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586 files changed, 30443 insertions, 4734 deletions
diff --git a/.gitignore b/.gitignore index 155e785b01..00fbff6a2c 100644 --- a/.gitignore +++ b/.gitignore @@ -36,3 +36,7 @@ streaming-tests.log dependency-reduced-pom.xml .ensime .ensime_lucene +checkpoint +derby.log +dist/ +spark-*-bin.tar.gz @@ -1,27 +1,202 @@ -Copyright (c) 2010, Regents of the University of California. -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - * Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - * Neither the name of the University of California, Berkeley nor the - names of its contributors may be used to endorse or promote - products derived from this software without specific prior written - permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -A PARTICULAR PURPOSE ARE DISCLAIMED. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed 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. diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000000..7cbb114b2a --- /dev/null +++ b/NOTICE @@ -0,0 +1,5 @@ +Apache Spark +Copyright 2013 The Apache Software Foundation. + +This product includes software developed at +The Apache Software Foundation (http://www.apache.org/). @@ -12,7 +12,7 @@ This README file only contains basic setup instructions. ## Building -Spark requires Scala 2.9.2 (Scala 2.10 is not yet supported). The project is +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: diff --git a/assembly/README b/assembly/README new file mode 100644 index 0000000000..6ee2a536d7 --- /dev/null +++ b/assembly/README @@ -0,0 +1,13 @@ +This is an assembly module for Spark project. + +It creates a single tar.gz file that includes all needed dependency of the project +except for org.apache.hadoop.* jars that are supposed to be available from the +deployed Hadoop cluster. + +This module is off by default to avoid spending extra time on top of repl-bin +module. To activate it specify the profile in the command line + -Passembly + +In case you want to avoid building time-expensive repl-bin module, that shaders +all the dependency into a big flat jar supplement maven command with + -DnoExpensive diff --git a/assembly/pom.xml b/assembly/pom.xml new file mode 100644 index 0000000000..cc5a4875af --- /dev/null +++ b/assembly/pom.xml @@ -0,0 +1,92 @@ +<?xml version="1.0" encoding="UTF-8"?> +<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.spark-project</groupId> + <artifactId>spark-parent</artifactId> + <version>0.8.0-SNAPSHOT</version> + <relativePath>../pom.xml</relativePath> + </parent> + + <groupId>org.spark-project</groupId> + <artifactId>spark-assembly</artifactId> + <name>Spark Project Assembly</name> + <url>http://spark-project.org/</url> + + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-assembly-plugin</artifactId> + <version>2.4</version> + <executions> + <execution> + <id>dist</id> + <phase>package</phase> + <goals> + <goal>single</goal> + </goals> + <configuration> + <descriptors> + <descriptor>src/main/assembly/assembly.xml</descriptor> + </descriptors> + </configuration> + </execution> + </executions> + </plugin> + </plugins> + </build> + + <profiles> + <profile> + <id>hadoop1</id> + <properties> + <classifier.name>hadoop1</classifier.name> + </properties> + </profile> + <profile> + <id>hadoop2</id> + <properties> + <classifier.name>hadoop2</classifier.name> + </properties> + </profile> + <profile> + <id>hadoop2-yarn</id> + <properties> + <classifier.name>hadoop2-yarn</classifier.name> + </properties> + </profile> + </profiles> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <classifier>${classifier.name}</classifier> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-bagel</artifactId> + <classifier>${classifier.name}</classifier> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-mllib</artifactId> + <classifier>${classifier.name}</classifier> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-repl</artifactId> + <classifier>${classifier.name}</classifier> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-streaming</artifactId> + <classifier>${classifier.name}</classifier> + <version>${project.version}</version> + </dependency> + </dependencies> +</project>
\ No newline at end of file diff --git a/assembly/src/main/assembly/assembly.xml b/assembly/src/main/assembly/assembly.xml new file mode 100644 index 0000000000..14485b7181 --- /dev/null +++ b/assembly/src/main/assembly/assembly.xml @@ -0,0 +1,68 @@ +<assembly> + <id>dist</id> + <formats> + <format>tar.gz</format> + <format>dir</format> + </formats> + <includeBaseDirectory>false</includeBaseDirectory> + + <fileSets> + <fileSet> + <includes> + <include>README</include> + </includes> + </fileSet> + <fileSet> + <directory> + ${project.parent.basedir}/core/src/main/resources/spark/ui/static/ + </directory> + <outputDirectory>/ui-resources/spark/ui/static</outputDirectory> + <includes> + <include>**/*</include> + </includes> + </fileSet> + <fileSet> + <directory> + ${project.parent.basedir}/bin/ + </directory> + <outputDirectory>/bin</outputDirectory> + <includes> + <include>**/*</include> + </includes> + </fileSet> + <fileSet> + <directory> + ${project.parent.basedir} + </directory> + <outputDirectory>/bin</outputDirectory> + <includes> + <include>run*</include> + <include>spark-shell*</include> + <include>spark-executor*</include> + </includes> + </fileSet> + </fileSets> + + <dependencySets> + <dependencySet> + <includes> + <include>org.spark-project:*:jar</include> + </includes> + <excludes> + <exclude>org.spark-project:spark-assembly:jar</exclude> + </excludes> + </dependencySet> + <dependencySet> + <outputDirectory>lib</outputDirectory> + <useTransitiveDependencies>true</useTransitiveDependencies> + <unpack>false</unpack> + <scope>runtime</scope> + <useProjectArtifact>false</useProjectArtifact> + <excludes> + <exclude>org.apache.hadoop:*:jar</exclude> + <exclude>org.spark-project:*:jar</exclude> + </excludes> + </dependencySet> + </dependencySets> + +</assembly> diff --git a/bagel/pom.xml b/bagel/pom.xml index 510cff4669..60bbc49e6c 100644 --- a/bagel/pom.xml +++ b/bagel/pom.xml @@ -1,10 +1,27 @@ <?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.spark-project</groupId> <artifactId>spark-parent</artifactId> - <version>0.7.1-SNAPSHOT</version> + <version>0.8.0-SNAPSHOT</version> <relativePath>../pom.xml</relativePath> </parent> @@ -102,5 +119,42 @@ </plugins> </build> </profile> + <profile> + <id>hadoop2-yarn</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2-yarn</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> </profiles> </project> diff --git a/bagel/src/main/scala/spark/bagel/Bagel.scala b/bagel/src/main/scala/spark/bagel/Bagel.scala index e10c03f6ba..80c8d53d2b 100644 --- a/bagel/src/main/scala/spark/bagel/Bagel.scala +++ b/bagel/src/main/scala/spark/bagel/Bagel.scala @@ -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. + */ + package spark.bagel import spark._ @@ -7,8 +24,7 @@ import scala.collection.mutable.ArrayBuffer import storage.StorageLevel object Bagel extends Logging { - - val DEFAULT_STORAGE_LEVEL = StorageLevel.MEMORY_ONLY + val DEFAULT_STORAGE_LEVEL = StorageLevel.MEMORY_AND_DISK /** * Runs a Bagel program. @@ -63,8 +79,9 @@ object Bagel extends Logging { val combinedMsgs = msgs.combineByKey( combiner.createCombiner _, combiner.mergeMsg _, combiner.mergeCombiners _, partitioner) val grouped = combinedMsgs.groupWith(verts) + val superstep_ = superstep // Create a read-only copy of superstep for capture in closure val (processed, numMsgs, numActiveVerts) = - comp[K, V, M, C](sc, grouped, compute(_, _, aggregated, superstep), storageLevel) + comp[K, V, M, C](sc, grouped, compute(_, _, aggregated, superstep_), storageLevel) val timeTaken = System.currentTimeMillis - startTime logInfo("Superstep %d took %d s".format(superstep, timeTaken / 1000)) diff --git a/bagel/src/main/scala/spark/bagel/examples/PageRankUtils.scala b/bagel/src/main/scala/spark/bagel/examples/PageRankUtils.scala index b97d786ed4..de65e27fe0 100644 --- a/bagel/src/main/scala/spark/bagel/examples/PageRankUtils.scala +++ b/bagel/src/main/scala/spark/bagel/examples/PageRankUtils.scala @@ -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. + */ + package spark.bagel.examples import spark._ diff --git a/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRank.scala b/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRank.scala index bc32663e0f..a0c5ac9c18 100644 --- a/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRank.scala +++ b/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRank.scala @@ -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. + */ + package spark.bagel.examples import spark._ diff --git a/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRankStandalone.scala b/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRankStandalone.scala index 9d9d80d809..3c54a85f42 100644 --- a/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRankStandalone.scala +++ b/bagel/src/main/scala/spark/bagel/examples/WikipediaPageRankStandalone.scala @@ -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. + */ + package spark.bagel.examples import spark._ diff --git a/bagel/src/test/resources/log4j.properties b/bagel/src/test/resources/log4j.properties index 83d05cab2f..5cdcf35b23 100644 --- a/bagel/src/test/resources/log4j.properties +++ b/bagel/src/test/resources/log4j.properties @@ -1,4 +1,21 @@ -# Set everything to be logged to the file bagel/target/unit-tests.log +# +# 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. +# + +# Set everything to be logged to the file bagel/target/unit-tests.log log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false diff --git a/bagel/src/test/scala/bagel/BagelSuite.scala b/bagel/src/test/scala/bagel/BagelSuite.scala index 25db395c22..ef2d57fbd0 100644 --- a/bagel/src/test/scala/bagel/BagelSuite.scala +++ b/bagel/src/test/scala/bagel/BagelSuite.scala @@ -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. + */ + package spark.bagel import org.scalatest.{FunSuite, Assertions, BeforeAndAfter} @@ -23,6 +40,7 @@ class BagelSuite extends FunSuite with Assertions with BeforeAndAfter with Timeo } // 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("halting by voting") { diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd new file mode 100644 index 0000000000..eb836b0ffd --- /dev/null +++ b/bin/compute-classpath.cmd @@ -0,0 +1,73 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +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 + +rem Figure out where the Spark framework is installed +set FWDIR=%~dp0..\ + +rem Load environment variables from conf\spark-env.cmd, if it exists +if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" + +set CORE_DIR=%FWDIR%core +set REPL_DIR=%FWDIR%repl +set EXAMPLES_DIR=%FWDIR%examples +set BAGEL_DIR=%FWDIR%bagel +set MLLIB_DIR=%FWDIR%mllib +set TOOLS_DIR=%FWDIR%tools +set STREAMING_DIR=%FWDIR%streaming +set PYSPARK_DIR=%FWDIR%python + +rem Build up classpath +set CLASSPATH=%SPARK_CLASSPATH%;%MESOS_CLASSPATH%;%FWDIR%conf;%CORE_DIR%\target\scala-%SCALA_VERSION%\classes +set CLASSPATH=%CLASSPATH%;%CORE_DIR%\target\scala-%SCALA_VERSION%\test-classes;%CORE_DIR%\src\main\resources +set CLASSPATH=%CLASSPATH%;%STREAMING_DIR%\target\scala-%SCALA_VERSION%\classes;%STREAMING_DIR%\target\scala-%SCALA_VERSION%\test-classes +set CLASSPATH=%CLASSPATH%;%STREAMING_DIR%\lib\org\apache\kafka\kafka\0.7.2-spark\* +set CLASSPATH=%CLASSPATH%;%REPL_DIR%\target\scala-%SCALA_VERSION%\classes;%EXAMPLES_DIR%\target\scala-%SCALA_VERSION%\classes +set CLASSPATH=%CLASSPATH%;%FWDIR%lib_managed\jars\* +set CLASSPATH=%CLASSPATH%;%FWDIR%lib_managed\bundles\* +set CLASSPATH=%CLASSPATH%;%FWDIR%repl\lib\* +set CLASSPATH=%CLASSPATH%;%FWDIR%python\lib\* +set CLASSPATH=%CLASSPATH%;%BAGEL_DIR%\target\scala-%SCALA_VERSION%\classes +set CLASSPATH=%CLASSPATH%;%MLLIB_DIR%\target\scala-%SCALA_VERSION%\classes +set CLASSPATH=%CLASSPATH%;%TOOLS_DIR%\target\scala-%SCALA_VERSION%\classes + +rem Add hadoop conf dir - else FileSystem.*, etc fail +rem Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts +rem the configurtion files. +if "x%HADOOP_CONF_DIR%"=="x" goto no_hadoop_conf_dir + set CLASSPATH=%CLASSPATH%;%HADOOP_CONF_DIR% +:no_hadoop_conf_dir + +if "x%YARN_CONF_DIR%"=="x" goto no_yarn_conf_dir + set CLASSPATH=%CLASSPATH%;%YARN_CONF_DIR% +:no_yarn_conf_dir + +rem Add Scala standard library +set CLASSPATH=%CLASSPATH%;%SCALA_HOME%\lib\scala-library.jar;%SCALA_HOME%\lib\scala-compiler.jar;%SCALA_HOME%\lib\jline.jar + +rem A bit of a hack to allow calling this script within run2.cmd without seeing output +if "%DONT_PRINT_CLASSPATH%"=="1" goto exit + +echo %CLASSPATH% + +:exit diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh new file mode 100755 index 0000000000..e4ce1ca848 --- /dev/null +++ b/bin/compute-classpath.sh @@ -0,0 +1,112 @@ +#!/usr/bin/env 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. +# + +# 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 + +# Figure out where Spark is installed +FWDIR="$(cd `dirname $0`/..; pwd)" + +# Load environment variables from conf/spark-env.sh, if it exists +if [ -e $FWDIR/conf/spark-env.sh ] ; then + . $FWDIR/conf/spark-env.sh +fi + +CORE_DIR="$FWDIR/core" +REPL_DIR="$FWDIR/repl" +REPL_BIN_DIR="$FWDIR/repl-bin" +EXAMPLES_DIR="$FWDIR/examples" +BAGEL_DIR="$FWDIR/bagel" +MLLIB_DIR="$FWDIR/mllib" +TOOLS_DIR="$FWDIR/tools" +STREAMING_DIR="$FWDIR/streaming" +PYSPARK_DIR="$FWDIR/python" + +# Build up classpath +CLASSPATH="$SPARK_CLASSPATH" + +function dev_classpath { + CLASSPATH="$CLASSPATH:$FWDIR/conf" + CLASSPATH="$CLASSPATH:$CORE_DIR/target/scala-$SCALA_VERSION/classes" + if [ -n "$SPARK_TESTING" ] ; then + CLASSPATH="$CLASSPATH:$CORE_DIR/target/scala-$SCALA_VERSION/test-classes" + CLASSPATH="$CLASSPATH:$STREAMING_DIR/target/scala-$SCALA_VERSION/test-classes" + fi + CLASSPATH="$CLASSPATH:$CORE_DIR/src/main/resources" + CLASSPATH="$CLASSPATH:$REPL_DIR/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$EXAMPLES_DIR/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$STREAMING_DIR/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$STREAMING_DIR/lib/org/apache/kafka/kafka/0.7.2-spark/*" # <-- our in-project Kafka Jar + if [ -e "$FWDIR/lib_managed" ]; then + CLASSPATH="$CLASSPATH:$FWDIR/lib_managed/jars/*" + CLASSPATH="$CLASSPATH:$FWDIR/lib_managed/bundles/*" + fi + CLASSPATH="$CLASSPATH:$REPL_DIR/lib/*" + # Add the shaded JAR for Maven builds + if [ -e $REPL_BIN_DIR/target ]; then + for jar in `find "$REPL_BIN_DIR/target" -name 'spark-repl-*-shaded-hadoop*.jar'`; do + CLASSPATH="$CLASSPATH:$jar" + done + # The shaded JAR doesn't contain examples, so include those separately + EXAMPLES_JAR=`ls "$EXAMPLES_DIR/target/spark-examples"*[0-9T].jar` + CLASSPATH+=":$EXAMPLES_JAR" + fi + CLASSPATH="$CLASSPATH:$BAGEL_DIR/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$MLLIB_DIR/target/scala-$SCALA_VERSION/classes" + CLASSPATH="$CLASSPATH:$TOOLS_DIR/target/scala-$SCALA_VERSION/classes" + for jar in `find $PYSPARK_DIR/lib -name '*jar'`; do + CLASSPATH="$CLASSPATH:$jar" + done + + # Add Scala standard library + if [ -z "$SCALA_LIBRARY_PATH" ]; then + if [ -z "$SCALA_HOME" ]; then + echo "SCALA_HOME is not set" >&2 + exit 1 + fi + SCALA_LIBRARY_PATH="$SCALA_HOME/lib" + fi + CLASSPATH="$CLASSPATH:$SCALA_LIBRARY_PATH/scala-library.jar" + CLASSPATH="$CLASSPATH:$SCALA_LIBRARY_PATH/scala-compiler.jar" + CLASSPATH="$CLASSPATH:$SCALA_LIBRARY_PATH/jline.jar" +} + +function release_classpath { + CLASSPATH="$CLASSPATH:$FWDIR/jars/*" +} + +if [ -f "$FWDIR/RELEASE" ]; then + release_classpath +else + dev_classpath +fi + +# Add hadoop conf dir - else FileSystem.*, etc fail ! +# Note, this assumes that there is either a HADOOP_CONF_DIR or YARN_CONF_DIR which hosts +# the configurtion files. +if [ "x" != "x$HADOOP_CONF_DIR" ]; then + CLASSPATH="$CLASSPATH:$HADOOP_CONF_DIR" +fi +if [ "x" != "x$YARN_CONF_DIR" ]; then + CLASSPATH="$CLASSPATH:$YARN_CONF_DIR" +fi + +echo "$CLASSPATH" diff --git a/bin/slaves.sh b/bin/slaves.sh index 030581e058..c8fb5ca473 100755 --- a/bin/slaves.sh +++ b/bin/slaves.sh @@ -1,7 +1,5 @@ #!/usr/bin/env bash -# This Spark deploy script is a modified version of the Apache Hadoop deploy -# script, available under the Apache 2 license: # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -10,13 +8,14 @@ # (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 +# 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. +# # Run a shell command on all slave hosts. # diff --git a/bin/spark-config.sh b/bin/spark-config.sh index d4b6558866..cd2c7b7b0d 100755 --- a/bin/spark-config.sh +++ b/bin/spark-config.sh @@ -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. +# + # included in all the spark scripts with source command # should not be executable directly # also should not be passed any arguments, since we need original $* @@ -16,4 +33,4 @@ this="$config_bin/$script" export SPARK_PREFIX=`dirname "$this"`/.. export SPARK_HOME=${SPARK_PREFIX} -export SPARK_CONF_DIR="$SPARK_HOME/conf"
\ No newline at end of file +export SPARK_CONF_DIR="$SPARK_HOME/conf" diff --git a/bin/spark-daemon.sh b/bin/spark-daemon.sh index 0c584055c7..a5b88ca785 100755 --- a/bin/spark-daemon.sh +++ b/bin/spark-daemon.sh @@ -1,7 +1,5 @@ #!/usr/bin/env bash -# This Spark deploy script is a modified version of the Apache Hadoop deploy -# script, available under the Apache 2 license: # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -10,13 +8,14 @@ # (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 +# 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. +# # Runs a Spark command as a daemon. # @@ -30,7 +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> <args...>" +usage="Usage: spark-daemon.sh [--config <conf-dir>] [--hosts hostlistfile] (start|stop) <spark-command> <spark-instance-number> <args...>" # if no args specified, show usage if [ $# -le 1 ]; then @@ -48,6 +47,8 @@ startStop=$1 shift command=$1 shift +instance=$1 +shift spark_rotate_log () { @@ -92,10 +93,10 @@ if [ "$SPARK_PID_DIR" = "" ]; then fi # some variables -export SPARK_LOGFILE=spark-$SPARK_IDENT_STRING-$command-$HOSTNAME.log +export SPARK_LOGFILE=spark-$SPARK_IDENT_STRING-$command-$instance-$HOSTNAME.log export SPARK_ROOT_LOGGER="INFO,DRFA" -log=$SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-$command-$HOSTNAME.out -pid=$SPARK_PID_DIR/spark-$SPARK_IDENT_STRING-$command.pid +log=$SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-$command-$instance-$HOSTNAME.out +pid=$SPARK_PID_DIR/spark-$SPARK_IDENT_STRING-$command-$instance.pid # Set default scheduling priority if [ "$SPARK_NICENESS" = "" ]; then diff --git a/bin/spark-daemons.sh b/bin/spark-daemons.sh index 4f9719ee80..354eb905a1 100755 --- a/bin/spark-daemons.sh +++ b/bin/spark-daemons.sh @@ -1,8 +1,25 @@ #!/usr/bin/env 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. +# + # Run a Spark command on all slave hosts. -usage="Usage: spark-daemons.sh [--config confdir] [--hosts hostlistfile] [start|stop] command args..." +usage="Usage: spark-daemons.sh [--config confdir] [--hosts hostlistfile] [start|stop] command instance-number args..." # if no args specified, show usage if [ $# -le 1 ]; then diff --git a/bin/start-all.sh b/bin/start-all.sh index b9891ad2f6..0182f1ab24 100755 --- a/bin/start-all.sh +++ b/bin/start-all.sh @@ -1,5 +1,22 @@ #!/usr/bin/env 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. +# + # Start all spark daemons. # Starts the master on this node. # Starts a worker on each node specified in conf/slaves diff --git a/bin/start-master.sh b/bin/start-master.sh index 87feb261fe..2288fb19d7 100755 --- a/bin/start-master.sh +++ b/bin/start-master.sh @@ -1,5 +1,22 @@ #!/usr/bin/env 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. +# + # Starts the master on the machine this script is executed on. bin=`dirname "$0"` @@ -32,4 +49,4 @@ if [ "$SPARK_PUBLIC_DNS" = "" ]; then fi fi -"$bin"/spark-daemon.sh start spark.deploy.master.Master --ip $SPARK_MASTER_IP --port $SPARK_MASTER_PORT --webui-port $SPARK_MASTER_WEBUI_PORT +"$bin"/spark-daemon.sh start spark.deploy.master.Master 1 --ip $SPARK_MASTER_IP --port $SPARK_MASTER_PORT --webui-port $SPARK_MASTER_WEBUI_PORT diff --git a/bin/start-slave.sh b/bin/start-slave.sh index 45a0cf7a6b..d6db16882d 100755 --- a/bin/start-slave.sh +++ b/bin/start-slave.sh @@ -1,14 +1,35 @@ #!/usr/bin/env 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. +# + +# Usage: start-slave.sh <worker#> <master-spark-URL> +# where <master-spark-URL> is like "spark://localhost:7077" + bin=`dirname "$0"` bin=`cd "$bin"; pwd` # Set SPARK_PUBLIC_DNS so slaves can be linked in master web UI if [ "$SPARK_PUBLIC_DNS" = "" ]; then # If we appear to be running on EC2, use the public address by default: - if [[ `hostname` == *ec2.internal ]]; then + # NOTE: ec2-metadata is installed on Amazon Linux AMI. Check based on that and hostname + if command -v ec2-metadata > /dev/null || [[ `hostname` == *ec2.internal ]]; then export SPARK_PUBLIC_DNS=`wget -q -O - http://instance-data.ec2.internal/latest/meta-data/public-hostname` fi fi -"$bin"/spark-daemon.sh start spark.deploy.worker.Worker $1 +"$bin"/spark-daemon.sh start spark.deploy.worker.Worker "$@" diff --git a/bin/start-slaves.sh b/bin/start-slaves.sh index 390247ca4a..dad7c3df76 100755 --- a/bin/start-slaves.sh +++ b/bin/start-slaves.sh @@ -1,5 +1,22 @@ #!/usr/bin/env 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. +# + bin=`dirname "$0"` bin=`cd "$bin"; pwd` @@ -21,4 +38,13 @@ fi echo "Master IP: $SPARK_MASTER_IP" # Launch the slaves -exec "$bin/slaves.sh" cd "$SPARK_HOME" \; "$bin/start-slave.sh" spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT +if [ "$SPARK_WORKER_INSTANCES" = "" ]; then + exec "$bin/slaves.sh" cd "$SPARK_HOME" \; "$bin/start-slave.sh" 1 spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT +else + if [ "$SPARK_WORKER_WEBUI_PORT" = "" ]; then + SPARK_WORKER_WEBUI_PORT=8081 + fi + for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do + "$bin/slaves.sh" cd "$SPARK_HOME" \; "$bin/start-slave.sh" $(( $i + 1 )) spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT --webui-port $(( $SPARK_WORKER_WEBUI_PORT + $i )) + done +fi diff --git a/bin/stop-all.sh b/bin/stop-all.sh index d352f6f631..a043ac0095 100755 --- a/bin/stop-all.sh +++ b/bin/stop-all.sh @@ -1,5 +1,22 @@ #!/usr/bin/env 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. +# + # Start all spark daemons. # Run this on the master nde diff --git a/bin/stop-master.sh b/bin/stop-master.sh index f75167dd2c..31a610bf9d 100755 --- a/bin/stop-master.sh +++ b/bin/stop-master.sh @@ -1,5 +1,22 @@ #!/usr/bin/env 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. +# + # Starts the master on the machine this script is executed on. bin=`dirname "$0"` @@ -7,4 +24,4 @@ bin=`cd "$bin"; pwd` . "$bin/spark-config.sh" -"$bin"/spark-daemon.sh stop spark.deploy.master.Master
\ No newline at end of file +"$bin"/spark-daemon.sh stop spark.deploy.master.Master 1 diff --git a/bin/stop-slaves.sh b/bin/stop-slaves.sh index 21c9ebf324..8e056f23d4 100755 --- a/bin/stop-slaves.sh +++ b/bin/stop-slaves.sh @@ -1,5 +1,22 @@ #!/usr/bin/env 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. +# + # Starts the master on the machine this script is executed on. bin=`dirname "$0"` @@ -7,4 +24,14 @@ bin=`cd "$bin"; pwd` . "$bin/spark-config.sh" -"$bin"/spark-daemons.sh stop spark.deploy.worker.Worker
\ No newline at end of file +if [ -f "${SPARK_CONF_DIR}/spark-env.sh" ]; then + . "${SPARK_CONF_DIR}/spark-env.sh" +fi + +if [ "$SPARK_WORKER_INSTANCES" = "" ]; then + "$bin"/spark-daemons.sh stop spark.deploy.worker.Worker 1 +else + for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do + "$bin"/spark-daemons.sh stop spark.deploy.worker.Worker $(( $i + 1 )) + done +fi diff --git a/conf/fairscheduler.xml.template b/conf/fairscheduler.xml.template new file mode 100644 index 0000000000..04a6b418dc --- /dev/null +++ b/conf/fairscheduler.xml.template @@ -0,0 +1,15 @@ +<?xml version="1.0"?> +<allocations> +<pool name="production"> + <minShare>2</minShare> + <weight>1</weight> + <schedulingMode>FAIR</schedulingMode> +</pool> +<pool name="test"> + <minShare>3</minShare> + <weight>2</weight> + <schedulingMode>FIFO</schedulingMode> +</pool> +<pool name="data"> +</pool> +</allocations> diff --git a/conf/metrics.properties.template b/conf/metrics.properties.template new file mode 100644 index 0000000000..63a5a2093e --- /dev/null +++ b/conf/metrics.properties.template @@ -0,0 +1,84 @@ +# syntax: [instance].sink|source.[name].[options]=[value] + +# This file configures Spark's internal metrics system. The metrics system is +# divided into instances which correspond to internal components. +# Each instance can be configured to report its metrics to one or more sinks. +# Accepted values for [instance] are "master", "worker", "executor", "driver", +# and "applications". A wild card "*" can be used as an instance name, in +# which case all instances will inherit the supplied property. +# +# Within an instance, a "source" specifies a particular set of grouped metrics. +# there are two kinds of sources: +# 1. Spark internal sources, like MasterSource, WorkerSource, etc, which will +# collect a Spark component's internal state. Each instance is paired with a +# Spark source that is added automatically. +# 2. Common sources, like JvmSource, which will collect low level state. +# These can be added through configuration options and are then loaded +# using reflection. +# +# A "sink" specifies where metrics are delivered to. Each instance can be +# assigned one or more sinks. +# +# The sink|source field specifies whether the property relates to a sink or +# source. +# +# The [name] field specifies the name of source or sink. +# +# The [options] field is the specific property of this source or sink. The +# source or sink is responsible for parsing this property. +# +# Notes: +# 1. To add a new sink, set the "class" option to a fully qualified class +# name (see examples below). +# 2. Some sinks involve a polling period. The minimum allowed polling period +# is 1 second. +# 3. Wild card properties can be overridden by more specific properties. +# For example, master.sink.console.period takes precedence over +# *.sink.console.period. +# 4. A metrics specific configuration +# "spark.metrics.conf=${SPARK_HOME}/conf/metrics.properties" should be +# added to Java properties using -Dspark.metrics.conf=xxx if you want to +# customize metrics system. You can also put the file in ${SPARK_HOME}/conf +# and it will be loaded automatically. + +# Enable JmxSink for all instances by class name +#*.sink.jmx.class=spark.metrics.sink.JmxSink + +# Enable ConsoleSink for all instances by class name +#*.sink.console.class=spark.metrics.sink.ConsoleSink + +# Polling period for ConsoleSink +#*.sink.console.period=10 + +#*.sink.console.unit=seconds + +# Master instance overlap polling period +#master.sink.console.period=15 + +#master.sink.console.unit=seconds + +# Enable CsvSink for all instances +#*.sink.csv.class=spark.metrics.sink.CsvSink + +# Polling period for CsvSink +#*.sink.csv.period=1 + +#*.sink.csv.unit=minutes + +# Polling directory for CsvSink +#*.sink.csv.directory=/tmp/ + +# Worker instance overlap polling period +#worker.sink.csv.period=10 + +#worker.sink.csv.unit=minutes + +# Enable jvm source for instance master, worker, driver and executor +#master.source.jvm.class=spark.metrics.source.JvmSource + +#worker.source.jvm.class=spark.metrics.source.JvmSource + +#driver.source.jvm.class=spark.metrics.source.JvmSource + +#executor.source.jvm.class=spark.metrics.source.JvmSource + diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template index 6d71ec5691..b8936314ec 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -3,8 +3,10 @@ # This file contains environment variables required to run Spark. Copy it as # spark-env.sh and edit that to configure Spark for your site. At a minimum, # the following two variables should be set: -# - MESOS_NATIVE_LIBRARY, to point to your Mesos native library (libmesos.so) -# - SCALA_HOME, to point to your Scala installation +# - SCALA_HOME, to point to your Scala installation, or SCALA_LIBRARY_PATH to +# point to the directory for Scala library JARs (if you install Scala as a +# Debian or RPM package, these are in a separate path, often /usr/share/java) +# - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos # # If using the standalone deploy mode, you can also set variables for it: # - SPARK_MASTER_IP, to bind the master to a different IP address @@ -12,13 +14,6 @@ # - SPARK_WORKER_CORES, to set the number of cores to use on this machine # - SPARK_WORKER_MEMORY, to set how much memory to use (e.g. 1000m, 2g) # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT -# -# Finally, Spark also relies on the following variables, but these can be set -# on just the *master* (i.e. in your driver program), and will automatically -# be propagated to workers: -# - SPARK_MEM, to change the amount of memory used per node (this should -# be in the same format as the JVM's -Xmx option, e.g. 300m or 1g) -# - SPARK_CLASSPATH, to add elements to Spark's classpath -# - SPARK_JAVA_OPTS, to add JVM options -# - SPARK_LIBRARY_PATH, to add extra search paths for native libraries. +# - SPARK_WORKER_INSTANCES, to set the number of worker instances/processes +# to be spawned on every slave machine diff --git a/core/pom.xml b/core/pom.xml index fe9c803728..ba0071f582 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -1,10 +1,27 @@ <?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.spark-project</groupId> <artifactId>spark-parent</artifactId> - <version>0.7.1-SNAPSHOT</version> + <version>0.8.0-SNAPSHOT</version> <relativePath>../pom.xml</relativePath> </parent> @@ -32,16 +49,26 @@ <artifactId>compress-lzf</artifactId> </dependency> <dependency> - <groupId>asm</groupId> - <artifactId>asm-all</artifactId> + <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>de.javakaffee</groupId> - <artifactId>kryo-serializers</artifactId> + <groupId>com.twitter</groupId> + <artifactId>chill_2.9.3</artifactId> + <version>0.3.0</version> + </dependency> + <dependency> + <groupId>com.twitter</groupId> + <artifactId>chill-java</artifactId> + <version>0.3.0</version> </dependency> <dependency> <groupId>com.typesafe.akka</groupId> @@ -56,43 +83,56 @@ <artifactId>akka-slf4j</artifactId> </dependency> <dependency> - <groupId>it.unimi.dsi</groupId> - <artifactId>fastutil</artifactId> - </dependency> - <dependency> - <groupId>colt</groupId> - <artifactId>colt</artifactId> + <groupId>org.scala-lang</groupId> + <artifactId>scalap</artifactId> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-can</artifactId> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-server</artifactId> + <groupId>net.liftweb</groupId> + <artifactId>lift-json_2.9.2</artifactId> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-json_${scala.version}</artifactId> + <groupId>it.unimi.dsi</groupId> + <artifactId>fastutil</artifactId> </dependency> <dependency> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-api</artifactId> + <groupId>colt</groupId> + <artifactId>colt</artifactId> </dependency> <dependency> <groupId>com.github.scala-incubator.io</groupId> - <artifactId>scala-io-file_${scala.version}</artifactId> + <artifactId>scala-io-file_2.9.2</artifactId> </dependency> <dependency> <groupId>org.apache.mesos</groupId> <artifactId>mesos</artifactId> </dependency> <dependency> + <groupId>io.netty</groupId> + <artifactId>netty-all</artifactId> + </dependency> + <dependency> <groupId>log4j</groupId> <artifactId>log4j</artifactId> </dependency> + <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-core</artifactId> + </dependency> + <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-jvm</artifactId> + </dependency> <dependency> + <groupId>org.apache.derby</groupId> + <artifactId>derby</artifactId> + <scope>test</scope> + </dependency> + <dependency> <groupId>org.scalatest</groupId> <artifactId>scalatest_${scala.version}</artifactId> <scope>test</scope> @@ -162,10 +202,6 @@ </environmentVariables> </configuration> </plugin> - <plugin> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-maven-plugin</artifactId> - </plugin> </plugins> </build> @@ -279,5 +315,72 @@ </plugins> </build> </profile> + <profile> + <id>hadoop2-yarn</id> + <dependencies> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-client</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.codehaus.mojo</groupId> + <artifactId>build-helper-maven-plugin</artifactId> + <executions> + <execution> + <id>add-source</id> + <phase>generate-sources</phase> + <goals> + <goal>add-source</goal> + </goals> + <configuration> + <sources> + <source>src/main/scala</source> + <source>src/hadoop2-yarn/scala</source> + </sources> + </configuration> + </execution> + <execution> + <id>add-scala-test-sources</id> + <phase>generate-test-sources</phase> + <goals> + <goal>add-test-source</goal> + </goals> + <configuration> + <sources> + <source>src/test/scala</source> + </sources> + </configuration> + </execution> + </executions> + </plugin> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2-yarn</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> </profiles> </project> diff --git a/core/src/hadoop1/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala b/core/src/hadoop1/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala index ca9f7219de..25386b2796 100644 --- a/core/src/hadoop1/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala +++ b/core/src/hadoop1/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala @@ -1,7 +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.hadoop.mapred trait HadoopMapRedUtil { def newJobContext(conf: JobConf, jobId: JobID): JobContext = new JobContext(conf, jobId) def newTaskAttemptContext(conf: JobConf, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContext(conf, attemptId) + + def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = new TaskAttemptID(jtIdentifier, + jobId, isMap, taskId, attemptId) } diff --git a/core/src/hadoop1/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala b/core/src/hadoop1/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala index de7b0f81e3..b1002e0cac 100644 --- a/core/src/hadoop1/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala +++ b/core/src/hadoop1/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala @@ -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. + */ + package org.apache.hadoop.mapreduce import org.apache.hadoop.conf.Configuration @@ -6,4 +23,7 @@ trait HadoopMapReduceUtil { def newJobContext(conf: Configuration, jobId: JobID): JobContext = new JobContext(conf, jobId) def newTaskAttemptContext(conf: Configuration, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContext(conf, attemptId) + + def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = new TaskAttemptID(jtIdentifier, + jobId, isMap, taskId, attemptId) } diff --git a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala new file mode 100644 index 0000000000..617954cb98 --- /dev/null +++ b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala @@ -0,0 +1,47 @@ +/* + * 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 spark.deploy +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.mapred.JobConf + + +/** + * Contains util methods to interact with Hadoop from spark. + */ +object SparkHadoopUtil { + + def getUserNameFromEnvironment(): String = { + // defaulting to -D ... + System.getProperty("user.name") + } + + def runAsUser(func: (Product) => Unit, args: Product) { + + // Add support, if exists - for now, simply run func ! + func(args) + } + + // 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 + def addCredentials(conf: JobConf) {} + + def isYarnMode(): Boolean = { false } + +} diff --git a/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala b/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala new file mode 100644 index 0000000000..0f972b7a0b --- /dev/null +++ b/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.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.hadoop.mapred + +import org.apache.hadoop.mapreduce.TaskType + +trait HadoopMapRedUtil { + def newJobContext(conf: JobConf, jobId: JobID): JobContext = new JobContextImpl(conf, jobId) + + def newTaskAttemptContext(conf: JobConf, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContextImpl(conf, attemptId) + + def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = + new TaskAttemptID(jtIdentifier, jobId, if (isMap) TaskType.MAP else TaskType.REDUCE, taskId, attemptId) +} diff --git a/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala b/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala new file mode 100644 index 0000000000..1a7cdf4788 --- /dev/null +++ b/core/src/hadoop2-yarn/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.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.hadoop.mapreduce + +import org.apache.hadoop.conf.Configuration +import task.{TaskAttemptContextImpl, JobContextImpl} + +trait HadoopMapReduceUtil { + def newJobContext(conf: Configuration, jobId: JobID): JobContext = new JobContextImpl(conf, jobId) + + def newTaskAttemptContext(conf: Configuration, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContextImpl(conf, attemptId) + + def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = + new TaskAttemptID(jtIdentifier, jobId, if (isMap) TaskType.MAP else TaskType.REDUCE, taskId, attemptId) +} diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala new file mode 100644 index 0000000000..6122fdced0 --- /dev/null +++ b/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.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 spark.deploy + +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. + */ +object SparkHadoopUtil { + + val yarnConf = newConfiguration() + + def getUserNameFromEnvironment(): String = { + // defaulting to env if -D is not present ... + val retval = System.getProperty(Environment.USER.name, System.getenv(Environment.USER.name)) + + // If nothing found, default to user we are running as + if (retval == null) System.getProperty("user.name") else retval + } + + def runAsUser(func: (Product) => Unit, args: Product) { + runAsUser(func, args, getUserNameFromEnvironment()) + } + + def runAsUser(func: (Product) => Unit, args: Product, user: String) { + func(args) + } + + // 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. + def isYarnMode(): Boolean = { + val yarnMode = System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE")) + java.lang.Boolean.valueOf(yarnMode) + } + + // Set an env variable indicating we are running in YARN mode. + // Note that anything with SPARK prefix gets propagated to all (remote) processes + def setYarnMode() { + System.setProperty("SPARK_YARN_MODE", "true") + } + + def setYarnMode(env: HashMap[String, String]) { + env("SPARK_YARN_MODE") = "true" + } + + // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems + // Always create a new config, dont reuse yarnConf. + 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 + def addCredentials(conf: JobConf) { + val jobCreds = conf.getCredentials(); + jobCreds.mergeAll(UserGroupInformation.getCurrentUser().getCredentials()) + } +} diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala new file mode 100644 index 0000000000..1b06169739 --- /dev/null +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala @@ -0,0 +1,351 @@ +/* + * 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 spark.deploy.yarn + +import java.net.Socket +import java.util.concurrent.CopyOnWriteArrayList +import java.util.concurrent.atomic.{AtomicInteger, AtomicReference} +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 scala.collection.JavaConversions._ +import spark.{SparkContext, Logging, 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 val yarnConf: YarnConfiguration = new YarnConfiguration(conf) + + private var yarnAllocator: YarnAllocationHandler = null + private var isFinished:Boolean = false + + 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 + } + } + + // Workaround until hadoop moves to something which has + // https://issues.apache.org/jira/browse/HADOOP-8406 + // 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 ? + // 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() + + // Allocate all containers + allocateWorkers() + + // Wait for the user class to Finish + userThread.join() + + 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 + while(!driverUp) { + val driverHost = System.getProperty("spark.driver.host") + val driverPort = System.getProperty("spark.driver.port") + try { + val socket = new Socket(driverHost, driverPort.toInt) + 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) + } + } + } + + 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 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 { + if (successed) { + ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) + } else { + ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.FAILED) + } + } + } + } + t.start() + return t + } + + private def allocateWorkers() { + logInfo("Waiting for spark context initialization") + + try { + var sparkContext: SparkContext = null + ApplicationMaster.sparkContextRef.synchronized { + var count = 0 + while (ApplicationMaster.sparkContextRef.get() == null) { + logInfo("Waiting for spark context initialization ... " + count) + count = count + 1 + ApplicationMaster.sparkContextRef.wait(10000L) + } + sparkContext = ApplicationMaster.sparkContextRef.get() + assert(sparkContext != null) + this.yarnAllocator = YarnAllocationHandler.newAllocator(yarnConf, resourceManager, appAttemptId, args, sparkContext.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 && + // If user thread exists, then quit ! + userThread.isAlive) { + + this.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 + 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 + if (userThread.isAlive) { + // 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)) + 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) { + 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 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) { + + synchronized { + if (isFinished) { + return + } + isFinished = true + } + + logInfo("finishApplicationMaster with " + status) + val finishReq = Records.newRecord(classOf[FinishApplicationMasterRequest]) + .asInstanceOf[FinishApplicationMasterRequest] + finishReq.setAppAttemptId(appAttemptId) + finishReq.setFinishApplicationStatus(status) + resourceManager.finishApplicationMaster(finishReq) + + } + +} + +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 + def incrementAllocatorLoop(by: Int) { + val count = yarnAllocatorLoop.getAndAdd(by) + if (count >= ALLOCATOR_LOOP_WAIT_COUNT) { + yarnAllocatorLoop.synchronized { + // to wake threads off wait ... + yarnAllocatorLoop.notifyAll() + } + } + } + + private val applicationMasters = new CopyOnWriteArrayList[ApplicationMaster]() + + def register(master: ApplicationMaster) { + applicationMasters.add(master) + } + + val sparkContextRef: AtomicReference[SparkContext] = new AtomicReference[SparkContext](null) + val yarnAllocatorLoop: AtomicInteger = new AtomicInteger(0) + + 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 Client declaring failure even though we exit'ed properly. + 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' + 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/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMasterArguments.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMasterArguments.scala new file mode 100644 index 0000000000..8de44b1f66 --- /dev/null +++ b/core/src/hadoop2-yarn/scala/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 spark.deploy.yarn + +import 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: 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/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala new file mode 100644 index 0000000000..8bcbfc2735 --- /dev/null +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala @@ -0,0 +1,327 @@ +/* + * 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 spark.deploy.yarn + +import java.net.{InetSocketAddress, URI} +import java.nio.ByteBuffer +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +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.protocolrecords._ +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 spark.{Logging, Utils} +import org.apache.hadoop.yarn.util.{Apps, Records, ConverterUtils} +import org.apache.hadoop.yarn.api.ApplicationConstants.Environment +import 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() { + init(yarnConf) + start() + logClusterResourceDetails() + + val newApp = super.getNewApplication() + val appId = newApp.getApplicationId() + + verifyClusterResources(newApp) + val appContext = createApplicationSubmissionContext(appId) + val localResources = prepareLocalResources(appId, "spark") + val env = setupLaunchEnv(localResources) + val amContainer = createContainerLaunchContext(newApp, localResources, env) + + appContext.setQueue(args.amQueue) + appContext.setAMContainerSpec(amContainer) + appContext.setUser(UserGroupInformation.getCurrentUser().getShortUserName()) + + submitApp(appContext) + + monitorApplication(appId) + System.exit(0) + } + + + def logClusterResourceDetails() { + val clusterMetrics: YarnClusterMetrics = super.getYarnClusterMetrics + 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) + } + + + 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("the worker size is to large to run on this cluster " + args.workerMemory); + System.exit(1) + } + val amMem = args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD + if (amMem > maxMem) { + logError("AM size is to large to run on this cluster " + amMem) + 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 + } + + 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") + return appContext + } + + def prepareLocalResources(appId: ApplicationId, appName: 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 + 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) + } + } + + Map("spark.jar" -> System.getenv("SPARK_JAR"), "app.jar" -> args.userJar, "log4j.properties" -> 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); + } + + 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 + } + } + UserGroupInformation.getCurrentUser().addCredentials(credentials); + return locaResources + } + + def setupLaunchEnv(localResources: HashMap[String, LocalResource]): HashMap[String, String] = { + logInfo("Setting up the launch environment") + val log4jConfLocalRes = localResources.getOrElse("log4j.properties", 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) + SparkHadoopUtil.setYarnMode(env) + 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() + } + + + // Add each SPARK-* key to the environment + System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k,v) => env(k) = v } + return 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) + + val minResMemory: Int = newApp.getMinimumResourceCapability().getMemory() + + var amMemory = ((args.amMemory / minResMemory) * minResMemory) + + (if (0 != (args.amMemory % minResMemory)) minResMemory else 0) - YarnAllocationHandler.MEMORY_OVERHEAD + + // Extra options for the JVM + var JAVA_OPTS = "" + + // Add Xmx for am memory + JAVA_OPTS += "-Xmx" + amMemory + "m " + + // 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 + 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()) { + javaCommand = Environment.JAVA_HOME.$() + "/bin/java" + } + + val commands = List[String](javaCommand + + " -server " + + JAVA_OPTS + + " spark.deploy.yarn.ApplicationMaster" + + " --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 the ApplicationMaster: " + commands(0)) + amContainer.setCommands(commands) + + val capability = Records.newRecord(classOf[Resource]).asInstanceOf[Resource] + // Memory for the ApplicationMaster + capability.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + amContainer.setResource(capability) + + // Setup security tokens + val dob = new DataOutputBuffer() + credentials.writeTokenStorageToStream(dob) + amContainer.setContainerTokens(ByteBuffer.wrap(dob.getData())) + + return 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 = { + while(true) { + Thread.sleep(1000) + val report = super.getApplicationReport(appId) + + logInfo("Application report from ASM: \n" + + "\t application identifier: " + appId.toString() + "\n" + + "\t appId: " + appId.getId() + "\n" + + "\t clientToken: " + report.getClientToken() + "\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 + } + } + return true + } +} + +object Client { + def main(argStrings: Array[String]) { + val args = new ClientArguments(argStrings) + SparkHadoopUtil.setYarnMode() + 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) + } + } +} diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala new file mode 100644 index 0000000000..67aff03781 --- /dev/null +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala @@ -0,0 +1,116 @@ +/* + * 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 spark.deploy.yarn + +import spark.util.MemoryParam +import spark.util.IntParam +import collection.mutable.{ArrayBuffer, HashMap} +import 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 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 amMemory: Int = 512 + // TODO + var inputFormatInfo: List[InputFormatInfo] = null + + 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-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 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: 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')" + ) + System.exit(exitCode) + } + +} diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala new file mode 100644 index 0000000000..f458f2f6a1 --- /dev/null +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala @@ -0,0 +1,217 @@ +/* + * 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 spark.deploy.yarn + +import java.net.URI +import java.nio.ByteBuffer +import java.security.PrivilegedExceptionAction + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileStatus, FileSystem, 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.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 spark.{Logging, Utils} + +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 + val workerMemoryString = workerMemory + "m" + JAVA_OPTS += "-Xms" + workerMemoryString + " -Xmx" + workerMemoryString + " " + if (env.isDefinedAt("SPARK_JAVA_OPTS")) { + JAVA_OPTS += env("SPARK_JAVA_OPTS") + " " + } + // 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 " + } +*/ + + ctx.setUser(UserGroupInformation.getCurrentUser().getShortUserName()) + + val credentials = UserGroupInformation.getCurrentUser().getCredentials() + val dob = new DataOutputBuffer() + credentials.writeTokenStorageToStream(dob) + ctx.setContainerTokens(ByteBuffer.wrap(dob.getData())) + + var javaCommand = "java"; + val javaHome = System.getenv("JAVA_HOME") + if (javaHome != null && !javaHome.isEmpty()) { + 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 + + " spark.executor.StandaloneExecutorBackend " + + 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 + val startReq = Records.newRecord(classOf[StartContainerRequest]) + .asInstanceOf[StartContainerRequest] + startReq.setContainerLaunchContext(ctx) + cm.startContainer(startReq) + } + + + 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 + } + + + logInfo("Prepared Local resources " + locaResources) + return locaResources + } + + 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, "./") + } + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./*") + Apps.addToEnvironment(env, Environment.CLASSPATH.name, "$CLASSPATH") + Client.populateHadoopClasspath(yarnConf, 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(); + if (containerToken != null) { + user.addToken(ProtoUtils.convertFromProtoFormat(containerToken, cmAddress)) + } + + val proxy = user + .doAs(new PrivilegedExceptionAction[ContainerManager] { + def run: ContainerManager = { + return rpc.getProxy(classOf[ContainerManager], + cmAddress, conf).asInstanceOf[ContainerManager] + } + }); + return proxy; + } + +} diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/YarnAllocationHandler.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/YarnAllocationHandler.scala new file mode 100644 index 0000000000..b0af8baf08 --- /dev/null +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/YarnAllocationHandler.scala @@ -0,0 +1,564 @@ +/* + * 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 spark.deploy.yarn + +import spark.{Logging, Utils} +import spark.scheduler.SplitInfo +import scala.collection +import org.apache.hadoop.yarn.api.records.{AMResponse, ApplicationAttemptId, ContainerId, Priority, Resource, ResourceRequest, ContainerStatus, Container} +import spark.scheduler.cluster.{ClusterScheduler, StandaloneSchedulerBackend} +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") { + 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. +// 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]) + 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] + + private val numWorkersRunning = new AtomicInteger() + // Used to generate a unique id per worker + private val workerIdCounter = new AtomicInteger() + private val lastResponseId = new AtomicInteger() + + def getNumWorkersRunning: Int = numWorkersRunning.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. + + // 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) + + val hostToContainers = new HashMap[String, ArrayBuffer[Container]]() + + // ignore if not satisfying constraints { + for (container <- _allocatedContainers) { + if (isResourceConstraintSatisfied(container)) { + // allocatedContainers += container + + val host = container.getNodeId.getHost + val containers = hostToContainers.getOrElseUpdate(host, new ArrayBuffer[Container]()) + + containers += container + } + // Add all ignored containers to released list + else releasedContainerList.add(container.getId()) + } + + // Find the appropriate containers to use + // Slightly non trivial groupBy I guess ... + 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) + + var remainingContainers = hostToContainers.get(candidateHost).getOrElse(null) + assert(remainingContainers != null) + + if (requiredHostCount >= remainingContainers.size){ + // Since we got <= required containers, add all to dataLocalContainers + dataLocalContainers.put(candidateHost, remainingContainers) + // all consumed + 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) + 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) + for (container <- remaining) releasedContainerList.add(container.getId()) + remainingContainers = null + } + + // now rack local + if (remainingContainers != null){ + val rack = YarnAllocationHandler.lookupRack(conf, candidateHost) + + if (rack != null){ + val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0) + val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) - + rackLocalContainers.get(rack).getOrElse(List()).size + + + if (requiredRackCount >= remainingContainers.size){ + // Add all to dataLocalContainers + dataLocalContainers.put(rack, remainingContainers) + // 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]()) + + existingRackLocal ++= rackLocal + remainingContainers = remaining + } + } + } + + // If still not consumed, then it is off rack host - add to that list. + if (remainingContainers != null){ + 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 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. + + val allocatedContainers = new ArrayBuffer[Container](_allocatedContainers.size) + allocatedContainers ++= ClusterScheduler.prioritizeContainers(dataLocalContainers) + allocatedContainers ++= ClusterScheduler.prioritizeContainers(rackLocalContainers) + allocatedContainers ++= ClusterScheduler.prioritizeContainers(offRackContainers) + + // Run each of the allocated containers + for (container <- allocatedContainers) { + val numWorkersRunningNow = numWorkersRunning.incrementAndGet() + val workerHostname = container.getNodeId.getHost + val containerId = container.getId + + 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") + 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) + 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) + + logInfo("launching container on " + containerId + " host " + workerHostname) + // 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]()) + + containerSet += containerId + allocatedContainerToHostMap.put(containerId, workerHostname) + if (rack != null) allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1) + } + + new Thread( + new WorkerRunnable(container, conf, driverUrl, workerId, + workerHostname, workerMemory, workerCores) + ).start() + } + } + logDebug("After allocated " + allocatedContainers.size + " containers (orig : " + + _allocatedContainers.size + "), current count " + numWorkersRunning.get() + + ", to-be-released " + releasedContainerList + ", pendingReleaseContainers : " + pendingReleaseContainers) + } + + + val completedContainers = amResp.getCompletedContainersStatuses() + if (completedContainers.size > 0){ + logDebug("Completed " + completedContainers.size + " containers, current count " + numWorkersRunning.get() + + ", to-be-released " + releasedContainerList + ", pendingReleaseContainers : " + pendingReleaseContainers) + + for (completedContainer <- completedContainers){ + val containerId = completedContainer.getContainerId + + // Was this released by us ? If yes, then simply remove from containerSet and move on. + if (pendingReleaseContainers.containsKey(containerId)) { + pendingReleaseContainers.remove(containerId) + } + else { + // 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) + } + + allocatedHostToContainersMap.synchronized { + if (allocatedContainerToHostMap.containsKey(containerId)) { + val host = allocatedContainerToHostMap.get(containerId).getOrElse(null) + assert (host != null) + + val containerSet = allocatedHostToContainersMap.get(host).getOrElse(null) + assert (containerSet != null) + + containerSet -= containerId + if (containerSet.isEmpty) allocatedHostToContainersMap.remove(host) + else allocatedHostToContainersMap.update(host, containerSet) + + allocatedContainerToHostMap -= containerId + + // 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 + if (rackCount > 0) allocatedRackCount.put(rack, rackCount) + else allocatedRackCount.remove(rack) + } + } + } + } + logDebug("After completed " + completedContainers.size + " containers, current count " + + numWorkersRunning.get() + ", to-be-released " + releasedContainerList + + ", pendingReleaseContainers : " + pendingReleaseContainers) + } + } + + def createRackResourceRequests(hostContainers: List[ResourceRequest]): List[ResourceRequest] = { + // First generate modified racks and new set of hosts under it : then issue requests + val rackToCounts = new HashMap[String, Int]() + + // Within this lock - used to read/write to the rack related maps too. + for (container <- hostContainers) { + val candidateHost = container.getHostName + val candidateNumContainers = container.getNumContainers + assert(YarnAllocationHandler.ANY_HOST != candidateHost) + + val rack = YarnAllocationHandler.lookupRack(conf, candidateHost) + if (rack != null) { + var count = rackToCounts.getOrElse(rack, 0) + count += candidateNumContainers + rackToCounts.put(rack, count) + } + } + + val requestedContainers: ArrayBuffer[ResourceRequest] = + new ArrayBuffer[ResourceRequest](rackToCounts.size) + for ((rack, count) <- rackToCounts){ + requestedContainers += + createResourceRequest(AllocationType.RACK, rack, count, YarnAllocationHandler.PRIORITY) + } + + requestedContainers.toList + } + + 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 + } + + private def allocateWorkerResources(numWorkers: Int): AllocateResponse = { + + var resourceRequests: List[ResourceRequest] = null + + // default. + if (numWorkers <= 0 || preferredHostToCount.isEmpty) { + logDebug("numWorkers: " + numWorkers + ", host preferences ? " + preferredHostToCount.isEmpty) + resourceRequests = List( + createResourceRequest(AllocationType.ANY, null, numWorkers, YarnAllocationHandler.PRIORITY)) + } + else { + // request for all hosts in preferred nodes and for numWorkers - + // candidates.size, request by default allocation policy. + val hostContainerRequests: ArrayBuffer[ResourceRequest] = + new ArrayBuffer[ResourceRequest](preferredHostToCount.size) + for ((candidateHost, candidateCount) <- preferredHostToCount) { + val requiredCount = candidateCount - allocatedContainersOnHost(candidateHost) + + if (requiredCount > 0) { + hostContainerRequests += + createResourceRequest(AllocationType.HOST, candidateHost, requiredCount, YarnAllocationHandler.PRIORITY) + } + } + val rackContainerRequests: List[ResourceRequest] = createRackResourceRequests(hostContainerRequests.toList) + + val anyContainerRequests: ResourceRequest = + createResourceRequest(AllocationType.ANY, null, numWorkers, YarnAllocationHandler.PRIORITY) + + val containerRequests: ArrayBuffer[ResourceRequest] = + new ArrayBuffer[ResourceRequest](hostContainerRequests.size() + rackContainerRequests.size() + 1) + + containerRequests ++= hostContainerRequests + containerRequests ++= rackContainerRequests + containerRequests += anyContainerRequests + + resourceRequests = containerRequests.toList + } + + val req = Records.newRecord(classOf[AllocateRequest]) + req.setResponseId(lastResponseId.incrementAndGet) + req.setApplicationAttemptId(appAttemptId) + + req.addAllAsks(resourceRequests) + + val releasedContainerList = createReleasedContainerList() + req.addAllReleases(releasedContainerList) + + + + if (numWorkers > 0) { + logInfo("Allocating " + numWorkers + " worker containers with " + (workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + " of memory each.") + } + 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) + } + resourceManager.allocate(req) + } + + + 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) + + val hostname = resource + val nodeLocal = createResourceRequestImpl(hostname, numWorkers, priority) + + // 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) + } + } + + private def createResourceRequestImpl(hostname:String, numWorkers: Int, priority: Int): ResourceRequest = { + + val rsrcRequest = Records.newRecord(classOf[ResourceRequest]) + val memCapability = Records.newRecord(classOf[Resource]) + // There probably is some overhead here, let's reserve a bit more memory. + memCapability.setMemory(workerMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + rsrcRequest.setCapability(memCapability) + + val pri = Records.newRecord(classOf[Priority]) + pri.setPriority(priority) + rsrcRequest.setPriority(pri) + + rsrcRequest.setHostName(hostname) + + rsrcRequest.setNumContainers(java.lang.Math.max(numWorkers, 0)) + rsrcRequest + } + + def createReleasedContainerList(): ArrayBuffer[ContainerId] = { + + val retval = new ArrayBuffer[ContainerId](1) + // iterator on COW list ... + for (container <- releasedContainerList.iterator()){ + retval += container + } + // remove from the original list. + if (! retval.isEmpty) { + releasedContainerList.removeAll(retval) + for (v <- retval) pendingReleaseContainers.put(v, true) + logInfo("Releasing " + retval.size + " containers. pendingReleaseContainers : " + + pendingReleaseContainers) + } + + retval + } +} + +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 RM 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, + 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) + } + + 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) + } + + // A simple method to copy the split info map. + private def generateNodeToWeight(conf: Configuration, input: collection.Map[String, collection.Set[SplitInfo]]) : + // host to count, rack to count + (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]] = { + val set = rackToHostSet.get(rack) + if (set == null) return None + + // No better way to get a Set[String] from JSet ? + val convertedSet: collection.mutable.Set[String] = set + Some(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) + + // 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/core/src/hadoop2-yarn/scala/spark/scheduler/cluster/YarnClusterScheduler.scala b/core/src/hadoop2-yarn/scala/spark/scheduler/cluster/YarnClusterScheduler.scala new file mode 100644 index 0000000000..307d96111c --- /dev/null +++ b/core/src/hadoop2-yarn/scala/spark/scheduler/cluster/YarnClusterScheduler.scala @@ -0,0 +1,59 @@ +/* + * 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 spark.scheduler.cluster + +import spark._ +import spark.deploy.yarn.{ApplicationMaster, YarnAllocationHandler} +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 ClusterScheduler(sc) { + + 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 + } + + // By default, if rack is unknown, return nothing + override def getCachedHostsForRack(rack: String): Option[Set[String]] = { + if (rack == None || rack == null) return None + + YarnAllocationHandler.fetchCachedHostsForRack(rack) + } + + 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/core/src/hadoop2/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala b/core/src/hadoop2/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala index 35300cea58..4b3d84670c 100644 --- a/core/src/hadoop2/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala +++ b/core/src/hadoop2/scala/org/apache/hadoop/mapred/HadoopMapRedUtil.scala @@ -1,7 +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.hadoop.mapred trait HadoopMapRedUtil { def newJobContext(conf: JobConf, jobId: JobID): JobContext = new JobContextImpl(conf, jobId) def newTaskAttemptContext(conf: JobConf, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContextImpl(conf, attemptId) + + def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = new TaskAttemptID(jtIdentifier, + jobId, isMap, taskId, attemptId) } diff --git a/core/src/hadoop2/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala b/core/src/hadoop2/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala index 7afdbff320..aa3b1ed3a5 100644 --- a/core/src/hadoop2/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala +++ b/core/src/hadoop2/scala/org/apache/hadoop/mapreduce/HadoopMapReduceUtil.scala @@ -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. + */ + package org.apache.hadoop.mapreduce import org.apache.hadoop.conf.Configuration @@ -7,4 +24,7 @@ trait HadoopMapReduceUtil { def newJobContext(conf: Configuration, jobId: JobID): JobContext = new JobContextImpl(conf, jobId) def newTaskAttemptContext(conf: Configuration, attemptId: TaskAttemptID): TaskAttemptContext = new TaskAttemptContextImpl(conf, attemptId) + + def newTaskAttemptID(jtIdentifier: String, jobId: Int, isMap: Boolean, taskId: Int, attemptId: Int) = new TaskAttemptID(jtIdentifier, + jobId, isMap, taskId, attemptId) } diff --git a/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala new file mode 100644 index 0000000000..617954cb98 --- /dev/null +++ b/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala @@ -0,0 +1,47 @@ +/* + * 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 spark.deploy +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.mapred.JobConf + + +/** + * Contains util methods to interact with Hadoop from spark. + */ +object SparkHadoopUtil { + + def getUserNameFromEnvironment(): String = { + // defaulting to -D ... + System.getProperty("user.name") + } + + def runAsUser(func: (Product) => Unit, args: Product) { + + // Add support, if exists - for now, simply run func ! + func(args) + } + + // 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 + def addCredentials(conf: JobConf) {} + + def isYarnMode(): Boolean = { false } + +} diff --git a/core/src/main/java/spark/network/netty/FileClient.java b/core/src/main/java/spark/network/netty/FileClient.java new file mode 100644 index 0000000000..0625a6d502 --- /dev/null +++ b/core/src/main/java/spark/network/netty/FileClient.java @@ -0,0 +1,89 @@ +/* + * 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 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.oio.OioEventLoopGroup; +import io.netty.channel.socket.oio.OioSocketChannel; + +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +class FileClient { + + private Logger LOG = LoggerFactory.getLogger(this.getClass().getName()); + private FileClientHandler handler = null; + private Channel channel = null; + private Bootstrap bootstrap = null; + private int connectTimeout = 60*1000; // 1 min + + public FileClient(FileClientHandler handler, int connectTimeout) { + this.handler = handler; + this.connectTimeout = connectTimeout; + } + + public void init() { + bootstrap = new Bootstrap(); + bootstrap.group(new OioEventLoopGroup()) + .channel(OioSocketChannel.class) + .option(ChannelOption.SO_KEEPALIVE, true) + .option(ChannelOption.TCP_NODELAY, true) + .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, connectTimeout) + .handler(new FileClientChannelInitializer(handler)); + } + + public void connect(String host, int port) { + try { + // Start the connection attempt. + channel = bootstrap.connect(host, port).sync().channel(); + // ChannelFuture cf = channel.closeFuture(); + //cf.addListener(new ChannelCloseListener(this)); + } catch (InterruptedException e) { + close(); + } + } + + public void waitForClose() { + try { + channel.closeFuture().sync(); + } catch (InterruptedException e) { + LOG.warn("FileClient interrupted", e); + } + } + + public void sendRequest(String file) { + //assert(file == null); + //assert(channel == null); + channel.write(file + "\r\n"); + } + + public void close() { + if(channel != null) { + channel.close(); + channel = null; + } + if ( bootstrap!=null) { + bootstrap.shutdown(); + bootstrap = null; + } + } +} diff --git a/core/src/main/java/spark/network/netty/FileClientChannelInitializer.java b/core/src/main/java/spark/network/netty/FileClientChannelInitializer.java new file mode 100644 index 0000000000..05ad4b61d7 --- /dev/null +++ b/core/src/main/java/spark/network/netty/FileClientChannelInitializer.java @@ -0,0 +1,41 @@ +/* + * 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 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; + + public FileClientChannelInitializer(FileClientHandler handler) { + fhandler = handler; + } + + @Override + public void initChannel(SocketChannel channel) { + // file no more than 2G + channel.pipeline() + .addLast("encoder", new StringEncoder(BufType.BYTE)) + .addLast("handler", fhandler); + } +} diff --git a/core/src/main/java/spark/network/netty/FileClientHandler.java b/core/src/main/java/spark/network/netty/FileClientHandler.java new file mode 100644 index 0000000000..e8cd9801f6 --- /dev/null +++ b/core/src/main/java/spark/network/netty/FileClientHandler.java @@ -0,0 +1,60 @@ +/* + * 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 spark.network.netty; + +import io.netty.buffer.ByteBuf; +import io.netty.channel.ChannelHandlerContext; +import io.netty.channel.ChannelInboundByteHandlerAdapter; + + +abstract class FileClientHandler extends ChannelInboundByteHandlerAdapter { + + private FileHeader currentHeader = null; + + private volatile boolean handlerCalled = false; + + public boolean isComplete() { + return handlerCalled; + } + + public abstract void handle(ChannelHandlerContext ctx, ByteBuf in, FileHeader header); + public abstract void handleError(String blockId); + + @Override + public ByteBuf newInboundBuffer(ChannelHandlerContext ctx) { + // Use direct buffer if possible. + return ctx.alloc().ioBuffer(); + } + + @Override + public void inboundBufferUpdated(ChannelHandlerContext ctx, ByteBuf in) { + // get header + if (currentHeader == null && in.readableBytes() >= FileHeader.HEADER_SIZE()) { + currentHeader = FileHeader.create(in.readBytes(FileHeader.HEADER_SIZE())); + } + // get file + if(in.readableBytes() >= currentHeader.fileLen()) { + handle(ctx, in, currentHeader); + handlerCalled = true; + currentHeader = null; + ctx.close(); + } + } + +} + diff --git a/core/src/main/java/spark/network/netty/FileServer.java b/core/src/main/java/spark/network/netty/FileServer.java new file mode 100644 index 0000000000..9f009a61d5 --- /dev/null +++ b/core/src/main/java/spark/network/netty/FileServer.java @@ -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 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.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. + */ +class FileServer { + + private Logger LOG = LoggerFactory.getLogger(this.getClass().getName()); + + private ServerBootstrap bootstrap = null; + private ChannelFuture channelFuture = null; + private int port = 0; + private Thread blockingThread = null; + + public FileServer(PathResolver pResolver, int port) { + InetSocketAddress addr = new InetSocketAddress(port); + + // Configure the server. + bootstrap = new ServerBootstrap(); + bootstrap.group(new OioEventLoopGroup(), new OioEventLoopGroup()) + .channel(OioServerSocketChannel.class) + .option(ChannelOption.SO_BACKLOG, 100) + .option(ChannelOption.SO_RCVBUF, 1500) + .childHandler(new FileServerChannelInitializer(pResolver)); + // Start the server. + channelFuture = bootstrap.bind(addr); + try { + // Get the address we bound to. + InetSocketAddress boundAddress = + ((InetSocketAddress) channelFuture.sync().channel().localAddress()); + this.port = boundAddress.getPort(); + } catch (InterruptedException ie) { + this.port = 0; + } + } + + /** + * Start the file server asynchronously in a new thread. + */ + public void start() { + blockingThread = new Thread() { + public void run() { + try { + channelFuture.channel().closeFuture().sync(); + LOG.info("FileServer exiting"); + } catch (InterruptedException e) { + LOG.error("File server start got interrupted", e); + } + // NOTE: bootstrap is shutdown in stop() + } + }; + blockingThread.setDaemon(true); + blockingThread.start(); + } + + public int getPort() { + return port; + } + + public void stop() { + // Close the bound channel. + if (channelFuture != null) { + channelFuture.channel().close(); + channelFuture = null; + } + // Shutdown bootstrap. + if (bootstrap != null) { + bootstrap.shutdown(); + bootstrap = null; + } + // TODO: Shutdown all accepted channels as well ? + } +} diff --git a/core/src/main/java/spark/network/netty/FileServerChannelInitializer.java b/core/src/main/java/spark/network/netty/FileServerChannelInitializer.java new file mode 100644 index 0000000000..50c57a81a3 --- /dev/null +++ b/core/src/main/java/spark/network/netty/FileServerChannelInitializer.java @@ -0,0 +1,42 @@ +/* + * 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 spark.network.netty; + +import io.netty.channel.ChannelInitializer; +import io.netty.channel.socket.SocketChannel; +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; + + public FileServerChannelInitializer(PathResolver pResolver) { + this.pResolver = pResolver; + } + + @Override + public void initChannel(SocketChannel channel) { + channel.pipeline() + .addLast("framer", new DelimiterBasedFrameDecoder(8192, Delimiters.lineDelimiter())) + .addLast("strDecoder", new StringDecoder()) + .addLast("handler", new FileServerHandler(pResolver)); + } +} diff --git a/core/src/main/java/spark/network/netty/FileServerHandler.java b/core/src/main/java/spark/network/netty/FileServerHandler.java new file mode 100644 index 0000000000..176ba8da49 --- /dev/null +++ b/core/src/main/java/spark/network/netty/FileServerHandler.java @@ -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 spark.network.netty; + +import java.io.File; +import java.io.FileInputStream; + +import io.netty.channel.ChannelHandlerContext; +import io.netty.channel.ChannelInboundMessageHandlerAdapter; +import io.netty.channel.DefaultFileRegion; + + +class FileServerHandler extends ChannelInboundMessageHandlerAdapter<String> { + + 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) { + //ctx.close(); + return; + } + File file = new File(path); + 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(); + 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())); + } catch (Exception e) { + //logger.warning("Exception when sending file : " + file.getAbsolutePath()); + e.printStackTrace(); + } + } else { + //logger.warning("File not found: " + file.getAbsolutePath()); + ctx.write(new FileHeader(0, blockId).buffer()); + } + ctx.flush(); + } + + @Override + public void exceptionCaught(ChannelHandlerContext ctx, Throwable cause) { + cause.printStackTrace(); + ctx.close(); + } +} diff --git a/core/src/main/java/spark/network/netty/PathResolver.java b/core/src/main/java/spark/network/netty/PathResolver.java new file mode 100755 index 0000000000..f446c55b19 --- /dev/null +++ b/core/src/main/java/spark/network/netty/PathResolver.java @@ -0,0 +1,29 @@ +/*
+ * 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 spark.network.netty;
+
+
+public interface PathResolver {
+ /**
+ * Get the absolute path of the file
+ *
+ * @param fileId
+ * @return the absolute path of file
+ */
+ public String getAbsolutePath(String fileId);
+}
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b/core/src/main/resources/spark/ui/static/spark-logo-77x50px-hd.png diff --git a/core/src/main/resources/spark/deploy/static/spark_logo.png b/core/src/main/resources/spark/ui/static/spark_logo.png Binary files differindex 4b18734779..4b18734779 100644 --- a/core/src/main/resources/spark/deploy/static/spark_logo.png +++ b/core/src/main/resources/spark/ui/static/spark_logo.png diff --git a/core/src/main/resources/spark/ui/static/webui.css b/core/src/main/resources/spark/ui/static/webui.css new file mode 100644 index 0000000000..8b9f4ee938 --- /dev/null +++ b/core/src/main/resources/spark/ui/static/webui.css @@ -0,0 +1,53 @@ +.navbar .brand { + height: 50px; + width: 110px; + margin-left: 1px; + padding: 0; +} + +.version { + line-height: 30px; + vertical-align: bottom; + font-size: 12px; + padding: 0; + margin: 0; + font-weight: bold; + color: #777; +} + +.navbar-inner { + padding-top: 2px; + height: 50px; +} + +.navbar-inner .nav { + margin-top: 5px; + font-size: 15px; +} + + +#infolist { + margin-left: 400px; + margin-top: 14px; +} + +#infolist li { + display: inline; + list-style-type: none; + list-style-position: outside; + padding-right: 20px; + padding-top: 10px; + padding-bottom: 10px; +} + +.progress-cell { + width: 134px; + border-right: 0; + padding: 0; + padding-top: 7px; + padding-left: 4px; +} + +.table td { + vertical-align: middle !important; +} diff --git a/core/src/main/scala/spark/Accumulators.scala b/core/src/main/scala/spark/Accumulators.scala index 57c6df35be..6ff92ce833 100644 --- a/core/src/main/scala/spark/Accumulators.scala +++ b/core/src/main/scala/spark/Accumulators.scala @@ -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. + */ + package spark import java.io._ diff --git a/core/src/main/scala/spark/Aggregator.scala b/core/src/main/scala/spark/Aggregator.scala index df8ce9c054..136b4da61e 100644 --- a/core/src/main/scala/spark/Aggregator.scala +++ b/core/src/main/scala/spark/Aggregator.scala @@ -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. + */ + package spark import java.util.{HashMap => JHashMap} diff --git a/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala b/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala index c27ed36406..8f6953b1f5 100644 --- a/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala +++ b/core/src/main/scala/spark/BlockStoreShuffleFetcher.scala @@ -1,14 +1,36 @@ +/* + * 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 spark -import executor.{ShuffleReadMetrics, TaskMetrics} import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap -import spark.storage.{DelegateBlockFetchTracker, BlockManagerId} -import util.{CompletionIterator, TimedIterator} +import spark.executor.{ShuffleReadMetrics, TaskMetrics} +import spark.serializer.Serializer +import spark.storage.BlockManagerId +import spark.util.CompletionIterator + private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Logging { - override def fetch[K, V](shuffleId: Int, reduceId: Int, metrics: TaskMetrics) = { + + override def fetch[K, V]( + shuffleId: Int, reduceId: Int, metrics: TaskMetrics, serializer: Serializer) = { + logDebug("Fetching outputs for shuffle %d, reduce %d".format(shuffleId, reduceId)) val blockManager = SparkEnv.get.blockManager @@ -48,18 +70,18 @@ private[spark] class BlockStoreShuffleFetcher extends ShuffleFetcher with Loggin } } - val blockFetcherItr = blockManager.getMultiple(blocksByAddress) - val itr = new TimedIterator(blockFetcherItr.flatMap(unpackBlock)) with DelegateBlockFetchTracker - itr.setDelegate(blockFetcherItr) + val blockFetcherItr = blockManager.getMultiple(blocksByAddress, serializer) + val itr = blockFetcherItr.flatMap(unpackBlock) + CompletionIterator[(K,V), Iterator[(K,V)]](itr, { val shuffleMetrics = new ShuffleReadMetrics - shuffleMetrics.shuffleReadMillis = itr.getNetMillis - shuffleMetrics.remoteFetchTime = itr.remoteFetchTime - shuffleMetrics.fetchWaitTime = itr.fetchWaitTime - shuffleMetrics.remoteBytesRead = itr.remoteBytesRead - shuffleMetrics.totalBlocksFetched = itr.totalBlocks - shuffleMetrics.localBlocksFetched = itr.numLocalBlocks - shuffleMetrics.remoteBlocksFetched = itr.numRemoteBlocks + shuffleMetrics.shuffleFinishTime = System.currentTimeMillis + shuffleMetrics.remoteFetchTime = blockFetcherItr.remoteFetchTime + shuffleMetrics.fetchWaitTime = blockFetcherItr.fetchWaitTime + shuffleMetrics.remoteBytesRead = blockFetcherItr.remoteBytesRead + shuffleMetrics.totalBlocksFetched = blockFetcherItr.totalBlocks + shuffleMetrics.localBlocksFetched = blockFetcherItr.numLocalBlocks + shuffleMetrics.remoteBlocksFetched = blockFetcherItr.numRemoteBlocks metrics.shuffleReadMetrics = Some(shuffleMetrics) }) } diff --git a/core/src/main/scala/spark/Cache.scala b/core/src/main/scala/spark/Cache.scala deleted file mode 100644 index 20d677a854..0000000000 --- a/core/src/main/scala/spark/Cache.scala +++ /dev/null @@ -1,63 +0,0 @@ -package spark - -import java.util.concurrent.atomic.AtomicInteger - -private[spark] sealed trait CachePutResponse -private[spark] case class CachePutSuccess(size: Long) extends CachePutResponse -private[spark] case class CachePutFailure() extends CachePutResponse - -/** - * An interface for caches in Spark, to allow for multiple implementations. Caches are used to store - * both partitions of cached RDDs and broadcast variables on Spark executors. Caches are also aware - * of which entries are part of the same dataset (for example, partitions in the same RDD). The key - * for each value in a cache is a (datasetID, partition) pair. - * - * A single Cache instance gets created on each machine and is shared by all caches (i.e. both the - * RDD split cache and the broadcast variable cache), to enable global replacement policies. - * However, because these several independent modules all perform caching, it is important to give - * them separate key namespaces, so that an RDD and a broadcast variable (for example) do not use - * the same key. For this purpose, Cache has the notion of KeySpaces. Each client module must first - * ask for a KeySpace, and then call get() and put() on that space using its own keys. - * - * This abstract class handles the creation of key spaces, so that subclasses need only deal with - * keys that are unique across modules. - */ -private[spark] abstract class Cache { - private val nextKeySpaceId = new AtomicInteger(0) - private def newKeySpaceId() = nextKeySpaceId.getAndIncrement() - - def newKeySpace() = new KeySpace(this, newKeySpaceId()) - - /** - * Get the value for a given (datasetId, partition), or null if it is not - * found. - */ - def get(datasetId: Any, partition: Int): Any - - /** - * Attempt to put a value in the cache; returns CachePutFailure if this was - * not successful (e.g. because the cache replacement policy forbids it), and - * CachePutSuccess if successful. If size estimation is available, the cache - * implementation should set the size field in CachePutSuccess. - */ - def put(datasetId: Any, partition: Int, value: Any): CachePutResponse - - /** - * Report the capacity of the cache partition. By default this just reports - * zero. Specific implementations can choose to provide the capacity number. - */ - def getCapacity: Long = 0L -} - -/** - * A key namespace in a Cache. - */ -private[spark] class KeySpace(cache: Cache, val keySpaceId: Int) { - def get(datasetId: Any, partition: Int): Any = - cache.get((keySpaceId, datasetId), partition) - - def put(datasetId: Any, partition: Int, value: Any): CachePutResponse = - cache.put((keySpaceId, datasetId), partition, value) - - def getCapacity: Long = cache.getCapacity -} diff --git a/core/src/main/scala/spark/CacheManager.scala b/core/src/main/scala/spark/CacheManager.scala index f7a2b7e802..81314805a9 100644 --- a/core/src/main/scala/spark/CacheManager.scala +++ b/core/src/main/scala/spark/CacheManager.scala @@ -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. + */ + package spark import scala.collection.mutable.{ArrayBuffer, HashSet} diff --git a/core/src/main/scala/spark/ClosureCleaner.scala b/core/src/main/scala/spark/ClosureCleaner.scala index 98525b99c8..8b39241095 100644 --- a/core/src/main/scala/spark/ClosureCleaner.scala +++ b/core/src/main/scala/spark/ClosureCleaner.scala @@ -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. + */ + package spark import java.lang.reflect.Field @@ -5,15 +22,22 @@ import java.lang.reflect.Field import scala.collection.mutable.Map import scala.collection.mutable.Set -import org.objectweb.asm.{ClassReader, MethodVisitor, Type} -import org.objectweb.asm.commons.EmptyVisitor +import org.objectweb.asm.{ClassReader, ClassVisitor, MethodVisitor, Type} import org.objectweb.asm.Opcodes._ +import java.io.{InputStream, IOException, ByteArrayOutputStream, ByteArrayInputStream, BufferedInputStream} private[spark] object ClosureCleaner extends Logging { // Get an ASM class reader for a given class from the JAR that loaded it private def getClassReader(cls: Class[_]): ClassReader = { - new ClassReader(cls.getResourceAsStream( - cls.getName.replaceFirst("^.*\\.", "") + ".class")) + // Copy data over, before delegating to ClassReader - else we can run out of open file handles. + val className = cls.getName.replaceFirst("^.*\\.", "") + ".class" + val resourceStream = cls.getResourceAsStream(className) + // todo: Fixme - continuing with earlier behavior ... + if (resourceStream == null) return new ClassReader(resourceStream) + + val baos = new ByteArrayOutputStream(128) + Utils.copyStream(resourceStream, baos, true) + new ClassReader(new ByteArrayInputStream(baos.toByteArray)) } // Check whether a class represents a Scala closure @@ -154,10 +178,10 @@ private[spark] object ClosureCleaner extends Logging { } } -private[spark] class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends EmptyVisitor { +private[spark] class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends ClassVisitor(ASM4) { override def visitMethod(access: Int, name: String, desc: String, sig: String, exceptions: Array[String]): MethodVisitor = { - return new EmptyVisitor { + return new MethodVisitor(ASM4) { override def visitFieldInsn(op: Int, owner: String, name: String, desc: String) { if (op == GETFIELD) { for (cl <- output.keys if cl.getName == owner.replace('/', '.')) { @@ -180,7 +204,7 @@ private[spark] class FieldAccessFinder(output: Map[Class[_], Set[String]]) exten } } -private[spark] class InnerClosureFinder(output: Set[Class[_]]) extends EmptyVisitor { +private[spark] class InnerClosureFinder(output: Set[Class[_]]) extends ClassVisitor(ASM4) { var myName: String = null override def visit(version: Int, access: Int, name: String, sig: String, @@ -190,7 +214,7 @@ private[spark] class InnerClosureFinder(output: Set[Class[_]]) extends EmptyVisi override def visitMethod(access: Int, name: String, desc: String, sig: String, exceptions: Array[String]): MethodVisitor = { - return new EmptyVisitor { + return new MethodVisitor(ASM4) { override def visitMethodInsn(op: Int, owner: String, name: String, desc: String) { val argTypes = Type.getArgumentTypes(desc) diff --git a/core/src/main/scala/spark/Dependency.scala b/core/src/main/scala/spark/Dependency.scala index 5eea907322..d17e70a4fa 100644 --- a/core/src/main/scala/spark/Dependency.scala +++ b/core/src/main/scala/spark/Dependency.scala @@ -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. + */ + package spark /** @@ -25,10 +42,12 @@ abstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) { * @param shuffleId the shuffle id * @param rdd the parent RDD * @param partitioner partitioner used to partition the shuffle output + * @param serializerClass class name of the serializer to use */ class ShuffleDependency[K, V]( @transient rdd: RDD[(K, V)], - val partitioner: Partitioner) + val partitioner: Partitioner, + val serializerClass: String = null) extends Dependency(rdd) { val shuffleId: Int = rdd.context.newShuffleId() diff --git a/core/src/main/scala/spark/DoubleRDDFunctions.scala b/core/src/main/scala/spark/DoubleRDDFunctions.scala index 178d31a73b..104168e61c 100644 --- a/core/src/main/scala/spark/DoubleRDDFunctions.scala +++ b/core/src/main/scala/spark/DoubleRDDFunctions.scala @@ -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. + */ + package spark import spark.partial.BoundedDouble @@ -37,7 +54,13 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * Compute the sample standard deviation of this RDD's elements (which corrects for bias in * estimating the standard deviation by dividing by N-1 instead of N). */ - def sampleStdev(): Double = stats().stdev + def sampleStdev(): Double = stats().sampleStdev + + /** + * Compute the sample variance of this RDD's elements (which corrects for bias in + * estimating the variance by dividing by N-1 instead of N). + */ + def sampleVariance(): Double = stats().sampleVariance /** (Experimental) Approximate operation to return the mean within a timeout. */ def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { diff --git a/core/src/main/scala/spark/FetchFailedException.scala b/core/src/main/scala/spark/FetchFailedException.scala index a953081d24..a2dae6cae9 100644 --- a/core/src/main/scala/spark/FetchFailedException.scala +++ b/core/src/main/scala/spark/FetchFailedException.scala @@ -1,20 +1,44 @@ +/* + * 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 spark import spark.storage.BlockManagerId private[spark] class FetchFailedException( - val bmAddress: BlockManagerId, - val shuffleId: Int, - val mapId: Int, - val reduceId: Int, + taskEndReason: TaskEndReason, + message: String, cause: Throwable) extends Exception { - - override def getMessage(): String = - "Fetch failed: %s %d %d %d".format(bmAddress, shuffleId, mapId, reduceId) + + def this (bmAddress: BlockManagerId, shuffleId: Int, mapId: Int, reduceId: Int, cause: Throwable) = + this(FetchFailed(bmAddress, shuffleId, mapId, reduceId), + "Fetch failed: %s %d %d %d".format(bmAddress, shuffleId, mapId, reduceId), + cause) + + def this (shuffleId: Int, reduceId: Int, cause: Throwable) = + this(FetchFailed(null, shuffleId, -1, reduceId), + "Unable to fetch locations from master: %d %d".format(shuffleId, reduceId), cause) + + override def getMessage(): String = message + override def getCause(): Throwable = cause - def toTaskEndReason: TaskEndReason = - FetchFailed(bmAddress, shuffleId, mapId, reduceId) + def toTaskEndReason: TaskEndReason = taskEndReason + } diff --git a/core/src/main/scala/spark/HadoopWriter.scala b/core/src/main/scala/spark/HadoopWriter.scala index afcf9f6db4..b1fe0075a3 100644 --- a/core/src/main/scala/spark/HadoopWriter.scala +++ b/core/src/main/scala/spark/HadoopWriter.scala @@ -1,15 +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.hadoop.mapred import org.apache.hadoop.fs.FileSystem import org.apache.hadoop.fs.Path -import org.apache.hadoop.util.ReflectionUtils -import org.apache.hadoop.io.NullWritable -import org.apache.hadoop.io.Text import java.text.SimpleDateFormat import java.text.NumberFormat import java.io.IOException -import java.net.URI import java.util.Date import spark.Logging @@ -24,7 +37,7 @@ import spark.SerializableWritable * a filename to write to, etc, exactly like in a Hadoop MapReduce job. */ class HadoopWriter(@transient jobConf: JobConf) extends Logging with HadoopMapRedUtil with Serializable { - + private val now = new Date() private val conf = new SerializableWritable(jobConf) @@ -106,6 +119,12 @@ class HadoopWriter(@transient jobConf: JobConf) extends Logging with HadoopMapRe } } + def commitJob() { + // always ? Or if cmtr.needsTaskCommit ? + val cmtr = getOutputCommitter() + cmtr.commitJob(getJobContext()) + } + def cleanup() { getOutputCommitter().cleanupJob(getJobContext()) } diff --git a/core/src/main/scala/spark/HttpFileServer.scala b/core/src/main/scala/spark/HttpFileServer.scala index 00901d95e2..a13a7a2859 100644 --- a/core/src/main/scala/spark/HttpFileServer.scala +++ b/core/src/main/scala/spark/HttpFileServer.scala @@ -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. + */ + package spark import java.io.{File} @@ -42,4 +59,4 @@ private[spark] class HttpFileServer extends Logging { return dir + "/" + file.getName } -}
\ No newline at end of file +} diff --git a/core/src/main/scala/spark/HttpServer.scala b/core/src/main/scala/spark/HttpServer.scala index 4e0507c080..c9dffbc631 100644 --- a/core/src/main/scala/spark/HttpServer.scala +++ b/core/src/main/scala/spark/HttpServer.scala @@ -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. + */ + package spark import java.io.File diff --git a/core/src/main/scala/spark/JavaSerializer.scala b/core/src/main/scala/spark/JavaSerializer.scala index b04a27d073..04c5f44e6b 100644 --- a/core/src/main/scala/spark/JavaSerializer.scala +++ b/core/src/main/scala/spark/JavaSerializer.scala @@ -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. + */ + package spark import java.io._ diff --git a/core/src/main/scala/spark/KryoSerializer.scala b/core/src/main/scala/spark/KryoSerializer.scala index d723ab7b1e..eeb2993d8a 100644 --- a/core/src/main/scala/spark/KryoSerializer.scala +++ b/core/src/main/scala/spark/KryoSerializer.scala @@ -1,25 +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 spark import java.io._ import java.nio.ByteBuffer -import java.nio.channels.Channels - -import scala.collection.immutable -import scala.collection.mutable - -import com.esotericsoftware.kryo._ -import com.esotericsoftware.kryo.{Serializer => KSerializer} +import com.esotericsoftware.kryo.{Kryo, KryoException} import com.esotericsoftware.kryo.io.{Input => KryoInput, Output => KryoOutput} import com.esotericsoftware.kryo.serializers.{JavaSerializer => KryoJavaSerializer} -import de.javakaffee.kryoserializers.KryoReflectionFactorySupport - +import com.twitter.chill.ScalaKryoInstantiator import serializer.{SerializerInstance, DeserializationStream, SerializationStream} import spark.broadcast._ import spark.storage._ private[spark] class KryoSerializationStream(kryo: Kryo, outStream: OutputStream) extends SerializationStream { - val output = new KryoOutput(outStream) def writeObject[T](t: T): SerializationStream = { @@ -33,7 +42,6 @@ class KryoSerializationStream(kryo: Kryo, outStream: OutputStream) extends Seria private[spark] class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends DeserializationStream { - val input = new KryoInput(inStream) def readObject[T](): T = { @@ -41,7 +49,7 @@ class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends Deser kryo.readClassAndObject(input).asInstanceOf[T] } catch { // DeserializationStream uses the EOF exception to indicate stopping condition. - case e: com.esotericsoftware.kryo.KryoException => throw new java.io.EOFException + case _: KryoException => throw new EOFException } } @@ -52,10 +60,9 @@ class KryoDeserializationStream(kryo: Kryo, inStream: InputStream) extends Deser } private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends SerializerInstance { - - val kryo = ks.kryo.get() - val output = ks.output.get() - val input = ks.input.get() + val kryo = ks.newKryo() + val output = ks.newKryoOutput() + val input = ks.newKryoInput() def serialize[T](t: T): ByteBuffer = { output.clear() @@ -91,130 +98,59 @@ private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends Serializ * serialization. */ trait KryoRegistrator { - def registerClasses(kryo: Kryo): Unit + def registerClasses(kryo: Kryo) } /** * A Spark serializer that uses the [[http://code.google.com/p/kryo/wiki/V1Documentation Kryo 1.x library]]. */ class KryoSerializer extends spark.serializer.Serializer with Logging { + private val bufferSize = System.getProperty("spark.kryoserializer.buffer.mb", "2").toInt * 1024 * 1024 - val bufferSize = System.getProperty("spark.kryoserializer.buffer.mb", "2").toInt * 1024 * 1024 - - val kryo = new ThreadLocal[Kryo] { - override def initialValue = createKryo() - } - - val output = new ThreadLocal[KryoOutput] { - override def initialValue = new KryoOutput(bufferSize) - } + def newKryoOutput() = new KryoOutput(bufferSize) - val input = new ThreadLocal[KryoInput] { - override def initialValue = new KryoInput(bufferSize) - } + def newKryoInput() = new KryoInput(bufferSize) - def createKryo(): Kryo = { - val kryo = new KryoReflectionFactorySupport() + def newKryo(): Kryo = { + val instantiator = new ScalaKryoInstantiator + val kryo = instantiator.newKryo() + val classLoader = Thread.currentThread.getContextClassLoader // Register some commonly used classes val toRegister: Seq[AnyRef] = Seq( - // Arrays - Array(1), Array(1.0), Array(1.0f), Array(1L), Array(""), Array(("", "")), - Array(new java.lang.Object), Array(1.toByte), Array(true), Array('c'), - // Specialized Tuple2s - ("", ""), ("", 1), (1, 1), (1.0, 1.0), (1L, 1L), - (1, 1.0), (1.0, 1), (1L, 1.0), (1.0, 1L), (1, 1L), (1L, 1), - // Scala collections - List(1), mutable.ArrayBuffer(1), - // Options and Either - Some(1), Left(1), Right(1), - // Higher-dimensional tuples - (1, 1, 1), (1, 1, 1, 1), (1, 1, 1, 1, 1), - None, ByteBuffer.allocate(1), StorageLevel.MEMORY_ONLY, PutBlock("1", ByteBuffer.allocate(1), StorageLevel.MEMORY_ONLY), GotBlock("1", ByteBuffer.allocate(1)), GetBlock("1") ) - for (obj <- toRegister) { - kryo.register(obj.getClass) - } + + for (obj <- toRegister) kryo.register(obj.getClass) // Allow sending SerializableWritable kryo.register(classOf[SerializableWritable[_]], new KryoJavaSerializer()) kryo.register(classOf[HttpBroadcast[_]], new KryoJavaSerializer()) - // Register some commonly used Scala singleton objects. Because these - // are singletons, we must return the exact same local object when we - // deserialize rather than returning a clone as FieldSerializer would. - class SingletonSerializer[T](obj: T) extends KSerializer[T] { - override def write(kryo: Kryo, output: KryoOutput, obj: T) {} - override def read(kryo: Kryo, input: KryoInput, cls: java.lang.Class[T]): T = obj - } - kryo.register(None.getClass, new SingletonSerializer[AnyRef](None)) - kryo.register(Nil.getClass, new SingletonSerializer[AnyRef](Nil)) - - // Register maps with a special serializer since they have complex internal structure - class ScalaMapSerializer(buildMap: Array[(Any, Any)] => scala.collection.Map[Any, Any]) - extends KSerializer[Array[(Any, Any)] => scala.collection.Map[Any, Any]] { - - //hack, look at https://groups.google.com/forum/#!msg/kryo-users/Eu5V4bxCfws/k-8UQ22y59AJ - private final val FAKE_REFERENCE = new Object() - override def write( - kryo: Kryo, - output: KryoOutput, - obj: Array[(Any, Any)] => scala.collection.Map[Any, Any]) { - val map = obj.asInstanceOf[scala.collection.Map[Any, Any]] - output.writeInt(map.size) - for ((k, v) <- map) { - kryo.writeClassAndObject(output, k) - kryo.writeClassAndObject(output, v) - } - } - override def read ( - kryo: Kryo, - input: KryoInput, - cls: Class[Array[(Any, Any)] => scala.collection.Map[Any, Any]]) - : Array[(Any, Any)] => scala.collection.Map[Any, Any] = { - kryo.reference(FAKE_REFERENCE) - val size = input.readInt() - val elems = new Array[(Any, Any)](size) - for (i <- 0 until size) { - val k = kryo.readClassAndObject(input) - val v = kryo.readClassAndObject(input) - elems(i)=(k,v) - } - buildMap(elems).asInstanceOf[Array[(Any, Any)] => scala.collection.Map[Any, Any]] + // Allow the user to register their own classes by setting spark.kryo.registrator + try { + Option(System.getProperty("spark.kryo.registrator")).foreach { regCls => + 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") } - kryo.register(mutable.HashMap().getClass, new ScalaMapSerializer(mutable.HashMap() ++ _)) - // TODO: add support for immutable maps too; this is more annoying because there are many - // subclasses of immutable.Map for small maps (with <= 4 entries) - val map1 = Map[Any, Any](1 -> 1) - val map2 = Map[Any, Any](1 -> 1, 2 -> 2) - val map3 = Map[Any, Any](1 -> 1, 2 -> 2, 3 -> 3) - val map4 = Map[Any, Any](1 -> 1, 2 -> 2, 3 -> 3, 4 -> 4) - val map5 = Map[Any, Any](1 -> 1, 2 -> 2, 3 -> 3, 4 -> 4, 5 -> 5) - kryo.register(map1.getClass, new ScalaMapSerializer(mutable.HashMap() ++ _ toMap)) - kryo.register(map2.getClass, new ScalaMapSerializer(mutable.HashMap() ++ _ toMap)) - kryo.register(map3.getClass, new ScalaMapSerializer(mutable.HashMap() ++ _ toMap)) - kryo.register(map4.getClass, new ScalaMapSerializer(mutable.HashMap() ++ _ toMap)) - kryo.register(map5.getClass, new ScalaMapSerializer(mutable.HashMap() ++ _ toMap)) - // Allow the user to register their own classes by setting spark.kryo.registrator - val regCls = System.getProperty("spark.kryo.registrator") - if (regCls != null) { - logInfo("Running user registrator: " + regCls) - val classLoader = Thread.currentThread.getContextClassLoader - val reg = Class.forName(regCls, true, classLoader).newInstance().asInstanceOf[KryoRegistrator] - reg.registerClasses(kryo) - } + 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) + kryo } def newInstance(): SerializerInstance = { - this.kryo.get().setClassLoader(Thread.currentThread().getContextClassLoader) new KryoSerializerInstance(this) } -} +}
\ No newline at end of file diff --git a/core/src/main/scala/spark/Logging.scala b/core/src/main/scala/spark/Logging.scala index 7c1c1bb144..79b0362830 100644 --- a/core/src/main/scala/spark/Logging.scala +++ b/core/src/main/scala/spark/Logging.scala @@ -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. + */ + package spark import org.slf4j.Logger @@ -68,6 +85,10 @@ trait Logging { if (log.isErrorEnabled) log.error(msg, throwable) } + protected def isTraceEnabled(): Boolean = { + 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 } diff --git a/core/src/main/scala/spark/MapOutputTracker.scala b/core/src/main/scala/spark/MapOutputTracker.scala index 866d630a6d..2c417e31db 100644 --- a/core/src/main/scala/spark/MapOutputTracker.scala +++ b/core/src/main/scala/spark/MapOutputTracker.scala @@ -1,7 +1,23 @@ +/* + * 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 spark import java.io._ -import java.util.concurrent.ConcurrentHashMap import java.util.zip.{GZIPInputStream, GZIPOutputStream} import scala.collection.mutable.HashMap @@ -12,8 +28,7 @@ import akka.dispatch._ import akka.pattern.ask import akka.remote._ import akka.util.Duration -import akka.util.Timeout -import akka.util.duration._ + import spark.scheduler.MapStatus import spark.storage.BlockManagerId @@ -40,10 +55,12 @@ private[spark] class MapOutputTrackerActor(tracker: MapOutputTracker) extends Ac private[spark] class MapOutputTracker extends Logging { + 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 mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]] + private var 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. @@ -52,7 +69,7 @@ private[spark] class MapOutputTracker extends Logging { // Cache a serialized version of the output statuses for each shuffle to send them out faster var cacheGeneration = generation - val cachedSerializedStatuses = new TimeStampedHashMap[Int, Array[Byte]] + private val cachedSerializedStatuses = new TimeStampedHashMap[Int, Array[Byte]] val metadataCleaner = new MetadataCleaner("MapOutputTracker", this.cleanup) @@ -60,7 +77,6 @@ private[spark] class MapOutputTracker extends Logging { // throw a SparkException if this fails. def askTracker(message: Any): Any = { try { - val timeout = 10.seconds val future = trackerActor.ask(message)(timeout) return Await.result(future, timeout) } catch { @@ -77,10 +93,9 @@ private[spark] class MapOutputTracker extends Logging { } def registerShuffle(shuffleId: Int, numMaps: Int) { - if (mapStatuses.get(shuffleId) != None) { + if (mapStatuses.putIfAbsent(shuffleId, new Array[MapStatus](numMaps)).isDefined) { throw new IllegalArgumentException("Shuffle ID " + shuffleId + " registered twice") } - mapStatuses.put(shuffleId, new Array[MapStatus](numMaps)) } def registerMapOutput(shuffleId: Int, mapId: Int, status: MapStatus) { @@ -101,8 +116,9 @@ private[spark] class MapOutputTracker extends Logging { } def unregisterMapOutput(shuffleId: Int, mapId: Int, bmAddress: BlockManagerId) { - var array = mapStatuses(shuffleId) - if (array != null) { + 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 @@ -115,13 +131,14 @@ private[spark] class MapOutputTracker extends Logging { } // Remembers which map output locations are currently being fetched on a worker - val fetching = new HashSet[Int] + private val fetching = new HashSet[Int] // Called on possibly remote nodes to get the server URIs and output sizes for a given shuffle def getServerStatuses(shuffleId: Int, reduceId: Int): Array[(BlockManagerId, Long)] = { val statuses = mapStatuses.get(shuffleId).orNull if (statuses == null) { logInfo("Don't have map outputs for shuffle " + shuffleId + ", fetching them") + var fetchedStatuses: Array[MapStatus] = null fetching.synchronized { if (fetching.contains(shuffleId)) { // Someone else is fetching it; wait for them to be done @@ -132,31 +149,48 @@ private[spark] class MapOutputTracker extends Logging { case e: InterruptedException => } } - return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, mapStatuses(shuffleId)) - } else { + } + + // Either while we waited the fetch happened successfully, or + // someone fetched it in between the get and the fetching.synchronized. + fetchedStatuses = mapStatuses.get(shuffleId).orNull + if (fetchedStatuses == null) { + // We have to do the fetch, get others to wait for us. fetching += shuffleId } } - // We won the race to fetch the output locs; do so - logInfo("Doing the fetch; tracker actor = " + trackerActor) - val host = System.getProperty("spark.hostname", Utils.localHostName) - // This try-finally prevents hangs due to timeouts: - var fetchedStatuses: Array[MapStatus] = null - try { - val fetchedBytes = - askTracker(GetMapOutputStatuses(shuffleId, host)).asInstanceOf[Array[Byte]] - fetchedStatuses = deserializeStatuses(fetchedBytes) - logInfo("Got the output locations") - mapStatuses.put(shuffleId, fetchedStatuses) - } finally { - fetching.synchronized { - fetching -= shuffleId - fetching.notifyAll() + + 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() + // This try-finally prevents hangs due to timeouts: + try { + val fetchedBytes = + askTracker(GetMapOutputStatuses(shuffleId, hostPort)).asInstanceOf[Array[Byte]] + fetchedStatuses = deserializeStatuses(fetchedBytes) + logInfo("Got the output locations") + mapStatuses.put(shuffleId, fetchedStatuses) + } finally { + fetching.synchronized { + fetching -= shuffleId + fetching.notifyAll() + } + } + } + if (fetchedStatuses != null) { + fetchedStatuses.synchronized { + return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, fetchedStatuses) } } - return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, fetchedStatuses) + else{ + throw new FetchFailedException(null, shuffleId, -1, reduceId, + new Exception("Missing all output locations for shuffle " + shuffleId)) + } } else { - return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, statuses) + statuses.synchronized { + return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, statuses) + } } } @@ -194,7 +228,8 @@ private[spark] class MapOutputTracker extends Logging { generationLock.synchronized { if (newGen > generation) { logInfo("Updating generation to " + newGen + " and clearing cache") - mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]] + // mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]] + mapStatuses.clear() generation = newGen } } @@ -232,10 +267,13 @@ private[spark] class MapOutputTracker extends Logging { // 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. - def serializeStatuses(statuses: Array[MapStatus]): Array[Byte] = { + private def serializeStatuses(statuses: Array[MapStatus]): Array[Byte] = { val out = new ByteArrayOutputStream val objOut = new ObjectOutputStream(new GZIPOutputStream(out)) - objOut.writeObject(statuses) + // Since statuses can be modified in parallel, sync on it + statuses.synchronized { + objOut.writeObject(statuses) + } objOut.close() out.toByteArray } @@ -243,7 +281,10 @@ private[spark] class MapOutputTracker extends Logging { // Opposite of serializeStatuses. def deserializeStatuses(bytes: Array[Byte]): Array[MapStatus] = { val objIn = new ObjectInputStream(new GZIPInputStream(new ByteArrayInputStream(bytes))) - objIn.readObject().asInstanceOf[Array[MapStatus]] + 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 ) } } @@ -253,14 +294,11 @@ private[spark] object MapOutputTracker { // 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), // throw a FetchFailedException. - def convertMapStatuses( + private def convertMapStatuses( shuffleId: Int, reduceId: Int, statuses: Array[MapStatus]): Array[(BlockManagerId, Long)] = { - if (statuses == null) { - throw new FetchFailedException(null, shuffleId, -1, reduceId, - new Exception("Missing all output locations for shuffle " + shuffleId)) - } + assert (statuses != null) statuses.map { status => if (status == null) { diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala index 07efba9e8d..6b0cc2fbf1 100644 --- a/core/src/main/scala/spark/PairRDDFunctions.scala +++ b/core/src/main/scala/spark/PairRDDFunctions.scala @@ -1,5 +1,23 @@ +/* + * 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 spark +import java.nio.ByteBuffer import java.util.{Date, HashMap => JHashMap} import java.text.SimpleDateFormat @@ -10,6 +28,8 @@ import scala.collection.JavaConversions._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path +import org.apache.hadoop.io.compress.CompressionCodec +import org.apache.hadoop.io.SequenceFile.CompressionType import org.apache.hadoop.mapred.FileOutputCommitter import org.apache.hadoop.mapred.FileOutputFormat import org.apache.hadoop.mapred.HadoopWriter @@ -17,7 +37,8 @@ import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapred.OutputFormat import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat} -import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat, RecordWriter => NewRecordWriter, Job => NewAPIHadoopJob, HadoopMapReduceUtil, TaskAttemptID, TaskAttemptContext} +import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat, RecordWriter => NewRecordWriter, Job => NewAPIHadoopJob, HadoopMapReduceUtil} +import org.apache.hadoop.security.UserGroupInformation import spark.partial.BoundedDouble import spark.partial.PartialResult @@ -52,7 +73,8 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( mergeValue: (C, V) => C, mergeCombiners: (C, C) => C, partitioner: Partitioner, - mapSideCombine: Boolean = true): RDD[(K, C)] = { + mapSideCombine: Boolean = true, + serializerClass: String = null): RDD[(K, C)] = { if (getKeyClass().isArray) { if (mapSideCombine) { throw new SparkException("Cannot use map-side combining with array keys.") @@ -61,19 +83,18 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( throw new SparkException("Default partitioner cannot partition array keys.") } } - val aggregator = - new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners) + val aggregator = new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners) if (self.partitioner == Some(partitioner)) { self.mapPartitions(aggregator.combineValuesByKey(_), true) } else if (mapSideCombine) { val mapSideCombined = self.mapPartitions(aggregator.combineValuesByKey(_), true) - val partitioned = new ShuffledRDD[K, C](mapSideCombined, partitioner) + val partitioned = new ShuffledRDD[K, C](mapSideCombined, partitioner, serializerClass) partitioned.mapPartitions(aggregator.combineCombinersByKey(_), 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](self, partitioner) + val values = new ShuffledRDD[K, V](self, partitioner, serializerClass) values.mapPartitions(aggregator.combineValuesByKey(_), true) } } @@ -94,7 +115,16 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( * list concatenation, 0 for addition, or 1 for multiplication.). */ def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = { - combineByKey[V]({v: V => func(zeroValue, v)}, func, func, partitioner) + // Serialize the zero value to a byte array so that we can get a new clone of it on each key + val zeroBuffer = SparkEnv.get.closureSerializer.newInstance().serialize(zeroValue) + val zeroArray = new Array[Byte](zeroBuffer.limit) + zeroBuffer.get(zeroArray) + + // When deserializing, use a lazy val to create just one instance of the serializer per task + lazy val cachedSerializer = SparkEnv.get.closureSerializer.newInstance() + def createZero() = cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray)) + + combineByKey[V]((v: V) => func(createZero(), v), func, func, partitioner) } /** @@ -184,11 +214,13 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( * partitioning of the resulting key-value pair RDD by passing a Partitioner. */ def groupByKey(partitioner: Partitioner): RDD[(K, Seq[V])] = { + // groupByKey shouldn't use map side combine because map side combine does not + // reduce the amount of data shuffled and requires all map side data be inserted + // into a hash table, leading to more objects in the old gen. def createCombiner(v: V) = ArrayBuffer(v) def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v - def mergeCombiners(b1: ArrayBuffer[V], b2: ArrayBuffer[V]) = b1 ++= b2 val bufs = combineByKey[ArrayBuffer[V]]( - createCombiner _, mergeValue _, mergeCombiners _, partitioner) + createCombiner _, mergeValue _, null, partitioner, mapSideCombine=false) bufs.asInstanceOf[RDD[(K, Seq[V])]] } @@ -469,7 +501,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( /** * Return an RDD with the pairs from `this` whose keys are not in `other`. - * + * * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ @@ -515,6 +547,16 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( } /** + * 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. Compress the result with the + * 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) + } + + /** * 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. */ @@ -545,8 +587,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( // 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 = new TaskAttemptID(jobtrackerID, - stageId, false, context.splitId, attemptNumber) + val attemptId = newTaskAttemptID(jobtrackerID, stageId, false, context.splitId, attemptNumber) val hadoopContext = newTaskAttemptContext(wrappedConf.value, attemptId) val format = outputFormatClass.newInstance val committer = format.getOutputCommitter(hadoopContext) @@ -565,16 +606,31 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( * however we're only going to use this local OutputCommitter for * setupJob/commitJob, so we just use a dummy "map" task. */ - val jobAttemptId = new TaskAttemptID(jobtrackerID, stageId, true, 0, 0) + val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, true, 0, 0) val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId) val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext) jobCommitter.setupJob(jobTaskContext) val count = self.context.runJob(self, writeShard _).sum + jobCommitter.commitJob(jobTaskContext) jobCommitter.cleanupJob(jobTaskContext) } /** * 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. Compress with the supplied codec. + */ + def saveAsHadoopFile( + path: String, + keyClass: Class[_], + valueClass: Class[_], + outputFormatClass: Class[_ <: OutputFormat[_, _]], + codec: Class[_ <: CompressionCodec]) { + saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, + new JobConf(self.context.hadoopConfiguration), Some(codec)) + } + + /** + * 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( @@ -582,11 +638,19 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], - conf: JobConf = new JobConf(self.context.hadoopConfiguration)) { + conf: JobConf = new JobConf(self.context.hadoopConfiguration), + codec: Option[Class[_ <: CompressionCodec]] = None) { conf.setOutputKeyClass(keyClass) conf.setOutputValueClass(valueClass) // conf.setOutputFormat(outputFormatClass) // Doesn't work in Scala 2.9 due to what may be a generics bug conf.set("mapred.output.format.class", outputFormatClass.getName) + for (c <- codec) { + conf.setCompressMapOutput(true) + conf.set("mapred.output.compress", "true") + conf.setMapOutputCompressorClass(c) + conf.set("mapred.output.compression.codec", c.getCanonicalName) + conf.set("mapred.output.compression.type", CompressionType.BLOCK.toString) + } conf.setOutputCommitter(classOf[FileOutputCommitter]) FileOutputFormat.setOutputPath(conf, HadoopWriter.createPathFromString(path, conf)) saveAsHadoopDataset(conf) @@ -637,6 +701,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( } self.context.runJob(self, writeToFile _) + writer.commitJob() writer.cleanup() } @@ -644,7 +709,7 @@ class PairRDDFunctions[K: ClassManifest, V: ClassManifest]( * Return an RDD with the keys of each tuple. */ def keys: RDD[K] = self.map(_._1) - + /** * Return an RDD with the values of each tuple. */ diff --git a/core/src/main/scala/spark/Partition.scala b/core/src/main/scala/spark/Partition.scala index e384308ef6..2a4edcec98 100644 --- a/core/src/main/scala/spark/Partition.scala +++ b/core/src/main/scala/spark/Partition.scala @@ -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. + */ + package spark /** diff --git a/core/src/main/scala/spark/Partitioner.scala b/core/src/main/scala/spark/Partitioner.scala index 6f8cd17c88..6035bc075e 100644 --- a/core/src/main/scala/spark/Partitioner.scala +++ b/core/src/main/scala/spark/Partitioner.scala @@ -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. + */ + package spark /** @@ -48,17 +65,9 @@ object Partitioner { class HashPartitioner(partitions: Int) extends Partitioner { def numPartitions = partitions - def getPartition(key: Any): Int = { - if (key == null) { - return 0 - } else { - val mod = key.hashCode % partitions - if (mod < 0) { - mod + partitions - } else { - mod // Guard against negative hash codes - } - } + def getPartition(key: Any): Int = key match { + case null => 0 + case _ => Utils.nonNegativeMod(key.hashCode, numPartitions) } override def equals(other: Any): Boolean = other match { diff --git a/core/src/main/scala/spark/RDD.scala b/core/src/main/scala/spark/RDD.scala index ed39732f13..ca7cdd622a 100644 --- a/core/src/main/scala/spark/RDD.scala +++ b/core/src/main/scala/spark/RDD.scala @@ -1,21 +1,37 @@ +/* + * 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 spark -import java.net.URL -import java.util.{Date, Random} -import java.util.{HashMap => JHashMap} +import java.util.Random import scala.collection.Map import scala.collection.JavaConversions.mapAsScalaMap import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap import org.apache.hadoop.io.BytesWritable +import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.io.NullWritable import org.apache.hadoop.io.Text import org.apache.hadoop.mapred.TextOutputFormat import it.unimi.dsi.fastutil.objects.{Object2LongOpenHashMap => OLMap} +import spark.broadcast.Broadcast import spark.Partitioner._ import spark.partial.BoundedDouble import spark.partial.CountEvaluator @@ -31,10 +47,14 @@ import spark.rdd.MapPartitionsRDD import spark.rdd.MapPartitionsWithIndexRDD import spark.rdd.PipedRDD import spark.rdd.SampledRDD -import spark.rdd.SubtractedRDD +import spark.rdd.ShuffledRDD import spark.rdd.UnionRDD import spark.rdd.ZippedRDD +import spark.rdd.ZippedPartitionsRDD2 +import spark.rdd.ZippedPartitionsRDD3 +import spark.rdd.ZippedPartitionsRDD4 import spark.storage.StorageLevel +import spark.util.BoundedPriorityQueue import SparkContext._ @@ -102,8 +122,11 @@ abstract class RDD[T: ClassManifest]( // Methods and fields available on all RDDs // ======================================================================= + /** The SparkContext that created this RDD. */ + def sparkContext: SparkContext = sc + /** A unique ID for this RDD (within its SparkContext). */ - val id = sc.newRddId() + val id: Int = sc.newRddId() /** A friendly name for this RDD */ var name: String = null @@ -114,9 +137,18 @@ abstract class RDD[T: ClassManifest]( this } + /** User-defined generator of this RDD*/ + var generator = Utils.getCallSiteInfo.firstUserClass + + /** Reset generator*/ + def setGenerator(_generator: String) = { + generator = _generator + } + /** * 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. + * it is computed. This can only be used to assign a new storage level if the RDD does not + * have a storage level set yet.. */ def persist(newLevel: StorageLevel): RDD[T] = { // TODO: Handle changes of StorageLevel @@ -136,6 +168,20 @@ abstract class RDD[T: ClassManifest]( /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */ def cache(): RDD[T] = persist() + /** + * 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. + * @return This RDD. + */ + def unpersist(blocking: Boolean = true): RDD[T] = { + logInfo("Removing RDD " + id + " from persistence list") + sc.env.blockManager.master.removeRdd(id, blocking) + sc.persistentRdds.remove(id) + storageLevel = StorageLevel.NONE + this + } + /** Get the RDD's current storage level, or StorageLevel.NONE if none is set. */ def getStorageLevel = storageLevel @@ -237,7 +283,14 @@ abstract class RDD[T: ClassManifest]( /** * Return a new RDD that is reduced into `numPartitions` partitions. */ - def coalesce(numPartitions: Int): RDD[T] = new CoalescedRDD(this, numPartitions) + def coalesce(numPartitions: Int, shuffle: Boolean = false): RDD[T] = { + if (shuffle) { + // include a shuffle step so that our upstream tasks are still distributed + new CoalescedRDD(new ShuffledRDD(map(x => (x, null)), new HashPartitioner(numPartitions)), numPartitions).keys + } else { + new CoalescedRDD(this, numPartitions) + } + } /** * Return a sampled subset of this RDD. @@ -249,30 +302,34 @@ abstract class RDD[T: ClassManifest]( var fraction = 0.0 var total = 0 var multiplier = 3.0 - var initialCount = count() + var initialCount = this.count() var maxSelected = 0 + if (num < 0) { + throw new IllegalArgumentException("Negative number of elements requested") + } + if (initialCount > Integer.MAX_VALUE - 1) { maxSelected = Integer.MAX_VALUE - 1 } else { maxSelected = initialCount.toInt } - if (num > initialCount) { + if (num > initialCount && !withReplacement) { total = maxSelected - fraction = math.min(multiplier * (maxSelected + 1) / initialCount, 1.0) - } else if (num < 0) { - throw(new IllegalArgumentException("Negative number of elements requested")) + fraction = multiplier * (maxSelected + 1) / initialCount } else { - fraction = math.min(multiplier * (num + 1) / initialCount, 1.0) + fraction = multiplier * (num + 1) / initialCount total = num } val rand = new Random(seed) - var samples = this.sample(withReplacement, fraction, rand.nextInt).collect() + var samples = this.sample(withReplacement, fraction, rand.nextInt()).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 thei initial size while (samples.length < total) { - samples = this.sample(withReplacement, fraction, rand.nextInt).collect() + samples = this.sample(withReplacement, fraction, rand.nextInt()).collect() } Utils.randomizeInPlace(samples, rand).take(total) @@ -330,13 +387,36 @@ abstract class RDD[T: ClassManifest]( /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: Seq[String]): RDD[String] = new PipedRDD(this, command) + 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. - */ - def pipe(command: Seq[String], env: Map[String, String]): RDD[String] = - new PipedRDD(this, command, env) + * The print behavior can be customized by providing two functions. + * + * @param command command to run in forked process. + * @param env environment variables to set. + * @param printPipeContext Before piping elements, this function is called as an oppotunity + * to pipe context data. Print line function (like out.println) will be + * passed as printPipeContext's parameter. + * @param printRDDElement Use this function to customize how to pipe elements. This function + * will be called with each RDD element as the 1st parameter, and the + * print line function (like out.println()) as the 2nd parameter. + * An example of pipe the RDD data of groupBy() in a streaming way, + * instead of constructing a huge String to concat all the elements: + * def printRDDElement(record:(String, Seq[String]), f:String=>Unit) = + * for (e <- record._2){f(e)} + * @return the result RDD + */ + def pipe( + command: Seq[String], + env: Map[String, String] = Map(), + printPipeContext: (String => Unit) => Unit = null, + 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. @@ -358,7 +438,7 @@ abstract class RDD[T: ClassManifest]( * 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") + @deprecated("use mapPartitionsWithIndex", "0.7.0") def mapPartitionsWithSplit[U: ClassManifest]( f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = @@ -428,6 +508,31 @@ abstract class RDD[T: ClassManifest]( */ def zip[U: ClassManifest](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 + * applying a function to the zipped partitions. Assumes that all the RDDs have the + * *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]( + f: (Iterator[T], Iterator[B]) => Iterator[V], + rdd2: RDD[B]): RDD[V] = + new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2) + + def zipPartitions[B: ClassManifest, C: ClassManifest, V: ClassManifest]( + f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V], + rdd2: RDD[B], + rdd3: RDD[C]): RDD[V] = + new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3) + + def zipPartitions[B: ClassManifest, C: ClassManifest, D: ClassManifest, V: ClassManifest]( + f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V], + rdd2: RDD[B], + rdd3: RDD[C], + rdd4: RDD[D]): RDD[V] = + new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4) + + // Actions (launch a job to return a value to the user program) /** @@ -443,7 +548,7 @@ abstract class RDD[T: ClassManifest]( */ def foreachPartition(f: Iterator[T] => Unit) { val cleanF = sc.clean(f) - sc.runJob(this, (iter: Iterator[T]) => f(iter)) + sc.runJob(this, (iter: Iterator[T]) => cleanF(iter)) } /** @@ -676,6 +781,34 @@ abstract class RDD[T: ClassManifest]( } /** + * Returns the top K elements from this RDD as defined by + * the specified implicit Ordering[T]. + * @param num the number of top elements to return + * @param ord the implicit ordering for T + * @return an array of top elements + */ + def top(num: Int)(implicit ord: Ordering[T]): Array[T] = { + mapPartitions { items => + val queue = new BoundedPriorityQueue[T](num) + queue ++= items + Iterator.single(queue) + }.reduce { (queue1, queue2) => + queue1 ++= queue2 + queue1 + }.toArray.sorted(ord.reverse) + } + + /** + * Returns the first K elements from this RDD as defined by + * the specified implicit Ordering[T] and maintains the + * ordering. + * @param num the number of top elements to return + * @param ord the implicit ordering for T + * @return an array of top elements + */ + def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = top(num)(ord.reverse) + + /** * Save this RDD as a text file, using string representations of elements. */ def saveAsTextFile(path: String) { @@ -684,6 +817,14 @@ abstract class RDD[T: ClassManifest]( } /** + * Save this RDD as a compressed text file, using string representations of elements. + */ + def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) { + this.map(x => (NullWritable.get(), new Text(x.toString))) + .saveAsHadoopFile[TextOutputFormat[NullWritable, Text]](path, codec) + } + + /** * Save this RDD as a SequenceFile of serialized objects. */ def saveAsObjectFile(path: String) { @@ -741,7 +882,7 @@ abstract class RDD[T: ClassManifest]( private var storageLevel: StorageLevel = StorageLevel.NONE /** Record user function generating this RDD. */ - private[spark] val origin = Utils.getSparkCallSite + private[spark] val origin = Utils.formatSparkCallSite private[spark] def elementClassManifest: ClassManifest[T] = classManifest[T] diff --git a/core/src/main/scala/spark/RDDCheckpointData.scala b/core/src/main/scala/spark/RDDCheckpointData.scala index d00092e984..b615f820eb 100644 --- a/core/src/main/scala/spark/RDDCheckpointData.scala +++ b/core/src/main/scala/spark/RDDCheckpointData.scala @@ -1,6 +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 spark import org.apache.hadoop.fs.Path +import org.apache.hadoop.conf.Configuration import rdd.{CheckpointRDD, CoalescedRDD} import scheduler.{ResultTask, ShuffleMapTask} @@ -62,14 +80,20 @@ 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()) + if (!fs.mkdirs(path)) { + throw new SparkException("Failed to create checkpoint path " + path) + } + // Save to file, and reload it as an RDD - val path = new Path(rdd.context.checkpointDir.get, "rdd-" + rdd.id).toString - rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path) _) - val newRDD = new CheckpointRDD[T](rdd.context, path) + rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path.toString) _) + val newRDD = new CheckpointRDD[T](rdd.context, path.toString) // Change the dependencies and partitions of the RDD RDDCheckpointData.synchronized { - cpFile = Some(path) + cpFile = Some(path.toString) cpRDD = Some(newRDD) rdd.markCheckpointed(newRDD) // Update the RDD's dependencies and partitions cpState = Checkpointed diff --git a/core/src/main/scala/spark/SequenceFileRDDFunctions.scala b/core/src/main/scala/spark/SequenceFileRDDFunctions.scala index 6b4a11d6d3..9f30b7f22f 100644 --- a/core/src/main/scala/spark/SequenceFileRDDFunctions.scala +++ b/core/src/main/scala/spark/SequenceFileRDDFunctions.scala @@ -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. + */ + package spark import java.io.EOFException @@ -18,6 +35,7 @@ import org.apache.hadoop.mapred.TextOutputFormat import org.apache.hadoop.mapred.SequenceFileOutputFormat import org.apache.hadoop.mapred.OutputCommitter import org.apache.hadoop.mapred.FileOutputCommitter +import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.io.Writable import org.apache.hadoop.io.NullWritable import org.apache.hadoop.io.BytesWritable @@ -36,17 +54,17 @@ class SequenceFileRDDFunctions[K <% Writable: ClassManifest, V <% Writable : Cla self: RDD[(K, V)]) extends Logging with Serializable { - + private def getWritableClass[T <% Writable: ClassManifest](): Class[_ <: Writable] = { val c = { - if (classOf[Writable].isAssignableFrom(classManifest[T].erasure)) { + if (classOf[Writable].isAssignableFrom(classManifest[T].erasure)) { classManifest[T].erasure } 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 - // is of the form "java.lang.Object apply(java.lang.Object)" + // is not of the form "java.lang.Object apply(java.lang.Object)" implicitly[T => Writable].getClass.getDeclaredMethods().filter( - m => m.getReturnType().toString != "java.lang.Object" && + m => m.getReturnType().toString != "class java.lang.Object" && m.getName() == "apply")(0).getReturnType } @@ -62,24 +80,28 @@ class SequenceFileRDDFunctions[K <% Writable: ClassManifest, V <% Writable : Cla * byte arrays to BytesWritable, and Strings to Text. The `path` can be on any Hadoop-supported * file system. */ - def saveAsSequenceFile(path: String) { + def saveAsSequenceFile(path: String, codec: Option[Class[_ <: CompressionCodec]] = None) { def anyToWritable[U <% Writable](u: U): Writable = u val keyClass = getWritableClass[K] val valueClass = getWritableClass[V] val convertKey = !classOf[Writable].isAssignableFrom(self.getKeyClass) val convertValue = !classOf[Writable].isAssignableFrom(self.getValueClass) - - logInfo("Saving as sequence file of type (" + keyClass.getSimpleName + "," + valueClass.getSimpleName + ")" ) + + logInfo("Saving as sequence file of type (" + keyClass.getSimpleName + "," + valueClass.getSimpleName + ")" ) val format = classOf[SequenceFileOutputFormat[Writable, Writable]] + val jobConf = new JobConf(self.context.hadoopConfiguration) if (!convertKey && !convertValue) { - self.saveAsHadoopFile(path, keyClass, valueClass, format) + self.saveAsHadoopFile(path, keyClass, valueClass, format, jobConf, codec) } else if (!convertKey && convertValue) { - self.map(x => (x._1,anyToWritable(x._2))).saveAsHadoopFile(path, keyClass, valueClass, format) + self.map(x => (x._1,anyToWritable(x._2))).saveAsHadoopFile( + path, keyClass, valueClass, format, jobConf, codec) } else if (convertKey && !convertValue) { - self.map(x => (anyToWritable(x._1),x._2)).saveAsHadoopFile(path, keyClass, valueClass, format) + self.map(x => (anyToWritable(x._1),x._2)).saveAsHadoopFile( + path, keyClass, valueClass, format, jobConf, codec) } else if (convertKey && convertValue) { - self.map(x => (anyToWritable(x._1),anyToWritable(x._2))).saveAsHadoopFile(path, keyClass, valueClass, format) - } + self.map(x => (anyToWritable(x._1),anyToWritable(x._2))).saveAsHadoopFile( + path, keyClass, valueClass, format, jobConf, codec) + } } } diff --git a/core/src/main/scala/spark/SerializableWritable.scala b/core/src/main/scala/spark/SerializableWritable.scala index 8306fbf570..0236611ef9 100644 --- a/core/src/main/scala/spark/SerializableWritable.scala +++ b/core/src/main/scala/spark/SerializableWritable.scala @@ -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. + */ + package spark import java.io._ diff --git a/core/src/main/scala/spark/ShuffleFetcher.scala b/core/src/main/scala/spark/ShuffleFetcher.scala index 442e9f0269..dcced035e7 100644 --- a/core/src/main/scala/spark/ShuffleFetcher.scala +++ b/core/src/main/scala/spark/ShuffleFetcher.scala @@ -1,13 +1,33 @@ +/* + * 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 spark -import executor.TaskMetrics +import spark.executor.TaskMetrics +import spark.serializer.Serializer + 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[K, V](shuffleId: Int, reduceId: Int, metrics: TaskMetrics) : Iterator[(K,V)] + def fetch[K, V](shuffleId: Int, reduceId: Int, metrics: TaskMetrics, + serializer: Serializer = SparkEnv.get.serializerManager.default): Iterator[(K,V)] /** Stop the fetcher */ def stop() {} diff --git a/core/src/main/scala/spark/SizeEstimator.scala b/core/src/main/scala/spark/SizeEstimator.scala index d4e1157250..6cc57566d7 100644 --- a/core/src/main/scala/spark/SizeEstimator.scala +++ b/core/src/main/scala/spark/SizeEstimator.scala @@ -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. + */ + package spark import java.lang.reflect.Field @@ -198,7 +215,7 @@ private[spark] object SizeEstimator extends Logging { val elem = JArray.get(array, index) size += SizeEstimator.estimate(elem, state.visited) } - state.size += ((length / 100.0) * size).toLong + state.size += ((length / (ARRAY_SAMPLE_SIZE * 1.0)) * size).toLong } } } diff --git a/core/src/main/scala/spark/SoftReferenceCache.scala b/core/src/main/scala/spark/SoftReferenceCache.scala deleted file mode 100644 index 3dd0a4b1f9..0000000000 --- a/core/src/main/scala/spark/SoftReferenceCache.scala +++ /dev/null @@ -1,18 +0,0 @@ -package spark - -import com.google.common.collect.MapMaker - -/** - * An implementation of Cache that uses soft references. - */ -private[spark] class SoftReferenceCache extends Cache { - val map = new MapMaker().softValues().makeMap[Any, Any]() - - override def get(datasetId: Any, partition: Int): Any = - map.get((datasetId, partition)) - - override def put(datasetId: Any, partition: Int, value: Any): CachePutResponse = { - map.put((datasetId, partition), value) - return CachePutSuccess(0) - } -} diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala index 4957a54c1b..40b30e4d23 100644 --- a/core/src/main/scala/spark/SparkContext.scala +++ b/core/src/main/scala/spark/SparkContext.scala @@ -1,47 +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 spark import java.io._ -import java.util.concurrent.atomic.AtomicInteger import java.net.URI +import java.util.Properties +import java.util.concurrent.ConcurrentHashMap +import java.util.concurrent.atomic.AtomicInteger +import scala.collection.JavaConversions._ import scala.collection.Map import scala.collection.generic.Growable import scala.collection.mutable.HashMap +import scala.collection.mutable.ArrayBuffer import scala.collection.JavaConversions._ +import scala.util.DynamicVariable +import scala.collection.mutable.{ConcurrentMap, HashMap} + +import akka.actor.Actor._ -import org.apache.hadoop.fs.Path import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.mapred.InputFormat -import org.apache.hadoop.mapred.SequenceFileInputFormat -import org.apache.hadoop.io.Writable -import org.apache.hadoop.io.IntWritable -import org.apache.hadoop.io.LongWritable -import org.apache.hadoop.io.FloatWritable -import org.apache.hadoop.io.DoubleWritable +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.ArrayWritable +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.lib.input.{FileInputFormat => NewFileInputFormat} import org.apache.hadoop.mapreduce.{Job => NewHadoopJob} +import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} +import org.apache.hadoop.security.UserGroupInformation + import org.apache.mesos.MesosNativeLibrary -import spark.deploy.LocalSparkCluster -import spark.partial.ApproximateEvaluator -import spark.partial.PartialResult +import spark.deploy.{LocalSparkCluster, SparkHadoopUtil} +import spark.partial.{ApproximateEvaluator, PartialResult} import spark.rdd.{CheckpointRDD, HadoopRDD, NewHadoopRDD, UnionRDD, ParallelCollectionRDD} -import spark.scheduler._ +import spark.scheduler.{DAGScheduler, DAGSchedulerSource, ResultTask, ShuffleMapTask, SparkListener, + SplitInfo, Stage, StageInfo, TaskScheduler, ActiveJob} +import spark.scheduler.cluster.{StandaloneSchedulerBackend, SparkDeploySchedulerBackend, + ClusterScheduler, Schedulable, SchedulingMode} import spark.scheduler.local.LocalScheduler -import spark.scheduler.cluster.{SparkDeploySchedulerBackend, SchedulerBackend, ClusterScheduler} import spark.scheduler.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend} -import spark.storage.BlockManagerUI +import spark.storage.{StorageStatus, StorageUtils, RDDInfo, BlockManagerSource} import spark.util.{MetadataCleaner, TimeStampedHashMap} -import spark.storage.{StorageStatus, StorageUtils, RDDInfo} +import ui.{SparkUI} +import spark.metrics._ /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark @@ -59,7 +88,10 @@ class SparkContext( val appName: String, val sparkHome: String = null, val jars: Seq[String] = Nil, - val environment: Map[String, String] = Map()) + 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()) extends Logging { // Ensure logging is initialized before we spawn any threads @@ -67,7 +99,7 @@ class SparkContext( // Set Spark driver host and port system properties if (System.getProperty("spark.driver.host") == null) { - System.setProperty("spark.driver.host", Utils.localIpAddress) + System.setProperty("spark.driver.host", Utils.localHostName()) } if (System.getProperty("spark.driver.port") == null) { System.setProperty("spark.driver.port", "0") @@ -84,34 +116,39 @@ class SparkContext( isLocal) SparkEnv.set(env) - // Start the BlockManager UI - private[spark] val ui = new BlockManagerUI( - env.actorSystem, env.blockManager.master.driverActor, this) - ui.start() - // Used to store a URL for each static file/jar together with the file's local timestamp private[spark] val addedFiles = HashMap[String, Long]() private[spark] val addedJars = HashMap[String, Long]() // Keeps track of all persisted RDDs - private[spark] val persistentRdds = new TimeStampedHashMap[Int, RDD[_]]() + private[spark] val persistentRdds = new TimeStampedHashMap[Int, RDD[_]] private[spark] val metadataCleaner = new MetadataCleaner("SparkContext", this.cleanup) + // Initalize the Spark UI + private[spark] val ui = new SparkUI(this) + ui.bind() + + val startTime = System.currentTimeMillis() // Add each JAR given through the constructor - jars.foreach { addJar(_) } + if (jars != null) { + jars.foreach { addJar(_) } + } // 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_MEM", "SPARK_CLASSPATH", "SPARK_LIBRARY_PATH", "SPARK_JAVA_OPTS", - "SPARK_TESTING")) { + for (key <- Seq("SPARK_CLASSPATH", "SPARK_LIBRARY_PATH", "SPARK_JAVA_OPTS", "SPARK_TESTING")) { val value = System.getenv(key) if (value != null) { executorEnvs(key) = value } } - executorEnvs ++= environment + // Since memory can be set with a system property too, use that + executorEnvs("SPARK_MEM") = SparkContext.executorMemoryRequested + "m" + if (environment != null) { + executorEnvs ++= environment + } // Create and start the scheduler private var taskScheduler: TaskScheduler = { @@ -143,14 +180,12 @@ class SparkContext( scheduler case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) => - // Check to make sure SPARK_MEM <= memoryPerSlave. Otherwise Spark will just hang. + // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang. val memoryPerSlaveInt = memoryPerSlave.toInt - val sparkMemEnv = System.getenv("SPARK_MEM") - val sparkMemEnvInt = if (sparkMemEnv != null) Utils.memoryStringToMb(sparkMemEnv) else 512 - if (sparkMemEnvInt > memoryPerSlaveInt) { + if (SparkContext.executorMemoryRequested > memoryPerSlaveInt) { throw new SparkException( - "Slave memory (%d MB) cannot be smaller than SPARK_MEM (%d MB)".format( - memoryPerSlaveInt, sparkMemEnvInt)) + "Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format( + memoryPerSlaveInt, SparkContext.executorMemoryRequested)) } val scheduler = new ClusterScheduler(this) @@ -164,6 +199,22 @@ class SparkContext( } scheduler + case "yarn-standalone" => + val scheduler = try { + val clazz = Class.forName("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)) @@ -183,12 +234,14 @@ class SparkContext( } taskScheduler.start() - private var dagScheduler = new DAGScheduler(taskScheduler) + @volatile private var dagScheduler = new DAGScheduler(taskScheduler) dagScheduler.start() + ui.start() + /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */ val hadoopConfiguration = { - val conf = new Configuration() + val conf = SparkHadoopUtil.