diff options
119 files changed, 4926 insertions, 1081 deletions
diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd index 6e7efbd334..44826f339c 100644 --- a/bin/compute-classpath.cmd +++ b/bin/compute-classpath.cmd @@ -15,6 +15,7 @@ set CORE_DIR=%FWDIR%core set REPL_DIR=%FWDIR%repl set EXAMPLES_DIR=%FWDIR%examples set BAGEL_DIR=%FWDIR%bagel +set MLLIB_DIR=%FWDIR%mllib set STREAMING_DIR=%FWDIR%streaming set PYSPARK_DIR=%FWDIR%python @@ -29,6 +30,7 @@ set CLASSPATH=%CLASSPATH%;%FWDIR%lib_managed\bundles\* set CLASSPATH=%CLASSPATH%;%FWDIR%repl\lib\* set CLASSPATH=%CLASSPATH%;%FWDIR%python\lib\* set CLASSPATH=%CLASSPATH%;%BAGEL_DIR%\target\scala-%SCALA_VERSION%\classes +set CLASSPATH=%CLASSPATH%;%MLLIB_DIR%\target\scala-%SCALA_VERSION%\classes 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 a0f35cb5eb..2caefe98c9 100755 --- a/bin/compute-classpath.sh +++ b/bin/compute-classpath.sh @@ -18,6 +18,7 @@ REPL_DIR="$FWDIR/repl" REPL_BIN_DIR="$FWDIR/repl-bin" EXAMPLES_DIR="$FWDIR/examples" BAGEL_DIR="$FWDIR/bagel" +MLLIB_DIR="$FWDIR/mllib" STREAMING_DIR="$FWDIR/streaming" PYSPARK_DIR="$FWDIR/python" @@ -49,6 +50,7 @@ if [ -e $REPL_BIN_DIR/target ]; then CLASSPATH+=":$EXAMPLES_JAR" fi CLASSPATH="$CLASSPATH:$BAGEL_DIR/target/scala-$SCALA_VERSION/classes" +CLASSPATH="$CLASSPATH:$MLLIB_DIR/target/scala-$SCALA_VERSION/classes" for jar in `find $PYSPARK_DIR/lib -name '*jar'`; do CLASSPATH="$CLASSPATH:$jar" done diff --git a/conf/fairscheduler.xml.template b/conf/fairscheduler.xml.template new file mode 100644 index 0000000000..04a6b418dc --- /dev/null +++ b/conf/fairscheduler.xml.template @@ -0,0 +1,15 @@ +<?xml version="1.0"?> +<allocations> +<pool name="production"> + <minShare>2</minShare> + <weight>1</weight> + <schedulingMode>FAIR</schedulingMode> +</pool> +<pool name="test"> + <minShare>3</minShare> + <weight>2</weight> + <schedulingMode>FIFO</schedulingMode> +</pool> +<pool name="data"> +</pool> +</allocations> diff --git a/core/pom.xml b/core/pom.xml index 385663a638..a55f58a9db 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -56,32 +56,24 @@ <artifactId>akka-slf4j_${scala.version}</artifactId> </dependency> <dependency> - <groupId>it.unimi.dsi</groupId> - <artifactId>fastutil</artifactId> - </dependency> - <dependency> - <groupId>colt</groupId> - <artifactId>colt</artifactId> - </dependency> - <dependency> - <groupId>io.spray</groupId> - <artifactId>spray-can</artifactId> + <groupId>org.scala-lang</groupId> + <artifactId>scalap</artifactId> </dependency> <dependency> - <groupId>io.spray</groupId> - <artifactId>spray-routing</artifactId> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> </dependency> <dependency> - <groupId>io.spray</groupId> - <artifactId>spray-io</artifactId> + <groupId>net.liftweb</groupId> + <artifactId>lift-json_2.10</artifactId> </dependency> <dependency> - <groupId>io.spray</groupId> - <artifactId>spray-json_${scala.version}</artifactId> + <groupId>it.unimi.dsi</groupId> + <artifactId>fastutil</artifactId> </dependency> <dependency> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-api</artifactId> + <groupId>colt</groupId> + <artifactId>colt</artifactId> </dependency> <dependency> <groupId>com.github.scala-incubator.io</groupId> @@ -175,10 +167,6 @@ </environmentVariables> </configuration> </plugin> - <plugin> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-maven-plugin</artifactId> - </plugin> </plugins> </build> diff --git a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala index a0fb4fe25d..f1c86de4cc 100644 --- a/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/hadoop1/scala/spark/deploy/SparkHadoopUtil.scala @@ -1,5 +1,6 @@ package spark.deploy import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.mapred.JobConf /** @@ -20,4 +21,7 @@ object SparkHadoopUtil { // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems def newConfiguration(): Configuration = new Configuration() + + // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster + def addCredentials(conf: JobConf) {} } diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala index ab1ab9d8a7..301a57fffa 100644 --- a/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/hadoop2-yarn/scala/spark/deploy/SparkHadoopUtil.scala @@ -1,6 +1,7 @@ package spark.deploy import collection.mutable.HashMap +import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.conf.Configuration @@ -27,18 +28,7 @@ object SparkHadoopUtil { } def runAsUser(func: (Product) => Unit, args: Product, user: String) { - - // println("running as user " + jobUserName) - - UserGroupInformation.setConfiguration(yarnConf) - val appMasterUgi: UserGroupInformation = UserGroupInformation.createRemoteUser(user) - appMasterUgi.doAs(new PrivilegedExceptionAction[AnyRef] { - def run: AnyRef = { - func(args) - // no return value ... - null - } - }) + func(args) } // Note that all params which start with SPARK are propagated all the way through, so if in yarn mode, this MUST be set to true. @@ -60,4 +50,10 @@ object SparkHadoopUtil { // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems // Always create a new config, dont reuse yarnConf. def newConfiguration(): Configuration = new YarnConfiguration(new Configuration()) + + // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster + def addCredentials(conf: JobConf) { + val jobCreds = conf.getCredentials(); + jobCreds.mergeAll(UserGroupInformation.getCurrentUser().getCredentials()) + } } diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala index aa72c1e5fe..f19648ec68 100644 --- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ApplicationMaster.scala @@ -30,23 +30,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e def run() { - // Initialization - val jobUserName = Utils.getUserNameFromEnvironment() - logInfo("running as user " + jobUserName) - - // run as user ... - UserGroupInformation.setConfiguration(yarnConf) - val appMasterUgi: UserGroupInformation = UserGroupInformation.createRemoteUser(jobUserName) - appMasterUgi.doAs(new PrivilegedExceptionAction[AnyRef] { - def run: AnyRef = { - runImpl() - return null - } - }) - } - - private def runImpl() { - appAttemptId = getApplicationAttemptId() resourceManager = registerWithResourceManager() val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster() diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala index 7a881e26df..514c17f241 100644 --- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/Client.scala @@ -1,9 +1,13 @@ package spark.deploy.yarn import java.net.{InetSocketAddress, URI} +import java.nio.ByteBuffer import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.mapred.Master import org.apache.hadoop.net.NetUtils +import org.apache.hadoop.io.DataOutputBuffer +import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.api.protocolrecords._ @@ -23,6 +27,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl var rpc: YarnRPC = YarnRPC.create(conf) val yarnConf: YarnConfiguration = new YarnConfiguration(conf) + val credentials = UserGroupInformation.getCurrentUser().getCredentials(); def run() { init(yarnConf) @@ -40,8 +45,8 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl appContext.setQueue(args.amQueue) appContext.setAMContainerSpec(amContainer) - appContext.setUser(args.amUser) - + appContext.setUser(UserGroupInformation.getCurrentUser().getShortUserName()) + submitApp(appContext) monitorApplication(appId) @@ -62,14 +67,21 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl def verifyClusterResources(app: GetNewApplicationResponse) = { val maxMem = app.getMaximumResourceCapability().getMemory() - logInfo("Max mem capabililty of resources in this cluster " + maxMem) + logInfo("Max mem capabililty of a single resource in this cluster " + maxMem) - // If the cluster does not have enough memory resources, exit. - val requestedMem = (args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD) + args.numWorkers * args.workerMemory - if (requestedMem > maxMem) { - logError("Cluster cannot satisfy memory resource request of " + requestedMem) + // if we have requested more then the clusters max for a single resource then exit. + if (args.workerMemory > maxMem) { + logError("the worker size is to large to run on this cluster " + args.workerMemory); + System.exit(1) + } + val amMem = args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD + if (amMem > maxMem) { + logError("AM size is to large to run on this cluster " + amMem) System.exit(1) } + + // We could add checks to make sure the entire cluster has enough resources but that involves getting + // all the node reports and computing ourselves } def createApplicationSubmissionContext(appId: ApplicationId): ApplicationSubmissionContext = { @@ -86,6 +98,15 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl // Upload Spark