers.size + " containers, current count " + - numWorkersRunning.get() + ", to-be-released " + releasedContainerList + - ", pendingReleaseContainers : " + pendingReleaseContainers) - logDebug("Cluster Resources: " + amResp.getAvailableResources) + if (_allocatedContainers.size > 0) { + logDebug(""" + Allocated containers: %d + Current worker count: %d + Containers released: %s + Containers to be released: %s + Cluster resources: %s + """.format( + _allocatedContainers.size, + numWorkersRunning.get(), + releasedContainerList, + pendingReleaseContainers, + amResp.getAvailableResources)) val hostToContainers = new HashMap[String, ArrayBuffer[Container]]() - // ignore if not satisfying constraints { + // Ignore if not satisfying constraints { for (container <- _allocatedContainers) { if (isResourceConstraintSatisfied(container)) { // allocatedContainers += container @@ -111,8 +136,7 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM else releasedContainerList.add(container.getId()) } - // Find the appropriate containers to use - // Slightly non trivial groupBy I guess ... + // Find the appropriate containers to use. Slightly non trivial groupBy ... val dataLocalContainers = new HashMap[String, ArrayBuffer[Container]]() val rackLocalContainers = new HashMap[String, ArrayBuffer[Container]]() val offRackContainers = new HashMap[String, ArrayBuffer[Container]]() @@ -132,21 +156,22 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM remainingContainers = null } else if (requiredHostCount > 0) { - // container list has more containers than we need for data locality. - // Split into two : data local container count of (remainingContainers.size - requiredHostCount) - // and rest as remainingContainer - val (dataLocal, remaining) = remainingContainers.splitAt(remainingContainers.size - requiredHostCount) + // Container list has more containers than we need for data locality. + // Split into two : data local container count of (remainingContainers.size - + // requiredHostCount) and rest as remainingContainer + val (dataLocal, remaining) = remainingContainers.splitAt( + remainingContainers.size - requiredHostCount) dataLocalContainers.put(candidateHost, dataLocal) // remainingContainers = remaining // yarn has nasty habit of allocating a tonne of containers on a host - discourage this : - // add remaining to release list. If we have insufficient containers, next allocation cycle - // will reallocate (but wont treat it as data local) + // add remaining to release list. If we have insufficient containers, next allocation + // cycle will reallocate (but wont treat it as data local) for (container <- remaining) releasedContainerList.add(container.getId()) remainingContainers = null } - // now rack local + // Now rack local if (remainingContainers != null){ val rack = YarnAllocationHandler.lookupRack(conf, candidateHost) @@ -159,15 +184,17 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM if (requiredRackCount >= remainingContainers.size){ // Add all to dataLocalContainers dataLocalContainers.put(rack, remainingContainers) - // all consumed + // All consumed remainingContainers = null } else if (requiredRackCount > 0) { // container list has more containers than we need for data locality. - // Split into two : data local container count of (remainingContainers.size - requiredRackCount) - // and rest as remainingContainer - val (rackLocal, remaining) = remainingContainers.splitAt(remainingContainers.size - requiredRackCount) - val existingRackLocal = rackLocalContainers.getOrElseUpdate(rack, new ArrayBuffer[Container]()) + // Split into two : data local container count of (remainingContainers.size - + // requiredRackCount) and rest as remainingContainer + val (rackLocal, remaining) = remainingContainers.splitAt( + remainingContainers.size - requiredRackCount) + val existingRackLocal = rackLocalContainers.getOrElseUpdate(rack, + new ArrayBuffer[Container]()) existingRackLocal ++= rackLocal remainingContainers = remaining @@ -183,13 +210,13 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM // Now that we have split the containers into various groups, go through them in order : // first host local, then rack local and then off rack (everything else). - // Note that the list we create below tries to ensure that not all containers end up within a host - // if there are sufficiently large number of hosts/containers. + // Note