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")) @@ -207,6 +260,38 @@ class SparkContext( private[spark] var checkpointDir: Option[String] = None + // Thread Local variable that can be used by users to pass information down the stack + private val localProperties = new DynamicVariable[Properties](null) + + def initLocalProperties() { + localProperties.value = new Properties() + } + + def addLocalProperty(key: String, value: String) { + if(localProperties.value == null) { + localProperties.value = new Properties() + } + localProperties.value.setProperty(key,value) + } + + /** Set a human readable description of the current job. */ + def setDescription(value: String) { + addLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, value) + } + + // Post init + taskScheduler.postStartHook() + + val dagSchedulerSource = new DAGSchedulerSource(this.dagScheduler) + val blockManagerSource = new BlockManagerSource(SparkEnv.get.blockManager) + + def initDriverMetrics() { + SparkEnv.get.metricsSystem.registerSource(dagSchedulerSource) + SparkEnv.get.metricsSystem.registerSource(blockManagerSource) + } + + initDriverMetrics() + // Methods for creating RDDs /** Distribute a local Scala collection to form an RDD. */ @@ -471,7 +556,7 @@ class SparkContext( */ def getExecutorMemoryStatus: Map[String, (Long, Long)] = { env.blockManager.master.getMemoryStatus.map { case(blockManagerId, mem) => - (blockManagerId.ip + ":" + blockManagerId.port, mem) + (blockManagerId.host + ":" + blockManagerId.port, mem) } } @@ -479,10 +564,16 @@ class SparkContext( * Return information about what RDDs are cached, if they are in mem or on disk, how much space * they take, etc. */ - def getRDDStorageInfo : Array[RDDInfo] = { + def getRDDStorageInfo: Array[RDDInfo] = { StorageUtils.rddInfoFromStorageStatus(getExecutorStorageStatus, this) } + /** + * Returns an immutable map of RDDs that have marked themselves as persistent via cache() call. + * Note that this does not necessarily mean the caching or computation was successful. + */ + def getPersistentRDDs: Map[Int, RDD[_]] = persistentRdds.toMap + def getStageInfo: Map[Stage,StageInfo] = { dagScheduler.stageToInfos } @@ -490,11 +581,33 @@ class SparkContext( /** * Return information about blocks stored in all of the slaves */ - def getExecutorStorageStatus : Array[StorageStatus] = { + def getExecutorStorageStatus: Array[StorageStatus] = { env.blockManager.master.getStorageStatus } /** + * Return pools for fair scheduler + * TODO(xiajunluan): We should take nested pools into account + */ + def getAllPools: ArrayBuffer[Schedulable] = { + taskScheduler.rootPool.schedulableQueue + } + + /** + * Return the pool associated with the given name, if one exists + */ + def getPoolForName(pool: String): Option[Schedulable] = { + taskScheduler.rootPool.schedulableNameToSchedulable.get(pool) + } + + /** + * Return current scheduling mode + */ + def getSchedulingMode: SchedulingMode.SchedulingMode = { + taskScheduler.schedulingMode + } + + /** * Clear the job's list of files added by `addFile` so that they do not get downloaded to * any new nodes. */ @@ -508,13 +621,23 @@ class SparkContext( * filesystems), or an HTTP, HTTPS or FTP URI. */ def addJar(path: String) { - val uri = new URI(path) - val key = uri.getScheme match { - case null | "file" => env.httpFileServer.addJar(new File(uri.getPath)) - case _ => path + if (null == path) { + logWarning("null specified as parameter to addJar", + new SparkException("null specified as parameter to addJar")) + } else { + val uri = new URI(path) + val key = uri.getScheme match { + case null | "file" => + if (SparkHadoopUtil.isYarnMode()) { + logWarning("local jar specified as parameter to addJar under Yarn mode") + return + } + env.httpFileServer.addJar(new File(uri.getPath)) + case _ => path + } + addedJars(key) = System.currentTimeMillis + logInfo("Added JAR " + path + " at " + key + " with timestamp " + addedJars(key)) } - addedJars(key) = System.currentTimeMillis - logInfo("Added JAR " + path + " at " + key + " with timestamp " + addedJars(key)) } /** @@ -527,10 +650,14 @@ class SparkContext( /** Shut down the SparkContext. */ def stop() { - if (dagScheduler != null) { + ui.stop() + // Do this only if not stopped already - best case effort. + // prevent NPE if stopped more than once. + val dagSchedulerCopy = dagScheduler + dagScheduler = null + if (dagSchedulerCopy != null) { metadataCleaner.cancel() - dagScheduler.stop() - dagScheduler = null + dagSchedulerCopy.stop() taskScheduler = null // TODO: Cache.stop()? env.stop() @@ -546,6 +673,7 @@ class SparkContext( } } + /** * Get Spark's home location from either a value set through the constructor, * or the spark.home Java property, or the SPARK_HOME environment variable @@ -575,10 +703,10 @@ class SparkContext( partitions: Seq[Int], allowLocal: Boolean, resultHandler: (Int, U) => Unit) { - val callSite = Utils.getSparkCallSite + val callSite = Utils.formatSparkCallSite logInfo("Starting job: " + callSite) val start = System.nanoTime - val result = dagScheduler.runJob(rdd, func, partitions, callSite, allowLocal, resultHandler) + val result = dagScheduler.runJob(rdd, func, partitions, callSite, allowLocal, resultHandler, localProperties.value) logInfo("Job finished: " + callSite + ", took " + (System.nanoTime - start) / 1e9 + " s") rdd.doCheckpoint() result @@ -657,12 +785,11 @@ class SparkContext( rdd: RDD[T], func: (TaskContext, Iterator[T]) => U, evaluator: ApproximateEvaluator[U, R], - timeout: Long - ): PartialResult[R] = { - val callSite = Utils.getSparkCallSite + timeout: Long): PartialResult[R] = { + val callSite = Utils.formatSparkCallSite logInfo("Starting job: " + callSite) val start = System.nanoTime - val result = dagScheduler.runApproximateJob(rdd, func, evaluator, callSite, timeout) + val result = dagScheduler.runApproximateJob(rdd, func, evaluator, callSite, timeout, localProperties.value) logInfo("Job finished: " + callSite + ", took " + (System.nanoTime - start) / 1e9 + " s") result } @@ -685,7 +812,7 @@ class SparkContext( */ def setCheckpointDir(dir: String, useExisting: Boolean = false) { val path = new Path(dir) - val fs = path.getFileSystem(new Configuration()) + val fs = path.getFileSystem(SparkHadoopUtil.newConfiguration()) if (!useExisting) { if (fs.exists(path)) { throw new Exception("Checkpoint directory '" + path + "' already exists.") @@ -722,6 +849,7 @@ class SparkContext( * various Spark features. */ object SparkContext { + val SPARK_JOB_DESCRIPTION = "spark.job.description" implicit object DoubleAccumulatorParam extends AccumulatorParam[Double] { def addInPlace(t1: Double, t2: Double): Double = t1 + t2 @@ -828,8 +956,16 @@ object SparkContext { /** Find the JAR that contains the class of a particular object */ 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) + } +} /** * A class encapsulating how to convert some type T to Writable. It stores both the Writable class @@ -842,3 +978,4 @@ private[spark] class WritableConverter[T]( val writableClass: ClassManifest[T] => Class[_ <: Writable], val convert: Writable => T) extends Serializable + diff --git a/core/src/main/scala/spark/SparkEnv.scala b/core/src/main/scala/spark/SparkEnv.scala index 7157fd2688..0adbf1d96e 100644 --- a/core/src/main/scala/spark/SparkEnv.scala +++ b/core/src/main/scala/spark/SparkEnv.scala @@ -1,14 +1,37 @@ +/* + * 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 spark +import collection.mutable +import serializer.Serializer + import akka.actor.{Actor, ActorRef, Props, ActorSystemImpl, ActorSystem} import akka.remote.RemoteActorRefProvider -import serializer.Serializer import spark.broadcast.BroadcastManager +import spark.metrics.MetricsSystem import spark.storage.BlockManager import spark.storage.BlockManagerMaster import spark.network.ConnectionManager +import spark.serializer.{Serializer, SerializerManager} import spark.util.AkkaUtils +import spark.api.python.PythonWorkerFactory + /** * Holds all the runtime environment objects for a running Spark instance (either master or worker), @@ -20,6 +43,7 @@ import spark.util.AkkaUtils class SparkEnv ( val executorId: String, val actorSystem: ActorSystem, + val serializerManager: SerializerManager, val serializer: Serializer, val closureSerializer: Serializer, val cacheManager: CacheManager, @@ -29,32 +53,70 @@ class SparkEnv ( val blockManager: BlockManager, val connectionManager: ConnectionManager, val httpFileServer: HttpFileServer, - val sparkFilesDir: String - ) { + val sparkFilesDir: String, + val metricsSystem: MetricsSystem, + // To be set only as part of initialization of SparkContext. + // (executorId, defaultHostPort) => executorHostPort + // If executorId is NOT found, return defaultHostPort + var executorIdToHostPort: Option[(String, String) => String]) { + + private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]() def stop() { + pythonWorkers.foreach { case(key, worker) => worker.stop() } httpFileServer.stop() mapOutputTracker.stop() shuffleFetcher.stop() broadcastManager.stop() blockManager.stop() blockManager.master.stop() + metricsSystem.stop() 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() } + + def createPythonWorker(pythonExec: String, envVars: Map[String, String]): java.net.Socket = { + synchronized { + val key = (pythonExec, envVars) + pythonWorkers.getOrElseUpdate(key, new PythonWorkerFactory(pythonExec, envVars)).create() + } + } + + def resolveExecutorIdToHostPort(executorId: String, defaultHostPort: String): String = { + val env = SparkEnv.get + if (env.executorIdToHostPort.isEmpty) { + // default to using host, not host port. Relevant to non cluster modes. + return defaultHostPort + } + + env.executorIdToHostPort.get(executorId, defaultHostPort) + } } object SparkEnv extends Logging { private val env = new ThreadLocal[SparkEnv] + @volatile private var lastSetSparkEnv : SparkEnv = _ def set(e: SparkEnv) { + lastSetSparkEnv = e env.set(e) } + /** + * Returns the ThreadLocal SparkEnv, if non-null. Else returns the SparkEnv + * previously set in any thread. + */ def get: SparkEnv = { - env.get() + Option(env.get()).getOrElse(lastSetSparkEnv) + } + + /** + * Returns the ThreadLocal SparkEnv. + */ + def getThreadLocal : SparkEnv = { + env.get() } def createFromSystemProperties( @@ -72,6 +134,16 @@ object SparkEnv extends Logging { System.setProperty("spark.driver.port", boundPort.toString) } + // set only if unset until now. + if (System.getProperty("spark.hostPort", null) == null) { + if (!isDriver){ + // unexpected + Utils.logErrorWithStack("Unexpected NOT to have spark.hostPort set") + } + Utils.checkHost(hostname) + System.setProperty("spark.hostPort", hostname + ":" + boundPort) + } + val classLoader = Thread.currentThread.getContextClassLoader // Create an instance of the class named by the given Java system property, or by @@ -81,16 +153,23 @@ object SparkEnv extends Logging { Class.forName(name, true, classLoader).newInstance().asInstanceOf[T] } - val serializer = instantiateClass[Serializer]("spark.serializer", "spark.JavaSerializer") - + val serializerManager = new SerializerManager + + val serializer = serializerManager.setDefault( + System.getProperty("spark.serializer", "spark.JavaSerializer")) + + val closureSerializer = serializerManager.get( + System.getProperty("spark.closure.serializer", "spark.JavaSerializer")) + def registerOrLookup(name: String, newActor: => Actor): ActorRef = { if (isDriver) { logInfo("Registering " + name) actorSystem.actorOf(Props(newActor), name = name) } else { - val driverIp: String = System.getProperty("spark.driver.host", "localhost") + val driverHost: String = System.getProperty("spark.driver.host", "localhost") val driverPort: Int = System.getProperty("spark.driver.port", "7077").toInt - val url = "akka://spark@%s:%s/user/%s".format(driverIp, driverPort, name) + Utils.checkHost(driverHost, "Expected hostname") + val url = "akka://spark@%s:%s/user/%s".format(driverHost, driverPort, name) logInfo("Connecting to " + name + ": " + url) actorSystem.actorFor(url) } @@ -105,9 +184,6 @@ object SparkEnv extends Logging { val broadcastManager = new BroadcastManager(isDriver) - val closureSerializer = instantiateClass[Serializer]( - "spark.closure.serializer", "spark.JavaSerializer") - val cacheManager = new CacheManager(blockManager) // Have to assign trackerActor after initialization as MapOutputTrackerActor @@ -124,6 +200,13 @@ object SparkEnv extends Logging { httpFileServer.initialize() System.setProperty("spark.fileserver.uri", httpFileServer.serverUri) + val metricsSystem = if (isDriver) { + MetricsSystem.createMetricsSystem("driver") + } else { + MetricsSystem.createMetricsSystem("executor") + } + metricsSystem.start() + // Set the sparkFiles directory, used when downloading dependencies. In local mode, // this is a temporary directory; in distributed mode, this is the executor's current working // directory. @@ -142,6 +225,7 @@ object SparkEnv extends Logging { new SparkEnv( executorId, actorSystem, + serializerManager, serializer, closureSerializer, cacheManager, @@ -151,7 +235,8 @@ object SparkEnv extends Logging { blockManager, connectionManager, httpFileServer, - sparkFilesDir) + sparkFilesDir, + metricsSystem, + None) } - } diff --git a/core/src/main/scala/spark/SparkException.scala b/core/src/main/scala/spark/SparkException.scala index aa7a16d7dd..b7045eea63 100644 --- a/core/src/main/scala/spark/SparkException.scala +++ b/core/src/main/scala/spark/SparkException.scala @@ -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. + */ + package spark class SparkException(message: String, cause: Throwable) diff --git a/core/src/main/scala/spark/SparkFiles.java b/core/src/main/scala/spark/SparkFiles.java index 566aec622c..f9b3f7965e 100644 --- a/core/src/main/scala/spark/SparkFiles.java +++ b/core/src/main/scala/spark/SparkFiles.java @@ -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. + */ + package spark; import java.io.File; diff --git a/core/src/main/scala/spark/TaskContext.scala b/core/src/main/scala/spark/TaskContext.scala index dd0609026a..b79f4ca813 100644 --- a/core/src/main/scala/spark/TaskContext.scala +++ b/core/src/main/scala/spark/TaskContext.scala @@ -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. + */ + package spark import executor.TaskMetrics diff --git a/core/src/main/scala/spark/TaskEndReason.scala b/core/src/main/scala/spark/TaskEndReason.scala index 420c54bc9a..3ad665da34 100644 --- a/core/src/main/scala/spark/TaskEndReason.scala +++ b/core/src/main/scala/spark/TaskEndReason.scala @@ -1,5 +1,23 @@ +/* + * 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 spark +import spark.executor.TaskMetrics import spark.storage.BlockManagerId /** @@ -14,9 +32,20 @@ private[spark] case object Success extends TaskEndReason private[spark] case object Resubmitted extends TaskEndReason // Task was finished earlier but we've now lost it -private[spark] -case class FetchFailed(bmAddress: BlockManagerId, shuffleId: Int, mapId: Int, reduceId: Int) extends TaskEndReason +private[spark] case class FetchFailed( + bmAddress: BlockManagerId, + shuffleId: Int, + mapId: Int, + reduceId: Int) + extends TaskEndReason -private[spark] case class ExceptionFailure(exception: Throwable) extends TaskEndReason +private[spark] case class ExceptionFailure( + className: String, + description: String, + stackTrace: Array[StackTraceElement], + metrics: Option[TaskMetrics]) + extends TaskEndReason private[spark] case class OtherFailure(message: String) extends TaskEndReason + +private[spark] case class TaskResultTooBigFailure() extends TaskEndReason diff --git a/core/src/main/scala/spark/TaskState.scala b/core/src/main/scala/spark/TaskState.scala index 78eb33a628..9df7d8277b 100644 --- a/core/src/main/scala/spark/TaskState.scala +++ b/core/src/main/scala/spark/TaskState.scala @@ -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. + */ + package spark import org.apache.mesos.Protos.{TaskState => MesosTaskState} diff --git a/core/src/main/scala/spark/Utils.scala b/core/src/main/scala/spark/Utils.scala index 81daacf958..ef598ae41b 100644 --- a/core/src/main/scala/spark/Utils.scala +++ b/core/src/main/scala/spark/Utils.scala @@ -1,23 +1,47 @@ +/* + * 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 spark import java.io._ -import java.net._ +import java.net.{InetAddress, URL, URI, NetworkInterface, Inet4Address, ServerSocket} import java.util.{Locale, Random, UUID} -import java.util.concurrent.{Executors, ThreadFactory, ThreadPoolExecutor} -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{Path, FileSystem, FileUtil} -import scala.collection.mutable.ArrayBuffer +import java.util.concurrent.{ConcurrentHashMap, Executors, ThreadFactory, ThreadPoolExecutor} +import java.util.regex.Pattern + +import scala.collection.Map +import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.collection.JavaConversions._ import scala.io.Source + import com.google.common.io.Files import com.google.common.util.concurrent.ThreadFactoryBuilder -import scala.Some + +import org.apache.hadoop.fs.{Path, FileSystem, FileUtil} + import spark.serializer.SerializerInstance +import spark.deploy.SparkHadoopUtil + /** * Various utility methods used by Spark. */ private object Utils extends Logging { + /** Serialize an object using Java serialization */ def serialize[T](o: T): Array[Byte] = { val bos = new ByteArrayOutputStream() @@ -68,6 +92,40 @@ private object Utils extends Logging { return buf } + private val shutdownDeletePaths = new collection.mutable.HashSet[String]() + + // Register the path to be deleted via shutdown hook + def registerShutdownDeleteDir(file: File) { + val absolutePath = file.getAbsolutePath() + shutdownDeletePaths.synchronized { + shutdownDeletePaths += absolutePath + } + } + + // Is the path already registered to be deleted via a shutdown hook ? + def hasShutdownDeleteDir(file: File): Boolean = { + val absolutePath = file.getAbsolutePath() + shutdownDeletePaths.synchronized { + shutdownDeletePaths.contains(absolutePath) + } + } + + // Note: if file is child of some registered path, while not equal to it, then return true; + // else false. This is to ensure that two shutdown hooks do not try to delete each others + // paths - resulting in IOException and incomplete cleanup. + def hasRootAsShutdownDeleteDir(file: File): Boolean = { + val absolutePath = file.getAbsolutePath() + val retval = shutdownDeletePaths.synchronized { + shutdownDeletePaths.find { path => + !absolutePath.equals(path) && absolutePath.startsWith(path) + }.isDefined + } + if (retval) { + logInfo("path = " + file + ", already present as root for deletion.") + } + retval + } + /** Create a temporary directory inside the given parent directory */ def createTempDir(root: String = System.getProperty("java.io.tmpdir")): File = { var attempts = 0 @@ -76,8 +134,8 @@ private object Utils extends Logging { while (dir == null) { attempts += 1 if (attempts > maxAttempts) { - throw new IOException("Failed to create a temp directory after " + maxAttempts + - " attempts!") + throw new IOException("Failed to create a temp directory (under " + root + ") after " + + maxAttempts + " attempts!") } try { dir = new File(root, "spark-" + UUID.randomUUID.toString) @@ -86,13 +144,17 @@ private object Utils extends Logging { } } catch { case e: IOException => ; } } + + registerShutdownDeleteDir(dir) + // Add a shutdown hook to delete the temp dir when the JVM exits Runtime.getRuntime.addShutdownHook(new Thread("delete Spark temp dir " + dir) { override def run() { - Utils.deleteRecursively(dir) + // Attempt to delete if some patch which is parent of this is not already registered. + if (! hasRootAsShutdownDeleteDir(dir)) Utils.deleteRecursively(dir) } }) - return dir + dir } /** Copy all data from an InputStream to an OutputStream */ @@ -135,40 +197,35 @@ private object Utils extends Logging { Utils.copyStream(in, out, true) if (targetFile.exists && !Files.equal(tempFile, targetFile)) { tempFile.delete() - throw new SparkException("File " + targetFile + " exists and does not match contents of" + - " " + url) + throw new SparkException( + "File " + targetFile + " exists and does not match contents of" + " " + url) } else { Files.move(tempFile, targetFile) } case "file" | null => - val sourceFile = if (uri.isAbsolute) { - new File(uri) - } else { - new File(url) - } - if (targetFile.exists && !Files.equal(sourceFile, targetFile)) { - throw new SparkException("File " + targetFile + " exists and does not match contents of" + - " " + url) - } else { - // Remove the file if it already exists - targetFile.delete() - // Symlink the file locally. - if (uri.isAbsolute) { - // url is absolute, i.e. it starts with "file:///". Extract the source - // file's absolute path from the url. - val sourceFile = new File(uri) - logInfo("Symlinking " + sourceFile.getAbsolutePath + " to " + targetFile.getAbsolutePath) - FileUtil.symLink(sourceFile.getAbsolutePath, targetFile.getAbsolutePath) + // In the case of a local file, copy the local file to the target directory. + // Note the difference between uri vs url. + val sourceFile = if (uri.isAbsolute) new File(uri) else new File(url) + if (targetFile.exists) { + // If the target file already exists, warn the user if + if (!Files.equal(sourceFile, targetFile)) { + throw new SparkException( + "File " + targetFile + " exists and does not match contents of" + " " + url) } else { - // url is not absolute, i.e. itself is the path to the source file. - logInfo("Symlinking " + url + " to " + targetFile.getAbsolutePath) - FileUtil.symLink(url, targetFile.getAbsolutePath) + // Do nothing if the file contents are the same, i.e. this file has been copied + // previously. + logInfo(sourceFile.getAbsolutePath + " has been previously copied to " + + targetFile.getAbsolutePath) } + } else { + // The file does not exist in the target directory. Copy it there. + logInfo("Copying " + sourceFile.getAbsolutePath + " to " + targetFile.getAbsolutePath) + Files.copy(sourceFile, targetFile) } case _ => // Use the Hadoop filesystem library, which supports file://, hdfs://, s3://, and others val uri = new URI(url) - val conf = new Configuration() + val conf = SparkHadoopUtil.newConfiguration() val fs = FileSystem.get(uri, conf) val in = fs.open(new Path(uri)) val out = new FileOutputStream(tempFile) @@ -227,8 +284,10 @@ private object Utils extends Logging { /** * Get the local host's IP address in dotted-quad format (e.g. 1.2.3.4). + * Note, this is typically not used from within core spark. */ lazy val localIpAddress: String = findLocalIpAddress() + lazy val localIpAddressHostname: String = getAddressHostName(localIpAddress) private def findLocalIpAddress(): String = { val defaultIpOverride = System.getenv("SPARK_LOCAL_IP") @@ -266,6 +325,8 @@ private object Utils extends Logging { * hostname it reports to the master. */ def setCustomHostname(hostname: String) { + // DEBUG code + Utils.checkHost(hostname) customHostname = Some(hostname) } @@ -273,7 +334,91 @@ private object Utils extends Logging { * Get the local machine's hostname. */ def localHostName(): String = { - customHostname.getOrElse(InetAddress.getLocalHost.getHostName) + customHostname.getOrElse(localIpAddressHostname) + } + + def getAddressHostName(address: String): String = { + InetAddress.getByName(address).getHostName + } + + def localHostPort(): String = { + val retval = System.getProperty("spark.hostPort", null) + if (retval == null) { + logErrorWithStack("spark.hostPort not set but invoking localHostPort") + return localHostName() + } + + retval + } + +/* + // Used by DEBUG code : remove when all testing done + private val ipPattern = Pattern.compile("^[0-9]+(\\.[0-9]+)*$") + def checkHost(host: String, message: String = "") { + // Currently catches only ipv4 pattern, this is just a debugging tool - not rigourous ! + // if (host.matches("^[0-9]+(\\.[0-9]+)*$")) { + if (ipPattern.matcher(host).matches()) { + Utils.logErrorWithStack("Unexpected to have host " + host + " which matches IP pattern. Message " + message) + } + if (Utils.parseHostPort(host)._2 != 0){ + Utils.logErrorWithStack("Unexpected to have host " + host + " which has port in it. Message " + message) + } + } + + // Used by DEBUG code : remove when all testing done + def checkHostPort(hostPort: String, message: String = "") { + val (host, port) = Utils.parseHostPort(hostPort) + checkHost(host) + if (port <= 0){ + Utils.logErrorWithStack("Unexpected to have port " + port + " which is not valid in " + hostPort + ". Message " + 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) } } + // temp code for debug + System.exit(-1) + } +*/ + + // Once testing is complete in various modes, replace with this ? + def checkHost(host: String, message: String = "") {} + def checkHostPort(hostPort: String, message: String = "") {} + + // Used by DEBUG code : remove when all testing done + def logErrorWithStack(msg: String) { + try { throw new Exception } catch { case ex: Exception => { logError(msg, ex) } } + } + + def getUserNameFromEnvironment(): String = { + SparkHadoopUtil.getUserNameFromEnvironment + } + + // Typically, this will be of order of number of nodes in cluster + // If not, we should change it to LRUCache or something. + private val hostPortParseResults = new ConcurrentHashMap[String, (String, Int)]() + + def parseHostPort(hostPort: String): (String, Int) = { + { + // Check cache first. + var cached = hostPortParseResults.get(hostPort) + if (cached != null) return cached + } + + val indx: Int = hostPort.lastIndexOf(':') + // This is potentially broken - when dealing with ipv6 addresses for example, sigh ... + // but then hadoop does not support ipv6 right now. + // For now, we assume that if port exists, then it is valid - not check if it is an int > 0 + if (-1 == indx) { + val retval = (hostPort, 0) + hostPortParseResults.put(hostPort, retval) + return retval + } + + val retval = (hostPort.substring(0, indx).trim(), hostPort.substring(indx + 1).trim().toInt) + hostPortParseResults.putIfAbsent(hostPort, retval) + hostPortParseResults.get(hostPort) } private[spark] val daemonThreadFactory: ThreadFactory = @@ -359,6 +504,26 @@ private object Utils extends Logging { } /** + * Returns a human-readable string representing a duration such as "35ms" + */ + def msDurationToString(ms: Long): String = { + val second = 1000 + val minute = 60 * second + val hour = 60 * minute + + ms match { + case t if t < second => + "%d ms".format(t) + case t if t < minute => + "%.1f s".format(t.toFloat / second) + case t if t < hour => + "%.1f m".format(t.toFloat / minute) + case t => + "%.2f h".format(t.toFloat / hour) + } + } + + /** * Convert a memory quantity in megabytes to a human-readable string such as "4.0 MB". */ def memoryMegabytesToString(megabytes: Long): String = { @@ -395,13 +560,57 @@ private object Utils extends Logging { execute(command, new File(".")) } + /** + * Execute a command and get its output, throwing an exception if it yields a code other than 0. + */ + def executeAndGetOutput(command: Seq[String], workingDir: File = new File("."), + extraEnvironment: Map[String, String] = Map.empty): String = { + val builder = new ProcessBuilder(command: _*) + .directory(workingDir) + val environment = builder.environment() + for ((key, value) <- extraEnvironment) { + environment.put(key, value) + } + val process = builder.start() + new Thread("read stderr for " + command(0)) { + override def run() { + for (line <- Source.fromInputStream(process.getErrorStream).getLines) { + System.err.println(line) + } + } + }.start() + val output = new StringBuffer + val stdoutThread = new Thread("read stdout for " + command(0)) { + override def run() { + for (line <- Source.fromInputStream(process.getInputStream).getLines) { + output.append(line) + } + } + } + stdoutThread.start() + val exitCode = process.waitFor() + stdoutThread.join() // Wait for it to finish reading output + if (exitCode != 0) { + throw new SparkException("Process " + command + " exited with code " + exitCode) + } + output.toString + } + + /** + * A regular expression to match classes of the "core" Spark API that we want to skip when + * finding the call site of a method. + */ + private val SPARK_CLASS_REGEX = """^spark(\.api\.java)?(\.rdd)?\.[A-Z]""".r + + private[spark] class CallSiteInfo(val lastSparkMethod: String, val firstUserFile: String, + val firstUserLine: Int, val firstUserClass: String) /** * When called inside a class in the spark package, returns the name of the user code class * (outside the spark package) that called into Spark, as well as which Spark method they called. * This is used, for example, to tell users where in their code each RDD got created. */ - def getSparkCallSite: String = { + def getCallSiteInfo: CallSiteInfo = { val trace = Thread.currentThread.getStackTrace().filter( el => (!el.getMethodName.contains("getStackTrace"))) @@ -413,10 +622,11 @@ private object Utils extends Logging { var firstUserFile = "<unknown>" var firstUserLine = 0 var finished = false + var firstUserClass = "<unknown>" for (el <- trace) { if (!finished) { - if (el.getClassName.startsWith("spark.") && !el.getClassName.startsWith("spark.examples.")) { + if (SPARK_CLASS_REGEX.findFirstIn(el.getClassName) != None) { lastSparkMethod = if (el.getMethodName == "<init>") { // Spark method is a constructor; get its class name el.getClassName.substring(el.getClassName.lastIndexOf('.') + 1) @@ -427,25 +637,33 @@ private object Utils extends Logging { else { firstUserLine = el.getLineNumber firstUserFile = el.getFileName + firstUserClass = el.getClassName finished = true } } } - "%s at %s:%s".format(lastSparkMethod, firstUserFile, firstUserLine) + new CallSiteInfo(lastSparkMethod, firstUserFile, firstUserLine, firstUserClass) } - /** - * Try to find a free port to bind to on the local host. This should ideally never be needed, - * except that, unfortunately, some of the networking libraries we currently rely on (e.g. Spray) - * don't let users bind to port 0 and then figure out which free port they actually bound to. - * We work around this by binding a ServerSocket and immediately unbinding it. This is *not* - * necessarily guaranteed to work, but it's the best we can do. - */ - def findFreePort(): Int = { - val socket = new ServerSocket(0) - val portBound = socket.getLocalPort - socket.close() - portBound + def formatSparkCallSite = { + val callSiteInfo = getCallSiteInfo + "%s at %s:%s".format(callSiteInfo.lastSparkMethod, callSiteInfo.firstUserFile, + callSiteInfo.firstUserLine) + } + + /** Return a string containing part of a file from byte 'start' to 'end'. */ + def offsetBytes(path: String, start: Long, end: Long): String = { + val file = new File(path) + val length = file.length() + val effectiveEnd = math.min(length, end) + val effectiveStart = math.max(0, start) + val buff = new Array[Byte]((effectiveEnd-effectiveStart).toInt) + val stream = new FileInputStream(file) + + stream.skip(effectiveStart) + stream.read(buff) + stream.close() + Source.fromBytes(buff).mkString } /** @@ -475,4 +693,76 @@ private object Utils extends Logging { } return false } + + def isSpace(c: Char): Boolean = { + " \t\r\n".indexOf(c) != -1 + } + + /** + * Split a string of potentially quoted arguments from the command line the way that a shell + * would do it to determine arguments to a command. For example, if the string is 'a "b c" d', + * then it would be parsed as three arguments: 'a', 'b c' and 'd'. + */ + def splitCommandString(s: String): Seq[String] = { + val buf = new ArrayBuffer[String] + var inWord = false + var inSingleQuote = false + var inDoubleQuote = false + var curWord = new StringBuilder + def endWord() { + buf += curWord.toString + curWord.clear() + } + var i = 0 + while (i < s.length) { + var nextChar = s.charAt(i) + if (inDoubleQuote) { + if (nextChar == '"') { + inDoubleQuote = false + } else if (nextChar == '\\') { + if (i < s.length - 1) { + // Append the next character directly, because only " and \ may be escaped in + // double quotes after the shell's own expansion + curWord.append(s.charAt(i + 1)) + i += 1 + } + } else { + curWord.append(nextChar) + } + } else if (inSingleQuote) { + if (nextChar == '\'') { + inSingleQuote = false + } else { + curWord.append(nextChar) + } + // Backslashes are not treated specially in single quotes + } else if (nextChar == '"') { + inWord = true + inDoubleQuote = true + } else if (nextChar == '\'') { + inWord = true + inSingleQuote = true + } else if (!isSpace(nextChar)) { + curWord.append(nextChar) + inWord = true + } else if (inWord && isSpace(nextChar)) { + endWord() + inWord = false + } + i += 1 + } + if (inWord || inDoubleQuote || inSingleQuote) { + endWord() + } + return buf + } + + /* Calculates 'x' modulo 'mod', takes to consideration sign of x, + * i.e. if 'x' is negative, than 'x' % 'mod' is negative too + * so function return (x % mod) + mod in that case. + */ + def nonNegativeMod(x: Int, mod: Int): Int = { + val rawMod = x % mod + rawMod + (if (rawMod < 0) mod else 0) + } } diff --git a/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala index ba00b6a844..8ce7df6213 100644 --- a/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala +++ b/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala @@ -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. + */ + package spark.api.java import spark.RDD @@ -58,6 +75,12 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav def coalesce(numPartitions: Int): JavaDoubleRDD = fromRDD(srdd.coalesce(numPartitions)) /** + * Return a new RDD that is reduced into `numPartitions` partitions. + */ + def coalesce(numPartitions: Int, shuffle: Boolean): JavaDoubleRDD = + fromRDD(srdd.coalesce(numPartitions, shuffle)) + + /** * 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 @@ -92,33 +115,48 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav // Double RDD functions - /** Return the sum of the elements in this RDD. */ + /** Add up the elements in this RDD. */ def sum(): Double = srdd.sum() - /** Return a [[spark.StatCounter]] describing the elements in this RDD. */ + /** + * Return a [[spark.util.StatCounter]] object that captures the mean, variance and count + * of the RDD's elements in one operation. + */ def stats(): StatCounter = srdd.stats() - /** Return the mean of the elements in this RDD. */ + /** Compute the mean of this RDD's elements. */ def mean(): Double = srdd.mean() - /** Return the variance of the elements in this RDD. */ + /** Compute the variance of this RDD's elements. */ def variance(): Double = srdd.variance() - /** Return the standard deviation of the elements in this RDD. */ + /** Compute the standard deviation of this RDD's elements. */ def stdev(): Double = srdd.stdev() + /** + * Compute the sample standard deviation of this RDD's elements (which corrects for bias in + * estimating the standard deviation by dividing by N-1 instead of N). + */ + def sampleStdev(): Double = srdd.sampleStdev() + + /** + * Compute the sample variance of this RDD's elements (which corrects for bias in + * estimating the standard variance by dividing by N-1 instead of N). + */ + def sampleVariance(): Double = srdd.sampleVariance() + /** Return the approximate mean of the elements in this RDD. */ def meanApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble] = srdd.meanApprox(timeout, confidence) - /** Return the approximate mean of the elements in this RDD. */ + /** (Experimental) Approximate operation to return the mean within a timeout. */ def meanApprox(timeout: Long): PartialResult[BoundedDouble] = srdd.meanApprox(timeout) - /** Return the approximate sum of the elements in this RDD. */ + /** (Experimental) Approximate operation to return the sum within a timeout. */ def sumApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble] = srdd.sumApprox(timeout, confidence) - - /** Return the approximate sum of the elements in this RDD. */ + + /** (Experimental) Approximate operation to return the sum within a timeout. */ def sumApprox(timeout: Long): PartialResult[BoundedDouble] = srdd.sumApprox(timeout) } diff --git a/core/src/main/scala/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/spark/api/java/JavaPairRDD.scala index 49aaabf835..ccc511dc5f 100644 --- a/core/src/main/scala/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/spark/api/java/JavaPairRDD.scala @@ -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. + */ + package spark.api.java import java.util.{List => JList} @@ -6,6 +23,7 @@ import java.util.Comparator import scala.Tuple2 import scala.collection.JavaConversions._ +import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapred.OutputFormat import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} @@ -66,7 +84,13 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif /** * Return a new RDD that is reduced into `numPartitions` partitions. */ - def coalesce(numPartitions: Int): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.coalesce(numPartitions)) + def coalesce(numPartitions: Int): JavaPairRDD[K, V] = fromRDD(rdd.coalesce(numPartitions)) + + /** + * Return a new RDD that is reduced into `numPartitions` partitions. + */ + def coalesce(numPartitions: Int, shuffle: Boolean): JavaPairRDD[K, V] = + fromRDD(rdd.coalesce(numPartitions, shuffle)) /** * Return a sampled subset of this RDD. @@ -453,6 +477,16 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass) } + /** Output the RDD to any Hadoop-supported file system, compressing with the supplied codec. */ + def saveAsHadoopFile[F <: OutputFormat[_, _]]( + path: String, + keyClass: Class[_], + valueClass: Class[_], + outputFormatClass: Class[F], + codec: Class[_ <: CompressionCodec]) { + rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, codec) + } + /** Output the RDD to any Hadoop-supported file system. */ def saveAsNewAPIHadoopFile[F <: NewOutputFormat[_, _]]( path: String, diff --git a/core/src/main/scala/spark/api/java/JavaRDD.scala b/core/src/main/scala/spark/api/java/JavaRDD.scala index 3016888898..c0bf2cf568 100644 --- a/core/src/main/scala/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/spark/api/java/JavaRDD.scala @@ -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. + */ + package spark.api.java import spark._ @@ -14,12 +31,18 @@ JavaRDDLike[T, JavaRDD[T]] { /** Persist this RDD with the default storage level (`MEMORY_ONLY`). */ def cache(): JavaRDD[T] = wrapRDD(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. + * it is computed. This can only be used to assign a new storage level if the RDD does not + * have a storage level set yet.. */ def persist(newLevel: StorageLevel): JavaRDD[T] = wrapRDD(rdd.persist(newLevel)) + /** + * Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. + */ + def unpersist(): JavaRDD[T] = wrapRDD(rdd.unpersist()) + // Transformations (return a new RDD) /** @@ -31,7 +54,7 @@ JavaRDDLike[T, JavaRDD[T]] { * Return a new RDD containing the distinct elements in this RDD. */ def distinct(numPartitions: Int): JavaRDD[T] = wrapRDD(rdd.distinct(numPartitions)) - + /** * Return a new RDD containing only the elements that satisfy a predicate. */ @@ -44,11 +67,17 @@ JavaRDDLike[T, JavaRDD[T]] { def coalesce(numPartitions: Int): JavaRDD[T] = rdd.coalesce(numPartitions) /** + * Return a new RDD that is reduced into `numPartitions` partitions. + */ + def coalesce(numPartitions: Int, shuffle: Boolean): JavaRDD[T] = + rdd.coalesce(numPartitions, shuffle) + + /** * Return a sampled subset of this RDD. */ def sample(withReplacement: Boolean, fraction: Double, seed: Int): JavaRDD[T] = wrapRDD(rdd.sample(withReplacement, fraction, seed)) - + /** * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). @@ -57,7 +86,7 @@ JavaRDDLike[T, JavaRDD[T]] { /** * 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. */ @@ -74,7 +103,6 @@ JavaRDDLike[T, JavaRDD[T]] { */ def subtract(other: JavaRDD[T], p: Partitioner): JavaRDD[T] = wrapRDD(rdd.subtract(other, p)) - } object JavaRDD { diff --git a/core/src/main/scala/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/spark/api/java/JavaRDDLike.scala index d884529d7a..21b5abf053 100644 --- a/core/src/main/scala/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/spark/api/java/JavaRDDLike.scala @@ -1,9 +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 spark.api.java -import java.util.{List => JList} +import java.util.{List => JList, Comparator} import scala.Tuple2 import scala.collection.JavaConversions._ +import org.apache.hadoop.io.compress.CompressionCodec import spark.{SparkContext, Partition, RDD, TaskContext} import spark.api.java.JavaPairRDD._ import spark.api.java.function.{Function2 => JFunction2, Function => JFunction, _} @@ -182,6 +200,21 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { JavaPairRDD.fromRDD(rdd.zip(other.rdd)(other.classManifest))(classManifest, other.classManifest) } + /** + * Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by + * applying a function to the zipped partitions. Assumes that all the RDDs have the + * *same number of partitions*, but does *not* require them to have the same number + * of elements in each partition. + */ + def zipPartitions[U, V]( + f: FlatMapFunction2[java.util.Iterator[T], java.util.Iterator[U], V], + other: JavaRDDLike[U, _]): JavaRDD[V] = { + def fn = (x: Iterator[T], y: Iterator[U]) => asScalaIterator( + f.apply(asJavaIterator(x), asJavaIterator(y)).iterator()) + JavaRDD.fromRDD( + rdd.zipPartitions(fn, other.rdd)(other.classManifest, f.elementType()))(f.elementType()) + } + // Actions (launch a job to return a value to the user program) /** @@ -295,6 +328,13 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def saveAsTextFile(path: String) = rdd.saveAsTextFile(path) + + /** + * Save this RDD as a compressed text file, using string representations of elements. + */ + def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) = + rdd.saveAsTextFile(path, codec) + /** * Save this RDD as a SequenceFile of serialized objects. */ @@ -336,4 +376,54 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def toDebugString(): String = { rdd.toDebugString } + + /** + * Returns the top K elements from this RDD as defined by + * the specified Comparator[T]. + * @param num the number of top elements to return + * @param comp the comparator that defines the order + * @return an array of top elements + */ + def top(num: Int, comp: Comparator[T]): JList[T] = { + import scala.collection.JavaConversions._ + val topElems = rdd.top(num)(Ordering.comparatorToOrdering(comp)) + val arr: java.util.Collection[T] = topElems.toSeq + new java.util.ArrayList(arr) + } + + /** + * Returns the top K elements from this RDD using the + * natural ordering for T. + * @param num the number of top elements to return + * @return an array of top elements + */ + def top(num: Int): JList[T] = { + val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[T]] + top(num, comp) + } + + /** + * Returns the first K elements from this RDD as defined by + * the specified Comparator[T] and maintains the order. + * @param num the number of top elements to return + * @param comp the comparator that defines the order + * @return an array of top elements + */ + def takeOrdered(num: Int, comp: Comparator[T]): JList[T] = { + import scala.collection.JavaConversions._ + val topElems = rdd.takeOrdered(num)(Ordering.comparatorToOrdering(comp)) + val arr: java.util.Collection[T] = topElems.toSeq + new java.util.ArrayList(arr) + } + + /** + * Returns the first K elements from this RDD using the + * natural ordering for T while maintain the order. + * @param num the number of top elements to return + * @return an array of top elements + */ + def takeOrdered(num: Int): JList[T] = { + val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[T]] + takeOrdered(num, comp) + } } diff --git a/core/src/main/scala/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/spark/api/java/JavaSparkContext.scala index 5f18b1e15b..fe182e7ab6 100644 --- a/core/src/main/scala/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/spark/api/java/JavaSparkContext.scala @@ -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. + */ + package spark.api.java import java.util.{Map => JMap} diff --git a/core/src/main/scala/spark/api/java/JavaSparkContextVarargsWorkaround.java b/core/src/main/scala/spark/api/java/JavaSparkContextVarargsWorkaround.java index 97344e73da..42b1de01b1 100644 --- a/core/src/main/scala/spark/api/java/JavaSparkContextVarargsWorkaround.java +++ b/core/src/main/scala/spark/api/java/JavaSparkContextVarargsWorkaround.java @@ -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. + */ + package spark.api.java; import java.util.Arrays; diff --git a/core/src/main/scala/spark/api/java/StorageLevels.java b/core/src/main/scala/spark/api/java/StorageLevels.java index 5e5845ac3a..f385636e83 100644 --- a/core/src/main/scala/spark/api/java/StorageLevels.java +++ b/core/src/main/scala/spark/api/java/StorageLevels.java @@ -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. + */ + package spark.api.java; import spark.storage.StorageLevel; diff --git a/core/src/main/scala/spark/api/java/function/DoubleFlatMapFunction.java b/core/src/main/scala/spark/api/java/function/DoubleFlatMapFunction.java index 3a8192be3a..8bc88d757f 100644 --- a/core/src/main/scala/spark/api/java/function/DoubleFlatMapFunction.java +++ b/core/src/main/scala/spark/api/java/function/DoubleFlatMapFunction.java @@ -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. + */ + package spark.api.java.function; diff --git a/core/src/main/scala/spark/api/java/function/DoubleFunction.java b/core/src/main/scala/spark/api/java/function/DoubleFunction.java index c6ef76d088..1aa1e5dae0 100644 --- a/core/src/main/scala/spark/api/java/function/DoubleFunction.java +++ b/core/src/main/scala/spark/api/java/function/DoubleFunction.java @@ -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. + */ + package spark.api.java.function; diff --git a/core/src/main/scala/spark/api/java/function/FlatMapFunction.scala b/core/src/main/scala/spark/api/java/function/FlatMapFunction.scala index e027cdacd3..9eb0cfe3f9 100644 --- a/core/src/main/scala/spark/api/java/function/FlatMapFunction.scala +++ b/core/src/main/scala/spark/api/java/function/FlatMapFunction.scala @@ -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. + */ + package spark.api.java.function /** diff --git a/core/src/main/scala/spark/api/java/function/FlatMapFunction2.scala b/core/src/main/scala/spark/api/java/function/FlatMapFunction2.scala new file mode 100644 index 0000000000..dda98710c2 --- /dev/null +++ b/core/src/main/scala/spark/api/java/function/FlatMapFunction2.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 spark.api.java.function + +/** + * 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]] +} diff --git a/core/src/main/scala/spark/api/java/function/Function.java b/core/src/main/scala/spark/api/java/function/Function.java index dae8295f21..2a2ea0aacf 100644 --- a/core/src/main/scala/spark/api/java/function/Function.java +++ b/core/src/main/scala/spark/api/java/function/Function.java @@ -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. + */ + package spark.api.java.function; import scala.reflect.ClassManifest; diff --git a/core/src/main/scala/spark/api/java/function/Function2.java b/core/src/main/scala/spark/api/java/function/Function2.java index 69bf12c8c9..952d31ece4 100644 --- a/core/src/main/scala/spark/api/java/function/Function2.java +++ b/core/src/main/scala/spark/api/java/function/Function2.java @@ -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. + */ + package spark.api.java.function; import scala.reflect.ClassManifest; diff --git a/core/src/main/scala/spark/api/java/function/PairFlatMapFunction.java b/core/src/main/scala/spark/api/java/function/PairFlatMapFunction.java index b3cc4df6aa..4aad602da3 100644 --- a/core/src/main/scala/spark/api/java/function/PairFlatMapFunction.java +++ b/core/src/main/scala/spark/api/java/function/PairFlatMapFunction.java @@ -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. + */ + package spark.api.java.function; import scala.Tuple2; diff --git a/core/src/main/scala/spark/api/java/function/PairFunction.java b/core/src/main/scala/spark/api/java/function/PairFunction.java index 9fc6df4b88..ccfe64ecf1 100644 --- a/core/src/main/scala/spark/api/java/function/PairFunction.java +++ b/core/src/main/scala/spark/api/java/function/PairFunction.java @@ -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. + */ + package spark.api.java.function; import scala.Tuple2; diff --git a/core/src/main/scala/spark/api/java/function/VoidFunction.scala b/core/src/main/scala/spark/api/java/function/VoidFunction.scala index b0096cf2bf..f6fc0b0f7d 100644 --- a/core/src/main/scala/spark/api/java/function/VoidFunction.scala +++ b/core/src/main/scala/spark/api/java/function/VoidFunction.scala @@ -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. + */ + package spark.api.java.function /** @@ -13,4 +30,4 @@ abstract class VoidFunction[T] extends Serializable { // return Unit), so it is implicitly converted to a Function1[T, Unit]: object VoidFunction { implicit def toFunction[T](f: VoidFunction[T]) : Function1[T, Unit] = ((x : T) => f.call(x)) -}
\ No newline at end of file +} diff --git a/core/src/main/scala/spark/api/java/function/WrappedFunction1.scala b/core/src/main/scala/spark/api/java/function/WrappedFunction1.scala index 923f5cdf4f..1758a38c4e 100644 --- a/core/src/main/scala/spark/api/java/function/WrappedFunction1.scala +++ b/core/src/main/scala/spark/api/java/function/WrappedFunction1.scala @@ -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. + */ + package spark.api.java.function import scala.runtime.AbstractFunction1 diff --git a/core/src/main/scala/spark/api/java/function/WrappedFunction2.scala b/core/src/main/scala/spark/api/java/function/WrappedFunction2.scala index 2c6e9b1571..b093567d2c 100644 --- a/core/src/main/scala/spark/api/java/function/WrappedFunction2.scala +++ b/core/src/main/scala/spark/api/java/function/WrappedFunction2.scala @@ -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. + */ + package spark.api.java.function import scala.runtime.AbstractFunction2 diff --git a/core/src/main/scala/spark/api/python/PythonPartitioner.scala b/core/src/main/scala/spark/api/python/PythonPartitioner.scala index d618c098c2..ac112b8c2c 100644 --- a/core/src/main/scala/spark/api/python/PythonPartitioner.scala +++ b/core/src/main/scala/spark/api/python/PythonPartitioner.scala @@ -1,7 +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 spark.api.python import spark.Partitioner - +import spark.Utils import java.util.Arrays /** @@ -18,25 +35,10 @@ private[spark] class PythonPartitioner( val pyPartitionFunctionId: Long) extends Partitioner { - override def getPartition(key: Any): Int = { - if (key == null) { - return 0 - } - else { - val hashCode = { - if (key.isInstanceOf[Array[Byte]]) { - Arrays.hashCode(key.asInstanceOf[Array[Byte]]) - } else { - key.hashCode() - } - } - val mod = hashCode % numPartitions - if (mod < 0) { - mod + numPartitions - } else { - mod // Guard against negative hash codes - } - } + override def getPartition(key: Any): Int = key match { + case null => 0 + case key: Array[Byte] => Utils.nonNegativeMod(Arrays.hashCode(key), numPartitions) + case _ => Utils.nonNegativeMod(key.hashCode(), numPartitions) } override def equals(other: Any): Boolean = other match { diff --git a/core/src/main/scala/spark/api/python/PythonRDD.scala b/core/src/main/scala/spark/api/python/PythonRDD.scala index 9b4d54ab4e..2dd79f7100 100644 --- a/core/src/main/scala/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/spark/api/python/PythonRDD.scala @@ -1,11 +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 spark.api.python import java.io._ import java.net._ -import java.util.{List => JList, ArrayList => JArrayList, Collections} +import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections} import scala.collection.JavaConversions._ -import scala.io.Source import spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD} import spark.broadcast.Broadcast @@ -16,16 +32,18 @@ import spark.rdd.PipedRDD private[spark] class PythonRDD[T: ClassManifest]( parent: RDD[T], command: Seq[String], - envVars: java.util.Map[String, String], + envVars: JMap[String, String], preservePartitoning: Boolean, pythonExec: String, broadcastVars: JList[Broadcast[Array[Byte]]], 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: java.util.Map[String, String], + def this(parent: RDD[T], command: String, envVars: JMap[String, String], preservePartitoning: Boolean, pythonExec: String, broadcastVars: JList[Broadcast[Array[Byte]]], accumulator: Accumulator[JList[Array[Byte]]]) = @@ -36,68 +54,65 @@ private[spark] class PythonRDD[T: ClassManifest]( override val partitioner = if (preservePartitoning) parent.partitioner else None - override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = { - val SPARK_HOME = new ProcessBuilder().environment().get("SPARK_HOME") - - val pb = new ProcessBuilder(Seq(pythonExec, SPARK_HOME + "/python/pyspark/worker.py")) - // Add the environmental variables to the process. - val currentEnvVars = pb.environment() - - for ((variable, value) <- envVars) { - currentEnvVars.put(variable, value) - } - val proc = pb.start() + override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = { + val startTime = System.currentTimeMillis val env = SparkEnv.get - - // Start a thread to print the process's stderr to ours - new Thread("stderr reader for " + pythonExec) { - override def run() { - for (line <- Source.fromInputStream(proc.getErrorStream).getLines) { - System.err.println(line) - } - } - }.start() + val worker = env.createPythonWorker(pythonExec, envVars.toMap) // Start a thread to feed the process input from our parent's iterator new Thread("stdin writer for " + pythonExec) { override def run() { - SparkEnv.set(env) - val out = new PrintWriter(proc.getOutputStream) - val dOut = new DataOutputStream(proc.getOutputStream) - // Partition index - dOut.writeInt(split.index) - // sparkFilesDir - PythonRDD.writeAsPickle(SparkFiles.getRootDirectory, dOut) - // Broadcast variables - dOut.writeInt(broadcastVars.length) - for (broadcast <- broadcastVars) { - dOut.writeLong(broadcast.id) - dOut.writeInt(broadcast.value.length) - dOut.write(broadcast.value) - dOut.flush() - } - // Serialized user code - for (elem <- command) { - out.println(elem) - } - out.flush() - // Data values - for (elem <- parent.iterator(split, context)) { - PythonRDD.writeAsPickle(elem, dOut) + try { + 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) + // Broadcast variables + dataOut.writeInt(broadcastVars.length) + for (broadcast <- broadcastVars) { + dataOut.writeLong(broadcast.id) + dataOut.writeInt(broadcast.value.length) + dataOut.write(broadcast.value) + } + dataOut.flush() + // Serialized user code + for (elem <- command) { + printOut.println(elem) + } + printOut.flush() + // Data values + for (elem <- parent.iterator(split, context)) { + PythonRDD.writeAsPickle(elem, dataOut) + } + dataOut.flush() + printOut.flush() + worker.shutdownOutput() + } catch { + case e: IOException => + // This can happen for legitimate reasons if the Python code stops returning data before we are done + // passing elements through, e.g., for take(). Just log a message to say it happened. + logInfo("stdin writer to Python finished early") + logDebug("stdin writer to Python finished early", e) } - dOut.flush() - out.flush() - proc.getOutputStream.close() } }.start() // Return an iterator that read lines from the process's stdout - val stream = new DataInputStream(proc.getInputStream) + val stream = new DataInputStream(new BufferedInputStream(worker.getInputStream, bufferSize)) return new Iterator[Array[Byte]] { def next(): Array[Byte] = { val obj = _nextObj - _nextObj = read() + if (hasNext) { + // FIXME: can deadlock if worker is waiting for us to + // respond to current message (currently irrelevant because + // output is shutdown before we read any input) + _nextObj = read() + } obj } @@ -108,6 +123,17 @@ private[spark] class PythonRDD[T: ClassManifest]( val obj = new Array[Byte](length) stream.readFully(obj) obj + case -3 => + // Timing data from worker + val bootTime = stream.readLong() + val initTime = stream.readLong() + val finishTime = stream.readLong() + val boot = bootTime - startTime + val init = initTime - bootTime + val finish = finishTime - initTime + val total = finishTime - startTime + logInfo("Times: total = %s, boot = %s, init = %s, finish = %s".format(total, boot, init, finish)) + read case -2 => // Signals that an exception has been thrown in python val exLength = stream.readInt() @@ -115,23 +141,21 @@ private[spark] class PythonRDD[T: ClassManifest]( stream.readFully(obj) throw new PythonException(new String(obj)) case -1 => - // 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 EOFException - while (true) { - val len2 = stream.readInt() + // 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) stream.readFully(update) accumulator += Collections.singletonList(update) + len2 = stream.readInt() } new Array[Byte](0) } } catch { case eof: EOFException => { - val exitStatus = proc.waitFor() - if (exitStatus != 0) { - throw new Exception("Subprocess exited with status " + exitStatus) - } - new Array[Byte](0) + throw new SparkException("Python worker exited unexpectedly (crashed)", eof) } case e => throw e } @@ -159,7 +183,7 @@ private class PairwiseRDD(prev: RDD[Array[Byte]]) extends override def compute(split: Partition, context: TaskContext) = prev.iterator(split, context).grouped(2).map { case Seq(a, b) => (a, b) - case x => throw new Exception("PairwiseRDD: unexpected value: " + x) + case x => throw new SparkException("PairwiseRDD: unexpected value: " + x) } val asJavaPairRDD : JavaPairRDD[Array[Byte], Array[Byte]] = JavaPairRDD.fromRDD(this) } @@ -215,7 +239,7 @@ private[spark] object PythonRDD { dOut.write(s) dOut.writeByte(Pickle.STOP) } else { - throw new Exception("Unexpected RDD type") + throw new SparkException("Unexpected RDD type") } } @@ -277,7 +301,11 @@ private class BytesToString extends spark.api.java.function.Function[Array[Byte] */ 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 + override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new JArrayList override def addInPlace(val1: JList[Array[Byte]], val2: JList[Array[Byte]]) @@ -290,7 +318,7 @@ class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int) // This happens on the master, where we pass the updates to Python through a socket val socket = new Socket(serverHost, serverPort) val in = socket.getInputStream - val out = new DataOutputStream(socket.getOutputStream) + val out = new DataOutputStream(new BufferedOutputStream(socket.getOutputStream, bufferSize)) out.writeInt(val2.size) for (array <- val2) { out.writeInt(array.length) diff --git a/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala new file mode 100644 index 0000000000..14f8320678 --- /dev/null +++ b/core/src/main/scala/spark/api/python/PythonWorkerFactory.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 spark.api.python + +import java.io.{File, DataInputStream, IOException} +import java.net.{Socket, SocketException, InetAddress} + +import scala.collection.JavaConversions._ + +import spark._ + +private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String, String]) + extends Logging { + var daemon: Process = null + val daemonHost = InetAddress.getByAddress(Array(127, 0, 0, 1)) + var daemonPort: Int = 0 + + def create(): Socket = { + synchronized { + // Start the daemon if it hasn't been started + startDaemon() + + // Attempt to connect, restart and retry once if it fails + try { + new Socket(daemonHost, daemonPort) + } catch { + case exc: SocketException => { + logWarning("Python daemon unexpectedly quit, attempting to restart") + stopDaemon() + startDaemon() + new Socket(daemonHost, daemonPort) + } + case e => throw e + } + } + } + + def stop() { + stopDaemon() + } + + private def startDaemon() { + synchronized { + // Is it already running? + if (daemon != null) { + return + } + + try { + // Create and start the daemon + val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME") + val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/daemon.py")) + val workerEnv = pb.environment() + workerEnv.putAll(envVars) + val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH") + workerEnv.put("PYTHONPATH", pythonPath) + daemon = pb.start() + + // Redirect the stderr to ours + new Thread("stderr reader for " + pythonExec) { + override def run() { + scala.util.control.Exception.ignoring(classOf[IOException]) { + // FIXME HACK: We copy the stream on the level of bytes to + // attempt to dodge encoding problems. + val in = daemon.getErrorStream + var buf = new Array[Byte](1024) + var len = in.read(buf) + while (len != -1) { + System.err.write(buf, 0, len) + len = in.read(buf) + } + } + } + }.start() + + val in = new DataInputStream(daemon.getInputStream) + daemonPort = in.readInt() + + // Redirect further stdout output to our stderr + new Thread("stdout reader for " + pythonExec) { + override def run() { + scala.util.control.Exception.ignoring(classOf[IOException]) { + // FIXME HACK: We copy the stream on the level of bytes to + // attempt to dodge encoding problems. + var buf = new Array[Byte](1024) + var len = in.read(buf) + while (len != -1) { + System.err.write(buf, 0, len) + len = in.read(buf) + } + } + } + }.start() + } catch { + case e => { + stopDaemon() + throw e + } + } + + // Important: don't close daemon's stdin (daemon.getOutputStream) so it can correctly + // detect our disappearance. + } + } + + private def stopDaemon() { + synchronized { + // Request shutdown of existing daemon by sending SIGTERM + if (daemon != null) { + daemon.destroy() + } + + daemon = null + daemonPort = 0 + } + } +} diff --git a/core/src/main/scala/spark/broadcast/BitTorrentBroadcast.scala b/core/src/main/scala/spark/broadcast/BitTorrentBroadcast.scala index adcb2d2415..6f7d385379 100644 --- a/core/src/main/scala/spark/broadcast/BitTorrentBroadcast.scala +++ b/core/src/main/scala/spark/broadcast/BitTorrentBroadcast.scala @@ -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. + */ + package spark.broadcast import java.io._ diff --git a/core/src/main/scala/spark/broadcast/Broadcast.scala b/core/src/main/scala/spark/broadcast/Broadcast.scala index 415bde5d67..aba56a60ca 100644 --- a/core/src/main/scala/spark/broadcast/Broadcast.scala +++ b/core/src/main/scala/spark/broadcast/Broadcast.scala @@ -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. + */ + package spark.broadcast import java.io._ diff --git a/core/src/main/scala/spark/broadcast/BroadcastFactory.scala b/core/src/main/scala/spark/broadcast/BroadcastFactory.scala index 5c6184c3c7..d33d95c7d9 100644 --- a/core/src/main/scala/spark/broadcast/BroadcastFactory.scala +++ b/core/src/main/scala/spark/broadcast/BroadcastFactory.scala @@ -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. + */ + package spark.broadcast /** diff --git a/core/src/main/scala/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/spark/broadcast/HttpBroadcast.scala index 7e30b8f7d2..138a8c21bc 100644 --- a/core/src/main/scala/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/spark/broadcast/HttpBroadcast.scala @@ -1,20 +1,36 @@ -package spark.broadcast +/* + * 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. + */ -import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} +package spark.broadcast -import java.io._ -import java.net._ -import java.util.UUID +import java.io.{File, FileOutputStream, ObjectInputStream, OutputStream} +import java.net.URL import it.unimi.dsi.fastutil.io.FastBufferedInputStream import it.unimi.dsi.fastutil.io.FastBufferedOutputStream -import spark._ +import spark.{HttpServer, Logging, SparkEnv, Utils} +import spark.io.CompressionCodec import spark.storage.StorageLevel -import util.{MetadataCleaner, TimeStampedHashSet} +import spark.util.{MetadataCleaner, TimeStampedHashSet} + private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long) -extends Broadcast[T](id) with Logging with Serializable { + extends Broadcast[T](id) with Logging with Serializable { def value = value_ @@ -68,6 +84,7 @@ private object HttpBroadcast extends Logging { private val files = new TimeStampedHashSet[String] private val cleaner = new MetadataCleaner("HttpBroadcast", cleanup) + private lazy val compressionCodec = CompressionCodec.createCodec() def initialize(isDriver: Boolean) { synchronized { @@ -105,10 +122,12 @@ private object HttpBroadcast extends Logging { def write(id: Long, value: Any) { val file = new File(broadcastDir, "broadcast-" + id) - val out: OutputStream = if (compress) { - new LZFOutputStream(new FileOutputStream(file)) // Does its own buffering - } else { - new FastBufferedOutputStream(new FileOutputStream(file), bufferSize) + val out: OutputStream = { + if (compress) { + compressionCodec.compressedOutputStream(new FileOutputStream(file)) + } else { + new FastBufferedOutputStream(new FileOutputStream(file), bufferSize) + } } val ser = SparkEnv.get.serializer.newInstance() val serOut = ser.serializeStream(out) @@ -119,10 +138,12 @@ private object HttpBroadcast extends Logging { def read[T](id: Long): T = { val url = serverUri + "/broadcast-" + id - var in = if (compress) { - new LZFInputStream(new URL(url).openStream()) // Does its own buffering - } else { - new FastBufferedInputStream(new URL(url).openStream(), bufferSize) + val in = { + if (compress) { + compressionCodec.compressedInputStream(new URL(url).openStream()) + } else { + new FastBufferedInputStream(new URL(url).openStream(), bufferSize) + } } val ser = SparkEnv.get.serializer.newInstance() val serIn = ser.deserializeStream(in) diff --git a/core/src/main/scala/spark/broadcast/MultiTracker.scala b/core/src/main/scala/spark/broadcast/MultiTracker.scala index 3fd77af73f..7855d44e9b 100644 --- a/core/src/main/scala/spark/broadcast/MultiTracker.scala +++ b/core/src/main/scala/spark/broadcast/MultiTracker.scala @@ -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. + */ + package spark.broadcast import java.io._ diff --git a/core/src/main/scala/spark/broadcast/SourceInfo.scala b/core/src/main/scala/spark/broadcast/SourceInfo.scala index c79bb93c38..b17ae63b5c 100644 --- a/core/src/main/scala/spark/broadcast/SourceInfo.scala +++ b/core/src/main/scala/spark/broadcast/SourceInfo.scala @@ -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. + */ + package spark.broadcast import java.util.BitSet diff --git a/core/src/main/scala/spark/broadcast/TreeBroadcast.scala b/core/src/main/scala/spark/broadcast/TreeBroadcast.scala index c55c476117..ea1e9a12c1 100644 --- a/core/src/main/scala/spark/broadcast/TreeBroadcast.scala +++ b/core/src/main/scala/spark/broadcast/TreeBroadcast.scala @@ -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. + */ + package spark.broadcast import java.io._ diff --git a/core/src/main/scala/spark/deploy/ApplicationDescription.scala b/core/src/main/scala/spark/deploy/ApplicationDescription.scala index 6659e53b25..a8b22fbef8 100644 --- a/core/src/main/scala/spark/deploy/ApplicationDescription.scala +++ b/core/src/main/scala/spark/deploy/ApplicationDescription.scala @@ -1,11 +1,29 @@ +/* + * 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 spark.deploy private[spark] class ApplicationDescription( val name: String, - val cores: Int, + val maxCores: Int, /* Integer.MAX_VALUE denotes an unlimited number of cores */ val memoryPerSlave: Int, val command: Command, - val sparkHome: String) + val sparkHome: String, + val appUiUrl: String) extends Serializable { val user = System.getProperty("user.name", "<unknown>") diff --git a/core/src/main/scala/spark/deploy/Command.scala b/core/src/main/scala/spark/deploy/Command.scala index 577101e3c3..bad629e965 100644 --- a/core/src/main/scala/spark/deploy/Command.scala +++ b/core/src/main/scala/spark/deploy/Command.scala @@ -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. + */ + package spark.deploy import scala.collection.Map diff --git a/core/src/main/scala/spark/deploy/DeployMessage.scala b/core/src/main/scala/spark/deploy/DeployMessage.scala index 8a3e64e4c2..31861f3ac2 100644 --- a/core/src/main/scala/spark/deploy/DeployMessage.scala +++ b/core/src/main/scala/spark/deploy/DeployMessage.scala @@ -1,95 +1,129 @@ +/* + * 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 spark.deploy +import scala.collection.immutable.List + +import spark.Utils import spark.deploy.ExecutorState.ExecutorState import spark.deploy.master.{WorkerInfo, ApplicationInfo} import spark.deploy.worker.ExecutorRunner -import scala.collection.immutable.List -private[spark] sealed trait DeployMessage extends Serializable +private[deploy] sealed trait DeployMessage extends Serializable -// Worker to Master +private[deploy] object DeployMessages { -private[spark] -case class RegisterWorker( - id: String, - host: String, - port: Int, - cores: Int, - memory: Int, - webUiPort: Int, - publicAddress: String) - extends DeployMessage + // Worker to Master -private[spark] -case class ExecutorStateChanged( - appId: String, - execId: Int, - state: ExecutorState, - message: Option[String], - exitStatus: Option[Int]) - extends DeployMessage + case class RegisterWorker( + id: String, + host: String, + port: Int, + cores: Int, + memory: Int, + webUiPort: Int, + publicAddress: String) + extends DeployMessage { + Utils.checkHost(host, "Required hostname") + assert (port > 0) + } -private[spark] case class Heartbeat(workerId: String) extends DeployMessage + case class ExecutorStateChanged( + appId: String, + execId: Int, + state: ExecutorState, + message: Option[String], + exitStatus: Option[Int]) + extends DeployMessage -// Master to Worker + case class Heartbeat(workerId: String) extends DeployMessage -private[spark] case class RegisteredWorker(masterWebUiUrl: String) extends DeployMessage -private[spark] case class RegisterWorkerFailed(message: String) extends DeployMessage -private[spark] case class KillExecutor(appId: String, execId: Int) extends DeployMessage + // Master to Worker -private[spark] case class LaunchExecutor( - appId: String, - execId: Int, - appDesc: ApplicationDescription, - cores: Int, - memory: Int, - sparkHome: String) - extends DeployMessage + case class RegisteredWorker(masterWebUiUrl: String) extends DeployMessage -// Client to Master + case class RegisterWorkerFailed(message: String) extends DeployMessage -private[spark] case class RegisterApplication(appDescription: ApplicationDescription) - extends DeployMessage + case class KillExecutor(appId: String, execId: Int) extends DeployMessage -// Master to Client + case class LaunchExecutor( + appId: String, + execId: Int, + appDesc: ApplicationDescription, + cores: Int, + memory: Int, + sparkHome: String) + extends DeployMessage -private[spark] -case class RegisteredApplication(appId: String) extends DeployMessage + // Client to Master -private[spark] -case class ExecutorAdded(id: Int, workerId: String, host: String, cores: Int, memory: Int) + case class RegisterApplication(appDescription: ApplicationDescription) + extends DeployMessage -private[spark] -case class ExecutorUpdated(id: Int, state: ExecutorState, message: Option[String], - exitStatus: Option[Int]) + // Master to Client -private[spark] -case class ApplicationRemoved(message: String) + case class RegisteredApplication(appId: String) extends DeployMessage -// Internal message in Client + case class ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) { + Utils.checkHostPort(hostPort, "Required hostport") + } -private[spark] case object StopClient + case class ExecutorUpdated(id: Int, state: ExecutorState, message: Option[String], + exitStatus: Option[Int]) -// MasterWebUI To Master + case class ApplicationRemoved(message: String) -private[spark] case object RequestMasterState + // Internal message in Client -// Master to MasterWebUI + case object StopClient -private[spark] -case class MasterState(host: String, port: Int, workers: Array[WorkerInfo], - activeApps: Array[ApplicationInfo], completedApps: Array[ApplicationInfo]) { + // MasterWebUI To Master - def uri = "spark://" + host + ":" + port -} + case object RequestMasterState + + // Master to MasterWebUI + + case class MasterStateResponse(host: String, port: Int, workers: Array[WorkerInfo], + activeApps: Array[ApplicationInfo], completedApps: Array[ApplicationInfo]) { -// WorkerWebUI to Worker -private[spark] case object RequestWorkerState + Utils.checkHost(host, "Required hostname") + assert (port > 0) -// Worker to WorkerWebUI + def uri = "spark://" + host + ":" + port + } -private[spark] -case class WorkerState(host: String, port: Int, workerId: String, executors: List[ExecutorRunner], - finishedExecutors: List[ExecutorRunner], masterUrl: String, cores: Int, memory: Int, - coresUsed: Int, memoryUsed: Int, masterWebUiUrl: String) + // WorkerWebUI to Worker + + case object RequestWorkerState + + // Worker to WorkerWebUI + + case class WorkerStateResponse(host: String, port: Int, workerId: String, + executors: List[ExecutorRunner], finishedExecutors: List[ExecutorRunner], masterUrl: String, + cores: Int, memory: Int, coresUsed: Int, memoryUsed: Int, masterWebUiUrl: String) { + + Utils.checkHost(host, "Required hostname") + assert (port > 0) + } + + // Actor System to Master + + case object CheckForWorkerTimeOut + +} diff --git a/core/src/main/scala/spark/deploy/ExecutorState.scala b/core/src/main/scala/spark/deploy/ExecutorState.scala index 5dc0c54552..08c9a3b725 100644 --- a/core/src/main/scala/spark/deploy/ExecutorState.scala +++ b/core/src/main/scala/spark/deploy/ExecutorState.scala @@ -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. + */ + package spark.deploy private[spark] object ExecutorState diff --git a/core/src/main/scala/spark/deploy/JsonProtocol.scala b/core/src/main/scala/spark/deploy/JsonProtocol.scala index 38a6ebfc24..bd1db7c294 100644 --- a/core/src/main/scala/spark/deploy/JsonProtocol.scala +++ b/core/src/main/scala/spark/deploy/JsonProtocol.scala @@ -1,78 +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 spark.deploy -import master.{ApplicationInfo, WorkerInfo} -import worker.ExecutorRunner -import cc.spray.json._ +import net.liftweb.json.JsonDSL._ -/** - * spray-json helper class containing implicit conversion to json for marshalling responses - */ -private[spark] object JsonProtocol extends DefaultJsonProtocol { - implicit object WorkerInfoJsonFormat extends RootJsonWriter[WorkerInfo] { - def write(obj: WorkerInfo) = JsObject( - "id" -> JsString(obj.id), - "host" -> JsString(obj.host), - "webuiaddress" -> JsString(obj.webUiAddress), - "cores" -> JsNumber(obj.cores), - "coresused" -> JsNumber(obj.coresUsed), - "memory" -> JsNumber(obj.memory), - "memoryused" -> JsNumber(obj.memoryUsed) - ) - } +import spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse} +import spark.deploy.master.{ApplicationInfo, WorkerInfo} +import spark.deploy.worker.ExecutorRunner + + +private[spark] object JsonProtocol { + def writeWorkerInfo(obj: WorkerInfo) = { + ("id" -> obj.id) ~ + ("host" -> obj.host) ~ + ("port" -> obj.port) ~ + ("webuiaddress" -> obj.webUiAddress) ~ + ("cores" -> obj.cores) ~ + ("coresused" -> obj.coresUsed) ~ + ("memory" -> obj.memory) ~ + ("memoryused" -> obj.memoryUsed) + } - implicit object AppInfoJsonFormat extends RootJsonWriter[ApplicationInfo] { - def write(obj: ApplicationInfo) = JsObject( - "starttime" -> JsNumber(obj.startTime), - "id" -> JsString(obj.id), - "name" -> JsString(obj.desc.name), - "cores" -> JsNumber(obj.desc.cores), - "user" -> JsString(obj.desc.user), - "memoryperslave" -> JsNumber(obj.desc.memoryPerSlave), - "submitdate" -> JsString(obj.submitDate.toString)) + def writeApplicationInfo(obj: ApplicationInfo) = { + ("starttime" -> obj.startTime) ~ + ("id" -> obj.id) ~ + ("name" -> obj.desc.name) ~ + ("cores" -> obj.desc.maxCores) ~ + ("user" -> obj.desc.user) ~ + ("memoryperslave" -> obj.desc.memoryPerSlave) ~ + ("submitdate" -> obj.submitDate.toString) } - implicit object AppDescriptionJsonFormat extends RootJsonWriter[ApplicationDescription] { - def write(obj: ApplicationDescription) = JsObject( - "name" -> JsString(obj.name), - "cores" -> JsNumber(obj.cores), - "memoryperslave" -> JsNumber(obj.memoryPerSlave), - "user" -> JsString(obj.user) - ) + def writeApplicationDescription(obj: ApplicationDescription) = { + ("name" -> obj.name) ~ + ("cores" -> obj.maxCores) ~ + ("memoryperslave" -> obj.memoryPerSlave) ~ + ("user" -> obj.user) } - implicit object ExecutorRunnerJsonFormat extends RootJsonWriter[ExecutorRunner] { - def write(obj: ExecutorRunner) = JsObject( - "id" -> JsNumber(obj.execId), - "memory" -> JsNumber(obj.memory), - "appid" -> JsString(obj.appId), - "appdesc" -> obj.appDesc.toJson.asJsObject - ) + def writeExecutorRunner(obj: ExecutorRunner) = { + ("id" -> obj.execId) ~ + ("memory" -> obj.memory) ~ + ("appid" -> obj.appId) ~ + ("appdesc" -> writeApplicationDescription(obj.appDesc)) } - implicit object MasterStateJsonFormat extends RootJsonWriter[MasterState] { - def write(obj: MasterState) = JsObject( - "url" -> JsString("spark://" + obj.uri), - "workers" -> JsArray(obj.workers.toList.map(_.toJson)), - "cores" -> JsNumber(obj.workers.map(_.cores).sum), - "coresused" -> JsNumber(obj.workers.map(_.coresUsed).sum), - "memory" -> JsNumber(obj.workers.map(_.memory).sum), - "memoryused" -> JsNumber(obj.workers.map(_.memoryUsed).sum), - "activeapps" -> JsArray(obj.activeApps.toList.map(_.toJson)), - "completedapps" -> JsArray(obj.completedApps.toList.map(_.toJson)) - ) + def writeMasterState(obj: MasterStateResponse) = { + ("url" -> ("spark://" + 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)) } - implicit object WorkerStateJsonFormat extends RootJsonWriter[WorkerState] { - def write(obj: WorkerState) = JsObject( - "id" -> JsString(obj.workerId), - "masterurl" -> JsString(obj.masterUrl), - "masterwebuiurl" -> JsString(obj.masterWebUiUrl), - "cores" -> JsNumber(obj.cores), - "coresused" -> JsNumber(obj.coresUsed), - "memory" -> JsNumber(obj.memory), - "memoryused" -> JsNumber(obj.memoryUsed), - "executors" -> JsArray(obj.executors.toList.map(_.toJson)), - "finishedexecutors" -> JsArray(obj.finishedExecutors.toList.map(_.toJson)) - ) + def writeWorkerState(obj: WorkerStateResponse) = { + ("id" -> obj.workerId) ~ + ("masterurl" -> obj.masterUrl) ~ + ("masterwebuiurl" -> obj.masterWebUiUrl) ~ + ("cores" -> obj.cores) ~ + ("coresused" -> obj.coresUsed) ~ + ("memory" -> obj.memory) ~ + ("memoryused" -> obj.memoryUsed) ~ + ("executors" -> obj.executors.toList.map(writeExecutorRunner)) ~ + ("finishedexecutors" -> obj.finishedExecutors.toList.map(writeExecutorRunner)) } } diff --git a/core/src/main/scala/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/spark/deploy/LocalSparkCluster.scala index 22319a96ca..6b8e9f27af 100644 --- a/core/src/main/scala/spark/deploy/LocalSparkCluster.scala +++ b/core/src/main/scala/spark/deploy/LocalSparkCluster.scala @@ -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. + */ + package spark.deploy import akka.actor.{ActorRef, Props, Actor, ActorSystem, Terminated} @@ -18,7 +35,7 @@ import scala.collection.mutable.ArrayBuffer private[spark] class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: Int) extends Logging { - private val localIpAddress = Utils.localIpAddress + private val localHostname = Utils.localHostName() private val masterActorSystems = ArrayBuffer[ActorSystem]() private val workerActorSystems = ArrayBuffer[ActorSystem]() @@ -26,13 +43,13 @@ class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: I logInfo("Starting a local Spark cluster with " + numWorkers + " workers.") /* Start the Master */ - val (masterSystem, masterPort) = Master.startSystemAndActor(localIpAddress, 0, 0) + val (masterSystem, masterPort) = Master.startSystemAndActor(localHostname, 0, 0) masterActorSystems += masterSystem - val masterUrl = "spark://" + localIpAddress + ":" + masterPort + val masterUrl = "spark://" + localHostname + ":" + masterPort /* Start the Workers */ for (workerNum <- 1 to numWorkers) { - val (workerSystem, _) = Worker.startSystemAndActor(localIpAddress, 0, 0, coresPerWorker, + val (workerSystem, _) = Worker.startSystemAndActor(localHostname, 0, 0, coresPerWorker, memoryPerWorker, masterUrl, null, Some(workerNum)) workerActorSystems += workerSystem } @@ -45,6 +62,7 @@ class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: I // Stop the workers before the master so they don't get upset that it disconnected workerActorSystems.foreach(_.shutdown()) workerActorSystems.foreach(_.awaitTermination()) + masterActorSystems.foreach(_.shutdown()) masterActorSystems.foreach(_.awaitTermination()) } diff --git a/core/src/main/scala/spark/deploy/WebUI.scala b/core/src/main/scala/spark/deploy/WebUI.scala index ad1a1092b2..8ea7792ef4 100644 --- a/core/src/main/scala/spark/deploy/WebUI.scala +++ b/core/src/main/scala/spark/deploy/WebUI.scala @@ -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. + */ + package spark.deploy import java.text.SimpleDateFormat @@ -6,7 +23,7 @@ import java.util.Date /** * Utilities used throughout the web UI. */ -private[spark] object WebUI { +private[spark] object DeployWebUI { val DATE_FORMAT = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") def formatDate(date: Date): String = DATE_FORMAT.format(date) diff --git a/core/src/main/scala/spark/deploy/client/Client.scala b/core/src/main/scala/spark/deploy/client/Client.scala index 2fc5e657f9..9d5ba8a796 100644 --- a/core/src/main/scala/spark/deploy/client/Client.scala +++ b/core/src/main/scala/spark/deploy/client/Client.scala @@ -1,19 +1,39 @@ +/* + * 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 spark.deploy.client -import spark.deploy._ +import java.util.concurrent.TimeoutException + import akka.actor._ +import akka.actor.Terminated import akka.pattern.ask -import akka.util.duration._ -import akka.pattern.AskTimeoutException -import spark.{SparkException, Logging} +import akka.util.Duration +import akka.remote.RemoteClientDisconnected import akka.remote.RemoteClientLifeCycleEvent import akka.remote.RemoteClientShutdown -import spark.deploy.RegisterApplication -import spark.deploy.master.Master -import akka.remote.RemoteClientDisconnected -import akka.actor.Terminated import akka.dispatch.Await +import spark.Logging +import spark.deploy.{ApplicationDescription, ExecutorState} +import spark.deploy.DeployMessages._ +import spark.deploy.master.Master + + /** * 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. @@ -59,10 +79,10 @@ private[spark] class Client( markDisconnected() context.stop(self) - case ExecutorAdded(id: Int, workerId: String, host: String, cores: Int, memory: Int) => + case ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) => val fullId = appId + "/" + id - logInfo("Executor added: %s on %s (%s) with %d cores".format(fullId, workerId, host, cores)) - listener.executorAdded(fullId, workerId, host, cores, memory) + logInfo("Executor added: %s on %s (%s) with %d cores".format(fullId, workerId, hostPort, cores)) + listener.executorAdded(fullId, workerId, hostPort, cores, memory) case ExecutorUpdated(id, state, message, exitStatus) => val fullId = appId + "/" + id @@ -112,11 +132,12 @@ private[spark] class Client( def stop() { if (actor != null) { try { - val timeout = 5.seconds + val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") val future = actor.ask(StopClient)(timeout) Await.result(future, timeout) } catch { - case e: AskTimeoutException => // Ignore it, maybe master went away + case e: TimeoutException => + logInfo("Stop request to Master timed out; it may already be shut down.") } actor = null } diff --git a/core/src/main/scala/spark/deploy/client/ClientListener.scala b/core/src/main/scala/spark/deploy/client/ClientListener.scala index b7008321df..064024455e 100644 --- a/core/src/main/scala/spark/deploy/client/ClientListener.scala +++ b/core/src/main/scala/spark/deploy/client/ClientListener.scala @@ -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. + */ + package spark.deploy.client /** @@ -12,7 +29,7 @@ private[spark] trait ClientListener { def disconnected(): Unit - def executorAdded(fullId: String, workerId: String, host: String, cores: Int, memory: Int): 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/spark/deploy/client/TestClient.scala b/core/src/main/scala/spark/deploy/client/TestClient.scala index dc004b59ca..4f4daa141a 100644 --- a/core/src/main/scala/spark/deploy/client/TestClient.scala +++ b/core/src/main/scala/spark/deploy/client/TestClient.scala @@ -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. + */ + package spark.deploy.client import spark.util.AkkaUtils @@ -16,7 +33,7 @@ private[spark] object TestClient { System.exit(0) } - def executorAdded(id: String, workerId: String, host: String, cores: Int, memory: Int) {} + def executorAdded(id: String, workerId: String, hostPort: String, cores: Int, memory: Int) {} def executorRemoved(id: String, message: String, exitStatus: Option[Int]) {} } @@ -25,7 +42,7 @@ private[spark] object TestClient { val url = args(0) val (actorSystem, port) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0) val desc = new ApplicationDescription( - "TestClient", 1, 512, Command("spark.deploy.client.TestExecutor", Seq(), Map()), "dummy-spark-home") + "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) client.start() diff --git a/core/src/main/scala/spark/deploy/client/TestExecutor.scala b/core/src/main/scala/spark/deploy/client/TestExecutor.scala index 0e46db2272..8a22b6b89f 100644 --- a/core/src/main/scala/spark/deploy/client/TestExecutor.scala +++ b/core/src/main/scala/spark/deploy/client/TestExecutor.scala @@ -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. + */ + package spark.deploy.client private[spark] object TestExecutor { diff --git a/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala index 3591a94072..6dd2f06126 100644 --- a/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala +++ b/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala @@ -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. + */ + package spark.deploy.master import spark.deploy.ApplicationDescription @@ -10,12 +27,14 @@ private[spark] class ApplicationInfo( val id: String, val desc: ApplicationDescription, val submitDate: Date, - val driver: ActorRef) + 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 @@ -33,11 +52,13 @@ private[spark] class ApplicationInfo( } def removeExecutor(exec: ExecutorInfo) { - executors -= exec.id - coresGranted -= exec.cores + if (executors.contains(exec.id)) { + executors -= exec.id + coresGranted -= exec.cores + } } - def coresLeft: Int = desc.cores - coresGranted + def coresLeft: Int = desc.maxCores - coresGranted private var _retryCount = 0 @@ -60,4 +81,5 @@ private[spark] class ApplicationInfo( System.currentTimeMillis() - startTime } } + } diff --git a/core/src/main/scala/spark/deploy/master/ApplicationSource.scala b/core/src/main/scala/spark/deploy/master/ApplicationSource.scala new file mode 100644 index 0000000000..4df2b6bfdd --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ApplicationSource.scala @@ -0,0 +1,24 @@ +package spark.deploy.master + +import com.codahale.metrics.{Gauge, MetricRegistry} + +import spark.metrics.source.Source + +class ApplicationSource(val application: ApplicationInfo) extends Source { + val metricRegistry = new MetricRegistry() + val sourceName = "%s.%s.%s".format("application", application.desc.name, + System.currentTimeMillis()) + + metricRegistry.register(MetricRegistry.name("status"), new Gauge[String] { + override def getValue: String = application.state.toString + }) + + metricRegistry.register(MetricRegistry.name("runtime_ms"), new Gauge[Long] { + override def getValue: Long = application.duration + }) + + metricRegistry.register(MetricRegistry.name("cores", "number"), new Gauge[Int] { + override def getValue: Int = application.coresGranted + }) + +} diff --git a/core/src/main/scala/spark/deploy/master/ApplicationState.scala b/core/src/main/scala/spark/deploy/master/ApplicationState.scala index 15016b388d..94f0ad8bae 100644 --- a/core/src/main/scala/spark/deploy/master/ApplicationState.scala +++ b/core/src/main/scala/spark/deploy/master/ApplicationState.scala @@ -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. + */ + package spark.deploy.master private[spark] object ApplicationState diff --git a/core/src/main/scala/spark/deploy/master/ExecutorInfo.scala b/core/src/main/scala/spark/deploy/master/ExecutorInfo.scala index 48e6055fb5..99b60f7d09 100644 --- a/core/src/main/scala/spark/deploy/master/ExecutorInfo.scala +++ b/core/src/main/scala/spark/deploy/master/ExecutorInfo.scala @@ -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. + */ + package spark.deploy.master import spark.deploy.ExecutorState diff --git a/core/src/main/scala/spark/deploy/master/Master.scala b/core/src/main/scala/spark/deploy/master/Master.scala index 71b9d0801d..4a4d9908a0 100644 --- a/core/src/main/scala/spark/deploy/master/Master.scala +++ b/core/src/main/scala/spark/deploy/master/Master.scala @@ -1,24 +1,46 @@ -package spark.deploy.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. + */ -import akka.actor._ -import akka.actor.Terminated -import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientDisconnected, RemoteClientShutdown} -import akka.util.duration._ +package spark.deploy.master import java.text.SimpleDateFormat import java.util.Date import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} -import spark.deploy._ +import akka.actor._ +import akka.actor.Terminated +import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientDisconnected, RemoteClientShutdown} +import akka.util.duration._ + import spark.{Logging, SparkException, Utils} +import spark.deploy.{ApplicationDescription, ExecutorState} +import spark.deploy.DeployMessages._ +import spark.deploy.master.ui.MasterWebUI +import spark.metrics.MetricsSystem import spark.util.AkkaUtils -private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor with Logging { +private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Actor with Logging { 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 + var nextAppNumber = 0 val workers = new HashSet[WorkerInfo] val idToWorker = new HashMap[String, WorkerInfo] @@ -35,9 +57,17 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor var firstApp: Option[ApplicationInfo] = None + val webUi = new MasterWebUI(self, webUiPort) + + Utils.checkHost(host, "Expected hostname") + + val masterMetricsSystem = MetricsSystem.createMetricsSystem("master") + val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications") + val masterSource = new MasterSource(this) + val masterPublicAddress = { val envVar = System.getenv("SPARK_PUBLIC_DNS") - if (envVar != null) envVar else ip + if (envVar != null) envVar else host } // As a temporary workaround before better ways of configuring memory, we allow users to set @@ -46,22 +76,21 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor val spreadOutApps = System.getProperty("spark.deploy.spreadOut", "true").toBoolean override def preStart() { - logInfo("Starting Spark master at spark://" + ip + ":" + port) + logInfo("Starting Spark master at spark://" + host + ":" + port) // Listen for remote client disconnection events, since they don't go through Akka's watch() context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) - startWebUi() - context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis)(timeOutDeadWorkers()) + webUi.start() + context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis, self, CheckForWorkerTimeOut) + + masterMetricsSystem.registerSource(masterSource) + masterMetricsSystem.start() + applicationMetricsSystem.start() } - def startWebUi() { - val webUi = new MasterWebUI(context.system, self) - try { - AkkaUtils.startSprayServer(context.system, "0.0.0.0", webUiPort, webUi.handler) - } catch { - case e: Exception => - logError("Failed to create web UI", e) - System.exit(1) - } + override def postStop() { + webUi.stop() + masterMetricsSystem.stop() + applicationMetricsSystem.stop() } override def receive = { @@ -73,7 +102,7 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor } 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 + ":" + webUiPort) + sender ! RegisteredWorker("http://" + masterPublicAddress + ":" + webUi.boundPort.get) schedule() } } @@ -145,7 +174,11 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor } case RequestMasterState => { - sender ! MasterState(ip, port, workers.toArray, apps.toArray, completedApps.toArray) + sender ! MasterStateResponse(host, port, workers.toArray, apps.toArray, completedApps.toArray) + } + + case CheckForWorkerTimeOut => { + timeOutDeadWorkers() } } @@ -210,20 +243,27 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor def launchExecutor(worker: WorkerInfo, exec: ExecutorInfo, sparkHome: String) { logInfo("Launching executor " + exec.fullId + " on worker " + worker.id) worker.addExecutor(exec) - worker.actor ! LaunchExecutor(exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory, sparkHome) - exec.application.driver ! ExecutorAdded(exec.id, worker.id, worker.host, exec.cores, exec.memory) + worker.actor ! LaunchExecutor( + 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 = { - // There may be one or more refs to dead workers on this same node (w/ different ID's), remove them. - workers.filter(w => (w.host == host) && (w.state == WorkerState.DEAD)).foreach(workers -= _) + // There may be one or more refs to dead workers on this same node (w/ different ID's), + // remove them. + workers.filter { w => + (w.host == host && w.port == port) && (w.state == WorkerState.DEAD) + }.foreach { w => + workers -= w + } val worker = new WorkerInfo(id, host, port, cores, memory, sender, webUiPort, publicAddress) workers += worker idToWorker(worker.id) = worker actorToWorker(sender) = worker addressToWorker(sender.path.address) = worker - return worker + worker } def removeWorker(worker: WorkerInfo) { @@ -234,7 +274,8 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor addressToWorker -= worker.actor.path.address for (exec <- worker.executors.values) { logInfo("Telling app of lost executor: " + exec.id) - exec.application.driver ! ExecutorUpdated(exec.id, ExecutorState.LOST, Some("worker lost"), None) + exec.application.driver ! ExecutorUpdated( + exec.id, ExecutorState.LOST, Some("worker lost"), None) exec.application.removeExecutor(exec) } } @@ -242,7 +283,8 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor def addApplication(desc: ApplicationDescription, driver: ActorRef): ApplicationInfo = { val now = System.currentTimeMillis() val date = new Date(now) - val app = new ApplicationInfo(now, newApplicationId(date), desc, date, driver) + val app = new ApplicationInfo(now, newApplicationId(date), desc, date, driver, desc.appUiUrl) + applicationMetricsSystem.registerSource(app.appSource) apps += app idToApp(app.id) = app actorToApp(driver) = app @@ -254,7 +296,7 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor if (workersAlive.size > 0 && !workersAlive.exists(_.memoryFree >= desc.memoryPerSlave)) { logWarning("Could not find any workers with enough memory for " + firstApp.get.id) } - return app + app } def finishApplication(app: ApplicationInfo) { @@ -268,14 +310,24 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor idToApp -= app.id actorToApp -= app.driver addressToApp -= app.driver.path.address - completedApps += app // Remember it in our history + if (completedApps.size >= RETAINED_APPLICATIONS) { + val toRemove = math.max(RETAINED_APPLICATIONS / 10, 1) + completedApps.take(toRemove).foreach( a => { + applicationMetricsSystem.removeSource(a.appSource) + }) + completedApps.trimStart(toRemove) + } + completedApps += app // Remember it in our history waitingApps -= app for (exec <- app.executors.values) { exec.worker.removeExecutor(exec) exec.worker.actor ! KillExecutor(exec.application.id, exec.id) + exec.state = ExecutorState.KILLED } app.markFinished(state) - app.driver ! ApplicationRemoved(state.toString) + if (state != ApplicationState.FINISHED) { + app.driver ! ApplicationRemoved(state.toString) + } schedule() } } @@ -290,12 +342,17 @@ private[spark] class Master(ip: String, port: Int, webUiPort: Int) extends Actor /** Check for, and remove, any timed-out workers */ def timeOutDeadWorkers() { // Copy the workers into an array so we don't modify the hashset while iterating through it - val expirationTime = System.currentTimeMillis() - WORKER_TIMEOUT - val toRemove = workers.filter(_.lastHeartbeat < expirationTime).toArray + val currentTime = System.currentTimeMillis() + val toRemove = workers.filter(_.lastHeartbeat < currentTime - WORKER_TIMEOUT).toArray for (worker <- toRemove) { - logWarning("Removing %s because we got no heartbeat in %d seconds".format( - worker.id, WORKER_TIMEOUT)) - removeWorker(worker) + if (worker.state != WorkerState.DEAD) { + logWarning("Removing %s because we got no heartbeat in %d seconds".format( + worker.id, WORKER_TIMEOUT/1000)) + 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 + } } } } @@ -307,7 +364,7 @@ private[spark] object Master { def main(argStrings: Array[String]) { val args = new MasterArguments(argStrings) - val (actorSystem, _) = startSystemAndActor(args.ip, args.port, args.webUiPort) + val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort) actorSystem.awaitTermination() } diff --git a/core/src/main/scala/spark/deploy/master/MasterArguments.scala b/core/src/main/scala/spark/deploy/master/MasterArguments.scala index 4ceab3fc03..0ae0160767 100644 --- a/core/src/main/scala/spark/deploy/master/MasterArguments.scala +++ b/core/src/main/scala/spark/deploy/master/MasterArguments.scala @@ -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. + */ + package spark.deploy.master import spark.util.IntParam @@ -7,13 +24,13 @@ import spark.Utils * Command-line parser for the master. */ private[spark] class MasterArguments(args: Array[String]) { - var ip = Utils.localHostName() + var host = Utils.localHostName() var port = 7077 var webUiPort = 8080 // Check for settings in environment variables - if (System.getenv("SPARK_MASTER_IP") != null) { - ip = System.getenv("SPARK_MASTER_IP") + if (System.getenv("SPARK_MASTER_HOST") != null) { + host = System.getenv("SPARK_MASTER_HOST") } if (System.getenv("SPARK_MASTER_PORT") != null) { port = System.getenv("SPARK_MASTER_PORT").toInt @@ -21,12 +38,21 @@ 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 + } + parse(args.toList) def parse(args: List[String]): Unit = args match { case ("--ip" | "-i") :: value :: tail => - ip = value + Utils.checkHost(value, "ip no longer supported, please use hostname " + value) + host = value + parse(tail) + + case ("--host" | "-h") :: value :: tail => + Utils.checkHost(value, "Please use hostname " + value) + host = value parse(tail) case ("--port" | "-p") :: IntParam(value) :: tail => @@ -54,7 +80,8 @@ private[spark] class MasterArguments(args: Array[String]) { "Usage: Master [options]\n" + "\n" + "Options:\n" + - " -i IP, --ip IP IP address or DNS name to listen on\n" + + " -i HOST, --ip HOST Hostname to listen on (deprecated, please use --host or -h) \n" + + " -h HOST, --host HOST Hostname to listen on\n" + " -p PORT, --port PORT Port to listen on (default: 7077)\n" + " --webui-port PORT Port for web UI (default: 8080)") System.exit(exitCode) diff --git a/core/src/main/scala/spark/deploy/master/MasterSource.scala b/core/src/main/scala/spark/deploy/master/MasterSource.scala new file mode 100644 index 0000000000..b8cfa6a773 --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/MasterSource.scala @@ -0,0 +1,25 @@ +package spark.deploy.master + +import com.codahale.metrics.{Gauge, MetricRegistry} + +import spark.metrics.source.Source + +private[spark] class MasterSource(val master: Master) extends Source { + val metricRegistry = new MetricRegistry() + val sourceName = "master" + + // Gauge for worker numbers in cluster + metricRegistry.register(MetricRegistry.name("workers","number"), 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] { + override def getValue: Int = master.apps.size + }) + + // Gauge for waiting application numbers in cluster + metricRegistry.register(MetricRegistry.name("waitingApps", "number"), new Gauge[Int] { + override def getValue: Int = master.waitingApps.size + }) +} diff --git a/core/src/main/scala/spark/deploy/master/MasterWebUI.scala b/core/src/main/scala/spark/deploy/master/MasterWebUI.scala deleted file mode 100644 index 54faa375fb..0000000000 --- a/core/src/main/scala/spark/deploy/master/MasterWebUI.scala +++ /dev/null @@ -1,74 +0,0 @@ -package spark.deploy.master - -import akka.actor.{ActorRef, ActorSystem} -import akka.dispatch.Await -import akka.pattern.ask -import akka.util.Timeout -import akka.util.duration._ -import cc.spray.Directives -import cc.spray.directives._ -import cc.spray.typeconversion.TwirlSupport._ -import cc.spray.http.MediaTypes -import cc.spray.typeconversion.SprayJsonSupport._ - -import spark.deploy._ -import spark.deploy.JsonProtocol._ - -/** - * Web UI server for the standalone master. - */ -private[spark] -class MasterWebUI(val actorSystem: ActorSystem, master: ActorRef) extends Directives { - val RESOURCE_DIR = "spark/deploy/master/webui" - val STATIC_RESOURCE_DIR = "spark/deploy/static" - - implicit val timeout = Timeout(10 seconds) - - val handler = { - get { - (path("") & parameters('format ?)) { - case Some(js) if js.equalsIgnoreCase("json") => - val future = master ? RequestMasterState - respondWithMediaType(MediaTypes.`application/json`) { ctx => - ctx.complete(future.mapTo[MasterState]) - } - case _ => - completeWith { - val future = master ? RequestMasterState - future.map { - masterState => spark.deploy.master.html.index.render(masterState.asInstanceOf[MasterState]) - } - } - } ~ - path("app") { - parameters("appId", 'format ?) { - case (appId, Some(js)) if (js.equalsIgnoreCase("json")) => - val future = master ? RequestMasterState - val appInfo = for (masterState <- future.mapTo[MasterState]) yield { - masterState.activeApps.find(_.id == appId).getOrElse({ - masterState.completedApps.find(_.id == appId).getOrElse(null) - }) - } - respondWithMediaType(MediaTypes.`application/json`) { ctx => - ctx.complete(appInfo.mapTo[ApplicationInfo]) - } - case (appId, _) => - completeWith { - val future = master ? RequestMasterState - future.map { state => - val masterState = state.asInstanceOf[MasterState] - val app = masterState.activeApps.find(_.id == appId).getOrElse({ - masterState.completedApps.find(_.id == appId).getOrElse(null) - }) - spark.deploy.master.html.app_details.render(app) - } - } - } - } ~ - pathPrefix("static") { - getFromResourceDirectory(STATIC_RESOURCE_DIR) - } ~ - getFromResourceDirectory(RESOURCE_DIR) - } - } -} diff --git a/core/src/main/scala/spark/deploy/master/WorkerInfo.scala b/core/src/main/scala/spark/deploy/master/WorkerInfo.scala index 23df1bb463..4135cfeb28 100644 --- a/core/src/main/scala/spark/deploy/master/WorkerInfo.scala +++ b/core/src/main/scala/spark/deploy/master/WorkerInfo.scala @@ -1,7 +1,25 @@ +/* + * 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 spark.deploy.master import akka.actor.ActorRef import scala.collection.mutable +import spark.Utils private[spark] class WorkerInfo( val id: String, @@ -13,6 +31,9 @@ private[spark] class WorkerInfo( val webUiPort: Int, val publicAddress: String) { + 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 @@ -23,6 +44,11 @@ private[spark] class WorkerInfo( def coresFree: Int = cores - coresUsed def memoryFree: Int = memory - memoryUsed + def hostPort: String = { + assert (port > 0) + host + ":" + port + } + def addExecutor(exec: ExecutorInfo) { executors(exec.fullId) = exec coresUsed += exec.cores diff --git a/core/src/main/scala/spark/deploy/master/WorkerState.scala b/core/src/main/scala/spark/deploy/master/WorkerState.scala index 0bf35014c8..3e50b7748d 100644 --- a/core/src/main/scala/spark/deploy/master/WorkerState.scala +++ b/core/src/main/scala/spark/deploy/master/WorkerState.scala @@ -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. + */ + package spark.deploy.master private[spark] object WorkerState extends Enumeration("ALIVE", "DEAD", "DECOMMISSIONED") { diff --git a/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala new file mode 100644 index 0000000000..b4c62bc224 --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala @@ -0,0 +1,118 @@ +/* + * 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 spark.deploy.master.ui + +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 spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} +import spark.deploy.JsonProtocol +import spark.deploy.master.ExecutorInfo +import spark.ui.UIUtils + +private[spark] class ApplicationPage(parent: MasterWebUI) { + val master = parent.master + implicit 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 app = state.activeApps.find(_.id == appId).getOrElse({ + state.completedApps.find(_.id == appId).getOrElse(null) + }) + JsonProtocol.writeApplicationInfo(app) + } + + /** Executor details for a particular application */ + 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 app = state.activeApps.find(_.id == appId).getOrElse({ + state.completedApps.find(_.id == appId).getOrElse(null) + }) + + val executorHeaders = Seq("ExecutorID", "Worker", "Cores", "Memory", "State", "Logs") + val executors = app.executors.values.toSeq + val executorTable = UIUtils.listingTable(executorHeaders, executorRow, executors) + + val content = + <hr /> + <div class="row"> + <div class="span12"> + <ul class="unstyled"> + <li><strong>ID:</strong> {app.id}</li> + <li><strong>Description:</strong> {app.desc.name}</li> + <li><strong>User:</strong> {app.desc.user}</li> + <li><strong>Cores:</strong> + { + if (app.desc.maxCores == Integer.MAX_VALUE) { + "Unlimited %s granted".format(app.coresGranted) + } else { + "%s (%s granted, %s left)".format( + app.desc.maxCores, app.coresGranted, app.coresLeft) + } + } + </li> + <li><strong>Memory per Slave:</strong> {app.desc.memoryPerSlave}</li> + <li><strong>Submit Date:</strong> {app.submitDate}</li> + <li><strong>State:</strong> {app.state}</li> + <li><strong><a href={app.appUiUrl}>Application Detail UI</a></strong></li> + </ul> + </div> + </div> + + <hr/> + + <div class="row"> <!-- Executors --> + <div class="span12"> + <h3> Executor Summary </h3> + <br/> + {executorTable} + </div> + </div>; + UIUtils.basicSparkPage(content, "Application Info: " + app.desc.name) + } + + def executorRow(executor: ExecutorInfo): Seq[Node] = { + <tr> + <td>{executor.id}</td> + <td> + <a href={executor.worker.webUiAddress}>{executor.worker.id}</a> + </td> + <td>{executor.cores}</td> + <td>{executor.memory}</td> + <td>{executor.state}</td> + <td> + <a href={"%s/logPage?appId=%s&executorId=%s&logType=stdout" + .format(executor.worker.webUiAddress, executor.application.id, executor.id)}>stdout</a> + <a href={"%s/logPage?appId=%s&executorId=%s&logType=stderr" + .format(executor.worker.webUiAddress, executor.application.id, executor.id)}>stderr</a> + </td> + </tr> + } +} diff --git a/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala new file mode 100644 index 0000000000..79fdb21024 --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala @@ -0,0 +1,148 @@ +/* + * 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 spark.deploy.master.ui + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import akka.dispatch.Await +import akka.pattern.ask +import akka.util.duration._ + +import net.liftweb.json.JsonAST.JValue + +import spark.Utils +import spark.deploy.DeployWebUI +import spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} +import spark.deploy.JsonProtocol +import spark.deploy.master.{ApplicationInfo, WorkerInfo} +import spark.ui.UIUtils + +private[spark] class IndexPage(parent: MasterWebUI) { + val master = parent.master + implicit val timeout = parent.timeout + + def renderJson(request: HttpServletRequest): JValue = { + val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse] + val state = Await.result(stateFuture, 30 seconds) + 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 workerHeaders = Seq("Id", "Address", "State", "Cores", "Memory") + val workers = state.workers.sortBy(_.id) + val workerTable = UIUtils.listingTable(workerHeaders, workerRow, workers) + + val appHeaders = Seq("ID", "Description", "Cores", "Memory per Node", "Submit Time", "User", + "State", "Duration") + val activeApps = state.activeApps.sortBy(_.startTime).reverse + val activeAppsTable = UIUtils.listingTable(appHeaders, appRow, activeApps) + val completedApps = state.completedApps.sortBy(_.endTime).reverse + val completedAppsTable = UIUtils.listingTable(appHeaders, appRow, completedApps) + + val content = + <hr /> + <div class="row"> + <div class="span12"> + <ul class="unstyled"> + <li><strong>URL:</strong> {state.uri}</li> + <li><strong>Workers:</strong> {state.workers.size}</li> + <li><strong>Cores:</strong> {state.workers.map(_.cores).sum} Total, + {state.workers.map(_.coresUsed).sum} Used</li> + <li><strong>Memory:</strong> + {Utils.memoryMegabytesToString(state.workers.map(_.memory).sum)} Total, + {Utils.memoryMegabytesToString(state.workers.map(_.memoryUsed).sum)} Used</li> + <li><strong>Applications:</strong> + {state.activeApps.size} Running, + {state.completedApps.size} Completed </li> + </ul> + </div> + </div> + + <div class="row"> + <div class="span12"> + <h3> Workers </h3> + <br/> + {workerTable} + </div> + </div> + + <hr/> + + <div class="row"> + <div class="span12"> + <h3> Running Applications </h3> + <br/> + {activeAppsTable} + </div> + </div> + + <hr/> + + <div class="row"> + <div class="span12"> + <h3> Completed Applications </h3> + <br/> + {completedAppsTable} + </div> + </div>; + UIUtils.basicSparkPage(content, "Spark Master: " + state.uri) + } + + def workerRow(worker: WorkerInfo): Seq[Node] = { + <tr> + <td> + <a href={worker.webUiAddress}>{worker.id}</a> + </td> + <td>{worker.host}:{worker.port}</td> + <td>{worker.state}</td> + <td>{worker.cores} ({worker.coresUsed} Used)</td> + <td sorttable_customkey={"%s.%s".format(worker.memory, worker.memoryUsed)}> + {Utils.memoryMegabytesToString(worker.memory)} + ({Utils.memoryMegabytesToString(worker.memoryUsed)} Used) + </td> + </tr> + } + + + def appRow(app: ApplicationInfo): Seq[Node] = { + <tr> + <td> + <a href={"app?appId=" + app.id}>{app.id}</a> + </td> + <td> + <a href={app.appUiUrl}>{app.desc.name}</a> + </td> + <td> + {app.coresGranted} + </td> + <td sorttable_customkey={app.desc.memoryPerSlave.toString}> + {Utils.memoryMegabytesToString(app.desc.memoryPerSlave)} + </td> + <td>{DeployWebUI.formatDate(app.submitDate)}</td> + <td>{app.desc.user}</td> + <td>{app.state.toString}</td> + <td>{DeployWebUI.formatDuration(app.duration)}</td> + </tr> + } +} diff --git a/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala new file mode 100644 index 0000000000..31bdb7854e --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.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 spark.deploy.master.ui + +import akka.actor.ActorRef +import akka.util.Duration + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.{Handler, Server} + +import spark.{Logging, Utils} +import spark.ui.JettyUtils +import spark.ui.JettyUtils._ + +/** + * Web UI server for the standalone master. + */ +private[spark] +class MasterWebUI(val master: ActorRef, requestedPort: Int) extends Logging { + implicit val timeout = Duration.create( + System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + val host = Utils.localHostName() + val port = requestedPort + + var server: Option[Server] = None + var boundPort: Option[Int] = None + + val applicationPage = new ApplicationPage(this) + val indexPage = new IndexPage(this) + + def start() { + try { + val (srv, bPort) = JettyUtils.startJettyServer("0.0.0.0", port, handlers) + server = Some(srv) + boundPort = Some(bPort) + logInfo("Started Master web UI at http://%s:%d".format(host, boundPort.get)) + } catch { + case e: Exception => + logError("Failed to create Master JettyUtils", e) + System.exit(1) + } + } + + val handlers = Array[(String, Handler)]( + ("/static", createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR)), + ("/app/json", (request: HttpServletRequest) => applicationPage.renderJson(request)), + ("/app", (request: HttpServletRequest) => applicationPage.render(request)), + ("/json", (request: HttpServletRequest) => indexPage.renderJson(request)), + ("*", (request: HttpServletRequest) => indexPage.render(request)) + ) + + def stop() { + server.foreach(_.stop()) + } +} + +private[spark] object MasterWebUI { + val STATIC_RESOURCE_DIR = "spark/ui/static" +} diff --git a/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala index de11771c8e..345dfe879c 100644 --- a/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala @@ -1,14 +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 spark.deploy.worker import java.io._ -import spark.deploy.{ExecutorState, ExecutorStateChanged, ApplicationDescription} +import java.lang.System.getenv + import akka.actor.ActorRef + import spark.{Utils, Logging} -import java.net.{URI, URL} -import org.apache.hadoop.fs.{Path, FileSystem} -import org.apache.hadoop.conf.Configuration -import scala.Some -import spark.deploy.ExecutorStateChanged +import spark.deploy.{ExecutorState, ApplicationDescription} +import spark.deploy.DeployMessages.ExecutorStateChanged /** * Manages the execution of one executor process. @@ -21,16 +37,21 @@ private[spark] class ExecutorRunner( val memory: Int, val worker: ActorRef, val workerId: String, - val hostname: String, + val hostPort: String, val sparkHome: File, val workDir: File) extends Logging { + Utils.checkHostPort(hostPort, "Expected hostport") + val fullId = appId + "/" + execId var workerThread: Thread = null var process: Process = null var shutdownHook: Thread = null + private def getAppEnv(key: String): Option[String] = + appDesc.command.environment.get(key).orElse(Option(getenv(key))) + def start() { workerThread = new Thread("ExecutorRunner for " + fullId) { override def run() { fetchAndRunExecutor() } @@ -38,7 +59,7 @@ private[spark] class ExecutorRunner( workerThread.start() // Shutdown hook that kills actors on shutdown. - shutdownHook = new Thread() { + shutdownHook = new Thread() { override def run() { if (process != null) { logInfo("Shutdown hook killing child process.") @@ -68,16 +89,38 @@ private[spark] class ExecutorRunner( /** Replace variables such as {{EXECUTOR_ID}} and {{CORES}} in a command argument passed to us */ def substituteVariables(argument: String): String = argument match { case "{{EXECUTOR_ID}}" => execId.toString - case "{{HOSTNAME}}" => hostname + case "{{HOSTNAME}}" => Utils.parseHostPort(hostPort)._1 case "{{CORES}}" => cores.toString case other => other } def buildCommandSeq(): Seq[String] = { val command = appDesc.command - val script = if (System.getProperty("os.name").startsWith("Windows")) "run.cmd" else "run" - val runScript = new File(sparkHome, script).getCanonicalPath - Seq(runScript, command.mainClass) ++ (command.arguments ++ Seq(appId)).map(substituteVariables) + val runner = getAppEnv("JAVA_HOME").map(_ + "/bin/java").getOrElse("java") + // 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) + } + + /** + * Attention: this must always be aligned with the environment variables in the run scripts and + * the way the JAVA_OPTS are assembled there. + */ + def buildJavaOpts(): Seq[String] = { + val libraryOpts = getAppEnv("SPARK_LIBRARY_PATH") + .map(p => List("-Djava.library.path=" + p)) + .getOrElse(Nil) + val userOpts = getAppEnv("SPARK_JAVA_OPTS").map(Utils.splitCommandString).getOrElse(Nil) + val memoryOpts = Seq("-Xms" + memory + "M", "-Xmx" + memory + "M") + + // Figure out our classpath with the external compute-classpath script + val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh" + val classPath = Utils.executeAndGetOutput( + Seq(sparkHome + "/bin/compute-classpath" + ext), + extraEnvironment=appDesc.command.environment) + + Seq("-cp", classPath) ++ libraryOpts ++ userOpts ++ memoryOpts } /** Spawn a thread that will redirect a given stream to a file */ @@ -113,7 +156,6 @@ private[spark] class ExecutorRunner( for ((key, value) <- appDesc.command.environment) { env.put(key, value) } - env.put("SPARK_MEM", memory.toString + "m") // In case we are running this from within the Spark Shell, avoid creating a "scala" // parent process for the executor command env.put("SPARK_LAUNCH_WITH_SCALA", "0") diff --git a/core/src/main/scala/spark/deploy/worker/Worker.scala b/core/src/main/scala/spark/deploy/worker/Worker.scala index da3f4f636c..0e46fa281e 100644 --- a/core/src/main/scala/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/spark/deploy/worker/Worker.scala @@ -1,22 +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 spark.deploy.worker -import scala.collection.mutable.{ArrayBuffer, HashMap} +import java.text.SimpleDateFormat +import java.util.Date +import java.io.File + +import scala.collection.mutable.HashMap + import akka.actor.{ActorRef, Props, Actor, ActorSystem, Terminated} +import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected} import akka.util.duration._ + import spark.{Logging, Utils} -import spark.util.AkkaUtils -import spark.deploy._ -import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected} -import java.text.SimpleDateFormat -import java.util.Date -import spark.deploy.RegisterWorker -import spark.deploy.LaunchExecutor -import spark.deploy.RegisterWorkerFailed +import spark.deploy.ExecutorState +import spark.deploy.DeployMessages._ import spark.deploy.master.Master -import java.io.File +import spark.deploy.worker.ui.WorkerWebUI +import spark.metrics.MetricsSystem +import spark.util.AkkaUtils + private[spark] class Worker( - ip: String, + host: String, port: Int, webUiPort: Int, cores: Int, @@ -25,6 +46,9 @@ private[spark] class Worker( workDirPath: String = null) extends Actor with Logging { + Utils.checkHost(host, "Expected hostname") + assert (port > 0) + val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss") // For worker and executor IDs // Send a heartbeat every (heartbeat timeout) / 4 milliseconds @@ -39,22 +63,30 @@ private[spark] class Worker( val finishedExecutors = new HashMap[String, ExecutorRunner] val publicAddress = { val envVar = System.getenv("SPARK_PUBLIC_DNS") - if (envVar != null) envVar else ip + if (envVar != null) envVar else host } + var webUi: WorkerWebUI = null var coresUsed = 0 var memoryUsed = 0 + val metricsSystem = MetricsSystem.createMetricsSystem("worker") + val workerSource = new WorkerSource(this) + def coresFree: Int = cores - coresUsed def memoryFree: Int = memory - memoryUsed def createWorkDir() { workDir = Option(workDirPath).map(new File(_)).getOrElse(new File(sparkHome, "work")) try { - if (!workDir.exists() && !workDir.mkdirs()) { + // This sporadically fails - not sure why ... !workDir.exists() && !workDir.mkdirs() + // So attempting to create and then check if directory was created or not. + workDir.mkdirs() + if ( !workDir.exists() || !workDir.isDirectory) { logError("Failed to create work directory " + workDir) System.exit(1) } + assert (workDir.isDirectory) } catch { case e: Exception => logError("Failed to create work directory " + workDir, e) @@ -64,33 +96,26 @@ private[spark] class Worker( override def preStart() { logInfo("Starting Spark worker %s:%d with %d cores, %s RAM".format( - ip, port, cores, Utils.memoryMegabytesToString(memory))) + host, port, cores, Utils.memoryMegabytesToString(memory))) sparkHome = new File(Option(System.getenv("SPARK_HOME")).getOrElse(".")) logInfo("Spark home: " + sparkHome) createWorkDir() + webUi = new WorkerWebUI(this, workDir, Some(webUiPort)) + webUi.start() connectToMaster() - startWebUi() + + metricsSystem.registerSource(workerSource) + metricsSystem.start() } def connectToMaster() { logInfo("Connecting to master " + masterUrl) master = context.actorFor(Master.toAkkaUrl(masterUrl)) - master ! RegisterWorker(workerId, ip, port, cores, memory, webUiPort, publicAddress) + 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 startWebUi() { - val webUi = new WorkerWebUI(context.system, self) - try { - AkkaUtils.startSprayServer(context.system, "0.0.0.0", webUiPort, webUi.handler) - } catch { - case e: Exception => - logError("Failed to create web UI", e) - System.exit(1) - } - } - override def receive = { case RegisteredWorker(url) => masterWebUiUrl = url @@ -106,7 +131,7 @@ private[spark] class Worker( 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, ip, new File(execSparkHome_), workDir) + appId, execId, appDesc, cores_, memory_, self, workerId, host + ":" + port, new File(execSparkHome_), workDir) executors(appId + "/" + execId) = manager manager.start() coresUsed += cores_ @@ -139,10 +164,10 @@ private[spark] class Worker( case Terminated(_) | RemoteClientDisconnected(_, _) | RemoteClientShutdown(_, _) => masterDisconnected() - + case RequestWorkerState => { - sender ! WorkerState(ip, port, workerId, executors.values.toList, - finishedExecutors.values.toList, masterUrl, cores, memory, + sender ! WorkerStateResponse(host, port, workerId, executors.values.toList, + finishedExecutors.values.toList, masterUrl, cores, memory, coresUsed, memoryUsed, masterWebUiUrl) } } @@ -156,18 +181,20 @@ private[spark] class Worker( } def generateWorkerId(): String = { - "worker-%s-%s-%d".format(DATE_FORMAT.format(new Date), ip, port) + "worker-%s-%s-%d".format(DATE_FORMAT.format(new Date), host, port) } override def postStop() { executors.values.foreach(_.kill()) + webUi.stop() + metricsSystem.stop() } } private[spark] object Worker { def main(argStrings: Array[String]) { val args = new WorkerArguments(argStrings) - val (actorSystem, _) = startSystemAndActor(args.ip, args.port, args.webUiPort, args.cores, + val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort, args.cores, args.memory, args.master, args.workDir) actorSystem.awaitTermination() } diff --git a/core/src/main/scala/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/spark/deploy/worker/WorkerArguments.scala index 08f02bad80..9fcd3260ca 100644 --- a/core/src/main/scala/spark/deploy/worker/WorkerArguments.scala +++ b/core/src/main/scala/spark/deploy/worker/WorkerArguments.scala @@ -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. + */ + package spark.deploy.worker import spark.util.IntParam @@ -9,7 +26,7 @@ import java.lang.management.ManagementFactory * Command-line parser for the master. */ private[spark] class WorkerArguments(args: Array[String]) { - var ip = Utils.localHostName() + var host = Utils.localHostName() var port = 0 var webUiPort = 8081 var cores = inferDefaultCores() @@ -38,7 +55,13 @@ private[spark] class WorkerArguments(args: Array[String]) { def parse(args: List[String]): Unit = args match { case ("--ip" | "-i") :: value :: tail => - ip = value + Utils.checkHost(value, "ip no longer supported, please use hostname " + value) + host = value + parse(tail) + + case ("--host" | "-h") :: value :: tail => + Utils.checkHost(value, "Please use hostname " + value) + host = value parse(tail) case ("--port" | "-p") :: IntParam(value) :: tail => @@ -93,7 +116,8 @@ private[spark] class WorkerArguments(args: Array[String]) { " -c CORES, --cores CORES Number of cores to use\n" + " -m MEM, --memory MEM Amount of memory to use (e.g. 1000M, 2G)\n" + " -d DIR, --work-dir DIR Directory to run apps in (default: SPARK_HOME/work)\n" + - " -i IP, --ip IP IP address or DNS name to listen on\n" + + " -i HOST, --ip IP Hostname to listen on (deprecated, please use --host or -h)\n" + + " -h HOST, --host HOST Hostname to listen on\n" + " -p PORT, --port PORT Port to listen on (default: random)\n" + " --webui-port PORT Port for web UI (default: 8081)") System.exit(exitCode) diff --git a/core/src/main/scala/spark/deploy/worker/WorkerSource.scala b/core/src/main/scala/spark/deploy/worker/WorkerSource.scala new file mode 100644 index 0000000000..39cb8e5690 --- /dev/null +++ b/core/src/main/scala/spark/deploy/worker/WorkerSource.scala @@ -0,0 +1,34 @@ +package spark.deploy.worker + +import com.codahale.metrics.{Gauge, MetricRegistry} + +import spark.metrics.source.Source + +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] { + override def getValue: Int = worker.executors.size + }) + + // Gauge for cores used of this worker + metricRegistry.register(MetricRegistry.name("coresUsed", "number"), 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] { + override def getValue: Int = worker.memoryUsed + }) + + // Gauge for cores free of this worker + metricRegistry.register(MetricRegistry.name("coresFree", "number"), 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] { + override def getValue: Int = worker.memoryFree + }) +} diff --git a/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala deleted file mode 100644 index 135cc2e86c..0000000000 --- a/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala +++ /dev/null @@ -1,56 +0,0 @@ -package spark.deploy.worker - -import akka.actor.{ActorRef, ActorSystem} -import akka.dispatch.Await -import akka.pattern.ask -import akka.util.Timeout -import akka.util.duration._ -import cc.spray.Directives -import cc.spray.typeconversion.TwirlSupport._ -import cc.spray.http.MediaTypes -import cc.spray.typeconversion.SprayJsonSupport._ - -import spark.deploy.{WorkerState, RequestWorkerState} -import spark.deploy.JsonProtocol._ - -/** - * Web UI server for the standalone worker. - */ -private[spark] -class WorkerWebUI(val actorSystem: ActorSystem, worker: ActorRef) extends Directives { - val RESOURCE_DIR = "spark/deploy/worker/webui" - val STATIC_RESOURCE_DIR = "spark/deploy/static" - - implicit val timeout = Timeout(10 seconds) - - val handler = { - get { - (path("") & parameters('format ?)) { - case Some(js) if js.equalsIgnoreCase("json") => { - val future = worker ? RequestWorkerState - respondWithMediaType(MediaTypes.`application/json`) { ctx => - ctx.complete(future.mapTo[WorkerState]) - } - } - case _ => - completeWith{ - val future = worker ? RequestWorkerState - future.map { workerState => - spark.deploy.worker.html.index(workerState.asInstanceOf[WorkerState]) - } - } - } ~ - path("log") { - parameters("appId", "executorId", "logType") { (appId, executorId, logType) => - respondWithMediaType(cc.spray.http.MediaTypes.`text/plain`) { - getFromFileName("work/" + appId + "/" + executorId + "/" + logType) - } - } - } ~ - pathPrefix("static") { - getFromResourceDirectory(STATIC_RESOURCE_DIR) - } ~ - getFromResourceDirectory(RESOURCE_DIR) - } - } -} diff --git a/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala new file mode 100644 index 0000000000..1619c6a4c2 --- /dev/null +++ b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala @@ -0,0 +1,120 @@ +/* + * 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 spark.deploy.worker.ui + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import akka.dispatch.Await +import akka.pattern.ask +import akka.util.duration._ + +import net.liftweb.json.JsonAST.JValue + +import spark.Utils +import spark.deploy.JsonProtocol +import spark.deploy.DeployMessages.{RequestWorkerState, WorkerStateResponse} +import spark.deploy.worker.ExecutorRunner +import spark.ui.UIUtils + + +private[spark] class IndexPage(parent: WorkerWebUI) { + val workerActor = parent.worker.self + val worker = parent.worker + val timeout = parent.timeout + + def renderJson(request: HttpServletRequest): JValue = { + val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse] + val workerState = Await.result(stateFuture, 30 seconds) + JsonProtocol.writeWorkerState(workerState) + } + + def render(request: HttpServletRequest): Seq[Node] = { + val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse] + val workerState = Await.result(stateFuture, 30 seconds) + + val executorHeaders = Seq("ExecutorID", "Cores", "Memory", "Job Details", "Logs") + val runningExecutorTable = + UIUtils.listingTable(executorHeaders, executorRow, workerState.executors) + val finishedExecutorTable = + UIUtils.listingTable(executorHeaders, executorRow, workerState.finishedExecutors) + + val content = + <hr /> + <div class="row"> <!-- Worker Details --> + <div class="span12"> + <ul class="unstyled"> + <li><strong>ID:</strong> {workerState.workerId}</li> + <li><strong> + Master URL:</strong> {workerState.masterUrl} + </li> + <li><strong>Cores:</strong> {workerState.cores} ({workerState.coresUsed} Used)</li> + <li><strong>Memory:</strong> {Utils.memoryMegabytesToString(workerState.memory)} + ({Utils.memoryMegabytesToString(workerState.memoryUsed)} Used)</li> + </ul> + <p><a href={workerState.masterWebUiUrl}>Back to Master</a></p> + </div> + </div> + <hr/> + + <div class="row"> <!-- Running Executors --> + <div class="span12"> + <h3> Running Executors {workerState.executors.size} </h3> + <br/> + {runningExecutorTable} + </div> + </div> + <hr/> + + <div class="row"> <!-- Finished Executors --> + <div class="span12"> + <h3> Finished Executors </h3> + <br/> + {finishedExecutorTable} + </div> + </div>; + + UIUtils.basicSparkPage(content, "Spark Worker on %s:%s".format( + workerState.host, workerState.port)) + } + + def executorRow(executor: ExecutorRunner): Seq[Node] = { + <tr> + <td>{executor.execId}</td> + <td>{executor.cores}</td> + <td sorttable_customkey={executor.memory.toString}> + {Utils.memoryMegabytesToString(executor.memory)} + </td> + <td> + <ul class="unstyled"> + <li><strong>ID:</strong> {executor.appId}</li> + <li><strong>Name:</strong> {executor.appDesc.name}</li> + <li><strong>User:</strong> {executor.appDesc.user}</li> + </ul> + </td> + <td> + <a href={"logPage?appId=%s&executorId=%s&logType=stdout" + .format(executor.appId, executor.execId)}>stdout</a> + <a href={"logPage?appId=%s&executorId=%s&logType=stderr" + .format(executor.appId, executor.execId)}>stderr</a> + </td> + </tr> + } + +} diff --git a/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala new file mode 100644 index 0000000000..61d4cd6d99 --- /dev/null +++ b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala @@ -0,0 +1,182 @@ +/* + * 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 spark.deploy.worker.ui + +import akka.actor.ActorRef +import akka.util.{Duration, Timeout} + +import java.io.{FileInputStream, File} + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.{Handler, Server} + +import spark.deploy.worker.Worker +import spark.{Utils, Logging} +import spark.ui.JettyUtils +import spark.ui.JettyUtils._ +import spark.ui.UIUtils + +/** + * Web UI server for the standalone worker. + */ +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 host = Utils.localHostName() + val port = requestedPort.getOrElse( + System.getProperty("worker.ui.port", WorkerWebUI.DEFAULT_PORT).toInt) + + var server: Option[Server] = None + var boundPort: Option[Int] = None + + val indexPage = new IndexPage(this) + + val handlers = Array[(String, Handler)]( + ("/static", createStaticHandler(WorkerWebUI.STATIC_RESOURCE_DIR)), + ("/log", (request: HttpServletRequest) => log(request)), + ("/logPage", (request: HttpServletRequest) => logPage(request)), + ("/json", (request: HttpServletRequest) => indexPage.renderJson(request)), + ("*", (request: HttpServletRequest) => indexPage.render(request)) + ) + + def start() { + try { + val (srv, bPort) = JettyUtils.startJettyServer("0.0.0.0", port, handlers) + server = Some(srv) + boundPort = Some(bPort) + logInfo("Started Worker web UI at http://%s:%d".format(host, bPort)) + } catch { + case e: Exception => + logError("Failed to create Worker JettyUtils", e) + System.exit(1) + } + } + + def log(request: HttpServletRequest): String = { + val defaultBytes = 100 * 1024 + val appId = request.getParameter("appId") + val executorId = request.getParameter("executorId") + val logType = request.getParameter("logType") + val offset = Option(request.getParameter("offset")).map(_.toLong) + val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes) + val path = "%s/%s/%s/%s".format(workDir.getPath, appId, executorId, logType) + + val (startByte, endByte) = getByteRange(path, offset, byteLength) + val file = new File(path) + val logLength = file.length + + val pre = "==== Bytes %s-%s of %s of %s/%s/%s ====\n" + .format(startByte, endByte, logLength, appId, executorId, logType) + pre + Utils.offsetBytes(path, startByte, endByte) + } + + def logPage(request: HttpServletRequest): Seq[scala.xml.Node] = { + val defaultBytes = 100 * 1024 + val appId = request.getParameter("appId") + val executorId = request.getParameter("executorId") + val logType = request.getParameter("logType") + val offset = Option(request.getParameter("offset")).map(_.toLong) + val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes) + val path = "%s/%s/%s/%s".format(workDir.getPath, appId, executorId, logType) + + val (startByte, endByte) = getByteRange(path, offset, byteLength) + val file = new File(path) + val logLength = file.length + + val logText = <node>{Utils.offsetBytes(path, startByte, endByte)}</node> + + val linkToMaster = <p><a href={worker.masterWebUiUrl}>Back to Master</a></p> + + val range = <span>Bytes {startByte.toString} - {endByte.toString} of {logLength}</span> + + val backButton = + if (startByte > 0) { + <a href={"?appId=%s&executorId=%s&logType=%s&offset=%s&byteLength=%s" + .format(appId, executorId, logType, math.max(startByte-byteLength, 0), + byteLength)}> + <button>Previous {Utils.memoryBytesToString(math.min(byteLength, startByte))}</button> + </a> + } + else { + <button disabled="disabled">Previous 0 B</button> + } + + val nextButton = + if (endByte < logLength) { + <a href={"?appId=%s&executorId=%s&logType=%s&offset=%s&byteLength=%s". + format(appId, executorId, logType, endByte, byteLength)}> + <button>Next {Utils.memoryBytesToString(math.min(byteLength, logLength-endByte))}</button> + </a> + } + else { + <button disabled="disabled">Next 0 B</button> + } + + val content = + <html> + <body> + {linkToMaster} + <hr /> + <div> + <div style="float:left;width:40%">{backButton}</div> + <div style="float:left;">{range}</div> + <div style="float:right;">{nextButton}</div> + </div> + <br /> + <div style="height:500px;overflow:auto;padding:5px;"> + <pre>{logText}</pre> + </div> + </body> + </html> + UIUtils.basicSparkPage(content, logType + " log page for " + appId) + } + + /** Determine the byte range for a log or log page. */ + def getByteRange(path: String, offset: Option[Long], byteLength: Int) + : (Long, Long) = { + val defaultBytes = 100 * 1024 + val maxBytes = 1024 * 1024 + + val file = new File(path) + val logLength = file.length() + val getOffset = offset.getOrElse(logLength-defaultBytes) + + val startByte = + if (getOffset < 0) 0L + else if (getOffset > logLength) logLength + else getOffset + + val logPageLength = math.min(byteLength, maxBytes) + + val endByte = math.min(startByte+logPageLength, logLength) + + (startByte, endByte) + } + + def stop() { + server.foreach(_.stop()) + } +} + +private[spark] object WorkerWebUI { + val STATIC_RESOURCE_DIR = "spark/ui/static" + val DEFAULT_PORT="8081" +} diff --git a/core/src/main/scala/spark/executor/Executor.scala b/core/src/main/scala/spark/executor/Executor.scala index 3e7407b58d..8a74a8d853 100644 --- a/core/src/main/scala/spark/executor/Executor.scala +++ b/core/src/main/scala/spark/executor/Executor.scala @@ -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. + */ + package spark.executor import java.io.{File, FileOutputStream} @@ -17,7 +34,7 @@ import java.nio.ByteBuffer * The Mesos executor for Spark. */ private[spark] class Executor(executorId: String, slaveHostname: String, properties: Seq[(String, String)]) extends Logging { - + // 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. private val currentFiles: HashMap[String, Long] = new HashMap[String, Long]() @@ -27,6 +44,11 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert initLogging() + // No ip or host:port - just hostname + Utils.checkHost(slaveHostname, "Expected executed slave to be a hostname") + // must not have port specified. + assert (0 == Utils.parseHostPort(slaveHostname)._2) + // Make sure the local hostname we report matches the cluster scheduler's name for this host Utils.setCustomHostname(slaveHostname) @@ -37,7 +59,8 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert // Create our ClassLoader and set it on this thread private val urlClassLoader = createClassLoader() - Thread.currentThread.setContextClassLoader(urlClassLoader) + 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. @@ -46,7 +69,7 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert 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()) { @@ -64,9 +87,14 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert } ) + val executorSource = new ExecutorSource(this) + // 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 akkaFrameSize = env.actorSystem.settings.config.getBytes("akka.remote.netty.message-frame-size") // Start worker thread pool val threadPool = new ThreadPoolExecutor( @@ -82,22 +110,26 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert override def run() { val startTime = System.currentTimeMillis() SparkEnv.set(env) - Thread.currentThread.setContextClassLoader(urlClassLoader) + Thread.currentThread.setContextClassLoader(replClassLoader) val ser = SparkEnv.get.closureSerializer.newInstance() logInfo("Running task ID " + taskId) context.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER) + var attemptedTask: Option[Task[Any]] = None + var taskStart: Long = 0 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) + attemptedTask = Some(task) logInfo("Its generation is " + task.generation) env.mapOutputTracker.updateGeneration(task.generation) - val taskStart = System.currentTimeMillis() + taskStart = System.currentTimeMillis() val value = task.run(taskId.toInt) val taskFinish = System.currentTimeMillis() task.metrics.foreach{ m => + m.hostname = Utils.localHostName m.executorDeserializeTime = (taskStart - startTime).toInt m.executorRunTime = (taskFinish - taskStart).toInt } @@ -108,6 +140,10 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert 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 + } context.statusUpdate(taskId, TaskState.FINISHED, serializedResult) logInfo("Finished task ID " + taskId) } catch { @@ -117,7 +153,10 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert } case t: Throwable => { - val reason = ExceptionFailure(t) + val serviceTime = (System.currentTimeMillis() - taskStart).toInt + val metrics = attemptedTask.flatMap(t => t.metrics) + metrics.foreach{m => m.executorRunTime = serviceTime} + val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics) context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) // TODO: Should we exit the whole executor here? On the one hand, the failed task may @@ -142,26 +181,31 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert val urls = currentJars.keySet.map { uri => new File(uri.split("/").last).toURI.toURL }.toArray - loader = new URLClassLoader(urls, loader) + new ExecutorURLClassLoader(urls, loader) + } - // If the REPL is in use, add another ClassLoader that will read - // new classes defined by the REPL as the user types code + /** + * If the REPL is in use, add another ClassLoader that will read + * 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") if (classUri != null) { logInfo("Using REPL class URI: " + classUri) - loader = { - try { - val klass = Class.forName("spark.repl.ExecutorClassLoader") - .asInstanceOf[Class[_ <: ClassLoader]] - val constructor = klass.getConstructor(classOf[String], classOf[ClassLoader]) - constructor.newInstance(classUri, loader) - } catch { - case _: ClassNotFoundException => loader - } + try { + val klass = Class.forName("spark.repl.ExecutorClassLoader") + .asInstanceOf[Class[_ <: ClassLoader]] + val constructor = klass.getConstructor(classOf[String], classOf[ClassLoader]) + return constructor.newInstance(classUri, parent) + } catch { + case _: ClassNotFoundException => + logError("Could not find spark.repl.ExecutorClassLoader on classpath!") + System.exit(1) + null } + } else { + return parent } - - return new ExecutorURLClassLoader(Array(), loader) } /** diff --git a/core/src/main/scala/spark/executor/ExecutorBackend.scala b/core/src/main/scala/spark/executor/ExecutorBackend.scala index e97e509700..33a6f8a824 100644 --- a/core/src/main/scala/spark/executor/ExecutorBackend.scala +++ b/core/src/main/scala/spark/executor/ExecutorBackend.scala @@ -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. + */ + package spark.executor import java.nio.ByteBuffer diff --git a/core/src/main/scala/spark/executor/ExecutorExitCode.scala b/core/src/main/scala/spark/executor/ExecutorExitCode.scala index fd76029cb3..64b9fb88f8 100644 --- a/core/src/main/scala/spark/executor/ExecutorExitCode.scala +++ b/core/src/main/scala/spark/executor/ExecutorExitCode.scala @@ -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. + */ + package spark.executor /** diff --git a/core/src/main/scala/spark/executor/ExecutorSource.scala b/core/src/main/scala/spark/executor/ExecutorSource.scala new file mode 100644 index 0000000000..94116edfcf --- /dev/null +++ b/core/src/main/scala/spark/executor/ExecutorSource.scala @@ -0,0 +1,30 @@ +package spark.executor + +import com.codahale.metrics.{Gauge, MetricRegistry} + +import spark.metrics.source.Source + +class ExecutorSource(val executor: Executor) extends Source { + val metricRegistry = new MetricRegistry() + val sourceName = "executor" + + // Gauge for executor thread pool's actively executing task counts + metricRegistry.register(MetricRegistry.name("threadpool", "activeTask", "count"), 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] { + 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] { + 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] { + override def getValue: Int = executor.threadPool.getMaximumPoolSize() + }) +} diff --git a/core/src/main/scala/spark/executor/ExecutorURLClassLoader.scala b/core/src/main/scala/spark/executor/ExecutorURLClassLoader.scala index 5beb4d049e..09d12fb65b 100644 --- a/core/src/main/scala/spark/executor/ExecutorURLClassLoader.scala +++ b/core/src/main/scala/spark/executor/ExecutorURLClassLoader.scala @@ -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. + */ + package spark.executor import java.net.{URLClassLoader, URL} diff --git a/core/src/main/scala/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/spark/executor/MesosExecutorBackend.scala index 10f3531df0..4961c42fad 100644 --- a/core/src/main/scala/spark/executor/MesosExecutorBackend.scala +++ b/core/src/main/scala/spark/executor/MesosExecutorBackend.scala @@ -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. + */ + package spark.executor import java.nio.ByteBuffer diff --git a/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala b/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala index 1047f71c6a..e47fe50021 100644 --- a/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala +++ b/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala @@ -1,34 +1,52 @@ +/* + * 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 spark.executor import java.nio.ByteBuffer -import spark.Logging -import spark.TaskState.TaskState -import spark.util.AkkaUtils + import akka.actor.{ActorRef, Actor, Props, Terminated} import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientShutdown, RemoteClientDisconnected} -import java.util.concurrent.{TimeUnit, ThreadPoolExecutor, SynchronousQueue} -import spark.scheduler.cluster._ -import spark.scheduler.cluster.RegisteredExecutor -import spark.scheduler.cluster.LaunchTask -import spark.scheduler.cluster.RegisterExecutorFailed -import spark.scheduler.cluster.RegisterExecutor + +import spark.{Logging, Utils} +import spark.TaskState.TaskState +import spark.deploy.SparkHadoopUtil +import spark.scheduler.cluster.StandaloneClusterMessages._ +import spark.util.AkkaUtils + private[spark] class StandaloneExecutorBackend( driverUrl: String, executorId: String, - hostname: String, + hostPort: String, cores: Int) extends Actor with ExecutorBackend with Logging { + Utils.checkHostPort(hostPort, "Expected hostport") + var executor: Executor = null var driver: ActorRef = null override def preStart() { logInfo("Connecting to driver: " + driverUrl) driver = context.actorFor(driverUrl) - driver ! RegisterExecutor(executorId, hostname, cores) + 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 } @@ -36,7 +54,8 @@ private[spark] class StandaloneExecutorBackend( override def receive = { case RegisteredExecutor(sparkProperties) => logInfo("Successfully registered with driver") - executor = new Executor(executorId, hostname, sparkProperties) + // Make this host instead of hostPort ? + executor = new Executor(executorId, Utils.parseHostPort(hostPort)._1, sparkProperties) case RegisterExecutorFailed(message) => logError("Slave registration failed: " + message) @@ -63,11 +82,30 @@ private[spark] class StandaloneExecutorBackend( private[spark] object StandaloneExecutorBackend { def run(driverUrl: String, executorId: String, hostname: String, cores: Int) { + SparkHadoopUtil.runAsUser(run0, Tuple4[Any, Any, Any, Any] (driverUrl, executorId, hostname, cores)) + } + + // This will be run 'as' the user + def run0(args: Product) { + assert(4 == args.productArity) + runImpl(args.productElement(0).asInstanceOf[String], + args.productElement(1).asInstanceOf[String], + args.productElement(2).asInstanceOf[String], + args.productElement(3).asInstanceOf[Int]) + } + + private def runImpl(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) + // set it + val sparkHostPort = hostname + ":" + boundPort + System.setProperty("spark.hostPort", sparkHostPort) val actor = actorSystem.actorOf( - Props(new StandaloneExecutorBackend(driverUrl, executorId, hostname, cores)), + Props(new StandaloneExecutorBackend(driverUrl, executorId, sparkHostPort, cores)), name = "Executor") actorSystem.awaitTermination() } diff --git a/core/src/main/scala/spark/executor/TaskMetrics.scala b/core/src/main/scala/spark/executor/TaskMetrics.scala index 93bbb6b458..3151627839 100644 --- a/core/src/main/scala/spark/executor/TaskMetrics.scala +++ b/core/src/main/scala/spark/executor/TaskMetrics.scala @@ -1,7 +1,29 @@ +/* + * 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 spark.executor class TaskMetrics extends Serializable { /** + * Host's name the task runs on + */ + var hostname: String = _ + + /** * Time taken on the executor to deserialize this task */ var executorDeserializeTime: Int = _ @@ -34,9 +56,14 @@ object TaskMetrics { class ShuffleReadMetrics extends Serializable { /** + * Time when shuffle finishs + */ + var shuffleFinishTime: Long = _ + + /** * Total number of blocks fetched in a shuffle (remote or local) */ - var totalBlocksFetched : Int = _ + var totalBlocksFetched: Int = _ /** * Number of remote blocks fetched in a shuffle @@ -49,11 +76,6 @@ class ShuffleReadMetrics extends Serializable { var localBlocksFetched: Int = _ /** - * Total time to read shuffle data - */ - var shuffleReadMillis: Long = _ - - /** * Total time that is spent blocked waiting for shuffle to fetch data */ var fetchWaitTime: Long = _ diff --git a/core/src/main/scala/spark/io/CompressionCodec.scala b/core/src/main/scala/spark/io/CompressionCodec.scala new file mode 100644 index 0000000000..0adebecadb --- /dev/null +++ b/core/src/main/scala/spark/io/CompressionCodec.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 spark.io + +import java.io.{InputStream, OutputStream} + +import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} + +import org.xerial.snappy.{SnappyInputStream, SnappyOutputStream} + + +/** + * CompressionCodec allows the customization of choosing different compression implementations + * to be used in block storage. + */ +trait CompressionCodec { + + def compressedOutputStream(s: OutputStream): OutputStream + + def compressedInputStream(s: InputStream): InputStream +} + + +private[spark] object CompressionCodec { + + def createCodec(): CompressionCodec = { + // Set the default codec to Snappy since the LZF implementation initializes a pretty large + // buffer for every stream, which results in a lot of memory overhead when the number of + // shuffle reduce buckets are large. + createCodec(classOf[SnappyCompressionCodec].getName) + } + + def createCodec(codecName: String): CompressionCodec = { + Class.forName( + System.getProperty("spark.io.compression.codec", codecName), + true, + Thread.currentThread.getContextClassLoader).newInstance().asInstanceOf[CompressionCodec] + } +} + + +/** + * LZF implementation of [[spark.io.CompressionCodec]]. + */ +class LZFCompressionCodec extends CompressionCodec { + + override def compressedOutputStream(s: OutputStream): OutputStream = { + new LZFOutputStream(s).setFinishBlockOnFlush(true) + } + + override def compressedInputStream(s: InputStream): InputStream = new LZFInputStream(s) +} + + +/** + * Snappy implementation of [[spark.io.CompressionCodec]]. + * Block size can be configured by spark.io.compression.snappy.block.size. + */ +class SnappyCompressionCodec extends CompressionCodec { + + override def compressedOutputStream(s: OutputStream): OutputStream = { + val blockSize = System.getProperty("spark.io.compression.snappy.block.size", "32768").toInt + new SnappyOutputStream(s, blockSize) + } + + override def compressedInputStream(s: InputStream): InputStream = new SnappyInputStream(s) +} diff --git a/core/src/main/scala/spark/metrics/MetricsConfig.scala b/core/src/main/scala/spark/metrics/MetricsConfig.scala new file mode 100644 index 0000000000..3e32e9c82f --- /dev/null +++ b/core/src/main/scala/spark/metrics/MetricsConfig.scala @@ -0,0 +1,96 @@ +/* + * 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 spark.metrics + +import java.util.Properties +import java.io.{File, FileInputStream, InputStream, IOException} + +import scala.collection.mutable +import scala.util.matching.Regex + +import spark.Logging + +private[spark] class MetricsConfig(val configFile: Option[String]) extends Logging { + initLogging() + + val DEFAULT_PREFIX = "*" + val INSTANCE_REGEX = "^(\\*|[a-zA-Z]+)\\.(.+)".r + val METRICS_CONF = "metrics.properties" + + val properties = new Properties() + var propertyCategories: mutable.HashMap[String, Properties] = null + + private def setDefaultProperties(prop: Properties) { + // empty function, any default property can be set here + } + + def initialize() { + //Add default properties in case there's no properties file + setDefaultProperties(properties) + + // If spark.metrics.conf is not set, try to get file in class path + var is: InputStream = null + try { + is = configFile match { + case Some(f) => new FileInputStream(f) + case None => getClass.getClassLoader.getResourceAsStream(METRICS_CONF) + } + + if (is != null) { + properties.load(is) + } + } catch { + case e: Exception => logError("Error loading configure file", e) + } finally { + if (is != null) is.close() + } + + propertyCategories = subProperties(properties, INSTANCE_REGEX) + if (propertyCategories.contains(DEFAULT_PREFIX)) { + import scala.collection.JavaConversions._ + + val defaultProperty = propertyCategories(DEFAULT_PREFIX) + for { (inst, prop) <- propertyCategories + if (inst != DEFAULT_PREFIX) + (k, v) <- defaultProperty + if (prop.getProperty(k) == null) } { + prop.setProperty(k, v) + } + } + } + + def subProperties(prop: Properties, regex: Regex): mutable.HashMap[String, Properties] = { + val subProperties = new mutable.HashMap[String, Properties] + import scala.collection.JavaConversions._ + prop.foreach { kv => + if (regex.findPrefixOf(kv._1) != None) { + val regex(prefix, suffix) = kv._1 + subProperties.getOrElseUpdate(prefix, new Properties).setProperty(suffix, kv._2) + } + } + subProperties + } + + def getInstance(inst: String): Properties = { + propertyCategories.get(inst) match { + case Some(s) => s + case None => propertyCategories.getOrElse(DEFAULT_PREFIX, new Properties) + } + } +} + diff --git a/core/src/main/scala/spark/metrics/MetricsSystem.scala b/core/src/main/scala/spark/metrics/MetricsSystem.scala new file mode 100644 index 0000000000..1dacafa135 --- /dev/null +++ b/core/src/main/scala/spark/metrics/MetricsSystem.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 spark.metrics + +import com.codahale.metrics.{Metric, MetricFilter, MetricRegistry} + +import java.util.Properties +import java.util.concurrent.TimeUnit + +import scala.collection.mutable + +import spark.Logging +import spark.metrics.sink.Sink +import spark.metrics.source.Source + +/** + * Spark Metrics System, created by specific "instance", combined by source, + * sink, periodically poll source metrics data to sink destinations. + * + * "instance" specify "who" (the role) use metrics system. In spark there are several roles + * like master, worker, executor, client driver, these roles will create metrics system + * for monitoring. So instance represents these roles. Currently in Spark, several instances + * have already implemented: master, worker, executor, driver. + * + * "source" specify "where" (source) to collect metrics data. In metrics system, there exists + * two kinds of source: + * 1. Spark internal source, like MasterSource, WorkerSource, etc, which will collect + * Spark component's internal state, these sources are related to instance and will be + * added after specific metrics system is created. + * 2. Common source, like JvmSource, which will collect low level state, is configured by + * configuration and loaded through reflection. + * + * "sink" specify "where" (destination) to output metrics data to. Several sinks can be + * coexisted and flush metrics to all these sinks. + * + * Metrics configuration format is like below: + * [instance].[sink|source].[name].[options] = xxxx + * + * [instance] can be "master", "worker", "executor", "driver", which means only the specified + * instance has this property. + * wild card "*" can be used to replace instance name, which means all the instances will have + * this property. + * + * [sink|source] means this property belongs to source or sink. This field can only be source or sink. + * + * [name] specify the name of sink or source, it is custom defined. + * + * [options] is the specific property of this source or sink. + */ +private[spark] class MetricsSystem private (val instance: String) extends Logging { + initLogging() + + val confFile = System.getProperty("spark.metrics.conf") + val metricsConfig = new MetricsConfig(Option(confFile)) + + val sinks = new mutable.ArrayBuffer[Sink] + val sources = new mutable.ArrayBuffer[Source] + val registry = new MetricRegistry() + + metricsConfig.initialize() + registerSources() + registerSinks() + + def start() { + sinks.foreach(_.start) + } + + def stop() { + sinks.foreach(_.stop) + } + + def registerSource(source: Source) { + sources += source + try { + registry.register(source.sourceName, source.metricRegistry) + } catch { + case e: IllegalArgumentException => logInfo("Metrics already registered", e) + } + } + + def removeSource(source: Source) { + sources -= source + registry.removeMatching(new MetricFilter { + def matches(name: String, metric: Metric): Boolean = name.startsWith(source.sourceName) + }) + } + + def registerSources() { + val instConfig = metricsConfig.getInstance(instance) + val sourceConfigs = metricsConfig.subProperties(instConfig, MetricsSystem.SOURCE_REGEX) + + // Register all the sources related to instance + sourceConfigs.foreach { kv => + val classPath = kv._2.getProperty("class") + try { + val source = Class.forName(classPath).newInstance() + registerSource(source.asInstanceOf[Source]) + } catch { + case e: Exception => logError("Source class " + classPath + " cannot be instantialized", e) + } + } + } + + def registerSinks() { + val instConfig = metricsConfig.getInstance(instance) + val sinkConfigs = metricsConfig.subProperties(instConfig, MetricsSystem.SINK_REGEX) + + sinkConfigs.foreach { kv => + val classPath = kv._2.getProperty("class") + try { + val sink = Class.forName(classPath) + .getConstructor(classOf[Properties], classOf[MetricRegistry]) + .newInstance(kv._2, registry) + sinks += sink.asInstanceOf[Sink] + } catch { + case e: Exception => logError("Sink class " + classPath + " cannot be instantialized", e) + } + } + } +} + +private[spark] object MetricsSystem { + val SINK_REGEX = "^sink\\.(.+)\\.(.+)".r + val SOURCE_REGEX = "^source\\.(.+)\\.(.+)".r + + val MINIMAL_POLL_UNIT = TimeUnit.SECONDS + val MINIMAL_POLL_PERIOD = 1 + + def checkMinimalPollingPeriod(pollUnit: TimeUnit, pollPeriod: Int) { + val period = MINIMAL_POLL_UNIT.convert(pollPeriod, pollUnit) + if (period < MINIMAL_POLL_PERIOD) { + throw new IllegalArgumentException("Polling period " + pollPeriod + " " + pollUnit + + " below than minimal polling period ") + } + } + + def createMetricsSystem(instance: String): MetricsSystem = new MetricsSystem(instance) +} diff --git a/core/src/main/scala/spark/metrics/sink/ConsoleSink.scala b/core/src/main/scala/spark/metrics/sink/ConsoleSink.scala new file mode 100644 index 0000000000..966ba37c20 --- /dev/null +++ b/core/src/main/scala/spark/metrics/sink/ConsoleSink.scala @@ -0,0 +1,59 @@ +/* + * 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 spark.metrics.sink + +import com.codahale.metrics.{ConsoleReporter, MetricRegistry} + +import java.util.Properties +import java.util.concurrent.TimeUnit + +import spark.metrics.MetricsSystem + +class ConsoleSink(val property: Properties, val registry: MetricRegistry) extends Sink { + val CONSOLE_DEFAULT_PERIOD = 10 + val CONSOLE_DEFAULT_UNIT = "SECONDS" + + val CONSOLE_KEY_PERIOD = "period" + val CONSOLE_KEY_UNIT = "unit" + + val pollPeriod = Option(property.getProperty(CONSOLE_KEY_PERIOD)) match { + case Some(s) => s.toInt + case None => CONSOLE_DEFAULT_PERIOD + } + + val pollUnit = Option(property.getProperty(CONSOLE_KEY_UNIT)) match { + case Some(s) => TimeUnit.valueOf(s.toUpperCase()) + case None => TimeUnit.valueOf(CONSOLE_DEFAULT_UNIT) + } + + MetricsSystem.checkMinimalPollingPeriod(pollUnit, pollPeriod) + + val reporter: ConsoleReporter = ConsoleReporter.forRegistry(registry) + .convertDurationsTo(TimeUnit.MILLISECONDS) + .convertRatesTo(TimeUnit.SECONDS) + .build() + + override def start() { + reporter.start(pollPeriod, pollUnit) + } + + override def stop() { + reporter.stop() + } +} + diff --git a/core/src/main/scala/spark/metrics/sink/CsvSink.scala b/core/src/main/scala/spark/metrics/sink/CsvSink.scala new file mode 100644 index 0000000000..cb990afdef --- /dev/null +++ b/core/src/main/scala/spark/metrics/sink/CsvSink.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 spark.metrics.sink + +import com.codahale.metrics.{CsvReporter, MetricRegistry} + +import java.io.File +import java.util.{Locale, Properties} +import java.util.concurrent.TimeUnit + +import spark.metrics.MetricsSystem + +class CsvSink(val property: Properties, val registry: MetricRegistry) extends Sink { + val CSV_KEY_PERIOD = "period" + val CSV_KEY_UNIT = "unit" + val CSV_KEY_DIR = "directory" + + val CSV_DEFAULT_PERIOD = 10 + val CSV_DEFAULT_UNIT = "SECONDS" + val CSV_DEFAULT_DIR = "/tmp/" + + val pollPeriod = Option(property.getProperty(CSV_KEY_PERIOD)) match { + case Some(s) => s.toInt + case None => CSV_DEFAULT_PERIOD + } + + val pollUnit = Option(property.getProperty(CSV_KEY_UNIT)) match { + case Some(s) => TimeUnit.valueOf(s.toUpperCase()) + case None => TimeUnit.valueOf(CSV_DEFAULT_UNIT) + } + + MetricsSystem.checkMinimalPollingPeriod(pollUnit, pollPeriod) + + val pollDir = Option(property.getProperty(CSV_KEY_DIR)) match { + case Some(s) => s + case None => CSV_DEFAULT_DIR + } + + val reporter: CsvReporter = CsvReporter.forRegistry(registry) + .formatFor(Locale.US) + .convertDurationsTo(TimeUnit.MILLISECONDS) + .convertRatesTo(TimeUnit.SECONDS) + .build(new File(pollDir)) + + override def start() { + reporter.start(pollPeriod, pollUnit) + } + + override def stop() { + reporter.stop() + } +} + diff --git a/core/src/main/scala/spark/metrics/sink/JmxSink.scala b/core/src/main/scala/spark/metrics/sink/JmxSink.scala new file mode 100644 index 0000000000..ee04544c0e --- /dev/null +++ b/core/src/main/scala/spark/metrics/sink/JmxSink.scala @@ -0,0 +1,35 @@ +/* + * 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 spark.metrics.sink + +import com.codahale.metrics.{JmxReporter, MetricRegistry} + +import java.util.Properties + +class JmxSink(val property: Properties, val registry: MetricRegistry) extends Sink { + val reporter: JmxReporter = JmxReporter.forRegistry(registry).build() + + override def start() { + reporter.start() + } + + override def stop() { + reporter.stop() + } + +} diff --git a/core/src/main/scala/spark/metrics/sink/Sink.scala b/core/src/main/scala/spark/metrics/sink/Sink.scala new file mode 100644 index 0000000000..dad1a7f0fe --- /dev/null +++ b/core/src/main/scala/spark/metrics/sink/Sink.scala @@ -0,0 +1,23 @@ +/* + * 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 spark.metrics.sink + +trait Sink { + def start: Unit + def stop: Unit +}
