diff options
210 files changed, 7425 insertions, 1842 deletions
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/bin/compute-classpath.cmd b/bin/compute-classpath.cmd index 835d1af794..eb836b0ffd 100644 --- a/bin/compute-classpath.cmd +++ b/bin/compute-classpath.cmd @@ -33,6 +33,7 @@ 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 @@ -48,6 +49,7 @@ 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 diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh index 4c539649a2..e4ce1ca848 100755 --- a/bin/compute-classpath.sh +++ b/bin/compute-classpath.sh @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # # Licensed to the Apache Software Foundation (ASF) under one or more @@ -36,6 +36,7 @@ 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" @@ -70,21 +71,11 @@ function dev_classpath { 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:$jar" done - # Figure out the JAR file that our examples were packaged into. This includes a bit of a hack - # to avoid the -sources and -doc packages that are built by publish-local. - if [ -e "$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 "$EXAMPLES_DIR/target/spark-examples"*[0-9T].jar` - fi - # Add Scala standard library if [ -z "$SCALA_LIBRARY_PATH" ]; then if [ -z "$SCALA_HOME" ]; then diff --git a/bin/spark-daemon.sh b/bin/spark-daemon.sh index a5b88ca785..96c71e66ca 100755 --- a/bin/spark-daemon.sh +++ b/bin/spark-daemon.sh @@ -75,6 +75,8 @@ if [ "$SPARK_IDENT_STRING" = "" ]; then export SPARK_IDENT_STRING="$USER" fi +export SPARK_PRINT_LAUNCH_COMMAND="1" + # get log directory if [ "$SPARK_LOG_DIR" = "" ]; then export SPARK_LOG_DIR="$SPARK_HOME/logs" @@ -124,8 +126,9 @@ case $startStop in spark_rotate_log $log echo starting $command, logging to $log + echo "Spark Daemon: $command" > $log cd "$SPARK_PREFIX" - nohup nice -n $SPARK_NICENESS "$SPARK_PREFIX"/run $command "$@" > "$log" 2>&1 < /dev/null & + nohup nice -n $SPARK_NICENESS "$SPARK_PREFIX"/run $command "$@" >> "$log" 2>&1 < /dev/null & echo $! > $pid sleep 1; head "$log" ;; 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 b8936314ec..c978db00d9 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -16,4 +16,9 @@ # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT # - SPARK_WORKER_INSTANCES, to set the number of worker instances/processes # to be spawned on every slave machine +# - SPARK_JAVA_OPTS, to set the jvm options for executor backend. Note: This is +# only for node-specific options, whereas app-specific options should be set +# in the application. +# Examples of node-speicic options : -Dspark.local.dir, GC related options. +# Examples of app-specific options : -Dspark.serializer diff --git a/core/pom.xml b/core/pom.xml index 6329b2fbd8..485aa29f83 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -49,6 +49,10 @@ <artifactId>compress-lzf</artifactId> </dependency> <dependency> + <groupId>org.xerial.snappy</groupId> + <artifactId>snappy-java</artifactId> + </dependency> + <dependency> <groupId>org.ow2.asm</groupId> <artifactId>asm</artifactId> </dependency> @@ -57,8 +61,14 @@ <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>0.3.1</version> + </dependency> + <dependency> + <groupId>com.twitter</groupId> + <artifactId>chill-java</artifactId> + <version>0.3.1</version> </dependency> <dependency> <groupId>com.typesafe.akka</groupId> @@ -108,6 +118,14 @@ <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> diff --git a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala index df55be1254..617954cb98 100644 --- a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala @@ -41,4 +41,7 @@ object SparkHadoopUtil { // 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/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala index df55be1254..617954cb98 100644 --- a/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala @@ -41,4 +41,7 @@ object SparkHadoopUtil { // 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/resources/spark/ui/static/bootstrap.min.css b/core/src/main/resources/spark/ui/static/bootstrap.min.css index b6428e6958..c177b6172a 100644 --- a/core/src/main/resources/spark/ui/static/bootstrap.min.css +++ b/core/src/main/resources/spark/ui/static/bootstrap.min.css @@ -1,9 +1,9 @@ -/*! +@import url('//fonts.googleapis.com/css?family=Open+Sans:400italic,700italic,400,700');/*! * Bootstrap v2.3.2 * - * Copyright 2012 Twitter, Inc + * Copyright 2013 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\ No newline at end of file diff --git a/core/src/main/resources/spark/ui/static/webui.css b/core/src/main/resources/spark/ui/static/webui.css index f7537bb766..b83f4109c0 100644 --- a/core/src/main/resources/spark/ui/static/webui.css +++ b/core/src/main/resources/spark/ui/static/webui.css @@ -5,6 +5,10 @@ padding: 0; } +body { + font-size: 15px !important; +} + .version { line-height: 30px; vertical-align: bottom; @@ -46,4 +50,33 @@ padding: 0; padding-top: 7px; padding-left: 4px; + line-height: 15px !important; +} + +.table td { + vertical-align: middle !important; +} + +.progress-completed .bar, +.progress .bar-completed { + background-color: #b3def9; + background-image: -moz-linear-gradient(top, #addfff, #badcf2); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#addfff), to(#badcf2)); + background-image: -webkit-linear-gradient(top, #addfff, #badcf2); + background-image: -o-linear-gradient(top, #addfff, #badcf2); + background-image: linear-gradient(to bottom, #addfff, #badcf2); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffaddfff', endColorstr='#ffbadcf2', GradientType=0); +} + +.progress-running .bar, +.progress .bar-running { + background-color: #c2ebfa; + background-image: -moz-linear-gradient(top, #bdedff, #c7e8f5); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#bdedff), to(#c7e8f5)); + background-image: -webkit-linear-gradient(top, #bdedff, #c7e8f5); + background-image: -o-linear-gradient(top, #bdedff, #c7e8f5); + background-image: linear-gradient(to bottom, #bdedff, #c7e8f5); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffbdedff', endColorstr='#ffc7e8f5', GradientType=0); } diff --git a/core/src/main/scala/spark/Cache.scala b/core/src/main/scala/spark/Cache.scala deleted file mode 100644 index b0c83ce59d..0000000000 --- a/core/src/main/scala/spark/Cache.scala +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package 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/DoubleRDDFunctions.scala b/core/src/main/scala/spark/DoubleRDDFunctions.scala index 93ef097702..104168e61c 100644 --- a/core/src/main/scala/spark/DoubleRDDFunctions.scala +++ b/core/src/main/scala/spark/DoubleRDDFunctions.scala @@ -54,7 +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/KryoSerializer.scala b/core/src/main/scala/spark/KryoSerializer.scala index ee37da7948..eeb2993d8a 100644 --- a/core/src/main/scala/spark/KryoSerializer.scala +++ b/core/src/main/scala/spark/KryoSerializer.scala @@ -19,24 +19,16 @@ 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 = { @@ -50,7 +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 = { @@ -58,7 +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 } } @@ -69,10 +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() @@ -108,125 +98,51 @@ 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 + def newKryoOutput() = new KryoOutput(bufferSize) - val kryo = new ThreadLocal[Kryo] { - override def initialValue = createKryo() - } - - val output = new ThreadLocal[KryoOutput] { - override def initialValue = 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) @@ -235,7 +151,6 @@ class KryoSerializer extends spark.serializer.Serializer with Logging { } 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/Partitioner.scala b/core/src/main/scala/spark/Partitioner.scala index 660af70d52..6035bc075e 100644 --- a/core/src/main/scala/spark/Partitioner.scala +++ b/core/src/main/scala/spark/Partitioner.scala @@ -65,17 +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 ca7cdd622a..160b3e9d83 100644 --- a/core/src/main/scala/spark/RDD.scala +++ b/core/src/main/scala/spark/RDD.scala @@ -31,6 +31,7 @@ import org.apache.hadoop.mapred.TextOutputFormat import it.unimi.dsi.fastutil.objects.{Object2LongOpenHashMap => OLMap} +import spark.api.java.JavaRDD import spark.broadcast.Broadcast import spark.Partitioner._ import spark.partial.BoundedDouble @@ -950,4 +951,8 @@ abstract class RDD[T: ClassManifest]( id, origin) + def toJavaRDD() : JavaRDD[T] = { + new JavaRDD(this)(elementClassManifest) + } + } diff --git a/core/src/main/scala/spark/SerializableWritable.scala b/core/src/main/scala/spark/SerializableWritable.scala index 0236611ef9..936d8e6241 100644 --- a/core/src/main/scala/spark/SerializableWritable.scala +++ b/core/src/main/scala/spark/SerializableWritable.scala @@ -21,7 +21,7 @@ import java.io._ import org.apache.hadoop.io.ObjectWritable import org.apache.hadoop.io.Writable -import org.apache.hadoop.mapred.JobConf +import org.apache.hadoop.conf.Configuration class SerializableWritable[T <: Writable](@transient var t: T) extends Serializable { def value = t @@ -35,7 +35,7 @@ class SerializableWritable[T <: Writable](@transient var t: T) extends Serializa private def readObject(in: ObjectInputStream) { in.defaultReadObject() val ow = new ObjectWritable() - ow.setConf(new JobConf()) + ow.setConf(new Configuration()) ow.readFields(in) t = ow.get().asInstanceOf[T] } diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala index 46b9935cb7..1069e27513 100644 --- a/core/src/main/scala/spark/SparkContext.scala +++ b/core/src/main/scala/spark/SparkContext.scala @@ -27,6 +27,7 @@ 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} @@ -60,13 +61,16 @@ import org.apache.mesos.MesosNativeLibrary import spark.deploy.{LocalSparkCluster, SparkHadoopUtil} import spark.partial.{ApproximateEvaluator, PartialResult} import spark.rdd.{CheckpointRDD, HadoopRDD, NewHadoopRDD, UnionRDD, ParallelCollectionRDD} -import spark.scheduler.{DAGScheduler, ResultTask, ShuffleMapTask, SparkListener, SplitInfo, Stage, StageInfo, TaskScheduler} -import spark.scheduler.cluster.{StandaloneSchedulerBackend, SparkDeploySchedulerBackend, ClusterScheduler} +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.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend} -import spark.storage.{StorageStatus, StorageUtils, RDDInfo} +import spark.storage.{StorageStatus, StorageUtils, RDDInfo, BlockManagerSource} import spark.util.{MetadataCleaner, TimeStampedHashMap} import ui.{SparkUI} +import spark.metrics._ /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark @@ -124,6 +128,8 @@ class SparkContext( private[spark] val ui = new SparkUI(this) ui.bind() + val startTime = System.currentTimeMillis() + // Add each JAR given through the constructor if (jars != null) { jars.foreach { addJar(_) } @@ -261,15 +267,31 @@ class SparkContext( localProperties.value = new Properties() } - def addLocalProperties(key: String, value: String) { + 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. */ @@ -525,7 +547,7 @@ class SparkContext( } def addSparkListener(listener: SparkListener) { - dagScheduler.sparkListeners += listener + dagScheduler.addSparkListener(listener) } /** @@ -546,6 +568,12 @@ class SparkContext( 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 } @@ -558,6 +586,28 @@ class SparkContext( } /** + * 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. */ @@ -577,7 +627,12 @@ class SparkContext( } else { val uri = new URI(path) val key = uri.getScheme match { - case null | "file" => env.httpFileServer.addJar(new File(uri.getPath)) + 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 @@ -794,6 +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 @@ -911,7 +967,6 @@ object SparkContext { } } - /** * A class encapsulating how to convert some type T to Writable. It stores both the Writable class * corresponding to T (e.g. IntWritable for Int) and a function for doing the conversion. @@ -923,3 +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 f2bdc11bdb..0adbf1d96e 100644 --- a/core/src/main/scala/spark/SparkEnv.scala +++ b/core/src/main/scala/spark/SparkEnv.scala @@ -24,6 +24,7 @@ import akka.actor.{Actor, ActorRef, Props, ActorSystemImpl, ActorSystem} import akka.remote.RemoteActorRefProvider import spark.broadcast.BroadcastManager +import spark.metrics.MetricsSystem import spark.storage.BlockManager import spark.storage.BlockManagerMaster import spark.network.ConnectionManager @@ -53,6 +54,7 @@ class SparkEnv ( val connectionManager: ConnectionManager, val httpFileServer: HttpFileServer, 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 @@ -68,6 +70,7 @@ class SparkEnv ( 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 @@ -94,13 +97,26 @@ class SparkEnv ( 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( @@ -184,6 +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. @@ -213,6 +236,7 @@ object SparkEnv extends Logging { connectionManager, httpFileServer, sparkFilesDir, + metricsSystem, None) } } diff --git a/core/src/main/scala/spark/Utils.scala b/core/src/main/scala/spark/Utils.scala index e6a96a5ec1..673f9a810d 100644 --- a/core/src/main/scala/spark/Utils.scala +++ b/core/src/main/scala/spark/Utils.scala @@ -33,8 +33,9 @@ import com.google.common.util.concurrent.ThreadFactoryBuilder import org.apache.hadoop.fs.{Path, FileSystem, FileUtil} -import spark.serializer.SerializerInstance +import spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance} import spark.deploy.SparkHadoopUtil +import java.nio.ByteBuffer /** @@ -68,6 +69,47 @@ private object Utils extends Logging { return ois.readObject.asInstanceOf[T] } + /** Serialize via nested stream using specific serializer */ + def serializeViaNestedStream(os: OutputStream, ser: SerializerInstance)(f: SerializationStream => Unit) = { + val osWrapper = ser.serializeStream(new OutputStream { + def write(b: Int) = os.write(b) + + override def write(b: Array[Byte], off: Int, len: Int) = os.write(b, off, len) + }) + try { + f(osWrapper) + } finally { + osWrapper.close() + } + } + + /** Deserialize via nested stream using specific serializer */ + def deserializeViaNestedStream(is: InputStream, ser: SerializerInstance)(f: DeserializationStream => Unit) = { + val isWrapper = ser.deserializeStream(new InputStream { + def read(): Int = is.read() + + override def read(b: Array[Byte], off: Int, len: Int): Int = is.read(b, off, len) + }) + try { + f(isWrapper) + } finally { + isWrapper.close() + } + } + + /** + * Primitive often used when writing {@link java.nio.ByteBuffer} to {@link java.io.DataOutput}. + */ + def writeByteBuffer(bb: ByteBuffer, out: ObjectOutput) = { + if (bb.hasArray) { + out.write(bb.array(), bb.arrayOffset() + bb.position(), bb.remaining()) + } else { + val bbval = new Array[Byte](bb.remaining()) + bb.get(bbval) + out.write(bbval) + } + } + def isAlpha(c: Char): Boolean = { (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z') } @@ -596,7 +638,7 @@ private object Utils extends Logging { 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. */ @@ -756,4 +798,13 @@ private object Utils extends Logging { } 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 392556f261..8ce7df6213 100644 --- a/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala +++ b/core/src/main/scala/spark/api/java/JavaDoubleRDD.scala @@ -115,33 +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 ccc511dc5f..ff12e8b76c 100644 --- a/core/src/main/scala/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/spark/api/java/JavaPairRDD.scala @@ -23,6 +23,7 @@ import java.util.Comparator import scala.Tuple2 import scala.collection.JavaConversions._ +import com.google.common.base.Optional import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapred.OutputFormat @@ -276,8 +277,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * partition the output RDD. */ def leftOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner) - : JavaPairRDD[K, (V, Option[W])] = - fromRDD(rdd.leftOuterJoin(other, partitioner)) + : JavaPairRDD[K, (V, Optional[W])] = { + val joinResult = rdd.leftOuterJoin(other, partitioner) + fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))}) + } /** * Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the @@ -286,8 +289,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * partition the output RDD. */ def rightOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner) - : JavaPairRDD[K, (Option[V], W)] = - fromRDD(rdd.rightOuterJoin(other, partitioner)) + : JavaPairRDD[K, (Optional[V], W)] = { + val joinResult = rdd.rightOuterJoin(other, partitioner) + fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)}) + } /** * Simplified version of combineByKey that hash-partitions the resulting RDD using the existing @@ -340,8 +345,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output * using the existing partitioner/parallelism level. */ - def leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Option[W])] = - fromRDD(rdd.leftOuterJoin(other)) + def leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Optional[W])] = { + val joinResult = rdd.leftOuterJoin(other) + fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))}) + } /** * Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the @@ -349,8 +356,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output * into `numPartitions` partitions. */ - def leftOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, Option[W])] = - fromRDD(rdd.leftOuterJoin(other, numPartitions)) + def leftOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (V, Optional[W])] = { + val joinResult = rdd.leftOuterJoin(other, numPartitions) + fromRDD(joinResult.mapValues{case (v, w) => (v, JavaUtils.optionToOptional(w))}) + } /** * Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the @@ -358,8 +367,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting * RDD using the existing partitioner/parallelism level. */ - def rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Option[V], W)] = - fromRDD(rdd.rightOuterJoin(other)) + def rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], W)] = { + val joinResult = rdd.rightOuterJoin(other) + fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)}) + } /** * Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the @@ -367,8 +378,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManif * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting * RDD into the given number of partitions. */ - def rightOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Option[V], W)] = - fromRDD(rdd.rightOuterJoin(other, numPartitions)) + def rightOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int): JavaPairRDD[K, (Optional[V], W)] = { + val joinResult = rdd.rightOuterJoin(other, numPartitions) + fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)}) + } /** * Return the key-value pairs in this RDD to the master as a Map. diff --git a/core/src/main/scala/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/spark/api/java/JavaRDDLike.scala index 21b5abf053..e0255ed23e 100644 --- a/core/src/main/scala/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/spark/api/java/JavaRDDLike.scala @@ -366,10 +366,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Gets the name of the file to which this RDD was checkpointed */ def getCheckpointFile(): Optional[String] = { - rdd.getCheckpointFile match { - case Some(file) => Optional.of(file) - case _ => Optional.absent() - } + JavaUtils.optionToOptional(rdd.getCheckpointFile) } /** A description of this RDD and its recursive dependencies for debugging. */ diff --git a/core/src/main/scala/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/spark/api/java/JavaSparkContext.scala index fe182e7ab6..29d57004b5 100644 --- a/core/src/main/scala/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/spark/api/java/JavaSparkContext.scala @@ -32,6 +32,8 @@ import spark.SparkContext.IntAccumulatorParam import spark.SparkContext.DoubleAccumulatorParam import spark.broadcast.Broadcast +import com.google.common.base.Optional + /** * A Java-friendly version of [[spark.SparkContext]] that returns [[spark.api.java.JavaRDD]]s and * works with Java collections instead of Scala ones. @@ -337,7 +339,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork * or the spark.home Java property, or the SPARK_HOME environment variable * (in that order of preference). If neither of these is set, return None. */ - def getSparkHome(): Option[String] = sc.getSparkHome() + def getSparkHome(): Optional[String] = JavaUtils.optionToOptional(sc.getSparkHome()) /** * Add a file to be downloaded with this Spark job on every node. diff --git a/core/src/main/scala/spark/api/java/JavaUtils.scala b/core/src/main/scala/spark/api/java/JavaUtils.scala new file mode 100644 index 0000000000..ffc131ac83 --- /dev/null +++ b/core/src/main/scala/spark/api/java/JavaUtils.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 + +import com.google.common.base.Optional + +object JavaUtils { + def optionToOptional[T](option: Option[T]): Optional[T] = + option match { + case Some(value) => Optional.of(value) + case None => Optional.absent() + } +} diff --git a/core/src/main/scala/spark/api/python/PythonPartitioner.scala b/core/src/main/scala/spark/api/python/PythonPartitioner.scala index 31a719fbff..ac112b8c2c 100644 --- a/core/src/main/scala/spark/api/python/PythonPartitioner.scala +++ b/core/src/main/scala/spark/api/python/PythonPartitioner.scala @@ -18,7 +18,7 @@ package spark.api.python import spark.Partitioner - +import spark.Utils import java.util.Arrays /** @@ -35,25 +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 af10822dbd..2dd79f7100 100644 --- a/core/src/main/scala/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/spark/api/python/PythonRDD.scala @@ -63,34 +63,42 @@ private[spark] class PythonRDD[T: ClassManifest]( // 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 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) + 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) } - dataOut.flush() - printOut.flush() - worker.shutdownOutput() } }.start() @@ -297,7 +305,7 @@ class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int) 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]]) diff --git a/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala index 078ad45ce8..14f8320678 100644 --- a/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala +++ b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala @@ -17,7 +17,7 @@ package spark.api.python -import java.io.{DataInputStream, IOException} +import java.io.{File, DataInputStream, IOException} import java.net.{Socket, SocketException, InetAddress} import scala.collection.JavaConversions._ @@ -67,6 +67,8 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String 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 diff --git a/core/src/main/scala/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/spark/broadcast/HttpBroadcast.scala index c565876950..138a8c21bc 100644 --- a/core/src/main/scala/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/spark/broadcast/HttpBroadcast.scala @@ -17,21 +17,20 @@ package spark.broadcast -import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} - -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_ @@ -85,6 +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 { @@ -122,10 +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) @@ -136,10 +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/deploy/DeployMessage.scala b/core/src/main/scala/spark/deploy/DeployMessage.scala index e1f8aff6f5..31861f3ac2 100644 --- a/core/src/main/scala/spark/deploy/DeployMessage.scala +++ b/core/src/main/scala/spark/deploy/DeployMessage.scala @@ -17,109 +17,113 @@ 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 -import spark.Utils -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 { - Utils.checkHost(host, "Required hostname") - assert (port > 0) -} + // 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, hostPort: String, cores: Int, memory: Int) { - Utils.checkHostPort(hostPort, "Required hostport") -} + 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 - Utils.checkHost(host, "Required hostname") - assert (port > 0) + case object RequestMasterState - def uri = "spark://" + host + ":" + port -} + // Master to MasterWebUI + + case class MasterStateResponse(host: String, port: Int, workers: Array[WorkerInfo], + activeApps: Array[ApplicationInfo], completedApps: Array[ApplicationInfo]) { + + Utils.checkHost(host, "Required hostname") + assert (port > 0) + + def uri = "spark://" + host + ":" + port + } + + // 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) { -// WorkerWebUI to Worker -private[spark] case object RequestWorkerState + Utils.checkHost(host, "Required hostname") + assert (port > 0) + } -// Worker to WorkerWebUI + // Actor System to Master -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) { + case object CheckForWorkerTimeOut - Utils.checkHost(host, "Required hostname") - assert (port > 0) } diff --git a/core/src/main/scala/spark/deploy/JsonProtocol.scala b/core/src/main/scala/spark/deploy/JsonProtocol.scala index 64f89623e1..bd1db7c294 100644 --- a/core/src/main/scala/spark/deploy/JsonProtocol.scala +++ b/core/src/main/scala/spark/deploy/JsonProtocol.scala @@ -17,9 +17,12 @@ package spark.deploy -import master.{ApplicationInfo, WorkerInfo} import net.liftweb.json.JsonDSL._ -import worker.ExecutorRunner + +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) = { @@ -57,7 +60,7 @@ private[spark] object JsonProtocol { ("appdesc" -> writeApplicationDescription(obj.appDesc)) } - def writeMasterState(obj: MasterState) = { + def writeMasterState(obj: MasterStateResponse) = { ("url" -> ("spark://" + obj.uri)) ~ ("workers" -> obj.workers.toList.map(writeWorkerInfo)) ~ ("cores" -> obj.workers.map(_.cores).sum) ~ @@ -68,7 +71,7 @@ private[spark] object JsonProtocol { ("completedapps" -> obj.completedApps.toList.map(writeApplicationInfo)) } - def writeWorkerState(obj: WorkerState) = { + def writeWorkerState(obj: WorkerStateResponse) = { ("id" -> obj.workerId) ~ ("masterurl" -> obj.masterUrl) ~ ("masterwebuiurl" -> obj.masterWebUiUrl) ~ diff --git a/core/src/main/scala/spark/deploy/client/Client.scala b/core/src/main/scala/spark/deploy/client/Client.scala index edefa0292d..9d5ba8a796 100644 --- a/core/src/main/scala/spark/deploy/client/Client.scala +++ b/core/src/main/scala/spark/deploy/client/Client.scala @@ -17,21 +17,23 @@ 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.util.duration._ -import akka.pattern.AskTimeoutException -import spark.{SparkException, Logging} +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. @@ -134,7 +136,8 @@ private[spark] class Client( 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/master/ApplicationInfo.scala b/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala index 15ff919738..6dd2f06126 100644 --- a/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala +++ b/core/src/main/scala/spark/deploy/master/ApplicationInfo.scala @@ -34,6 +34,7 @@ private[spark] class ApplicationInfo( var executors = new mutable.HashMap[Int, ExecutorInfo] var coresGranted = 0 var endTime = -1L + val appSource = new ApplicationSource(this) private var nextExecutorId = 0 @@ -51,8 +52,10 @@ 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.maxCores - coresGranted 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/Master.scala b/core/src/main/scala/spark/deploy/master/Master.scala index e5a7a87e2e..4a4d9908a0 100644 --- a/core/src/main/scala/spark/deploy/master/Master.scala +++ b/core/src/main/scala/spark/deploy/master/Master.scala @@ -17,26 +17,30 @@ package spark.deploy.master -import akka.actor._ -import akka.actor.Terminated -import akka.remote.