and the application JAR to the remote file system // Add them as local resources to the AM val fs = FileSystem.get(conf) + + val delegTokenRenewer = Master.getMasterPrincipal(conf); + if (UserGroupInformation.isSecurityEnabled()) { + if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { + logError("Can't get Master Kerberos principal for use as renewer") + System.exit(1) + } + } + Map("spark.jar" -> System.getenv("SPARK_JAR"), "app.jar" -> args.userJar, "log4j.properties" -> System.getenv("SPARK_LOG4J_CONF")) .foreach { case(destName, _localPath) => val localPath: String = if (_localPath != null) _localPath.trim() else "" @@ -97,6 +118,11 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl fs.copyFromLocalFile(false, true, src, dst) val destStatus = fs.getFileStatus(dst) + // get tokens for anything we upload to hdfs + if (UserGroupInformation.isSecurityEnabled()) { + fs.addDelegationTokens(delegTokenRenewer, credentials); + } + val amJarRsrc = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource] amJarRsrc.setType(LocalResourceType.FILE) amJarRsrc.setVisibility(LocalResourceVisibility.APPLICATION) @@ -106,6 +132,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl locaResources(destName) = amJarRsrc } } + UserGroupInformation.getCurrentUser().addCredentials(credentials); return locaResources } @@ -114,7 +141,6 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val log4jConfLocalRes = localResources.getOrElse("log4j.properties", null) val env = new HashMap[String, String]() - Apps.addToEnvironment(env, Environment.USER.name, args.amUser) // If log4j present, ensure ours overrides all others if (log4jConfLocalRes != null) Apps.addToEnvironment(env, Environment.CLASSPATH.name, "./") @@ -142,6 +168,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl env("SPARK_YARN_LOG4J_SIZE") = log4jConfLocalRes.getSize().toString() } + // Add each SPARK-* key to the environment System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k,v) => env(k) = v } return env @@ -195,7 +222,13 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } // Command for the ApplicationMaster - val commands = List[String]("java " + + var javaCommand = "java"; + val javaHome = System.getenv("JAVA_HOME") + if (javaHome != null && !javaHome.isEmpty()) { + javaCommand = Environment.JAVA_HOME.$() + "/bin/java" + } + + val commands = List[String](javaCommand + " -server " + JAVA_OPTS + " spark.deploy.yarn.ApplicationMaster" + @@ -214,7 +247,12 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl // Memory for the ApplicationMaster capability.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD) amContainer.setResource(capability) - + + // Setup security tokens + val dob = new DataOutputBuffer() + credentials.writeTokenStorageToStream(dob) + amContainer.setContainerTokens(ByteBuffer.wrap(dob.getData())) + return amContainer } diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala index 24110558e7..07e7edea36 100644 --- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/ClientArguments.scala @@ -13,7 +13,6 @@ class ClientArguments(val args: Array[String]) { var workerMemory = 1024 var workerCores = 1 var numWorkers = 2 - var amUser = System.getProperty("user.name") var amQueue = System.getProperty("QUEUE", "default") var amMemory: Int = 512 // TODO @@ -58,10 +57,6 @@ class ClientArguments(val args: Array[String]) { workerCores = value args = tail - case ("--user") :: value :: tail => - amUser = value - args = tail - case ("--queue") :: value :: tail => amQueue = value args = tail @@ -96,8 +91,7 @@ class ClientArguments(val args: Array[String]) { " --worker-cores NUM Number of cores for the workers (Default: 1). This is unsused right now.\n" + " --master-memory MEM Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)\n" + " --worker-memory MEM Memory per Worker (e.g. 1000M, 2G) (Default: 1G)\n" + - " --queue QUEUE The hadoop queue to use for allocation requests (Default: 'default')\n" + - " --user USERNAME Run the ApplicationMaster (and slaves) as a different user\n" + " --queue QUEUE The hadoop queue to use for allocation requests (Default: 'default')" ) System.exit(exitCode) } diff --git a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala index a2bf0af762..cc6f3344a1 100644 --- a/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala +++ b/core/src/hadoop2-yarn/scala/spark/deploy/yarn/WorkerRunnable.scala @@ -1,9 +1,12 @@ package spark.deploy.yarn import java.net.URI +import java.nio.ByteBuffer +import java.security.PrivilegedExceptionAction import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.io.DataOutputBuffer import org.apache.hadoop.net.NetUtils import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.api._ @@ -11,7 +14,7 @@ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.api.protocolrecords._ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC -import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records} +import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records, ProtoUtils} import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import scala.collection.JavaConversions._ @@ -76,7 +79,19 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S */ ctx.setUser(UserGroupInformation.getCurrentUser().getShortUserName()) - val commands = List[String]("java " + + + val credentials = UserGroupInformation.getCurrentUser().getCredentials() + val dob = new DataOutputBuffer() + credentials.writeTokenStorageToStream(dob) + ctx.setContainerTokens(ByteBuffer.wrap(dob.getData())) + + var javaCommand = "java"; + val javaHome = System.getenv("JAVA_HOME") + if (javaHome != null && !javaHome.isEmpty()) { + javaCommand = Environment.JAVA_HOME.$() + "/bin/java" + } + + val commands = List[String](javaCommand + " -server " + // Kill if OOM is raised - leverage yarn's failure handling to cause rescheduling. // Not killing the task leaves various aspects of the worker and (to some extent) the jvm in an inconsistent state. @@ -143,8 +158,6 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S def prepareEnvironment: HashMap[String, String] = { val env = new HashMap[String, String]() - // should we add this ? - Apps.addToEnvironment(env, Environment.USER.name, Utils.getUserNameFromEnvironment()) // If log4j present, ensure ours overrides all others if (System.getenv("SPARK_YARN_LOG4J_PATH") != null) { @@ -165,7 +178,23 @@ class WorkerRunnable(container: Container, conf: Configuration, masterAddress: S val cmHostPortStr = container.getNodeId().getHost() + ":" + container.getNodeId().getPort() val cmAddress = NetUtils.createSocketAddr(cmHostPortStr) logInfo("Connecting to ContainerManager at " + cmHostPortStr) - return rpc.getProxy(classOf[ContainerManager], cmAddress, conf).asInstanceOf[ContainerManager] + + // use doAs and remoteUser here so we can add the container token and not + // pollute the current users credentials with all of the individual container tokens + val user = UserGroupInformation.createRemoteUser(container.getId().toString()); + val containerToken = container.getContainerToken(); + if (containerToken != null) { + user.addToken(ProtoUtils.convertFromProtoFormat(containerToken, cmAddress)) + } + + val proxy = user + .doAs(new PrivilegedExceptionAction[ContainerManager] { + def run: ContainerManager = { + return rpc.getProxy(classOf[ContainerManager], + cmAddress, conf).asInstanceOf[ContainerManager] + } + }); + return proxy; } } diff --git a/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala b/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala index a0fb4fe25d..f1c86de4cc 100644 --- a/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/hadoop2/scala/spark/deploy/SparkHadoopUtil.scala @@ -1,5 +1,6 @@ package spark.deploy import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.mapred.JobConf /** @@ -20,4 +21,7 @@ object SparkHadoopUtil { // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems def newConfiguration(): Configuration = new Configuration() + + // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster + def addCredentials(conf: JobConf) {} } diff --git a/core/src/main/resources/spark/deploy/static/bootstrap-responsive.min.css b/core/src/main/resources/spark/deploy/static/bootstrap-responsive.min.css deleted file mode 100644 index 1f55036a07..0000000000 --- a/core/src/main/resources/spark/deploy/static/bootstrap-responsive.min.css +++ /dev/null @@ -1,9 +0,0 @@ -/*! - * Bootstrap Responsive v2.0.4 - * - * Copyright 2012 Twitter, Inc - * Licensed under the Apache License v2.0 - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Designed and built with all the love in the world @twitter by @mdo and @fat. - */.