\ No newline at end of file diff --git a/core/src/main/scala/spark/metrics/source/JvmSource.scala b/core/src/main/scala/spark/metrics/source/JvmSource.scala new file mode 100644 index 0000000000..e771008557 --- /dev/null +++ b/core/src/main/scala/spark/metrics/source/JvmSource.scala @@ -0,0 +1,32 @@ +/* + * 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 spark.metrics.source + +import com.codahale.metrics.MetricRegistry +import com.codahale.metrics.jvm.{GarbageCollectorMetricSet, MemoryUsageGaugeSet} + +class JvmSource extends Source { + val sourceName = "jvm" + val metricRegistry = new MetricRegistry() + + val gcMetricSet = new GarbageCollectorMetricSet + val memGaugeSet = new MemoryUsageGaugeSet + + metricRegistry.registerAll(gcMetricSet) + metricRegistry.registerAll(memGaugeSet) +} diff --git a/core/src/main/scala/spark/metrics/source/Source.scala b/core/src/main/scala/spark/metrics/source/Source.scala new file mode 100644 index 0000000000..76199a004b --- /dev/null +++ b/core/src/main/scala/spark/metrics/source/Source.scala @@ -0,0 +1,25 @@ +/* + * 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 spark.metrics.source + +import com.codahale.metrics.MetricRegistry + +trait Source { + def sourceName: String + def metricRegistry: MetricRegistry +} diff --git a/core/src/main/scala/spark/network/BufferMessage.scala b/core/src/main/scala/spark/network/BufferMessage.scala new file mode 100644 index 0000000000..e566aeac13 --- /dev/null +++ b/core/src/main/scala/spark/network/BufferMessage.scala @@ -0,0 +1,111 @@ +/* + * 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 spark.network + +import java.nio.ByteBuffer + +import scala.collection.mutable.ArrayBuffer + +import spark.storage.BlockManager + + +private[spark] +class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: Int) + extends Message(Message.BUFFER_MESSAGE, id_) { + + val initialSize = currentSize() + var gotChunkForSendingOnce = false + + def size = initialSize + + def currentSize() = { + if (buffers == null || buffers.isEmpty) { + 0 + } else { + buffers.map(_.remaining).reduceLeft(_ + _) + } + } + + def getChunkForSending(maxChunkSize: Int): Option[MessageChunk] = { + if (maxChunkSize <= 0) { + throw new Exception("Max chunk size is " + maxChunkSize) + } + + if (size == 0 && gotChunkForSendingOnce == false) { + val newChunk = new MessageChunk( + new MessageChunkHeader(typ, id, 0, 0, ackId, senderAddress), null) + gotChunkForSendingOnce = true + return Some(newChunk) + } + + while(!buffers.isEmpty) { + val buffer = buffers(0) + if (buffer.remaining == 0) { + BlockManager.dispose(buffer) + buffers -= buffer + } else { + val newBuffer = if (buffer.remaining <= maxChunkSize) { + buffer.duplicate() + } else { + buffer.slice().limit(maxChunkSize).asInstanceOf[ByteBuffer] + } + buffer.position(buffer.position + newBuffer.remaining) + val newChunk = new MessageChunk(new MessageChunkHeader( + typ, id, size, newBuffer.remaining, ackId, senderAddress), newBuffer) + gotChunkForSendingOnce = true + return Some(newChunk) + } + } + None + } + + def getChunkForReceiving(chunkSize: Int): Option[MessageChunk] = { + // STRONG ASSUMPTION: BufferMessage created when receiving data has ONLY ONE data buffer + if (buffers.size > 1) { + throw new Exception("Attempting to get chunk from message with multiple data buffers") + } + val buffer = buffers(0) + if (buffer.remaining > 0) { + if (buffer.remaining < chunkSize) { + throw new Exception("Not enough space in data buffer for receiving chunk") + } + val newBuffer = buffer.slice().limit(chunkSize).asInstanceOf[ByteBuffer] + buffer.position(buffer.position + newBuffer.remaining) + val newChunk = new MessageChunk(new MessageChunkHeader( + typ, id, size, newBuffer.remaining, ackId, senderAddress), newBuffer) + return Some(newChunk) + } + None + } + + def flip() { + buffers.foreach(_.flip) + } + + def hasAckId() = (ackId != 0) + + def isCompletelyReceived() = !buffers(0).hasRemaining + + override def toString = { + if (hasAckId) { + "BufferAckMessage(aid = " + ackId + ", id = " + id + ", size = " + size + ")" + } else { + "BufferMessage(id = " + id + ", size = " + size + ")" + } + } +} diff --git a/core/src/main/scala/spark/network/Connection.scala b/core/src/main/scala/spark/network/Connection.scala index d1451bc212..b66c00b58c 100644 --- a/core/src/main/scala/spark/network/Connection.scala +++ b/core/src/main/scala/spark/network/Connection.scala @@ -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. + */ + package spark.network import spark._ @@ -13,12 +30,13 @@ import java.net._ private[spark] abstract class Connection(val channel: SocketChannel, val selector: Selector, - val remoteConnectionManagerId: ConnectionManagerId) extends Logging { + val socketRemoteConnectionManagerId: ConnectionManagerId) + extends Logging { + def this(channel_ : SocketChannel, selector_ : Selector) = { this(channel_, selector_, - ConnectionManagerId.fromSocketAddress( - channel_.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress] - )) + ConnectionManagerId.fromSocketAddress( + channel_.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress])) } channel.configureBlocking(false) @@ -33,16 +51,47 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, val remoteAddress = getRemoteAddress() + // Read channels typically do not register for write and write does not for read + // Now, we do have write registering for read too (temporarily), but this is to detect + // channel close NOT to actually read/consume data on it ! + // How does this work if/when we move to SSL ? + + // What is the interest to register with selector for when we want this connection to be selected + def registerInterest() + + // What is the interest to register with selector for when we want this connection to + // be de-selected + // Traditionally, 0 - but in our case, for example, for close-detection on SendingConnection hack, + // it will be SelectionKey.OP_READ (until we fix it properly) + def unregisterInterest() + + // On receiving a read event, should we change the interest for this channel or not ? + // Will be true for ReceivingConnection, false for SendingConnection. + def changeInterestForRead(): Boolean + + // On receiving a write event, should we change the interest for this channel or not ? + // Will be false for ReceivingConnection, true for SendingConnection. + // Actually, for now, should not get triggered for ReceivingConnection + def changeInterestForWrite(): Boolean + + def getRemoteConnectionManagerId(): ConnectionManagerId = { + socketRemoteConnectionManagerId + } + def key() = channel.keyFor(selector) def getRemoteAddress() = channel.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress] - def read() { - throw new UnsupportedOperationException("Cannot read on connection of type " + this.getClass.toString) + // Returns whether we have to register for further reads or not. + def read(): Boolean = { + throw new UnsupportedOperationException( + "Cannot read on connection of type " + this.getClass.toString) } - - def write() { - throw new UnsupportedOperationException("Cannot write on connection of type " + this.getClass.toString) + + // Returns whether we have to register for further writes or not. + def write(): Boolean = { + throw new UnsupportedOperationException( + "Cannot write on connection of type " + this.getClass.toString) } def close() { @@ -54,26 +103,32 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, callOnCloseCallback() } - def onClose(callback: Connection => Unit) {onCloseCallback = callback} + def onClose(callback: Connection => Unit) { + onCloseCallback = callback + } - def onException(callback: (Connection, Exception) => Unit) {onExceptionCallback = callback} + def onException(callback: (Connection, Exception) => Unit) { + onExceptionCallback = callback + } - def onKeyInterestChange(callback: (Connection, Int) => Unit) {onKeyInterestChangeCallback = callback} + def onKeyInterestChange(callback: (Connection, Int) => Unit) { + onKeyInterestChangeCallback = callback + } def callOnExceptionCallback(e: Exception) { if (onExceptionCallback != null) { onExceptionCallback(this, e) } else { - logError("Error in connection to " + remoteConnectionManagerId + + logError("Error in connection to " + getRemoteConnectionManagerId() + " and OnExceptionCallback not registered", e) } } - + def callOnCloseCallback() { if (onCloseCallback != null) { onCloseCallback(this) } else { - logWarning("Connection to " + remoteConnectionManagerId + + logWarning("Connection to " + getRemoteConnectionManagerId() + " closed and OnExceptionCallback not registered") } @@ -81,7 +136,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, def changeConnectionKeyInterest(ops: Int) { if (onKeyInterestChangeCallback != null) { - onKeyInterestChangeCallback(this, ops) + onKeyInterestChangeCallback(this, ops) } else { throw new Exception("OnKeyInterestChangeCallback not registered") } @@ -105,24 +160,25 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, print(" (" + position + ", " + length + ")") buffer.position(curPosition) } - } -private[spark] class SendingConnection(val address: InetSocketAddress, selector_ : Selector, - remoteId_ : ConnectionManagerId) -extends Connection(SocketChannel.open, selector_, remoteId_) { +private[spark] +class SendingConnection(val address: InetSocketAddress, selector_ : Selector, + remoteId_ : ConnectionManagerId) + extends Connection(SocketChannel.open, selector_, remoteId_) { class Outbox(fair: Int = 0) { val messages = new Queue[Message]() - val defaultChunkSize = 65536 //32768 //16384 + val defaultChunkSize = 65536 //32768 //16384 var nextMessageToBeUsed = 0 def addMessage(message: Message) { - messages.synchronized{ + messages.synchronized{ /*messages += message*/ messages.enqueue(message) - logDebug("Added [" + message + "] to outbox for sending to [" + remoteConnectionManagerId + "]") + logDebug("Added [" + message + "] to outbox for sending to " + + "[" + getRemoteConnectionManagerId() + "]") } } @@ -147,18 +203,18 @@ extends Connection(SocketChannel.open, selector_, remoteId_) { message.started = true message.startTime = System.currentTimeMillis } - return chunk + return chunk } else { - /*logInfo("Finished sending [" + message + "] to [" + remoteConnectionManagerId + "]")*/ + /*logInfo("Finished sending [" + message + "] to [" + getRemoteConnectionManagerId() + "]")*/ message.finishTime = System.currentTimeMillis - logDebug("Finished sending [" + message + "] to [" + remoteConnectionManagerId + + logDebug("Finished sending [" + message + "] to [" + getRemoteConnectionManagerId() + "] in " + message.timeTaken ) } } } None } - + private def getChunkRR(): Option[MessageChunk] = { messages.synchronized { while (!messages.isEmpty) { @@ -170,15 +226,17 @@ extends Connection(SocketChannel.open, selector_, remoteId_) { messages.enqueue(message) nextMessageToBeUsed = nextMessageToBeUsed + 1 if (!message.started) { - logDebug("Starting to send [" + message + "] to [" + remoteConnectionManagerId + "]") + logDebug( + "Starting to send [" + message + "] to [" + getRemoteConnectionManagerId() + "]") message.started = true message.startTime = System.currentTimeMillis } - logTrace("Sending chunk from [" + message+ "] to [" + remoteConnectionManagerId + "]") - return chunk + logTrace( + "Sending chunk from [" + message+ "] to [" + getRemoteConnectionManagerId() + "]") + return chunk } else { message.finishTime = System.currentTimeMillis - logDebug("Finished sending [" + message + "] to [" + remoteConnectionManagerId + + logDebug("Finished sending [" + message + "] to [" + getRemoteConnectionManagerId() + "] in " + message.timeTaken ) } } @@ -186,27 +244,40 @@ extends Connection(SocketChannel.open, selector_, remoteId_) { None } } - - val outbox = new Outbox(1) + + private val outbox = new Outbox(1) val currentBuffers = new ArrayBuffer[ByteBuffer]() /*channel.socket.setSendBufferSize(256 * 1024)*/ - override def getRemoteAddress() = address + override def getRemoteAddress() = address + + val DEFAULT_INTEREST = SelectionKey.OP_READ + + override def registerInterest() { + // Registering read too - does not really help in most cases, but for some + // it does - so let us keep it for now. + changeConnectionKeyInterest(SelectionKey.OP_WRITE | DEFAULT_INTEREST) + } + + override def unregisterInterest() { + changeConnectionKeyInterest(DEFAULT_INTEREST) + } def send(message: Message) { outbox.synchronized { outbox.addMessage(message) if (channel.isConnected) { - changeConnectionKeyInterest(SelectionKey.OP_WRITE | SelectionKey.OP_READ) + registerInterest() } } } + // MUST be called within the selector loop def connect() { try{ - channel.connect(address) channel.register(selector, SelectionKey.OP_CONNECT) + channel.connect(address) logInfo("Initiating connection to [" + address + "]") } catch { case e: Exception => { @@ -216,36 +287,52 @@ extends Connection(SocketChannel.open, selector_, remoteId_) { } } - def finishConnect() { + def finishConnect(force: Boolean): Boolean = { try { - channel.finishConnect - changeConnectionKeyInterest(SelectionKey.OP_WRITE | SelectionKey.OP_READ) + // Typically, this should finish immediately since it was triggered by a connect + // selection - though need not necessarily always complete successfully. + val connected = channel.finishConnect + if (!force && !connected) { + logInfo( + "finish connect failed [" + address + "], " + outbox.messages.size + " messages pending") + return false + } + + // Fallback to previous behavior - assume finishConnect completed + // This will happen only when finishConnect failed for some repeated number of times + // (10 or so) + // Is highly unlikely unless there was an unclean close of socket, etc + registerInterest() logInfo("Connected to [" + address + "], " + outbox.messages.size + " messages pending") + return true } catch { case e: Exception => { logWarning("Error finishing connection to " + address, e) callOnExceptionCallback(e) + // ignore + return true } } } - override def write() { - try{ - while(true) { + override def write(): Boolean = { + try { + while (true) { if (currentBuffers.size == 0) { outbox.synchronized { outbox.getChunk() match { case Some(chunk) => { - currentBuffers ++= chunk.buffers + currentBuffers ++= chunk.buffers } case None => { - changeConnectionKeyInterest(SelectionKey.OP_READ) - return + // changeConnectionKeyInterest(0) + /*key.interestOps(0)*/ + return false } } } } - + if (currentBuffers.size > 0) { val buffer = currentBuffers(0) val remainingBytes = buffer.remaining @@ -254,69 +341,109 @@ extends Connection(SocketChannel.open, selector_, remoteId_) { currentBuffers -= buffer } if (writtenBytes < remainingBytes) { - return + // re-register for write. + return true } } } } catch { - case e: Exception => { - logWarning("Error writing in connection to " + remoteConnectionManagerId, e) + case e: Exception => { + logWarning("Error writing in connection to " + getRemoteConnectionManagerId(), e) callOnExceptionCallback(e) close() + return false } } + // should not happen - to keep scala compiler happy + return true } - override def read() { + // This is a hack to determine if remote socket was closed or not. + // SendingConnection DOES NOT expect to receive any data - if it does, it is an error + // For a bunch of cases, read will return -1 in case remote socket is closed : hence we + // register for reads to determine that. + override def read(): Boolean = { // We don't expect the other side to send anything; so, we just read to detect an error or EOF. try { val length = channel.read(ByteBuffer.allocate(1)) if (length == -1) { // EOF close() } else if (length > 0) { - logWarning("Unexpected data read from SendingConnection to " + remoteConnectionManagerId) + logWarning( + "Unexpected data read from SendingConnection to " + getRemoteConnectionManagerId()) } } catch { case e: Exception => - logError("Exception while reading SendingConnection to " + remoteConnectionManagerId, e) + logError("Exception while reading SendingConnection to " + getRemoteConnectionManagerId(), e) callOnExceptionCallback(e) close() } + + false } + + override def changeInterestForRead(): Boolean = false + + override def changeInterestForWrite(): Boolean = true } -private[spark] class ReceivingConnection(channel_ : SocketChannel, selector_ : Selector) -extends Connection(channel_, selector_) { - +// Must be created within selector loop - else deadlock +private[spark] class ReceivingConnection(channel_ : SocketChannel, selector_ : Selector) + extends Connection(channel_, selector_) { + class Inbox() { val messages = new HashMap[Int, BufferMessage]() - + def getChunk(header: MessageChunkHeader): Option[MessageChunk] = { - + def createNewMessage: BufferMessage = { val newMessage = Message.create(header).asInstanceOf[BufferMessage] newMessage.started = true newMessage.startTime = System.currentTimeMillis - logDebug("Starting to receive [" + newMessage + "] from [" + remoteConnectionManagerId + "]") + logDebug( + "Starting to receive [" + newMessage + "] from [" + getRemoteConnectionManagerId() + "]") messages += ((newMessage.id, newMessage)) newMessage } - + val message = messages.getOrElseUpdate(header.id, createNewMessage) - logTrace("Receiving chunk of [" + message + "] from [" + remoteConnectionManagerId + "]") + logTrace( + "Receiving chunk of [" + message + "] from [" + getRemoteConnectionManagerId() + "]") message.getChunkForReceiving(header.chunkSize) } - + def getMessageForChunk(chunk: MessageChunk): Option[BufferMessage] = { - messages.get(chunk.header.id) + messages.get(chunk.header.id) } def removeMessage(message: Message) { messages -= message.id } } - + + @volatile private var inferredRemoteManagerId: ConnectionManagerId = null + + override def getRemoteConnectionManagerId(): ConnectionManagerId = { + val currId = inferredRemoteManagerId + if (currId != null) currId else super.getRemoteConnectionManagerId() + } + + // The reciever's remote address is the local socket on remote side : which is NOT + // the connection manager id of the receiver. + // We infer that from the messages we receive on the receiver socket. + private def processConnectionManagerId(header: MessageChunkHeader) { + val currId = inferredRemoteManagerId + if (header.address == null || currId != null) return + + val managerId = ConnectionManagerId.fromSocketAddress(header.address) + + if (managerId != null) { + inferredRemoteManagerId = managerId + } + } + + val inbox = new Inbox() val headerBuffer: ByteBuffer = ByteBuffer.allocate(MessageChunkHeader.HEADER_SIZE) var onReceiveCallback: (Connection , Message) => Unit = null @@ -324,24 +451,29 @@ extends Connection(channel_, selector_) { channel.register(selector, SelectionKey.OP_READ) - override def read() { + override def read(): Boolean = { try { while (true) { if (currentChunk == null) { val headerBytesRead = channel.read(headerBuffer) if (headerBytesRead == -1) { close() - return + return false } if (headerBuffer.remaining > 0) { - return + // re-register for read event ... + return true } headerBuffer.flip if (headerBuffer.remaining != MessageChunkHeader.HEADER_SIZE) { - throw new Exception("Unexpected number of bytes (" + headerBuffer.remaining + ") in the header") + throw new Exception( + "Unexpected number of bytes (" + headerBuffer.remaining + ") in the header") } val header = MessageChunkHeader.create(headerBuffer) headerBuffer.clear() + + processConnectionManagerId(header) + header.typ match { case Message.BUFFER_MESSAGE => { if (header.totalSize == 0) { @@ -349,7 +481,8 @@ extends Connection(channel_, selector_) { onReceiveCallback(this, Message.create(header)) } currentChunk = null - return + // re-register for read event ... + return true } else { currentChunk = inbox.getChunk(header).orNull } @@ -357,26 +490,28 @@ extends Connection(channel_, selector_) { case _ => throw new Exception("Message of unknown type received") } } - + if (currentChunk == null) throw new Exception("No message chunk to receive data") - + val bytesRead = channel.read(currentChunk.buffer) if (bytesRead == 0) { - return + // re-register for read event ... + return true } else if (bytesRead == -1) { close() - return + return false } /*logDebug("Read " + bytesRead + " bytes for the buffer")*/ - + if (currentChunk.buffer.remaining == 0) { /*println("Filled buffer at " + System.currentTimeMillis)*/ val bufferMessage = inbox.getMessageForChunk(currentChunk).get if (bufferMessage.isCompletelyReceived) { bufferMessage.flip bufferMessage.finishTime = System.currentTimeMillis - logDebug("Finished receiving [" + bufferMessage + "] from [" + remoteConnectionManagerId + "] in " + bufferMessage.timeTaken) + logDebug("Finished receiving [" + bufferMessage + "] from " + + "[" + getRemoteConnectionManagerId() + "] in " + bufferMessage.timeTaken) if (onReceiveCallback != null) { onReceiveCallback(this, bufferMessage) } @@ -386,13 +521,32 @@ extends Connection(channel_, selector_) { } } } catch { - case e: Exception => { - logWarning("Error reading from connection to " + remoteConnectionManagerId, e) + case e: Exception => { + logWarning("Error reading from connection to " + getRemoteConnectionManagerId(), e) callOnExceptionCallback(e) close() + return false } } + // should not happen - to keep scala compiler happy + return true } - + def onReceive(callback: (Connection, Message) => Unit) {onReceiveCallback = callback} + + override def changeInterestForRead(): Boolean = true + + override def changeInterestForWrite(): Boolean = { + throw new IllegalStateException("Unexpected invocation right now") + } + + override def registerInterest() { + // Registering read too - does not really help in most cases, but for some + // it does - so let us keep it for now. + changeConnectionKeyInterest(SelectionKey.OP_READ) + } + + override def unregisterInterest() { + changeConnectionKeyInterest(0) + } } diff --git a/core/src/main/scala/spark/network/ConnectionManager.scala b/core/src/main/scala/spark/network/ConnectionManager.scala index b6ec664d7e..6c4e7dc03e 100644 --- a/core/src/main/scala/spark/network/ConnectionManager.scala +++ b/core/src/main/scala/spark/network/ConnectionManager.scala @@ -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. + */ + package spark.network import spark._ @@ -6,28 +23,19 @@ import java.nio._ import java.nio.channels._ import java.nio.channels.spi._ import java.net._ -import java.util.concurrent.Executors +import java.util.concurrent.{LinkedBlockingDeque, TimeUnit, ThreadPoolExecutor} +import scala.collection.mutable.HashSet import scala.collection.mutable.HashMap import scala.collection.mutable.SynchronizedMap import scala.collection.mutable.SynchronizedQueue -import scala.collection.mutable.Queue import scala.collection.mutable.ArrayBuffer import akka.dispatch.{Await, Promise, ExecutionContext, Future} import akka.util.Duration import akka.util.duration._ -private[spark] case class ConnectionManagerId(host: String, port: Int) { - def toSocketAddress() = new InetSocketAddress(host, port) -} -private[spark] object ConnectionManagerId { - def fromSocketAddress(socketAddress: InetSocketAddress): ConnectionManagerId = { - new ConnectionManagerId(socketAddress.getHostName(), socketAddress.getPort()) - } -} - private[spark] class ConnectionManager(port: Int) extends Logging { class MessageStatus( @@ -41,73 +49,263 @@ private[spark] class ConnectionManager(port: Int) extends Logging { def markDone() { completionHandler(this) } } - - val selector = SelectorProvider.provider.openSelector() - val handleMessageExecutor = Executors.newFixedThreadPool(System.getProperty("spark.core.connection.handler.threads","20").toInt) - val serverChannel = ServerSocketChannel.open() - val connectionsByKey = new HashMap[SelectionKey, Connection] with SynchronizedMap[SelectionKey, Connection] - val connectionsById = new HashMap[ConnectionManagerId, SendingConnection] with SynchronizedMap[ConnectionManagerId, SendingConnection] - val messageStatuses = new HashMap[Int, MessageStatus] - val connectionRequests = new HashMap[ConnectionManagerId, SendingConnection] with SynchronizedMap[ConnectionManagerId, SendingConnection] - val keyInterestChangeRequests = new SynchronizedQueue[(SelectionKey, Int)] - val sendMessageRequests = new Queue[(Message, SendingConnection)] + + 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, + 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, + 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, + new LinkedBlockingDeque[Runnable]()) + + private val serverChannel = ServerSocketChannel.open() + private val connectionsByKey = new HashMap[SelectionKey, Connection] with SynchronizedMap[SelectionKey, Connection] + private val connectionsById = new HashMap[ConnectionManagerId, SendingConnection] with SynchronizedMap[ConnectionManagerId, SendingConnection] + private val messageStatuses = new HashMap[Int, MessageStatus] + private val keyInterestChangeRequests = new SynchronizedQueue[(SelectionKey, Int)] + private val registerRequests = new SynchronizedQueue[SendingConnection] implicit val futureExecContext = ExecutionContext.fromExecutor(Utils.newDaemonCachedThreadPool()) - var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message]= null + private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message]= null serverChannel.configureBlocking(false) serverChannel.socket.setReuseAddress(true) - serverChannel.socket.setReceiveBufferSize(256 * 1024) + serverChannel.socket.setReceiveBufferSize(256 * 1024) serverChannel.socket.bind(new InetSocketAddress(port)) serverChannel.register(selector, SelectionKey.OP_ACCEPT) val id = new ConnectionManagerId(Utils.localHostName, serverChannel.socket.getLocalPort) logInfo("Bound socket to port " + serverChannel.socket.getLocalPort() + " with id = " + id) - - val selectorThread = new Thread("connection-manager-thread") { + + private val selectorThread = new Thread("connection-manager-thread") { override def run() = ConnectionManager.this.run() } selectorThread.setDaemon(true) selectorThread.start() - private def run() { - try { - while(!selectorThread.isInterrupted) { - for ((connectionManagerId, sendingConnection) <- connectionRequests) { - sendingConnection.connect() - addConnection(sendingConnection) - connectionRequests -= connectionManagerId + private val writeRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]() + + private def triggerWrite(key: SelectionKey) { + val conn = connectionsByKey.getOrElse(key, null) + if (conn == null) return + + writeRunnableStarted.synchronized { + // So that we do not trigger more write events while processing this one. + // The write method will re-register when done. + if (conn.changeInterestForWrite()) conn.unregisterInterest() + if (writeRunnableStarted.contains(key)) { + // key.interestOps(key.interestOps() & ~ SelectionKey.OP_WRITE) + return + } + + writeRunnableStarted += key + } + handleReadWriteExecutor.execute(new Runnable { + override def run() { + var register: Boolean = false + try { + register = conn.write() + } finally { + writeRunnableStarted.synchronized { + writeRunnableStarted -= key + if (register && conn.changeInterestForWrite()) { + conn.registerInterest() + } + } } - sendMessageRequests.synchronized { - while (!sendMessageRequests.isEmpty) { - val (message, connection) = sendMessageRequests.dequeue - connection.send(message) + } + } ) + } + + private val readRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]() + + private def triggerRead(key: SelectionKey) { + val conn = connectionsByKey.getOrElse(key, null) + if (conn == null) return + + readRunnableStarted.synchronized { + // So that we do not trigger more read events while processing this one. + // The read method will re-register when done. + if (conn.changeInterestForRead())conn.unregisterInterest() + if (readRunnableStarted.contains(key)) { + return + } + + readRunnableStarted += key + } + handleReadWriteExecutor.execute(new Runnable { + override def run() { + var register: Boolean = false + try { + register = conn.read() + } finally { + readRunnableStarted.synchronized { + readRunnableStarted -= key + if (register && conn.changeInterestForRead()) { + conn.registerInterest() + } } } + } + } ) + } + + private def triggerConnect(key: SelectionKey) { + val conn = connectionsByKey.getOrElse(key, null).asInstanceOf[SendingConnection] + if (conn == null) return + + // prevent other events from being triggered + // Since we are still trying to connect, we do not need to do the additional steps in triggerWrite + conn.changeConnectionKeyInterest(0) + + handleConnectExecutor.execute(new Runnable { + override def run() { + + var tries: Int = 10 + while (tries >= 0) { + if (conn.finishConnect(false)) return + // Sleep ? + Thread.sleep(1) + tries -= 1 + } + + // fallback to previous behavior : we should not really come here since this method was + // triggered since channel became connectable : but at times, the first finishConnect need not + // succeed : hence the loop to retry a few 'times'. + conn.finishConnect(true) + } + } ) + } + + // MUST be called within selector loop - else deadlock. + private def triggerForceCloseByException(key: SelectionKey, e: Exception) { + try { + key.interestOps(0) + } catch { + // ignore exceptions + case e: Exception => logDebug("Ignoring exception", e) + } - while (!keyInterestChangeRequests.isEmpty) { + val conn = connectionsByKey.getOrElse(key, null) + if (conn == null) return + + // Pushing to connect threadpool + handleConnectExecutor.execute(new Runnable { + override def run() { + try { + conn.callOnExceptionCallback(e) + } catch { + // ignore exceptions + case e: Exception => logDebug("Ignoring exception", e) + } + try { + conn.close() + } catch { + // ignore exceptions + case e: Exception => logDebug("Ignoring exception", e) + } + } + }) + } + + + def run() { + try { + while(!selectorThread.isInterrupted) { + while (! registerRequests.isEmpty) { + val conn: SendingConnection = registerRequests.dequeue + addListeners(conn) + conn.connect() + addConnection(conn) + } + + while(!keyInterestChangeRequests.isEmpty) { val (key, ops) = keyInterestChangeRequests.dequeue - val connection = connectionsByKey(key) - val lastOps = key.interestOps() - key.interestOps(ops) - - def intToOpStr(op: Int): String = { - val opStrs = ArrayBuffer[String]() - if ((op & SelectionKey.OP_READ) != 0) opStrs += "READ" - if ((op & SelectionKey.OP_WRITE) != 0) opStrs += "WRITE" - if ((op & SelectionKey.OP_CONNECT) != 0) opStrs += "CONNECT" - if ((op & SelectionKey.OP_ACCEPT) != 0) opStrs += "ACCEPT" - if (opStrs.size > 0) opStrs.reduceLeft(_ + " | " + _) else " " + + try { + if (key.isValid) { + val connection = connectionsByKey.getOrElse(key, null) + if (connection != null) { + val lastOps = key.interestOps() + key.interestOps(ops) + + // hot loop - prevent materialization of string if trace not enabled. + if (isTraceEnabled()) { + def intToOpStr(op: Int): String = { + val opStrs = ArrayBuffer[String]() + if ((op & SelectionKey.OP_READ) != 0) opStrs += "READ" + if ((op & SelectionKey.OP_WRITE) != 0) opStrs += "WRITE" + if ((op & SelectionKey.OP_CONNECT) != 0) opStrs += "CONNECT" + if ((op & SelectionKey.OP_ACCEPT) != 0) opStrs += "ACCEPT" + if (opStrs.size > 0) opStrs.reduceLeft(_ + " | " + _) else " " + } + + logTrace("Changed key for connection to [" + connection.getRemoteConnectionManagerId() + + "] changed from [" + intToOpStr(lastOps) + "] to [" + intToOpStr(ops) + "]") + } + } + } else { + logInfo("Key not valid ? " + key) + throw new CancelledKeyException() + } + } catch { + case e: CancelledKeyException => { + logInfo("key already cancelled ? " + key, e) + triggerForceCloseByException(key, e) + } + case e: Exception => { + logError("Exception processing key " + key, e) + triggerForceCloseByException(key, e) + } } - - logTrace("Changed key for connection to [" + connection.remoteConnectionManagerId + - "] changed from [" + intToOpStr(lastOps) + "] to [" + intToOpStr(ops) + "]") - } - val selectedKeysCount = selector.select() + val selectedKeysCount = + try { + selector.select() + } catch { + // Explicitly only dealing with CancelledKeyException here since other exceptions should be dealt with differently. + case e: CancelledKeyException => { + // Some keys within the selectors list are invalid/closed. clear them. + val allKeys = selector.keys().iterator() + + while (allKeys.hasNext()) { + val key = allKeys.next() + try { + if (! key.isValid) { + logInfo("Key not valid ? " + key) + throw new CancelledKeyException() + } + } catch { + case e: CancelledKeyException => { + logInfo("key already cancelled ? " + key, e) + triggerForceCloseByException(key, e) + } + case e: Exception => { + logError("Exception processing key " + key, e) + triggerForceCloseByException(key, e) + } + } + } + } + 0 + } + if (selectedKeysCount == 0) { logDebug("Selector selected " + selectedKeysCount + " of " + selector.keys.size + " keys") } @@ -115,20 +313,40 @@ private[spark] class ConnectionManager(port: Int) extends Logging { logInfo("Selector thread was interrupted!") return } - - val selectedKeys = selector.selectedKeys().iterator() - while (selectedKeys.hasNext()) { - val key = selectedKeys.next - selectedKeys.remove() - if (key.isValid) { - if (key.isAcceptable) { - acceptConnection(key) - } else if (key.isConnectable) { - connectionsByKey(key).asInstanceOf[SendingConnection].finishConnect() - } else if (key.isReadable) { - connectionsByKey(key).read() - } else if (key.isWritable) { - connectionsByKey(key).write() + + if (0 != selectedKeysCount) { + val selectedKeys = selector.selectedKeys().iterator() + while (selectedKeys.hasNext()) { + val key = selectedKeys.next + selectedKeys.remove() + try { + if (key.isValid) { + if (key.isAcceptable) { + acceptConnection(key) + } else + if (key.isConnectable) { + triggerConnect(key) + } else + if (key.isReadable) { + triggerRead(key) + } else + if (key.isWritable) { + triggerWrite(key) + } + } else { + logInfo("Key not valid ? " + key) + throw new CancelledKeyException() + } + } catch { + // weird, but we saw this happening - even though key.isValid was true, key.isAcceptable would throw CancelledKeyException. + case e: CancelledKeyException => { + logInfo("key already cancelled ? " + key, e) + triggerForceCloseByException(key, e) + } + case e: Exception => { + logError("Exception processing key " + key, e) + triggerForceCloseByException(key, e) + } } } } @@ -137,97 +355,119 @@ private[spark] class ConnectionManager(port: Int) extends Logging { case e: Exception => logError("Error in select loop", e) } } - - private def acceptConnection(key: SelectionKey) { + + def acceptConnection(key: SelectionKey) { val serverChannel = key.channel.asInstanceOf[ServerSocketChannel] - val newChannel = serverChannel.accept() - val newConnection = new ReceivingConnection(newChannel, selector) - newConnection.onReceive(receiveMessage) - newConnection.onClose(removeConnection) - addConnection(newConnection) - logInfo("Accepted connection from [" + newConnection.remoteAddress.getAddress + "]") - } - private def addConnection(connection: Connection) { - connectionsByKey += ((connection.key, connection)) - if (connection.isInstanceOf[SendingConnection]) { - val sendingConnection = connection.asInstanceOf[SendingConnection] - connectionsById += ((sendingConnection.remoteConnectionManagerId, sendingConnection)) + var newChannel = serverChannel.accept() + + // accept them all in a tight loop. non blocking accept with no processing, should be fine + while (newChannel != null) { + try { + val newConnection = new ReceivingConnection(newChannel, selector) + newConnection.onReceive(receiveMessage) + addListeners(newConnection) + addConnection(newConnection) + logInfo("Accepted connection from [" + newConnection.remoteAddress.getAddress + "]") + } catch { + // might happen in case of issues with registering with selector + case e: Exception => logError("Error in accept loop", e) + } + + newChannel = serverChannel.accept() } + } + + private def addListeners(connection: Connection) { connection.onKeyInterestChange(changeConnectionKeyInterest) connection.onException(handleConnectionError) connection.onClose(removeConnection) } - private def removeConnection(connection: Connection) { + def addConnection(connection: Connection) { + connectionsByKey += ((connection.key, connection)) + } + + def removeConnection(connection: Connection) { connectionsByKey -= connection.key - if (connection.isInstanceOf[SendingConnection]) { - val sendingConnection = connection.asInstanceOf[SendingConnection] - val sendingConnectionManagerId = sendingConnection.remoteConnectionManagerId - logInfo("Removing SendingConnection to " + sendingConnectionManagerId) - - connectionsById -= sendingConnectionManagerId - - messageStatuses.synchronized { - messageStatuses - .values.filter(_.connectionManagerId == sendingConnectionManagerId).foreach(status => { - logInfo("Notifying " + status) - status.synchronized { - status.attempted = true - status.acked = false - status.markDone() - } + + try { + if (connection.isInstanceOf[SendingConnection]) { + val sendingConnection = connection.asInstanceOf[SendingConnection] + val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId() + logInfo("Removing SendingConnection to " + sendingConnectionManagerId) + + connectionsById -= sendingConnectionManagerId + + messageStatuses.synchronized { + messageStatuses + .values.filter(_.connectionManagerId == sendingConnectionManagerId).foreach(status => { + logInfo("Notifying " + status) + status.synchronized { + status.attempted = true + status.acked = false + status.markDone() + } + }) + + messageStatuses.retain((i, status) => { + status.connectionManagerId != sendingConnectionManagerId }) + } + } else if (connection.isInstanceOf[ReceivingConnection]) { + val receivingConnection = connection.asInstanceOf[ReceivingConnection] + val remoteConnectionManagerId = receivingConnection.getRemoteConnectionManagerId() + logInfo("Removing ReceivingConnection to " + remoteConnectionManagerId) + + val sendingConnectionOpt = connectionsById.get(remoteConnectionManagerId) + if (! sendingConnectionOpt.isDefined) { + logError("Corresponding SendingConnectionManagerId not found") + return + } - messageStatuses.retain((i, status) => { - status.connectionManagerId != sendingConnectionManagerId - }) - } - } else if (connection.isInstanceOf[ReceivingConnection]) { - val receivingConnection = connection.asInstanceOf[ReceivingConnection] - val remoteConnectionManagerId = receivingConnection.remoteConnectionManagerId - logInfo("Removing ReceivingConnection to " + remoteConnectionManagerId) - - val sendingConnectionManagerId = connectionsById.keys.find(_.host == remoteConnectionManagerId.host).orNull - if (sendingConnectionManagerId == null) { - logError("Corresponding SendingConnectionManagerId not found") - return - } - logInfo("Corresponding SendingConnectionManagerId is " + sendingConnectionManagerId) - - val sendingConnection = connectionsById(sendingConnectionManagerId) - sendingConnection.close() - connectionsById -= sendingConnectionManagerId - - messageStatuses.synchronized { - for (s <- messageStatuses.values if s.connectionManagerId == sendingConnectionManagerId) { - logInfo("Notifying " + s) - s.synchronized { - s.attempted = true - s.acked = false - s.markDone() + val sendingConnection = sendingConnectionOpt.get + connectionsById -= remoteConnectionManagerId + sendingConnection.close() + + val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId() + + assert (sendingConnectionManagerId == remoteConnectionManagerId) + + messageStatuses.synchronized { + for (s <- messageStatuses.values if s.connectionManagerId == sendingConnectionManagerId) { + logInfo("Notifying " + s) + s.synchronized { + s.attempted = true + s.acked = false + s.markDone() + } } - } - messageStatuses.retain((i, status) => { - status.connectionManagerId != sendingConnectionManagerId - }) + messageStatuses.retain((i, status) => { + status.connectionManagerId != sendingConnectionManagerId + }) + } } + } finally { + // So that the selection keys can be removed. + wakeupSelector() } } - private def handleConnectionError(connection: Connection, e: Exception) { - logInfo("Handling connection error on connection to " + connection.remoteConnectionManagerId) + def handleConnectionError(connection: Connection, e: Exception) { + logInfo("Handling connection error on connection to " + connection.getRemoteConnectionManagerId()) removeConnection(connection) } - private def changeConnectionKeyInterest(connection: Connection, ops: Int) { - keyInterestChangeRequests += ((connection.key, ops)) + def changeConnectionKeyInterest(connection: Connection, ops: Int) { + keyInterestChangeRequests += ((connection.key, ops)) + // so that registerations happen ! + wakeupSelector() } - private def receiveMessage(connection: Connection, message: Message) { + def receiveMessage(connection: Connection, message: Message) { val connectionManagerId = ConnectionManagerId.fromSocketAddress(message.senderAddress) - logDebug("Received [" + message + "] from [" + connectionManagerId + "]") + logDebug("Received [" + message + "] from [" + connectionManagerId + "]") val runnable = new Runnable() { val creationTime = System.currentTimeMillis def run() { @@ -247,11 +487,11 @@ private[spark] class ConnectionManager(port: Int) extends Logging { if (bufferMessage.hasAckId) { val sentMessageStatus = messageStatuses.synchronized { messageStatuses.get(bufferMessage.ackId) match { - case Some(status) => { - messageStatuses -= bufferMessage.ackId + case Some(status) => { + messageStatuses -= bufferMessage.ackId status } - case None => { + case None => { throw new Exception("Could not find reference for received ack message " + message.id) null } @@ -271,7 +511,7 @@ private[spark] class ConnectionManager(port: Int) extends Logging { logDebug("Not calling back as callback is null") None } - + if (ackMessage.isDefined) { if (!ackMessage.get.isInstanceOf[BufferMessage]) { logDebug("Response to " + bufferMessage + " is not a buffer message, it is of type " + ackMessage.get.getClass()) @@ -281,7 +521,7 @@ private[spark] class ConnectionManager(port: Int) extends Logging { } } - sendMessage(connectionManagerId, ackMessage.getOrElse { + sendMessage(connectionManagerId, ackMessage.getOrElse { Message.createBufferMessage(bufferMessage.id) }) } @@ -293,18 +533,22 @@ private[spark] class ConnectionManager(port: Int) extends Logging { private def sendMessage(connectionManagerId: ConnectionManagerId, message: Message) { def startNewConnection(): SendingConnection = { val inetSocketAddress = new InetSocketAddress(connectionManagerId.host, connectionManagerId.port) - val newConnection = connectionRequests.getOrElseUpdate(connectionManagerId, - new SendingConnection(inetSocketAddress, selector, connectionManagerId)) - newConnection + val newConnection = new SendingConnection(inetSocketAddress, selector, connectionManagerId) + registerRequests.enqueue(newConnection) + + newConnection } - val lookupKey = ConnectionManagerId.fromSocketAddress(connectionManagerId.toSocketAddress) - val connection = connectionsById.getOrElse(lookupKey, startNewConnection()) + // I removed the lookupKey stuff as part of merge ... should I re-add it ? We did not find it useful in our test-env ... + // If we do re-add it, we should consistently use it everywhere I guess ? + val connection = connectionsById.getOrElseUpdate(connectionManagerId, startNewConnection()) message.senderAddress = id.toSocketAddress() logDebug("Sending [" + message + "] to [" + connectionManagerId + "]") - /*connection.send(message)*/ - sendMessageRequests.synchronized { - sendMessageRequests += ((message, connection)) - } + connection.send(message) + + wakeupSelector() + } + + private def wakeupSelector() { selector.wakeup() } @@ -337,6 +581,8 @@ private[spark] class ConnectionManager(port: Int) extends Logging { logWarning("All connections not cleaned up") } handleMessageExecutor.shutdown() + handleReadWriteExecutor.shutdown() + handleConnectExecutor.shutdown() logInfo("ConnectionManager stopped") } } @@ -346,17 +592,17 @@ private[spark] object ConnectionManager { def main(args: Array[String]) { val manager = new ConnectionManager(9999) - manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { + manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { println("Received [" + msg + "] from [" + id + "]") None }) - + /*testSequentialSending(manager)*/ /*System.gc()*/ /*testParallelSending(manager)*/ /*System.gc()*/ - + /*testParallelDecreasingSending(manager)*/ /*System.gc()*/ @@ -368,9 +614,9 @@ private[spark] object ConnectionManager { println("--------------------------") println("Sequential Sending") println("--------------------------") - val size = 10 * 1024 * 1024 + val size = 10 * 1024 * 1024 val count = 10 - + val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte)) buffer.flip @@ -386,7 +632,7 @@ private[spark] object ConnectionManager { println("--------------------------") println("Parallel Sending") println("--------------------------") - val size = 10 * 1024 * 1024 + val size = 10 * 1024 * 1024 val count = 10 val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte)) @@ -401,12 +647,12 @@ private[spark] object ConnectionManager { if (!g.isDefined) println("Failed") }) val finishTime = System.currentTimeMillis - + val mb = size * count / 1024.0 / 1024.0 val ms = finishTime - startTime val tput = mb * 1000.0 / ms println("--------------------------") - println("Started at " + startTime + ", finished at " + finishTime) + println("Started at " + startTime + ", finished at " + finishTime) println("Sent " + count + " messages of size " + size + " in " + ms + " ms (" + tput + " MB/s)") println("--------------------------") println() @@ -416,7 +662,7 @@ private[spark] object ConnectionManager { println("--------------------------") println("Parallel Decreasing Sending") println("--------------------------") - val size = 10 * 1024 * 1024 + val size = 10 * 1024 * 1024 val count = 10 val buffers = Array.tabulate(count)(i => ByteBuffer.allocate(size * (i + 1)).put(Array.tabulate[Byte](size * (i + 1))(x => x.toByte))) buffers.foreach(_.flip) @@ -431,7 +677,7 @@ private[spark] object ConnectionManager { if (!g.isDefined) println("Failed") }) val finishTime = System.currentTimeMillis - + val ms = finishTime - startTime val tput = mb * 1000.0 / ms println("--------------------------") @@ -445,7 +691,7 @@ private[spark] object ConnectionManager { println("--------------------------") println("Continuous Sending") println("--------------------------") - val size = 10 * 1024 * 1024 + val size = 10 * 1024 * 1024 val count = 10 val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte)) diff --git a/core/src/main/scala/spark/network/ConnectionManagerId.scala b/core/src/main/scala/spark/network/ConnectionManagerId.scala new file mode 100644 index 0000000000..9d5c518293 --- /dev/null +++ b/core/src/main/scala/spark/network/ConnectionManagerId.scala @@ -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. + */ + +package spark.network + +import java.net.InetSocketAddress + +import spark.Utils + + +private[spark] case class ConnectionManagerId(host: String, port: Int) { + // DEBUG code + Utils.checkHost(host) + assert (port > 0) + + def toSocketAddress() = new InetSocketAddress(host, port) +} + + +private[spark] object ConnectionManagerId { + def fromSocketAddress(socketAddress: InetSocketAddress): ConnectionManagerId = { + new ConnectionManagerId(socketAddress.getHostName(), socketAddress.getPort()) + } +} diff --git a/core/src/main/scala/spark/network/ConnectionManagerTest.scala b/core/src/main/scala/spark/network/ConnectionManagerTest.scala index 533e4610f3..9e3827aaf5 100644 --- a/core/src/main/scala/spark/network/ConnectionManagerTest.scala +++ b/core/src/main/scala/spark/network/ConnectionManagerTest.scala @@ -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. + */ + package spark.network import spark._ diff --git a/core/src/main/scala/spark/network/Message.scala b/core/src/main/scala/spark/network/Message.scala index 525751b5bf..a25457ea35 100644 --- a/core/src/main/scala/spark/network/Message.scala +++ b/core/src/main/scala/spark/network/Message.scala @@ -1,55 +1,27 @@ -package spark.network - -import spark._ +/* + * 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. + */ -import scala.collection.mutable.ArrayBuffer +package spark.network import java.nio.ByteBuffer -import java.net.InetAddress import java.net.InetSocketAddress -import storage.BlockManager - -private[spark] class MessageChunkHeader( - val typ: Long, - val id: Int, - val totalSize: Int, - val chunkSize: Int, - val other: Int, - val address: InetSocketAddress) { - lazy val buffer = { - val ip = address.getAddress.getAddress() - val port = address.getPort() - ByteBuffer. - allocate(MessageChunkHeader.HEADER_SIZE). - putLong(typ). - putInt(id). - putInt(totalSize). - putInt(chunkSize). - putInt(other). - putInt(ip.size). - put(ip). - putInt(port). - position(MessageChunkHeader.HEADER_SIZE). - flip.asInstanceOf[ByteBuffer] - } - - override def toString = "" + this.getClass.getSimpleName + ":" + id + " of type " + typ + - " and sizes " + totalSize + " / " + chunkSize + " bytes" -} -private[spark] class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) { - val size = if (buffer == null) 0 else buffer.remaining - lazy val buffers = { - val ab = new ArrayBuffer[ByteBuffer]() - ab += header.buffer - if (buffer != null) { - ab += buffer - } - ab - } +import scala.collection.mutable.ArrayBuffer - override def toString = "" + this.getClass.getSimpleName + " (id = " + header.id + ", size = " + size + ")" -} private[spark] abstract class Message(val typ: Long, val id: Int) { var senderAddress: InetSocketAddress = null @@ -58,120 +30,16 @@ private[spark] abstract class Message(val typ: Long, val id: Int) { var finishTime = -1L def size: Int - + def getChunkForSending(maxChunkSize: Int): Option[MessageChunk] - + def getChunkForReceiving(chunkSize: Int): Option[MessageChunk] - + def timeTaken(): String = (finishTime - startTime).toString + " ms" override def toString = this.getClass.getSimpleName + "(id = " + id + ", size = " + size + ")" } -private[spark] class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: Int) -extends Message(Message.BUFFER_MESSAGE, id_) { - - val initialSize = currentSize() - var gotChunkForSendingOnce = false - - def size = initialSize - - def currentSize() = { - if (buffers == null || buffers.isEmpty) { - 0 - } else { - buffers.map(_.remaining).reduceLeft(_ + _) - } - } - - def getChunkForSending(maxChunkSize: Int): Option[MessageChunk] = { - if (maxChunkSize <= 0) { - throw new Exception("Max chunk size is " + maxChunkSize) - } - - if (size == 0 && gotChunkForSendingOnce == false) { - val newChunk = new MessageChunk(new MessageChunkHeader(typ, id, 0, 0, ackId, senderAddress), null) - gotChunkForSendingOnce = true - return Some(newChunk) - } - - while(!buffers.isEmpty) { - val buffer = buffers(0) - if (buffer.remaining == 0) { - BlockManager.dispose(buffer) - buffers -= buffer - } else { - val newBuffer = if (buffer.remaining <= maxChunkSize) { - buffer.duplicate() - } else { - buffer.slice().limit(maxChunkSize).asInstanceOf[ByteBuffer] - } - buffer.position(buffer.position + newBuffer.remaining) - val newChunk = new MessageChunk(new MessageChunkHeader( - typ, id, size, newBuffer.remaining, ackId, senderAddress), newBuffer) - gotChunkForSendingOnce = true - return Some(newChunk) - } - } - None - } - - def getChunkForReceiving(chunkSize: Int): Option[MessageChunk] = { - // STRONG ASSUMPTION: BufferMessage created when receiving data has ONLY ONE data buffer - if (buffers.size > 1) { - throw new Exception("Attempting to get chunk from message with multiple data buffers") - } - val buffer = buffers(0) - if (buffer.remaining > 0) { - if (buffer.remaining < chunkSize) { - throw new Exception("Not enough space in data buffer for receiving chunk") - } - val newBuffer = buffer.slice().limit(chunkSize).asInstanceOf[ByteBuffer] - buffer.position(buffer.position + newBuffer.remaining) - val newChunk = new MessageChunk(new MessageChunkHeader( - typ, id, size, newBuffer.remaining, ackId, senderAddress), newBuffer) - return Some(newChunk) - } - None - } - - def flip() { - buffers.foreach(_.flip) - } - - def hasAckId() = (ackId != 0) - - def isCompletelyReceived() = !buffers(0).hasRemaining - - override def toString = { - if (hasAckId) { - "BufferAckMessage(aid = " + ackId + ", id = " + id + ", size = " + size + ")" - } else { - "BufferMessage(id = " + id + ", size = " + size + ")" - } - } -} - -private[spark] object MessageChunkHeader { - val HEADER_SIZE = 40 - - def create(buffer: ByteBuffer): MessageChunkHeader = { - if (buffer.remaining != HEADER_SIZE) { - throw new IllegalArgumentException("Cannot convert buffer data to Message") - } - val typ = buffer.getLong() - val id = buffer.getInt() - val totalSize = buffer.getInt() - val chunkSize = buffer.getInt() - val other = buffer.getInt() - val ipSize = buffer.getInt() - val ipBytes = new Array[Byte](ipSize) - buffer.get(ipBytes) - val ip = InetAddress.getByAddress(ipBytes) - val port = buffer.getInt() - new MessageChunkHeader(typ, id, totalSize, chunkSize, other, new InetSocketAddress(ip, port)) - } -} private[spark] object Message { val BUFFER_MESSAGE = 1111111111L @@ -180,14 +48,16 @@ private[spark] object Message { def getNewId() = synchronized { lastId += 1 - if (lastId == 0) lastId += 1 + if (lastId == 0) { + lastId += 1 + } lastId } def createBufferMessage(dataBuffers: Seq[ByteBuffer], ackId: Int): BufferMessage = { if (dataBuffers == null) { return new BufferMessage(getNewId(), new ArrayBuffer[ByteBuffer], ackId) - } + } if (dataBuffers.exists(_ == null)) { throw new Exception("Attempting to create buffer message with null buffer") } @@ -196,7 +66,7 @@ private[spark] object Message { def createBufferMessage(dataBuffers: Seq[ByteBuffer]): BufferMessage = createBufferMessage(dataBuffers, 0) - + def createBufferMessage(dataBuffer: ByteBuffer, ackId: Int): BufferMessage = { if (dataBuffer == null) { return createBufferMessage(Array(ByteBuffer.allocate(0)), ackId) @@ -204,15 +74,18 @@ private[spark] object Message { return createBufferMessage(Array(dataBuffer), ackId) } } - - def createBufferMessage(dataBuffer: ByteBuffer): BufferMessage = + + def createBufferMessage(dataBuffer: ByteBuffer): BufferMessage = createBufferMessage(dataBuffer, 0) - - def createBufferMessage(ackId: Int): BufferMessage = createBufferMessage(new Array[ByteBuffer](0), ackId) + + def createBufferMessage(ackId: Int): BufferMessage = { + createBufferMessage(new Array[ByteBuffer](0), ackId) + } def create(header: MessageChunkHeader): Message = { val newMessage: Message = header.typ match { - case BUFFER_MESSAGE => new BufferMessage(header.id, ArrayBuffer(ByteBuffer.allocate(header.totalSize)), header.other) + case BUFFER_MESSAGE => new BufferMessage(header.id, + ArrayBuffer(ByteBuffer.allocate(header.totalSize)), header.other) } newMessage.senderAddress = header.address newMessage diff --git a/core/src/main/scala/spark/network/MessageChunk.scala b/core/src/main/scala/spark/network/MessageChunk.scala new file mode 100644 index 0000000000..784db5ab62 --- /dev/null +++ b/core/src/main/scala/spark/network/MessageChunk.scala @@ -0,0 +1,42 @@ +/* + * 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 spark.network + +import java.nio.ByteBuffer + +import scala.collection.mutable.ArrayBuffer + + +private[network] +class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) { + + val size = if (buffer == null) 0 else buffer.remaining + + lazy val buffers = { + val ab = new ArrayBuffer[ByteBuffer]() + ab += header.buffer + if (buffer != null) { + ab += buffer + } + ab + } + + override def toString = { + "" + this.getClass.getSimpleName + " (id = " + header.id + ", size = " + size + ")" + } +} diff --git a/core/src/main/scala/spark/network/MessageChunkHeader.scala b/core/src/main/scala/spark/network/MessageChunkHeader.scala new file mode 100644 index 0000000000..18d0cbcc14 --- /dev/null +++ b/core/src/main/scala/spark/network/MessageChunkHeader.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 spark.network + +import java.net.InetAddress +import java.net.InetSocketAddress +import java.nio.ByteBuffer + + +private[spark] class MessageChunkHeader( + val typ: Long, + val id: Int, + val totalSize: Int, + val chunkSize: Int, + val other: Int, + val address: InetSocketAddress) { + lazy val buffer = { + // No need to change this, at 'use' time, we do a reverse lookup of the hostname. + // Refer to network.Connection + val ip = address.getAddress.getAddress() + val port = address.getPort() + ByteBuffer. + allocate(MessageChunkHeader.HEADER_SIZE). + putLong(typ). + putInt(id). + putInt(totalSize). + putInt(chunkSize). + putInt(other). + putInt(ip.size). + put(ip). + putInt(port). + position(MessageChunkHeader.HEADER_SIZE). + flip.asInstanceOf[ByteBuffer] + } + + override def toString = "" + this.getClass.getSimpleName + ":" + id + " of type " + typ + + " and sizes " + totalSize + " / " + chunkSize + " bytes" +} + + +private[spark] object MessageChunkHeader { + val HEADER_SIZE = 40 + + def create(buffer: ByteBuffer): MessageChunkHeader = { + if (buffer.remaining != HEADER_SIZE) { + throw new IllegalArgumentException("Cannot convert buffer data to Message") + } + val typ = buffer.getLong() + val id = buffer.getInt() + val totalSize = buffer.getInt() + val chunkSize = buffer.getInt() + val other = buffer.getInt() + val ipSize = buffer.getInt() + val ipBytes = new Array[Byte](ipSize) + buffer.get(ipBytes) + val ip = InetAddress.getByAddress(ipBytes) + val port = buffer.getInt() + new MessageChunkHeader(typ, id, totalSize, chunkSize, other, new InetSocketAddress(ip, port)) + } +} diff --git a/core/src/main/scala/spark/network/ReceiverTest.scala b/core/src/main/scala/spark/network/ReceiverTest.scala index a174d5f403..2bbc736f40 100644 --- a/core/src/main/scala/spark/network/ReceiverTest.scala +++ b/core/src/main/scala/spark/network/ReceiverTest.scala @@ -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. + */ + package spark.network import java.nio.ByteBuffer diff --git a/core/src/main/scala/spark/network/SenderTest.scala b/core/src/main/scala/spark/network/SenderTest.scala index a4ff69e4d2..542c54c36b 100644 --- a/core/src/main/scala/spark/network/SenderTest.scala +++ b/core/src/main/scala/spark/network/SenderTest.scala @@ -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. + */ + package spark.network import java.nio.ByteBuffer diff --git a/core/src/main/scala/spark/network/netty/FileHeader.scala b/core/src/main/scala/spark/network/netty/FileHeader.scala new file mode 100644 index 0000000000..bf46d32aa3 --- /dev/null +++ b/core/src/main/scala/spark/network/netty/FileHeader.scala @@ -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. + */ + +package spark.network.netty + +import io.netty.buffer._ + +import spark.Logging + +private[spark] class FileHeader ( + val fileLen: Int, + val blockId: String) 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)) + //padding the rest of header + if (FileHeader.HEADER_SIZE - buf.readableBytes > 0 ) { + buf.writeZero(FileHeader.HEADER_SIZE - buf.readableBytes) + } else { + throw new Exception("too long header " + buf.readableBytes) + logInfo("too long header") + } + buf + } + +} + +private[spark] object FileHeader { + + val HEADER_SIZE = 40 + + def getFileLenOffset = 0 + def getFileLenSize = Integer.SIZE/8 + + def create(buf: ByteBuf): FileHeader = { + val length = buf.readInt + val idLength = buf.readInt + val idBuilder = new StringBuilder(idLength) + for (i <- 1 to idLength) { + idBuilder += buf.readByte().asInstanceOf[Char] + } + val 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) + + } +} + diff --git a/core/src/main/scala/spark/network/netty/ShuffleCopier.scala b/core/src/main/scala/spark/network/netty/ShuffleCopier.scala new file mode 100644 index 0000000000..b01f6369f6 --- /dev/null +++ b/core/src/main/scala/spark/network/netty/ShuffleCopier.scala @@ -0,0 +1,118 @@ +/* + * 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 spark.network.netty + +import java.util.concurrent.Executors + +import io.netty.buffer.ByteBuf +import io.netty.channel.ChannelHandlerContext +import io.netty.util.CharsetUtil + +import spark.Logging +import spark.network.ConnectionManagerId + +import scala.collection.JavaConverters._ + + +private[spark] class ShuffleCopier extends Logging { + + def getBlock(host: String, port: Int, blockId: String, + resultCollectCallback: (String, Long, ByteBuf) => Unit) { + + val handler = new ShuffleCopier.ShuffleClientHandler(resultCollectCallback) + val connectTimeout = System.getProperty("spark.shuffle.netty.connect.timeout", "60000").toInt + val fc = new FileClient(handler, connectTimeout) + + try { + fc.init() + fc.connect(host, port) + fc.sendRequest(blockId) + fc.waitForClose() + fc.close() + } catch { + // Handle any socket-related exceptions in FileClient + case e: Exception => { + logError("Shuffle copy of block " + blockId + " from " + host + ":" + port + " failed", e) + handler.handleError(blockId) + } + } + } + + def getBlock(cmId: ConnectionManagerId, blockId: String, + resultCollectCallback: (String, Long, ByteBuf) => Unit) { + getBlock(cmId.host, cmId.port, blockId, resultCollectCallback) + } + + def getBlocks(cmId: ConnectionManagerId, + blocks: Seq[(String, Long)], + resultCollectCallback: (String, Long, ByteBuf) => Unit) { + + for ((blockId, size) <- blocks) { + getBlock(cmId, blockId, resultCollectCallback) + } + } +} + + +private[spark] object ShuffleCopier extends Logging { + + private class ShuffleClientHandler(resultCollectCallBack: (String, Long, ByteBuf) => Unit) + extends FileClientHandler with Logging { + + override def handle(ctx: ChannelHandlerContext, in: ByteBuf, header: FileHeader) { + logDebug("Received Block: " + header.blockId + " (" + header.fileLen + "B)"); + resultCollectCallBack(header.blockId, header.fileLen.toLong, in.readBytes(header.fileLen)) + } + + override def handleError(blockId: String) { + if (!isComplete) { + resultCollectCallBack(blockId, -1, null) + } + } + } + + def echoResultCollectCallBack(blockId: String, size: Long, content: ByteBuf) { + if (size != -1) { + logInfo("File: " + blockId + " content is : \" " + content.toString(CharsetUtil.UTF_8) + "\"") + } + } + + def main(args: Array[String]) { + if (args.length < 3) { + System.err.println("Usage: ShuffleCopier <host> <port> <shuffle_block_id> <threads>") + System.exit(1) + } + val host = args(0) + val port = args(1).toInt + val file = 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 { + Executors.callable(new Runnable() { + def run() { + val copier = new ShuffleCopier() + copier.getBlock(host, port, file, echoResultCollectCallBack) + } + }) + }).asJava + copiers.invokeAll(tasks) + copiers.shutdown + System.exit(0) + } +} diff --git a/core/src/main/scala/spark/network/netty/ShuffleSender.scala b/core/src/main/scala/spark/network/netty/ShuffleSender.scala new file mode 100644 index 0000000000..cdf88b03a0 --- /dev/null +++ b/core/src/main/scala/spark/network/netty/ShuffleSender.scala @@ -0,0 +1,70 @@ +/* + * 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 spark.network.netty + +import java.io.File + +import spark.Logging + + +private[spark] class ShuffleSender(portIn: Int, val pResolver: PathResolver) extends Logging { + + val server = new FileServer(pResolver, portIn) + server.start() + + def stop() { + server.stop() + } + + def port: Int = server.getPort() +} + + +/** + * An application for testing the shuffle sender as a standalone program. + */ +private[spark] object ShuffleSender { + + def main(args: Array[String]) { + if (args.length < 3) { + System.err.println( + "Usage: ShuffleSender <port> <subDirsPerLocalDir> <list of shuffle_block_directories>") + System.exit(1) + } + + val port = args(0).toInt + val subDirsPerLocalDir = args(1).toInt + val localDirs = args.drop(2).map(new File(_)) + + val pResovler = new PathResolver { + override def getAbsolutePath(blockId: String): String = { + if (!blockId.startsWith("shuffle_")) { + throw new Exception("Block " + blockId + " is not a shuffle block") + } + // Figure out which local directory it hashes to, and which subdirectory in that + val hash = math.abs(blockId.hashCode) + 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 sender = new ShuffleSender(port, pResovler) + } +} diff --git a/core/src/main/scala/spark/package.scala b/core/src/main/scala/spark/package.scala index 389ec4da3e..b244bfbf06 100644 --- a/core/src/main/scala/spark/package.scala +++ b/core/src/main/scala/spark/package.scala @@ -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. + */ + /** * Core Spark functionality. [[spark.SparkContext]] serves as the main entry point to Spark, while * [[spark.RDD]] is the data type representing a distributed collection, and provides most diff --git a/core/src/main/scala/spark/partial/ApproximateActionListener.scala b/core/src/main/scala/spark/partial/ApproximateActionListener.scala index de2dce161a..691d939150 100644 --- a/core/src/main/scala/spark/partial/ApproximateActionListener.scala +++ b/core/src/main/scala/spark/partial/ApproximateActionListener.scala @@ -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. + */ + package spark.partial import spark._ diff --git a/core/src/main/scala/spark/partial/ApproximateEvaluator.scala b/core/src/main/scala/spark/partial/ApproximateEvaluator.scala index 75713b2eaa..5eae144dfb 100644 --- a/core/src/main/scala/spark/partial/ApproximateEvaluator.scala +++ b/core/src/main/scala/spark/partial/ApproximateEvaluator.scala @@ -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. + */ + package spark.partial /** diff --git a/core/src/main/scala/spark/partial/BoundedDouble.scala b/core/src/main/scala/spark/partial/BoundedDouble.scala index 463c33d6e2..8bdbe6c012 100644 --- a/core/src/main/scala/spark/partial/BoundedDouble.scala +++ b/core/src/main/scala/spark/partial/BoundedDouble.scala @@ -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. + */ + package spark.partial /** diff --git a/core/src/main/scala/spark/partial/CountEvaluator.scala b/core/src/main/scala/spark/partial/CountEvaluator.scala index daf2c5170c..6aa92094eb 100644 --- a/core/src/main/scala/spark/partial/CountEvaluator.scala +++ b/core/src/main/scala/spark/partial/CountEvaluator.scala @@ -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. + */ + package spark.partial import cern.jet.stat.Probability diff --git a/core/src/main/scala/spark/partial/GroupedCountEvaluator.scala b/core/src/main/scala/spark/partial/GroupedCountEvaluator.scala index 01fbb8a11b..ebe2e5a1e3 100644 --- a/core/src/main/scala/spark/partial/GroupedCountEvaluator.scala +++ b/core/src/main/scala/spark/partial/GroupedCountEvaluator.scala @@ -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. + */ + package spark.partial import java.util.{HashMap => JHashMap} diff --git a/core/src/main/scala/spark/partial/GroupedMeanEvaluator.scala b/core/src/main/scala/spark/partial/GroupedMeanEvaluator.scala index c622df5220..2dadbbd5fb 100644 --- a/core/src/main/scala/spark/partial/GroupedMeanEvaluator.scala +++ b/core/src/main/scala/spark/partial/GroupedMeanEvaluator.scala @@ -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. + */ + package spark.partial import java.util.{HashMap => JHashMap} diff --git a/core/src/main/scala/spark/partial/GroupedSumEvaluator.scala b/core/src/main/scala/spark/partial/GroupedSumEvaluator.scala index 20fa55cff2..ae2b63f7cb 100644 --- a/core/src/main/scala/spark/partial/GroupedSumEvaluator.scala +++ b/core/src/main/scala/spark/partial/GroupedSumEvaluator.scala @@ -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. + */ + package spark.partial import java.util.{HashMap => JHashMap} diff --git a/core/src/main/scala/spark/partial/MeanEvaluator.scala b/core/src/main/scala/spark/partial/MeanEvaluator.scala index 762c85400d..5ddcad7075 100644 --- a/core/src/main/scala/spark/partial/MeanEvaluator.scala +++ b/core/src/main/scala/spark/partial/MeanEvaluator.scala @@ -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. + */ + package spark.partial import cern.jet.stat.Probability diff --git a/core/src/main/scala/spark/partial/PartialResult.scala b/core/src/main/scala/spark/partial/PartialResult.scala index 200ed4ea1e..922a9f9bc6 100644 --- a/core/src/main/scala/spark/partial/PartialResult.scala +++ b/core/src/main/scala/spark/partial/PartialResult.scala @@ -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. + */ + package spark.partial class PartialResult[R](initialVal: R, isFinal: Boolean) { diff --git a/core/src/main/scala/spark/partial/StudentTCacher.scala b/core/src/main/scala/spark/partial/StudentTCacher.scala index 443abba5cd..f3bb987d46 100644 --- a/core/src/main/scala/spark/partial/StudentTCacher.scala +++ b/core/src/main/scala/spark/partial/StudentTCacher.scala @@ -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. + */ + package spark.partial import cern.jet.stat.Probability diff --git a/core/src/main/scala/spark/partial/SumEvaluator.scala b/core/src/main/scala/spark/partial/SumEvaluator.scala index 58fb60f441..4083abef03 100644 --- a/core/src/main/scala/spark/partial/SumEvaluator.scala +++ b/core/src/main/scala/spark/partial/SumEvaluator.scala @@ -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. + */ + package spark.partial import cern.jet.stat.Probability diff --git a/core/src/main/scala/spark/rdd/BlockRDD.scala b/core/src/main/scala/spark/rdd/BlockRDD.scala index 7348c4f15b..0ebb722d73 100644 --- a/core/src/main/scala/spark/rdd/BlockRDD.scala +++ b/core/src/main/scala/spark/rdd/BlockRDD.scala @@ -1,7 +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 spark.rdd -import scala.collection.mutable.HashMap import spark.{RDD, SparkContext, SparkEnv, Partition, TaskContext} +import spark.storage.BlockManager private[spark] class BlockRDDPartition(val blockId: String, idx: Int) extends Partition { val index = idx @@ -11,12 +28,7 @@ private[spark] class BlockRDD[T: ClassManifest](sc: SparkContext, @transient blockIds: Array[String]) extends RDD[T](sc, Nil) { - @transient lazy val locations_ = { - val blockManager = SparkEnv.get.blockManager - /*val locations = blockIds.map(id => blockManager.getLocations(id))*/ - val locations = blockManager.getLocations(blockIds) - HashMap(blockIds.zip(locations):_*) - } + @transient lazy val locations_ = BlockManager.blockIdsToExecutorLocations(blockIds, SparkEnv.get) override def getPartitions: Array[Partition] = (0 until blockIds.size).map(i => { new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition] diff --git a/core/src/main/scala/spark/rdd/CartesianRDD.scala b/core/src/main/scala/spark/rdd/CartesianRDD.scala index 38600b8be4..150e5bca29 100644 --- a/core/src/main/scala/spark/rdd/CartesianRDD.scala +++ b/core/src/main/scala/spark/rdd/CartesianRDD.scala @@ -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. + */ + package spark.rdd import java.io.{ObjectOutputStream, IOException} diff --git a/core/src/main/scala/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/spark/rdd/CheckpointRDD.scala index 9e37bdf659..6794e0e201 100644 --- a/core/src/main/scala/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/spark/rdd/CheckpointRDD.scala @@ -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. + */ + package spark.rdd import spark._ @@ -8,6 +25,7 @@ import org.apache.hadoop.util.ReflectionUtils import org.apache.hadoop.fs.Path import java.io.{File, IOException, EOFException} import java.text.NumberFormat +import spark.deploy.SparkHadoopUtil private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {} @@ -21,13 +39,20 @@ class CheckpointRDD[T: ClassManifest](sc: SparkContext, val checkpointPath: Stri @transient val fs = new Path(checkpointPath).getFileSystem(sc.hadoopConfiguration) override def getPartitions: Array[Partition] = { - val dirContents = fs.listStatus(new Path(checkpointPath)) - val partitionFiles = dirContents.map(_.getPath.toString).filter(_.contains("part-")).sorted - val numPartitions = partitionFiles.size - if (numPartitions > 0 && (! partitionFiles(0).endsWith(CheckpointRDD.splitIdToFile(0)) || - ! partitionFiles(numPartitions-1).endsWith(CheckpointRDD.splitIdToFile(numPartitions-1)))) { - throw new SparkException("Invalid checkpoint directory: " + checkpointPath) - } + val cpath = new Path(checkpointPath) + val numPartitions = + // listStatus can throw exception if path does not exist. + if (fs.exists(cpath)) { + val dirContents = fs.listStatus(cpath) + val partitionFiles = dirContents.map(_.getPath.toString).filter(_.contains("part-")).sorted + val numPart = partitionFiles.size + if (numPart > 0 && (! partitionFiles(0).endsWith(CheckpointRDD.splitIdToFile(0)) || + ! partitionFiles(numPart-1).endsWith(CheckpointRDD.splitIdToFile(numPart-1)))) { + throw new SparkException("Invalid checkpoint directory: " + checkpointPath) + } + numPart + } else 0 + Array.tabulate(numPartitions)(i => new CheckpointRDDPartition(i)) } @@ -35,7 +60,7 @@ class CheckpointRDD[T: ClassManifest](sc: SparkContext, val checkpointPath: Stri checkpointData.get.cpFile = Some(checkpointPath) override def getPreferredLocations(split: Partition): Seq[String] = { - val status = fs.getFileStatus(new Path(checkpointPath)) + val status = fs.getFileStatus(new Path(checkpointPath, CheckpointRDD.splitIdToFile(split.index))) val locations = fs.getFileBlockLocations(status, 0, status.getLen) locations.headOption.toList.flatMap(_.getHosts).filter(_ != "localhost") } @@ -58,7 +83,7 @@ private[spark] object CheckpointRDD extends Logging { def writeToFile[T](path: String, blockSize: Int = -1)(ctx: TaskContext, iterator: Iterator[T]) { val outputDir = new Path(path) - val fs = outputDir.getFileSystem(new Configuration()) + val fs = outputDir.getFileSystem(SparkHadoopUtil.newConfiguration()) val finalOutputName = splitIdToFile(ctx.splitId) val finalOutputPath = new Path(outputDir, finalOutputName) @@ -83,6 +108,7 @@ private[spark] object CheckpointRDD extends Logging { if (!fs.rename(tempOutputPath, finalOutputPath)) { if (!fs.exists(finalOutputPath)) { + logInfo("Deleting tempOutputPath " + tempOutputPath) fs.delete(tempOutputPath, false) throw new IOException("Checkpoint failed: failed to save output of task: " + ctx.attemptId + " and final output path does not exist") @@ -95,7 +121,7 @@ private[spark] object CheckpointRDD extends Logging { } def readFromFile[T](path: Path, context: TaskContext): Iterator[T] = { - val fs = path.getFileSystem(new Configuration()) + val fs = path.getFileSystem(SparkHadoopUtil.newConfiguration()) val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt val fileInputStream = fs.open(path, bufferSize) val serializer = SparkEnv.get.serializer.newInstance() @@ -117,11 +143,11 @@ 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(new Configuration()) + val fs = path.getFileSystem(SparkHadoopUtil.newConfiguration()) sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, 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") - fs.delete(path) + fs.delete(path, true) } } diff --git a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala index 9213513e80..c540cd36eb 100644 --- a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala +++ b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala @@ -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. + */ + package spark.rdd import java.io.{ObjectOutputStream, IOException} @@ -6,7 +23,7 @@ import java.util.{HashMap => JHashMap} import scala.collection.JavaConversions import scala.collection.mutable.ArrayBuffer -import spark.{Aggregator, Logging, Partition, Partitioner, RDD, SparkEnv, TaskContext} +import spark.{Aggregator, Partition, Partitioner, RDD, SparkEnv, TaskContext} import spark.{Dependency, OneToOneDependency, ShuffleDependency} @@ -29,7 +46,7 @@ private[spark] case class NarrowCoGroupSplitDep( private[spark] case class ShuffleCoGroupSplitDep(shuffleId: Int) extends CoGroupSplitDep private[spark] -class CoGroupPartition(idx: Int, val deps: Seq[CoGroupSplitDep]) +class CoGroupPartition(idx: Int, val deps: Array[CoGroupSplitDep]) extends Partition with Serializable { override val index: Int = idx override def hashCode(): Int = idx @@ -49,12 +66,17 @@ private[spark] class CoGroupAggregator * * @param rdds parent RDDs. * @param part partitioner used to partition the shuffle output. - * @param mapSideCombine flag indicating whether to merge values before shuffle step. + * @param mapSideCombine flag indicating whether to merge values before shuffle step. If the flag + * is on, Spark does an extra pass over the data on the map side to merge + * all values belonging to the same key together. This can reduce the amount + * of data shuffled if and only if the number of distinct keys is very small, + * and the ratio of key size to value size is also very small. */ class CoGroupedRDD[K]( @transient var rdds: Seq[RDD[(K, _)]], part: Partitioner, - val mapSideCombine: Boolean = true) + val mapSideCombine: Boolean = false, + val serializerClass: String = null) extends RDD[(K, Seq[Seq[_]])](rdds.head.context, Nil) { private val aggr = new CoGroupAggregator @@ -68,9 +90,9 @@ class CoGroupedRDD[K]( logInfo("Adding shuffle dependency with " + rdd) if (mapSideCombine) { val mapSideCombinedRDD = rdd.mapPartitions(aggr.combineValuesByKey(_), true) - new ShuffleDependency[Any, ArrayBuffer[Any]](mapSideCombinedRDD, part) + new ShuffleDependency[Any, ArrayBuffer[Any]](mapSideCombinedRDD, part, serializerClass) } else { - new ShuffleDependency[Any, Any](rdd.asInstanceOf[RDD[(Any, Any)]], part) + new ShuffleDependency[Any, Any](rdd.asInstanceOf[RDD[(Any, Any)]], part, serializerClass) } } } @@ -88,7 +110,7 @@ class CoGroupedRDD[K]( case _ => new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i)) } - }.toList) + }.toArray) } array } @@ -112,6 +134,7 @@ class CoGroupedRDD[K]( } } + val ser = SparkEnv.get.serializerManager.get(serializerClass) for ((dep, depNum) <- split.deps.zipWithIndex) dep match { case NarrowCoGroupSplitDep(rdd, _, itsSplit) => { // Read them from the parent @@ -124,12 +147,12 @@ class CoGroupedRDD[K]( val fetcher = SparkEnv.get.shuffleFetcher if (mapSideCombine) { // With map side combine on, for each key, the shuffle fetcher returns a list of values. - fetcher.fetch[K, Seq[Any]](shuffleId, split.index, context.taskMetrics).foreach { + fetcher.fetch[K, Seq[Any]](shuffleId, split.index, context.taskMetrics, ser).foreach { case (key, values) => getSeq(key)(depNum) ++= values } } else { // With map side combine off, for each key the shuffle fetcher returns a single value. - fetcher.fetch[K, Any](shuffleId, split.index, context.taskMetrics).foreach { + fetcher.fetch[K, Any](shuffleId, split.index, context.taskMetrics, ser).foreach { case (key, value) => getSeq(key)(depNum) += value } } diff --git a/core/src/main/scala/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/spark/rdd/CoalescedRDD.scala index 6d862c0c28..2b5bf18541 100644 --- a/core/src/main/scala/spark/rdd/CoalescedRDD.scala +++ b/core/src/main/scala/spark/rdd/CoalescedRDD.scala @@ -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. + */ + package spark.rdd import spark.{Dependency, OneToOneDependency, NarrowDependency, RDD, Partition, TaskContext} diff --git a/core/src/main/scala/spark/rdd/EmptyRDD.scala b/core/src/main/scala/spark/rdd/EmptyRDD.scala new file mode 100644 index 0000000000..d7d4db5d30 --- /dev/null +++ b/core/src/main/scala/spark/rdd/EmptyRDD.scala @@ -0,0 +1,33 @@ +/* + * 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 spark.rdd + +import spark.{RDD, SparkContext, SparkEnv, Partition, TaskContext} + + +/** + * An RDD that is empty, i.e. has no element in it. + */ +class EmptyRDD[T: ClassManifest](sc: SparkContext) extends RDD[T](sc, Nil) { + + override def getPartitions: Array[Partition] = Array.empty + + override def compute(split: Partition, context: TaskContext): Iterator[T] = { + throw new UnsupportedOperationException("empty RDD") + } +} diff --git a/core/src/main/scala/spark/rdd/FilteredRDD.scala b/core/src/main/scala/spark/rdd/FilteredRDD.scala index c84ec39d21..783508cfd1 100644 --- a/core/src/main/scala/spark/rdd/FilteredRDD.scala +++ b/core/src/main/scala/spark/rdd/FilteredRDD.scala @@ -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. + */ + package spark.rdd import spark.{OneToOneDependency, RDD, Partition, TaskContext} diff --git a/core/src/main/scala/spark/rdd/FlatMappedRDD.scala b/core/src/main/scala/spark/rdd/FlatMappedRDD.scala index 8ebc778925..ed75eac3ff 100644 --- a/core/src/main/scala/spark/rdd/FlatMappedRDD.scala +++ b/core/src/main/scala/spark/rdd/FlatMappedRDD.scala @@ -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. + */ + package spark.rdd import spark.{RDD, Partition, TaskContext} diff --git a/core/src/main/scala/spark/rdd/GlommedRDD.scala b/core/src/main/scala/spark/rdd/GlommedRDD.scala index e16c7ba881..1573f8a289 100644 --- a/core/src/main/scala/spark/rdd/GlommedRDD.scala +++ b/core/src/main/scala/spark/rdd/GlommedRDD.scala @@ -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. + */ + package spark.rdd import spark.{RDD, Partition, TaskContext} diff --git a/core/src/main/scala/spark/rdd/HadoopRDD.scala b/core/src/main/scala/spark/rdd/HadoopRDD.scala index cbf5512e24..fd00d59c77 100644 --- a/core/src/main/scala/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/spark/rdd/HadoopRDD.scala @@ -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. + */ + package spark.rdd import java.io.EOFException @@ -15,6 +32,7 @@ import org.apache.hadoop.mapred.RecordReader import org.apache.hadoop.mapred.Reporter import org.apache.hadoop.util.ReflectionUtils +import spark.deploy.SparkHadoopUtil import spark.{Dependency, Logging, Partition, RDD, SerializableWritable, SparkContext, TaskContext} import spark.util.NextIterator import org.apache.hadoop.conf.Configurable @@ -50,6 +68,7 @@ class HadoopRDD[K, V]( private val confBroadcast = sc.broadcast(new SerializableWritable(conf)) override def getPartitions: Array[Partition] = { + SparkHadoopUtil.addCredentials(conf); val inputFormat = createInputFormat(conf) if (inputFormat.isInstanceOf[Configurable]) { inputFormat.asInstanceOf[Configurable].setConf(conf) @@ -69,6 +88,7 @@ class HadoopRDD[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 diff --git a/core/src/main/scala/spark/rdd/JdbcRDD.scala b/core/src/main/scala/spark/rdd/JdbcRDD.scala new file mode 100644 index 0000000000..59132437d2 --- /dev/null +++ b/core/src/main/scala/spark/rdd/JdbcRDD.scala @@ -0,0 +1,120 @@ +/* + * 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 spark.rdd + +import java.sql.{Connection, ResultSet} + +import spark.{Logging, Partition, RDD, SparkContext, TaskContext} +import spark.util.NextIterator + +private[spark] class JdbcPartition(idx: Int, val lower: Long, val upper: Long) extends Partition { + override def index = idx +} + +/** + * An RDD that executes an SQL query on a JDBC connection and reads results. + * For usage example, see test case JdbcRDDSuite. + * + * @param getConnection a function that returns an open Connection. + * The RDD takes care of closing the connection. + * @param sql the text of the query. + * The query must contain two ? placeholders for parameters used to partition the results. + * E.g. "select title, author from books where ? <= id and id <= ?" + * @param lowerBound the minimum value of the first placeholder + * @param upperBound the maximum value of the second placeholder + * The lower and upper bounds are inclusive. + * @param numPartitions the number of partitions. + * Given a lowerBound of 1, an upperBound of 20, and a numPartitions of 2, + * the query would be executed twice, once with (1, 10) and once with (11, 20) + * @param mapRow a function from a ResultSet to a single row of the desired result type(s). + * 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]( + sc: SparkContext, + getConnection: () => Connection, + sql: String, + lowerBound: Long, + upperBound: Long, + numPartitions: Int, + mapRow: (ResultSet) => T = JdbcRDD.resultSetToObjectArray _) + extends RDD[T](sc, Nil) with Logging { + + override def getPartitions: Array[Partition] = { + // bounds are inclusive, hence the + 1 here and - 1 on end + val length = 1 + upperBound - lowerBound + (0 until numPartitions).map(i => { + val start = lowerBound + ((i * length) / numPartitions).toLong + val end = lowerBound + (((i + 1) * length) / numPartitions).toLong - 1 + new JdbcPartition(i, start, end) + }).toArray + } + + override def compute(thePart: Partition, context: TaskContext) = new NextIterator[T] { + context.addOnCompleteCallback{ () => closeIfNeeded() } + val part = thePart.asInstanceOf[JdbcPartition] + val conn = getConnection() + val stmt = conn.prepareStatement(sql, ResultSet.TYPE_FORWARD_ONLY, ResultSet.CONCUR_READ_ONLY) + + // setFetchSize(Integer.MIN_VALUE) is a mysql driver specific way to force streaming results, + // rather than pulling entire resultset into memory. + // see http://dev.mysql.com/doc/refman/5.0/en/connector-j-reference-implementation-notes.html + if (conn.getMetaData.getURL.matches("jdbc:mysql:.*")) { + stmt.setFetchSize(Integer.MIN_VALUE) + logInfo("statement fetch size set to: " + stmt.getFetchSize + " to force MySQL streaming ") + } + + stmt.setLong(1, part.lower) + stmt.setLong(2, part.upper) + val rs = stmt.executeQuery() + + override def getNext: T = { + if (rs.next()) { + mapRow(rs) + } else { + finished = true + null.asInstanceOf[T] + } + } + + override def close() { + try { + if (null != rs && ! rs.isClosed()) rs.close() + } catch { + case e: Exception => logWarning("Exception closing resultset", e) + } + try { + if (null != stmt && ! stmt.isClosed()) stmt.close() + } catch { + case e: Exception => logWarning("Exception closing statement", e) + } + try { + if (null != conn && ! stmt.isClosed()) conn.close() + logInfo("closed connection") + } catch { + case e: Exception => logWarning("Exception closing connection", e) + } + } + } +} + +object JdbcRDD { + def resultSetToObjectArray(rs: ResultSet) = { + Array.tabulate[Object](rs.getMetaData.getColumnCount)(i => rs.getObject(i + 1)) + } +} diff --git a/core/src/main/scala/spark/rdd/MapPartitionsRDD.scala b/core/src/main/scala/spark/rdd/MapPartitionsRDD.scala index d283c5b2bb..af8f0a112f 100644 --- a/core/src/main/scala/spark/rdd/MapPartitionsRDD.scala +++ b/core/src/main/scala/spark/rdd/MapPartitionsRDD.scala @@ -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. + */ + package spark.rdd import spark.{RDD, Partition, TaskContext} diff --git a/core/src/main/scala/spark/rdd/MapPartitionsWithIndexRDD.scala b/core/src/main/scala/spark/rdd/MapPartitionsWithIndexRDD.scala index afb7504ba1..3b4e9518fd 100644 --- a/core/src/main/scala/spark/rdd/MapPartitionsWithIndexRDD.scala +++ b/core/src/main/scala/spark/rdd/MapPartitionsWithIndexRDD.scala @@ -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. + */ + package spark.rdd import spark.{RDD, Partition, TaskContext} diff --git a/core/src/main/scala/spark/rdd/MappedRDD.scala b/core/src/main/scala/spark/rdd/MappedRDD.scala index af07311b6d..8b411dd85d 100644 --- a/core/src/main/scala/spark/rdd/MappedRDD.scala +++ b/core/src/main/scala/spark/rdd/MappedRDD.scala @@ -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. + */ + package spark.rdd import spark.{RDD, Partition, TaskContext} diff --git a/core/src/main/scala/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/spark/rdd/NewHadoopRDD.scala index bdd974590a..0b71608169 100644 --- a/core/src/main/scala/spark/rdd/NewHadoopRDD.scala +++ b/core/src/main/scala/spark/rdd/NewHadoopRDD.scala @@ -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. + */ + package spark.rdd import java.text.SimpleDateFormat @@ -56,8 +73,9 @@ class NewHadoopRDD[K, V]( 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 = new TaskAttemptID(jobtrackerId, id, true, split.index, 0) + val attemptId = newTaskAttemptID(jobtrackerId, id, true, split.index, 0) val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId) val format = inputFormatClass.newInstance if (format.isInstanceOf[Configurable]) { diff --git a/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala index 07585a88ce..16ba0c26f8 100644 --- a/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala +++ b/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala @@ -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. + */ + package spark.rdd import scala.collection.immutable.NumericRange diff --git a/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala index 41ff62dd22..d8700becb0 100644 --- a/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala +++ b/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala @@ -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. + */ + package spark.rdd import spark.{NarrowDependency, RDD, SparkEnv, Partition, TaskContext} @@ -16,8 +33,9 @@ class PruneDependency[T](rdd: RDD[T], @transient partitionFilterFunc: Int => Boo extends NarrowDependency[T](rdd) { @transient - val partitions: Array[Partition] = rdd.partitions.filter(s => partitionFilterFunc(s.index)) - .zipWithIndex.map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition } + val partitions: Array[Partition] = rdd.partitions.zipWithIndex + .filter(s => partitionFilterFunc(s._2)) + .map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition } override def getParents(partitionId: Int) = List(partitions(partitionId).index) } diff --git a/core/src/main/scala/spark/rdd/PipedRDD.scala b/core/src/main/scala/spark/rdd/PipedRDD.scala index 962a1b21ad..2cefdc78b0 100644 --- a/core/src/main/scala/spark/rdd/PipedRDD.scala +++ b/core/src/main/scala/spark/rdd/PipedRDD.scala @@ -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. + */ + package spark.rdd import java.io.PrintWriter @@ -9,6 +26,7 @@ import scala.collection.mutable.ArrayBuffer import scala.io.Source import spark.{RDD, SparkEnv, Partition, TaskContext} +import spark.broadcast.Broadcast /** @@ -18,14 +36,21 @@ import spark.{RDD, SparkEnv, Partition, TaskContext} class PipedRDD[T: ClassManifest]( prev: RDD[T], command: Seq[String], - envVars: Map[String, String]) + envVars: Map[String, String], + printPipeContext: (String => Unit) => Unit, + printRDDElement: (T, String => Unit) => Unit) extends RDD[String](prev) { - def this(prev: RDD[T], command: Seq[String]) = this(prev, command, Map()) - // 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(prev: RDD[T], command: String) = this(prev, PipedRDD.tokenize(command)) + def this( + prev: RDD[T], + command: String, + envVars: Map[String, String] = Map(), + printPipeContext: (String => Unit) => Unit = null, + printRDDElement: (T, String => Unit) => Unit = null) = + this(prev, PipedRDD.tokenize(command), envVars, printPipeContext, printRDDElement) + override def getPartitions: Array[Partition] = firstParent[T].partitions @@ -52,8 +77,17 @@ class PipedRDD[T: ClassManifest]( override def run() { SparkEnv.set(env) val out = new PrintWriter(proc.getOutputStream) + + // input the pipe context firstly + if (printPipeContext != null) { + printPipeContext(out.println(_)) + } for (elem <- firstParent[T].iterator(split, context)) { - out.println(elem) + if (printRDDElement != null) { + printRDDElement(elem, out.println(_)) + } else { + out.println(elem) + } } out.close() } diff --git a/core/src/main/scala/spark/rdd/SampledRDD.scala b/core/src/main/scala/spark/rdd/SampledRDD.scala index 243673f151..574c9b141d 100644 --- a/core/src/main/scala/spark/rdd/SampledRDD.scala +++ b/core/src/main/scala/spark/rdd/SampledRDD.scala @@ -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. + */ + package spark.rdd import java.util.Random diff --git a/core/src/main/scala/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/spark/rdd/ShuffledRDD.scala index 51f02409b6..0137f80953 100644 --- a/core/src/main/scala/spark/rdd/ShuffledRDD.scala +++ b/core/src/main/scala/spark/rdd/ShuffledRDD.scala @@ -1,8 +1,26 @@ +/* + * 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 spark.rdd import spark.{Partitioner, RDD, SparkEnv, ShuffleDependency, Partition, TaskContext} import spark.SparkContext._ + private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition { override val index = idx override def hashCode(): Int = idx @@ -12,13 +30,15 @@ private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition { * The resulting RDD from a shuffle (e.g. repartitioning of data). * @param prev the parent RDD. * @param part the partitioner used to partition the RDD + * @param serializerClass class name of the serializer to use. * @tparam K the key class. * @tparam V the value class. */ class ShuffledRDD[K, V]( - prev: RDD[(K, V)], - part: Partitioner) - extends RDD[(K, V)](prev.context, List(new ShuffleDependency(prev, part))) { + @transient prev: RDD[(K, V)], + part: Partitioner, + serializerClass: String = null) + extends RDD[(K, V)](prev.context, List(new ShuffleDependency(prev, part, serializerClass))) { override val partitioner = Some(part) @@ -28,6 +48,7 @@ class ShuffledRDD[K, V]( override def compute(split: Partition, context: TaskContext): Iterator[(K, V)] = { val shuffledId = dependencies.head.asInstanceOf[ShuffleDependency[K, V]].shuffleId - SparkEnv.get.shuffleFetcher.fetch[K, V](shuffledId, split.index, context.taskMetrics) + SparkEnv.get.shuffleFetcher.fetch[K, V](shuffledId, split.index, context.taskMetrics, + SparkEnv.get.serializerManager.get(serializerClass)) } } diff --git a/core/src/main/scala/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/spark/rdd/SubtractedRDD.scala index 0a02561062..0402b9f250 100644 --- a/core/src/main/scala/spark/rdd/SubtractedRDD.scala +++ b/core/src/main/scala/spark/rdd/SubtractedRDD.scala @@ -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. + */ + package spark.rdd import java.util.{HashMap => JHashMap} @@ -12,6 +29,7 @@ import spark.SparkEnv import spark.ShuffleDependency import spark.OneToOneDependency + /** * An optimized version of cogroup for set difference/subtraction. * @@ -31,7 +49,9 @@ import spark.OneToOneDependency private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassManifest]( @transient var rdd1: RDD[(K, V)], @transient var rdd2: RDD[(K, W)], - part: Partitioner) extends RDD[(K, V)](rdd1.context, Nil) { + part: Partitioner, + val serializerClass: String = null) + extends RDD[(K, V)](rdd1.context, Nil) { override def getDependencies: Seq[Dependency[_]] = { Seq(rdd1, rdd2).map { rdd => @@ -40,7 +60,7 @@ private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassM new OneToOneDependency(rdd) } else { logInfo("Adding shuffle dependency with " + rdd) - new ShuffleDependency(rdd.asInstanceOf[RDD[(K, Any)]], part) + new ShuffleDependency(rdd.asInstanceOf[RDD[(K, Any)]], part, serializerClass) } } } @@ -56,7 +76,7 @@ private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassM case _ => new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i)) } - }.toList) + }.toArray) } array } @@ -65,6 +85,7 @@ 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 map = new JHashMap[K, ArrayBuffer[V]] def getSeq(k: K): ArrayBuffer[V] = { val seq = map.get(k) @@ -77,12 +98,16 @@ private[spark] class SubtractedRDD[K: ClassManifest, V: ClassManifest, W: ClassM } } def integrate(dep: CoGroupSplitDep, op: ((K, V)) => Unit) = dep match { - case NarrowCoGroupSplitDep(rdd, _, itsSplit) => + case NarrowCoGroupSplitDep(rdd, _, itsSplit) => { for (t <- rdd.iterator(itsSplit, context)) op(t.asInstanceOf[(K, V)]) - case ShuffleCoGroupSplitDep(shuffleId) => - for (t <- SparkEnv.get.shuffleFetcher.fetch(shuffleId, partition.index, context.taskMetrics)) + } + case ShuffleCoGroupSplitDep(shuffleId) => { + val iter = SparkEnv.get.shuffleFetcher.fetch(shuffleId, partition.index, + context.taskMetrics, serializer) + for (t <- iter) op(t.asInstanceOf[(K, V)]) + } } // the first dep is rdd1; add all values to the map integrate(partition.deps(0), t => getSeq(t._1) += t._2) diff --git a/core/src/main/scala/spark/rdd/UnionRDD.scala b/core/src/main/scala/spark/rdd/UnionRDD.scala index 2c52a67e22..2776826f18 100644 --- a/core/src/main/scala/spark/rdd/UnionRDD.scala +++ b/core/src/main/scala/spark/rdd/UnionRDD.scala @@ -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. + */ + package spark.rdd import scala.collection.mutable.ArrayBuffer diff --git a/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala new file mode 100644 index 0000000000..6a4fa13ad6 --- /dev/null +++ b/core/src/main/scala/spark/rdd/ZippedPartitionsRDD.scala @@ -0,0 +1,155 @@ +/* + * 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 spark.rdd + +import spark.{Utils, OneToOneDependency, RDD, SparkContext, Partition, TaskContext} +import java.io.{ObjectOutputStream, IOException} + +private[spark] class ZippedPartitionsPartition( + idx: Int, + @transient rdds: Seq[RDD[_]]) + extends Partition { + + override val index: Int = idx + var partitionValues = rdds.map(rdd => rdd.partitions(idx)) + def partitions = partitionValues + + @throws(classOf[IOException]) + private def writeObject(oos: ObjectOutputStream) { + // Update the reference to parent split at the time of task serialization + partitionValues = rdds.map(rdd => rdd.partitions(idx)) + oos.defaultWriteObject() + } +} + +abstract class ZippedPartitionsBaseRDD[V: ClassManifest]( + sc: SparkContext, + var rdds: Seq[RDD[_]]) + extends RDD[V](sc, rdds.map(x => new OneToOneDependency(x))) { + + override def getPartitions: Array[Partition] = { + val sizes = rdds.map(x => x.partitions.size) + if (!sizes.forall(x => x == sizes(0))) { + 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 + } + + override def getPreferredLocations(s: Partition): Seq[String] = { + // Note that as number of rdd's increase and/or number of slaves in cluster increase, the computed preferredLocations below + // become diminishingly small : so we might need to look at alternate strategies to alleviate this. + // If there are no (or very small number of preferred locations), we will end up transferred the blocks to 'any' node in the + // cluster - paying with n/w and cache cost. + // Maybe pick a node which figures max amount of time ? + // Choose node which is hosting 'larger' of some subset of blocks ? + // Look at rack locality to ensure chosen host is atleast rack local to both hosting node ?, etc (would be good to defer this if possible) + val splits = s.asInstanceOf[ZippedPartitionsPartition].partitions + val rddSplitZip = rdds.zip(splits) + + // exact match. + val exactMatchPreferredLocations = rddSplitZip.map(x => x._1.preferredLocations(x._2)) + val exactMatchLocations = exactMatchPreferredLocations.reduce((x, y) => x.intersect(y)) + + // Remove exact match and then do host local match. + val exactMatchHosts = exactMatchLocations.map(Utils.parseHostPort(_)._1) + val matchPreferredHosts = exactMatchPreferredLocations.map(locs => locs.map(Utils.parseHostPort(_)._1)) + .reduce((x, y) => x.intersect(y)) + val otherNodeLocalLocations = matchPreferredHosts.filter { s => !exactMatchHosts.contains(s) } + + otherNodeLocalLocations ++ exactMatchLocations + } + + override def clearDependencies() { + super.clearDependencies() + rdds = null + } +} + +class ZippedPartitionsRDD2[A: ClassManifest, B: ClassManifest, V: ClassManifest]( + sc: SparkContext, + f: (Iterator[A], Iterator[B]) => Iterator[V], + var rdd1: RDD[A], + var rdd2: RDD[B]) + extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2)) { + + override def compute(s: Partition, context: TaskContext): Iterator[V] = { + val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions + f(rdd1.iterator(partitions(0), context), rdd2.iterator(partitions(1), context)) + } + + override def clearDependencies() { + super.clearDependencies() + rdd1 = null + rdd2 = null + } +} + +class ZippedPartitionsRDD3 + [A: ClassManifest, B: ClassManifest, C: ClassManifest, V: ClassManifest]( + 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)) { + + override def compute(s: Partition, context: TaskContext): Iterator[V] = { + val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions + f(rdd1.iterator(partitions(0), context), + rdd2.iterator(partitions(1), context), + rdd3.iterator(partitions(2), context)) + } + + override def clearDependencies() { + super.clearDependencies() + rdd1 = null + rdd2 = null + rdd3 = null + } +} + +class ZippedPartitionsRDD4 + [A: ClassManifest, B: ClassManifest, C: ClassManifest, D:ClassManifest, V: ClassManifest]( + 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)) { + + override def compute(s: Partition, context: TaskContext): Iterator[V] = { + val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions + f(rdd1.iterator(partitions(0), context), + rdd2.iterator(partitions(1), context), + rdd3.iterator(partitions(2), context), + rdd4.iterator(partitions(3), context)) + } + + override def clearDependencies() { + super.clearDependencies() + rdd1 = null + rdd2 = null + rdd3 = null + rdd4 = null + } +} diff --git a/core/src/main/scala/spark/rdd/ZippedRDD.scala b/core/src/main/scala/spark/rdd/ZippedRDD.scala index e80ec17aa5..b1c43b3195 100644 --- a/core/src/main/scala/spark/rdd/ZippedRDD.scala +++ b/core/src/main/scala/spark/rdd/ZippedRDD.scala @@ -1,6 +1,23 @@ +/* + * 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 spark.rdd -import spark.{OneToOneDependency, RDD, SparkContext, Partition, TaskContext} +import spark.{Utils, OneToOneDependency, RDD, SparkContext, Partition, TaskContext} import java.io.{ObjectOutputStream, IOException} @@ -10,17 +27,17 @@ private[spark] class ZippedPartition[T: ClassManifest, U: ClassManifest]( @transient rdd2: RDD[U] ) extends Partition { - var split1 = rdd1.partitions(idx) - var split2 = rdd1.partitions(idx) + var partition1 = rdd1.partitions(idx) + var partition2 = rdd2.partitions(idx) override val index: Int = idx - def splits = (split1, split2) + def partitions = (partition1, partition2) @throws(classOf[IOException]) private def writeObject(oos: ObjectOutputStream) { - // Update the reference to parent split at the time of task serialization - split1 = rdd1.partitions(idx) - split2 = rdd2.partitions(idx) + // Update the reference to parent partition at the time of task serialization + partition1 = rdd1.partitions(idx) + partition2 = rdd2.partitions(idx) oos.defaultWriteObject() } } @@ -43,13 +60,32 @@ class ZippedRDD[T: ClassManifest, U: ClassManifest]( } override def compute(s: Partition, context: TaskContext): Iterator[(T, U)] = { - val (split1, split2) = s.asInstanceOf[ZippedPartition[T, U]].splits - rdd1.iterator(split1, context).zip(rdd2.iterator(split2, context)) + val (partition1, partition2) = s.asInstanceOf[ZippedPartition[T, U]].partitions + rdd1.iterator(partition1, context).zip(rdd2.iterator(partition2, context)) } override def getPreferredLocations(s: Partition): Seq[String] = { - val (split1, split2) = s.asInstanceOf[ZippedPartition[T, U]].splits - rdd1.preferredLocations(split1).intersect(rdd2.preferredLocations(split2)) + // Note that as number of slaves in cluster increase, the computed preferredLocations can become small : so we might need + // to look at alternate strategies to alleviate this. (If there are no (or very small number of preferred locations), we + // will end up transferred the blocks to 'any' node in the cluster - paying with n/w and cache cost. + // Maybe pick one or the other ? (so that atleast one block is local ?). + // Choose node which is hosting 'larger' of the blocks ? + // Look at rack locality to ensure chosen host is atleast rack local to both hosting node ?, etc (would be good to defer this if possible) + val (partition1, partition2) = s.asInstanceOf[ZippedPartition[T, U]].partitions + val pref1 = rdd1.preferredLocations(partition1) + val pref2 = rdd2.preferredLocations(partition2) + + // exact match - instance local and host local. + val exactMatchLocations = pref1.intersect(pref2) + + // remove locations which are already handled via exactMatchLocations, and intersect where both partitions are node local. + val otherNodeLocalPref1 = pref1.filter(loc => ! exactMatchLocations.contains(loc)).map(loc => Utils.parseHostPort(loc)._1) + val otherNodeLocalPref2 = pref2.filter(loc => ! exactMatchLocations.contains(loc)).map(loc => Utils.parseHostPort(loc)._1) + val otherNodeLocalLocations = otherNodeLocalPref1.intersect(otherNodeLocalPref2) + + + // Can have mix of instance local (hostPort) and node local (host) locations as preference ! + exactMatchLocations ++ otherNodeLocalLocations } override def clearDependencies() { diff --git a/core/src/main/scala/spark/scheduler/ActiveJob.scala b/core/src/main/scala/spark/scheduler/ActiveJob.scala index 5a4e9a582d..71cc94edb6 100644 --- a/core/src/main/scala/spark/scheduler/ActiveJob.scala +++ b/core/src/main/scala/spark/scheduler/ActiveJob.scala @@ -1,7 +1,26 @@ +/* + * 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 spark.scheduler import spark.TaskContext +import java.util.Properties + /** * Tracks information about an active job in the DAGScheduler. */ @@ -11,7 +30,8 @@ private[spark] class ActiveJob( val func: (TaskContext, Iterator[_]) => _, val partitions: Array[Int], val callSite: String, - val listener: JobListener) { + val listener: JobListener, + val properties: Properties) { val numPartitions = partitions.length val finished = Array.fill[Boolean](numPartitions)(false) diff --git a/core/src/main/scala/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/spark/scheduler/DAGScheduler.scala index c54dce51d7..89c51a44c9 100644 --- a/core/src/main/scala/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/spark/scheduler/DAGScheduler.scala @@ -1,18 +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 spark.scheduler -import cluster.TaskInfo +import java.io.NotSerializableException +import java.util.Properties +import java.util.concurrent.{LinkedBlockingQueue, TimeUnit} import java.util.concurrent.atomic.AtomicInteger -import java.util.concurrent.LinkedBlockingQueue -import java.util.concurrent.TimeUnit import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map} import spark._ import spark.executor.TaskMetrics -import spark.partial.ApproximateActionListener -import spark.partial.ApproximateEvaluator -import spark.partial.PartialResult -import spark.storage.BlockManagerMaster +import spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult} +import spark.scheduler.cluster.TaskInfo +import spark.storage.{BlockManager, BlockManagerMaster} import spark.util.{MetadataCleaner, TimeStampedHashMap} /** @@ -34,6 +50,11 @@ class DAGScheduler( } taskSched.setListener(this) + // Called by TaskScheduler to report task's starting. + override def taskStarted(task: Task[_], taskInfo: TaskInfo) { + eventQueue.put(BeginEvent(task, taskInfo)) + } + // Called by TaskScheduler to report task completions or failures. override def taskEnded( task: Task[_], @@ -50,6 +71,11 @@ class DAGScheduler( eventQueue.put(ExecutorLost(execId)) } + // Called by TaskScheduler when a host is added + override def executorGained(execId: String, hostPort: String) { + eventQueue.put(ExecutorGained(execId, hostPort)) + } + // Called by TaskScheduler to cancel an entire TaskSet due to repeated failures. override def taskSetFailed(taskSet: TaskSet, reason: String) { eventQueue.put(TaskSetFailed(taskSet, reason)) @@ -88,6 +114,8 @@ class DAGScheduler( // stray messages to detect. val failedGeneration = new HashMap[String, Long] + 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 @@ -112,9 +140,8 @@ class DAGScheduler( private def getCacheLocs(rdd: RDD[_]): Array[List[String]] = { if (!cacheLocs.contains(rdd.id)) { val blockIds = rdd.partitions.indices.map(index=> "rdd_%d_%d".format(rdd.id, index)).toArray - cacheLocs(rdd.id) = blockManagerMaster.getLocations(blockIds).map { - locations => locations.map(_.ip).toList - }.toArray + val locs = BlockManager.blockIdsToExecutorLocations(blockIds, env, blockManagerMaster) + cacheLocs(rdd.id) = blockIds.map(locs.getOrElse(_, Nil)) } cacheLocs(rdd.id) } @@ -143,7 +170,13 @@ class DAGScheduler( * as a result stage for the final RDD used directly in an action. The stage will also be given * the provided priority. */ - private def newStage(rdd: RDD[_], shuffleDep: Option[ShuffleDependency[_,_]], priority: Int): Stage = { + private def newStage( + rdd: RDD[_], + shuffleDep: Option[ShuffleDependency[_,_]], + priority: Int, + callSite: Option[String] = None) + : Stage = + { if (shuffleDep != None) { // 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 @@ -151,7 +184,7 @@ class DAGScheduler( mapOutputTracker.registerShuffle(shuffleDep.get.shuffleId, rdd.partitions.size) } val id = nextStageId.getAndIncrement() - val stage = new Stage(id, rdd, shuffleDep, getParentStages(rdd, priority), priority) + val stage = new Stage(id, rdd, shuffleDep, getParentStages(rdd, priority), priority, callSite) idToStage(id) = stage stageToInfos(stage) = StageInfo(stage) stage @@ -221,13 +254,15 @@ class DAGScheduler( partitions: Seq[Int], callSite: String, allowLocal: Boolean, - resultHandler: (Int, U) => Unit) + resultHandler: (Int, U) => Unit, + properties: Properties = null) : (JobSubmitted, JobWaiter[U]) = { 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) + val toSubmit = JobSubmitted(finalRdd, func2, partitions.toArray, allowLocal, callSite, waiter, + properties) return (toSubmit, waiter) } @@ -237,17 +272,27 @@ class DAGScheduler( partitions: Seq[Int], callSite: String, allowLocal: Boolean, - resultHandler: (Int, U) => Unit) + resultHandler: (Int, U) => Unit, + properties: Properties = null) { if (partitions.size == 0) { return } - val (toSubmit, waiter) = prepareJob( - finalRdd, func, partitions, callSite, allowLocal, resultHandler) + + // 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) waiter.awaitResult() match { case JobSucceeded => {} - case JobFailed(exception: Exception) => + case JobFailed(exception: Exception, _) => logInfo("Failed to run " + callSite) throw exception } @@ -258,13 +303,14 @@ class DAGScheduler( func: (TaskContext, Iterator[T]) => U, evaluator: ApproximateEvaluator[U, R], callSite: String, - timeout: Long) + timeout: Long, + properties: Properties = null) : PartialResult[R] = { 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, false, callSite, listener)) + eventQueue.put(JobSubmitted(rdd, func2, partitions, false, callSite, listener, properties)) return listener.awaitResult() // Will throw an exception if the job fails } @@ -274,29 +320,39 @@ class DAGScheduler( */ private[scheduler] def processEvent(event: DAGSchedulerEvent): Boolean = { event match { - case JobSubmitted(finalRDD, func, partitions, allowLocal, callSite, listener) => + case JobSubmitted(finalRDD, func, partitions, allowLocal, callSite, listener, properties) => val runId = nextRunId.getAndIncrement() - val finalStage = newStage(finalRDD, None, runId) - val job = new ActiveJob(runId, finalStage, func, partitions, callSite, listener) + val finalStage = newStage(finalRDD, None, runId, Some(callSite)) + val job = new ActiveJob(runId, finalStage, func, partitions, callSite, listener, properties) clearCacheLocs() logInfo("Got job " + job.runId + " (" + callSite + ") with " + partitions.length + " output partitions (allowLocal=" + allowLocal + ")") - logInfo("Final stage: " + finalStage + " (" + finalStage.origin + ")") + logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")") logInfo("Parents of final stage: " + finalStage.parents) 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. runLocally(job) } else { + sparkListeners.foreach(_.onJobStart(SparkListenerJobStart(job, properties))) + idToActiveJob(runId) = job activeJobs += job resultStageToJob(finalStage) = job submitStage(finalStage) } + case ExecutorGained(execId, hostPort) => + handleExecutorGained(execId, hostPort) + case ExecutorLost(execId) => handleExecutorLost(execId) + case begin: BeginEvent => + sparkListeners.foreach(_.onTaskStart(SparkListenerTaskStart(begin.task, begin.taskInfo))) + case completion: CompletionEvent => + sparkListeners.foreach(_.onTaskEnd(SparkListenerTaskEnd(completion.task, + completion.reason, completion.taskInfo, completion.taskMetrics))) handleTaskCompletion(completion) case TaskSetFailed(taskSet, reason) => @@ -307,6 +363,7 @@ class DAGScheduler( for (job <- activeJobs) { val error = new SparkException("Job cancelled because SparkContext was shut down") job.listener.jobFailed(error) + sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error, None)))) } return true } @@ -326,7 +383,7 @@ class DAGScheduler( submitStage(stage) } } - + /** * Check for waiting or failed stages which are now eligible for resubmission. * Ordinarily run on every iteration of the event loop. @@ -453,12 +510,31 @@ class DAGScheduler( tasks += new ResultTask(stage.id, stage.rdd, job.func, partition, locs, id) } } + // must be run listener before possible NotSerializableException + // should be "StageSubmitted" first and then "JobEnded" + val properties = idToActiveJob(stage.priority).properties + sparkListeners.foreach(_.onStageSubmitted( + SparkListenerStageSubmitted(stage, tasks.size, properties))) + if (tasks.size > 0) { + // Preemptively serialize a task to make sure it can be serialized. We are catching this + // exception here because it would be fairly hard to catch the non-serializable exception + // down the road, where we have several different implementations for local scheduler and + // cluster schedulers. + try { + SparkEnv.get.closureSerializer.newInstance().serialize(tasks.head) + } catch { + case e: NotSerializableException => + abortStage(stage, e.toString) + running -= stage + return + } + logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")") myPending ++= tasks logDebug("New pending tasks: " + myPending) taskSched.submitTasks( - new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.priority)) + new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.priority, properties)) if (!stage.submissionTime.isDefined) { stage.submissionTime = Some(System.currentTimeMillis()) } @@ -482,7 +558,8 @@ class DAGScheduler( case Some(t) => "%.03f".format((System.currentTimeMillis() - t) / 1000.0) case _ => "Unkown" } - logInfo("%s (%s) finished in %s s".format(stage, stage.origin, serviceTime)) + logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime)) + stage.completionTime = Some(System.currentTimeMillis) val stageComp = StageCompleted(stageToInfos(stage)) sparkListeners.foreach{_.onStageCompleted(stageComp)} running -= stage @@ -504,9 +581,11 @@ class DAGScheduler( job.numFinished += 1 // If the whole job has finished, remove it if (job.numFinished == job.numPartitions) { + idToActiveJob -= stage.priority activeJobs -= job resultStageToJob -= stage markStageAsFinished(stage) + sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobSucceeded))) } job.listener.taskSucceeded(rt.outputId, event.result) } @@ -545,7 +624,7 @@ class DAGScheduler( if (stage.outputLocs.count(_ == Nil) != 0) { // Some tasks had failed; let's resubmit this stage // TODO: Lower-level scheduler should also deal with this - logInfo("Resubmitting " + stage + " (" + stage.origin + + logInfo("Resubmitting " + stage + " (" + stage.name + ") because some of its tasks had failed: " + stage.outputLocs.zipWithIndex.filter(_._1 == Nil).map(_._2).mkString(", ")) submitStage(stage) @@ -577,7 +656,7 @@ class DAGScheduler( running -= failedStage failed += failedStage // TODO: Cancel running tasks in the stage - logInfo("Marking " + failedStage + " (" + failedStage.origin + + logInfo("Marking " + failedStage + " (" + failedStage.name + ") for resubmision due to a fetch failure") // Mark the map whose fetch failed as broken in the map stage val mapStage = shuffleToMapStage(shuffleId) @@ -585,7 +664,7 @@ class DAGScheduler( mapStage.removeOutputLoc(mapId, bmAddress) mapOutputTracker.unregisterMapOutput(shuffleId, mapId, bmAddress) } - logInfo("The failed fetch was from " + mapStage + " (" + mapStage.origin + + logInfo("The failed fetch was from " + mapStage + " (" + mapStage.name + "); marking it for resubmission") failed += mapStage // Remember that a fetch failed now; this is used to resubmit the broken @@ -596,8 +675,11 @@ class DAGScheduler( handleExecutorLost(bmAddress.executorId, Some(task.generation)) } + case ExceptionFailure(className, description, stackTrace, metrics) => + // Do nothing here, left up to the TaskScheduler to decide how to handle user failures + case other => - // Non-fetch failure -- probably a bug in user code; abort all jobs depending on this stage + // Unrecognized failure - abort all jobs depending on this stage abortStage(idToStage(task.stageId), task + " failed: " + other) } } @@ -631,15 +713,27 @@ class DAGScheduler( } } + private def handleExecutorGained(execId: String, hostPort: String) { + // remove from failedGeneration(execId) ? + if (failedGeneration.contains(execId)) { + logInfo("Host gained which was in lost list earlier: " + hostPort) + failedGeneration -= execId + } + } + /** * 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. */ private def abortStage(failedStage: Stage, reason: String) { val dependentStages = resultStageToJob.keys.filter(x => stageDependsOn(x, failedStage)).toSeq + failedStage.completionTime = Some(System.currentTimeMillis()) for (resultStage <- dependentStages) { val job = resultStageToJob(resultStage) - job.listener.jobFailed(new SparkException("Job failed: " + reason)) + val error = new SparkException("Job failed: " + reason) + job.listener.jobFailed(error) + sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage))))) + idToActiveJob -= resultStage.priority activeJobs -= job resultStageToJob -= resultStage } @@ -712,10 +806,14 @@ class DAGScheduler( 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) } def stop() { diff --git a/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala index ed0b9bf178..3b4ee6287a 100644 --- a/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala +++ b/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala @@ -1,5 +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 spark.scheduler +import java.util.Properties + import spark.scheduler.cluster.TaskInfo import scala.collection.mutable.Map @@ -20,9 +39,12 @@ private[spark] case class JobSubmitted( partitions: Array[Int], allowLocal: Boolean, callSite: String, - listener: JobListener) + listener: JobListener, + properties: Properties = null) extends DAGSchedulerEvent +private[spark] case class BeginEvent(task: Task[_], taskInfo: TaskInfo) extends DAGSchedulerEvent + private[spark] case class CompletionEvent( task: Task[_], reason: TaskEndReason, @@ -32,6 +54,10 @@ private[spark] case class CompletionEvent( taskMetrics: TaskMetrics) extends DAGSchedulerEvent +private[spark] case class ExecutorGained(execId: String, hostPort: String) extends DAGSchedulerEvent { + Utils.checkHostPort(hostPort, "Required hostport") +} + private[spark] case class ExecutorLost(execId: String) extends DAGSchedulerEvent private[spark] case class TaskSetFailed(taskSet: TaskSet, reason: String) extends DAGSchedulerEvent diff --git a/core/src/main/scala/spark/scheduler/DAGSchedulerSource.scala b/core/src/main/scala/spark/scheduler/DAGSchedulerSource.scala new file mode 100644 index 0000000000..87d27cc70d --- /dev/null +++ b/core/src/main/scala/spark/scheduler/DAGSchedulerSource.scala @@ -0,0 +1,30 @@ +package spark.scheduler + +import com.codahale.metrics.{Gauge,MetricRegistry} + +import spark.metrics.source.Source + +private[spark] class DAGSchedulerSource(val dagScheduler: DAGScheduler) extends Source { + val metricRegistry = new MetricRegistry() + val sourceName = "DAGScheduler" + + metricRegistry.register(MetricRegistry.name("stage", "failedStages", "number"), new Gauge[Int] { + override def getValue: Int = dagScheduler.failed.size + }) + + metricRegistry.register(MetricRegistry.name("stage", "runningStages", "number"), new Gauge[Int] { + override def getValue: Int = dagScheduler.running.size + }) + + metricRegistry.register(MetricRegistry.name("stage", "waitingStages", "number"), 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.nextRunId.get() + }) + + metricRegistry.register(MetricRegistry.name("job", "activeJobs", "number"), new Gauge[Int] { + override def getValue: Int = dagScheduler.activeJobs.size + }) +} diff --git a/core/src/main/scala/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/spark/scheduler/InputFormatInfo.scala new file mode 100644 index 0000000000..65f8c3200e --- /dev/null +++ b/core/src/main/scala/spark/scheduler/InputFormatInfo.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 spark.scheduler + +import spark.Logging +import scala.collection.immutable.Set +import org.apache.hadoop.mapred.{FileInputFormat, JobConf} +import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.util.ReflectionUtils +import org.apache.hadoop.mapreduce.Job +import org.apache.hadoop.conf.Configuration +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} +import scala.collection.JavaConversions._ +import spark.deploy.SparkHadoopUtil + + +/** + * Parses and holds information about inputFormat (and files) specified as a parameter. + */ +class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Class[_], + val path: String) extends Logging { + + var mapreduceInputFormat: Boolean = false + var mapredInputFormat: Boolean = false + + validate() + + override def toString(): String = { + "InputFormatInfo " + super.toString + " .. inputFormatClazz " + inputFormatClazz + ", path : " + path + } + + override def hashCode(): Int = { + var hashCode = inputFormatClazz.hashCode + hashCode = hashCode * 31 + path.hashCode + hashCode + } + + // Since we are not doing canonicalization of path, this can be wrong : like relative vs absolute path + // .. which is fine, this is best case effort to remove duplicates - right ? + override def equals(other: Any): Boolean = other match { + case that: InputFormatInfo => { + // not checking config - that should be fine, right ? + this.inputFormatClazz == that.inputFormatClazz && + this.path == that.path + } + case _ => false + } + + private def validate() { + logDebug("validate InputFormatInfo : " + inputFormatClazz + ", path " + path) + + try { + if (classOf[org.apache.hadoop.mapreduce.InputFormat[_, _]].isAssignableFrom(inputFormatClazz)) { + logDebug("inputformat is from mapreduce package") + mapreduceInputFormat = true + } + else if (classOf[org.apache.hadoop.mapred.InputFormat[_, _]].isAssignableFrom(inputFormatClazz)) { + logDebug("inputformat is from mapred package") + mapredInputFormat = true + } + else { + throw new IllegalArgumentException("Specified inputformat " + inputFormatClazz + + " is NOT a supported input format ? does not implement either of the supported hadoop api's") + } + } + catch { + case e: ClassNotFoundException => { + throw new IllegalArgumentException("Specified inputformat " + inputFormatClazz + " cannot be found ?", e) + } + } + } + + + // This method does not expect failures, since validate has already passed ... + private def prefLocsFromMapreduceInputFormat(): Set[SplitInfo] = { + val conf = new JobConf(configuration) + SparkHadoopUtil.addCredentials(conf); + FileInputFormat.setInputPaths(conf, path) + + val instance: org.apache.hadoop.mapreduce.InputFormat[_, _] = + ReflectionUtils.newInstance(inputFormatClazz.asInstanceOf[Class[_]], conf).asInstanceOf[ + org.apache.hadoop.mapreduce.InputFormat[_, _]] + val job = new Job(conf) + + val retval = new ArrayBuffer[SplitInfo]() + val list = instance.getSplits(job) + for (split <- list) { + retval ++= SplitInfo.toSplitInfo(inputFormatClazz, path, split) + } + + return retval.toSet + } + + // This method does not expect failures, since validate has already passed ... + private def prefLocsFromMapredInputFormat(): Set[SplitInfo] = { + val jobConf = new JobConf(configuration) + SparkHadoopUtil.addCredentials(jobConf); + FileInputFormat.setInputPaths(jobConf, path) + + val instance: org.apache.hadoop.mapred.InputFormat[_, _] = + ReflectionUtils.newInstance(inputFormatClazz.asInstanceOf[Class[_]], jobConf).asInstanceOf[ + org.apache.hadoop.mapred.InputFormat[_, _]] + + val retval = new ArrayBuffer[SplitInfo]() + instance.getSplits(jobConf, jobConf.getNumMapTasks()).foreach( + elem => retval ++= SplitInfo.toSplitInfo(inputFormatClazz, path, elem) + ) + + return retval.toSet + } + + private def findPreferredLocations(): Set[SplitInfo] = { + logDebug("mapreduceInputFormat : " + mapreduceInputFormat + ", mapredInputFormat : " + mapredInputFormat + + ", inputFormatClazz : " + inputFormatClazz) + if (mapreduceInputFormat) { + return prefLocsFromMapreduceInputFormat() + } + else { + assert(mapredInputFormat) + return prefLocsFromMapredInputFormat() + } + } +} + + + + +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) : + 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 + (or small set of) hosts go down. + + go to (a) until required nodes are allocated. + + If a node 'dies', follow same procedure. + + PS: I know the wording here is weird, hopefully it makes some sense ! + */ + def computePreferredLocations(formats: Seq[InputFormatInfo]): HashMap[String, HashSet[SplitInfo]] = { + + val nodeToSplit = new HashMap[String, HashSet[SplitInfo]] + for (inputSplit <- formats) { + val splits = inputSplit.findPreferredLocations() + + for (split <- splits){ + val location = split.hostLocation + val set = nodeToSplit.getOrElseUpdate(location, new HashSet[SplitInfo]) + set += split + } + } + + nodeToSplit + } +} diff --git a/core/src/main/scala/spark/scheduler/JobListener.scala b/core/src/main/scala/spark/scheduler/JobListener.scala index f46b9d551d..af108b8fec 100644 --- a/core/src/main/scala/spark/scheduler/JobListener.scala +++ b/core/src/main/scala/spark/scheduler/JobListener.scala @@ -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. + */ + package spark.scheduler /** diff --git a/core/src/main/scala/spark/scheduler/JobLogger.scala b/core/src/main/scala/spark/scheduler/JobLogger.scala new file mode 100644 index 0000000000..ad2efcec63 --- /dev/null +++ b/core/src/main/scala/spark/scheduler/JobLogger.scala @@ -0,0 +1,338 @@ +/*
+ * 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 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 spark._
+import spark.SparkContext
+import spark.executor.TaskMetrics
+import spark.scheduler.cluster.TaskInfo
+
+// 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
+
+ new Thread("JobLogger") {
+ setDaemon(true)
+ override def run() {
+ while (true) {
+ val event = eventQueue.take
+ logDebug("Got event of type " + event.getClass.getName)
+ event match {
+ case SparkListenerJobStart(job, properties) =>
+ processJobStartEvent(job, properties)
+ case SparkListenerStageSubmitted(stage, taskSize, properties) =>
+ processStageSubmittedEvent(stage, taskSize)
+ case StageCompleted(stageInfo) =>
+ processStageCompletedEvent(stageInfo)
+ case SparkListenerJobEnd(job, result) =>
+ processJobEndEvent(job, result)
+ case SparkListenerTaskStart(task, taskInfo) =>
+ processTaskStartEvent(task, taskInfo)
+ case SparkListenerTaskEnd(task, reason, taskInfo, taskMetrics) =>
+ processTaskEndEvent(task, reason, taskInfo, taskMetrics)
+ case _ =>
+ }
+ }
+ }
+ }.start()
+
+ // 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.priority == 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.priority == 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.priority, 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) {
+ eventQueue.put(stageSubmitted)
+ }
+
+ protected def processStageSubmittedEvent(stage: Stage, taskSize: Int) {
+ stageLogInfo(stage.id, "STAGE_ID=" + stage.id + " STATUS=SUBMITTED" + " TASK_SIZE=" + taskSize)
+ }
+
+ override def onStageCompleted(stageCompleted: StageCompleted) {
+ eventQueue.put(stageCompleted)
+ }
+
+ protected def processStageCompletedEvent(stageInfo: StageInfo) {
+ stageLogInfo(stageInfo.stage.id, "STAGE_ID=" +
+ stageInfo.stage.id + " STATUS=COMPLETED")
+
+ }
+
+ override def onTaskStart(taskStart: SparkListenerTaskStart) {
+ eventQueue.put(taskStart)
+ }
+
+ protected def processTaskStartEvent(task: Task[_], taskInfo: TaskInfo) {
+ var taskStatus = ""
+ task match {
+ case resultTask: ResultTask[_, _] => taskStatus = "TASK_TYPE=RESULT_TASK"
+ case shuffleMapTask: ShuffleMapTask => taskStatus = "TASK_TYPE=SHUFFLE_MAP_TASK"
+ }
+ }
+
+ override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
+ eventQueue.put(taskEnd)
+ }
+
+ protected def processTaskEndEvent(task: Task[_], reason: TaskEndReason,
+ taskInfo: TaskInfo, taskMetrics: TaskMetrics) {
+ var taskStatus = ""
+ task match {
+ case resultTask: ResultTask[_, _] => taskStatus = "TASK_TYPE=RESULT_TASK"
+ case shuffleMapTask: ShuffleMapTask => taskStatus = "TASK_TYPE=SHUFFLE_MAP_TASK"
+ }
+ reason match {
+ case Success => taskStatus += " STATUS=SUCCESS"
+ recordTaskMetrics(task.stageId, taskStatus, taskInfo, 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) {
+ eventQueue.put(jobEnd)
+ }
+
+ protected def processJobEndEvent(job: ActiveJob, reason: JobResult) {
+ var info = "JOB_ID=" + job.runId
+ reason match {
+ case JobSucceeded => info += " STATUS=SUCCESS"
+ case JobFailed(exception, _) =>
+ info += " STATUS=FAILED REASON="
+ exception.getMessage.split("\\s+").foreach(info += _ + "_")
+ case _ =>
+ }
+ jobLogInfo(job.runId, info.substring(0, info.length - 1).toUpperCase)
+ closeLogWriter(job.runId)
+ }
+
+ 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) {
+ eventQueue.put(jobStart)
+ }
+
+ protected def processJobStartEvent(job: ActiveJob, properties: Properties) {
+ createLogWriter(job.runId)
+ recordJobProperties(job.runId, properties)
+ buildJobDep(job.runId, job.finalStage)
+ recordStageDep(job.runId)
+ recordStageDepGraph(job.runId, job.finalStage)
+ jobLogInfo(job.runId, "JOB_ID=" + job.runId + " STATUS=STARTED")
+ }
+}