that the list we create below tries to ensure that not all containers end up within a + // host if there are sufficiently large number of hosts/containers. val allocatedContainers = new ArrayBuffer[Container](_allocatedContainers.size) - allocatedContainers ++= ClusterScheduler.prioritizeContainers(dataLocalContainers) - allocatedContainers ++= ClusterScheduler.prioritizeContainers(rackLocalContainers) - allocatedContainers ++= ClusterScheduler.prioritizeContainers(offRackContainers) + allocatedContainers ++= TaskSchedulerImpl.prioritizeContainers(dataLocalContainers) + allocatedContainers ++= TaskSchedulerImpl.prioritizeContainers(rackLocalContainers) + allocatedContainers ++= TaskSchedulerImpl.prioritizeContainers(offRackContainers) // Run each of the allocated containers for (container <- allocatedContainers) { @@ -197,33 +224,39 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM val workerHostname = container.getNodeId.getHost val containerId = container.getId - assert (container.getResource.getMemory >= (workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD)) + assert( + container.getResource.getMemory >= (workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD)) if (numWorkersRunningNow > maxWorkers) { - logInfo("Ignoring container " + containerId + " at host " + workerHostname + - " .. we already have required number of containers") + logInfo("""Ignoring container %s at host %s, since we already have the required number of + containers for it.""".format(containerId, workerHostname)) releasedContainerList.add(containerId) // reset counter back to old value. numWorkersRunning.decrementAndGet() } else { - // deallocate + allocate can result in reusing id's wrongly - so use a different counter (workerIdCounter) + // Deallocate + allocate can result in reusing id's wrongly - so use a different counter + // (workerIdCounter) val workerId = workerIdCounter.incrementAndGet().toString - val driverUrl = "akka://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), System.getProperty("spark.driver.port"), - StandaloneSchedulerBackend.ACTOR_NAME) + val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( + conf.get("spark.driver.host"), conf.get("spark.driver.port"), + CoarseGrainedSchedulerBackend.ACTOR_NAME) logInfo("launching container on " + containerId + " host " + workerHostname) - // just to be safe, simply remove it from pendingReleaseContainers. Should not be there, but .. + // Just to be safe, simply remove it from pendingReleaseContainers. + // Should not be there, but .. pendingReleaseContainers.remove(containerId) val rack = YarnAllocationHandler.lookupRack(conf, workerHostname) allocatedHostToContainersMap.synchronized { - val containerSet = allocatedHostToContainersMap.getOrElseUpdate(workerHostname, new HashSet[ContainerId]()) + val containerSet = allocatedHostToContainersMap.getOrElseUpdate(workerHostname, + new HashSet[ContainerId]()) containerSet += containerId allocatedContainerToHostMap.put(containerId, workerHostname) - if (rack != null) allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1) + if (rack != null) { + allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1) + } } new Thread( @@ -232,17 +265,23 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM ).start() } } - logDebug("After allocated " + allocatedContainers.size + " containers (orig : " + - _allocatedContainers.size + "), current count " + numWorkersRunning.get() + - ", to-be-released " + releasedContainerList + ", pendingReleaseContainers : " + pendingReleaseContainers) + logDebug(""" + Finished processing %d containers. + Current number of workers running: %d, + releasedContainerList: %s, + pendingReleaseContainers: %s + """.format( + allocatedContainers.size, + numWorkersRunning.get(), + releasedContainerList, + pendingReleaseContainers)) } val completedContainers = amResp.getCompletedContainersStatuses() if (completedContainers.size > 0){ - logDebug("Completed " + completedContainers.size + " containers, current count " + numWorkersRunning.get() + - ", to-be-released " + releasedContainerList + ", pendingReleaseContainers : " + pendingReleaseContainers) - + logDebug("Completed %d containers, to-be-released: %s".format( + completedContainers.size, releasedContainerList)) for (completedContainer <- completedContainers){ val containerId = completedContainer.getContainerId @@ -251,10 +290,19 