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 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b/core/src/main/resources/spark/ui/static/spark-logo-77x50px-hd.png diff --git a/core/src/main/resources/spark/deploy/static/spark_logo.png b/core/src/main/resources/spark/ui/static/spark_logo.png Binary files differindex 4b18734779..4b18734779 100644 --- a/core/src/main/resources/spark/deploy/static/spark_logo.png +++ b/core/src/main/resources/spark/ui/static/spark_logo.png diff --git a/core/src/main/resources/spark/ui/static/webui.css b/core/src/main/resources/spark/ui/static/webui.css new file mode 100644 index 0000000000..f7537bb766 --- /dev/null +++ b/core/src/main/resources/spark/ui/static/webui.css @@ -0,0 +1,49 @@ +.navbar .brand { + height: 50px; + width: 110px; + margin-left: 1px; + padding: 0; +} + +.version { + line-height: 30px; + vertical-align: bottom; + font-size: 12px; + padding: 0; + margin: 0; + font-weight: bold; + color: #777; +} + +.navbar-inner { + padding-top: 2px; + height: 50px; +} + +.navbar-inner .nav { + margin-top: 5px; + font-size: 15px; +} + + +#infolist { + margin-left: 400px; + margin-top: 14px; +} + +#infolist li { + display: inline; + list-style-type: none; + list-style-position: outside; + padding-right: 20px; + padding-top: 10px; + padding-bottom: 10px; +} + +.progress-cell { + width: 134px; + border-right: 0; + padding: 0; + padding-top: 7px; + padding-left: 4px; +} diff --git a/core/src/main/scala/spark/PairRDDFunctions.scala b/core/src/main/scala/spark/PairRDDFunctions.scala index fe812fe530..0095b868a8 100644 --- a/core/src/main/scala/spark/PairRDDFunctions.scala +++ b/core/src/main/scala/spark/PairRDDFunctions.scala @@ -22,6 +22,7 @@ import org.apache.hadoop.mapred.OutputFormat import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat, RecordWriter => NewRecordWriter, Job => NewAPIHadoopJob, HadoopMapReduceUtil} +import org.apache.hadoop.security.UserGroupInformation import spark.partial.BoundedDouble import spark.partial.PartialResult diff --git a/core/src/main/scala/spark/RDD.scala b/core/src/main/scala/spark/RDD.scala index e88290fdb2..8dde5391d3 100644 --- a/core/src/main/scala/spark/RDD.scala +++ b/core/src/main/scala/spark/RDD.scala @@ -108,6 +108,9 @@ abstract class RDD[T: ClassTag]( // Methods and fields available on all RDDs // ======================================================================= + /** The SparkContext that created this RDD. */ + def sparkContext: SparkContext = sc + /** A unique ID for this RDD (within its SparkContext). */ val id: Int = sc.newRddId() @@ -284,31 +287,35 @@ abstract class RDD[T: ClassTag]( def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[T] = { var fraction = 0.0 var total = 0 - val multiplier = 3.0 - val initialCount = count() + var multiplier = 3.0 + var initialCount = this.count() var maxSelected = 0 + if (num < 0) { + throw new IllegalArgumentException("Negative number of elements requested") + } + if (initialCount > Integer.MAX_VALUE - 1) { maxSelected = Integer.MAX_VALUE - 1 } else { maxSelected = initialCount.toInt } - if (num > initialCount) { + if (num > initialCount && !withReplacement) { total = maxSelected - fraction = math.min(multiplier * (maxSelected + 1) / initialCount, 1.0) - } else if (num < 0) { - throw(new IllegalArgumentException("Negative number of elements requested")) + fraction = multiplier * (maxSelected + 1) / initialCount } else { - fraction = math.min(multiplier * (num + 1) / initialCount, 1.0) + fraction = multiplier * (num + 1) / initialCount total = num } val rand = new Random(seed) - var samples = this.sample(withReplacement, fraction, rand.nextInt).collect() + var samples = this.sample(withReplacement, fraction, rand.nextInt()).collect() + // If the first sample didn't turn out large enough, keep trying to take samples; + // this shouldn't happen often because we use a big multiplier for thei initial size while (samples.length < total) { - samples = this.sample(withReplacement, fraction, rand.nextInt).collect() + samples = this.sample(withReplacement, fraction, rand.nextInt()).collect() } Utils.randomizeInPlace(samples, rand).take(total) diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala index ef6de87193..01d9a70a78 100644 --- a/core/src/main/scala/spark/SparkContext.scala +++ b/core/src/main/scala/spark/SparkContext.scala @@ -39,6 +39,7 @@ import org.apache.hadoop.mapred.TextInputFormat import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import org.apache.hadoop.mapreduce.{Job => NewHadoopJob} import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} +import org.apache.hadoop.security.UserGroupInformation import org.apache.mesos.MesosNativeLibrary @@ -49,8 +50,9 @@ import spark.scheduler.{DAGScheduler, ResultTask, ShuffleMapTask, SparkListener, import spark.scheduler.cluster.{StandaloneSchedulerBackend, SparkDeploySchedulerBackend, ClusterScheduler} import spark.scheduler.local.LocalScheduler import spark.scheduler.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend} -import spark.storage.{BlockManagerUI, StorageStatus, StorageUtils, RDDInfo} +import spark.storage.{StorageStatus, StorageUtils, RDDInfo} import spark.util.{MetadataCleaner, TimeStampedHashMap} +import ui.{SparkUI} /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark @@ -96,11 +98,6 @@ class SparkContext( isLocal) SparkEnv.set(env) - // Start the BlockManager UI - private[spark] val ui = new BlockManagerUI( - env.actorSystem, env.blockManager.master.driverActor, this) - ui.start() - // Used to store a URL for each static file/jar together with the file's local timestamp private[spark] val addedFiles = HashMap[String, Long]() private[spark] val addedJars = HashMap[String, Long]() @@ -109,6 +106,9 @@ class SparkContext( private[spark] val persistentRdds = new TimeStampedHashMap[Int, RDD[_]] private[spark] val metadataCleaner = new MetadataCleaner("SparkContext", this.cleanup) + // Initalize the Spark UI + private[spark] val ui = new SparkUI(this) + ui.bind() // Add each JAR given through the constructor if (jars != null) { @@ -217,6 +217,8 @@ class SparkContext( @volatile private var dagScheduler = new DAGScheduler(taskScheduler) dagScheduler.start() + ui.start() + /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */ val hadoopConfiguration = { val conf = SparkHadoopUtil.newConfiguration() @@ -579,6 +581,7 @@ class SparkContext( /** Shut down the SparkContext. */ def stop() { + ui.stop() // Do this only if not stopped already - best case effort. // prevent NPE if stopped more than once. val dagSchedulerCopy = dagScheduler diff --git a/core/src/main/scala/spark/TaskEndReason.scala b/core/src/main/scala/spark/TaskEndReason.scala index 8140cba084..bb75ec208c 100644 --- a/core/src/main/scala/spark/TaskEndReason.scala +++ b/core/src/main/scala/spark/TaskEndReason.scala @@ -1,5 +1,6 @@ package spark +import spark.executor.TaskMetrics import spark.storage.BlockManagerId /** @@ -24,7 +25,8 @@ private[spark] case class FetchFailed( private[spark] case class ExceptionFailure( className: String, description: String, - stackTrace: Array[StackTraceElement]) + stackTrace: Array[StackTraceElement], + metrics: Option[TaskMetrics]) extends TaskEndReason private[spark] case class OtherFailure(message: String) extends TaskEndReason diff --git a/core/src/main/scala/spark/Utils.scala b/core/src/main/scala/spark/Utils.scala index e02507f83e..f90b2ccaa1 100644 --- a/core/src/main/scala/spark/Utils.scala +++ b/core/src/main/scala/spark/Utils.scala @@ -490,6 +490,26 @@ private object Utils extends Logging { } /** + * Returns a human-readable string representing a duration such as "35ms" + */ + def msDurationToString(ms: Long): String = { + val second = 1000 + val minute = 60 * second + val hour = 60 * minute + + ms match { + case t if t < second => + "%d ms".format(t) + case t if t < minute => + "%.1f s".format(t.toFloat / second) + case t if t < hour => + "%.1f m".format(t.toFloat / minute) + case t => + "%.2f h".format(t.toFloat / hour) + } + } + + /** * Convert a memory quantity in megabytes to a human-readable string such as "4.0 MB". */ def memoryMegabytesToString(megabytes: Long): String = { @@ -604,18 +624,19 @@ private object Utils extends Logging { "%s at %s:%s".format(callSiteInfo.lastSparkMethod, callSiteInfo.firstUserFile, callSiteInfo.firstUserLine) } - /** - * Try to find a free port to bind to on the local host. This should ideally never be needed, - * except that, unfortunately, some of the networking libraries we currently rely on (e.g. Spray) - * don't let users bind to port 0 and then figure out which free port they actually bound to. - * We work around this by binding a ServerSocket and immediately unbinding it. This is *not* - * necessarily guaranteed to work, but it's the best we can do. - */ - def findFreePort(): Int = { - val socket = new ServerSocket(0) - val portBound = socket.getLocalPort - socket.close() - portBound + + /** Return a string containing the last `n` bytes of a file. */ + def lastNBytes(path: String, n: Int): String = { + val file = new File(path) + val length = file.length() + val buff = new Array[Byte](math.min(n, length.toInt)) + val skip = math.max(0, length - n) + val stream = new FileInputStream(file) + + stream.skip(skip) + stream.read(buff) + stream.close() + Source.fromBytes(buff).mkString } /** diff --git a/core/src/main/scala/spark/deploy/JsonProtocol.scala b/core/src/main/scala/spark/deploy/JsonProtocol.scala index 88b03a007c..607d34d111 100644 --- a/core/src/main/scala/spark/deploy/JsonProtocol.scala +++ b/core/src/main/scala/spark/deploy/JsonProtocol.scala @@ -1,79 +1,65 @@ package spark.deploy import master.