diff --git a/core/src/main/scala/spark/scheduler/JobResult.scala b/core/src/main/scala/spark/scheduler/JobResult.scala index 654131ee84..a61b335152 100644 --- a/core/src/main/scala/spark/scheduler/JobResult.scala +++ b/core/src/main/scala/spark/scheduler/JobResult.scala @@ -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. + */ + package spark.scheduler /** @@ -6,4 +23,4 @@ package spark.scheduler private[spark] sealed trait JobResult private[spark] case object JobSucceeded extends JobResult -private[spark] case class JobFailed(exception: Exception) extends JobResult +private[spark] case class JobFailed(exception: Exception, failedStage: Option[Stage]) extends JobResult diff --git a/core/src/main/scala/spark/scheduler/JobWaiter.scala b/core/src/main/scala/spark/scheduler/JobWaiter.scala index 3cc6a86345..69cd161c1f 100644 --- a/core/src/main/scala/spark/scheduler/JobWaiter.scala +++ b/core/src/main/scala/spark/scheduler/JobWaiter.scala @@ -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. + */ + package spark.scheduler import scala.collection.mutable.ArrayBuffer @@ -35,7 +52,7 @@ private[spark] class JobWaiter[T](totalTasks: Int, resultHandler: (Int, T) => Un throw new UnsupportedOperationException("jobFailed() called on a finished JobWaiter") } jobFinished = true - jobResult = JobFailed(exception) + jobResult = JobFailed(exception, None) this.notifyAll() } } diff --git a/core/src/main/scala/spark/scheduler/MapStatus.scala b/core/src/main/scala/spark/scheduler/MapStatus.scala index 203abb917b..2f6a68ee85 100644 --- a/core/src/main/scala/spark/scheduler/MapStatus.scala +++ b/core/src/main/scala/spark/scheduler/MapStatus.scala @@ -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. + */ + package spark.scheduler import spark.storage.BlockManagerId diff --git a/core/src/main/scala/spark/scheduler/ResultTask.scala b/core/src/main/scala/spark/scheduler/ResultTask.scala index beb21a76fe..1ced6f9524 100644 --- a/core/src/main/scala/spark/scheduler/ResultTask.scala +++ b/core/src/main/scala/spark/scheduler/ResultTask.scala @@ -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. + */ + package spark.scheduler import spark._ @@ -70,6 +87,13 @@ private[spark] class ResultTask[T, U]( rdd.partitions(partition) } + private val preferredLocs: Seq[String] = if (locs == null) Nil else locs.toSet.toSeq + + { + // DEBUG code + preferredLocs.foreach (hostPort => Utils.checkHost(Utils.parseHostPort(hostPort)._1, "preferredLocs : " + preferredLocs)) + } + override def run(attemptId: Long): U = { val context = new TaskContext(stageId, partition, attemptId) metrics = Some(context.taskMetrics) @@ -80,7 +104,7 @@ private[spark] class ResultTask[T, U]( } } - override def preferredLocations: Seq[String] = locs + override def preferredLocations: Seq[String] = preferredLocs override def toString = "ResultTask(" + stageId + ", " + partition + ")" @@ -94,6 +118,7 @@ private[spark] class ResultTask[T, U]( out.write(bytes) out.writeInt(partition) out.writeInt(outputId) + out.writeLong(generation) out.writeObject(split) } } @@ -108,6 +133,7 @@ private[spark] class ResultTask[T, U]( func = func_.asInstanceOf[(TaskContext, Iterator[T]) => U] partition = in.readInt() val outputId = in.readInt() + generation = in.readLong() split = in.readObject().asInstanceOf[Partition] } } diff --git a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala index 36d087a4d0..e3bb6d1e60 100644 --- a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala @@ -1,21 +1,32 @@ +/* + * 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 spark.scheduler import java.io._ -import java.util.{HashMap => JHashMap} import java.util.zip.{GZIPInputStream, GZIPOutputStream} -import scala.collection.mutable.{ArrayBuffer, HashMap} -import scala.collection.JavaConversions._ - -import it.unimi.dsi.fastutil.io.FastBufferedOutputStream - -import com.ning.compress.lzf.LZFInputStream -import com.ning.compress.lzf.LZFOutputStream +import scala.collection.mutable.HashMap import spark._ -import executor.ShuffleWriteMetrics +import spark.executor.ShuffleWriteMetrics import spark.storage._ -import util.{TimeStampedHashMap, MetadataCleaner} +import spark.util.{TimeStampedHashMap, MetadataCleaner} + private[spark] object ShuffleMapTask { @@ -77,19 +88,22 @@ private[spark] class ShuffleMapTask( var rdd: RDD[_], var dep: ShuffleDependency[_,_], var partition: Int, - @transient var locs: Seq[String]) + @transient private var locs: Seq[String]) extends Task[MapStatus](stageId) with Externalizable with Logging { protected def this() = this(0, null, null, 0, null) - var split = if (rdd == null) { - null - } else { - rdd.partitions(partition) + @transient private val preferredLocs: Seq[String] = if (locs == null) Nil else locs.toSet.toSeq + + { + // DEBUG code + preferredLocs.foreach (hostPort => Utils.checkHost(Utils.parseHostPort(hostPort)._1, "preferredLocs : " + preferredLocs)) } + var split = if (rdd == null) null else rdd.partitions(partition) + override def writeExternal(out: ObjectOutput) { RDDCheckpointData.synchronized { split = rdd.partitions(partition) @@ -121,40 +135,58 @@ private[spark] class ShuffleMapTask( val taskContext = new TaskContext(stageId, partition, attemptId) metrics = Some(taskContext.taskMetrics) + + val blockManager = SparkEnv.get.blockManager + var shuffle: ShuffleBlocks = null + var buckets: ShuffleWriterGroup = null + try { - // Partition the map output. - val buckets = Array.fill(numOutputSplits)(new ArrayBuffer[(Any, Any)]) + // 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) + + // Write the map output to its associated buckets. for (elem <- rdd.iterator(split, taskContext)) { val pair = elem.asInstanceOf[(Any, Any)] val bucketId = dep.partitioner.getPartition(pair._1) - buckets(bucketId) += pair + buckets.writers(bucketId).write(pair) } - val compressedSizes = new Array[Byte](numOutputSplits) - - var totalBytes = 0l - - val blockManager = SparkEnv.get.blockManager - for (i <- 0 until numOutputSplits) { - val blockId = "shuffle_" + dep.shuffleId + "_" + partition + "_" + i - // Get a Scala iterator from Java map - val iter: Iterator[(Any, Any)] = buckets(i).iterator - val size = blockManager.put(blockId, iter, StorageLevel.DISK_ONLY, false) + // 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 => + writer.commit() + writer.close() + val size = writer.size() totalBytes += size - compressedSizes(i) = MapOutputTracker.compressSize(size) + MapOutputTracker.compressSize(size) } + + // Update shuffle metrics. val shuffleMetrics = new ShuffleWriteMetrics shuffleMetrics.shuffleBytesWritten = totalBytes metrics.get.shuffleWriteMetrics = Some(shuffleMetrics) return 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()) + } + throw e } finally { + // Release the writers back to the shuffle block manager. + if (shuffle != null && buckets != null) { + shuffle.releaseWriters(buckets) + } // Execute the callbacks on task completion. taskContext.executeOnCompleteCallbacks() } } - override def preferredLocations: Seq[String] = locs + override def preferredLocations: Seq[String] = preferredLocs override def toString = "ShuffleMapTask(%d, %d)".format(stageId, partition) } diff --git a/core/src/main/scala/spark/scheduler/SparkListener.scala b/core/src/main/scala/spark/scheduler/SparkListener.scala index a65140b145..2a09a956ad 100644 --- a/core/src/main/scala/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/spark/scheduler/SparkListener.scala @@ -1,27 +1,84 @@ +/* + * 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 spark.scheduler +import java.util.Properties import spark.scheduler.cluster.TaskInfo import spark.util.Distribution -import spark.{Utils, Logging} +import spark.{Logging, SparkContext, TaskEndReason, Utils} import spark.executor.TaskMetrics +sealed trait SparkListenerEvents + +case class SparkListenerStageSubmitted(stage: Stage, taskSize: Int, properties: Properties) + extends SparkListenerEvents + +case class StageCompleted(val stageInfo: StageInfo) extends SparkListenerEvents + +case class SparkListenerTaskStart(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) + extends SparkListenerEvents + +case class SparkListenerJobEnd(job: ActiveJob, jobResult: JobResult) + extends SparkListenerEvents + trait SparkListener { /** - * called when a stage is completed, with information on the completed stage + * Called when a stage is completed, with information on the completed stage */ - def onStageCompleted(stageCompleted: StageCompleted) -} + def onStageCompleted(stageCompleted: StageCompleted) { } -sealed trait SparkListenerEvents + /** + * Called when a stage is submitted + */ + def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) { } -case class StageCompleted(val stageInfo: StageInfo) extends SparkListenerEvents + /** + * Called when a task starts + */ + def onTaskStart(taskEnd: SparkListenerTaskStart) { } + + /** + * Called when a task ends + */ + def onTaskEnd(taskEnd: SparkListenerTaskEnd) { } + + /** + * Called when a job starts + */ + def onJobStart(jobStart: SparkListenerJobStart) { } + /** + * Called when a job ends + */ + def onJobEnd(jobEnd: SparkListenerJobEnd) { } + +} /** * Simple SparkListener that logs a few summary statistics when each stage completes */ class StatsReportListener extends SparkListener with Logging { - def onStageCompleted(stageCompleted: StageCompleted) { + override def onStageCompleted(stageCompleted: StageCompleted) { import spark.scheduler.StatsReportListener._ implicit val sc = stageCompleted this.logInfo("Finished stage: " + stageCompleted.stageInfo) @@ -36,6 +93,7 @@ class StatsReportListener extends SparkListener with Logging { showBytesDistribution("task result size:", (_, metric) => Some(metric.resultSize)) //runtime breakdown + val runtimePcts = stageCompleted.stageInfo.taskInfos.map{ case (info, metrics) => RuntimePercentage(info.duration, metrics) } diff --git a/core/src/main/scala/spark/scheduler/SplitInfo.scala b/core/src/main/scala/spark/scheduler/SplitInfo.scala new file mode 100644 index 0000000000..4e3661ec5d --- /dev/null +++ b/core/src/main/scala/spark/scheduler/SplitInfo.scala @@ -0,0 +1,78 @@ +/* + * 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 spark.scheduler + +import collection.mutable.ArrayBuffer + +// information about a specific split instance : handles both split instances. +// So that we do not need to worry about the differences. +class SplitInfo(val inputFormatClazz: Class[_], val hostLocation: String, val path: String, + val length: Long, val underlyingSplit: Any) { + override def toString(): String = { + "SplitInfo " + super.toString + " .. inputFormatClazz " + inputFormatClazz + + ", hostLocation : " + hostLocation + ", path : " + path + + ", length : " + length + ", underlyingSplit " + underlyingSplit + } + + override def hashCode(): Int = { + var hashCode = inputFormatClazz.hashCode + hashCode = hashCode * 31 + hostLocation.hashCode + hashCode = hashCode * 31 + path.hashCode + // ignore overflow ? It is hashcode anyway ! + hashCode = hashCode * 31 + (length & 0x7fffffff).toInt + hashCode + } + + // This is practically useless since most of the Split impl's dont seem to implement equals :-( + // So unless there is identity equality between underlyingSplits, it will always fail even if it + // is pointing to same block. + override def equals(other: Any): Boolean = other match { + case that: SplitInfo => { + this.hostLocation == that.hostLocation && + this.inputFormatClazz == that.inputFormatClazz && + this.path == that.path && + this.length == that.length && + // other split specific checks (like start for FileSplit) + this.underlyingSplit == that.underlyingSplit + } + case _ => false + } +} + +object SplitInfo { + + def toSplitInfo(inputFormatClazz: Class[_], path: String, + mapredSplit: org.apache.hadoop.mapred.InputSplit): Seq[SplitInfo] = { + val retval = new ArrayBuffer[SplitInfo]() + val length = mapredSplit.getLength + for (host <- mapredSplit.getLocations) { + retval += new SplitInfo(inputFormatClazz, host, path, length, mapredSplit) + } + retval + } + + def toSplitInfo(inputFormatClazz: Class[_], path: String, + mapreduceSplit: org.apache.hadoop.mapreduce.InputSplit): Seq[SplitInfo] = { + val retval = new ArrayBuffer[SplitInfo]() + val length = mapreduceSplit.getLength + for (host <- mapreduceSplit.getLocations) { + retval += new SplitInfo(inputFormatClazz, host, path, length, mapreduceSplit) + } + retval + } +} diff --git a/core/src/main/scala/spark/scheduler/Stage.scala b/core/src/main/scala/spark/scheduler/Stage.scala index 552061e46b..5428daeb94 100644 --- a/core/src/main/scala/spark/scheduler/Stage.scala +++ b/core/src/main/scala/spark/scheduler/Stage.scala @@ -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. + */ + package spark.scheduler import java.net.URI @@ -24,9 +41,10 @@ private[spark] class Stage( val rdd: RDD[_], val shuffleDep: Option[ShuffleDependency[_,_]], // Output shuffle if stage is a map stage val parents: List[Stage], - val priority: Int) + val priority: Int, + callSite: Option[String]) extends Logging { - + val isShuffleMap = shuffleDep != None val numPartitions = rdd.partitions.size val outputLocs = Array.fill[List[MapStatus]](numPartitions)(Nil) @@ -34,6 +52,7 @@ private[spark] class Stage( /** When first task was submitted to scheduler. */ var submissionTime: Option[Long] = None + var completionTime: Option[Long] = None private var nextAttemptId = 0 @@ -60,7 +79,7 @@ private[spark] class Stage( numAvailableOutputs -= 1 } } - + def removeOutputsOnExecutor(execId: String) { var becameUnavailable = false for (partition <- 0 until numPartitions) { @@ -84,7 +103,7 @@ private[spark] class Stage( return id } - def origin: String = rdd.origin + val name = callSite.getOrElse(rdd.origin) override def toString = "Stage " + id diff --git a/core/src/main/scala/spark/scheduler/StageInfo.scala b/core/src/main/scala/spark/scheduler/StageInfo.scala index 8d83ff10c4..c4026f995a 100644 --- a/core/src/main/scala/spark/scheduler/StageInfo.scala +++ b/core/src/main/scala/spark/scheduler/StageInfo.scala @@ -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. + */ + package spark.scheduler import spark.scheduler.cluster.TaskInfo @@ -9,4 +26,4 @@ case class StageInfo( val taskInfos: mutable.Buffer[(TaskInfo, TaskMetrics)] = mutable.Buffer[(TaskInfo, TaskMetrics)]() ) { override def toString = stage.rdd.toString -}
\ No newline at end of file +} diff --git a/core/src/main/scala/spark/scheduler/Task.scala b/core/src/main/scala/spark/scheduler/Task.scala index a6462c6968..50768d43e0 100644 --- a/core/src/main/scala/spark/scheduler/Task.scala +++ b/core/src/main/scala/spark/scheduler/Task.scala @@ -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. + */ + package spark.scheduler import spark.serializer.SerializerInstance diff --git a/core/src/main/scala/spark/scheduler/TaskResult.scala b/core/src/main/scala/spark/scheduler/TaskResult.scala index 6de0aa7adf..dc0621ea7b 100644 --- a/core/src/main/scala/spark/scheduler/TaskResult.scala +++ b/core/src/main/scala/spark/scheduler/TaskResult.scala @@ -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. + */ + package spark.scheduler import java.io._ diff --git a/core/src/main/scala/spark/scheduler/TaskScheduler.scala b/core/src/main/scala/spark/scheduler/TaskScheduler.scala index d549b184b0..4943d58e25 100644 --- a/core/src/main/scala/spark/scheduler/TaskScheduler.scala +++ b/core/src/main/scala/spark/scheduler/TaskScheduler.scala @@ -1,5 +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 spark.scheduler +import spark.scheduler.cluster.Pool +import spark.scheduler.cluster.SchedulingMode.SchedulingMode /** * Low-level task scheduler interface, implemented by both ClusterScheduler and LocalScheduler. * These schedulers get sets of tasks submitted to them from the DAGScheduler for each stage, @@ -8,8 +27,17 @@ package spark.scheduler * the TaskSchedulerListener interface. */ private[spark] trait TaskScheduler { + + def rootPool: Pool + + def schedulingMode: SchedulingMode + def start(): Unit + // Invoked after system has successfully initialized (typically in spark context). + // Yarn uses this to bootstrap allocation of resources based on preferred locations, wait for slave registerations, etc. + def postStartHook() { } + // Disconnect from the cluster. def stop(): Unit diff --git a/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala b/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala index 771518dddf..2cdeb1c8c0 100644 --- a/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala +++ b/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala @@ -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. + */ + package spark.scheduler import spark.scheduler.cluster.TaskInfo @@ -10,10 +27,16 @@ import 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, hostPort: String): Unit + // A node was lost from the cluster. def executorLost(execId: String): Unit diff --git a/core/src/main/scala/spark/scheduler/TaskSet.scala b/core/src/main/scala/spark/scheduler/TaskSet.scala index a3002ca477..dc3550dd0b 100644 --- a/core/src/main/scala/spark/scheduler/TaskSet.scala +++ b/core/src/main/scala/spark/scheduler/TaskSet.scala @@ -1,11 +1,35 @@ +/* + * 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 spark.scheduler +import java.util.Properties + /** * A set of tasks submitted together to the low-level TaskScheduler, usually representing * missing partitions of a particular stage. */ -private[spark] class TaskSet(val tasks: Array[Task[_]], val stageId: Int, val attempt: Int, val priority: Int) { - val id: String = stageId + "." + attempt +private[spark] class TaskSet( + val tasks: Array[Task[_]], + val stageId: Int, + val attempt: Int, + val priority: Int, + val properties: Properties) { + val id: String = stageId + "." + attempt override def toString: String = "TaskSet " + id } diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala index 26fdef101b..96568e0d27 100644 --- a/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala +++ b/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala @@ -1,6 +1,23 @@ +/* + * 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 spark.scheduler.cluster -import java.io.{File, FileInputStream, FileOutputStream} +import java.lang.{Boolean => JBoolean} import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap @@ -9,6 +26,7 @@ import scala.collection.mutable.HashSet import spark._ import spark.TaskState.TaskState import spark.scheduler._ +import spark.scheduler.cluster.SchedulingMode.SchedulingMode import java.nio.ByteBuffer import java.util.concurrent.atomic.AtomicLong import java.util.{TimerTask, Timer} @@ -25,17 +43,45 @@ private[spark] class ClusterScheduler(val sc: SparkContext) val SPECULATION_INTERVAL = System.getProperty("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 + // How often to revive offers in case there are pending tasks - that is how often to try to get + // tasks scheduled in case there are nodes available : default 0 is to disable it - to preserve existing behavior + // Note that this is required due to delayed scheduling due to data locality waits, etc. + // TODO: rename property ? + val TASK_REVIVAL_INTERVAL = System.getProperty("spark.tasks.revive.interval", "0").toLong + + /* + This property controls how aggressive we should be to modulate waiting for node local task scheduling. + To elaborate, currently there is a time limit (3 sec def) to ensure that spark attempts to wait for node locality of tasks before + scheduling on other nodes. We have modified this in yarn branch such that offers to task set happen in prioritized order : + node-local, rack-local and then others + But once all available node local (and no pref) tasks are scheduled, instead of waiting for 3 sec before + scheduling to other nodes (which degrades performance for time sensitive tasks and on larger clusters), we can + modulate that : to also allow rack local nodes or any node. The default is still set to HOST - so that previous behavior is + maintained. This is to allow tuning the tension between pulling rdd data off node and scheduling computation asap. + + TODO: rename property ? The value is one of + - NODE_LOCAL (default, no change w.r.t current behavior), + - RACK_LOCAL and + - ANY + + Note that this property makes more sense when used in conjugation with spark.tasks.revive.interval > 0 : else it is not very effective. + + Additional Note: For non trivial clusters, there is a 4x - 5x reduction in running time (in some of our experiments) based on whether + it is left at default NODE_LOCAL, RACK_LOCAL (if cluster is configured to be rack aware) or ANY. + If cluster is rack aware, then setting it to RACK_LOCAL gives best tradeoff and a 3x - 4x performance improvement while minimizing IO impact. + Also, it brings down the variance in running time drastically. + */ + val TASK_SCHEDULING_AGGRESSION = TaskLocality.parse(System.getProperty("spark.tasks.schedule.aggression", "NODE_LOCAL")) val activeTaskSets = new HashMap[String, TaskSetManager] - var activeTaskSetsQueue = new ArrayBuffer[TaskSetManager] val taskIdToTaskSetId = new HashMap[Long, String] val taskIdToExecutorId = new HashMap[Long, String] val taskSetTaskIds = new HashMap[String, HashSet[Long]] - var hasReceivedTask = false - var hasLaunchedTask = false - val starvationTimer = new Timer(true) + @volatile private var hasReceivedTask = false + @volatile private var hasLaunchedTask = false + private val starvationTimer = new Timer(true) // Incrementing Mesos task IDs val nextTaskId = new AtomicLong(0) @@ -43,11 +89,16 @@ private[spark] class ClusterScheduler(val sc: SparkContext) // Which executor IDs we have executors on val activeExecutorIds = new HashSet[String] + // TODO: We might want to remove this and merge it with execId datastructures - but later. + // Which hosts in the cluster are alive (contains hostPort's) - used for process local and node local task locality. + private val hostPortsAlive = new HashSet[String] + private val hostToAliveHostPorts = new HashMap[String, HashSet[String]] + // The set of executors we have on each host; this is used to compute hostsAlive, which // in turn is used to decide when we can attain data locality on a given host - val executorsByHost = new HashMap[String, HashSet[String]] + private val executorsByHostPort = new HashMap[String, HashSet[String]] - val executorIdToHost = new HashMap[String, String] + private val executorIdToHostPort = new HashMap[String, String] // JAR server, if any JARs were added by the user to the SparkContext var jarServer: HttpServer = null @@ -62,24 +113,51 @@ private[spark] class ClusterScheduler(val sc: SparkContext) val mapOutputTracker = SparkEnv.get.mapOutputTracker + var schedulableBuilder: SchedulableBuilder = null + var rootPool: Pool = null + // default scheduler is FIFO + val schedulingMode: SchedulingMode = SchedulingMode.withName( + System.getProperty("spark.cluster.schedulingmode", "FIFO")) + override def setListener(listener: TaskSchedulerListener) { this.listener = listener } def initialize(context: SchedulerBackend) { backend = context + // 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() + // resolve executorId to hostPort mapping. + def executorToHostPort(executorId: String, defaultHostPort: String): String = { + executorIdToHostPort.getOrElse(executorId, defaultHostPort) + } + + // Unfortunately, this means that SparkEnv is indirectly referencing ClusterScheduler + // Will that be a design violation ? + SparkEnv.get.executorIdToHostPort = Some(executorToHostPort) } + def newTaskId(): Long = nextTaskId.getAndIncrement() override def start() { backend.start() - if (System.getProperty("spark.speculation", "false") == "true") { + if (JBoolean.getBoolean("spark.speculation")) { new Thread("ClusterScheduler speculation check") { setDaemon(true) override def run() { + logInfo("Starting speculative execution thread") while (true) { try { Thread.sleep(SPECULATION_INTERVAL) @@ -91,15 +169,36 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } }.start() } + + + // Change to always run with some default if TASK_REVIVAL_INTERVAL <= 0 ? + if (TASK_REVIVAL_INTERVAL > 0) { + new Thread("ClusterScheduler task offer revival check") { + setDaemon(true) + + override def run() { + logInfo("Starting speculative task offer revival thread") + while (true) { + try { + Thread.sleep(TASK_REVIVAL_INTERVAL) + } catch { + case e: InterruptedException => {} + } + + if (hasPendingTasks()) backend.reviveOffers() + } + } + }.start() + } } override def submitTasks(taskSet: TaskSet) { val tasks = taskSet.tasks logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks") this.synchronized { - val manager = new TaskSetManager(this, taskSet) + val manager = new ClusterTaskSetManager(this, taskSet) activeTaskSets(taskSet.id) = manager - activeTaskSetsQueue += manager + schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties) taskSetTaskIds(taskSet.id) = new HashSet[Long]() if (hasReceivedTask == false) { @@ -107,7 +206,8 @@ private[spark] class ClusterScheduler(val sc: SparkContext) override def run() { if (!hasLaunchedTask) { logWarning("Initial job has not accepted any resources; " + - "check your cluster UI to ensure that workers are registered") + "check your cluster UI to ensure that workers are registered " + + "and have sufficient memory") } else { this.cancel() } @@ -122,7 +222,8 @@ private[spark] class ClusterScheduler(val sc: SparkContext) def taskSetFinished(manager: TaskSetManager) { this.synchronized { activeTaskSets -= manager.taskSet.id - activeTaskSetsQueue -= manager + manager.parent.removeSchedulable(manager) + logInfo("Remove TaskSet %s from pool %s".format(manager.taskSet.id, manager.parent.name)) taskIdToTaskSetId --= taskSetTaskIds(manager.taskSet.id) taskIdToExecutorId --= taskSetTaskIds(manager.taskSet.id) taskSetTaskIds.remove(manager.taskSet.id) @@ -139,22 +240,130 @@ private[spark] class ClusterScheduler(val sc: SparkContext) SparkEnv.set(sc.env) // Mark each slave as alive and remember its hostname for (o <- offers) { - executorIdToHost(o.executorId) = o.hostname - if (!executorsByHost.contains(o.hostname)) { - executorsByHost(o.hostname) = new HashSet() + // DEBUG Code + Utils.checkHostPort(o.hostPort) + + executorIdToHostPort(o.executorId) = o.hostPort + if (! executorsByHostPort.contains(o.hostPort)) { + executorsByHostPort(o.hostPort) = new HashSet[String]() } + + hostPortsAlive += o.hostPort + hostToAliveHostPorts.getOrElseUpdate(Utils.parseHostPort(o.hostPort)._1, new HashSet[String]).add(o.hostPort) + executorGained(o.executorId, o.hostPort) } // Build a list of tasks to assign to each slave val tasks = offers.map(o => new ArrayBuffer[TaskDescription](o.cores)) + // merge availableCpus into nodeToAvailableCpus block ? val availableCpus = offers.map(o => o.cores).toArray + val nodeToAvailableCpus = { + val map = new HashMap[String, Int]() + for (offer <- offers) { + val hostPort = offer.hostPort + val cores = offer.cores + // DEBUG code + Utils.checkHostPort(hostPort) + + val host = Utils.parseHostPort(hostPort)._1 + + map.put(host, map.getOrElse(host, 0) + cores) + } + + map + } var launchedTask = false - for (manager <- activeTaskSetsQueue.sortBy(m => (m.taskSet.priority, m.taskSet.stageId))) { + val sortedTaskSetQueue = rootPool.getSortedTaskSetQueue() + + for (manager <- sortedTaskSetQueue) { + logDebug("parentName:%s, name:%s, runningTasks:%s".format( + manager.parent.name, manager.name, manager.runningTasks)) + } + + for (manager <- sortedTaskSetQueue) { + + // Split offers based on node local, rack local and off-rack tasks. + val processLocalOffers = new HashMap[String, ArrayBuffer[Int]]() + val nodeLocalOffers = new HashMap[String, ArrayBuffer[Int]]() + val rackLocalOffers = new HashMap[String, ArrayBuffer[Int]]() + val otherOffers = new HashMap[String, ArrayBuffer[Int]]() + + for (i <- 0 until offers.size) { + val hostPort = offers(i).hostPort + // DEBUG code + Utils.checkHostPort(hostPort) + + val numProcessLocalTasks = math.max(0, math.min(manager.numPendingTasksForHostPort(hostPort), availableCpus(i))) + if (numProcessLocalTasks > 0){ + val list = processLocalOffers.getOrElseUpdate(hostPort, new ArrayBuffer[Int]) + for (j <- 0 until numProcessLocalTasks) list += i + } + + val host = Utils.parseHostPort(hostPort)._1 + val numNodeLocalTasks = math.max(0, + // Remove process local tasks (which are also host local btw !) from this + math.min(manager.numPendingTasksForHost(hostPort) - numProcessLocalTasks, nodeToAvailableCpus(host))) + if (numNodeLocalTasks > 0){ + val list = nodeLocalOffers.getOrElseUpdate(host, new ArrayBuffer[Int]) + for (j <- 0 until numNodeLocalTasks) list += i + } + + val numRackLocalTasks = math.max(0, + // Remove node local tasks (which are also rack local btw !) from this + math.min(manager.numRackLocalPendingTasksForHost(hostPort) - numProcessLocalTasks - numNodeLocalTasks, nodeToAvailableCpus(host))) + if (numRackLocalTasks > 0){ + val list = rackLocalOffers.getOrElseUpdate(host, new ArrayBuffer[Int]) + for (j <- 0 until numRackLocalTasks) list += i + } + if (numNodeLocalTasks <= 0 && numRackLocalTasks <= 0){ + // add to others list - spread even this across cluster. + val list = otherOffers.getOrElseUpdate(host, new ArrayBuffer[Int]) + list += i + } + } + + val offersPriorityList = new ArrayBuffer[Int]( + processLocalOffers.size + nodeLocalOffers.size + rackLocalOffers.size + otherOffers.size) + + // First process local, then host local, then rack, then others + + // numNodeLocalOffers contains count of both process local and host offers. + val numNodeLocalOffers = { + val processLocalPriorityList = ClusterScheduler.prioritizeContainers(processLocalOffers) + offersPriorityList ++= processLocalPriorityList + + val nodeLocalPriorityList = ClusterScheduler.prioritizeContainers(nodeLocalOffers) + offersPriorityList ++= nodeLocalPriorityList + + processLocalPriorityList.size + nodeLocalPriorityList.size + } + val numRackLocalOffers = { + val rackLocalPriorityList = ClusterScheduler.prioritizeContainers(rackLocalOffers) + offersPriorityList ++= rackLocalPriorityList + rackLocalPriorityList.size + } + offersPriorityList ++= ClusterScheduler.prioritizeContainers(otherOffers) + + var lastLoop = false + val lastLoopIndex = TASK_SCHEDULING_AGGRESSION match { + case TaskLocality.NODE_LOCAL => numNodeLocalOffers + case TaskLocality.RACK_LOCAL => numRackLocalOffers + numNodeLocalOffers + case TaskLocality.ANY => offersPriorityList.size + } + do { launchedTask = false - for (i <- 0 until offers.size) { + var loopCount = 0 + for (i <- offersPriorityList) { val execId = offers(i).executorId - val host = offers(i).hostname - manager.slaveOffer(execId, host, availableCpus(i)) match { + val hostPort = offers(i).hostPort + + // If last loop and within the lastLoopIndex, expand scope - else use null (which will use default/existing) + val overrideLocality = if (lastLoop && loopCount < lastLoopIndex) TASK_SCHEDULING_AGGRESSION else null + + // If last loop, override waiting for host locality - we scheduled all local tasks already and there might be more available ... + loopCount += 1 + + manager.slaveOffer(execId, hostPort, availableCpus(i), overrideLocality) match { case Some(task) => tasks(i) += task val tid = task.taskId @@ -162,15 +371,31 @@ private[spark] class ClusterScheduler(val sc: SparkContext) taskSetTaskIds(manager.taskSet.id) += tid taskIdToExecutorId(tid) = execId activeExecutorIds += execId - executorsByHost(host) += execId + executorsByHostPort(hostPort) += execId availableCpus(i) -= 1 launchedTask = true case None => {} + } + } + // Loop once more - when lastLoop = true, then we try to schedule task on all nodes irrespective of + // data locality (we still go in order of priority : but that would not change anything since + // if data local tasks had been available, we would have scheduled them already) + if (lastLoop) { + // prevent more looping + launchedTask = false + } else if (!lastLoop && !launchedTask) { + // Do this only if TASK_SCHEDULING_AGGRESSION != NODE_LOCAL + if (TASK_SCHEDULING_AGGRESSION != TaskLocality.NODE_LOCAL) { + // fudge launchedTask to ensure we loop once more + launchedTask = true + // dont loop anymore + lastLoop = true } } } while (launchedTask) } + if (tasks.size > 0) { hasLaunchedTask = true } @@ -223,6 +448,7 @@ private[spark] class ClusterScheduler(val sc: SparkContext) backend.reviveOffers() } if (taskFailed) { + // Also revive offers if a task had failed for some reason other than host lost backend.reviveOffers() } @@ -256,29 +482,40 @@ private[spark] class ClusterScheduler(val sc: SparkContext) if (jarServer != null) { jarServer.stop() } + + // sleeping for an arbitrary 5 seconds : to ensure that messages are sent out. + // TODO: Do something better ! + Thread.sleep(5000L) } override def defaultParallelism() = backend.defaultParallelism() + // Check for speculatable tasks in all our active jobs. def checkSpeculatableTasks() { var shouldRevive = false synchronized { - for (ts <- activeTaskSetsQueue) { - shouldRevive |= ts.checkSpeculatableTasks() - } + shouldRevive = rootPool.checkSpeculatableTasks() } if (shouldRevive) { backend.reviveOffers() } } + // 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 + synchronized { if (activeExecutorIds.contains(executorId)) { - val host = executorIdToHost(executorId) - logError("Lost executor %s on %s: %s".format(executorId, host, reason)) + val hostPort = executorIdToHostPort(executorId) + logError("Lost executor %s on %s: %s".format(executorId, hostPort, reason)) removeExecutor(executorId) failedExecutor = Some(executorId) } else { @@ -296,19 +533,104 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } } - /** Get a list of hosts that currently have executors */ - def hostsAlive: scala.collection.Set[String] = executorsByHost.keySet - /** Remove an executor from all our data structures and mark it as lost */ private def removeExecutor(executorId: String) { activeExecutorIds -= executorId - val host = executorIdToHost(executorId) - val execs = executorsByHost.getOrElse(host, new HashSet) + val hostPort = executorIdToHostPort(executorId) + if (hostPortsAlive.contains(hostPort)) { + // DEBUG Code + Utils.checkHostPort(hostPort) + + hostPortsAlive -= hostPort + hostToAliveHostPorts.getOrElseUpdate(Utils.parseHostPort(hostPort)._1, new HashSet[String]).remove(hostPort) + } + + val execs = executorsByHostPort.getOrElse(hostPort, new HashSet) execs -= executorId if (execs.isEmpty) { - executorsByHost -= host + executorsByHostPort -= hostPort + } + executorIdToHostPort -= executorId + rootPool.executorLost(executorId, hostPort) + } + + def executorGained(execId: String, hostPort: String) { + listener.executorGained(execId, hostPort) + } + + def getExecutorsAliveOnHost(host: String): Option[Set[String]] = { + Utils.checkHost(host) + + val retval = hostToAliveHostPorts.get(host) + if (retval.isDefined) { + return Some(retval.get.toSet) } - executorIdToHost -= executorId - activeTaskSetsQueue.foreach(_.executorLost(executorId, host)) + + None + } + + def isExecutorAliveOnHostPort(hostPort: String): Boolean = { + // Even if hostPort is a host, it does not matter - it is just a specific check. + // But we do have to ensure that only hostPort get into hostPortsAlive ! + // So no check against Utils.checkHostPort + hostPortsAlive.contains(hostPort) + } + + // By default, rack is unknown + def getRackForHost(value: String): Option[String] = None + + // By default, (cached) hosts for rack is unknown + def getCachedHostsForRack(rack: String): Option[Set[String]] = None +} + + +object ClusterScheduler { + + // Used to 'spray' available containers across the available set to ensure too many containers on same host + // are not used up. Used in yarn mode and in task scheduling (when there are multiple containers available + // to execute a task) + // For example: yarn can returns more containers than we would have requested under ANY, this method + // prioritizes how to use the allocated containers. + // flatten the map such that the array buffer entries are spread out across the returned value. + // given <host, list[container]> == <h1, [c1 .. c5]>, <h2, [c1 .. c3]>, <h3, [c1, c2]>, <h4, c1>, <h5, c1>, i + // the return value would be something like : h1c1, h2c1, h3c1, h4c1, h5c1, h1c2, h2c2, h3c2, h1c3, h2c3, h1c4, h1c5 + // We then 'use' the containers in this order (consuming only the top K from this list where + // K = number to be user). This is to ensure that if we have multiple eligible allocations, + // they dont end up allocating all containers on a small number of hosts - increasing probability of + // multiple container failure when a host goes down. + // Note, there is bias for keys with higher number of entries in value to be picked first (by design) + // Also note that invocation of this method is expected to have containers of same 'type' + // (host-local, rack-local, off-rack) and not across types : so that reordering is simply better from + // the available list - everything else being same. + // That is, we we first consume data local, then rack local and finally off rack nodes. So the + // prioritization from this method applies to within each category + def prioritizeContainers[K, T] (map: HashMap[K, ArrayBuffer[T]]): List[T] = { + val _keyList = new ArrayBuffer[K](map.size) + _keyList ++= map.keys + + // order keyList based on population of value in map + val keyList = _keyList.sortWith( + (left, right) => map.get(left).getOrElse(Set()).size > map.get(right).getOrElse(Set()).size + ) + + val retval = new ArrayBuffer[T](keyList.size * 2) + var index = 0 + var found = true + + while (found){ + found = false + for (key <- keyList) { + val containerList: ArrayBuffer[T] = map.get(key).getOrElse(null) + assert(containerList != null) + // Get the index'th entry for this host - if present + if (index < containerList.size){ + retval += containerList.apply(index) + found = true + } + } + index += 1 + } + + retval.toList } } diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala new file mode 100644 index 0000000000..d2110bd098 --- /dev/null +++ b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala @@ -0,0 +1,802 @@ +/* + * 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 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 spark.{FetchFailed, Logging, Resubmitted, SparkEnv, Success, TaskEndReason, TaskState, Utils} +import spark.{ExceptionFailure, SparkException, TaskResultTooBigFailure} +import spark.TaskState.TaskState +import spark.scheduler.{ShuffleMapTask, Task, TaskResult, TaskSet} + + +private[spark] object TaskLocality + extends Enumeration("PROCESS_LOCAL", "NODE_LOCAL", "RACK_LOCAL", "ANY") with Logging { + + // process local is expected to be used ONLY within tasksetmanager for now. + val PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY = Value + + type TaskLocality = Value + + def isAllowed(constraint: TaskLocality, condition: TaskLocality): Boolean = { + + // Must not be the constraint. + assert (constraint != TaskLocality.PROCESS_LOCAL) + + constraint match { + case TaskLocality.NODE_LOCAL => + condition == TaskLocality.NODE_LOCAL + case TaskLocality.RACK_LOCAL => + condition == TaskLocality.NODE_LOCAL || condition == TaskLocality.RACK_LOCAL + // For anything else, allow + case _ => true + } + } + + def parse(str: String): TaskLocality = { + // better way to do this ? + try { + val retval = TaskLocality.withName(str) + // Must not specify PROCESS_LOCAL ! + assert (retval != TaskLocality.PROCESS_LOCAL) + retval + } catch { + case nEx: NoSuchElementException => { + logWarning("Invalid task locality specified '" + str + "', defaulting to NODE_LOCAL") + // default to preserve earlier behavior + NODE_LOCAL + } + } + } +} + +/** + * Schedules the tasks within a single TaskSet in the ClusterScheduler. + */ +private[spark] class ClusterTaskSetManager(sched: ClusterScheduler, val taskSet: TaskSet) + extends TaskSetManager with Logging { + + // Maximum time to wait to run a task in a preferred location (in ms) + val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong + + // CPUs to request per task + val CPUS_PER_TASK = System.getProperty("spark.task.cpus", "1").toDouble + + // 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 ser = SparkEnv.get.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 + // Last time when we launched a preferred task (for delay scheduling) + var lastPreferredLaunchTime = System.currentTimeMillis + + // List of pending tasks for each node (process local to container). + // 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 pendingTasksForHostPort = new HashMap[String, ArrayBuffer[Int]] + + // List of pending tasks for each node. + // Essentially, similar to pendingTasksForHostPort, except at host level + private val pendingTasksForHost = new HashMap[String, ArrayBuffer[Int]] + + // List of pending tasks for each node based on rack locality. + // Essentially, similar to pendingTasksForHost, except at rack level + private val pendingRackLocalTasksForHost = new HashMap[String, ArrayBuffer[Int]] + + // List containing pending tasks with no locality preferences + val pendingTasksWithNoPrefs = new ArrayBuffer[Int] + + // List 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 job 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 generation and set it on all tasks + val generation = sched.mapOutputTracker.getGeneration + logDebug("Generation for " + taskSet.id + ": " + generation) + for (t <- tasks) { + t.generation = generation + } + + // 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) + } + + // Note that it follows the hierarchy. + // if we search for NODE_LOCAL, the output will include PROCESS_LOCAL and + // if we search for RACK_LOCAL, it will include PROCESS_LOCAL & NODE_LOCAL + private def findPreferredLocations( + _taskPreferredLocations: Seq[String], + scheduler: ClusterScheduler, + taskLocality: TaskLocality.TaskLocality): HashSet[String] = + { + if (TaskLocality.PROCESS_LOCAL == taskLocality) { + // straight forward comparison ! Special case it. + val retval = new HashSet[String]() + scheduler.synchronized { + for (location <- _taskPreferredLocations) { + if (scheduler.isExecutorAliveOnHostPort(location)) { + retval += location + } + } + } + + return retval + } + + val taskPreferredLocations = { + if (TaskLocality.NODE_LOCAL == taskLocality) { + _taskPreferredLocations + } else { + assert (TaskLocality.RACK_LOCAL == taskLocality) + // Expand set to include all 'seen' rack local hosts. + // This works since container allocation/management happens within master - + // so any rack locality information is updated in msater. + // Best case effort, and maybe sort of kludge for now ... rework it later ? + val hosts = new HashSet[String] + _taskPreferredLocations.foreach(h => { + val rackOpt = scheduler.getRackForHost(h) + if (rackOpt.isDefined) { + val hostsOpt = scheduler.getCachedHostsForRack(rackOpt.get) + if (hostsOpt.isDefined) { + hosts ++= hostsOpt.get + } + } + + // Ensure that irrespective of what scheduler says, host is always added ! + hosts += h + }) + + hosts + } + } + + val retval = new HashSet[String] + scheduler.synchronized { + for (prefLocation <- taskPreferredLocations) { + val aliveLocationsOpt = scheduler.getExecutorsAliveOnHost(Utils.parseHostPort(prefLocation)._1) + if (aliveLocationsOpt.isDefined) { + retval ++= aliveLocationsOpt.get + } + } + } + + retval + } + + // Add a task to all the pending-task lists that it should be on. + private def addPendingTask(index: Int) { + // We can infer hostLocalLocations from rackLocalLocations by joining it against + // tasks(index).preferredLocations (with appropriate hostPort <-> host conversion). + // But not doing it for simplicity sake. If this becomes a performance issue, modify it. + val locs = tasks(index).preferredLocations + val processLocalLocations = findPreferredLocations(locs, sched, TaskLocality.PROCESS_LOCAL) + val hostLocalLocations = findPreferredLocations(locs, sched, TaskLocality.NODE_LOCAL) + val rackLocalLocations = findPreferredLocations(locs, sched, TaskLocality.RACK_LOCAL) + + if (rackLocalLocations.size == 0) { + // Current impl ensures this. + assert (processLocalLocations.size == 0) + assert (hostLocalLocations.size == 0) + pendingTasksWithNoPrefs += index + } else { + + // process local locality + for (hostPort <- processLocalLocations) { + // DEBUG Code + Utils.checkHostPort(hostPort) + + val hostPortList = pendingTasksForHostPort.getOrElseUpdate(hostPort, ArrayBuffer()) + hostPortList += index + } + + // host locality (includes process local) + for (hostPort <- hostLocalLocations) { + // DEBUG Code + Utils.checkHostPort(hostPort) + + val host = Utils.parseHostPort(hostPort)._1 + val hostList = pendingTasksForHost.getOrElseUpdate(host, ArrayBuffer()) + hostList += index + } + + // rack locality (includes process local and host local) + for (rackLocalHostPort <- rackLocalLocations) { + // DEBUG Code + Utils.checkHostPort(rackLocalHostPort) + + val rackLocalHost = Utils.parseHostPort(rackLocalHostPort)._1 + val list = pendingRackLocalTasksForHost.getOrElseUpdate(rackLocalHost, ArrayBuffer()) + list += index + } + } + + allPendingTasks += index + } + + // Return the pending tasks list for a given host port (process local), or an empty list if + // there is no map entry for that host + private def getPendingTasksForHostPort(hostPort: String): ArrayBuffer[Int] = { + // DEBUG Code + Utils.checkHostPort(hostPort) + pendingTasksForHostPort.getOrElse(hostPort, 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(hostPort: String): ArrayBuffer[Int] = { + val host = Utils.parseHostPort(hostPort)._1 + pendingTasksForHost.getOrElse(host, ArrayBuffer()) + } + + // Return the pending tasks (rack level) list for a given host, or an empty list if + // there is no map entry for that host + private def getRackLocalPendingTasksForHost(hostPort: String): ArrayBuffer[Int] = { + val host = Utils.parseHostPort(hostPort)._1 + pendingRackLocalTasksForHost.getOrElse(host, ArrayBuffer()) + } + + // Number of pending tasks for a given host Port (which would be process local) + override def numPendingTasksForHostPort(hostPort: String): Int = { + getPendingTasksForHostPort(hostPort).count { index => + copiesRunning(index) == 0 && !finished(index) + } + } + + // Number of pending tasks for a given host (which would be data local) + override def numPendingTasksForHost(hostPort: String): Int = { + getPendingTasksForHost(hostPort).count { index => + copiesRunning(index) == 0 && !finished(index) + } + } + + // Number of pending rack local tasks for a given host + override def numRackLocalPendingTasksForHost(hostPort: String): Int = { + getRackLocalPendingTasksForHost(hostPort).count { index => + copiesRunning(index) == 0 && !finished(index) + } + } + + + // 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 + } + + // Return a speculative task for a given host if any are available. The task should not have an + // attempt running on this host, in case the host is slow. In addition, if locality is set, the + // task must have a preference for this host/rack/no preferred locations at all. + private def findSpeculativeTask(hostPort: String, locality: TaskLocality.TaskLocality): Option[Int] = { + + assert (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) + speculatableTasks.retain(index => !finished(index)) // Remove finished tasks from set + + if (speculatableTasks.size > 0) { + val localTask = speculatableTasks.find { index => + val locations = findPreferredLocations(tasks(index).preferredLocations, sched, + TaskLocality.NODE_LOCAL) + val attemptLocs = taskAttempts(index).map(_.hostPort) + (locations.size == 0 || locations.contains(hostPort)) && !attemptLocs.contains(hostPort) + } + + if (localTask != None) { + speculatableTasks -= localTask.get + return localTask + } + + // check for rack locality + if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { + val rackTask = speculatableTasks.find { index => + val locations = findPreferredLocations(tasks(index).preferredLocations, sched, + TaskLocality.RACK_LOCAL) + val attemptLocs = taskAttempts(index).map(_.hostPort) + locations.contains(hostPort) && !attemptLocs.contains(hostPort) + } + + if (rackTask != None) { + speculatableTasks -= rackTask.get + return rackTask + } + } + + // Any task ... + if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { + // Check for attemptLocs also ? + val nonLocalTask = speculatableTasks.find { i => + !taskAttempts(i).map(_.hostPort).contains(hostPort) + } + if (nonLocalTask != None) { + speculatableTasks -= nonLocalTask.get + return nonLocalTask + } + } + } + return None + } + + // Dequeue a pending task for a given node and return its index. + // If localOnly is set to false, allow non-local tasks as well. + private def findTask(hostPort: String, locality: TaskLocality.TaskLocality): Option[Int] = { + val processLocalTask = findTaskFromList(getPendingTasksForHostPort(hostPort)) + if (processLocalTask != None) { + return processLocalTask + } + + val localTask = findTaskFromList(getPendingTasksForHost(hostPort)) + if (localTask != None) { + return localTask + } + + if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) { + val rackLocalTask = findTaskFromList(getRackLocalPendingTasksForHost(hostPort)) + if (rackLocalTask != None) { + return rackLocalTask + } + } + + // Look for no pref tasks AFTER rack local tasks - this has side effect that we will get to + // failed tasks later rather than sooner. + // TODO: That code path needs to be revisited (adding to no prefs list when host:port goes down). + val noPrefTask = findTaskFromList(pendingTasksWithNoPrefs) + if (noPrefTask != None) { + return noPrefTask + } + + if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { + val nonLocalTask = findTaskFromList(allPendingTasks) + if (nonLocalTask != None) { + return nonLocalTask + } + } + + // Finally, if all else has failed, find a speculative task + return findSpeculativeTask(hostPort, locality) + } + + private def isProcessLocalLocation(task: Task[_], hostPort: String): Boolean = { + Utils.checkHostPort(hostPort) + + val locs = task.preferredLocations + + locs.contains(hostPort) + } + + private def isHostLocalLocation(task: Task[_], hostPort: String): Boolean = { + val locs = task.preferredLocations + + // If no preference, consider it as host local + if (locs.isEmpty) return true + + val host = Utils.parseHostPort(hostPort)._1 + locs.find(h => Utils.parseHostPort(h)._1 == host).isDefined + } + + // Does a host count as a rack local preferred location for a task? + // (assumes host is NOT preferred location). + // This is true if either the task has preferred locations and this host is one, or it has + // no preferred locations (in which we still count the launch as preferred). + private def isRackLocalLocation(task: Task[_], hostPort: String): Boolean = { + + val locs = task.preferredLocations + + val preferredRacks = new HashSet[String]() + for (preferredHost <- locs) { + val rack = sched.getRackForHost(preferredHost) + if (None != rack) preferredRacks += rack.get + } + + if (preferredRacks.isEmpty) return false + + val hostRack = sched.getRackForHost(hostPort) + + return None != hostRack && preferredRacks.contains(hostRack.get) + } + + // Respond to an offer of a single slave from the scheduler by finding a task + override def slaveOffer( + execId: String, + hostPort: String, + availableCpus: Double, + overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] = + { + if (tasksFinished < numTasks && availableCpus >= CPUS_PER_TASK) { + // If explicitly specified, use that + val locality = if (overrideLocality != null) overrideLocality else { + // expand only if we have waited for more than LOCALITY_WAIT for a host local task ... + val time = System.currentTimeMillis + if (time - lastPreferredLaunchTime < LOCALITY_WAIT) { + TaskLocality.NODE_LOCAL + } else { + TaskLocality.ANY + } + } + + findTask(hostPort, locality) match { + case Some(index) => { + // Found a task; do some bookkeeping and return a Mesos task for it + val task = tasks(index) + val taskId = sched.newTaskId() + // Figure out whether this should count as a preferred launch + val taskLocality = + if (isProcessLocalLocation(task, hostPort)) TaskLocality.PROCESS_LOCAL + else if (isHostLocalLocation(task, hostPort)) TaskLocality.NODE_LOCAL + else if (isRackLocalLocation(task, hostPort)) TaskLocality.RACK_LOCAL + else TaskLocality.ANY + val prefStr = taskLocality.toString + logInfo("Starting task %s:%d as TID %s on slave %s: %s (%s)".format( + taskSet.id, index, taskId, execId, hostPort, prefStr)) + // Do various bookkeeping + copiesRunning(index) += 1 + val time = System.currentTimeMillis + val info = new TaskInfo(taskId, index, time, execId, hostPort, taskLocality) + taskInfos(taskId) = info + taskAttempts(index) = info :: taskAttempts(index) + if (taskLocality == TaskLocality.PROCESS_LOCAL || taskLocality == TaskLocality.NODE_LOCAL) { + lastPreferredLaunchTime = time + } + // Serialize and return the task + val startTime = System.currentTimeMillis + // 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 = System.currentTimeMillis - 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, serializedTask)) + } + case _ => + } + } + return None + } + + override def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { + 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.hostPort, 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 = System.currentTimeMillis + 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) + } + } + + // TODO(xiajunluan): for now we just find Pool not TaskSetManager + // we can extend this function in future if needed + override def getSchedulableByName(name: String): Schedulable = { + return null + } + + override def addSchedulable(schedulable:Schedulable) { + // nothing + } + + override def removeSchedulable(schedulable:Schedulable) { + // nothing + } + + override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { + var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager] + sortedTaskSetQueue += this + return sortedTaskSetQueue + } + + override def executorLost(execId: String, hostPort: String) { + logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id) + + // If some task has preferred locations only on hostname, and there are no more executors there, + // put it in the no-prefs list to avoid the wait from delay scheduling + + // host local tasks - should we push this to rack local or no pref list ? For now, preserving + // behavior and moving to no prefs list. Note, this was done due to impliations related to + // 'waiting' for data local tasks, etc. + // Note: NOT checking process local list - since host local list is super set of that. We need + // to ad to no prefs only if there is no host local node for the task (not if there is no + // process local node for the task) + for (index <- getPendingTasksForHost(Utils.parseHostPort(hostPort)._1)) { + val newLocs = findPreferredLocations( + tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) + if (newLocs.isEmpty) { + pendingTasksWithNoPrefs += index + } + } + + // 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 = System.currentTimeMillis() + 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.hostPort, threshold)) + speculatableTasks += index + foundTasks = true + } + } + } + return foundTasks + } + + override def hasPendingTasks(): Boolean = { + numTasks > 0 && tasksFinished < numTasks + } +} diff --git a/core/src/main/scala/spark/scheduler/cluster/ExecutorLossReason.scala b/core/src/main/scala/spark/scheduler/cluster/ExecutorLossReason.scala index 8bf838209f..8825f2dd24 100644 --- a/core/src/main/scala/spark/scheduler/cluster/ExecutorLossReason.scala +++ b/core/src/main/scala/spark/scheduler/cluster/ExecutorLossReason.scala @@ -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. + */ + package spark.scheduler.cluster import spark.executor.ExecutorExitCode diff --git a/core/src/main/scala/spark/scheduler/cluster/Pool.scala b/core/src/main/scala/spark/scheduler/cluster/Pool.scala new file mode 100644 index 0000000000..83708f07e1 --- /dev/null +++ b/core/src/main/scala/spark/scheduler/cluster/Pool.scala @@ -0,0 +1,121 @@ +/* + * 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 spark.scheduler.cluster + +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap + +import spark.Logging +import spark.scheduler.cluster.SchedulingMode.SchedulingMode + +/** + * An Schedulable entity that represent collection of Pools or TaskSetManagers + */ + +private[spark] class Pool( + val poolName: String, + val schedulingMode: SchedulingMode, + initMinShare: Int, + initWeight: Int) + extends Schedulable + with Logging { + + var schedulableQueue = new ArrayBuffer[Schedulable] + var schedulableNameToSchedulable = new HashMap[String, Schedulable] + + var weight = initWeight + var minShare = initMinShare + var runningTasks = 0 + + var priority = 0 + var stageId = 0 + var name = poolName + var parent:Schedulable = null + + var taskSetSchedulingAlgorithm: SchedulingAlgorithm = { + schedulingMode match { + case SchedulingMode.FAIR => + new FairSchedulingAlgorithm() + case SchedulingMode.FIFO => + new FIFOSchedulingAlgorithm() + } + } + + override def addSchedulable(schedulable: Schedulable) { + schedulableQueue += schedulable + schedulableNameToSchedulable(schedulable.name) = schedulable + schedulable.parent= this + } + + override def removeSchedulable(schedulable: Schedulable) { + schedulableQueue -= schedulable + schedulableNameToSchedulable -= schedulable.name + } + + override def getSchedulableByName(schedulableName: String): Schedulable = { + if (schedulableNameToSchedulable.contains(schedulableName)) { + return schedulableNameToSchedulable(schedulableName) + } + for (schedulable <- schedulableQueue) { + var sched = schedulable.getSchedulableByName(schedulableName) + if (sched != null) { + return sched + } + } + return null + } + + override def executorLost(executorId: String, host: String) { + schedulableQueue.foreach(_.executorLost(executorId, host)) + } + + override def checkSpeculatableTasks(): Boolean = { + var shouldRevive = false + for (schedulable <- schedulableQueue) { + shouldRevive |= schedulable.checkSpeculatableTasks() + } + return shouldRevive + } + + override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { + var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager] + val sortedSchedulableQueue = schedulableQueue.sortWith(taskSetSchedulingAlgorithm.comparator) + for (schedulable <- sortedSchedulableQueue) { + sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue() + } + return sortedTaskSetQueue + } + + 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 hasPendingTasks(): Boolean = { + schedulableQueue.exists(_.hasPendingTasks()) + } +} diff --git a/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala b/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala new file mode 100644 index 0000000000..e77e8e4162 --- /dev/null +++ b/core/src/main/scala/spark/scheduler/cluster/Schedulable.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 spark.scheduler.cluster + +import spark.scheduler.cluster.SchedulingMode.SchedulingMode + +import scala.collection.mutable.ArrayBuffer +/** + * An interface for schedulable entities. + * there are two type of Schedulable entities(Pools and TaskSetManagers) + */ +private[spark] trait Schedulable { + var parent: Schedulable + // child queues + def schedulableQueue: ArrayBuffer[Schedulable] + def schedulingMode: SchedulingMode + def weight: Int + def minShare: Int + def runningTasks: Int + def priority: Int + 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/spark/scheduler/cluster/SchedulableBuilder.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala new file mode 100644 index 0000000000..b2d089f31d --- /dev/null +++ b/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.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 spark.scheduler.cluster + +import java.io.{File, FileInputStream, FileOutputStream} + +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap +import scala.collection.mutable.HashSet +import scala.util.control.Breaks._ +import scala.xml._ + +import spark.Logging +import spark.scheduler.cluster.SchedulingMode.SchedulingMode + +import java.util.Properties + +/** + * An interface to build Schedulable tree + * buildPools: build the tree nodes(pools) + * addTaskSetManager: build the leaf nodes(TaskSetManagers) + */ +private[spark] trait SchedulableBuilder { + def buildPools() + def addTaskSetManager(manager: Schedulable, properties: Properties) +} + +private[spark] class FIFOSchedulableBuilder(val rootPool: Pool) + extends SchedulableBuilder with Logging { + + override def buildPools() { + // nothing + } + + override def addTaskSetManager(manager: Schedulable, properties: Properties) { + rootPool.addSchedulable(manager) + } +} + +private[spark] class FairSchedulableBuilder(val rootPool: Pool) + extends SchedulableBuilder with Logging { + + val schedulerAllocFile = System.getProperty("spark.fairscheduler.allocation.file","unspecified") + val FAIR_SCHEDULER_PROPERTIES = "spark.scheduler.cluster.fair.pool" + val DEFAULT_POOL_NAME = "default" + val MINIMUM_SHARES_PROPERTY = "minShare" + val SCHEDULING_MODE_PROPERTY = "schedulingMode" + val WEIGHT_PROPERTY = "weight" + val POOL_NAME_PROPERTY = "@name" + val POOLS_PROPERTY = "pool" + val DEFAULT_SCHEDULING_MODE = SchedulingMode.FIFO + val DEFAULT_MINIMUM_SHARE = 2 + val DEFAULT_WEIGHT = 1 + + override def buildPools() { + val file = new File(schedulerAllocFile) + if (file.exists()) { + val xml = XML.loadFile(file) + for (poolNode <- (xml \\ POOLS_PROPERTY)) { + + val poolName = (poolNode \ POOL_NAME_PROPERTY).text + var schedulingMode = DEFAULT_SCHEDULING_MODE + var minShare = DEFAULT_MINIMUM_SHARE + var weight = DEFAULT_WEIGHT + + val xmlSchedulingMode = (poolNode \ SCHEDULING_MODE_PROPERTY).text + if (xmlSchedulingMode != "") { + try { + schedulingMode = SchedulingMode.withName(xmlSchedulingMode) + } catch { + case e: Exception => logInfo("Error xml schedulingMode, using default schedulingMode") + } + } + + val xmlMinShare = (poolNode \ MINIMUM_SHARES_PROPERTY).text + if (xmlMinShare != "") { + minShare = xmlMinShare.toInt + } + + val xmlWeight = (poolNode \ WEIGHT_PROPERTY).text + if (xmlWeight != "") { + weight = xmlWeight.toInt + } + + val pool = new Pool(poolName, schedulingMode, minShare, weight) + rootPool.addSchedulable(pool) + logInfo("Create new pool with name:%s,schedulingMode:%s,minShare:%d,weight:%d".format( + poolName, schedulingMode, minShare, weight)) + } + } + + // finally create "default" pool + if (rootPool.getSchedulableByName(DEFAULT_POOL_NAME) == null) { + val pool = new Pool(DEFAULT_POOL_NAME, DEFAULT_SCHEDULING_MODE, + DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT) + rootPool.addSchedulable(pool) + logInfo("Create default pool with name:%s,schedulingMode:%s,minShare:%d,weight:%d".format( + DEFAULT_POOL_NAME, DEFAULT_SCHEDULING_MODE, DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT)) + } + } + + override def addTaskSetManager(manager: Schedulable, properties: Properties) { + var poolName = DEFAULT_POOL_NAME + var parentPool = rootPool.getSchedulableByName(poolName) + if (properties != null) { + poolName = properties.getProperty(FAIR_SCHEDULER_PROPERTIES, DEFAULT_POOL_NAME) + parentPool = rootPool.getSchedulableByName(poolName) + if (parentPool == null) { + // we will create a new pool that user has configured in app + // instead of being defined in xml file + parentPool = new Pool(poolName, DEFAULT_SCHEDULING_MODE, + DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT) + rootPool.addSchedulable(parentPool) + logInfo("Create pool with name:%s,schedulingMode:%s,minShare:%d,weight:%d".format( + poolName, DEFAULT_SCHEDULING_MODE, DEFAULT_MINIMUM_SHARE, DEFAULT_WEIGHT)) + } + } + parentPool.addSchedulable(manager) + logInfo("Added task set " + manager.name + " tasks to pool "+poolName) + } +} diff --git a/core/src/main/scala/spark/scheduler/cluster/SchedulerBackend.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulerBackend.scala index 9ac875de3a..4431744ec3 100644 --- a/core/src/main/scala/spark/scheduler/cluster/SchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/cluster/SchedulerBackend.scala @@ -1,6 +1,23 @@ +/* + * 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 spark.scheduler.cluster -import spark.Utils +import spark.{SparkContext, Utils} /** * A backend interface for cluster scheduling systems that allows plugging in different ones under @@ -14,14 +31,7 @@ private[spark] trait SchedulerBackend { def defaultParallelism(): Int // Memory used by each executor (in megabytes) - protected val executorMemory = { - // 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) - } - + protected val executorMemory: Int = SparkContext.executorMemoryRequested // TODO: Probably want to add a killTask too } diff --git a/core/src/main/scala/spark/scheduler/cluster/SchedulingAlgorithm.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulingAlgorithm.scala new file mode 100644 index 0000000000..69e0ac2a6b --- /dev/null +++ b/core/src/main/scala/spark/scheduler/cluster/SchedulingAlgorithm.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 spark.scheduler.cluster + +/** + * An interface for sort algorithm + * FIFO: FIFO algorithm between TaskSetManagers + * FS: FS algorithm between Pools, and FIFO or FS within Pools + */ +private[spark] trait SchedulingAlgorithm { + def comparator(s1: Schedulable, s2: Schedulable): Boolean +} + +private[spark] class FIFOSchedulingAlgorithm extends SchedulingAlgorithm { + override def comparator(s1: Schedulable, s2: Schedulable): Boolean = { + val priority1 = s1.priority + val priority2 = s2.priority + var res = math.signum(priority1 - priority2) + if (res == 0) { + val stageId1 = s1.stageId + val stageId2 = s2.stageId + res = math.signum(stageId1 - stageId2) + } + if (res < 0) { + return true + } else { + return false + } + } +} + +private[spark] class FairSchedulingAlgorithm extends SchedulingAlgorithm { + override def comparator(s1: Schedulable, s2: Schedulable): Boolean = { + val minShare1 = s1.minShare + val minShare2 = s2.minShare + val runningTasks1 = s1.runningTasks + val runningTasks2 = s2.runningTasks + val s1Needy = runningTasks1 < minShare1 + val s2Needy = runningTasks2 < minShare2 + val minShareRatio1 = runningTasks1.toDouble / math.max(minShare1, 1.0).toDouble + val minShareRatio2 = runningTasks2.toDouble / math.max(minShare2, 1.0).toDouble + val taskToWeightRatio1 = runningTasks1.toDouble / s1.weight.toDouble + val taskToWeightRatio2 = runningTasks2.toDouble / s2.weight.toDouble + var res:Boolean = true + var compare:Int = 0 + + if (s1Needy && !s2Needy) { + return true + } else if (!s1Needy && s2Needy) { + return false + } else if (s1Needy && s2Needy) { + compare = minShareRatio1.compareTo(minShareRatio2) + } else { + compare = taskToWeightRatio1.compareTo(taskToWeightRatio2) + } + + if (compare < 0) { + return true + } else if (compare > 0) { + return false + } else { + return s1.name < s2.name + } + } +} + diff --git a/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala new file mode 100644 index 0000000000..55cdf4791f --- /dev/null +++ b/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala @@ -0,0 +1,29 @@ +/* + * 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 spark.scheduler.cluster + +/** + * "FAIR" and "FIFO" determines which policy is used + * 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") { + + type SchedulingMode = Value + val FAIR,FIFO,NONE = Value +} diff --git a/core/src/main/scala/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala index bb289c9cf3..55d6c0a47e 100644 --- a/core/src/main/scala/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala @@ -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. + */ + package spark.scheduler.cluster import spark.{Utils, Logging, SparkContext} @@ -31,7 +48,8 @@ private[spark] class SparkDeploySchedulerBackend( val command = Command("spark.executor.StandaloneExecutorBackend", args, sc.executorEnvs) val sparkHome = sc.getSparkHome().getOrElse( throw new IllegalArgumentException("must supply spark home for spark standalone")) - val appDesc = new ApplicationDescription(appName, maxCores, executorMemory, command, sparkHome) + val appDesc = new ApplicationDescription(appName, maxCores, executorMemory, command, sparkHome, + sc.ui.appUIAddress) client = new Client(sc.env.actorSystem, master, appDesc, this) client.start() @@ -57,9 +75,9 @@ private[spark] class SparkDeploySchedulerBackend( } } - override def executorAdded(executorId: String, workerId: String, host: String, cores: Int, memory: Int) { - logInfo("Granted executor ID %s on host %s with %d cores, %s RAM".format( - executorId, host, cores, Utils.memoryMegabytesToString(memory))) + override def executorAdded(executorId: String, workerId: String, hostPort: String, cores: Int, memory: Int) { + logInfo("Granted executor ID %s on hostPort %s with %d cores, %s RAM".format( + executorId, hostPort, cores, Utils.memoryMegabytesToString(memory))) } override def executorRemoved(executorId: String, message: String, exitStatus: Option[Int]) { diff --git a/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala b/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala index d766067824..05c29eb72f 100644 --- a/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala +++ b/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala @@ -1,42 +1,63 @@ +/* + * 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 spark.scheduler.cluster -import spark.TaskState.TaskState import java.nio.ByteBuffer + +import spark.TaskState.TaskState +import spark.Utils import spark.util.SerializableBuffer + private[spark] sealed trait StandaloneClusterMessage extends Serializable -// Driver to executors -private[spark] -case class LaunchTask(task: TaskDescription) extends StandaloneClusterMessage +private[spark] object StandaloneClusterMessages { -private[spark] -case class RegisteredExecutor(sparkProperties: Seq[(String, String)]) - extends StandaloneClusterMessage + // Driver to executors + case class LaunchTask(task: TaskDescription) extends StandaloneClusterMessage -private[spark] -case class RegisterExecutorFailed(message: String) extends StandaloneClusterMessage + case class RegisteredExecutor(sparkProperties: Seq[(String, String)]) + extends StandaloneClusterMessage -// Executors to driver -private[spark] -case class RegisterExecutor(executorId: String, host: String, cores: Int) - extends StandaloneClusterMessage + case class RegisterExecutorFailed(message: String) extends StandaloneClusterMessage -private[spark] -case class StatusUpdate(executorId: String, taskId: Long, state: TaskState, data: SerializableBuffer) - extends StandaloneClusterMessage + // Executors to driver + case class RegisterExecutor(executorId: String, hostPort: String, cores: Int) + extends StandaloneClusterMessage { + Utils.checkHostPort(hostPort, "Expected host port") + } -private[spark] -object StatusUpdate { - /** Alternate factory method that takes a ByteBuffer directly for the data field */ - def apply(executorId: String, taskId: Long, state: TaskState, data: ByteBuffer): StatusUpdate = { - StatusUpdate(executorId, taskId, state, new SerializableBuffer(data)) + case class StatusUpdate(executorId: String, taskId: Long, state: TaskState, + data: SerializableBuffer) extends StandaloneClusterMessage + + object StatusUpdate { + /** Alternate factory method that takes a ByteBuffer directly for the data field */ + def apply(executorId: String, taskId: Long, state: TaskState, data: ByteBuffer) + : StatusUpdate = { + StatusUpdate(executorId, taskId, state, new SerializableBuffer(data)) + } } -} -// Internal messages in driver -private[spark] case object ReviveOffers extends StandaloneClusterMessage -private[spark] case object StopDriver extends StandaloneClusterMessage + // Internal messages in driver + case object ReviveOffers extends StandaloneClusterMessage + + case object StopDriver extends StandaloneClusterMessage -private[spark] case class RemoveExecutor(executorId: String, reason: String) - extends StandaloneClusterMessage + case class RemoveExecutor(executorId: String, reason: String) extends StandaloneClusterMessage + +} diff --git a/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala b/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala index 7a428e3361..075a7cbf7e 100644 --- a/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala @@ -1,15 +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 spark.scheduler.cluster +import java.util.concurrent.atomic.AtomicInteger + import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} import akka.actor._ -import akka.util.duration._ -import akka.pattern.ask - -import spark.{SparkException, Logging, TaskState} import akka.dispatch.Await -import java.util.concurrent.atomic.AtomicInteger +import akka.pattern.ask import akka.remote.{RemoteClientShutdown, RemoteClientDisconnected, RemoteClientLifeCycleEvent} +import akka.util.Duration + +import spark.{Utils, SparkException, Logging, TaskState} +import spark.scheduler.cluster.StandaloneClusterMessages._ /** * A standalone scheduler backend, which waits for standalone executors to connect to it through @@ -24,12 +43,12 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor var totalCoreCount = new AtomicInteger(0) class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor { - val executorActor = new HashMap[String, ActorRef] - val executorAddress = new HashMap[String, Address] - val executorHost = new HashMap[String, String] - val freeCores = new HashMap[String, Int] - val actorToExecutorId = new HashMap[ActorRef, String] - val addressToExecutorId = new HashMap[Address, String] + private val executorActor = new HashMap[String, ActorRef] + private val executorAddress = new HashMap[String, Address] + private val executorHostPort = 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() @@ -37,7 +56,8 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor } def receive = { - case RegisterExecutor(executorId, host, cores) => + case RegisterExecutor(executorId, hostPort, cores) => + Utils.checkHostPort(hostPort, "Host port expected " + hostPort) if (executorActor.contains(executorId)) { sender ! RegisterExecutorFailed("Duplicate executor ID: " + executorId) } else { @@ -45,7 +65,7 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor sender ! RegisteredExecutor(sparkProperties) context.watch(sender) executorActor(executorId) = sender - executorHost(executorId) = host + executorHostPort(executorId) = hostPort freeCores(executorId) = cores executorAddress(executorId) = sender.path.address actorToExecutorId(sender) = executorId @@ -85,13 +105,13 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor // Make fake resource offers on all executors def makeOffers() { launchTasks(scheduler.resourceOffers( - executorHost.toArray.map {case (id, host) => new WorkerOffer(id, host, freeCores(id))})) + executorHostPort.toArray.map {case (id, hostPort) => new WorkerOffer(id, hostPort, freeCores(id))})) } // Make fake resource offers on just one executor def makeOffers(executorId: String) { launchTasks(scheduler.resourceOffers( - Seq(new WorkerOffer(executorId, executorHost(executorId), freeCores(executorId))))) + Seq(new WorkerOffer(executorId, executorHostPort(executorId), freeCores(executorId))))) } // Launch tasks returned by a set of resource offers @@ -110,9 +130,9 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor actorToExecutorId -= executorActor(executorId) addressToExecutorId -= executorAddress(executorId) executorActor -= executorId - executorHost -= executorId + executorHostPort -= executorId freeCores -= executorId - executorHost -= executorId + executorHostPort -= executorId totalCoreCount.addAndGet(-numCores) scheduler.executorLost(executorId, SlaveLost(reason)) } @@ -128,7 +148,7 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor while (iterator.hasNext) { val entry = iterator.next val (key, value) = (entry.getKey.toString, entry.getValue.toString) - if (key.startsWith("spark.")) { + if (key.startsWith("spark.") && !key.equals("spark.hostPort")) { properties += ((key, value)) } } @@ -136,10 +156,11 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor Props(new DriverActor(properties)), name = StandaloneSchedulerBackend.ACTOR_NAME) } + private val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + override def stop() { try { if (driverActor != null) { - val timeout = 5.seconds val future = driverActor.ask(StopDriver)(timeout) Await.result(future, timeout) } @@ -159,7 +180,6 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor // Called by subclasses when notified of a lost worker def removeExecutor(executorId: String, reason: String) { try { - val timeout = 5.seconds val future = driverActor.ask(RemoveExecutor(executorId, reason))(timeout) Await.result(future, timeout) } catch { diff --git a/core/src/main/scala/spark/scheduler/cluster/TaskDescription.scala b/core/src/main/scala/spark/scheduler/cluster/TaskDescription.scala index b41e951be9..761fdf6919 100644 --- a/core/src/main/scala/spark/scheduler/cluster/TaskDescription.scala +++ b/core/src/main/scala/spark/scheduler/cluster/TaskDescription.scala @@ -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. + */ + package spark.scheduler.cluster import java.nio.ByteBuffer diff --git a/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala b/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala index dfe3c5a85b..c693b722ac 100644 --- a/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala +++ b/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala @@ -1,5 +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 spark.scheduler.cluster +import spark.Utils + /** * Information about a running task attempt inside a TaskSet. */ @@ -9,8 +28,11 @@ class TaskInfo( val index: Int, val launchTime: Long, val executorId: String, - val host: String, - val preferred: Boolean) { + val hostPort: String, + val taskLocality: TaskLocality.TaskLocality) { + + Utils.checkHostPort(hostPort, "Expected hostport") + var finishTime: Long = 0 var failed = false @@ -29,6 +51,17 @@ class TaskInfo( def running: Boolean = !finished + def status: String = { + if (running) + "RUNNING" + else if (failed) + "FAILED" + else if (successful) + "SUCCESS" + else + "UNKNOWN" + } + def duration: Long = { if (!finished) { throw new UnsupportedOperationException("duration() called on unfinished tasks") diff --git a/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala index c9f2c48804..1a92a5ed6f 100644 --- a/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala +++ b/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala @@ -1,430 +1,47 @@ -package spark.scheduler.cluster - -import java.util.Arrays -import java.util.{HashMap => JHashMap} - -import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap -import scala.collection.mutable.HashSet -import scala.math.max -import scala.math.min - -import spark._ -import spark.scheduler._ -import spark.TaskState.TaskState -import java.nio.ByteBuffer - -/** - * Schedules the tasks within a single TaskSet in the ClusterScheduler. +/* + * 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. */ -private[spark] class TaskSetManager(sched: ClusterScheduler, val taskSet: TaskSet) extends Logging { - - // Maximum time to wait to run a task in a preferred location (in ms) - val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong - - // CPUs to request per task - val CPUS_PER_TASK = System.getProperty("spark.task.cpus", "1").toDouble - - // Maximum times a task is allowed to fail before failing the job - val MAX_TASK_FAILURES = 4 - - // 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 ser = SparkEnv.get.closureSerializer.newInstance() - - val priority = taskSet.priority - 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 - - // Last time when we launched a preferred task (for delay scheduling) - var lastPreferredLaunchTime = System.currentTimeMillis - - // List of pending tasks for each node. 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. - val pendingTasksForHost = new HashMap[String, ArrayBuffer[Int]] - - // List containing pending tasks with no locality preferences - val pendingTasksWithNoPrefs = new ArrayBuffer[Int] - - // List 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 job 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 generation and set it on all tasks - val generation = sched.mapOutputTracker.getGeneration - logDebug("Generation for " + taskSet.id + ": " + generation) - for (t <- tasks) { - t.generation = generation - } - - // 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) - } - - // Add a task to all the pending-task lists that it should be on. - private def addPendingTask(index: Int) { - val locations = tasks(index).preferredLocations.toSet & sched.hostsAlive - if (locations.size == 0) { - pendingTasksWithNoPrefs += index - } else { - for (host <- locations) { - val list = pendingTasksForHost.getOrElseUpdate(host, ArrayBuffer()) - list += index - } - } - allPendingTasks += index - } - - // 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()) - } - - // 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 - } - - // Return a speculative task for a given host if any are available. The task should not have an - // attempt running on this host, in case the host is slow. In addition, if localOnly is set, the - // task must have a preference for this host (or no preferred locations at all). - private def findSpeculativeTask(host: String, localOnly: Boolean): Option[Int] = { - val hostsAlive = sched.hostsAlive - speculatableTasks.retain(index => !finished(index)) // Remove finished tasks from set - val localTask = speculatableTasks.find { - index => - val locations = tasks(index).preferredLocations.toSet & hostsAlive - val attemptLocs = taskAttempts(index).map(_.host) - (locations.size == 0 || locations.contains(host)) && !attemptLocs.contains(host) - } - if (localTask != None) { - speculatableTasks -= localTask.get - return localTask - } - if (!localOnly && speculatableTasks.size > 0) { - val nonLocalTask = speculatableTasks.find(i => !taskAttempts(i).map(_.host).contains(host)) - if (nonLocalTask != None) { - speculatableTasks -= nonLocalTask.get - return nonLocalTask - } - } - return None - } - - // Dequeue a pending task for a given node and return its index. - // If localOnly is set to false, allow non-local tasks as well. - private def findTask(host: String, localOnly: Boolean): Option[Int] = { - val localTask = findTaskFromList(getPendingTasksForHost(host)) - if (localTask != None) { - return localTask - } - val noPrefTask = findTaskFromList(pendingTasksWithNoPrefs) - if (noPrefTask != None) { - return noPrefTask - } - if (!localOnly) { - val nonLocalTask = findTaskFromList(allPendingTasks) - if (nonLocalTask != None) { - return nonLocalTask - } - } - // Finally, if all else has failed, find a speculative task - return findSpeculativeTask(host, localOnly) - } - - // Does a host count as a preferred location for a task? This is true if - // either the task has preferred locations and this host is one, or it has - // no preferred locations (in which we still count the launch as preferred). - private def isPreferredLocation(task: Task[_], host: String): Boolean = { - val locs = task.preferredLocations - return (locs.contains(host) || locs.isEmpty) - } - - // Respond to an offer of a single slave from the scheduler by finding a task - def slaveOffer(execId: String, host: String, availableCpus: Double): Option[TaskDescription] = { - if (tasksFinished < numTasks && availableCpus >= CPUS_PER_TASK) { - val time = System.currentTimeMillis - val localOnly = (time - lastPreferredLaunchTime < LOCALITY_WAIT) - - findTask(host, localOnly) match { - case Some(index) => { - // Found a task; do some bookkeeping and return a Mesos task for it - val task = tasks(index) - val taskId = sched.newTaskId() - // Figure out whether this should count as a preferred launch - val preferred = isPreferredLocation(task, host) - val prefStr = if (preferred) { - "preferred" - } else { - "non-preferred, not one of " + task.preferredLocations.mkString(", ") - } - logInfo("Starting task %s:%d as TID %s on executor %s: %s (%s)".format( - taskSet.id, index, taskId, execId, host, prefStr)) - // Do various bookkeeping - copiesRunning(index) += 1 - val info = new TaskInfo(taskId, index, time, execId, host, preferred) - taskInfos(taskId) = info - taskAttempts(index) = info :: taskAttempts(index) - if (preferred) { - lastPreferredLaunchTime = time - } - // Serialize and return the task - val startTime = System.currentTimeMillis - val serializedTask = Task.serializeWithDependencies( - task, sched.sc.addedFiles, sched.sc.addedJars, ser) - val timeTaken = System.currentTimeMillis - startTime - 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) - return Some(new TaskDescription(taskId, execId, taskName, serializedTask)) - } - case _ => - } - } - return None - } +package spark.scheduler.cluster - def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { - 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 _ => - } - } +import java.nio.ByteBuffer - 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() - if (!finished(index)) { - tasksFinished += 1 - logInfo("Finished TID %s in %d ms (progress: %d/%d)".format( - tid, info.duration, tasksFinished, numTasks)) - // Deserialize task result and pass it to the scheduler - val result = ser.deserialize[TaskResult[_]](serializedData, getClass.getClassLoader) - result.metrics.resultSize = serializedData.limit() - sched.listener.taskEnded(tasks(index), Success, result.value, result.accumUpdates, info, result.metrics) - // 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") - } - } +import spark.TaskState.TaskState +import spark.scheduler.TaskSet - 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() - 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) - return +private[spark] trait TaskSetManager extends Schedulable { + def schedulableQueue = null + + def schedulingMode = SchedulingMode.NONE + + def taskSet: TaskSet - case ef: ExceptionFailure => - val key = ef.exception.toString - val now = System.currentTimeMillis - 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.exception.getStackTrace.map(loc => "\tat %s".format(loc.toString)) - logInfo("Loss was due to %s\n%s".format(ef.exception.toString, locs.mkString("\n"))) - } else { - logInfo("Loss was due to %s [duplicate %d]".format(ef.exception.toString, dupCount)) - } + def slaveOffer( + execId: String, + hostPort: String, + availableCpus: Double, + overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] - 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") - } - } + def numPendingTasksForHostPort(hostPort: String): Int - def error(message: String) { - // Save the error message - abort("Error: " + message) - } + def numRackLocalPendingTasksForHost(hostPort: String): Int - 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) - sched.taskSetFinished(this) - } + def numPendingTasksForHost(hostPort: String): Int - def executorLost(execId: String, hostname: String) { - logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id) - val newHostsAlive = sched.hostsAlive - // If some task has preferred locations only on hostname, and there are no more executors there, - // put it in the no-prefs list to avoid the wait from delay scheduling - if (!newHostsAlive.contains(hostname)) { - for (index <- getPendingTasksForHost(hostname)) { - val newLocs = tasks(index).preferredLocations.toSet & newHostsAlive - if (newLocs.isEmpty) { - pendingTasksWithNoPrefs += index - } - } - } - // 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) - } - } + def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) - /** - * 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. - */ - 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 = System.currentTimeMillis() - 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 - } + def error(message: String) } diff --git a/core/src/main/scala/spark/scheduler/cluster/WorkerOffer.scala b/core/src/main/scala/spark/scheduler/cluster/WorkerOffer.scala index 3c3afcbb14..06d1203f70 100644 --- a/core/src/main/scala/spark/scheduler/cluster/WorkerOffer.scala +++ b/core/src/main/scala/spark/scheduler/cluster/WorkerOffer.scala @@ -1,8 +1,25 @@ +/* + * 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 spark.scheduler.cluster /** * Represents free resources available on an executor. */ private[spark] -class WorkerOffer(val executorId: String, val hostname: String, val cores: Int) { +class WorkerOffer(val executorId: String, val hostPort: String, val cores: Int) { } diff --git a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala index 9e1bde3fbe..bb0c836e86 100644 --- a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala +++ b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala @@ -1,20 +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. + */ + package spark.scheduler.local import java.io.File import java.util.concurrent.atomic.AtomicInteger +import java.nio.ByteBuffer +import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap +import scala.collection.mutable.HashSet import spark._ +import spark.TaskState.TaskState import spark.executor.ExecutorURLClassLoader import spark.scheduler._ -import spark.scheduler.cluster.TaskInfo +import spark.scheduler.cluster._ +import spark.scheduler.cluster.SchedulingMode.SchedulingMode +import akka.actor._ /** - * A simple TaskScheduler implementation that runs tasks locally in a thread pool. Optionally + * 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] class LocalScheduler(threads: Int, maxFailures: Int, sc: SparkContext) + +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 { @@ -30,87 +84,135 @@ private[spark] class LocalScheduler(threads: Int, maxFailures: Int, sc: SparkCon val classLoader = new ExecutorURLClassLoader(Array(), Thread.currentThread.getContextClassLoader) - // TODO: Need to take into account stage priority in scheduling + var schedulableBuilder: SchedulableBuilder = null + var rootPool: Pool = null + val schedulingMode: SchedulingMode = SchedulingMode.withName( + System.getProperty("spark.cluster.schedulingmode", "FIFO")) + val activeTaskSets = new HashMap[String, TaskSetManager] + val taskIdToTaskSetId = new HashMap[Long, String] + val taskSetTaskIds = new HashMap[String, HashSet[Long]] - override def start() { } + 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) { - val tasks = taskSet.tasks - val failCount = new Array[Int](tasks.size) - - def submitTask(task: Task[_], idInJob: Int) { - val myAttemptId = attemptId.getAndIncrement() - threadPool.submit(new Runnable { - def run() { - runTask(task, idInJob, myAttemptId) - } - }) + 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 runTask(task: Task[_], idInJob: Int, attemptId: Int) { - logInfo("Running " + task) - val info = new TaskInfo(attemptId, idInJob, System.currentTimeMillis(), "local", "local", true) - // Set the Spark execution environment for the worker thread - SparkEnv.set(env) - 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 ser = SparkEnv.get.closureSerializer.newInstance() - val bytes = Task.serializeWithDependencies(task, sc.addedFiles, sc.addedJars, ser) - logInfo("Size of task " + idInJob + " is " + bytes.limit + " bytes") - val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(bytes) - updateDependencies(taskFiles, taskJars) // Download any files added with addFile - val deserStart = System.currentTimeMillis() - val deserializedTask = ser.deserialize[Task[_]]( - taskBytes, Thread.currentThread.getContextClassLoader) - val deserTime = System.currentTimeMillis() - deserStart - - // Run it - val result: Any = deserializedTask.run(attemptId) - - // 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 = ser.serialize(result) - deserializedTask.metrics.get.resultSize = serResult.limit() - val resultToReturn = ser.deserialize[Any](serResult) - val accumUpdates = ser.deserialize[collection.mutable.Map[Long, Any]]( - ser.serialize(Accumulators.values)) - logInfo("Finished " + task) - info.markSuccessful() - deserializedTask.metrics.get.executorRunTime = info.duration.toInt //close enough - deserializedTask.metrics.get.executorDeserializeTime = deserTime.toInt - - // If the threadpool has not already been shutdown, notify DAGScheduler - if (!Thread.currentThread().isInterrupted) - listener.taskEnded(task, Success, resultToReturn, accumUpdates, info, deserializedTask.metrics.getOrElse(null)) - } catch { - case t: Throwable => { - logError("Exception in task " + idInJob, t) - failCount.synchronized { - failCount(idInJob) += 1 - if (failCount(idInJob) <= maxFailures) { - submitTask(task, idInJob) - } else { - // TODO: Do something nicer here to return all the way to the user - if (!Thread.currentThread().isInterrupted) - listener.taskEnded(task, new ExceptionFailure(t), null, null, info, null) - } + 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.slaveOffer(null, null, freeCpuCores) 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() + var attemptedTask: Option[Task[_]] = None + val start = System.currentTimeMillis() + var taskStart: Long = 0 + try { + Accumulators.clear() + Thread.currentThread().setContextClassLoader(classLoader) - for ((task, i) <- tasks.zipWithIndex) { - submitTask(task, i) + // 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 = ser.serialize(result) + deserializedTask.metrics.get.resultSize = serResult.limit() + val resultToReturn = ser.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.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) + metrics.foreach{m => m.executorRunTime = serviceTime.toInt} + val failure = new ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics) + localActor ! LocalStatusUpdate(taskId, TaskState.FAILED, ser.serialize(failure)) + } } } @@ -126,6 +228,7 @@ private[spark] class LocalScheduler(threads: Int, maxFailures: Int, sc: SparkCon 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)) @@ -141,7 +244,16 @@ private[spark] class LocalScheduler(threads: Int, maxFailures: Int, sc: SparkCon } } - override def stop() { + 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() } diff --git a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala new file mode 100644 index 0000000000..4ab15532cf --- /dev/null +++ b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala @@ -0,0 +1,203 @@ +/* + * 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 spark.scheduler.local + +import java.nio.ByteBuffer +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap + +import spark.{ExceptionFailure, Logging, SparkEnv, Success, TaskState} +import spark.TaskState.TaskState +import spark.scheduler.{Task, TaskResult, TaskSet} +import spark.scheduler.cluster.{Schedulable, TaskDescription, TaskInfo, TaskLocality, 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 ser = SparkEnv.get.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 slaveOffer( + execId: String, + hostPort: String, + availableCpus: Double, + overrideLocality: TaskLocality.TaskLocality = null): 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, bytes)) + case None => {} + } + } + return None + } + + override def numPendingTasksForHostPort(hostPort: String): Int = { + return 0 + } + + override def numRackLocalPendingTasksForHost(hostPort :String): Int = { + return 0 + } + + override def numPendingTasksForHost(hostPort: String): Int = { + return 0 + } + + override def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { + 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/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala index f4a2994b6d..6ebbb5ec9b 100644 --- a/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala @@ -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. + */ + package spark.scheduler.mesos import com.google.protobuf.ByteString @@ -93,12 +110,6 @@ private[spark] class CoarseMesosSchedulerBackend( } def createCommand(offer: Offer, numCores: Int): CommandInfo = { - val runScript = new File(sparkHome, "run").getCanonicalPath - val driverUrl = "akka://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), System.getProperty("spark.driver.port"), - StandaloneSchedulerBackend.ACTOR_NAME) - val command = "\"%s\" spark.executor.StandaloneExecutorBackend %s %s %s %d".format( - runScript, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores) val environment = Environment.newBuilder() sc.executorEnvs.foreach { case (key, value) => environment.addVariables(Environment.Variable.newBuilder() @@ -106,7 +117,26 @@ private[spark] class CoarseMesosSchedulerBackend( .setValue(value) .build()) } - return CommandInfo.newBuilder().setValue(command).setEnvironment(environment).build() + 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") + if (uri == null) { + val runScript = new File(sparkHome, "run").getCanonicalPath + command.setValue("\"%s\" spark.executor.StandaloneExecutorBackend %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*; ./run spark.executor.StandaloneExecutorBackend %s %s %s %d".format( + basename, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores)) + command.addUris(CommandInfo.URI.newBuilder().setValue(uri)) + } + return command.build() } override def offerRescinded(d: SchedulerDriver, o: OfferID) {} diff --git a/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala index ca7fab4cc5..f6069a5775 100644 --- a/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala @@ -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. + */ + package spark.scheduler.mesos import com.google.protobuf.ByteString @@ -43,8 +60,12 @@ private[spark] class MesosSchedulerBackend( // An ExecutorInfo for our tasks var execArgs: Array[Byte] = null + var classLoader: ClassLoader = null + override def start() { synchronized { + classLoader = Thread.currentThread.getContextClassLoader + new Thread("MesosSchedulerBackend driver") { setDaemon(true) override def run() { @@ -68,7 +89,6 @@ private[spark] class MesosSchedulerBackend( val sparkHome = sc.getSparkHome().getOrElse(throw new SparkException( "Spark home is not set; set it through the spark.home system " + "property, the SPARK_HOME environment variable or the SparkContext constructor")) - val execScript = new File(sparkHome, "spark-executor").getCanonicalPath val environment = Environment.newBuilder() sc.executorEnvs.foreach { case (key, value) => environment.addVariables(Environment.Variable.newBuilder() @@ -76,15 +96,23 @@ private[spark] class MesosSchedulerBackend( .setValue(value) .build()) } + val command = CommandInfo.newBuilder() + .setEnvironment(environment) + val uri = System.getProperty("spark.executor.uri") + if (uri == null) { + command.setValue(new File(sparkHome, "spark-executor").getCanonicalPath) + } 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*; ./spark-executor".format(basename)) + command.addUris(CommandInfo.URI.newBuilder().setValue(uri)) + } val memory = Resource.newBuilder() .setName("mem") .setType(Value.Type.SCALAR) .setScalar(Value.Scalar.newBuilder().setValue(executorMemory).build()) .build() - val command = CommandInfo.newBuilder() - .setValue(execScript) - .setEnvironment(environment) - .build() ExecutorInfo.newBuilder() .setExecutorId(ExecutorID.newBuilder().setValue(execId).build()) .setCommand(command) @@ -114,13 +142,28 @@ private[spark] class MesosSchedulerBackend( return execArgs } + private def setClassLoader(): ClassLoader = { + val oldClassLoader = Thread.currentThread.getContextClassLoader + Thread.currentThread.setContextClassLoader(classLoader) + return oldClassLoader + } + + private def restoreClassLoader(oldClassLoader: ClassLoader) { + Thread.currentThread.setContextClassLoader(oldClassLoader) + } + override def offerRescinded(d: SchedulerDriver, o: OfferID) {} override def registered(d: SchedulerDriver, frameworkId: FrameworkID, masterInfo: MasterInfo) { - logInfo("Registered as framework ID " + frameworkId.getValue) - registeredLock.synchronized { - isRegistered = true - registeredLock.notifyAll() + val oldClassLoader = setClassLoader() + try { + logInfo("Registered as framework ID " + frameworkId.getValue) + registeredLock.synchronized { + isRegistered = true + registeredLock.notifyAll() + } + } finally { + restoreClassLoader(oldClassLoader) } } @@ -142,49 +185,54 @@ private[spark] class MesosSchedulerBackend( * tasks are balanced across the cluster. */ override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) { - synchronized { - // Build a big list of the offerable workers, and remember their indices so that we can - // figure out which Offer to reply to for each worker - val offerableIndices = new ArrayBuffer[Int] - val offerableWorkers = new ArrayBuffer[WorkerOffer] - - def enoughMemory(o: Offer) = { - val mem = getResource(o.getResourcesList, "mem") - val slaveId = o.getSlaveId.getValue - mem >= executorMemory || slaveIdsWithExecutors.contains(slaveId) - } + val oldClassLoader = setClassLoader() + try { + synchronized { + // Build a big list of the offerable workers, and remember their indices so that we can + // figure out which Offer to reply to for each worker + val offerableIndices = new ArrayBuffer[Int] + val offerableWorkers = new ArrayBuffer[WorkerOffer] + + def enoughMemory(o: Offer) = { + val mem = getResource(o.getResourcesList, "mem") + val slaveId = o.getSlaveId.getValue + mem >= executorMemory || slaveIdsWithExecutors.contains(slaveId) + } - for ((offer, index) <- offers.zipWithIndex if enoughMemory(offer)) { - offerableIndices += index - offerableWorkers += new WorkerOffer( - offer.getSlaveId.getValue, - offer.getHostname, - getResource(offer.getResourcesList, "cpus").toInt) - } + for ((offer, index) <- offers.zipWithIndex if enoughMemory(offer)) { + offerableIndices += index + offerableWorkers += new WorkerOffer( + offer.getSlaveId.getValue, + offer.getHostname, + getResource(offer.getResourcesList, "cpus").toInt) + } - // Call into the ClusterScheduler - val taskLists = scheduler.resourceOffers(offerableWorkers) - - // Build a list of Mesos tasks for each slave - val mesosTasks = offers.map(o => Collections.emptyList[MesosTaskInfo]()) - for ((taskList, index) <- taskLists.zipWithIndex) { - if (!taskList.isEmpty) { - val offerNum = offerableIndices(index) - val slaveId = offers(offerNum).getSlaveId.getValue - slaveIdsWithExecutors += slaveId - mesosTasks(offerNum) = new JArrayList[MesosTaskInfo](taskList.size) - for (taskDesc <- taskList) { - taskIdToSlaveId(taskDesc.taskId) = slaveId - mesosTasks(offerNum).add(createMesosTask(taskDesc, slaveId)) + // Call into the ClusterScheduler + val taskLists = scheduler.resourceOffers(offerableWorkers) + + // Build a list of Mesos tasks for each slave + val mesosTasks = offers.map(o => Collections.emptyList[MesosTaskInfo]()) + for ((taskList, index) <- taskLists.zipWithIndex) { + if (!taskList.isEmpty) { + val offerNum = offerableIndices(index) + val slaveId = offers(offerNum).getSlaveId.getValue + slaveIdsWithExecutors += slaveId + mesosTasks(offerNum) = new JArrayList[MesosTaskInfo](taskList.size) + for (taskDesc <- taskList) { + taskIdToSlaveId(taskDesc.taskId) = slaveId + mesosTasks(offerNum).add(createMesosTask(taskDesc, slaveId)) + } } } - } - // Reply to the offers - val filters = Filters.newBuilder().setRefuseSeconds(1).build() // TODO: lower timeout? - for (i <- 0 until offers.size) { - d.launchTasks(offers(i).getId, mesosTasks(i), filters) + // Reply to the offers + val filters = Filters.newBuilder().setRefuseSeconds(1).build() // TODO: lower timeout? + for (i <- 0 until offers.size) { + d.launchTasks(offers(i).getId, mesosTasks(i), filters) + } } + } finally { + restoreClassLoader(oldClassLoader) } } @@ -224,23 +272,33 @@ private[spark] class MesosSchedulerBackend( } override def statusUpdate(d: SchedulerDriver, status: TaskStatus) { - val tid = status.getTaskId.getValue.toLong - val state = TaskState.fromMesos(status.getState) - synchronized { - if (status.getState == MesosTaskState.TASK_LOST && taskIdToSlaveId.contains(tid)) { - // We lost the executor on this slave, so remember that it's gone - slaveIdsWithExecutors -= taskIdToSlaveId(tid) - } - if (isFinished(status.getState)) { - taskIdToSlaveId.remove(tid) + val oldClassLoader = setClassLoader() + try { + val tid = status.getTaskId.getValue.toLong + val state = TaskState.fromMesos(status.getState) + synchronized { + if (status.getState == MesosTaskState.TASK_LOST && taskIdToSlaveId.contains(tid)) { + // We lost the executor on this slave, so remember that it's gone + slaveIdsWithExecutors -= taskIdToSlaveId(tid) + } + if (isFinished(status.getState)) { + taskIdToSlaveId.remove(tid) + } } + scheduler.statusUpdate(tid, state, status.getData.asReadOnlyByteBuffer) + } finally { + restoreClassLoader(oldClassLoader) } - scheduler.statusUpdate(tid, state, status.getData.asReadOnlyByteBuffer) } override def error(d: SchedulerDriver, message: String) { - logError("Mesos error: " + message) - scheduler.error(message) + val oldClassLoader = setClassLoader() + try { + logError("Mesos error: " + message) + scheduler.error(message) + } finally { + restoreClassLoader(oldClassLoader) + } } override def stop() { @@ -256,11 +314,16 @@ private[spark] class MesosSchedulerBackend( override def frameworkMessage(d: SchedulerDriver, e: ExecutorID, s: SlaveID, b: Array[Byte]) {} private def recordSlaveLost(d: SchedulerDriver, slaveId: SlaveID, reason: ExecutorLossReason) { - logInfo("Mesos slave lost: " + slaveId.getValue) - synchronized { - slaveIdsWithExecutors -= slaveId.getValue + val oldClassLoader = setClassLoader() + try { + logInfo("Mesos slave lost: " + slaveId.getValue) + synchronized { + slaveIdsWithExecutors -= slaveId.getValue + } + scheduler.executorLost(slaveId.getValue, reason) + } finally { + restoreClassLoader(oldClassLoader) } - scheduler.executorLost(slaveId.getValue, reason) } override def slaveLost(d: SchedulerDriver, slaveId: SlaveID) { diff --git a/core/src/main/scala/spark/serializer/Serializer.scala b/core/src/main/scala/spark/serializer/Serializer.scala index aca86ab6f0..dc94d42bb6 100644 --- a/core/src/main/scala/spark/serializer/Serializer.scala +++ b/core/src/main/scala/spark/serializer/Serializer.scala @@ -1,10 +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 spark.serializer -import java.nio.ByteBuffer import java.io.{EOFException, InputStream, OutputStream} +import java.nio.ByteBuffer + import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream + import spark.util.ByteBufferInputStream + /** * A serializer. Because some serialization libraries are not thread safe, this class is used to * create [[spark.serializer.SerializerInstance]] objects that do the actual serialization and are @@ -14,6 +34,7 @@ trait Serializer { def newInstance(): SerializerInstance } + /** * An instance of a serializer, for use by one thread at a time. */ @@ -45,6 +66,7 @@ trait SerializerInstance { } } + /** * A stream for writing serialized objects. */ @@ -61,6 +83,7 @@ trait SerializationStream { } } + /** * A stream for reading serialized objects. */ diff --git a/core/src/main/scala/spark/serializer/SerializerManager.scala b/core/src/main/scala/spark/serializer/SerializerManager.scala new file mode 100644 index 0000000000..b7b24705a2 --- /dev/null +++ b/core/src/main/scala/spark/serializer/SerializerManager.scala @@ -0,0 +1,62 @@ +/* + * 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 spark.serializer + +import java.util.concurrent.ConcurrentHashMap + + +/** + * A service that returns a serializer object given the serializer's class name. If a previous + * instance of the serializer object has been created, the get method returns that instead of + * creating a new one. + */ +private[spark] class SerializerManager { + + private val serializers = new ConcurrentHashMap[String, Serializer] + private var _default: Serializer = _ + + def default = _default + + def setDefault(clsName: String): Serializer = { + _default = get(clsName) + _default + } + + def get(clsName: String): Serializer = { + if (clsName == null) { + default + } else { + var serializer = serializers.get(clsName) + if (serializer != null) { + // If the serializer has been created previously, reuse that. + serializer + } else this.synchronized { + // Otherwise, create a new one. But make sure no other thread has attempted + // to create another new one at the same time. + serializer = serializers.get(clsName) + if (serializer == null) { + val clsLoader = Thread.currentThread.getContextClassLoader + serializer = + Class.forName(clsName, true, clsLoader).newInstance().asInstanceOf[Serializer] + serializers.put(clsName, serializer) + } + serializer + } + } + } +} diff --git a/core/src/main/scala/spark/storage/BlockException.scala b/core/src/main/scala/spark/storage/BlockException.scala new file mode 100644 index 0000000000..8ebfaf3cbf --- /dev/null +++ b/core/src/main/scala/spark/storage/BlockException.scala @@ -0,0 +1,22 @@ +/* + * 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 spark.storage + +private[spark] +case class BlockException(blockId: String, message: String) extends Exception(message) + diff --git a/core/src/main/scala/spark/storage/BlockFetchTracker.scala b/core/src/main/scala/spark/storage/BlockFetchTracker.scala index 993aece1f7..265e554ad8 100644 --- a/core/src/main/scala/spark/storage/BlockFetchTracker.scala +++ b/core/src/main/scala/spark/storage/BlockFetchTracker.scala @@ -1,10 +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 spark.storage private[spark] trait BlockFetchTracker { - def totalBlocks : Int - def numLocalBlocks: Int - def numRemoteBlocks: Int - def remoteFetchTime : Long - def fetchWaitTime: Long - def remoteBytesRead : Long + def totalBlocks : Int + def numLocalBlocks: Int + def numRemoteBlocks: Int + def remoteFetchTime : Long + def fetchWaitTime: Long + def remoteBytesRead : Long } diff --git a/core/src/main/scala/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/spark/storage/BlockFetcherIterator.scala new file mode 100644 index 0000000000..1965c5bc19 --- /dev/null +++ b/core/src/main/scala/spark/storage/BlockFetcherIterator.scala @@ -0,0 +1,347 @@ +/* + * 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 spark.storage + +import java.nio.ByteBuffer +import java.util.concurrent.LinkedBlockingQueue + +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashSet +import scala.collection.mutable.Queue + +import io.netty.buffer.ByteBuf + +import spark.Logging +import spark.Utils +import spark.SparkException +import spark.network.BufferMessage +import spark.network.ConnectionManagerId +import spark.network.netty.ShuffleCopier +import spark.serializer.Serializer + + +/** + * A block fetcher iterator interface. There are two implementations: + * + * BasicBlockFetcherIterator: uses a custom-built NIO communication layer. + * NettyBlockFetcherIterator: uses Netty (OIO) as the communication layer. + * + * Eventually we would like the two to converge and use a single NIO-based communication layer, + * but extensive tests show that under some circumstances (e.g. large shuffles with lots of cores), + * NIO would perform poorly and thus the need for the Netty OIO one. + */ + +private[storage] +trait BlockFetcherIterator extends Iterator[(String, Option[Iterator[Any]])] + with Logging with BlockFetchTracker { + def initialize() +} + + +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)]) { + 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]) { + def failed: Boolean = size == -1 + } + + class BasicBlockFetcherIterator( + private val blockManager: BlockManager, + val blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])], + serializer: Serializer) + extends BlockFetcherIterator { + + import blockManager._ + + private var _remoteBytesRead = 0l + private var _remoteFetchTime = 0l + private var _fetchWaitTime = 0l + + if (blocksByAddress == null) { + throw new IllegalArgumentException("BlocksByAddress is null") + } + + // Total number blocks fetched (local + remote). Also number of FetchResults expected + protected var _numBlocksToFetch = 0 + + protected var startTime = System.currentTimeMillis + + // 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]() + + // 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]() + + // A queue to hold our results. + protected val results = new LinkedBlockingQueue[FetchResult] + + // Queue of fetch requests to issue; we'll pull requests off this gradually to make sure that + // the number of bytes in flight is limited to maxBytesInFlight + private val fetchRequests = new Queue[FetchRequest] + + // Current bytes in flight from our requests + private var bytesInFlight = 0L + + protected def sendRequest(req: FetchRequest) { + logDebug("Sending request for %d blocks (%s) from %s".format( + req.blocks.size, Utils.memoryBytesToString(req.size), req.address.hostPort)) + val cmId = new ConnectionManagerId(req.address.host, req.address.port) + val blockMessageArray = new BlockMessageArray(req.blocks.map { + case (blockId, size) => BlockMessage.fromGetBlock(GetBlock(blockId)) + }) + bytesInFlight += req.size + val sizeMap = req.blocks.toMap // so we can look up the size of each blockID + val fetchStart = System.currentTimeMillis() + val future = connectionManager.sendMessageReliably(cmId, blockMessageArray.toBufferMessage) + future.onSuccess { + case Some(message) => { + val fetchDone = System.currentTimeMillis() + _remoteFetchTime += fetchDone - fetchStart + val bufferMessage = message.asInstanceOf[BufferMessage] + val blockMessageArray = BlockMessageArray.fromBufferMessage(bufferMessage) + for (blockMessage <- blockMessageArray) { + if (blockMessage.getType != BlockMessage.TYPE_GOT_BLOCK) { + throw new SparkException( + "Unexpected message " + blockMessage.getType + " received from " + cmId) + } + val blockId = blockMessage.getId + results.put(new FetchResult(blockId, sizeMap(blockId), + () => dataDeserialize(blockId, blockMessage.getData, serializer))) + _remoteBytesRead += req.size + logDebug("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime)) + } + } + case None => { + logError("Could not get block(s) from " + cmId) + for ((blockId, size) <- req.blocks) { + results.put(new FetchResult(blockId, -1, null)) + } + } + } + } + + protected def splitLocalRemoteBlocks(): ArrayBuffer[FetchRequest] = { + // Split local and remote blocks. Remote blocks are further split into FetchRequests of size + // at most maxBytesInFlight in order to limit the amount of data in flight. + val remoteRequests = new ArrayBuffer[FetchRequest] + for ((address, blockInfos) <- blocksByAddress) { + if (address == blockManagerId) { + numLocal = blockInfos.size + // Filter out zero-sized blocks + localBlocksToFetch ++= blockInfos.filter(_._2 != 0).map(_._1) + _numBlocksToFetch += localBlocksToFetch.size + } else { + numRemote += blockInfos.size + // Make our requests at least maxBytesInFlight / 5 in length; the reason to keep them + // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5 + // nodes, rather than blocking on reading output from one node. + val minRequestSize = math.max(maxBytesInFlight / 5, 1L) + logInfo("maxBytesInFlight: " + maxBytesInFlight + ", minRequest: " + minRequestSize) + val iterator = blockInfos.iterator + var curRequestSize = 0L + var curBlocks = new ArrayBuffer[(String, Long)] + while (iterator.hasNext) { + val (blockId, size) = iterator.next() + // Skip empty blocks + if (size > 0) { + curBlocks += ((blockId, size)) + remoteBlocksToFetch += blockId + _numBlocksToFetch += 1 + curRequestSize += size + } else if (size < 0) { + throw new BlockException(blockId, "Negative block size " + size) + } + if (curRequestSize >= minRequestSize) { + // Add this FetchRequest + remoteRequests += new FetchRequest(address, curBlocks) + curRequestSize = 0 + curBlocks = new ArrayBuffer[(String, Long)] + } + } + // Add in the final request + if (!curBlocks.isEmpty) { + remoteRequests += new FetchRequest(address, curBlocks) + } + } + } + logInfo("Getting " + _numBlocksToFetch + " non-zero-bytes blocks out of " + + totalBlocks + " blocks") + remoteRequests + } + + protected def getLocalBlocks() { + // Get the local blocks while remote blocks are being fetched. Note that it's okay to do + // these all at once because they will just memory-map some files, so they won't consume + // any memory that might exceed our maxBytesInFlight + for (id <- localBlocksToFetch) { + getLocalFromDisk(id, serializer) match { + case Some(iter) => { + // Pass 0 as size since it's not in flight + results.put(new FetchResult(id, 0, () => iter)) + logDebug("Got local block " + id) + } + case None => { + throw new BlockException(id, "Could not get block " + id + " from local machine") + } + } + } + } + + override def initialize() { + // Split local and remote blocks. + val remoteRequests = splitLocalRemoteBlocks() + // Add the remote requests into our queue in a random order + fetchRequests ++= Utils.randomize(remoteRequests) + + // Send out initial requests for blocks, up to our maxBytesInFlight + while (!fetchRequests.isEmpty && + (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) { + sendRequest(fetchRequests.dequeue()) + } + + val numGets = remoteRequests.size - fetchRequests.size + logInfo("Started " + numGets + " remote gets in " + Utils.getUsedTimeMs(startTime)) + + // Get Local Blocks + startTime = System.currentTimeMillis + getLocalBlocks() + logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime) + " ms") + } + + //an iterator that will read fetched blocks off the queue as they arrive. + @volatile protected var resultsGotten = 0 + + override def hasNext: Boolean = resultsGotten < _numBlocksToFetch + + override def next(): (String, Option[Iterator[Any]]) = { + resultsGotten += 1 + val startFetchWait = System.currentTimeMillis() + val result = results.take() + val stopFetchWait = System.currentTimeMillis() + _fetchWaitTime += (stopFetchWait - startFetchWait) + if (! result.failed) bytesInFlight -= result.size + while (!fetchRequests.isEmpty && + (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) { + sendRequest(fetchRequests.dequeue()) + } + (result.blockId, if (result.failed) None else Some(result.deserialize())) + } + + // Implementing BlockFetchTracker trait. + override def totalBlocks: Int = numLocal + numRemote + override def numLocalBlocks: Int = numLocal + override def numRemoteBlocks: Int = numRemote + override def remoteFetchTime: Long = _remoteFetchTime + override def fetchWaitTime: Long = _fetchWaitTime + override def remoteBytesRead: Long = _remoteBytesRead + } + // End of BasicBlockFetcherIterator + + class NettyBlockFetcherIterator( + blockManager: BlockManager, + blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])], + serializer: Serializer) + extends BasicBlockFetcherIterator(blockManager, blocksByAddress, serializer) { + + import blockManager._ + + val fetchRequestsSync = new LinkedBlockingQueue[FetchRequest] + + private def startCopiers(numCopiers: Int): List[_ <: Thread] = { + (for ( i <- Range(0,numCopiers) ) yield { + val copier = new Thread { + override def run(){ + try { + while(!isInterrupted && !fetchRequestsSync.isEmpty) { + sendRequest(fetchRequestsSync.take()) + } + } catch { + case x: InterruptedException => logInfo("Copier Interrupted") + //case _ => throw new SparkException("Exception Throw in Shuffle Copier") + } + } + } + copier.start + copier + }).toList + } + + // keep this to interrupt the threads when necessary + private def stopCopiers() { + for (copier <- copiers) { + copier.interrupt() + } + } + + override protected def sendRequest(req: FetchRequest) { + + def putResult(blockId: String, blockSize: Long, blockData: ByteBuf) { + val fetchResult = new FetchResult(blockId, blockSize, + () => dataDeserialize(blockId, blockData.nioBuffer, serializer)) + results.put(fetchResult) + } + + logDebug("Sending request for %d blocks (%s) from %s".format( + req.blocks.size, Utils.memoryBytesToString(req.size), req.address.host)) + val cmId = new ConnectionManagerId(req.address.host, req.address.nettyPort) + val cpier = new ShuffleCopier + cpier.getBlocks(cmId, req.blocks, putResult) + logDebug("Sent request for remote blocks " + req.blocks + " from " + req.address.host ) + } + + private var copiers: List[_ <: Thread] = null + + override def initialize() { + // Split Local Remote Blocks and set numBlocksToFetch + val remoteRequests = splitLocalRemoteBlocks() + // Add the remote requests into our queue in a random order + for (request <- Utils.randomize(remoteRequests)) { + fetchRequestsSync.put(request) + } + + copiers = startCopiers(System.getProperty("spark.shuffle.copier.threads", "6").toInt) + logInfo("Started " + fetchRequestsSync.size + " remote gets in " + + Utils.getUsedTimeMs(startTime)) + + // Get Local Blocks + startTime = System.currentTimeMillis + getLocalBlocks() + logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime) + " ms") + } + + override def next(): (String, Option[Iterator[Any]]) = { + resultsGotten += 1 + val result = results.take() + // If all the results has been retrieved, copiers will exit automatically + (result.blockId, if (result.failed) None else Some(result.deserialize())) + } + } + // End of NettyBlockFetcherIterator +} diff --git a/core/src/main/scala/spark/storage/BlockManager.scala b/core/src/main/scala/spark/storage/BlockManager.scala index 210061e972..3a72474419 100644 --- a/core/src/main/scala/spark/storage/BlockManager.scala +++ b/core/src/main/scala/spark/storage/BlockManager.scala @@ -1,22 +1,36 @@ +/* + * 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 spark.storage import java.io.{InputStream, OutputStream} import java.nio.{ByteBuffer, MappedByteBuffer} -import java.util.concurrent.{ConcurrentHashMap, LinkedBlockingQueue} -import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Queue} -import scala.collection.JavaConversions._ +import scala.collection.mutable.{HashMap, ArrayBuffer, HashSet} import akka.actor.{ActorSystem, Cancellable, Props} import akka.dispatch.{Await, Future} import akka.util.Duration import akka.util.duration._ -import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} - import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream -import spark.{Logging, SizeEstimator, SparkEnv, SparkException, Utils} +import spark.{Logging, SparkEnv, SparkException, Utils} +import spark.io.CompressionCodec import spark.network._ import spark.serializer.Serializer import spark.util.{ByteBufferInputStream, IdGenerator, MetadataCleaner, TimeStampedHashMap} @@ -24,30 +38,35 @@ import spark.util.{ByteBufferInputStream, IdGenerator, MetadataCleaner, TimeStam import sun.nio.ch.DirectBuffer -private[spark] -case class BlockException(blockId: String, message: String, ex: Exception = null) -extends Exception(message) - -private[spark] -class BlockManager( +private[spark] class BlockManager( executorId: String, actorSystem: ActorSystem, val master: BlockManagerMaster, - val serializer: Serializer, + val defaultSerializer: Serializer, maxMemory: Long) extends Logging { - class BlockInfo(val level: StorageLevel, val tellMaster: Boolean) { - var pending: Boolean = true - var size: Long = -1L - var failed: Boolean = false + 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 (pending) { + if (initThread != Thread.currentThread() && pending) { synchronized { while (pending) this.wait() } @@ -57,35 +76,51 @@ class BlockManager( /** 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 { - pending = false - failed = false - size = sizeInBytes this.notifyAll() } } /** Mark this BlockInfo as ready but failed */ def markFailure() { + assert (pending) + size = 0 + initThread = null + failed = true + initThread = null + pending = false synchronized { - failed = true - pending = false this.notifyAll() } } } + val shuffleBlockManager = new ShuffleBlockManager(this) + private val blockInfo = new TimeStampedHashMap[String, BlockInfo] private[storage] val memoryStore: BlockStore = new MemoryStore(this, maxMemory) - private[storage] val diskStore: BlockStore = + private[storage] val diskStore: DiskStore = new DiskStore(this, System.getProperty("spark.local.dir", System.getProperty("java.io.tmpdir"))) + // 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 connectionManager = new ConnectionManager(0) implicit val futureExecContext = connectionManager.futureExecContext val blockManagerId = BlockManagerId( - executorId, connectionManager.id.host, connectionManager.id.port) + executorId, connectionManager.id.host, connectionManager.id.port, nettyPort) // Max megabytes of data to keep in flight per reducer (to avoid over-allocating memory // for receiving shuffle outputs) @@ -101,7 +136,7 @@ class BlockManager( val heartBeatFrequency = BlockManager.getHeartBeatFrequencyFromSystemProperties - val host = System.getProperty("spark.hostname", Utils.localHostName()) + val hostPort = Utils.localHostPort() val slaveActor = actorSystem.actorOf(Props(new BlockManagerSlaveActor(this)), name = "BlockManagerActor" + BlockManager.ID_GENERATOR.next) @@ -122,6 +157,13 @@ class BlockManager( val metadataCleaner = new MetadataCleaner("BlockManager", this.dropOldBlocks) initialize() + // The compression codec to use. Note that the "lazy" val is necessary because we want to delay + // the initialization of the compression codec until it is first used. The reason is that a Spark + // 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() + /** * Construct a BlockManager with a memory limit set based on system properties. */ @@ -212,9 +254,12 @@ class BlockManager( * Tell the master about the current storage status of a block. This will send a block update * message reflecting the current status, *not* the desired storage level in its block info. * For example, a block with MEMORY_AND_DISK set might have fallen out to be only on disk. + * + * 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) { - val needReregister = !tryToReportBlockStatus(blockId, info) + def reportBlockStatus(blockId: String, info: BlockInfo, droppedMemorySize: Long = 0L) { + val needReregister = !tryToReportBlockStatus(blockId, info, droppedMemorySize) if (needReregister) { logInfo("Got told to reregister updating block " + blockId) // Reregistering will report our new block for free. @@ -228,7 +273,7 @@ class BlockManager( * 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): Boolean = { + private def tryToReportBlockStatus(blockId: String, info: BlockInfo, droppedMemorySize: Long = 0L): Boolean = { val (curLevel, inMemSize, onDiskSize, tellMaster) = info.synchronized { info.level match { case null => @@ -237,7 +282,7 @@ class BlockManager( val inMem = level.useMemory && memoryStore.contains(blockId) val onDisk = level.useDisk && diskStore.contains(blockId) val storageLevel = StorageLevel(onDisk, inMem, level.deserialized, level.replication) - val memSize = if (inMem) memoryStore.getSize(blockId) else 0L + val memSize = if (inMem) memoryStore.getSize(blockId) else droppedMemorySize val diskSize = if (onDisk) diskStore.getSize(blockId) else 0L (storageLevel, memSize, diskSize, info.tellMaster) } @@ -250,26 +295,24 @@ class BlockManager( } } - /** - * Get locations of the block. + * Get locations of an array of blocks. */ - def getLocations(blockId: String): Seq[String] = { + def getLocationBlockIds(blockIds: Array[String]): Array[Seq[BlockManagerId]] = { val startTimeMs = System.currentTimeMillis - var managers = master.getLocations(blockId) - val locations = managers.map(_.ip) - logDebug("Got block locations in " + Utils.getUsedTimeMs(startTimeMs)) - return locations + val locations = master.getLocations(blockIds).toArray + logDebug("Got multiple block location in " + Utils.getUsedTimeMs(startTimeMs)) + locations } /** - * Get locations of an array of blocks. + * A short-circuited method to get blocks directly from disk. This is used for getting + * shuffle blocks. It is safe to do so without a lock on block info since disk store + * never deletes (recent) items. */ - def getLocations(blockIds: Array[String]): Array[Seq[String]] = { - val startTimeMs = System.currentTimeMillis - val locations = master.getLocations(blockIds).map(_.map(_.ip).toSeq).toArray - logDebug("Got multiple block location in " + Utils.getUsedTimeMs(startTimeMs)) - return locations + def getLocalFromDisk(blockId: String, serializer: Serializer): Option[Iterator[Any]] = { + diskStore.getValues(blockId, serializer).orElse( + sys.error("Block " + blockId + " not found on disk, though it should be")) } /** @@ -277,18 +320,6 @@ class BlockManager( */ def getLocal(blockId: String): Option[Iterator[Any]] = { logDebug("Getting local block " + blockId) - - // 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 (blockId.startsWith("shuffle_")) { - return diskStore.getValues(blockId) match { - case Some(iterator) => - Some(iterator) - case None => - throw new Exception("Block " + blockId + " not found on disk, though it should be") - } - } - val info = blockInfo.get(blockId).orNull if (info != null) { info.synchronized { @@ -339,6 +370,8 @@ class BlockManager( 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) @@ -372,7 +405,7 @@ class BlockManager( // 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 (blockId.startsWith("shuffle_")) { + if (ShuffleBlockManager.isShuffle(blockId)) { return diskStore.getBytes(blockId) match { case Some(bytes) => Some(bytes) @@ -411,6 +444,7 @@ class BlockManager( // 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) @@ -450,7 +484,7 @@ class BlockManager( for (loc <- locations) { logDebug("Getting remote block " + blockId + " from " + loc) val data = BlockManagerWorker.syncGetBlock( - GetBlock(blockId), ConnectionManagerId(loc.ip, loc.port)) + GetBlock(blockId), ConnectionManagerId(loc.host, loc.port)) if (data != null) { return Some(dataDeserialize(blockId, data)) } @@ -473,9 +507,19 @@ class BlockManager( * fashion as they're received. Expects a size in bytes to be provided for each block fetched, * so that we can control the maxMegabytesInFlight for the fetch. */ - def getMultiple(blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])]) + def getMultiple( + blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])], serializer: Serializer) : BlockFetcherIterator = { - return new BlockFetcherIterator(this, blocksByAddress) + + val iter = + if (System.getProperty("spark.shuffle.use.netty", "false").toBoolean) { + new BlockFetcherIterator.NettyBlockFetcherIterator(this, blocksByAddress, serializer) + } else { + new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer) + } + + iter.initialize() + iter } def put(blockId: String, values: Iterator[Any], level: StorageLevel, tellMaster: Boolean) @@ -486,6 +530,22 @@ class BlockManager( } /** + * A short circuited method to get a block writer that can write data directly to disk. + * This is currently used for writing shuffle files out. Callers should handle error + * cases. + */ + def getDiskBlockWriter(blockId: String, 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 + } + + /** * 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, @@ -501,17 +561,26 @@ class BlockManager( throw new IllegalArgumentException("Storage level is null or invalid") } - val oldBlock = blockInfo.get(blockId).orNull - if (oldBlock != null && oldBlock.waitForReady()) { - logWarning("Block " + blockId + " already exists on this machine; not re-adding it") - return oldBlock.size - } - // 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 = new BlockInfo(level, tellMaster) - blockInfo.put(blockId, myInfo) + 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 oldBlockOpt.get.size + } + + // 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 @@ -531,6 +600,7 @@ class BlockManager( 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 @@ -555,26 +625,25 @@ class BlockManager( // 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(size) if (tellMaster) { reportBlockStatus(blockId, myInfo) } - } catch { + } 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. - case e: Exception => { + 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", e) - throw e + logWarning("Putting block " + blockId + " failed") } } } logDebug("Put block " + blockId + " locally took " + Utils.getUsedTimeMs(startTimeMs)) - // Replicate block if required if (level.replication > 1) { val remoteStartTime = System.currentTimeMillis @@ -611,16 +680,26 @@ class BlockManager( throw new IllegalArgumentException("Storage level is null or invalid") } - if (blockInfo.contains(blockId)) { - logWarning("Block " + blockId + " already exists on this machine; not re-adding it") - return - } - // 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 = new BlockInfo(level, tellMaster) - blockInfo.put(blockId, myInfo) + 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 @@ -639,6 +718,7 @@ class BlockManager( 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 @@ -649,22 +729,24 @@ class BlockManager( 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) } - } catch { + } 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. - case e: Exception => { + 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", e) - throw e + logWarning("Putting block " + blockId + " failed") } } } @@ -698,7 +780,7 @@ class BlockManager( logDebug("Try to replicate BlockId " + blockId + " once; The size of the data is " + data.limit() + " Bytes. To node: " + peer) if (!BlockManagerWorker.syncPutBlock(PutBlock(blockId, data, tLevel), - new ConnectionManagerId(peer.ip, peer.port))) { + new ConnectionManagerId(peer.host, peer.port))) { logError("Failed to call syncPutBlock to " + peer) } logDebug("Replicated BlockId " + blockId + " once used " + @@ -730,6 +812,14 @@ class BlockManager( val info = blockInfo.get(blockId).orNull if (info != null) { info.synchronized { + // required ? As of now, this will be invoked only for blocks which are ready + // But in case this changes in future, adding for consistency sake. + if (! info.waitForReady() ) { + // If we get here, the block write failed. + logWarning("Block " + blockId + " was marked as failure. Nothing to drop") + return + } + val level = info.level if (level.useDisk && !diskStore.contains(blockId)) { logInfo("Writing block " + blockId + " to disk") @@ -740,12 +830,13 @@ class BlockManager( diskStore.putBytes(blockId, bytes, level) } } + val droppedMemorySize = if (memoryStore.contains(blockId)) memoryStore.getSize(blockId) else 0L val blockWasRemoved = memoryStore.remove(blockId) if (!blockWasRemoved) { logWarning("Block " + blockId + " could not be dropped from memory as it does not exist") } if (info.tellMaster) { - reportBlockStatus(blockId, info) + reportBlockStatus(blockId, info, droppedMemorySize) } if (!level.useDisk) { // The block is completely gone from this node; forget it so we can put() it again later. @@ -758,9 +849,23 @@ class BlockManager( } /** + * Remove all blocks belonging to the given RDD. + * @return The number of blocks removed. + */ + def removeRdd(rddId: Int): Int = { + // 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)) + blocksToRemove.size + } + + /** * Remove a block from both memory and disk. */ - def removeBlock(blockId: String) { + def removeBlock(blockId: String, tellMaster: Boolean = true) { logInfo("Removing block " + blockId) val info = blockInfo.get(blockId).orNull if (info != null) info.synchronized { @@ -772,7 +877,7 @@ class BlockManager( "the disk or memory store") } blockInfo.remove(blockId) - if (info.tellMaster) { + if (tellMaster && info.tellMaster) { reportBlockStatus(blockId, info) } } else { @@ -805,7 +910,7 @@ class BlockManager( } def shouldCompress(blockId: String): Boolean = { - if (blockId.startsWith("shuffle_")) { + if (ShuffleBlockManager.isShuffle(blockId)) { compressShuffle } else if (blockId.startsWith("broadcast_")) { compressBroadcast @@ -820,17 +925,20 @@ class BlockManager( * Wrap an output stream for compression if block compression is enabled for its block type */ def wrapForCompression(blockId: String, s: OutputStream): OutputStream = { - if (shouldCompress(blockId)) new LZFOutputStream(s) else s + 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 = { - if (shouldCompress(blockId)) new LZFInputStream(s) else s + if (shouldCompress(blockId)) compressionCodec.compressedInputStream(s) else s } - def dataSerialize(blockId: String, values: Iterator[Any]): ByteBuffer = { + def dataSerialize( + blockId: String, + values: Iterator[Any], + serializer: Serializer = defaultSerializer): ByteBuffer = { val byteStream = new FastByteArrayOutputStream(4096) val ser = serializer.newInstance() ser.serializeStream(wrapForCompression(blockId, byteStream)).writeAll(values).close() @@ -842,7 +950,10 @@ class BlockManager( * Deserializes a ByteBuffer into an iterator of values and disposes of it when the end of * the iterator is reached. */ - def dataDeserialize(blockId: String, bytes: ByteBuffer): Iterator[Any] = { + def dataDeserialize( + blockId: String, + bytes: ByteBuffer, + serializer: Serializer = defaultSerializer): Iterator[Any] = { bytes.rewind() val stream = wrapForCompression(blockId, new ByteBufferInputStream(bytes, true)) serializer.newInstance().deserializeStream(stream).asIterator @@ -862,8 +973,8 @@ class BlockManager( } } -private[spark] -object BlockManager extends Logging { + +private[spark] object BlockManager extends Logging { val ID_GENERATOR = new IdGenerator @@ -873,7 +984,7 @@ object BlockManager extends Logging { } def getHeartBeatFrequencyFromSystemProperties: Long = - System.getProperty("spark.storage.blockManagerHeartBeatMs", "5000").toLong + System.getProperty("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong / 4 def getDisableHeartBeatsForTesting: Boolean = System.getProperty("spark.test.disableBlockManagerHeartBeat", "false").toBoolean @@ -892,177 +1003,44 @@ object BlockManager extends Logging { } } } -} - -class BlockFetcherIterator( - private val blockManager: BlockManager, - val blocksByAddress: Seq[(BlockManagerId, Seq[(String, Long)])] -) extends Iterator[(String, Option[Iterator[Any]])] with Logging with BlockFetchTracker { - - import blockManager._ - private var _remoteBytesRead = 0l - private var _remoteFetchTime = 0l - private var _fetchWaitTime = 0l - - if (blocksByAddress == null) { - throw new IllegalArgumentException("BlocksByAddress is null") - } - val totalBlocks = blocksByAddress.map(_._2.size).sum - logDebug("Getting " + totalBlocks + " blocks") - var startTime = System.currentTimeMillis - val localBlockIds = new ArrayBuffer[String]() - val remoteBlockIds = new HashSet[String]() - - // 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]) { - def failed: Boolean = size == -1 - } - - // A queue to hold our results. - val results = new LinkedBlockingQueue[FetchResult] - - // A request to fetch one or more blocks, complete with their sizes - class FetchRequest(val address: BlockManagerId, val blocks: Seq[(String, Long)]) { - val size = blocks.map(_._2).sum - } + def blockIdsToExecutorLocations(blockIds: Array[String], env: SparkEnv, blockManagerMaster: BlockManagerMaster = null): HashMap[String, List[String]] = { + // env == null and blockManagerMaster != null is used in tests + assert (env != null || blockManagerMaster != null) + val locationBlockIds: Seq[Seq[BlockManagerId]] = + if (env != null) { + env.blockManager.getLocationBlockIds(blockIds) + } else { + blockManagerMaster.getLocations(blockIds) + } - // Queue of fetch requests to issue; we'll pull requests off this gradually to make sure that - // the number of bytes in flight is limited to maxBytesInFlight - val fetchRequests = new Queue[FetchRequest] + // Convert from block master locations to executor locations (we need that for task scheduling) + val executorLocations = new HashMap[String, List[String]]() + for (i <- 0 until blockIds.length) { + val blockId = blockIds(i) + val blockLocations = locationBlockIds(i) - // Current bytes in flight from our requests - var bytesInFlight = 0L + val executors = new HashSet[String]() - def sendRequest(req: FetchRequest) { - logDebug("Sending request for %d blocks (%s) from %s".format( - req.blocks.size, Utils.memoryBytesToString(req.size), req.address.ip)) - val cmId = new ConnectionManagerId(req.address.ip, req.address.port) - val blockMessageArray = new BlockMessageArray(req.blocks.map { - case (blockId, size) => BlockMessage.fromGetBlock(GetBlock(blockId)) - }) - bytesInFlight += req.size - val sizeMap = req.blocks.toMap // so we can look up the size of each blockID - val fetchStart = System.currentTimeMillis() - val future = connectionManager.sendMessageReliably(cmId, blockMessageArray.toBufferMessage) - future.onSuccess { - case Some(message) => { - val fetchDone = System.currentTimeMillis() - _remoteFetchTime += fetchDone - fetchStart - val bufferMessage = message.asInstanceOf[BufferMessage] - val blockMessageArray = BlockMessageArray.fromBufferMessage(bufferMessage) - for (blockMessage <- blockMessageArray) { - if (blockMessage.getType != BlockMessage.TYPE_GOT_BLOCK) { - throw new SparkException( - "Unexpected message " + blockMessage.getType + " received from " + cmId) - } - val blockId = blockMessage.getId - results.put(new FetchResult( - blockId, sizeMap(blockId), () => dataDeserialize(blockId, blockMessage.getData))) - _remoteBytesRead += req.size - logDebug("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime)) + if (env != null) { + for (bkLocation <- blockLocations) { + val executorHostPort = env.resolveExecutorIdToHostPort(bkLocation.executorId, bkLocation.host) + executors += executorHostPort + // logInfo("bkLocation = " + bkLocation + ", executorHostPort = " + executorHostPort) } - } - case None => { - logError("Could not get block(s) from " + cmId) - for ((blockId, size) <- req.blocks) { - results.put(new FetchResult(blockId, -1, null)) + } else { + // Typically while testing, etc - revert to simply using host. + for (bkLocation <- blockLocations) { + executors += bkLocation.host + // logInfo("bkLocation = " + bkLocation + ", executorHostPort = " + executorHostPort) } } - } - } - // Split local and remote blocks. Remote blocks are further split into FetchRequests of size - // at most maxBytesInFlight in order to limit the amount of data in flight. - val remoteRequests = new ArrayBuffer[FetchRequest] - for ((address, blockInfos) <- blocksByAddress) { - if (address == blockManagerId) { - localBlockIds ++= blockInfos.map(_._1) - } else { - remoteBlockIds ++= blockInfos.map(_._1) - // Make our requests at least maxBytesInFlight / 5 in length; the reason to keep them - // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5 - // nodes, rather than blocking on reading output from one node. - val minRequestSize = math.max(maxBytesInFlight / 5, 1L) - logInfo("maxBytesInFlight: " + maxBytesInFlight + ", minRequest: " + minRequestSize) - val iterator = blockInfos.iterator - var curRequestSize = 0L - var curBlocks = new ArrayBuffer[(String, Long)] - while (iterator.hasNext) { - val (blockId, size) = iterator.next() - curBlocks += ((blockId, size)) - curRequestSize += size - if (curRequestSize >= minRequestSize) { - // Add this FetchRequest - remoteRequests += new FetchRequest(address, curBlocks) - curRequestSize = 0 - curBlocks = new ArrayBuffer[(String, Long)] - } - } - // Add in the final request - if (!curBlocks.isEmpty) { - remoteRequests += new FetchRequest(address, curBlocks) - } + executorLocations.put(blockId, executors.toSeq.toList) } - } - // Add the remote requests into our queue in a random order - fetchRequests ++= Utils.randomize(remoteRequests) - // Send out initial requests for blocks, up to our maxBytesInFlight - while (!fetchRequests.isEmpty && - (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) { - sendRequest(fetchRequests.dequeue()) + executorLocations } - val numGets = remoteBlockIds.size - fetchRequests.size - logInfo("Started " + numGets + " remote gets in " + Utils.getUsedTimeMs(startTime)) - - // Get the local blocks while remote blocks are being fetched. Note that it's okay to do - // these all at once because they will just memory-map some files, so they won't consume - // any memory that might exceed our maxBytesInFlight - startTime = System.currentTimeMillis - for (id <- localBlockIds) { - getLocal(id) match { - case Some(iter) => { - results.put(new FetchResult(id, 0, () => iter)) // Pass 0 as size since it's not in flight - logDebug("Got local block " + id) - } - case None => { - throw new BlockException(id, "Could not get block " + id + " from local machine") - } - } - } - logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime) + " ms") - - //an iterator that will read fetched blocks off the queue as they arrive. - var resultsGotten = 0 - - def hasNext: Boolean = resultsGotten < totalBlocks - - def next(): (String, Option[Iterator[Any]]) = { - resultsGotten += 1 - val startFetchWait = System.currentTimeMillis() - val result = results.take() - val stopFetchWait = System.currentTimeMillis() - _fetchWaitTime += (stopFetchWait - startFetchWait) - bytesInFlight -= result.size - while (!fetchRequests.isEmpty && - (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) { - sendRequest(fetchRequests.dequeue()) - } - (result.blockId, if (result.failed) None else Some(result.deserialize())) - } - - - //methods to profile the block fetching - def numLocalBlocks = localBlockIds.size - def numRemoteBlocks = remoteBlockIds.size - - def remoteFetchTime = _remoteFetchTime - def fetchWaitTime = _fetchWaitTime - - def remoteBytesRead = _remoteBytesRead - } + diff --git a/core/src/main/scala/spark/storage/BlockManagerId.scala b/core/src/main/scala/spark/storage/BlockManagerId.scala index f2f1e77d41..b36a6176c0 100644 --- a/core/src/main/scala/spark/storage/BlockManagerId.scala +++ b/core/src/main/scala/spark/storage/BlockManagerId.scala @@ -1,52 +1,88 @@ +/* + * 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 spark.storage import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput} import java.util.concurrent.ConcurrentHashMap +import spark.Utils /** * This class represent an unique identifier for a BlockManager. * The first 2 constructors of this class is made private to ensure that - * BlockManagerId objects can be created only using the factory method in - * [[spark.storage.BlockManager$]]. This allows de-duplication of ID objects. + * BlockManagerId objects can be created only using the apply method in + * the companion object. This allows de-duplication of ID objects. * Also, constructor parameters are private to ensure that parameters cannot * be modified from outside this class. */ private[spark] class BlockManagerId private ( private var executorId_ : String, - private var ip_ : String, - private var port_ : Int + private var host_ : String, + private var port_ : Int, + private var nettyPort_ : Int ) extends Externalizable { - private def this() = this(null, null, 0) // For deserialization only + private def this() = this(null, null, 0, 0) // For deserialization only def executorId: String = executorId_ - def ip: String = ip_ + if (null != host_){ + Utils.checkHost(host_, "Expected hostname") + assert (port_ > 0) + } + + def hostPort: String = { + // DEBUG code + Utils.checkHost(host) + assert (port > 0) + + host + ":" + port + } + + def host: String = host_ def port: Int = port_ + def nettyPort: Int = nettyPort_ + override def writeExternal(out: ObjectOutput) { out.writeUTF(executorId_) - out.writeUTF(ip_) + out.writeUTF(host_) out.writeInt(port_) + out.writeInt(nettyPort_) } override def readExternal(in: ObjectInput) { executorId_ = in.readUTF() - ip_ = in.readUTF() + host_ = in.readUTF() port_ = in.readInt() + nettyPort_ = in.readInt() } @throws(classOf[IOException]) private def readResolve(): Object = BlockManagerId.getCachedBlockManagerId(this) - override def toString = "BlockManagerId(%s, %s, %d)".format(executorId, ip, port) + override def toString = "BlockManagerId(%s, %s, %d, %d)".format(executorId, host, port, nettyPort) - override def hashCode: Int = (executorId.hashCode * 41 + ip.hashCode) * 41 + port + override def hashCode: Int = (executorId.hashCode * 41 + host.hashCode) * 41 + port + nettyPort override def equals(that: Any) = that match { case id: BlockManagerId => - executorId == id.executorId && port == id.port && ip == id.ip + executorId == id.executorId && port == id.port && host == id.host && nettyPort == id.nettyPort case _ => false } @@ -55,8 +91,17 @@ private[spark] class BlockManagerId private ( private[spark] object BlockManagerId { - def apply(execId: String, ip: String, port: Int) = - getCachedBlockManagerId(new BlockManagerId(execId, ip, port)) + /** + * Returns a [[spark.storage.BlockManagerId]] for the given configuraiton. + * + * @param execId ID of the executor. + * @param host Host name of the block manager. + * @param port Port of the block manager. + * @param nettyPort Optional port for the Netty-based shuffle sender. + * @return A new [[spark.storage.BlockManagerId]]. + */ + def apply(execId: String, host: String, port: Int, nettyPort: Int) = + getCachedBlockManagerId(new BlockManagerId(execId, host, port, nettyPort)) def apply(in: ObjectInput) = { val obj = new BlockManagerId() @@ -67,11 +112,7 @@ private[spark] object BlockManagerId { val blockManagerIdCache = new ConcurrentHashMap[BlockManagerId, BlockManagerId]() def getCachedBlockManagerId(id: BlockManagerId): BlockManagerId = { - if (blockManagerIdCache.containsKey(id)) { - blockManagerIdCache.get(id) - } else { - blockManagerIdCache.put(id, id) - id - } + blockManagerIdCache.putIfAbsent(id, id) + blockManagerIdCache.get(id) } } diff --git a/core/src/main/scala/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/spark/storage/BlockManagerMaster.scala index 036fdc3480..76128e8cff 100644 --- a/core/src/main/scala/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/spark/storage/BlockManagerMaster.scala @@ -1,19 +1,30 @@ -package spark.storage - -import java.io._ -import java.util.