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM pendingReleaseContainers.remove(containerId) } else { - // simply decrement count - next iteration of ReporterThread will take care of allocating ! + // Simply decrement count - next iteration of ReporterThread will take care of allocating. numWorkersRunning.decrementAndGet() - logInfo("Container completed ? nodeId: " + containerId + ", state " + completedContainer.getState + - " httpaddress: " + completedContainer.getDiagnostics) + logInfo("Completed container %s (state: %s, exit status: %s)".format( + containerId, + completedContainer.getState, + completedContainer.getExitStatus())) + // Hadoop 2.2.X added a ContainerExitStatus we should switch to use + // there are some exit status' we shouldn't necessarily count against us, but for + // now I think its ok as none of the containers are expected to exit + if (completedContainer.getExitStatus() != 0) { + logInfo("Container marked as failed: " + containerId) + numWorkersFailed.incrementAndGet() + } } allocatedHostToContainersMap.synchronized { @@ -271,7 +319,7 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM allocatedContainerToHostMap -= containerId - // doing this within locked context, sigh ... move to outside ? + // Doing this within locked context, sigh ... move to outside ? val rack = YarnAllocationHandler.lookupRack(conf, host) if (rack != null) { val rackCount = allocatedRackCount.getOrElse(rack, 0) - 1 @@ -281,9 +329,16 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM } } } - logDebug("After completed " + completedContainers.size + " containers, current count " + - numWorkersRunning.get() + ", to-be-released " + releasedContainerList + - ", pendingReleaseContainers : " + pendingReleaseContainers) + logDebug(""" + Finished processing %d completed containers. + Current number of workers running: %d, + releasedContainerList: %s, + pendingReleaseContainers: %s + """.format( + completedContainers.size, + numWorkersRunning.get(), + releasedContainerList, + pendingReleaseContainers)) } } @@ -337,7 +392,7 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM // default. if (numWorkers <= 0 || preferredHostToCount.isEmpty) { - logDebug("numWorkers: " + numWorkers + ", host preferences ? " + preferredHostToCount.isEmpty) + logDebug("numWorkers: " + numWorkers + ", host preferences: " + preferredHostToCount.isEmpty) resourceRequests = List( createResourceRequest(AllocationType.ANY, null, numWorkers, YarnAllocationHandler.PRIORITY)) } @@ -350,17 +405,24 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM val requiredCount = candidateCount - allocatedContainersOnHost(candidateHost) if (requiredCount > 0) { - hostContainerRequests += - createResourceRequest(AllocationType.HOST, candidateHost, requiredCount, YarnAllocationHandler.PRIORITY) + hostContainerRequests += createResourceRequest( + AllocationType.HOST, + candidateHost, + requiredCount, + YarnAllocationHandler.PRIORITY) } } - val rackContainerRequests: List[ResourceRequest] = createRackResourceRequests(hostContainerRequests.toList) + val rackContainerRequests: List[ResourceRequest] = createRackResourceRequests( + hostContainerRequests.toList) - val anyContainerRequests: ResourceRequest = - createResourceRequest(AllocationType.ANY, null, numWorkers, YarnAllocationHandler.PRIORITY) + val anyContainerRequests: ResourceRequest = createResourceRequest( + AllocationType.ANY, + resource = null, + numWorkers, + YarnAllocationHandler.PRIORITY) - val containerRequests: ArrayBuffer[ResourceRequest] = - new ArrayBuffer[ResourceRequest](hostContainerRequests.size() + rackContainerRequests.size() + 1) + val containerRequests: ArrayBuffer[ResourceRequest] = new ArrayBuffer[ResourceRequest]( + hostContainerRequests.size + rackContainerRequests.size + 1) containerRequests ++= hostContainerRequests containerRequests ++= rackContainerRequests @@ -378,55 +440,60 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM val releasedContainerList = createReleasedContainerList() req.addAllReleases(releasedContainerList) - - if (numWorkers > 0) { - logInfo("Allocating " + numWorkers + " worker containers with " + (workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + " of memory each.") + logInfo("Allocating %d worker containers with %d of memory each.".format(numWorkers, + workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD)) } else { logDebug("Empty