{RemoteClientLifeCycleEvent, RemoteClientDisconnected, RemoteClientShutdown} -import akka.util.duration._ - 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 -import ui.MasterWebUI 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] @@ -53,10 +57,14 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act var firstApp: Option[ApplicationInfo] = None - val webUi = new MasterWebUI(self) + 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 host @@ -72,11 +80,17 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act // Listen for remote client disconnection events, since they don't go through Akka's watch() context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) webUi.start() - context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis)(timeOutDeadWorkers()) + context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis, self, CheckForWorkerTimeOut) + + masterMetricsSystem.registerSource(masterSource) + masterMetricsSystem.start() + applicationMetricsSystem.start() } override def postStop() { webUi.stop() + masterMetricsSystem.stop() + applicationMetricsSystem.stop() } override def receive = { @@ -160,7 +174,11 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act } case RequestMasterState => { - sender ! MasterState(host, port, workers.toArray, apps.toArray, completedApps.toArray) + sender ! MasterStateResponse(host, port, workers.toArray, apps.toArray, completedApps.toArray) + } + + case CheckForWorkerTimeOut => { + timeOutDeadWorkers() } } @@ -225,20 +243,27 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act def launchExecutor(worker: WorkerInfo, exec: ExecutorInfo, sparkHome: String) { logInfo("Launching executor " + exec.fullId + " on worker " + worker.id) worker.addExecutor(exec) - worker.actor ! LaunchExecutor(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) + 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.port == port) && (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) { @@ -249,7 +274,8 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act 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) } } @@ -258,6 +284,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act val now = System.currentTimeMillis() val date = new Date(now) 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 @@ -269,7 +296,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act 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) { @@ -283,7 +310,14 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act 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) @@ -308,12 +342,17 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act /** 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 + } } } } diff --git a/core/src/main/scala/spark/deploy/master/MasterArguments.scala b/core/src/main/scala/spark/deploy/master/MasterArguments.scala index d0ec3d5ea0..0ae0160767 100644 --- a/core/src/main/scala/spark/deploy/master/MasterArguments.scala +++ b/core/src/main/scala/spark/deploy/master/MasterArguments.scala @@ -38,7 +38,10 @@ 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 { 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/ui/ApplicationPage.scala b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala index 32264af393..36a1e91b24 100644 --- a/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala +++ b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala @@ -17,6 +17,8 @@ package spark.deploy.master.ui +import scala.xml.Node + import akka.dispatch.Await import akka.pattern.ask import akka.util.duration._ @@ -25,9 +27,8 @@ import javax.servlet.http.HttpServletRequest import net.liftweb.json.JsonAST.JValue -import scala.xml.Node - -import spark.deploy.{RequestMasterState, JsonProtocol, MasterState} +import spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} +import spark.deploy.JsonProtocol import spark.deploy.master.ExecutorInfo import spark.ui.UIUtils @@ -38,7 +39,7 @@ private[spark] class ApplicationPage(parent: MasterWebUI) { /** Executor details for a particular application */ def renderJson(request: HttpServletRequest): JValue = { val appId = request.getParameter("appId") - val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState] + 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) @@ -49,7 +50,7 @@ private[spark] class ApplicationPage(parent: MasterWebUI) { /** Executor details for a particular application */ def render(request: HttpServletRequest): Seq[Node] = { val appId = request.getParameter("appId") - val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState] + 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) @@ -89,8 +90,7 @@ private[spark] class ApplicationPage(parent: MasterWebUI) { <div class="row"> <!-- Executors --> <div class="span12"> - <h3> Executor Summary </h3> - <br/> + <h4> Executor Summary </h4> {executorTable} </div> </div>; diff --git a/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala index b05197c1b9..d3b10f197b 100644 --- a/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala +++ b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala @@ -17,26 +17,36 @@ 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 javax.servlet.http.HttpServletRequest - -import scala.xml.Node +import net.liftweb.json.JsonAST.JValue -import spark.deploy.{RequestMasterState, DeployWebUI, MasterState} import spark.Utils -import spark.ui.UIUtils +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[MasterState] + val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse] val state = Await.result(stateFuture, 30 seconds) val workerHeaders = Seq("Id", "Address", "State", "Cores", "Memory") @@ -71,8 +81,7 @@ private[spark] class IndexPage(parent: MasterWebUI) { <div class="row"> <div class="span12"> - <h3> Workers </h3> - <br/> + <h4> Workers </h4> {workerTable} </div> </div> @@ -81,8 +90,8 @@ private[spark] class IndexPage(parent: MasterWebUI) { <div class="row"> <div class="span12"> - <h3> Running Applications </h3> - <br/> + <h4> Running Applications </h4> + {activeAppsTable} </div> </div> @@ -91,8 +100,7 @@ private[spark] class IndexPage(parent: MasterWebUI) { <div class="row"> <div class="span12"> - <h3> Completed Applications </h3> - <br/> + <h4> Completed Applications </h4> {completedAppsTable} </div> </div>; diff --git a/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala index 04b32c7968..31bdb7854e 100644 --- a/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala @@ -32,12 +32,11 @@ import spark.ui.JettyUtils._ * Web UI server for the standalone master. */ private[spark] -class MasterWebUI(val master: ActorRef, requestedPort: Option[Int] = None) extends Logging { +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.getOrElse( - System.getProperty("master.ui.port", MasterWebUI.DEFAULT_PORT).toInt) + val port = requestedPort var server: Option[Server] = None var boundPort: Option[Int] = None @@ -62,6 +61,7 @@ class MasterWebUI(val master: ActorRef, requestedPort: Option[Int] = None) exten ("/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)) ) @@ -72,5 +72,4 @@ class MasterWebUI(val master: ActorRef, requestedPort: Option[Int] = None) exten private[spark] object MasterWebUI { val STATIC_RESOURCE_DIR = "spark/ui/static" - val DEFAULT_PORT = "8080" } diff --git a/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala index 8b51ff1c3a..4537c8305c 100644 --- a/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala @@ -19,14 +19,15 @@ package spark.deploy.worker import java.io._ import java.lang.System.getenv -import spark.deploy.{ExecutorState, ExecutorStateChanged, ApplicationDescription} + import akka.actor.ActorRef + +import com.google.common.base.Charsets +import com.google.common.io.Files + 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. @@ -51,6 +52,9 @@ private[spark] class ExecutorRunner( 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() } @@ -95,7 +99,7 @@ private[spark] class ExecutorRunner( def buildCommandSeq(): Seq[String] = { val command = appDesc.command - val runner = Option(getenv("JAVA_HOME")).map(_ + "/bin/java").getOrElse("java") + 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) ++ @@ -107,10 +111,11 @@ private[spark] class ExecutorRunner( * the way the JAVA_OPTS are assembled there. */ def buildJavaOpts(): Seq[String] = { - val libraryOpts = Option(getenv("SPARK_LIBRARY_PATH")) + val libraryOpts = getAppEnv("SPARK_LIBRARY_PATH") .map(p => List("-Djava.library.path=" + p)) .getOrElse(Nil) - val userOpts = Option(getenv("SPARK_JAVA_OPTS")).map(Utils.splitCommandString).getOrElse(Nil) + val workerLocalOpts = Option(getenv("SPARK_JAVA_OPTS")).map(Utils.splitCommandString).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 @@ -119,12 +124,12 @@ private[spark] class ExecutorRunner( Seq(sparkHome + "/bin/compute-classpath" + ext), extraEnvironment=appDesc.command.environment) - Seq("-cp", classPath) ++ libraryOpts ++ userOpts ++ memoryOpts + Seq("-cp", classPath) ++ libraryOpts ++ workerLocalOpts ++ userOpts ++ memoryOpts } /** Spawn a thread that will redirect a given stream to a file */ def redirectStream(in: InputStream, file: File) { - val out = new FileOutputStream(file) + val out = new FileOutputStream(file, true) new Thread("redirect output to " + file) { override def run() { try { @@ -160,9 +165,16 @@ private[spark] class ExecutorRunner( env.put("SPARK_LAUNCH_WITH_SCALA", "0") process = builder.start() + val header = "Spark Executor Command: %s\n%s\n\n".format( + command.mkString("\"", "\" \"", "\""), "=" * 40) + // Redirect its stdout and stderr to files - redirectStream(process.getInputStream, new File(executorDir, "stdout")) - redirectStream(process.getErrorStream, new File(executorDir, "stderr")) + val stdout = new File(executorDir, "stdout") + Files.write(header, stdout, Charsets.UTF_8) + redirectStream(process.getInputStream, stdout) + + val stderr = new File(executorDir, "stderr") + redirectStream(process.getErrorStream, stderr) // Wait for it to exit; this is actually a bad thing if it happens, because we expect to run // long-lived processes only. However, in the future, we might restart the executor a few diff --git a/core/src/main/scala/spark/deploy/worker/Worker.scala b/core/src/main/scala/spark/deploy/worker/Worker.scala index 0bd88ea253..0e46fa281e 100644 --- a/core/src/main/scala/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/spark/deploy/worker/Worker.scala @@ -17,21 +17,24 @@ 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 ui.WorkerWebUI +import spark.deploy.worker.ui.WorkerWebUI +import spark.metrics.MetricsSystem +import spark.util.AkkaUtils + private[spark] class Worker( host: String, @@ -67,6 +70,9 @@ private[spark] class Worker( 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 @@ -97,6 +103,9 @@ private[spark] class Worker( webUi = new WorkerWebUI(this, workDir, Some(webUiPort)) webUi.start() connectToMaster() + + metricsSystem.registerSource(workerSource) + metricsSystem.start() } def connectToMaster() { @@ -155,10 +164,10 @@ private[spark] class Worker( case Terminated(_) | RemoteClientDisconnected(_, _) | RemoteClientShutdown(_, _) => masterDisconnected() - + case RequestWorkerState => { - sender ! WorkerState(host, 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) } } @@ -178,6 +187,7 @@ private[spark] class Worker( override def postStop() { executors.values.foreach(_.kill()) webUi.stop() + metricsSystem.stop() } } 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/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala index 7548a26c2e..700eb22d96 100644 --- a/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala +++ b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala @@ -17,34 +17,36 @@ 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 javax.servlet.http.HttpServletRequest - import net.liftweb.json.JsonAST.JValue -import scala.xml.Node - -import spark.deploy.{RequestWorkerState, JsonProtocol, WorkerState} -import spark.deploy.worker.ExecutorRunner 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[WorkerState] + 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[WorkerState] + val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerStateResponse] val workerState = Await.result(stateFuture, 30 seconds) val executorHeaders = Seq("ExecutorID", "Cores", "Memory", "Job Details", "Logs") @@ -69,12 +71,11 @@ private[spark] class IndexPage(parent: WorkerWebUI) { <p><a href={workerState.masterWebUiUrl}>Back to Master</a></p> </div> </div> - <hr/> + <hr/> <div class="row"> <!-- Running Executors --> <div class="span12"> - <h3> Running Executors {workerState.executors.size} </h3> - <br/> + <h4> Running Executors {workerState.executors.size} </h4> {runningExecutorTable} </div> </div> @@ -82,13 +83,13 @@ private[spark] class IndexPage(parent: WorkerWebUI) { <div class="row"> <!-- Finished Executors --> <div class="span12"> - <h3> Finished Executors </h3> - <br/> + <h4> Finished Executors </h4> {finishedExecutorTable} </div> </div>; - UIUtils.basicSparkPage(content, "Spark Worker on %s:%s".format(workerState.host, workerState.port)) + UIUtils.basicSparkPage(content, "Spark Worker on %s:%s".format( + workerState.host, workerState.port)) } def executorRow(executor: ExecutorRunner): Seq[Node] = { diff --git a/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala index 61d4cd6d99..742e0a5fb6 100644 --- a/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala +++ b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala @@ -37,7 +37,7 @@ import spark.ui.UIUtils */ private[spark] class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[Int] = None) - extends Logging { + extends Logging { implicit val timeout = Timeout( Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")) val host = Utils.localHostName() @@ -111,23 +111,31 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I 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> + byteLength)}> + <button type="button" class="btn btn-default"> + Previous {Utils.memoryBytesToString(math.min(byteLength, startByte))} + </button> </a> } else { - <button disabled="disabled">Previous 0 B</button> + <button type="button" class="btn btn-default" 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> + <button type="button" class="btn btn-default"> + Next {Utils.memoryBytesToString(math.min(byteLength, logLength-endByte))} + </button> </a> } else { - <button disabled="disabled">Next 0 B</button> + <button type="button" class="btn btn-default" disabled="disabled"> + Next 0 B + </button> } val content = diff --git a/core/src/main/scala/spark/executor/Executor.scala b/core/src/main/scala/spark/executor/Executor.scala index 2e81151882..8a74a8d853 100644 --- a/core/src/main/scala/spark/executor/Executor.scala +++ b/core/src/main/scala/spark/executor/Executor.scala @@ -69,7 +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()) { @@ -87,9 +87,13 @@ 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 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..d491a3c0c9 --- /dev/null +++ b/core/src/main/scala/spark/executor/ExecutorSource.scala @@ -0,0 +1,55 @@ +package spark.executor + +import com.codahale.metrics.{Gauge, MetricRegistry} + +import org.apache.hadoop.fs.FileSystem +import org.apache.hadoop.hdfs.DistributedFileSystem +import org.apache.hadoop.fs.LocalFileSystem + +import scala.collection.JavaConversions._ + +import spark.metrics.source.Source + +class ExecutorSource(val executor: Executor) extends Source { + private def fileStats(scheme: String) : Option[FileSystem.Statistics] = + FileSystem.getAllStatistics().filter(s => s.getScheme.equals(scheme)).headOption + + private def registerFileSystemStat[T]( + scheme: String, name: String, f: FileSystem.Statistics => T, defaultValue: T) = { + metricRegistry.register(MetricRegistry.name("filesystem", scheme, name), new Gauge[T] { + override def getValue: T = fileStats(scheme).map(f).getOrElse(defaultValue) + }) + } + + 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() + }) + + // Gauge for file system stats of this executor + for (scheme <- Array("hdfs", "file")) { + registerFileSystemStat(scheme, "bytesRead", _.getBytesRead(), 0L) + registerFileSystemStat(scheme, "bytesWritten", _.getBytesWritten(), 0L) + registerFileSystemStat(scheme, "readOps", _.getReadOps(), 0) + registerFileSystemStat(scheme, "largeReadOps", _.getLargeReadOps(), 0) + registerFileSystemStat(scheme, "writeOps", _.getWriteOps(), 0) + } +} diff --git a/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala b/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala index f4003da732..e47fe50021 100644 --- a/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala +++ b/core/src/main/scala/spark/executor/StandaloneExecutorBackend.scala @@ -18,19 +18,16 @@ 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.Utils + +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, 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/SoftReferenceCache.scala b/core/src/main/scala/spark/metrics/sink/JmxSink.scala index f41a379582..ee04544c0e 100644 --- a/core/src/main/scala/spark/SoftReferenceCache.scala +++ b/core/src/main/scala/spark/metrics/sink/JmxSink.scala @@ -15,21 +15,21 @@ * limitations under the License. */ -package spark +package spark.metrics.sink -import com.google.common.collect.MapMaker +import com.codahale.metrics.{JmxReporter, MetricRegistry} -/** - * An implementation of Cache that uses soft references. - */ -private[spark] class SoftReferenceCache extends Cache { - val map = new MapMaker().softValues().makeMap[Any, Any]() +import java.util.Properties + +class JmxSink(val property: Properties, val registry: MetricRegistry) extends Sink { + val reporter: JmxReporter = JmxReporter.forRegistry(registry).build() - override def get(datasetId: Any, partition: Int): Any = - map.get((datasetId, partition)) + override def start() { + reporter.start() + } - override def put(datasetId: Any, partition: Int, value: Any): CachePutResponse = { - map.put((datasetId, partition), value) - return CachePutSuccess(0) + 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/Connection.scala b/core/src/main/scala/spark/network/Connection.scala index b66c00b58c..1e571d39ae 100644 --- a/core/src/main/scala/spark/network/Connection.scala +++ b/core/src/main/scala/spark/network/Connection.scala @@ -45,12 +45,15 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, channel.socket.setKeepAlive(true) /*channel.socket.setReceiveBufferSize(32768) */ + @volatile private var closed = false var onCloseCallback: Connection => Unit = null var onExceptionCallback: (Connection, Exception) => Unit = null var onKeyInterestChangeCallback: (Connection, Int) => Unit = null val remoteAddress = getRemoteAddress() + def resetForceReregister(): Boolean + // 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 ! @@ -95,6 +98,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, } def close() { + closed = true val k = key() if (k != null) { k.cancel() @@ -103,6 +107,8 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, callOnCloseCallback() } + protected def isClosed: Boolean = closed + def onClose(callback: Connection => Unit) { onCloseCallback = callback } @@ -168,7 +174,7 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector, remoteId_ : ConnectionManagerId) extends Connection(SocketChannel.open, selector_, remoteId_) { - class Outbox(fair: Int = 0) { + private class Outbox(fair: Int = 0) { val messages = new Queue[Message]() val defaultChunkSize = 65536 //32768 //16384 var nextMessageToBeUsed = 0 @@ -245,7 +251,17 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector, } } + // outbox is used as a lock - ensure that it is always used as a leaf (since methods which + // lock it are invoked in context of other locks) private val outbox = new Outbox(1) + /* + This is orthogonal to whether we have pending bytes to write or not - and satisfies a slightly + different purpose. This flag is to see if we need to force reregister for write even when we + do not have any pending bytes to write to socket. + This can happen due to a race between adding pending buffers, and checking for existing of + data as detailed in https://github.com/mesos/spark/pull/791 + */ + private var needForceReregister = false val currentBuffers = new ArrayBuffer[ByteBuffer]() /*channel.socket.setSendBufferSize(256 * 1024)*/ @@ -267,9 +283,19 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector, def send(message: Message) { outbox.synchronized { outbox.addMessage(message) - if (channel.isConnected) { - registerInterest() - } + needForceReregister = true + } + if (channel.isConnected) { + registerInterest() + } + } + + // return previous value after resetting it. + def resetForceReregister(): Boolean = { + outbox.synchronized { + val result = needForceReregister + needForceReregister = false + result } } @@ -322,7 +348,11 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector, outbox.synchronized { outbox.getChunk() match { case Some(chunk) => { - currentBuffers ++= chunk.buffers + val buffers = chunk.buffers + // If we have 'seen' pending messages, then reset flag - since we handle that as normal + // registering of event (below) + if (needForceReregister && buffers.exists(_.remaining() > 0)) resetForceReregister() + currentBuffers ++= buffers } case None => { // changeConnectionKeyInterest(0) @@ -384,7 +414,7 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector, override def changeInterestForRead(): Boolean = false - override def changeInterestForWrite(): Boolean = true + override def changeInterestForWrite(): Boolean = ! isClosed } @@ -534,6 +564,7 @@ private[spark] class ReceivingConnection(channel_ : SocketChannel, selector_ : S def onReceive(callback: (Connection, Message) => Unit) {onReceiveCallback = callback} + // override def changeInterestForRead(): Boolean = ! isClosed override def changeInterestForRead(): Boolean = true override def changeInterestForWrite(): Boolean = { @@ -549,4 +580,7 @@ private[spark] class ReceivingConnection(channel_ : SocketChannel, selector_ : S override def unregisterInterest() { changeConnectionKeyInterest(0) } + + // For read conn, always false. + override def resetForceReregister(): Boolean = false } diff --git a/core/src/main/scala/spark/network/ConnectionManager.scala b/core/src/main/scala/spark/network/ConnectionManager.scala index 6c4e7dc03e..8b9f3ae18c 100644 --- a/core/src/main/scala/spark/network/ConnectionManager.scala +++ b/core/src/main/scala/spark/network/ConnectionManager.scala @@ -123,7 +123,8 @@ private[spark] class ConnectionManager(port: Int) extends Logging { } finally { writeRunnableStarted.synchronized { writeRunnableStarted -= key - if (register && conn.changeInterestForWrite()) { + val needReregister = register || conn.resetForceReregister() + if (needReregister && conn.changeInterestForWrite()) { conn.registerInterest() } } diff --git a/core/src/main/scala/spark/rdd/HadoopRDD.scala b/core/src/main/scala/spark/rdd/HadoopRDD.scala index d0fdeb741e..fd00d59c77 100644 --- a/core/src/main/scala/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/spark/rdd/HadoopRDD.scala @@ -88,6 +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/NewHadoopRDD.scala b/core/src/main/scala/spark/rdd/NewHadoopRDD.scala index 17fe805fd4..0b71608169 100644 --- a/core/src/main/scala/spark/rdd/NewHadoopRDD.scala +++ b/core/src/main/scala/spark/rdd/NewHadoopRDD.scala @@ -73,6 +73,7 @@ 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 = newTaskAttemptID(jobtrackerId, id, true, split.index, 0) val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId) diff --git a/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala index 16ba0c26f8..33079cd539 100644 --- a/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala +++ b/core/src/main/scala/spark/rdd/ParallelCollectionRDD.scala @@ -20,13 +20,15 @@ package spark.rdd import scala.collection.immutable.NumericRange import scala.collection.mutable.ArrayBuffer import scala.collection.Map -import spark.{RDD, TaskContext, SparkContext, Partition} +import spark._ +import java.io._ +import scala.Serializable private[spark] class ParallelCollectionPartition[T: ClassManifest]( - val rddId: Long, - val slice: Int, - values: Seq[T]) - extends Partition with Serializable { + var rddId: Long, + var slice: Int, + var values: Seq[T]) + extends Partition with Serializable { def iterator: Iterator[T] = values.iterator @@ -37,15 +39,49 @@ private[spark] class ParallelCollectionPartition[T: ClassManifest]( case _ => false } - override val index: Int = slice + override def index: Int = slice + + @throws(classOf[IOException]) + private def writeObject(out: ObjectOutputStream): Unit = { + + val sfactory = SparkEnv.get.serializer + + // Treat java serializer with default action rather than going thru serialization, to avoid a + // separate serialization header. + + sfactory match { + case js: JavaSerializer => out.defaultWriteObject() + case _ => + out.writeLong(rddId) + out.writeInt(slice) + + val ser = sfactory.newInstance() + Utils.serializeViaNestedStream(out, ser)(_.writeObject(values)) + } + } + + @throws(classOf[IOException]) + private def readObject(in: ObjectInputStream): Unit = { + + val sfactory = SparkEnv.get.serializer + sfactory match { + case js: JavaSerializer => in.defaultReadObject() + case _ => + rddId = in.readLong() + slice = in.readInt() + + val ser = sfactory.newInstance() + Utils.deserializeViaNestedStream(in, ser)(ds => values = ds.readObject()) + } + } } private[spark] class ParallelCollectionRDD[T: ClassManifest]( @transient sc: SparkContext, @transient data: Seq[T], numSlices: Int, - locationPrefs: Map[Int,Seq[String]]) - extends RDD[T](sc, Nil) { + locationPrefs: Map[Int, Seq[String]]) + extends RDD[T](sc, Nil) { // TODO: Right now, each split sends along its full data, even if later down the RDD chain it gets // cached. It might be worthwhile to write the data to a file in the DFS and read it in the split // instead. @@ -82,16 +118,17 @@ private object ParallelCollectionRDD { 1 } slice(new Range( - r.start, r.end + sign, r.step).asInstanceOf[Seq[T]], numSlices) + r.start, r.end + sign, r.step).asInstanceOf[Seq[T]], numSlices) } case r: Range => { (0 until numSlices).map(i => { val start = ((i * r.length.toLong) / numSlices).toInt - val end = (((i+1) * r.length.toLong) / numSlices).toInt + val end = (((i + 1) * r.length.toLong) / numSlices).toInt new Range(r.start + start * r.step, r.start + end * r.step, r.step) }).asInstanceOf[Seq[Seq[T]]] } - case nr: NumericRange[_] => { // For ranges of Long, Double, BigInteger, etc + case nr: NumericRange[_] => { + // For ranges of Long, Double, BigInteger, etc val slices = new ArrayBuffer[Seq[T]](numSlices) val sliceSize = (nr.size + numSlices - 1) / numSlices // Round up to catch everything var r = nr @@ -102,10 +139,10 @@ private object ParallelCollectionRDD { slices } case _ => { - val array = seq.toArray // To prevent O(n^2) operations for List etc + val array = seq.toArray // To prevent O(n^2) operations for List etc (0 until numSlices).map(i => { val start = ((i * array.length.toLong) / numSlices).toInt - val end = (((i+1) * array.length.toLong) / numSlices).toInt + val end = (((i + 1) * array.length.toLong) / numSlices).toInt array.slice(start, end).toSeq }) } diff --git a/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala index 191cfde565..d8700becb0 100644 --- a/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala +++ b/core/src/main/scala/spark/rdd/PartitionPruningRDD.scala @@ -33,8 +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/scheduler/DAGScheduler.scala b/core/src/main/scala/spark/scheduler/DAGScheduler.scala index 29e879aa42..fbf3f4c807 100644 --- a/core/src/main/scala/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/spark/scheduler/DAGScheduler.scala @@ -17,19 +17,17 @@ package spark.scheduler -import cluster.TaskInfo -import java.util.concurrent.atomic.AtomicInteger -import java.util.concurrent.LinkedBlockingQueue -import java.util.concurrent.TimeUnit +import java.io.NotSerializableException import java.util.Properties +import java.util.concurrent.{LinkedBlockingQueue, TimeUnit} +import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map} import spark._ import spark.executor.TaskMetrics -import spark.partial.ApproximateActionListener -import spark.partial.ApproximateEvaluator -import spark.partial.PartialResult +import spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult} +import spark.scheduler.cluster.TaskInfo import spark.storage.{BlockManager, BlockManagerMaster} import spark.util.{MetadataCleaner, TimeStampedHashMap} @@ -52,6 +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[_], @@ -99,7 +102,7 @@ class DAGScheduler( private[spark] val stageToInfos = new TimeStampedHashMap[Stage, StageInfo] - private[spark] val sparkListeners = ArrayBuffer[SparkListener]() + private val listenerBus = new SparkListenerBus() var cacheLocs = new HashMap[Int, Array[List[String]]] @@ -134,6 +137,10 @@ class DAGScheduler( }.start() } + def addSparkListener(listener: SparkListener) { + listenerBus.addListener(listener) + } + 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 @@ -258,7 +265,8 @@ class DAGScheduler( assert(partitions.size > 0) val waiter = new JobWaiter(partitions.size, resultHandler) val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] - val toSubmit = JobSubmitted(finalRdd, func2, partitions.toArray, allowLocal, callSite, waiter, properties) + val toSubmit = JobSubmitted(finalRdd, func2, partitions.toArray, allowLocal, callSite, waiter, + properties) return (toSubmit, waiter) } @@ -283,7 +291,7 @@ class DAGScheduler( "Total number of partitions: " + maxPartitions) } - val (toSubmit, waiter) = prepareJob( + val (toSubmit: JobSubmitted, waiter: JobWaiter[_]) = prepareJob( finalRdd, func, partitions, callSite, allowLocal, resultHandler, properties) eventQueue.put(toSubmit) waiter.awaitResult() match { @@ -330,7 +338,7 @@ class DAGScheduler( // Compute very short actions like first() or take() with no parent stages locally. runLocally(job) } else { - sparkListeners.foreach(_.onJobStart(SparkListenerJobStart(job, properties))) + listenerBus.post(SparkListenerJobStart(job, properties)) idToActiveJob(runId) = job activeJobs += job resultStageToJob(finalStage) = job @@ -343,9 +351,12 @@ class DAGScheduler( case ExecutorLost(execId) => handleExecutorLost(execId) + case begin: BeginEvent => + listenerBus.post(SparkListenerTaskStart(begin.task, begin.taskInfo)) + case completion: CompletionEvent => - sparkListeners.foreach(_.onTaskEnd(SparkListenerTaskEnd(completion.task, - completion.reason, completion.taskInfo, completion.taskMetrics))) + listenerBus.post(SparkListenerTaskEnd( + completion.task, completion.reason, completion.taskInfo, completion.taskMetrics)) handleTaskCompletion(completion) case TaskSetFailed(taskSet, reason) => @@ -356,7 +367,7 @@ 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)))) + listenerBus.post(SparkListenerJobEnd(job, JobFailed(error, None))) } return true } @@ -503,12 +514,28 @@ 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 + listenerBus.post(SparkListenerStageSubmitted(stage, tasks.size, properties)) + if (tasks.size > 0) { - sparkListeners.foreach(_.onStageSubmitted(SparkListenerStageSubmitted(stage, tasks.size))) + // 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) - val properties = idToActiveJob(stage.priority).properties taskSched.submitTasks( new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.priority, properties)) if (!stage.submissionTime.isDefined) { @@ -536,8 +563,7 @@ class DAGScheduler( } 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)} + listenerBus.post(StageCompleted(stageToInfos(stage))) running -= stage } event.reason match { @@ -561,7 +587,7 @@ class DAGScheduler( activeJobs -= job resultStageToJob -= stage markStageAsFinished(stage) - sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobSucceeded))) + listenerBus.post(SparkListenerJobEnd(job, JobSucceeded)) } job.listener.taskSucceeded(rt.outputId, event.result) } @@ -708,7 +734,7 @@ class DAGScheduler( val job = resultStageToJob(resultStage) val error = new SparkException("Job failed: " + reason) job.listener.jobFailed(error) - sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage))))) + listenerBus.post(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage)))) idToActiveJob -= resultStage.priority activeJobs -= job resultStageToJob -= resultStage diff --git a/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala index 506c87f65b..3b4ee6287a 100644 --- a/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala +++ b/core/src/main/scala/spark/scheduler/DAGSchedulerEvent.scala @@ -43,6 +43,8 @@ private[spark] case class JobSubmitted( 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, 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/JobLogger.scala b/core/src/main/scala/spark/scheduler/JobLogger.scala index 85b5ddd4a8..7194fcaa49 100644 --- a/core/src/main/scala/spark/scheduler/JobLogger.scala +++ b/core/src/main/scala/spark/scheduler/JobLogger.scala @@ -23,8 +23,10 @@ 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.executor.TaskMetrics