{HashMap => JHashMap} +/* + * 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. + */ -import scala.collection.JavaConverters._ -import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} -import scala.util.Random +package spark.storage -import akka.actor.{Actor, ActorRef, ActorSystem, Props} -import akka.dispatch.Await +import akka.actor.ActorRef +import akka.dispatch.{Await, Future} import akka.pattern.ask -import akka.util.{Duration, Timeout} -import akka.util.duration._ +import akka.util.Duration + +import spark.{Logging, SparkException} +import spark.storage.BlockManagerMessages._ -import spark.{Logging, SparkException, Utils} private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Logging { @@ -22,7 +33,7 @@ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Loggi val DRIVER_AKKA_ACTOR_NAME = "BlockManagerMaster" - val timeout = 10.seconds + val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") /** Remove a dead executor from the driver actor. This is only called on the driver side. */ def removeExecutor(execId: String) { @@ -88,6 +99,19 @@ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Loggi } /** + * Remove all blocks belonging to the given RDD. + */ + def removeRdd(rddId: Int, blocking: Boolean) { + val future = askDriverWithReply[Future[Seq[Int]]](RemoveRdd(rddId)) + future onFailure { + case e: Throwable => logError("Failed to remove RDD " + rddId, e) + } + if (blocking) { + Await.result(future, timeout) + } + } + + /** * Return the memory status for each block manager, in the form of a map from * the block manager's id to two long values. The first value is the maximum * amount of memory allocated for the block manager, while the second is the @@ -98,7 +122,7 @@ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Loggi } def getStorageStatus: Array[StorageStatus] = { - askDriverWithReply[ArrayBuffer[StorageStatus]](GetStorageStatus).toArray + askDriverWithReply[Array[StorageStatus]](GetStorageStatus) } /** Stop the driver actor, called only on the Spark driver node */ @@ -135,7 +159,7 @@ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Loggi val future = driverActor.ask(message)(timeout) val result = Await.result(future, timeout) if (result == null) { - throw new Exception("BlockManagerMaster returned null") + throw new SparkException("BlockManagerMaster returned null") } return result.asInstanceOf[T] } catch { diff --git a/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala index 2830bc6297..011bb6b83d 100644 --- a/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala @@ -1,16 +1,36 @@ +/* + * 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 spark.storage import java.util.{HashMap => JHashMap} -import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} +import scala.collection.mutable import scala.collection.JavaConversions._ -import scala.util.Random import akka.actor.{Actor, ActorRef, Cancellable} -import akka.util.{Duration, Timeout} +import akka.dispatch.Future +import akka.pattern.ask +import akka.util.Duration import akka.util.duration._ -import spark.{Logging, Utils} +import spark.{Logging, Utils, SparkException} +import spark.storage.BlockManagerMessages._ + /** * BlockManagerMasterActor is an actor on the master node to track statuses of @@ -21,13 +41,16 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { // Mapping from block manager id to the block manager's information. private val blockManagerInfo = - new HashMap[BlockManagerId, BlockManagerMasterActor.BlockManagerInfo] + new mutable.HashMap[BlockManagerId, BlockManagerMasterActor.BlockManagerInfo] // Mapping from executor ID to block manager ID. - private val blockManagerIdByExecutor = new HashMap[String, BlockManagerId] + 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, Pair[Int, HashSet[BlockManagerId]]] + private val blockLocations = new JHashMap[String, mutable.HashSet[BlockManagerId]] + + val akkaTimeout = Duration.create( + System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") initLogging() @@ -35,7 +58,7 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { "" + (BlockManager.getHeartBeatFrequencyFromSystemProperties * 3)).toLong val checkTimeoutInterval = System.getProperty("spark.storage.blockManagerTimeoutIntervalMs", - "5000").toLong + "60000").toLong var timeoutCheckingTask: Cancellable = null @@ -50,28 +73,34 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { def receive = { case RegisterBlockManager(blockManagerId, maxMemSize, slaveActor) => register(blockManagerId, maxMemSize, slaveActor) + sender ! true case UpdateBlockInfo(blockManagerId, blockId, storageLevel, deserializedSize, size) => + // TODO: Ideally we want to handle all the message replies in receive instead of in the + // individual private methods. updateBlockInfo(blockManagerId, blockId, storageLevel, deserializedSize, size) case GetLocations(blockId) => - getLocations(blockId) + sender ! getLocations(blockId) case GetLocationsMultipleBlockIds(blockIds) => - getLocationsMultipleBlockIds(blockIds) + sender ! getLocationsMultipleBlockIds(blockIds) case GetPeers(blockManagerId, size) => - getPeersDeterministic(blockManagerId, size) - /*getPeers(blockManagerId, size)*/ + sender ! getPeers(blockManagerId, size) case GetMemoryStatus => - getMemoryStatus + sender ! memoryStatus case GetStorageStatus => - getStorageStatus + sender ! storageStatus + + case RemoveRdd(rddId) => + sender ! removeRdd(rddId) case RemoveBlock(blockId) => - removeBlock(blockId) + removeBlockFromWorkers(blockId) + sender ! true case RemoveExecutor(execId) => removeExecutor(execId) @@ -81,7 +110,7 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { logInfo("Stopping BlockManagerMaster") sender ! true if (timeoutCheckingTask != null) { - timeoutCheckingTask.cancel + timeoutCheckingTask.cancel() } context.stop(self) @@ -89,13 +118,36 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { expireDeadHosts() case HeartBeat(blockManagerId) => - heartBeat(blockManagerId) + sender ! heartBeat(blockManagerId) case other => - logInfo("Got unknown message: " + other) + logWarning("Got unknown message: " + other) + } + + private def removeRdd(rddId: Int): Future[Seq[Int]] = { + // 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)) + blocks.foreach { blockId => + val bms: mutable.HashSet[BlockManagerId] = blockLocations.get(blockId) + bms.foreach(bm => blockManagerInfo.get(bm).foreach(_.removeBlock(blockId))) + blockLocations.remove(blockId) + } + + // Ask the slaves to remove the RDD, and put the result in a sequence of Futures. + // The dispatcher is used as an implicit argument into the Future sequence construction. + import context.dispatcher + val removeMsg = RemoveRdd(rddId) + Future.sequence(blockManagerInfo.values.map { bm => + bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Int] + }.toSeq) } - def removeBlockManager(blockManagerId: BlockManagerId) { + private def removeBlockManager(blockManagerId: BlockManagerId) { val info = blockManagerInfo(blockManagerId) // Remove the block manager from blockManagerIdByExecutor. @@ -106,7 +158,7 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { val iterator = info.blocks.keySet.iterator while (iterator.hasNext) { val blockId = iterator.next - val locations = blockLocations.get(blockId)._2 + val locations = blockLocations.get(blockId) locations -= blockManagerId if (locations.size == 0) { blockLocations.remove(locations) @@ -114,45 +166,41 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { } } - def expireDeadHosts() { + private def expireDeadHosts() { logTrace("Checking for hosts with no recent heart beats in BlockManagerMaster.") val now = System.currentTimeMillis() val minSeenTime = now - slaveTimeout - val toRemove = new HashSet[BlockManagerId] + val toRemove = new mutable.HashSet[BlockManagerId] for (info <- blockManagerInfo.values) { if (info.lastSeenMs < minSeenTime) { - logWarning("Removing BlockManager " + info.blockManagerId + " with no recent heart beats") + logWarning("Removing BlockManager " + info.blockManagerId + " with no recent heart beats: " + + (now - info.lastSeenMs) + "ms exceeds " + slaveTimeout + "ms") toRemove += info.blockManagerId } } toRemove.foreach(removeBlockManager) } - def removeExecutor(execId: String) { + private def removeExecutor(execId: String) { logInfo("Trying to remove executor " + execId + " from BlockManagerMaster.") blockManagerIdByExecutor.get(execId).foreach(removeBlockManager) - sender ! true } - def heartBeat(blockManagerId: BlockManagerId) { + private def heartBeat(blockManagerId: BlockManagerId): Boolean = { if (!blockManagerInfo.contains(blockManagerId)) { - if (blockManagerId.executorId == "<driver>" && !isLocal) { - sender ! true - } else { - sender ! false - } + blockManagerId.executorId == "<driver>" && !isLocal } else { blockManagerInfo(blockManagerId).updateLastSeenMs() - sender ! true + true } } // Remove a block from the slaves that have it. This can only be used to remove // blocks that the master knows about. - private def removeBlock(blockId: String) { - val block = blockLocations.get(blockId) - if (block != null) { - block._2.foreach { blockManagerId: BlockManagerId => + private def removeBlockFromWorkers(blockId: String) { + val locations = blockLocations.get(blockId) + if (locations != null) { + locations.foreach { blockManagerId: BlockManagerId => val blockManager = blockManagerInfo.get(blockManagerId) if (blockManager.isDefined) { // Remove the block from the slave's BlockManager. @@ -162,23 +210,20 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { } } } - sender ! true } // Return a map from the block manager id to max memory and remaining memory. - private def getMemoryStatus() { - val res = blockManagerInfo.map { case(blockManagerId, info) => + private def memoryStatus: Map[BlockManagerId, (Long, Long)] = { + blockManagerInfo.map { case(blockManagerId, info) => (blockManagerId, (info.maxMem, info.remainingMem)) }.toMap - sender ! res } - private def getStorageStatus() { - val res = blockManagerInfo.map { case(blockManagerId, info) => + private def storageStatus: Array[StorageStatus] = { + blockManagerInfo.map { case(blockManagerId, info) => import collection.JavaConverters._ StorageStatus(blockManagerId, info.maxMem, info.blocks.asScala.toMap) - } - sender ! res + }.toArray } private def register(id: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) { @@ -187,7 +232,8 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { } else if (!blockManagerInfo.contains(id)) { blockManagerIdByExecutor.get(id.executorId) match { case Some(manager) => - // A block manager of the same host name already exists + // A block manager of the same executor already exists. + // This should never happen. Let's just quit. logError("Got two different block manager registrations on " + id.executorId) System.exit(1) case None => @@ -196,7 +242,6 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { blockManagerInfo(id) = new BlockManagerMasterActor.BlockManagerInfo( id, System.currentTimeMillis(), maxMemSize, slaveActor) } - sender ! true } private def updateBlockInfo( @@ -225,12 +270,12 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { blockManagerInfo(blockManagerId).updateBlockInfo(blockId, storageLevel, memSize, diskSize) - var locations: HashSet[BlockManagerId] = null + var locations: mutable.HashSet[BlockManagerId] = null if (blockLocations.containsKey(blockId)) { - locations = blockLocations.get(blockId)._2 + locations = blockLocations.get(blockId) } else { - locations = new HashSet[BlockManagerId] - blockLocations.put(blockId, (storageLevel.replication, locations)) + locations = new mutable.HashSet[BlockManagerId] + blockLocations.put(blockId, locations) } if (storageLevel.isValid) { @@ -246,70 +291,24 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { sender ! true } - private def getLocations(blockId: String) { - val startTimeMs = System.currentTimeMillis() - val tmp = " " + blockId + " " - if (blockLocations.containsKey(blockId)) { - var res: ArrayBuffer[BlockManagerId] = new ArrayBuffer[BlockManagerId] - res.appendAll(blockLocations.get(blockId)._2) - sender ! res.toSeq - } else { - var res: ArrayBuffer[BlockManagerId] = new ArrayBuffer[BlockManagerId] - sender ! res - } - } - - private def getLocationsMultipleBlockIds(blockIds: Array[String]) { - def getLocations(blockId: String): Seq[BlockManagerId] = { - val tmp = blockId - if (blockLocations.containsKey(blockId)) { - var res: ArrayBuffer[BlockManagerId] = new ArrayBuffer[BlockManagerId] - res.appendAll(blockLocations.get(blockId)._2) - return res.toSeq - } else { - var res: ArrayBuffer[BlockManagerId] = new ArrayBuffer[BlockManagerId] - return res.toSeq - } - } - - var res: ArrayBuffer[Seq[BlockManagerId]] = new ArrayBuffer[Seq[BlockManagerId]] - for (blockId <- blockIds) { - res.append(getLocations(blockId)) - } - sender ! res.toSeq + private def getLocations(blockId: String): Seq[BlockManagerId] = { + if (blockLocations.containsKey(blockId)) blockLocations.get(blockId).toSeq else Seq.empty } - private def getPeers(blockManagerId: BlockManagerId, size: Int) { - var peers: Array[BlockManagerId] = blockManagerInfo.keySet.toArray - var res: ArrayBuffer[BlockManagerId] = new ArrayBuffer[BlockManagerId] - res.appendAll(peers) - res -= blockManagerId - val rand = new Random(System.currentTimeMillis()) - while (res.length > size) { - res.remove(rand.nextInt(res.length)) - } - sender ! res.toSeq + private def getLocationsMultipleBlockIds(blockIds: Array[String]): Seq[Seq[BlockManagerId]] = { + blockIds.map(blockId => getLocations(blockId)) } - private def getPeersDeterministic(blockManagerId: BlockManagerId, size: Int) { - var peers: Array[BlockManagerId] = blockManagerInfo.keySet.toArray - var res: ArrayBuffer[BlockManagerId] = new ArrayBuffer[BlockManagerId] + private def getPeers(blockManagerId: BlockManagerId, size: Int): Seq[BlockManagerId] = { + val peers: Array[BlockManagerId] = blockManagerInfo.keySet.toArray val selfIndex = peers.indexOf(blockManagerId) if (selfIndex == -1) { - throw new Exception("Self index for " + blockManagerId + " not found") + throw new SparkException("Self index for " + blockManagerId + " not found") } // Note that this logic will select the same node multiple times if there aren't enough peers - var index = selfIndex - while (res.size < size) { - index += 1 - if (index == selfIndex) { - throw new Exception("More peer expected than available") - } - res += peers(index % peers.size) - } - sender ! res.toSeq + Array.tabulate[BlockManagerId](size) { i => peers((selfIndex + i + 1) % peers.length) }.toSeq } } @@ -332,8 +331,8 @@ object BlockManagerMasterActor { // Mapping from block id to its status. private val _blocks = new JHashMap[String, BlockStatus] - logInfo("Registering block manager %s:%d with %s RAM".format( - blockManagerId.ip, blockManagerId.port, Utils.memoryBytesToString(maxMem))) + logInfo("Registering block manager %s with %s RAM".format( + blockManagerId.hostPort, Utils.memoryBytesToString(maxMem))) def updateLastSeenMs() { _lastSeenMs = System.currentTimeMillis() @@ -358,13 +357,13 @@ object BlockManagerMasterActor { _blocks.put(blockId, BlockStatus(storageLevel, memSize, diskSize)) if (storageLevel.useMemory) { _remainingMem -= memSize - logInfo("Added %s in memory on %s:%d (size: %s, free: %s)".format( - blockId, blockManagerId.ip, blockManagerId.port, Utils.memoryBytesToString(memSize), + logInfo("Added %s in memory on %s (size: %s, free: %s)".format( + blockId, blockManagerId.hostPort, Utils.memoryBytesToString(memSize), Utils.memoryBytesToString(_remainingMem))) } if (storageLevel.useDisk) { - logInfo("Added %s on disk on %s:%d (size: %s)".format( - blockId, blockManagerId.ip, blockManagerId.port, Utils.memoryBytesToString(diskSize))) + logInfo("Added %s on disk on %s (size: %s)".format( + blockId, blockManagerId.hostPort, Utils.memoryBytesToString(diskSize))) } } else if (_blocks.containsKey(blockId)) { // If isValid is not true, drop the block. @@ -372,17 +371,24 @@ object BlockManagerMasterActor { _blocks.remove(blockId) if (blockStatus.storageLevel.useMemory) { _remainingMem += blockStatus.memSize - logInfo("Removed %s on %s:%d in memory (size: %s, free: %s)".format( - blockId, blockManagerId.ip, blockManagerId.port, Utils.memoryBytesToString(memSize), + logInfo("Removed %s on %s in memory (size: %s, free: %s)".format( + blockId, blockManagerId.hostPort, Utils.memoryBytesToString(memSize), Utils.memoryBytesToString(_remainingMem))) } if (blockStatus.storageLevel.useDisk) { - logInfo("Removed %s on %s:%d on disk (size: %s)".format( - blockId, blockManagerId.ip, blockManagerId.port, Utils.memoryBytesToString(diskSize))) + logInfo("Removed %s on %s on disk (size: %s)".format( + blockId, blockManagerId.hostPort, Utils.memoryBytesToString(diskSize))) } } } + def removeBlock(blockId: String) { + if (_blocks.containsKey(blockId)) { + _remainingMem += _blocks.get(blockId).memSize + _blocks.remove(blockId) + } + } + def remainingMem: Long = _remainingMem def lastSeenMs: Long = _lastSeenMs diff --git a/core/src/main/scala/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/spark/storage/BlockManagerMessages.scala index cff48d9909..9375a9ca54 100644 --- a/core/src/main/scala/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/spark/storage/BlockManagerMessages.scala @@ -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. + */ + package spark.storage import java.io.{Externalizable, ObjectInput, ObjectOutput} @@ -5,99 +22,89 @@ import java.io.{Externalizable, ObjectInput, ObjectOutput} import akka.actor.ActorRef -////////////////////////////////////////////////////////////////////////////////// -// Messages from the master to slaves. -////////////////////////////////////////////////////////////////////////////////// -private[spark] -sealed trait ToBlockManagerSlave - -// Remove a block from the slaves that have it. This can only be used to remove -// blocks that the master knows about. -private[spark] -case class RemoveBlock(blockId: String) extends ToBlockManagerSlave - - -////////////////////////////////////////////////////////////////////////////////// -// Messages from slaves to the master. -////////////////////////////////////////////////////////////////////////////////// -private[spark] -sealed trait ToBlockManagerMaster - -private[spark] -case class RegisterBlockManager( - blockManagerId: BlockManagerId, - maxMemSize: Long, - sender: ActorRef) - extends ToBlockManagerMaster - -private[spark] -case class HeartBeat(blockManagerId: BlockManagerId) extends ToBlockManagerMaster - -private[spark] -class UpdateBlockInfo( - var blockManagerId: BlockManagerId, - var blockId: String, - var storageLevel: StorageLevel, - var memSize: Long, - var diskSize: Long) - extends ToBlockManagerMaster - with Externalizable { - - def this() = this(null, null, null, 0, 0) // For deserialization only - - override def writeExternal(out: ObjectOutput) { - blockManagerId.writeExternal(out) - out.writeUTF(blockId) - storageLevel.writeExternal(out) - out.writeLong(memSize) - out.writeLong(diskSize) - } - - override def readExternal(in: ObjectInput) { - blockManagerId = BlockManagerId(in) - blockId = in.readUTF() - storageLevel = StorageLevel(in) - memSize = in.readLong() - diskSize = in.readLong() - } -} - -private[spark] -object UpdateBlockInfo { - def apply(blockManagerId: BlockManagerId, - blockId: String, - storageLevel: StorageLevel, - memSize: Long, - diskSize: Long): UpdateBlockInfo = { - new UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize) +private[storage] object BlockManagerMessages { + ////////////////////////////////////////////////////////////////////////////////// + // Messages from the master to slaves. + ////////////////////////////////////////////////////////////////////////////////// + sealed trait ToBlockManagerSlave + + // 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 + + // Remove all blocks belonging to a specific RDD. + case class RemoveRdd(rddId: Int) extends ToBlockManagerSlave + + + ////////////////////////////////////////////////////////////////////////////////// + // Messages from slaves to the master. + ////////////////////////////////////////////////////////////////////////////////// + sealed trait ToBlockManagerMaster + + case class RegisterBlockManager( + blockManagerId: BlockManagerId, + maxMemSize: Long, + sender: ActorRef) + extends ToBlockManagerMaster + + case class HeartBeat(blockManagerId: BlockManagerId) extends ToBlockManagerMaster + + class UpdateBlockInfo( + var blockManagerId: BlockManagerId, + var blockId: String, + var storageLevel: StorageLevel, + var memSize: Long, + var diskSize: Long) + extends ToBlockManagerMaster + with Externalizable { + + def this() = this(null, null, null, 0, 0) // For deserialization only + + override def writeExternal(out: ObjectOutput) { + blockManagerId.writeExternal(out) + out.writeUTF(blockId) + storageLevel.writeExternal(out) + out.writeLong(memSize) + out.writeLong(diskSize) + } + + override def readExternal(in: ObjectInput) { + blockManagerId = BlockManagerId(in) + blockId = in.readUTF() + storageLevel = StorageLevel(in) + memSize = in.readLong() + diskSize = in.readLong() + } } - // For pattern-matching - def unapply(h: UpdateBlockInfo): Option[(BlockManagerId, String, StorageLevel, Long, Long)] = { - Some((h.blockManagerId, h.blockId, h.storageLevel, h.memSize, h.diskSize)) + object UpdateBlockInfo { + def apply(blockManagerId: BlockManagerId, + blockId: String, + storageLevel: StorageLevel, + memSize: Long, + diskSize: Long): UpdateBlockInfo = { + new UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize) + } + + // For pattern-matching + def unapply(h: UpdateBlockInfo): Option[(BlockManagerId, String, StorageLevel, Long, Long)] = { + Some((h.blockManagerId, h.blockId, h.storageLevel, h.memSize, h.diskSize)) + } } -} -private[spark] -case class GetLocations(blockId: String) extends ToBlockManagerMaster + case class GetLocations(blockId: String) extends ToBlockManagerMaster -private[spark] -case class GetLocationsMultipleBlockIds(blockIds: Array[String]) extends ToBlockManagerMaster + case class GetLocationsMultipleBlockIds(blockIds: Array[String]) extends ToBlockManagerMaster -private[spark] -case class GetPeers(blockManagerId: BlockManagerId, size: Int) extends ToBlockManagerMaster + case class GetPeers(blockManagerId: BlockManagerId, size: Int) extends ToBlockManagerMaster -private[spark] -case class RemoveExecutor(execId: String) extends ToBlockManagerMaster + case class RemoveExecutor(execId: String) extends ToBlockManagerMaster -private[spark] -case object StopBlockManagerMaster extends ToBlockManagerMaster + case object StopBlockManagerMaster extends ToBlockManagerMaster -private[spark] -case object GetMemoryStatus extends ToBlockManagerMaster + case object GetMemoryStatus extends ToBlockManagerMaster -private[spark] -case object ExpireDeadHosts extends ToBlockManagerMaster + case object ExpireDeadHosts extends ToBlockManagerMaster -private[spark] -case object GetStorageStatus extends ToBlockManagerMaster + case object GetStorageStatus extends ToBlockManagerMaster +} diff --git a/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala b/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala index f570cdc52d..6e5fb43732 100644 --- a/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala +++ b/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala @@ -1,8 +1,25 @@ +/* + * 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 spark.storage import akka.actor.Actor -import spark.{Logging, SparkException, Utils} +import spark.storage.BlockManagerMessages._ /** @@ -11,6 +28,12 @@ import spark.{Logging, SparkException, Utils} */ class BlockManagerSlaveActor(blockManager: BlockManager) extends Actor { override def receive = { - case RemoveBlock(blockId) => blockManager.removeBlock(blockId) + + case RemoveBlock(blockId) => + blockManager.removeBlock(blockId) + + case RemoveRdd(rddId) => + val numBlocksRemoved = blockManager.removeRdd(rddId) + sender ! numBlocksRemoved } } diff --git a/core/src/main/scala/spark/storage/BlockManagerSource.scala b/core/src/main/scala/spark/storage/BlockManagerSource.scala new file mode 100644 index 0000000000..2aecd1ea71 --- /dev/null +++ b/core/src/main/scala/spark/storage/BlockManagerSource.scala @@ -0,0 +1,48 @@ +package spark.storage + +import com.codahale.metrics.{Gauge,MetricRegistry} + +import spark.metrics.source.Source + + +private[spark] class BlockManagerSource(val blockManager: BlockManager) extends Source { + val metricRegistry = new MetricRegistry() + val sourceName = "BlockManager" + + metricRegistry.register(MetricRegistry.name("memory", "maxMem", "MBytes"), new Gauge[Long] { + override def getValue: Long = { + val storageStatusList = blockManager.master.getStorageStatus + val maxMem = storageStatusList.map(_.maxMem).reduce(_ + _) + maxMem / 1024 / 1024 + } + }) + + metricRegistry.register(MetricRegistry.name("memory", "remainingMem", "MBytes"), new Gauge[Long] { + override def getValue: Long = { + val storageStatusList = blockManager.master.getStorageStatus + val remainingMem = storageStatusList.map(_.memRemaining).reduce(_ + _) + remainingMem / 1024 / 1024 + } + }) + + metricRegistry.register(MetricRegistry.name("memory", "memUsed", "MBytes"), new Gauge[Long] { + override def getValue: Long = { + val storageStatusList = blockManager.master.getStorageStatus + val maxMem = storageStatusList.map(_.maxMem).reduce(_ + _) + val remainingMem = storageStatusList.map(_.memRemaining).reduce(_ + _) + (maxMem - remainingMem) / 1024 / 1024 + } + }) + + metricRegistry.register(MetricRegistry.name("disk", "diskSpaceUsed", "MBytes"), new Gauge[Long] { + override def getValue: Long = { + val storageStatusList = blockManager.master.getStorageStatus + val diskSpaceUsed = storageStatusList + .flatMap(_.blocks.values.map(_.diskSize)) + .reduceOption(_ + _) + .getOrElse(0L) + + diskSpaceUsed / 1024 / 1024 + } + }) +} diff --git a/core/src/main/scala/spark/storage/BlockManagerUI.scala b/core/src/main/scala/spark/storage/BlockManagerUI.scala deleted file mode 100644 index 9e6721ec17..0000000000 --- a/core/src/main/scala/spark/storage/BlockManagerUI.scala +++ /dev/null @@ -1,76 +0,0 @@ -package spark.storage - -import akka.actor.{ActorRef, ActorSystem} -import akka.util.Timeout -import akka.util.duration._ -import cc.spray.typeconversion.TwirlSupport._ -import cc.spray.Directives -import spark.{Logging, SparkContext} -import spark.util.AkkaUtils -import spark.Utils - - -/** - * Web UI server for the BlockManager inside each SparkContext. - */ -private[spark] -class BlockManagerUI(val actorSystem: ActorSystem, blockManagerMaster: ActorRef, sc: SparkContext) - extends Directives with Logging { - - val STATIC_RESOURCE_DIR = "spark/deploy/static" - - implicit val timeout = Timeout(10 seconds) - - /** Start a HTTP server to run the Web interface */ - def start() { - try { - val port = if (System.getProperty("spark.ui.port") != null) { - System.getProperty("spark.ui.port").toInt - } else { - // TODO: Unfortunately, it's not possible to pass port 0 to spray and figure out which - // random port it bound to, so we have to try to find a local one by creating a socket. - Utils.findFreePort() - } - AkkaUtils.startSprayServer(actorSystem, "0.0.0.0", port, handler, "BlockManagerHTTPServer") - logInfo("Started BlockManager web UI at http://%s:%d".format(Utils.localHostName(), port)) - } catch { - case e: Exception => - logError("Failed to create BlockManager WebUI", e) - System.exit(1) - } - } - - val handler = { - get { - path("") { - completeWith { - // Request the current storage status from the Master - val storageStatusList = sc.getExecutorStorageStatus - // Calculate macro-level statistics - val maxMem = storageStatusList.map(_.maxMem).reduce(_+_) - val remainingMem = storageStatusList.map(_.memRemaining).reduce(_+_) - val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize)) - .reduceOption(_+_).getOrElse(0L) - val rdds = StorageUtils.rddInfoFromStorageStatus(storageStatusList, sc) - spark.storage.html.index. - render(maxMem, remainingMem, diskSpaceUsed, rdds, storageStatusList) - } - } ~ - path("rdd") { - parameter("id") { id => - completeWith { - val prefix = "rdd_" + id.toString - val storageStatusList = sc.getExecutorStorageStatus - val filteredStorageStatusList = StorageUtils. - filterStorageStatusByPrefix(storageStatusList, prefix) - val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head - spark.storage.html.rdd.render(rddInfo, filteredStorageStatusList) - } - } - } ~ - pathPrefix("static") { - getFromResourceDirectory(STATIC_RESOURCE_DIR) - } - } - } -} diff --git a/core/src/main/scala/spark/storage/BlockManagerWorker.scala b/core/src/main/scala/spark/storage/BlockManagerWorker.scala index d2985559c1..39064bce92 100644 --- a/core/src/main/scala/spark/storage/BlockManagerWorker.scala +++ b/core/src/main/scala/spark/storage/BlockManagerWorker.scala @@ -1,14 +1,25 @@ +/* + * 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 spark.storage import java.nio.ByteBuffer -import scala.actors._ -import scala.actors.Actor._ -import scala.actors.remote._ -import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} -import scala.util.Random - -import spark.{Logging, Utils, SparkEnv} +import spark.{Logging, Utils} import spark.network._ /** @@ -19,7 +30,7 @@ import spark.network._ */ private[spark] class BlockManagerWorker(val blockManager: BlockManager) extends Logging { initLogging() - + blockManager.connectionManager.onReceiveMessage(onBlockMessageReceive) def onBlockMessageReceive(msg: Message, id: ConnectionManagerId): Option[Message] = { @@ -51,7 +62,7 @@ private[spark] class BlockManagerWorker(val blockManager: BlockManager) extends logDebug("Received [" + pB + "]") putBlock(pB.id, pB.data, pB.level) return None - } + } case BlockMessage.TYPE_GET_BLOCK => { val gB = new GetBlock(blockMessage.getId) logDebug("Received [" + gB + "]") @@ -88,30 +99,26 @@ private[spark] class BlockManagerWorker(val blockManager: BlockManager) extends private[spark] object BlockManagerWorker extends Logging { private var blockManagerWorker: BlockManagerWorker = null - private val DATA_TRANSFER_TIME_OUT_MS: Long = 500 - private val REQUEST_RETRY_INTERVAL_MS: Long = 1000 - + initLogging() - + def startBlockManagerWorker(manager: BlockManager) { blockManagerWorker = new BlockManagerWorker(manager) } - + def syncPutBlock(msg: PutBlock, toConnManagerId: ConnectionManagerId): Boolean = { val blockManager = blockManagerWorker.blockManager - val connectionManager = blockManager.connectionManager - val serializer = blockManager.serializer + val connectionManager = blockManager.connectionManager val blockMessage = BlockMessage.fromPutBlock(msg) val blockMessageArray = new BlockMessageArray(blockMessage) val resultMessage = connectionManager.sendMessageReliablySync( toConnManagerId, blockMessageArray.toBufferMessage) return (resultMessage != None) } - + def syncGetBlock(msg: GetBlock, toConnManagerId: ConnectionManagerId): ByteBuffer = { val blockManager = blockManagerWorker.blockManager - val connectionManager = blockManager.connectionManager - val serializer = blockManager.serializer + val connectionManager = blockManager.connectionManager val blockMessage = BlockMessage.fromGetBlock(msg) val blockMessageArray = new BlockMessageArray(blockMessage) val responseMessage = connectionManager.sendMessageReliablySync( diff --git a/core/src/main/scala/spark/storage/BlockMessage.scala b/core/src/main/scala/spark/storage/BlockMessage.scala index 30d7500e01..bcce26b7c1 100644 --- a/core/src/main/scala/spark/storage/BlockMessage.scala +++ b/core/src/main/scala/spark/storage/BlockMessage.scala @@ -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. + */ + package spark.storage import java.nio.ByteBuffer @@ -5,7 +22,6 @@ import java.nio.ByteBuffer import scala.collection.mutable.StringBuilder import scala.collection.mutable.ArrayBuffer -import spark._ import spark.network._ private[spark] case class GetBlock(id: String) diff --git a/core/src/main/scala/spark/storage/BlockMessageArray.scala b/core/src/main/scala/spark/storage/BlockMessageArray.scala index a25decb123..ee2fc167d5 100644 --- a/core/src/main/scala/spark/storage/BlockMessageArray.scala +++ b/core/src/main/scala/spark/storage/BlockMessageArray.scala @@ -1,8 +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 spark.storage import java.nio.ByteBuffer -import scala.collection.mutable.StringBuilder import scala.collection.mutable.ArrayBuffer import spark._ @@ -96,7 +112,7 @@ private[spark] object BlockMessageArray { def main(args: Array[String]) { val blockMessages = - (0 until 10).map(i => { + (0 until 10).map { i => if (i % 2 == 0) { val buffer = ByteBuffer.allocate(100) buffer.clear @@ -104,7 +120,7 @@ private[spark] object BlockMessageArray { } else { BlockMessage.fromGetBlock(GetBlock(i.toString)) } - }) + } val blockMessageArray = new BlockMessageArray(blockMessages) println("Block message array created") @@ -115,6 +131,7 @@ private[spark] object BlockMessageArray { val newBuffer = ByteBuffer.allocate(totalSize) newBuffer.clear() bufferMessage.buffers.foreach(buffer => { + assert (0 == buffer.position()) newBuffer.put(buffer) buffer.rewind() }) diff --git a/core/src/main/scala/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/spark/storage/BlockObjectWriter.scala new file mode 100644 index 0000000000..3812009ca1 --- /dev/null +++ b/core/src/main/scala/spark/storage/BlockObjectWriter.scala @@ -0,0 +1,65 @@ +/* + * 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 spark.storage + + +/** + * An interface for writing JVM objects to some underlying storage. This interface allows + * appending data to an existing block, and can guarantee atomicity in the case of faults + * as it allows the caller to revert partial writes. + * + * This interface does not support concurrent writes. + */ +abstract class BlockObjectWriter(val blockId: String) { + + var closeEventHandler: () => Unit = _ + + def open(): BlockObjectWriter + + def close() { + closeEventHandler() + } + + 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. + */ + def commit(): Long + + /** + * Reverts writes that haven't been flushed yet. Callers should invoke this function + * when there are runtime exceptions. + */ + def revertPartialWrites() + + /** + * Writes an object. + */ + def write(value: Any) + + /** + * Size of the valid writes, in bytes. + */ + def size(): Long +} diff --git a/core/src/main/scala/spark/storage/BlockStore.scala b/core/src/main/scala/spark/storage/BlockStore.scala index 8188d3595e..c8db0022b0 100644 --- a/core/src/main/scala/spark/storage/BlockStore.scala +++ b/core/src/main/scala/spark/storage/BlockStore.scala @@ -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. + */ + package spark.storage import java.nio.ByteBuffer diff --git a/core/src/main/scala/spark/storage/DelegateBlockFetchTracker.scala b/core/src/main/scala/spark/storage/DelegateBlockFetchTracker.scala deleted file mode 100644 index f6c28dce52..0000000000 --- a/core/src/main/scala/spark/storage/DelegateBlockFetchTracker.scala +++ /dev/null @@ -1,12 +0,0 @@ -package spark.storage - -private[spark] trait DelegateBlockFetchTracker extends BlockFetchTracker { - var delegate : BlockFetchTracker = _ - def setDelegate(d: BlockFetchTracker) {delegate = d} - def totalBlocks = delegate.totalBlocks - def numLocalBlocks = delegate.numLocalBlocks - def numRemoteBlocks = delegate.numRemoteBlocks - def remoteFetchTime = delegate.remoteFetchTime - def fetchWaitTime = delegate.fetchWaitTime - def remoteBytesRead = delegate.remoteBytesRead -} diff --git a/core/src/main/scala/spark/storage/DiskStore.scala b/core/src/main/scala/spark/storage/DiskStore.scala index ddbf8821ad..3ebfe173b1 100644 --- a/core/src/main/scala/spark/storage/DiskStore.scala +++ b/core/src/main/scala/spark/storage/DiskStore.scala @@ -1,41 +1,142 @@ +/* + * 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 spark.storage +import java.io.{File, FileOutputStream, OutputStream, RandomAccessFile} import java.nio.ByteBuffer -import java.io.{File, FileOutputStream, RandomAccessFile} +import java.nio.channels.FileChannel import java.nio.channels.FileChannel.MapMode import java.util.{Random, Date} import java.text.SimpleDateFormat -import it.unimi.dsi.fastutil.io.FastBufferedOutputStream - import scala.collection.mutable.ArrayBuffer -import spark.executor.ExecutorExitCode +import it.unimi.dsi.fastutil.io.FastBufferedOutputStream import spark.Utils +import spark.executor.ExecutorExitCode +import spark.serializer.{Serializer, SerializationStream} +import spark.Logging +import spark.network.netty.ShuffleSender +import spark.network.netty.PathResolver + /** * Stores BlockManager blocks on disk. */ private class DiskStore(blockManager: BlockManager, rootDirs: String) - extends BlockStore(blockManager) { + 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 + } - val MAX_DIR_CREATION_ATTEMPTS: Int = 10 - val subDirsPerLocalDir = System.getProperty("spark.diskStore.subDirectories", "64").toInt + 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. - val localDirs = createLocalDirs() - val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir)) + 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: String): Long = { getFile(blockId).length() } - override def putBytes(blockId: String, bytes: ByteBuffer, level: StorageLevel) { + override def putBytes(blockId: String, _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) @@ -49,6 +150,18 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) blockId, Utils.memoryBytesToString(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 + } + override def putValues( blockId: String, values: ArrayBuffer[Any], @@ -61,18 +174,18 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) val file = createFile(blockId) val fileOut = blockManager.wrapForCompression(blockId, new FastBufferedOutputStream(new FileOutputStream(file))) - val objOut = blockManager.serializer.newInstance().serializeStream(fileOut) + val objOut = blockManager.defaultSerializer.newInstance().serializeStream(fileOut) objOut.writeAll(values.iterator) objOut.close() val length = file.length() + + val timeTaken = System.currentTimeMillis - startTime logDebug("Block %s stored as %s file on disk in %d ms".format( - blockId, Utils.memoryBytesToString(length), (System.currentTimeMillis - startTime))) + blockId, Utils.memoryBytesToString(length), timeTaken)) if (returnValues) { // Return a byte buffer for the contents of the file - val channel = new RandomAccessFile(file, "r").getChannel() - val buffer = channel.map(MapMode.READ_ONLY, 0, length) - channel.close() + val buffer = getFileBytes(file) PutResult(length, Right(buffer)) } else { PutResult(length, null) @@ -81,10 +194,7 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) override def getBytes(blockId: String): Option[ByteBuffer] = { val file = getFile(blockId) - val length = file.length().toInt - val channel = new RandomAccessFile(file, "r").getChannel() - val bytes = channel.map(MapMode.READ_ONLY, 0, length) - channel.close() + val bytes = getFileBytes(file) Some(bytes) } @@ -92,11 +202,18 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) getBytes(blockId).map(bytes => blockManager.dataDeserialize(blockId, bytes)) } + /** + * 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]] = { + getBytes(blockId).map(bytes => blockManager.dataDeserialize(blockId, bytes, serializer)) + } + override def remove(blockId: String): Boolean = { val file = getFile(blockId) if (file.exists()) { file.delete() - true } else { false } @@ -106,10 +223,13 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) getFile(blockId).exists() } - private def createFile(blockId: String): File = { + private def createFile(blockId: String, allowAppendExisting: Boolean = false): File = { val file = getFile(blockId) - if (file.exists()) { - throw new Exception("File for block " + blockId + " already exists on disk: " + file) + 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 } @@ -144,8 +264,8 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) private def createLocalDirs(): Array[File] = { logDebug("Creating local directories at root dirs '" + rootDirs + "'") val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss") - rootDirs.split(",").map(rootDir => { - var foundLocalDir: Boolean = false + rootDirs.split(",").map { rootDir => + var foundLocalDir = false var localDir: File = null var localDirId: String = null var tries = 0 @@ -156,12 +276,11 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) localDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) localDir = new File(rootDir, "spark-local-" + localDirId) if (!localDir.exists) { - localDir.mkdirs() - foundLocalDir = true + foundLocalDir = localDir.mkdirs() } } catch { case e: Exception => - logWarning("Attempt " + tries + " to create local dir failed", e) + logWarning("Attempt " + tries + " to create local dir " + localDir + " failed", e) } } if (!foundLocalDir) { @@ -171,19 +290,40 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) } 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") - try { - localDirs.foreach(localDir => Utils.deleteRecursively(localDir)) - } catch { - case t: Throwable => logError("Exception while deleting local spark dirs", t) + 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 + } } diff --git a/core/src/main/scala/spark/storage/MemoryStore.scala b/core/src/main/scala/spark/storage/MemoryStore.scala index 949588476c..b5a86b85a7 100644 --- a/core/src/main/scala/spark/storage/MemoryStore.scala +++ b/core/src/main/scala/spark/storage/MemoryStore.scala @@ -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. + */ + package spark.storage import java.util.LinkedHashMap @@ -31,7 +48,9 @@ private class MemoryStore(blockManager: BlockManager, maxMemory: Long) } } - override def putBytes(blockId: String, bytes: ByteBuffer, level: StorageLevel) { + override def putBytes(blockId: String, _bytes: ByteBuffer, level: StorageLevel) { + // Work on a duplicate - since the original input might be used elsewhere. + val bytes = _bytes.duplicate() bytes.rewind() if (level.deserialized) { val values = blockManager.dataDeserialize(blockId, bytes) diff --git a/core/src/main/scala/spark/storage/PutResult.scala b/core/src/main/scala/spark/storage/PutResult.scala index 76f236057b..3a0974fe15 100644 --- a/core/src/main/scala/spark/storage/PutResult.scala +++ b/core/src/main/scala/spark/storage/PutResult.scala @@ -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. + */ + package spark.storage import java.nio.ByteBuffer diff --git a/core/src/main/scala/spark/storage/ShuffleBlockManager.scala b/core/src/main/scala/spark/storage/ShuffleBlockManager.scala new file mode 100644 index 0000000000..8a7a6f9ed3 --- /dev/null +++ b/core/src/main/scala/spark/storage/ShuffleBlockManager.scala @@ -0,0 +1,67 @@ +/* + * 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 spark.storage + +import spark.serializer.Serializer + + +private[spark] +class ShuffleWriterGroup(val id: Int, val writers: Array[BlockObjectWriter]) + + +private[spark] +trait ShuffleBlocks { + def acquireWriters(mapId: Int): ShuffleWriterGroup + def releaseWriters(group: ShuffleWriterGroup) +} + + +private[spark] +class ShuffleBlockManager(blockManager: BlockManager) { + + 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) + } + new ShuffleWriterGroup(mapId, writers) + } + + override def releaseWriters(group: ShuffleWriterGroup) = { + // Nothing really to release here. + } + } + } +} + + +private[spark] +object ShuffleBlockManager { + + // Returns the block id for a given shuffle block. + def blockId(shuffleId: Int, bucketId: Int, groupId: Int): String = { + "shuffle_" + shuffleId + "_" + groupId + "_" + bucketId + } + + // Returns true if the block is a shuffle block. + def isShuffle(blockId: String): Boolean = blockId.startsWith("shuffle_") +} diff --git a/core/src/main/scala/spark/storage/StorageLevel.scala b/core/src/main/scala/spark/storage/StorageLevel.scala index 3b5a77ab22..f52650988c 100644 --- a/core/src/main/scala/spark/storage/StorageLevel.scala +++ b/core/src/main/scala/spark/storage/StorageLevel.scala @@ -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. + */ + package spark.storage import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput} @@ -123,11 +140,7 @@ object StorageLevel { val storageLevelCache = new java.util.concurrent.ConcurrentHashMap[StorageLevel, StorageLevel]() private[spark] def getCachedStorageLevel(level: StorageLevel): StorageLevel = { - if (storageLevelCache.containsKey(level)) { - storageLevelCache.get(level) - } else { - storageLevelCache.put(level, level) - level - } + storageLevelCache.putIfAbsent(level, level) + storageLevelCache.get(level) } } diff --git a/core/src/main/scala/spark/storage/StorageUtils.scala b/core/src/main/scala/spark/storage/StorageUtils.scala index dec47a9d41..2aeed4ea3c 100644 --- a/core/src/main/scala/spark/storage/StorageUtils.scala +++ b/core/src/main/scala/spark/storage/StorageUtils.scala @@ -1,12 +1,29 @@ +/* + * 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 spark.storage import spark.{Utils, SparkContext} import BlockManagerMasterActor.BlockStatus private[spark] -case class StorageStatus(blockManagerId: BlockManagerId, maxMem: Long, +case class StorageStatus(blockManagerId: BlockManagerId, maxMem: Long, blocks: Map[String, BlockStatus]) { - + def memUsed(blockPrefix: String = "") = { blocks.filterKeys(_.startsWith(blockPrefix)).values.map(_.memSize). reduceOption(_+_).getOrElse(0l) @@ -22,53 +39,69 @@ case class StorageStatus(blockManagerId: BlockManagerId, maxMem: Long, } case class RDDInfo(id: Int, name: String, storageLevel: StorageLevel, - numCachedPartitions: Int, numPartitions: Int, memSize: Long, diskSize: Long) { + numCachedPartitions: Int, numPartitions: Int, memSize: Long, diskSize: Long) + extends Ordered[RDDInfo] { override def toString = { import Utils.memoryBytesToString "RDD \"%s\" (%d) Storage: %s; CachedPartitions: %d; TotalPartitions: %d; MemorySize: %s; DiskSize: %s".format(name, id, storageLevel.toString, numCachedPartitions, numPartitions, memoryBytesToString(memSize), memoryBytesToString(diskSize)) } + + override def compare(that: RDDInfo) = { + this.id - that.id + } } /* Helper methods for storage-related objects */ private[spark] object StorageUtils { - /* Given the current storage status of the BlockManager, returns information for each RDD */ - def rddInfoFromStorageStatus(storageStatusList: Array[StorageStatus], + /* 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(_.blocks).toMap, sc) } - /* Given a list of BlockStatus objets, returns information for each RDD */ - def rddInfoFromBlockStatusList(infos: Map[String, BlockStatus], + /* Returns a map of blocks to their locations, compiled from a list of StorageStatus objects */ + def blockLocationsFromStorageStatus(storageStatusList: Seq[StorageStatus]) = { + val blockLocationPairs = storageStatusList + .flatMap(s => s.blocks.map(b => (b._1, s.blockManagerId.hostPort))) + blockLocationPairs.groupBy(_._1).map{case (k, v) => (k, v.unzip._2)}.toMap + } + + /* Given a list of BlockStatus objets, returns information for each RDD */ + def rddInfoFromBlockStatusList(infos: Map[String, BlockStatus], sc: SparkContext) : Array[RDDInfo] = { // Group by rddId, ignore the partition name - val groupedRddBlocks = infos.groupBy { case(k, v) => + val groupedRddBlocks = infos.filterKeys(_.startsWith("rdd_")).groupBy { case(k, v) => k.substring(0,k.lastIndexOf('_')) }.mapValues(_.values.toArray) // For each RDD, generate an RDDInfo object - groupedRddBlocks.map { case(rddKey, rddBlocks) => - + val rddInfos = groupedRddBlocks.map { case (rddKey, 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 for the rdd, if available. - val rdd = sc.persistentRdds(rddId) - val rddName = Option(rdd.name).getOrElse(rddKey) - val rddStorageLevel = rdd.getStorageLevel - RDDInfo(rddId, rddName, rddStorageLevel, rddBlocks.length, rdd.partitions.size, memSize, diskSize) - }.toArray + // 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 rddStorageLevel = r.getStorageLevel + RDDInfo(rddId, rddName, rddStorageLevel, rddBlocks.length, r.partitions.size, memSize, diskSize) + } + }.flatten.toArray + + scala.util.Sorting.quickSort(rddInfos) + + rddInfos } - /* Removes all BlockStatus object that are not part of a block prefix */ - def filterStorageStatusByPrefix(storageStatusList: Array[StorageStatus], + /* Removes all BlockStatus object that are not part of a block prefix */ + def filterStorageStatusByPrefix(storageStatusList: Array[StorageStatus], prefix: String) : Array[StorageStatus] = { storageStatusList.map { status => diff --git a/core/src/main/scala/spark/storage/ThreadingTest.scala b/core/src/main/scala/spark/storage/ThreadingTest.scala index 5c406e68cb..b3ab1ff4b4 100644 --- a/core/src/main/scala/spark/storage/ThreadingTest.scala +++ b/core/src/main/scala/spark/storage/ThreadingTest.scala @@ -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. + */ + package spark.storage import akka.actor._ diff --git a/core/src/main/scala/spark/ui/JettyUtils.scala b/core/src/main/scala/spark/ui/JettyUtils.scala new file mode 100644 index 0000000000..ca6088ad93 --- /dev/null +++ b/core/src/main/scala/spark/ui/JettyUtils.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 spark.ui + +import annotation.tailrec + +import javax.servlet.http.{HttpServletResponse, HttpServletRequest} + +import net.liftweb.json.{JValue, pretty, render} + +import org.eclipse.jetty.server.{Server, Request, Handler} +import org.eclipse.jetty.server.handler.{ResourceHandler, HandlerList, ContextHandler, AbstractHandler} +import org.eclipse.jetty.util.thread.QueuedThreadPool + +import scala.util.{Try, Success, Failure} +import scala.xml.Node + +import spark.Logging + +/** Utilities for launching a web server using Jetty's HTTP Server class */ +private[spark] object JettyUtils extends Logging { + // Base type for a function that returns something based on an HTTP request. Allows for + // implicit conversion from many types of functions to jetty Handlers. + type Responder[T] = HttpServletRequest => T + + // Conversions from various types of Responder's to jetty Handlers + implicit def jsonResponderToHandler(responder: Responder[JValue]): Handler = + createHandler(responder, "text/json", (in: JValue) => pretty(render(in))) + + implicit def htmlResponderToHandler(responder: Responder[Seq[Node]]): Handler = + createHandler(responder, "text/html", (in: Seq[Node]) => "<!DOCTYPE html>" + in.toString) + + implicit def textResponderToHandler(responder: Responder[String]): Handler = + createHandler(responder, "text/plain") + + private def createHandler[T <% AnyRef](responder: Responder[T], contentType: String, + extractFn: T => String = (in: Any) => in.toString): Handler = { + new AbstractHandler { + def handle(target: String, + baseRequest: Request, + request: HttpServletRequest, + response: HttpServletResponse) { + response.setContentType("%s;charset=utf-8".format(contentType)) + response.setStatus(HttpServletResponse.SC_OK) + baseRequest.setHandled(true) + val result = responder(request) + response.setHeader("Cache-Control", "no-cache, no-store, must-revalidate") + response.getWriter().println(extractFn(result)) + } + } + } + + /** Creates a handler that always redirects the user to a given path */ + def createRedirectHandler(newPath: String): Handler = { + new AbstractHandler { + def handle(target: String, + baseRequest: Request, + request: HttpServletRequest, + response: HttpServletResponse) { + response.setStatus(302) + response.setHeader("Location", baseRequest.getRootURL + newPath) + baseRequest.setHandled(true) + } + } + } + + /** Creates a handler for serving files from a static directory */ + def createStaticHandler(resourceBase: String): ResourceHandler = { + val staticHandler = new ResourceHandler + Option(getClass.getClassLoader.getResource(resourceBase)) match { + case Some(res) => + staticHandler.setResourceBase(res.toString) + case None => + throw new Exception("Could not find resource path for Web UI: " + resourceBase) + } + staticHandler + } + + /** + * Attempts to start a Jetty server at the supplied ip:port which uses the supplied handlers. + * + * If the desired port number is contented, continues incrementing ports until a free port is + * found. Returns the chosen port and the jetty Server object. + */ + def startJettyServer(ip: String, port: Int, handlers: Seq[(String, Handler)]): (Server, Int) = { + val handlersToRegister = handlers.map { case(path, handler) => + val contextHandler = new ContextHandler(path) + contextHandler.setHandler(handler) + contextHandler.asInstanceOf[org.eclipse.jetty.server.Handler] + } + + val handlerList = new HandlerList + handlerList.setHandlers(handlersToRegister.toArray) + + @tailrec + def connect(currentPort: Int): (Server, Int) = { + val server = new Server(currentPort) + val pool = new QueuedThreadPool + pool.setDaemon(true) + server.setThreadPool(pool) + server.setHandler(handlerList) + + Try { server.start() } match { + case s: Success[_] => + sys.addShutdownHook(server.stop()) // Be kind, un-bind + (server, server.getConnectors.head.getLocalPort) + case f: Failure[_] => + server.stop() + logInfo("Failed to create UI at port, %s. Trying again.".format(currentPort)) + logInfo("Error was: " + f.toString) + connect((currentPort + 1) % 65536) + } + } + + connect(port) + } +} diff --git a/core/src/main/scala/spark/ui/Page.scala b/core/src/main/scala/spark/ui/Page.scala new file mode 100644 index 0000000000..03034a4520 --- /dev/null +++ b/core/src/main/scala/spark/ui/Page.scala @@ -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. + */ + +package spark.ui + +private[spark] object Page extends Enumeration { val Storage, Jobs, Environment, Executors = Value } diff --git a/core/src/main/scala/spark/ui/SparkUI.scala b/core/src/main/scala/spark/ui/SparkUI.scala new file mode 100644 index 0000000000..7599f82a94 --- /dev/null +++ b/core/src/main/scala/spark/ui/SparkUI.scala @@ -0,0 +1,83 @@ +/* + * 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 spark.ui + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.{Handler, Server} + +import spark.{Logging, SparkContext, Utils} +import spark.ui.env.EnvironmentUI +import spark.ui.exec.ExecutorsUI +import spark.ui.storage.BlockManagerUI +import spark.ui.jobs.JobProgressUI +import spark.ui.JettyUtils._ + +/** Top level user interface for Spark */ +private[spark] class SparkUI(sc: SparkContext) extends Logging { + val host = Utils.localHostName() + val port = Option(System.getProperty("spark.ui.port")).getOrElse(SparkUI.DEFAULT_PORT).toInt + var boundPort: Option[Int] = None + var server: Option[Server] = None + + val handlers = Seq[(String, Handler)]( + ("/static", createStaticHandler(SparkUI.STATIC_RESOURCE_DIR)), + ("/", createRedirectHandler("/stages")) + ) + val storage = new BlockManagerUI(sc) + val jobs = new JobProgressUI(sc) + val env = new EnvironmentUI(sc) + val exec = new ExecutorsUI(sc) + val allHandlers = storage.getHandlers ++ jobs.getHandlers ++ env.getHandlers ++ + exec.getHandlers ++ handlers + + /** Bind the HTTP server which backs this web interface */ + def bind() { + try { + val (srv, usedPort) = JettyUtils.startJettyServer("0.0.0.0", port, allHandlers) + logInfo("Started Spark Web UI at http://%s:%d".format(host, usedPort)) + server = Some(srv) + boundPort = Some(usedPort) + } catch { + case e: Exception => + logError("Failed to create Spark JettyUtils", e) + System.exit(1) + } + } + + /** Initialize all components of the server */ + def start() { + // NOTE: This is decoupled from bind() because of the following dependency cycle: + // DAGScheduler() requires that the port of this server is known + // This server must register all handlers, including JobProgressUI, before binding + // JobProgressUI registers a listener with SparkContext, which requires sc to initialize + jobs.start() + exec.start() + } + + def stop() { + server.foreach(_.stop()) + } + + private[spark] def appUIAddress = "http://" + host + ":" + boundPort.getOrElse("-1") +} + +private[spark] object SparkUI { + val DEFAULT_PORT = "33000" + val STATIC_RESOURCE_DIR = "spark/ui/static" +} diff --git a/core/src/main/scala/spark/ui/UIUtils.scala b/core/src/main/scala/spark/ui/UIUtils.scala new file mode 100644 index 0000000000..cff26d3168 --- /dev/null +++ b/core/src/main/scala/spark/ui/UIUtils.scala @@ -0,0 +1,139 @@ +/* + * 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 spark.ui + +import scala.xml.Node + +import spark.SparkContext + +/** Utility functions for generating XML pages with spark content. */ +private[spark] object UIUtils { + import Page._ + + /** Returns a spark page with correctly formatted headers */ + def headerSparkPage(content: => Seq[Node], sc: SparkContext, title: String, page: Page.Value) + : Seq[Node] = { + val storage = page match { + case Storage => <li class="active"><a href="/storage">Storage</a></li> + case _ => <li><a href="/storage">Storage</a></li> + } + val jobs = page match { + case Jobs => <li class="active"><a href="/stages">Jobs</a></li> + case _ => <li><a href="/stages">Jobs</a></li> + } + val environment = page match { + case Environment => <li class="active"><a href="/environment">Environment</a></li> + case _ => <li><a href="/environment">Environment</a></li> + } + val executors = page match { + case Executors => <li class="active"><a href="/executors">Executors</a></li> + case _ => <li><a href="/executors">Executors</a></li> + } + + <html> + <head> + <meta http-equiv="Content-type" content="text/html; charset=utf-8" /> + <link rel="stylesheet" href="/static/bootstrap.min.css" type="text/css" /> + <link rel="stylesheet" href="/static/webui.css" type="text/css" /> + <link rel="stylesheet" href="/static/bootstrap-responsive.min.css" type="text/css" /> + <script src="/static/sorttable.js"></script> + <title>{sc.appName} - {title}</title> + <style type="text/css"> + table.sortable thead {{ cursor: pointer; }} + </style> + </head> + <body> + <div class="container"> + + <div class="row"> + <div class="span12"> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <div class="brand"><img src="/static/spark-logo-77x50px-hd.png" /></div> + <ul class="nav"> + {storage} + {jobs} + {environment} + {executors} + </ul> + <ul id="infolist"> + <li>Application: <strong>{sc.appName}</strong></li> + <li>Executors: <strong>{sc.getExecutorStorageStatus.size}</strong></li> + </ul> + </div> + </div> + </div> + </div> + </div> + + <div class="row" style="padding-top: 5px;"> + <div class="span12"> + <h1 style="vertical-align: bottom; display: inline-block;"> + {title} + </h1> + </div> + </div> + <hr/> + {content} + </div> + </body> + </html> + } + + /** Returns a page with the spark css/js and a simple format. Used for scheduler UI. */ + def basicSparkPage(content: => Seq[Node], title: String): Seq[Node] = { + <html> + <head> + <meta http-equiv="Content-type" content="text/html; charset=utf-8" /> + <link rel="stylesheet" href="/static/bootstrap.min.css" type="text/css" /> + <link rel="stylesheet" href="/static/bootstrap-responsive.min.css" type="text/css" /> + <script src="/static/sorttable.js"></script> + <title>{title}</title> + <style type="text/css"> + table.sortable thead {{ cursor: pointer; }} + </style> + </head> + <body> + <div class="container"> + <div class="row"> + <div class="span2"> + <img src="/static/spark_logo.png" /> + </div> + <div class="span10"> + <h3 style="vertical-align: bottom; margin-top: 40px; display: inline-block;"> + {title} + </h3> + </div> + </div> + {content} + </div> + </body> + </html> + } + + /** Returns an HTML table constructed by generating a row for each object in a sequence. */ + def listingTable[T](headers: Seq[String], makeRow: T => Seq[Node], rows: Seq[T]): Seq[Node] = { + <table class="table table-bordered table-striped table-condensed sortable"> + <thead>{headers.map(h => <th>{h}</th>)}</thead> + <tbody> + {rows.map(r => makeRow(r))} + </tbody> + </table> + } +} diff --git a/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala new file mode 100644 index 0000000000..97ea644021 --- /dev/null +++ b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala @@ -0,0 +1,104 @@ +/* + * 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 spark.ui + +import scala.util.Random + +import spark.SparkContext +import spark.SparkContext._ +import spark.scheduler.cluster.SchedulingMode +import spark.scheduler.cluster.SchedulingMode.SchedulingMode +/** + * Continuously generates jobs that expose various features of the WebUI (internal testing tool). + * + * 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: ./run spark.ui.UIWorkloadGenerator [master] [FIFO|FAIR]") + System.exit(1) + } + val master = args(0) + val schedulingMode = SchedulingMode.withName(args(1)) + val appName = "Spark UI Tester" + + if (schedulingMode == SchedulingMode.FAIR) { + System.setProperty("spark.cluster.schedulingmode", "FAIR") + } + val sc = new SparkContext(master, appName) + + def setProperties(s: String) = { + if(schedulingMode == SchedulingMode.FAIR) { + sc.addLocalProperty("spark.scheduler.cluster.fair.pool", s) + } + sc.addLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, s) + } + + val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS) + def nextFloat() = (new Random()).nextFloat() + + val jobs = Seq[(String, () => Long)]( + ("Count", baseData.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", { + baseData.map{x => + val probFailure = (4.0 / NUM_PARTITIONS) + if (nextFloat() < probFailure) { + throw new Exception("This is a task failure") + } + 1 + }.count + }), + ("Partially failed phase (longer tasks)", { + baseData.map{x => + val probFailure = (4.0 / NUM_PARTITIONS) + if (nextFloat() < probFailure) { + Thread.sleep(100) + throw new Exception("This is a task failure") + } + 1 + }.count + }), + ("Job with delays", baseData.map(x => Thread.sleep(100)).count) + ) + + while (true) { + for ((desc, job) <- jobs) { + new Thread { + override def run() { + try { + setProperties(desc) + job() + println("Job funished: " + desc) + } catch { + case e: Exception => + println("Job Failed: " + desc) + } + } + }.start + Thread.sleep(INTER_JOB_WAIT_MS) + } + } + } +} diff --git a/core/src/main/scala/spark/ui/env/EnvironmentUI.scala b/core/src/main/scala/spark/ui/env/EnvironmentUI.scala new file mode 100644 index 0000000000..5ae7935ed4 --- /dev/null +++ b/core/src/main/scala/spark/ui/env/EnvironmentUI.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 spark.ui.env + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.Handler + +import scala.collection.JavaConversions._ +import scala.util.Properties + +import spark.ui.JettyUtils._ +import spark.ui.UIUtils.headerSparkPage +import spark.ui.Page.Environment +import spark.SparkContext +import spark.ui.UIUtils + +import scala.xml.Node + +private[spark] class EnvironmentUI(sc: SparkContext) { + + def getHandlers = Seq[(String, Handler)]( + ("/environment", (request: HttpServletRequest) => envDetails(request)) + ) + + def envDetails(request: HttpServletRequest): Seq[Node] = { + val jvmInformation = Seq( + ("Java Version", "%s (%s)".format(Properties.javaVersion, Properties.javaVendor)), + ("Java Home", Properties.javaHome), + ("Scala Version", Properties.versionString), + ("Scala Home", Properties.scalaHome) + ) + def jvmRow(kv: (String, String)) = <tr><td>{kv._1}</td><td>{kv._2}</td></tr> + def jvmTable = UIUtils.listingTable(Seq("Name", "Value"), jvmRow, jvmInformation) + + val properties = System.getProperties.iterator.toSeq + val classPathProperty = properties + .filter{case (k, v) => k.contains("java.class.path")} + .headOption + .getOrElse("", "") + val sparkProperties = properties.filter(_._1.startsWith("spark")) + val otherProperties = properties.diff(sparkProperties :+ classPathProperty) + + val propertyHeaders = Seq("Name", "Value") + def propertyRow(kv: (String, String)) = <tr><td>{kv._1}</td><td>{kv._2}</td></tr> + val sparkPropertyTable = UIUtils.listingTable(propertyHeaders, propertyRow, sparkProperties) + val otherPropertyTable = UIUtils.listingTable(propertyHeaders, propertyRow, otherProperties) + + val classPathEntries = classPathProperty._2 + .split(System.getProperty("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")} + val addedFiles = sc.addedFiles.iterator.toSeq.map{case (path, time) => (path, "Added By User")} + val classPath = addedJars ++ addedFiles ++ classPathEntries + + val classPathHeaders = Seq("Resource", "Source") + def classPathRow(data: (String, String)) = <tr><td>{data._1}</td><td>{data._2}</td></tr> + val classPathTable = UIUtils.listingTable(classPathHeaders, classPathRow, classPath) + + val content = + <span> + <h2>Runtime Information</h2> {jvmTable} + <h2>Spark Properties</h2> {sparkPropertyTable} + <h2>System Properties</h2> {otherPropertyTable} + <h2>Classpath Entries</h2> {classPathTable} + </span> + + headerSparkPage(content, sc, "Environment", Environment) + } +} diff --git a/core/src/main/scala/spark/ui/exec/ExecutorsUI.scala b/core/src/main/scala/spark/ui/exec/ExecutorsUI.scala new file mode 100644 index 0000000000..4be2bfa413 --- /dev/null +++ b/core/src/main/scala/spark/ui/exec/ExecutorsUI.scala @@ -0,0 +1,153 @@ +package spark.ui.exec + + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.Handler + +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} +import scala.util.Properties + +import spark.{ExceptionFailure, Logging, SparkContext, Success, Utils} +import spark.executor.TaskMetrics +import spark.scheduler.cluster.TaskInfo +import spark.scheduler._ +import spark.SparkContext +import spark.storage.{StorageStatus, StorageUtils} +import spark.ui.JettyUtils._ +import spark.ui.Page.Executors +import spark.ui.UIUtils.headerSparkPage +import spark.ui.UIUtils + +import scala.xml.{Node, XML} + +private[spark] class ExecutorsUI(val sc: SparkContext) { + + private var _listener: Option[ExecutorsListener] = None + def listener = _listener.get + + def start() { + _listener = Some(new ExecutorsListener) + sc.addSparkListener(listener) + } + + def getHandlers = Seq[(String, Handler)]( + ("/executors", (request: HttpServletRequest) => render(request)) + ) + + def render(request: HttpServletRequest): Seq[Node] = { + val storageStatusList = sc.getExecutorStorageStatus + + val maxMem = storageStatusList.map(_.maxMem).reduce(_+_) + val memUsed = storageStatusList.map(_.memUsed()).reduce(_+_) + val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize)) + .reduceOption(_+_).getOrElse(0L) + + val execHead = Seq("Executor ID", "Address", "RDD blocks", "Memory used", "Disk used", + "Active tasks", "Failed tasks", "Complete tasks", "Total tasks") + def execRow(kv: Seq[String]) = + <tr> + <td>{kv(0)}</td> + <td>{kv(1)}</td> + <td>{kv(2)}</td> + <td sorttable_customkey={kv(3)}> + {Utils.memoryBytesToString(kv(3).toLong)} / {Utils.memoryBytesToString(kv(4).toLong)} + </td> + <td sorttable_customkey={kv(5)}> + {Utils.memoryBytesToString(kv(5).toLong)} + </td> + <td>{kv(6)}</td> + <td>{kv(7)}</td> + <td>{kv(8)}</td> + <td>{kv(9)}</td> + </tr> + val execInfo = + for (b <- 0 until storageStatusList.size) + yield getExecInfo(b) + val execTable = UIUtils.listingTable(execHead, execRow, execInfo) + + val content = + <div class="row"> + <div class="span12"> + <ul class="unstyled"> + <li><strong>Memory:</strong> + {Utils.memoryBytesToString(memUsed)} Used + ({Utils.memoryBytesToString(maxMem)} Total) </li> + <li><strong>Disk:</strong> {Utils.memoryBytesToString(diskSpaceUsed)} Used </li> + </ul> + </div> + </div> + <div class = "row"> + <div class="span12"> + {execTable} + </div> + </div>; + + headerSparkPage(content, sc, "Executors", 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).toString + val failedTasks = listener.executorToTasksFailed.getOrElse(a.toString, 0).toString + val completedTasks = listener.executorToTasksComplete.getOrElse(a.toString, 0).toString + val totalTasks = listener.executorToTaskInfos(a.toString).size.toString + + Seq( + execId, + hostPort, + rddBlocks, + memUsed, + maxMem, + diskUsed, + activeTasks, + failedTasks, + completedTasks, + totalTasks + ) + } + + private[spark] class ExecutorsListener extends SparkListener with Logging { + val executorToTasksActive = HashMap[String, HashSet[TaskInfo]]() + val executorToTasksComplete = HashMap[String, Int]() + val executorToTasksFailed = HashMap[String, Int]() + val executorToTaskInfos = + HashMap[String, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]]() + + override def onTaskStart(taskStart: SparkListenerTaskStart) { + val eid = taskStart.taskInfo.executorId + val activeTasks = executorToTasksActive.getOrElseUpdate(eid, new HashSet[TaskInfo]()) + activeTasks += taskStart.taskInfo + val taskList = executorToTaskInfos.getOrElse( + eid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) + taskList += ((taskStart.taskInfo, None, None)) + executorToTaskInfos(eid) = taskList + } + + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) { + val eid = taskEnd.taskInfo.executorId + val activeTasks = executorToTasksActive.getOrElseUpdate(eid, new HashSet[TaskInfo]()) + activeTasks -= taskEnd.taskInfo + val (failureInfo, metrics): (Option[ExceptionFailure], Option[TaskMetrics]) = + taskEnd.reason match { + case e: ExceptionFailure => + executorToTasksFailed(eid) = executorToTasksFailed.getOrElse(eid, 0) + 1 + (Some(e), e.metrics) + case _ => + executorToTasksComplete(eid) = executorToTasksComplete.getOrElse(eid, 0) + 1 + (None, Option(taskEnd.taskMetrics)) + } + val taskList = executorToTaskInfos.getOrElse( + eid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) + taskList -= ((taskEnd.taskInfo, None, None)) + taskList += ((taskEnd.taskInfo, metrics, failureInfo)) + executorToTaskInfos(eid) = taskList + } + } +} diff --git a/core/src/main/scala/spark/ui/jobs/IndexPage.scala b/core/src/main/scala/spark/ui/jobs/IndexPage.scala new file mode 100644 index 0000000000..2da2155e09 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/IndexPage.scala @@ -0,0 +1,95 @@ +/* + * 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 spark.ui.jobs + +import javax.servlet.http.HttpServletRequest + +import scala.xml.{NodeSeq, Node} + +import spark.scheduler.cluster.SchedulingMode +import spark.ui.Page._ +import spark.ui.UIUtils._ +import spark.Utils + +/** Page showing list of all ongoing and recently finished stages and pools*/ +private[spark] class IndexPage(parent: JobProgressUI) { + def listener = parent.listener + + def render(request: HttpServletRequest): Seq[Node] = { + val activeStages = listener.activeStages.toSeq + val completedStages = listener.completedStages.reverse.toSeq + val failedStages = listener.failedStages.reverse.toSeq + val now = System.currentTimeMillis() + + var activeTime = 0L + for (tasks <- listener.stageToTasksActive.values; t <- tasks) { + activeTime += t.timeRunning(now) + } + + val activeStagesTable = new StageTable(activeStages, parent) + val completedStagesTable = new StageTable(completedStages, parent) + val failedStagesTable = new StageTable(failedStages, parent) + + val poolTable = new PoolTable(listener.sc.getAllPools, listener) + val summary: NodeSeq = + <div> + <ul class="unstyled"> + <li> + <strong>Duration: </strong> + {parent.formatDuration(now - listener.sc.startTime)} + </li> + <li> + <strong>CPU time: </strong> + {parent.formatDuration(listener.totalTime + activeTime)} + </li> + {if (listener.totalShuffleRead > 0) + <li> + <strong>Shuffle read: </strong> + {Utils.memoryBytesToString(listener.totalShuffleRead)} + </li> + } + {if (listener.totalShuffleWrite > 0) + <li> + <strong>Shuffle write: </strong> + {Utils.memoryBytesToString(listener.totalShuffleWrite)} + </li> + } + <li><strong>Active Stages Number:</strong> {activeStages.size} </li> + <li><strong>Completed Stages Number:</strong> {completedStages.size} </li> + <li><strong>Failed Stages Number:</strong> {failedStages.size} </li> + <li><strong>Scheduling Mode:</strong> {parent.sc.getSchedulingMode}</li> + + </ul> + </div> + + val content = summary ++ + {if (listener.sc.getSchedulingMode == SchedulingMode.FAIR) { + <h3>Pools</h3> ++ poolTable.toNodeSeq + } else { + Seq() + }} ++ + <h3>Active Stages : {activeStages.size}</h3> ++ + activeStagesTable.toNodeSeq++ + <h3>Completed Stages : {completedStages.size}</h3> ++ + completedStagesTable.toNodeSeq++ + <h3>Failed Stages : {failedStages.size}</h3> ++ + failedStagesTable.toNodeSeq + + headerSparkPage(content, parent.sc, "Spark Stages", Jobs) + } +} diff --git a/core/src/main/scala/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/spark/ui/jobs/JobProgressListener.scala new file mode 100644 index 0000000000..f22c4e39e3 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/JobProgressListener.scala @@ -0,0 +1,167 @@ +package spark.ui.jobs + +import scala.Seq +import scala.collection.mutable.{HashSet, ListBuffer, HashMap, ArrayBuffer} + +import spark.{ExceptionFailure, SparkContext, Success, Utils} +import spark.scheduler._ +import spark.scheduler.cluster.TaskInfo +import spark.executor.TaskMetrics +import collection.mutable + +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 DEFAULT_POOL_NAME = "default" + + val stageToPool = new HashMap[Stage, String]() + val stageToDescription = new HashMap[Stage, String]() + val poolToActiveStages = new HashMap[String, HashSet[Stage]]() + + val activeStages = HashSet[Stage]() + val completedStages = ListBuffer[Stage]() + val failedStages = ListBuffer[Stage]() + + // 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 = + HashMap[Int, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]]() + + override def onJobStart(jobStart: SparkListenerJobStart) {} + + override def onStageCompleted(stageCompleted: StageCompleted) = { + val stage = stageCompleted.stageInfo.stage + poolToActiveStages(stageToPool(stage)) -= stage + activeStages -= stage + completedStages += stage + trimIfNecessary(completedStages) + } + + /** If stages is too large, remove and garbage collect old stages */ + def trimIfNecessary(stages: ListBuffer[Stage]) { + 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)} + }) + stages.trimEnd(toRemove) + } + } + + /** For FIFO, all stages are contained by "default" pool but "default" pool here is meaningless */ + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = { + val stage = stageSubmitted.stage + activeStages += stage + + val poolName = Option(stageSubmitted.properties).map { + p => p.getProperty("spark.scheduler.cluster.fair.pool", DEFAULT_POOL_NAME) + }.getOrElse(DEFAULT_POOL_NAME) + stageToPool(stage) = poolName + + val description = Option(stageSubmitted.properties).flatMap { + p => Option(p.getProperty(SparkContext.SPARK_JOB_DESCRIPTION)) + } + description.map(d => stageToDescription(stage) = d) + + val stages = poolToActiveStages.getOrElseUpdate(poolName, new HashSet[Stage]()) + stages += stage + } + + override def onTaskStart(taskStart: SparkListenerTaskStart) { + val sid = taskStart.task.stageId + val tasksActive = stageToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]()) + tasksActive += taskStart.taskInfo + val taskList = stageToTaskInfos.getOrElse( + sid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) + taskList += ((taskStart.taskInfo, None, None)) + stageToTaskInfos(sid) = taskList + } + + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) { + val sid = taskEnd.task.stageId + val tasksActive = stageToTasksActive.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 + (Some(e), e.metrics) + case _ => + stageToTasksComplete(sid) = stageToTasksComplete.getOrElse(sid, 0) + 1 + (None, Option(taskEnd.taskMetrics)) + } + + stageToTime.getOrElseUpdate(sid, 0L) + val time = metrics.map(m => m.executorRunTime).getOrElse(0) + stageToTime(sid) += time + totalTime += time + + stageToShuffleRead.getOrElseUpdate(sid, 0L) + val shuffleRead = metrics.flatMap(m => m.shuffleReadMetrics).map(s => + s.remoteBytesRead).getOrElse(0L) + stageToShuffleRead(sid) += shuffleRead + totalShuffleRead += shuffleRead + + stageToShuffleWrite.getOrElseUpdate(sid, 0L) + val shuffleWrite = metrics.flatMap(m => m.shuffleWriteMetrics).map(s => + s.shuffleBytesWritten).getOrElse(0L) + stageToShuffleWrite(sid) += shuffleWrite + totalShuffleWrite += shuffleWrite + + val taskList = stageToTaskInfos.getOrElse( + sid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) + taskList -= ((taskEnd.taskInfo, None, None)) + taskList += ((taskEnd.taskInfo, metrics, failureInfo)) + stageToTaskInfos(sid) = taskList + } + + override def onJobEnd(jobEnd: SparkListenerJobEnd) { + jobEnd match { + case end: SparkListenerJobEnd => + end.jobResult match { + case JobFailed(ex, Some(stage)) => + activeStages -= stage + poolToActiveStages(stageToPool(stage)) -= stage + failedStages += stage + trimIfNecessary(failedStages) + case _ => + } + case _ => + } + } + + /** Is this stage's input from a shuffle read. */ + def hasShuffleRead(stageID: Int): Boolean = { + // This is written in a slightly complicated way to avoid having to scan all tasks + for (s <- stageToTaskInfos.get(stageID).getOrElse(Seq())) { + if (s._2 != null) return s._2.flatMap(m => m.shuffleReadMetrics).isDefined + } + return false // No tasks have finished for this stage + } + + /** Is this stage's output to a shuffle write. */ + def hasShuffleWrite(stageID: Int): Boolean = { + // This is written in a slightly complicated way to avoid having to scan all tasks + for (s <- stageToTaskInfos.get(stageID).getOrElse(Seq())) { + if (s._2 != null) return s._2.flatMap(m => m.shuffleWriteMetrics).isDefined + } + return false // No tasks have finished for this stage + } +} diff --git a/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala new file mode 100644 index 0000000000..c83f102ff3 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala @@ -0,0 +1,60 @@ +/* + * 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 spark.ui.jobs + +import akka.util.Duration + +import java.text.SimpleDateFormat + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.Handler + +import scala.Seq +import scala.collection.mutable.{HashSet, ListBuffer, HashMap, ArrayBuffer} + +import spark.ui.JettyUtils._ +import spark.{ExceptionFailure, SparkContext, Success, Utils} +import spark.scheduler._ +import collection.mutable +import spark.scheduler.cluster.SchedulingMode +import spark.scheduler.cluster.SchedulingMode.SchedulingMode + +/** Web UI showing progress status of all jobs in the given SparkContext. */ +private[spark] class JobProgressUI(val sc: SparkContext) { + private var _listener: Option[JobProgressListener] = None + def listener = _listener.get + val dateFmt = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") + + private val indexPage = new IndexPage(this) + private val stagePage = new StagePage(this) + private val poolPage = new PoolPage(this) + + def start() { + _listener = Some(new JobProgressListener(sc)) + sc.addSparkListener(listener) + } + + def formatDuration(ms: Long) = Utils.msDurationToString(ms) + + def getHandlers = Seq[(String, Handler)]( + ("/stages/stage", (request: HttpServletRequest) => stagePage.render(request)), + ("/stages/pool", (request: HttpServletRequest) => poolPage.render(request)), + ("/stages", (request: HttpServletRequest) => indexPage.render(request)) + ) +} diff --git a/core/src/main/scala/spark/ui/jobs/PoolPage.scala b/core/src/main/scala/spark/ui/jobs/PoolPage.scala new file mode 100644 index 0000000000..ee5a6a6a48 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/PoolPage.scala @@ -0,0 +1,30 @@ +package spark.ui.jobs + +import javax.servlet.http.HttpServletRequest + +import scala.xml.{NodeSeq, Node} +import scala.collection.mutable.HashSet + +import spark.scheduler.Stage +import spark.ui.UIUtils._ +import spark.ui.Page._ + +/** Page showing specific pool details */ +private[spark] class PoolPage(parent: JobProgressUI) { + def listener = parent.listener + + def render(request: HttpServletRequest): Seq[Node] = { + val poolName = request.getParameter("poolname") + val poolToActiveStages = listener.poolToActiveStages + val activeStages = poolToActiveStages.getOrElseUpdate(poolName, new HashSet[Stage]).toSeq + val activeStagesTable = new StageTable(activeStages, parent) + + val pool = listener.sc.getPoolForName(poolName).get + val poolTable = new PoolTable(Seq(pool), listener) + + val content = <h3>Pool </h3> ++ poolTable.toNodeSeq() ++ + <h3>Active Stages : {activeStages.size}</h3> ++ activeStagesTable.toNodeSeq() + + headerSparkPage(content, parent.sc, "Spark Pool Details", Jobs) + } +} diff --git a/core/src/main/scala/spark/ui/jobs/PoolTable.scala b/core/src/main/scala/spark/ui/jobs/PoolTable.scala new file mode 100644 index 0000000000..9cfe0d68f0 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/PoolTable.scala @@ -0,0 +1,49 @@ +package spark.ui.jobs + +import scala.xml.Node +import scala.collection.mutable.HashMap +import scala.collection.mutable.HashSet + +import spark.scheduler.Stage +import spark.scheduler.cluster.Schedulable + +/** Table showing list of pools */ +private[spark] class PoolTable(pools: Seq[Schedulable], listener: JobProgressListener) { + + var poolToActiveStages: HashMap[String, HashSet[Stage]] = listener.poolToActiveStages + + def toNodeSeq(): Seq[Node] = { + poolTable(poolRow, pools) + } + + // pool tables + def poolTable(makeRow: (Schedulable, HashMap[String, HashSet[Stage]]) => Seq[Node], + rows: Seq[Schedulable] + ): Seq[Node] = { + <table class="table table-bordered table-striped table-condensed sortable"> + <thead> + <th>Pool Name</th> + <th>Minimum Share</th> + <th>Pool Weight</th> + <td>Active Stages</td> + <td>Running Tasks</td> + <td>SchedulingMode</td> + </thead> + <tbody> + {rows.map(r => makeRow(r, poolToActiveStages))} + </tbody> + </table> + } + + def poolRow(p: Schedulable, poolToActiveStages: HashMap[String, HashSet[Stage]]): Seq[Node] = { + <tr> + <td><a href={"/stages/pool?poolname=%s".format(p.name)}>{p.name}</a></td> + <td>{p.minShare}</td> + <td>{p.weight}</td> + <td>{poolToActiveStages.getOrElseUpdate(p.name, new HashSet[Stage]()).size}</td> + <td>{p.runningTasks}</td> + <td>{p.schedulingMode}</td> + </tr> + } +} + diff --git a/core/src/main/scala/spark/ui/jobs/StagePage.scala b/core/src/main/scala/spark/ui/jobs/StagePage.scala new file mode 100644 index 0000000000..e327cb3947 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/StagePage.scala @@ -0,0 +1,167 @@ +/* + * 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 spark.ui.jobs + +import java.util.Date + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import spark.ui.UIUtils._ +import spark.ui.Page._ +import spark.util.Distribution +import spark.{ExceptionFailure, Utils} +import spark.scheduler.cluster.TaskInfo +import spark.executor.TaskMetrics + +/** Page showing statistics and task list for a given stage */ +private[spark] class StagePage(parent: JobProgressUI) { + def listener = parent.listener + val dateFmt = parent.dateFmt + + def render(request: HttpServletRequest): Seq[Node] = { + val stageId = request.getParameter("id").toInt + val now = System.currentTimeMillis() + + if (!listener.stageToTaskInfos.contains(stageId)) { + val content = + <div> + <h2>Summary Metrics</h2> No tasks have started yet + <h2>Tasks</h2> No tasks have started yet + </div> + return headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs) + } + + val tasks = listener.stageToTaskInfos(stageId) + + val shuffleRead = listener.stageToShuffleRead(stageId) > 0 + val shuffleWrite = listener.stageToShuffleWrite(stageId) > 0 + + var activeTime = 0L + listener.stageToTasksActive(stageId).foreach { t => + activeTime += t.timeRunning(now) + } + + val summary = + <div> + <ul class="unstyled"> + <li> + <strong>CPU time: </strong> + {parent.formatDuration(listener.stageToTime(stageId) + activeTime)} + </li> + {if (shuffleRead) + <li> + <strong>Shuffle read: </strong> + {Utils.memoryBytesToString(listener.stageToShuffleRead(stageId))} + </li> + } + {if (shuffleWrite) + <li> + <strong>Shuffle write: </strong> + {Utils.memoryBytesToString(listener.stageToShuffleWrite(stageId))} + </li> + } + </ul> + </div> + + val taskHeaders: Seq[String] = + Seq("Task ID", "Status", "Duration", "Locality Level", "Worker", "Launch Time") ++ + {if (shuffleRead) Seq("Shuffle Read") else Nil} ++ + {if (shuffleWrite) Seq("Shuffle Write") else Nil} ++ + Seq("Details") + + val taskTable = listingTable(taskHeaders, taskRow, tasks) + + // Excludes tasks which failed and have incomplete metrics + val validTasks = tasks.filter(t => t._1.status == "SUCCESS" && (Option(t._2).isDefined)) + + val summaryTable: Option[Seq[Node]] = + if (validTasks.size == 0) { + None + } + else { + 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)) + + def getQuantileCols(data: Seq[Double]) = + Distribution(data).get.getQuantiles().map(d => Utils.memoryBytesToString(d.toLong)) + + val shuffleReadSizes = validTasks.map { + case(info, metrics, exception) => + metrics.get.shuffleReadMetrics.map(_.remoteBytesRead).getOrElse(0L).toDouble + } + val shuffleReadQuantiles = "Shuffle Read (Remote)" +: getQuantileCols(shuffleReadSizes) + + val shuffleWriteSizes = validTasks.map { + case(info, metrics, exception) => + metrics.get.shuffleWriteMetrics.map(_.shuffleBytesWritten).getOrElse(0L).toDouble + } + val shuffleWriteQuantiles = "Shuffle Write" +: getQuantileCols(shuffleWriteSizes) + + val listings: Seq[Seq[String]] = Seq(serviceQuantiles, + if (shuffleRead) shuffleReadQuantiles else Nil, + if (shuffleWrite) shuffleWriteQuantiles else Nil) + + val quantileHeaders = Seq("Metric", "Min", "25%", "50%", "75%", "Max") + def quantileRow(data: Seq[String]): Seq[Node] = <tr> {data.map(d => <td>{d}</td>)} </tr> + Some(listingTable(quantileHeaders, quantileRow, listings)) + } + + val content = + summary ++ <h2>Summary Metrics</h2> ++ summaryTable.getOrElse(Nil) ++ + <h2>Tasks</h2> ++ taskTable; + + headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs) + } + + + def taskRow(taskData: (TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])): Seq[Node] = { + def fmtStackTrace(trace: Seq[StackTraceElement]): Seq[Node] = + trace.map(e => <span style="display:block;">{e.toString}</span>) + val (info, metrics, exception) = taskData + + val duration = if (info.status == "RUNNING") info.timeRunning(System.currentTimeMillis()) + else metrics.map(m => m.executorRunTime).getOrElse(1) + val formatDuration = if (info.status == "RUNNING") parent.formatDuration(duration) + else metrics.map(m => parent.formatDuration(m.executorRunTime)).getOrElse("") + + <tr> + <td>{info.taskId}</td> + <td>{info.status}</td> + <td sorttable_customkey={duration.toString}> + {formatDuration} + </td> + <td>{info.taskLocality}</td> + <td>{info.hostPort}</td> + <td>{dateFmt.format(new Date(info.launchTime))}</td> + {metrics.flatMap{m => m.shuffleReadMetrics}.map{s => + <td>{Utils.memoryBytesToString(s.remoteBytesRead)}</td>}.getOrElse("")} + {metrics.flatMap{m => m.shuffleWriteMetrics}.map{s => + <td>{Utils.memoryBytesToString(s.shuffleBytesWritten)}</td>}.getOrElse("")} + <td>{exception.map(e => + <span> + {e.className} ({e.description})<br/> + {fmtStackTrace(e.stackTrace)} + </span>).getOrElse("")} + </td> + </tr> + } +} diff --git a/core/src/main/scala/spark/ui/jobs/StageTable.scala b/core/src/main/scala/spark/ui/jobs/StageTable.scala new file mode 100644 index 0000000000..38fa3bcbcd --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/StageTable.scala @@ -0,0 +1,120 @@ +package spark.ui.jobs + +import java.util.Date +import java.text.SimpleDateFormat + +import javax.servlet.http.HttpServletRequest + +import scala.Some +import scala.xml.{NodeSeq, Node} +import scala.collection.mutable.HashMap +import scala.collection.mutable.HashSet + +import spark.scheduler.cluster.{SchedulingMode, TaskInfo} +import spark.scheduler.Stage +import spark.ui.UIUtils._ +import spark.ui.Page._ +import spark.Utils +import spark.storage.StorageLevel + +/** Page showing list of all ongoing and recently finished stages */ +private[spark] class StageTable(val stages: Seq[Stage], val parent: JobProgressUI) { + + val listener = parent.listener + val dateFmt = parent.dateFmt + val isFairScheduler = listener.sc.getSchedulingMode == SchedulingMode.FAIR + + def toNodeSeq(): Seq[Node] = { + stageTable(stageRow, stages) + } + + /** Special table which merges two header cells. */ + def stageTable[T](makeRow: T => Seq[Node], rows: Seq[T]): Seq[Node] = { + <table class="table table-bordered table-striped table-condensed sortable"> + <thead> + <th>Stage Id</th> + {if (isFairScheduler) {<th>Pool Name</th>} else {}} + <th>Description</th> + <th>Submitted</th> + <td>Duration</td> + <td colspan="2">Tasks: Complete/Total</td> + <td>Shuffle Read</td> + <td>Shuffle Write</td> + <td>Stored RDD</td> + </thead> + <tbody> + {rows.map(r => makeRow(r))} + </tbody> + </table> + } + + def getElapsedTime(submitted: Option[Long], completed: Long): String = { + submitted match { + case Some(t) => parent.formatDuration(completed - t) + case _ => "Unknown" + } + } + + def makeProgressBar(started: Int, completed: Int, total: Int): Seq[Node] = { + val completeWidth = "width: %s%%".format((completed.toDouble/total)*100) + val startWidth = "width: %s%%".format((started.toDouble/total)*100) + + <div class="progress" style="height: 15px; margin-bottom: 0px"> + <div class="bar" style={completeWidth}></div> + <div class="bar bar-info" style={startWidth}></div> + </div> + } + + + def stageRow(s: Stage): 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 { + case 0 => "" + case b => Utils.memoryBytesToString(b) + } + val shuffleWrite = listener.stageToShuffleWrite.getOrElse(s.id, 0L) match { + case 0 => "" + case b => Utils.memoryBytesToString(b) + } + + val startedTasks = listener.stageToTasksActive.getOrElse(s.id, HashSet[TaskInfo]()).size + val completedTasks = listener.stageToTasksComplete.getOrElse(s.id, 0) + val totalTasks = s.numPartitions + + val poolName = listener.stageToPool.get(s) + + val nameLink = <a href={"/stages/stage?id=%s".format(s.id)}>{s.name}</a> + val description = listener.stageToDescription.get(s) + .map(d => <div><em>{d}</em></div><div>{nameLink}</div>).getOrElse(nameLink) + + <tr> + <td>{s.id}</td> + {if (isFairScheduler) { + <td><a href={"/stages/pool?poolname=%s".format(poolName.get)}>{poolName.get}</a></td>} + } + <td>{description}</td> + <td valign="middle">{submissionTime}</td> + <td>{getElapsedTime(s.submissionTime, + s.completionTime.getOrElse(System.currentTimeMillis()))}</td> + <td class="progress-cell">{makeProgressBar(startedTasks, completedTasks, totalTasks)}</td> + <td style="border-left: 0; text-align: center;">{completedTasks} / {totalTasks} + {listener.stageToTasksFailed.getOrElse(s.id, 0) match { + case f if f > 0 => "(%s failed)".format(f) + case _ => + }} + </td> + <td>{shuffleRead}</td> + <td>{shuffleWrite}</td> + <td>{if (s.rdd.getStorageLevel != StorageLevel.NONE) { + <a href={"/storage/rdd?id=%s".format(s.rdd.id)}> + {Option(s.rdd.name).getOrElse(s.rdd.id)} + </a> + }} + </td> + </tr> + } +} diff --git a/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala b/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala new file mode 100644 index 0000000000..49ed069c75 --- /dev/null +++ b/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala @@ -0,0 +1,41 @@ +/* + * 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 spark.ui.storage + +import akka.util.Duration + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.Handler + +import spark.{Logging, SparkContext} +import 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) + + def getHandlers = Seq[(String, Handler)]( + ("/storage/rdd", (request: HttpServletRequest) => rddPage.render(request)), + ("/storage", (request: HttpServletRequest) => indexPage.render(request)) + ) +} diff --git a/core/src/main/scala/spark/ui/storage/IndexPage.scala b/core/src/main/scala/spark/ui/storage/IndexPage.scala new file mode 100644 index 0000000000..f76192eba8 --- /dev/null +++ b/core/src/main/scala/spark/ui/storage/IndexPage.scala @@ -0,0 +1,65 @@ +/* + * 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 spark.ui.storage + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import spark.storage.{RDDInfo, StorageUtils} +import spark.Utils +import spark.ui.UIUtils._ +import spark.ui.Page._ + +/** Page showing list of RDD's currently stored in the cluster */ +private[spark] class IndexPage(parent: BlockManagerUI) { + val sc = parent.sc + + def render(request: HttpServletRequest): Seq[Node] = { + val storageStatusList = sc.getExecutorStorageStatus + // Calculate macro-level statistics + + val rddHeaders = Seq( + "RDD Name", + "Storage Level", + "Cached Partitions", + "Fraction Partitions Cached", + "Size in Memory", + "Size on Disk") + val rdds = StorageUtils.rddInfoFromStorageStatus(storageStatusList, sc) + val content = listingTable(rddHeaders, rddRow, rdds) + + headerSparkPage(content, parent.sc, "Spark Storage ", Storage) + } + + def rddRow(rdd: RDDInfo): Seq[Node] = { + <tr> + <td> + <a href={"/storage/rdd?id=%s".format(rdd.id)}> + {rdd.name} + </a> + </td> + <td>{rdd.storageLevel.description} + </td> + <td>{rdd.numCachedPartitions}</td> + <td>{rdd.numCachedPartitions / rdd.numPartitions.toDouble}</td> + <td>{Utils.memoryBytesToString(rdd.memSize)}</td> + <td>{Utils.memoryBytesToString(rdd.diskSize)}</td> + </tr> + } +} diff --git a/core/src/main/scala/spark/ui/storage/RDDPage.scala b/core/src/main/scala/spark/ui/storage/RDDPage.scala new file mode 100644 index 0000000000..003be54ad8 --- /dev/null +++ b/core/src/main/scala/spark/ui/storage/RDDPage.scala @@ -0,0 +1,130 @@ +/* + * 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 spark.ui.storage + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import spark.storage.{StorageStatus, StorageUtils} +import spark.ui.UIUtils._ +import spark.Utils +import spark.storage.BlockManagerMasterActor.BlockStatus +import spark.ui.Page._ + +/** Page showing storage details for a given RDD */ +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 storageStatusList = sc.getExecutorStorageStatus + val filteredStorageStatusList = StorageUtils. + filterStorageStatusByPrefix(storageStatusList, prefix) + val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head + + val workerHeaders = Seq("Host", "Memory Usage", "Disk Usage") + val workers = filteredStorageStatusList.map((prefix, _)) + val workerTable = listingTable(workerHeaders, workerRow, workers) + + val blockHeaders = Seq("Block Name", "Storage Level", "Size in Memory", "Size on Disk", + "Locations") + + val blockStatuses = filteredStorageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1) + val blockLocations = StorageUtils.blockLocationsFromStorageStatus(filteredStorageStatusList) + val blocks = blockStatuses.map { + case(id, status) => (id, status, blockLocations.get(id).getOrElse(Seq("UNKNOWN"))) + } + val blockTable = listingTable(blockHeaders, blockRow, blocks) + + val content = + <div class="row"> + <div class="span12"> + <ul class="unstyled"> + <li> + <strong>Storage Level:</strong> + {rddInfo.storageLevel.description} + </li> + <li> + <strong>Cached Partitions:</strong> + {rddInfo.numCachedPartitions} + </li> + <li> + <strong>Total Partitions:</strong> + {rddInfo.numPartitions} + </li> + <li> + <strong>Memory Size:</strong> + {Utils.memoryBytesToString(rddInfo.memSize)} + </li> + <li> + <strong>Disk Size:</strong> + {Utils.memoryBytesToString(rddInfo.diskSize)} + </li> + </ul> + </div> + </div> + <hr/> + <div class="row"> + <div class="span12"> + {workerTable} + </div> + </div> + <hr/> + <div class="row"> + <div class="span12"> + <h3> RDD Summary </h3> + {blockTable} + </div> + </div>; + + headerSparkPage(content, parent.sc, "RDD Info: " + rddInfo.name, Jobs) + } + + def blockRow(row: (String, BlockStatus, Seq[String])): Seq[Node] = { + val (id, block, locations) = row + <tr> + <td>{id}</td> + <td> + {block.storageLevel.description} + </td> + <td sorttable_customkey={block.memSize.toString}> + {Utils.memoryBytesToString(block.memSize)} + </td> + <td sorttable_customkey={block.diskSize.toString}> + {Utils.memoryBytesToString(block.diskSize)} + </td> + <td> + {locations.map(l => <span>{l}<br/></span>)} + </td> + </tr> + } + + def workerRow(worker: (String, StorageStatus)): Seq[Node] = { + val (prefix, status) = worker + <tr> + <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td> + <td> + {Utils.memoryBytesToString(status.memUsed(prefix))} + ({Utils.memoryBytesToString(status.memRemaining)} Total Available) + </td> + <td>{Utils.memoryBytesToString(status.diskUsed(prefix))}</td> + </tr> + } +} diff --git a/core/src/main/scala/spark/util/AkkaUtils.scala b/core/src/main/scala/spark/util/AkkaUtils.scala index 3e805b7831..9233277bdb 100644 --- a/core/src/main/scala/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/spark/util/AkkaUtils.scala @@ -1,18 +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 spark.util -import akka.actor.{ActorRef, Props, ActorSystemImpl, ActorSystem} +import akka.actor.{ActorSystem, ExtendedActorSystem} import com.typesafe.config.ConfigFactory import akka.util.duration._ -import akka.pattern.ask import akka.remote.RemoteActorRefProvider -import cc.spray.Route -import cc.spray.io.IoWorker -import cc.spray.{SprayCanRootService, HttpService} -import cc.spray.can.server.HttpServer -import cc.spray.io.pipelines.MessageHandlerDispatch.SingletonHandler -import akka.dispatch.Await -import spark.SparkException -import java.util.concurrent.TimeoutException + /** * Various utility classes for working with Akka. @@ -29,9 +38,12 @@ private[spark] object AkkaUtils { 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", "20").toInt + val akkaTimeout = System.getProperty("spark.akka.timeout", "60").toInt val akkaFrameSize = System.getProperty("spark.akka.frameSize", "10").toInt - val lifecycleEvents = System.getProperty("spark.akka.logLifecycleEvents", "false").toBoolean + 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"] @@ -45,42 +57,16 @@ private[spark] object AkkaUtils { 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, - if (lifecycleEvents) "on" else "off")) + lifecycleEvents, akkaWriteTimeout)) - val actorSystem = ActorSystem(name, akkaConf, getClass.getClassLoader) + val actorSystem = 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[ActorSystemImpl].provider + val provider = actorSystem.asInstanceOf[ExtendedActorSystem].provider val boundPort = provider.asInstanceOf[RemoteActorRefProvider].transport.address.port.get return (actorSystem, boundPort) } - - /** - * Creates a Spray HTTP server bound to a given IP and port with a given Spray Route object to - * handle requests. Returns the bound port or throws a SparkException on failure. - */ - def startSprayServer(actorSystem: ActorSystem, ip: String, port: Int, route: Route, - name: String = "HttpServer"): ActorRef = { - val ioWorker = new IoWorker(actorSystem).start() - val httpService = actorSystem.actorOf(Props(new HttpService(route))) - val rootService = actorSystem.actorOf(Props(new SprayCanRootService(httpService))) - val server = actorSystem.actorOf( - Props(new HttpServer(ioWorker, SingletonHandler(rootService))), name = name) - actorSystem.registerOnTermination { ioWorker.stop() } - val timeout = 3.seconds - val future = server.ask(HttpServer.Bind(ip, port))(timeout) - try { - Await.result(future, timeout) match { - case bound: HttpServer.Bound => - return server - case other: Any => - throw new SparkException("Failed to bind web UI to port " + port + ": " + other) - } - } catch { - case e: TimeoutException => - throw new SparkException("Failed to bind web UI to port " + port) - } - } } diff --git a/core/src/main/scala/spark/util/BoundedPriorityQueue.scala b/core/src/main/scala/spark/util/BoundedPriorityQueue.scala new file mode 100644 index 0000000000..0575497f5d --- /dev/null +++ b/core/src/main/scala/spark/util/BoundedPriorityQueue.scala @@ -0,0 +1,62 @@ +/* + * 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 spark.util + +import java.io.Serializable +import java.util.{PriorityQueue => JPriorityQueue} +import scala.collection.generic.Growable +import scala.collection.JavaConverters._ + +/** + * Bounded priority queue. This class wraps the original PriorityQueue + * class and modifies it such that only the top K elements are retained. + * The top K elements are defined by an implicit Ordering[A]. + */ +class BoundedPriorityQueue[A](maxSize: Int)(implicit ord: Ordering[A]) + extends Iterable[A] with Growable[A] with Serializable { + + private val underlying = new JPriorityQueue[A](maxSize, ord) + + override def iterator: Iterator[A] = underlying.iterator.asScala + + override def ++=(xs: TraversableOnce[A]): this.type = { + xs.foreach { this += _ } + this + } + + override def +=(elem: A): this.type = { + if (size < maxSize) underlying.offer(elem) + else maybeReplaceLowest(elem) + this + } + + override def +=(elem1: A, elem2: A, elems: A*): this.type = { + this += elem1 += elem2 ++= elems + } + + override def clear() { underlying.clear() } + + private def maybeReplaceLowest(a: A): Boolean = { + val head = underlying.peek() + if (head != null && ord.gt(a, head)) { + underlying.poll() + underlying.offer(a) + } else false + } +} + diff --git a/core/src/main/scala/spark/util/ByteBufferInputStream.scala b/core/src/main/scala/spark/util/ByteBufferInputStream.scala index d7e67497fe..47a28e2f76 100644 --- a/core/src/main/scala/spark/util/ByteBufferInputStream.scala +++ b/core/src/main/scala/spark/util/ByteBufferInputStream.scala @@ -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. + */ + package spark.util import java.io.InputStream diff --git a/core/src/main/scala/spark/util/CompletionIterator.scala b/core/src/main/scala/spark/util/CompletionIterator.scala index 8139183780..210450892b 100644 --- a/core/src/main/scala/spark/util/CompletionIterator.scala +++ b/core/src/main/scala/spark/util/CompletionIterator.scala @@ -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. + */ + package spark.util /** @@ -22,4 +39,4 @@ object CompletionIterator { def completion() = completionFunction } } -}
\ No newline at end of file +} diff --git a/core/src/main/scala/spark/util/Distribution.scala b/core/src/main/scala/spark/util/Distribution.scala index 24738b4307..5d4d7a6c50 100644 --- a/core/src/main/scala/spark/util/Distribution.scala +++ b/core/src/main/scala/spark/util/Distribution.scala @@ -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. + */ + package spark.util import java.io.PrintStream @@ -62,4 +79,4 @@ object Distribution { quantiles.foreach{q => out.print(q + "\t")} out.println } -}
\ No newline at end of file +} diff --git a/core/src/main/scala/spark/util/IdGenerator.scala b/core/src/main/scala/spark/util/IdGenerator.scala index b6e309fe1a..3422280559 100644 --- a/core/src/main/scala/spark/util/IdGenerator.scala +++ b/core/src/main/scala/spark/util/IdGenerator.scala @@ -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. + */ + package spark.util import java.util.concurrent.atomic.AtomicInteger diff --git a/core/src/main/scala/spark/util/IntParam.scala b/core/src/main/scala/spark/util/IntParam.scala index 0427646747..daf0d58fa2 100644 --- a/core/src/main/scala/spark/util/IntParam.scala +++ b/core/src/main/scala/spark/util/IntParam.scala @@ -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. + */ + package spark.util /** diff --git a/core/src/main/scala/spark/util/MemoryParam.scala b/core/src/main/scala/spark/util/MemoryParam.scala index 3726738842..298562323a 100644 --- a/core/src/main/scala/spark/util/MemoryParam.scala +++ b/core/src/main/scala/spark/util/MemoryParam.scala @@ -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. + */ + package spark.util import spark.Utils diff --git a/core/src/main/scala/spark/util/MetadataCleaner.scala b/core/src/main/scala/spark/util/MetadataCleaner.scala index dafa906712..92909e0959 100644 --- a/core/src/main/scala/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/spark/util/MetadataCleaner.scala @@ -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. + */ + package spark.util import java.util.concurrent.{TimeUnit, ScheduledFuture, Executors} diff --git a/core/src/main/scala/spark/util/NextIterator.scala b/core/src/main/scala/spark/util/NextIterator.scala index 48b5018ddd..22163ece8d 100644 --- a/core/src/main/scala/spark/util/NextIterator.scala +++ b/core/src/main/scala/spark/util/NextIterator.scala @@ -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. + */ + package spark.util /** Provides a basic/boilerplate Iterator implementation. */ @@ -68,4 +85,4 @@ private[spark] abstract class NextIterator[U] extends Iterator[U] { gotNext = false nextValue } -}
\ No newline at end of file +} diff --git a/core/src/main/scala/spark/util/RateLimitedOutputStream.scala b/core/src/main/scala/spark/util/RateLimitedOutputStream.scala index e3f00ea8c7..00f782bbe7 100644 --- a/core/src/main/scala/spark/util/RateLimitedOutputStream.scala +++ b/core/src/main/scala/spark/util/RateLimitedOutputStream.scala @@ -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. + */ + package spark.util import scala.annotation.tailrec diff --git a/core/src/main/scala/spark/util/SerializableBuffer.scala b/core/src/main/scala/spark/util/SerializableBuffer.scala index 09d588fe1c..7e6842628a 100644 --- a/core/src/main/scala/spark/util/SerializableBuffer.scala +++ b/core/src/main/scala/spark/util/SerializableBuffer.scala @@ -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. + */ + package spark.util import java.nio.ByteBuffer diff --git a/core/src/main/scala/spark/util/StatCounter.scala b/core/src/main/scala/spark/util/StatCounter.scala index 5f80180339..76358d4151 100644 --- a/core/src/main/scala/spark/util/StatCounter.scala +++ b/core/src/main/scala/spark/util/StatCounter.scala @@ -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. + */ + package spark.util /** @@ -37,17 +54,23 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable { if (other == this) { merge(other.copy()) // Avoid overwriting fields in a weird order } else { - val delta = other.mu - mu - if (other.n * 10 < n) { - mu = mu + (delta * other.n) / (n + other.n) - } else if (n * 10 < other.n) { - mu = other.mu - (delta * n) / (n + other.n) - } else { - mu = (mu * n + other.mu * other.n) / (n + other.n) + if (n == 0) { + mu = other.mu + m2 = other.m2 + n = other.n + } else if (other.n != 0) { + val delta = other.mu - mu + if (other.n * 10 < n) { + mu = mu + (delta * other.n) / (n + other.n) + } else if (n * 10 < other.n) { + mu = other.mu - (delta * n) / (n + other.n) + } else { + mu = (mu * n + other.mu * other.n) / (n + other.n) + } + m2 += other.m2 + (delta * delta * n * other.n) / (n + other.n) + n += other.n } - m2 += other.m2 + (delta * delta * n * other.n) / (n + other.n) - n += other.n - this + this } } diff --git a/core/src/main/scala/spark/util/TimeStampedHashMap.scala b/core/src/main/scala/spark/util/TimeStampedHashMap.scala index 188f8910da..07772a0afb 100644 --- a/core/src/main/scala/spark/util/TimeStampedHashMap.scala +++ b/core/src/main/scala/spark/util/TimeStampedHashMap.scala @@ -1,8 +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 spark.util import java.util.concurrent.ConcurrentHashMap import scala.collection.JavaConversions import scala.collection.mutable.Map +import scala.collection.immutable +import spark.scheduler.MapStatus /** * This is a custom implementation of scala.collection.mutable.Map which stores the insertion @@ -42,6 +61,13 @@ class TimeStampedHashMap[A, B] extends Map[A, B]() with spark.Logging { this } + // Should we return previous value directly or as Option ? + def putIfAbsent(key: A, value: B): Option[B] = { + val prev = internalMap.putIfAbsent(key, (value, currentTime)) + if (prev != null) Some(prev._1) else None + } + + override def -= (key: A): this.type = { internalMap.remove(key) this @@ -74,6 +100,8 @@ class TimeStampedHashMap[A, B] extends Map[A, B]() with spark.Logging { } } + def toMap: immutable.Map[A, B] = iterator.toMap + /** * Removes old key-value pairs that have timestamp earlier than `threshTime` */ diff --git a/core/src/main/scala/spark/util/TimeStampedHashSet.scala b/core/src/main/scala/spark/util/TimeStampedHashSet.scala index 5f1cc93752..41e3fd8cba 100644 --- a/core/src/main/scala/spark/util/TimeStampedHashSet.scala +++ b/core/src/main/scala/spark/util/TimeStampedHashSet.scala @@ -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. + */ + package spark.util import scala.collection.mutable.Set diff --git a/core/src/main/scala/spark/util/TimedIterator.scala b/core/src/main/scala/spark/util/TimedIterator.scala deleted file mode 100644 index 539b01f4ce..0000000000 --- a/core/src/main/scala/spark/util/TimedIterator.scala +++ /dev/null @@ -1,32 +0,0 @@ -package spark.util - -/** - * A utility for tracking the total time an iterator takes to iterate through its elements. - * - * In general, this should only be used if you expect it to take a considerable amount of time - * (eg. milliseconds) to get each element -- otherwise, the timing won't be very accurate, - * and you are probably just adding more overhead - */ -class TimedIterator[+A](val sub: Iterator[A]) extends Iterator[A] { - private var netMillis = 0l - private var nElems = 0 - def hasNext = { - val start = System.currentTimeMillis() - val r = sub.hasNext - val end = System.currentTimeMillis() - netMillis += (end - start) - r - } - def next = { - val start = System.currentTimeMillis() - val r = sub.next - val end = System.currentTimeMillis() - netMillis += (end - start) - nElems += 1 - r - } - - def getNetMillis = netMillis - def getAverageTimePerItem = netMillis / nElems.toDouble - -} diff --git a/core/src/main/scala/spark/util/Vector.scala b/core/src/main/scala/spark/util/Vector.scala index 835822edb2..a47cac3b96 100644 --- a/core/src/main/scala/spark/util/Vector.scala +++ b/core/src/main/scala/spark/util/Vector.scala @@ -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. + */ + package spark.util class Vector(val elements: Array[Double]) extends Serializable { @@ -56,7 +73,6 @@ class Vector(val elements: Array[Double]) extends Serializable { def += (other: Vector): Vector = { if (length != other.length) throw new IllegalArgumentException("Vectors of different length") - var ans = 0.0 var i = 0 while (i < length) { elements(i) += other(i) @@ -100,9 +116,7 @@ object Vector { def apply(elements: Double*) = new Vector(elements.toArray) def apply(length: Int, initializer: Int => Double): Vector = { - val elements = new Array[Double](length) - for (i <- 0 until length) - elements(i) = initializer(i) + val elements: Array[Double] = Array.tabulate(length)(initializer) return new Vector(elements) } diff --git a/core/src/main/twirl/spark/common/layout.scala.html b/core/src/main/twirl/spark/common/layout.scala.html deleted file mode 100644 index b9192060aa..0000000000 --- a/core/src/main/twirl/spark/common/layout.scala.html +++ /dev/null @@ -1,35 +0,0 @@ -@(title: String)(content: Html) - -<!DOCTYPE html> -<html> - - <head> - <meta http-equiv="Content-type" content="text/html; charset=utf-8"> - <link rel="stylesheet" href="static/bootstrap.min.css" type="text/css"> - <link rel="stylesheet" href="static/bootstrap-responsive.min.css" type="text/css"> - <script src="static/sorttable.js"></script> - <title>@title</title> - <style type="text/css"> - table.sortable thead { cursor: pointer; } - </style> - </head> - - <body> - <div class="container"> - - <!-- HEADER --> - <div class="row"> - <div class="span12"> - <img src="static/spark_logo.png"> - <h1 style="vertical-align: bottom; margin-bottom: 10px; margin-left: 30px; display: inline-block;"> @title </h1> - </div> - </div> - - <hr/> - - @content - - </div> - </body> - -</html>
\ No newline at end of file diff --git a/core/src/main/twirl/spark/deploy/master/app_details.scala.html b/core/src/main/twirl/spark/deploy/master/app_details.scala.html deleted file mode 100644 index 301a7e2124..0000000000 --- a/core/src/main/twirl/spark/deploy/master/app_details.scala.html +++ /dev/null @@ -1,40 +0,0 @@ -@(app: spark.deploy.master.ApplicationInfo) - -@spark.common.html.layout(title = "Application Details") { - - <!-- Application Details --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li><strong>ID:</strong> @app.id</li> - <li><strong>Description:</strong> @app.desc.name</li> - <li><strong>User:</strong> @app.desc.user</li> - <li><strong>Cores:</strong> - @app.desc.cores - (@app.coresGranted Granted - @if(app.desc.cores == Integer.MAX_VALUE) { - - } else { - , @app.coresLeft - } - ) - </li> - <li><strong>Memory per Slave:</strong> @app.desc.memoryPerSlave</li> - <li><strong>Submit Date:</strong> @app.submitDate</li> - <li><strong>State:</strong> @app.state</li> - </ul> - </div> - </div> - - <hr/> - - <!-- Executors --> - <div class="row"> - <div class="span12"> - <h3> Executor Summary </h3> - <br/> - @executors_table(app.executors.values.toList) - </div> - </div> - -} diff --git a/core/src/main/twirl/spark/deploy/master/app_row.scala.html b/core/src/main/twirl/spark/deploy/master/app_row.scala.html deleted file mode 100644 index feb306f35c..0000000000 --- a/core/src/main/twirl/spark/deploy/master/app_row.scala.html +++ /dev/null @@ -1,20 +0,0 @@ -@(app: spark.deploy.master.ApplicationInfo) - -@import spark.Utils -@import spark.deploy.WebUI.formatDate -@import spark.deploy.WebUI.formatDuration - -<tr> - <td> - <a href="app?appId=@(app.id)">@app.id</a> - </td> - <td>@app.desc.name</td> - <td> - @app.coresGranted - </td> - <td>@Utils.memoryMegabytesToString(app.desc.memoryPerSlave)</td> - <td>@formatDate(app.submitDate)</td> - <td>@app.desc.user</td> - <td>@app.state.toString()</td> - <td>@formatDuration(app.duration)</td> -</tr> diff --git a/core/src/main/twirl/spark/deploy/master/app_table.scala.html b/core/src/main/twirl/spark/deploy/master/app_table.scala.html deleted file mode 100644 index f789cee0f1..0000000000 --- a/core/src/main/twirl/spark/deploy/master/app_table.scala.html +++ /dev/null @@ -1,21 +0,0 @@ -@(apps: Array[spark.deploy.master.ApplicationInfo]) - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>ID</th> - <th>Description</th> - <th>Cores</th> - <th>Memory per Node</th> - <th>Submit Time</th> - <th>User</th> - <th>State</th> - <th>Duration</th> - </tr> - </thead> - <tbody> - @for(j <- apps) { - @app_row(j) - } - </tbody> -</table> diff --git a/core/src/main/twirl/spark/deploy/master/executor_row.scala.html b/core/src/main/twirl/spark/deploy/master/executor_row.scala.html deleted file mode 100644 index d2d80fad48..0000000000 --- a/core/src/main/twirl/spark/deploy/master/executor_row.scala.html +++ /dev/null @@ -1,15 +0,0 @@ -@(executor: spark.deploy.master.ExecutorInfo) - -<tr> - <td>@executor.id</td> - <td> - <a href="@executor.worker.webUiAddress">@executor.worker.id</href> - </td> - <td>@executor.cores</td> - <td>@executor.memory</td> - <td>@executor.state</td> - <td> - <a href="@(executor.worker.webUiAddress)/log?appId=@(executor.application.id)&executorId=@(executor.id)&logType=stdout">stdout</a> - <a href="@(executor.worker.webUiAddress)/log?appId=@(executor.application.id)&executorId=@(executor.id)&logType=stderr">stderr</a> - </td> -</tr> diff --git a/core/src/main/twirl/spark/deploy/master/executors_table.scala.html b/core/src/main/twirl/spark/deploy/master/executors_table.scala.html deleted file mode 100644 index cafc42c80e..0000000000 --- a/core/src/main/twirl/spark/deploy/master/executors_table.scala.html +++ /dev/null @@ -1,19 +0,0 @@ -@(executors: List[spark.deploy.master.ExecutorInfo]) - -<table class="table table-bordered table-striped table-condensed"> - <thead> - <tr> - <th>ExecutorID</th> - <th>Worker</th> - <th>Cores</th> - <th>Memory</th> - <th>State</th> - <th>Logs</th> - </tr> - </thead> - <tbody> - @for(e <- executors) { - @executor_row(e) - } - </tbody> -</table>
\ No newline at end of file diff --git a/core/src/main/twirl/spark/deploy/master/index.scala.html b/core/src/main/twirl/spark/deploy/master/index.scala.html deleted file mode 100644 index ac51a39a51..0000000000 --- a/core/src/main/twirl/spark/deploy/master/index.scala.html +++ /dev/null @@ -1,53 +0,0 @@ -@(state: spark.deploy.MasterState) -@import spark.deploy.master._ -@import spark.Utils - -@spark.common.html.layout(title = "Spark Master on " + state.host) { - - <!-- Cluster Details --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li><strong>URL:</strong> @(state.uri)</li> - <li><strong>Workers:</strong> @state.workers.size </li> - <li><strong>Cores:</strong> @{state.workers.map(_.cores).sum} Total, - @{state.workers.map(_.coresUsed).sum} Used</li> - <li><strong>Memory:</strong> @{Utils.memoryMegabytesToString(state.workers.map(_.memory).sum)} Total, - @{Utils.memoryMegabytesToString(state.workers.map(_.memoryUsed).sum)} Used</li> - <li><strong>Applications:</strong> @state.activeApps.size Running, @state.completedApps.size Completed </li> - </ul> - </div> - </div> - - <!-- Worker Summary --> - <div class="row"> - <div class="span12"> - <h3> Workers </h3> - <br/> - @worker_table(state.workers.sortBy(_.id)) - </div> - </div> - - <hr/> - - <!-- App Summary (Running) --> - <div class="row"> - <div class="span12"> - <h3> Running Applications </h3> - <br/> - @app_table(state.activeApps.sortBy(_.startTime).reverse) - </div> - </div> - - <hr/> - - <!-- App Summary (Completed) --> - <div class="row"> - <div class="span12"> - <h3> Completed Applications </h3> - <br/> - @app_table(state.completedApps.sortBy(_.endTime).reverse) - </div> - </div> - -} diff --git a/core/src/main/twirl/spark/deploy/master/worker_row.scala.html b/core/src/main/twirl/spark/deploy/master/worker_row.scala.html deleted file mode 100644 index be69e9bf02..0000000000 --- a/core/src/main/twirl/spark/deploy/master/worker_row.scala.html +++ /dev/null @@ -1,14 +0,0 @@ -@(worker: spark.deploy.master.WorkerInfo) - -@import spark.Utils - -<tr> - <td> - <a href="@worker.webUiAddress">@worker.id</href> - </td> - <td>@{worker.host}:@{worker.port}</td> - <td>@worker.state</td> - <td>@worker.cores (@worker.coresUsed Used)</td> - <td>@{Utils.memoryMegabytesToString(worker.memory)} - (@{Utils.memoryMegabytesToString(worker.memoryUsed)} Used)</td> -</tr> diff --git a/core/src/main/twirl/spark/deploy/master/worker_table.scala.html b/core/src/main/twirl/spark/deploy/master/worker_table.scala.html deleted file mode 100644 index b249411a62..0000000000 --- a/core/src/main/twirl/spark/deploy/master/worker_table.scala.html +++ /dev/null @@ -1,18 +0,0 @@ -@(workers: Array[spark.deploy.master.WorkerInfo]) - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>ID</th> - <th>Address</th> - <th>State</th> - <th>Cores</th> - <th>Memory</th> - </tr> - </thead> - <tbody> - @for(w <- workers) { - @worker_row(w) - } - </tbody> -</table> diff --git a/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html b/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html deleted file mode 100644 index dad0a89080..0000000000 --- a/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html +++ /dev/null @@ -1,20 +0,0 @@ -@(executor: spark.deploy.worker.ExecutorRunner) - -@import spark.Utils - -<tr> - <td>@executor.execId</td> - <td>@executor.cores</td> - <td>@Utils.memoryMegabytesToString(executor.memory)</td> - <td> - <ul class="unstyled"> - <li><strong>ID:</strong> @executor.appId</li> - <li><strong>Name:</strong> @executor.appDesc.name</li> - <li><strong>User:</strong> @executor.appDesc.user</li> - </ul> - </td> - <td> - <a href="log?appId=@(executor.appId)&executorId=@(executor.execId)&logType=stdout">stdout</a> - <a href="log?appId=@(executor.appId)&executorId=@(executor.execId)&logType=stderr">stderr</a> - </td> -</tr> diff --git a/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html b/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html deleted file mode 100644 index 327a2399c7..0000000000 --- a/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html +++ /dev/null @@ -1,18 +0,0 @@ -@(executors: List[spark.deploy.worker.ExecutorRunner]) - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>ExecutorID</th> - <th>Cores</th> - <th>Memory</th> - <th>Job Details</th> - <th>Logs</th> - </tr> - </thead> - <tbody> - @for(e <- executors) { - @executor_row(e) - } - </tbody> -</table>
\ No newline at end of file diff --git a/core/src/main/twirl/spark/deploy/worker/index.scala.html b/core/src/main/twirl/spark/deploy/worker/index.scala.html deleted file mode 100644 index c39f769a73..0000000000 --- a/core/src/main/twirl/spark/deploy/worker/index.scala.html +++ /dev/null @@ -1,44 +0,0 @@ -@(worker: spark.deploy.WorkerState) -@import spark.Utils - -@spark.common.html.layout(title = "Spark Worker on " + worker.host) { - - <!-- Worker Details --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li><strong>ID:</strong> @worker.workerId</li> - <li><strong> - Master URL:</strong> @worker.masterUrl - </li> - <li><strong>Cores:</strong> @worker.cores (@worker.coresUsed Used)</li> - <li><strong>Memory:</strong> @{Utils.memoryMegabytesToString(worker.memory)} - (@{Utils.memoryMegabytesToString(worker.memoryUsed)} Used)</li> - </ul> - <p><a href="@worker.masterWebUiUrl">Back to Master</a></p> - </div> - </div> - - <hr/> - - <!-- Running Executors --> - <div class="row"> - <div class="span12"> - <h3> Running Executors </h3> - <br/> - @executors_table(worker.executors) - </div> - </div> - - <hr/> - - <!-- Finished Executors --> - <div class="row"> - <div class="span12"> - <h3> Finished Executors </h3> - <br/> - @executors_table(worker.finishedExecutors) - </div> - </div> - -} diff --git a/core/src/main/twirl/spark/storage/index.scala.html b/core/src/main/twirl/spark/storage/index.scala.html deleted file mode 100644 index 2b337f6133..0000000000 --- a/core/src/main/twirl/spark/storage/index.scala.html +++ /dev/null @@ -1,40 +0,0 @@ -@(maxMem: Long, remainingMem: Long, diskSpaceUsed: Long, rdds: Array[spark.storage.RDDInfo], storageStatusList: Array[spark.storage.StorageStatus]) -@import spark.Utils - -@spark.common.html.layout(title = "Storage Dashboard") { - - <!-- High-Level Information --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li><strong>Memory:</strong> - @{Utils.memoryBytesToString(maxMem - remainingMem)} Used - (@{Utils.memoryBytesToString(remainingMem)} Available) </li> - <li><strong>Disk:</strong> @{Utils.memoryBytesToString(diskSpaceUsed)} Used </li> - </ul> - </div> - </div> - - <hr/> - - <!-- RDD Summary --> - <div class="row"> - <div class="span12"> - <h3> RDD Summary </h3> - <br/> - @rdd_table(rdds) - </div> - </div> - - <hr/> - - <!-- Worker Summary --> - <div class="row"> - <div class="span12"> - <h3> Worker Summary </h3> - <br/> - @worker_table(storageStatusList) - </div> - </div> - -}
\ No newline at end of file diff --git a/core/src/main/twirl/spark/storage/rdd.scala.html b/core/src/main/twirl/spark/storage/rdd.scala.html deleted file mode 100644 index d85addeb17..0000000000 --- a/core/src/main/twirl/spark/storage/rdd.scala.html +++ /dev/null @@ -1,81 +0,0 @@ -@(rddInfo: spark.storage.RDDInfo, storageStatusList: Array[spark.storage.StorageStatus]) -@import spark.Utils - -@spark.common.html.layout(title = "RDD Info ") { - - <!-- High-Level Information --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li> - <strong>Storage Level:</strong> - @(rddInfo.storageLevel.description) - <li> - <strong>Cached Partitions:</strong> - @(rddInfo.numCachedPartitions) - </li> - <li> - <strong>Total Partitions:</strong> - @(rddInfo.numPartitions) - </li> - <li> - <strong>Memory Size:</strong> - @{Utils.memoryBytesToString(rddInfo.memSize)} - </li> - <li> - <strong>Disk Size:</strong> - @{Utils.memoryBytesToString(rddInfo.diskSize)} - </li> - </ul> - </div> - </div> - - <hr/> - - <!-- RDD Summary --> - <div class="row"> - <div class="span12"> - <h3> RDD Summary </h3> - <br/> - - - <!-- Block Table Summary --> - <table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>Block Name</th> - <th>Storage Level</th> - <th>Size in Memory</th> - <th>Size on Disk</th> - </tr> - </thead> - <tbody> - @storageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1).map { case (k,v) => - <tr> - <td>@k</td> - <td> - @(v.storageLevel.description) - </td> - <td>@{Utils.memoryBytesToString(v.memSize)}</td> - <td>@{Utils.memoryBytesToString(v.diskSize)}</td> - </tr> - } - </tbody> - </table> - - - </div> - </div> - - <hr/> - - <!-- Worker Table --> - <div class="row"> - <div class="span12"> - <h3> Worker Summary </h3> - <br/> - @worker_table(storageStatusList, "rdd_" + rddInfo.id ) - </div> - </div> - -}
\ No newline at end of file diff --git a/core/src/main/twirl/spark/storage/rdd_table.scala.html b/core/src/main/twirl/spark/storage/rdd_table.scala.html deleted file mode 100644 index a51e64aed0..0000000000 --- a/core/src/main/twirl/spark/storage/rdd_table.scala.html +++ /dev/null @@ -1,32 +0,0 @@ -@(rdds: Array[spark.storage.RDDInfo]) -@import spark.Utils - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>RDD Name</th> - <th>Storage Level</th> - <th>Cached Partitions</th> - <th>Fraction Partitions Cached</th> - <th>Size in Memory</th> - <th>Size on Disk</th> - </tr> - </thead> - <tbody> - @for(rdd <- rdds) { - <tr> - <td> - <a href="rdd?id=@(rdd.id)"> - @rdd.name - </a> - </td> - <td>@(rdd.storageLevel.description) - </td> - <td>@rdd.numCachedPartitions</td> - <td>@(rdd.numCachedPartitions / rdd.numPartitions.toDouble)</td> - <td>@{Utils.memoryBytesToString(rdd.memSize)}</td> - <td>@{Utils.memoryBytesToString(rdd.diskSize)}</td> - </tr> - } - </tbody> -</table>
\ No newline at end of file diff --git a/core/src/main/twirl/spark/storage/worker_table.scala.html b/core/src/main/twirl/spark/storage/worker_table.scala.html deleted file mode 100644 index d54b8de4cc..0000000000 --- a/core/src/main/twirl/spark/storage/worker_table.scala.html +++ /dev/null @@ -1,24 +0,0 @@ -@(workersStatusList: Array[spark.storage.StorageStatus], prefix: String = "") -@import spark.Utils - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>Host</th> - <th>Memory Usage</th> - <th>Disk Usage</th> - </tr> - </thead> - <tbody> - @for(status <- workersStatusList) { - <tr> - <td>@(status.blockManagerId.ip + ":" + status.blockManagerId.port)</td> - <td> - @(Utils.memoryBytesToString(status.memUsed(prefix))) - (@(Utils.memoryBytesToString(status.memRemaining)) Total Available) - </td> - <td>@(Utils.memoryBytesToString(status.diskUsed(prefix)))</td> - </tr> - } - </tbody> -</table>
\ No newline at end of file diff --git a/core/src/test/resources/fairscheduler.xml b/core/src/test/resources/fairscheduler.xml new file mode 100644 index 0000000000..996ffb1864 --- /dev/null +++ b/core/src/test/resources/fairscheduler.xml @@ -0,0 +1,32 @@ +<?xml version="1.0"?> +<!-- + ~ 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. + --> + +<allocations> +<pool name="1"> + <minShare>2</minShare> + <weight>1</weight> + <schedulingMode>FIFO</schedulingMode> +</pool> +<pool name="2"> + <minShare>3</minShare> + <weight>1</weight> + <schedulingMode>FIFO</schedulingMode> +</pool> +<pool name="3"> +</pool> +</allocations> diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties index 6ec89c0184..f6fef03689 100644 --- a/core/src/test/resources/log4j.properties +++ b/core/src/test/resources/log4j.properties @@ -1,4 +1,21 @@ -# Set everything to be logged to the file core/target/unit-tests.log +# +# 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. +# + +# Set everything to be logged to the file core/target/unit-tests.log log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false @@ -8,3 +25,4 @@ log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: # Ignore messages below warning level from Jetty, because it's a bit verbose log4j.logger.org.eclipse.jetty=WARN +org.eclipse.jetty.LEVEL=WARN diff --git a/core/src/test/resources/test_metrics_config.properties b/core/src/test/resources/test_metrics_config.properties new file mode 100644 index 0000000000..2b31ddf2eb --- /dev/null +++ b/core/src/test/resources/test_metrics_config.properties @@ -0,0 +1,6 @@ +*.sink.console.period = 10 +*.sink.console.unit = seconds +*.source.jvm.class = spark.metrics.source.JvmSource +master.sink.console.period = 20 +master.sink.console.unit = minutes + diff --git a/core/src/test/resources/test_metrics_system.properties b/core/src/test/resources/test_metrics_system.properties new file mode 100644 index 0000000000..d5479f0298 --- /dev/null +++ b/core/src/test/resources/test_metrics_system.properties @@ -0,0 +1,7 @@ +*.sink.console.period = 10 +*.sink.console.unit = seconds +test.sink.console.class = spark.metrics.sink.ConsoleSink +test.sink.dummy.class = spark.metrics.sink.DummySink +test.source.dummy.class = spark.metrics.source.DummySource +test.sink.console.period = 20 +test.sink.console.unit = minutes diff --git a/core/src/test/scala/spark/AccumulatorSuite.scala b/core/src/test/scala/spark/AccumulatorSuite.scala index ac8ae7d308..0af175f316 100644 --- a/core/src/test/scala/spark/AccumulatorSuite.scala +++ b/core/src/test/scala/spark/AccumulatorSuite.scala @@ -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. + */ + package spark import org.scalatest.FunSuite diff --git a/core/src/test/scala/spark/BroadcastSuite.scala b/core/src/test/scala/spark/BroadcastSuite.scala index 362a31fb0d..785721ece8 100644 --- a/core/src/test/scala/spark/BroadcastSuite.scala +++ b/core/src/test/scala/spark/BroadcastSuite.scala @@ -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. + */ + package spark import org.scalatest.FunSuite diff --git a/core/src/test/scala/spark/CheckpointSuite.scala b/core/src/test/scala/spark/CheckpointSuite.scala index ca385972fb..a84c89e3c9 100644 --- a/core/src/test/scala/spark/CheckpointSuite.scala +++ b/core/src/test/scala/spark/CheckpointSuite.scala @@ -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. + */ + package spark import org.scalatest.FunSuite @@ -27,6 +44,16 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { } } + test("basic checkpointing") { + val parCollection = sc.makeRDD(1 to 4) + val flatMappedRDD = parCollection.flatMap(x => 1 to x) + flatMappedRDD.checkpoint() + assert(flatMappedRDD.dependencies.head.rdd == parCollection) + val result = flatMappedRDD.collect() + assert(flatMappedRDD.dependencies.head.rdd != parCollection) + assert(flatMappedRDD.collect() === result) + } + test("RDDs with one-to-one dependencies") { testCheckpointing(_.map(x => x.toString)) testCheckpointing(_.flatMap(x => 1 to x)) diff --git a/core/src/test/scala/spark/ClosureCleanerSuite.scala b/core/src/test/scala/spark/ClosureCleanerSuite.scala index b2d0dd4627..7d2831e19c 100644 --- a/core/src/test/scala/spark/ClosureCleanerSuite.scala +++ b/core/src/test/scala/spark/ClosureCleanerSuite.scala @@ -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. + */ + package spark import java.io.NotSerializableException diff --git a/core/src/test/scala/spark/DistributedSuite.scala b/core/src/test/scala/spark/DistributedSuite.scala index 4104b33c8b..e11efe459c 100644 --- a/core/src/test/scala/spark/DistributedSuite.scala +++ b/core/src/test/scala/spark/DistributedSuite.scala @@ -1,13 +1,33 @@ +/* + * 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 spark import network.ConnectionManagerId import org.scalatest.FunSuite import org.scalatest.BeforeAndAfter +import org.scalatest.concurrent.Timeouts._ 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 @@ -15,8 +35,15 @@ import scala.collection.mutable.ArrayBuffer import SparkContext._ import storage.{GetBlock, BlockManagerWorker, StorageLevel} +import ui.JettyUtils + + +class NotSerializableClass +class NotSerializableExn(val notSer: NotSerializableClass) extends Throwable() {} -class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter with LocalSparkContext { + +class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter + with LocalSparkContext { val clusterUrl = "local-cluster[2,1,512]" @@ -25,6 +52,24 @@ class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter System.clearProperty("spark.storage.memoryFraction") } + test("task throws not serializable exception") { + // Ensures that executors do not crash when an exn is not serializable. If executors crash, + // this test will hang. Correct behavior is that executors don't crash but fail tasks + // and the scheduler throws a SparkException. + + // numSlaves must be less than numPartitions + val numSlaves = 3 + val numPartitions = 10 + + sc = new SparkContext("local-cluster[%s,1,512]".format(numSlaves), "test") + val data = sc.parallelize(1 to 100, numPartitions). + map(x => throw new NotSerializableExn(new NotSerializableClass)) + intercept[SparkException] { + data.count() + } + resetSparkContext() + } + test("local-cluster format") { sc = new SparkContext("local-cluster[2,1,512]", "test") assert(sc.parallelize(1 to 2, 2).count() == 2) @@ -153,7 +198,7 @@ class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter val blockManager = SparkEnv.get.blockManager blockManager.master.getLocations(blockId).foreach(id => { val bytes = BlockManagerWorker.syncGetBlock( - GetBlock(blockId), ConnectionManagerId(id.ip, id.port)) + GetBlock(blockId), ConnectionManagerId(id.host, id.port)) val deserialized = blockManager.dataDeserialize(blockId, bytes).asInstanceOf[Iterator[Int]].toList assert(deserialized === (1 to 100).toList) }) @@ -196,7 +241,6 @@ class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter sc = new SparkContext(clusterUrl, "test") val data = sc.parallelize(Seq(true, true), 2) assert(data.count === 2) // force executors to start - val masterId = SparkEnv.get.blockManager.blockManagerId assert(data.map(markNodeIfIdentity).collect.size === 2) assert(data.map(failOnMarkedIdentity).collect.size === 2) } @@ -252,12 +296,48 @@ class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter assert(data2.count === 2) } } + + test("unpersist RDDs") { + DistributedSuite.amMaster = true + sc = new SparkContext("local-cluster[3,1,512]", "test") + val data = sc.parallelize(Seq(true, false, false, false), 4) + data.persist(StorageLevel.MEMORY_ONLY_2) + data.count + assert(sc.persistentRdds.isEmpty === false) + data.unpersist() + assert(sc.persistentRdds.isEmpty === true) + + failAfter(Span(3000, Millis)) { + try { + while (! sc.getRDDStorageInfo.isEmpty) { + Thread.sleep(200) + } + } catch { + case _ => { 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 { // Indicates whether this JVM is marked for failure. var mark = false - + // Set by test to remember if we are in the driver program so we can assert // that we are not. var amMaster = false @@ -274,9 +354,9 @@ object DistributedSuite { // Act like an identity function, but if mark was set to true previously, fail, // crashing the entire JVM. def failOnMarkedIdentity(item: Boolean): Boolean = { - if (mark) { + if (mark) { System.exit(42) - } + } item - } + } } diff --git a/core/src/test/scala/spark/DriverSuite.scala b/core/src/test/scala/spark/DriverSuite.scala index 5e84b3a66a..ed16b9d8ef 100644 --- a/core/src/test/scala/spark/DriverSuite.scala +++ b/core/src/test/scala/spark/DriverSuite.scala @@ -1,7 +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 spark import java.io.File +import org.apache.log4j.Logger +import org.apache.log4j.Level + import org.scalatest.FunSuite import org.scalatest.concurrent.Timeouts import org.scalatest.prop.TableDrivenPropertyChecks._ @@ -27,6 +47,7 @@ class DriverSuite extends FunSuite with Timeouts { */ object DriverWithoutCleanup { def main(args: Array[String]) { + Logger.getRootLogger().setLevel(Level.WARN) val sc = new SparkContext(args(0), "DriverWithoutCleanup") sc.parallelize(1 to 100, 4).count() } diff --git a/core/src/test/scala/spark/FailureSuite.scala b/core/src/test/scala/spark/FailureSuite.scala index 8c1445a465..5b133cdd6e 100644 --- a/core/src/test/scala/spark/FailureSuite.scala +++ b/core/src/test/scala/spark/FailureSuite.scala @@ -1,9 +1,23 @@ +/* + * 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 spark import org.scalatest.FunSuite -import org.scalatest.prop.Checkers - -import scala.collection.mutable.ArrayBuffer import SparkContext._ @@ -23,7 +37,7 @@ object FailureSuiteState { } 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") { @@ -49,7 +63,7 @@ class FailureSuite extends FunSuite with LocalSparkContext { test("failure in a two-stage job") { sc = new SparkContext("local[1,1]", "test") val results = sc.makeRDD(1 to 3).map(x => (x, x)).groupByKey(3).map { - case (k, v) => + case (k, v) => FailureSuiteState.synchronized { FailureSuiteState.tasksRun += 1 if (k == 1 && FailureSuiteState.tasksFailed == 0) { @@ -70,15 +84,43 @@ class FailureSuite extends FunSuite with LocalSparkContext { sc = new SparkContext("local[1,1]", "test") val results = sc.makeRDD(1 to 3).map(x => new NonSerializable) - val thrown = intercept[spark.SparkException] { + val thrown = intercept[SparkException] { results.collect() } - assert(thrown.getClass === classOf[spark.SparkException]) + assert(thrown.getClass === classOf[SparkException]) assert(thrown.getMessage.contains("NotSerializableException")) FailureSuiteState.clear() } + test("failure because task closure is not serializable") { + sc = new SparkContext("local[1,1]", "test") + val a = new NonSerializable + + // Non-serializable closure in the final result stage + val thrown = intercept[SparkException] { + sc.parallelize(1 to 10, 2).map(x => a).count() + } + assert(thrown.getClass === classOf[SparkException]) + assert(thrown.getMessage.contains("NotSerializableException")) + + // Non-serializable closure in an earlier stage + val thrown1 = intercept[SparkException] { + sc.parallelize(1 to 10, 2).map(x => (x, a)).partitionBy(new HashPartitioner(3)).count() + } + assert(thrown1.getClass === classOf[SparkException]) + assert(thrown1.getMessage.contains("NotSerializableException")) + + // Non-serializable closure in foreach function + val thrown2 = intercept[SparkException] { + sc.parallelize(1 to 10, 2).foreach(x => println(a)) + } + assert(thrown2.getClass === classOf[SparkException]) + assert(thrown2.getMessage.contains("NotSerializableException")) + + FailureSuiteState.clear() + } + // TODO: Need to add tests with shuffle fetch failures. } diff --git a/core/src/test/scala/spark/FileServerSuite.scala b/core/src/test/scala/spark/FileServerSuite.scala index f1a35bced3..242ae971f8 100644 --- a/core/src/test/scala/spark/FileServerSuite.scala +++ b/core/src/test/scala/spark/FileServerSuite.scala @@ -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. + */ + package spark import com.google.common.io.Files @@ -35,26 +52,26 @@ class FileServerSuite extends FunSuite with LocalSparkContext { val testData = Array((1,1), (1,1), (2,1), (3,5), (2,2), (3,0)) val result = sc.parallelize(testData).reduceByKey { val path = SparkFiles.get("FileServerSuite.txt") - val in = new BufferedReader(new FileReader(path)) + val in = new BufferedReader(new FileReader(path)) val fileVal = in.readLine().toInt in.close() _ * fileVal + _ * fileVal - }.collect + }.collect() assert(result.toSet === Set((1,200), (2,300), (3,500))) } test("Distributing files locally using URL as input") { // addFile("file:///....") sc = new SparkContext("local[4]", "test") - sc.addFile((new File(tmpFile.toString)).toURL.toString) + sc.addFile(new File(tmpFile.toString).toURI.toString) val testData = Array((1,1), (1,1), (2,1), (3,5), (2,2), (3,0)) val result = sc.parallelize(testData).reduceByKey { val path = SparkFiles.get("FileServerSuite.txt") - val in = new BufferedReader(new FileReader(path)) + val in = new BufferedReader(new FileReader(path)) val fileVal = in.readLine().toInt in.close() _ * fileVal + _ * fileVal - }.collect + }.collect() assert(result.toSet === Set((1,200), (2,300), (3,500))) } @@ -80,12 +97,11 @@ class FileServerSuite extends FunSuite with LocalSparkContext { val testData = Array((1,1), (1,1), (2,1), (3,5), (2,2), (3,0)) val result = sc.parallelize(testData).reduceByKey { val path = SparkFiles.get("FileServerSuite.txt") - val in = new BufferedReader(new FileReader(path)) + val in = new BufferedReader(new FileReader(path)) val fileVal = in.readLine().toInt in.close() _ * fileVal + _ * fileVal - }.collect - println(result) + }.collect() assert(result.toSet === Set((1,200), (2,300), (3,500))) } diff --git a/core/src/test/scala/spark/FileSuite.scala b/core/src/test/scala/spark/FileSuite.scala index 91b48c7456..1e2c257c4b 100644 --- a/core/src/test/scala/spark/FileSuite.scala +++ b/core/src/test/scala/spark/FileSuite.scala @@ -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. + */ + package spark import java.io.{FileWriter, PrintWriter, File} @@ -7,6 +24,8 @@ import scala.io.Source import com.google.common.io.Files import org.scalatest.FunSuite import org.apache.hadoop.io._ +import org.apache.hadoop.io.compress.{DefaultCodec, CompressionCodec, GzipCodec} + import SparkContext._ @@ -26,6 +45,28 @@ class FileSuite extends FunSuite with LocalSparkContext { assert(sc.textFile(outputDir).collect().toList === List("1", "2", "3", "4")) } + test("text files (compressed)") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val normalDir = new File(tempDir, "output_normal").getAbsolutePath + val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath + val codec = new DefaultCodec() + + val data = sc.parallelize("a" * 10000, 1) + data.saveAsTextFile(normalDir) + data.saveAsTextFile(compressedOutputDir, classOf[DefaultCodec]) + + val normalFile = new File(normalDir, "part-00000") + val normalContent = sc.textFile(normalDir).collect + assert(normalContent === Array.fill(10000)("a")) + + val compressedFile = new File(compressedOutputDir, "part-00000" + codec.getDefaultExtension) + val compressedContent = sc.textFile(compressedOutputDir).collect + assert(compressedContent === Array.fill(10000)("a")) + + assert(compressedFile.length < normalFile.length) + } + test("SequenceFiles") { sc = new SparkContext("local", "test") val tempDir = Files.createTempDir() @@ -37,6 +78,28 @@ class FileSuite extends FunSuite with LocalSparkContext { assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) } + test("SequenceFile (compressed)") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val normalDir = new File(tempDir, "output_normal").getAbsolutePath + val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath + val codec = new DefaultCodec() + + val data = sc.parallelize(Seq.fill(100)("abc"), 1).map(x => (x, x)) + data.saveAsSequenceFile(normalDir) + data.saveAsSequenceFile(compressedOutputDir, Some(classOf[DefaultCodec])) + + val normalFile = new File(normalDir, "part-00000") + val normalContent = sc.sequenceFile[String, String](normalDir).collect + assert(normalContent === Array.fill(100)("abc", "abc")) + + val compressedFile = new File(compressedOutputDir, "part-00000" + codec.getDefaultExtension) + val compressedContent = sc.sequenceFile[String, String](compressedOutputDir).collect + assert(compressedContent === Array.fill(100)("abc", "abc")) + + assert(compressedFile.length < normalFile.length) + } + test("SequenceFile with writable key") { sc = new SparkContext("local", "test") val tempDir = Files.createTempDir() diff --git a/core/src/test/scala/spark/JavaAPISuite.java b/core/src/test/scala/spark/JavaAPISuite.java index d3dcd3bbeb..5e2bf2d231 100644 --- a/core/src/test/scala/spark/JavaAPISuite.java +++ b/core/src/test/scala/spark/JavaAPISuite.java @@ -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. + */ + package spark; import java.io.File; @@ -8,6 +25,7 @@ import java.util.*; import scala.Tuple2; import com.google.common.base.Charsets; +import org.apache.hadoop.io.compress.DefaultCodec; import com.google.common.io.Files; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; @@ -308,7 +326,9 @@ public class JavaAPISuite implements Serializable { Assert.assertEquals(20/6.0, rdd.mean(), 0.01); Assert.assertEquals(20/6.0, rdd.mean(), 0.01); Assert.assertEquals(6.22222, rdd.variance(), 0.01); + Assert.assertEquals(7.46667, rdd.sampleVariance(), 0.01); Assert.assertEquals(2.49444, rdd.stdev(), 0.01); + Assert.assertEquals(2.73252, rdd.sampleStdev(), 0.01); Double first = rdd.first(); List<Double> take = rdd.take(5); @@ -474,6 +494,19 @@ public class JavaAPISuite implements Serializable { } @Test + public void textFilesCompressed() throws IOException { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + rdd.saveAsTextFile(outputDir, DefaultCodec.class); + + // Try reading it in as a text file RDD + List<String> expected = Arrays.asList("1", "2", "3", "4"); + JavaRDD<String> readRDD = sc.textFile(outputDir); + Assert.assertEquals(expected, readRDD.collect()); + } + + @Test public void sequenceFile() { File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); @@ -620,6 +653,37 @@ public class JavaAPISuite implements Serializable { } @Test + public void hadoopFileCompressed() { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output_compressed").getAbsolutePath(); + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs); + + rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() { + @Override + public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) { + return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2())); + } + }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class, + DefaultCodec.class); + + JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir, + SequenceFileInputFormat.class, IntWritable.class, Text.class); + + Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, + String>() { + @Override + public String call(Tuple2<IntWritable, Text> x) { + return x.toString(); + } + }).collect().toString()); + } + + @Test public void zip() { JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); JavaDoubleRDD doubles = rdd.map(new DoubleFunction<Integer>() { @@ -633,6 +697,32 @@ public class JavaAPISuite implements Serializable { } @Test + public void zipPartitions() { + JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6), 2); + JavaRDD<String> rdd2 = sc.parallelize(Arrays.asList("1", "2", "3", "4"), 2); + FlatMapFunction2<Iterator<Integer>, Iterator<String>, Integer> sizesFn = + new FlatMapFunction2<Iterator<Integer>, Iterator<String>, Integer>() { + @Override + public Iterable<Integer> call(Iterator<Integer> i, Iterator<String> s) { + int sizeI = 0; + int sizeS = 0; + while (i.hasNext()) { + sizeI += 1; + i.next(); + } + while (s.hasNext()) { + sizeS += 1; + s.next(); + } + return Arrays.asList(sizeI, sizeS); + } + }; + + JavaRDD<Integer> sizes = rdd1.zipPartitions(sizesFn, rdd2); + Assert.assertEquals("[3, 2, 3, 2]", sizes.collect().toString()); + } + + @Test public void accumulators() { JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); diff --git a/core/src/test/scala/spark/KryoSerializerSuite.scala b/core/src/test/scala/spark/KryoSerializerSuite.scala index 327e2ff848..30d2d5282b 100644 --- a/core/src/test/scala/spark/KryoSerializerSuite.scala +++ b/core/src/test/scala/spark/KryoSerializerSuite.scala @@ -1,13 +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 spark import scala.collection.mutable -import scala.collection.immutable import org.scalatest.FunSuite import com.esotericsoftware.kryo._ -import SparkContext._ - class KryoSerializerSuite extends FunSuite { test("basic types") { val ser = (new KryoSerializer).newInstance() @@ -36,6 +50,7 @@ class KryoSerializerSuite extends FunSuite { check(Array(true, false, true)) check(Array('a', 'b', 'c')) check(Array[Int]()) + check(Array(Array("1", "2"), Array("1", "2", "3", "4"))) } test("pairs") { @@ -86,7 +101,7 @@ class KryoSerializerSuite extends FunSuite { } test("custom registrator") { - import spark.test._ + import KryoTest._ System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName) val ser = (new KryoSerializer).newInstance() @@ -106,14 +121,14 @@ class KryoSerializerSuite extends FunSuite { val hashMap = new java.util.HashMap[String, String] hashMap.put("foo", "bar") check(hashMap) - + System.clearProperty("spark.kryo.registrator") } } -package test { +object KryoTest { case class CaseClass(i: Int, s: String) {} - + class ClassWithNoArgConstructor { var x: Int = 0 override def equals(other: Any) = other match { @@ -137,4 +152,4 @@ package test { k.register(classOf[java.util.HashMap[_, _]]) } } -} +}