allocation req .. release : " + releasedContainerList) } - for (req <- resourceRequests) { - logInfo("rsrcRequest ... host : " + req.getHostName + ", numContainers : " + req.getNumContainers + - ", p = " + req.getPriority().getPriority + ", capability: " + req.getCapability) + for (request <- resourceRequests) { + logInfo("ResourceRequest (host : %s, num containers: %d, priority = %s , capability : %s)". + format( + request.getHostName, + request.getNumContainers, + request.getPriority, + request.getCapability)) } resourceManager.allocate(req) } - private def createResourceRequest(requestType: AllocationType.AllocationType, - resource:String, numWorkers: Int, priority: Int): ResourceRequest = { + private def createResourceRequest( + requestType: AllocationType.AllocationType, + resource:String, + numWorkers: Int, + priority: Int): ResourceRequest = { // If hostname specified, we need atleast two requests - node local and rack local. // There must be a third request - which is ANY : that will be specially handled. requestType match { case AllocationType.HOST => { - assert (YarnAllocationHandler.ANY_HOST != resource) - + assert(YarnAllocationHandler.ANY_HOST != resource) val hostname = resource val nodeLocal = createResourceRequestImpl(hostname, numWorkers, priority) - // add to host->rack mapping + // Add to host->rack mapping YarnAllocationHandler.populateRackInfo(conf, hostname) nodeLocal } - case AllocationType.RACK => { val rack = resource createResourceRequestImpl(rack, numWorkers, priority) } - - case AllocationType.ANY => { - createResourceRequestImpl(YarnAllocationHandler.ANY_HOST, numWorkers, priority) - } - - case _ => throw new IllegalArgumentException("Unexpected/unsupported request type .. " + requestType) + case AllocationType.ANY => createResourceRequestImpl( + YarnAllocationHandler.ANY_HOST, numWorkers, priority) + case _ => throw new IllegalArgumentException( + "Unexpected/unsupported request type: " + requestType) } } - private def createResourceRequestImpl(hostname:String, numWorkers: Int, priority: Int): ResourceRequest = { + private def createResourceRequestImpl( + hostname:String, + numWorkers: Int, + priority: Int): ResourceRequest = { val rsrcRequest = Records.newRecord(classOf[ResourceRequest]) val memCapability = Records.newRecord(classOf[Resource]) @@ -447,11 +514,11 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM def createReleasedContainerList(): ArrayBuffer[ContainerId] = { val retval = new ArrayBuffer[ContainerId](1) - // iterator on COW list ... + // Iterator on COW list ... for (container <- releasedContainerList.iterator()){ retval += container } - // remove from the original list. + // Remove from the original list. if (! retval.isEmpty) { releasedContainerList.removeAll(retval) for (v <- retval) pendingReleaseContainers.put(v, true) @@ -466,14 +533,14 @@ private[yarn] class YarnAllocationHandler(val conf: Configuration, val resourceM object YarnAllocationHandler { val ANY_HOST = "*" - // all requests are issued with same priority : we do not (yet) have any distinction between + // All requests are issued with same priority : we do not (yet) have any distinction between // request types (like map/reduce in hadoop for example) val PRIORITY = 1 // Additional memory overhead - in mb val MEMORY_OVERHEAD = 384 - // host to rack map - saved from allocation requests + // Host to rack map - saved from allocation requests // We are expecting this not to change. // Note that it is possible for this to change : and RM will indicate that to us via update // response to allocate. But we are punting on handling that for now. @@ -481,38 +548,69 @@ object YarnAllocationHandler { private val rackToHostSet = new ConcurrentHashMap[String, JSet[String]]() - def newAllocator(conf: Configuration, - resourceManager: AMRMProtocol, appAttemptId: ApplicationAttemptId, - args: ApplicationMasterArguments): YarnAllocationHandler = { - - new YarnAllocationHandler(conf, resourceManager, appAttemptId, args.numWorkers, - args.workerMemory, args.workerCores, Map[String, Int](), Map[String, Int]()) + def newAllocator( + conf: Configuration, + resourceManager: AMRMProtocol, + appAttemptId: ApplicationAttemptId, + args: ApplicationMasterArguments): YarnAllocationHandler = { + + new YarnAllocationHandler( + conf, + resourceManager, + appAttemptId, + args.numWorkers, + args.workerMemory, + args.workerCores, + Map[String, Int](), + Map[String, Int]()) } - def