{ApplicationInfo, WorkerInfo} +import net.liftweb.json.JsonDSL._ import worker.ExecutorRunner -import spray.json._ -/** - * spray-json helper class containing implicit conversion to json for marshalling responses - */ -private[spark] object JsonProtocol extends DefaultJsonProtocol { - implicit object WorkerInfoJsonFormat extends RootJsonWriter[WorkerInfo] { - def write(obj: WorkerInfo) = JsObject( - "id" -> JsString(obj.id), - "host" -> JsString(obj.host), - "port" -> JsNumber(obj.port), - "webuiaddress" -> JsString(obj.webUiAddress), - "cores" -> JsNumber(obj.cores), - "coresused" -> JsNumber(obj.coresUsed), - "memory" -> JsNumber(obj.memory), - "memoryused" -> JsNumber(obj.memoryUsed) - ) - } +private[spark] object JsonProtocol { + def writeWorkerInfo(obj: WorkerInfo) = { + ("id" -> obj.id) ~ + ("host" -> obj.host) ~ + ("port" -> obj.port) ~ + ("webuiaddress" -> obj.webUiAddress) ~ + ("cores" -> obj.cores) ~ + ("coresused" -> obj.coresUsed) ~ + ("memory" -> obj.memory) ~ + ("memoryused" -> obj.memoryUsed) + } - implicit object AppInfoJsonFormat extends RootJsonWriter[ApplicationInfo] { - def write(obj: ApplicationInfo) = JsObject( - "starttime" -> JsNumber(obj.startTime), - "id" -> JsString(obj.id), - "name" -> JsString(obj.desc.name), - "cores" -> JsNumber(obj.desc.maxCores), - "user" -> JsString(obj.desc.user), - "memoryperslave" -> JsNumber(obj.desc.memoryPerSlave), - "submitdate" -> JsString(obj.submitDate.toString)) + def writeApplicationInfo(obj: ApplicationInfo) = { + ("starttime" -> obj.startTime) ~ + ("id" -> obj.id) ~ + ("name" -> obj.desc.name) ~ + ("cores" -> obj.desc.maxCores) ~ + ("user" -> obj.desc.user) ~ + ("memoryperslave" -> obj.desc.memoryPerSlave) ~ + ("submitdate" -> obj.submitDate.toString) } - implicit object AppDescriptionJsonFormat extends RootJsonWriter[ApplicationDescription] { - def write(obj: ApplicationDescription) = JsObject( - "name" -> JsString(obj.name), - "cores" -> JsNumber(obj.maxCores), - "memoryperslave" -> JsNumber(obj.memoryPerSlave), - "user" -> JsString(obj.user) - ) + def writeApplicationDescription(obj: ApplicationDescription) = { + ("name" -> obj.name) ~ + ("cores" -> obj.maxCores) ~ + ("memoryperslave" -> obj.memoryPerSlave) ~ + ("user" -> obj.user) } - implicit object ExecutorRunnerJsonFormat extends RootJsonWriter[ExecutorRunner] { - def write(obj: ExecutorRunner) = JsObject( - "id" -> JsNumber(obj.execId), - "memory" -> JsNumber(obj.memory), - "appid" -> JsString(obj.appId), - "appdesc" -> obj.appDesc.toJson.asJsObject - ) + def writeExecutorRunner(obj: ExecutorRunner) = { + ("id" -> obj.execId) ~ + ("memory" -> obj.memory) ~ + ("appid" -> obj.appId) ~ + ("appdesc" -> writeApplicationDescription(obj.appDesc)) } - implicit object MasterStateJsonFormat extends RootJsonWriter[MasterState] { - def write(obj: MasterState) = JsObject( - "url" -> JsString("spark://" + obj.uri), - "workers" -> JsArray(obj.workers.toList.map(_.toJson)), - "cores" -> JsNumber(obj.workers.map(_.cores).sum), - "coresused" -> JsNumber(obj.workers.map(_.coresUsed).sum), - "memory" -> JsNumber(obj.workers.map(_.memory).sum), - "memoryused" -> JsNumber(obj.workers.map(_.memoryUsed).sum), - "activeapps" -> JsArray(obj.activeApps.toList.map(_.toJson)), - "completedapps" -> JsArray(obj.completedApps.toList.map(_.toJson)) - ) + def writeMasterState(obj: MasterState) = { + ("url" -> ("spark://" + obj.uri)) ~ + ("workers" -> obj.workers.toList.map(writeWorkerInfo)) ~ + ("cores" -> obj.workers.map(_.cores).sum) ~ + ("coresused" -> obj.workers.map(_.coresUsed).sum) ~ + ("memory" -> obj.workers.map(_.memory).sum) ~ + ("memoryused" -> obj.workers.map(_.memoryUsed).sum) ~ + ("activeapps" -> obj.activeApps.toList.map(writeApplicationInfo)) ~ + ("completedapps" -> obj.completedApps.toList.map(writeApplicationInfo)) } - implicit object WorkerStateJsonFormat extends RootJsonWriter[WorkerState] { - def write(obj: WorkerState) = JsObject( - "id" -> JsString(obj.workerId), - "masterurl" -> JsString(obj.masterUrl), - "masterwebuiurl" -> JsString(obj.masterWebUiUrl), - "cores" -> JsNumber(obj.cores), - "coresused" -> JsNumber(obj.coresUsed), - "memory" -> JsNumber(obj.memory), - "memoryused" -> JsNumber(obj.memoryUsed), - "executors" -> JsArray(obj.executors.toList.map(_.toJson)), - "finishedexecutors" -> JsArray(obj.finishedExecutors.toList.map(_.toJson)) - ) + def writeWorkerState(obj: WorkerState) = { + ("id" -> obj.workerId) ~ + ("masterurl" -> obj.masterUrl) ~ + ("masterwebuiurl" -> obj.masterWebUiUrl) ~ + ("cores" -> obj.cores) ~ + ("coresused" -> obj.coresUsed) ~ + ("memory" -> obj.memory) ~ + ("memoryused" -> obj.memoryUsed) ~ + ("executors" -> obj.executors.toList.map(writeExecutorRunner)) ~ + ("finishedexecutors" -> obj.finishedExecutors.toList.map(writeExecutorRunner)) } } diff --git a/core/src/main/scala/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/spark/deploy/LocalSparkCluster.scala index 2b0b3b10e7..494b1cc6f0 100644 --- a/core/src/main/scala/spark/deploy/LocalSparkCluster.scala +++ b/core/src/main/scala/spark/deploy/LocalSparkCluster.scala @@ -17,11 +17,11 @@ import scala.collection.mutable.ArrayBuffer */ private[spark] class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: Int) extends Logging { - + private val localHostname = Utils.localHostName() private val masterActorSystems = ArrayBuffer[ActorSystem]() private val workerActorSystems = ArrayBuffer[ActorSystem]() - + def start(): String = { logInfo("Starting a local Spark cluster with " + numWorkers + " workers.") diff --git a/core/src/main/scala/spark/deploy/WebUI.scala b/core/src/main/scala/spark/deploy/WebUI.scala index ad1a1092b2..844c4142c7 100644 --- a/core/src/main/scala/spark/deploy/WebUI.scala +++ b/core/src/main/scala/spark/deploy/WebUI.scala @@ -6,7 +6,7 @@ import java.util.Date /** * Utilities used throughout the web UI. */ -private[spark] object WebUI { +private[spark] object DeployWebUI { val DATE_FORMAT = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") def formatDate(date: Date): String = DATE_FORMAT.format(date) diff --git a/core/src/main/scala/spark/deploy/master/Master.scala b/core/src/main/scala/spark/deploy/master/Master.scala index 770cfe9d05..d31e6735b7 100644 --- a/core/src/main/scala/spark/deploy/master/Master.scala +++ b/core/src/main/scala/spark/deploy/master/Master.scala @@ -13,6 +13,7 @@ import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} import spark.deploy._ import spark.{Logging, SparkException, Utils} import spark.util.AkkaUtils +import ui.MasterWebUI private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Actor with Logging { @@ -35,6 +36,8 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act var firstApp: Option[ApplicationInfo] = None + val webUi = new MasterWebUI(self) + Utils.checkHost(host, "Expected hostname") val masterPublicAddress = { @@ -51,20 +54,13 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act logInfo("Starting Spark master at spark://" + host + ":" + port) // Listen for remote client disconnection events, since they don't go through Akka's watch() context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) - startWebUi() + webUi.start() import context.dispatcher context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis)(timeOutDeadWorkers()) } - def startWebUi() { - val webUi = new MasterWebUI(self) - try { - AkkaUtils.startSprayServer(context.system, "0.0.0.0", webUiPort, webUi.handler) - } catch { - case e: Exception => - logError("Failed to create web UI", e) - System.exit(1) - } + override def postStop() { + webUi.stop() } override def receive = { @@ -76,7 +72,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act } else { addWorker(id, host, workerPort, cores, memory, worker_webUiPort, publicAddress) context.watch(sender) // This doesn't work with remote actors but helps for testing - sender ! RegisteredWorker("http://" + masterPublicAddress + ":" + webUiPort) + sender ! RegisteredWorker("http://" + masterPublicAddress + ":" + webUi.boundPort) schedule() } } @@ -279,7 +275,9 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act exec.state = ExecutorState.KILLED } app.markFinished(state) - app.driver ! ApplicationRemoved(state.toString) + if (state != ApplicationState.FINISHED) { + app.driver ! ApplicationRemoved(state.toString) + } schedule() } } diff --git a/core/src/main/scala/spark/deploy/master/MasterWebUI.scala b/core/src/main/scala/spark/deploy/master/MasterWebUI.scala deleted file mode 100644 index 34cee87853..0000000000 --- a/core/src/main/scala/spark/deploy/master/MasterWebUI.scala +++ /dev/null @@ -1,78 +0,0 @@ -package spark.deploy.master - -import akka.actor.