\ No newline at end of file diff --git a/core/src/test/scala/spark/LocalSparkContext.scala b/core/src/test/scala/spark/LocalSparkContext.scala index ff00dd05dd..ddc212d290 100644 --- a/core/src/test/scala/spark/LocalSparkContext.scala +++ b/core/src/test/scala/spark/LocalSparkContext.scala @@ -1,13 +1,39 @@ +/* + * 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 spark import org.scalatest.Suite import org.scalatest.BeforeAndAfterEach +import org.scalatest.BeforeAndAfterAll + +import org.jboss.netty.logging.InternalLoggerFactory +import org.jboss.netty.logging.Slf4JLoggerFactory /** Manages a local `sc` {@link SparkContext} variable, correctly stopping it after each test. */ -trait LocalSparkContext extends BeforeAndAfterEach { self: Suite => +trait LocalSparkContext extends BeforeAndAfterEach with BeforeAndAfterAll { self: Suite => @transient var sc: SparkContext = _ + override def beforeAll() { + InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory()); + super.beforeAll() + } + override def afterEach() { resetSparkContext() super.afterEach() @@ -27,6 +53,7 @@ object LocalSparkContext { sc.stop() // 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") } /** Runs `f` by passing in `sc` and ensures that `sc` is stopped. */ @@ -38,4 +65,4 @@ object LocalSparkContext { } } -}
\ No newline at end of file +} diff --git a/core/src/test/scala/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/spark/MapOutputTrackerSuite.scala index 3abc584b6a..ce6cec0451 100644 --- a/core/src/test/scala/spark/MapOutputTrackerSuite.scala +++ b/core/src/test/scala/spark/MapOutputTrackerSuite.scala @@ -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. + */ + package spark import org.scalatest.FunSuite @@ -8,7 +25,7 @@ import spark.storage.BlockManagerId import spark.util.AkkaUtils class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { - + test("compressSize") { assert(MapOutputTracker.compressSize(0L) === 0) assert(MapOutputTracker.compressSize(1L) === 1) @@ -45,13 +62,13 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { 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), + tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000, 0), Array(compressedSize1000, compressedSize10000))) - tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000), + tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0), Array(compressedSize10000, compressedSize1000))) val statuses = tracker.getServerStatuses(10, 0) - assert(statuses.toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000), - (BlockManagerId("b", "hostB", 1000), size10000))) + assert(statuses.toSeq === Seq((BlockManagerId("a", "hostA", 1000, 0), size1000), + (BlockManagerId("b", "hostB", 1000, 0), size10000))) tracker.stop() } @@ -64,14 +81,14 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { 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), + 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), + tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0), Array(compressedSize10000, compressedSize1000, compressedSize1000))) // As if we had two simulatenous fetch failures - tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000)) - tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000)) + tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0)) + tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0)) // The remaining reduce task might try to grab the output despite the shuffle failure; // this should cause it to fail, and the scheduler will ignore the failure due to the @@ -80,16 +97,20 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { } test("remote fetch") { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", "localhost", 0) + val hostname = "localhost" + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0) + 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 (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", "localhost", 0) + + val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0) val slaveTracker = new MapOutputTracker() slaveTracker.trackerActor = slaveSystem.actorFor( "akka://spark@localhost:" + boundPort + "/user/MapOutputTracker") - + masterTracker.registerShuffle(10, 1) masterTracker.incrementGeneration() slaveTracker.updateGeneration(masterTracker.getGeneration) @@ -98,13 +119,13 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { val compressedSize1000 = MapOutputTracker.compressSize(1000L) val size1000 = MapOutputTracker.decompressSize(compressedSize1000) masterTracker.registerMapOutput(10, 0, new MapStatus( - BlockManagerId("a", "hostA", 1000), Array(compressedSize1000))) + BlockManagerId("a", "hostA", 1000, 0), Array(compressedSize1000))) masterTracker.incrementGeneration() slaveTracker.updateGeneration(masterTracker.getGeneration) assert(slaveTracker.getServerStatuses(10, 0).toSeq === - Seq((BlockManagerId("a", "hostA", 1000), size1000))) + Seq((BlockManagerId("a", "hostA", 1000, 0), size1000))) - masterTracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000)) + masterTracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0)) masterTracker.incrementGeneration() slaveTracker.updateGeneration(masterTracker.getGeneration) intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) } diff --git a/core/src/test/scala/spark/PairRDDFunctionsSuite.scala b/core/src/test/scala/spark/PairRDDFunctionsSuite.scala new file mode 100644 index 0000000000..b102eaf4e6 --- /dev/null +++ b/core/src/test/scala/spark/PairRDDFunctionsSuite.scala @@ -0,0 +1,304 @@ +/* + * 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 spark + +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashSet + +import org.scalatest.FunSuite +import org.scalatest.prop.Checkers +import org.scalacheck.Arbitrary._ +import org.scalacheck.Gen +import org.scalacheck.Prop._ + +import com.google.common.io.Files + +import spark.rdd.ShuffledRDD +import spark.SparkContext._ + +class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { + test("groupByKey") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1))) + val groups = pairs.groupByKey().collect() + assert(groups.size === 2) + val valuesFor1 = groups.find(_._1 == 1).get._2 + assert(valuesFor1.toList.sorted === List(1, 2, 3)) + val valuesFor2 = groups.find(_._1 == 2).get._2 + assert(valuesFor2.toList.sorted === List(1)) + } + + test("groupByKey with duplicates") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val groups = pairs.groupByKey().collect() + assert(groups.size === 2) + val valuesFor1 = groups.find(_._1 == 1).get._2 + assert(valuesFor1.toList.sorted === List(1, 1, 2, 3)) + val valuesFor2 = groups.find(_._1 == 2).get._2 + assert(valuesFor2.toList.sorted === List(1)) + } + + test("groupByKey with negative key hash codes") { + val pairs = sc.parallelize(Array((-1, 1), (-1, 2), (-1, 3), (2, 1))) + val groups = pairs.groupByKey().collect() + assert(groups.size === 2) + val valuesForMinus1 = groups.find(_._1 == -1).get._2 + assert(valuesForMinus1.toList.sorted === List(1, 2, 3)) + val valuesFor2 = groups.find(_._1 == 2).get._2 + assert(valuesFor2.toList.sorted === List(1)) + } + + test("groupByKey with many output partitions") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1))) + val groups = pairs.groupByKey(10).collect() + assert(groups.size === 2) + val valuesFor1 = groups.find(_._1 == 1).get._2 + assert(valuesFor1.toList.sorted === List(1, 2, 3)) + val valuesFor2 = groups.find(_._1 == 2).get._2 + assert(valuesFor2.toList.sorted === List(1)) + } + + test("reduceByKey") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.reduceByKey(_+_).collect() + assert(sums.toSet === Set((1, 7), (2, 1))) + } + + test("reduceByKey with collectAsMap") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.reduceByKey(_+_).collectAsMap() + assert(sums.size === 2) + assert(sums(1) === 7) + assert(sums(2) === 1) + } + + test("reduceByKey with many output partitons") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.reduceByKey(_+_, 10).collect() + assert(sums.toSet === Set((1, 7), (2, 1))) + } + + test("reduceByKey with partitioner") { + val p = new Partitioner() { + def numPartitions = 2 + def getPartition(key: Any) = key.asInstanceOf[Int] + } + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 1), (0, 1))).partitionBy(p) + val sums = pairs.reduceByKey(_+_) + assert(sums.collect().toSet === Set((1, 4), (0, 1))) + assert(sums.partitioner === Some(p)) + // count the dependencies to make sure there is only 1 ShuffledRDD + val deps = new HashSet[RDD[_]]() + def visit(r: RDD[_]) { + for (dep <- r.dependencies) { + deps += dep.rdd + visit(dep.rdd) + } + } + visit(sums) + assert(deps.size === 2) // ShuffledRDD, ParallelCollection + } + + test("join") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.join(rdd2).collect() + assert(joined.size === 4) + assert(joined.toSet === Set( + (1, (1, 'x')), + (1, (2, 'x')), + (2, (1, 'y')), + (2, (1, 'z')) + )) + } + + test("join all-to-all") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (1, 3))) + val rdd2 = sc.parallelize(Array((1, 'x'), (1, 'y'))) + val joined = rdd1.join(rdd2).collect() + assert(joined.size === 6) + assert(joined.toSet === Set( + (1, (1, 'x')), + (1, (1, 'y')), + (1, (2, 'x')), + (1, (2, 'y')), + (1, (3, 'x')), + (1, (3, 'y')) + )) + } + + test("leftOuterJoin") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.leftOuterJoin(rdd2).collect() + assert(joined.size === 5) + assert(joined.toSet === Set( + (1, (1, Some('x'))), + (1, (2, Some('x'))), + (2, (1, Some('y'))), + (2, (1, Some('z'))), + (3, (1, None)) + )) + } + + test("rightOuterJoin") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.rightOuterJoin(rdd2).collect() + assert(joined.size === 5) + assert(joined.toSet === Set( + (1, (Some(1), 'x')), + (1, (Some(2), 'x')), + (2, (Some(1), 'y')), + (2, (Some(1), 'z')), + (4, (None, 'w')) + )) + } + + test("join with no matches") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((4, 'x'), (5, 'y'), (5, 'z'), (6, 'w'))) + val joined = rdd1.join(rdd2).collect() + assert(joined.size === 0) + } + + test("join with many output partitions") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.join(rdd2, 10).collect() + assert(joined.size === 4) + assert(joined.toSet === Set( + (1, (1, 'x')), + (1, (2, 'x')), + (2, (1, 'y')), + (2, (1, 'z')) + )) + } + + test("groupWith") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.groupWith(rdd2).collect() + assert(joined.size === 4) + assert(joined.toSet === Set( + (1, (ArrayBuffer(1, 2), ArrayBuffer('x'))), + (2, (ArrayBuffer(1), ArrayBuffer('y', 'z'))), + (3, (ArrayBuffer(1), ArrayBuffer())), + (4, (ArrayBuffer(), ArrayBuffer('w'))) + )) + } + + test("zero-partition RDD") { + val emptyDir = Files.createTempDir() + val file = sc.textFile(emptyDir.getAbsolutePath) + assert(file.partitions.size == 0) + assert(file.collect().toList === Nil) + // Test that a shuffle on the file works, because this used to be a bug + assert(file.map(line => (line, 1)).reduceByKey(_ + _).collect().toList === Nil) + } + + test("keys and values") { + val rdd = sc.parallelize(Array((1, "a"), (2, "b"))) + assert(rdd.keys.collect().toList === List(1, 2)) + assert(rdd.values.collect().toList === List("a", "b")) + } + + test("default partitioner uses partition size") { + // specify 2000 partitions + val a = sc.makeRDD(Array(1, 2, 3, 4), 2000) + // do a map, which loses the partitioner + val b = a.map(a => (a, (a * 2).toString)) + // then a group by, and see we didn't revert to 2 partitions + val c = b.groupByKey() + assert(c.partitions.size === 2000) + } + + test("default partitioner uses largest partitioner") { + val a = sc.makeRDD(Array((1, "a"), (2, "b")), 2) + val b = sc.makeRDD(Array((1, "a"), (2, "b")), 2000) + val c = a.join(b) + assert(c.partitions.size === 2000) + } + + test("subtract") { + val a = sc.parallelize(Array(1, 2, 3), 2) + val b = sc.parallelize(Array(2, 3, 4), 4) + val c = a.subtract(b) + assert(c.collect().toSet === Set(1)) + assert(c.partitions.size === a.partitions.size) + } + + test("subtract with narrow dependency") { + // use a deterministic partitioner + val p = new Partitioner() { + def numPartitions = 5 + def getPartition(key: Any) = key.asInstanceOf[Int] + } + // partitionBy so we have a narrow dependency + val a = sc.parallelize(Array((1, "a"), (2, "b"), (3, "c"))).partitionBy(p) + // more partitions/no partitioner so a shuffle dependency + val b = sc.parallelize(Array((2, "b"), (3, "cc"), (4, "d")), 4) + val c = a.subtract(b) + assert(c.collect().toSet === Set((1, "a"), (3, "c"))) + // Ideally we could keep the original partitioner... + assert(c.partitioner === None) + } + + test("subtractByKey") { + val a = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 2) + val b = sc.parallelize(Array((2, 20), (3, 30), (4, 40)), 4) + val c = a.subtractByKey(b) + assert(c.collect().toSet === Set((1, "a"), (1, "a"))) + assert(c.partitions.size === a.partitions.size) + } + + test("subtractByKey with narrow dependency") { + // use a deterministic partitioner + val p = new Partitioner() { + def numPartitions = 5 + def getPartition(key: Any) = key.asInstanceOf[Int] + } + // partitionBy so we have a narrow dependency + val a = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c"))).partitionBy(p) + // more partitions/no partitioner so a shuffle dependency + val b = sc.parallelize(Array((2, "b"), (3, "cc"), (4, "d")), 4) + val c = a.subtractByKey(b) + assert(c.collect().toSet === Set((1, "a"), (1, "a"))) + assert(c.partitioner.get === p) + } + + test("foldByKey") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.foldByKey(0)(_+_).collect() + assert(sums.toSet === Set((1, 7), (2, 1))) + } + + test("foldByKey with mutable result type") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val bufs = pairs.mapValues(v => ArrayBuffer(v)).cache() + // Fold the values using in-place mutation + val sums = bufs.foldByKey(new ArrayBuffer[Int])(_ ++= _).collect() + assert(sums.toSet === Set((1, ArrayBuffer(1, 2, 3, 1)), (2, ArrayBuffer(1)))) + // Check that the mutable objects in the original RDD were not changed + assert(bufs.collect().toSet === Set( + (1, ArrayBuffer(1)), + (1, ArrayBuffer(2)), + (1, ArrayBuffer(3)), + (1, ArrayBuffer(1)), + (2, ArrayBuffer(1)))) + } +} diff --git a/core/src/test/scala/spark/PartitionPruningRDDSuite.scala b/core/src/test/scala/spark/PartitionPruningRDDSuite.scala new file mode 100644 index 0000000000..88352b639f --- /dev/null +++ b/core/src/test/scala/spark/PartitionPruningRDDSuite.scala @@ -0,0 +1,28 @@ +package spark + +import org.scalatest.FunSuite +import spark.SparkContext._ +import spark.rdd.PartitionPruningRDD + + +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) + } +} diff --git a/core/src/test/scala/spark/PartitioningSuite.scala b/core/src/test/scala/spark/PartitioningSuite.scala index 60db759c25..b1e0b2b4d0 100644 --- a/core/src/test/scala/spark/PartitioningSuite.scala +++ b/core/src/test/scala/spark/PartitioningSuite.scala @@ -1,13 +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 spark import org.scalatest.FunSuite - import scala.collection.mutable.ArrayBuffer - import SparkContext._ +import spark.util.StatCounter +import scala.math.abs + +class PartitioningSuite extends FunSuite with SharedSparkContext { -class PartitioningSuite extends FunSuite with LocalSparkContext { - test("HashPartitioner equality") { val p2 = new HashPartitioner(2) val p4 = new HashPartitioner(4) @@ -21,8 +38,6 @@ class PartitioningSuite extends FunSuite with LocalSparkContext { } test("RangePartitioner equality") { - sc = new SparkContext("local", "test") - // Make an RDD where all the elements are the same so that the partition range bounds // are deterministically all the same. val rdd = sc.parallelize(Seq(1, 1, 1, 1)).map(x => (x, x)) @@ -50,7 +65,6 @@ class PartitioningSuite extends FunSuite with LocalSparkContext { } test("HashPartitioner not equal to RangePartitioner") { - sc = new SparkContext("local", "test") val rdd = sc.parallelize(1 to 10).map(x => (x, x)) val rangeP2 = new RangePartitioner(2, rdd) val hashP2 = new HashPartitioner(2) @@ -61,8 +75,6 @@ class PartitioningSuite extends FunSuite with LocalSparkContext { } test("partitioner preservation") { - sc = new SparkContext("local", "test") - val rdd = sc.parallelize(1 to 10, 4).map(x => (x, x)) val grouped2 = rdd.groupByKey(2) @@ -101,7 +113,6 @@ class PartitioningSuite extends FunSuite with LocalSparkContext { } test("partitioning Java arrays should fail") { - sc = new SparkContext("local", "test") val arrs: RDD[Array[Int]] = sc.parallelize(Array(1, 2, 3, 4), 2).map(x => Array(x)) val arrPairs: RDD[(Array[Int], Int)] = sc.parallelize(Array(1, 2, 3, 4), 2).map(x => (Array(x), x)) @@ -120,4 +131,20 @@ class PartitioningSuite extends FunSuite with LocalSparkContext { assert(intercept[SparkException]{ arrPairs.reduceByKeyLocally(_ + _) }.getMessage.contains("array")) assert(intercept[SparkException]{ arrPairs.reduceByKey(_ + _) }.getMessage.contains("array")) } + + test("zero-length partitions should be correctly handled") { + // Create RDD with some consecutive empty partitions (including the "first" one) + val rdd: RDD[Double] = sc + .parallelize(Array(-1.0, -1.0, -1.0, -1.0, 2.0, 4.0, -1.0, -1.0), 8) + .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); + + // Add other tests here for classes that should be able to handle empty partitions correctly + } } diff --git a/core/src/test/scala/spark/PipedRDDSuite.scala b/core/src/test/scala/spark/PipedRDDSuite.scala index a6344edf8f..35c04710a3 100644 --- a/core/src/test/scala/spark/PipedRDDSuite.scala +++ b/core/src/test/scala/spark/PipedRDDSuite.scala @@ -1,12 +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 spark import org.scalatest.FunSuite import SparkContext._ -class PipedRDDSuite extends FunSuite with LocalSparkContext { - +class PipedRDDSuite extends FunSuite with SharedSparkContext { + test("basic pipe") { - sc = new SparkContext("local", "test") val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) val piped = nums.pipe(Seq("cat")) @@ -19,8 +35,45 @@ class PipedRDDSuite extends FunSuite with LocalSparkContext { assert(c(3) === "4") } + test("advanced pipe") { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + val bl = sc.broadcast(List("0")) + + val piped = nums.pipe(Seq("cat"), + Map[String, String](), + (f: String => Unit) => {bl.value.map(f(_));f("\u0001")}, + (i:Int, f: String=> Unit) => f(i + "_")) + + val c = piped.collect() + + assert(c.size === 8) + assert(c(0) === "0") + assert(c(1) === "\u0001") + assert(c(2) === "1_") + assert(c(3) === "2_") + assert(c(4) === "0") + assert(c(5) === "\u0001") + assert(c(6) === "3_") + assert(c(7) === "4_") + + val nums1 = sc.makeRDD(Array("a\t1", "b\t2", "a\t3", "b\t4"), 2) + val d = nums1.groupBy(str=>str.split("\t")(0)). + pipe(Seq("cat"), + Map[String, String](), + (f: String => Unit) => {bl.value.map(f(_));f("\u0001")}, + (i:Tuple2[String, Seq[String]], f: String=> Unit) => {for (e <- i._2){ f(e + "_")}}).collect() + assert(d.size === 8) + assert(d(0) === "0") + assert(d(1) === "\u0001") + assert(d(2) === "b\t2_") + assert(d(3) === "b\t4_") + assert(d(4) === "0") + assert(d(5) === "\u0001") + assert(d(6) === "a\t1_") + assert(d(7) === "a\t3_") + } + test("pipe with env variable") { - sc = new SparkContext("local", "test") val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) val piped = nums.pipe(Seq("printenv", "MY_TEST_ENV"), Map("MY_TEST_ENV" -> "LALALA")) val c = piped.collect() @@ -30,9 +83,8 @@ class PipedRDDSuite extends FunSuite with LocalSparkContext { } test("pipe with non-zero exit status") { - sc = new SparkContext("local", "test") val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) - val piped = nums.pipe("cat nonexistent_file") + val piped = nums.pipe(Seq("cat nonexistent_file", "2>", "/dev/null")) intercept[SparkException] { piped.collect() } diff --git a/core/src/test/scala/spark/RDDSuite.scala b/core/src/test/scala/spark/RDDSuite.scala index 53635b1de6..cbddf4e523 100644 --- a/core/src/test/scala/spark/RDDSuite.scala +++ b/core/src/test/scala/spark/RDDSuite.scala @@ -1,14 +1,32 @@ +/* + * 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 spark import scala.collection.mutable.HashMap import org.scalatest.FunSuite +import org.scalatest.concurrent.Timeouts._ +import org.scalatest.time.{Span, Millis} import spark.SparkContext._ -import spark.rdd.{CoalescedRDD, CoGroupedRDD, PartitionPruningRDD} +import spark.rdd.{CoalescedRDD, CoGroupedRDD, EmptyRDD, PartitionPruningRDD, ShuffledRDD} -class RDDSuite extends FunSuite with LocalSparkContext { +class RDDSuite extends FunSuite with SharedSparkContext { test("basic operations") { - sc = new SparkContext("local", "test") val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) assert(nums.collect().toList === List(1, 2, 3, 4)) val dups = sc.makeRDD(Array(1, 1, 2, 2, 3, 3, 4, 4), 2) @@ -44,7 +62,6 @@ class RDDSuite extends FunSuite with LocalSparkContext { } test("SparkContext.union") { - sc = new SparkContext("local", "test") val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) assert(sc.union(nums).collect().toList === List(1, 2, 3, 4)) assert(sc.union(nums, nums).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4)) @@ -53,7 +70,6 @@ class RDDSuite extends FunSuite with LocalSparkContext { } test("aggregate") { - sc = new SparkContext("local", "test") val pairs = sc.makeRDD(Array(("a", 1), ("b", 2), ("a", 2), ("c", 5), ("a", 3))) type StringMap = HashMap[String, Int] val emptyMap = new StringMap { @@ -73,27 +89,7 @@ class RDDSuite extends FunSuite with LocalSparkContext { assert(result.toSet === Set(("a", 6), ("b", 2), ("c", 5))) } - test("basic checkpointing") { - import java.io.File - val checkpointDir = File.createTempFile("temp", "") - checkpointDir.delete() - - sc = new SparkContext("local", "test") - sc.setCheckpointDir(checkpointDir.toString) - val parCollection = sc.makeRDD(1 to 4) - val flatMappedRDD = parCollection.flatMap(x => 1 to x) - flatMappedRDD.checkpoint() - assert(flatMappedRDD.dependencies.head.rdd == parCollection) - val result = flatMappedRDD.collect() - Thread.sleep(1000) - assert(flatMappedRDD.dependencies.head.rdd != parCollection) - assert(flatMappedRDD.collect() === result) - - checkpointDir.deleteOnExit() - } - test("basic caching") { - sc = new SparkContext("local", "test") val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2).cache() assert(rdd.collect().toList === List(1, 2, 3, 4)) assert(rdd.collect().toList === List(1, 2, 3, 4)) @@ -101,7 +97,6 @@ class RDDSuite extends FunSuite with LocalSparkContext { } test("caching with failures") { - sc = new SparkContext("local", "test") val onlySplit = new Partition { override def index: Int = 0 } var shouldFail = true val rdd = new RDD[Int](sc, Nil) { @@ -123,38 +118,26 @@ class RDDSuite extends FunSuite with LocalSparkContext { assert(rdd.collect().toList === List(1, 2, 3, 4)) } - test("cogrouped RDDs") { - sc = new SparkContext("local", "test") - val rdd1 = sc.makeRDD(Array((1, "one"), (1, "another one"), (2, "two"), (3, "three")), 2) - val rdd2 = sc.makeRDD(Array((1, "one1"), (1, "another one1"), (2, "two1")), 2) - - // Use cogroup function - val cogrouped = rdd1.cogroup(rdd2).collectAsMap() - assert(cogrouped(1) === (Seq("one", "another one"), Seq("one1", "another one1"))) - assert(cogrouped(2) === (Seq("two"), Seq("two1"))) - assert(cogrouped(3) === (Seq("three"), Seq())) - - // Construct CoGroupedRDD directly, with map side combine enabled - val cogrouped1 = new CoGroupedRDD[Int]( - Seq(rdd1.asInstanceOf[RDD[(Int, Any)]], rdd2.asInstanceOf[RDD[(Int, Any)]]), - new HashPartitioner(3), - true).collectAsMap() - assert(cogrouped1(1).toSeq === Seq(Seq("one", "another one"), Seq("one1", "another one1"))) - assert(cogrouped1(2).toSeq === Seq(Seq("two"), Seq("two1"))) - assert(cogrouped1(3).toSeq === Seq(Seq("three"), Seq())) - - // Construct CoGroupedRDD directly, with map side combine disabled - val cogrouped2 = new CoGroupedRDD[Int]( - Seq(rdd1.asInstanceOf[RDD[(Int, Any)]], rdd2.asInstanceOf[RDD[(Int, Any)]]), - new HashPartitioner(3), - false).collectAsMap() - assert(cogrouped2(1).toSeq === Seq(Seq("one", "another one"), Seq("one1", "another one1"))) - assert(cogrouped2(2).toSeq === Seq(Seq("two"), Seq("two1"))) - assert(cogrouped2(3).toSeq === Seq(Seq("three"), Seq())) + test("empty RDD") { + val empty = new EmptyRDD[Int](sc) + assert(empty.count === 0) + assert(empty.collect().size === 0) + + val thrown = intercept[UnsupportedOperationException]{ + empty.reduce(_+_) + } + assert(thrown.getMessage.contains("empty")) + + val emptyKv = new EmptyRDD[(Int, Int)](sc) + val rdd = sc.parallelize(1 to 2, 2).map(x => (x, x)) + assert(rdd.join(emptyKv).collect().size === 0) + assert(rdd.rightOuterJoin(emptyKv).collect().size === 0) + assert(rdd.leftOuterJoin(emptyKv).collect().size === 2) + assert(rdd.cogroup(emptyKv).collect().size === 2) + assert(rdd.union(emptyKv).collect().size === 2) } - test("coalesced RDDs") { - sc = new SparkContext("local", "test") + test("cogrouped RDDs") { val data = sc.parallelize(1 to 10, 10) val coalesced1 = data.coalesce(2) @@ -184,10 +167,14 @@ class RDDSuite extends FunSuite with LocalSparkContext { assert(coalesced4.collect().toList === (1 to 10).toList) assert(coalesced4.glom().collect().map(_.toList).toList === (1 to 10).map(x => List(x)).toList) + + // we can optionally shuffle to keep the upstream parallel + val coalesced5 = data.coalesce(1, shuffle = true) + assert(coalesced5.dependencies.head.rdd.dependencies.head.rdd.asInstanceOf[ShuffledRDD[_, _]] != + null) } test("zipped RDDs") { - sc = new SparkContext("local", "test") val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) val zipped = nums.zip(nums.map(_ + 1.0)) assert(zipped.glom().map(_.toList).collect().toList === @@ -199,7 +186,6 @@ class RDDSuite extends FunSuite with LocalSparkContext { } test("partition pruning") { - sc = new SparkContext("local", "test") val data = sc.parallelize(1 to 10, 10) // Note that split number starts from 0, so > 8 means only 10th partition left. val prunedRdd = new PartitionPruningRDD(data, splitNum => splitNum > 8) @@ -211,7 +197,6 @@ class RDDSuite extends FunSuite with LocalSparkContext { test("mapWith") { import java.util.Random - sc = new SparkContext("local", "test") val ones = sc.makeRDD(Array(1, 1, 1, 1, 1, 1), 2) val randoms = ones.mapWith( (index: Int) => new Random(index + 42)) @@ -230,7 +215,6 @@ class RDDSuite extends FunSuite with LocalSparkContext { test("flatMapWith") { import java.util.Random - sc = new SparkContext("local", "test") val ones = sc.makeRDD(Array(1, 1, 1, 1, 1, 1), 2) val randoms = ones.flatMapWith( (index: Int) => new Random(index + 42)) @@ -252,7 +236,6 @@ class RDDSuite extends FunSuite with LocalSparkContext { test("filterWith") { import java.util.Random - sc = new SparkContext("local", "test") val ints = sc.makeRDD(Array(1, 2, 3, 4, 5, 6), 2) val sample = ints.filterWith( (index: Int) => new Random(index + 42)) @@ -268,4 +251,78 @@ class RDDSuite extends FunSuite with LocalSparkContext { assert(sample.size === checkSample.size) for (i <- 0 until sample.size) assert(sample(i) === checkSample(i)) } + + test("top with predefined ordering") { + val nums = Array.range(1, 100000) + val ints = sc.makeRDD(scala.util.Random.shuffle(nums), 2) + val topK = ints.top(5) + assert(topK.size === 5) + assert(topK === nums.reverse.take(5)) + } + + test("top with custom ordering") { + val words = Vector("a", "b", "c", "d") + implicit val ord = implicitly[Ordering[String]].reverse + val rdd = sc.makeRDD(words, 2) + val topK = rdd.top(2) + assert(topK.size === 2) + assert(topK.sorted === Array("b", "a")) + } + + test("takeOrdered with predefined ordering") { + val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + val rdd = sc.makeRDD(nums, 2) + val sortedLowerK = rdd.takeOrdered(5) + assert(sortedLowerK.size === 5) + assert(sortedLowerK === Array(1, 2, 3, 4, 5)) + } + + test("takeOrdered with custom ordering") { + val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + implicit val ord = implicitly[Ordering[Int]].reverse + val rdd = sc.makeRDD(nums, 2) + val sortedTopK = rdd.takeOrdered(5) + assert(sortedTopK.size === 5) + assert(sortedTopK === Array(10, 9, 8, 7, 6)) + assert(sortedTopK === nums.sorted(ord).take(5)) + } + + test("takeSample") { + val data = sc.parallelize(1 to 100, 2) + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=false, 20, seed) + assert(sample.size === 20) // Got exactly 20 elements + assert(sample.toSet.size === 20) // Elements are distinct + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=false, 200, seed) + assert(sample.size === 100) // Got only 100 elements + assert(sample.toSet.size === 100) // Elements are distinct + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 20, seed) + assert(sample.size === 20) // Got exactly 20 elements + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 100, seed) + assert(sample.size === 100) // Got exactly 100 elements + // Chance of getting all distinct elements is astronomically low, so test we got < 100 + assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 200, seed) + assert(sample.size === 200) // Got exactly 200 elements + // Chance of getting all distinct elements is still quite low, so test we got < 100 + assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements") + } + } + + test("runJob on an invalid partition") { + intercept[IllegalArgumentException] { + sc.runJob(sc.parallelize(1 to 10, 2), {iter: Iterator[Int] => iter.size}, Seq(0, 1, 2), false) + } + } } diff --git a/core/src/test/scala/spark/SharedSparkContext.scala b/core/src/test/scala/spark/SharedSparkContext.scala new file mode 100644 index 0000000000..70c24515be --- /dev/null +++ b/core/src/test/scala/spark/SharedSparkContext.scala @@ -0,0 +1,42 @@ +/* + * 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 spark + +import org.scalatest.Suite +import org.scalatest.BeforeAndAfterAll + +/** Shares a local `SparkContext` between all tests in a suite and closes it at the end */ +trait SharedSparkContext extends BeforeAndAfterAll { self: Suite => + + @transient private var _sc: SparkContext = _ + + def sc: SparkContext = _sc + + override def beforeAll() { + _sc = new SparkContext("local", "test") + super.beforeAll() + } + + override def afterAll() { + if (_sc != null) { + LocalSparkContext.stop(_sc) + _sc = null + } + super.afterAll() + } +} diff --git a/core/src/test/scala/spark/ShuffleNettySuite.scala b/core/src/test/scala/spark/ShuffleNettySuite.scala new file mode 100644 index 0000000000..6bad6c1d13 --- /dev/null +++ b/core/src/test/scala/spark/ShuffleNettySuite.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 spark + +import org.scalatest.BeforeAndAfterAll + + +class ShuffleNettySuite extends ShuffleSuite with BeforeAndAfterAll { + + // This test suite should run all tests in ShuffleSuite with Netty shuffle mode. + + override def beforeAll(configMap: Map[String, Any]) { + System.setProperty("spark.shuffle.use.netty", "true") + } + + override def afterAll(configMap: Map[String, Any]) { + System.setProperty("spark.shuffle.use.netty", "false") + } +} diff --git a/core/src/test/scala/spark/ShuffleSuite.scala b/core/src/test/scala/spark/ShuffleSuite.scala index 2b2a90defa..752e4b85e6 100644 --- a/core/src/test/scala/spark/ShuffleSuite.scala +++ b/core/src/test/scala/spark/ShuffleSuite.scala @@ -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. + */ + package spark import scala.collection.mutable.ArrayBuffer @@ -16,54 +33,9 @@ import spark.rdd.ShuffledRDD import spark.SparkContext._ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext { - - test("groupByKey") { - sc = new SparkContext("local", "test") - val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1))) - val groups = pairs.groupByKey().collect() - assert(groups.size === 2) - val valuesFor1 = groups.find(_._1 == 1).get._2 - assert(valuesFor1.toList.sorted === List(1, 2, 3)) - val valuesFor2 = groups.find(_._1 == 2).get._2 - assert(valuesFor2.toList.sorted === List(1)) - } - - test("groupByKey with duplicates") { - sc = new SparkContext("local", "test") - val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val groups = pairs.groupByKey().collect() - assert(groups.size === 2) - val valuesFor1 = groups.find(_._1 == 1).get._2 - assert(valuesFor1.toList.sorted === List(1, 1, 2, 3)) - val valuesFor2 = groups.find(_._1 == 2).get._2 - assert(valuesFor2.toList.sorted === List(1)) - } - - test("groupByKey with negative key hash codes") { - sc = new SparkContext("local", "test") - val pairs = sc.parallelize(Array((-1, 1), (-1, 2), (-1, 3), (2, 1))) - val groups = pairs.groupByKey().collect() - assert(groups.size === 2) - val valuesForMinus1 = groups.find(_._1 == -1).get._2 - assert(valuesForMinus1.toList.sorted === List(1, 2, 3)) - val valuesFor2 = groups.find(_._1 == 2).get._2 - assert(valuesFor2.toList.sorted === List(1)) - } - - test("groupByKey with many output partitions") { - sc = new SparkContext("local", "test") - val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1))) - val groups = pairs.groupByKey(10).collect() - assert(groups.size === 2) - val valuesFor1 = groups.find(_._1 == 1).get._2 - assert(valuesFor1.toList.sorted === List(1, 2, 3)) - val valuesFor2 = groups.find(_._1 == 2).get._2 - assert(valuesFor2.toList.sorted === List(1)) - } - - test("groupByKey with compression") { + test("groupByKey without compression") { try { - System.setProperty("spark.blockManager.compress", "true") + System.setProperty("spark.shuffle.compress", "false") sc = new SparkContext("local", "test") val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)), 4) val groups = pairs.groupByKey(4).collect() @@ -73,243 +45,104 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext { val valuesFor2 = groups.find(_._1 == 2).get._2 assert(valuesFor2.toList.sorted === List(1)) } finally { - System.setProperty("spark.blockManager.compress", "false") + System.setProperty("spark.shuffle.compress", "true") } } - test("reduceByKey") { - sc = new SparkContext("local", "test") - val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_).collect() - assert(sums.toSet === Set((1, 7), (2, 1))) - } - - test("reduceByKey with collectAsMap") { - sc = new SparkContext("local", "test") - val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_).collectAsMap() - assert(sums.size === 2) - assert(sums(1) === 7) - assert(sums(2) === 1) - } + test("shuffle non-zero block size") { + sc = new SparkContext("local-cluster[2,1,512]", "test") + val NUM_BLOCKS = 3 - test("reduceByKey with many output partitons") { - sc = new SparkContext("local", "test") - val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_, 10).collect() - assert(sums.toSet === Set((1, 7), (2, 1))) - } - - test("reduceByKey with partitioner") { - sc = new SparkContext("local", "test") - val p = new Partitioner() { - def numPartitions = 2 - def getPartition(key: Any) = key.asInstanceOf[Int] + val a = sc.parallelize(1 to 10, 2) + val b = a.map { x => + (x, new ShuffleSuite.NonJavaSerializableClass(x * 2)) } - val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 1), (0, 1))).partitionBy(p) - val sums = pairs.reduceByKey(_+_) - assert(sums.collect().toSet === Set((1, 4), (0, 1))) - assert(sums.partitioner === Some(p)) - // count the dependencies to make sure there is only 1 ShuffledRDD - val deps = new HashSet[RDD[_]]() - def visit(r: RDD[_]) { - for (dep <- r.dependencies) { - deps += dep.rdd - visit(dep.rdd) - } + // If the Kryo serializer is not used correctly, the shuffle would fail because the + // default Java serializer cannot handle the non serializable class. + val c = new ShuffledRDD(b, new HashPartitioner(NUM_BLOCKS), + classOf[spark.KryoSerializer].getName) + val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId + + assert(c.count === 10) + + // All blocks must have non-zero size + (0 until NUM_BLOCKS).foreach { id => + val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id) + assert(statuses.forall(s => s._2 > 0)) } - visit(sums) - assert(deps.size === 2) // ShuffledRDD, ParallelCollection - } - - test("join") { - sc = new SparkContext("local", "test") - val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) - val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) - val joined = rdd1.join(rdd2).collect() - assert(joined.size === 4) - assert(joined.toSet === Set( - (1, (1, 'x')), - (1, (2, 'x')), - (2, (1, 'y')), - (2, (1, 'z')) - )) } - test("join all-to-all") { - sc = new SparkContext("local", "test") - val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (1, 3))) - val rdd2 = sc.parallelize(Array((1, 'x'), (1, 'y'))) - val joined = rdd1.join(rdd2).collect() - assert(joined.size === 6) - assert(joined.toSet === Set( - (1, (1, 'x')), - (1, (1, 'y')), - (1, (2, 'x')), - (1, (2, 'y')), - (1, (3, 'x')), - (1, (3, 'y')) - )) + test("shuffle serializer") { + // Use a local cluster with 2 processes to make sure there are both local and remote blocks + sc = new SparkContext("local-cluster[2,1,512]", "test") + val a = sc.parallelize(1 to 10, 2) + val b = a.map { x => + (x, new ShuffleSuite.NonJavaSerializableClass(x * 2)) + } + // If the Kryo serializer is not used correctly, the shuffle would fail because the + // default Java serializer cannot handle the non serializable class. + val c = new ShuffledRDD(b, new HashPartitioner(3), classOf[spark.KryoSerializer].getName) + assert(c.count === 10) } - test("leftOuterJoin") { - sc = new SparkContext("local", "test") - val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) - val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) - val joined = rdd1.leftOuterJoin(rdd2).collect() - assert(joined.size === 5) - assert(joined.toSet === Set( - (1, (1, Some('x'))), - (1, (2, Some('x'))), - (2, (1, Some('y'))), - (2, (1, Some('z'))), - (3, (1, None)) - )) - } + test("zero sized blocks") { + // Use a local cluster with 2 processes to make sure there are both local and remote blocks + sc = new SparkContext("local-cluster[2,1,512]", "test") - test("rightOuterJoin") { - sc = new SparkContext("local", "test") - val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) - val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) - val joined = rdd1.rightOuterJoin(rdd2).collect() - assert(joined.size === 5) - assert(joined.toSet === Set( - (1, (Some(1), 'x')), - (1, (Some(2), 'x')), - (2, (Some(1), 'y')), - (2, (Some(1), 'z')), - (4, (None, 'w')) - )) - } + // 10 partitions from 4 keys + val NUM_BLOCKS = 10 + val a = sc.parallelize(1 to 4, NUM_BLOCKS) + val b = a.map(x => (x, x*2)) - test("join with no matches") { - sc = new SparkContext("local", "test") - val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) - val rdd2 = sc.parallelize(Array((4, 'x'), (5, 'y'), (5, 'z'), (6, 'w'))) - val joined = rdd1.join(rdd2).collect() - assert(joined.size === 0) - } + // NOTE: The default Java serializer doesn't create zero-sized blocks. + // So, use Kryo + val c = new ShuffledRDD(b, new HashPartitioner(10), classOf[spark.KryoSerializer].getName) - test("join with many output partitions") { - sc = new SparkContext("local", "test") - val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) - val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) - val joined = rdd1.join(rdd2, 10).collect() - assert(joined.size === 4) - assert(joined.toSet === Set( - (1, (1, 'x')), - (1, (2, 'x')), - (2, (1, 'y')), - (2, (1, 'z')) - )) - } + val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId + assert(c.count === 4) - test("groupWith") { - sc = new SparkContext("local", "test") - val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) - val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) - val joined = rdd1.groupWith(rdd2).collect() - assert(joined.size === 4) - assert(joined.toSet === Set( - (1, (ArrayBuffer(1, 2), ArrayBuffer('x'))), - (2, (ArrayBuffer(1), ArrayBuffer('y', 'z'))), - (3, (ArrayBuffer(1), ArrayBuffer())), - (4, (ArrayBuffer(), ArrayBuffer('w'))) - )) - } + val blockSizes = (0 until NUM_BLOCKS).flatMap { id => + val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id) + statuses.map(x => x._2) + } + val nonEmptyBlocks = blockSizes.filter(x => x > 0) - test("zero-partition RDD") { - sc = new SparkContext("local", "test") - val emptyDir = Files.createTempDir() - val file = sc.textFile(emptyDir.getAbsolutePath) - assert(file.partitions.size == 0) - assert(file.collect().toList === Nil) - // Test that a shuffle on the file works, because this used to be a bug - assert(file.map(line => (line, 1)).reduceByKey(_ + _).collect().toList === Nil) + // We should have at most 4 non-zero sized partitions + assert(nonEmptyBlocks.size <= 4) } - test("keys and values") { - sc = new SparkContext("local", "test") - val rdd = sc.parallelize(Array((1, "a"), (2, "b"))) - assert(rdd.keys.collect().toList === List(1, 2)) - assert(rdd.values.collect().toList === List("a", "b")) - } + test("zero sized blocks without kryo") { + // Use a local cluster with 2 processes to make sure there are both local and remote blocks + sc = new SparkContext("local-cluster[2,1,512]", "test") - test("default partitioner uses partition size") { - sc = new SparkContext("local", "test") - // specify 2000 partitions - val a = sc.makeRDD(Array(1, 2, 3, 4), 2000) - // do a map, which loses the partitioner - val b = a.map(a => (a, (a * 2).toString)) - // then a group by, and see we didn't revert to 2 partitions - val c = b.groupByKey() - assert(c.partitions.size === 2000) - } + // 10 partitions from 4 keys + val NUM_BLOCKS = 10 + val a = sc.parallelize(1 to 4, NUM_BLOCKS) + val b = a.map(x => (x, x*2)) - test("default partitioner uses largest partitioner") { - sc = new SparkContext("local", "test") - val a = sc.makeRDD(Array((1, "a"), (2, "b")), 2) - val b = sc.makeRDD(Array((1, "a"), (2, "b")), 2000) - val c = a.join(b) - assert(c.partitions.size === 2000) - } + // NOTE: The default Java serializer should create zero-sized blocks + val c = new ShuffledRDD(b, new HashPartitioner(10)) - test("subtract") { - sc = new SparkContext("local", "test") - val a = sc.parallelize(Array(1, 2, 3), 2) - val b = sc.parallelize(Array(2, 3, 4), 4) - val c = a.subtract(b) - assert(c.collect().toSet === Set(1)) - assert(c.partitions.size === a.partitions.size) - } + val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId + assert(c.count === 4) - test("subtract with narrow dependency") { - sc = new SparkContext("local", "test") - // use a deterministic partitioner - val p = new Partitioner() { - def numPartitions = 5 - def getPartition(key: Any) = key.asInstanceOf[Int] + val blockSizes = (0 until NUM_BLOCKS).flatMap { id => + val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id) + statuses.map(x => x._2) } - // partitionBy so we have a narrow dependency - val a = sc.parallelize(Array((1, "a"), (2, "b"), (3, "c"))).partitionBy(p) - // more partitions/no partitioner so a shuffle dependency - val b = sc.parallelize(Array((2, "b"), (3, "cc"), (4, "d")), 4) - val c = a.subtract(b) - assert(c.collect().toSet === Set((1, "a"), (3, "c"))) - // Ideally we could keep the original partitioner... - assert(c.partitioner === None) - } - - test("subtractByKey") { - sc = new SparkContext("local", "test") - val a = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 2) - val b = sc.parallelize(Array((2, 20), (3, 30), (4, 40)), 4) - val c = a.subtractByKey(b) - assert(c.collect().toSet === Set((1, "a"), (1, "a"))) - assert(c.partitions.size === a.partitions.size) - } + val nonEmptyBlocks = blockSizes.filter(x => x > 0) - test("subtractByKey with narrow dependency") { - sc = new SparkContext("local", "test") - // use a deterministic partitioner - val p = new Partitioner() { - def numPartitions = 5 - def getPartition(key: Any) = key.asInstanceOf[Int] - } - // partitionBy so we have a narrow dependency - val a = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c"))).partitionBy(p) - // more partitions/no partitioner so a shuffle dependency - val b = sc.parallelize(Array((2, "b"), (3, "cc"), (4, "d")), 4) - val c = a.subtractByKey(b) - assert(c.collect().toSet === Set((1, "a"), (1, "a"))) - assert(c.partitioner.get === p) + // We should have at most 4 non-zero sized partitions + assert(nonEmptyBlocks.size <= 4) } - } object ShuffleSuite { + def mergeCombineException(x: Int, y: Int): Int = { throw new SparkException("Exception for map-side combine.") x + y } + + class NonJavaSerializableClass(val value: Int) } diff --git a/core/src/test/scala/spark/SizeEstimatorSuite.scala b/core/src/test/scala/spark/SizeEstimatorSuite.scala index e235ef2f67..1ef812dfbd 100644 --- a/core/src/test/scala/spark/SizeEstimatorSuite.scala +++ b/core/src/test/scala/spark/SizeEstimatorSuite.scala @@ -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. + */ + package spark import org.scalatest.FunSuite @@ -35,7 +52,7 @@ class SizeEstimatorSuite var oldOops: String = _ override def beforeAll() { - // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case + // 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") } @@ -46,54 +63,54 @@ class SizeEstimatorSuite } test("simple classes") { - expect(16)(SizeEstimator.estimate(new DummyClass1)) - expect(16)(SizeEstimator.estimate(new DummyClass2)) - expect(24)(SizeEstimator.estimate(new DummyClass3)) - expect(24)(SizeEstimator.estimate(new DummyClass4(null))) - expect(48)(SizeEstimator.estimate(new DummyClass4(new DummyClass3))) + 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) } // 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") { - expect(40)(SizeEstimator.estimate(DummyString(""))) - expect(48)(SizeEstimator.estimate(DummyString("a"))) - expect(48)(SizeEstimator.estimate(DummyString("ab"))) - expect(56)(SizeEstimator.estimate(DummyString("abcdefgh"))) + assert(SizeEstimator.estimate(DummyString("")) === 40) + assert(SizeEstimator.estimate(DummyString("a")) === 48) + assert(SizeEstimator.estimate(DummyString("ab")) === 48) + assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 56) } test("primitive arrays") { - expect(32)(SizeEstimator.estimate(new Array[Byte](10))) - expect(40)(SizeEstimator.estimate(new Array[Char](10))) - expect(40)(SizeEstimator.estimate(new Array[Short](10))) - expect(56)(SizeEstimator.estimate(new Array[Int](10))) - expect(96)(SizeEstimator.estimate(new Array[Long](10))) - expect(56)(SizeEstimator.estimate(new Array[Float](10))) - expect(96)(SizeEstimator.estimate(new Array[Double](10))) - expect(4016)(SizeEstimator.estimate(new Array[Int](1000))) - expect(8016)(SizeEstimator.estimate(new Array[Long](1000))) + 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) } test("object arrays") { // Arrays containing nulls should just have one pointer per element - expect(56)(SizeEstimator.estimate(new Array[String](10))) - expect(56)(SizeEstimator.estimate(new Array[AnyRef](10))) + assert(SizeEstimator.estimate(new Array[String](10)) === 56) + assert(SizeEstimator.estimate(new Array[AnyRef](10)) === 56) // 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.) - expect(216)(SizeEstimator.estimate(Array.fill(10)(new DummyClass1))) - expect(216)(SizeEstimator.estimate(Array.fill(10)(new DummyClass2))) - expect(296)(SizeEstimator.estimate(Array.fill(10)(new DummyClass3))) - expect(56)(SizeEstimator.estimate(Array(new DummyClass1, new DummyClass2))) + 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) // Past size 100, our samples 100 elements, but we should still get the right size. - expect(28016)(SizeEstimator.estimate(Array.fill(1000)(new DummyClass3))) + assert(SizeEstimator.estimate(Array.fill(1000)(new DummyClass3)) === 28016) // If an array contains the *same* element many times, we should only count it once. val d1 = new DummyClass1 - expect(72)(SizeEstimator.estimate(Array.fill(10)(d1))) // 10 pointers plus 8-byte object - expect(432)(SizeEstimator.estimate(Array.fill(100)(d1))) // 100 pointers plus 8-byte object + 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 // 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 @@ -111,10 +128,10 @@ class SizeEstimatorSuite val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() - expect(40)(SizeEstimator.estimate(DummyString(""))) - expect(48)(SizeEstimator.estimate(DummyString("a"))) - expect(48)(SizeEstimator.estimate(DummyString("ab"))) - expect(56)(SizeEstimator.estimate(DummyString("abcdefgh"))) + assert(SizeEstimator.estimate(DummyString("")) === 40) + assert(SizeEstimator.estimate(DummyString("a")) === 48) + assert(SizeEstimator.estimate(DummyString("ab")) === 48) + assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 56) resetOrClear("os.arch", arch) } @@ -128,10 +145,10 @@ class SizeEstimatorSuite val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() - expect(56)(SizeEstimator.estimate(DummyString(""))) - expect(64)(SizeEstimator.estimate(DummyString("a"))) - expect(64)(SizeEstimator.estimate(DummyString("ab"))) - expect(72)(SizeEstimator.estimate(DummyString("abcdefgh"))) + assert(SizeEstimator.estimate(DummyString("")) === 56) + assert(SizeEstimator.estimate(DummyString("a")) === 64) + assert(SizeEstimator.estimate(DummyString("ab")) === 64) + assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 72) resetOrClear("os.arch", arch) resetOrClear("spark.test.useCompressedOops", oops) diff --git a/core/src/test/scala/spark/SortingSuite.scala b/core/src/test/scala/spark/SortingSuite.scala index 495f957e53..b933c4aab8 100644 --- a/core/src/test/scala/spark/SortingSuite.scala +++ b/core/src/test/scala/spark/SortingSuite.scala @@ -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. + */ + package spark import org.scalatest.FunSuite @@ -5,16 +22,14 @@ import org.scalatest.BeforeAndAfter import org.scalatest.matchers.ShouldMatchers import SparkContext._ -class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers with Logging { - +class SortingSuite extends FunSuite with SharedSparkContext with ShouldMatchers with Logging { + test("sortByKey") { - sc = new SparkContext("local", "test") val pairs = sc.parallelize(Array((1, 0), (2, 0), (0, 0), (3, 0)), 2) - assert(pairs.sortByKey().collect() === Array((0,0), (1,0), (2,0), (3,0))) + assert(pairs.sortByKey().collect() === Array((0,0), (1,0), (2,0), (3,0))) } test("large array") { - sc = new SparkContext("local", "test") val rand = new scala.util.Random() val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } val pairs = sc.parallelize(pairArr, 2) @@ -24,7 +39,6 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w } test("large array with one split") { - sc = new SparkContext("local", "test") val rand = new scala.util.Random() val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } val pairs = sc.parallelize(pairArr, 2) @@ -32,9 +46,8 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w assert(sorted.partitions.size === 1) assert(sorted.collect() === pairArr.sortBy(_._1)) } - + test("large array with many partitions") { - sc = new SparkContext("local", "test") val rand = new scala.util.Random() val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } val pairs = sc.parallelize(pairArr, 2) @@ -42,9 +55,8 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w assert(sorted.partitions.size === 20) assert(sorted.collect() === pairArr.sortBy(_._1)) } - + test("sort descending") { - sc = new SparkContext("local", "test") val rand = new scala.util.Random() val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } val pairs = sc.parallelize(pairArr, 2) @@ -52,15 +64,13 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w } test("sort descending with one split") { - sc = new SparkContext("local", "test") val rand = new scala.util.Random() val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } val pairs = sc.parallelize(pairArr, 1) assert(pairs.sortByKey(false, 1).collect() === pairArr.sortWith((x, y) => x._1 > y._1)) } - + test("sort descending with many partitions") { - sc = new SparkContext("local", "test") val rand = new scala.util.Random() val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } val pairs = sc.parallelize(pairArr, 2) @@ -68,7 +78,6 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w } test("more partitions than elements") { - sc = new SparkContext("local", "test") val rand = new scala.util.Random() val pairArr = Array.fill(10) { (rand.nextInt(), rand.nextInt()) } val pairs = sc.parallelize(pairArr, 30) @@ -76,14 +85,12 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w } test("empty RDD") { - sc = new SparkContext("local", "test") val pairArr = new Array[(Int, Int)](0) val pairs = sc.parallelize(pairArr, 2) assert(pairs.sortByKey().collect() === pairArr.sortBy(_._1)) } test("partition balancing") { - sc = new SparkContext("local", "test") val pairArr = (1 to 1000).map(x => (x, x)).toArray val sorted = sc.parallelize(pairArr, 4).sortByKey() assert(sorted.collect() === pairArr.sortBy(_._1)) @@ -99,7 +106,6 @@ class SortingSuite extends FunSuite with LocalSparkContext with ShouldMatchers w } test("partition balancing for descending sort") { - sc = new SparkContext("local", "test") val pairArr = (1 to 1000).map(x => (x, x)).toArray val sorted = sc.parallelize(pairArr, 4).sortByKey(false) assert(sorted.collect() === pairArr.sortBy(_._1).reverse) diff --git a/core/src/test/scala/spark/SparkContextInfoSuite.scala b/core/src/test/scala/spark/SparkContextInfoSuite.scala new file mode 100644 index 0000000000..6d50bf5e1b --- /dev/null +++ b/core/src/test/scala/spark/SparkContextInfoSuite.scala @@ -0,0 +1,60 @@ +/* + * 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 spark + +import org.scalatest.FunSuite +import spark.SparkContext._ + +class SparkContextInfoSuite extends FunSuite with LocalSparkContext { + test("getPersistentRDDs only returns RDDs that are marked as cached") { + sc = new SparkContext("local", "test") + assert(sc.getPersistentRDDs.isEmpty === true) + + val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2) + assert(sc.getPersistentRDDs.isEmpty === true) + + rdd.cache() + assert(sc.getPersistentRDDs.size === 1) + assert(sc.getPersistentRDDs.values.head === rdd) + } + + test("getPersistentRDDs returns an immutable map") { + sc = new SparkContext("local", "test") + val rdd1 = sc.makeRDD(Array(1, 2, 3, 4), 2).cache() + + val myRdds = sc.getPersistentRDDs + assert(myRdds.size === 1) + assert(myRdds.values.head === rdd1) + + val rdd2 = sc.makeRDD(Array(5, 6, 7, 8), 1).cache() + + // getPersistentRDDs should have 2 RDDs, but myRdds should not change + assert(sc.getPersistentRDDs.size === 2) + assert(myRdds.size === 1) + } + + test("getRDDStorageInfo only reports on RDDs that actually persist data") { + sc = new SparkContext("local", "test") + val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2).cache() + + assert(sc.getRDDStorageInfo.size === 0) + + rdd.collect() + assert(sc.getRDDStorageInfo.size === 1) + } +}
\ No newline at end of file diff --git a/core/src/test/scala/spark/ThreadingSuite.scala b/core/src/test/scala/spark/ThreadingSuite.scala index ff315b6693..f2acd0bd3c 100644 --- a/core/src/test/scala/spark/ThreadingSuite.scala +++ b/core/src/test/scala/spark/ThreadingSuite.scala @@ -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. + */ + package spark import java.util.concurrent.Semaphore diff --git a/core/src/test/scala/spark/UnpersistSuite.scala b/core/src/test/scala/spark/UnpersistSuite.scala new file mode 100644 index 0000000000..93977d16f4 --- /dev/null +++ b/core/src/test/scala/spark/UnpersistSuite.scala @@ -0,0 +1,47 @@ +/* + * 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 spark + +import org.scalatest.FunSuite +import org.scalatest.concurrent.Timeouts._ +import org.scalatest.time.{Span, Millis} +import spark.SparkContext._ + +class UnpersistSuite extends FunSuite with LocalSparkContext { + test("unpersist RDD") { + sc = new SparkContext("local", "test") + val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2).cache() + rdd.count + assert(sc.persistentRdds.isEmpty === false) + rdd.unpersist() + assert(sc.persistentRdds.isEmpty === true) + + failAfter(Span(3000, Millis)) { + try { + while (! sc.getRDDStorageInfo.isEmpty) { + Thread.sleep(200) + } + } catch { + case _ => { Thread.sleep(10) } + // Do nothing. We might see exceptions because block manager + // is racing this thread to remove entries from the driver. + } + } + assert(sc.getRDDStorageInfo.isEmpty === true) + } +} diff --git a/core/src/test/scala/spark/UtilsSuite.scala b/core/src/test/scala/spark/UtilsSuite.scala index ed4701574f..31c3b25c50 100644 --- a/core/src/test/scala/spark/UtilsSuite.scala +++ b/core/src/test/scala/spark/UtilsSuite.scala @@ -1,7 +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 spark +import com.google.common.base.Charsets +import com.google.common.io.Files +import java.io.{ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream, File} import org.scalatest.FunSuite -import java.io.{ByteArrayOutputStream, ByteArrayInputStream} +import org.apache.commons.io.FileUtils import scala.util.Random class UtilsSuite extends FunSuite { @@ -27,24 +47,93 @@ class UtilsSuite extends FunSuite { assert(os.toByteArray.toList.equals(bytes.toList)) } - test("memoryStringToMb"){ - assert(Utils.memoryStringToMb("1") == 0) - assert(Utils.memoryStringToMb("1048575") == 0) - assert(Utils.memoryStringToMb("3145728") == 3) + test("memoryStringToMb") { + assert(Utils.memoryStringToMb("1") === 0) + assert(Utils.memoryStringToMb("1048575") === 0) + assert(Utils.memoryStringToMb("3145728") === 3) + + assert(Utils.memoryStringToMb("1024k") === 1) + assert(Utils.memoryStringToMb("5000k") === 4) + assert(Utils.memoryStringToMb("4024k") === Utils.memoryStringToMb("4024K")) + + assert(Utils.memoryStringToMb("1024m") === 1024) + assert(Utils.memoryStringToMb("5000m") === 5000) + assert(Utils.memoryStringToMb("4024m") === Utils.memoryStringToMb("4024M")) + + assert(Utils.memoryStringToMb("2g") === 2048) + assert(Utils.memoryStringToMb("3g") === Utils.memoryStringToMb("3G")) + + assert(Utils.memoryStringToMb("2t") === 2097152) + assert(Utils.memoryStringToMb("3t") === Utils.memoryStringToMb("3T")) + } + + test("splitCommandString") { + assert(Utils.splitCommandString("") === Seq()) + assert(Utils.splitCommandString("a") === Seq("a")) + assert(Utils.splitCommandString("aaa") === Seq("aaa")) + assert(Utils.splitCommandString("a b c") === Seq("a", "b", "c")) + assert(Utils.splitCommandString(" a b\t c ") === Seq("a", "b", "c")) + assert(Utils.splitCommandString("a 'b c'") === Seq("a", "b c")) + assert(Utils.splitCommandString("a 'b c' d") === Seq("a", "b c", "d")) + assert(Utils.splitCommandString("'b c'") === Seq("b c")) + assert(Utils.splitCommandString("a \"b c\"") === Seq("a", "b c")) + assert(Utils.splitCommandString("a \"b c\" d") === Seq("a", "b c", "d")) + assert(Utils.splitCommandString("\"b c\"") === Seq("b c")) + assert(Utils.splitCommandString("a 'b\" c' \"d' e\"") === Seq("a", "b\" c", "d' e")) + assert(Utils.splitCommandString("a\t'b\nc'\nd") === Seq("a", "b\nc", "d")) + assert(Utils.splitCommandString("a \"b\\\\c\"") === Seq("a", "b\\c")) + assert(Utils.splitCommandString("a \"b\\\"c\"") === Seq("a", "b\"c")) + assert(Utils.splitCommandString("a 'b\\\"c'") === Seq("a", "b\\\"c")) + assert(Utils.splitCommandString("'a'b") === Seq("ab")) + assert(Utils.splitCommandString("'a''b'") === Seq("ab")) + assert(Utils.splitCommandString("\"a\"b") === Seq("ab")) + assert(Utils.splitCommandString("\"a\"\"b\"") === Seq("ab")) + assert(Utils.splitCommandString("''") === Seq("")) + assert(Utils.splitCommandString("\"\"") === Seq("")) + } + + test("string formatting of time durations") { + val second = 1000 + val minute = second * 60 + val hour = minute * 60 + def str = Utils.msDurationToString(_) + + assert(str(123) === "123 ms") + assert(str(second) === "1.0 s") + assert(str(second + 462) === "1.5 s") + assert(str(hour) === "1.00 h") + assert(str(minute) === "1.0 m") + assert(str(minute + 4 * second + 34) === "1.1 m") + assert(str(10 * hour + minute + 4 * second) === "10.02 h") + assert(str(10 * hour + 59 * minute + 59 * second + 999) === "11.00 h") + } + + test("reading offset bytes of a file") { + val tmpDir2 = Files.createTempDir() + val f1Path = tmpDir2 + "/f1" + val f1 = new FileOutputStream(f1Path) + f1.write("1\n2\n3\n4\n5\n6\n7\n8\n9\n".getBytes(Charsets.UTF_8)) + f1.close() + + // Read first few bytes + assert(Utils.offsetBytes(f1Path, 0, 5) === "1\n2\n3") + + // Read some middle bytes + assert(Utils.offsetBytes(f1Path, 4, 11) === "3\n4\n5\n6") + + // Read last few bytes + assert(Utils.offsetBytes(f1Path, 12, 18) === "7\n8\n9\n") - assert(Utils.memoryStringToMb("1024k") == 1) - assert(Utils.memoryStringToMb("5000k") == 4) - assert(Utils.memoryStringToMb("4024k") == Utils.memoryStringToMb("4024K")) + // Read some nonexistent bytes in the beginning + assert(Utils.offsetBytes(f1Path, -5, 5) === "1\n2\n3") - assert(Utils.memoryStringToMb("1024m") == 1024) - assert(Utils.memoryStringToMb("5000m") == 5000) - assert(Utils.memoryStringToMb("4024m") == Utils.memoryStringToMb("4024M")) + // Read some nonexistent bytes at the end + assert(Utils.offsetBytes(f1Path, 12, 22) === "7\n8\n9\n") - assert(Utils.memoryStringToMb("2g") == 2048) - assert(Utils.memoryStringToMb("3g") == Utils.memoryStringToMb("3G")) + // Read some nonexistent bytes on both ends + assert(Utils.offsetBytes(f1Path, -3, 25) === "1\n2\n3\n4\n5\n6\n7\n8\n9\n") - assert(Utils.memoryStringToMb("2t") == 2097152) - assert(Utils.memoryStringToMb("3t") == Utils.memoryStringToMb("3T")) + FileUtils.deleteDirectory(tmpDir2) } } diff --git a/core/src/test/scala/spark/ZippedPartitionsSuite.scala b/core/src/test/scala/spark/ZippedPartitionsSuite.scala new file mode 100644 index 0000000000..5e6d7b09d8 --- /dev/null +++ b/core/src/test/scala/spark/ZippedPartitionsSuite.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 spark + +import scala.collection.immutable.NumericRange + +import org.scalatest.FunSuite +import org.scalatest.prop.Checkers +import org.scalacheck.Arbitrary._ +import org.scalacheck.Gen +import org.scalacheck.Prop._ + +import SparkContext._ + + +object ZippedPartitionsSuite { + def procZippedData(i: Iterator[Int], s: Iterator[String], d: Iterator[Double]) : Iterator[Int] = { + Iterator(i.toArray.size, s.toArray.size, d.toArray.size) + } +} + +class ZippedPartitionsSuite extends FunSuite with SharedSparkContext { + test("print sizes") { + val data1 = sc.makeRDD(Array(1, 2, 3, 4), 2) + val data2 = sc.makeRDD(Array("1", "2", "3", "4", "5", "6"), 2) + val data3 = sc.makeRDD(Array(1.0, 2.0), 2) + + val zippedRDD = data1.zipPartitions(ZippedPartitionsSuite.procZippedData, data2, data3) + + val obtainedSizes = zippedRDD.collect() + val expectedSizes = Array(2, 3, 1, 2, 3, 1) + assert(obtainedSizes.size == 6) + assert(obtainedSizes.zip(expectedSizes).forall(x => x._1 == x._2)) + } +} diff --git a/core/src/test/scala/spark/io/CompressionCodecSuite.scala b/core/src/test/scala/spark/io/CompressionCodecSuite.scala new file mode 100644 index 0000000000..1ba82fe2b9 --- /dev/null +++ b/core/src/test/scala/spark/io/CompressionCodecSuite.scala @@ -0,0 +1,62 @@ +/* + * 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 spark.io + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream} + +import org.scalatest.FunSuite + + +class CompressionCodecSuite extends FunSuite { + + def testCodec(codec: CompressionCodec) { + // Write 1000 integers to the output stream, compressed. + val outputStream = new ByteArrayOutputStream() + val out = codec.compressedOutputStream(outputStream) + for (i <- 1 until 1000) { + out.write(i % 256) + } + out.close() + + // Read the 1000 integers back. + val inputStream = new ByteArrayInputStream(outputStream.toByteArray) + val in = codec.compressedInputStream(inputStream) + for (i <- 1 until 1000) { + assert(in.read() === i % 256) + } + in.close() + } + + test("default compression codec") { + val codec = CompressionCodec.createCodec() + assert(codec.getClass === classOf[SnappyCompressionCodec]) + testCodec(codec) + } + + test("lzf compression codec") { + val codec = CompressionCodec.createCodec(classOf[LZFCompressionCodec].getName) + assert(codec.getClass === classOf[LZFCompressionCodec]) + testCodec(codec) + } + + test("snappy compression codec") { + val codec = CompressionCodec.createCodec(classOf[SnappyCompressionCodec].getName) + assert(codec.getClass === classOf[SnappyCompressionCodec]) + testCodec(codec) + } +} diff --git a/core/src/test/scala/spark/metrics/MetricsConfigSuite.scala b/core/src/test/scala/spark/metrics/MetricsConfigSuite.scala new file mode 100644 index 0000000000..87cd2ffad2 --- /dev/null +++ b/core/src/test/scala/spark/metrics/MetricsConfigSuite.scala @@ -0,0 +1,64 @@ +package spark.metrics + +import java.util.Properties +import java.io.{File, FileOutputStream} + +import org.scalatest.{BeforeAndAfter, FunSuite} + +import spark.metrics._ + +class MetricsConfigSuite extends FunSuite with BeforeAndAfter { + var filePath: String = _ + + before { + filePath = getClass.getClassLoader.getResource("test_metrics_config.properties").getFile() + } + + test("MetricsConfig with default properties") { + val conf = new MetricsConfig(Option("dummy-file")) + conf.initialize() + + assert(conf.properties.size() === 0) + assert(conf.properties.getProperty("test-for-dummy") === null) + + val property = conf.getInstance("random") + assert(property.size() === 0) + } + + test("MetricsConfig with properties set") { + val conf = new MetricsConfig(Option(filePath)) + conf.initialize() + + val masterProp = conf.getInstance("master") + assert(masterProp.size() === 3) + assert(masterProp.getProperty("sink.console.period") === "20") + assert(masterProp.getProperty("sink.console.unit") === "minutes") + assert(masterProp.getProperty("source.jvm.class") === "spark.metrics.source.JvmSource") + + val workerProp = conf.getInstance("worker") + assert(workerProp.size() === 3) + assert(workerProp.getProperty("sink.console.period") === "10") + assert(workerProp.getProperty("sink.console.unit") === "seconds") + assert(masterProp.getProperty("source.jvm.class") === "spark.metrics.source.JvmSource") + } + + test("MetricsConfig with subProperties") { + val conf = new MetricsConfig(Option(filePath)) + conf.initialize() + + val propCategories = conf.propertyCategories + assert(propCategories.size === 2) + + val masterProp = conf.getInstance("master") + val sourceProps = conf.subProperties(masterProp, MetricsSystem.SOURCE_REGEX) + assert(sourceProps.size === 1) + assert(sourceProps("jvm").getProperty("class") === "spark.metrics.source.JvmSource") + + val sinkProps = conf.subProperties(masterProp, MetricsSystem.SINK_REGEX) + assert(sinkProps.size === 1) + assert(sinkProps.contains("console")) + + val consoleProps = sinkProps("console") + assert(consoleProps.size() === 2) + } +} diff --git a/core/src/test/scala/spark/metrics/MetricsSystemSuite.scala b/core/src/test/scala/spark/metrics/MetricsSystemSuite.scala new file mode 100644 index 0000000000..c189996417 --- /dev/null +++ b/core/src/test/scala/spark/metrics/MetricsSystemSuite.scala @@ -0,0 +1,39 @@ +package spark.metrics + +import java.util.Properties +import java.io.{File, FileOutputStream} + +import org.scalatest.{BeforeAndAfter, FunSuite} + +import spark.metrics._ + +class MetricsSystemSuite extends FunSuite with BeforeAndAfter { + var filePath: String = _ + + before { + filePath = getClass.getClassLoader.getResource("test_metrics_system.properties").getFile() + System.setProperty("spark.metrics.conf", filePath) + } + + test("MetricsSystem with default config") { + val metricsSystem = MetricsSystem.createMetricsSystem("default") + val sources = metricsSystem.sources + val sinks = metricsSystem.sinks + + assert(sources.length === 0) + assert(sinks.length === 0) + } + + test("MetricsSystem with sources add") { + val metricsSystem = MetricsSystem.createMetricsSystem("test") + val sources = metricsSystem.sources + val sinks = metricsSystem.sinks + + assert(sources.length === 0) + assert(sinks.length === 1) + + val source = new spark.deploy.master.MasterSource(null) + metricsSystem.registerSource(source) + assert(sources.length === 1) + } +} diff --git a/core/src/test/scala/spark/rdd/JdbcRDDSuite.scala b/core/src/test/scala/spark/rdd/JdbcRDDSuite.scala new file mode 100644 index 0000000000..dc8ca941c1 --- /dev/null +++ b/core/src/test/scala/spark/rdd/JdbcRDDSuite.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 spark + +import org.scalatest.{ BeforeAndAfter, FunSuite } +import spark.SparkContext._ +import spark.rdd.JdbcRDD +import java.sql._ + +class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { + + before { + Class.forName("org.apache.derby.jdbc.EmbeddedDriver") + val conn = DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb;create=true") + try { + val create = conn.createStatement + create.execute(""" + CREATE TABLE FOO( + ID INTEGER NOT NULL GENERATED ALWAYS AS IDENTITY (START WITH 1, INCREMENT BY 1), + DATA INTEGER + )""") + create.close + val insert = conn.prepareStatement("INSERT INTO FOO(DATA) VALUES(?)") + (1 to 100).foreach { i => + insert.setInt(1, i * 2) + insert.executeUpdate + } + insert.close + } catch { + case e: SQLException if e.getSQLState == "X0Y32" => + // table exists + } finally { + conn.close + } + } + + test("basic functionality") { + sc = new SparkContext("local", "test") + val rdd = new JdbcRDD( + sc, + () => { DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb") }, + "SELECT DATA FROM FOO WHERE ? <= ID AND ID <= ?", + 1, 100, 3, + (r: ResultSet) => { r.getInt(1) } ).cache + + assert(rdd.count === 100) + assert(rdd.reduce(_+_) === 10100) + } + + after { + try { + DriverManager.getConnection("jdbc:derby:;shutdown=true") + } catch { + case se: SQLException if se.getSQLState == "XJ015" => + // normal shutdown + } + } +} diff --git a/core/src/test/scala/spark/rdd/ParallelCollectionSplitSuite.scala b/core/src/test/scala/spark/rdd/ParallelCollectionSplitSuite.scala index d27a2538e4..d1276d541f 100644 --- a/core/src/test/scala/spark/rdd/ParallelCollectionSplitSuite.scala +++ b/core/src/test/scala/spark/rdd/ParallelCollectionSplitSuite.scala @@ -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. + */ + package spark.rdd import scala.collection.immutable.NumericRange diff --git a/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala new file mode 100644 index 0000000000..05afcd6567 --- /dev/null +++ b/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala @@ -0,0 +1,267 @@ +/* + * 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 spark.scheduler + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter + +import spark._ +import spark.scheduler._ +import spark.scheduler.cluster._ +import scala.collection.mutable.ArrayBuffer + +import java.util.Properties + +class DummyTaskSetManager( + initPriority: Int, + initStageId: Int, + initNumTasks: Int, + clusterScheduler: ClusterScheduler, + taskSet: TaskSet) + extends ClusterTaskSetManager(clusterScheduler, taskSet) { + + parent = null + weight = 1 + minShare = 2 + runningTasks = 0 + priority = initPriority + stageId = initStageId + name = "TaskSet_"+stageId + override val numTasks = initNumTasks + tasksFinished = 0 + + 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 addSchedulable(schedulable: Schedulable) { + } + + override def removeSchedulable(schedulable: Schedulable) { + } + + override def getSchedulableByName(name: String): Schedulable = { + return null + } + + override def executorLost(executorId: String, host: String): Unit = { + } + + override def slaveOffer(execId: String, host: String, avaiableCpus: Double, overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] = { + if (tasksFinished + runningTasks < numTasks) { + increaseRunningTasks(1) + return Some(new TaskDescription(0, execId, "task 0:0", null)) + } + return None + } + + override def checkSpeculatableTasks(): Boolean = { + return true + } + + def taskFinished() { + decreaseRunningTasks(1) + tasksFinished +=1 + if (tasksFinished == numTasks) { + parent.removeSchedulable(this) + } + } + + def abort() { + decreaseRunningTasks(runningTasks) + parent.removeSchedulable(this) + } +} + +class DummyTask(stageId: Int) extends Task[Int](stageId) +{ + def run(attemptId: Long): Int = { + return 0 + } +} + +class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging { + + def createDummyTaskSetManager(priority: Int, stage: Int, numTasks: Int, cs: ClusterScheduler, taskSet: TaskSet): DummyTaskSetManager = { + new DummyTaskSetManager(priority, stage, numTasks, cs , taskSet) + } + + def resourceOffer(rootPool: Pool): Int = { + 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)) + } + for (taskSet <- taskSetQueue) { + taskSet.slaveOffer("execId_1", "hostname_1", 1) match { + case Some(task) => + return taskSet.stageId + case None => {} + } + } + -1 + } + + def checkTaskSetId(rootPool: Pool, expectedTaskSetId: Int) { + assert(resourceOffer(rootPool) === expectedTaskSetId) + } + + test("FIFO Scheduler Test") { + sc = new SparkContext("local", "ClusterSchedulerSuite") + val clusterScheduler = new ClusterScheduler(sc) + var tasks = ArrayBuffer[Task[_]]() + val task = new DummyTask(0) + tasks += task + val taskSet = new TaskSet(tasks.toArray,0,0,0,null) + + val rootPool = new Pool("", SchedulingMode.FIFO, 0, 0) + val schedulableBuilder = new FIFOSchedulableBuilder(rootPool) + schedulableBuilder.buildPools() + + val taskSetManager0 = createDummyTaskSetManager(0, 0, 2, clusterScheduler, taskSet) + val taskSetManager1 = createDummyTaskSetManager(0, 1, 2, clusterScheduler, taskSet) + val taskSetManager2 = createDummyTaskSetManager(0, 2, 2, clusterScheduler, taskSet) + schedulableBuilder.addTaskSetManager(taskSetManager0, null) + schedulableBuilder.addTaskSetManager(taskSetManager1, null) + schedulableBuilder.addTaskSetManager(taskSetManager2, null) + + checkTaskSetId(rootPool, 0) + resourceOffer(rootPool) + checkTaskSetId(rootPool, 1) + resourceOffer(rootPool) + taskSetManager1.abort() + checkTaskSetId(rootPool, 2) + } + + test("Fair Scheduler Test") { + sc = new SparkContext("local", "ClusterSchedulerSuite") + val clusterScheduler = new ClusterScheduler(sc) + var tasks = ArrayBuffer[Task[_]]() + val task = new DummyTask(0) + tasks += task + val taskSet = new TaskSet(tasks.toArray,0,0,0,null) + + val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() + System.setProperty("spark.fairscheduler.allocation.file", xmlPath) + val rootPool = new Pool("", SchedulingMode.FAIR, 0, 0) + val schedulableBuilder = new FairSchedulableBuilder(rootPool) + schedulableBuilder.buildPools() + + assert(rootPool.getSchedulableByName("default") != null) + assert(rootPool.getSchedulableByName("1") != null) + assert(rootPool.getSchedulableByName("2") != null) + assert(rootPool.getSchedulableByName("3") != null) + assert(rootPool.getSchedulableByName("1").minShare === 2) + assert(rootPool.getSchedulableByName("1").weight === 1) + assert(rootPool.getSchedulableByName("2").minShare === 3) + assert(rootPool.getSchedulableByName("2").weight === 1) + assert(rootPool.getSchedulableByName("3").minShare === 2) + assert(rootPool.getSchedulableByName("3").weight === 1) + + val properties1 = new Properties() + properties1.setProperty("spark.scheduler.cluster.fair.pool","1") + val properties2 = new Properties() + properties2.setProperty("spark.scheduler.cluster.fair.pool","2") + + val taskSetManager10 = createDummyTaskSetManager(1, 0, 1, clusterScheduler, taskSet) + val taskSetManager11 = createDummyTaskSetManager(1, 1, 1, clusterScheduler, taskSet) + val taskSetManager12 = createDummyTaskSetManager(1, 2, 2, clusterScheduler, taskSet) + schedulableBuilder.addTaskSetManager(taskSetManager10, properties1) + schedulableBuilder.addTaskSetManager(taskSetManager11, properties1) + schedulableBuilder.addTaskSetManager(taskSetManager12, properties1) + + val taskSetManager23 = createDummyTaskSetManager(2, 3, 2, clusterScheduler, taskSet) + val taskSetManager24 = createDummyTaskSetManager(2, 4, 2, clusterScheduler, taskSet) + schedulableBuilder.addTaskSetManager(taskSetManager23, properties2) + schedulableBuilder.addTaskSetManager(taskSetManager24, properties2) + + checkTaskSetId(rootPool, 0) + checkTaskSetId(rootPool, 3) + checkTaskSetId(rootPool, 3) + checkTaskSetId(rootPool, 1) + checkTaskSetId(rootPool, 4) + checkTaskSetId(rootPool, 2) + checkTaskSetId(rootPool, 2) + checkTaskSetId(rootPool, 4) + + taskSetManager12.taskFinished() + assert(rootPool.getSchedulableByName("1").runningTasks === 3) + taskSetManager24.abort() + assert(rootPool.getSchedulableByName("2").runningTasks === 2) + } + + test("Nested Pool Test") { + sc = new SparkContext("local", "ClusterSchedulerSuite") + val clusterScheduler = new ClusterScheduler(sc) + var tasks = ArrayBuffer[Task[_]]() + val task = new DummyTask(0) + tasks += task + val taskSet = new TaskSet(tasks.toArray,0,0,0,null) + + val rootPool = new Pool("", SchedulingMode.FAIR, 0, 0) + val pool0 = new Pool("0", SchedulingMode.FAIR, 3, 1) + val pool1 = new Pool("1", SchedulingMode.FAIR, 4, 1) + rootPool.addSchedulable(pool0) + rootPool.addSchedulable(pool1) + + val pool00 = new Pool("00", SchedulingMode.FAIR, 2, 2) + val pool01 = new Pool("01", SchedulingMode.FAIR, 1, 1) + pool0.addSchedulable(pool00) + pool0.addSchedulable(pool01) + + val pool10 = new Pool("10", SchedulingMode.FAIR, 2, 2) + val pool11 = new Pool("11", SchedulingMode.FAIR, 2, 1) + pool1.addSchedulable(pool10) + pool1.addSchedulable(pool11) + + val taskSetManager000 = createDummyTaskSetManager(0, 0, 5, clusterScheduler, taskSet) + val taskSetManager001 = createDummyTaskSetManager(0, 1, 5, clusterScheduler, taskSet) + pool00.addSchedulable(taskSetManager000) + pool00.addSchedulable(taskSetManager001) + + val taskSetManager010 = createDummyTaskSetManager(1, 2, 5, clusterScheduler, taskSet) + val taskSetManager011 = createDummyTaskSetManager(1, 3, 5, clusterScheduler, taskSet) + pool01.addSchedulable(taskSetManager010) + pool01.addSchedulable(taskSetManager011) + + val taskSetManager100 = createDummyTaskSetManager(2, 4, 5, clusterScheduler, taskSet) + val taskSetManager101 = createDummyTaskSetManager(2, 5, 5, clusterScheduler, taskSet) + pool10.addSchedulable(taskSetManager100) + pool10.addSchedulable(taskSetManager101) + + val taskSetManager110 = createDummyTaskSetManager(3, 6, 5, clusterScheduler, taskSet) + val taskSetManager111 = createDummyTaskSetManager(3, 7, 5, clusterScheduler, taskSet) + pool11.addSchedulable(taskSetManager110) + pool11.addSchedulable(taskSetManager111) + + checkTaskSetId(rootPool, 0) + checkTaskSetId(rootPool, 4) + checkTaskSetId(rootPool, 6) + checkTaskSetId(rootPool, 2) + } +} diff --git a/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala index 6da58a0f6e..caaf3209fd 100644 --- a/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala @@ -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. + */ + package spark.scheduler import scala.collection.mutable.{Map, HashMap} @@ -6,21 +23,18 @@ import org.scalatest.FunSuite import org.scalatest.BeforeAndAfter import spark.LocalSparkContext - -import spark.storage.BlockManager -import spark.storage.BlockManagerId -import spark.storage.BlockManagerMaster -import spark.{Dependency, ShuffleDependency, OneToOneDependency} -import spark.FetchFailedException import spark.MapOutputTracker import spark.RDD import spark.SparkContext -import spark.SparkException import spark.Partition import spark.TaskContext -import spark.TaskEndReason +import spark.{Dependency, ShuffleDependency, OneToOneDependency} +import spark.{FetchFailed, Success, TaskEndReason} +import spark.storage.{BlockManagerId, BlockManagerMaster} -import spark.{FetchFailed, Success} +import spark.scheduler.cluster.Pool +import spark.scheduler.cluster.SchedulingMode +import spark.scheduler.cluster.SchedulingMode.SchedulingMode /** * Tests for DAGScheduler. These tests directly call the event processing functions in DAGScheduler @@ -39,12 +53,14 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont /** Set of TaskSets the DAGScheduler has requested executed. */ val taskSets = scala.collection.mutable.Buffer[TaskSet]() val taskScheduler = new TaskScheduler() { + override def rootPool: Pool = null + override def schedulingMode: SchedulingMode = SchedulingMode.NONE override def start() = {} override def stop() = {} override def submitTasks(taskSet: TaskSet) = { // normally done by TaskSetManager taskSet.tasks.foreach(_.generation = mapOutputTracker.getGeneration) - taskSets += taskSet + taskSets += taskSet } override def setListener(listener: TaskSchedulerListener) = {} override def defaultParallelism() = 2 @@ -164,7 +180,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont } } } - + /** Sends the rdd to the scheduler for scheduling. */ private def submit( rdd: RDD[_], @@ -174,7 +190,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont listener: JobListener = listener) { runEvent(JobSubmitted(rdd, func, partitions, allowLocal, null, listener)) } - + /** Sends TaskSetFailed to the scheduler. */ private def failed(taskSet: TaskSet, message: String) { runEvent(TaskSetFailed(taskSet, message)) @@ -209,11 +225,11 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont runEvent(JobSubmitted(rdd, jobComputeFunc, Array(0), true, null, listener)) assert(results === Map(0 -> 42)) } - + test("run trivial job w/ dependency") { val baseRdd = makeRdd(1, Nil) val finalRdd = makeRdd(1, List(new OneToOneDependency(baseRdd))) - submit(finalRdd, Array(0)) + submit(finalRdd, Array(0)) complete(taskSets(0), Seq((Success, 42))) assert(results === Map(0 -> 42)) } @@ -250,7 +266,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont complete(taskSets(1), Seq((Success, 42))) assert(results === Map(0 -> 42)) } - + test("run trivial shuffle with fetch failure") { val shuffleMapRdd = makeRdd(2, Nil) val shuffleDep = new ShuffleDependency(shuffleMapRdd, null) @@ -271,7 +287,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont // have the 2nd attempt pass complete(taskSets(2), Seq((Success, makeMapStatus("hostA", 1)))) // we can see both result blocks now - assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.ip) === Array("hostA", "hostB")) + assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) === Array("hostA", "hostB")) complete(taskSets(3), Seq((Success, 43))) assert(results === Map(0 -> 42, 1 -> 43)) } @@ -385,12 +401,12 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont assert(results === Map(0 -> 42)) } - /** Assert that the supplied TaskSet has exactly the given preferredLocations. */ + /** Assert that the supplied TaskSet has exactly the given preferredLocations. Note, converts taskSet's locations to host only. */ private def assertLocations(taskSet: TaskSet, locations: Seq[Seq[String]]) { assert(locations.size === taskSet.tasks.size) for ((expectLocs, taskLocs) <- taskSet.tasks.map(_.preferredLocations).zip(locations)) { - assert(expectLocs === taskLocs) + assert(expectLocs.map(loc => spark.Utils.parseHostPort(loc)._1) === taskLocs) } } @@ -398,6 +414,6 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont new MapStatus(makeBlockManagerId(host), Array.fill[Byte](reduces)(2)) private def makeBlockManagerId(host: String): BlockManagerId = - BlockManagerId("exec-" + host, host, 12345) + BlockManagerId("exec-" + host, host, 12345, 0) } diff --git a/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala b/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala new file mode 100644 index 0000000000..bb9e715f95 --- /dev/null +++ b/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala @@ -0,0 +1,121 @@ +/* + * 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 spark.scheduler + +import java.util.Properties +import java.util.concurrent.LinkedBlockingQueue +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers +import scala.collection.mutable +import spark._ +import spark.SparkContext._ + + +class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers { + + test("inner method") { + sc = new SparkContext("local", "joblogger") + val joblogger = new JobLogger { + def createLogWriterTest(jobID: Int) = createLogWriter(jobID) + def closeLogWriterTest(jobID: Int) = closeLogWriter(jobID) + def getRddNameTest(rdd: RDD[_]) = getRddName(rdd) + def buildJobDepTest(jobID: Int, stage: Stage) = buildJobDep(jobID, stage) + } + type MyRDD = RDD[(Int, Int)] + def makeRdd( + numPartitions: Int, + dependencies: List[Dependency[_]] + ): MyRDD = { + val maxPartition = numPartitions - 1 + return new MyRDD(sc, dependencies) { + override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = + throw new RuntimeException("should not be reached") + override def getPartitions = (0 to maxPartition).map(i => new Partition { + override def index = i + }).toArray + } + } + val jobID = 5 + 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) + parentRdd.setName("MyRDD") + joblogger.getRddNameTest(parentRdd) should be ("MyRDD") + joblogger.createLogWriterTest(jobID) + joblogger.getJobIDtoPrintWriter.size should be (1) + joblogger.buildJobDepTest(jobID, rootStage) + joblogger.getJobIDToStages.get(jobID).get.size should be (2) + joblogger.getStageIDToJobID.get(0) should be (Some(jobID)) + joblogger.getStageIDToJobID.get(1) should be (Some(jobID)) + joblogger.closeLogWriterTest(jobID) + joblogger.getStageIDToJobID.size should be (0) + joblogger.getJobIDToStages.size should be (0) + joblogger.getJobIDtoPrintWriter.size should be (0) + } + + test("inner variables") { + sc = new SparkContext("local[4]", "joblogger") + val joblogger = new JobLogger { + override protected def closeLogWriter(jobID: Int) = + getJobIDtoPrintWriter.get(jobID).foreach { fileWriter => + fileWriter.close() + } + } + sc.addSparkListener(joblogger) + val rdd = sc.parallelize(1 to 1e2.toInt, 4).map{ i => (i % 12, 2 * i) } + rdd.reduceByKey(_+_).collect() + + joblogger.getLogDir should be ("/tmp/spark") + joblogger.getJobIDtoPrintWriter.size should be (1) + joblogger.getStageIDToJobID.size should be (2) + joblogger.getStageIDToJobID.get(0) should be (Some(0)) + joblogger.getStageIDToJobID.get(1) should be (Some(0)) + joblogger.getJobIDToStages.size should be (1) + } + + + test("interface functions") { + sc = new SparkContext("local[4]", "joblogger") + val joblogger = new JobLogger { + var onTaskEndCount = 0 + var onJobEndCount = 0 + var onJobStartCount = 0 + var onStageCompletedCount = 0 + var onStageSubmittedCount = 0 + 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 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() + + joblogger.onJobStartCount should be (1) + joblogger.onJobEndCount should be (1) + joblogger.onTaskEndCount should be (8) + joblogger.onStageSubmittedCount should be (2) + joblogger.onStageCompletedCount should be (2) + } +} diff --git a/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala new file mode 100644 index 0000000000..66fd59e8bb --- /dev/null +++ b/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala @@ -0,0 +1,223 @@ +/* + * 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 spark.scheduler + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter + +import spark._ +import spark.scheduler._ +import 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.addLocalProperty("spark.scheduler.cluster.fair.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.cluster.schedulingmode", "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.cluster.schedulingmode", "FAIR") + val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() + System.setProperty("spark.fairscheduler.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/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/spark/scheduler/SparkListenerSuite.scala index 2f5af10e69..392d67d67b 100644 --- a/core/src/test/scala/spark/scheduler/SparkListenerSuite.scala +++ b/core/src/test/scala/spark/scheduler/SparkListenerSuite.scala @@ -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. + */ + package spark.scheduler import org.scalatest.FunSuite @@ -57,7 +74,6 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc taskMetrics.shuffleReadMetrics should be ('defined) val sm = taskMetrics.shuffleReadMetrics.get sm.totalBlocksFetched should be > (0) - sm.shuffleReadMillis should be > (0l) sm.localBlocksFetched should be > (0) sm.remoteBlocksFetched should be (0) sm.remoteBytesRead should be (0l) @@ -78,7 +94,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc class SaveStageInfo extends SparkListener { val stageInfos = mutable.Buffer[StageInfo]() - def onStageCompleted(stage: StageCompleted) { + override def onStageCompleted(stage: StageCompleted) { stageInfos += stage.stageInfo } } diff --git a/core/src/test/scala/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/spark/scheduler/TaskContextSuite.scala index 647bcaf860..95a6eee2fc 100644 --- a/core/src/test/scala/spark/scheduler/TaskContextSuite.scala +++ b/core/src/test/scala/spark/scheduler/TaskContextSuite.scala @@ -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. + */ + package spark.scheduler import org.scalatest.FunSuite diff --git a/core/src/test/scala/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/spark/storage/BlockManagerSuite.scala index b8c0f6fb76..b719d65342 100644 --- a/core/src/test/scala/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/spark/storage/BlockManagerSuite.scala @@ -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. + */ + package spark.storage import java.nio.ByteBuffer @@ -15,8 +32,10 @@ import org.scalatest.time.SpanSugar._ import spark.JavaSerializer import spark.KryoSerializer import spark.SizeEstimator +import spark.util.AkkaUtils import spark.util.ByteBufferInputStream + class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodTester { var store: BlockManager = null var store2: BlockManager = null @@ -31,7 +50,11 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT val serializer = new KryoSerializer before { - actorSystem = ActorSystem("test") + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("test", "localhost", 0) + this.actorSystem = actorSystem + System.setProperty("spark.driver.port", boundPort.toString) + System.setProperty("spark.hostPort", "localhost:" + boundPort) + master = new BlockManagerMaster( actorSystem.actorOf(Props(new spark.storage.BlockManagerMasterActor(true)))) @@ -41,9 +64,14 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT oldHeartBeat = System.setProperty("spark.storage.disableBlockManagerHeartBeat", "true") val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() + // Set some value ... + System.setProperty("spark.hostPort", spark.Utils.localHostName() + ":" + 1111) } after { + System.clearProperty("spark.driver.port") + System.clearProperty("spark.hostPort") + if (store != null) { store.stop() store = null @@ -88,9 +116,9 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("BlockManagerId object caching") { - val id1 = BlockManagerId("e1", "XXX", 1) - val id2 = BlockManagerId("e1", "XXX", 1) // this should return the same object as id1 - val id3 = BlockManagerId("e1", "XXX", 2) // this should return a different object + val id1 = BlockManagerId("e1", "XXX", 1, 0) + val id2 = BlockManagerId("e1", "XXX", 1, 0) // this should return the same object as id1 + val id3 = BlockManagerId("e1", "XXX", 2, 0) // this should return a different object assert(id2 === id1, "id2 is not same as id1") assert(id2.eq(id1), "id2 is not the same object as id1") assert(id3 != id1, "id3 is same as id1") @@ -113,7 +141,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT // Putting a1, a2 and a3 in memory and telling master only about a1 and a2 store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY) - store.putSingle("a3", a3, StorageLevel.MEMORY_ONLY, false) + store.putSingle("a3", a3, StorageLevel.MEMORY_ONLY, tellMaster = false) // Checking whether blocks are in memory assert(store.getSingle("a1") != None, "a1 was not in store") @@ -159,7 +187,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT // Putting a1, a2 and a3 in memory and telling master only about a1 and a2 store.putSingle("a1-to-remove", a1, StorageLevel.MEMORY_ONLY) store.putSingle("a2-to-remove", a2, StorageLevel.MEMORY_ONLY) - store.putSingle("a3-to-remove", a3, StorageLevel.MEMORY_ONLY, false) + store.putSingle("a3-to-remove", a3, StorageLevel.MEMORY_ONLY, tellMaster = false) // Checking whether blocks are in memory and memory size val memStatus = master.getMemoryStatus.head._2 @@ -198,6 +226,39 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } } + test("removing rdd") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + 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("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 + } + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + 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) + 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 + } + test("reregistration on heart beat") { val heartBeat = PrivateMethod[Unit]('heartBeat) store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) @@ -226,7 +287,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT master.removeExecutor(store.blockManagerId.executorId) assert(master.getLocations("a1").size == 0, "a1 was not removed from master") - store.putSingle("a2", a1, StorageLevel.MEMORY_ONLY) + store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY) store.waitForAsyncReregister() assert(master.getLocations("a1").size > 0, "a1 was not reregistered with master") @@ -244,7 +305,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT master.removeExecutor(store.blockManagerId.executorId) val t1 = new Thread { override def run() { - store.put("a2", a2.iterator, StorageLevel.MEMORY_ONLY, true) + store.put("a2", a2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) } } val t2 = new Thread { @@ -454,9 +515,9 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT 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)) - store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, true) - store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY, true) - store.put("list3", list3.iterator, StorageLevel.MEMORY_ONLY, true) + store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + store.put("list3", list3.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) assert(store.get("list2") != None, "list2 was not in store") assert(store.get("list2").get.size == 2) assert(store.get("list3") != None, "list3 was not in store") @@ -465,7 +526,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT assert(store.get("list2") != None, "list2 was not in store") assert(store.get("list2").get.size == 2) // At this point list2 was gotten last, so LRU will getSingle rid of list3 - store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, true) + store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) assert(store.get("list1") != None, "list1 was not in store") assert(store.get("list1").get.size == 2) assert(store.get("list2") != None, "list2 was not in store") @@ -480,9 +541,9 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT val list3 = List(new Array[Byte](200), new Array[Byte](200)) val list4 = List(new Array[Byte](200), new Array[Byte](200)) // First store list1 and list2, both in memory, and list3, on disk only - store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY_SER, true) - store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY_SER, true) - store.put("list3", list3.iterator, StorageLevel.DISK_ONLY, true) + store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true) + store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true) + store.put("list3", list3.iterator, StorageLevel.DISK_ONLY, tellMaster = true) // At this point LRU should not kick in because list3 is only on disk assert(store.get("list1") != None, "list2 was not in store") assert(store.get("list1").get.size === 2) @@ -497,7 +558,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT assert(store.get("list3") != None, "list1 was not in store") assert(store.get("list3").get.size === 2) // Now let's add in list4, which uses both disk and memory; list1 should drop out - store.put("list4", list4.iterator, StorageLevel.MEMORY_AND_DISK_SER, true) + store.put("list4", list4.iterator, StorageLevel.MEMORY_AND_DISK_SER, tellMaster = true) assert(store.get("list1") === None, "list1 was in store") assert(store.get("list2") != None, "list3 was not in store") assert(store.get("list2").get.size === 2) diff --git a/core/src/test/scala/spark/ui/UISuite.scala b/core/src/test/scala/spark/ui/UISuite.scala new file mode 100644 index 0000000000..56c1fed6ad --- /dev/null +++ b/core/src/test/scala/spark/ui/UISuite.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 spark.ui + +import scala.util.{Failure, Success, Try} +import java.net.ServerSocket +import org.scalatest.FunSuite +import org.eclipse.jetty.server.Server + +class UISuite extends FunSuite { + test("jetty port increases under contention") { + val startPort = 33333 + val server = new Server(startPort) + server.start() + val (jettyServer1, boundPort1) = JettyUtils.startJettyServer("localhost", startPort, Seq()) + val (jettyServer2, boundPort2) = JettyUtils.startJettyServer("localhost", startPort, Seq()) + + assert(boundPort1 === startPort + 1) + assert(boundPort2 === startPort + 2) + } + + test("jetty binds to port 0 correctly") { + val (jettyServer, boundPort) = JettyUtils.startJettyServer("localhost", 0, Seq()) + assert(jettyServer.getState === "STARTED") + assert(boundPort != 0) + Try {new ServerSocket(boundPort)} match { + case Success(s) => fail("Port %s doesn't seem used by jetty server".format(boundPort)) + case Failure (e) => + } + } +} diff --git a/core/src/test/scala/spark/util/DistributionSuite.scala b/core/src/test/scala/spark/util/DistributionSuite.scala index cc6249b1dd..6578b55e82 100644 --- a/core/src/test/scala/spark/util/DistributionSuite.scala +++ b/core/src/test/scala/spark/util/DistributionSuite.scala @@ -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. + */ + package spark.util import org.scalatest.FunSuite diff --git a/core/src/test/scala/spark/util/NextIteratorSuite.scala b/core/src/test/scala/spark/util/NextIteratorSuite.scala index ed5b36da73..fdbd43d941 100644 --- a/core/src/test/scala/spark/util/NextIteratorSuite.scala +++ b/core/src/test/scala/spark/util/NextIteratorSuite.scala @@ -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. + */ + package spark.util import org.scalatest.FunSuite diff --git a/core/src/test/scala/spark/util/RateLimitedOutputStreamSuite.scala b/core/src/test/scala/spark/util/RateLimitedOutputStreamSuite.scala index 794063fb6d..4c0044202f 100644 --- a/core/src/test/scala/spark/util/RateLimitedOutputStreamSuite.scala +++ b/core/src/test/scala/spark/util/RateLimitedOutputStreamSuite.scala @@ -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. + */ + package spark.util import org.scalatest.FunSuite diff --git a/docs/_config.yml b/docs/_config.yml index f99d5bb376..5c135a0242 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -3,8 +3,8 @@ markdown: kramdown # These allow the documentation to be updated with nerw releases # of Spark, Scala, and Mesos. -SPARK_VERSION: 0.7.1-SNAPSHOT -SPARK_VERSION_SHORT: 0.7.1 -SCALA_VERSION: 2.9.2 +SPARK_VERSION: 0.8.0-SNAPSHOT +SPARK_VERSION_SHORT: 0.8.0 +SCALA_VERSION: 2.9.3 MESOS_VERSION: 0.9.0-incubating SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb index d77e53963c..45ef4bba82 100644 --- a/docs/_plugins/copy_api_dirs.rb +++ b/docs/_plugins/copy_api_dirs.rb @@ -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. +# + require 'fileutils' include FileUtils @@ -18,7 +35,7 @@ if ENV['SKIP_API'] != '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-2.9.2/api" + source = "../" + project_name + "/target/scala-2.9.3/api" dest = "api/" + project_name puts "echo making directory " + dest diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md index c2eeafd07a..04cd79d039 100644 --- a/docs/building-with-maven.md +++ b/docs/building-with-maven.md @@ -42,10 +42,10 @@ To run a specific test suite: You might run into the following errors if you're using a vanilla installation of Maven: - [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/andyk/Development/spark/core/target/scala-2.9.2/classes... + [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_VERSION}}/classes... [ERROR] PermGen space -> [Help 1] - [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/andyk/Development/spark/core/target/scala-2.9.2/classes... + [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_VERSION}}/classes... [ERROR] Java heap space -> [Help 1] To fix these, you can do the following: diff --git a/docs/configuration.md b/docs/configuration.md index 17fdbf04d1..99624a44aa 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -22,26 +22,30 @@ the copy executable. Inside `spark-env.sh`, you *must* set at least the following two variables: -* `SCALA_HOME`, to point to your Scala installation. +* `SCALA_HOME`, to point to your Scala installation, or `SCALA_LIBRARY_PATH` to point to the directory for Scala + library JARs (if you install Scala as a Debian or RPM package, there is no `SCALA_HOME`, but these libraries + are in a separate path, typically /usr/share/java; look for `scala-library.jar`). * `MESOS_NATIVE_LIBRARY`, if you are [running on a Mesos cluster](running-on-mesos.html). -In addition, there are four other variables that control execution. These can be set *either in `spark-env.sh` -or in each job's driver program*, because they will automatically be propagated to workers from the driver. -For a multi-user environment, we recommend setting the in the driver program instead of `spark-env.sh`, so -that different user jobs can use different amounts of memory, JVM options, etc. +In addition, there are four other variables that control execution. These should be set *in the environment that +launches the job's driver program* instead of `spark-env.sh`, because they will be automatically propagated to +workers. Setting these per-job instead of in `spark-env.sh` ensures that different jobs can have different settings +for these variables. -* `SPARK_MEM`, to set the amount of memory used per node (this should be in the same format as the - JVM's -Xmx option, e.g. `300m` or `1g`) * `SPARK_JAVA_OPTS`, to add JVM options. This includes any system properties that you'd like to pass with `-D`. * `SPARK_CLASSPATH`, to add elements to Spark's classpath. * `SPARK_LIBRARY_PATH`, to add search directories for native libraries. +* `SPARK_MEM`, to set the amount of memory used per node. This should be in the same format as the + JVM's -Xmx option, e.g. `300m` or `1g`. Note that this option will soon be deprecated in favor of + the `spark.executor.memory` system property, so we recommend using that in new code. -Note that if you do set these in `spark-env.sh`, they will override the values set by user programs, which -is undesirable; you can choose to have `spark-env.sh` set them only if the user program hasn't, as follows: +Beware that if you do set these variables in `spark-env.sh`, they will override the values set by user programs, +which is undesirable; if you prefer, you can choose to have `spark-env.sh` set them only if the user program +hasn't, as follows: {% highlight bash %} -if [ -z "$SPARK_MEM" ] ; then - SPARK_MEM="1g" +if [ -z "$SPARK_JAVA_OPTS" ] ; then + SPARK_JAVA_OPTS="-verbose:gc" fi {% endhighlight %} @@ -55,18 +59,25 @@ val sc = new SparkContext(...) {% endhighlight %} Most of the configurable system properties control internal settings that have reasonable default values. However, -there are at least four properties that you will commonly want to control: +there are at least five properties that you will commonly want to control: <table class="table"> <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr> <tr> + <td>spark.executor.memory</td> + <td>512m</td> + <td> + Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. `512m`, `2g`). + </td> +</tr> +<tr> <td>spark.serializer</td> <td>spark.JavaSerializer</td> <td> Class to use for serializing objects that will be sent over the network or need to be cached in serialized form. The default of Java serialization works with any Serializable Java object but is quite slow, so we recommend <a href="tuning.html">using <code>spark.KryoSerializer</code> - and configuring Kryo serialization</a> when speed is necessary. Can be any subclass of + and configuring Kryo serialization</a> when speed is necessary. Can be any subclass of <a href="api/core/index.html#spark.Serializer"><code>spark.Serializer</code></a>). </td> </tr> @@ -75,7 +86,7 @@ there are at least four properties that you will commonly want to control: <td>(none)</td> <td> If you use Kryo serialization, set this class to register your custom classes with Kryo. - You need to set it to a class that extends + You need to set it to a class that extends <a href="api/core/index.html#spark.KryoRegistrator"><code>spark.KryoRegistrator</code></a>). See the <a href="tuning.html#data-serialization">tuning guide</a> for more details. </td> @@ -135,9 +146,16 @@ Apart from these, the following properties are also available, and may be useful </tr> <tr> <td>spark.ui.port</td> - <td>(random)</td> + <td>33000</td> + <td> + Port for your application's dashboard, which shows memory and workload data + </td> +</tr> +<tr> + <td>spark.ui.retained_stages</td> + <td>1000</td> <td> - Port for your application's dashboard, which shows memory usage of each RDD. + How many stages the Spark UI remembers before garbage collecting. </td> </tr> <tr> @@ -163,6 +181,21 @@ Apart from these, the following properties are also available, and may be useful </td> </tr> <tr> + <td>spark.io.compression.codec</td> + <td>spark.io.SnappyCompressionCodec</td> + <td> + The compression codec class to use for various compressions. By default, Spark provides two + codecs: <code>spark.io.LZFCompressionCodec</code> and <code>spark.io.SnappyCompressionCodec</code>. + </td> +</tr> +<tr> + <td>spark.io.compression.snappy.block.size</td> + <td>32768</td> + <td> + Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is used. + </td> +</tr> +<tr> <td>spark.reducer.maxMbInFlight</td> <td>48</td> <td> @@ -180,8 +213,18 @@ Apart from these, the following properties are also available, and may be useful </td> </tr> <tr> + <td>spark.kryo.referenceTracking</td> + <td>true</td> + <td> + Whether to track references to the same object when serializing data with Kryo, which is + necessary if your object graphs have loops and useful for efficiency if they contain multiple + copies of the same object. Can be disabled to improve performance if you know this is not the + case. + </td> +</tr> +<tr> <td>spark.kryoserializer.buffer.mb</td> - <td>32</td> + <td>2</td> <td> Maximum object size to allow within Kryo (the library needs to create a buffer at least as large as the largest single object you'll serialize). Increase this if you get a "buffer limit @@ -260,6 +303,21 @@ Apart from these, the following properties are also available, and may be useful applications). Note that any RDD that persists in memory for more than this duration will be cleared as well. </td> </tr> +<tr> + <td>spark.streaming.blockInterval</td> + <td>200</td> + <td> + Duration (milliseconds) of how long to batch new objects coming from network receivers. + </td> +</tr> +<tr> + <td>spark.task.maxFailures</td> + <td>4</td> + <td> + Number of individual task failures before giving up on the job. + Should be greater than or equal to 1. Number of allowed retries = this value - 1. + </td> +</tr> </table> diff --git a/docs/ec2-scripts.md b/docs/ec2-scripts.md index dc57035eba..eab8a0ff20 100644 --- a/docs/ec2-scripts.md +++ b/docs/ec2-scripts.md @@ -106,9 +106,8 @@ permissions on your private key file, you can run `launch` with the # Configuration You can edit `/root/spark/conf/spark-env.sh` on each machine to set Spark configuration options, such -as JVM options and, most crucially, the amount of memory to use per machine (`SPARK_MEM`). -This file needs to be copied to **every machine** to reflect the change. The easiest way to do this -is to use a script we provide called `copy-dir`. First edit your `spark-env.sh` file on the master, +as JVM options. This file needs to be copied to **every machine** to reflect the change. The easiest way to +do this is to use a script we provide called `copy-dir`. First edit your `spark-env.sh` file on the master, then run `~/spark-ec2/copy-dir /root/spark/conf` to RSYNC it to all the workers. The [configuration guide](configuration.html) describes the available configuration options. diff --git a/docs/index.md b/docs/index.md index 51d505e1fa..0c4add45dc 100644 --- a/docs/index.md +++ b/docs/index.md @@ -18,7 +18,7 @@ or you will need to set the `SCALA_HOME` environment variable to point to where you've installed Scala. Scala must also be accessible through one of these methods on slave nodes on your cluster. -Spark uses [Simple Build Tool](https://github.com/harrah/xsbt/wiki), which is bundled with it. To compile the code, go into the top-level Spark directory and run +Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with it. To compile the code, go into the top-level Spark directory and run sbt/sbt package diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index 3a7a8db4a6..794bff5647 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -10,6 +10,7 @@ To learn the basics of Spark, we recommend reading through the easy to follow even if you don't know Scala. This guide will show how to use the Spark features described there in Python. + # Key Differences in the Python API There are a few key differences between the Python and Scala APIs: @@ -17,24 +18,23 @@ There are a few key differences between the Python and Scala APIs: * Python is dynamically typed, so RDDs can hold objects of different types. * PySpark does not currently support the following Spark features: - Special functions on RDDs of doubles, such as `mean` and `stdev` - - `lookup` + - `lookup`, `sample` and `sort` - `persist` at storage levels other than `MEMORY_ONLY` - - `sample` - - `sort` + - Execution on Windows -- this is slated for a future release 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: {% highlight python %} logData = sc.textFile(logFile).cache() -errors = logData.filter(lambda s: 'ERROR' in s.split()) +errors = logData.filter(lambda line: "ERROR" in line) {% endhighlight %} You can also pass functions that are defined using the `def` keyword; this is useful for more complicated functions that cannot be expressed using `lambda`: {% highlight python %} def is_error(line): - return 'ERROR' in line.split() + return "ERROR" in line errors = logData.filter(is_error) {% endhighlight %} @@ -43,8 +43,7 @@ Functions can access objects in enclosing scopes, although modifications to thos {% highlight python %} error_keywords = ["Exception", "Error"] def is_error(line): - words = line.split() - return any(keyword in words for keyword in error_keywords) + return any(keyword in line for keyword in error_keywords) errors = logData.filter(is_error) {% endhighlight %} @@ -52,6 +51,7 @@ PySpark will automatically ship these functions to workers, along with any objec Instances of classes will be serialized and shipped to workers by PySpark, but classes themselves cannot be automatically distributed to workers. The [Standalone Use](#standalone-use) section describes how to ship code dependencies to workers. + # Installing and Configuring PySpark PySpark requires Python 2.6 or higher. @@ -83,16 +83,41 @@ The Python shell can be used explore data interactively and is a simple way to l >>> help(pyspark) # Show all pyspark functions {% endhighlight %} -By default, the `pyspark` shell creates SparkContext that runs jobs locally. -To connect to a non-local cluster, set the `MASTER` environment variable. +By default, the `pyspark` shell creates SparkContext that runs jobs locally on a single core. +To connect to a non-local cluster, or use multiple cores, set the `MASTER` environment variable. For example, to use the `pyspark` shell with a [standalone Spark cluster](spark-standalone.html): {% highlight bash %} $ MASTER=spark://IP:PORT ./pyspark {% endhighlight %} +Or, to use four cores on the local machine: + +{% highlight bash %} +$ MASTER=local[4] ./pyspark +{% endhighlight %} + + +## IPython + +It is also possible to launch PySpark in [IPython](http://ipython.org), the enhanced Python interpreter. +To do this, simply set the `IPYTHON` variable to `1` when running `pyspark`: + +{% highlight bash %} +$ IPYTHON=1 ./pyspark +{% endhighlight %} + +Alternatively, you can customize the `ipython` command by setting `IPYTHON_OPTS`. For example, to launch +the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing support: -# Standalone Use +{% highlight bash %} +$ IPYTHON_OPTS="notebook --pylab inline" ./pyspark +{% endhighlight %} + +IPython also works on a cluster or on multiple cores if you set the `MASTER` environment variable. + + +# Standalone Programs PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `pyspark`. The Quick Start guide includes a [complete example](quick-start.html#a-standalone-job-in-python) of a standalone Python job. @@ -107,6 +132,7 @@ sc = SparkContext("local", "Job 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. + # Where to Go from Here PySpark includes several sample programs in the [`python/examples` folder](https://github.com/mesos/spark/tree/master/python/examples). diff --git a/docs/quick-start.md b/docs/quick-start.md index 216f7c9cc5..335643536a 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -53,8 +53,8 @@ scala> textFile.filter(line => line.contains("Spark")).count() // How many lines res3: Long = 15 {% endhighlight %} -## Transformations -RDD transformations can be used for more complex computations. Let's say we want to find the line with the most words: +## More On RDD Operations +RDD actions and transformations can be used for more complex computations. Let's say we want to find the line with the most words: {% highlight scala %} scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b) @@ -111,18 +111,20 @@ We'll create a very simple Spark job in Scala. So simple, in fact, that it's nam import spark.SparkContext import SparkContext._ -object SimpleJob extends Application { - val logFile = "/var/log/syslog" // Should be some file on your system - val sc = new SparkContext("local", "Simple Job", "$YOUR_SPARK_HOME", - List("target/scala-{{site.SCALA_VERSION}}/simple-project_{{site.SCALA_VERSION}}-1.0.jar")) - val logData = sc.textFile(logFile, 2).cache() - val numAs = logData.filter(line => line.contains("a")).count() - val numBs = logData.filter(line => line.contains("b")).count() - println("Lines with a: %s, Lines with b: %s".format(numAs, numBs)) +object SimpleJob { + def main(args: Array[String]) { + val logFile = "$YOUR_SPARK_HOME/README.md" // Should be some file on your system + val sc = new SparkContext("local", "Simple Job", "YOUR_SPARK_HOME", + List("target/scala-{{site.SCALA_VERSION}}/simple-project_{{site.SCALA_VERSION}}-1.0.jar")) + val logData = sc.textFile(logFile, 2).cache() + val numAs = logData.filter(line => line.contains("a")).count() + val numBs = logData.filter(line => line.contains("b")).count() + println("Lines with a: %s, Lines with b: %s".format(numAs, numBs)) + } } {% endhighlight %} -This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. 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 job, the directory where Spark is installed, and a name for the jar file containing the job's sources. 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 job 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 job. 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 job, the directory where Spark is installed, and a name for the jar file containing the job's sources. 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 two repositories which host Spark dependencies: @@ -154,7 +156,7 @@ $ find . $ sbt package $ sbt run ... -Lines with a: 8422, Lines with b: 1836 +Lines with a: 46, Lines with b: 23 {% endhighlight %} This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS. @@ -171,7 +173,7 @@ import spark.api.java.function.Function; public class SimpleJob { public static void main(String[] args) { - String logFile = "/var/log/syslog"; // Should be some file on your system + String logFile = "$YOUR_SPARK_HOME/README.md"; // Should be some file on your system JavaSparkContext sc = new JavaSparkContext("local", "Simple Job", "$YOUR_SPARK_HOME", new String[]{"target/simple-project-1.0.jar"}); JavaRDD<String> logData = sc.textFile(logFile).cache(); @@ -189,7 +191,7 @@ public class SimpleJob { } {% endhighlight %} -This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that like in 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 job 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. To build the job, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version. @@ -237,7 +239,7 @@ Now, we can execute the job using Maven: $ mvn package $ mvn exec:java -Dexec.mainClass="SimpleJob" ... -Lines with a: 8422, Lines with b: 1836 +Lines with a: 46, Lines with b: 23 {% endhighlight %} This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS. @@ -251,7 +253,7 @@ As an example, we'll create a simple Spark job, `SimpleJob.py`: """SimpleJob.py""" from pyspark import SparkContext -logFile = "/var/log/syslog" # Should be some file on your system +logFile = "$YOUR_SPARK_HOME/README.md" # Should be some file on your system sc = SparkContext("local", "Simple job") logData = sc.textFile(logFile).cache() @@ -263,7 +265,8 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs) This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. -Like in the Scala and Java examples, we use a SparkContext to create RDDs. +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. For jobs that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide.html). `SimpleJob` is simple enough that we do not need to specify any code dependencies. @@ -274,7 +277,7 @@ We can run this job using the `pyspark` script: $ cd $SPARK_HOME $ ./pyspark SimpleJob.py ... -Lines with a: 8422, Lines with b: 1836 +Lines with a: 46, Lines with b: 23 {% endhighlight python %} This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS. diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index c2957e6cb4..66fb8d73e8 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -5,24 +5,54 @@ title: Launching Spark on YARN Experimental support for running over a [YARN (Hadoop NextGen)](http://hadoop.apache.org/docs/r2.0.2-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html) -cluster was added to Spark in version 0.6.0. Because YARN depends on version -2.0 of the Hadoop libraries, this currently requires checking out a separate -branch of Spark, called `yarn`, which you can do as follows: +cluster was added to Spark in version 0.6.0. This was merged into master as part of 0.7 effort. +To build spark core with YARN support, please use the hadoop2-yarn profile. +Ex: mvn -Phadoop2-yarn clean install - git clone git://github.com/mesos/spark - cd spark - git checkout -b yarn --track origin/yarn +# Building spark core consolidated jar. + +We need a consolidated spark core jar (which bundles all the required dependencies) to run Spark jobs on a yarn cluster. +This can be built either through sbt or via maven. + +- Building spark assembled jar via sbt. + It is a manual process of enabling it in project/SparkBuild.scala. +Please comment out the + HADOOP_VERSION, HADOOP_MAJOR_VERSION and HADOOP_YARN +variables before the line 'For Hadoop 2 YARN support' +Next, uncomment the subsequent 3 variable declaration lines (for these three variables) which enable hadoop yarn support. + +Assembly of the jar Ex: + + ./sbt/sbt clean assembly + +The assembled jar would typically be something like : +`./core/target/spark-core-assembly-0.8.0-SNAPSHOT.jar` + + +- Building spark assembled jar via Maven. + Use the hadoop2-yarn profile and execute the package target. + +Something like this. Ex: + + mvn -Phadoop2-yarn clean package -DskipTests=true + + +This will build the shaded (consolidated) jar. Typically something like : +`./repl-bin/target/spark-repl-bin-<VERSION>-shaded-hadoop2-yarn.jar` # Preparations -- In order to distribute Spark within the cluster, it must be packaged into a single JAR file. This can be done by running `sbt/sbt assembly` +- Building spark core assembled jar (see above). - 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 package`. 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. # 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. + The command to launch the YARN Client is as follows: SPARK_JAR=<SPARK_YAR_FILE> ./run spark.deploy.yarn.Client \ @@ -30,22 +60,28 @@ 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-memory <MEMORY_FOR_MASTER> \ --worker-memory <MEMORY_PER_WORKER> \ - --worker-cores <CORES_PER_WORKER> + --worker-cores <CORES_PER_WORKER> \ + --user <hadoop_user> \ + --queue <queue_name> For example: SPARK_JAR=./core/target/spark-core-assembly-{{site.SPARK_VERSION}}.jar ./run spark.deploy.yarn.Client \ --jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}.jar \ --class spark.examples.SparkPi \ - --args standalone \ + --args yarn-standalone \ --num-workers 3 \ + --master-memory 4g \ --worker-memory 2g \ - --worker-cores 2 + --worker-cores 1 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. # Important Notes -- When your application instantiates a Spark context it must use a special "standalone" master url. This starts the scheduler without forcing it to connect to a cluster. A good way to handle this is to pass "standalone" as an argument to your program, as shown in the example above. -- YARN does not support requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed. +- 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. +- Currently, we have not yet integrated with hadoop security. If --user is present, the hadoop_user specified will be used to run the tasks on the cluster. If unspecified, current user will be used (which should be valid in cluster). + Once hadoop security support is added, and if hadoop cluster is enabled with security, additional restrictions would apply via delegation tokens passed. diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index 2315aadbdf..e9cf9ef36f 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -43,12 +43,18 @@ new SparkContext(master, appName, [sparkHome], [jars]) 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. For example, to run on four cores, use +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 {% highlight bash %} $ MASTER=local[4] ./spark-shell {% endhighlight %} +Or, to also add `code.jar` to its classpath, use: + +{% highlight bash %} +$ MASTER=local[4] ADD_JARS=code.jar ./spark-shell +{% endhighlight %} + ### Master URLs The master URL passed to Spark can be in one of the following formats: @@ -67,6 +73,8 @@ The master URL passed to Spark can be in one of the following formats: </td></tr> </table> +If no master URL is specified, the spark shell defaults to "local". + For running on YARN, Spark launches an instance of the standalone deploy cluster within YARN; see [running on YARN](running-on-yarn.html) for details. ### Deploying Code on a Cluster @@ -76,7 +84,7 @@ If you want to run your job on a cluster, you will need to specify the two optio * `sparkHome`: The path at which Spark is installed on your worker machines (it should be the same on all of them). * `jars`: A list of JAR files on the local machine containing your job's code and any dependencies, which Spark will deploy to all the worker nodes. You'll need to package your job into a set of JARs using your build system. For example, if you're using SBT, the [sbt-assembly](https://github.com/sbt/sbt-assembly) plugin is a good way to make a single JAR with your code and dependencies. -If you run `spark-shell` on a cluster, any classes you define in the shell will automatically be distributed. +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) diff --git a/docs/spark-simple-tutorial.md b/docs/spark-simple-tutorial.md index 9875de62bd..fbdbc7d19d 100644 --- a/docs/spark-simple-tutorial.md +++ b/docs/spark-simple-tutorial.md @@ -13,7 +13,7 @@ title: Tutorial - Running a Simple Spark Application 3. Edit the ~/SparkTest/sbt/sbt file to look like this: - #!/bin/bash + #!/usr/bin/env bash java -Xmx800M -XX:MaxPermSize=150m -jar $(dirname $0)/sbt-launch-*.jar "$@" 4. To build a Spark application, you need Spark and its dependencies in a single Java archive (JAR) file. Create this JAR in Spark's main directory with sbt as: diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index b30699cf3d..8cd1b0cd66 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -7,7 +7,7 @@ title: Spark Streaming Programming Guide {:toc} # Overview -A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collections of elements) representing a continuous stream of data. DStreams can be created from live incoming data (such as data from a socket, Kafka, etc.) or can be generated by transformong existing DStreams using parallel operators like `map`, `reduce`, and `window`. The basic processing model is as follows: +A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collections of elements) representing a continuous stream of data. DStreams can be created from live incoming data (such as data from a socket, Kafka, etc.) or can be generated by transforming existing DStreams using parallel operators like `map`, `reduce`, and `window`. The basic processing model is as follows: (i) While a Spark Streaming driver program is running, the system receives data from various sources and and divides it into batches. Each batch of data is treated as an RDD, that is, an immutable parallel collection of data. These input RDDs are saved in memory and replicated to two nodes for fault-tolerance. This sequence of RDDs is collectively called an InputDStream. (ii) Data received by InputDStreams are processed using DStream operations. Since all data is represented as RDDs and all DStream operations as RDD operations, data is automatically recovered in the event of node failures. @@ -20,7 +20,7 @@ The first thing a Spark Streaming program must do is create a `StreamingContext` new StreamingContext(master, appName, batchDuration, [sparkHome], [jars]) {% endhighlight %} -The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls) and can be "local" for local testing. The `appName` is a name of your program, which will be shown on your cluster's web UI. The `batchDuration` is the size of the batches (as explained earlier). This must be set carefully such the cluster can keep up with the processing of the data streams. Start with something conservative like 5 seconds. See the [Performance Tuning](#setting-the-right-batch-size) section for a detailed discussion. Finally, `sparkHome` and `jars` are necessary when running on a cluster to specify the location of your code, as described in the [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster). +The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls) and can be "local" for local testing. The `appName` is a name of your program, which will be shown on your cluster's web UI. The `batchDuration` is the size of the batches (as explained earlier). This must be set carefully such that the cluster can keep up with the processing of the data streams. Start with something conservative like 5 seconds. See the [Performance Tuning](#setting-the-right-batch-size) section for a detailed discussion. Finally, `sparkHome` and `jars` are necessary when running on a cluster to specify the location of your code, as described in the [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster). This constructor creates a SparkContext for your job as well, which can be accessed with `streamingContext.sparkContext`. @@ -83,7 +83,7 @@ DStreams support many of the transformations available on normal Spark RDD's: <tr> <td> <b>groupByKey</b>([<i>numTasks</i>]) </td> <td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, Seq[V]) pairs by grouping together all the values of each key in the RDDs of the source DStream. <br /> - <b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluser) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks. + <b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks. </td> </tr> <tr> @@ -132,7 +132,7 @@ Spark Streaming features windowed computations, which allow you to apply transfo <td> <b>groupByKeyAndWindow</b>(<i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>]) </td> <td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, Seq[V]) pairs by grouping together values of each key over batches in a sliding window. <br /> -<b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluser) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td> +<b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td> </tr> <tr> <td> <b>reduceByKeyAndWindow</b>(<i>func</i>, <i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>]) </td> diff --git a/docs/tuning.md b/docs/tuning.md index 843380b9a2..5ffca54481 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -49,7 +49,7 @@ Finally, to register your classes with Kryo, create a public class that extends {% highlight scala %} import com.esotericsoftware.kryo.Kryo -class MyRegistrator extends KryoRegistrator { +class MyRegistrator extends spark.KryoRegistrator { override def registerClasses(kryo: Kryo) { kryo.register(classOf[MyClass1]) kryo.register(classOf[MyClass2]) @@ -157,9 +157,9 @@ their work directories), *not* on your driver program. **Cache Size Tuning** -One important configuration parameter for GC is the amount of memory that should be used for -caching RDDs. By default, Spark uses 66% of the configured memory (`SPARK_MEM`) to cache RDDs. This means that - 33% of memory is available for any objects created during task execution. +One important configuration parameter for GC is the amount of memory that should be used for caching RDDs. +By default, Spark uses 66% of the configured executor memory (`spark.executor.memory` or `SPARK_MEM`) to +cache RDDs. This means that 33% of memory is available for any objects created during task execution. 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 diff --git a/ec2/deploy.generic/root/mesos-ec2/ec2-variables.sh b/ec2/deploy.generic/root/mesos-ec2/ec2-variables.sh index 50ecf83404..ede6c78428 100644 --- a/ec2/deploy.generic/root/mesos-ec2/ec2-variables.sh +++ b/ec2/deploy.generic/root/mesos-ec2/ec2-variables.sh @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # These variables are automatically filled in by the mesos-ec2 script. export MESOS_MASTERS="{{master_list}}" diff --git a/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh b/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh index 166a884c88..685ed8be8c 100644 --- a/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh +++ b/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # These variables are automatically filled in by the mesos-ec2 script. export MESOS_MASTERS="{{master_list}}" diff --git a/ec2/spark-ec2 b/ec2/spark-ec2 index 2714f19ba3..454057aa0d 100755 --- a/ec2/spark-ec2 +++ b/ec2/spark-ec2 @@ -1,5 +1,6 @@ #!/bin/sh +# # 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 @@ -15,6 +16,7 @@ # 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. +# cd "`dirname $0`" PYTHONPATH="./third_party/boto-2.4.1.zip/boto-2.4.1:$PYTHONPATH" python ./spark_ec2.py $@ diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 571d27fde6..740ec08542 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -1,6 +1,7 @@ #!/usr/bin/env python # -*- coding: 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 @@ -8,14 +9,15 @@ # 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 __future__ import with_statement @@ -51,7 +53,7 @@ def parse_args(): help="Seconds to wait for nodes to start (default: 120)") parser.add_option("-k", "--key-pair", help="Key pair to use on instances") - parser.add_option("-i", "--identity-file", + parser.add_option("-i", "--identity-file", help="SSH private key file to use for logging into instances") parser.add_option("-t", "--instance-type", default="m1.large", help="Type of instance to launch (default: m1.large). " + @@ -67,7 +69,7 @@ def parse_args(): parser.add_option("-a", "--ami", default="latest", help="Amazon Machine Image ID to use, or 'latest' to use latest " + "available AMI (default: latest)") - 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, @@ -97,20 +99,20 @@ 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") - + (opts, args) = parser.parse_args() if len(args) != 2: parser.print_help() sys.exit(1) (action, cluster_name) = args - if opts.identity_file == None and action in ['launch', 'login']: + if opts.identity_file == None and action in ['launch', 'login', 'start']: print >> stderr, ("ERROR: The -i or --identity-file argument is " + "required for " + action) sys.exit(1) if opts.cluster_type not in ["mesos", "standalone"] and action == "launch": print >> stderr, ("ERROR: Invalid cluster type: " + opts.cluster_type) sys.exit(1) - + # Boto config check # http://boto.cloudhackers.com/en/latest/boto_config_tut.html home_dir = os.getenv('HOME') @@ -176,6 +178,7 @@ def launch_cluster(conn, opts, cluster_name): 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') + master_group.authorize('tcp', 33000, 33010, '0.0.0.0/0') if opts.cluster_type == "mesos": master_group.authorize('tcp', 38090, 38090, '0.0.0.0/0') if opts.ganglia: @@ -255,7 +258,7 @@ def launch_cluster(conn, opts, cluster_name): block_device_map = block_map) my_req_ids += [req.id for req in slave_reqs] i += 1 - + print "Waiting for spot instances to be granted..." try: while True: @@ -411,7 +414,7 @@ def setup_standalone_cluster(master, slave_nodes, opts): slave_ips = '\n'.join([i.public_dns_name for i in slave_nodes]) ssh(master, opts, "echo \"%s\" > spark/conf/slaves" % (slave_ips)) ssh(master, opts, "/root/spark/bin/start-all.sh") - + def setup_spark_cluster(master, opts): ssh(master, opts, "chmod u+x spark-ec2/setup.sh") ssh(master, opts, "spark-ec2/setup.sh") @@ -526,7 +529,7 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, zoo_nodes, dest.write(text) dest.close() # rsync the whole directory over to the master machine - command = (("rsync -rv -e 'ssh -o StrictHostKeyChecking=no -i %s' " + + command = (("rsync -rv -e 'ssh -o StrictHostKeyChecking=no -i %s' " + "'%s/' '%s@%s:/'") % (opts.identity_file, tmp_dir, opts.user, active_master)) subprocess.check_call(command, shell=True) # Remove the temp directory we created above @@ -540,11 +543,24 @@ def scp(host, opts, local_file, dest_file): (opts.identity_file, local_file, opts.user, host, dest_file), shell=True) -# Run a command on a host through ssh, throwing an exception if ssh fails +# Run a command on a host through ssh, retrying up to two times +# and then throwing an exception if ssh continues to fail. def ssh(host, opts, command): - subprocess.check_call( - "ssh -t -o StrictHostKeyChecking=no -i %s %s@%s '%s'" % - (opts.identity_file, opts.user, host, command), shell=True) + tries = 0 + while True: + try: + return subprocess.check_call( + "ssh -t -o StrictHostKeyChecking=no -i %s %s@%s '%s'" % + (opts.identity_file, opts.user, host, command), shell=True) + except subprocess.CalledProcessError as e: + if (tries > 2): + raise e + print "Error connecting to host {0}, sleeping 30".format(e) + time.sleep(30) + tries = tries + 1 + + + # Gets a list of zones to launch instances in @@ -603,12 +619,12 @@ def main(): print "Terminating zoo..." for inst in zoo_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", cluster_name + "-zoo"] - + attempt = 1; while attempt <= 3: print "Attempt %d" % attempt @@ -624,7 +640,7 @@ def main(): from_port=rule.from_port, to_port=rule.to_port, src_group=grant) - + # Sleep for AWS eventual-consistency to catch up, and for instances # to terminate time.sleep(30) # Yes, it does have to be this long :-( @@ -635,13 +651,13 @@ def main(): except boto.exception.EC2ResponseError: success = False; print "Failed to delete security group " + group.name - + # Unfortunately, group.revoke() returns True even if a rule was not # deleted, so this needs to be rerun if something fails if success: break; - + attempt += 1 - + if not success: print "Failed to delete all security groups after 3 tries." print "Try re-running in a few minutes." @@ -664,7 +680,7 @@ def main(): elif action == "stop": response = raw_input("Are you sure you want to stop the cluster " + cluster_name + "?