newAllocator(conf: Configuration, - resourceManager: AMRMProtocol, appAttemptId: ApplicationAttemptId, - args: ApplicationMasterArguments, - map: collection.Map[String, collection.Set[SplitInfo]]): YarnAllocationHandler = { + def newAllocator( + conf: Configuration, + resourceManager: AMRMProtocol, + appAttemptId: ApplicationAttemptId, + args: ApplicationMasterArguments, + map: collection.Map[String, + collection.Set[SplitInfo]]): YarnAllocationHandler = { val (hostToCount, rackToCount) = generateNodeToWeight(conf, map) - - new YarnAllocationHandler(conf, resourceManager, appAttemptId, args.numWorkers, - args.workerMemory, args.workerCores, hostToCount, rackToCount) + new YarnAllocationHandler( + conf, + resourceManager, + appAttemptId, + args.numWorkers, + args.workerMemory, + args.workerCores, + hostToCount, + rackToCount) } - def newAllocator(conf: Configuration, - resourceManager: AMRMProtocol, appAttemptId: ApplicationAttemptId, - maxWorkers: Int, workerMemory: Int, workerCores: Int, - map: collection.Map[String, collection.Set[SplitInfo]]): YarnAllocationHandler = { + def newAllocator( + conf: Configuration, + resourceManager: AMRMProtocol, + appAttemptId: ApplicationAttemptId, + maxWorkers: Int, + workerMemory: Int, + workerCores: Int, + map: collection.Map[String, collection.Set[SplitInfo]]): YarnAllocationHandler = { val (hostToCount, rackToCount) = generateNodeToWeight(conf, map) - new YarnAllocationHandler(conf, resourceManager, appAttemptId, maxWorkers, - workerMemory, workerCores, hostToCount, rackToCount) + new YarnAllocationHandler( + conf, + resourceManager, + appAttemptId, + maxWorkers, + workerMemory, + workerCores, + hostToCount, + rackToCount) } // A simple method to copy the split info map. - private def generateNodeToWeight(conf: Configuration, input: collection.Map[String, collection.Set[SplitInfo]]) : + private def generateNodeToWeight( + conf: Configuration, + input: collection.Map[String, collection.Set[SplitInfo]]) : // host to count, rack to count (Map[String, Int], Map[String, Int]) = { @@ -536,7 +634,7 @@ object YarnAllocationHandler { } def lookupRack(conf: Configuration, host: String): String = { - if (! hostToRack.contains(host)) populateRackInfo(conf, host) + if (!hostToRack.contains(host)) populateRackInfo(conf, host) hostToRack.get(host) } @@ -559,10 +657,12 @@ object YarnAllocationHandler { val rack = rackInfo.getNetworkLocation hostToRack.put(hostname, rack) if (! rackToHostSet.containsKey(rack)) { - rackToHostSet.putIfAbsent(rack, Collections.newSetFromMap(new ConcurrentHashMap[String, JBoolean]())) + rackToHostSet.putIfAbsent(rack, + Collections.newSetFromMap(new ConcurrentHashMap[String, JBoolean]())) } rackToHostSet.get(rack).add(hostname) + // TODO(harvey): Figure out this comment... // Since RackResolver caches, we are disabling this for now ... } /* else { // right ? Else we will keep calling rack resolver in case we cant resolve rack info ... diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala index ca2f1e2565..2ba2366ead 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -18,13 +18,10 @@ package org.apache.spark.deploy.yarn import org.apache.spark.deploy.SparkHadoopUtil -import collection.mutable.HashMap import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.yarn.api.ApplicationConstants.Environment -import java.security.PrivilegedExceptionAction /** * Contains util methods to interact with Hadoop from spark. @@ -40,7 +37,7 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil { // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster override def addCredentials(conf: JobConf) { - val jobCreds = conf.getCredentials(); + val jobCreds = conf.getCredentials() jobCreds.mergeAll(UserGroupInformation.getCurrentUser().getCredentials()) } } diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala new file mode 100644 index 0000000000..522e0a9ad7 --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala @@ -0,0 +1,48 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster + +import org.apache.spark._ +import org.apache.hadoop.conf.Configuration +import org.apache.spark.deploy.yarn.YarnAllocationHandler +import org.apache.spark.scheduler.TaskSchedulerImpl +import org.apache.spark.util.Utils + +/** + * + * This scheduler launch worker through Yarn - by call into Client to launch