{ActorRef, ActorContext, ActorRefFactory} -import scala.concurrent.Await -import akka.pattern.ask - -import akka.util.Timeout -import scala.concurrent.duration._ -import spray.routing.Directives -import spray.routing.directives._ -import spray.httpx.TwirlSupport._ -import spray.httpx.SprayJsonSupport._ -import spray.http.MediaTypes._ - -import spark.deploy._ -import spark.deploy.JsonProtocol._ - -/** - * Web UI server for the standalone master. - */ -private[spark] -class MasterWebUI(master: ActorRef)(implicit val context: ActorContext) extends Directives { - import context.dispatcher - - val actorSystem = context.system - val RESOURCE_DIR = "spark/deploy/master/webui" - val STATIC_RESOURCE_DIR = "spark/deploy/static" - - implicit val timeout = Timeout(Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")) - - val handler = { - get { - (path("") & parameters('format ?)) { - case Some(js) if js.equalsIgnoreCase("json") => - val future = (master ? RequestMasterState).mapTo[MasterState] - respondWithMediaType(`application/json`) { ctx => - ctx.complete(future.mapTo[MasterState]) - } - case _ => - complete { - val future = (master ? RequestMasterState).mapTo[MasterState] - future.map { - masterState => spark.deploy.master.html.index.render(masterState) - } - } - } ~ - path("app") { - parameters("appId", 'format ?) { - case (appId, Some(js)) if (js.equalsIgnoreCase("json")) => - val future = master ? RequestMasterState - val appInfo = for (masterState <- future.mapTo[MasterState]) yield { - masterState.activeApps.find(_.id == appId).getOrElse({ - masterState.completedApps.find(_.id == appId).getOrElse(null) - }) - } - respondWithMediaType(`application/json`) { ctx => - ctx.complete(appInfo.mapTo[ApplicationInfo]) - } - case (appId, _) => - complete { - val future = master ? RequestMasterState - future.map { state => - val masterState = state.asInstanceOf[MasterState] - val app = masterState.activeApps.find(_.id == appId).getOrElse({ - masterState.completedApps.find(_.id == appId).getOrElse(null) - }) - spark.deploy.master.html.app_details.render(app) - } - } - } - } ~ - pathPrefix("static") { - getFromResourceDirectory(STATIC_RESOURCE_DIR) - } ~ - getFromResourceDirectory(RESOURCE_DIR) - } - } -} diff --git a/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala new file mode 100644 index 0000000000..939f8c9587 --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala @@ -0,0 +1,101 @@ +package spark.deploy.master.ui + +import akka.pattern.ask + +import scala.concurrent.Await +import scala.concurrent.duration._ + +import javax.servlet.http.HttpServletRequest + +import net.liftweb.json.JsonAST.JValue + +import scala.xml.Node + +import spark.deploy.{RequestMasterState, JsonProtocol, MasterState} +import spark.deploy.master.ExecutorInfo +import spark.ui.UIUtils + +private[spark] class ApplicationPage(parent: MasterWebUI) { + val master = parent.master + implicit val timeout = parent.timeout + + /** Executor details for a particular application */ + def renderJson(request: HttpServletRequest): JValue = { + val appId = request.getParameter("appId") + val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState] + val state = Await.result(stateFuture, 30 seconds) + val app = state.activeApps.find(_.id == appId).getOrElse({ + state.completedApps.find(_.id == appId).getOrElse(null) + }) + JsonProtocol.writeApplicationInfo(app) + } + + /** Executor details for a particular application */ + def render(request: HttpServletRequest): Seq[Node] = { + val appId = request.getParameter("appId") + val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState] + val state = Await.result(stateFuture, 30 seconds) + val app = state.activeApps.find(_.id == appId).getOrElse({ + state.completedApps.find(_.id == appId).getOrElse(null) + }) + + val executorHeaders = Seq("ExecutorID", "Worker", "Cores", "Memory", "State", "Logs") + val executors = app.executors.values.toSeq + val executorTable = UIUtils.listingTable(executorHeaders, executorRow, executors) + + val content = + <hr /> + <div class="row"> + <div class="span12"> + <ul class="unstyled"> + <li><strong>ID:</strong> {app.id}</li> + <li><strong>Description:</strong> {app.desc.name}</li> + <li><strong>User:</strong> {app.desc.user}</li> + <li><strong>Cores:</strong> + { + if (app.desc.maxCores == Integer.MAX_VALUE) { + "Unlimited %s granted".format(app.coresGranted) + } else { + "%s (%s granted, %s left)".format( + app.desc.maxCores, app.coresGranted, app.coresLeft) + } + } + </li> + <li><strong>Memory per Slave:</strong> {app.desc.memoryPerSlave}</li> + <li><strong>Submit Date:</strong> {app.submitDate}</li> + <li><strong>State:</strong> {app.state}</li> + <li><strong><a href={app.appUiUrl}>Application Detail UI</a></strong></li> + </ul> + </div> + </div> + + <hr/> + + <div class="row"> <!-- Executors --> + <div class="span12"> + <h3> Executor Summary </h3> + <br/> + {executorTable} + </div> + </div>; + UIUtils.basicSparkPage(content, "Application Info: " + app.desc.name) + } + + def executorRow(executor: ExecutorInfo): Seq[Node] = { + <tr> + <td>{executor.id}</td> + <td> + <a href={executor.worker.webUiAddress}>{executor.worker.id}</a> + </td> + <td>{executor.cores}</td> + <td>{executor.memory}</td> + <td>{executor.state}</td> + <td> + <a href={"%s/log?appId=%s&executorId=%s&logType=stdout" + .format(executor.worker.webUiAddress, executor.application.id, executor.id)}>stdout</a> + <a href={"%s/log?appId=%s&executorId=%s&logType=stderr" + .format(executor.worker.webUiAddress, executor.application.id, executor.id)}>stderr</a> + </td> + </tr> + } +} diff --git a/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala new file mode 100644 index 0000000000..73e378f988 --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala @@ -0,0 +1,119 @@ +package spark.deploy.master.ui + +import scala.concurrent.Await +import akka.pattern.ask +import scala.concurrent.duration._ + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import spark.deploy.{RequestMasterState, DeployWebUI, MasterState} +import spark.Utils +import spark.ui.UIUtils +import spark.deploy.master.{ApplicationInfo, WorkerInfo} + +private[spark] class IndexPage(parent: MasterWebUI) { + val master = parent.master + implicit val timeout = parent.timeout + + /** Index view listing applications and executors */ + def render(request: HttpServletRequest): Seq[Node] = { + val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterState] + val state = Await.result(stateFuture, 30 seconds) + + val workerHeaders = Seq("Id", "Address", "State", "Cores", "Memory") + val workers = state.workers.sortBy(_.id) + val workerTable = UIUtils.listingTable(workerHeaders, workerRow, workers) + + val appHeaders = Seq("ID", "Description", "Cores", "Memory per Node", "Submit Time", "User", + "State", "Duration") + val activeApps = state.activeApps.sortBy(_.startTime).reverse + val activeAppsTable = UIUtils.listingTable(appHeaders, appRow, activeApps) + val completedApps = state.completedApps.sortBy(_.endTime).reverse + val completedAppsTable = UIUtils.listingTable(appHeaders, appRow, completedApps) + + val content = + <hr /> + <div class="row"> + <div class="span12"> + <ul class="unstyled"> + <li><strong>URL:</strong>{state.uri}</li> + <li><strong>Workers:</strong>{state.workers.size}</li> + <li><strong>Cores:</strong> {state.workers.map(_.cores).sum} Total, + {state.workers.map(_.coresUsed).sum} Used</li> + <li><strong>Memory:</strong> + {Utils.memoryMegabytesToString(state.workers.map(_.memory).sum)} Total, + {Utils.memoryMegabytesToString(state.workers.map(_.memoryUsed).sum)} Used</li> + <li><strong>Applications:</strong> + {state.activeApps.size} Running, + {state.completedApps.size} Completed </li> + </ul> + </div> + </div> + + <div class="row"> + <div class="span12"> + <h3> Workers </h3> + <br/> + {workerTable} + </div> + </div> + + <hr/> + + <div class="row"> + <div class="span12"> + <h3> Running Applications </h3> + <br/> + {activeAppsTable} + </div> + </div> + + <hr/> + + <div class="row"> + <div class="span12"> + <h3> Completed Applications </h3> + <br/> + {completedAppsTable} + </div> + </div>; + UIUtils.basicSparkPage(content, "Spark Master: " + state.uri) + } + + def workerRow(worker: WorkerInfo): Seq[Node] = { + <tr> + <td> + <a href={worker.webUiAddress}>{worker.id}</a> + </td> + <td>{worker.host}:{worker.port}</td> + <td>{worker.state}</td> + <td>{worker.cores} ({worker.coresUsed} Used)</td> + <td sorttable_customkey={"%s.