\nDATA ON EPHEMERAL DISKS WILL BE LOST, " + - "BUT THE CLUSTER WILL KEEP USING SPACE ON\n" + + "BUT THE CLUSTER WILL KEEP USING SPACE ON\n" + "AMAZON EBS IF IT IS EBS-BACKED!!\n" + "Stop cluster " + cluster_name + " (y/N): ") if response == "y": diff --git a/examples/pom.xml b/examples/pom.xml index 39cc47c709..7a8d08fade 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -1,10 +1,27 @@ <?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.spark-project</groupId> <artifactId>spark-parent</artifactId> - <version>0.7.1-SNAPSHOT</version> + <version>0.8.0-SNAPSHOT</version> <relativePath>../pom.xml</relativePath> </parent> @@ -16,13 +33,17 @@ <dependencies> <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> + </dependency> + <dependency> <groupId>org.eclipse.jetty</groupId> <artifactId>jetty-server</artifactId> </dependency> <dependency> <groupId>com.twitter</groupId> <artifactId>algebird-core_2.9.2</artifactId> - <version>0.1.8</version> + <version>0.1.11</version> </dependency> <dependency> <groupId>org.scalatest</groupId> @@ -34,6 +55,41 @@ <artifactId>scalacheck_${scala.version}</artifactId> <scope>test</scope> </dependency> + <dependency> + <groupId>org.apache.cassandra</groupId> + <artifactId>cassandra-all</artifactId> + <version>1.2.5</version> + <exclusions> + <exclusion> + <groupId>com.google.guava</groupId> + <artifactId>guava</artifactId> + </exclusion> + <exclusion> + <groupId>com.googlecode.concurrentlinkedhashmap</groupId> + <artifactId>concurrentlinkedhashmap-lru</artifactId> + </exclusion> + <exclusion> + <groupId>com.ning</groupId> + <artifactId>compress-lzf</artifactId> + </exclusion> + <exclusion> + <groupId>io.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + <exclusion> + <groupId>jline</groupId> + <artifactId>jline</artifactId> + </exclusion> + <exclusion> + <groupId>log4j</groupId> + <artifactId>log4j</artifactId> + </exclusion> + <exclusion> + <groupId>org.apache.cassandra.deps</groupId> + <artifactId>avro</artifactId> + </exclusion> + </exclusions> + </dependency> </dependencies> <build> <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> @@ -67,6 +123,11 @@ <artifactId>hadoop-core</artifactId> <scope>provided</scope> </dependency> + <dependency> + <groupId>org.apache.hbase</groupId> + <artifactId>hbase</artifactId> + <version>0.94.6</version> + </dependency> </dependencies> <build> <plugins> @@ -105,6 +166,11 @@ <artifactId>hadoop-client</artifactId> <scope>provided</scope> </dependency> + <dependency> + <groupId>org.apache.hbase</groupId> + <artifactId>hbase</artifactId> + <version>0.94.6</version> + </dependency> </dependencies> <build> <plugins> @@ -118,5 +184,53 @@ </plugins> </build> </profile> + <profile> + <id>hadoop2-yarn</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-streaming</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hbase</groupId> + <artifactId>hbase</artifactId> + <version>0.94.6</version> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2-yarn</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> </profiles> </project> diff --git a/examples/src/main/java/spark/examples/JavaHdfsLR.java b/examples/src/main/java/spark/examples/JavaHdfsLR.java index 8b0a9b6808..9485e0cfa9 100644 --- a/examples/src/main/java/spark/examples/JavaHdfsLR.java +++ b/examples/src/main/java/spark/examples/JavaHdfsLR.java @@ -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. + */ + package spark.examples; import spark.api.java.JavaRDD; diff --git a/examples/src/main/java/spark/examples/JavaKMeans.java b/examples/src/main/java/spark/examples/JavaKMeans.java index 626034eb0d..2d34776177 100644 --- a/examples/src/main/java/spark/examples/JavaKMeans.java +++ b/examples/src/main/java/spark/examples/JavaKMeans.java @@ -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. + */ + package spark.examples; import scala.Tuple2; diff --git a/examples/src/main/java/spark/examples/JavaLogQuery.java b/examples/src/main/java/spark/examples/JavaLogQuery.java index 6b22e7120c..d22684d980 100644 --- a/examples/src/main/java/spark/examples/JavaLogQuery.java +++ b/examples/src/main/java/spark/examples/JavaLogQuery.java @@ -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. + */ + package spark.examples; import com.google.common.collect.Lists; diff --git a/examples/src/main/java/spark/examples/JavaPageRank.java b/examples/src/main/java/spark/examples/JavaPageRank.java new file mode 100644 index 0000000000..9d90ef9174 --- /dev/null +++ b/examples/src/main/java/spark/examples/JavaPageRank.java @@ -0,0 +1,116 @@ +/* + * 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 spark.examples; + +import scala.Tuple2; +import spark.api.java.JavaPairRDD; +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; +import spark.api.java.function.FlatMapFunction; +import spark.api.java.function.Function; +import spark.api.java.function.PairFlatMapFunction; +import spark.api.java.function.PairFunction; + +import java.util.List; +import java.util.ArrayList; + +/** + * Computes the PageRank of URLs from an input file. Input file should + * be in format of: + * URL neighbor URL + * URL neighbor URL + * URL neighbor URL + * ... + * where URL and their neighbors are separated by space(s). + */ +public class JavaPageRank { + private static double sum(List<Double> numbers) { + double out = 0.0; + for (double number : numbers) { + out += number; + } + return out; + } + + public static void main(String[] args) throws Exception { + if (args.length < 3) { + System.err.println("Usage: JavaPageRank <master> <file> <number_of_iterations>"); + System.exit(1); + } + + JavaSparkContext ctx = new JavaSparkContext(args[0], "JavaPageRank", + System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR")); + + // Loads in input file. It should be in format of: + // URL neighbor URL + // URL neighbor URL + // URL neighbor URL + // ... + JavaRDD<String> lines = ctx.textFile(args[1], 1); + + // Loads all URLs from input file and initialize their neighbors. + JavaPairRDD<String, List<String>> links = lines.map(new PairFunction<String, String, String>() { + @Override + public Tuple2<String, String> call(String s) { + String[] parts = s.split("\\s+"); + return new Tuple2<String, String>(parts[0], parts[1]); + } + }).distinct().groupByKey().cache(); + + // Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one. + JavaPairRDD<String, Double> ranks = links.mapValues(new Function<List<String>, Double>() { + @Override + public Double call(List<String> rs) throws Exception { + return 1.0; + } + }); + + // Calculates and updates URL ranks continuously using PageRank algorithm. + for (int current = 0; current < Integer.parseInt(args[2]); current++) { + // Calculates URL contributions to the rank of other URLs. + JavaPairRDD<String, Double> contribs = links.join(ranks).values() + .flatMap(new PairFlatMapFunction<Tuple2<List<String>, Double>, String, Double>() { + @Override + public Iterable<Tuple2<String, Double>> call(Tuple2<List<String>, Double> s) { + List<Tuple2<String, Double>> results = new ArrayList<Tuple2<String, Double>>(); + for (String n : s._1) { + results.add(new Tuple2<String, Double>(n, s._2 / s._1.size())); + } + + return results; + } + }); + + // Re-calculates URL ranks based on neighbor contributions. + ranks = contribs.groupByKey().mapValues(new Function<List<Double>, Double>() { + @Override + public Double call(List<Double> cs) throws Exception { + return 0.15 + sum(cs) * 0.85; + } + }); + } + + // Collects all URL ranks and dump them to console. + List<Tuple2<String, Double>> output = ranks.collect(); + for (Tuple2 tuple : output) { + System.out.println(tuple._1 + " has rank: " + tuple._2 + "."); + } + + System.exit(0); + } +} diff --git a/examples/src/main/java/spark/examples/JavaSparkPi.java b/examples/src/main/java/spark/examples/JavaSparkPi.java index a15a967de8..d5f42fbb38 100644 --- a/examples/src/main/java/spark/examples/JavaSparkPi.java +++ b/examples/src/main/java/spark/examples/JavaSparkPi.java @@ -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. + */ + package spark.examples; import spark.api.java.JavaRDD; diff --git a/examples/src/main/java/spark/examples/JavaTC.java b/examples/src/main/java/spark/examples/JavaTC.java index b319bdab44..559d7f9e53 100644 --- a/examples/src/main/java/spark/examples/JavaTC.java +++ b/examples/src/main/java/spark/examples/JavaTC.java @@ -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. + */ + package spark.examples; import scala.Tuple2; diff --git a/examples/src/main/java/spark/examples/JavaWordCount.java b/examples/src/main/java/spark/examples/JavaWordCount.java index 9d4c7a252d..1af370c1c3 100644 --- a/examples/src/main/java/spark/examples/JavaWordCount.java +++ b/examples/src/main/java/spark/examples/JavaWordCount.java @@ -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. + */ + package spark.examples; import scala.Tuple2; diff --git a/examples/src/main/java/spark/streaming/examples/JavaFlumeEventCount.java b/examples/src/main/java/spark/streaming/examples/JavaFlumeEventCount.java index e24c6ddaa7..096a9ae219 100644 --- a/examples/src/main/java/spark/streaming/examples/JavaFlumeEventCount.java +++ b/examples/src/main/java/spark/streaming/examples/JavaFlumeEventCount.java @@ -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. + */ + package spark.streaming.examples; import spark.api.java.function.Function; diff --git a/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java b/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java index 3e57580fd4..c54d3f3d59 100644 --- a/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java +++ b/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java @@ -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. + */ + package spark.streaming.examples; import com.google.common.collect.Lists; diff --git a/examples/src/main/java/spark/streaming/examples/JavaQueueStream.java b/examples/src/main/java/spark/streaming/examples/JavaQueueStream.java index 15b82c8da1..1f4a991542 100644 --- a/examples/src/main/java/spark/streaming/examples/JavaQueueStream.java +++ b/examples/src/main/java/spark/streaming/examples/JavaQueueStream.java @@ -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. + */ + package spark.streaming.examples; import com.google.common.collect.Lists; diff --git a/examples/src/main/scala/spark/examples/BroadcastTest.scala b/examples/src/main/scala/spark/examples/BroadcastTest.scala index ba59be1687..911490cb6c 100644 --- a/examples/src/main/scala/spark/examples/BroadcastTest.scala +++ b/examples/src/main/scala/spark/examples/BroadcastTest.scala @@ -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. + */ + package spark.examples import spark.SparkContext diff --git a/examples/src/main/scala/spark/examples/CassandraTest.scala b/examples/src/main/scala/spark/examples/CassandraTest.scala new file mode 100644 index 0000000000..104bfd5204 --- /dev/null +++ b/examples/src/main/scala/spark/examples/CassandraTest.scala @@ -0,0 +1,213 @@ +/* + * 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 spark.examples + +import org.apache.hadoop.mapreduce.Job +import org.apache.cassandra.hadoop.ColumnFamilyOutputFormat +import org.apache.cassandra.hadoop.ConfigHelper +import org.apache.cassandra.hadoop.ColumnFamilyInputFormat +import org.apache.cassandra.thrift._ +import spark.SparkContext +import spark.SparkContext._ +import java.nio.ByteBuffer +import java.util.SortedMap +import org.apache.cassandra.db.IColumn +import org.apache.cassandra.utils.ByteBufferUtil +import scala.collection.JavaConversions._ + + +/* + * This example demonstrates using Spark with Cassandra with the New Hadoop API and Cassandra + * support for Hadoop. + * + * To run this example, run this file with the following command params - + * <spark_master> <cassandra_node> <cassandra_port> + * + * So if you want to run this on localhost this will be, + * local[3] localhost 9160 + * + * The example makes some assumptions: + * 1. You have already created a keyspace called casDemo and it has a column family named Words + * 2. There are column family has a column named "para" which has test content. + * + * You can create the content by running the following script at the bottom of this file with + * cassandra-cli. + * + */ +object CassandraTest { + + def main(args: Array[String]) { + + // Get a SparkContext + val sc = new SparkContext(args(0), "casDemo") + + // Build the job configuration with ConfigHelper provided by Cassandra + val job = new Job() + job.setInputFormatClass(classOf[ColumnFamilyInputFormat]) + + val host: String = args(1) + val port: String = args(2) + + ConfigHelper.setInputInitialAddress(job.getConfiguration(), host) + ConfigHelper.setInputRpcPort(job.getConfiguration(), port) + ConfigHelper.setOutputInitialAddress(job.getConfiguration(), host) + ConfigHelper.setOutputRpcPort(job.getConfiguration(), port) + ConfigHelper.setInputColumnFamily(job.getConfiguration(), "casDemo", "Words") + ConfigHelper.setOutputColumnFamily(job.getConfiguration(), "casDemo", "WordCount") + + val predicate = new SlicePredicate() + val sliceRange = new SliceRange() + sliceRange.setStart(Array.empty[Byte]) + sliceRange.setFinish(Array.empty[Byte]) + predicate.setSlice_range(sliceRange) + ConfigHelper.setInputSlicePredicate(job.getConfiguration(), predicate) + + ConfigHelper.setInputPartitioner(job.getConfiguration(), "Murmur3Partitioner") + ConfigHelper.setOutputPartitioner(job.getConfiguration(), "Murmur3Partitioner") + + // Make a new Hadoop RDD + val casRdd = sc.newAPIHadoopRDD( + job.getConfiguration(), + classOf[ColumnFamilyInputFormat], + classOf[ByteBuffer], + classOf[SortedMap[ByteBuffer, IColumn]]) + + // Let us first get all the paragraphs from the retrieved rows + val paraRdd = casRdd.map { + case (key, value) => { + ByteBufferUtil.string(value.get(ByteBufferUtil.bytes("para")).value()) + } + } + + // Lets get the word count in paras + val counts = paraRdd.flatMap(p => p.split(" ")).map(word => (word, 1)).reduceByKey(_ + _) + + counts.collect().foreach { + case (word, count) => println(word + ":" + count) + } + + counts.map { + case (word, count) => { + val colWord = new org.apache.cassandra.thrift.Column() + colWord.setName(ByteBufferUtil.bytes("word")) + colWord.setValue(ByteBufferUtil.bytes(word)) + colWord.setTimestamp(System.currentTimeMillis) + + val colCount = new org.apache.cassandra.thrift.Column() + colCount.setName(ByteBufferUtil.bytes("wcount")) + colCount.setValue(ByteBufferUtil.bytes(count.toLong)) + colCount.setTimestamp(System.currentTimeMillis) + + val outputkey = ByteBufferUtil.bytes(word + "-COUNT-" + System.currentTimeMillis) + + val mutations: java.util.List[Mutation] = new Mutation() :: new Mutation() :: Nil + mutations.get(0).setColumn_or_supercolumn(new ColumnOrSuperColumn()) + mutations.get(0).column_or_supercolumn.setColumn(colWord) + mutations.get(1).setColumn_or_supercolumn(new ColumnOrSuperColumn()) + mutations.get(1).column_or_supercolumn.setColumn(colCount) + (outputkey, mutations) + } + }.saveAsNewAPIHadoopFile("casDemo", classOf[ByteBuffer], classOf[List[Mutation]], + classOf[ColumnFamilyOutputFormat], job.getConfiguration) + } +} + +/* +create keyspace casDemo; +use casDemo; + +create column family WordCount with comparator = UTF8Type; +update column family WordCount with column_metadata = + [{column_name: word, validation_class: UTF8Type}, + {column_name: wcount, validation_class: LongType}]; + +create column family Words with comparator = UTF8Type; +update column family Words with column_metadata = + [{column_name: book, validation_class: UTF8Type}, + {column_name: para, validation_class: UTF8Type}]; + +assume Words keys as utf8; + +set Words['3musk001']['book'] = 'The Three Musketeers'; +set Words['3musk001']['para'] = 'On the first Monday of the month of April, 1625, the market + town of Meung, in which the author of ROMANCE OF THE ROSE was born, appeared to + be in as perfect a state of revolution as if the Huguenots had just made + a second La Rochelle of it. Many citizens, seeing the women flying + toward the High Street, leaving their children crying at the open doors, + hastened to don the cuirass, and supporting their somewhat uncertain + courage with a musket or a partisan, directed their steps toward the + hostelry of the Jolly Miller, before which was gathered, increasing + every minute, a compact group, vociferous and full of curiosity.'; + +set Words['3musk002']['book'] = 'The Three Musketeers'; +set Words['3musk002']['para'] = 'In those times panics were common, and few days passed without + some city or other registering in its archives an event of this kind. There were + nobles, who made war against each other; there was the king, who made + war against the cardinal; there was Spain, which made war against the + king. Then, in addition to these concealed or public, secret or open + wars, there were robbers, mendicants, Huguenots, wolves, and scoundrels, + who made war upon everybody. The citizens always took up arms readily + against thieves, wolves or scoundrels, often against nobles or + Huguenots, sometimes against the king, but never against cardinal or + Spain. It resulted, then, from this habit that on the said first Monday + of April, 1625, the citizens, on hearing the clamor, and seeing neither + the red-and-yellow standard nor the livery of the Duc de Richelieu, + rushed toward the hostel of the Jolly Miller. When arrived there, the + cause of the hubbub was apparent to all'; + +set Words['3musk003']['book'] = 'The Three Musketeers'; +set Words['3musk003']['para'] = 'You ought, I say, then, to husband the means you have, however + large the sum may be; but you ought also to endeavor to perfect yourself in + the exercises becoming a gentleman. I will write a letter today to the + Director of the Royal Academy, and tomorrow he will admit you without + any expense to yourself. Do not refuse this little service. Our + best-born and richest gentlemen sometimes solicit it without being able + to obtain it. You will learn horsemanship, swordsmanship in all its + branches, and dancing. You will make some desirable acquaintances; and + from time to time you can call upon me, just to tell me how you are + getting on, and to say whether I can be of further service to you.'; + + +set Words['thelostworld001']['book'] = 'The Lost World'; +set Words['thelostworld001']['para'] = 'She sat with that proud, delicate profile of hers outlined + against the red curtain. How beautiful she was! And yet how aloof! We had been + friends, quite good friends; but never could I get beyond the same + comradeship which I might have established with one of my + fellow-reporters upon the Gazette,--perfectly frank, perfectly kindly, + and perfectly unsexual. My instincts are all against a woman being too + frank and at her ease with me. It is no compliment to a man. Where + the real sex feeling begins, timidity and distrust are its companions, + heritage from old wicked days when love and violence went often hand in + hand. The bent head, the averted eye, the faltering voice, the wincing + figure--these, and not the unshrinking gaze and frank reply, are the + true signals of passion. Even in my short life I had learned as much + as that--or had inherited it in that race memory which we call instinct.'; + +set Words['thelostworld002']['book'] = 'The Lost World'; +set Words['thelostworld002']['para'] = 'I always liked McArdle, the crabbed, old, round-backed, + red-headed news editor, and I rather hoped that he liked me. Of course, Beaumont was + the real boss; but he lived in the rarefied atmosphere of some Olympian + height from which he could distinguish nothing smaller than an + international crisis or a split in the Cabinet. Sometimes we saw him + passing in lonely majesty to his inner sanctum, with his eyes staring + vaguely and his mind hovering over the Balkans or the Persian Gulf. He + was above and beyond us. But McArdle was his first lieutenant, and it + was he that we knew. The old man nodded as I entered the room, and he + pushed his spectacles far up on his bald forehead.'; + +*/ diff --git a/examples/src/main/scala/spark/examples/ExceptionHandlingTest.scala b/examples/src/main/scala/spark/examples/ExceptionHandlingTest.scala index 21a90f2e5a..67ddaec8d2 100644 --- a/examples/src/main/scala/spark/examples/ExceptionHandlingTest.scala +++ b/examples/src/main/scala/spark/examples/ExceptionHandlingTest.scala @@ -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. + */ + package spark.examples import spark.SparkContext diff --git a/examples/src/main/scala/spark/examples/GroupByTest.scala b/examples/src/main/scala/spark/examples/GroupByTest.scala index a6603653f1..5cee413615 100644 --- a/examples/src/main/scala/spark/examples/GroupByTest.scala +++ b/examples/src/main/scala/spark/examples/GroupByTest.scala @@ -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. + */ + package spark.examples import spark.SparkContext diff --git a/examples/src/main/scala/spark/examples/HBaseTest.scala b/examples/src/main/scala/spark/examples/HBaseTest.scala new file mode 100644 index 0000000000..4dd6c243ac --- /dev/null +++ b/examples/src/main/scala/spark/examples/HBaseTest.scala @@ -0,0 +1,52 @@ +/* + * 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 spark.examples + +import spark._ +import spark.rdd.NewHadoopRDD +import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor} +import org.apache.hadoop.hbase.client.HBaseAdmin +import org.apache.hadoop.hbase.mapreduce.TableInputFormat + +object HBaseTest { + def main(args: Array[String]) { + val sc = new SparkContext(args(0), "HBaseTest", + System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) + + val conf = HBaseConfiguration.create() + + // Other options for configuring scan behavior are available. More information available at + // http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html + conf.set(TableInputFormat.INPUT_TABLE, args(1)) + + // Initialize hBase table if necessary + val admin = new HBaseAdmin(conf) + if(!admin.isTableAvailable(args(1))) { + val tableDesc = new HTableDescriptor(args(1)) + admin.createTable(tableDesc) + } + + val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], + classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], + classOf[org.apache.hadoop.hbase.client.Result]) + + hBaseRDD.count() + + System.exit(0) + } +} diff --git a/examples/src/main/scala/spark/examples/HdfsTest.scala b/examples/src/main/scala/spark/examples/HdfsTest.scala index dd61c467f7..23258336e2 100644 --- a/examples/src/main/scala/spark/examples/HdfsTest.scala +++ b/examples/src/main/scala/spark/examples/HdfsTest.scala @@ -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. + */ + package spark.examples import spark._ diff --git a/examples/src/main/scala/spark/examples/LocalALS.scala b/examples/src/main/scala/spark/examples/LocalALS.scala index 2de810e062..7a449a9d72 100644 --- a/examples/src/main/scala/spark/examples/LocalALS.scala +++ b/examples/src/main/scala/spark/examples/LocalALS.scala @@ -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. + */ + package spark.examples import scala.math.sqrt diff --git a/examples/src/main/scala/spark/examples/LocalFileLR.scala b/examples/src/main/scala/spark/examples/LocalFileLR.scala index f958ef9f72..c1f8d32aa8 100644 --- a/examples/src/main/scala/spark/examples/LocalFileLR.scala +++ b/examples/src/main/scala/spark/examples/LocalFileLR.scala @@ -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. + */ + package spark.examples import java.util.Random diff --git a/examples/src/main/scala/spark/examples/LocalKMeans.scala b/examples/src/main/scala/spark/examples/LocalKMeans.scala index b07e799cef..0a0bc6f476 100644 --- a/examples/src/main/scala/spark/examples/LocalKMeans.scala +++ b/examples/src/main/scala/spark/examples/LocalKMeans.scala @@ -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. + */ + package spark.examples import java.util.Random @@ -10,73 +27,73 @@ import scala.collection.mutable.HashSet * K-means clustering. */ object LocalKMeans { - val N = 1000 - val R = 1000 // Scaling factor - val D = 10 - val K = 10 - val convergeDist = 0.001 - val rand = new Random(42) - - def generateData = { - def generatePoint(i: Int) = { - Vector(D, _ => rand.nextDouble * R) - } - Array.tabulate(N)(generatePoint) - } - - def closestPoint(p: Vector, centers: HashMap[Int, Vector]): Int = { - var index = 0 - var bestIndex = 0 - var closest = Double.PositiveInfinity - - for (i <- 1 to centers.size) { - val vCurr = centers.get(i).get - val tempDist = p.squaredDist(vCurr) - if (tempDist < closest) { - closest = tempDist - bestIndex = i - } - } - - return bestIndex - } - - def main(args: Array[String]) { - val data = generateData - var points = new HashSet[Vector] - var kPoints = new HashMap[Int, Vector] - var tempDist = 1.0 - - while (points.size < K) { - points.add(data(rand.nextInt(N))) - } - - val iter = points.iterator - for (i <- 1 to points.size) { - kPoints.put(i, iter.next()) - } - - println("Initial centers: " + kPoints) - - while(tempDist > convergeDist) { - var closest = data.map (p => (closestPoint(p, kPoints), (p, 1))) - - var mappings = closest.groupBy[Int] (x => x._1) - - var pointStats = mappings.map(pair => pair._2.reduceLeft [(Int, (Vector, Int))] {case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1+y2))}) - - var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)} - - tempDist = 0.0 - for (mapping <- newPoints) { - tempDist += kPoints.get(mapping._1).get.squaredDist(mapping._2) - } - - for (newP <- newPoints) { - kPoints.put(newP._1, newP._2) - } - } - - println("Final centers: " + kPoints) - } + val N = 1000 + val R = 1000 // Scaling factor + val D = 10 + val K = 10 + val convergeDist = 0.001 + val rand = new Random(42) + + def generateData = { + def generatePoint(i: Int) = { + Vector(D, _ => rand.nextDouble * R) + } + Array.tabulate(N)(generatePoint) + } + + def closestPoint(p: Vector, centers: HashMap[Int, Vector]): Int = { + var index = 0 + var bestIndex = 0 + var closest = Double.PositiveInfinity + + for (i <- 1 to centers.size) { + val vCurr = centers.get(i).get + val tempDist = p.squaredDist(vCurr) + if (tempDist < closest) { + closest = tempDist + bestIndex = i + } + } + + return bestIndex + } + + def main(args: Array[String]) { + val data = generateData + var points = new HashSet[Vector] + var kPoints = new HashMap[Int, Vector] + var tempDist = 1.0 + + while (points.size < K) { + points.add(data(rand.nextInt(N))) + } + + val iter = points.iterator + for (i <- 1 to points.size) { + kPoints.put(i, iter.next()) + } + + println("Initial centers: " + kPoints) + + while(tempDist > convergeDist) { + var closest = data.map (p => (closestPoint(p, kPoints), (p, 1))) + + var mappings = closest.groupBy[Int] (x => x._1) + + var pointStats = mappings.map(pair => pair._2.reduceLeft [(Int, (Vector, Int))] {case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1+y2))}) + + var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)} + + tempDist = 0.0 + for (mapping <- newPoints) { + tempDist += kPoints.get(mapping._1).get.squaredDist(mapping._2) + } + + for (newP <- newPoints) { + kPoints.put(newP._1, newP._2) + } + } + + println("Final centers: " + kPoints) + } } diff --git a/examples/src/main/scala/spark/examples/LocalLR.scala b/examples/src/main/scala/spark/examples/LocalLR.scala index cd73f553d6..ab99bf1fbe 100644 --- a/examples/src/main/scala/spark/examples/LocalLR.scala +++ b/examples/src/main/scala/spark/examples/LocalLR.scala @@ -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. + */ + package spark.examples import java.util.Random diff --git a/examples/src/main/scala/spark/examples/LocalPi.scala b/examples/src/main/scala/spark/examples/LocalPi.scala index 9457472f2d..ccd69695df 100644 --- a/examples/src/main/scala/spark/examples/LocalPi.scala +++ b/examples/src/main/scala/spark/examples/LocalPi.scala @@ -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. + */ + package spark.examples import scala.math.random diff --git a/examples/src/main/scala/spark/examples/LogQuery.scala b/examples/src/main/scala/spark/examples/LogQuery.scala index 6497596d35..e815ececf7 100644 --- a/examples/src/main/scala/spark/examples/LogQuery.scala +++ b/examples/src/main/scala/spark/examples/LogQuery.scala @@ -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. + */ + package spark.examples import spark.SparkContext diff --git a/examples/src/main/scala/spark/examples/MultiBroadcastTest.scala b/examples/src/main/scala/spark/examples/MultiBroadcastTest.scala index 92cd81c487..d0b1cf06e5 100644 --- a/examples/src/main/scala/spark/examples/MultiBroadcastTest.scala +++ b/examples/src/main/scala/spark/examples/MultiBroadcastTest.scala @@ -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. + */ + package spark.examples import spark.SparkContext @@ -8,7 +25,7 @@ object MultiBroadcastTest { System.err.println("Usage: BroadcastTest <master> [<slices>] [numElem]") System.exit(1) } - + val sc = new SparkContext(args(0), "Broadcast Test", System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) @@ -19,7 +36,7 @@ object MultiBroadcastTest { for (i <- 0 until arr1.length) { arr1(i) = i } - + var arr2 = new Array[Int](num) for (i <- 0 until arr2.length) { arr2(i) = i @@ -30,7 +47,7 @@ object MultiBroadcastTest { sc.parallelize(1 to 10, slices).foreach { i => println(barr1.value.size + barr2.value.size) } - + System.exit(0) } } diff --git a/examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala b/examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala index 0d17bda004..d197bbaf7c 100644 --- a/examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala +++ b/examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala @@ -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. + */ + package spark.examples import spark.SparkContext @@ -11,7 +28,7 @@ object SimpleSkewedGroupByTest { "[numMappers] [numKVPairs] [valSize] [numReducers] [ratio]") System.exit(1) } - + var numMappers = if (args.length > 1) args(1).toInt else 2 var numKVPairs = if (args.length > 2) args(2).toInt else 1000 var valSize = if (args.length > 3) args(3).toInt else 1000 @@ -20,7 +37,7 @@ object SimpleSkewedGroupByTest { val sc = new SparkContext(args(0), "GroupBy Test", System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - + val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p => val ranGen = new Random var result = new Array[(Int, Array[Byte])](numKVPairs) diff --git a/examples/src/main/scala/spark/examples/SkewedGroupByTest.scala b/examples/src/main/scala/spark/examples/SkewedGroupByTest.scala index 83be3fc27b..4641b82444 100644 --- a/examples/src/main/scala/spark/examples/SkewedGroupByTest.scala +++ b/examples/src/main/scala/spark/examples/SkewedGroupByTest.scala @@ -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. + */ + package spark.examples import spark.SparkContext @@ -10,7 +27,7 @@ object SkewedGroupByTest { System.err.println("Usage: GroupByTest <master> [numMappers] [numKVPairs] [KeySize] [numReducers]") System.exit(1) } - + var numMappers = if (args.length > 1) args(1).toInt else 2 var numKVPairs = if (args.length > 2) args(2).toInt else 1000 var valSize = if (args.length > 3) args(3).toInt else 1000 @@ -18,7 +35,7 @@ object SkewedGroupByTest { val sc = new SparkContext(args(0), "GroupBy Test", System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - + val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p => val ranGen = new Random diff --git a/examples/src/main/scala/spark/examples/SparkALS.scala b/examples/src/main/scala/spark/examples/SparkALS.scala index 8fb3b0fb2a..ba0dfd8f9b 100644 --- a/examples/src/main/scala/spark/examples/SparkALS.scala +++ b/examples/src/main/scala/spark/examples/SparkALS.scala @@ -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. + */ + package spark.examples import scala.math.sqrt diff --git a/examples/src/main/scala/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/spark/examples/SparkHdfsLR.scala index 0f42f405a0..ef6e09a8e8 100644 --- a/examples/src/main/scala/spark/examples/SparkHdfsLR.scala +++ b/examples/src/main/scala/spark/examples/SparkHdfsLR.scala @@ -1,9 +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 spark.examples import java.util.Random import scala.math.exp import spark.util.Vector import spark._ +import spark.deploy.SparkHadoopUtil +import spark.scheduler.InputFormatInfo /** * Logistic regression based classification. @@ -32,9 +51,13 @@ object SparkHdfsLR { System.err.println("Usage: SparkHdfsLR <master> <file> <iters>") System.exit(1) } + val inputPath = args(1) + val conf = SparkHadoopUtil.newConfiguration() val sc = new SparkContext(args(0), "SparkHdfsLR", - System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - val lines = sc.textFile(args(1)) + System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")), Map(), + InputFormatInfo.computePreferredLocations( + Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath)))) + val lines = sc.textFile(inputPath) val points = lines.map(parsePoint _).cache() val ITERATIONS = args(2).toInt diff --git a/examples/src/main/scala/spark/examples/SparkKMeans.scala b/examples/src/main/scala/spark/examples/SparkKMeans.scala index 4161c59fea..38ed3b149a 100644 --- a/examples/src/main/scala/spark/examples/SparkKMeans.scala +++ b/examples/src/main/scala/spark/examples/SparkKMeans.scala @@ -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. + */ + package spark.examples import java.util.Random diff --git a/examples/src/main/scala/spark/examples/SparkLR.scala b/examples/src/main/scala/spark/examples/SparkLR.scala index 2f41aeb376..52a0d69744 100644 --- a/examples/src/main/scala/spark/examples/SparkLR.scala +++ b/examples/src/main/scala/spark/examples/SparkLR.scala @@ -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. + */ + package spark.examples import java.util.Random diff --git a/examples/src/main/scala/spark/examples/SparkPi.scala b/examples/src/main/scala/spark/examples/SparkPi.scala index f598d2ff9c..00560ac9d1 100644 --- a/examples/src/main/scala/spark/examples/SparkPi.scala +++ b/examples/src/main/scala/spark/examples/SparkPi.scala @@ -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. + */ + package spark.examples import scala.math.random diff --git a/examples/src/main/scala/spark/examples/SparkTC.scala b/examples/src/main/scala/spark/examples/SparkTC.scala index 911ae8f168..bf988a953b 100644 --- a/examples/src/main/scala/spark/examples/SparkTC.scala +++ b/examples/src/main/scala/spark/examples/SparkTC.scala @@ -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. + */ + package spark.examples import spark._ diff --git a/examples/src/main/scala/spark/streaming/examples/ActorWordCount.scala b/examples/src/main/scala/spark/streaming/examples/ActorWordCount.scala index 3b847fe603..f97174aeae 100644 --- a/examples/src/main/scala/spark/streaming/examples/ActorWordCount.scala +++ b/examples/src/main/scala/spark/streaming/examples/ActorWordCount.scala @@ -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. + */ + package spark.streaming.examples import scala.collection.mutable.LinkedList diff --git a/examples/src/main/scala/spark/streaming/examples/FlumeEventCount.scala b/examples/src/main/scala/spark/streaming/examples/FlumeEventCount.scala index 39c76fd98a..3ab4fc2c37 100644 --- a/examples/src/main/scala/spark/streaming/examples/FlumeEventCount.scala +++ b/examples/src/main/scala/spark/streaming/examples/FlumeEventCount.scala @@ -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. + */ + package spark.streaming.examples import spark.util.IntParam diff --git a/examples/src/main/scala/spark/streaming/examples/HdfsWordCount.scala b/examples/src/main/scala/spark/streaming/examples/HdfsWordCount.scala index 9389f8a38d..f5baec242d 100644 --- a/examples/src/main/scala/spark/streaming/examples/HdfsWordCount.scala +++ b/examples/src/main/scala/spark/streaming/examples/HdfsWordCount.scala @@ -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. + */ + package spark.streaming.examples import spark.streaming.{Seconds, StreamingContext} diff --git a/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala index c3a9e491ba..4929703ba2 100644 --- a/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala +++ b/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala @@ -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. + */ + package spark.streaming.examples import java.util.Properties @@ -37,7 +54,7 @@ object KafkaWordCount { ssc.checkpoint("checkpoint") val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap - val lines = ssc.kafkaStream[String](zkQuorum, group, topicpMap) + val lines = ssc.kafkaStream(zkQuorum, group, topicpMap) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1l)).reduceByKeyAndWindow(add _, subtract _, Minutes(10), Seconds(2), 2) wordCounts.print() diff --git a/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala b/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala index 704540c2bf..150fb5eb9c 100644 --- a/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala +++ b/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala @@ -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. + */ + package spark.streaming.examples import spark.streaming.{Seconds, StreamingContext} diff --git a/examples/src/main/scala/spark/streaming/examples/QueueStream.scala b/examples/src/main/scala/spark/streaming/examples/QueueStream.scala index f450e21040..da36c8c23c 100644 --- a/examples/src/main/scala/spark/streaming/examples/QueueStream.scala +++ b/examples/src/main/scala/spark/streaming/examples/QueueStream.scala @@ -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. + */ + package spark.streaming.examples import spark.RDD diff --git a/examples/src/main/scala/spark/streaming/examples/RawNetworkGrep.scala b/examples/src/main/scala/spark/streaming/examples/RawNetworkGrep.scala index 175281e095..7fb680bcc3 100644 --- a/examples/src/main/scala/spark/streaming/examples/RawNetworkGrep.scala +++ b/examples/src/main/scala/spark/streaming/examples/RawNetworkGrep.scala @@ -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. + */ + package spark.streaming.examples import spark.util.IntParam diff --git a/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala b/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala new file mode 100644 index 0000000000..33ab324732 --- /dev/null +++ b/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala @@ -0,0 +1,67 @@ +/* + * 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 spark.streaming.examples + +import spark.streaming._ +import spark.streaming.StreamingContext._ + +/** + * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every second. + * Usage: StatefulNetworkWordCount <master> <hostname> <port> + * <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1. + * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data. + * + * To run this on your local machine, you need to first run a Netcat server + * `$ nc -lk 9999` + * and then run the example + * `$ ./run spark.streaming.examples.StatefulNetworkWordCount local[2] localhost 9999` + */ +object StatefulNetworkWordCount { + def main(args: Array[String]) { + if (args.length < 3) { + System.err.println("Usage: StatefulNetworkWordCount <master> <hostname> <port>\n" + + "In local mode, <master> should be 'local[n]' with n > 1") + System.exit(1) + } + + val updateFunc = (values: Seq[Int], state: Option[Int]) => { + val currentCount = values.foldLeft(0)(_ + _) + + val previousCount = state.getOrElse(0) + + Some(currentCount + previousCount) + } + + // Create the context with a 1 second batch size + val ssc = new StreamingContext(args(0), "NetworkWordCumulativeCountUpdateStateByKey", Seconds(1), + System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) + ssc.checkpoint(".") + + // Create a NetworkInputDStream on target ip:port and count the + // words in input stream of \n delimited test (eg. generated by 'nc') + val lines = ssc.socketTextStream(args(1), args(2).toInt) + val words = lines.flatMap(_.split(" ")) + val wordDstream = words.map(x => (x, 1)) + + // Update the cumulative count using updateStateByKey + // This will give a Dstream made of state (which is the cumulative count of the words) + val stateDstream = wordDstream.updateStateByKey[Int](updateFunc) + stateDstream.print() + ssc.start() + } +} diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala index 483aae452b..8770abd57e 100644 --- a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala +++ b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala @@ -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. + */ + package spark.streaming.examples import spark.streaming.{Seconds, StreamingContext} @@ -26,8 +43,8 @@ import spark.SparkContext._ */ object TwitterAlgebirdCMS { def main(args: Array[String]) { - if (args.length < 3) { - System.err.println("Usage: TwitterAlgebirdCMS <master> <twitter_username> <twitter_password>" + + if (args.length < 1) { + System.err.println("Usage: TwitterAlgebirdCMS <master>" + " [filter1] [filter2] ... [filter n]") System.exit(1) } @@ -40,16 +57,15 @@ object TwitterAlgebirdCMS { // K highest frequency elements to take val TOPK = 10 - val Array(master, username, password) = args.slice(0, 3) - val filters = args.slice(3, args.length) + val (master, filters) = (args.head, args.tail) val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10), System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - val stream = ssc.twitterStream(username, password, filters, StorageLevel.MEMORY_ONLY_SER) + val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER) val users = stream.map(status => status.getUser.getId) - val cms = new CountMinSketchMonoid(DELTA, EPS, SEED, PERC) + val cms = new CountMinSketchMonoid(EPS, DELTA, SEED, PERC) var globalCMS = cms.zero val mm = new MapMonoid[Long, Int]() var globalExact = Map[Long, Int]() diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala index f3288bfb85..cba5c986be 100644 --- a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala +++ b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala @@ -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. + */ + package spark.streaming.examples import spark.streaming.{Seconds, StreamingContext} @@ -21,20 +38,19 @@ import spark.streaming.dstream.TwitterInputDStream */ object TwitterAlgebirdHLL { def main(args: Array[String]) { - if (args.length < 3) { - System.err.println("Usage: TwitterAlgebirdHLL <master> <twitter_username> <twitter_password>" + + if (args.length < 1) { + System.err.println("Usage: TwitterAlgebirdHLL <master>" + " [filter1] [filter2] ... [filter n]") System.exit(1) } /** Bit size parameter for HyperLogLog, trades off accuracy vs size */ val BIT_SIZE = 12 - val Array(master, username, password) = args.slice(0, 3) - val filters = args.slice(3, args.length) + val (master, filters) = (args.head, args.tail) val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5), System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - val stream = ssc.twitterStream(username, password, filters, StorageLevel.MEMORY_ONLY_SER) + val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER) val users = stream.map(status => status.getUser.getId) diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala b/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala index 9d4494c6f2..682b99f75e 100644 --- a/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala +++ b/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala @@ -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. + */ + package spark.streaming.examples import spark.streaming.{Seconds, StreamingContext} @@ -12,18 +29,17 @@ import spark.SparkContext._ */ object TwitterPopularTags { def main(args: Array[String]) { - if (args.length < 3) { - System.err.println("Usage: TwitterPopularTags <master> <twitter_username> <twitter_password>" + + if (args.length < 1) { + System.err.println("Usage: TwitterPopularTags <master>" + " [filter1] [filter2] ... [filter n]") System.exit(1) } - val Array(master, username, password) = args.slice(0, 3) - val filters = args.slice(3, args.length) + val (master, filters) = (args.head, args.tail) val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2), System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - val stream = ssc.twitterStream(username, password, filters) + val stream = ssc.twitterStream(None, filters) val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#"))) diff --git a/examples/src/main/scala/spark/streaming/examples/ZeroMQWordCount.scala b/examples/src/main/scala/spark/streaming/examples/ZeroMQWordCount.scala index 74d0d338b7..e264fae609 100644 --- a/examples/src/main/scala/spark/streaming/examples/ZeroMQWordCount.scala +++ b/examples/src/main/scala/spark/streaming/examples/ZeroMQWordCount.scala @@ -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. + */ + package spark.streaming.examples import akka.actor.ActorSystem diff --git a/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewGenerator.scala b/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewGenerator.scala index 4c6e08bc74..375d5c9d22 100644 --- a/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewGenerator.scala +++ b/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewGenerator.scala @@ -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. + */ + package spark.streaming.examples.clickstream import java.net.{InetAddress,ServerSocket,Socket,SocketException} diff --git a/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewStream.scala b/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewStream.scala index e226a4a73a..a24342bebf 100644 --- a/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewStream.scala +++ b/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewStream.scala @@ -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. + */ + package spark.streaming.examples.clickstream import spark.streaming.{Seconds, StreamingContext} diff --git a/make-distribution.sh b/make-distribution.sh new file mode 100755 index 0000000000..0a8941c1f8 --- /dev/null +++ b/make-distribution.sh @@ -0,0 +1,77 @@ +#!/usr/bin/env 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. +# + +# +# Script to create a binary distribution for easy deploys of Spark. +# The distribution directory defaults to dist/ but can be overridden below. +# The distribution contains fat (assembly) jars that include the Scala library, +# so it is completely self contained. +# It does not contain source or *.class files. +# +# Arguments +# (none): Creates dist/ directory +# tgz: Additionally creates spark-$VERSION-bin.tar.gz +# +# Recommended deploy/testing procedure (standalone mode): +# 1) Rsync / deploy the dist/ dir to one host +# 2) cd to deploy dir; ./bin/start-master.sh +# 3) Verify master is up by visiting web page, ie http://master-ip:8080. Note the spark:// URL. +# 4) ./bin/start-slave.sh 1 <<spark:// URL>> +# 5) MASTER="spark://my-master-ip:7077" ./spark-shell +# + +# Figure out where the Spark framework is installed +FWDIR="$(cd `dirname $0`; pwd)" +DISTDIR="$FWDIR/dist" + +# Get version from SBT +export TERM=dumb # Prevents color codes in SBT output +VERSION=$($FWDIR/sbt/sbt "show version" | tail -1 | cut -f 2 | sed 's/^\([a-zA-Z0-9.-]*\).*/\1/') + +if [ "$1" == "tgz" ]; then + echo "Making spark-$VERSION-bin.tar.gz" +else + echo "Making distribution for Spark $VERSION in $DISTDIR..." +fi + + +# Build fat JAR +$FWDIR/sbt/sbt "repl/assembly" + +# Make directories +rm -rf "$DISTDIR" +mkdir -p "$DISTDIR/jars" +echo "$VERSION" >$DISTDIR/RELEASE + +# Copy jars +cp $FWDIR/repl/target/*.jar "$DISTDIR/jars/" + +# Copy other things +cp -r "$FWDIR/bin" "$DISTDIR" +cp -r "$FWDIR/conf" "$DISTDIR" +cp "$FWDIR/run" "$FWDIR/spark-shell" "$DISTDIR" +cp "$FWDIR/spark-executor" "$DISTDIR" + + +if [ "$1" == "tgz" ]; then + TARDIR="$FWDIR/spark-$VERSION" + cp -r $DISTDIR $TARDIR + tar -zcf spark-$VERSION-bin.tar.gz -C $FWDIR spark-$VERSION + rm -rf $TARDIR +fi diff --git a/mllib/data/als/test.data b/mllib/data/als/test.data new file mode 100644 index 0000000000..e476cc23e0 --- /dev/null +++ b/mllib/data/als/test.data @@ -0,0 +1,16 @@ +1,1,5.0 +1,2,1.0 +1,3,5.0 +1,4,1.0 +2,1,5.0 +2,2,1.0 +2,3,5.0 +2,4,1.0 +3,1,1.0 +3,2,5.0 +3,3,1.0 +3,4,5.0 +4,1,1.0 +4,2,5.0 +4,3,1.0 +4,4,5.0 diff --git a/mllib/data/lr-data/random.data b/mllib/data/lr-data/random.data new file mode 100755 index 0000000000..29bcb8acba --- /dev/null +++ b/mllib/data/lr-data/random.data @@ -0,0 +1,1000 @@ +0.0,-0.19138793197590276 0.7834675900121327 +1.0,3.712420417753061 3.55967640829891 +0.0,-0.3173743619974614 0.9034702789806682 +1.0,4.759494447180777 3.407011867344781 +0.0,-0.7078607074437426 -0.7866705652344417 +1.0,2.6708084832010215 2.5322909406378016 +0.0,-0.07553885038446313 -0.1297104483563081 +1.0,2.759487072285262 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b/mllib/pom.xml @@ -0,0 +1,165 @@ +<?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.spark-project</groupId> + <artifactId>spark-parent</artifactId> + <version>0.8.0-SNAPSHOT</version> + <relativePath>../pom.xml</relativePath> + </parent> + + <groupId>org.spark-project</groupId> + <artifactId>spark-mllib</artifactId> + <packaging>jar</packaging> + <name>Spark Project ML Library</name> + <url>http://spark-project.org/</url> + + <dependencies> + <dependency> + <groupId>org.eclipse.jetty</groupId> + <artifactId>jetty-server</artifactId> + </dependency> + <dependency> + <groupId>org.jblas</groupId> + <artifactId>jblas</artifactId> + <version>1.2.3</version> + </dependency> + + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalacheck</groupId> + <artifactId>scalacheck_${scala.version}</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + <build> + <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + </plugin> + </plugins> + </build> + + <profiles> + <profile> + <id>hadoop1</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop1</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-core</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop1</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> + <profile> + <id>hadoop2</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-core</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> + <profile> + <id>hadoop2-yarn</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2-yarn</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> + </profiles> +</project> diff --git a/mllib/src/main/scala/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/spark/mllib/classification/ClassificationModel.scala new file mode 100644 index 0000000000..d6154b66ae --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/classification/ClassificationModel.scala @@ -0,0 +1,21 @@ +package spark.mllib.classification + +import spark.RDD + +trait ClassificationModel extends Serializable { + /** + * Predict values for the given data set using the model trained. + * + * @param testData RDD representing data points to be predicted + * @return RDD[Int] where each entry contains the corresponding prediction + */ + def predict(testData: RDD[Array[Double]]): RDD[Int] + + /** + * Predict values for a single data point using the model trained. + * + * @param testData array representing a single data point + * @return Int prediction from the trained model + */ + def predict(testData: Array[Double]): Int +} diff --git a/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala new file mode 100644 index 0000000000..203aa8fdd4 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala @@ -0,0 +1,233 @@ +/* + * 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 spark.mllib.classification + +import spark.{Logging, RDD, SparkContext} +import spark.mllib.optimization._ +import spark.mllib.util.MLUtils + +import scala.math.round + +import org.jblas.DoubleMatrix + +/** + * Logistic Regression using Stochastic Gradient Descent. + * Based on Matlab code written by John Duchi. + */ +class LogisticRegressionModel( + val weights: Array[Double], + val intercept: Double, + val stochasticLosses: Array[Double]) extends ClassificationModel { + + // Create a column vector that can be used for predictions + private val weightsMatrix = new DoubleMatrix(weights.length, 1, weights:_*) + + override def predict(testData: spark.RDD[Array[Double]]): RDD[Int] = { + // A small optimization to avoid serializing the entire model. Only the weightsMatrix + // and intercept is needed. + val localWeights = weightsMatrix + val localIntercept = intercept + testData.map { x => + val margin = new DoubleMatrix(1, x.length, x:_*).mmul(localWeights).get(0) + localIntercept + round(1.0/ (1.0 + math.exp(margin * -1))).toInt + } + } + + override def predict(testData: Array[Double]): Int = { + val dataMat = new DoubleMatrix(1, testData.length, testData:_*) + val margin = dataMat.mmul(weightsMatrix).get(0) + this.intercept + round(1.0/ (1.0 + math.exp(margin * -1))).toInt + } +} + +class LogisticRegressionLocalRandomSGD private (var stepSize: Double, var miniBatchFraction: Double, + var numIters: Int) + extends Logging { + + /** + * Construct a LogisticRegression object with default parameters + */ + def this() = this(1.0, 1.0, 100) + + /** + * Set the step size per-iteration of SGD. Default 1.0. + */ + def setStepSize(step: Double) = { + this.stepSize = step + this + } + + /** + * Set fraction of data to be used for each SGD iteration. Default 1.0. + */ + def setMiniBatchFraction(fraction: Double) = { + this.miniBatchFraction = fraction + this + } + + /** + * Set the number of iterations for SGD. Default 100. + */ + def setNumIterations(iters: Int) = { + this.numIters = iters + this + } + + def train(input: RDD[(Int, Array[Double])]): LogisticRegressionModel = { + val nfeatures: Int = input.take(1)(0)._2.length + val initialWeights = Array.fill(nfeatures)(1.0) + train(input, initialWeights) + } + + def train( + input: RDD[(Int, Array[Double])], + initialWeights: Array[Double]): LogisticRegressionModel = { + + // Add a extra variable consisting of all 1.0's for the intercept. + val data = input.map { case (y, features) => + (y.toDouble, Array(1.0, features:_*)) + } + + val initalWeightsWithIntercept = Array(1.0, initialWeights:_*) + + val (weights, stochasticLosses) = GradientDescent.runMiniBatchSGD( + data, + new LogisticGradient(), + new SimpleUpdater(), + stepSize, + numIters, + 0.0, + initalWeightsWithIntercept, + miniBatchFraction) + + val intercept = weights(0) + val weightsScaled = weights.tail + + val model = new LogisticRegressionModel(weightsScaled, intercept, stochasticLosses) + + logInfo("Final model weights " + model.weights.mkString(",")) + logInfo("Final model intercept " + model.intercept) + logInfo("Last 10 stochastic losses " + model.stochasticLosses.takeRight(10).mkString(", ")) + model + } +} + +/** + * Top-level methods for calling Logistic Regression. + * NOTE(shivaram): We use multiple train methods instead of default arguments to support + * Java programs. + */ +object LogisticRegressionLocalRandomSGD { + + /** + * Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed + * number of iterations of gradient descent using the specified step size. Each iteration uses + * `miniBatchFraction` fraction of the data to calculate the gradient. The weights used in + * gradient descent are initialized using the initial weights provided. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @param stepSize Step size to be used for each iteration of gradient descent. + * @param miniBatchFraction Fraction of data to be used per iteration. + * @param initialWeights Initial set of weights to be used. Array should be equal in size to + * the number of features in the data. + */ + def train( + input: RDD[(Int, Array[Double])], + numIterations: Int, + stepSize: Double, + miniBatchFraction: Double, + initialWeights: Array[Double]) + : LogisticRegressionModel = + { + new LogisticRegressionLocalRandomSGD(stepSize, miniBatchFraction, numIterations).train( + input, initialWeights) + } + + /** + * Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed + * number of iterations of gradient descent using the specified step size. Each iteration uses + * `miniBatchFraction` fraction of the data to calculate the gradient. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @param stepSize Step size to be used for each iteration of gradient descent. + + * @param miniBatchFraction Fraction of data to be used per iteration. + */ + def train( + input: RDD[(Int, Array[Double])], + numIterations: Int, + stepSize: Double, + miniBatchFraction: Double) + : LogisticRegressionModel = + { + new LogisticRegressionLocalRandomSGD(stepSize, miniBatchFraction, numIterations).train(input) + } + + /** + * Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed + * number of iterations of gradient descent using the specified step size. We use the entire data + * set to update the gradient in each iteration. + * + * @param input RDD of (label, array of features) pairs. + * @param stepSize Step size to be used for each iteration of Gradient Descent. + + * @param numIterations Number of iterations of gradient descent to run. + * @return a LogisticRegressionModel which has the weights and offset from training. + */ + def train( + input: RDD[(Int, Array[Double])], + numIterations: Int, + stepSize: Double) + : LogisticRegressionModel = + { + train(input, numIterations, stepSize, 1.0) + } + + /** + * Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed + * number of iterations of gradient descent using a step size of 1.0. We use the entire data set + * to update the gradient in each iteration. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @return a LogisticRegressionModel which has the weights and offset from training. + */ + def train( + input: RDD[(Int, Array[Double])], + numIterations: Int) + : LogisticRegressionModel = + { + train(input, numIterations, 1.0, 1.0) + } + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: LogisticRegression <master> <input_dir> <step_size> " + + "<regularization_parameter> <niters>") + System.exit(1) + } + val sc = new SparkContext(args(0), "LogisticRegression") + val data = MLUtils.loadLabeledData(sc, args(1)).map(yx => (yx._1.toInt, yx._2)) + val model = LogisticRegressionLocalRandomSGD.train( + data, args(4).toInt, args(2).toDouble, args(3).toDouble) + + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/spark/mllib/classification/SVM.scala new file mode 100644 index 0000000000..3a6a12814a --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/classification/SVM.scala @@ -0,0 +1,241 @@ +/* + * 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 spark.mllib.classification + +import scala.math.signum +import spark.{Logging, RDD, SparkContext} +import spark.mllib.optimization._ +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix + +/** + * SVM using Stochastic Gradient Descent. + */ +class SVMModel( + val weights: Array[Double], + val intercept: Double, + val stochasticLosses: Array[Double]) extends ClassificationModel { + + // Create a column vector that can be used for predictions + private val weightsMatrix = new DoubleMatrix(weights.length, 1, weights:_*) + + override def predict(testData: spark.RDD[Array[Double]]): RDD[Int] = { + // A small optimization to avoid serializing the entire model. Only the weightsMatrix + // and intercept is needed. + val localWeights = weightsMatrix + val localIntercept = intercept + testData.map { x => + signum(new DoubleMatrix(1, x.length, x:_*).dot(localWeights) + localIntercept).toInt + } + } + + override def predict(testData: Array[Double]): Int = { + val dataMat = new DoubleMatrix(1, testData.length, testData:_*) + signum(dataMat.dot(weightsMatrix) + this.intercept).toInt + } +} + + + +class SVMLocalRandomSGD private (var stepSize: Double, var regParam: Double, + var miniBatchFraction: Double, var numIters: Int) + extends Logging { + + /** + * Construct a SVM object with default parameters + */ + def this() = this(1.0, 1.0, 1.0, 100) + + /** + * Set the step size per-iteration of SGD. Default 1.0. + */ + def setStepSize(step: Double) = { + this.stepSize = step + this + } + + /** + * Set the regularization parameter. Default 1.0. + */ + def setRegParam(param: Double) = { + this.regParam = param + this + } + + /** + * Set fraction of data to be used for each SGD iteration. Default 1.0. + */ + def setMiniBatchFraction(fraction: Double) = { + this.miniBatchFraction = fraction + this + } + + /** + * Set the number of iterations for SGD. Default 100. + */ + def setNumIterations(iters: Int) = { + this.numIters = iters + this + } + + def train(input: RDD[(Int, Array[Double])]): SVMModel = { + val nfeatures: Int = input.take(1)(0)._2.length + val initialWeights = Array.fill(nfeatures)(1.0) + train(input, initialWeights) + } + + def train( + input: RDD[(Int, Array[Double])], + initialWeights: Array[Double]): SVMModel = { + + // Add a extra variable consisting of all 1.0's for the intercept. + val data = input.map { case (y, features) => + (y.toDouble, Array(1.0, features:_*)) + } + + val initalWeightsWithIntercept = Array(1.0, initialWeights:_*) + + val (weights, stochasticLosses) = GradientDescent.runMiniBatchSGD( + data, + new HingeGradient(), + new SquaredL2Updater(), + stepSize, + numIters, + regParam, + initalWeightsWithIntercept, + miniBatchFraction) + + val intercept = weights(0) + val weightsScaled = weights.tail + + val model = new SVMModel(weightsScaled, intercept, stochasticLosses) + + logInfo("Final model weights " + model.weights.mkString(",")) + logInfo("Final model intercept " + model.intercept) + logInfo("Last 10 stochasticLosses " + model.stochasticLosses.takeRight(10).mkString(", ")) + model + } +} + +/** + * Top-level methods for calling SVM. + + + */ +object SVMLocalRandomSGD { + + /** + * Train a SVM model given an RDD of (label, features) pairs. We run a fixed number + * of iterations of gradient descent using the specified step size. Each iteration uses + * `miniBatchFraction` fraction of the data to calculate the gradient. The weights used in + * gradient descent are initialized using the initial weights provided. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @param stepSize Step size to be used for each iteration of gradient descent. + * @param regParam Regularization parameter. + * @param miniBatchFraction Fraction of data to be used per iteration. + * @param initialWeights Initial set of weights to be used. Array should be equal in size to + * the number of features in the data. + */ + def train( + input: RDD[(Int, Array[Double])], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double, + initialWeights: Array[Double]) + : SVMModel = + { + new SVMLocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train( + input, initialWeights) + } + + /** + * Train a SVM model given an RDD of (label, features) pairs. We run a fixed number + * of iterations of gradient descent using the specified step size. Each iteration uses + * `miniBatchFraction` fraction of the data to calculate the gradient. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @param stepSize Step size to be used for each iteration of gradient descent. + * @param regParam Regularization parameter. + * @param miniBatchFraction Fraction of data to be used per iteration. + */ + def train( + input: RDD[(Int, Array[Double])], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double) + : SVMModel = + { + new SVMLocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input) + } + + /** + * Train a SVM model given an RDD of (label, features) pairs. We run a fixed number + * of iterations of gradient descent using the specified step size. We use the entire data set to + * update the gradient in each iteration. + * + * @param input RDD of (label, array of features) pairs. + * @param stepSize Step size to be used for each iteration of Gradient Descent. + * @param regParam Regularization parameter. + * @param numIterations Number of iterations of gradient descent to run. + * @return a SVMModel which has the weights and offset from training. + */ + def train( + input: RDD[(Int, Array[Double])], + numIterations: Int, + stepSize: Double, + regParam: Double) + : SVMModel = + { + train(input, numIterations, stepSize, regParam, 1.0) + } + + /** + * Train a SVM model given an RDD of (label, features) pairs. We run a fixed number + * of iterations of gradient descent using a step size of 1.0. We use the entire data set to + * update the gradient in each iteration. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @return a SVMModel which has the weights and offset from training. + */ + def train( + input: RDD[(Int, Array[Double])], + numIterations: Int) + : SVMModel = + { + train(input, numIterations, 1.0, 1.0, 1.0) + } + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: SVM <master> <input_dir> <step_size> <regularization_parameter> <niters>") + System.exit(1) + } + val sc = new SparkContext(args(0), "SVM") + val data = MLUtils.loadLabeledData(sc, args(1)).map(yx => (yx._1.toInt, yx._2)) + val model = SVMLocalRandomSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble) + + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala new file mode 100644 index 0000000000..b402c71ed2 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala @@ -0,0 +1,335 @@ +/* + * 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 spark.mllib.clustering + +import scala.collection.mutable.ArrayBuffer +import scala.util.Random + +import spark.{SparkContext, RDD} +import spark.SparkContext._ +import spark.Logging +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix + + +/** + * K-means clustering with support for multiple parallel runs and a k-means++ like initialization + * mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested, + * they are executed together with joint passes over the data for efficiency. + * + * This is an iterative algorithm that will make multiple passes over the data, so any RDDs given + * to it should be cached by the user. + */ +class KMeans private ( + var k: Int, + var maxIterations: Int, + var runs: Int, + var initializationMode: String, + var initializationSteps: Int, + var epsilon: Double) + extends Serializable with Logging +{ + private type ClusterCenters = Array[Array[Double]] + + def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4) + + /** Set the number of clusters to create (k). Default: 2. */ + def setK(k: Int): KMeans = { + this.k = k + this + } + + /** Set maximum number of iterations to run. Default: 20. */ + def setMaxIterations(maxIterations: Int): KMeans = { + this.maxIterations = maxIterations + this + } + + /** + * Set the initialization algorithm. This can be either "random" to choose random points as + * initial cluster centers, or "k-means||" to use a parallel variant of k-means++ + * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||. + */ + def setInitializationMode(initializationMode: String): KMeans = { + if (initializationMode != KMeans.RANDOM && initializationMode != KMeans.K_MEANS_PARALLEL) { + throw new IllegalArgumentException("Invalid initialization mode: " + initializationMode) + } + this.initializationMode = initializationMode + this + } + + /** + * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm + * this many times with random starting conditions (configured by the initialization mode), then + * return the best clustering found over any run. Default: 1. + */ + def setRuns(runs: Int): KMeans = { + if (runs <= 0) { + throw new IllegalArgumentException("Number of runs must be positive") + } + this.runs = runs + this + } + + /** + * Set the number of steps for the k-means|| initialization mode. This is an advanced + * setting -- the default of 5 is almost always enough. Default: 5. + */ + def setInitializationSteps(initializationSteps: Int): KMeans = { + if (initializationSteps <= 0) { + throw new IllegalArgumentException("Number of initialization steps must be positive") + } + this.initializationSteps = initializationSteps + this + } + + /** + * Set the distance threshold within which we've consider centers to have converged. + * If all centers move less than this Euclidean distance, we stop iterating one run. + */ + def setEpsilon(epsilon: Double): KMeans = { + this.epsilon = epsilon + this + } + + /** + * Train a K-means model on the given set of points; `data` should be cached for high + * performance, because this is an iterative algorithm. + */ + def train(data: RDD[Array[Double]]): KMeansModel = { + // TODO: check whether data is persistent; this needs RDD.storageLevel to be publicly readable + + val sc = data.sparkContext + + val centers = if (initializationMode == KMeans.RANDOM) { + initRandom(data) + } else { + initKMeansParallel(data) + } + + val active = Array.fill(runs)(true) + val costs = Array.fill(runs)(0.0) + + var activeRuns = new ArrayBuffer[Int] ++ (0 until runs) + var iteration = 0 + + // Execute iterations of Lloyd's algorithm until all runs have converged + while (iteration < maxIterations && !activeRuns.isEmpty) { + type WeightedPoint = (DoubleMatrix, Long) + def mergeContribs(p1: WeightedPoint, p2: WeightedPoint): WeightedPoint = { + (p1._1.addi(p2._1), p1._2 + p2._2) + } + + val activeCenters = activeRuns.map(r => centers(r)).toArray + val costAccums = activeRuns.map(_ => sc.accumulator(0.0)) + + // Find the sum and count of points mapping to each center + val totalContribs = data.mapPartitions { points => + val runs = activeCenters.length + val k = activeCenters(0).length + val dims = activeCenters(0)(0).length + + val sums = Array.fill(runs, k)(new DoubleMatrix(dims)) + val counts = Array.fill(runs, k)(0L) + + for (point <- points; (centers, runIndex) <- activeCenters.zipWithIndex) { + val (bestCenter, cost) = KMeans.findClosest(centers, point) + costAccums(runIndex) += cost + sums(runIndex)(bestCenter).addi(new DoubleMatrix(point)) + counts(runIndex)(bestCenter) += 1 + } + + val contribs = for (i <- 0 until runs; j <- 0 until k) yield { + ((i, j), (sums(i)(j), counts(i)(j))) + } + contribs.iterator + }.reduceByKey(mergeContribs).collectAsMap() + + // Update the cluster centers and costs for each active run + for ((run, i) <- activeRuns.zipWithIndex) { + var changed = false + for (j <- 0 until k) { + val (sum, count) = totalContribs((i, j)) + if (count != 0) { + val newCenter = sum.divi(count).data + if (MLUtils.squaredDistance(newCenter, centers(run)(j)) > epsilon * epsilon) { + changed = true + } + centers(run)(j) = newCenter + } + } + if (!changed) { + active(run) = false + logInfo("Run " + run + " finished in " + (iteration + 1) + " iterations") + } + costs(run) = costAccums(i).value + } + + activeRuns = activeRuns.filter(active(_)) + iteration += 1 + } + + val bestRun = costs.zipWithIndex.min._2 + new KMeansModel(centers(bestRun)) + } + + /** + * Initialize `runs` sets of cluster centers at random. + */ + 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()) + Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k)) + } + + /** + * Initialize `runs` sets of cluster centers using the k-means|| algorithm by Bahmani et al. + * (Bahmani et al., Scalable K-Means++, VLDB 2012). This is a variant of k-means++ that tries + * to find with dissimilar cluster centers by starting with a random center and then doing + * passes where more centers are chosen with probability proportional to their squared distance + * to the current cluster set. It results in a provable approximation to an optimal clustering. + * + * The original paper can be found at http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf. + */ + private def initKMeansParallel(data: RDD[Array[Double]]): Array[ClusterCenters] = { + // Initialize each run's center to a random point + val seed = new Random().nextInt() + val sample = data.takeSample(true, runs, seed) + val centers = Array.tabulate(runs)(r => ArrayBuffer(sample(r))) + + // On each step, sample 2 * k points on average for each run with probability proportional + // to their squared distance from that run's current centers + for (step <- 0 until initializationSteps) { + val centerArrays = centers.map(_.toArray) + val sumCosts = data.flatMap { point => + 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) + for { + p <- points + r <- 0 until runs + if rand.nextDouble() < KMeans.pointCost(centerArrays(r), p) * 2 * k / sumCosts(r) + } yield (r, p) + }.collect() + for ((r, p) <- chosen) { + centers(r) += p + } + } + + // Finally, we might have a set of more than k candidate centers for each run; weigh each + // candidate by the number of points in the dataset mapping to it and run a local k-means++ + // on the weighted centers to pick just k of them + val centerArrays = centers.map(_.toArray) + val weightMap = data.flatMap { p => + for (r <- 0 until runs) yield ((r, KMeans.findClosest(centerArrays(r), p)._1), 1.0) + }.reduceByKey(_ + _).collectAsMap() + val finalCenters = (0 until runs).map { r => + val myCenters = centers(r).toArray + val myWeights = (0 until myCenters.length).map(i => weightMap.getOrElse((r, i), 0.0)).toArray + LocalKMeans.kMeansPlusPlus(r, myCenters, myWeights, k, 30) + } + + finalCenters.toArray + } +} + + +/** + * Top-level methods for calling K-means clustering. + */ +object KMeans { + // Initialization mode names + val RANDOM = "random" + val K_MEANS_PARALLEL = "k-means||" + + def train( + data: RDD[Array[Double]], + k: Int, + maxIterations: Int, + runs: Int, + initializationMode: String) + : KMeansModel = + { + new KMeans().setK(k) + .setMaxIterations(maxIterations) + .setRuns(runs) + .setInitializationMode(initializationMode) + .train(data) + } + + def train(data: RDD[Array[Double]], k: Int, maxIterations: Int, runs: Int): KMeansModel = { + train(data, k, maxIterations, runs, K_MEANS_PARALLEL) + } + + def train(data: RDD[Array[Double]], k: Int, maxIterations: Int): KMeansModel = { + train(data, k, maxIterations, 1, K_MEANS_PARALLEL) + } + + /** + * Return the index of the closest point in `centers` to `point`, as well as its distance. + */ + private[mllib] def findClosest(centers: Array[Array[Double]], point: Array[Double]) + : (Int, Double) = + { + var bestDistance = Double.PositiveInfinity + var bestIndex = 0 + for (i <- 0 until centers.length) { + val distance = MLUtils.squaredDistance(point, centers(i)) + if (distance < bestDistance) { + bestDistance = distance + bestIndex = i + } + } + (bestIndex, bestDistance) + } + + /** + * Return the K-means cost of a given point against the given cluster centers. + */ + private[mllib] def pointCost(centers: Array[Array[Double]], point: Array[Double]): Double = { + var bestDistance = Double.PositiveInfinity + for (i <- 0 until centers.length) { + val distance = MLUtils.squaredDistance(point, centers(i)) + if (distance < bestDistance) { + bestDistance = distance + } + } + bestDistance + } + + def main(args: Array[String]) { + if (args.length < 4) { + println("Usage: KMeans <master> <input_file> <k> <max_iterations> [<runs>]") + System.exit(1) + } + val (master, inputFile, k, iters) = (args(0), args(1), args(2).toInt, args(3).toInt) + val runs = if (args.length >= 5) args(4).toInt else 1 + val sc = new SparkContext(master, "KMeans") + val data = sc.textFile(inputFile).map(line => line.split(' ').map(_.toDouble)).cache() + val model = KMeans.train(data, k, iters, runs) + val cost = model.computeCost(data) + println("Cluster centers:") + for (c <- model.clusterCenters) { + println(" " + c.mkString(" ")) + } + println("Cost: " + cost) + System.exit(0) + } +} diff --git a/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala new file mode 100644 index 0000000000..b8f80e80cd --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala @@ -0,0 +1,44 @@ +/* + * 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 spark.mllib.clustering + +import spark.RDD +import spark.SparkContext._ +import spark.mllib.util.MLUtils + + +/** + * A clustering model for K-means. Each point belongs to the cluster with the closest center. + */ +class KMeansModel(val clusterCenters: Array[Array[Double]]) extends Serializable { + /** Total number of clusters. */ + def k: Int = clusterCenters.length + + /** Return the cluster index that a given point belongs to. */ + def predict(point: Array[Double]): Int = { + KMeans.findClosest(clusterCenters, point)._1 + } + + /** + * Return the K-means cost (sum of squared distances of points to their nearest center) for this + * model on the given data. + */ + def computeCost(data: RDD[Array[Double]]): Double = { + data.map(p => KMeans.pointCost(clusterCenters, p)).sum + } +} diff --git a/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala b/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala new file mode 100644 index 0000000000..89fe7d7e85 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala @@ -0,0 +1,105 @@ +/* + * 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 spark.mllib.clustering + +import scala.util.Random + +import org.jblas.{DoubleMatrix, SimpleBlas} + +/** + * An utility object to run K-means locally. This is private to the ML package because it's used + * in the initialization of KMeans but not meant to be publicly exposed. + */ +private[mllib] object LocalKMeans { + /** + * Run K-means++ on the weighted point set `points`. This first does the K-means++ + * initialization procedure and then roudns of Lloyd's algorithm. + */ + def kMeansPlusPlus( + seed: Int, + points: Array[Array[Double]], + weights: Array[Double], + k: Int, + maxIterations: Int) + : Array[Array[Double]] = + { + val rand = new Random(seed) + val dimensions = points(0).length + val centers = new Array[Array[Double]](k) + + // Initialize centers by sampling using the k-means++ procedure + centers(0) = pickWeighted(rand, points, weights) + for (i <- 1 until k) { + // Pick the next center with a probability proportional to cost under current centers + val curCenters = centers.slice(0, i) + val sum = points.zip(weights).map { case (p, w) => + w * KMeans.pointCost(curCenters, p) + }.sum + val r = rand.nextDouble() * sum + var cumulativeScore = 0.0 + var j = 0 + while (j < points.length && cumulativeScore < r) { + cumulativeScore += weights(j) * KMeans.pointCost(curCenters, points(j)) + j += 1 + } + centers(i) = points(j-1) + } + + // Run up to maxIterations iterations of Lloyd's algorithm + val oldClosest = Array.fill(points.length)(-1) + var iteration = 0 + var moved = true + while (moved && iteration < maxIterations) { + moved = false + val sums = Array.fill(k)(new DoubleMatrix(dimensions)) + val counts = Array.fill(k)(0.0) + for ((p, i) <- points.zipWithIndex) { + val index = KMeans.findClosest(centers, p)._1 + SimpleBlas.axpy(weights(i), new DoubleMatrix(p), sums(index)) + counts(index) += weights(i) + if (index != oldClosest(i)) { + moved = true + oldClosest(i) = index + } + } + // Update centers + for (i <- 0 until k) { + if (counts(i) == 0.0) { + // Assign center to a random point + centers(i) = points(rand.nextInt(points.length)) + } else { + centers(i) = sums(i).divi(counts(i)).data + } + } + iteration += 1 + } + + centers + } + + private def pickWeighted[T](rand: Random, data: Array[T], weights: Array[Double]): T = { + val r = rand.nextDouble() * weights.sum + var i = 0 + var curWeight = 0.0 + while (i < data.length && curWeight < r) { + curWeight += weights(i) + i += 1 + } + data(i - 1) + } +} diff --git a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala new file mode 100644 index 0000000000..22b2ec5ed6 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala @@ -0,0 +1,77 @@ +/* + * 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 spark.mllib.optimization + +import org.jblas.DoubleMatrix + +abstract class Gradient extends Serializable { + /** + * Compute the gradient for a given row of data. + * + * @param data - One row of data. Row matrix of size 1xn where n is the number of features. + * @param label - Label for this data item. + * @param weights - Column matrix containing weights for every feature. + */ + def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): + (DoubleMatrix, Double) +} + +class LogisticGradient extends Gradient { + override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): + (DoubleMatrix, Double) = { + val margin: Double = -1.0 * data.dot(weights) + val gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label + + val gradient = data.mul(gradientMultiplier) + val loss = + if (margin > 0) { + math.log(1 + math.exp(0 - margin)) + } else { + math.log(1 + math.exp(margin)) - margin + } + + (gradient, loss) + } +} + + +class SquaredGradient extends Gradient { + override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): + (DoubleMatrix, Double) = { + val diff: Double = data.dot(weights) - label + + val loss = 0.5 * diff * diff + val gradient = data.mul(diff) + + (gradient, loss) + } +} + + +class HingeGradient extends Gradient { + override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): + (DoubleMatrix, Double) = { + + val dotProduct = data.dot(weights) + + if (1.0 > label * dotProduct) + (data.mul(-label), 1.0 - label * dotProduct) + else + (DoubleMatrix.zeros(1,weights.length), 0.0) + } +} diff --git a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala new file mode 100644 index 0000000000..19cda26446 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.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 spark.mllib.optimization + +import spark.{Logging, RDD, SparkContext} +import spark.SparkContext._ + +import org.jblas.DoubleMatrix + +import scala.collection.mutable.ArrayBuffer + + +object GradientDescent { + + /** + * Run gradient descent in parallel using mini batches. + * Based on Matlab code written by John Duchi. + * + * @param data - Input data for SGD. RDD of form (label, [feature values]). + * @param gradient - Gradient object that will be used to compute the gradient. + * @param updater - Updater object that will be used to update the model. + * @param stepSize - stepSize to be used during update. + * @param numIters - number of iterations that SGD should be run. + * @param regParam - regularization parameter + * @param miniBatchFraction - fraction of the input data set that should be used for + * one iteration of SGD. Default value 1.0. + * + * @return A tuple containing two elements. The first element is a column matrix containing + * weights for every feature, and the second element is an array containing the stochastic + * loss computed for every iteration. + */ + def runMiniBatchSGD( + data: RDD[(Double, Array[Double])], + gradient: Gradient, + updater: Updater, + stepSize: Double, + numIters: Int, + regParam: Double, + initialWeights: Array[Double], + miniBatchFraction: Double=1.0) : (Array[Double], Array[Double]) = { + + val stochasticLossHistory = new ArrayBuffer[Double](numIters) + + val nexamples: Long = data.count() + val miniBatchSize = nexamples * miniBatchFraction + + // Initialize weights as a column vector + var weights = new DoubleMatrix(initialWeights.length, 1, initialWeights:_*) + var regVal = 0.0 + + for (i <- 1 to numIters) { + val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42+i).map { + case (y, features) => + val featuresRow = new DoubleMatrix(features.length, 1, features:_*) + val (grad, loss) = gradient.compute(featuresRow, y, weights) + (grad, loss) + }.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2)) + + /** + * NOTE(Xinghao): lossSum is computed using the weights from the previous iteration + * and regVal is the regularization value computed in the previous iteration as well. + */ + stochasticLossHistory.append(lossSum / miniBatchSize + regVal) + val update = updater.compute(weights, gradientSum.div(miniBatchSize), stepSize, i, regParam) + weights = update._1 + regVal = update._2 + } + + (weights.toArray, stochasticLossHistory.toArray) + } +} diff --git a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala new file mode 100644 index 0000000000..3ebc1409b6 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala @@ -0,0 +1,84 @@ +/* + * 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 spark.mllib.optimization + +import scala.math._ +import org.jblas.DoubleMatrix + +abstract class Updater extends Serializable { + /** + * Compute an updated value for weights given the gradient, stepSize and iteration number. + * Also returns the regularization value computed using the *updated* weights. + * + * @param weightsOld - Column matrix of size nx1 where n is the number of features. + * @param gradient - Column matrix of size nx1 where n is the number of features. + * @param stepSize - step size across iterations + * @param iter - Iteration number + * @param regParam - Regularization parameter + * + * @return A tuple of 2 elements. The first element is a column matrix containing updated weights, + * and the second element is the regularization value computed using updated weights. + */ + def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int, + regParam: Double): (DoubleMatrix, Double) +} + +class SimpleUpdater extends Updater { + override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, + stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = { + val thisIterStepSize = stepSize / math.sqrt(iter) + val normGradient = gradient.mul(thisIterStepSize) + (weightsOld.sub(normGradient), 0) + } +} + +/** + * L1 regularization -- corresponding proximal operator is the soft-thresholding function + * That is, each weight component is shrunk towards 0 by shrinkageVal + * If w > shrinkageVal, set weight component to w-shrinkageVal. + * If w < -shrinkageVal, set weight component to w+shrinkageVal. + * If -shrinkageVal < w < shrinkageVal, set weight component to 0. + * Equivalently, set weight component to signum(w) * max(0.0, abs(w) - shrinkageVal) + */ +class L1Updater extends Updater { + override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, + stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = { + val thisIterStepSize = stepSize / math.sqrt(iter) + val normGradient = gradient.mul(thisIterStepSize) + // Take gradient step + val newWeights = weightsOld.sub(normGradient) + // Soft thresholding + val shrinkageVal = regParam * thisIterStepSize + (0 until newWeights.length).foreach { i => + val wi = newWeights.get(i) + newWeights.put(i, signum(wi) * max(0.0, abs(wi) - shrinkageVal)) + } + (newWeights, newWeights.norm1 * regParam) + } +} + +class SquaredL2Updater extends Updater { + override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, + stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = { + val thisIterStepSize = stepSize / math.sqrt(iter) + val normGradient = gradient.mul(thisIterStepSize) + val newWeights = weightsOld.sub(normGradient).div(2.0 * thisIterStepSize * regParam + 1.0) + (newWeights, pow(newWeights.norm2, 2.0) * regParam) + } +} + diff --git a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala new file mode 100644 index 0000000000..6ecf0151a1 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala @@ -0,0 +1,436 @@ +/* + * 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 spark.mllib.recommendation + +import scala.collection.mutable.{ArrayBuffer, BitSet} +import scala.util.Random +import scala.util.Sorting + +import spark.{HashPartitioner, Partitioner, SparkContext, RDD} +import spark.storage.StorageLevel +import spark.KryoRegistrator +import spark.SparkContext._ + +import com.esotericsoftware.kryo.Kryo +import org.jblas.{DoubleMatrix, SimpleBlas, Solve} + + +/** + * Out-link information for a user or product block. This includes the original user/product IDs + * of the elements within this block, and the list of destination blocks that each user or + * product will need to send its feature vector to. + */ +private[recommendation] case class OutLinkBlock(elementIds: Array[Int], shouldSend: Array[BitSet]) + + +/** + * In-link information for a user (or product) block. This includes the original user/product IDs + * of the elements within this block, as well as an array of indices and ratings that specify + * which user in the block will be rated by which products from each product block (or vice-versa). + * Specifically, if this InLinkBlock is for users, ratingsForBlock(b)(i) will contain two arrays, + * indices and ratings, for the i'th product that will be sent to us by product block b (call this + * P). These arrays represent the users that product P had ratings for (by their index in this + * block), as well as the corresponding rating for each one. We can thus use this information when + * we get product block b's message to update the corresponding users. + */ +private[recommendation] case class InLinkBlock( + elementIds: Array[Int], ratingsForBlock: Array[Array[(Array[Int], Array[Double])]]) + + +/** + * A more compact class to represent a rating than Tuple3[Int, Int, Double]. + */ +private[recommendation] case class Rating(user: Int, product: Int, rating: Double) + + +/** + * Alternating Least Squares matrix factorization. + * + * 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 + * product blocks that need that user's feature vector. This is achieved by precomputing some + * information about the ratings matrix to determine the "out-links" of each user (which blocks of + * products it will contribute to) and "in-link" information for each product (which of the feature + * 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. + */ +class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var lambda: Double) + extends Serializable +{ + def this() = this(-1, 10, 10, 0.01) + + /** + * Set the number of blocks to parallelize the computation into; pass -1 for an auto-configured + * number of blocks. Default: -1. + */ + def setBlocks(numBlocks: Int): ALS = { + this.numBlocks = numBlocks + this + } + + /** Set the rank of the feature matrices computed (number of features). Default: 10. */ + def setRank(rank: Int): ALS = { + this.rank = rank + this + } + + /** Set the number of iterations to run. Default: 10. */ + def setIterations(iterations: Int): ALS = { + this.iterations = iterations + this + } + + /** Set the regularization parameter, lambda. Default: 0.01. */ + def setLambda(lambda: Double): ALS = { + this.lambda = lambda + 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. + */ + def train(ratings: RDD[(Int, Int, Double)]): MatrixFactorizationModel = { + val numBlocks = if (this.numBlocks == -1) { + math.max(ratings.context.defaultParallelism, ratings.partitions.size / 2) + } else { + this.numBlocks + } + + val partitioner = new HashPartitioner(numBlocks) + + val ratingsByUserBlock = ratings.map{ case (u, p, r) => (u % numBlocks, Rating(u, p, r)) } + val ratingsByProductBlock = ratings.map{ case (u, p, r) => (p % numBlocks, Rating(p, u, r)) } + + val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock) + val (productInLinks, productOutLinks) = makeLinkRDDs(numBlocks, ratingsByProductBlock) + + // Initialize user and product factors randomly + val seed = new Random().nextInt() + var users = userOutLinks.mapValues(_.elementIds.map(u => randomFactor(rank, seed ^ u))) + var products = productOutLinks.mapValues(_.elementIds.map(p => randomFactor(rank, seed ^ ~p))) + + for (iter <- 0 until iterations) { + // perform ALS update + products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda) + users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda) + } + + // 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)) + } + + usersOut.persist() + productsOut.persist() + + new MatrixFactorizationModel(rank, usersOut, productsOut) + } + + /** + * 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). + */ + private def makeOutLinkBlock(numBlocks: Int, ratings: Array[Rating]): OutLinkBlock = { + val userIds = ratings.map(_.user).distinct.sorted + val numUsers = userIds.length + val userIdToPos = userIds.zipWithIndex.toMap + val shouldSend = Array.fill(numUsers)(new BitSet(numBlocks)) + for (r <- ratings) { + shouldSend(userIdToPos(r.user))(r.product % numBlocks) = true + } + OutLinkBlock(userIds, shouldSend) + } + + /** + * Make the in-links table for a block of the users (or products) dataset given a list of + * (user, product, rating) values for the users in that block (or the opposite for products). + */ + private def makeInLinkBlock(numBlocks: Int, ratings: Array[Rating]): InLinkBlock = { + val userIds = ratings.map(_.user).distinct.sorted + val numUsers = userIds.length + val userIdToPos = userIds.zipWithIndex.toMap + // Split out our ratings by product block + val blockRatings = Array.fill(numBlocks)(new ArrayBuffer[Rating]) + for (r <- ratings) { + blockRatings(r.product % numBlocks) += r + } + val ratingsForBlock = new Array[Array[(Array[Int], Array[Double])]](numBlocks) + for (productBlock <- 0 until numBlocks) { + // Create an array of (product, Seq(Rating)) ratings + val groupedRatings = blockRatings(productBlock).groupBy(_.product).toArray + // Sort them by product ID + val ordering = new Ordering[(Int, ArrayBuffer[Rating])] { + def compare(a: (Int, ArrayBuffer[Rating]), b: (Int, ArrayBuffer[Rating])): Int = a._1 - b._1 + } + Sorting.quickSort(groupedRatings)(ordering) + // Translate the user IDs to indices based on userIdToPos + ratingsForBlock(productBlock) = groupedRatings.map { case (p, rs) => + (rs.view.map(r => userIdToPos(r.user)).toArray, rs.view.map(_.rating).toArray) + } + } + InLinkBlock(userIds, ratingsForBlock) + } + + /** + * Make RDDs of InLinkBlocks and OutLinkBlocks given an RDD of (blockId, (u, p, r)) values for + * the users (or (blockId, (p, u, r)) for the products). We create these simultaneously to avoid + * having to shuffle the (blockId, (u, p, r)) RDD twice, or to cache it. + */ + private def makeLinkRDDs(numBlocks: Int, ratings: RDD[(Int, Rating)]) + : (RDD[(Int, InLinkBlock)], RDD[(Int, OutLinkBlock)]) = + { + val grouped = ratings.partitionBy(new HashPartitioner(numBlocks)) + val links = grouped.mapPartitionsWithIndex((blockId, elements) => { + val ratings = elements.map{_._2}.toArray + val inLinkBlock = makeInLinkBlock(numBlocks, ratings) + val outLinkBlock = makeOutLinkBlock(numBlocks, ratings) + Iterator.single((blockId, (inLinkBlock, outLinkBlock))) + }, true) + links.persist(StorageLevel.MEMORY_AND_DISK) + (links.mapValues(_._1), links.mapValues(_._2)) + } + + /** + * Make a random factor vector with the given seed. + * TODO: Initialize things using mapPartitionsWithIndex to make it faster? + */ + private def randomFactor(rank: Int, seed: Int): Array[Double] = { + val rand = new Random(seed) + Array.fill(rank)(rand.nextDouble) + } + + /** + * Compute the user feature vectors given the current products (or vice-versa). This first joins + * the products with their out-links to generate a set of messages to each destination block + * (specifically, the features for the products that user block cares about), then groups these + * by destination and joins them with the in-link info to figure out how to update each user. + * It returns an RDD of new feature vectors for each user block. + */ + private def updateFeatures( + products: RDD[(Int, Array[Array[Double]])], + productOutLinks: RDD[(Int, OutLinkBlock)], + userInLinks: RDD[(Int, InLinkBlock)], + partitioner: Partitioner, + rank: Int, + lambda: Double) + : RDD[(Int, Array[Array[Double]])] = + { + val numBlocks = products.partitions.size + productOutLinks.join(products).flatMap { case (bid, (outLinkBlock, factors)) => + val toSend = Array.fill(numBlocks)(new ArrayBuffer[Array[Double]]) + for (p <- 0 until outLinkBlock.elementIds.length; userBlock <- 0 until numBlocks) { + if (outLinkBlock.shouldSend(p)(userBlock)) { + toSend(userBlock) += factors(p) + } + } + toSend.zipWithIndex.map{ case (buf, idx) => (idx, (bid, buf.toArray)) } + }.groupByKey(partitioner) + .join(userInLinks) + .mapValues{ case (messages, inLinkBlock) => updateBlock(messages, inLinkBlock, rank, lambda) } + } + + /** + * Compute the new feature vectors for a block of the users matrix given the list of factors + * it received from each product and its InLinkBlock. + */ + def updateBlock(messages: Seq[(Int, Array[Array[Double]])], inLinkBlock: InLinkBlock, + rank: Int, lambda: Double) + : Array[Array[Double]] = + { + // Sort the incoming block factor messages by block ID and make them an array + val blockFactors = messages.sortBy(_._1).map(_._2).toArray // Array[Array[Double]] + val numBlocks = blockFactors.length + val numUsers = inLinkBlock.elementIds.length + + // We'll sum up the XtXes using vectors that represent only the lower-triangular part, since + // the matrices are symmetric + val triangleSize = rank * (rank + 1) / 2 + val userXtX = Array.fill(numUsers)(DoubleMatrix.zeros(triangleSize)) + val userXy = Array.fill(numUsers)(DoubleMatrix.zeros(rank)) + + // Some temp variables to avoid memory allocation + val tempXtX = DoubleMatrix.zeros(triangleSize) + val fullXtX = DoubleMatrix.zeros(rank, rank) + + // Compute the XtX and Xy values for each user by adding products it rated in each product block + for (productBlock <- 0 until numBlocks) { + for (p <- 0 until blockFactors(productBlock).length) { + val x = new DoubleMatrix(blockFactors(productBlock)(p)) + 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))) + } + } + } + + // Solve the least-squares problem for each user and return the new feature vectors + userXtX.zipWithIndex.map{ case (triangularXtX, index) => + // Compute the full XtX matrix from the lower-triangular part we got above + fillFullMatrix(triangularXtX, fullXtX) + // 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 + } + } + + /** + * Set xtxDest to the lower-triangular part of x transpose * x. For efficiency in summing + * these matrices, we store xtxDest as only rank * (rank+1) / 2 values, namely the values + * at (0,0), (1,0), (1,1), (2,0), (2,1), (2,2), etc in that order. + */ + private def fillXtX(x: DoubleMatrix, xtxDest: DoubleMatrix) { + var i = 0 + var pos = 0 + while (i < x.length) { + var j = 0 + while (j <= i) { + xtxDest.data(pos) = x.data(i) * x.data(j) + pos += 1 + j += 1 + } + i += 1 + } + } + + /** + * Given a triangular matrix in the order of fillXtX above, compute the full symmetric square + * matrix that it represents, storing it into destMatrix. + */ + private def fillFullMatrix(triangularMatrix: DoubleMatrix, destMatrix: DoubleMatrix) { + val rank = destMatrix.rows + var i = 0 + var pos = 0 + while (i < rank) { + var j = 0 + while (j <= i) { + destMatrix.data(i*rank + j) = triangularMatrix.data(pos) + destMatrix.data(j*rank + i) = triangularMatrix.data(pos) + pos += 1 + j += 1 + } + i += 1 + } + } +} + + +/** + * Top-level methods for calling Alternating Least Squares (ALS) matrix factorizaton. + */ +object ALS { + /** + * Train a matrix factorization model given an RDD of 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. 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 + */ + def train( + ratings: RDD[(Int, Int, Double)], + rank: Int, + iterations: Int, + lambda: Double, + blocks: Int) + : MatrixFactorizationModel = + { + new ALS(blocks, rank, iterations, lambda).train(ratings) + } + + /** + * Train a matrix factorization model given an RDD of 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`. + * + * @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 train(ratings: RDD[(Int, Int, Double)], rank: Int, iterations: Int, lambda: Double) + : MatrixFactorizationModel = + { + train(ratings, rank, iterations, lambda, -1) + } + + /** + * Train a matrix factorization model given an RDD of 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`. + * + * @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 train(ratings: RDD[(Int, Int, Double)], rank: Int, iterations: Int) + : MatrixFactorizationModel = + { + train(ratings, rank, iterations, 0.01, -1) + } + + private class ALSRegistrator extends KryoRegistrator { + override def registerClasses(kryo: Kryo) { + kryo.register(classOf[Rating]) + } + } + + def main(args: Array[String]) { + if (args.length != 5 && args.length != 6) { + println("Usage: ALS <master> <ratings_file> <rank> <iterations> <output_dir> [<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", "spark.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 sc = new SparkContext(master, "ALS") + val ratings = sc.textFile(ratingsFile).map { line => + val fields = line.split(',') + (fields(0).toInt, fields(1).toInt, fields(2).toDouble) + } + val model = ALS.train(ratings, rank, iters, 0.01, blocks) + 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) + } +} diff --git a/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala new file mode 100644 index 0000000000..38637b3dd1 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -0,0 +1,40 @@ +/* + * 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 spark.mllib.recommendation + +import spark.RDD +import spark.SparkContext._ + +import org.jblas._ + +class MatrixFactorizationModel( + val rank: Int, + val userFeatures: RDD[(Int, Array[Double])], + val productFeatures: RDD[(Int, Array[Double])]) + extends Serializable +{ + /** Predict the rating of one user for one product. */ + def predict(user: Int, product: Int): Double = { + val userVector = new DoubleMatrix(userFeatures.lookup(user).head) + val productVector = new DoubleMatrix(productFeatures.lookup(product).head) + userVector.dot(productVector) + } + + // TODO: Figure out what good bulk prediction methods would look like. + // Probably want a way to get the top users for a product or vice-versa. +} diff --git a/mllib/src/main/scala/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/spark/mllib/regression/Lasso.scala new file mode 100644 index 0000000000..e8b1ed8a48 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/Lasso.scala @@ -0,0 +1,241 @@ +/* + * 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 spark.mllib.regression + +import spark.{Logging, RDD, SparkContext} +import spark.mllib.optimization._ +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix + +/** + * Lasso using Stochastic Gradient Descent. + * + */ +class LassoModel( + val weights: Array[Double], + val intercept: Double, + val stochasticLosses: Array[Double]) extends RegressionModel { + + // Create a column vector that can be used for predictions + private val weightsMatrix = new DoubleMatrix(weights.length, 1, weights:_*) + + override def predict(testData: spark.RDD[Array[Double]]) = { + // A small optimization to avoid serializing the entire model. Only the weightsMatrix + // and intercept is needed. + val localWeights = weightsMatrix + val localIntercept = intercept + testData.map { x => + new DoubleMatrix(1, x.length, x:_*).dot(localWeights) + localIntercept + } + } + + + override def predict(testData: Array[Double]): Double = { + val dataMat = new DoubleMatrix(1, testData.length, testData:_*) + dataMat.dot(weightsMatrix) + this.intercept + } +} + + +class LassoLocalRandomSGD private (var stepSize: Double, var regParam: Double, + var miniBatchFraction: Double, var numIters: Int) + extends Logging { + + /** + * Construct a Lasso object with default parameters + */ + def this() = this(1.0, 1.0, 1.0, 100) + + /** + * Set the step size per-iteration of SGD. Default 1.0. + */ + def setStepSize(step: Double) = { + this.stepSize = step + this + } + + /** + * Set the regularization parameter. Default 1.0. + */ + def setRegParam(param: Double) = { + this.regParam = param + this + } + + /** + * Set fraction of data to be used for each SGD iteration. Default 1.0. + */ + def setMiniBatchFraction(fraction: Double) = { + this.miniBatchFraction = fraction + this + } + + /** + * Set the number of iterations for SGD. Default 100. + */ + def setNumIterations(iters: Int) = { + this.numIters = iters + this + } + + def train(input: RDD[(Double, Array[Double])]): LassoModel = { + val nfeatures: Int = input.take(1)(0)._2.length + val initialWeights = Array.fill(nfeatures)(1.0) + train(input, initialWeights) + } + + def train( + input: RDD[(Double, Array[Double])], + initialWeights: Array[Double]): LassoModel = { + + // Add a extra variable consisting of all 1.0's for the intercept. + val data = input.map { case (y, features) => + (y, Array(1.0, features:_*)) + } + + val initalWeightsWithIntercept = Array(1.0, initialWeights:_*) + + val (weights, stochasticLosses) = GradientDescent.runMiniBatchSGD( + data, + new SquaredGradient(), + new L1Updater(), + stepSize, + numIters, + regParam, + initalWeightsWithIntercept, + miniBatchFraction) + + val intercept = weights(0) + val weightsScaled = weights.tail + + val model = new LassoModel(weightsScaled, intercept, stochasticLosses) + + logInfo("Final model weights " + model.weights.mkString(",")) + logInfo("Final model intercept " + model.intercept) + logInfo("Last 10 stochasticLosses " + model.stochasticLosses.takeRight(10).mkString(", ")) + model + } +} + +/** + * Top-level methods for calling Lasso. + * + * + */ +object LassoLocalRandomSGD { + + /** + * Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number + * of iterations of gradient descent using the specified step size. Each iteration uses + * `miniBatchFraction` fraction of the data to calculate the gradient. The weights used in + * gradient descent are initialized using the initial weights provided. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @param stepSize Step size to be used for each iteration of gradient descent. + * @param regParam Regularization parameter. + * @param miniBatchFraction Fraction of data to be used per iteration. + * @param initialWeights Initial set of weights to be used. Array should be equal in size to + * the number of features in the data. + */ + def train( + input: RDD[(Double, Array[Double])], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double, + initialWeights: Array[Double]) + : LassoModel = + { + new LassoLocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train( + input, initialWeights) + } + + /** + * Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number + * of iterations of gradient descent using the specified step size. Each iteration uses + * `miniBatchFraction` fraction of the data to calculate the gradient. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @param stepSize Step size to be used for each iteration of gradient descent. + * @param regParam Regularization parameter. + * @param miniBatchFraction Fraction of data to be used per iteration. + */ + def train( + input: RDD[(Double, Array[Double])], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double) + : LassoModel = + { + new LassoLocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input) + } + + /** + * Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number + * of iterations of gradient descent using the specified step size. We use the entire data set to + * update the gradient in each iteration. + * + * @param input RDD of (label, array of features) pairs. + * @param stepSize Step size to be used for each iteration of Gradient Descent. + * @param regParam Regularization parameter. + * @param numIterations Number of iterations of gradient descent to run. + * @return a LassoModel which has the weights and offset from training. + */ + def train( + input: RDD[(Double, Array[Double])], + numIterations: Int, + stepSize: Double, + regParam: Double) + : LassoModel = + { + train(input, numIterations, stepSize, regParam, 1.0) + } + + /** + * Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number + * of iterations of gradient descent using a step size of 1.0. We use the entire data set to + * update the gradient in each iteration. + * + * @param input RDD of (label, array of features) pairs. + * @param numIterations Number of iterations of gradient descent to run. + * @return a LassoModel which has the weights and offset from training. + */ + def train( + input: RDD[(Double, Array[Double])], + numIterations: Int) + : LassoModel = + { + train(input, numIterations, 1.0, 1.0, 1.0) + } + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: Lasso <master> <input_dir> <step_size> <regularization_parameter> <niters>") + System.exit(1) + } + val sc = new SparkContext(args(0), "Lasso") + val data = MLUtils.loadLabeledData(sc, args(1)) + val model = LassoLocalRandomSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble) + + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/spark/mllib/regression/RegressionModel.scala new file mode 100644 index 0000000000..b845ba1a89 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/RegressionModel.scala @@ -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. + */ + +package spark.mllib.regression + +import spark.RDD + +trait RegressionModel extends Serializable { + /** + * Predict values for the given data set using the model trained. + * + * @param testData RDD representing data points to be predicted + * @return RDD[Double] where each entry contains the corresponding prediction + */ + def predict(testData: RDD[Array[Double]]): RDD[Double] + + /** + * Predict values for a single data point using the model trained. + * + * @param testData array representing a single data point + * @return Double prediction from the trained model + */ + def predict(testData: Array[Double]): Double +} diff --git a/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala new file mode 100644 index 0000000000..6ba141e8fb --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala @@ -0,0 +1,216 @@ +/* + * 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 spark.mllib.regression + +import spark.{Logging, RDD, SparkContext} +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix +import org.jblas.Solve + +import scala.annotation.tailrec +import scala.collection.mutable + +/** + * Ridge Regression from Joseph Gonzalez's implementation in MLBase + */ +class RidgeRegressionModel( + val weights: DoubleMatrix, + val intercept: Double, + val lambdaOpt: Double, + val lambdas: Seq[(Double, Double, DoubleMatrix)]) + extends RegressionModel { + + override def predict(testData: RDD[Array[Double]]): RDD[Double] = { + // A small optimization to avoid serializing the entire model. + val localIntercept = this.intercept + val localWeights = this.weights + testData.map { x => + (new DoubleMatrix(1, x.length, x:_*).mmul(localWeights)).get(0) + localIntercept + } + } + + override def predict(testData: Array[Double]): Double = { + (new DoubleMatrix(1, testData.length, testData:_*).mmul(this.weights)).get(0) + this.intercept + } +} + +class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double) + extends Logging { + + def this() = this(0.0, 100.0) + + /** + * Set the lower bound on binary search for lambda's. Default is 0. + */ + def setLowLambda(low: Double) = { + this.lambdaLow = low + this + } + + /** + * Set the upper bound on binary search for lambda's. Default is 100.0. + */ + def setHighLambda(hi: Double) = { + this.lambdaHigh = hi + this + } + + def train(input: RDD[(Double, Array[Double])]): RidgeRegressionModel = { + val nfeatures: Int = input.take(1)(0)._2.length + val nexamples: Long = input.count() + + val (yMean, xColMean, xColSd) = MLUtils.computeStats(input, nfeatures, nexamples) + + val data = input.map { case(y, features) => + val yNormalized = y - yMean + val featuresMat = new DoubleMatrix(nfeatures, 1, features:_*) + val featuresNormalized = featuresMat.sub(xColMean).divi(xColSd) + (yNormalized, featuresNormalized.toArray) + } + + // Compute XtX - Size of XtX is nfeatures by nfeatures + val XtX: DoubleMatrix = data.map { case (y, features) => + val x = new DoubleMatrix(1, features.length, features:_*) + x.transpose().mmul(x) + }.reduce(_.addi(_)) + + // Compute Xt*y - Size of Xty is nfeatures by 1 + val Xty: DoubleMatrix = data.map { case (y, features) => + new DoubleMatrix(features.length, 1, features:_*).mul(y) + }.reduce(_.addi(_)) + + // Define a function to compute the leave one out cross validation error + // for a single example + def crossValidate(lambda: Double): (Double, Double, DoubleMatrix) = { + // Compute the MLE ridge regression parameter value + + // Ridge Regression parameter = inv(XtX + \lambda*I) * Xty + val XtXlambda = DoubleMatrix.eye(nfeatures).muli(lambda).addi(XtX) + val w = Solve.solveSymmetric(XtXlambda, Xty) + + val invXtX = Solve.solveSymmetric(XtXlambda, DoubleMatrix.eye(nfeatures)) + + // compute the generalized cross validation score + val cverror = data.map { + case (y, features) => + val x = new DoubleMatrix(features.length, 1, features:_*) + val yhat = w.transpose().mmul(x).get(0) + val H_ii = x.transpose().mmul(invXtX).mmul(x).get(0) + val residual = (y - yhat) / (1.0 - H_ii) + residual * residual + }.reduce(_ + _) / nexamples + + (lambda, cverror, w) + } + + // Binary search for the best assignment to lambda. + def binSearch(low: Double, high: Double): Seq[(Double, Double, DoubleMatrix)] = { + val buffer = mutable.ListBuffer.empty[(Double, Double, DoubleMatrix)] + + @tailrec + def loop(low: Double, high: Double): Seq[(Double, Double, DoubleMatrix)] = { + val mid = (high - low) / 2 + low + val lowValue = crossValidate((mid - low) / 2 + low) + val highValue = crossValidate((high - mid) / 2 + mid) + val (newLow, newHigh) = if (lowValue._2 < highValue._2) { + (low, mid + (high-low)/4) + } else { + (mid - (high-low)/4, high) + } + if (newHigh - newLow > 1.0E-7) { + buffer += lowValue += highValue + loop(newLow, newHigh) + } else { + buffer += lowValue += highValue + buffer.result() + } + } + + loop(low, high) + } + + // Actually compute the best lambda + val lambdas = binSearch(lambdaLow, lambdaHigh).sortBy(_._1) + + // Find the best parameter set by taking the lowest cverror. + val (lambdaOpt, cverror, weights) = lambdas.reduce((a, b) => if (a._2 < b._2) a else b) + + // Return the model which contains the solution + val weightsScaled = weights.div(xColSd) + val intercept = yMean - (weights.transpose().mmul(xColMean.div(xColSd)).get(0)) + val model = new RidgeRegressionModel(weightsScaled, intercept, lambdaOpt, lambdas) + + logInfo("RidgeRegression: optimal lambda " + model.lambdaOpt) + logInfo("RidgeRegression: optimal weights " + model.weights) + logInfo("RidgeRegression: optimal intercept " + model.intercept) + logInfo("RidgeRegression: cross-validation error " + cverror) + + model + } +} + +/** + * Top-level methods for calling Ridge Regression. + * NOTE(shivaram): We use multiple train methods instead of default arguments to support + * Java programs. + */ +object RidgeRegression { + + /** + * Train a ridge regression model given an RDD of (response, features) pairs. + * We use the closed form solution to compute the cross-validation score for + * a given lambda. The optimal lambda is computed by performing binary search + * between the provided bounds of lambda. + * + * @param input RDD of (response, array of features) pairs. + * @param lambdaLow lower bound used in binary search for lambda + * @param lambdaHigh upper bound used in binary search for lambda + */ + def train( + input: RDD[(Double, Array[Double])], + lambdaLow: Double, + lambdaHigh: Double) + : RidgeRegressionModel = + { + new RidgeRegression(lambdaLow, lambdaHigh).train(input) + } + + /** + * Train a ridge regression model given an RDD of (response, features) pairs. + * We use the closed form solution to compute the cross-validation score for + * a given lambda. The optimal lambda is computed by performing binary search + * between lambda values of 0 and 100. + * + * @param input RDD of (response, array of features) pairs. + */ + def train(input: RDD[(Double, Array[Double])]) : RidgeRegressionModel = { + train(input, 0.0, 100.0) + } + + def main(args: Array[String]) { + if (args.length != 2) { + println("Usage: RidgeRegression <master> <input_dir>") + System.exit(1) + } + val sc = new SparkContext(args(0), "RidgeRegression") + val data = MLUtils.loadLabeledData(sc, args(1)) + val model = RidgeRegression.train(data, 0, 1000) + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala new file mode 100644 index 0000000000..8f95cf7479 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.scala @@ -0,0 +1,80 @@ +/* + * 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 spark.mllib.util + +import scala.util.Random + +import spark.{RDD, SparkContext} + +object KMeansDataGenerator { + + /** + * Generate an RDD containing test data for KMeans. This function chooses k cluster centers + * from a d-dimensional Gaussian distribution scaled by factor r, then creates a Gaussian + * cluster with scale 1 around each center. + * + * @param sc SparkContext to use for creating the RDD + * @param numPoints Number of points that will be contained in the RDD + * @param k Number of clusters + * @param d Number of dimensions + * @parak r Scaling factor for the distribution of the initial centers + * @param numPartitions Number of partitions of the generated RDD; default 2 + */ + def generateKMeansRDD( + sc: SparkContext, + numPoints: Int, + k: Int, + d: Int, + r: Double, + numPartitions: Int = 2) + : RDD[Array[Double]] = + { + // First, generate some centers + val rand = new Random(42) + val centers = Array.fill(k)(Array.fill(d)(rand.nextGaussian() * r)) + // Then generate points around each center + sc.parallelize(0 until numPoints, numPartitions).map { idx => + val center = centers(idx % k) + val rand2 = new Random(42 + idx) + Array.tabulate(d)(i => center(i) + rand2.nextGaussian()) + } + } + + def main(args: Array[String]) { + if (args.length < 6) { + println("Usage: KMeansGenerator " + + "<master> <output_dir> <num_points> <k> <d> <r> [<num_partitions>]") + System.exit(1) + } + + val sparkMaster = args(0) + val outputPath = args(1) + val numPoints = args(2).toInt + val k = args(3).toInt + val d = args(4).toInt + val r = args(5).toDouble + val parts = if (args.length >= 7) args(6).toInt else 2 + + val sc = new SparkContext(sparkMaster, "KMeansDataGenerator") + val data = generateKMeansRDD(sc, numPoints, k, d, r, parts) + data.map(_.mkString(" ")).saveAsTextFile(outputPath) + + System.exit(0) + } +} + diff --git a/mllib/src/main/scala/spark/mllib/util/LassoDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/LassoDataGenerator.scala new file mode 100644 index 0000000000..ef4f42a494 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/LassoDataGenerator.scala @@ -0,0 +1,45 @@ +package spark.mllib.regression + +import scala.util.Random + +import org.jblas.DoubleMatrix + +import spark.{RDD, SparkContext} +import spark.mllib.util.MLUtils + +object LassoGenerator { + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: LassoGenerator " + + "<master> <output_dir> <num_examples> <num_features> <num_partitions>") + System.exit(1) + } + + val sparkMaster: String = args(0) + val outputPath: String = args(1) + val nexamples: Int = if (args.length > 2) args(2).toInt else 1000 + val nfeatures: Int = if (args.length > 3) args(3).toInt else 2 + val parts: Int = if (args.length > 4) args(4).toInt else 2 + val eps = 3 + + val sc = new SparkContext(sparkMaster, "LassoGenerator") + + val globalRnd = new Random(94720) + val trueWeights = new DoubleMatrix(1, nfeatures+1, + Array.fill[Double](nfeatures + 1) { globalRnd.nextGaussian() }:_*) + + val data: RDD[(Double, Array[Double])] = sc.parallelize(0 until nexamples, parts).map { idx => + val rnd = new Random(42 + idx) + + val x = Array.fill[Double](nfeatures) { + rnd.nextDouble() * 2.0 - 1.0 + } + val y = (new DoubleMatrix(1, x.length, x:_*)).dot(trueWeights) + rnd.nextGaussian() * 0.1 + (y, x) + } + + MLUtils.saveLabeledData(data, outputPath) + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.scala new file mode 100644 index 0000000000..8d659cd97c --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.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 spark.mllib.util + +import scala.util.Random + +import spark.{RDD, SparkContext} + +object LogisticRegressionDataGenerator { + + /** + * Generate an RDD containing test data for LogisticRegression. This function chooses + * positive labels with probability `probOne` and scales positive examples by `eps`. + * + * @param sc SparkContext to use for creating the RDD. + * @param nexamples Number of examples that will be contained in the RDD. + * @param nfeatures Number of features to generate for each example. + * @param eps Epsilon factor by which positive examples are scaled. + * @param nparts Number of partitions of the generated RDD. Default value is 2. + * @param probOne Probability that a label is 1 (and not 0). Default value is 0.5. + */ + def generateLogisticRDD( + sc: SparkContext, + nexamples: Int, + nfeatures: Int, + eps: Double, + nparts: Int = 2, + probOne: Double = 0.5): RDD[(Double, Array[Double])] = { + val data = sc.parallelize(0 until nexamples, nparts).map { idx => + val rnd = new Random(42 + idx) + + val y = if (idx % 2 == 0) 0.0 else 1.0 + val x = Array.fill[Double](nfeatures) { + rnd.nextGaussian() + (y * eps) + } + (y, x) + } + data + } + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: LogisticRegressionGenerator " + + "<master> <output_dir> <num_examples> <num_features> <num_partitions>") + System.exit(1) + } + + val sparkMaster: String = args(0) + val outputPath: String = args(1) + val nexamples: Int = if (args.length > 2) args(2).toInt else 1000 + val nfeatures: Int = if (args.length > 3) args(3).toInt else 2 + val parts: Int = if (args.length > 4) args(4).toInt else 2 + val eps = 3 + + val sc = new SparkContext(sparkMaster, "LogisticRegressionDataGenerator") + val data = generateLogisticRDD(sc, nexamples, nfeatures, eps, parts) + + MLUtils.saveLabeledData(data, outputPath) + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala new file mode 100644 index 0000000000..88992cde0c --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/MFDataGenerator.scala @@ -0,0 +1,113 @@ +/* + * 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 spark.mllib.recommendation + +import scala.util.Random + +import org.jblas.DoubleMatrix + +import spark.{RDD, SparkContext} +import spark.mllib.util.MLUtils + +/** +* Generate RDD(s) containing data for Matrix Factorization. +* +* This method samples training entries according to the oversampling factor +* 'trainSampFact', which is a multiplicative factor of the number of +* degrees of freedom of the matrix: rank*(m+n-rank). +* +* It optionally samples entries for a testing matrix using +* 'testSampFact', the percentage of the number of training entries +* to use for testing. +* +* This method takes the following inputs: +* sparkMaster (String) The master URL. +* outputPath (String) Directory to save output. +* m (Int) Number of rows in data matrix. +* n (Int) Number of columns in data matrix. +* rank (Int) Underlying rank of data matrix. +* trainSampFact (Double) Oversampling factor. +* noise (Boolean) Whether to add gaussian noise to training data. +* sigma (Double) Standard deviation of added gaussian noise. +* test (Boolean) Whether to create testing RDD. +* testSampFact (Double) Percentage of training data to use as test data. +*/ + +object MFDataGenerator{ + + def main(args: Array[String]) { + if (args.length < 2) { + println("Usage: MFDataGenerator " + + "<master> <outputDir> [m] [n] [rank] [trainSampFact] [noise] [sigma] [test] [testSampFact]") + System.exit(1) + } + + val sparkMaster: String = args(0) + val outputPath: String = args(1) + val m: Int = if (args.length > 2) args(2).toInt else 100 + val n: Int = if (args.length > 3) args(3).toInt else 100 + val rank: Int = if (args.length > 4) args(4).toInt else 10 + val trainSampFact: Double = if (args.length > 5) args(5).toDouble else 1.0 + val noise: Boolean = if (args.length > 6) args(6).toBoolean else false + val sigma: Double = if (args.length > 7) args(7).toDouble else 0.1 + val test: Boolean = if (args.length > 8) args(8).toBoolean else false + val testSampFact: Double = if (args.length > 9) args(9).toDouble else 0.1 + + val sc = new SparkContext(sparkMaster, "MFDataGenerator") + + val A = DoubleMatrix.randn(m, rank) + val B = DoubleMatrix.randn(rank, n) + val z = 1 / (scala.math.sqrt(scala.math.sqrt(rank))) + A.mmuli(z) + B.mmuli(z) + val fullData = A.mmul(B) + + val df = rank * (m + n - rank) + val sampSize = scala.math.min(scala.math.round(trainSampFact * df), + scala.math.round(.99 * m * n)).toInt + val rand = new Random() + val mn = m * n + val shuffled = rand.shuffle(1 to mn toIterable) + + val omega = shuffled.slice(0, sampSize) + val ordered = omega.sortWith(_ < _).toArray + val trainData: RDD[(Int, Int, Double)] = sc.parallelize(ordered) + .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1))) + + // optionally add gaussian noise + if (noise) { + trainData.map(x => (x._1, x._2, x._3 + rand.nextGaussian * sigma)) + } + + trainData.map(x => x._1 + "," + x._2 + "," + x._3).saveAsTextFile(outputPath) + + // optionally generate testing data + if (test) { + val testSampSize = scala.math + .min(scala.math.round(sampSize * testSampFact),scala.math.round(mn - sampSize)).toInt + val testOmega = shuffled.slice(sampSize, sampSize + testSampSize) + val testOrdered = testOmega.sortWith(_ < _).toArray + val testData: RDD[(Int, Int, Double)] = sc.parallelize(testOrdered) + .map(x => (fullData.indexRows(x - 1), fullData.indexColumns(x - 1), fullData.get(x - 1))) + testData.map(x => x._1 + "," + x._2 + "," + x._3).saveAsTextFile(outputPath) + } + + sc.stop() + + } +}