WorkerLauncher as AM. + */ +private[spark] class YarnClientClusterScheduler(sc: SparkContext, conf: Configuration) extends TaskSchedulerImpl(sc) { + + def this(sc: SparkContext) = this(sc, new Configuration()) + + // By default, rack is unknown + override def getRackForHost(hostPort: String): Option[String] = { + val host = Utils.parseHostPort(hostPort)._1 + val retval = YarnAllocationHandler.lookupRack(conf, host) + if (retval != null) Some(retval) else None + } + + override def postStartHook() { + + // The yarn application is running, but the worker might not yet ready + // Wait for a few seconds for the slaves to bootstrap and register with master - best case attempt + Thread.sleep(2000L) + logInfo("YarnClientClusterScheduler.postStartHook done") + } +} diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala new file mode 100644 index 0000000000..4b69f5078b --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster + +import org.apache.hadoop.yarn.api.records.{ApplicationId, YarnApplicationState} +import org.apache.spark.{SparkException, Logging, SparkContext} +import org.apache.spark.deploy.yarn.{Client, ClientArguments} +import org.apache.spark.scheduler.TaskSchedulerImpl + +private[spark] class YarnClientSchedulerBackend( + scheduler: TaskSchedulerImpl, + sc: SparkContext) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + with Logging { + + var client: Client = null + var appId: ApplicationId = null + + override def start() { + super.start() + + val defalutWorkerCores = "2" + val defalutWorkerMemory = "512m" + val defaultWorkerNumber = "1" + + val userJar = System.getenv("SPARK_YARN_APP_JAR") + var workerCores = System.getenv("SPARK_WORKER_CORES") + var workerMemory = System.getenv("SPARK_WORKER_MEMORY") + var workerNumber = System.getenv("SPARK_WORKER_INSTANCES") + + if (userJar == null) + throw new SparkException("env SPARK_YARN_APP_JAR is not set") + + if (workerCores == null) + workerCores = defalutWorkerCores + if (workerMemory == null) + workerMemory = defalutWorkerMemory + if (workerNumber == null) + workerNumber = defaultWorkerNumber + + val driverHost = conf.get("spark.driver.host") + val driverPort = conf.get("spark.driver.port") + val hostport = driverHost + ":" + driverPort + + val argsArray = Array[String]( + "--class", "notused", + "--jar", userJar, + "--args", hostport, + "--worker-memory", workerMemory, + "--worker-cores", workerCores, + "--num-workers", workerNumber, + "--master-class", "org.apache.spark.deploy.yarn.WorkerLauncher" + ) + + val args = new ClientArguments(argsArray) + client = new Client(args) + appId = client.runApp() + waitForApp() + } + + def waitForApp() { + + // TODO : need a better way to find out whether the workers are ready or not + // maybe by resource usage report? + while(true) { + val report = client.getApplicationReport(appId) + + logInfo("Application report from ASM: \n" + + "\t appMasterRpcPort: " + report.getRpcPort() + "\n" + + "\t appStartTime: " + report.getStartTime() + "\n" + + "\t yarnAppState: " + report.getYarnApplicationState() + "\n" + ) + + // Ready to go, or already gone. + val state = report.getYarnApplicationState() + if (state == YarnApplicationState.RUNNING) { + return + } else if (state == YarnApplicationState.FINISHED || + state == YarnApplicationState.FAILED || + state == YarnApplicationState.KILLED) { + throw new SparkException("Yarn application already ended," + + "might be killed or not able to launch application master.") + } + + Thread.sleep(1000) + } + } + + override def stop() { + super.stop() + client.stop() + logInfo("Stoped") + } + +} diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala index 29b3f22e13..2d9fbcb400 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala @@ -17,16 +17,20 @@ package org.apache.spark.scheduler.cluster +import org.apache.hadoop.conf.Configuration + import org.apache.spark._ import org.apache.spark.deploy.yarn.