import spark.scheduler.cluster.TaskInfo
@@ -53,29 +55,6 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging { 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) =>
- processStageSubmittedEvent(stage, taskSize)
- case StageCompleted(stageInfo) =>
- processStageCompletedEvent(stageInfo)
- case SparkListenerJobEnd(job, result) =>
- processJobEndEvent(job, result)
- 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 + "/")
@@ -236,37 +215,32 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging { }
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)
+ stageLogInfo(
+ stageSubmitted.stage.id,
+ "STAGE_ID=%d STATUS=SUBMITTED TASK_SIZE=%d".format(
+ stageSubmitted.stage.id, stageSubmitted.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")
+ stageLogInfo(
+ stageCompleted.stageInfo.stage.id,
+ "STAGE_ID=%d STATUS=COMPLETED".format(stageCompleted.stageInfo.stage.id))
}
-
- override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
- eventQueue.put(taskEnd)
- }
- protected def processTaskEndEvent(task: Task[_], reason: TaskEndReason,
- taskInfo: TaskInfo, taskMetrics: TaskMetrics) {
+ override def onTaskStart(taskStart: SparkListenerTaskStart) { }
+
+ override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
+ val task = taskEnd.task
+ val taskInfo = taskEnd.taskInfo
var taskStatus = ""
task match {
case resultTask: ResultTask[_, _] => taskStatus = "TASK_TYPE=RESULT_TASK"
case shuffleMapTask: ShuffleMapTask => taskStatus = "TASK_TYPE=SHUFFLE_MAP_TASK"
}
- reason match {
+ taskEnd.reason match {
case Success => taskStatus += " STATUS=SUCCESS"
- recordTaskMetrics(task.stageId, taskStatus, taskInfo, taskMetrics)
+ recordTaskMetrics(task.stageId, taskStatus, taskInfo, taskEnd.taskMetrics)
case Resubmitted =>
taskStatus += " STATUS=RESUBMITTED TID=" + taskInfo.taskId +
" STAGE_ID=" + task.stageId
@@ -285,12 +259,9 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging { }
override def onJobEnd(jobEnd: SparkListenerJobEnd) {
- eventQueue.put(jobEnd)
- }
-
- protected def processJobEndEvent(job: ActiveJob, reason: JobResult) {
+ val job = jobEnd.job
var info = "JOB_ID=" + job.runId
- reason match {
+ jobEnd.jobResult match {
case JobSucceeded => info += " STATUS=SUCCESS"
case JobFailed(exception, _) =>
info += " STATUS=FAILED REASON="
@@ -303,16 +274,14 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging { protected def recordJobProperties(jobID: Int, properties: Properties) {
if(properties != null) {
- val annotation = properties.getProperty("spark.job.annotation", "")
- jobLogInfo(jobID, annotation, false)
+ 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) {
+ val job = jobStart.job
+ val properties = jobStart.properties
createLogWriter(job.runId)
recordJobProperties(job.runId, properties)
buildJobDep(job.runId, job.finalStage)
diff --git a/core/src/main/scala/spark/scheduler/ResultTask.scala b/core/src/main/scala/spark/scheduler/ResultTask.scala index 361b1e6b91..832ca18b8c 100644 --- a/core/src/main/scala/spark/scheduler/ResultTask.scala +++ b/core/src/main/scala/spark/scheduler/ResultTask.scala @@ -51,15 +51,13 @@ private[spark] object ResultTask { } def deserializeInfo(stageId: Int, bytes: Array[Byte]): (RDD[_], (TaskContext, Iterator[_]) => _) = { - synchronized { - val loader = Thread.currentThread.getContextClassLoader - val in = new GZIPInputStream(new ByteArrayInputStream(bytes)) - val ser = SparkEnv.get.closureSerializer.newInstance - val objIn = ser.deserializeStream(in) - val rdd = objIn.readObject().asInstanceOf[RDD[_]] - val func = objIn.readObject().asInstanceOf[(TaskContext, Iterator[_]) => _] - return (rdd, func) - } + val loader = Thread.currentThread.getContextClassLoader + val in = new GZIPInputStream(new ByteArrayInputStream(bytes)) + val ser = SparkEnv.get.closureSerializer.newInstance + val objIn = ser.deserializeStream(in) + val rdd = objIn.readObject().asInstanceOf[RDD[_]] + val func = objIn.readObject().asInstanceOf[(TaskContext, Iterator[_]) => _] + return (rdd, func) } def clearCache() { @@ -118,6 +116,7 @@ private[spark] class ResultTask[T, U]( out.write(bytes) out.writeInt(partition) out.writeInt(outputId) + out.writeLong(generation) out.writeObject(split) } } @@ -132,6 +131,7 @@ 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 1c25605f75..e3bb6d1e60 100644 --- a/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/spark/scheduler/ShuffleMapTask.scala @@ -18,16 +18,9 @@ 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 spark.executor.ShuffleWriteMetrics @@ -109,11 +102,7 @@ private[spark] class ShuffleMapTask( preferredLocs.foreach (hostPort => Utils.checkHost(Utils.parseHostPort(hostPort)._1, "preferredLocs : " + preferredLocs)) } - var split = if (rdd == null) { - null - } else { - rdd.partitions(partition) - } + var split = if (rdd == null) null else rdd.partitions(partition) override def writeExternal(out: ObjectOutput) { RDDCheckpointData.synchronized { diff --git a/core/src/main/scala/spark/scheduler/SparkListener.scala b/core/src/main/scala/spark/scheduler/SparkListener.scala index 4fb1c5d42d..2a09a956ad 100644 --- a/core/src/main/scala/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/spark/scheduler/SparkListener.scala @@ -25,17 +25,20 @@ import spark.executor.TaskMetrics sealed trait SparkListenerEvents -case class SparkListenerStageSubmitted(stage: Stage, taskSize: Int) extends 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) +case class SparkListenerJobStart(job: ActiveJob, properties: Properties = null) extends SparkListenerEvents -case class SparkListenerJobEnd(job: ActiveJob, jobResult: JobResult) +case class SparkListenerJobEnd(job: ActiveJob, jobResult: JobResult) extends SparkListenerEvents trait SparkListener { @@ -43,12 +46,17 @@ trait SparkListener { * Called when a stage is completed, with information on the completed stage */ def onStageCompleted(stageCompleted: StageCompleted) { } - + /** * Called when a stage is submitted */ def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) { } - + + /** + * Called when a task starts + */ + def onTaskStart(taskEnd: SparkListenerTaskStart) { } + /** * Called when a task ends */ @@ -58,12 +66,12 @@ trait SparkListener { * Called when a job starts */ def onJobStart(jobStart: SparkListenerJobStart) { } - + /** * Called when a job ends */ def onJobEnd(jobEnd: SparkListenerJobEnd) { } - + } /** diff --git a/core/src/main/scala/spark/scheduler/SparkListenerBus.scala b/core/src/main/scala/spark/scheduler/SparkListenerBus.scala new file mode 100644 index 0000000000..f55ed455ed --- /dev/null +++ b/core/src/main/scala/spark/scheduler/SparkListenerBus.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.scheduler
+
+import java.util.concurrent.LinkedBlockingQueue
+
+import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
+
+import spark.Logging
+
+/** Asynchronously passes SparkListenerEvents to registered SparkListeners. */
+private[spark] class SparkListenerBus() extends Logging {
+ private val sparkListeners = new ArrayBuffer[SparkListener]() with SynchronizedBuffer[SparkListener]
+
+ /* Cap the capacity of the SparkListenerEvent queue so we get an explicit error (rather than
+ * an OOM exception) if it's perpetually being added to more quickly than it's being drained. */
+ private val EVENT_QUEUE_CAPACITY = 10000
+ private val eventQueue = new LinkedBlockingQueue[SparkListenerEvents](EVENT_QUEUE_CAPACITY)
+ private var queueFullErrorMessageLogged = false
+
+ new Thread("SparkListenerBus") {
+ setDaemon(true)
+ override def run() {
+ while (true) {
+ val event = eventQueue.take
+ event match {
+ case stageSubmitted: SparkListenerStageSubmitted =>
+ sparkListeners.foreach(_.onStageSubmitted(stageSubmitted))
+ case stageCompleted: StageCompleted =>
+ sparkListeners.foreach(_.onStageCompleted(stageCompleted))
+ case jobStart: SparkListenerJobStart =>
+ sparkListeners.foreach(_.onJobStart(jobStart))
+ case jobEnd: SparkListenerJobEnd =>
+ sparkListeners.foreach(_.onJobEnd(jobEnd))
+ case taskStart: SparkListenerTaskStart =>
+ sparkListeners.foreach(_.onTaskStart(taskStart))
+ case taskEnd: SparkListenerTaskEnd =>
+ sparkListeners.foreach(_.onTaskEnd(taskEnd))
+ case _ =>
+ }
+ }
+ }
+ }.start()
+
+ def addListener(listener: SparkListener) {
+ sparkListeners += listener
+ }
+
+ def post(event: SparkListenerEvents) {
+ val eventAdded = eventQueue.offer(event)
+ if (!eventAdded && !queueFullErrorMessageLogged) {
+ logError("Dropping SparkListenerEvent because no remaining room in event queue. " +
+ "This likely means one of the SparkListeners is too slow and cannot keep up with the " +
+ "rate at which tasks are being started by the scheduler.")
+ queueFullErrorMessageLogged = true
+ }
+ }
+}
+
diff --git a/core/src/main/scala/spark/scheduler/TaskResult.scala b/core/src/main/scala/spark/scheduler/TaskResult.scala index dc0621ea7b..89793e0e82 100644 --- a/core/src/main/scala/spark/scheduler/TaskResult.scala +++ b/core/src/main/scala/spark/scheduler/TaskResult.scala @@ -21,6 +21,8 @@ import java.io._ import scala.collection.mutable.Map import spark.executor.TaskMetrics +import spark.{Utils, SparkEnv} +import java.nio.ByteBuffer // Task result. Also contains updates to accumulator variables. // TODO: Use of distributed cache to return result is a hack to get around @@ -30,7 +32,13 @@ class TaskResult[T](var value: T, var accumUpdates: Map[Long, Any], var metrics: def this() = this(null.asInstanceOf[T], null, null) override def writeExternal(out: ObjectOutput) { - out.writeObject(value) + + val objectSer = SparkEnv.get.serializer.newInstance() + val bb = objectSer.serialize(value) + + out.writeInt(bb.remaining()) + Utils.writeByteBuffer(bb, out) + out.writeInt(accumUpdates.size) for ((key, value) <- accumUpdates) { out.writeLong(key) @@ -40,7 +48,14 @@ class TaskResult[T](var value: T, var accumUpdates: Map[Long, Any], var metrics: } override def readExternal(in: ObjectInput) { - value = in.readObject().asInstanceOf[T] + + val objectSer = SparkEnv.get.serializer.newInstance() + + val blen = in.readInt() + val byteVal = new Array[Byte](blen) + in.readFully(byteVal) + value = objectSer.deserialize(ByteBuffer.wrap(byteVal)) + val numUpdates = in.readInt if (numUpdates == 0) { accumUpdates = null diff --git a/core/src/main/scala/spark/scheduler/TaskScheduler.scala b/core/src/main/scala/spark/scheduler/TaskScheduler.scala index 5188308006..4943d58e25 100644 --- a/core/src/main/scala/spark/scheduler/TaskScheduler.scala +++ b/core/src/main/scala/spark/scheduler/TaskScheduler.scala @@ -17,6 +17,8 @@ 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, @@ -25,6 +27,11 @@ 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). diff --git a/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala b/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala index 245e7ccb52..2cdeb1c8c0 100644 --- a/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala +++ b/core/src/main/scala/spark/scheduler/TaskSchedulerListener.scala @@ -27,6 +27,9 @@ 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 diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala index 7c10074dc7..96568e0d27 100644 --- a/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala +++ b/core/src/main/scala/spark/scheduler/cluster/ClusterScheduler.scala @@ -26,6 +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} @@ -114,6 +115,9 @@ private[spark] class ClusterScheduler(val sc: SparkContext) 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 @@ -121,15 +125,13 @@ private[spark] class ClusterScheduler(val sc: SparkContext) def initialize(context: SchedulerBackend) { backend = context - //default scheduler is FIFO - val schedulingMode = System.getProperty("spark.cluster.schedulingmode", "FIFO") - //temporarily set rootPool name to empty - rootPool = new Pool("", SchedulingMode.withName(schedulingMode), 0, 0) + // temporarily set rootPool name to empty + rootPool = new Pool("", schedulingMode, 0, 0) schedulableBuilder = { schedulingMode match { - case "FIFO" => + case SchedulingMode.FIFO => new FIFOSchedulableBuilder(rootPool) - case "FAIR" => + case SchedulingMode.FAIR => new FairSchedulableBuilder(rootPool) } } @@ -204,7 +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() } @@ -270,10 +273,12 @@ private[spark] class ClusterScheduler(val sc: SparkContext) } var launchedTask = false val sortedTaskSetQueue = rootPool.getSortedTaskSetQueue() - for (manager <- sortedTaskSetQueue) - { - logInfo("parentName:%s,name:%s,runningTasks:%s".format(manager.parent.name, manager.name, manager.runningTasks)) + + 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. diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala index 3d06520675..7f855cd345 100644 --- a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala +++ b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala @@ -17,7 +17,8 @@ package spark.scheduler.cluster -import java.util.{HashMap => JHashMap, NoSuchElementException, Arrays} +import java.nio.ByteBuffer +import java.util.{Arrays, NoSuchElementException} import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap @@ -25,12 +26,14 @@ import scala.collection.mutable.HashSet import scala.math.max import scala.math.min -import spark._ -import spark.scheduler._ +import spark.{FetchFailed, Logging, Resubmitted, SparkEnv, Success, TaskEndReason, TaskState, Utils} +import spark.{ExceptionFailure, SparkException, TaskResultTooBigFailure} import spark.TaskState.TaskState -import java.nio.ByteBuffer +import spark.scheduler.{ShuffleMapTask, Task, TaskResult, TaskSet} + -private[spark] object TaskLocality extends Enumeration("PROCESS_LOCAL", "NODE_LOCAL", "RACK_LOCAL", "ANY") with Logging { +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 @@ -43,8 +46,10 @@ private[spark] object TaskLocality extends Enumeration("PROCESS_LOCAL", "NODE_LO 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 + 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 } @@ -56,11 +61,10 @@ private[spark] object TaskLocality extends Enumeration("PROCESS_LOCAL", "NODE_LO 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"); + logWarning("Invalid task locality specified '" + str + "', defaulting to NODE_LOCAL") // default to preserve earlier behavior NODE_LOCAL } @@ -71,11 +75,8 @@ private[spark] object TaskLocality extends Enumeration("PROCESS_LOCAL", "NODE_LO /** * Schedules the tasks within a single TaskSet in the ClusterScheduler. */ -private[spark] class ClusterTaskSetManager( - sched: ClusterScheduler, - val taskSet: TaskSet) - extends TaskSetManager - with Logging { +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 @@ -84,14 +85,15 @@ private[spark] class ClusterTaskSetManager( 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 + 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 env = SparkEnv.get + val ser = env.closureSerializer.newInstance() val tasks = taskSet.tasks val numTasks = tasks.length @@ -107,12 +109,12 @@ private[spark] class ClusterTaskSetManager( var priority = taskSet.priority var stageId = taskSet.stageId var name = "TaskSet_"+taskSet.stageId.toString - var parent:Schedulable = null - + 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 + // 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 @@ -172,9 +174,11 @@ private[spark] class ClusterTaskSetManager( // 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] = { - + 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]() @@ -189,13 +193,14 @@ private[spark] class ClusterTaskSetManager( return retval } - val taskPreferredLocations = + 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. + // 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 => { @@ -213,6 +218,7 @@ private[spark] class ClusterTaskSetManager( hosts } + } val retval = new HashSet[String] scheduler.synchronized { @@ -229,11 +235,13 @@ private[spark] class ClusterTaskSetManager( // 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 processLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.PROCESS_LOCAL) - val hostLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) - val rackLocalLocations = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.RACK_LOCAL) + // 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. @@ -298,18 +306,24 @@ private[spark] class ClusterTaskSetManager( } // Number of pending tasks for a given host Port (which would be process local) - def numPendingTasksForHostPort(hostPort: String): Int = { - getPendingTasksForHostPort(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) + 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) - def numPendingTasksForHost(hostPort: String): Int = { - getPendingTasksForHost(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) + 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 - def numRackLocalPendingTasksForHost(hostPort: String): Int = { - getRackLocalPendingTasksForHost(hostPort).count( index => copiesRunning(index) == 0 && !finished(index) ) + override def numRackLocalPendingTasksForHost(hostPort: String): Int = { + getRackLocalPendingTasksForHost(hostPort).count { index => + copiesRunning(index) == 0 && !finished(index) + } } @@ -337,12 +351,12 @@ private[spark] class ClusterTaskSetManager( 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) - } + 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 @@ -351,11 +365,11 @@ private[spark] class ClusterTaskSetManager( // 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) + 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) { @@ -367,7 +381,9 @@ private[spark] class ClusterTaskSetManager( // Any task ... if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) { // Check for attemptLocs also ? - val nonLocalTask = speculatableTasks.find(i => !taskAttempts(i).map(_.hostPort).contains(hostPort)) + val nonLocalTask = speculatableTasks.find { i => + !taskAttempts(i).map(_.hostPort).contains(hostPort) + } if (nonLocalTask != None) { speculatableTasks -= nonLocalTask.get return nonLocalTask @@ -397,7 +413,8 @@ private[spark] class ClusterTaskSetManager( } } - // Look for no pref tasks AFTER rack local tasks - this has side effect that we will get to failed tasks later rather than sooner. + // 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) { @@ -433,7 +450,8 @@ private[spark] class ClusterTaskSetManager( 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). + // 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 = { @@ -454,14 +472,22 @@ private[spark] class ClusterTaskSetManager( } // Respond to an offer of a single slave from the scheduler by finding a task - def slaveOffer(execId: String, hostPort: String, availableCpus: Double, overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] = { - + 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 + if (time - lastPreferredLaunchTime < LOCALITY_WAIT) { + TaskLocality.NODE_LOCAL + } else { + TaskLocality.ANY + } } findTask(hostPort, locality) match { @@ -489,6 +515,8 @@ private[spark] class ClusterTaskSetManager( } // 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 @@ -496,6 +524,8 @@ private[spark] class ClusterTaskSetManager( 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 _ => @@ -504,7 +534,8 @@ private[spark] class ClusterTaskSetManager( return None } - def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { + override def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { + SparkEnv.set(env) state match { case TaskState.FINISHED => taskFinished(tid, state, serializedData) @@ -518,6 +549,10 @@ private[spark] class ClusterTaskSetManager( } } + 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) { @@ -530,13 +565,14 @@ private[spark] class ClusterTaskSetManager( decreaseRunningTasks(1) if (!finished(index)) { tasksFinished += 1 - logInfo("Finished TID %s in %d ms (progress: %d/%d)".format( - tid, info.duration, tasksFinished, numTasks)) + 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) + sched.listener.taskEnded( + tasks(index), Success, result.value, result.accumUpdates, info, result.metrics) } catch { case cnf: ClassNotFoundException => val loader = Thread.currentThread().getContextClassLoader @@ -582,8 +618,8 @@ private[spark] class ClusterTaskSetManager( return case taskResultTooBig: TaskResultTooBigFailure => - logInfo("Loss was due to task %s result exceeding Akka frame size; " + - "aborting job".format(tid)) + 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 @@ -634,7 +670,7 @@ private[spark] class ClusterTaskSetManager( } } - def error(message: String) { + override def error(message: String) { // Save the error message abort("Error: " + message) } @@ -662,17 +698,18 @@ private[spark] class ClusterTaskSetManager( } } - //TODO: for now we just find Pool not TaskSetManager, we can extend this function in future if needed + // 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 + // nothing } override def removeSchedulable(schedulable:Schedulable) { - //nothing + // nothing } override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { @@ -687,13 +724,15 @@ private[spark] class ClusterTaskSetManager( // 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) + // 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.RACK_LOCAL) - val newLocs = findPreferredLocations(tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) + val newLocs = findPreferredLocations( + tasks(index).preferredLocations, sched, TaskLocality.NODE_LOCAL) if (newLocs.isEmpty) { pendingTasksWithNoPrefs += index } diff --git a/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala b/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala index f557b142c4..e77e8e4162 100644 --- a/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala +++ b/core/src/main/scala/spark/scheduler/cluster/Schedulable.scala @@ -17,14 +17,18 @@ package spark.scheduler.cluster -import scala.collection.mutable.ArrayBuffer +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 diff --git a/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala index 95554023c0..b2d089f31d 100644 --- a/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala +++ b/core/src/main/scala/spark/scheduler/cluster/SchedulableBuilder.scala @@ -41,10 +41,11 @@ private[spark] trait SchedulableBuilder { def addTaskSetManager(manager: Schedulable, properties: Properties) } -private[spark] class FIFOSchedulableBuilder(val rootPool: Pool) extends SchedulableBuilder with Logging { +private[spark] class FIFOSchedulableBuilder(val rootPool: Pool) + extends SchedulableBuilder with Logging { override def buildPools() { - //nothing + // nothing } override def addTaskSetManager(manager: Schedulable, properties: Properties) { @@ -52,7 +53,8 @@ private[spark] class FIFOSchedulableBuilder(val rootPool: Pool) extends Schedula } } -private[spark] class FairSchedulableBuilder(val rootPool: Pool) extends SchedulableBuilder with Logging { +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" @@ -103,9 +105,10 @@ private[spark] class FairSchedulableBuilder(val rootPool: Pool) extends Schedula } } - //finally create "default" pool + // 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) + 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)) @@ -119,8 +122,10 @@ private[spark] class FairSchedulableBuilder(val rootPool: Pool) extends Schedula 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) + // 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)) diff --git a/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala index 4b3e3e50e1..55cdf4791f 100644 --- a/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala +++ b/core/src/main/scala/spark/scheduler/cluster/SchedulingMode.scala @@ -17,8 +17,13 @@ package spark.scheduler.cluster -object SchedulingMode extends Enumeration("FAIR","FIFO"){ +/** + * "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 = Value + val FAIR,FIFO,NONE = Value } diff --git a/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala b/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala index ac9e5ef94d..05c29eb72f 100644 --- a/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala +++ b/core/src/main/scala/spark/scheduler/cluster/StandaloneClusterMessage.scala @@ -17,46 +17,47 @@ package spark.scheduler.cluster -import spark.TaskState.TaskState import java.nio.ByteBuffer -import spark.util.SerializableBuffer + +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, hostPort: String, cores: Int) - extends StandaloneClusterMessage { - Utils.checkHostPort(hostPort, "Expected host port") -} + 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") + } + + case class StatusUpdate(executorId: String, taskId: Long, state: TaskState, + data: SerializableBuffer) extends StandaloneClusterMessage -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)) + 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 -private[spark] case class RemoveExecutor(executorId: String, reason: String) - extends StandaloneClusterMessage + case object StopDriver 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 03a64e0192..075a7cbf7e 100644 --- a/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala @@ -17,17 +17,18 @@ 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.dispatch.Await import akka.pattern.ask +import akka.remote.{RemoteClientShutdown, RemoteClientDisconnected, RemoteClientLifeCycleEvent} import akka.util.Duration import spark.{Utils, SparkException, Logging, TaskState} -import akka.dispatch.Await -import java.util.concurrent.atomic.AtomicInteger -import akka.remote.