%s".format(worker.memory, worker.memoryUsed)}> + {Utils.memoryMegabytesToString(worker.memory)} + ({Utils.memoryMegabytesToString(worker.memoryUsed)} Used) + </td> + </tr> + } + + + def appRow(app: ApplicationInfo): Seq[Node] = { + <tr> + <td> + <a href={"app?appId=" + app.id}>{app.id}</a> + </td> + <td>{app.desc.name}</td> + <td> + {app.coresGranted} + </td> + <td sorttable_customkey={app.desc.memoryPerSlave.toString}> + {Utils.memoryMegabytesToString(app.desc.memoryPerSlave)} + </td> + <td>{DeployWebUI.formatDate(app.submitDate)}</td> + <td>{app.desc.user}</td> + <td>{app.state.toString}</td> + <td>{DeployWebUI.formatDuration(app.duration)}</td> + </tr> + } +} diff --git a/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala new file mode 100644 index 0000000000..bcc4d49234 --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ui/MasterWebUI.scala @@ -0,0 +1,59 @@ +package spark.deploy.master.ui + +import akka.actor.ActorRef +import scala.concurrent.duration._ + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.{Handler, Server} + +import spark.{Logging, Utils} +import spark.ui.JettyUtils +import spark.ui.JettyUtils._ + +/** + * Web UI server for the standalone master. + */ +private[spark] +class MasterWebUI(val master: ActorRef, requestedPort: Option[Int] = None) 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) + + var server: Option[Server] = None + var boundPort: Option[Int] = None + + val applicationPage = new ApplicationPage(this) + val indexPage = new IndexPage(this) + + def start() { + try { + val (srv, bPort) = JettyUtils.startJettyServer("0.0.0.0", port, handlers) + server = Some(srv) + boundPort = Some(bPort) + logInfo("Started Master web UI at http://%s:%d".format(host, boundPort.get)) + } catch { + case e: Exception => + logError("Failed to create Master JettyUtils", e) + System.exit(1) + } + } + + val handlers = Array[(String, Handler)]( + ("/static", createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR)), + ("/app/json", (request: HttpServletRequest) => applicationPage.renderJson(request)), + ("/app", (request: HttpServletRequest) => applicationPage.render(request)), + ("*", (request: HttpServletRequest) => indexPage.render(request)) + ) + + def stop() { + server.foreach(_.stop()) + } +} + +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/Worker.scala b/core/src/main/scala/spark/deploy/worker/Worker.scala index b5dfd16e67..7c1871e047 100644 --- a/core/src/main/scala/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/spark/deploy/worker/Worker.scala @@ -14,7 +14,7 @@ import spark.deploy.LaunchExecutor import spark.deploy.RegisterWorkerFailed import spark.deploy.master.Master import java.io.File - +import ui.WorkerWebUI private[spark] class Worker( host: String, @@ -45,6 +45,7 @@ private[spark] class Worker( val envVar = System.getenv("SPARK_PUBLIC_DNS") if (envVar != null) envVar else host } + var webUi: WorkerWebUI = null var coresUsed = 0 var memoryUsed = 0 @@ -76,35 +77,26 @@ private[spark] class Worker( sparkHome = new File(Option(System.getenv("SPARK_HOME")).getOrElse(".")) logInfo("Spark home: " + sparkHome) createWorkDir() + webUi = new WorkerWebUI(self, workDir, Some(webUiPort)) + webUi.start() connectToMaster() - startWebUi() } def connectToMaster() { logInfo("Connecting to master " + masterUrl) master = context.actorFor(Master.toAkkaUrl(masterUrl)) - master ! RegisterWorker(workerId, host, port, cores, memory, webUiPort, publicAddress) + master ! RegisterWorker(workerId, host, port, cores, memory, webUi.boundPort.get, publicAddress) context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent]) context.watch(master) // Doesn't work with remote actors, but useful for testing } - def startWebUi() { - val webUi = new WorkerWebUI(self, workDir) - try { - AkkaUtils.startSprayServer(context.system, "0.0.0.0", webUiPort, webUi.handler) - } catch { - case e: Exception => - logError("Failed to create web UI", e) - System.exit(1) - } - } + import context.dispatcher override def receive = { case RegisteredWorker(url) => masterWebUiUrl = url logInfo("Successfully registered with master") - import context.dispatcher - context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis) { + context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis) { master ! Heartbeat(workerId) } @@ -170,6 +162,7 @@ private[spark] class Worker( override def postStop() { executors.values.foreach(_.kill()) + webUi.stop() } } diff --git a/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala deleted file mode 100644 index cc2ab6187a..0000000000 --- a/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala +++ /dev/null @@ -1,61 +0,0 @@ -package spark.deploy.worker - -import akka.actor.{ActorRef, ActorContext} -import scala.concurrent.Await -import akka.pattern.ask - -import akka.util.Timeout -import scala.concurrent.duration._ -import spray.routing.Directives -import spray.httpx.TwirlSupport._ -import spray.httpx.SprayJsonSupport._ -import spray.http.MediaTypes._ - -import spark.deploy.{WorkerState, RequestWorkerState} -import spark.deploy.JsonProtocol._ -import java.io.File - -/** - * Web UI server for the standalone worker. - */ -private[spark] -class WorkerWebUI(worker: ActorRef, workDir: File)(implicit val context: ActorContext) extends Directives { - import context.dispatcher - - val actorSystem = context.system - val RESOURCE_DIR = "spark/deploy/worker/webui" - val STATIC_RESOURCE_DIR = "spark/deploy/static" - - implicit val timeout = Timeout(Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")) - - val handler = { - get { - (path("") & parameters('format ?)) { - case Some(js) if js.equalsIgnoreCase("json") => { - val future = (worker ? RequestWorkerState).mapTo[WorkerState] - respondWithMediaType(`application/json`) { ctx => - ctx.complete(future) - } - } - case _ => - complete { - val future = (worker ? RequestWorkerState).mapTo[WorkerState] - future.map { workerState => - spark.deploy.worker.html.index(workerState) - } - } - } ~ - path("log") { - parameters("appId", "executorId", "logType") { (appId, executorId, logType) => - respondWithMediaType(`text/plain`) { - getFromFile(workDir.getPath() + "/" + appId + "/" + executorId + "/" + logType) - } - } - } ~ - pathPrefix("static") { - getFromResourceDirectory(STATIC_RESOURCE_DIR) - } ~ - getFromResourceDirectory(RESOURCE_DIR) - } - } -} diff --git a/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala new file mode 100644 index 0000000000..8cb74632aa --- /dev/null +++ b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala @@ -0,0 +1,100 @@ +package spark.deploy.worker.ui + +import scala.concurrent.duration._ +import scala.concurrent.Await + +import akka.pattern.ask + +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.ui.UIUtils + +private[spark] class IndexPage(parent: WorkerWebUI) { + val worker = parent.worker + val timeout = parent.timeout + + def renderJson(request: HttpServletRequest): JValue = { + val stateFuture = (worker ? RequestWorkerState)(timeout).mapTo[WorkerState] + val workerState = Await.result(stateFuture, 30 seconds) + JsonProtocol.writeWorkerState(workerState) + } + + def render(request: HttpServletRequest): Seq[Node] = { + val stateFuture = (worker ? RequestWorkerState)(timeout).mapTo[WorkerState] + val workerState = Await.result(stateFuture, 30 seconds) + + val executorHeaders = Seq("ExecutorID", "Cores", "Memory", "Job Details", "Logs") + val runningExecutorTable = + UIUtils.listingTable(executorHeaders, executorRow, workerState.executors) + val finishedExecutorTable = + UIUtils.listingTable(executorHeaders, executorRow, workerState.finishedExecutors) + + val content = + <hr /> + <div class="row"> <!-- Worker Details --> + <div class="span12"> + <ul class="unstyled"> + <li><strong>ID:</strong> {workerState.workerId}</li> + <li><strong> + Master URL:</strong> {workerState.masterUrl} + </li> + <li><strong>Cores:</strong> {workerState.cores} ({workerState.coresUsed} Used)</li> + <li><strong>Memory:</strong> {Utils.memoryMegabytesToString(workerState.memory)} + ({Utils.memoryMegabytesToString(workerState.memoryUsed)} Used)</li> + </ul> + <p><a href={workerState.masterWebUiUrl}>Back to Master</a></p> + </div> + </div> + <hr/> + + <div class="row"> <!-- Running Executors --> + <div class="span12"> + <h3> Running Executors {workerState.executors.size} </h3> + <br/> + {runningExecutorTable} + </div> + </div> + <hr/> + + <div class="row"> <!