{ApplicationMaster, YarnAllocationHandler} +import org.apache.spark.scheduler.TaskSchedulerImpl import org.apache.spark.util.Utils -import org.apache.hadoop.conf.Configuration /** * - * This is a simple extension to ClusterScheduler - to ensure that appropriate initialization of ApplicationMaster, etc is done + * This is a simple extension to ClusterScheduler - to ensure that appropriate initialization of + * ApplicationMaster, etc. is done */ -private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration) extends ClusterScheduler(sc) { +private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration) + extends TaskSchedulerImpl(sc) { logInfo("Created YarnClusterScheduler") diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala new file mode 100644 index 0000000000..2941356bc5 --- /dev/null +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientDistributedCacheManagerSuite.scala @@ -0,0 +1,220 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.yarn + +import java.net.URI + +import org.scalatest.FunSuite +import org.scalatest.mock.MockitoSugar +import org.mockito.Mockito.when + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.FileStatus +import org.apache.hadoop.fs.FileSystem +import org.apache.hadoop.fs.Path +import org.apache.hadoop.fs.permission.FsAction +import org.apache.hadoop.yarn.api.records.LocalResource +import org.apache.hadoop.yarn.api.records.LocalResourceVisibility +import org.apache.hadoop.yarn.api.records.LocalResourceType +import org.apache.hadoop.yarn.util.{Records, ConverterUtils} + +import scala.collection.mutable.HashMap +import scala.collection.mutable.Map + + +class ClientDistributedCacheManagerSuite extends FunSuite with MockitoSugar { + + class MockClientDistributedCacheManager extends ClientDistributedCacheManager { + override def getVisibility(conf: Configuration, uri: URI, statCache: Map[URI, FileStatus]): + LocalResourceVisibility = { + return LocalResourceVisibility.PRIVATE + } + } + + test("test getFileStatus empty") { + val distMgr = new ClientDistributedCacheManager() + val fs = mock[FileSystem] + val uri = new URI("/tmp/testing") + when(fs.getFileStatus(new Path(uri))).thenReturn(new FileStatus()) + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + val stat = distMgr.getFileStatus(fs, uri, statCache) + assert(stat.getPath() === null) + } + + test("test getFileStatus cached") { + val distMgr = new ClientDistributedCacheManager() + val fs = mock[FileSystem] + val uri = new URI("/tmp/testing") + val realFileStatus = new FileStatus(10, false, 1, 1024, 10, 10, null, "testOwner", + null, new Path("/tmp/testing")) + when(fs.getFileStatus(new Path(uri))).thenReturn(new FileStatus()) + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus](uri -> realFileStatus) + val stat = distMgr.getFileStatus(fs, uri, statCache) + assert(stat.getPath().toString() === "/tmp/testing") + } + + test("test addResource") { + val distMgr = new MockClientDistributedCacheManager() + val fs = mock[FileSystem] + val conf = new Configuration() + val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") + val localResources = HashMap[String, LocalResource]() + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + when(fs.getFileStatus(destPath)).thenReturn(new FileStatus()) + + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, "link", + statCache, false) + val resource = localResources("link") + assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) + assert(ConverterUtils.getPathFromYarnURL(resource.getResource()) === destPath) + assert(resource.getTimestamp() === 0) + assert(resource.getSize() === 0) + assert(resource.getType() === LocalResourceType.FILE) + + val env = new HashMap[String, String]() + distMgr.setDistFilesEnv(env) + assert(env("SPARK_YARN_CACHE_FILES") === "file:/foo.invalid.com:8080/tmp/testing#link") + assert(env("SPARK_YARN_CACHE_FILES_TIME_STAMPS") === "0") + assert(env("SPARK_YARN_CACHE_FILES_FILE_SIZES") === "0") + assert(env("SPARK_YARN_CACHE_FILES_VISIBILITIES") === LocalResourceVisibility.PRIVATE.name()) + + distMgr.setDistArchivesEnv(env) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") === None) + + //add another one and verify both there and order correct + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + null, new Path("/tmp/testing2")) + val destPath2 = new Path("file:///foo.invalid.com:8080/tmp/testing2") + when(fs.getFileStatus(destPath2)).thenReturn(realFileStatus) + distMgr.addResource(fs, conf, destPath2, localResources, LocalResourceType.FILE, "link2", + statCache, false) + val resource2 = localResources("link2") + assert(resource2.getVisibility() === LocalResourceVisibility.PRIVATE) + assert(ConverterUtils.getPathFromYarnURL(resource2.getResource()) === destPath2) + assert(resource2.getTimestamp() === 10) + assert(resource2.getSize() === 20) + assert(resource2.getType() === LocalResourceType.FILE) + + val