{RemoteClientShutdown, RemoteClientDisconnected, RemoteClientLifeCycleEvent} +import spark.scheduler.cluster.StandaloneClusterMessages._ /** * A standalone scheduler backend, which waits for standalone executors to connect to it through diff --git a/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala b/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala index a1ebd48b01..c693b722ac 100644 --- a/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala +++ b/core/src/main/scala/spark/scheduler/cluster/TaskInfo.scala @@ -51,6 +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 07c3ddcc7e..1a92a5ed6f 100644 --- a/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala +++ b/core/src/main/scala/spark/scheduler/cluster/TaskSetManager.scala @@ -17,18 +17,31 @@ package spark.scheduler.cluster -import scala.collection.mutable.ArrayBuffer -import spark.scheduler._ -import spark.TaskState.TaskState import java.nio.ByteBuffer +import spark.TaskState.TaskState +import spark.scheduler.TaskSet + private[spark] trait TaskSetManager extends Schedulable { + def schedulableQueue = null + + def schedulingMode = SchedulingMode.NONE + def taskSet: TaskSet - def slaveOffer(execId: String, hostPort: String, availableCpus: Double, - overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] + + def slaveOffer( + execId: String, + hostPort: String, + availableCpus: Double, + overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] + def numPendingTasksForHostPort(hostPort: String): Int - def numRackLocalPendingTasksForHost(hostPort :String): Int + + def numRackLocalPendingTasksForHost(hostPort: String): Int + def numPendingTasksForHost(hostPort: String): Int + def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) + def error(message: String) } diff --git a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala index 1f73cb99a7..f274b1a767 100644 --- a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala +++ b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala @@ -29,6 +29,7 @@ import spark.TaskState.TaskState import spark.executor.ExecutorURLClassLoader import spark.scheduler._ import spark.scheduler.cluster._ +import spark.scheduler.cluster.SchedulingMode.SchedulingMode import akka.actor._ /** @@ -37,10 +38,15 @@ import akka.actor._ * testing fault recovery. */ -private[spark] case class LocalReviveOffers() -private[spark] case class LocalStatusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) +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 { -private[spark] class LocalActor(localScheduler: LocalScheduler, var freeCores: Int) extends Actor with Logging { def receive = { case LocalReviveOffers => launchTask(localScheduler.resourceOffer(freeCores)) @@ -55,7 +61,7 @@ private[spark] class LocalActor(localScheduler: LocalScheduler, var freeCores: I freeCores -= 1 localScheduler.threadPool.submit(new Runnable { def run() { - localScheduler.runTask(task.taskId,task.serializedTask) + localScheduler.runTask(task.taskId, task.serializedTask) } }) } @@ -80,6 +86,8 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: 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]] @@ -87,15 +95,13 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: var localActor: ActorRef = null override def start() { - //default scheduler is FIFO - val schedulingMode = System.getProperty("spark.cluster.schedulingmode", "FIFO") - //temporarily set rootPool name to empty - rootPool = new Pool("", SchedulingMode.withName(schedulingMode), 0, 0) + // temporarily set rootPool name to empty + rootPool = new Pool("", schedulingMode, 0, 0) schedulableBuilder = { schedulingMode match { - case "FIFO" => + case SchedulingMode.FIFO => new FIFOSchedulableBuilder(rootPool) - case "FAIR" => + case SchedulingMode.FAIR => new FairSchedulableBuilder(rootPool) } } @@ -110,7 +116,7 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: override def submitTasks(taskSet: TaskSet) { synchronized { - var manager = new LocalTaskSetManager(this, taskSet) + val manager = new LocalTaskSetManager(this, taskSet) schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties) activeTaskSets(taskSet.id) = manager taskSetTaskIds(taskSet.id) = new HashSet[Long]() @@ -124,14 +130,15 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: 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)) + 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 { + manager.slaveOffer(null, null, freeCpuCores) match { case Some(task) => tasks += task taskIdToTaskSetId(task.taskId) = manager.taskSet.id @@ -139,7 +146,7 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: freeCpuCores -= 1 launchTask = true case None => {} - } + } } while(launchTask) } return tasks @@ -162,7 +169,8 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: // Set the Spark execution environment for the worker thread SparkEnv.set(env) val ser = SparkEnv.get.closureSerializer.newInstance() - var attemptedTask: Option[Task[_]] = None + val objectSer = SparkEnv.get.serializer.newInstance() + var attemptedTask: Option[Task[_]] = None val start = System.currentTimeMillis() var taskStart: Long = 0 try { @@ -186,9 +194,9 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: // 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) + val serResult = objectSer.serialize(result) deserializedTask.metrics.get.resultSize = serResult.limit() - val resultToReturn = ser.deserialize[Any](serResult) + val resultToReturn = objectSer.deserialize[Any](serResult) val accumUpdates = ser.deserialize[collection.mutable.Map[Long, Any]]( ser.serialize(Accumulators.values)) val serviceTime = System.currentTimeMillis() - taskStart diff --git a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala index e662ad6709..c38eeb9e11 100644 --- a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala +++ b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala @@ -17,81 +17,77 @@ 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.{ExceptionFailure, Logging, SparkEnv, Success, TaskState} import spark.TaskState.TaskState -import spark.scheduler._ -import spark.scheduler.cluster._ +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 { -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 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 env = SparkEnv.get + val ser = env.closureSerializer.newInstance() val copiesRunning = new Array[Int](numTasks) val finished = new Array[Boolean](numTasks) val numFailures = new Array[Int](numTasks) val MAX_TASK_FAILURES = sched.maxFailures - def increaseRunningTasks(taskNum: Int): Unit = { - runningTasks += taskNum - if (parent != null) { - parent.increaseRunningTasks(taskNum) - } + override def increaseRunningTasks(taskNum: Int): Unit = { + runningTasks += taskNum + if (parent != null) { + parent.increaseRunningTasks(taskNum) + } } - def decreaseRunningTasks(taskNum: Int): Unit = { + override def decreaseRunningTasks(taskNum: Int): Unit = { runningTasks -= taskNum if (parent != null) { parent.decreaseRunningTasks(taskNum) } } - def addSchedulable(schedulable: Schedulable): Unit = { - //nothing + override def addSchedulable(schedulable: Schedulable): Unit = { + // nothing } - def removeSchedulable(schedulable: Schedulable): Unit = { - //nothing + override def removeSchedulable(schedulable: Schedulable): Unit = { + // nothing } - def getSchedulableByName(name: String): Schedulable = { + override def getSchedulableByName(name: String): Schedulable = { return null } - def executorLost(executorId: String, host: String): Unit = { - //nothing + override def executorLost(executorId: String, host: String): Unit = { + // nothing } - def checkSpeculatableTasks(): Boolean = { - return true - } + override def checkSpeculatableTasks() = true - def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { + override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager] sortedTaskSetQueue += this return sortedTaskSetQueue } - def hasPendingTasks(): Boolean = { - return true - } + override def hasPendingTasks() = true def findTask(): Option[Int] = { for (i <- 0 to numTasks-1) { @@ -102,21 +98,32 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas return None } - def slaveOffer(execId: String, hostPort: String, availableCpus: Double, overrideLocality: TaskLocality.TaskLocality = null): Option[TaskDescription] = { + 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)) + 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) + val info = new TaskInfo(taskId, index, System.currentTimeMillis(), "local", "local:1", + TaskLocality.NODE_LOCAL) taskInfos(taskId) = info - val bytes = Task.serializeWithDependencies(task, sched.sc.addedFiles, sched.sc.addedJars, ser) + // 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 => {} } @@ -124,19 +131,20 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas return None } - def numPendingTasksForHostPort(hostPort: String): Int = { + override def numPendingTasksForHostPort(hostPort: String): Int = { return 0 } - def numRackLocalPendingTasksForHost(hostPort :String): Int = { + override def numRackLocalPendingTasksForHost(hostPort :String): Int = { return 0 } - def numPendingTasksForHost(hostPort: String): Int = { + override def numPendingTasksForHost(hostPort: String): Int = { return 0 } - def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { + override def statusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer) { + SparkEnv.set(env) state match { case TaskState.FINISHED => taskEnded(tid, state, serializedData) @@ -146,6 +154,10 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas } } + 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 @@ -168,15 +180,18 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas val task = taskSet.tasks(index) info.markFailed() decreaseRunningTasks(1) - val reason: ExceptionFailure = ser.deserialize[ExceptionFailure](serializedData, getClass.getClassLoader) + 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"))) + 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) + 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 @@ -185,6 +200,6 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas } } - def error(message: String) { + 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 7bc6040544..6ebbb5ec9b 100644 --- a/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/mesos/CoarseMesosSchedulerBackend.scala @@ -110,12 +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() @@ -123,7 +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 75b8268b55..f6069a5775 100644 --- a/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala @@ -89,7 +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() @@ -97,15 +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) diff --git a/core/src/main/scala/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/spark/storage/BlockFetcherIterator.scala index 1965c5bc19..07e3db30fe 100644 --- a/core/src/main/scala/spark/storage/BlockFetcherIterator.scala +++ b/core/src/main/scala/spark/storage/BlockFetcherIterator.scala @@ -132,9 +132,10 @@ object BlockFetcherIterator { "Unexpected message " + blockMessage.getType + " received from " + cmId) } val blockId = blockMessage.getId + val networkSize = blockMessage.getData.limit() results.put(new FetchResult(blockId, sizeMap(blockId), () => dataDeserialize(blockId, blockMessage.getData, serializer))) - _remoteBytesRead += req.size + _remoteBytesRead += networkSize logDebug("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime)) } } diff --git a/core/src/main/scala/spark/storage/BlockManager.scala b/core/src/main/scala/spark/storage/BlockManager.scala index e4ffa57ad2..3a72474419 100644 --- a/core/src/main/scala/spark/storage/BlockManager.scala +++ b/core/src/main/scala/spark/storage/BlockManager.scala @@ -27,11 +27,10 @@ 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, SparkEnv, SparkException, Utils} +import spark.io.CompressionCodec import spark.network._ import spark.serializer.Serializer import spark.util.{ByteBufferInputStream, IdGenerator, MetadataCleaner, TimeStampedHashMap} @@ -158,6 +157,13 @@ private[spark] 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. */ @@ -919,18 +925,14 @@ private[spark] 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)).setFinishBlockOnFlush(true) - } 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( diff --git a/core/src/main/scala/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/spark/storage/BlockManagerMaster.scala index 3186f7c85b..76128e8cff 100644 --- a/core/src/main/scala/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/spark/storage/BlockManagerMaster.scala @@ -23,6 +23,7 @@ import akka.pattern.ask import akka.util.Duration import spark.{Logging, SparkException} +import spark.storage.BlockManagerMessages._ private[spark] class BlockManagerMaster(var driverActor: ActorRef) extends Logging { diff --git a/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala index 244000d952..011bb6b83d 100644 --- a/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/spark/storage/BlockManagerMasterActor.scala @@ -29,6 +29,8 @@ import akka.util.Duration import akka.util.duration._ import spark.{Logging, Utils, SparkException} +import spark.storage.BlockManagerMessages._ + /** * BlockManagerMasterActor is an actor on the master node to track statuses of diff --git a/core/src/main/scala/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/spark/storage/BlockManagerMessages.scala index 01de4ccb8f..9375a9ca54 100644 --- a/core/src/main/scala/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/spark/storage/BlockManagerMessages.scala @@ -22,102 +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 - -// Remove all blocks belonging to a specific RDD. -private[spark] case class RemoveRdd(rddId: Int) 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) +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() + } } - override def readExternal(in: ObjectInput) { - blockManagerId = BlockManagerId(in) - blockId = in.readUTF() - storageLevel = StorageLevel(in) - memSize = in.readLong() - diskSize = in.readLong() + 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] -object UpdateBlockInfo { - def apply(blockManagerId: BlockManagerId, - blockId: String, - storageLevel: StorageLevel, - memSize: Long, - diskSize: Long): UpdateBlockInfo = { - new UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize) - } + case class GetLocations(blockId: String) extends ToBlockManagerMaster - // For pattern-matching - def unapply(h: UpdateBlockInfo): Option[(BlockManagerId, String, StorageLevel, Long, Long)] = { - Some((h.blockManagerId, h.blockId, h.storageLevel, h.memSize, h.diskSize)) - } -} + case class GetLocationsMultipleBlockIds(blockIds: Array[String]) extends ToBlockManagerMaster -private[spark] -case class GetLocations(blockId: String) extends ToBlockManagerMaster + case class GetPeers(blockManagerId: BlockManagerId, size: Int) extends ToBlockManagerMaster -private[spark] -case class GetLocationsMultipleBlockIds(blockIds: Array[String]) extends ToBlockManagerMaster + case class RemoveExecutor(execId: String) extends ToBlockManagerMaster -private[spark] -case class GetPeers(blockManagerId: BlockManagerId, size: Int) extends ToBlockManagerMaster + case object StopBlockManagerMaster extends ToBlockManagerMaster -private[spark] -case class RemoveExecutor(execId: String) extends ToBlockManagerMaster + case object GetMemoryStatus extends ToBlockManagerMaster -private[spark] -case object StopBlockManagerMaster extends ToBlockManagerMaster + case object ExpireDeadHosts extends ToBlockManagerMaster -private[spark] -case object GetMemoryStatus extends ToBlockManagerMaster - -private[spark] -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 45cffad810..6e5fb43732 100644 --- a/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala +++ b/core/src/main/scala/spark/storage/BlockManagerSlaveActor.scala @@ -19,7 +19,7 @@ package spark.storage import akka.actor.Actor -import spark.{Logging, SparkException, Utils} +import spark.storage.BlockManagerMessages._ /** 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/BlockMessage.scala b/core/src/main/scala/spark/storage/BlockMessage.scala index ab72dbb62b..bcce26b7c1 100644 --- a/core/src/main/scala/spark/storage/BlockMessage.scala +++ b/core/src/main/scala/spark/storage/BlockMessage.scala @@ -22,7 +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 b0229d6124..ee2fc167d5 100644 --- a/core/src/main/scala/spark/storage/BlockMessageArray.scala +++ b/core/src/main/scala/spark/storage/BlockMessageArray.scala @@ -19,7 +19,6 @@ package spark.storage import java.nio.ByteBuffer -import scala.collection.mutable.StringBuilder import scala.collection.mutable.ArrayBuffer import spark._ @@ -113,7 +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 @@ -121,7 +120,7 @@ private[spark] object BlockMessageArray { } else { BlockMessage.fromGetBlock(GetBlock(i.toString)) } - }) + } val blockMessageArray = new BlockMessageArray(blockMessages) println("Block message array created") diff --git a/core/src/main/scala/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/spark/storage/BlockObjectWriter.scala index 01ed6e8c1f..3812009ca1 100644 --- a/core/src/main/scala/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/spark/storage/BlockObjectWriter.scala @@ -17,8 +17,6 @@ package spark.storage -import java.nio.ByteBuffer - /** * An interface for writing JVM objects to some underlying storage. This interface allows diff --git a/core/src/main/scala/spark/storage/DiskStore.scala b/core/src/main/scala/spark/storage/DiskStore.scala index 3495d653bd..3ebfe173b1 100644 --- a/core/src/main/scala/spark/storage/DiskStore.scala +++ b/core/src/main/scala/spark/storage/DiskStore.scala @@ -66,7 +66,6 @@ private class DiskStore(blockManager: BlockManager, rootDirs: String) override def close() { if (initialized) { objOut.close() - bs.close() channel = null bs = null objOut = null diff --git a/core/src/main/scala/spark/ui/Page.scala b/core/src/main/scala/spark/ui/Page.scala index a31e750d06..03034a4520 100644 --- a/core/src/main/scala/spark/ui/Page.scala +++ b/core/src/main/scala/spark/ui/Page.scala @@ -17,4 +17,4 @@ package spark.ui -private[spark] object Page extends Enumeration { val Storage, Jobs, Environment = Value } +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 index 9396f22063..7599f82a94 100644 --- a/core/src/main/scala/spark/ui/SparkUI.scala +++ b/core/src/main/scala/spark/ui/SparkUI.scala @@ -23,6 +23,7 @@ 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._ @@ -41,7 +42,9 @@ private[spark] class SparkUI(sc: SparkContext) extends Logging { val storage = new BlockManagerUI(sc) val jobs = new JobProgressUI(sc) val env = new EnvironmentUI(sc) - val allHandlers = storage.getHandlers ++ jobs.getHandlers ++ env.getHandlers ++ handlers + 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() { @@ -64,6 +67,7 @@ private[spark] class SparkUI(sc: SparkContext) extends Logging { // 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() { diff --git a/core/src/main/scala/spark/ui/UIUtils.scala b/core/src/main/scala/spark/ui/UIUtils.scala index b1d11954dd..ee7a8b482e 100644 --- a/core/src/main/scala/spark/ui/UIUtils.scala +++ b/core/src/main/scala/spark/ui/UIUtils.scala @@ -28,18 +28,22 @@ private[spark] object UIUtils { /** 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 storage = page match { + case Storage => <li class="active"><a href="/storage">Storage</a></li> + case _ => <li><a href="/storage">Storage</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> @@ -61,17 +65,14 @@ private[spark] object UIUtils { <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} + <a href="/" class="brand"><img src="/static/spark-logo-77x50px-hd.png" /></a> + <ul class="nav nav-pills"> {jobs} + {storage} {environment} + {executors} </ul> - <ul id="infolist"> - <li>Application: <strong>{sc.appName}</strong></li> - <li>Master: <strong>{sc.master}</strong></li> - <li>Executors: <strong>{sc.getExecutorStorageStatus.size}</strong></li> - </ul> + <p class="navbar-text pull-right">Application: <strong>{sc.appName}</strong></p> </div> </div> </div> @@ -80,9 +81,9 @@ private[spark] object UIUtils { <div class="row" style="padding-top: 5px;"> <div class="span12"> - <h1 style="vertical-align: bottom; display: inline-block;"> + <h3 style="vertical-align: bottom; display: inline-block;"> {title} - </h1> + </h3> </div> </div> <hr/> @@ -112,9 +113,9 @@ private[spark] object UIUtils { <img src="/static/spark_logo.png" /> </div> <div class="span10"> - <h1 style="vertical-align: bottom; margin-top: 40px; display: inline-block;"> + <h3 style="vertical-align: bottom; margin-top: 40px; display: inline-block;"> {title} - </h1> + </h3> </div> </div> {content} diff --git a/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala index a80e2d7002..97ea644021 100644 --- a/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala +++ b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala @@ -21,7 +21,8 @@ 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). * @@ -29,18 +30,29 @@ import spark.SparkContext._ */ private[spark] object UIWorkloadGenerator { val NUM_PARTITIONS = 100 - val INTER_JOB_WAIT_MS = 500 + 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) - // NOTE: Right now there is no easy way for us to show spark.job.annotation for a given phase, - // but we pass it here anyways since it will be useful once we do. - def setName(s: String) = { - sc.addLocalProperties("spark.job.annotation", s) + 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() @@ -73,14 +85,18 @@ private[spark] object UIWorkloadGenerator { while (true) { for ((desc, job) <- jobs) { - try { - setName(desc) - job() - println("Job funished: " + desc) - } catch { - case e: Exception => - println("Job Failed: " + desc) - } + 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 index 5ae7935ed4..dc39b91648 100644 --- a/core/src/main/scala/spark/ui/env/EnvironmentUI.scala +++ b/core/src/main/scala/spark/ui/env/EnvironmentUI.scala @@ -44,7 +44,7 @@ private[spark] class EnvironmentUI(sc: SparkContext) { ("Java Home", Properties.javaHome), ("Scala Version", Properties.versionString), ("Scala Home", Properties.scalaHome) - ) + ).sorted def jvmRow(kv: (String, String)) = <tr><td>{kv._1}</td><td>{kv._2}</td></tr> def jvmTable = UIUtils.listingTable(Seq("Name", "Value"), jvmRow, jvmInformation) @@ -53,8 +53,8 @@ private[spark] class EnvironmentUI(sc: SparkContext) { .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 sparkProperties = properties.filter(_._1.startsWith("spark")).sorted + val otherProperties = properties.diff(sparkProperties :+ classPathProperty).sorted val propertyHeaders = Seq("Name", "Value") def propertyRow(kv: (String, String)) = <tr><td>{kv._1}</td><td>{kv._2}</td></tr> @@ -67,7 +67,7 @@ private[spark] class EnvironmentUI(sc: SparkContext) { .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 classPath = (addedJars ++ addedFiles ++ classPathEntries).sorted val classPathHeaders = Seq("Resource", "Source") def classPathRow(data: (String, String)) = <tr><td>{data._1}</td><td>{data._2}</td></tr> @@ -75,10 +75,10 @@ private[spark] class EnvironmentUI(sc: SparkContext) { val content = <span> - <h2>Runtime Information</h2> {jvmTable} - <h2>Spark Properties</h2> {sparkPropertyTable} - <h2>System Properties</h2> {otherPropertyTable} - <h2>Classpath Entries</h2> {classPathTable} + <h4>Runtime Information</h4> {jvmTable} + <h4>Spark Properties</h4> {sparkPropertyTable} + <h4>System Properties</h4> {otherPropertyTable} + <h4>Classpath Entries</h4> {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..6ec48f70a4 --- /dev/null +++ b/core/src/main/scala/spark/ui/exec/ExecutorsUI.scala @@ -0,0 +1,142 @@ +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 = activeTasks + failedTasks + completedTasks + + 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]() + + override def onTaskStart(taskStart: SparkListenerTaskStart) { + val eid = taskStart.taskInfo.executorId + val activeTasks = executorToTasksActive.getOrElseUpdate(eid, new HashSet[TaskInfo]()) + activeTasks += taskStart.taskInfo + } + + 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)) + } + } + } +} diff --git a/core/src/main/scala/spark/ui/jobs/IndexPage.scala b/core/src/main/scala/spark/ui/jobs/IndexPage.scala index f31af3cda6..9724671a03 100644 --- a/core/src/main/scala/spark/ui/jobs/IndexPage.scala +++ b/core/src/main/scala/spark/ui/jobs/IndexPage.scala @@ -17,113 +17,77 @@ package spark.ui.jobs -import java.util.Date - import javax.servlet.http.HttpServletRequest -import scala.Some import scala.xml.{NodeSeq, Node} -import spark.scheduler.Stage -import spark.ui.UIUtils._ +import spark.scheduler.cluster.SchedulingMode import spark.ui.Page._ -import spark.storage.StorageLevel +import spark.ui.UIUtils._ +import spark.Utils -/** Page showing list of all ongoing and recently finished stages */ +/** Page showing list of all ongoing and recently finished stages and pools*/ private[spark] class IndexPage(parent: JobProgressUI) { def listener = parent.listener - val dateFmt = parent.dateFmt def render(request: HttpServletRequest): Seq[Node] = { - val activeStages = listener.activeStages.toSeq - val completedStages = listener.completedStages.reverse.toSeq - val failedStages = listener.failedStages.reverse.toSeq - - /** 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> - <th>Origin</th> - <th>Submitted</th> - <th>Duration</th> - <th colspan="2">Tasks: Complete/Total</th> - <th>Shuffle Activity</th> - <th>Stored RDD</th> - </thead> - <tbody> - {rows.map(r => makeRow(r))} - </tbody> - </table> - } - - val activeStageTable: NodeSeq = stageTable(stageRow, activeStages) - val completedStageTable = stageTable(stageRow, completedStages) - val failedStageTable: NodeSeq = stageTable(stageRow, failedStages) - - val content = <h2>Active Stages</h2> ++ activeStageTable ++ - <h2>Completed Stages</h2> ++ completedStageTable ++ - <h2>Failed Stages</h2> ++ failedStageTable - - headerSparkPage(content, parent.sc, "Spark Stages", Jobs) - } - - def getElapsedTime(submitted: Option[Long], completed: Long): String = { - submitted match { - case Some(t) => parent.formatDuration(completed - t) - case _ => "Unknown" + listener.synchronized { + 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.sortBy(_.submissionTime).reverse, parent) + val completedStagesTable = new StageTable(completedStages.sortBy(_.submissionTime).reverse, parent) + val failedStagesTable = new StageTable(failedStages.sortBy(_.submissionTime).reverse, 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> + <li><strong>Scheduling Mode:</strong> {parent.sc.getSchedulingMode}</li> + <li> + <a href="#active"><strong>Active Stages:</strong></a> + {activeStages.size} + </li> + <li> + <a href="#completed"><strong>Completed Stages:</strong></a> + {completedStages.size} + </li> + <li> + <a href="#failed"><strong>Failed Stages:</strong></a> + {failedStages.size} + </li> + </ul> + </div> + + val content = summary ++ + {if (listener.sc.getSchedulingMode == SchedulingMode.FAIR) { + <h4>Pools</h4> ++ poolTable.toNodeSeq + } else { + Seq() + }} ++ + <h4 id="active">Active Stages: {activeStages.size}</h4> ++ + activeStagesTable.toNodeSeq++ + <h4 id="completed">Completed Stages: {completedStages.size}</h4> ++ + completedStagesTable.toNodeSeq++ + <h4 id ="failed">Failed Stages: {failedStages.size}</h4> ++ + failedStagesTable.toNodeSeq + + headerSparkPage(content, parent.sc, "Spark Stages", Jobs) } } - - def makeProgressBar(completed: Int, total: Int): Seq[Node] = { - val width=130 - val height=15 - val completeWidth = (completed.toDouble / total) * width - - <svg width={width.toString} height={height.toString}> - <rect width={width.toString} height={height.toString} - fill="white" stroke="rgb(51,51,51)" stroke-width="1" /> - <rect width={completeWidth.toString} height={height.toString} - fill="rgb(0,136,204)" stroke="black" stroke-width="1" /> - </svg> - } - - - def stageRow(s: Stage): Seq[Node] = { - val submissionTime = s.submissionTime match { - case Some(t) => dateFmt.format(new Date(t)) - case None => "Unknown" - } - val (read, write) = (listener.hasShuffleRead(s.id), listener.hasShuffleWrite(s.id)) - val shuffleInfo = (read, write) match { - case (true, true) => "Read/Write" - case (true, false) => "Read" - case (false, true) => "Write" - case _ => "" - } - val completedTasks = listener.stageToTasksComplete.getOrElse(s.id, 0) - val totalTasks = s.numPartitions - - <tr> - <td>{s.id}</td> - <td><a href={"/stages/stage?id=%s".format(s.id)}>{s.name}</a></td> - <td>{submissionTime}</td> - <td>{getElapsedTime(s.submissionTime, - s.completionTime.getOrElse(System.currentTimeMillis()))}</td> - <td class="progress-cell">{makeProgressBar(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>{shuffleInfo}</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/jobs/JobProgressListener.scala b/core/src/main/scala/spark/ui/jobs/JobProgressListener.scala new file mode 100644 index 0000000000..1d9767a83c --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/JobProgressListener.scala @@ -0,0 +1,156 @@ +package spark.ui.jobs + +import scala.Seq +import scala.collection.mutable.{ListBuffer, HashMap, HashSet} + +import spark.{ExceptionFailure, SparkContext, Success, Utils} +import spark.scheduler._ +import spark.scheduler.cluster.TaskInfo +import spark.executor.TaskMetrics +import collection.mutable + +/** + * Tracks task-level information to be displayed in the UI. + * + * All access to the data structures in this class must be synchronized on the + * class, since the UI thread and the DAGScheduler event loop may otherwise + * be reading/updating the internal data structures concurrently. + */ +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, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]]() + + override def onJobStart(jobStart: SparkListenerJobStart) {} + + override def onStageCompleted(stageCompleted: StageCompleted) = synchronized { + 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]) = synchronized { + if (stages.size > RETAINED_STAGES) { + val toRemove = RETAINED_STAGES / 10 + stages.takeRight(toRemove).foreach( s => { + stageToTaskInfos.remove(s.id) + stageToTime.remove(s.id) + stageToShuffleRead.remove(s.id) + stageToShuffleWrite.remove(s.id) + stageToTasksActive.remove(s.id) + stageToTasksComplete.remove(s.id) + stageToTasksFailed.remove(s.id) + stageToPool.remove(s) + if (stageToDescription.contains(s)) {stageToDescription.remove(s)} + }) + stages.trimEnd(toRemove) + } + } + + /** For FIFO, all stages are contained by "default" pool but "default" pool here is meaningless */ + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = synchronized { + 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) = synchronized { + val sid = taskStart.task.stageId + val tasksActive = stageToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]()) + tasksActive += taskStart.taskInfo + val taskList = stageToTaskInfos.getOrElse( + sid, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) + taskList += ((taskStart.taskInfo, None, None)) + stageToTaskInfos(sid) = taskList + } + + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + 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, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) + taskList -= ((taskEnd.taskInfo, None, None)) + taskList += ((taskEnd.taskInfo, metrics, failureInfo)) + stageToTaskInfos(sid) = taskList + } + + override def onJobEnd(jobEnd: SparkListenerJobEnd) = synchronized { + 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 _ => + } + } +} diff --git a/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala index 44dcf82d11..c83f102ff3 100644 --- a/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala +++ b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala @@ -31,9 +31,9 @@ import scala.collection.mutable.