-- Finished Executors --> + <div class="span12"> + <h3> Finished Executors </h3> + <br/> + {finishedExecutorTable} + </div> + </div>; + + UIUtils.basicSparkPage(content, "Spark Worker on %s:%s".format(workerState.host, workerState.port)) + } + + def executorRow(executor: ExecutorRunner): Seq[Node] = { + <tr> + <td>{executor.execId}</td> + <td>{executor.cores}</td> + <td sorttable_customkey={executor.memory.toString}> + {Utils.memoryMegabytesToString(executor.memory)} + </td> + <td> + <ul class="unstyled"> + <li><strong>ID:</strong> {executor.appId}</li> + <li><strong>Name:</strong> {executor.appDesc.name}</li> + <li><strong>User:</strong> {executor.appDesc.user}</li> + </ul> + </td> + <td> + <a href={"log?appId=%s&executorId=%s&logType=stdout" + .format(executor.appId, executor.execId)}>stdout</a> + <a href={"log?appId=%s&executorId=%s&logType=stderr" + .format(executor.appId, executor.execId)}>stderr</a> + </td> + </tr> + } + +} diff --git a/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala new file mode 100644 index 0000000000..b1336dd1af --- /dev/null +++ b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala @@ -0,0 +1,78 @@ +package spark.deploy.worker.ui + +import akka.actor.ActorRef +import akka.util.Timeout + +import scala.concurrent.duration._ + +import java.io.{FileInputStream, File} + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.{Handler, Server} + +import spark.{Utils, Logging} +import spark.ui.JettyUtils +import spark.ui.JettyUtils._ + +/** + * Web UI server for the standalone worker. + */ +private[spark] +class WorkerWebUI(val worker: ActorRef, val workDir: File, requestedPort: Option[Int] = None) + extends Logging { + implicit val timeout = Timeout( + Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")) + val host = Utils.localHostName() + val port = requestedPort.getOrElse( + System.getProperty("worker.ui.port", WorkerWebUI.DEFAULT_PORT).toInt) + + var server: Option[Server] = None + var boundPort: Option[Int] = None + + val indexPage = new IndexPage(this) + + val handlers = Array[(String, Handler)]( + ("/static", createStaticHandler(WorkerWebUI.STATIC_RESOURCE_DIR)), + ("/log", (request: HttpServletRequest) => log(request)), + ("/json", (request: HttpServletRequest) => indexPage.renderJson(request)), + ("*", (request: HttpServletRequest) => indexPage.render(request)) + ) + + def start() { + try { + val (srv, bPort) = JettyUtils.startJettyServer("0.0.0.0", port, handlers) + server = Some(srv) + boundPort = Some(bPort) + logInfo("Started Worker web UI at http://%s:%d".format(host, bPort)) + } catch { + case e: Exception => + logError("Failed to create Worker JettyUtils", e) + System.exit(1) + } + } + + def log(request: HttpServletRequest): String = { + val appId = request.getParameter("appId") + val executorId = request.getParameter("executorId") + val logType = request.getParameter("logType") + + val maxBytes = 1024 * 1024 // Guard against OOM + val defaultBytes = 100 * 1024 + val numBytes = Option(request.getParameter("numBytes")) + .flatMap(s => Some(s.toInt)).getOrElse(defaultBytes) + + val path = "%s/%s/%s/%s".format(workDir.getPath, appId, executorId, logType) + val pre = "==== Last %s bytes of %s/%s/%s ====\n".format(numBytes, appId, executorId, logType) + pre + Utils.lastNBytes(path, math.min(numBytes, maxBytes)) + } + + def stop() { + server.foreach(_.stop()) + } +} + +private[spark] object WorkerWebUI { + val STATIC_RESOURCE_DIR = "spark/ui/static" + val DEFAULT_PORT="8081" +} diff --git a/core/src/main/scala/spark/executor/Executor.scala b/core/src/main/scala/spark/executor/Executor.scala index 2bf55ea9a9..8360547a74 100644 --- a/core/src/main/scala/spark/executor/Executor.scala +++ b/core/src/main/scala/spark/executor/Executor.scala @@ -93,15 +93,18 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert val ser = SparkEnv.get.closureSerializer.newInstance() logInfo("Running task ID " + taskId) context.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER) + var attemptedTask: Option[Task[Any]] = None + var taskStart: Long = 0 try { SparkEnv.set(env) Accumulators.clear() val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask) updateDependencies(taskFiles, taskJars) val task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader) + attemptedTask = Some(task) logInfo("Its generation is " + task.generation) env.mapOutputTracker.updateGeneration(task.generation) - val taskStart = System.currentTimeMillis() + taskStart = System.currentTimeMillis() val value = task.run(taskId.toInt) val taskFinish = System.currentTimeMillis() task.metrics.foreach{ m => @@ -129,7 +132,10 @@ private[spark] class Executor(executorId: String, slaveHostname: String, propert } case t: Throwable => { - val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace) + val serviceTime = (System.currentTimeMillis() - taskStart).toInt + val metrics = attemptedTask.flatMap(t => t.metrics) + metrics.foreach{m => m.executorRunTime = serviceTime} + val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics) context.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) // TODO: Should we exit the whole executor here? On the one hand, the failed task may diff --git a/core/src/main/scala/spark/rdd/HadoopRDD.scala b/core/src/main/scala/spark/rdd/HadoopRDD.scala index cbf5512e24..07c103503c 100644 --- a/core/src/main/scala/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/spark/rdd/HadoopRDD.scala @@ -15,6 +15,7 @@ import org.apache.hadoop.mapred.RecordReader import org.apache.hadoop.mapred.Reporter import org.apache.hadoop.util.ReflectionUtils +import spark.deploy.SparkHadoopUtil import spark.{Dependency, Logging, Partition, RDD, SerializableWritable, SparkContext, TaskContext} import spark.util.NextIterator import org.apache.hadoop.conf.Configurable @@ -50,6 +51,7 @@ class HadoopRDD[K, V]( private val confBroadcast = sc.broadcast(new SerializableWritable(conf)) override def getPartitions: Array[Partition] = { + SparkHadoopUtil.addCredentials(conf); val inputFormat = createInputFormat(conf) if (inputFormat.isInstanceOf[Configurable]) { inputFormat.asInstanceOf[Configurable].setConf(conf) diff --git a/core/src/main/scala/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/spark/scheduler/DAGScheduler.scala index 1164c40c43..d4bda150cb 100644 --- a/core/src/main/scala/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/spark/scheduler/DAGScheduler.scala @@ -257,7 +257,7 @@ class DAGScheduler( eventQueue.put(toSubmit) waiter.awaitResult() match { case JobSucceeded => {} - case JobFailed(exception: Exception) => + case JobFailed(exception: Exception, _) => logInfo("Failed to run " + callSite) throw exception } @@ -313,7 +313,7 @@ class DAGScheduler( handleExecutorLost(execId) case completion: CompletionEvent => - sparkListeners.foreach(_.onTaskEnd(SparkListenerTaskEnd(completion.task, + sparkListeners.foreach(_.onTaskEnd(SparkListenerTaskEnd(completion.task, completion.reason, completion.taskInfo, completion.taskMetrics))) handleTaskCompletion(completion) @@ -325,7 +325,7 @@ 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)))) + sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error, None)))) } return true } @@ -504,6 +504,7 @@ class DAGScheduler( case _ => "Unkown" } logInfo("%s (%s) finished in %s s".format(stage, stage.origin, serviceTime)) + stage.completionTime = Some(System.currentTimeMillis) val stageComp = StageCompleted(stageToInfos(stage)) sparkListeners.foreach{_.onStageCompleted(stageComp)} running -= stage @@ -525,6 +526,7 @@ class DAGScheduler( job.numFinished += 1 // If the whole job has finished, remove it if (job.numFinished == job.numPartitions) { + idToActiveJob -= stage.priority activeJobs -= job resultStageToJob -= stage markStageAsFinished(stage) @@ -618,8 +620,11 @@ class DAGScheduler( handleExecutorLost(bmAddress.executorId, Some(task.generation)) } + case ExceptionFailure(className, description, stackTrace, metrics) => + // Do nothing here, left up to the TaskScheduler to decide how to handle user failures + case other => - // Non-fetch failure -- probably a bug in user code; abort all jobs depending on this stage + // Unrecognized failure - abort all jobs depending on this stage abortStage(idToStage(task.stageId), task + " failed: " + other) } } @@ -652,7 +657,7 @@ class DAGScheduler( "(generation " + currentGeneration + ")") } } - + private def handleExecutorGained(execId: String, hostPort: String) { // remove from failedGeneration(execId) ? if (failedGeneration.contains(execId)) { @@ -667,11 +672,13 @@ class DAGScheduler( */ private def abortStage(failedStage: Stage, reason: String) { val dependentStages = resultStageToJob.keys.filter(x => stageDependsOn(x, failedStage)).toSeq + failedStage.completionTime = Some(System.currentTimeMillis()) for (resultStage <- dependentStages) { val job = resultStageToJob(resultStage) val error = new SparkException("Job failed: " + reason) job.listener.jobFailed(error) - sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error)))) + sparkListeners.foreach(_.onJobEnd(SparkListenerJobEnd(job, JobFailed(error, Some(failedStage))))) + idToActiveJob -= resultStage.priority activeJobs -= job resultStageToJob -= resultStage } @@ -748,6 +755,10 @@ class DAGScheduler( sizeBefore = pendingTasks.size pendingTasks.clearOldValues(cleanupTime) logInfo("pendingTasks " + sizeBefore + " --> " + pendingTasks.size) + + sizeBefore = stageToInfos.size + stageToInfos.clearOldValues(cleanupTime) + logInfo("stageToInfos " + sizeBefore + " --> " + stageToInfos.size) } def stop() { diff --git a/core/src/main/scala/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/spark/scheduler/InputFormatInfo.scala index 287f731787..17d0ea4f80 100644 --- a/core/src/main/scala/spark/scheduler/InputFormatInfo.scala +++ b/core/src/main/scala/spark/scheduler/InputFormatInfo.scala @@ -3,11 +3,13 @@ package spark.scheduler import spark.Logging import scala.collection.immutable.Set import org.apache.hadoop.mapred.