env2 = new HashMap[String, String]() + distMgr.setDistFilesEnv(env2) + val timestamps = env2("SPARK_YARN_CACHE_FILES_TIME_STAMPS").split(',') + val files = env2("SPARK_YARN_CACHE_FILES").split(',') + val sizes = env2("SPARK_YARN_CACHE_FILES_FILE_SIZES").split(',') + val visibilities = env2("SPARK_YARN_CACHE_FILES_VISIBILITIES") .split(',') + assert(files(0) === "file:/foo.invalid.com:8080/tmp/testing#link") + assert(timestamps(0) === "0") + assert(sizes(0) === "0") + assert(visibilities(0) === LocalResourceVisibility.PRIVATE.name()) + + assert(files(1) === "file:/foo.invalid.com:8080/tmp/testing2#link2") + assert(timestamps(1) === "10") + assert(sizes(1) === "20") + assert(visibilities(1) === LocalResourceVisibility.PRIVATE.name()) + } + + test("test addResource link null") { + val distMgr = new MockClientDistributedCacheManager() + val fs = mock[FileSystem] + val conf = new Configuration() + val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") + val localResources = HashMap[String, LocalResource]() + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + when(fs.getFileStatus(destPath)).thenReturn(new FileStatus()) + + intercept[Exception] { + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, null, + statCache, false) + } + assert(localResources.get("link") === None) + assert(localResources.size === 0) + } + + test("test addResource appmaster only") { + val distMgr = new MockClientDistributedCacheManager() + val fs = mock[FileSystem] + val conf = new Configuration() + val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") + val localResources = HashMap[String, LocalResource]() + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + null, new Path("/tmp/testing")) + when(fs.getFileStatus(destPath)).thenReturn(realFileStatus) + + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, "link", + statCache, true) + val resource = localResources("link") + assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) + assert(ConverterUtils.getPathFromYarnURL(resource.getResource()) === destPath) + assert(resource.getTimestamp() === 10) + assert(resource.getSize() === 20) + assert(resource.getType() === LocalResourceType.ARCHIVE) + + val env = new HashMap[String, String]() + distMgr.setDistFilesEnv(env) + assert(env.get("SPARK_YARN_CACHE_FILES") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_TIME_STAMPS") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_FILE_SIZES") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_VISIBILITIES") === None) + + distMgr.setDistArchivesEnv(env) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") === None) + assert(env.get("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") === None) + } + + test("test addResource archive") { + val distMgr = new MockClientDistributedCacheManager() + val fs = mock[FileSystem] + val conf = new Configuration() + val destPath = new Path("file:///foo.invalid.com:8080/tmp/testing") + val localResources = HashMap[String, LocalResource]() + val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() + val realFileStatus = new FileStatus(20, false, 1, 1024, 10, 30, null, "testOwner", + null, new Path("/tmp/testing")) + when(fs.getFileStatus(destPath)).thenReturn(realFileStatus) + + distMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, "link", + statCache, false) + val resource = localResources("link") + assert(resource.getVisibility() === LocalResourceVisibility.PRIVATE) + assert(ConverterUtils.getPathFromYarnURL(resource.getResource()) === destPath) + assert(resource.getTimestamp() === 10) + assert(resource.getSize() === 20) + assert(resource.getType() === LocalResourceType.ARCHIVE) + + val env = new HashMap[String, String]() + + distMgr.setDistArchivesEnv(env) + assert(env("SPARK_YARN_CACHE_ARCHIVES") === "file:/foo.invalid.com:8080/tmp/testing#link") + assert(env("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS") === "10") + assert(env("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES") === "20") + assert(env("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES") === LocalResourceVisibility.PRIVATE.name()) + + distMgr.setDistFilesEnv(env) + assert(env.get("SPARK_YARN_CACHE_FILES") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_TIME_STAMPS") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_FILE_SIZES") === None) + assert(env.get("SPARK_YARN_CACHE_FILES_VISIBILITIES") === None) + } + + +} |