\ No newline at end of file diff --git a/mllib/src/main/scala/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala new file mode 100644 index 0000000000..25d9673004 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/MLUtils.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 spark.mllib.util + +import spark.{RDD, SparkContext} +import spark.SparkContext._ + +import org.jblas.DoubleMatrix + +/** + * Helper methods to load and save data + * Data format: + * <l>, <f1> <f2> ... + * where <f1>, <f2> are feature values in Double and <l> is the corresponding label as Double. + */ +object MLUtils { + + /** + * @param sc SparkContext + * @param dir Directory to the input data files. + * @return An RDD of tuples. For each tuple, the first element is the label, and the second + * element represents the feature values (an array of Double). + */ + def loadLabeledData(sc: SparkContext, dir: String): RDD[(Double, Array[Double])] = { + sc.textFile(dir).map { line => + val parts = line.split(',') + val label = parts(0).toDouble + val features = parts(1).trim().split(' ').map(_.toDouble) + (label, features) + } + } + + def saveLabeledData(data: RDD[(Double, Array[Double])], dir: String) { + val dataStr = data.map(x => x._1 + "," + x._2.mkString(" ")) + dataStr.saveAsTextFile(dir) + } + + /** + * Utility function to compute mean and standard deviation on a given dataset. + * + * @param data - input data set whose statistics are computed + * @param nfeatures - number of features + * @param nexamples - number of examples in input dataset + * + * @return (yMean, xColMean, xColSd) - Tuple consisting of + * yMean - mean of the labels + * xColMean - Row vector with mean for every column (or feature) of the input data + * xColSd - Row vector standard deviation for every column (or feature) of the input data. + */ + def computeStats(data: RDD[(Double, Array[Double])], nfeatures: Int, nexamples: Long): + (Double, DoubleMatrix, DoubleMatrix) = { + val yMean: Double = data.map { case (y, features) => y }.reduce(_ + _) / nexamples + + // NOTE: We shuffle X by column here to compute column sum and sum of squares. + val xColSumSq: RDD[(Int, (Double, Double))] = data.flatMap { case(y, features) => + val nCols = features.length + // Traverse over every column and emit (col, value, value^2) + Iterator.tabulate(nCols) { i => + (i, (features(i), features(i)*features(i))) + } + }.reduceByKey { case(x1, x2) => + (x1._1 + x2._1, x1._2 + x2._2) + } + val xColSumsMap = xColSumSq.collectAsMap() + + val xColMean = DoubleMatrix.zeros(nfeatures, 1) + val xColSd = DoubleMatrix.zeros(nfeatures, 1) + + // Compute mean and unbiased variance using column sums + var col = 0 + while (col < nfeatures) { + xColMean.put(col, xColSumsMap(col)._1 / nexamples) + val variance = + (xColSumsMap(col)._2 - (math.pow(xColSumsMap(col)._1, 2) / nexamples)) / (nexamples) + xColSd.put(col, math.sqrt(variance)) + col += 1 + } + + (yMean, xColMean, xColSd) + } + + /** + * Return the squared Euclidean distance between two vectors. + */ + def squaredDistance(v1: Array[Double], v2: Array[Double]): Double = { + if (v1.length != v2.length) { + throw new IllegalArgumentException("Vector sizes don't match") + } + var i = 0 + var sum = 0.0 + while (i < v1.length) { + sum += (v1(i) - v2(i)) * (v1(i) - v2(i)) + i += 1 + } + sum + } +} diff --git a/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.scala new file mode 100644 index 0000000000..c5b8a29942 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.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 spark.mllib.util + +import scala.util.Random + +import org.jblas.DoubleMatrix + +import spark.{RDD, SparkContext} + +object RidgeRegressionDataGenerator { + + /** + * Generate an RDD containing test data used for RidgeRegression. This function generates + * uniformly random values for every feature and adds Gaussian noise with mean `eps` to the + * response variable `Y`. + * + * @param sc SparkContext to be used for generating the RDD. + * @param nexamples Number of examples that will be contained in the RDD. + * @param nfeatures Number of features to generate for each example. + * @param eps Epsilon factor by which examples are scaled. + * @param nparts Number of partitions in the RDD. Default value is 2. + */ + def generateRidgeRDD( + sc: SparkContext, + nexamples: Int, + nfeatures: Int, + eps: Double, + nparts: Int = 2) : RDD[(Double, Array[Double])] = { + org.jblas.util.Random.seed(42) + // Random values distributed uniformly in [-0.5, 0.5] + val w = DoubleMatrix.rand(nfeatures, 1).subi(0.5) + w.put(0, 0, 10) + w.put(1, 0, 10) + + val data: RDD[(Double, Array[Double])] = sc.parallelize(0 until nparts, nparts).flatMap { p => + org.jblas.util.Random.seed(42 + p) + val examplesInPartition = nexamples / nparts + + val X = DoubleMatrix.rand(examplesInPartition, nfeatures) + val y = X.mmul(w) + + val rnd = new Random(42 + p) + + val normalValues = Array.fill[Double](examplesInPartition)(rnd.nextGaussian() * eps) + val yObs = new DoubleMatrix(normalValues).addi(y) + + Iterator.tabulate(examplesInPartition) { i => + (yObs.get(i, 0), X.getRow(i).toArray) + } + } + data + } + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: RidgeRegressionGenerator " + + "<master> <output_dir> <num_examples> <num_features> <num_partitions>") + System.exit(1) + } + + val sparkMaster: String = args(0) + val outputPath: String = args(1) + val nexamples: Int = if (args.length > 2) args(2).toInt else 1000 + val nfeatures: Int = if (args.length > 3) args(3).toInt else 100 + val parts: Int = if (args.length > 4) args(4).toInt else 2 + val eps = 10 + + val sc = new SparkContext(sparkMaster, "RidgeRegressionDataGenerator") + val data = generateRidgeRDD(sc, nexamples, nfeatures, eps, parts) + + MLUtils.saveLabeledData(data, outputPath) + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/SVMDataGenerator.scala new file mode 100644 index 0000000000..00a54d9a70 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/SVMDataGenerator.scala @@ -0,0 +1,48 @@ +package spark.mllib.classification + +import scala.util.Random +import scala.math.signum + +import org.jblas.DoubleMatrix + +import spark.{RDD, SparkContext} +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix + +object SVMGenerator { + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: SVMGenerator " + + "<master> <output_dir> <num_examples> <num_features> <num_partitions>") + System.exit(1) + } + + val sparkMaster: String = args(0) + val outputPath: String = args(1) + val nexamples: Int = if (args.length > 2) args(2).toInt else 1000 + val nfeatures: Int = if (args.length > 3) args(3).toInt else 2 + val parts: Int = if (args.length > 4) args(4).toInt else 2 + val eps = 3 + + val sc = new SparkContext(sparkMaster, "SVMGenerator") + + val globalRnd = new Random(94720) + val trueWeights = new DoubleMatrix(1, nfeatures+1, + Array.fill[Double](nfeatures + 1) { globalRnd.nextGaussian() }:_*) + + val data: RDD[(Double, Array[Double])] = sc.parallelize(0 until nexamples, parts).map { idx => + val rnd = new Random(42 + idx) + + val x = Array.fill[Double](nfeatures) { + rnd.nextDouble() * 2.0 - 1.0 + } + val y = signum((new DoubleMatrix(1, x.length, x:_*)).dot(trueWeights) + rnd.nextGaussian() * 0.1) + (y, x) + } + + MLUtils.saveLabeledData(data, outputPath) + sc.stop() + } +} diff --git a/mllib/src/test/resources/log4j.properties b/mllib/src/test/resources/log4j.properties new file mode 100644 index 0000000000..4265ba6e5d --- /dev/null +++ b/mllib/src/test/resources/log4j.properties @@ -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. +# + +# Set everything to be logged to the file core/target/unit-tests.log +log4j.rootCategory=INFO, file +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=false +log4j.appender.file.file=mllib/target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %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/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala new file mode 100644 index 0000000000..8664263935 --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala @@ -0,0 +1,130 @@ +/* + * 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 spark.mllib.classification + +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers + +import spark.SparkContext + + +class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll with ShouldMatchers { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + // Generate input of the form Y = logistic(offset + scale*X) + def generateLogisticInput( + offset: Double, + scale: Double, + nPoints: Int, + seed: Int): Seq[(Int, Array[Double])] = { + val rnd = new Random(seed) + val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian()) + + // NOTE: if U is uniform[0, 1] then ln(u) - ln(1-u) is Logistic(0,1) + val unifRand = new scala.util.Random(45) + val rLogis = (0 until nPoints).map { i => + val u = unifRand.nextDouble() + math.log(u) - math.log(1.0-u) + } + + // y <- A + B*x + rLogis() + // y <- as.numeric(y > 0) + val y: Seq[Int] = (0 until nPoints).map { i => + val yVal = offset + scale * x1(i) + rLogis(i) + if (yVal > 0) 1 else 0 + } + + val testData = (0 until nPoints).map(i => (y(i), Array(x1(i)))) + testData + } + + def validatePrediction(predictions: Seq[Int], input: Seq[(Int, Array[Double])]) { + val numOffPredictions = predictions.zip(input).filter { case (prediction, (expected, _)) => + (prediction != expected) + }.size + // At least 83% of the predictions should be on. + ((input.length - numOffPredictions).toDouble / input.length) should be > 0.83 + } + + // Test if we can correctly learn A, B where Y = logistic(A + B*X) + test("logistic regression") { + val nPoints = 10000 + val A = 2.0 + val B = -1.5 + + val testData = generateLogisticInput(A, B, nPoints, 42) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + val lr = new LogisticRegressionLocalRandomSGD().setStepSize(10.0).setNumIterations(20) + + val model = lr.train(testRDD) + + // Test the weights + val weight0 = model.weights(0) + assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]") + assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]") + + val validationData = generateLogisticInput(A, B, nPoints, 17) + val validationRDD = sc.parallelize(validationData, 2) + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row._2)), validationData) + } + + test("logistic regression with initial weights") { + val nPoints = 10000 + val A = 2.0 + val B = -1.5 + + val testData = generateLogisticInput(A, B, nPoints, 42) + + val initialB = -1.0 + val initialWeights = Array(initialB) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + + // Use half as many iterations as the previous test. + val lr = new LogisticRegressionLocalRandomSGD().setStepSize(10.0).setNumIterations(10) + + val model = lr.train(testRDD, initialWeights) + + val weight0 = model.weights(0) + assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]") + assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]") + + val validationData = generateLogisticInput(A, B, nPoints, 17) + val validationRDD = sc.parallelize(validationData, 2) + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row._2)), validationData) + } +} diff --git a/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala new file mode 100644 index 0000000000..d546e0729e --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala @@ -0,0 +1,116 @@ +/* + * 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 spark.mllib.classification + +import scala.util.Random +import scala.math.signum + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext + +import org.jblas.DoubleMatrix + +class SVMSuite extends FunSuite with BeforeAndAfterAll { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + // Generate noisy input of the form Y = signum(x.dot(weights) + intercept + noise) + def generateSVMInput( + intercept: Double, + weights: Array[Double], + nPoints: Int, + seed: Int): Seq[(Int, Array[Double])] = { + val rnd = new Random(seed) + val weightsMat = new DoubleMatrix(1, weights.length, weights:_*) + val x = Array.fill[Array[Double]](nPoints)(Array.fill[Double](weights.length)(rnd.nextGaussian())) + val y = x.map(xi => + signum((new DoubleMatrix(1, xi.length, xi:_*)).dot(weightsMat) + intercept + 0.1 * rnd.nextGaussian()).toInt + ) + y zip x + } + + def validatePrediction(predictions: Seq[Int], input: Seq[(Int, Array[Double])]) { + val numOffPredictions = predictions.zip(input).filter { case (prediction, (expected, _)) => + (prediction != expected) + }.size + // At least 80% of the predictions should be on. + assert(numOffPredictions < input.length / 5) + } + + test("SVMLocalRandomSGD") { + val nPoints = 10000 + + val A = 2.0 + val B = -1.5 + val C = 1.0 + + val testData = generateSVMInput(A, Array[Double](B,C), nPoints, 42) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + + val svm = new SVMLocalRandomSGD().setStepSize(1.0).setRegParam(1.0).setNumIterations(100) + + val model = svm.train(testRDD) + + val validationData = generateSVMInput(A, Array[Double](B,C), nPoints, 17) + val validationRDD = sc.parallelize(validationData,2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row._2)), validationData) + } + + test("SVMLocalRandomSGD with initial weights") { + val nPoints = 10000 + + val A = 2.0 + val B = -1.5 + val C = 1.0 + + val testData = generateSVMInput(A, Array[Double](B,C), nPoints, 42) + + val initialB = -1.0 + val initialC = -1.0 + val initialWeights = Array(initialB,initialC) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + + val svm = new SVMLocalRandomSGD().setStepSize(1.0).setRegParam(1.0).setNumIterations(100) + + val model = svm.train(testRDD, initialWeights) + + val validationData = generateSVMInput(A, Array[Double](B,C), nPoints, 17) + val validationRDD = sc.parallelize(validationData,2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row._2)), validationData) + } +} diff --git a/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala new file mode 100644 index 0000000000..bebade9afb --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala @@ -0,0 +1,170 @@ +/* + * 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 spark.mllib.clustering + +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext +import spark.SparkContext._ + +import org.jblas._ + + +class KMeansSuite extends FunSuite with BeforeAndAfterAll { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + val EPSILON = 1e-4 + + import KMeans.{RANDOM, K_MEANS_PARALLEL} + + def prettyPrint(point: Array[Double]): String = point.mkString("(", ", ", ")") + + def prettyPrint(points: Array[Array[Double]]): String = { + points.map(prettyPrint).mkString("(", "; ", ")") + } + + // L1 distance between two points + def distance1(v1: Array[Double], v2: Array[Double]): Double = { + v1.zip(v2).map{ case (a, b) => math.abs(a-b) }.max + } + + // Assert that two vectors are equal within tolerance EPSILON + def assertEqual(v1: Array[Double], v2: Array[Double]) { + def errorMessage = prettyPrint(v1) + " did not equal " + prettyPrint(v2) + assert(v1.length == v2.length, errorMessage) + assert(distance1(v1, v2) <= EPSILON, errorMessage) + } + + // Assert that two sets of points are equal, within EPSILON tolerance + def assertSetsEqual(set1: Array[Array[Double]], set2: Array[Array[Double]]) { + def errorMessage = prettyPrint(set1) + " did not equal " + prettyPrint(set2) + assert(set1.length == set2.length, errorMessage) + for (v <- set1) { + val closestDistance = set2.map(w => distance1(v, w)).min + if (closestDistance > EPSILON) { + fail(errorMessage) + } + } + for (v <- set2) { + val closestDistance = set1.map(w => distance1(v, w)).min + if (closestDistance > EPSILON) { + fail(errorMessage) + } + } + } + + test("single cluster") { + val data = sc.parallelize(Array( + Array(1.0, 2.0, 6.0), + Array(1.0, 3.0, 0.0), + Array(1.0, 4.0, 6.0) + )) + + // No matter how many runs or iterations we use, we should get one cluster, + // centered at the mean of the points + + var model = KMeans.train(data, k=1, maxIterations=1) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=2) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train( + data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + } + + test("single cluster with big dataset") { + val smallData = Array( + Array(1.0, 2.0, 6.0), + Array(1.0, 3.0, 0.0), + Array(1.0, 4.0, 6.0) + ) + val data = sc.parallelize((1 to 100).flatMap(_ => smallData), 4) + + // No matter how many runs or iterations we use, we should get one cluster, + // centered at the mean of the points + + var model = KMeans.train(data, k=1, maxIterations=1) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=2) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + } + + test("k-means|| initialization") { + val points = Array( + Array(1.0, 2.0, 6.0), + Array(1.0, 3.0, 0.0), + Array(1.0, 4.0, 6.0), + Array(1.0, 0.0, 1.0), + Array(1.0, 1.0, 1.0) + ) + val rdd = sc.parallelize(points) + + // K-means|| initialization should place all clusters into distinct centers because + // it will make at least five passes, and it will give non-zero probability to each + // unselected point as long as it hasn't yet selected all of them + + var model = KMeans.train(rdd, k=5, maxIterations=1) + assertSetsEqual(model.clusterCenters, points) + + // Iterations of Lloyd's should not change the answer either + model = KMeans.train(rdd, k=5, maxIterations=10) + assertSetsEqual(model.clusterCenters, points) + + // Neither should more runs + model = KMeans.train(rdd, k=5, maxIterations=10, runs=5) + assertSetsEqual(model.clusterCenters, points) + } +} diff --git a/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala new file mode 100644 index 0000000000..f98590b8d9 --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala @@ -0,0 +1,97 @@ +/* + * 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 spark.mllib.recommendation + +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext +import spark.SparkContext._ + +import org.jblas._ + + +class ALSSuite extends FunSuite with BeforeAndAfterAll { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + test("rank-1 matrices") { + testALS(10, 20, 1, 15, 0.7, 0.3) + } + + test("rank-2 matrices") { + testALS(20, 30, 2, 15, 0.7, 0.3) + } + + /** + * Test if we can correctly factorize R = U * P where U and P are of known rank. + * + * @param users number of users + * @param products number of products + * @param features number of features (rank of problem) + * @param iterations number of iterations to run + * @param samplingRate what fraction of the user-product pairs are known + * @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) + { + val rand = new Random(42) + + // Create a random matrix with uniform values from -1 to 1 + def randomMatrix(m: Int, n: Int) = + new DoubleMatrix(m, n, Array.fill(m * n)(rand.nextDouble() * 2 - 1): _*) + + val userMatrix = randomMatrix(users, features) + val productMatrix = randomMatrix(features, products) + val trueRatings = userMatrix.mmul(productMatrix) + + val sampledRatings = { + for (u <- 0 until users; p <- 0 until products if rand.nextDouble() < samplingRate) + yield (u, p, trueRatings.get(u, p)) + } + + val model = ALS.train(sc.parallelize(sampledRatings), features, iterations) + + val predictedU = new DoubleMatrix(users, features) + for ((u, vec) <- model.userFeatures.collect(); i <- 0 until features) { + predictedU.put(u, i, vec(i)) + } + val predictedP = new DoubleMatrix(products, features) + for ((p, vec) <- model.productFeatures.collect(); i <- 0 until features) { + predictedP.put(p, i, vec(i)) + } + 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)) + } + } + } +} + diff --git a/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala new file mode 100644 index 0000000000..cf2b067d40 --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala @@ -0,0 +1,127 @@ +/* + * 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 spark.mllib.regression + +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext + +import org.jblas.DoubleMatrix + + +class LassoSuite extends FunSuite with BeforeAndAfterAll { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + // Generate noisy input of the form Y = x.dot(weights) + intercept + noise + def generateLassoInput( + intercept: Double, + weights: Array[Double], + nPoints: Int, + seed: Int): Seq[(Double, Array[Double])] = { + val rnd = new Random(seed) + val weightsMat = new DoubleMatrix(1, weights.length, weights:_*) + val x = Array.fill[Array[Double]](nPoints)(Array.fill[Double](weights.length)(rnd.nextGaussian())) + val y = x.map(xi => + (new DoubleMatrix(1, xi.length, xi:_*)).dot(weightsMat) + intercept + 0.1 * rnd.nextGaussian() + ) + y zip x + } + + def validatePrediction(predictions: Seq[Double], input: Seq[(Double, Array[Double])]) { + val numOffPredictions = predictions.zip(input).filter { case (prediction, (expected, _)) => + // A prediction is off if the prediction is more than 0.5 away from expected value. + math.abs(prediction - expected) > 0.5 + }.size + // At least 80% of the predictions should be on. + assert(numOffPredictions < input.length / 5) + } + + test("LassoLocalRandomSGD") { + val nPoints = 10000 + + val A = 2.0 + val B = -1.5 + val C = 1.0e-2 + + val testData = generateLassoInput(A, Array[Double](B,C), nPoints, 42) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + val ls = new LassoLocalRandomSGD().setStepSize(1.0).setRegParam(0.01).setNumIterations(20) + + val model = ls.train(testRDD) + + val weight0 = model.weights(0) + val weight1 = model.weights(1) + assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]") + assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]") + assert(weight1 >= -1.0e-3 && weight1 <= 1.0e-3, weight1 + " not in [-0.001, 0.001]") + + val validationData = generateLassoInput(A, Array[Double](B,C), nPoints, 17) + val validationRDD = sc.parallelize(validationData,2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row._2)), validationData) + } + + test("LassoLocalRandomSGD with initial weights") { + val nPoints = 10000 + + val A = 2.0 + val B = -1.5 + val C = 1.0e-2 + + val testData = generateLassoInput(A, Array[Double](B,C), nPoints, 42) + + val initialB = -1.0 + val initialC = -1.0 + val initialWeights = Array(initialB,initialC) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + val ls = new LassoLocalRandomSGD().setStepSize(1.0).setRegParam(0.01).setNumIterations(20) + + val model = ls.train(testRDD, initialWeights) + + val weight0 = model.weights(0) + val weight1 = model.weights(1) + assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]") + assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]") + assert(weight1 >= -1.0e-3 && weight1 <= 1.0e-3, weight1 + " not in [-0.001, 0.001]") + + val validationData = generateLassoInput(A, Array[Double](B,C), nPoints, 17) + val validationRDD = sc.parallelize(validationData,2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_._2)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row._2)), validationData) + } +} diff --git a/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala new file mode 100644 index 0000000000..3c588c6162 --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala @@ -0,0 +1,64 @@ +/* + * 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 spark.mllib.regression + +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext +import spark.SparkContext._ + + +class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + // Test if we can correctly learn Y = 3 + X1 + X2 when + // X1 and X2 are collinear. + test("multi-collinear variables") { + val rnd = new Random(43) + val x1 = Array.fill[Double](20)(rnd.nextGaussian()) + + // Pick a mean close to mean of x1 + val rnd1 = new Random(42) //new NormalDistribution(0.1, 0.01) + val x2 = Array.fill[Double](20)(0.1 + rnd1.nextGaussian() * 0.01) + + val xMat = (0 until 20).map(i => Array(x1(i), x2(i))).toArray + + val y = xMat.map(i => 3 + i(0) + i(1)) + val testData = (0 until 20).map(i => (y(i), xMat(i))).toArray + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + val ridgeReg = new RidgeRegression().setLowLambda(0) + .setHighLambda(10) + + val model = ridgeReg.train(testRDD) + + assert(model.intercept >= 2.9 && model.intercept <= 3.1) + assert(model.weights.length === 2) + assert(model.weights.get(0) >= 0.9 && model.weights.get(0) <= 1.1) + assert(model.weights.get(1) >= 0.9 && model.weights.get(1) <= 1.1) + } +} @@ -1,9 +1,26 @@ <?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> <groupId>org.spark-project</groupId> <artifactId>spark-parent</artifactId> - <version>0.7.1-SNAPSHOT</version> + <version>0.8.0-SNAPSHOT</version> <packaging>pom</packaging> <name>Spark Project Parent POM</name> <url>http://spark-project.org/</url> @@ -41,9 +58,10 @@ <module>core</module> <module>bagel</module> <module>examples</module> + <module>mllib</module> + <module>tools</module> <module>streaming</module> <module>repl</module> - <module>repl-bin</module> </modules> <properties> @@ -51,14 +69,15 @@ <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> <java.version>1.5</java.version> - <scala.version>2.9.2</scala.version> + <scala.version>2.9.3</scala.version> <mesos.version>0.9.0-incubating</mesos.version> <akka.version>2.0.3</akka.version> - <spray.version>1.0-M2.1</spray.version> - <spray.json.version>1.1.1</spray.json.version> - <slf4j.version>1.6.1</slf4j.version> + <slf4j.version>1.7.2</slf4j.version> <cdh.version>4.1.2</cdh.version> <log4j.version>1.2.17</log4j.version> + + <PermGen>64m</PermGen> + <MaxPermGen>512m</MaxPermGen> </properties> <repositories> @@ -95,28 +114,6 @@ <enabled>false</enabled> </snapshots> </repository> - <repository> - <id>spray-repo</id> - <name>Spray Repository</name> - <url>http://repo.spray.cc/</url> - <releases> - <enabled>true</enabled> - </releases> - <snapshots> - <enabled>false</enabled> - </snapshots> - </repository> - <repository> - <id>twitter4j-repo</id> - <name>Twitter4J Repository</name> - <url>http://twitter4j.org/maven2/</url> - <releases> - <enabled>true</enabled> - </releases> - <snapshots> - <enabled>false</enabled> - </snapshots> - </repository> </repositories> <pluginRepositories> <pluginRepository> @@ -187,9 +184,14 @@ <version>0.8.4</version> </dependency> <dependency> - <groupId>asm</groupId> - <artifactId>asm-all</artifactId> - <version>3.3.1</version> + <groupId>org.xerial.snappy</groupId> + <artifactId>snappy-java</artifactId> + <version>1.0.5</version> + </dependency> + <dependency> + <groupId>org.ow2.asm</groupId> + <artifactId>asm</artifactId> + <version>4.0</version> </dependency> <dependency> <groupId>com.google.protobuf</groupId> @@ -197,9 +199,14 @@ <version>2.4.1</version> </dependency> <dependency> - <groupId>de.javakaffee</groupId> - <artifactId>kryo-serializers</artifactId> - <version>0.22</version> + <groupId>com.twitter</groupId> + <artifactId>chill_2.9.3</artifactId> + <version>0.3.0</version> + </dependency> + <dependency> + <groupId>com.twitter</groupId> + <artifactId>chill-java</artifactId> + <version>0.3.0</version> </dependency> <dependency> <groupId>com.typesafe.akka</groupId> @@ -227,36 +234,41 @@ <version>1.2.0</version> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-can</artifactId> - <version>${spray.version}</version> + <groupId>com.github.scala-incubator.io</groupId> + <artifactId>scala-io-file_2.9.2</artifactId> + <version>0.4.1</version> + </dependency> + <dependency> + <groupId>org.apache.mesos</groupId> + <artifactId>mesos</artifactId> + <version>${mesos.version}</version> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-server</artifactId> - <version>${spray.version}</version> + <groupId>io.netty</groupId> + <artifactId>netty-all</artifactId> + <version>4.0.0.Beta2</version> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-json_${scala.version}</artifactId> - <version>${spray.json.version}</version> + <groupId>org.apache.derby</groupId> + <artifactId>derby</artifactId> + <version>10.4.2.0</version> + <scope>test</scope> </dependency> <dependency> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-api</artifactId> - <version>1.0.2</version> + <groupId>net.liftweb</groupId> + <artifactId>lift-json_2.9.2</artifactId> + <version>2.5</version> </dependency> <dependency> - <groupId>com.github.scala-incubator.io</groupId> - <artifactId>scala-io-file_${scala.version}</artifactId> - <version>0.4.1</version> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-core</artifactId> + <version>3.0.0</version> </dependency> <dependency> - <groupId>org.apache.mesos</groupId> - <artifactId>mesos</artifactId> - <version>${mesos.version}</version> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-jvm</artifactId> + <version>3.0.0</version> </dependency> - <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-compiler</artifactId> @@ -267,6 +279,16 @@ <artifactId>jline</artifactId> <version>${scala.version}</version> </dependency> + <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> + <version>${scala.version}</version> + </dependency> + <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scalap</artifactId> + <version>${scala.version}</version> + </dependency> <dependency> <groupId>log4j</groupId> @@ -277,7 +299,7 @@ <dependency> <groupId>org.scalatest</groupId> <artifactId>scalatest_${scala.version}</artifactId> - <version>1.8</version> + <version>1.9.1</version> <scope>test</scope> </dependency> <dependency> @@ -289,7 +311,7 @@ <dependency> <groupId>org.scalacheck</groupId> <artifactId>scalacheck_${scala.version}</artifactId> - <version>1.9</version> + <version>1.10.0</version> <scope>test</scope> </dependency> <dependency> @@ -333,21 +355,9 @@ <version>1.7</version> </plugin> <plugin> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-maven-plugin</artifactId> - <version>1.0.1</version> - <executions> - <execution> - <goals> - <goal>generate</goal> - </goals> - </execution> - </executions> - </plugin> - <plugin> <groupId>net.alchim31.maven</groupId> <artifactId>scala-maven-plugin</artifactId> - <version>3.1.0</version> + <version>3.1.5</version> <executions> <execution> <id>scala-compile-first</id> @@ -374,13 +384,17 @@ <configuration> <scalaVersion>${scala.version}</scalaVersion> <recompileMode>incremental</recompileMode> + <useZincServer>true</useZincServer> <args> <arg>-unchecked</arg> <arg>-optimise</arg> </args> <jvmArgs> <jvmArg>-Xms64m</jvmArg> + <jvmArg>-Xms1024m</jvmArg> <jvmArg>-Xmx1024m</jvmArg> + <jvmArg>-XX:PermSize=${PermGen}</jvmArg> + <jvmArg>-XX:MaxPermSize=${MaxPermGen}</jvmArg> </jvmArgs> <javacArgs> <javacArg>-source</javacArg> @@ -417,8 +431,9 @@ <configuration> <reportsDirectory>${project.build.directory}/surefire-reports</reportsDirectory> <junitxml>.</junitxml> - <filereports>WDF TestSuite.txt</filereports> + <filereports>${project.build.directory}/SparkTestSuite.txt</filereports> <argLine>-Xms64m -Xmx1024m</argLine> + <stderr/> </configuration> <executions> <execution> @@ -512,7 +527,6 @@ <profiles> <profile> <id>hadoop1</id> - <properties> <hadoop.major.version>1</hadoop.major.version> </properties> @@ -548,15 +562,102 @@ <dependency> <groupId>org.apache.avro</groupId> <artifactId>avro</artifactId> - <version>1.7.1.cloudera.2</version> + <version>1.7.4</version> </dependency> <dependency> <groupId>org.apache.avro</groupId> <artifactId>avro-ipc</artifactId> - <version>1.7.1.cloudera.2</version> + <version>1.7.4</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> </dependency> </dependencies> </dependencyManagement> </profile> + + <profile> + <id>hadoop2-yarn</id> + <properties> + <hadoop.major.version>2</hadoop.major.version> + <!-- 0.23.* is same as 2.0.* - except hardened to run production jobs --> + <!-- <yarn.version>0.23.7</yarn.version> --> + <yarn.version>2.0.2-alpha</yarn.version> + </properties> + + <repositories> + <repository> + <id>maven-root</id> + <name>Maven root repository</name> + <url>http://repo1.maven.org/maven2/</url> + <releases> + <enabled>true</enabled> + </releases> + <snapshots> + <enabled>false</enabled> + </snapshots> + </repository> + </repositories> + + <dependencyManagement> + <dependencies> + <!-- TODO: check versions, bringover from yarn branch ! --> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <version>${yarn.version}</version> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + <version>${yarn.version}</version> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + <version>${yarn.version}</version> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-client</artifactId> + <version>${yarn.version}</version> + </dependency> + <!-- Specify Avro version because Kafka also has it as a dependency --> + <dependency> + <groupId>org.apache.avro</groupId> + <artifactId>avro</artifactId> + <version>1.7.4</version> + </dependency> + <dependency> + <groupId>org.apache.avro</groupId> + <artifactId>avro-ipc</artifactId> + <version>1.7.4</version> + </dependency> + </dependencies> + </dependencyManagement> + </profile> + <profile> + <id>assembly</id> + <activation> + <activeByDefault>false</activeByDefault> + </activation> + <modules> + <module>assembly</module> + </modules> + </profile> + <profile> + <id>expensive-modules</id> + <activation> + <property> + <name>!noExpensive</name> + </property> + </activation> + <modules> + <module>repl-bin</module> + </modules> + </profile> </profiles> </project> diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 5f378b2398..c822f49e78 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -1,9 +1,25 @@ +/* + * 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. + */ + import sbt._ import sbt.Classpaths.publishTask import Keys._ import sbtassembly.Plugin._ import AssemblyKeys._ -import twirl.sbt.TwirlPlugin._ // For Sonatype publishing //import com.jsuereth.pgp.sbtplugin.PgpKeys._ @@ -12,31 +28,42 @@ object SparkBuild extends Build { // "1.0.4" for Apache releases, or "0.20.2-cdh3u5" for Cloudera Hadoop. val HADOOP_VERSION = "1.0.4" val HADOOP_MAJOR_VERSION = "1" + val HADOOP_YARN = false // For Hadoop 2 versions such as "2.0.0-mr1-cdh4.1.1", set the HADOOP_MAJOR_VERSION to "2" //val HADOOP_VERSION = "2.0.0-mr1-cdh4.1.1" //val HADOOP_MAJOR_VERSION = "2" + //val HADOOP_YARN = false + + // For Hadoop 2 YARN support + //val HADOOP_VERSION = "2.0.2-alpha" + //val HADOOP_MAJOR_VERSION = "2" + //val HADOOP_YARN = true - lazy val root = Project("root", file("."), settings = rootSettings) aggregate(core, repl, examples, bagel, streaming) + lazy val root = Project("root", file("."), settings = rootSettings) aggregate(core, repl, examples, bagel, streaming, mllib, tools) lazy val core = Project("core", file("core"), settings = coreSettings) - lazy val repl = Project("repl", file("repl"), settings = replSettings) dependsOn (core) dependsOn (streaming) + lazy val repl = Project("repl", file("repl"), settings = replSettings) dependsOn (core) dependsOn(bagel) dependsOn(mllib) lazy val examples = Project("examples", file("examples"), settings = examplesSettings) dependsOn (core) dependsOn (streaming) + lazy val tools = Project("tools", file("tools"), settings = examplesSettings) dependsOn (core) dependsOn (streaming) + lazy val bagel = Project("bagel", file("bagel"), settings = bagelSettings) dependsOn (core) lazy val streaming = Project("streaming", file("streaming"), settings = streamingSettings) dependsOn (core) + lazy val mllib = Project("mllib", file("mllib"), settings = mllibSettings) dependsOn (core) + // 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") def sharedSettings = Defaults.defaultSettings ++ Seq( organization := "org.spark-project", - version := "0.7.1-SNAPSHOT", - scalaVersion := "2.9.2", + version := "0.8.0-SNAPSHOT", + scalaVersion := "2.9.3", scalacOptions := Seq("-unchecked", "-optimize", "-deprecation"), unmanagedJars in Compile <<= baseDirectory map { base => (base / "lib" ** "*.jar").classpath }, retrieveManaged := true, @@ -44,7 +71,14 @@ object SparkBuild extends Build { transitiveClassifiers in Scope.GlobalScope := Seq("sources"), testListeners <<= target.map(t => Seq(new eu.henkelmann.sbt.JUnitXmlTestsListener(t.getAbsolutePath))), - // shared between both core and streaming. + // Fork new JVMs for tests and set Java options for those + fork := true, + javaOptions += "-Xmx2500m", + + // 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/"), // For Sonatype publishing @@ -92,13 +126,13 @@ object SparkBuild extends Build { */ libraryDependencies ++= Seq( - "org.eclipse.jetty" % "jetty-server" % "7.5.3.v20111011", - "org.scalatest" %% "scalatest" % "1.8" % "test", - "org.scalacheck" %% "scalacheck" % "1.9" % "test", - "com.novocode" % "junit-interface" % "0.8" % "test", + "io.netty" % "netty" % "3.5.3.Final", + "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" ), - parallelExecution := false, /* 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) }, @@ -110,41 +144,74 @@ object SparkBuild extends Build { publishMavenStyle in MavenCompile := true, publishLocal in MavenCompile <<= publishTask(publishLocalConfiguration in MavenCompile, deliverLocal), publishLocalBoth <<= Seq(publishLocal in MavenCompile, publishLocal).dependOn - ) + ) ++ net.virtualvoid.sbt.graph.Plugin.graphSettings - val slf4jVersion = "1.6.1" + val slf4jVersion = "1.7.2" + + val excludeJackson = ExclusionRule(organization = "org.codehaus.jackson") + val excludeNetty = ExclusionRule(organization = "org.jboss.netty") + val excludeAsm = ExclusionRule(organization = "asm") + val excludeSnappy = ExclusionRule(organization = "org.xerial.snappy") def coreSettings = sharedSettings ++ Seq( name := "spark-core", resolvers ++= Seq( "JBoss Repository" at "http://repository.jboss.org/nexus/content/repositories/releases/", "Spray Repository" at "http://repo.spray.cc/", - "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/", - "Twitter4J Repository" at "http://twitter4j.org/maven2/" + "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/" ), libraryDependencies ++= Seq( - "com.google.guava" % "guava" % "11.0.1", + "com.google.guava" % "guava" % "14.0.1", + "com.google.code.findbugs" % "jsr305" % "1.3.9", "log4j" % "log4j" % "1.2.16", "org.slf4j" % "slf4j-api" % slf4jVersion, "org.slf4j" % "slf4j-log4j12" % slf4jVersion, + "commons-daemon" % "commons-daemon" % "1.0.10", "com.ning" % "compress-lzf" % "0.8.4", - "org.apache.hadoop" % "hadoop-core" % HADOOP_VERSION, - "asm" % "asm-all" % "3.3.1", + "org.xerial.snappy" % "snappy-java" % "1.0.5", + "org.ow2.asm" % "asm" % "4.0", "com.google.protobuf" % "protobuf-java" % "2.4.1", - "de.javakaffee" % "kryo-serializers" % "0.22", - "com.typesafe.akka" % "akka-actor" % "2.0.3", - "com.typesafe.akka" % "akka-remote" % "2.0.3", - "com.typesafe.akka" % "akka-slf4j" % "2.0.3", + "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", - "cc.spray" % "spray-can" % "1.0-M2.1", - "cc.spray" % "spray-server" % "1.0-M2.1", - "cc.spray" %% "spray-json" % "1.1.1", - "org.apache.mesos" % "mesos" % "0.9.0-incubating" - ) ++ (if (HADOOP_MAJOR_VERSION == "2") Some("org.apache.hadoop" % "hadoop-client" % HADOOP_VERSION) else None).toSeq, - unmanagedSourceDirectories in Compile <+= baseDirectory{ _ / ("src/hadoop" + HADOOP_MAJOR_VERSION + "/scala") } - ) ++ assemblySettings ++ extraAssemblySettings ++ Twirl.settings + "net.liftweb" % "lift-json_2.9.2" % "2.5", + "org.apache.mesos" % "mesos" % "0.9.0-incubating", + "io.netty" % "netty-all" % "4.0.0.Beta2", + "org.apache.derby" % "derby" % "10.4.2.0" % "test", + "com.codahale.metrics" % "metrics-core" % "3.0.0", + "com.codahale.metrics" % "metrics-jvm" % "3.0.0", + "com.twitter" % "chill_2.9.3" % "0.3.0", + "com.twitter" % "chill-java" % "0.3.0" + ) ++ ( + if (HADOOP_MAJOR_VERSION == "2") { + if (HADOOP_YARN) { + Seq( + // Exclude rule required for all ? + "org.apache.hadoop" % "hadoop-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm), + "org.apache.hadoop" % "hadoop-yarn-api" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm), + "org.apache.hadoop" % "hadoop-yarn-common" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm), + "org.apache.hadoop" % "hadoop-yarn-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm) + ) + } else { + Seq( + "org.apache.hadoop" % "hadoop-core" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm), + "org.apache.hadoop" % "hadoop-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm) + ) + } + } else { + Seq("org.apache.hadoop" % "hadoop-core" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty) ) + }), + unmanagedSourceDirectories in Compile <+= baseDirectory{ _ / + ( if (HADOOP_YARN && HADOOP_MAJOR_VERSION == "2") { + "src/hadoop2-yarn/scala" + } else { + "src/hadoop" + HADOOP_MAJOR_VERSION + "/scala" + } ) + } + ) ++ assemblySettings ++ extraAssemblySettings def rootSettings = sharedSettings ++ Seq( publish := {} @@ -153,28 +220,59 @@ object SparkBuild extends Build { def replSettings = sharedSettings ++ Seq( name := "spark-repl", libraryDependencies <+= scalaVersion("org.scala-lang" % "scala-compiler" % _) - ) + ) ++ assemblySettings ++ extraAssemblySettings def examplesSettings = sharedSettings ++ Seq( name := "spark-examples", - libraryDependencies ++= Seq("com.twitter" % "algebird-core_2.9.2" % "0.1.8") + libraryDependencies ++= Seq( + "com.twitter" % "algebird-core_2.9.2" % "0.1.11", + + "org.apache.hbase" % "hbase" % "0.94.6" excludeAll(excludeNetty, excludeAsm), + + "org.apache.cassandra" % "cassandra-all" % "1.2.5" + exclude("com.google.guava", "guava") + exclude("com.googlecode.concurrentlinkedhashmap", "concurrentlinkedhashmap-lru") + exclude("com.ning","compress-lzf") + exclude("io.netty", "netty") + exclude("jline","jline") + exclude("log4j","log4j") + exclude("org.apache.cassandra.deps", "avro") + excludeAll(excludeSnappy) + ) ) - def bagelSettings = sharedSettings ++ Seq(name := "spark-bagel") + def toolsSettings = sharedSettings ++ Seq( + name := "spark-tools" + ) + + def bagelSettings = sharedSettings ++ Seq( + name := "spark-bagel" + ) + + def mllibSettings = sharedSettings ++ Seq( + name := "spark-mllib", + libraryDependencies ++= Seq( + "org.jblas" % "jblas" % "1.2.3" + ) + ) def streamingSettings = sharedSettings ++ Seq( name := "spark-streaming", + resolvers ++= Seq( + "Akka Repository" at "http://repo.akka.io/releases/" + ), libraryDependencies ++= Seq( - "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile", - "com.github.sgroschupf" % "zkclient" % "0.1", - "org.twitter4j" % "twitter4j-stream" % "3.0.3", - "com.typesafe.akka" % "akka-zeromq" % "2.0.3" + "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) ) ) ++ assemblySettings ++ extraAssemblySettings def extraAssemblySettings() = Seq(test in assembly := {}) ++ Seq( 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 "reference.conf" => MergeStrategy.concat case _ => MergeStrategy.first } diff --git a/project/build.properties b/project/build.properties index d4287112c6..08e17131f6 100644 --- a/project/build.properties +++ b/project/build.properties @@ -1 +1,18 @@ -sbt.version=0.11.3 +# +# 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. +# + +sbt.version=0.12.3 diff --git a/project/plugins.sbt b/project/plugins.sbt index 4d0e696a11..1b0f879b94 100644 --- a/project/plugins.sbt +++ b/project/plugins.sbt @@ -4,15 +4,15 @@ resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/release resolvers += "Spray Repository" at "http://repo.spray.cc/" -addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.8.3") +addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.8.5") -addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.1.0-RC1") +addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.1.1") -addSbtPlugin("com.github.mpeltonen" % "sbt-idea" % "1.0.0") - -addSbtPlugin("cc.spray" %% "sbt-twirl" % "0.5.2") +addSbtPlugin("com.github.mpeltonen" % "sbt-idea" % "1.2.0") // For Sonatype publishing //resolvers += Resolver.url("sbt-plugin-releases", new URL("http://scalasbt.artifactoryonline.com/scalasbt/sbt-plugin-releases/"))(Resolver.ivyStylePatterns) //addSbtPlugin("com.jsuereth" % "xsbt-gpg-plugin" % "0.6") + +addSbtPlugin("net.virtual-void" % "sbt-dependency-graph" % "0.7.3") @@ -1,5 +1,22 @@ #!/usr/bin/env 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. +# + # Figure out where the Scala framework is installed FWDIR="$(cd `dirname $0`; pwd)" @@ -36,9 +53,13 @@ if [[ "$SPARK_LAUNCH_WITH_SCALA" != "0" ]] ; then export SPARK_LAUNCH_WITH_SCALA=1 fi +if [ -n "$IPYTHON_OPTS" ]; then + IPYTHON=1 +fi + if [[ "$IPYTHON" = "1" ]] ; then - export PYSPARK_PYTHON="ipython" - exec "$PYSPARK_PYTHON" -i -c "%run $PYTHONSTARTUP" + IPYTHON_OPTS=${IPYTHON_OPTS:--i} + exec ipython "$IPYTHON_OPTS" -c "%run $PYTHONSTARTUP" else - exec "$PYSPARK_PYTHON" "$@" + exec "$PYSPARK_PYTHON" "$@" fi diff --git a/python/epydoc.conf b/python/epydoc.conf index 45102cd9fe..d5d5aa5454 100644 --- a/python/epydoc.conf +++ b/python/epydoc.conf @@ -1,5 +1,22 @@ [epydoc] # Epydoc section marker (required by ConfigParser) +# +# 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. +# + # Information about the project. name: PySpark url: http://spark-project.org diff --git a/python/examples/als.py b/python/examples/als.py index 010f80097f..a77dfb2577 100755 --- a/python/examples/als.py +++ b/python/examples/als.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. +# + """ This example requires numpy (http://www.numpy.org/) """ @@ -31,8 +48,7 @@ def update(i, vec, mat, ratings): if __name__ == "__main__": if len(sys.argv) < 2: - print >> sys.stderr, \ - "Usage: PythonALS <master> <M> <U> <F> <iters> <slices>" + print >> sys.stderr, "Usage: als <master> <M> <U> <F> <iters> <slices>" exit(-1) sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)]) M = int(sys.argv[2]) if len(sys.argv) > 2 else 100 @@ -67,5 +83,5 @@ if __name__ == "__main__": usb = sc.broadcast(us) error = rmse(R, ms, us) - print "Iteration %d:" % i + print "Iteration %d:" % i print "\nRMSE: %5.4f\n" % error diff --git a/python/examples/kmeans.py b/python/examples/kmeans.py index 72cf9f88c6..ba31af92fc 100644..100755 --- a/python/examples/kmeans.py +++ b/python/examples/kmeans.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. +# + """ This example requires numpy (http://www.numpy.org/) """ @@ -24,8 +41,7 @@ def closestPoint(p, centers): if __name__ == "__main__": if len(sys.argv) < 5: - print >> sys.stderr, \ - "Usage: PythonKMeans <master> <file> <k> <convergeDist>" + print >> sys.stderr, "Usage: kmeans <master> <file> <k> <convergeDist>" exit(-1) sc = SparkContext(sys.argv[1], "PythonKMeans") lines = sc.textFile(sys.argv[2]) diff --git a/python/examples/logistic_regression.py b/python/examples/logistic_regression.py index f13698a86f..3ac1bae4e9 100755 --- a/python/examples/logistic_regression.py +++ b/python/examples/logistic_regression.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. +# + """ This example requires numpy (http://www.numpy.org/) """ @@ -18,7 +35,7 @@ np.random.seed(42) DataPoint = namedtuple("DataPoint", ['x', 'y']) -from lr import DataPoint # So that DataPoint is properly serialized +from logistic_regression import DataPoint # So that DataPoint is properly serialized def generateData(): @@ -31,8 +48,7 @@ def generateData(): if __name__ == "__main__": if len(sys.argv) == 1: - print >> sys.stderr, \ - "Usage: PythonLR <master> [<slices>]" + print >> sys.stderr, "Usage: logistic_regression <master> [<slices>]" exit(-1) sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)]) slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 diff --git a/python/examples/pi.py b/python/examples/pi.py index 127cba029b..ab0645fc2f 100644..100755 --- a/python/examples/pi.py +++ b/python/examples/pi.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. +# + import sys from random import random from operator import add @@ -7,8 +24,7 @@ from pyspark import SparkContext if __name__ == "__main__": if len(sys.argv) == 1: - print >> sys.stderr, \ - "Usage: PythonPi <master> [<slices>]" + print >> sys.stderr, "Usage: pi <master> [<slices>]" exit(-1) sc = SparkContext(sys.argv[1], "PythonPi") slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 diff --git a/python/examples/transitive_closure.py b/python/examples/transitive_closure.py index 73f7f8fbaf..744cce6651 100644..100755 --- a/python/examples/transitive_closure.py +++ b/python/examples/transitive_closure.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. +# + import sys from random import Random @@ -20,11 +37,10 @@ def generateGraph(): if __name__ == "__main__": if len(sys.argv) == 1: - print >> sys.stderr, \ - "Usage: PythonTC <master> [<slices>]" + print >> sys.stderr, "Usage: transitive_closure <master> [<slices>]" exit(-1) - sc = SparkContext(sys.argv[1], "PythonTC") - slices = sys.argv[2] if len(sys.argv) > 2 else 2 + sc = SparkContext(sys.argv[1], "PythonTransitiveClosure") + slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 tc = sc.parallelize(generateGraph(), slices).cache() # Linear transitive closure: each round grows paths by one edge, diff --git a/python/examples/wordcount.py b/python/examples/wordcount.py index 857160624b..a6de22766a 100644..100755 --- a/python/examples/wordcount.py +++ b/python/examples/wordcount.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. +# + import sys from operator import add @@ -6,8 +23,7 @@ from pyspark import SparkContext if __name__ == "__main__": if len(sys.argv) < 3: - print >> sys.stderr, \ - "Usage: PythonWordCount <master> <file>" + print >> sys.stderr, "Usage: wordcount <master> <file>" exit(-1) sc = SparkContext(sys.argv[1], "PythonWordCount") lines = sc.textFile(sys.argv[2], 1) diff --git a/python/pyspark/accumulators.py b/python/pyspark/accumulators.py index 3e9d7d36da..d367f91967 100644 --- a/python/pyspark/accumulators.py +++ b/python/pyspark/accumulators.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. +# + """ >>> from pyspark.context import SparkContext >>> sc = SparkContext('local', 'test') diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py index def810dd46..dfdaba274f 100644 --- a/python/pyspark/broadcast.py +++ b/python/pyspark/broadcast.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. +# + """ >>> from pyspark.context import SparkContext >>> sc = SparkContext('local', 'test') diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 657fe6f989..c2b49ff37a 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.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. +# + import os import shutil import sys @@ -124,14 +141,21 @@ class SparkContext(object): def parallelize(self, c, numSlices=None): """ Distribute a local Python collection to form an RDD. + + >>> sc.parallelize(range(5), 5).glom().collect() + [[0], [1], [2], [3], [4]] """ numSlices = numSlices or self.defaultParallelism # Calling the Java parallelize() method with an ArrayList is too slow, # because it sends O(n) Py4J commands. As an alternative, serialized # objects are written to a file and loaded through textFile(). tempFile = NamedTemporaryFile(delete=False, dir=self._temp_dir) - if self.batchSize != 1: - c = batched(c, self.batchSize) + # 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) + if batchSize > 1: + c = batched(c, batchSize) for x in c: write_with_length(dump_pickle(x), tempFile) tempFile.close() diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py new file mode 100644 index 0000000000..eb18ec08c9 --- /dev/null +++ b/python/pyspark/daemon.py @@ -0,0 +1,181 @@ +# +# 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. +# + +import os +import signal +import socket +import sys +import traceback +import multiprocessing +from ctypes import c_bool +from errno import EINTR, ECHILD +from socket import AF_INET, SOCK_STREAM, SOMAXCONN +from signal import SIGHUP, SIGTERM, SIGCHLD, SIG_DFL, SIG_IGN +from pyspark.worker import main as worker_main +from pyspark.serializers import write_int + +try: + POOLSIZE = multiprocessing.cpu_count() +except NotImplementedError: + POOLSIZE = 4 + +exit_flag = multiprocessing.Value(c_bool, False) + + +def should_exit(): + global exit_flag + return exit_flag.value + + +def compute_real_exit_code(exit_code): + # SystemExit's code can be integer or string, but os._exit only accepts integers + import numbers + if isinstance(exit_code, numbers.Integral): + return exit_code + else: + return 1 + + +def worker(listen_sock): + # Redirect stdout to stderr + os.dup2(2, 1) + sys.stdout = sys.stderr # The sys.stdout object is different from file descriptor 1 + + # Manager sends SIGHUP to request termination of workers in the pool + def handle_sighup(*args): + assert should_exit() + signal.signal(SIGHUP, handle_sighup) + + # Cleanup zombie children + def handle_sigchld(*args): + pid = status = None + try: + while (pid, status) != (0, 0): + pid, status = os.waitpid(0, os.WNOHANG) + except EnvironmentError as err: + if err.errno == EINTR: + # retry + handle_sigchld() + elif err.errno != ECHILD: + raise + signal.signal(SIGCHLD, handle_sigchld) + + # Handle clients + while not should_exit(): + # Wait until a client arrives or we have to exit + sock = None + while not should_exit() and sock is None: + try: + sock, addr = listen_sock.accept() + except EnvironmentError as err: + if err.errno != EINTR: + raise + + if sock is not None: + # Fork a child to handle the client. + # The client is handled in the child so that the manager + # never receives SIGCHLD unless a worker crashes. + if os.fork() == 0: + # Leave the worker pool + signal.signal(SIGHUP, SIG_DFL) + listen_sock.close() + # Read the socket using fdopen instead of socket.makefile() because the latter + # seems to be very slow; note that we need to dup() the file descriptor because + # otherwise writes also cause a seek that makes us miss data on the read side. + infile = os.fdopen(os.dup(sock.fileno()), "a+", 65536) + outfile = os.fdopen(os.dup(sock.fileno()), "a+", 65536) + exit_code = 0 + try: + worker_main(infile, outfile) + except SystemExit as exc: + exit_code = exc.code + finally: + outfile.flush() + sock.close() + os._exit(compute_real_exit_code(exit_code)) + else: + sock.close() + + +def launch_worker(listen_sock): + if os.fork() == 0: + try: + worker(listen_sock) + except Exception as err: + traceback.print_exc() + os._exit(1) + else: + assert should_exit() + os._exit(0) + + +def manager(): + # Create a new process group to corral our children + os.setpgid(0, 0) + + # Create a listening socket on the AF_INET loopback interface + listen_sock = socket.socket(AF_INET, SOCK_STREAM) + listen_sock.bind(('127.0.0.1', 0)) + listen_sock.listen(max(1024, 2 * POOLSIZE, SOMAXCONN)) + listen_host, listen_port = listen_sock.getsockname() + write_int(listen_port, sys.stdout) + + # Launch initial worker pool + for idx in range(POOLSIZE): + launch_worker(listen_sock) + listen_sock.close() + + def shutdown(): + global exit_flag + exit_flag.value = True + + # Gracefully exit on SIGTERM, don't die on SIGHUP + signal.signal(SIGTERM, lambda signum, frame: shutdown()) + signal.signal(SIGHUP, SIG_IGN) + + # Cleanup zombie children + def handle_sigchld(*args): + try: + pid, status = os.waitpid(0, os.WNOHANG) + if status != 0 and not should_exit(): + raise RuntimeError("worker crashed: %s, %s" % (pid, status)) + except EnvironmentError as err: + if err.errno not in (ECHILD, EINTR): + raise + signal.signal(SIGCHLD, handle_sigchld) + + # Initialization complete + sys.stdout.close() + try: + while not should_exit(): + try: + # Spark tells us to exit by closing stdin + if os.read(0, 512) == '': + shutdown() + except EnvironmentError as err: + if err.errno != EINTR: + shutdown() + raise + finally: + signal.signal(SIGTERM, SIG_DFL) + exit_flag.value = True + # Send SIGHUP to notify workers of shutdown + os.kill(0, SIGHUP) + + +if __name__ == '__main__': + manager() diff --git a/python/pyspark/files.py b/python/pyspark/files.py index 001b7a28b6..89bcbcfe06 100644 --- a/python/pyspark/files.py +++ b/python/pyspark/files.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. +# + import os diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index 2329e536cc..e503fb7621 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.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. +# + import os import sys from subprocess import Popen, PIPE diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 172ed85fab..51c2cb9806 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.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. +# + from base64 import standard_b64encode as b64enc import copy from collections import defaultdict @@ -143,7 +160,7 @@ class RDD(object): >>> sorted(sc.parallelize([1, 1, 2, 3]).distinct().collect()) [1, 2, 3] """ - return self.map(lambda x: (x, "")) \ + return self.map(lambda x: (x, None)) \ .reduceByKey(lambda x, _: x) \ .map(lambda (x, _): x) @@ -250,7 +267,11 @@ class RDD(object): >>> def f(x): print x >>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f) """ - self.map(f).collect() # Force evaluation + def processPartition(iterator): + for x in iterator: + f(x) + yield None + self.mapPartitions(processPartition).collect() # Force evaluation def collect(self): """ @@ -369,13 +390,16 @@ class RDD(object): >>> sc.parallelize([2, 3, 4, 5, 6]).take(10) [2, 3, 4, 5, 6] """ + def takeUpToNum(iterator): + taken = 0 + while taken < num: + yield next(iterator) + taken += 1 + # Take only up to num elements from each partition we try + mapped = self.mapPartitions(takeUpToNum) items = [] - for partition in range(self._jrdd.splits().size()): - iterator = self.ctx._takePartition(self._jrdd.rdd(), partition) - # Each item in the iterator is a string, Python object, batch of - # Python objects. Regardless, it is sufficient to take `num` - # of these objects in order to collect `num` Python objects: - iterator = iterator.take(num) + for partition in range(mapped._jrdd.splits().size()): + iterator = self.ctx._takePartition(mapped._jrdd.rdd(), partition) items.extend(self._collect_iterator_through_file(iterator)) if len(items) >= num: break @@ -399,7 +423,7 @@ class RDD(object): >>> sc.parallelize(range(10)).saveAsTextFile(tempFile.name) >>> from fileinput import input >>> from glob import glob - >>> ''.join(input(glob(tempFile.name + "/part-0000*"))) + >>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*")))) '0\\n1\\n2\\n3\\n4\\n5\\n6\\n7\\n8\\n9\\n' """ def func(split, iterator): @@ -732,9 +756,8 @@ class PipelinedRDD(RDD): self.ctx._gateway._gateway_client) self.ctx._pickled_broadcast_vars.clear() class_manifest = self._prev_jrdd.classManifest() - env = copy.copy(self.ctx.environment) - env['PYTHONPATH'] = os.environ.get("PYTHONPATH", "") - env = MapConverter().convert(env, self.ctx._gateway._gateway_client) + env = MapConverter().convert(self.ctx.environment, + self.ctx._gateway._gateway_client) python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(), pipe_command, env, self.preservesPartitioning, self.ctx.pythonExec, broadcast_vars, self.ctx._javaAccumulator, class_manifest) diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 115cf28cc2..fecacd1241 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.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. +# + import struct import cPickle @@ -46,6 +63,10 @@ def read_long(stream): return struct.unpack("!q", length)[0] +def write_long(value, stream): + stream.write(struct.pack("!q", value)) + + def read_int(stream): length = stream.read(4) if length == "": diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index 54ff1bf8e7..cc8cd9e3c4 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.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. +# + """ An interactive shell. diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index 6a1962d267..dfd841b10a 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.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. +# + """ Unit tests for PySpark; additional tests are implemented as doctests in individual modules. @@ -12,6 +29,7 @@ import unittest from pyspark.context import SparkContext from pyspark.files import SparkFiles from pyspark.java_gateway import SPARK_HOME +from pyspark.serializers import read_int class PySparkTestCase(unittest.TestCase): @@ -117,5 +135,47 @@ class TestIO(PySparkTestCase): self.sc.parallelize([1]).foreach(func) +class TestDaemon(unittest.TestCase): + def connect(self, port): + from socket import socket, AF_INET, SOCK_STREAM + sock = socket(AF_INET, SOCK_STREAM) + sock.connect(('127.0.0.1', port)) + # send a split index of -1 to shutdown the worker + sock.send("\xFF\xFF\xFF\xFF") + sock.close() + return True + + def do_termination_test(self, terminator): + from subprocess import Popen, PIPE + from errno import ECONNREFUSED + + # start daemon + daemon_path = os.path.join(os.path.dirname(__file__), "daemon.py") + daemon = Popen([sys.executable, daemon_path], stdin=PIPE, stdout=PIPE) + + # read the port number + port = read_int(daemon.stdout) + + # daemon should accept connections + self.assertTrue(self.connect(port)) + + # request shutdown + terminator(daemon) + time.sleep(1) + + # daemon should no longer accept connections + with self.assertRaises(EnvironmentError) as trap: + self.connect(port) + self.assertEqual(trap.exception.errno, ECONNREFUSED) + + def test_termination_stdin(self): + """Ensure that daemon and workers terminate when stdin is closed.""" + self.do_termination_test(lambda daemon: daemon.stdin.close()) + + def test_termination_sigterm(self): + """Ensure that daemon and workers terminate on SIGTERM.""" + from signal import SIGTERM + self.do_termination_test(lambda daemon: os.kill(daemon.pid, SIGTERM)) + if __name__ == "__main__": unittest.main() diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py index 812e7a9da5..75d692beeb 100644 --- a/python/pyspark/worker.py +++ b/python/pyspark/worker.py @@ -1,8 +1,26 @@ +# +# 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. +# + """ Worker that receives input from Piped RDD. """ import os import sys +import time import traceback from base64 import standard_b64decode # CloudPickler needs to be imported so that depicklers are registered using the @@ -12,48 +30,60 @@ 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, read_int, dump_pickle, load_pickle, read_from_pickle_file + read_long, write_long, read_int, dump_pickle, load_pickle, read_from_pickle_file -# Redirect stdout to stderr so that users must return values from functions. -old_stdout = os.fdopen(os.dup(1), 'w') -os.dup2(2, 1) +def load_obj(infile): + return load_pickle(standard_b64decode(infile.readline().strip())) -def load_obj(): - return load_pickle(standard_b64decode(sys.stdin.readline().strip())) +def report_times(outfile, boot, init, finish): + write_int(-3, outfile) + write_long(1000 * boot, outfile) + write_long(1000 * init, outfile) + write_long(1000 * finish, outfile) -def main(): - split_index = read_int(sys.stdin) - spark_files_dir = load_pickle(read_with_length(sys.stdin)) +def main(infile, outfile): + boot_time = time.time() + split_index = read_int(infile) + if split_index == -1: # for unit tests + return + spark_files_dir = load_pickle(read_with_length(infile)) SparkFiles._root_directory = spark_files_dir SparkFiles._is_running_on_worker = True sys.path.append(spark_files_dir) - num_broadcast_variables = read_int(sys.stdin) + num_broadcast_variables = read_int(infile) for _ in range(num_broadcast_variables): - bid = read_long(sys.stdin) - value = read_with_length(sys.stdin) + bid = read_long(infile) + value = read_with_length(infile) _broadcastRegistry[bid] = Broadcast(bid, load_pickle(value)) - func = load_obj() - bypassSerializer = load_obj() + func = load_obj(infile) + bypassSerializer = load_obj(infile) if bypassSerializer: dumps = lambda x: x else: dumps = dump_pickle - iterator = read_from_pickle_file(sys.stdin) + init_time = time.time() + iterator = read_from_pickle_file(infile) try: for obj in func(split_index, iterator): - write_with_length(dumps(obj), old_stdout) + write_with_length(dumps(obj), outfile) except Exception as e: - write_int(-2, old_stdout) - write_with_length(traceback.format_exc(), old_stdout) + write_int(-2, 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, old_stdout) + write_int(-1, outfile) for aid, accum in _accumulatorRegistry.items(): - write_with_length(dump_pickle((aid, accum._value)), old_stdout) + write_with_length(dump_pickle((aid, accum._value)), outfile) + write_int(-1, outfile) if __name__ == '__main__': - main() + # Redirect stdout to stderr so that users must return values from functions. + old_stdout = os.fdopen(os.dup(1), 'w') + os.dup2(2, 1) + main(sys.stdin, old_stdout) diff --git a/python/run-tests b/python/run-tests index a3a9ff5dcb..6643faa2e0 100755 --- a/python/run-tests +++ b/python/run-tests @@ -1,8 +1,29 @@ #!/usr/bin/env bash -# Figure out where the Scala framework is installed +# +# 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. +# + + +# Figure out where the Spark framework is installed FWDIR="$(cd `dirname $0`; cd ../; pwd)" +# CD into the python directory to find things on the right path +cd "$FWDIR/python" + FAILED=0 $FWDIR/pyspark pyspark/rdd.py diff --git a/repl-bin/pom.xml b/repl-bin/pom.xml index dd720e2291..7c4e722cc1 100644 --- a/repl-bin/pom.xml +++ b/repl-bin/pom.xml @@ -1,10 +1,27 @@ <?xml version="1.0" encoding="UTF-8"?> +<!-- + ~ Licensed to the Apache Software Foundation (ASF) under one or more + ~ contributor license agreements. See the NOTICE file distributed with + ~ this work for additional information regarding copyright ownership. + ~ The ASF licenses this file to You under the Apache License, Version 2.0 + ~ (the "License"); you may not use this file except in compliance with + ~ the License. You may obtain a copy of the License at + ~ + ~ http://www.apache.org/licenses/LICENSE-2.0 + ~ + ~ Unless required by applicable law or agreed to in writing, software + ~ distributed under the License is distributed on an "AS IS" BASIS, + ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + ~ See the License for the specific language governing permissions and + ~ limitations under the License. + --> + <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>org.spark-project</groupId> <artifactId>spark-parent</artifactId> - <version>0.7.1-SNAPSHOT</version> + <version>0.8.0-SNAPSHOT</version> <relativePath>../pom.xml</relativePath> </parent> @@ -154,6 +171,61 @@ </dependencies> </profile> <profile> + <id>hadoop2-yarn</id> + <properties> + <classifier>hadoop2-yarn</classifier> + </properties> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-bagel</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + <scope>runtime</scope> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-examples</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + <scope>runtime</scope> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-repl</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + <scope>runtime</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>runtime</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + <scope>runtime</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + <scope>runtime</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-client</artifactId> + <scope>runtime</scope> + </dependency> + </dependencies> + </profile> + <profile> <id>deb</id> <build> <plugins> diff --git a/repl-bin/src/deb/bin/run b/repl-bin/src/deb/bin/run index c54c9e97a0..8b5d8300f2 100755 --- a/repl-bin/src/deb/bin/run +++ b/repl-bin/src/deb/bin/run @@ -1,6 +1,23 @@ -#!/bin/bash - -SCALA_VERSION=2.9.2 +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +SCALA_VERSION=2.9.3 # Figure out where the Scala framework is installed FWDIR="$(cd `dirname $0`; pwd)" diff --git a/repl-bin/src/deb/bin/spark-executor b/repl-bin/src/deb/bin/spark-executor index 47b9cccdfe..bcfae22677 100755 --- a/repl-bin/src/deb/bin/spark-executor +++ b/repl-bin/src/deb/bin/spark-executor @@ -1,4 +1,21 @@ -#!/bin/bash +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# FWDIR="$(cd `dirname $0`; pwd)" echo "Running spark-executor with framework dir = $FWDIR" diff --git a/repl-bin/src/deb/bin/spark-shell b/repl-bin/src/deb/bin/spark-shell index 219c66eb0b..ec7e33e1e3 100755 --- a/repl-bin/src/deb/bin/spark-shell +++ b/repl-bin/src/deb/bin/spark-shell @@ -1,4 +1,21 @@ -#!/bin/bash +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# FWDIR="$(cd `dirname $0`; pwd)" exec $FWDIR/run spark.repl.Main "$@" diff --git a/repl/pom.xml b/repl/pom.xml index a3e4606edc..7d8da03254 100644 --- a/repl/pom.xml +++ b/repl/pom.xml @@ -1,10 +1,27 @@ <?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.spark-project</groupId> <artifactId>spark-parent</artifactId> - <version>0.7.1-SNAPSHOT</version> + <version>0.8.0-SNAPSHOT</version> <relativePath>../pom.xml</relativePath> </parent> @@ -97,13 +114,6 @@ <scope>runtime</scope> </dependency> <dependency> - <groupId>org.spark-project</groupId> - <artifactId>spark-streaming</artifactId> - <version>${project.version}</version> - <classifier>hadoop1</classifier> - <scope>runtime</scope> - </dependency> - <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-core</artifactId> <scope>provided</scope> @@ -148,20 +158,84 @@ <scope>runtime</scope> </dependency> <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-core</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.avro</groupId> + <artifactId>avro</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.avro</groupId> + <artifactId>avro-ipc</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> + <profile> + <id>hadoop2-yarn</id> + <properties> + <classifier>hadoop2-yarn</classifier> + </properties> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-bagel</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + <scope>runtime</scope> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-examples</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + <scope>runtime</scope> + </dependency> + <dependency> <groupId>org.spark-project</groupId> <artifactId>spark-streaming</artifactId> <version>${project.version}</version> - <classifier>hadoop2</classifier> + <classifier>hadoop2-yarn</classifier> <scope>runtime</scope> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> - <artifactId>hadoop-core</artifactId> + <artifactId>hadoop-client</artifactId> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> - <artifactId>hadoop-client</artifactId> + <artifactId>hadoop-yarn-api</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> <scope>provided</scope> </dependency> <dependency> @@ -181,7 +255,7 @@ <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-jar-plugin</artifactId> <configuration> - <classifier>hadoop2</classifier> + <classifier>hadoop2-yarn</classifier> </configuration> </plugin> </plugins> diff --git a/repl/src/main/scala/spark/repl/ExecutorClassLoader.scala b/repl/src/main/scala/spark/repl/ExecutorClassLoader.scala index 13d81ec1cf..274bc585db 100644 --- a/repl/src/main/scala/spark/repl/ExecutorClassLoader.scala +++ b/repl/src/main/scala/spark/repl/ExecutorClassLoader.scala @@ -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. + */ + package spark.repl import java.io.{ByteArrayOutputStream, InputStream} @@ -8,7 +25,6 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path} import org.objectweb.asm._ -import org.objectweb.asm.commons.EmptyVisitor import org.objectweb.asm.Opcodes._ @@ -83,7 +99,7 @@ extends ClassLoader(parent) { } class ConstructorCleaner(className: String, cv: ClassVisitor) -extends ClassAdapter(cv) { +extends ClassVisitor(ASM4, cv) { override def visitMethod(access: Int, name: String, desc: String, sig: String, exceptions: Array[String]): MethodVisitor = { val mv = cv.visitMethod(access, name, desc, sig, exceptions) diff --git a/repl/src/main/scala/spark/repl/Main.scala b/repl/src/main/scala/spark/repl/Main.scala index 58809ab646..d824d62fd1 100644 --- a/repl/src/main/scala/spark/repl/Main.scala +++ b/repl/src/main/scala/spark/repl/Main.scala @@ -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. + */ + package spark.repl import scala.collection.mutable.Set diff --git a/repl/src/main/scala/spark/repl/SparkILoop.scala b/repl/src/main/scala/spark/repl/SparkILoop.scala index cd7b5128b2..0cecbd71ad 100644 --- a/repl/src/main/scala/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/spark/repl/SparkILoop.scala @@ -200,7 +200,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ - /___/ .__/\_,_/_/ /_/\_\ version 0.7.1 + /___/ .__/\_,_/_/ /_/\_\ version 0.8.0 /_/ """) import Properties._ @@ -822,7 +822,7 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: spark.repl.Main.interp.out.println("Spark context available as sc."); spark.repl.Main.interp.out.flush(); """) - command("import spark.SparkContext._"); + command("import spark.SparkContext._") } echo("Type in expressions to have them evaluated.") echo("Type :help for more information.") @@ -831,6 +831,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: 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 => { @@ -838,7 +842,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: if (prop != null) prop else "local" } } - sparkContext = new SparkContext(master, "Spark shell") + 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 } @@ -850,6 +857,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: 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(_)) + this.settings = settings createInterpreter() diff --git a/repl/src/test/resources/log4j.properties b/repl/src/test/resources/log4j.properties index cfb1a390e6..a6d33e69d2 100644 --- a/repl/src/test/resources/log4j.properties +++ b/repl/src/test/resources/log4j.properties @@ -1,4 +1,21 @@ -# Set everything to be logged to the repl/target/unit-tests.log +# +# 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. +# + +# Set everything to be logged to the repl/target/unit-tests.log log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false diff --git a/repl/src/test/scala/spark/repl/ReplSuite.scala b/repl/src/test/scala/spark/repl/ReplSuite.scala index 43559b96d3..80ae605558 100644 --- a/repl/src/test/scala/spark/repl/ReplSuite.scala +++ b/repl/src/test/scala/spark/repl/ReplSuite.scala @@ -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. + */ + package spark.repl import java.io._ @@ -28,23 +45,25 @@ class ReplSuite extends FunSuite { val separator = System.getProperty("path.separator") interp.process(Array("-classpath", paths.mkString(separator))) spark.repl.Main.interp = null - if (interp.sparkContext != null) + if (interp.sparkContext != null) { interp.sparkContext.stop() + } // 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") return out.toString } - + def assertContains(message: String, output: String) { - assert(output contains message, + assert(output.contains(message), "Interpreter output did not contain '" + message + "':\n" + output) } - + def assertDoesNotContain(message: String, output: String) { - assert(!(output contains message), + assert(!output.contains(message), "Interpreter output contained '" + message + "':\n" + output) } - + test ("simple foreach with accumulator") { val output = runInterpreter("local", """ val accum = sc.accumulator(0) @@ -55,7 +74,7 @@ class ReplSuite extends FunSuite { assertDoesNotContain("Exception", output) assertContains("res1: Int = 55", output) } - + test ("external vars") { val output = runInterpreter("local", """ var v = 7 @@ -104,7 +123,7 @@ class ReplSuite extends FunSuite { assertContains("res0: Int = 70", output) assertContains("res1: Int = 100", output) } - + 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 @@ -142,6 +161,27 @@ 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 + """) + assertDoesNotContain("error:", output) + assertDoesNotContain("Exception", output) + assertContains("res0: Int = 70", output) + assertContains("res1: Int = 100", output) + assertContains("res2: Array[Int] = Array(0, 0, 0, 0, 0)", output) + assertContains("res4: Array[Int] = Array(0, 0, 0, 0, 0)", output) + } + if (System.getenv("MESOS_NATIVE_LIBRARY") != null) { test ("running on Mesos") { val output = runInterpreter("localquiet", """ @@ -1,6 +1,23 @@ -#!/bin/bash - -SCALA_VERSION=2.9.2 +#!/usr/bin/env 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. +# + +SCALA_VERSION=2.9.3 # Figure out where the Scala framework is installed FWDIR="$(cd `dirname $0`; pwd)" @@ -22,50 +39,65 @@ fi # values for that; it doesn't need a lot if [ "$1" = "spark.deploy.master.Master" -o "$1" = "spark.deploy.worker.Worker" ]; then SPARK_MEM=${SPARK_DAEMON_MEMORY:-512m} - SPARK_JAVA_OPTS=$SPARK_DAEMON_JAVA_OPTS # Empty by default + SPARK_DAEMON_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS -Dspark.akka.logLifecycleEvents=true" + # Do not overwrite SPARK_JAVA_OPTS environment variable in this script + OUR_JAVA_OPTS="$SPARK_DAEMON_JAVA_OPTS" # Empty by default +else + OUR_JAVA_OPTS="$SPARK_JAVA_OPTS" fi # Add java opts for master, worker, executor. The opts maybe null case "$1" in 'spark.deploy.master.Master') - SPARK_JAVA_OPTS+=" $SPARK_MASTER_OPTS" + OUR_JAVA_OPTS="$OUR_JAVA_OPTS $SPARK_MASTER_OPTS" ;; 'spark.deploy.worker.Worker') - SPARK_JAVA_OPTS+=" $SPARK_WORKER_OPTS" + OUR_JAVA_OPTS="$OUR_JAVA_OPTS $SPARK_WORKER_OPTS" ;; 'spark.executor.StandaloneExecutorBackend') - SPARK_JAVA_OPTS+=" $SPARK_EXECUTOR_OPTS" + OUR_JAVA_OPTS="$OUR_JAVA_OPTS $SPARK_EXECUTOR_OPTS" ;; 'spark.executor.MesosExecutorBackend') - SPARK_JAVA_OPTS+=" $SPARK_EXECUTOR_OPTS" + OUR_JAVA_OPTS="$OUR_JAVA_OPTS $SPARK_EXECUTOR_OPTS" ;; 'spark.repl.Main') - SPARK_JAVA_OPTS+=" $SPARK_REPL_OPTS" + OUR_JAVA_OPTS="$OUR_JAVA_OPTS $SPARK_REPL_OPTS" ;; esac -if [ "$SPARK_LAUNCH_WITH_SCALA" == "1" ]; then - if [ `command -v scala` ]; then - RUNNER="scala" +# Figure out whether to run our class with java or with the scala launcher. +# In most cases, we'd prefer to execute our process with java because scala +# creates a shell script as the parent of its Java process, which makes it +# hard to kill the child with stuff like Process.destroy(). However, for +# the Spark shell, the wrapper is necessary to properly reset the terminal +# when we exit, so we allow it to set a variable to launch with scala. +# We still fall back on java for the shell if this is a "release" created +# from make-distribution.sh since it's possible scala is not installed +# but we have everything we need to run the shell. +if [[ "$SPARK_LAUNCH_WITH_SCALA" == "1" && ! -f "$FWDIR/RELEASE" ]]; then + if [ "$SCALA_HOME" ]; then + RUNNER="${SCALA_HOME}/bin/scala" else - if [ -z "$SCALA_HOME" ]; then - echo "SCALA_HOME is not set" >&2 + if [ `command -v scala` ]; then + RUNNER="scala" + else + echo "SCALA_HOME is not set and scala is not in PATH" >&2 exit 1 fi - RUNNER="${SCALA_HOME}/bin/scala" fi else - if [ `command -v java` ]; then - RUNNER="java" + if [ -n "${JAVA_HOME}" ]; then + RUNNER="${JAVA_HOME}/bin/java" else - if [ -z "$JAVA_HOME" ]; then + if [ `command -v java` ]; then + RUNNER="java" + else echo "JAVA_HOME is not set" >&2 exit 1 fi - RUNNER="${JAVA_HOME}/bin/java" fi - if [ -z "$SCALA_LIBRARY_PATH" ]; then + if [[ ! -f "$FWDIR/RELEASE" && -z "$SCALA_LIBRARY_PATH" ]]; then if [ -z "$SCALA_HOME" ]; then echo "SCALA_HOME is not set" >&2 exit 1 @@ -82,83 +114,54 @@ fi export SPARK_MEM # Set JAVA_OPTS to be able to load native libraries and to set heap size -JAVA_OPTS="$SPARK_JAVA_OPTS" -JAVA_OPTS+=" -Djava.library.path=$SPARK_LIBRARY_PATH" -JAVA_OPTS+=" -Xms$SPARK_MEM -Xmx$SPARK_MEM" +JAVA_OPTS="$OUR_JAVA_OPTS" +JAVA_OPTS="$JAVA_OPTS -Djava.library.path=$SPARK_LIBRARY_PATH" +JAVA_OPTS="$JAVA_OPTS -Xms$SPARK_MEM -Xmx$SPARK_MEM" # Load extra JAVA_OPTS from conf/java-opts, if it exists if [ -e $FWDIR/conf/java-opts ] ; then - JAVA_OPTS+=" `cat $FWDIR/conf/java-opts`" + JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`" fi export JAVA_OPTS +# Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in ExecutorRunner.scala! + +if [ ! -f "$FWDIR/RELEASE" ]; then + CORE_DIR="$FWDIR/core" + EXAMPLES_DIR="$FWDIR/examples" + REPL_DIR="$FWDIR/repl" + + # Exit if the user hasn't compiled Spark + if [ ! -e "$CORE_DIR/target" ]; then + echo "Failed to find Spark classes in $CORE_DIR/target" >&2 + echo "You need to compile Spark before running this program" >&2 + exit 1 + fi -CORE_DIR="$FWDIR/core" -REPL_DIR="$FWDIR/repl" -EXAMPLES_DIR="$FWDIR/examples" -BAGEL_DIR="$FWDIR/bagel" -STREAMING_DIR="$FWDIR/streaming" -PYSPARK_DIR="$FWDIR/python" - -# Exit if the user hasn't compiled Spark -if [ ! -e "$REPL_DIR/target" ]; then - echo "Failed to find Spark classes in $REPL_DIR/target" >&2 - echo "You need to compile Spark before running this program" >&2 - exit 1 -fi + if [[ "$@" = *repl* && ! -e "$REPL_DIR/target" ]]; then + echo "Failed to find Spark classes in $REPL_DIR/target" >&2 + echo "You need to compile Spark repl module before running this program" >&2 + exit 1 + fi -# Build up classpath -CLASSPATH="$SPARK_CLASSPATH" -CLASSPATH+=":$FWDIR/conf" -CLASSPATH+=":$CORE_DIR/target/scala-$SCALA_VERSION/classes" -if [ -n "$SPARK_TESTING" ] ; then - CLASSPATH+=":$CORE_DIR/target/scala-$SCALA_VERSION/test-classes" - CLASSPATH+=":$STREAMING_DIR/target/scala-$SCALA_VERSION/test-classes" -fi -CLASSPATH+=":$CORE_DIR/src/main/resources" -CLASSPATH+=":$REPL_DIR/target/scala-$SCALA_VERSION/classes" -CLASSPATH+=":$EXAMPLES_DIR/target/scala-$SCALA_VERSION/classes" -CLASSPATH+=":$STREAMING_DIR/target/scala-$SCALA_VERSION/classes" -CLASSPATH+=":$STREAMING_DIR/lib/org/apache/kafka/kafka/0.7.2-spark/*" # <-- our in-project Kafka Jar -if [ -e "$FWDIR/lib_managed" ]; then - CLASSPATH+=":$FWDIR/lib_managed/jars/*" - CLASSPATH+=":$FWDIR/lib_managed/bundles/*" -fi -CLASSPATH+=":$REPL_DIR/lib/*" -if [ -e repl-bin/target ]; then - for jar in `find "repl-bin/target" -name 'spark-repl-*-shaded-hadoop*.jar'`; do - CLASSPATH+=":$jar" - done -fi -CLASSPATH+=":$BAGEL_DIR/target/scala-$SCALA_VERSION/classes" -for jar in `find $PYSPARK_DIR/lib -name '*jar'`; do - CLASSPATH+=":$jar" -done -export CLASSPATH # Needed for spark-shell - -# Figure out the JAR file that our examples were packaged into. This includes a bit of a hack -# to avoid the -sources and -doc packages that are built by publish-local. -if [ -e "$EXAMPLES_DIR/target/scala-$SCALA_VERSION/spark-examples"*[0-9T].jar ]; then - # Use the JAR from the SBT build - export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR/target/scala-$SCALA_VERSION/spark-examples"*[0-9T].jar` -fi -if [ -e "$EXAMPLES_DIR/target/spark-examples-"*hadoop[12].jar ]; then - # Use the JAR from the Maven build - export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR/target/spark-examples-"*hadoop[12].jar` + # Figure out the JAR file that our examples were packaged into. This includes a bit of a hack + # to avoid the -sources and -doc packages that are built by publish-local. + if [ -e "$EXAMPLES_DIR/target/scala-$SCALA_VERSION/spark-examples"*[0-9T].jar ]; then + # Use the JAR from the SBT build + export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR/target/scala-$SCALA_VERSION/spark-examples"*[0-9T].jar` + fi + if [ -e "$EXAMPLES_DIR/target/spark-examples"*[0-9T].jar ]; then + # Use the JAR from the Maven build + export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR/target/spark-examples"*[0-9T].jar` + fi fi -# Figure out whether to run our class with java or with the scala launcher. -# In most cases, we'd prefer to execute our process with java because scala -# creates a shell script as the parent of its Java process, which makes it -# hard to kill the child with stuff like Process.destroy(). However, for -# the Spark shell, the wrapper is necessary to properly reset the terminal -# when we exit, so we allow it to set a variable to launch with scala. +# Compute classpath using external script +CLASSPATH=`$FWDIR/bin/compute-classpath.sh` +export CLASSPATH + if [ "$SPARK_LAUNCH_WITH_SCALA" == "1" ]; then EXTRA_ARGS="" # Java options will be passed to scala as JAVA_OPTS else - CLASSPATH+=":$SCALA_LIBRARY_PATH/scala-library.jar" - CLASSPATH+=":$SCALA_LIBRARY_PATH/scala-compiler.jar" - CLASSPATH+=":$SCALA_LIBRARY_PATH/jline.jar" # The JVM doesn't read JAVA_OPTS by default so we need to pass it in EXTRA_ARGS="$JAVA_OPTS" fi - exec "$RUNNER" -cp "$CLASSPATH" $EXTRA_ARGS "$@" @@ -1,2 +1,20 @@ @echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + cmd /V /E /C %~dp0run2.cmd %* @@ -1,6 +1,23 @@ @echo off -set SCALA_VERSION=2.9.2 +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +set SCALA_VERSION=2.9.3 rem Figure out where the Spark framework is installed set FWDIR=%~dp0 @@ -21,8 +38,11 @@ set RUNNING_DAEMON=0 if "%1"=="spark.deploy.master.Master" set RUNNING_DAEMON=1 if "%1"=="spark.deploy.worker.Worker" set RUNNING_DAEMON=1 if "x%SPARK_DAEMON_MEMORY%" == "x" set SPARK_DAEMON_MEMORY=512m +set SPARK_DAEMON_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% -Dspark.akka.logLifecycleEvents=true if "%RUNNING_DAEMON%"=="1" set SPARK_MEM=%SPARK_DAEMON_MEMORY% -if "%RUNNING_DAEMON%"=="1" set SPARK_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% +rem Do not overwrite SPARK_JAVA_OPTS environment variable in this script +if "%RUNNING_DAEMON%"=="0" set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% +if "%RUNNING_DAEMON%"=="1" set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% rem Check that SCALA_HOME has been specified if not "x%SCALA_HOME%"=="x" goto scala_exists @@ -30,37 +50,22 @@ if not "x%SCALA_HOME%"=="x" goto scala_exists goto exit :scala_exists -rem If the user specifies a Mesos JAR, put it before our included one on the classpath -set MESOS_CLASSPATH= -if not "x%MESOS_JAR%"=="x" set MESOS_CLASSPATH=%MESOS_JAR% - rem Figure out how much memory to use per executor and set it as an environment rem variable so that our process sees it and can report it to Mesos if "x%SPARK_MEM%"=="x" set SPARK_MEM=512m rem Set JAVA_OPTS to be able to load native libraries and to set heap size -set JAVA_OPTS=%SPARK_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%SPARK_MEM% -Xmx%SPARK_MEM% -rem Load extra JAVA_OPTS from conf/java-opts, if it exists -if exist "%FWDIR%conf\java-opts.cmd" call "%FWDIR%conf\java-opts.cmd" +set JAVA_OPTS=%OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%SPARK_MEM% -Xmx%SPARK_MEM% +rem Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in ExecutorRunner.scala! set CORE_DIR=%FWDIR%core -set REPL_DIR=%FWDIR%repl set EXAMPLES_DIR=%FWDIR%examples -set BAGEL_DIR=%FWDIR%bagel -set STREAMING_DIR=%FWDIR%streaming -set PYSPARK_DIR=%FWDIR%python +set REPL_DIR=%FWDIR%repl -rem Build up classpath -set CLASSPATH=%SPARK_CLASSPATH%;%MESOS_CLASSPATH%;%FWDIR%conf;%CORE_DIR%\target\scala-%SCALA_VERSION%\classes -set CLASSPATH=%CLASSPATH%;%CORE_DIR%\target\scala-%SCALA_VERSION%\test-classes;%CORE_DIR%\src\main\resources -set CLASSPATH=%CLASSPATH%;%STREAMING_DIR%\target\scala-%SCALA_VERSION%\classes;%STREAMING_DIR%\target\scala-%SCALA_VERSION%\test-classes -set CLASSPATH=%CLASSPATH%;%STREAMING_DIR%\lib\org\apache\kafka\kafka\0.7.2-spark\* -set CLASSPATH=%CLASSPATH%;%REPL_DIR%\target\scala-%SCALA_VERSION%\classes;%EXAMPLES_DIR%\target\scala-%SCALA_VERSION%\classes -set CLASSPATH=%CLASSPATH%;%FWDIR%lib_managed\jars\* -set CLASSPATH=%CLASSPATH%;%FWDIR%lib_managed\bundles\* -set CLASSPATH=%CLASSPATH%;%FWDIR%repl\lib\* -set CLASSPATH=%CLASSPATH%;%FWDIR%python\lib\* -set CLASSPATH=%CLASSPATH%;%BAGEL_DIR%\target\scala-%SCALA_VERSION%\classes +rem Compute classpath using external script +set DONT_PRINT_CLASSPATH=1 +call "%FWDIR%bin\compute-classpath.cmd" +set DONT_PRINT_CLASSPATH=0 rem Figure out the JAR file that our examples were packaged into. rem First search in the build path from SBT: @@ -1,8 +1,28 @@ -#!/bin/bash +#!/usr/bin/env 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. +# + EXTRA_ARGS="" if [ "$MESOS_HOME" != "" ]; then EXTRA_ARGS="-Djava.library.path=$MESOS_HOME/lib/java" fi -export SPARK_HOME=$(cd "$(dirname $0)/.."; pwd) + +export SPARK_HOME=$(cd "$(dirname $0)/.." 2>&1 >/dev/null ; pwd) export SPARK_TESTING=1 # To put test classes on classpath -java -Xmx1200M -XX:MaxPermSize=250m $EXTRA_ARGS -jar $SPARK_HOME/sbt/sbt-launch-*.jar "$@" + +java -Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=128m $EXTRA_ARGS -jar $SPARK_HOME/sbt/sbt-launch-*.jar "$@" diff --git a/sbt/sbt.cmd b/sbt/sbt.cmd index ce3ae70174..56a16d77f2 100644 --- a/sbt/sbt.cmd +++ b/sbt/sbt.cmd @@ -1,5 +1,25 @@ @echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + set EXTRA_ARGS= if not "%MESOS_HOME%x"=="x" set EXTRA_ARGS=-Djava.library.path=%MESOS_HOME%\lib\java + set SPARK_HOME=%~dp0.. + java -Xmx1200M -XX:MaxPermSize=200m %EXTRA_ARGS% -jar %SPARK_HOME%\sbt\sbt-launch-0.11.3-2.jar "%*" diff --git a/spark-executor b/spark-executor index b66c374ca8..feccbf5cc2 100755 --- a/spark-executor +++ b/spark-executor @@ -1,4 +1,22 @@ #!/bin/sh + +# +# 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. +# + FWDIR="`dirname $0`" echo "Running spark-executor with framework dir = $FWDIR" exec $FWDIR/run spark.executor.MesosExecutorBackend diff --git a/spark-shell b/spark-shell index 574ae2104d..62fc18550d 100755 --- a/spark-shell +++ b/spark-shell @@ -1,4 +1,88 @@ -#!/bin/sh +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# +# Shell script for starting the Spark Shell REPL +# Note that it will set MASTER to spark://${SPARK_MASTER_IP}:${SPARK_MASTER_PORT} +# 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 +# + +# Enter posix mode for bash +set -o posix + FWDIR="`dirname $0`" -export SPARK_LAUNCH_WITH_SCALA=1 -exec $FWDIR/run spark.repl.Main "$@" + +for o in "$@"; do + if [ "$1" = "-c" -o "$1" = "--cores" ]; then + shift + if [ -n "$1" ]; then + OPTIONS="-Dspark.cores.max=$1" + shift + fi + fi +done + +# Set MASTER from spark-env if possible +if [ -z "$MASTER" ]; then + if [ -e "$FWDIR/conf/spark-env.sh" ]; then + . "$FWDIR/conf/spark-env.sh" + fi + if [[ "x" != "x$SPARK_MASTER_IP" && "y" != "y$SPARK_MASTER_PORT" ]]; then + MASTER="spark://${SPARK_MASTER_IP}:${SPARK_MASTER_PORT}" + export MASTER + fi +fi + +# Copy restore-TTY-on-exit functions from Scala script so spark-shell exits properly even in +# binary distribution of Spark where Scala is not installed +exit_status=127 +saved_stty="" + +# restore stty settings (echo in particular) +function restoreSttySettings() { + stty $saved_stty + saved_stty="" +} + +function onExit() { + if [[ "$saved_stty" != "" ]]; then + restoreSttySettings + fi + exit $exit_status +} + +# to reenable echo if we are interrupted before completing. +trap onExit INT + +# save terminal settings +saved_stty=$(stty -g 2>/dev/null) +# clear on error so we don't later try to restore them +if [[ ! $? ]]; then + saved_stty="" +fi + +export SPARK_LAUNCH_WITH_SCALA=${SPARK_LAUNCH_WITH_SCALA:-1} +$FWDIR/run $OPTIONS spark.repl.Main "$@" + +# record the exit status lest it be overwritten: +# then reenable echo and propagate the code. +exit_status=$? +onExit diff --git a/spark-shell.cmd b/spark-shell.cmd index 34697d52d7..ec65eabb74 100644 --- a/spark-shell.cmd +++ b/spark-shell.cmd @@ -1,4 +1,22 @@ @echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + set FWDIR=%~dp0 set SPARK_LAUNCH_WITH_SCALA=1 cmd /V /E /C %FWDIR%run2.cmd spark.repl.Main %* diff --git a/streaming/pom.xml b/streaming/pom.xml index ec077e8089..7e6b06d772 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -1,10 +1,27 @@ <?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.spark-project</groupId> <artifactId>spark-parent</artifactId> - <version>0.7.1-SNAPSHOT</version> + <version>0.8.0-SNAPSHOT</version> <relativePath>../pom.xml</relativePath> </parent> @@ -41,6 +58,12 @@ <groupId>org.apache.flume</groupId> <artifactId>flume-ng-sdk</artifactId> <version>1.2.0</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> </dependency> <dependency> <groupId>com.github.sgroschupf</groupId> @@ -53,6 +76,10 @@ <version>3.0.3</version> </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> @@ -149,5 +176,42 @@ </plugins> </build> </profile> + <profile> + <id>hadoop2-yarn</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2-yarn</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> </profiles> </project> diff --git a/streaming/src/main/scala/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/spark/streaming/Checkpoint.scala index e303e33e5e..070d930b5e 100644 --- a/streaming/src/main/scala/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/spark/streaming/Checkpoint.scala @@ -1,13 +1,31 @@ +/* + * 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 spark.streaming -import spark.{Logging, Utils} +import java.io._ +import java.util.concurrent.Executors +import java.util.concurrent.RejectedExecutionException -import org.apache.hadoop.fs.{FileUtil, Path} +import org.apache.hadoop.fs.Path import org.apache.hadoop.conf.Configuration -import java.io._ -import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} -import java.util.concurrent.Executors +import spark.Logging +import spark.io.CompressionCodec private[streaming] @@ -32,17 +50,29 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) } } + /** * Convenience class to speed up the writing of graph checkpoint to file */ private[streaming] class CheckpointWriter(checkpointDir: String) 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 executor = Executors.newFixedThreadPool(1) + private val compressionCodec = CompressionCodec.createCodec() + + // 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 @@ -51,15 +81,17 @@ class CheckpointWriter(checkpointDir: String) extends Logging { attempts += 1 try { logDebug("Saving checkpoint for time " + checkpointTime + " to file '" + file + "'") - if (fs.exists(file)) { - val bkFile = new Path(file.getParent, file.getName + ".bk") - FileUtil.copy(fs, file, fs, bkFile, true, true, conf) - logDebug("Moved existing checkpoint file to " + bkFile) - } - val fos = fs.create(file) + // This is inherently thread unsafe .. so alleviating it by writing to '.new' and then doing moves : which should be pretty fast. + val fos = fs.create(writeFile) fos.write(bytes) fos.close() - fos.close() + if (fs.exists(file) && fs.rename(file, bakFile)) { + logDebug("Moved existing checkpoint file to " + bakFile) + } + // paranoia + fs.delete(file, false) + fs.rename(writeFile, file) + val finishTime = System.currentTimeMillis(); logInfo("Checkpoint for time " + checkpointTime + " saved to file '" + file + "', took " + bytes.length + " bytes and " + (finishTime - startTime) + " milliseconds") @@ -75,16 +107,29 @@ class CheckpointWriter(checkpointDir: String) extends Logging { def write(checkpoint: Checkpoint) { val bos = new ByteArrayOutputStream() - val zos = new LZFOutputStream(bos) + val zos = compressionCodec.compressedOutputStream(bos) val oos = new ObjectOutputStream(zos) oos.writeObject(checkpoint) oos.close() bos.close() - executor.execute(new CheckpointWriteHandler(checkpoint.checkpointTime, bos.toByteArray)) + try { + executor.execute(new CheckpointWriteHandler(checkpoint.checkpointTime, bos.toByteArray)) + } catch { + case rej: RejectedExecutionException => + logError("Could not submit checkpoint task to the thread pool executor", rej) + } } def stop() { + synchronized { + 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.") } } @@ -96,6 +141,8 @@ object CheckpointReader extends Logging { 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 compressionCodec = CompressionCodec.createCodec() + attempts.foreach(file => { if (fs.exists(file)) { logInfo("Attempting to load checkpoint from file '" + file + "'") @@ -106,7 +153,7 @@ object CheckpointReader extends Logging { // of ObjectInputStream is used to explicitly use the current thread's default class // 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 = new LZFInputStream(fis) + val zis = compressionCodec.compressedInputStream(fis) val ois = new ObjectInputStreamWithLoader(zis, Thread.currentThread().getContextClassLoader) val cp = ois.readObject.asInstanceOf[Checkpoint] ois.close() @@ -129,7 +176,9 @@ object CheckpointReader extends Logging { } private[streaming] -class ObjectInputStreamWithLoader(inputStream_ : InputStream, loader: ClassLoader) extends ObjectInputStream(inputStream_) { +class ObjectInputStreamWithLoader(inputStream_ : InputStream, loader: ClassLoader) + extends ObjectInputStream(inputStream_) { + override def resolveClass(desc: ObjectStreamClass): Class[_] = { try { return loader.loadClass(desc.getName()) diff --git a/streaming/src/main/scala/spark/streaming/DStream.scala b/streaming/src/main/scala/spark/streaming/DStream.scala index e1be5ef51c..684d3abb56 100644 --- a/streaming/src/main/scala/spark/streaming/DStream.scala +++ b/streaming/src/main/scala/spark/streaming/DStream.scala @@ -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. + */ + package spark.streaming import spark.streaming.dstream._ @@ -441,7 +458,12 @@ abstract class DStream[T: ClassManifest] ( * Return a new DStream in which each RDD has a single element generated by counting each RDD * of this DStream. */ - def count(): DStream[Long] = this.map(_ => 1L).reduce(_ + _) + def count(): DStream[Long] = { + this.map(_ => (null, 1L)) + .transform(_.union(context.sparkContext.makeRDD(Seq((null, 0L)), 1))) + .reduceByKey(_ + _) + .map(_._2) + } /** * Return a new DStream in which each RDD contains the counts of each distinct value in @@ -457,7 +479,7 @@ abstract class DStream[T: ClassManifest] ( * this DStream will be registered as an output stream and therefore materialized. */ def foreach(foreachFunc: RDD[T] => Unit) { - foreach((r: RDD[T], t: Time) => foreachFunc(r)) + this.foreach((r: RDD[T], t: Time) => foreachFunc(r)) } /** diff --git a/streaming/src/main/scala/spark/streaming/DStreamCheckpointData.scala b/streaming/src/main/scala/spark/streaming/DStreamCheckpointData.scala index 6b0fade7c6..399ca1c63d 100644 --- a/streaming/src/main/scala/spark/streaming/DStreamCheckpointData.scala +++ b/streaming/src/main/scala/spark/streaming/DStreamCheckpointData.scala @@ -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. + */ + package spark.streaming import org.apache.hadoop.fs.Path diff --git a/streaming/src/main/scala/spark/streaming/DStreamGraph.scala b/streaming/src/main/scala/spark/streaming/DStreamGraph.scala index adb7f3a24d..c09a332d44 100644 --- a/streaming/src/main/scala/spark/streaming/DStreamGraph.scala +++ b/streaming/src/main/scala/spark/streaming/DStreamGraph.scala @@ -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. + */ + package spark.streaming import dstream.InputDStream @@ -54,8 +71,8 @@ final private[streaming] class DStreamGraph extends Serializable with Logging { throw new Exception("Batch duration already set as " + batchDuration + ". cannot set it again.") } + batchDuration = duration } - batchDuration = duration } def remember(duration: Duration) { diff --git a/streaming/src/main/scala/spark/streaming/Duration.scala b/streaming/src/main/scala/spark/streaming/Duration.scala index ee26206e24..12a14e233d 100644 --- a/streaming/src/main/scala/spark/streaming/Duration.scala +++ b/streaming/src/main/scala/spark/streaming/Duration.scala @@ -1,5 +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 spark.streaming +import spark.Utils + case class Duration (private val millis: Long) { def < (that: Duration): Boolean = (this.millis < that.millis) @@ -32,8 +51,10 @@ case class Duration (private val millis: Long) { def toFormattedString: String = millis.toString def milliseconds: Long = millis -} + def prettyPrint = Utils.msDurationToString(millis) + +} /** * Helper object that creates instance of [[spark.streaming.Duration]] representing diff --git a/streaming/src/main/scala/spark/streaming/Interval.scala b/streaming/src/main/scala/spark/streaming/Interval.scala index 6a8b81760e..b30cd969e9 100644 --- a/streaming/src/main/scala/spark/streaming/Interval.scala +++ b/streaming/src/main/scala/spark/streaming/Interval.scala @@ -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. + */ + package spark.streaming private[streaming] diff --git a/streaming/src/main/scala/spark/streaming/Job.scala b/streaming/src/main/scala/spark/streaming/Job.scala index 67bd8388bc..ceb3f92b65 100644 --- a/streaming/src/main/scala/spark/streaming/Job.scala +++ b/streaming/src/main/scala/spark/streaming/Job.scala @@ -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. + */ + package spark.streaming import java.util.concurrent.atomic.AtomicLong diff --git a/streaming/src/main/scala/spark/streaming/JobManager.scala b/streaming/src/main/scala/spark/streaming/JobManager.scala index 7696c4a592..a31230689f 100644 --- a/streaming/src/main/scala/spark/streaming/JobManager.scala +++ b/streaming/src/main/scala/spark/streaming/JobManager.scala @@ -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. + */ + package spark.streaming import spark.Logging diff --git a/streaming/src/main/scala/spark/streaming/NetworkInputTracker.scala b/streaming/src/main/scala/spark/streaming/NetworkInputTracker.scala index b159d26c02..d4cf2e568c 100644 --- a/streaming/src/main/scala/spark/streaming/NetworkInputTracker.scala +++ b/streaming/src/main/scala/spark/streaming/NetworkInputTracker.scala @@ -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. + */ + package spark.streaming import spark.streaming.dstream.{NetworkInputDStream, NetworkReceiver} diff --git a/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala b/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala index 3ec922957d..47bf07bee1 100644 --- a/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala +++ b/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala @@ -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. + */ + package spark.streaming import spark.streaming.StreamingContext._ @@ -14,6 +31,7 @@ import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.mapred.{JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} 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)]) @@ -470,7 +488,7 @@ extends Serializable { valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: JobConf = new JobConf - ) { + ) { val saveFunc = (rdd: RDD[(K, V)], time: Time) => { val file = rddToFileName(prefix, suffix, time) rdd.saveAsHadoopFile(file, keyClass, valueClass, outputFormatClass, conf) diff --git a/streaming/src/main/scala/spark/streaming/Scheduler.scala b/streaming/src/main/scala/spark/streaming/Scheduler.scala index 1c4b22a898..252cc2a303 100644 --- a/streaming/src/main/scala/spark/streaming/Scheduler.scala +++ b/streaming/src/main/scala/spark/streaming/Scheduler.scala @@ -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. + */ + package spark.streaming import util.{ManualClock, RecurringTimer, Clock} diff --git a/streaming/src/main/scala/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/spark/streaming/StreamingContext.scala index b8b60aab43..ffd656227d 100644 --- a/streaming/src/main/scala/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/spark/streaming/StreamingContext.scala @@ -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. + */ + package spark.streaming import akka.actor.Props @@ -27,6 +44,8 @@ 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 @@ -186,10 +205,11 @@ class StreamingContext private ( * should be same. */ def actorStream[T: ClassManifest]( - props: Props, - name: String, - storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2, - supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy): DStream[T] = { + props: Props, + name: String, + storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2, + supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy + ): DStream[T] = { networkStream(new ActorReceiver[T](props, name, storageLevel, supervisorStrategy)) } @@ -197,9 +217,10 @@ class StreamingContext private ( * Create an input stream that receives messages pushed by a zeromq publisher. * @param publisherUrl Url of remote zeromq publisher * @param subscribe topic to subscribe to - * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence - * of byte thus it needs the converter(which might be deserializer of bytes) - * to translate from sequence of sequence of bytes, where sequence refer to a frame + * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic + * and each frame has sequence of byte thus it needs the converter + * (which might be deserializer of bytes) to translate from sequence + * of sequence of bytes, where sequence refer to a frame * and sub sequence refer to its payload. * @param storageLevel RDD storage level. Defaults to memory-only. */ @@ -215,24 +236,39 @@ class StreamingContext private ( } /** - * Create an input stream that pulls messages form a Kafka Broker. + * Create an input stream that pulls messages from a Kafka Broker. * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). * @param groupId The group id for this consumer. * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - * @param initialOffsets Optional initial offsets for each of the partitions to consume. - * By default the value is pulled from zookeper. + * in its own thread. * @param storageLevel Storage level to use for storing the received objects * (default: StorageLevel.MEMORY_AND_DISK_SER_2) */ - def kafkaStream[T: ClassManifest]( + def kafkaStream( zkQuorum: String, groupId: String, topics: Map[String, Int], - initialOffsets: Map[KafkaPartitionKey, Long] = Map[KafkaPartitionKey, Long](), storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2 + ): DStream[String] = { + val kafkaParams = Map[String, String]( + "zk.connect" -> zkQuorum, "groupid" -> groupId, "zk.connectiontimeout.ms" -> "10000") + kafkaStream[String, kafka.serializer.StringDecoder](kafkaParams, topics, storageLevel) + } + + /** + * Create an 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 Storage level to use for storing the received objects + */ + def kafkaStream[T: ClassManifest, D <: kafka.serializer.Decoder[_]: Manifest]( + kafkaParams: Map[String, String], + topics: Map[String, Int], + storageLevel: StorageLevel ): DStream[T] = { - val inputStream = new KafkaInputDStream[T](this, zkQuorum, groupId, topics, initialOffsets, storageLevel) + val inputStream = new KafkaInputDStream[T, D](this, kafkaParams, topics, storageLevel) registerInputStream(inputStream) inputStream } @@ -362,18 +398,18 @@ class StreamingContext private ( /** * Create a input stream that returns tweets received from Twitter. - * @param username Twitter username - * @param password Twitter password + * @param twitterAuth Twitter4J authentication, or None to use Twitter4J's default OAuth + * authorization; this uses the system properties twitter4j.oauth.consumerKey, + * .consumerSecret, .accessToken and .accessTokenSecret. * @param filters Set of filter strings to get only those tweets that match them * @param storageLevel Storage level to use for storing the received objects */ def twitterStream( - username: String, - password: String, + twitterAuth: Option[Authorization] = None, filters: Seq[String] = Nil, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 ): DStream[Status] = { - val inputStream = new TwitterInputDStream(this, username, password, filters, storageLevel) + val inputStream = new TwitterInputDStream(this, twitterAuth, filters, storageLevel) registerInputStream(inputStream) inputStream } @@ -397,7 +433,8 @@ class StreamingContext private ( * it will process either one or all of the RDDs returned by the queue. * @param queue Queue of RDDs * @param oneAtATime Whether only one RDD should be consumed from the queue in every interval - * @param defaultRDD Default RDD is returned by the DStream when the queue is empty. Set as null if no RDD should be returned when empty + * @param defaultRDD Default RDD is returned by the DStream when the queue is empty. + * Set as null if no RDD should be returned when empty * @tparam T Type of objects in the RDD */ def queueStream[T: ClassManifest]( diff --git a/streaming/src/main/scala/spark/streaming/Time.scala b/streaming/src/main/scala/spark/streaming/Time.scala index f14decf08b..ad5eab9dd2 100644 --- a/streaming/src/main/scala/spark/streaming/Time.scala +++ b/streaming/src/main/scala/spark/streaming/Time.scala @@ -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. + */ + package spark.streaming /** @@ -52,4 +69,4 @@ case class Time(private val millis: Long) { object Time { val ordering = Ordering.by((time: Time) => time.millis) -}
\ No newline at end of file +} diff --git a/streaming/src/main/scala/spark/streaming/api/java/JavaDStream.scala b/streaming/src/main/scala/spark/streaming/api/java/JavaDStream.scala index 4d93f0a5f7..7dcb1d713d 100644 --- a/streaming/src/main/scala/spark/streaming/api/java/JavaDStream.scala +++ b/streaming/src/main/scala/spark/streaming/api/java/JavaDStream.scala @@ -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. + */ + package spark.streaming.api.java import spark.streaming.{Duration, Time, DStream} @@ -82,4 +99,4 @@ class JavaDStream[T](val dstream: DStream[T])(implicit val classManifest: ClassM object JavaDStream { implicit def fromDStream[T: ClassManifest](dstream: DStream[T]): JavaDStream[T] = new JavaDStream[T](dstream) -}
\ No newline at end of file +} diff --git a/streaming/src/main/scala/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/spark/streaming/api/java/JavaDStreamLike.scala index 548809a359..3ab5c1fdde 100644 --- a/streaming/src/main/scala/spark/streaming/api/java/JavaDStreamLike.scala +++ b/streaming/src/main/scala/spark/streaming/api/java/JavaDStreamLike.scala @@ -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. + */ + package spark.streaming.api.java import java.util.{List => JList} @@ -296,4 +313,4 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T def checkpoint(interval: Duration) = { dstream.checkpoint(interval) } -}
\ No newline at end of file +} diff --git a/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala index 30240cad98..ccd15563b0 100644 --- a/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala @@ -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. + */ + package spark.streaming.api.java import java.util.{List => JList} diff --git a/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala index 3d149a742c..b7720ad0ea 100644 --- a/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala @@ -1,26 +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. + */ + package spark.streaming.api.java import spark.streaming._ import receivers.{ActorReceiver, ReceiverSupervisorStrategy} import spark.streaming.dstream._ import spark.storage.StorageLevel - import spark.api.java.function.{Function => JFunction, Function2 => JFunction2} import spark.api.java.{JavaSparkContext, JavaRDD} - import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} - import twitter4j.Status - import akka.actor.Props import akka.actor.SupervisorStrategy import akka.zeromq.Subscribe - import scala.collection.JavaConversions._ - import java.lang.{Long => JLong, Integer => JInt} import java.io.InputStream import java.util.{Map => JMap} +import twitter4j.auth.Authorization /** * A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic @@ -121,14 +133,15 @@ class JavaStreamingContext(val ssc: StreamingContext) { * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed * in its own thread. */ - def kafkaStream[T]( + def kafkaStream( zkQuorum: String, groupId: String, topics: JMap[String, JInt]) - : JavaDStream[T] = { - implicit val cmt: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - ssc.kafkaStream[T](zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*)) + : JavaDStream[String] = { + implicit val cmt: ClassManifest[String] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[String]] + ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), + StorageLevel.MEMORY_ONLY_SER_2) } /** @@ -136,49 +149,45 @@ class JavaStreamingContext(val ssc: StreamingContext) { * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). * @param groupId The group id for this consumer. * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - * @param initialOffsets Optional initial offsets for each of the partitions to consume. - * By default the value is pulled from zookeper. + * in its own thread. + * @param storageLevel RDD storage level. Defaults to memory-only + * */ - def kafkaStream[T]( + def kafkaStream( zkQuorum: String, groupId: String, topics: JMap[String, JInt], - initialOffsets: JMap[KafkaPartitionKey, JLong]) - : JavaDStream[T] = { - implicit val cmt: ClassManifest[T] = - implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - ssc.kafkaStream[T]( - zkQuorum, - groupId, - Map(topics.mapValues(_.intValue()).toSeq: _*), - Map(initialOffsets.mapValues(_.longValue()).toSeq: _*)) + storageLevel: StorageLevel) + : JavaDStream[String] = { + implicit val cmt: ClassManifest[String] = + implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[String]] + ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), + storageLevel) } /** * Create an input stream that pulls messages form a Kafka Broker. - * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). - * @param groupId The group id for this consumer. + * @param typeClass Type of RDD + * @param decoderClass Type of kafka 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 initialOffsets Optional initial offsets for each of the partitions to consume. - * By default the value is pulled from zookeper. * @param storageLevel RDD storage level. Defaults to memory-only */ - def kafkaStream[T]( - zkQuorum: String, - groupId: String, + def kafkaStream[T, D <: kafka.serializer.Decoder[_]]( + typeClass: Class[T], + decoderClass: Class[D], + kafkaParams: JMap[String, String], topics: JMap[String, JInt], - initialOffsets: JMap[KafkaPartitionKey, JLong], storageLevel: StorageLevel) : JavaDStream[T] = { implicit val cmt: ClassManifest[T] = implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]] - ssc.kafkaStream[T]( - zkQuorum, - groupId, + implicit val cmd: Manifest[D] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[D]] + ssc.kafkaStream[T, D]( + kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), - Map(initialOffsets.mapValues(_.longValue()).toSeq: _*), storageLevel) } @@ -315,47 +324,76 @@ class JavaStreamingContext(val ssc: StreamingContext) { /** * Create a input stream that returns tweets received from Twitter. - * @param username Twitter username - * @param password Twitter password + * @param twitterAuth Twitter4J Authorization object + * @param filters Set of filter strings to get only those tweets that match them + * @param storageLevel Storage level to use for storing the received objects + */ + def twitterStream( + twitterAuth: Authorization, + filters: Array[String], + storageLevel: StorageLevel + ): JavaDStream[Status] = { + ssc.twitterStream(Some(twitterAuth), filters, storageLevel) + } + + /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * .consumerSecret, .accessToken and .accessTokenSecret to be set. * @param filters Set of filter strings to get only those tweets that match them * @param storageLevel Storage level to use for storing the received objects */ def twitterStream( - username: String, - password: String, filters: Array[String], storageLevel: StorageLevel ): JavaDStream[Status] = { - ssc.twitterStream(username, password, filters, storageLevel) + ssc.twitterStream(None, filters, storageLevel) } /** * Create a input stream that returns tweets received from Twitter. - * @param username Twitter username - * @param password Twitter password + * @param twitterAuth Twitter4J Authorization * @param filters Set of filter strings to get only those tweets that match them */ def twitterStream( - username: String, - password: String, + twitterAuth: Authorization, filters: Array[String] ): JavaDStream[Status] = { - ssc.twitterStream(username, password, filters) + ssc.twitterStream(Some(twitterAuth), filters) + } + + /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * .consumerSecret, .accessToken and .accessTokenSecret to be set. + * @param filters Set of filter strings to get only those tweets that match them + */ + def twitterStream( + filters: Array[String] + ): JavaDStream[Status] = { + ssc.twitterStream(None, filters) } /** * Create a input stream that returns tweets received from Twitter. - * @param username Twitter username - * @param password Twitter password + * @param twitterAuth Twitter4J Authorization */ def twitterStream( - username: String, - password: String + twitterAuth: Authorization ): JavaDStream[Status] = { - ssc.twitterStream(username, password) + ssc.twitterStream(Some(twitterAuth)) } /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * .consumerSecret, .accessToken and .accessTokenSecret to be set. + */ + 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 * @param name Name of the actor diff --git a/streaming/src/main/scala/spark/streaming/dstream/CoGroupedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/CoGroupedDStream.scala index 4ef4bb7de1..99553d295d 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/CoGroupedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/CoGroupedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.{RDD, Partitioner} diff --git a/streaming/src/main/scala/spark/streaming/dstream/ConstantInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/ConstantInputDStream.scala index 41c3af4694..095137092a 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/ConstantInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/ConstantInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.RDD @@ -16,4 +33,4 @@ class ConstantInputDStream[T: ClassManifest](ssc_ : StreamingContext, rdd: RDD[T override def compute(validTime: Time): Option[RDD[T]] = { Some(rdd) } -}
\ No newline at end of file +} diff --git a/streaming/src/main/scala/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/FileInputDStream.scala index 41b9bd9461..de0536125d 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/FileInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/FileInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.RDD diff --git a/streaming/src/main/scala/spark/streaming/dstream/FilteredDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/FilteredDStream.scala index e993164f99..9d8c5c3175 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/FilteredDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/FilteredDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Duration, DStream, Time} diff --git a/streaming/src/main/scala/spark/streaming/dstream/FlatMapValuedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/FlatMapValuedDStream.scala index cabd34f5f2..78d7117f0f 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/FlatMapValuedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/FlatMapValuedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Duration, DStream, Time} diff --git a/streaming/src/main/scala/spark/streaming/dstream/FlatMappedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/FlatMappedDStream.scala index a69af60589..d13bebb10f 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/FlatMappedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/FlatMappedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Duration, DStream, Time} diff --git a/streaming/src/main/scala/spark/streaming/dstream/FlumeInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/FlumeInputDStream.scala index c9644b3a83..4906f503c2 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/FlumeInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/FlumeInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.StreamingContext diff --git a/streaming/src/main/scala/spark/streaming/dstream/ForEachDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/ForEachDStream.scala index ee69ea5177..7df537eb56 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/ForEachDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/ForEachDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.RDD diff --git a/streaming/src/main/scala/spark/streaming/dstream/GlommedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/GlommedDStream.scala index b589cbd4d5..06fda6fe8e 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/GlommedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/GlommedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Duration, DStream, Time} diff --git a/streaming/src/main/scala/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/InputDStream.scala index 3c5d43a609..4dbdec459d 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/InputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/InputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Time, Duration, StreamingContext, DStream} diff --git a/streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala index ddd9becf32..6ee588af15 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.Logging @@ -9,58 +26,51 @@ import java.util.concurrent.Executors import kafka.consumer._ import kafka.message.{Message, MessageSet, MessageAndMetadata} -import kafka.serializer.StringDecoder +import kafka.serializer.Decoder import kafka.utils.{Utils, ZKGroupTopicDirs} import kafka.utils.ZkUtils._ +import kafka.utils.ZKStringSerializer +import org.I0Itec.zkclient._ import scala.collection.Map import scala.collection.mutable.HashMap import scala.collection.JavaConversions._ -// Key for a specific Kafka Partition: (broker, topic, group, part) -case class KafkaPartitionKey(brokerId: Int, topic: String, groupId: String, partId: Int) - /** * Input stream that pulls messages from a Kafka Broker. * - * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). - * @param groupId The group id for this consumer. + * @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 initialOffsets Optional initial offsets for each of the partitions to consume. - * By default the value is pulled from zookeper. * @param storageLevel RDD storage level. */ private[streaming] -class KafkaInputDStream[T: ClassManifest]( +class KafkaInputDStream[T: ClassManifest, D <: Decoder[_]: Manifest]( @transient ssc_ : StreamingContext, - zkQuorum: String, - groupId: String, + kafkaParams: Map[String, String], topics: Map[String, Int], - initialOffsets: Map[KafkaPartitionKey, Long], storageLevel: StorageLevel ) extends NetworkInputDStream[T](ssc_ ) with Logging { def getReceiver(): NetworkReceiver[T] = { - new KafkaReceiver(zkQuorum, groupId, topics, initialOffsets, storageLevel) + new KafkaReceiver[T, D](kafkaParams, topics, storageLevel) .asInstanceOf[NetworkReceiver[T]] } } private[streaming] -class KafkaReceiver(zkQuorum: String, groupId: String, - topics: Map[String, Int], initialOffsets: Map[KafkaPartitionKey, Long], - storageLevel: StorageLevel) extends NetworkReceiver[Any] { - - // Timeout for establishing a connection to Zookeper in ms. - val ZK_TIMEOUT = 10000 +class KafkaReceiver[T: ClassManifest, D <: Decoder[_]: Manifest]( + kafkaParams: Map[String, String], + topics: Map[String, Int], + storageLevel: StorageLevel + ) extends NetworkReceiver[Any] { // Handles pushing data into the BlockManager lazy protected val blockGenerator = new BlockGenerator(storageLevel) // Connection to Kafka - var consumerConnector : ZookeeperConsumerConnector = null + var consumerConnector : ConsumerConnector = null def onStop() { blockGenerator.stop() @@ -73,54 +83,59 @@ class KafkaReceiver(zkQuorum: String, groupId: String, // 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: " + groupId) - logInfo("Initial offsets: " + initialOffsets.toString) + logInfo("Starting Kafka Consumer Stream with group: " + kafkaParams("groupid")) - // Zookeper connection properties + // Kafka connection properties val props = new Properties() - props.put("zk.connect", zkQuorum) - props.put("zk.connectiontimeout.ms", ZK_TIMEOUT.toString) - props.put("groupid", groupId) + kafkaParams.foreach(param => props.put(param._1, param._2)) // Create the connection to the cluster - logInfo("Connecting to Zookeper: " + zkQuorum) + logInfo("Connecting to Zookeper: " + kafkaParams("zk.connect")) val consumerConfig = new ConsumerConfig(props) - consumerConnector = Consumer.create(consumerConfig).asInstanceOf[ZookeeperConsumerConnector] - logInfo("Connected to " + zkQuorum) + consumerConnector = Consumer.create(consumerConfig) + logInfo("Connected to " + kafkaParams("zk.connect")) - // If specified, set the topic offset - setOffsets(initialOffsets) + // 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")) + } // Create Threads for each Topic/Message Stream we are listening - val topicMessageStreams = consumerConnector.createMessageStreams(topics, new StringDecoder()) + val decoder = manifest[D].erasure.newInstance.asInstanceOf[Decoder[T]] + val topicMessageStreams = consumerConnector.createMessageStreams(topics, decoder) // Start the messages handler for each partition topicMessageStreams.values.foreach { streams => streams.foreach { stream => executorPool.submit(new MessageHandler(stream)) } } - - } - - // Overwrites the offets in Zookeper. - private def setOffsets(offsets: Map[KafkaPartitionKey, Long]) { - offsets.foreach { case(key, offset) => - val topicDirs = new ZKGroupTopicDirs(key.groupId, key.topic) - val partitionName = key.brokerId + "-" + key.partId - updatePersistentPath(consumerConnector.zkClient, - topicDirs.consumerOffsetDir + "/" + partitionName, offset.toString) - } } // Handles Kafka Messages - private class MessageHandler(stream: KafkaStream[String]) extends Runnable { + private class MessageHandler[T: ClassManifest](stream: KafkaStream[T]) extends Runnable { def run() { logInfo("Starting MessageHandler.") - stream.takeWhile { msgAndMetadata => + for (msgAndMetadata <- stream) { blockGenerator += msgAndMetadata.message - // Keep on handling messages - - true } } } + + // It is our responsibility to delete the consumer group when specifying autooffset.reset. This is because + // Kafka 0.7.2 only honors this param when the group is not in zookeeper. + // + // The kafka high level consumer doesn't expose setting offsets currently, this is a trick copied from Kafkas' + // ConsoleConsumer. See code related to 'autooffset.reset' when it is set to 'smallest'/'largest': + // https://github.com/apache/kafka/blob/0.7.2/core/src/main/scala/kafka/consumer/ConsoleConsumer.scala + private def tryZookeeperConsumerGroupCleanup(zkUrl: String, groupId: String) { + try { + val dir = "/consumers/" + groupId + logInfo("Cleaning up temporary zookeeper data under " + dir + ".") + val zk = new ZkClient(zkUrl, 30*1000, 30*1000, ZKStringSerializer) + zk.deleteRecursive(dir) + zk.close() + } catch { + case _ => // swallow + } + } } diff --git a/streaming/src/main/scala/spark/streaming/dstream/MapPartitionedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/MapPartitionedDStream.scala index 848afecfad..af41a1b9ac 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/MapPartitionedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/MapPartitionedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Duration, DStream, Time} diff --git a/streaming/src/main/scala/spark/streaming/dstream/MapValuedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/MapValuedDStream.scala index 6055aa6a05..8d8a6161c6 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/MapValuedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/MapValuedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Duration, DStream, Time} diff --git a/streaming/src/main/scala/spark/streaming/dstream/MappedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/MappedDStream.scala index 20818a0cab..3fda84a38a 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/MappedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/MappedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Duration, DStream, Time} diff --git a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala index 7385474963..344b41c4d0 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Time, StreamingContext, AddBlocks, RegisterReceiver, DeregisterReceiver} @@ -140,12 +157,10 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log /** - * Pushes a block (as iterator of values) into the block manager. + * Pushes a block (as an ArrayBuffer filled with data) into the block manager. */ - def pushBlock(blockId: String, iterator: Iterator[T], metadata: Any, level: StorageLevel) { - val buffer = new ArrayBuffer[T] ++ iterator - env.blockManager.put(blockId, buffer.asInstanceOf[ArrayBuffer[Any]], level) - + def pushBlock(blockId: String, arrayBuffer: ArrayBuffer[T], metadata: Any, level: StorageLevel) { + env.blockManager.put(blockId, arrayBuffer.asInstanceOf[ArrayBuffer[Any]], level) actor ! ReportBlock(blockId, metadata) } @@ -195,10 +210,10 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log class BlockGenerator(storageLevel: StorageLevel) extends Serializable with Logging { - case class Block(id: String, iterator: Iterator[T], metadata: Any = null) + case class Block(id: String, buffer: ArrayBuffer[T], metadata: Any = null) val clock = new SystemClock() - val blockInterval = 200L + val blockInterval = System.getProperty("spark.streaming.blockInterval", "200").toLong val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer) val blockStorageLevel = storageLevel val blocksForPushing = new ArrayBlockingQueue[Block](1000) @@ -222,17 +237,13 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log currentBuffer += obj } - private def createBlock(blockId: String, iterator: Iterator[T]) : Block = { - new Block(blockId, iterator) - } - private def updateCurrentBuffer(time: Long) { try { val newBlockBuffer = currentBuffer currentBuffer = new ArrayBuffer[T] if (newBlockBuffer.size > 0) { val blockId = "input-" + NetworkReceiver.this.streamId + "-" + (time - blockInterval) - val newBlock = createBlock(blockId, newBlockBuffer.toIterator) + val newBlock = new Block(blockId, newBlockBuffer) blocksForPushing.add(newBlock) } } catch { @@ -248,7 +259,7 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log try { while(true) { val block = blocksForPushing.take() - NetworkReceiver.this.pushBlock(block.id, block.iterator, block.metadata, storageLevel) + NetworkReceiver.this.pushBlock(block.id, block.buffer, block.metadata, storageLevel) } } catch { case ie: InterruptedException => diff --git a/streaming/src/main/scala/spark/streaming/dstream/PluggableInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/PluggableInputDStream.scala index 3c2a81947b..33f7cd063f 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/PluggableInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/PluggableInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.StreamingContext diff --git a/streaming/src/main/scala/spark/streaming/dstream/QueueInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/QueueInputDStream.scala index da224ad6f7..b269061b73 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/QueueInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/QueueInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.RDD diff --git a/streaming/src/main/scala/spark/streaming/dstream/RawInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/RawInputDStream.scala index 1b2fa56779..236f74f575 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/RawInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/RawInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.Logging diff --git a/streaming/src/main/scala/spark/streaming/dstream/ReducedWindowedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/ReducedWindowedDStream.scala index 343b6915e7..96260501ab 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/ReducedWindowedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/ReducedWindowedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.StreamingContext._ diff --git a/streaming/src/main/scala/spark/streaming/dstream/ShuffledDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/ShuffledDStream.scala index 1f9548bfb8..83b57b27f7 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/ShuffledDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/ShuffledDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.{RDD, Partitioner} diff --git a/streaming/src/main/scala/spark/streaming/dstream/SocketInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/SocketInputDStream.scala index 1408af0afa..5877b10e0e 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/SocketInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/SocketInputDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.StreamingContext diff --git a/streaming/src/main/scala/spark/streaming/dstream/StateDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/StateDStream.scala index db62955036..4b46613d5e 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/StateDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/StateDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.RDD diff --git a/streaming/src/main/scala/spark/streaming/dstream/TransformedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/TransformedDStream.scala index 99660d9dee..e7fbc5bbcf 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/TransformedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/TransformedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.RDD diff --git a/streaming/src/main/scala/spark/streaming/dstream/TwitterInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/TwitterInputDStream.scala index c697498862..f09a8b9f90 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/TwitterInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/TwitterInputDStream.scala @@ -1,36 +1,64 @@ +/* + * 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 spark.streaming.dstream import spark._ import spark.streaming._ import storage.StorageLevel - import twitter4j._ -import twitter4j.auth.BasicAuthorization +import twitter4j.auth.Authorization +import java.util.prefs.Preferences +import twitter4j.conf.ConfigurationBuilder +import twitter4j.conf.PropertyConfiguration +import twitter4j.auth.OAuthAuthorization +import twitter4j.auth.AccessToken /* A stream of Twitter statuses, potentially filtered by one or more keywords. * -* @constructor create a new Twitter stream using the supplied username and password to authenticate. +* @constructor create a new Twitter stream using the supplied Twitter4J authentication credentials. * An optional set of string filters can be used to restrict the set of tweets. The Twitter API is * such that this may return a sampled subset of all tweets during each interval. +* +* If no Authorization object is provided, initializes OAuth authorization using the system +* properties twitter4j.oauth.consumerKey, .consumerSecret, .accessToken and .accessTokenSecret. */ private[streaming] class TwitterInputDStream( @transient ssc_ : StreamingContext, - username: String, - password: String, + twitterAuth: Option[Authorization], filters: Seq[String], storageLevel: StorageLevel ) extends NetworkInputDStream[Status](ssc_) { + + private def createOAuthAuthorization(): Authorization = { + new OAuthAuthorization(new ConfigurationBuilder().build()) + } + private val authorization = twitterAuth.getOrElse(createOAuthAuthorization()) + override def getReceiver(): NetworkReceiver[Status] = { - new TwitterReceiver(username, password, filters, storageLevel) + new TwitterReceiver(authorization, filters, storageLevel) } } private[streaming] class TwitterReceiver( - username: String, - password: String, + twitterAuth: Authorization, filters: Seq[String], storageLevel: StorageLevel ) extends NetworkReceiver[Status] { @@ -40,8 +68,7 @@ class TwitterReceiver( protected override def onStart() { blockGenerator.start() - twitterStream = new TwitterStreamFactory() - .getInstance(new BasicAuthorization(username, password)) + twitterStream = new TwitterStreamFactory().getInstance(twitterAuth) twitterStream.addListener(new StatusListener { def onStatus(status: Status) = { blockGenerator += status diff --git a/streaming/src/main/scala/spark/streaming/dstream/UnionDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/UnionDStream.scala index 00bad5da34..3eaa9a7e7f 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/UnionDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/UnionDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.streaming.{Duration, DStream, Time} diff --git a/streaming/src/main/scala/spark/streaming/dstream/WindowedDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/WindowedDStream.scala index cbf0c88108..fd24d61730 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/WindowedDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/WindowedDStream.scala @@ -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. + */ + package spark.streaming.dstream import spark.RDD diff --git a/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala b/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala index b3201d0b28..2d9937eab8 100644 --- a/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala +++ b/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala @@ -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. + */ + package spark.streaming.receivers import akka.actor.{ Actor, PoisonPill, Props, SupervisorStrategy } @@ -9,6 +26,8 @@ import spark.streaming.dstream.NetworkReceiver import java.util.concurrent.atomic.AtomicInteger +import scala.collection.mutable.ArrayBuffer + /** A helper with set of defaults for supervisor strategy **/ object ReceiverSupervisorStrategy { @@ -136,8 +155,9 @@ private[streaming] class ActorReceiver[T: ClassManifest]( } protected def pushBlock(iter: Iterator[T]) { - pushBlock("block-" + streamId + "-" + System.nanoTime(), - iter, null, storageLevel) + val buffer = new ArrayBuffer[T] + buffer ++= iter + pushBlock("block-" + streamId + "-" + System.nanoTime(), buffer, null, storageLevel) } protected def onStart() = { diff --git a/streaming/src/main/scala/spark/streaming/receivers/ZeroMQReceiver.scala b/streaming/src/main/scala/spark/streaming/receivers/ZeroMQReceiver.scala index 5533c3cf1e..22d554e7e4 100644 --- a/streaming/src/main/scala/spark/streaming/receivers/ZeroMQReceiver.scala +++ b/streaming/src/main/scala/spark/streaming/receivers/ZeroMQReceiver.scala @@ -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. + */ + package spark.streaming.receivers import akka.actor.Actor diff --git a/streaming/src/main/scala/spark/streaming/util/Clock.scala b/streaming/src/main/scala/spark/streaming/util/Clock.scala index 974651f9f6..d9ac722df5 100644 --- a/streaming/src/main/scala/spark/streaming/util/Clock.scala +++ b/streaming/src/main/scala/spark/streaming/util/Clock.scala @@ -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. + */ + package spark.streaming.util private[streaming] diff --git a/streaming/src/main/scala/spark/streaming/util/MasterFailureTest.scala b/streaming/src/main/scala/spark/streaming/util/MasterFailureTest.scala index f673e5be15..8ce5d8daf5 100644 --- a/streaming/src/main/scala/spark/streaming/util/MasterFailureTest.scala +++ b/streaming/src/main/scala/spark/streaming/util/MasterFailureTest.scala @@ -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. + */ + package spark.streaming.util import spark.{Logging, RDD} @@ -159,6 +176,7 @@ object MasterFailureTest extends Logging { // Setup the streaming computation with the given operation System.clearProperty("spark.driver.port") + System.clearProperty("spark.hostPort") var ssc = new StreamingContext("local[4]", "MasterFailureTest", batchDuration, null, Nil, Map()) ssc.checkpoint(checkpointDir.toString) val inputStream = ssc.textFileStream(testDir.toString) @@ -205,6 +223,7 @@ object MasterFailureTest extends Logging { // (iii) Its not timed out yet System.clearProperty("spark.streaming.clock") System.clearProperty("spark.driver.port") + System.clearProperty("spark.hostPort") ssc.start() val startTime = System.currentTimeMillis() while (!killed && !isLastOutputGenerated && !isTimedOut) { @@ -357,13 +376,16 @@ class FileGeneratingThread(input: Seq[String], testDir: Path, interval: Long) // Write the data to a local file and then move it to the target test directory val localFile = new File(localTestDir, (i+1).toString) val hadoopFile = new Path(testDir, (i+1).toString) + 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), hadoopFile) + fs.copyFromLocalFile(new Path(localFile.toString), tempHadoopFile) + fs.rename(tempHadoopFile, hadoopFile) done = true } catch { case ioe: IOException => { diff --git a/streaming/src/main/scala/spark/streaming/util/RawTextHelper.scala b/streaming/src/main/scala/spark/streaming/util/RawTextHelper.scala index 03749d4a94..bf04120293 100644 --- a/streaming/src/main/scala/spark/streaming/util/RawTextHelper.scala +++ b/streaming/src/main/scala/spark/streaming/util/RawTextHelper.scala @@ -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. + */ + package spark.streaming.util import spark.SparkContext diff --git a/streaming/src/main/scala/spark/streaming/util/RawTextSender.scala b/streaming/src/main/scala/spark/streaming/util/RawTextSender.scala index d8b987ec86..5cc6ad9dee 100644 --- a/streaming/src/main/scala/spark/streaming/util/RawTextSender.scala +++ b/streaming/src/main/scala/spark/streaming/util/RawTextSender.scala @@ -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. + */ + package spark.streaming.util import java.nio.ByteBuffer @@ -5,7 +22,7 @@ import spark.util.{RateLimitedOutputStream, IntParam} import java.net.ServerSocket import spark.{Logging, KryoSerializer} import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream -import io.Source +import scala.io.Source import java.io.IOException /** diff --git a/streaming/src/main/scala/spark/streaming/util/RecurringTimer.scala b/streaming/src/main/scala/spark/streaming/util/RecurringTimer.scala index 8e10276deb..7ecc44236d 100644 --- a/streaming/src/main/scala/spark/streaming/util/RecurringTimer.scala +++ b/streaming/src/main/scala/spark/streaming/util/RecurringTimer.scala @@ -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. + */ + package spark.streaming.util private[streaming] diff --git a/streaming/src/test/java/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/spark/streaming/JavaAPISuite.java index 3bed500f73..3b93790baa 100644 --- a/streaming/src/test/java/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/spark/streaming/JavaAPISuite.java @@ -1,9 +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 spark.streaming; 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.junit.After; import org.junit.Assert; @@ -23,7 +41,6 @@ import spark.streaming.api.java.JavaPairDStream; import spark.streaming.api.java.JavaStreamingContext; import spark.streaming.JavaTestUtils; import spark.streaming.JavaCheckpointTestUtils; -import spark.streaming.dstream.KafkaPartitionKey; import spark.streaming.InputStreamsSuite; import java.io.*; @@ -1203,10 +1220,14 @@ public class JavaAPISuite implements Serializable { @Test public void testKafkaStream() { HashMap<String, Integer> topics = Maps.newHashMap(); - HashMap<KafkaPartitionKey, Long> offsets = Maps.newHashMap(); JavaDStream test1 = ssc.kafkaStream("localhost:12345", "group", topics); - JavaDStream test2 = ssc.kafkaStream("localhost:12345", "group", topics, offsets); - JavaDStream test3 = ssc.kafkaStream("localhost:12345", "group", topics, offsets, + JavaDStream 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, StorageLevel.MEMORY_AND_DISK()); } @@ -1263,7 +1284,7 @@ public class JavaAPISuite implements Serializable { @Test public void testTwitterStream() { String[] filters = new String[] { "good", "bad", "ugly" }; - JavaDStream test = ssc.twitterStream("username", "password", filters, StorageLevel.MEMORY_ONLY()); + JavaDStream test = ssc.twitterStream(filters, StorageLevel.MEMORY_ONLY()); } @Test diff --git a/streaming/src/test/java/spark/streaming/JavaTestUtils.scala b/streaming/src/test/java/spark/streaming/JavaTestUtils.scala index 64a7e7cbf9..f9d25db8da 100644 --- a/streaming/src/test/java/spark/streaming/JavaTestUtils.scala +++ b/streaming/src/test/java/spark/streaming/JavaTestUtils.scala @@ -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. + */ + package spark.streaming import collection.mutable.{SynchronizedBuffer, ArrayBuffer} @@ -64,4 +81,4 @@ object JavaTestUtils extends JavaTestBase { object JavaCheckpointTestUtils extends JavaTestBase { override def actuallyWait = true -}
\ No newline at end of file +} diff --git a/streaming/src/test/resources/log4j.properties b/streaming/src/test/resources/log4j.properties index 59c445e63f..063529a9cb 100644 --- a/streaming/src/test/resources/log4j.properties +++ b/streaming/src/test/resources/log4j.properties @@ -1,4 +1,21 @@ -# Set everything to be logged to the file streaming/target/unit-tests.log +# +# 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. +# + +# Set everything to be logged to the file streaming/target/unit-tests.log log4j.rootCategory=INFO, file # log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file=org.apache.log4j.FileAppender diff --git a/streaming/src/test/scala/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/spark/streaming/BasicOperationsSuite.scala index 8fce91853c..67e3e0cd30 100644 --- a/streaming/src/test/scala/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/spark/streaming/BasicOperationsSuite.scala @@ -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. + */ + package spark.streaming import spark.streaming.StreamingContext._ @@ -6,13 +23,16 @@ import util.ManualClock class BasicOperationsSuite extends TestSuiteBase { - System.setProperty("spark.streaming.clock", "spark.streaming.util.ManualClock") - override def framework() = "BasicOperationsSuite" + before { + System.setProperty("spark.streaming.clock", "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("map") { @@ -90,9 +110,9 @@ class BasicOperationsSuite extends TestSuiteBase { test("count") { testOperation( - Seq(1 to 1, 1 to 2, 1 to 3, 1 to 4), + Seq(Seq(), 1 to 1, 1 to 2, 1 to 3, 1 to 4), (s: DStream[Int]) => s.count(), - Seq(Seq(1L), Seq(2L), Seq(3L), Seq(4L)) + Seq(Seq(0L), Seq(1L), Seq(2L), Seq(3L), Seq(4L)) ) } diff --git a/streaming/src/test/scala/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/spark/streaming/CheckpointSuite.scala index cac86deeaf..8c639648f0 100644 --- a/streaming/src/test/scala/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/spark/streaming/CheckpointSuite.scala @@ -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. + */ + package spark.streaming import dstream.FileInputDStream @@ -31,6 +48,7 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { // 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 @@ -325,6 +343,7 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { ) ssc = new StreamingContext(checkpointDir) System.clearProperty("spark.driver.port") + System.clearProperty("spark.hostPort") ssc.start() val outputNew = advanceTimeWithRealDelay[V](ssc, nextNumBatches) // the first element will be re-processed data of the last batch before restart @@ -350,4 +369,4 @@ class CheckpointSuite extends TestSuiteBase with BeforeAndAfter { val outputStream = ssc.graph.getOutputStreams.head.asInstanceOf[TestOutputStream[V]] outputStream.output } -}
\ No newline at end of file +} diff --git a/streaming/src/test/scala/spark/streaming/FailureSuite.scala b/streaming/src/test/scala/spark/streaming/FailureSuite.scala index a5fa7ab92d..7fc649fe27 100644 --- a/streaming/src/test/scala/spark/streaming/FailureSuite.scala +++ b/streaming/src/test/scala/spark/streaming/FailureSuite.scala @@ -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. + */ + package spark.streaming import spark.Logging diff --git a/streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala index 4d33857b25..1c5419b16d 100644 --- a/streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/spark/streaming/InputStreamsSuite.scala @@ -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. + */ + package spark.streaming import akka.actor.Actor @@ -30,15 +47,18 @@ import com.google.common.io.Files class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { - System.setProperty("spark.streaming.clock", "spark.streaming.util.ManualClock") - val testPort = 9999 override def checkpointDir = "checkpoint" + before { + System.setProperty("spark.streaming.clock", "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") } @@ -240,6 +260,17 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { assert(output(i) === expectedOutput(i)) } } + + test("kafka input stream") { + val ssc = new StreamingContext(master, framework, 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) + } } diff --git a/streaming/src/test/scala/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/spark/streaming/TestSuiteBase.scala index ad6aa79d10..cb34b5a7cc 100644 --- a/streaming/src/test/scala/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/spark/streaming/TestSuiteBase.scala @@ -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. + */ + package spark.streaming import spark.streaming.dstream.{InputDStream, ForEachDStream} diff --git a/streaming/src/test/scala/spark/streaming/WindowOperationsSuite.scala b/streaming/src/test/scala/spark/streaming/WindowOperationsSuite.scala index 1b66f3bda2..894b765fc6 100644 --- a/streaming/src/test/scala/spark/streaming/WindowOperationsSuite.scala +++ b/streaming/src/test/scala/spark/streaming/WindowOperationsSuite.scala @@ -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. + */ + package spark.streaming import spark.streaming.StreamingContext._ @@ -16,6 +33,7 @@ class WindowOperationsSuite extends TestSuiteBase { 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") } val largerSlideInput = Seq( diff --git a/tools/pom.xml b/tools/pom.xml new file mode 100644 index 0000000000..878eb82f18 --- /dev/null +++ b/tools/pom.xml @@ -0,0 +1,176 @@ +<?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.spark-project</groupId> + <artifactId>spark-parent</artifactId> + <version>0.8.0-SNAPSHOT</version> + <relativePath>../pom.xml</relativePath> + </parent> + + <groupId>org.spark-project</groupId> + <artifactId>spark-tools</artifactId> + <packaging>jar</packaging> + <name>Spark Project Tools</name> + <url>http://spark-project.org/</url> + + <dependencies> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.version}</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + + <build> + <outputDirectory>target/scala-${scala.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-source-plugin</artifactId> + </plugin> + <plugin> + <groupId>org.codehaus.mojo</groupId> + <artifactId>build-helper-maven-plugin</artifactId> + </plugin> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + </plugin> + </plugins> + </build> + + <profiles> + <profile> + <id>hadoop1</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop1</classifier> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-streaming</artifactId> + <version>${project.version}</version> + <classifier>hadoop1</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-core</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop1</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> + <profile> + <id>hadoop2</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2</classifier> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-streaming</artifactId> + <version>${project.version}</version> + <classifier>hadoop2</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-core</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> + <profile> + <id>hadoop2-yarn</id> + <dependencies> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-core</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-streaming</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-api</artifactId> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-yarn-common</artifactId> + <scope>provided</scope> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <configuration> + <classifier>hadoop2-yarn</classifier> + </configuration> + </plugin> + </plugins> + </build> + </profile> + </profiles> +</project> diff --git a/tools/src/main/scala/spark/tools/JavaAPICompletenessChecker.scala b/tools/src/main/scala/spark/tools/JavaAPICompletenessChecker.scala new file mode 100644 index 0000000000..3a55f50812 --- /dev/null +++ b/tools/src/main/scala/spark/tools/JavaAPICompletenessChecker.scala @@ -0,0 +1,354 @@ +/* + * 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 spark.tools + +import spark._ +import java.lang.reflect.Method +import scala.collection.mutable.ArrayBuffer +import spark.api.java._ +import spark.streaming.{PairDStreamFunctions, DStream, StreamingContext} +import spark.streaming.api.java.{JavaPairDStream, JavaDStream, JavaStreamingContext} +import scala.Tuple2 + + +private[spark] abstract class SparkType(val name: String) + +private[spark] case class BaseType(override val name: String) extends SparkType(name) { + override def toString: String = { + name + } +} + +private[spark] +case class ParameterizedType(override val name: String, + parameters: Seq[SparkType], + typebounds: String = "") extends SparkType(name) { + override def toString: String = { + if (typebounds != "") { + typebounds + " " + name + "<" + parameters.mkString(", ") + ">" + } else { + name + "<" + parameters.mkString(", ") + ">" + } + } +} + +private[spark] +case class SparkMethod(name: String, returnType: SparkType, parameters: Seq[SparkType]) { + override def toString: String = { + returnType + " " + name + "(" + parameters.mkString(", ") + ")" + } +} + +/** + * A tool for identifying methods that need to be ported from Scala to the Java API. + * + * It uses reflection to find methods in the Scala API and rewrites those methods' signatures + * into appropriate Java equivalents. If those equivalent methods have not been implemented in + * the Java API, they are printed. + */ +object JavaAPICompletenessChecker { + + private def parseType(typeStr: String): SparkType = { + if (!typeStr.contains("<")) { + // Base types might begin with "class" or "interface", so we have to strip that off: + BaseType(typeStr.trim.split(" ").last) + } else if (typeStr.endsWith("[]")) { + ParameterizedType("Array", Seq(parseType(typeStr.stripSuffix("[]")))) + } else { + val parts = typeStr.split("<", 2) + val name = parts(0).trim + assert (parts(1).last == '>') + val parameters = parts(1).dropRight(1) + ParameterizedType(name, parseTypeList(parameters)) + } + } + + private def parseTypeList(typeStr: String): Seq[SparkType] = { + val types: ArrayBuffer[SparkType] = new ArrayBuffer[SparkType] + var stack = 0 + var token: StringBuffer = new StringBuffer() + for (c <- typeStr.trim) { + if (c == ',' && stack == 0) { + types += parseType(token.toString) + token = new StringBuffer() + } else if (c == ' ' && stack != 0) { + // continue + } else { + if (c == '<') { + stack += 1 + } else if (c == '>') { + stack -= 1 + } + token.append(c) + } + } + assert (stack == 0) + if (token.toString != "") { + types += parseType(token.toString) + } + types.toSeq + } + + private def parseReturnType(typeStr: String): SparkType = { + if (typeStr(0) == '<') { + val parts = typeStr.drop(0).split(">", 2) + val parsed = parseType(parts(1)).asInstanceOf[ParameterizedType] + ParameterizedType(parsed.name, parsed.parameters, parts(0)) + } else { + parseType(typeStr) + } + } + + private def toSparkMethod(method: Method): SparkMethod = { + val returnType = parseReturnType(method.getGenericReturnType.toString) + val name = method.getName + val parameters = method.getGenericParameterTypes.map(t => parseType(t.toString)) + SparkMethod(name, returnType, parameters) + } + + private def toJavaType(scalaType: SparkType): SparkType = { + val renameSubstitutions = Map( + "scala.collection.Map" -> "java.util.Map", + // TODO: the JavaStreamingContext API accepts Array arguments + // instead of Lists, so this isn't a trivial translation / sub: + "scala.collection.Seq" -> "java.util.List", + "scala.Function2" -> "spark.api.java.function.Function2", + "scala.collection.Iterator" -> "java.util.Iterator", + "scala.collection.mutable.Queue" -> "java.util.Queue", + "double" -> "java.lang.Double" + ) + // Keep applying the substitutions until we've reached a fixedpoint. + def applySubs(scalaType: SparkType): SparkType = { + scalaType match { + case ParameterizedType(name, parameters, typebounds) => + name match { + case "spark.RDD" => + if (parameters(0).name == classOf[Tuple2[_, _]].getName) { + val tupleParams = + parameters(0).asInstanceOf[ParameterizedType].parameters.map(toJavaType) + ParameterizedType(classOf[JavaPairRDD[_, _]].getName, tupleParams) + } else { + ParameterizedType(classOf[JavaRDD[_]].getName, parameters.map(toJavaType)) + } + case "spark.streaming.DStream" => + if (parameters(0).name == classOf[Tuple2[_, _]].getName) { + val tupleParams = + parameters(0).asInstanceOf[ParameterizedType].parameters.map(toJavaType) + ParameterizedType("spark.streaming.api.java.JavaPairDStream", tupleParams) + } else { + ParameterizedType("spark.streaming.api.java.JavaDStream", + parameters.map(toJavaType)) + } + // TODO: Spark Streaming uses Guava's Optional in place of Option, leading to some + // false-positives here: + case "scala.Option" => + toJavaType(parameters(0)) + case "scala.Function1" => + val firstParamName = parameters.last.name + if (firstParamName.startsWith("scala.collection.Traversable") || + firstParamName.startsWith("scala.collection.Iterator")) { + ParameterizedType("spark.api.java.function.FlatMapFunction", + Seq(parameters(0), + parameters.last.asInstanceOf[ParameterizedType].parameters(0)).map(toJavaType)) + } else if (firstParamName == "scala.runtime.BoxedUnit") { + ParameterizedType("spark.api.java.function.VoidFunction", + parameters.dropRight(1).map(toJavaType)) + } else { + ParameterizedType("spark.api.java.function.Function", parameters.map(toJavaType)) + } + case _ => + ParameterizedType(renameSubstitutions.getOrElse(name, name), + parameters.map(toJavaType)) + } + case BaseType(name) => + if (renameSubstitutions.contains(name)) { + BaseType(renameSubstitutions(name)) + } else { + scalaType + } + } + } + var oldType = scalaType + var newType = applySubs(scalaType) + while (oldType != newType) { + oldType = newType + newType = applySubs(scalaType) + } + newType + } + + private def toJavaMethod(method: SparkMethod): SparkMethod = { + val params = method.parameters + .filterNot(_.name == "scala.reflect.ClassManifest").map(toJavaType) + SparkMethod(method.name, toJavaType(method.returnType), params) + } + + private def isExcludedByName(method: Method): Boolean = { + val name = method.getDeclaringClass.getName + "." + method.getName + // Scala methods that are declared as private[mypackage] become public in the resulting + // Java bytecode. As a result, we need to manually exclude those methods here. + // This list also includes a few methods that are only used by the web UI or other + // internal Spark components. + val excludedNames = Seq( + "spark.RDD.origin", + "spark.RDD.elementClassManifest", + "spark.RDD.checkpointData", + "spark.RDD.partitioner", + "spark.RDD.partitions", + "spark.RDD.firstParent", + "spark.RDD.doCheckpoint", + "spark.RDD.markCheckpointed", + "spark.RDD.clearDependencies", + "spark.RDD.getDependencies", + "spark.RDD.getPartitions", + "spark.RDD.dependencies", + "spark.RDD.getPreferredLocations", + "spark.RDD.collectPartitions", + "spark.RDD.computeOrReadCheckpoint", + "spark.PairRDDFunctions.getKeyClass", + "spark.PairRDDFunctions.getValueClass", + "spark.SparkContext.stringToText", + "spark.SparkContext.makeRDD", + "spark.SparkContext.runJob", + "spark.SparkContext.runApproximateJob", + "spark.SparkContext.clean", + "spark.SparkContext.metadataCleaner", + "spark.SparkContext.ui", + "spark.SparkContext.newShuffleId", + "spark.SparkContext.newRddId", + "spark.SparkContext.cleanup", + "spark.SparkContext.receiverJobThread", + "spark.SparkContext.getRDDStorageInfo", + "spark.SparkContext.addedFiles", + "spark.SparkContext.addedJars", + "spark.SparkContext.persistentRdds", + "spark.SparkContext.executorEnvs", + "spark.SparkContext.checkpointDir", + "spark.SparkContext.getSparkHome", + "spark.SparkContext.executorMemoryRequested", + "spark.SparkContext.getExecutorStorageStatus", + "spark.streaming.DStream.generatedRDDs", + "spark.streaming.DStream.zeroTime", + "spark.streaming.DStream.rememberDuration", + "spark.streaming.DStream.storageLevel", + "spark.streaming.DStream.mustCheckpoint", + "spark.streaming.DStream.checkpointDuration", + "spark.streaming.DStream.checkpointData", + "spark.streaming.DStream.graph", + "spark.streaming.DStream.isInitialized", + "spark.streaming.DStream.parentRememberDuration", + "spark.streaming.DStream.initialize", + "spark.streaming.DStream.validate", + "spark.streaming.DStream.setContext", + "spark.streaming.DStream.setGraph", + "spark.streaming.DStream.remember", + "spark.streaming.DStream.getOrCompute", + "spark.streaming.DStream.generateJob", + "spark.streaming.DStream.clearOldMetadata", + "spark.streaming.DStream.addMetadata", + "spark.streaming.DStream.updateCheckpointData", + "spark.streaming.DStream.restoreCheckpointData", + "spark.streaming.DStream.isTimeValid", + "spark.streaming.StreamingContext.nextNetworkInputStreamId", + "spark.streaming.StreamingContext.networkInputTracker", + "spark.streaming.StreamingContext.checkpointDir", + "spark.streaming.StreamingContext.checkpointDuration", + "spark.streaming.StreamingContext.receiverJobThread", + "spark.streaming.StreamingContext.scheduler", + "spark.streaming.StreamingContext.initialCheckpoint", + "spark.streaming.StreamingContext.getNewNetworkStreamId", + "spark.streaming.StreamingContext.validate", + "spark.streaming.StreamingContext.createNewSparkContext", + "spark.streaming.StreamingContext.rddToFileName", + "spark.streaming.StreamingContext.getSparkCheckpointDir", + "spark.streaming.StreamingContext.env", + "spark.streaming.StreamingContext.graph", + "spark.streaming.StreamingContext.isCheckpointPresent" + ) + val excludedPatterns = Seq( + """^spark\.SparkContext\..*To.*Functions""", + """^spark\.SparkContext\..*WritableConverter""", + """^spark\.SparkContext\..*To.*Writable""" + ).map(_.r) + lazy val excludedByPattern = + !excludedPatterns.map(_.findFirstIn(name)).filter(_.isDefined).isEmpty + name.contains("$") || excludedNames.contains(name) || excludedByPattern + } + + private def isExcludedByInterface(method: Method): Boolean = { + val excludedInterfaces = + Set("spark.Logging", "org.apache.hadoop.mapreduce.HadoopMapReduceUtil") + def toComparisionKey(method: Method) = + (method.getReturnType, method.getName, method.getGenericReturnType) + val interfaces = method.getDeclaringClass.getInterfaces.filter { i => + excludedInterfaces.contains(i.getName) + } + val excludedMethods = interfaces.flatMap(_.getMethods.map(toComparisionKey)) + excludedMethods.contains(toComparisionKey(method)) + } + + private def printMissingMethods(scalaClass: Class[_], javaClass: Class[_]) { + val methods = scalaClass.getMethods + .filterNot(_.isAccessible) + .filterNot(isExcludedByName) + .filterNot(isExcludedByInterface) + val javaEquivalents = methods.map(m => toJavaMethod(toSparkMethod(m))).toSet + + val javaMethods = javaClass.getMethods.map(toSparkMethod).toSet + + val missingMethods = javaEquivalents -- javaMethods + + for (method <- missingMethods) { + println(method) + } + } + + def main(args: Array[String]) { + println("Missing RDD methods") + printMissingMethods(classOf[RDD[_]], classOf[JavaRDD[_]]) + println() + + println("Missing PairRDD methods") + printMissingMethods(classOf[PairRDDFunctions[_, _]], classOf[JavaPairRDD[_, _]]) + println() + + println("Missing DoubleRDD methods") + printMissingMethods(classOf[DoubleRDDFunctions], classOf[JavaDoubleRDD]) + println() + + println("Missing OrderedRDD methods") + printMissingMethods(classOf[OrderedRDDFunctions[_, _]], classOf[JavaPairRDD[_, _]]) + println() + + println("Missing SparkContext methods") + printMissingMethods(classOf[SparkContext], classOf[JavaSparkContext]) + println() + + println("Missing StreamingContext methods") + printMissingMethods(classOf[StreamingContext], classOf[JavaStreamingContext]) + println() + + println("Missing DStream methods") + printMissingMethods(classOf[DStream[_]], classOf[JavaDStream[_]]) + println() + + println("Missing PairDStream methods") + printMissingMethods(classOf[PairDStreamFunctions[_, _]], classOf[JavaPairDStream[_, _]]) + println() + } +} |