{HashSet, ListBuffer, HashMap, ArrayBuffer} import spark.ui.JettyUtils._ import spark.{ExceptionFailure, SparkContext, Success, Utils} import spark.scheduler._ -import spark.scheduler.cluster.TaskInfo -import spark.executor.TaskMetrics 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) { @@ -43,9 +43,10 @@ private[spark] class JobProgressUI(val sc: SparkContext) { private val indexPage = new IndexPage(this) private val stagePage = new StagePage(this) + private val poolPage = new PoolPage(this) def start() { - _listener = Some(new JobProgressListener) + _listener = Some(new JobProgressListener(sc)) sc.addSparkListener(listener) } @@ -53,92 +54,7 @@ private[spark] class JobProgressUI(val sc: SparkContext) { 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)) ) } - -private[spark] class JobProgressListener extends SparkListener { - // How many stages to remember - val RETAINED_STAGES = System.getProperty("spark.ui.retained_stages", "1000").toInt - - val activeStages = HashSet[Stage]() - val completedStages = ListBuffer[Stage]() - val failedStages = ListBuffer[Stage]() - - 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 - 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) - }) - stages.trimEnd(toRemove) - } - } - - override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = - activeStages += stageSubmitted.stage - - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) { - val sid = taskEnd.task.stageId - 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, Some(taskEnd.taskMetrics)) - } - val taskList = stageToTaskInfos.getOrElse( - sid, ArrayBuffer[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]()) - 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 - 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/PoolPage.scala b/core/src/main/scala/spark/ui/jobs/PoolPage.scala new file mode 100644 index 0000000000..04ef35c800 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/PoolPage.scala @@ -0,0 +1,32 @@ +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] = { + listener.synchronized { + val poolName = request.getParameter("poolname") + val poolToActiveStages = listener.poolToActiveStages + val activeStages = poolToActiveStages.get(poolName).toSeq.flatten + val activeStagesTable = new StageTable(activeStages.sortBy(_.submissionTime).reverse, 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..21ebcef63a --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/PoolTable.scala @@ -0,0 +1,54 @@ +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] = { + listener.synchronized { + poolTable(poolRow, pools) + } + } + + private 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> + } + + private def poolRow(p: Schedulable, poolToActiveStages: HashMap[String, HashSet[Stage]]): Seq[Node] = { + val activeStages = poolToActiveStages.get(p.name) match { + case Some(stages) => stages.size + case None => 0 + } + <tr> + <td><a href={"/stages/pool?poolname=%s".format(p.name)}>{p.name}</a></td> + <td>{p.minShare}</td> + <td>{p.weight}</td> + <td>{activeStages}</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 index 292966f23a..1b071a91e5 100644 --- a/core/src/main/scala/spark/ui/jobs/StagePage.scala +++ b/core/src/main/scala/spark/ui/jobs/StagePage.scala @@ -36,71 +36,102 @@ private[spark] class StagePage(parent: JobProgressUI) { val dateFmt = parent.dateFmt def render(request: HttpServletRequest): Seq[Node] = { - val stageId = request.getParameter("id").toInt + listener.synchronized { + val stageId = request.getParameter("id").toInt + val now = System.currentTimeMillis() + + if (!listener.stageToTaskInfos.contains(stageId)) { + val content = + <div> + <h4>Summary Metrics</h4> No tasks have started yet + <h4>Tasks</h4> No tasks have started yet + </div> + return headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs) + } - if (!listener.stageToTaskInfos.contains(stageId)) { - val content = - <div> - <h2>Summary Metrics</h2> No tasks have finished yet - <h2>Tasks</h2> No tasks have finished yet - </div> - return headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs) - } + val tasks = listener.stageToTaskInfos(stageId).toSeq.sortBy(_._1.launchTime) - val tasks = listener.stageToTaskInfos(stageId) + val shuffleReadBytes = listener.stageToShuffleRead.getOrElse(stageId, 0L) + val hasShuffleRead = shuffleReadBytes > 0 + val shuffleWriteBytes = listener.stageToShuffleWrite.getOrElse(stageId, 0L) + val hasShuffleWrite = shuffleWriteBytes > 0 - val shuffleRead = listener.hasShuffleRead(stageId) - val shuffleWrite = listener.hasShuffleWrite(stageId) + var activeTime = 0L + listener.stageToTasksActive(stageId).foreach(activeTime += _.timeRunning(now)) - val taskHeaders: Seq[String] = - Seq("Task ID", "Duration", "Locality Level", "Worker", "Launch Time") ++ - {if (shuffleRead) Seq("Shuffle Read") else Nil} ++ - {if (shuffleWrite) Seq("Shuffle Write") else Nil} ++ - Seq("Details") + val summary = + <div> + <ul class="unstyled"> + <li> + <strong>CPU time: </strong> + {parent.formatDuration(listener.stageToTime.getOrElse(stageId, 0L) + activeTime)} + </li> + {if (hasShuffleRead) + <li> + <strong>Shuffle read: </strong> + {Utils.memoryBytesToString(shuffleReadBytes)} + </li> + } + {if (hasShuffleWrite) + <li> + <strong>Shuffle write: </strong> + {Utils.memoryBytesToString(shuffleWriteBytes)} + </li> + } + </ul> + </div> - val taskTable = listingTable(taskHeaders, taskRow, tasks) + val taskHeaders: Seq[String] = + Seq("Task ID", "Status", "Duration", "Locality Level", "Worker", "Launch Time") ++ + {if (hasShuffleRead) Seq("Shuffle Read") else Nil} ++ + {if (hasShuffleWrite) Seq("Shuffle Write") else Nil} ++ + Seq("Details") - // Excludes tasks which failed and have incomplete metrics - val validTasks = tasks.filter(t => Option(t._2).isDefined) + val taskTable = listingTable(taskHeaders, taskRow, tasks) - 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) + // Excludes tasks which failed and have incomplete metrics + val validTasks = tasks.filter(t => t._1.status == "SUCCESS" && (t._2.isDefined)) - val shuffleWriteSizes = validTasks.map { - case(info, metrics, exception) => - metrics.get.shuffleWriteMetrics.map(_.shuffleBytesWritten).getOrElse(0L).toDouble + 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 (hasShuffleRead) shuffleReadQuantiles else Nil, + if (hasShuffleWrite) 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 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 = - <h2>Summary Metrics</h2> ++ summaryTable.getOrElse(Nil) ++ <h2>Tasks</h2> ++ taskTable; + val content = + summary ++ <h2>Summary Metrics</h2> ++ summaryTable.getOrElse(Nil) ++ + <h2>Tasks</h2> ++ taskTable; - headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs) + headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs) + } } @@ -108,10 +139,17 @@ private[spark] class StagePage(parent: JobProgressUI) { 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 sorttable_customkey={metrics.map{m => m.executorRunTime.toString}.getOrElse("1")}> - {metrics.map{m => parent.formatDuration(m.executorRunTime)}.getOrElse("")} + <td>{info.status}</td> + <td sorttable_customkey={duration.toString}> + {formatDuration} </td> <td>{info.taskLocality}</td> <td>{info.hostPort}</td> 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..5068a025fa --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/StageTable.scala @@ -0,0 +1,118 @@ +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] = { + listener.synchronized { + stageTable(stageRow, stages) + } + } + + /** Special table which merges two header cells. */ + private 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>Tasks: Succeeded/Total</td> + <td>Shuffle Read</td> + <td>Shuffle Write</td> + </thead> + <tbody> + {rows.map(r => makeRow(r))} + </tbody> + </table> + } + + private def getElapsedTime(submitted: Option[Long], completed: Long): String = { + submitted match { + case Some(t) => parent.formatDuration(completed - t) + case _ => "Unknown" + } + } + + private def makeProgressBar(started: Int, completed: Int, failed: String, 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; position: relative"> + <span style="text-align:center; position:absolute; width:100%;"> + {completed}/{total} {failed} + </span> + <div class="bar bar-completed" style={completeWidth}></div> + <div class="bar bar-running" style={startWidth}></div> + </div> + } + + + private 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 failedTasks = listener.stageToTasksFailed.getOrElse(s.id, 0) match { + case f if f > 0 => "(%s failed)".format(f) + case _ => "" + } + 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, failedTasks, totalTasks)} + </td> + <td>{shuffleRead}</td> + <td>{shuffleWrite}</td> + </tr> + } +} diff --git a/core/src/main/scala/spark/ui/storage/IndexPage.scala b/core/src/main/scala/spark/ui/storage/IndexPage.scala index 4e0360d19a..f76192eba8 100644 --- a/core/src/main/scala/spark/ui/storage/IndexPage.scala +++ b/core/src/main/scala/spark/ui/storage/IndexPage.scala @@ -33,10 +33,6 @@ private[spark] class IndexPage(parent: BlockManagerUI) { def render(request: HttpServletRequest): Seq[Node] = { 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 rddHeaders = Seq( "RDD Name", @@ -46,19 +42,7 @@ private[spark] class IndexPage(parent: BlockManagerUI) { "Size in Memory", "Size on Disk") val rdds = StorageUtils.rddInfoFromStorageStatus(storageStatusList, sc) - val rddTable = listingTable(rddHeaders, rddRow, rdds) - - val content = - <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> ++ {rddTable}; + val content = listingTable(rddHeaders, rddRow, rdds) headerSparkPage(content, parent.sc, "Spark Storage ", Storage) } diff --git a/core/src/main/scala/spark/ui/storage/RDDPage.scala b/core/src/main/scala/spark/ui/storage/RDDPage.scala index 003be54ad8..4c3ee12c98 100644 --- a/core/src/main/scala/spark/ui/storage/RDDPage.scala +++ b/core/src/main/scala/spark/ui/storage/RDDPage.scala @@ -83,18 +83,19 @@ private[spark] class RDDPage(parent: BlockManagerUI) { <hr/> <div class="row"> <div class="span12"> + <h3> Data Distribution Summary </h3> {workerTable} </div> </div> <hr/> <div class="row"> <div class="span12"> - <h3> RDD Summary </h3> + <h4> Partitions </h4> {blockTable} </div> </div>; - headerSparkPage(content, parent.sc, "RDD Info: " + rddInfo.name, Jobs) + headerSparkPage(content, parent.sc, "RDD Info: " + rddInfo.name, Storage) } def blockRow(row: (String, BlockStatus, Seq[String])): Seq[Node] = { diff --git a/core/src/main/scala/spark/util/TimeStampedHashMap.scala b/core/src/main/scala/spark/util/TimeStampedHashMap.scala index cc7909194a..07772a0afb 100644 --- a/core/src/main/scala/spark/util/TimeStampedHashMap.scala +++ b/core/src/main/scala/spark/util/TimeStampedHashMap.scala @@ -20,6 +20,7 @@ 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 /** @@ -99,6 +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/Vector.scala b/core/src/main/scala/spark/util/Vector.scala index ed49386f18..a47cac3b96 100644 --- a/core/src/main/scala/spark/util/Vector.scala +++ b/core/src/main/scala/spark/util/Vector.scala @@ -73,7 +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) @@ -117,9 +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/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/FailureSuite.scala b/core/src/test/scala/spark/FailureSuite.scala index 6c847b8fef..5b133cdd6e 100644 --- a/core/src/test/scala/spark/FailureSuite.scala +++ b/core/src/test/scala/spark/FailureSuite.scala @@ -18,9 +18,6 @@ package spark import org.scalatest.FunSuite -import org.scalatest.prop.Checkers - -import scala.collection.mutable.ArrayBuffer import SparkContext._ @@ -40,7 +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") { @@ -66,7 +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) { @@ -87,12 +84,40 @@ 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() } diff --git a/core/src/test/scala/spark/JavaAPISuite.java b/core/src/test/scala/spark/JavaAPISuite.java index aaf03e683b..4ab271de1a 100644 --- a/core/src/test/scala/spark/JavaAPISuite.java +++ b/core/src/test/scala/spark/JavaAPISuite.java @@ -22,6 +22,7 @@ import java.io.IOException; import java.io.Serializable; import java.util.*; +import com.google.common.base.Optional; import scala.Tuple2; import com.google.common.base.Charsets; @@ -198,6 +199,35 @@ public class JavaAPISuite implements Serializable { } @Test + public void leftOuterJoin() { + JavaPairRDD<Integer, Integer> rdd1 = sc.parallelizePairs(Arrays.asList( + new Tuple2<Integer, Integer>(1, 1), + new Tuple2<Integer, Integer>(1, 2), + new Tuple2<Integer, Integer>(2, 1), + new Tuple2<Integer, Integer>(3, 1) + )); + JavaPairRDD<Integer, Character> rdd2 = sc.parallelizePairs(Arrays.asList( + new Tuple2<Integer, Character>(1, 'x'), + new Tuple2<Integer, Character>(2, 'y'), + new Tuple2<Integer, Character>(2, 'z'), + new Tuple2<Integer, Character>(4, 'w') + )); + List<Tuple2<Integer,Tuple2<Integer,Optional<Character>>>> joined = + rdd1.leftOuterJoin(rdd2).collect(); + Assert.assertEquals(5, joined.size()); + Tuple2<Integer,Tuple2<Integer,Optional<Character>>> firstUnmatched = + rdd1.leftOuterJoin(rdd2).filter( + new Function<Tuple2<Integer, Tuple2<Integer, Optional<Character>>>, Boolean>() { + @Override + public Boolean call(Tuple2<Integer, Tuple2<Integer, Optional<Character>>> tup) + throws Exception { + return !tup._2()._2().isPresent(); + } + }).first(); + Assert.assertEquals(3, firstUnmatched._1().intValue()); + } + + @Test public void foldReduce() { JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13)); Function2<Integer, Integer, Integer> add = new Function2<Integer, Integer, Integer>() { @@ -326,7 +356,9 @@ 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); diff --git a/core/src/test/scala/spark/KryoSerializerSuite.scala b/core/src/test/scala/spark/KryoSerializerSuite.scala index c3323dcbb3..7568a0bf65 100644 --- a/core/src/test/scala/spark/KryoSerializerSuite.scala +++ b/core/src/test/scala/spark/KryoSerializerSuite.scala @@ -18,14 +18,13 @@ package spark import scala.collection.mutable -import scala.collection.immutable import org.scalatest.FunSuite import com.esotericsoftware.kryo._ -import SparkContext._ +import KryoTest._ -class KryoSerializerSuite extends FunSuite { +class KryoSerializerSuite extends FunSuite with SharedSparkContext { test("basic types") { val ser = (new KryoSerializer).newInstance() def check[T](t: T) { @@ -53,6 +52,7 @@ 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") { @@ -103,7 +103,7 @@ 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() @@ -123,14 +123,65 @@ class KryoSerializerSuite extends FunSuite { val hashMap = new java.util.HashMap[String, String] hashMap.put("foo", "bar") check(hashMap) - + + System.clearProperty("spark.kryo.registrator") + } + + test("kryo with collect") { + val control = 1 :: 2 :: Nil + val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)).collect().map(_.x) + assert(control === result.toSeq) + } + + test("kryo with parallelize") { + val control = 1 :: 2 :: Nil + val result = sc.parallelize(control.map(new ClassWithoutNoArgConstructor(_))).map(_.x).collect() + assert (control === result.toSeq) + } + + test("kryo with parallelize for specialized tuples") { + assert (sc.parallelize( Array((1, 11), (2, 22), (3, 33)) ).count === 3) + } + + test("kryo with parallelize for primitive arrays") { + assert (sc.parallelize( Array(1, 2, 3) ).count === 3) + } + + test("kryo with collect for specialized tuples") { + assert (sc.parallelize( Array((1, 11), (2, 22), (3, 33)) ).collect().head === (1, 11)) + } + + test("kryo with reduce") { + val control = 1 :: 2 :: Nil + val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)) + .reduce((t1, t2) => new ClassWithoutNoArgConstructor(t1.x + t2.x)).x + assert(control.sum === result) + } + + // TODO: this still doesn't work + ignore("kryo with fold") { + val control = 1 :: 2 :: Nil + val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)) + .fold(new ClassWithoutNoArgConstructor(10))((t1, t2) => new ClassWithoutNoArgConstructor(t1.x + t2.x)).x + assert(10 + control.sum === result) + } + + override def beforeAll() { + System.setProperty("spark.serializer", "spark.KryoSerializer") + System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName) + super.beforeAll() + } + + override def afterAll() { + super.afterAll() System.clearProperty("spark.kryo.registrator") + System.clearProperty("spark.serializer") } } -package test { +object KryoTest { case class CaseClass(i: Int, s: String) {} - + class ClassWithNoArgConstructor { var x: Int = 0 override def equals(other: Any) = other match { 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/ShuffleSuite.scala b/core/src/test/scala/spark/ShuffleSuite.scala index 3a56c26bef..752e4b85e6 100644 --- a/core/src/test/scala/spark/ShuffleSuite.scala +++ b/core/src/test/scala/spark/ShuffleSuite.scala @@ -33,9 +33,9 @@ import spark.rdd.ShuffledRDD import spark.SparkContext._ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext { - test("groupByKey with compression") { + test("groupByKey without compression") { try { - System.setProperty("spark.shuffle.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() @@ -45,7 +45,7 @@ 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") } } 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/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/scheduler/ClusterSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala index 8f81d0b6ee..05afcd6567 100644 --- a/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala +++ b/core/src/test/scala/spark/scheduler/ClusterSchedulerSuite.scala @@ -33,7 +33,7 @@ class DummyTaskSetManager( initNumTasks: Int, clusterScheduler: ClusterScheduler, taskSet: TaskSet) - extends ClusterTaskSetManager(clusterScheduler,taskSet) { + extends ClusterTaskSetManager(clusterScheduler, taskSet) { parent = null weight = 1 diff --git a/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala index f802b66cf1..caaf3209fd 100644 --- a/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/spark/scheduler/DAGSchedulerSuite.scala @@ -23,21 +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 @@ -56,6 +53,8 @@ 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) = { diff --git a/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala b/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala index 0f855c38da..bb9e715f95 100644 --- a/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala +++ b/core/src/test/scala/spark/scheduler/JobLoggerSuite.scala @@ -57,7 +57,7 @@ class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers 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)) + joblogger.onStageSubmitted(SparkListenerStageSubmitted(rootStage, 4, null)) joblogger.getRddNameTest(parentRdd) should be (parentRdd.getClass.getName) parentRdd.setName("MyRDD") joblogger.getRddNameTest(parentRdd) should be ("MyRDD") diff --git a/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala b/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala index 14bb58731b..66fd59e8bb 100644 --- a/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala +++ b/core/src/test/scala/spark/scheduler/LocalSchedulerSuite.scala @@ -73,7 +73,7 @@ class LocalSchedulerSuite extends FunSuite with LocalSparkContext { TaskThreadInfo.threadToStarted(threadIndex) = new CountDownLatch(1) new Thread { if (poolName != null) { - sc.addLocalProperties("spark.scheduler.cluster.fair.pool",poolName) + sc.addLocalProperty("spark.scheduler.cluster.fair.pool", poolName) } override def run() { val ans = nums.map(number => { diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index f06ab2d5b0..a76346f428 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -74,6 +74,7 @@ <li><a href="api/core/index.html">Spark Java/Scala (Scaladoc)</a></li> <li><a href="api/pyspark/index.html">Spark Python (Epydoc)</a></li> <li><a href="api/streaming/index.html">Spark Streaming Java/Scala (Scaladoc) </a></li> + <li><a href="api/mllib/index.html">Spark ML Library (Scaladoc) </a></li> </ul> </li> diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb index 45ef4bba82..217254c59f 100644 --- a/docs/_plugins/copy_api_dirs.rb +++ b/docs/_plugins/copy_api_dirs.rb @@ -20,7 +20,7 @@ include FileUtils if ENV['SKIP_API'] != '1' # Build Scaladoc for Java/Scala - projects = ["core", "examples", "repl", "bagel", "streaming"] + projects = ["core", "examples", "repl", "bagel", "streaming", "mllib"] puts "Moving to project root and building scaladoc." curr_dir = pwd diff --git a/docs/configuration.md b/docs/configuration.md index 5c06897cae..99624a44aa 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -35,7 +35,7 @@ for these variables. * `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 +* `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. @@ -77,7 +77,7 @@ there are at least five properties that you will commonly want to control: 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> @@ -86,7 +86,7 @@ there are at least five 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> @@ -181,6 +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> @@ -295,6 +310,14 @@ Apart from these, the following properties are also available, and may be useful 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/python-programming-guide.md b/docs/python-programming-guide.md index e8aaac74d0..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: @@ -50,6 +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. @@ -81,16 +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. @@ -105,6 +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/spark-simple-tutorial.md b/docs/spark-simple-tutorial.md deleted file mode 100644 index 9875de62bd..0000000000 --- a/docs/spark-simple-tutorial.md +++ /dev/null @@ -1,41 +0,0 @@ ---- -layout: global -title: Tutorial - Running a Simple Spark Application ---- - -1. Create directory for spark demo: - - ~$ mkdir SparkTest - -2. Copy the sbt files in ~/spark/sbt directory: - - ~/SparkTest$ cp -r ../spark/sbt . - -3. Edit the ~/SparkTest/sbt/sbt file to look like this: - - #!/bin/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: - - ~/spark$ sbt/sbt assembly - -5. create a source file in ~/SparkTest/src/main/scala directory: - - ~/SparkTest/src/main/scala$ vi Test1.scala - -6. Make the contain of the Test1.scala file like this: - - import spark.SparkContext - import spark.SparkContext._ - object Test1 { - def main(args: Array[String]) { - val sc = new SparkContext("local", "SparkTest") - println(sc.parallelize(1 to 10).reduce(_ + _)) - System.exit(0) - } - } - -7. Run the Test1.scala file: - - ~/SparkTest$ sbt/sbt run diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index 3986c0c79d..7463844a4e 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -43,7 +43,7 @@ Finally, the following configuration options can be passed to the master and wor </tr> <tr> <td><code>-p PORT</code>, <code>--port PORT</code></td> - <td>IP address or DNS name to listen on (default: 7077 for master, random for worker)</td> + <td>Port for service to listen on (default: 7077 for master, random for worker)</td> </tr> <tr> <td><code>--webui-port PORT</code></td> 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.py b/ec2/spark_ec2.py index 2ec3c007fb..740ec08542 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -9,9 +9,9 @@ # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at -# +# # http://www.apache.org/licenses/LICENSE-2.0 -# +# # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. @@ -53,7 +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). " + @@ -69,7 +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, @@ -99,7 +99,7 @@ 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() @@ -112,7 +112,7 @@ def parse_args(): 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') @@ -178,6 +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: @@ -257,7 +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: @@ -413,7 +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") @@ -528,7 +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 @@ -557,9 +558,9 @@ def ssh(host, opts, command): 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 @@ -618,12 +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 @@ -639,7 +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 :-( @@ -650,13 +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." @@ -679,7 +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 7a8d08fade..a051da8a77 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -119,6 +119,12 @@ <classifier>hadoop1</classifier> </dependency> <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-mllib</artifactId> + <version>${project.version}</version> + <classifier>hadoop1</classifier> + </dependency> + <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-core</artifactId> <scope>provided</scope> @@ -157,6 +163,12 @@ <classifier>hadoop2</classifier> </dependency> <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-mllib</artifactId> + <version>${project.version}</version> + <classifier>hadoop2</classifier> + </dependency> + <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-core</artifactId> <scope>provided</scope> @@ -200,6 +212,12 @@ <classifier>hadoop2-yarn</classifier> </dependency> <dependency> + <groupId>org.spark-project</groupId> + <artifactId>spark-mllib</artifactId> + <version>${project.version}</version> + <classifier>hadoop2-yarn</classifier> + </dependency> + <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <scope>provided</scope> 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..75df1af2e3 --- /dev/null +++ b/examples/src/main/java/spark/examples/JavaPageRank.java @@ -0,0 +1,115 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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.Function2; +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 class Sum extends Function2<Double, Double, Double> { + @Override + public Double call(Double a, Double b) { + return a + b; + } + } + + 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.reduceByKey(new Sum()).mapValues(new Function<Double, Double>() { + @Override + public Double call(Double sum) throws Exception { + return 0.15 + sum * 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/mllib/examples/JavaALS.java b/examples/src/main/java/spark/mllib/examples/JavaALS.java new file mode 100644 index 0000000000..b48f459cb7 --- /dev/null +++ b/examples/src/main/java/spark/mllib/examples/JavaALS.java @@ -0,0 +1,87 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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.examples; + +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; +import spark.api.java.function.Function; + +import spark.mllib.recommendation.ALS; +import spark.mllib.recommendation.MatrixFactorizationModel; +import spark.mllib.recommendation.Rating; + +import java.io.Serializable; +import java.util.Arrays; +import java.util.StringTokenizer; + +import scala.Tuple2; + +/** + * Example using MLLib ALS from Java. + */ +public class JavaALS { + + static class ParseRating extends Function<String, Rating> { + public Rating call(String line) { + StringTokenizer tok = new StringTokenizer(line, ","); + int x = Integer.parseInt(tok.nextToken()); + int y = Integer.parseInt(tok.nextToken()); + double rating = Double.parseDouble(tok.nextToken()); + return new Rating(x, y, rating); + } + } + + static class FeaturesToString extends Function<Tuple2<Object, double[]>, String> { + public String call(Tuple2<Object, double[]> element) { + return element._1().toString() + "," + Arrays.toString(element._2()); + } + } + + public static void main(String[] args) { + + if (args.length != 5 && args.length != 6) { + System.err.println( + "Usage: JavaALS <master> <ratings_file> <rank> <iterations> <output_dir> [<blocks>]"); + System.exit(1); + } + + int rank = Integer.parseInt(args[2]); + int iterations = Integer.parseInt(args[3]); + String outputDir = args[4]; + int blocks = -1; + if (args.length == 6) { + blocks = Integer.parseInt(args[5]); + } + + JavaSparkContext sc = new JavaSparkContext(args[0], "JavaALS", + System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR")); + JavaRDD<String> lines = sc.textFile(args[1]); + + JavaRDD<Rating> ratings = lines.map(new ParseRating()); + + MatrixFactorizationModel model = ALS.train(ratings.rdd(), rank, iterations, 0.01, blocks); + + model.userFeatures().toJavaRDD().map(new FeaturesToString()).saveAsTextFile( + outputDir + "/userFeatures"); + model.productFeatures().toJavaRDD().map(new FeaturesToString()).saveAsTextFile( + outputDir + "/productFeatures"); + System.out.println("Final user/product features written to " + outputDir); + + System.exit(0); + } +} diff --git a/examples/src/main/java/spark/mllib/examples/JavaKMeans.java b/examples/src/main/java/spark/mllib/examples/JavaKMeans.java new file mode 100644 index 0000000000..02f40438b8 --- /dev/null +++ b/examples/src/main/java/spark/mllib/examples/JavaKMeans.java @@ -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.mllib.examples; + +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; +import spark.api.java.function.Function; + +import spark.mllib.clustering.KMeans; +import spark.mllib.clustering.KMeansModel; + +import java.util.Arrays; +import java.util.StringTokenizer; + +/** + * Example using MLLib KMeans from Java. + */ +public class JavaKMeans { + + static class ParsePoint extends Function<String, double[]> { + public double[] call(String line) { + StringTokenizer tok = new StringTokenizer(line, " "); + int numTokens = tok.countTokens(); + double[] point = new double[numTokens]; + for (int i = 0; i < numTokens; ++i) { + point[i] = Double.parseDouble(tok.nextToken()); + } + return point; + } + } + + public static void main(String[] args) { + + if (args.length < 4) { + System.err.println( + "Usage: JavaKMeans <master> <input_file> <k> <max_iterations> [<runs>]"); + System.exit(1); + } + + String inputFile = args[1]; + int k = Integer.parseInt(args[2]); + int iterations = Integer.parseInt(args[3]); + int runs = 1; + + if (args.length >= 5) { + runs = Integer.parseInt(args[4]); + } + + JavaSparkContext sc = new JavaSparkContext(args[0], "JavaKMeans", + System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR")); + JavaRDD<String> lines = sc.textFile(args[1]); + + JavaRDD<double[]> points = lines.map(new ParsePoint()); + + KMeansModel model = KMeans.train(points.rdd(), k, iterations, runs); + + System.out.println("Cluster centers:"); + for (double[] center : model.clusterCenters()) { + System.out.println(" " + Arrays.toString(center)); + } + double cost = model.computeCost(points.rdd()); + System.out.println("Cost: " + cost); + + System.exit(0); + } +} diff --git a/examples/src/main/java/spark/mllib/examples/JavaLR.java b/examples/src/main/java/spark/mllib/examples/JavaLR.java new file mode 100644 index 0000000000..bf4aeaf40f --- /dev/null +++ b/examples/src/main/java/spark/mllib/examples/JavaLR.java @@ -0,0 +1,85 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package spark.mllib.examples; + + +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; +import spark.api.java.function.Function; + +import spark.mllib.classification.LogisticRegressionWithSGD; +import spark.mllib.classification.LogisticRegressionModel; +import spark.mllib.regression.LabeledPoint; + +import java.util.Arrays; +import java.util.StringTokenizer; + +/** + * Logistic regression based classification using ML Lib. + */ +public class JavaLR { + + static class ParsePoint extends Function<String, LabeledPoint> { + public LabeledPoint call(String line) { + String[] parts = line.split(","); + double y = Double.parseDouble(parts[0]); + StringTokenizer tok = new StringTokenizer(parts[1], " "); + int numTokens = tok.countTokens(); + double[] x = new double[numTokens]; + for (int i = 0; i < numTokens; ++i) { + x[i] = Double.parseDouble(tok.nextToken()); + } + return new LabeledPoint(y, x); + } + } + + public static void printWeights(double[] a) { + System.out.println(Arrays.toString(a)); + } + + public static void main(String[] args) { + if (args.length != 4) { + System.err.println("Usage: JavaLR <master> <input_dir> <step_size> <niters>"); + System.exit(1); + } + + JavaSparkContext sc = new JavaSparkContext(args[0], "JavaLR", + System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR")); + JavaRDD<String> lines = sc.textFile(args[1]); + JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache(); + double stepSize = Double.parseDouble(args[2]); + int iterations = Integer.parseInt(args[3]); + + // Another way to configure LogisticRegression + // + // LogisticRegressionWithSGD lr = new LogisticRegressionWithSGD(); + // lr.optimizer().setNumIterations(iterations) + // .setStepSize(stepSize) + // .setMiniBatchFraction(1.0); + // lr.setIntercept(true); + // LogisticRegressionModel model = lr.train(points.rdd()); + + LogisticRegressionModel model = LogisticRegressionWithSGD.train(points.rdd(), + iterations, stepSize); + + System.out.print("Final w: "); + printWeights(model.weights()); + + System.exit(0); + } +} diff --git a/examples/src/main/scala/spark/examples/SparkPageRank.scala b/examples/src/main/scala/spark/examples/SparkPageRank.scala new file mode 100644 index 0000000000..dedbbd01a3 --- /dev/null +++ b/examples/src/main/scala/spark/examples/SparkPageRank.scala @@ -0,0 +1,46 @@ +package spark.examples + +import spark.SparkContext._ +import spark.SparkContext + + +/** + * 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). + */ +object SparkPageRank { + def main(args: Array[String]) { + if (args.length < 3) { + System.err.println("Usage: PageRank <master> <file> <number_of_iterations>") + System.exit(1) + } + var iters = args(2).toInt + val ctx = new SparkContext(args(0), "PageRank", + System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) + val lines = ctx.textFile(args(1), 1) + val links = lines.map{ s => + val parts = s.split("\\s+") + (parts(0), parts(1)) + }.distinct().groupByKey().cache() + var ranks = links.mapValues(v => 1.0) + + for (i <- 1 to iters) { + val contribs = links.join(ranks).values.flatMap{ case (urls, rank) => + val size = urls.size + urls.map(url => (url, rank / size)) + } + ranks = contribs.reduceByKey(_ + _).mapValues(0.15 + 0.85 * _) + } + + val output = ranks.collect() + output.foreach(tup => println(tup._1 + " has rank: " + tup._2 + ".")) + + System.exit(0) + } +} + diff --git a/make-distribution.sh b/make-distribution.sh index 4374e0e8c4..0a8941c1f8 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -66,6 +66,7 @@ cp $FWDIR/repl/target/*.jar "$DISTDIR/jars/" cp -r "$FWDIR/bin" "$DISTDIR" cp -r "$FWDIR/conf" "$DISTDIR" cp "$FWDIR/run" "$FWDIR/spark-shell" "$DISTDIR" +cp "$FWDIR/spark-executor" "$DISTDIR" if [ "$1" == "tgz" ]; then diff --git a/mllib/pom.xml b/mllib/pom.xml new file mode 100644 index 0000000000..a07480fbe2 --- /dev/null +++ b/mllib/pom.xml @@ -0,0 +1,170 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- + ~ Licensed to the Apache Software Foundation (ASF) under one or more + ~ contributor license agreements. See the NOTICE file distributed with + ~ this work for additional information regarding copyright ownership. + ~ The ASF licenses this file to You under the Apache License, Version 2.0 + ~ (the "License"); you may not use this file except in compliance with + ~ the License. You may obtain a copy of the License at + ~ + ~ http://www.apache.org/licenses/LICENSE-2.0 + ~ + ~ Unless required by applicable law or agreed to in writing, software + ~ distributed under the License is distributed on an "AS IS" BASIS, + ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + ~ See the License for the specific language governing permissions and + ~ limitations under the License. + --> + +<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>org.spark-project</groupId> + <artifactId>spark-parent</artifactId> + <version>0.8.0-SNAPSHOT</version> + <relativePath>../pom.xml</relativePath> + </parent> + + <groupId>org.spark-project</groupId> + <artifactId>spark-mllib</artifactId> + <packaging>jar</packaging> + <name>Spark Project ML Library</name> + <url>http://spark-project.org/</url> + + <dependencies> + <dependency> + <groupId>org.eclipse.jetty</groupId> + <artifactId>jetty-server</artifactId> + </dependency> + <dependency> + <groupId>org.jblas</groupId> + <artifactId>jblas</artifactId> + <version>1.2.3</version> + </dependency> + + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalacheck</groupId> + <artifactId>scalacheck_${scala.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.novocode</groupId> + <artifactId>junit-interface</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + <build> + <outputDirectory>target/scala-${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..70fae8c15a --- /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[Double] + + /** + * 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]): Double +} 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..30ee0ab0ff --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala @@ -0,0 +1,179 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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.regression._ +import spark.mllib.util.MLUtils + +import scala.math.round + +import org.jblas.DoubleMatrix + +/** + * Classification model trained using Logistic Regression. + * + * @param weights Weights computed for every feature. + * @param intercept Intercept computed for this model. + */ +class LogisticRegressionModel( + override val weights: Array[Double], + override val intercept: Double) + extends GeneralizedLinearModel(weights, intercept) + with ClassificationModel with Serializable { + + override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix, + intercept: Double) = { + val margin = dataMatrix.mmul(weightMatrix).get(0) + intercept + round(1.0/ (1.0 + math.exp(margin * -1))) + } +} + +/** + * Train a classification model for Logistic Regression using Stochastic Gradient Descent. + */ +class LogisticRegressionWithSGD private ( + var stepSize: Double, + var numIterations: Int, + var regParam: Double, + var miniBatchFraction: Double, + var addIntercept: Boolean) + extends GeneralizedLinearAlgorithm[LogisticRegressionModel] + with Serializable { + + val gradient = new LogisticGradient() + val updater = new SimpleUpdater() + val optimizer = new GradientDescent(gradient, updater).setStepSize(stepSize) + .setNumIterations(numIterations) + .setRegParam(regParam) + .setMiniBatchFraction(miniBatchFraction) + /** + * Construct a LogisticRegression object with default parameters + */ + def this() = this(1.0, 100, 0.0, 1.0, true) + + def createModel(weights: Array[Double], intercept: Double) = { + new LogisticRegressionModel(weights, intercept) + } +} + +/** + * Top-level methods for calling Logistic Regression. + */ +object LogisticRegressionWithSGD { + // NOTE(shivaram): We use multiple train methods instead of default arguments to support + // Java programs. + + /** + * 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[LabeledPoint], + numIterations: Int, + stepSize: Double, + miniBatchFraction: Double, + initialWeights: Array[Double]) + : LogisticRegressionModel = + { + new LogisticRegressionWithSGD(stepSize, numIterations, 0.0, miniBatchFraction, true).run( + 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[LabeledPoint], + numIterations: Int, + stepSize: Double, + miniBatchFraction: Double) + : LogisticRegressionModel = + { + new LogisticRegressionWithSGD(stepSize, numIterations, 0.0, miniBatchFraction, true).run( + 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[LabeledPoint], + 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[LabeledPoint], + numIterations: Int) + : LogisticRegressionModel = + { + train(input, numIterations, 1.0, 1.0) + } + + def main(args: Array[String]) { + if (args.length != 4) { + println("Usage: LogisticRegression <master> <input_dir> <step_size> " + + "<niters>") + System.exit(1) + } + val sc = new SparkContext(args(0), "LogisticRegression") + val data = MLUtils.loadLabeledData(sc, args(1)) + val model = LogisticRegressionWithSGD.train(data, args(3).toInt, args(2).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..f799cb2829 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/classification/SVM.scala @@ -0,0 +1,176 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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.regression._ +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix + +/** + * Model built using SVM. + * + * @param weights Weights computed for every feature. + * @param intercept Intercept computed for this model. + */ +class SVMModel( + override val weights: Array[Double], + override val intercept: Double) + extends GeneralizedLinearModel(weights, intercept) + with ClassificationModel with Serializable { + + override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix, + intercept: Double) = { + signum(dataMatrix.dot(weightMatrix) + intercept) + } +} + +/** + * Train an SVM using Stochastic Gradient Descent. + */ +class SVMWithSGD private ( + var stepSize: Double, + var numIterations: Int, + var regParam: Double, + var miniBatchFraction: Double, + var addIntercept: Boolean) + extends GeneralizedLinearAlgorithm[SVMModel] with Serializable { + + val gradient = new HingeGradient() + val updater = new SquaredL2Updater() + val optimizer = new GradientDescent(gradient, updater).setStepSize(stepSize) + .setNumIterations(numIterations) + .setRegParam(regParam) + .setMiniBatchFraction(miniBatchFraction) + /** + * Construct a SVM object with default parameters + */ + def this() = this(1.0, 100, 1.0, 1.0, true) + + def createModel(weights: Array[Double], intercept: Double) = { + new SVMModel(weights, intercept) + } +} + +/** + * Top-level methods for calling SVM. + */ +object SVMWithSGD { + + /** + * 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[LabeledPoint], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double, + initialWeights: Array[Double]) + : SVMModel = + { + new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(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[LabeledPoint], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double) + : SVMModel = + { + new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(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[LabeledPoint], + 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[LabeledPoint], + 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)) + val model = SVMWithSGD.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 index d875d6de50..97e3d110ae 100644 --- a/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala +++ b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala @@ -112,7 +112,7 @@ class KMeans private ( * 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 = { + def run(data: RDD[Array[Double]]): KMeansModel = { // TODO: check whether data is persistent; this needs RDD.storageLevel to be publicly readable val sc = data.sparkContext @@ -194,8 +194,8 @@ class KMeans private ( */ private def initRandom(data: RDD[Array[Double]]): Array[ClusterCenters] = { // Sample all the cluster centers in one pass to avoid repeated scans - val sample = data.takeSample(true, runs * k, new Random().nextInt()) - Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k)) + val sample = data.takeSample(true, runs * k, new Random().nextInt()).toSeq + Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k).toArray) } /** @@ -210,7 +210,7 @@ class KMeans private ( private def initKMeansParallel(data: RDD[Array[Double]]): Array[ClusterCenters] = { // Initialize each run's center to a random point val seed = new Random().nextInt() - val sample = data.takeSample(true, runs, seed) + val sample = data.takeSample(true, runs, seed).toSeq val centers = Array.tabulate(runs)(r => ArrayBuffer(sample(r))) // On each step, sample 2 * k points on average for each run with probability proportional @@ -271,7 +271,7 @@ object KMeans { .setMaxIterations(maxIterations) .setRuns(runs) .setInitializationMode(initializationMode) - .train(data) + .run(data) } def train(data: RDD[Array[Double]], k: Int, maxIterations: Int, runs: Int): KMeansModel = { @@ -315,14 +315,15 @@ object KMeans { } def main(args: Array[String]) { - if (args.length != 4) { - println("Usage: KMeans <master> <input_file> <k> <max_iterations>") + 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)) - val model = KMeans.train(data, k, iters) + 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) { diff --git a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala index 2fb0c8136f..e72b8b3a92 100644 --- a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala +++ b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala @@ -19,18 +19,29 @@ package spark.mllib.optimization import org.jblas.DoubleMatrix +/** + * Class used to compute the gradient for a loss function, given a single data point. + */ abstract class Gradient extends Serializable { /** - * Compute the gradient for a given row of data. + * Compute the gradient and loss given features of a single data point. * - * @param data - One row of data. Row matrix of size 1xn where n is the number of features. + * @param data - Feature values for one data point. Column matrix of size nx1 + * where n is the number of features. * @param label - Label for this data item. * @param weights - Column matrix containing weights for every feature. + * + * @return A tuple of 2 elements. The first element is a column matrix containing the computed + * gradient and the second element is the loss computed at this data point. + * */ def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): (DoubleMatrix, Double) } +/** + * Compute gradient and loss for a logistic loss function. + */ class LogisticGradient extends Gradient { override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): (DoubleMatrix, Double) = { @@ -48,3 +59,34 @@ class LogisticGradient extends Gradient { (gradient, loss) } } + +/** + * Compute gradient and loss for a Least-squared loss function. + */ +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) + } +} + +/** + * Compute gradient and loss for a Hinge loss function. + */ +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 index e1b73bc25e..31917df7e8 100644 --- a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala +++ b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala @@ -24,56 +24,143 @@ import org.jblas.DoubleMatrix import scala.collection.mutable.ArrayBuffer +/** + * Class used to solve an optimization problem using Gradient Descent. + * @param gradient Gradient function to be used. + * @param updater Updater to be used to update weights after every iteration. + */ +class GradientDescent(var gradient: Gradient, var updater: Updater) extends Optimizer { + + private var stepSize: Double = 1.0 + private var numIterations: Int = 100 + private var regParam: Double = 0.0 + private var miniBatchFraction: Double = 1.0 + + /** + * Set the step size per-iteration of SGD. Default 1.0. + */ + def setStepSize(step: Double): this.type = { + this.stepSize = step + this + } + + /** + * Set fraction of data to be used for each SGD iteration. Default 1.0. + */ + def setMiniBatchFraction(fraction: Double): this.type = { + this.miniBatchFraction = fraction + this + } + + /** + * Set the number of iterations for SGD. Default 100. + */ + def setNumIterations(iters: Int): this.type = { + this.numIterations = iters + this + } + + /** + * Set the regularization parameter used for SGD. Default 0.0. + */ + def setRegParam(regParam: Double): this.type = { + this.regParam = regParam + this + } + + /** + * Set the gradient function to be used for SGD. + */ + def setGradient(gradient: Gradient): this.type = { + this.gradient = gradient + this + } + + + /** + * Set the updater function to be used for SGD. + */ + def setUpdater(updater: Updater): this.type = { + this.updater = updater + this + } + + def optimize(data: RDD[(Double, Array[Double])], initialWeights: Array[Double]) + : Array[Double] = { -object GradientDescent { + val (weights, stochasticLossHistory) = GradientDescent.runMiniBatchSGD( + data, + gradient, + updater, + stepSize, + numIterations, + regParam, + miniBatchFraction, + initialWeights) + weights + } + +} +// Top-level method to run gradient descent. +object GradientDescent extends Logging { /** * 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 numIterations - 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 weights - Column matrix containing weights for every feature. - * @return lossHistory - Array containing the loss computed for every iteration. + * @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, - miniBatchFraction: Double=1.0) : (DoubleMatrix, Array[Double]) = { + numIterations: Int, + regParam: Double, + miniBatchFraction: Double, + initialWeights: Array[Double]) : (Array[Double], Array[Double]) = { - val lossHistory = new ArrayBuffer[Double](numIters) + val stochasticLossHistory = new ArrayBuffer[Double](numIterations) - val nfeatures: Int = data.take(1)(0)._2.length val nexamples: Long = data.count() val miniBatchSize = nexamples * miniBatchFraction - // Initialize weights as a column matrix - var weights = DoubleMatrix.ones(nfeatures) - var reg_val = 0.0 + // Initialize weights as a column vector + var weights = new DoubleMatrix(initialWeights.length, 1, initialWeights:_*) + var regVal = 0.0 - for (i <- 1 to numIters) { + for (i <- 1 to numIterations) { 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) + val featuresCol = new DoubleMatrix(features.length, 1, features:_*) + val (grad, loss) = gradient.compute(featuresCol, y, weights) (grad, loss) }.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2)) - lossHistory.append(lossSum / miniBatchSize + reg_val) - val update = updater.compute(weights, gradientSum.div(miniBatchSize), stepSize, i) + /** + * 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 - reg_val = update._2 + regVal = update._2 } - (weights, lossHistory.toArray) + logInfo("GradientDescent finished. Last 10 stochastic losses %s".format( + stochasticLossHistory.takeRight(10).mkString(", "))) + + (weights.toArray, stochasticLossHistory.toArray) } } diff --git a/mllib/src/main/scala/spark/mllib/optimization/Optimizer.scala b/mllib/src/main/scala/spark/mllib/optimization/Optimizer.scala new file mode 100644 index 0000000000..76a519c338 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/optimization/Optimizer.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.mllib.optimization + +import spark.RDD + +trait Optimizer { + + /** + * Solve the provided convex optimization problem. + */ + def optimize(data: RDD[(Double, Array[Double])], initialWeights: Array[Double]): Array[Double] + +} diff --git a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala index b864fd4634..db67d6b0bc 100644 --- a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala +++ b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala @@ -17,28 +17,83 @@ package spark.mllib.optimization +import scala.math._ import org.jblas.DoubleMatrix +/** + * Class used to update weights used in Gradient Descent. + */ abstract class Updater extends Serializable { /** - * Compute an updated value for weights given the gradient, stepSize and iteration number. + * Compute an updated value for weights given the gradient, stepSize, iteration number and + * regularization parameter. 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 weightsNew - Column matrix containing updated weights - * @return reg_val - regularization value + * @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(weightsOlds: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int): - (DoubleMatrix, Double) + def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int, + regParam: Double): (DoubleMatrix, Double) } +/** + * A simple updater that adaptively adjusts the learning rate the + * square root of the number of iterations. Does not perform any regularization. + */ class SimpleUpdater extends Updater { override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, - stepSize: Double, iter: Int): (DoubleMatrix, Double) = { - val normGradient = gradient.mul(stepSize / math.sqrt(iter)) + stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = { + val thisIterStepSize = stepSize / math.sqrt(iter) + val normGradient = gradient.mul(thisIterStepSize) (weightsOld.sub(normGradient), 0) } } + +/** + * Updater that adjusts learning rate and performs L1 regularization. + * + * The corresponding proximal operator used 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) + } +} + +/** + * Updater that adjusts the learning rate and performs L2 regularization + */ +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 index 7da96397a6..6c71dc1f32 100644 --- a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala @@ -35,8 +35,7 @@ import org.jblas.{DoubleMatrix, SimpleBlas, Solve} * 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]) +private[recommendation] case class OutLinkBlock(elementIds: Array[Int], shouldSend: Array[BitSet]) /** @@ -56,8 +55,7 @@ private[recommendation] case class InLinkBlock( /** * A more compact class to represent a rating than Tuple3[Int, Int, Double]. */ -private[recommendation] case class Rating(user: Int, product: Int, rating: Double) - +case class Rating(val user: Int, val product: Int, val rating: Double) /** * Alternating Least Squares matrix factorization. @@ -105,10 +103,10 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l } /** - * Run ALS with the configured parmeters on an input RDD of (user, product, rating) triples. + * 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 = { + def run(ratings: RDD[Rating]): MatrixFactorizationModel = { val numBlocks = if (this.numBlocks == -1) { math.max(ratings.context.defaultParallelism, ratings.partitions.size / 2) } else { @@ -117,8 +115,10 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l 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 ratingsByUserBlock = ratings.map{ rating => (rating.user % numBlocks, rating) } + val ratingsByProductBlock = ratings.map{ rating => + (rating.product % numBlocks, Rating(rating.product, rating.user, rating.rating)) + } val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock) val (productInLinks, productOutLinks) = makeLinkRDDs(numBlocks, ratingsByProductBlock) @@ -357,14 +357,14 @@ object ALS { * @param blocks level of parallelism to split computation into */ def train( - ratings: RDD[(Int, Int, Double)], + ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int) : MatrixFactorizationModel = { - new ALS(blocks, rank, iterations, lambda).train(ratings) + new ALS(blocks, rank, iterations, lambda).run(ratings) } /** @@ -379,7 +379,7 @@ object ALS { * @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) + def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double) : MatrixFactorizationModel = { train(ratings, rank, iterations, lambda, -1) @@ -396,7 +396,7 @@ object ALS { * @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) + def train(ratings: RDD[Rating], rank: Int, iterations: Int) : MatrixFactorizationModel = { train(ratings, rank, iterations, 0.01, -1) @@ -419,11 +419,12 @@ object ALS { 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) + Rating(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(" ") } diff --git a/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala index 38637b3dd1..5e21717da5 100644 --- a/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala +++ b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -22,6 +22,15 @@ import spark.SparkContext._ import org.jblas._ +/** + * Model representing the result of matrix factorization. + * + * @param rank Rank for the features in this model. + * @param userFeatures RDD of tuples where each tuple represents the userId and + * the features computed for this user. + * @param productFeatures RDD of tuples where each tuple represents the productId + * and the features computed for this product. + */ class MatrixFactorizationModel( val rank: Int, val userFeatures: RDD[(Int, Array[Double])], diff --git a/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala new file mode 100644 index 0000000000..4ecafff08b --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -0,0 +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.mllib.regression + +import spark.{Logging, RDD} +import spark.mllib.optimization._ + +import org.jblas.DoubleMatrix + +/** + * GeneralizedLinearModel (GLM) represents a model trained using + * GeneralizedLinearAlgorithm. GLMs consist of a weight vector and + * an intercept. + * + * @param weights Weights computed for every feature. + * @param intercept Intercept computed for this model. + */ +abstract class GeneralizedLinearModel(val weights: Array[Double], val intercept: Double) + extends Serializable { + + // Create a column vector that can be used for predictions + private val weightsMatrix = new DoubleMatrix(weights.length, 1, weights:_*) + + /** + * Predict the result given a data point and the weights learned. + * + * @param dataMatrix Row vector containing the features for this data point + * @param weightMatrix Column vector containing the weights of the model + * @param intercept Intercept of the model. + */ + def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix, + intercept: Double): Double + + /** + * 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: spark.RDD[Array[Double]]): RDD[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 => + val dataMatrix = new DoubleMatrix(1, x.length, x:_*) + predictPoint(dataMatrix, localWeights, localIntercept) + } + } + + /** + * 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 = { + val dataMat = new DoubleMatrix(1, testData.length, testData:_*) + predictPoint(dataMat, weightsMatrix, intercept) + } +} + +/** + * GeneralizedLinearAlgorithm implements methods to train a Genearalized Linear Model (GLM). + * This class should be extended with an Optimizer to create a new GLM. + */ +abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] + extends Logging with Serializable { + + val optimizer: Optimizer + + /** + * Create a model given the weights and intercept + */ + protected def createModel(weights: Array[Double], intercept: Double): M + + protected var addIntercept: Boolean + + /** + * Set if the algorithm should add an intercept. Default true. + */ + def setIntercept(addIntercept: Boolean): this.type = { + this.addIntercept = addIntercept + this + } + + /** + * Run the algorithm with the configured parameters on an input + * RDD of LabeledPoint entries. + */ + def run(input: RDD[LabeledPoint]) : M = { + val nfeatures: Int = input.first().features.length + val initialWeights = Array.fill(nfeatures)(1.0) + run(input, initialWeights) + } + + /** + * Run the algorithm with the configured parameters on an input RDD + * of LabeledPoint entries starting from the initial weights provided. + */ + def run(input: RDD[LabeledPoint], initialWeights: Array[Double]) : M = { + + // Add a extra variable consisting of all 1.0's for the intercept. + val data = if (addIntercept) { + input.map(labeledPoint => (labeledPoint.label, Array(1.0, labeledPoint.features:_*))) + } else { + input.map(labeledPoint => (labeledPoint.label, labeledPoint.features)) + } + + val initialWeightsWithIntercept = if (addIntercept) { + Array(1.0, initialWeights:_*) + } else { + initialWeights + } + + val weights = optimizer.optimize(data, initialWeightsWithIntercept) + val intercept = weights(0) + val weightsScaled = weights.tail + + val model = createModel(weightsScaled, intercept) + + logInfo("Final model weights " + model.weights.mkString(",")) + logInfo("Final model intercept " + model.intercept) + model + } +} diff --git a/mllib/src/main/scala/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/spark/mllib/regression/LabeledPoint.scala new file mode 100644 index 0000000000..3de60482c5 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/LabeledPoint.scala @@ -0,0 +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.mllib.regression + +/** + * Class that represents the features and labels of a data point. + * + * @param label Label for this data point. + * @param features List of features for this data point. + */ +case class LabeledPoint(val label: Double, val features: Array[Double]) 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..6bbc990a5a --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/Lasso.scala @@ -0,0 +1,176 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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 + +/** + * Regression model trained using Lasso. + * + * @param weights Weights computed for every feature. + * @param intercept Intercept computed for this model. + */ +class LassoModel( + override val weights: Array[Double], + override val intercept: Double) + extends GeneralizedLinearModel(weights, intercept) + with RegressionModel with Serializable { + + override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix, + intercept: Double) = { + dataMatrix.dot(weightMatrix) + intercept + } +} + +/** + * Train a regression model with L1-regularization using Stochastic Gradient Descent. + */ +class LassoWithSGD private ( + var stepSize: Double, + var numIterations: Int, + var regParam: Double, + var miniBatchFraction: Double, + var addIntercept: Boolean) + extends GeneralizedLinearAlgorithm[LassoModel] + with Serializable { + + val gradient = new SquaredGradient() + val updater = new L1Updater() + val optimizer = new GradientDescent(gradient, updater).setStepSize(stepSize) + .setNumIterations(numIterations) + .setRegParam(regParam) + .setMiniBatchFraction(miniBatchFraction) + + /** + * Construct a Lasso object with default parameters + */ + def this() = this(1.0, 100, 1.0, 1.0, true) + + def createModel(weights: Array[Double], intercept: Double) = { + new LassoModel(weights, intercept) + } +} + +/** + * Top-level methods for calling Lasso. + */ +object LassoWithSGD { + + /** + * 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[LabeledPoint], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double, + initialWeights: Array[Double]) + : LassoModel = + { + new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(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[LabeledPoint], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double) + : LassoModel = + { + new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(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[LabeledPoint], + 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[LabeledPoint], + 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 = LassoWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble) + + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala deleted file mode 100644 index bb294c2257..0000000000 --- a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala +++ /dev/null @@ -1,175 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package spark.mllib.regression - -import spark.{Logging, RDD, SparkContext} -import spark.mllib.optimization._ -import spark.mllib.util.MLUtils - -import org.jblas.DoubleMatrix - -/** - * Logistic Regression using Stochastic Gradient Descent. - * Based on Matlab code written by John Duchi. - */ -class LogisticRegressionModel( - val weights: DoubleMatrix, - val intercept: Double, - val losses: Array[Double]) extends RegressionModel { - - override def predict(testData: spark.RDD[Array[Double]]) = { - testData.map { x => - val margin = new DoubleMatrix(1, x.length, x:_*).mmul(this.weights).get(0) + this.intercept - 1.0/ (1.0 + math.exp(margin * -1)) - } - } - - override def predict(testData: Array[Double]): Double = { - val dataMat = new DoubleMatrix(1, testData.length, testData:_*) - val margin = dataMat.mmul(this.weights).get(0) + this.intercept - 1.0/ (1.0 + math.exp(margin * -1)) - } -} - -class LogisticRegression 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[(Double, Array[Double])]): LogisticRegressionModel = { - // 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 (weights, losses) = GradientDescent.runMiniBatchSGD( - data, new LogisticGradient(), new SimpleUpdater(), stepSize, numIters, miniBatchFraction) - - val weightsScaled = weights.getRange(1, weights.length) - val intercept = weights.get(0) - - val model = new LogisticRegressionModel(weightsScaled, intercept, losses) - - logInfo("Final model weights " + model.weights) - logInfo("Final model intercept " + model.intercept) - logInfo("Last 10 losses " + model.losses.takeRight(10).mkString(", ")) - model - } -} - -/** - * Top-level methods for calling Logistic Regression. - */ -object LogisticRegression { - - /** - * 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[(Double, Array[Double])], - numIterations: Int, - stepSize: Double, - miniBatchFraction: Double) - : LogisticRegressionModel = - { - new LogisticRegression(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[(Double, 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[(Double, Array[Double])], - numIterations: Int) - : LogisticRegressionModel = - { - train(input, numIterations, 1.0, 1.0) - } - - def main(args: Array[String]) { - if (args.length != 4) { - println("Usage: LogisticRegression <master> <input_dir> <step_size> <niters>") - System.exit(1) - } - val sc = new SparkContext(args(0), "LogisticRegression") - val data = MLUtils.loadLabeledData(sc, args(1)) - val model = LogisticRegression.train(data, args(3).toInt, args(2).toDouble) - - sc.stop() - } -} diff --git a/mllib/src/main/scala/spark/mllib/regression/Regression.scala b/mllib/src/main/scala/spark/mllib/regression/RegressionModel.scala index 645204ddf3..b845ba1a89 100644 --- a/mllib/src/main/scala/spark/mllib/regression/Regression.scala +++ b/mllib/src/main/scala/spark/mllib/regression/RegressionModel.scala @@ -19,7 +19,7 @@ package spark.mllib.regression import spark.RDD -trait RegressionModel { +trait RegressionModel extends Serializable { /** * Predict values for the given data set using the model trained. * diff --git a/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala index 7c7f912b43..b42d94af41 100644 --- a/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala @@ -37,8 +37,11 @@ class RidgeRegressionModel( 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(this.weights)).get(0) + this.intercept + (new DoubleMatrix(1, x.length, x:_*).mmul(localWeights)).get(0) + localIntercept } } @@ -68,7 +71,8 @@ class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double) this } - def train(input: RDD[(Double, Array[Double])]): RidgeRegressionModel = { + def train(inputLabeled: RDD[LabeledPoint]): RidgeRegressionModel = { + val input = inputLabeled.map(labeledPoint => (labeledPoint.label, labeledPoint.features)) val nfeatures: Int = input.take(1)(0)._2.length val nexamples: Long = input.count() @@ -166,6 +170,8 @@ class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double) * Top-level methods for calling Ridge Regression. */ object RidgeRegression { + // NOTE(shivaram): We use multiple train methods instead of default arguments to support + // Java programs. /** * Train a ridge regression model given an RDD of (response, features) pairs. @@ -178,7 +184,7 @@ object RidgeRegression { * @param lambdaHigh upper bound used in binary search for lambda */ def train( - input: RDD[(Double, Array[Double])], + input: RDD[LabeledPoint], lambdaLow: Double, lambdaHigh: Double) : RidgeRegressionModel = @@ -194,7 +200,7 @@ object RidgeRegression { * * @param input RDD of (response, array of features) pairs. */ - def train(input: RDD[(Double, Array[Double])]) : RidgeRegressionModel = { + def train(input: RDD[LabeledPoint]) : RidgeRegressionModel = { train(input, 0.0, 100.0) } 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..672b63f65a --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/KMeansDataGenerator.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.util + +import scala.util.Random + +import spark.{RDD, SparkContext} + +/** + * Generate test data for KMeans. This class first chooses k cluster centers + * from a d-dimensional Gaussian distribution scaled by factor r and then creates a Gaussian + * cluster with scale 1 around each center. + */ + +object KMeansDataGenerator { + + /** + * Generate an RDD containing test data for KMeans. + * + * @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 + * @param 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..eeb14fc4e3 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/LassoDataGenerator.scala @@ -0,0 +1,48 @@ +package spark.mllib.util + +import scala.util.Random + +import org.jblas.DoubleMatrix + +import spark.{RDD, SparkContext} +import spark.mllib.regression.LabeledPoint + +/** + * Generate sample data used for Lasso Regression. This class generates uniform random values + * for the features and adds Gaussian noise with weight 0.1 to generate response variables. + */ +object LassoDataGenerator { + + def main(args: Array[String]) { + if (args.length < 2) { + 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 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[LabeledPoint] = 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 + LabeledPoint(y, x) + } + + MLUtils.saveLabeledData(data, outputPath) + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/regression/LogisticRegressionGenerator.scala b/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.scala index 8094d22405..d6402f23e2 100644 --- a/mllib/src/main/scala/spark/mllib/regression/LogisticRegressionGenerator.scala +++ b/mllib/src/main/scala/spark/mllib/util/LogisticRegressionDataGenerator.scala @@ -15,16 +15,48 @@ * limitations under the License. */ -package spark.mllib.regression +package spark.mllib.util import scala.util.Random -import org.jblas.DoubleMatrix - import spark.{RDD, SparkContext} -import spark.mllib.util.MLUtils +import spark.mllib.regression.LabeledPoint + +/** + * Generate test data for LogisticRegression. This class chooses positive labels + * with probability `probOne` and scales features for positive examples by `eps`. + */ -object LogisticRegressionGenerator { +object LogisticRegressionDataGenerator { + + /** + * Generate an RDD containing test data for LogisticRegression. + * + * @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[LabeledPoint] = { + 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) + } + LabeledPoint(y, x) + } + data + } def main(args: Array[String]) { if (args.length != 5) { @@ -40,17 +72,8 @@ object LogisticRegressionGenerator { val parts: Int = if (args.length > 4) args(4).toInt else 2 val eps = 3 - val sc = new SparkContext(sparkMaster, "LogisticRegressionGenerator") - - val data: RDD[(Double, Array[Double])] = sc.parallelize(0 until nexamples, parts).map { idx => - val rnd = new Random(42 + idx) - - val y = if (idx % 2 == 0) 0 else 1 - val x = Array.fill[Double](nfeatures) { - rnd.nextGaussian() + (y * eps) - } - (y, x) - } + 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 index b5e564df6d..4e030a81b4 100644 --- a/mllib/src/main/scala/spark/mllib/util/MLUtils.scala +++ b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala @@ -21,32 +21,42 @@ import spark.{RDD, SparkContext} import spark.SparkContext._ import org.jblas.DoubleMatrix +import spark.mllib.regression.LabeledPoint /** - * 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. + * Helper methods to load, save and pre-process data used in ML Lib. */ object MLUtils { /** + * Load labeled data from a file. The data format used here is + * <L>, <f1> <f2> ... + * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double. + * * @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). + * @return An RDD of LabeledPoint. Each labeled point has two elements: 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])] = { + def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = { sc.textFile(dir).map { line => - val parts = line.split(",") + val parts = line.split(',') val label = parts(0).toDouble - val features = parts(1).trim().split(" ").map(_.toDouble) - (label, features) + val features = parts(1).trim().split(' ').map(_.toDouble) + LabeledPoint(label, features) } } - def saveLabeledData(data: RDD[(Double, Array[Double])], dir: String) { - val dataStr = data.map(x => x._1 + "," + x._2.mkString(" ")) + /** + * Save labeled data to a file. The data format used here is + * <L>, <f1> <f2> ... + * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double. + * + * @param data An RDD of LabeledPoints containing data to be saved. + * @param dir Directory to save the data. + */ + def saveLabeledData(data: RDD[LabeledPoint], dir: String) { + val dataStr = data.map(x => x.label + "," + x.features.mkString(" ")) dataStr.saveAsTextFile(dir) } diff --git a/mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala b/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.scala index c2260ae286..4d329168be 100644 --- a/mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala +++ b/mllib/src/main/scala/spark/mllib/util/RidgeRegressionDataGenerator.scala @@ -15,43 +15,49 @@ * limitations under the License. */ -package spark.mllib.regression +package spark.mllib.util import scala.util.Random import org.jblas.DoubleMatrix import spark.{RDD, SparkContext} -import spark.mllib.util.MLUtils - - -object RidgeRegressionGenerator { - - 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 +import spark.mllib.regression.LabeledPoint +/** + * Generate sample data used for RidgeRegression. This class generates + * uniformly random values for every feature and adds Gaussian noise with mean `eps` to the + * response variable `Y`. + * + */ +object RidgeRegressionDataGenerator { + + /** + * Generate an RDD containing sample data for RidgeRegression. + * + * @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. + * + * @return RDD of LabeledPoint containing sample data. + */ + def generateRidgeRDD( + sc: SparkContext, + nexamples: Int, + nfeatures: Int, + eps: Double, + nparts: Int = 2) : RDD[LabeledPoint] = { org.jblas.util.Random.seed(42) - val sc = new SparkContext(sparkMaster, "RidgeRegressionGenerator") - // 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 parts, parts).flatMap { p => + val data: RDD[LabeledPoint] = sc.parallelize(0 until nparts, nparts).flatMap { p => org.jblas.util.Random.seed(42 + p) - val examplesInPartition = nexamples / parts + val examplesInPartition = nexamples / nparts val X = DoubleMatrix.rand(examplesInPartition, nfeatures) val y = X.mmul(w) @@ -62,9 +68,28 @@ object RidgeRegressionGenerator { val yObs = new DoubleMatrix(normalValues).addi(y) Iterator.tabulate(examplesInPartition) { i => - (yObs.get(i, 0), X.getRow(i).toArray) + LabeledPoint(yObs.get(i, 0), X.getRow(i).toArray) } } + data + } + + def main(args: Array[String]) { + if (args.length < 2) { + 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..e02bd190f6 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/SVMDataGenerator.scala @@ -0,0 +1,49 @@ +package spark.mllib.util + +import scala.util.Random +import scala.math.signum + +import spark.{RDD, SparkContext} + +import org.jblas.DoubleMatrix +import spark.mllib.regression.LabeledPoint + +/** + * Generate sample data used for SVM. This class generates uniform random values + * for the features and adds Gaussian noise with weight 0.1 to generate labels. + */ +object SVMDataGenerator { + + def main(args: Array[String]) { + if (args.length < 2) { + 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 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[LabeledPoint] = 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) + LabeledPoint(y, x) + } + + MLUtils.saveLabeledData(data, outputPath) + sc.stop() + } +} diff --git a/mllib/src/test/java/spark/mllib/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/spark/mllib/classification/JavaLogisticRegressionSuite.java new file mode 100644 index 0000000000..e0ebd45cd8 --- /dev/null +++ b/mllib/src/test/java/spark/mllib/classification/JavaLogisticRegressionSuite.java @@ -0,0 +1,98 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package spark.mllib.classification; + +import java.io.Serializable; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; + +import spark.mllib.regression.LabeledPoint; + +public class JavaLogisticRegressionSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaLogisticRegressionSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + System.clearProperty("spark.driver.port"); + } + + int validatePrediction(List<LabeledPoint> validationData, LogisticRegressionModel model) { + int numAccurate = 0; + for (LabeledPoint point: validationData) { + Double prediction = model.predict(point.features()); + if (prediction == point.label()) { + numAccurate++; + } + } + return numAccurate; + } + + @Test + public void runLRUsingConstructor() { + int nPoints = 10000; + double A = 2.0; + double B = -1.5; + + JavaRDD<LabeledPoint> testRDD = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + List<LabeledPoint> validationData = + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 17); + + LogisticRegressionWithSGD lrImpl = new LogisticRegressionWithSGD(); + lrImpl.optimizer().setStepSize(1.0) + .setRegParam(1.0) + .setNumIterations(100); + LogisticRegressionModel model = lrImpl.run(testRDD.rdd()); + + int numAccurate = validatePrediction(validationData, model); + Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0); + } + + @Test + public void runLRUsingStaticMethods() { + int nPoints = 10000; + double A = 2.0; + double B = -1.5; + + JavaRDD<LabeledPoint> testRDD = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + List<LabeledPoint> validationData = + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 17); + + LogisticRegressionModel model = LogisticRegressionWithSGD.train( + testRDD.rdd(), 100, 1.0, 1.0); + + int numAccurate = validatePrediction(validationData, model); + Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0); + } + +} diff --git a/mllib/src/test/java/spark/mllib/classification/JavaSVMSuite.java b/mllib/src/test/java/spark/mllib/classification/JavaSVMSuite.java new file mode 100644 index 0000000000..7881b3c38f --- /dev/null +++ b/mllib/src/test/java/spark/mllib/classification/JavaSVMSuite.java @@ -0,0 +1,98 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package spark.mllib.classification; + + +import java.io.Serializable; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; + +import spark.mllib.regression.LabeledPoint; + +public class JavaSVMSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaSVMSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + System.clearProperty("spark.driver.port"); + } + + int validatePrediction(List<LabeledPoint> validationData, SVMModel model) { + int numAccurate = 0; + for (LabeledPoint point: validationData) { + Double prediction = model.predict(point.features()); + if (prediction == point.label()) { + numAccurate++; + } + } + return numAccurate; + } + + @Test + public void runSVMUsingConstructor() { + int nPoints = 10000; + double A = 2.0; + double[] weights = {-1.5, 1.0}; + + JavaRDD<LabeledPoint> testRDD = sc.parallelize(SVMSuite.generateSVMInputAsList(A, + weights, nPoints, 42), 2).cache(); + List<LabeledPoint> validationData = + SVMSuite.generateSVMInputAsList(A, weights, nPoints, 17); + + SVMWithSGD svmSGDImpl = new SVMWithSGD(); + svmSGDImpl.optimizer().setStepSize(1.0) + .setRegParam(1.0) + .setNumIterations(100); + SVMModel model = svmSGDImpl.run(testRDD.rdd()); + + int numAccurate = validatePrediction(validationData, model); + Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0); + } + + @Test + public void runSVMUsingStaticMethods() { + int nPoints = 10000; + double A = 2.0; + double[] weights = {-1.5, 1.0}; + + JavaRDD<LabeledPoint> testRDD = sc.parallelize(SVMSuite.generateSVMInputAsList(A, + weights, nPoints, 42), 2).cache(); + List<LabeledPoint> validationData = + SVMSuite.generateSVMInputAsList(A, weights, nPoints, 17); + + SVMModel model = SVMWithSGD.train(testRDD.rdd(), 100, 1.0, 1.0, 1.0); + + int numAccurate = validatePrediction(validationData, model); + Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0); + } + +} diff --git a/mllib/src/test/java/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/spark/mllib/clustering/JavaKMeansSuite.java new file mode 100644 index 0000000000..3f2d82bfb4 --- /dev/null +++ b/mllib/src/test/java/spark/mllib/clustering/JavaKMeansSuite.java @@ -0,0 +1,115 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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 java.io.Serializable; +import java.util.ArrayList; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; + +public class JavaKMeansSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaKMeans"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + System.clearProperty("spark.driver.port"); + } + + // L1 distance between two points + double distance1(double[] v1, double[] v2) { + double distance = 0.0; + for (int i = 0; i < v1.length; ++i) { + distance = Math.max(distance, Math.abs(v1[i] - v2[i])); + } + return distance; + } + + // Assert that two sets of points are equal, within EPSILON tolerance + void assertSetsEqual(double[][] v1, double[][] v2) { + double EPSILON = 1e-4; + Assert.assertTrue(v1.length == v2.length); + for (int i = 0; i < v1.length; ++i) { + double minDistance = Double.MAX_VALUE; + for (int j = 0; j < v2.length; ++j) { + minDistance = Math.min(minDistance, distance1(v1[i], v2[j])); + } + Assert.assertTrue(minDistance <= EPSILON); + } + + for (int i = 0; i < v2.length; ++i) { + double minDistance = Double.MAX_VALUE; + for (int j = 0; j < v1.length; ++j) { + minDistance = Math.min(minDistance, distance1(v2[i], v1[j])); + } + Assert.assertTrue(minDistance <= EPSILON); + } + } + + + @Test + public void runKMeansUsingStaticMethods() { + List<double[]> points = new ArrayList(); + points.add(new double[]{1.0, 2.0, 6.0}); + points.add(new double[]{1.0, 3.0, 0.0}); + points.add(new double[]{1.0, 4.0, 6.0}); + + double[][] expectedCenter = { {1.0, 3.0, 4.0} }; + + JavaRDD<double[]> data = sc.parallelize(points, 2); + KMeansModel model = KMeans.train(data.rdd(), 1, 1); + assertSetsEqual(model.clusterCenters(), expectedCenter); + + model = KMeans.train(data.rdd(), 1, 1, 1, KMeans.RANDOM()); + assertSetsEqual(model.clusterCenters(), expectedCenter); + } + + @Test + public void runKMeansUsingConstructor() { + List<double[]> points = new ArrayList(); + points.add(new double[]{1.0, 2.0, 6.0}); + points.add(new double[]{1.0, 3.0, 0.0}); + points.add(new double[]{1.0, 4.0, 6.0}); + + double[][] expectedCenter = { {1.0, 3.0, 4.0} }; + + JavaRDD<double[]> data = sc.parallelize(points, 2); + KMeansModel model = new KMeans().setK(1).setMaxIterations(5).run(data.rdd()); + assertSetsEqual(model.clusterCenters(), expectedCenter); + + model = new KMeans().setK(1) + .setMaxIterations(1) + .setRuns(1) + .setInitializationMode(KMeans.RANDOM()) + .run(data.rdd()); + assertSetsEqual(model.clusterCenters(), expectedCenter); + } +} diff --git a/mllib/src/test/java/spark/mllib/recommendation/JavaALSSuite.java b/mllib/src/test/java/spark/mllib/recommendation/JavaALSSuite.java new file mode 100644 index 0000000000..7993629a6d --- /dev/null +++ b/mllib/src/test/java/spark/mllib/recommendation/JavaALSSuite.java @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package spark.mllib.recommendation; + +import java.io.Serializable; +import java.util.List; + +import scala.Tuple2; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; + +import org.jblas.DoubleMatrix; + +public class JavaALSSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaALS"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + System.clearProperty("spark.driver.port"); + } + + void validatePrediction(MatrixFactorizationModel model, int users, int products, int features, + DoubleMatrix trueRatings, double matchThreshold) { + DoubleMatrix predictedU = new DoubleMatrix(users, features); + List<scala.Tuple2<Object, double[]>> userFeatures = model.userFeatures().toJavaRDD().collect(); + for (int i = 0; i < features; ++i) { + for (scala.Tuple2<Object, double[]> userFeature : userFeatures) { + predictedU.put((Integer)userFeature._1(), i, userFeature._2()[i]); + } + } + DoubleMatrix predictedP = new DoubleMatrix(products, features); + + List<scala.Tuple2<Object, double[]>> productFeatures = + model.productFeatures().toJavaRDD().collect(); + for (int i = 0; i < features; ++i) { + for (scala.Tuple2<Object, double[]> productFeature : productFeatures) { + predictedP.put((Integer)productFeature._1(), i, productFeature._2()[i]); + } + } + + DoubleMatrix predictedRatings = predictedU.mmul(predictedP.transpose()); + + for (int u = 0; u < users; ++u) { + for (int p = 0; p < products; ++p) { + double prediction = predictedRatings.get(u, p); + double correct = trueRatings.get(u, p); + Assert.assertTrue(Math.abs(prediction - correct) < matchThreshold); + } + } + } + + @Test + public void runALSUsingStaticMethods() { + int features = 1; + int iterations = 15; + int users = 10; + int products = 10; + scala.Tuple2<List<Rating>, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( + users, products, features, 0.7); + + JavaRDD<Rating> data = sc.parallelize(testData._1()); + MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations); + validatePrediction(model, users, products, features, testData._2(), 0.3); + } + + @Test + public void runALSUsingConstructor() { + int features = 2; + int iterations = 15; + int users = 20; + int products = 30; + scala.Tuple2<List<Rating>, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( + users, products, features, 0.7); + + JavaRDD<Rating> data = sc.parallelize(testData._1()); + + MatrixFactorizationModel model = new ALS().setRank(features) + .setIterations(iterations) + .run(data.rdd()); + validatePrediction(model, users, products, features, testData._2(), 0.3); + } +} diff --git a/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java b/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java new file mode 100644 index 0000000000..e26d7b385c --- /dev/null +++ b/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java @@ -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.mllib.regression; + +import java.io.Serializable; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import spark.api.java.JavaRDD; +import spark.api.java.JavaSparkContext; + +public class JavaLassoSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaLassoSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + System.clearProperty("spark.driver.port"); + } + + int validatePrediction(List<LabeledPoint> validationData, LassoModel model) { + int numAccurate = 0; + for (LabeledPoint point: validationData) { + Double prediction = model.predict(point.features()); + // A prediction is off if the prediction is more than 0.5 away from expected value. + if (Math.abs(prediction - point.label()) <= 0.5) { + numAccurate++; + } + } + return numAccurate; + } + + @Test + public void runLassoUsingConstructor() { + int nPoints = 10000; + double A = 2.0; + double[] weights = {-1.5, 1.0e-2}; + + JavaRDD<LabeledPoint> testRDD = sc.parallelize(LassoSuite.generateLassoInputAsList(A, + weights, nPoints, 42), 2).cache(); + List<LabeledPoint> validationData = + LassoSuite.generateLassoInputAsList(A, weights, nPoints, 17); + + LassoWithSGD svmSGDImpl = new LassoWithSGD(); + svmSGDImpl.optimizer().setStepSize(1.0) + .setRegParam(0.01) + .setNumIterations(20); + LassoModel model = svmSGDImpl.run(testRDD.rdd()); + + int numAccurate = validatePrediction(validationData, model); + Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0); + } + + @Test + public void runLassoUsingStaticMethods() { + int nPoints = 10000; + double A = 2.0; + double[] weights = {-1.5, 1.0e-2}; + + JavaRDD<LabeledPoint> testRDD = sc.parallelize(LassoSuite.generateLassoInputAsList(A, + weights, nPoints, 42), 2).cache(); + List<LabeledPoint> validationData = + LassoSuite.generateLassoInputAsList(A, weights, nPoints, 17); + + LassoModel model = LassoWithSGD.train(testRDD.rdd(), 100, 1.0, 0.01, 1.0); + + int numAccurate = validatePrediction(validationData, model); + Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0); + } + +} diff --git a/mllib/src/test/resources/log4j.properties b/mllib/src/test/resources/log4j.properties index a112e0b506..4265ba6e5d 100644 --- a/mllib/src/test/resources/log4j.properties +++ b/mllib/src/test/resources/log4j.properties @@ -19,7 +19,7 @@ log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=ml/target/unit-tests.log +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 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..bd87c528c3 --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala @@ -0,0 +1,150 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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.collection.JavaConversions._ + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers + +import spark.SparkContext +import spark.mllib.regression._ + +object LogisticRegressionSuite { + + def generateLogisticInputAsList( + offset: Double, + scale: Double, + nPoints: Int, + seed: Int): java.util.List[LabeledPoint] = { + seqAsJavaList(generateLogisticInput(offset, scale, nPoints, seed)) + } + + // Generate input of the form Y = logistic(offset + scale*X) + def generateLogisticInput( + offset: Double, + scale: Double, + nPoints: Int, + seed: Int): Seq[LabeledPoint] = { + 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 => LabeledPoint(y(i), Array(x1(i)))) + testData + } + +} + +class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll with ShouldMatchers { + @transient private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local", "test") + } + + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) { + val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) => + (prediction != expected.label) + }.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 = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 42) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + val lr = new LogisticRegressionWithSGD() + lr.optimizer.setStepSize(10.0).setNumIterations(20) + + val model = lr.run(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 = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17) + val validationRDD = sc.parallelize(validationData, 2) + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row.features)), validationData) + } + + test("logistic regression with initial weights") { + val nPoints = 10000 + val A = 2.0 + val B = -1.5 + + val testData = LogisticRegressionSuite.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 LogisticRegressionWithSGD() + lr.optimizer.setStepSize(10.0).setNumIterations(10) + + val model = lr.run(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 = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17) + val validationRDD = sc.parallelize(validationData, 2) + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row.features)), 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..04f631d80f --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala @@ -0,0 +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.mllib.classification + +import scala.util.Random +import scala.math.signum +import scala.collection.JavaConversions._ + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext +import spark.mllib.regression._ + +import org.jblas.DoubleMatrix + +object SVMSuite { + + def generateSVMInputAsList( + intercept: Double, + weights: Array[Double], + nPoints: Int, + seed: Int): java.util.List[LabeledPoint] = { + seqAsJavaList(generateSVMInput(intercept, weights, nPoints, seed)) + } + + // 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[LabeledPoint] = { + 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).map(p => LabeledPoint(p._1, p._2)) + } + +} + +class SVMSuite extends FunSuite with BeforeAndAfterAll { + @transient private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local", "test") + } + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) { + val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) => + (prediction != expected.label) + }.size + // At least 80% of the predictions should be on. + assert(numOffPredictions < input.length / 5) + } + + + test("SVM using local random SGD") { + val nPoints = 10000 + + val A = 2.0 + val B = -1.5 + val C = 1.0 + + val testData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 42) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + + val svm = new SVMWithSGD() + svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100) + + val model = svm.run(testRDD) + + val validationData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 17) + val validationRDD = sc.parallelize(validationData,2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row.features)), validationData) + } + + test("SVM local random SGD with initial weights") { + val nPoints = 10000 + + val A = 2.0 + val B = -1.5 + val C = 1.0 + + val testData = SVMSuite.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 SVMWithSGD() + svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100) + + val model = svm.run(testRDD, initialWeights) + + val validationData = SVMSuite.generateSVMInput(A, Array[Double](B,C), nPoints, 17) + val validationRDD = sc.parallelize(validationData,2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row.features)), validationData) + } +} diff --git a/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala index bebade9afb..d5d95c8639 100644 --- a/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala +++ b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala @@ -27,9 +27,12 @@ import spark.SparkContext._ import org.jblas._ - class KMeansSuite extends FunSuite with BeforeAndAfterAll { - val sc = new SparkContext("local", "test") + @transient private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local", "test") + } override def afterAll() { sc.stop() diff --git a/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala index f98590b8d9..15a60efda6 100644 --- a/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala @@ -17,6 +17,7 @@ package spark.mllib.recommendation +import scala.collection.JavaConversions._ import scala.util.Random import org.scalatest.BeforeAndAfterAll @@ -27,9 +28,49 @@ import spark.SparkContext._ import org.jblas._ +object ALSSuite { + + def generateRatingsAsJavaList( + users: Int, + products: Int, + features: Int, + samplingRate: Double): (java.util.List[Rating], DoubleMatrix) = { + val (sampledRatings, trueRatings) = generateRatings(users, products, features, samplingRate) + (seqAsJavaList(sampledRatings), trueRatings) + } + + def generateRatings( + users: Int, + products: Int, + features: Int, + samplingRate: Double): (Seq[Rating], DoubleMatrix) = { + 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 Rating(u, p, trueRatings.get(u, p)) + } + + (sampledRatings, trueRatings) + } + +} + class ALSSuite extends FunSuite with BeforeAndAfterAll { - val sc = new SparkContext("local", "test") + @transient private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local", "test") + } override def afterAll() { sc.stop() @@ -57,21 +98,8 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll { 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 (sampledRatings, trueRatings) = ALSSuite.generateRatings(users, products, + features, samplingRate) val model = ALS.train(sc.parallelize(sampledRatings), features, iterations) val predictedU = new DoubleMatrix(users, features) 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..55a738f1e4 --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala @@ -0,0 +1,150 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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.collection.JavaConversions._ +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext + +import org.jblas.DoubleMatrix + +object LassoSuite { + + def generateLassoInputAsList( + intercept: Double, + weights: Array[Double], + nPoints: Int, + seed: Int): java.util.List[LabeledPoint] = { + seqAsJavaList(generateLassoInput(intercept, weights, nPoints, seed)) + } + + + // Generate noisy input of the form Y = x.dot(weights) + intercept + noise + def generateLassoInput( + intercept: Double, + weights: Array[Double], + nPoints: Int, + seed: Int): Seq[LabeledPoint] = { + 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).map(p => LabeledPoint(p._1, p._2)) + } + +} + +class LassoSuite extends FunSuite with BeforeAndAfterAll { + @transient private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local", "test") + } + + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) { + 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.label) > 0.5 + }.size + // At least 80% of the predictions should be on. + assert(numOffPredictions < input.length / 5) + } + + test("Lasso local random SGD") { + val nPoints = 10000 + + val A = 2.0 + val B = -1.5 + val C = 1.0e-2 + + val testData = LassoSuite.generateLassoInput(A, Array[Double](B,C), nPoints, 42) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + + val ls = new LassoWithSGD() + ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(20) + + val model = ls.run(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 = LassoSuite.generateLassoInput(A, Array[Double](B,C), nPoints, 17) + val validationRDD = sc.parallelize(validationData, 2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row.features)), validationData) + } + + test("Lasso local random SGD with initial weights") { + val nPoints = 10000 + + val A = 2.0 + val B = -1.5 + val C = 1.0e-2 + + val testData = LassoSuite.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 LassoWithSGD() + ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(20) + + val model = ls.run(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 = LassoSuite.generateLassoInput(A, Array[Double](B,C), nPoints, 17) + val validationRDD = sc.parallelize(validationData,2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) + + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row.features)), validationData) + } +} diff --git a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala deleted file mode 100644 index bc9bfd054f..0000000000 --- a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package spark.mllib.regression - -import scala.util.Random - -import org.scalatest.BeforeAndAfterAll -import org.scalatest.FunSuite - -import spark.SparkContext -import spark.SparkContext._ - - -class LogisticRegressionSuite 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 A, B where Y = logistic(A + B*X) - test("logistic regression") { - val nPoints = 10000 - val rnd = new Random(42) - - val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian()) - - val A = 2.0 - val B = -1.5 - - // 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(100) - // y <- as.numeric(y > 0) - val y = (0 until nPoints).map { i => - val yVal = A + B * x1(i) + rLogis(i) - if (yVal > 0) 1.0 else 0.0 - } - - val testData = (0 until nPoints).map(i => (y(i).toDouble, Array(x1(i)))).toArray - - val testRDD = sc.parallelize(testData, 2) - testRDD.cache() - val lr = new LogisticRegression().setStepSize(10.0) - .setNumIterations(20) - - val model = lr.train(testRDD) - - val weight0 = model.weights.get(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]") - } -} diff --git a/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala index 3c588c6162..e2b244894d 100644 --- a/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala +++ b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala @@ -27,7 +27,11 @@ import spark.SparkContext._ class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll { - val sc = new SparkContext("local", "test") + @transient private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local", "test") + } override def afterAll() { sc.stop() @@ -47,7 +51,7 @@ class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll { 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 testData = (0 until 20).map(i => LabeledPoint(y(i), xMat(i))).toArray val testRDD = sc.parallelize(testData, 2) testRDD.cache() diff --git a/pagerank_data.txt b/pagerank_data.txt new file mode 100644 index 0000000000..95755ab8f5 --- /dev/null +++ b/pagerank_data.txt @@ -0,0 +1,6 @@ +1 2 +1 3 +1 4 +2 1 +3 1 +4 1 @@ -58,9 +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> @@ -183,6 +184,11 @@ <version>0.8.4</version> </dependency> <dependency> + <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> @@ -193,9 +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>0.3.1</version> + </dependency> + <dependency> + <groupId>com.twitter</groupId> + <artifactId>chill-java</artifactId> + <version>0.3.1</version> </dependency> <dependency> <groupId>com.typesafe.akka</groupId> @@ -249,6 +260,16 @@ <version>2.5</version> </dependency> <dependency> + <groupId>com.codahale.metrics</groupId> + <artifactId>metrics-core</artifactId> + <version>3.0.0</version> + </dependency> + <dependency> + <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> <version>${scala.version}</version> @@ -564,7 +585,7 @@ <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> + <yarn.version>2.0.5-alpha</yarn.version> </properties> <repositories> @@ -618,5 +639,25 @@ </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 e9819b877d..f860925650 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -40,13 +40,18 @@ object SparkBuild extends Build { //val HADOOP_MAJOR_VERSION = "2" //val HADOOP_YARN = true - lazy val root = Project("root", file("."), settings = rootSettings) aggregate(core, repl, examples, bagel, streaming, mllib) + // HBase version; set as appropriate. + val HBASE_VERSION = "0.94.6" + + 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) + 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) 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) @@ -149,6 +154,7 @@ object SparkBuild extends Build { 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", @@ -166,9 +172,9 @@ object SparkBuild extends Build { "org.slf4j" % "slf4j-log4j12" % slf4jVersion, "commons-daemon" % "commons-daemon" % "1.0.10", "com.ning" % "compress-lzf" % "0.8.4", + "org.xerial.snappy" % "snappy-java" % "1.0.5", "org.ow2.asm" % "asm" % "4.0", "com.google.protobuf" % "protobuf-java" % "2.4.1", - "de.javakaffee" % "kryo-serializers" % "0.22", "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), @@ -177,21 +183,25 @@ object SparkBuild extends Build { "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" + "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" % "0.3.1", + "com.twitter" % "chill-java" % "0.3.1" ) ++ ( if (HADOOP_MAJOR_VERSION == "2") { if (HADOOP_YARN) { Seq( // Exclude rule required for all ? - "org.apache.hadoop" % "hadoop-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty), - "org.apache.hadoop" % "hadoop-yarn-api" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty), - "org.apache.hadoop" % "hadoop-yarn-common" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty), - "org.apache.hadoop" % "hadoop-yarn-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty) + "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), - "org.apache.hadoop" % "hadoop-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty) + "org.apache.hadoop" % "hadoop-core" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm), + "org.apache.hadoop" % "hadoop-client" % HADOOP_VERSION excludeAll(excludeJackson, excludeNetty, excludeAsm) ) } } else { @@ -220,7 +230,7 @@ object SparkBuild extends Build { libraryDependencies ++= Seq( "com.twitter" % "algebird-core_2.9.2" % "0.1.11", - "org.apache.hbase" % "hbase" % "0.94.6" excludeAll(excludeNetty, excludeAsm), + "org.apache.hbase" % "hbase" % HBASE_VERSION excludeAll(excludeNetty, excludeAsm), "org.apache.cassandra" % "cassandra-all" % "1.2.5" exclude("com.google.guava", "guava") @@ -230,10 +240,17 @@ object SparkBuild extends Build { 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", @@ -248,7 +265,7 @@ object SparkBuild extends Build { "Akka Repository" at "http://repo.akka.io/releases/" ), libraryDependencies ++= Seq( - "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty), + "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) @@ -53,9 +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/examples/als.py b/python/examples/als.py index f2b2eee64c..a77dfb2577 100755 --- a/python/examples/als.py +++ b/python/examples/als.py @@ -48,8 +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 @@ -84,5 +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 c670556f2b..ba31af92fc 100644..100755 --- a/python/examples/kmeans.py +++ b/python/examples/kmeans.py @@ -41,8 +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 54d227d0d3..1117dea538 100755 --- a/python/examples/logistic_regression.py +++ b/python/examples/logistic_regression.py @@ -16,7 +16,8 @@ # """ -This example requires numpy (http://www.numpy.org/) +A logistic regression implementation that uses NumPy (http://www.numpy.org) to act on batches +of input data using efficient matrix operations. """ from collections import namedtuple from math import exp @@ -27,48 +28,45 @@ import numpy as np from pyspark import SparkContext -N = 100000 # Number of data points D = 10 # Number of dimensions -R = 0.7 # Scaling factor -ITERATIONS = 5 -np.random.seed(42) -DataPoint = namedtuple("DataPoint", ['x', 'y']) -from lr import DataPoint # So that DataPoint is properly serialized - - -def generateData(): - def generatePoint(i): - y = -1 if i % 2 == 0 else 1 - x = np.random.normal(size=D) + (y * R) - return DataPoint(x, y) - return [generatePoint(i) for i in range(N)] - +# Read a batch of points from the input file into a NumPy matrix object. We operate on batches to +# make further computations faster. +# The data file contains lines of the form <label> <x1> <x2> ... <xD>. We load each block of these +# into a NumPy array of size numLines * (D + 1) and pull out column 0 vs the others in gradient(). +def readPointBatch(iterator): + strs = list(iterator) + matrix = np.zeros((len(strs), D + 1)) + for i in xrange(len(strs)): + matrix[i] = np.fromstring(strs[i].replace(',', ' '), dtype=np.float32, sep=' ') + return [matrix] if __name__ == "__main__": - if len(sys.argv) == 1: - print >> sys.stderr, \ - "Usage: PythonLR <master> [<slices>]" + if len(sys.argv) != 4: + print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>" exit(-1) sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)]) - slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 - points = sc.parallelize(generateData(), slices).cache() + points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache() + iterations = int(sys.argv[3]) # Initialize w to a random value w = 2 * np.random.ranf(size=D) - 1 print "Initial w: " + str(w) + # Compute logistic regression gradient for a matrix of data points + def gradient(matrix, w): + Y = matrix[:,0] # point labels (first column of input file) + X = matrix[:,1:] # point coordinates + # For each point (x, y), compute gradient function, then sum these up + return ((1.0 / (1.0 + np.exp(-Y * X.dot(w))) - 1.0) * Y * X.T).sum(1) + def add(x, y): x += y return x - for i in range(1, ITERATIONS + 1): - print "On iteration %i" % i - - gradient = points.map(lambda p: - (1.0 / (1.0 + exp(-p.y * np.dot(w, p.x)))) * p.y * p.x - ).reduce(add) - w -= gradient + for i in range(iterations): + print "On iteration %i" % (i + 1) + w -= points.map(lambda m: gradient(m, w)).reduce(add) print "Final w: " + str(w) diff --git a/python/examples/pi.py b/python/examples/pi.py index 33c026e824..ab0645fc2f 100644..100755 --- a/python/examples/pi.py +++ b/python/examples/pi.py @@ -24,8 +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 40be3b5000..744cce6651 100644..100755 --- a/python/examples/transitive_closure.py +++ b/python/examples/transitive_closure.py @@ -37,10 +37,9 @@ 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") + sc = SparkContext(sys.argv[1], "PythonTransitiveClosure") slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 tc = sc.parallelize(generateGraph(), slices).cache() diff --git a/python/examples/wordcount.py b/python/examples/wordcount.py index 41c846ba79..a6de22766a 100644..100755 --- a/python/examples/wordcount.py +++ b/python/examples/wordcount.py @@ -23,8 +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/context.py b/python/pyspark/context.py index 2f741cb345..c2b49ff37a 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -141,14 +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/rdd.py b/python/pyspark/rdd.py index c6a6b24c5a..51c2cb9806 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -160,7 +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) @@ -267,7 +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): """ @@ -386,13 +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 @@ -749,9 +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/shell.py b/python/pyspark/shell.py index cc8cd9e3c4..9b4b4e78cb 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -24,10 +24,15 @@ import os import pyspark from pyspark.context import SparkContext +# this is the equivalent of ADD_JARS +add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") != None else None -sc = SparkContext(os.environ.get("MASTER", "local"), "PySparkShell") +sc = SparkContext(os.environ.get("MASTER", "local"), "PySparkShell", pyFiles=add_files) print "Spark context avaiable as sc." +if add_files != None: + print "Adding files: [%s]" % ", ".join(add_files) + # The ./pyspark script stores the old PYTHONSTARTUP value in OLD_PYTHONSTARTUP, # which allows us to execute the user's PYTHONSTARTUP file: _pythonstartup = os.environ.get('OLD_PYTHONSTARTUP') diff --git a/python/run-tests b/python/run-tests index 1ee947d414..6643faa2e0 100755 --- a/python/run-tests +++ b/python/run-tests @@ -21,6 +21,9 @@ # 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 $FWDIR/pyspark pyspark/rdd.py diff --git a/repl-bin/src/deb/bin/run b/repl-bin/src/deb/bin/run index 0e5100e00b..8b5d8300f2 100755 --- a/repl-bin/src/deb/bin/run +++ b/repl-bin/src/deb/bin/run @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # # Licensed to the Apache Software Foundation (ASF) under one or more diff --git a/repl-bin/src/deb/bin/spark-executor b/repl-bin/src/deb/bin/spark-executor index d3c1c23413..bcfae22677 100755 --- a/repl-bin/src/deb/bin/spark-executor +++ b/repl-bin/src/deb/bin/spark-executor @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # # Licensed to the Apache Software Foundation (ASF) under one or more diff --git a/repl-bin/src/deb/bin/spark-shell b/repl-bin/src/deb/bin/spark-shell index 8b258543db..ec7e33e1e3 100755 --- a/repl-bin/src/deb/bin/spark-shell +++ b/repl-bin/src/deb/bin/spark-shell @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # # Licensed to the Apache Software Foundation (ASF) under one or more diff --git a/repl/src/main/scala/spark/repl/SparkILoop.scala b/repl/src/main/scala/spark/repl/SparkILoop.scala index 59f9d05683..0cecbd71ad 100644 --- a/repl/src/main/scala/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/spark/repl/SparkILoop.scala @@ -831,6 +831,10 @@ class SparkILoop(in0: Option[BufferedReader], val out: PrintWriter, val master: var sparkContext: SparkContext = null def createSparkContext(): SparkContext = { + val uri = System.getenv("SPARK_EXECUTOR_URI") + if (uri != null) { + System.setProperty("spark.executor.uri", uri) + } val master = this.master match { case Some(m) => m case None => { @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # # Licensed to the Apache Software Foundation (ASF) under one or more @@ -17,6 +17,8 @@ # limitations under the License. # +SCALA_VERSION=2.9.3 + # Figure out where the Scala framework is installed FWDIR="$(cd `dirname $0`; pwd)" @@ -70,7 +72,10 @@ esac # hard to kill the child with stuff like Process.destroy(). However, for # the Spark shell, the wrapper is necessary to properly reset the terminal # when we exit, so we allow it to set a variable to launch with scala. -if [ "$SPARK_LAUNCH_WITH_SCALA" == "1" ]; then +# 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 @@ -136,6 +141,17 @@ if [ ! -f "$FWDIR/RELEASE" ]; then echo "You need to compile Spark repl module before running this program" >&2 exit 1 fi + + # Figure out the JAR file that our examples were packaged into. This includes a bit of a hack + # to avoid the -sources and -doc packages that are built by publish-local. + if [ -e "$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 "$EXAMPLES_DIR/target/spark-examples"*[0-9T].jar` + fi fi # Compute classpath using external script @@ -148,4 +164,12 @@ else # The JVM doesn't read JAVA_OPTS by default so we need to pass it in EXTRA_ARGS="$JAVA_OPTS" fi -exec "$RUNNER" -cp "$CLASSPATH" $EXTRA_ARGS "$@" + +command="$RUNNER -cp \"$CLASSPATH\" $EXTRA_ARGS $@" +if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then + echo "Spark Command: $command" + echo "========================================" + echo +fi + +exec $command @@ -1,4 +1,4 @@ -#!/bin/bash +#!/usr/bin/env bash # # Licensed to the Apache Software Foundation (ASF) under one or more @@ -22,7 +22,7 @@ if [ "$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=350m -XX:ReservedCodeCacheSize=128m $EXTRA_ARGS -jar $SPARK_HOME/sbt/sbt-launch-*.jar "$@" diff --git a/spark-shell b/spark-shell index 5371fc540a..62fc18550d 100755 --- a/spark-shell +++ b/spark-shell @@ -1,4 +1,4 @@ -#!/bin/bash --posix +#!/usr/bin/env bash # # Licensed to the Apache Software Foundation (ASF) under one or more @@ -25,6 +25,9 @@ # -c <cores> Set the number of cores for REPL to use # +# Enter posix mode for bash +set -o posix + FWDIR="`dirname $0`" for o in "$@"; do @@ -76,6 +79,7 @@ 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: diff --git a/streaming/src/main/scala/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/spark/streaming/Checkpoint.scala index 1e4c1e3742..070d930b5e 100644 --- a/streaming/src/main/scala/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/spark/streaming/Checkpoint.scala @@ -17,16 +17,17 @@ package spark.streaming -import spark.{Logging, Utils} - -import org.apache.hadoop.fs.{FileUtil, Path} -import org.apache.hadoop.conf.Configuration - import java.io._ -import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} import java.util.concurrent.Executors import java.util.concurrent.RejectedExecutionException +import org.apache.hadoop.fs.Path +import org.apache.hadoop.conf.Configuration + +import spark.Logging +import spark.io.CompressionCodec + + private[streaming] class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) extends Logging with Serializable { @@ -49,6 +50,7 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) } } + /** * Convenience class to speed up the writing of graph checkpoint to file */ @@ -66,6 +68,8 @@ class CheckpointWriter(checkpointDir: String) extends Logging { 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 ? @@ -103,7 +107,7 @@ 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() @@ -137,6 +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 + "'") @@ -147,7 +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() @@ -170,7 +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/api/java/JavaPairDStream.scala b/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala index ccd15563b0..ea08fb3826 100644 --- a/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/spark/streaming/api/java/JavaPairDStream.scala @@ -29,7 +29,7 @@ import spark.{RDD, Partitioner} import org.apache.hadoop.mapred.{JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} import org.apache.hadoop.conf.Configuration -import spark.api.java.{JavaRDD, JavaPairRDD} +import spark.api.java.{JavaUtils, JavaRDD, JavaPairRDD} import spark.storage.StorageLevel import com.google.common.base.Optional import spark.RDD @@ -401,10 +401,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( (Seq[V], Option[S]) => Option[S] = { val scalaFunc: (Seq[V], Option[S]) => Option[S] = (values, state) => { val list: JList[V] = values - val scalaState: Optional[S] = state match { - case Some(s) => Optional.of(s) - case _ => Optional.absent() - } + val scalaState: Optional[S] = JavaUtils.optionToOptional(state) val result: Optional[S] = in.apply(list, scalaState) result.isPresent match { case true => Some(result.get()) 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..30fded12f0 --- /dev/null +++ b/tools/src/main/scala/spark/tools/JavaAPICompletenessChecker.scala @@ -0,0 +1,358 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR 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, isReturnType: Boolean): 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(applySubs) + ParameterizedType(classOf[JavaPairRDD[_, _]].getName, tupleParams) + } else { + ParameterizedType(classOf[JavaRDD[_]].getName, parameters.map(applySubs)) + } + case "spark.streaming.DStream" => + if (parameters(0).name == classOf[Tuple2[_, _]].getName) { + val tupleParams = + parameters(0).asInstanceOf[ParameterizedType].parameters.map(applySubs) + ParameterizedType("spark.streaming.api.java.JavaPairDStream", tupleParams) + } else { + ParameterizedType("spark.streaming.api.java.JavaDStream", + parameters.map(applySubs)) + } + case "scala.Option" => { + if (isReturnType) { + ParameterizedType("com.google.common.base.Optional", parameters.map(applySubs)) + } else { + applySubs(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(applySubs)) + } else if (firstParamName == "scala.runtime.BoxedUnit") { + ParameterizedType("spark.api.java.function.VoidFunction", + parameters.dropRight(1).map(applySubs)) + } else { + ParameterizedType("spark.api.java.function.Function", parameters.map(applySubs)) + } + case _ => + ParameterizedType(renameSubstitutions.getOrElse(name, name), + parameters.map(applySubs)) + } + 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(_, isReturnType = false)) + SparkMethod(method.name, toJavaType(method.returnType, isReturnType = true), 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() + } +} |