{FileInputFormat, JobConf} +import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.util.ReflectionUtils import org.apache.hadoop.mapreduce.Job import org.apache.hadoop.conf.Configuration import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} import scala.collection.JavaConversions._ +import spark.deploy.SparkHadoopUtil /** @@ -70,6 +72,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl // This method does not expect failures, since validate has already passed ... private def prefLocsFromMapreduceInputFormat(): Set[SplitInfo] = { val conf = new JobConf(configuration) + SparkHadoopUtil.addCredentials(conf); FileInputFormat.setInputPaths(conf, path) val instance: org.apache.hadoop.mapreduce.InputFormat[_, _] = @@ -89,6 +92,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl // This method does not expect failures, since validate has already passed ... private def prefLocsFromMapredInputFormat(): Set[SplitInfo] = { val jobConf = new JobConf(configuration) + SparkHadoopUtil.addCredentials(jobConf); FileInputFormat.setInputPaths(jobConf, path) val instance: org.apache.hadoop.mapred.InputFormat[_, _] = diff --git a/core/src/main/scala/spark/scheduler/JobLogger.scala b/core/src/main/scala/spark/scheduler/JobLogger.scala index 178bfaba3d..6a9d52f356 100644 --- a/core/src/main/scala/spark/scheduler/JobLogger.scala +++ b/core/src/main/scala/spark/scheduler/JobLogger.scala @@ -275,7 +275,7 @@ class JobLogger(val logDirName: String) extends SparkListener with Logging { var info = "JOB_ID=" + job.runId
reason match {
case JobSucceeded => info += " STATUS=SUCCESS"
- case JobFailed(exception) =>
+ case JobFailed(exception, _) =>
info += " STATUS=FAILED REASON="
exception.getMessage.split("\\s+").foreach(info += _ + "_")
case _ =>
diff --git a/core/src/main/scala/spark/scheduler/JobResult.scala b/core/src/main/scala/spark/scheduler/JobResult.scala index 654131ee84..a0fdf391e6 100644 --- a/core/src/main/scala/spark/scheduler/JobResult.scala +++ b/core/src/main/scala/spark/scheduler/JobResult.scala @@ -6,4 +6,4 @@ package spark.scheduler private[spark] sealed trait JobResult private[spark] case object JobSucceeded extends JobResult -private[spark] case class JobFailed(exception: Exception) extends JobResult +private[spark] case class JobFailed(exception: Exception, failedStage: Option[Stage]) extends JobResult diff --git a/core/src/main/scala/spark/scheduler/JobWaiter.scala b/core/src/main/scala/spark/scheduler/JobWaiter.scala index 3cc6a86345..6ff2e29434 100644 --- a/core/src/main/scala/spark/scheduler/JobWaiter.scala +++ b/core/src/main/scala/spark/scheduler/JobWaiter.scala @@ -35,7 +35,7 @@ private[spark] class JobWaiter[T](totalTasks: Int, resultHandler: (Int, T) => Un throw new UnsupportedOperationException("jobFailed() called on a finished JobWaiter") } jobFinished = true - jobResult = JobFailed(exception) + jobResult = JobFailed(exception, None) this.notifyAll() } } diff --git a/core/src/main/scala/spark/scheduler/SparkListener.scala b/core/src/main/scala/spark/scheduler/SparkListener.scala index bac984b5c9..8de3aa91a4 100644 --- a/core/src/main/scala/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/spark/scheduler/SparkListener.scala @@ -68,6 +68,7 @@ class StatsReportListener extends SparkListener with Logging { showBytesDistribution("task result size:", (_, metric) => Some(metric.resultSize)) //runtime breakdown + val runtimePcts = stageCompleted.stageInfo.taskInfos.map{ case (info, metrics) => RuntimePercentage(info.duration, metrics) } diff --git a/core/src/main/scala/spark/scheduler/Stage.scala b/core/src/main/scala/spark/scheduler/Stage.scala index 7fc9e13fd9..539cf8233b 100644 --- a/core/src/main/scala/spark/scheduler/Stage.scala +++ b/core/src/main/scala/spark/scheduler/Stage.scala @@ -34,6 +34,7 @@ private[spark] class Stage( /** When first task was submitted to scheduler. */ var submissionTime: Option[Long] = None + var completionTime: Option[Long] = None private var nextAttemptId = 0 diff --git a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala index d72b0bfc9f..fe6420a522 100644 --- a/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala +++ b/core/src/main/scala/spark/scheduler/cluster/ClusterTaskSetManager.scala @@ -571,6 +571,7 @@ private[spark] class ClusterTaskSetManager( return case ef: ExceptionFailure => + sched.listener.taskEnded(tasks(index), ef, null, null, info, ef.metrics.getOrElse(null)) val key = ef.description val now = System.currentTimeMillis val (printFull, dupCount) = { diff --git a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala index 93d4318b29..b000e328e6 100644 --- a/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala +++ b/core/src/main/scala/spark/scheduler/local/LocalScheduler.scala @@ -145,6 +145,9 @@ 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 start = System.currentTimeMillis() + var taskStart: Long = 0 try { Accumulators.clear() Thread.currentThread().setContextClassLoader(classLoader) @@ -153,10 +156,11 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: // this adds a bit of unnecessary overhead but matches how the Mesos Executor works. val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(bytes) updateDependencies(taskFiles, taskJars) // Download any files added with addFile - val deserStart = System.currentTimeMillis() val deserializedTask = ser.deserialize[Task[_]]( taskBytes, Thread.currentThread.getContextClassLoader) - val deserTime = System.currentTimeMillis() - deserStart + attemptedTask = Some(deserializedTask) + val deserTime = System.currentTimeMillis() - start + taskStart = System.currentTimeMillis() // Run it val result: Any = deserializedTask.run(taskId) @@ -170,16 +174,19 @@ private[spark] class LocalScheduler(threads: Int, val maxFailures: Int, val sc: val resultToReturn = ser.deserialize[Any](serResult) val accumUpdates = ser.deserialize[collection.mutable.Map[Long, Any]]( ser.serialize(Accumulators.values)) + val serviceTime = System.currentTimeMillis() - taskStart logInfo("Finished " + taskId) - deserializedTask.metrics.get.executorRunTime = deserTime.toInt//info.duration.toInt //close enough + deserializedTask.metrics.get.executorRunTime = serviceTime.toInt deserializedTask.metrics.get.executorDeserializeTime = deserTime.toInt - val taskResult = new TaskResult(result, accumUpdates, deserializedTask.metrics.getOrElse(null)) val serializedResult = ser.serialize(taskResult) localActor ! LocalStatusUpdate(taskId, TaskState.FINISHED, serializedResult) } catch { case t: Throwable => { - val failure = new ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace) + val serviceTime = System.currentTimeMillis() - taskStart + val metrics = attemptedTask.flatMap(t => t.metrics) + metrics.foreach{m => m.executorRunTime = serviceTime.toInt} + val failure = new ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics) localActor ! LocalStatusUpdate(taskId, TaskState.FAILED, ser.serialize(failure)) } } diff --git a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala index 70b69bb26f..f12fec41d5 100644 --- a/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala +++ b/core/src/main/scala/spark/scheduler/local/LocalTaskSetManager.scala @@ -152,6 +152,7 @@ private[spark] class LocalTaskSetManager(sched: LocalScheduler, val taskSet: Tas info.markFailed() decreaseRunningTasks(1) val reason: ExceptionFailure = ser.deserialize[ExceptionFailure](serializedData, getClass.getClassLoader) + sched.listener.taskEnded(task, reason, null, null, info, reason.metrics.getOrElse(null)) if (!finished(index)) { copiesRunning(index) -= 1 numFailures(index) += 1 diff --git a/core/src/main/scala/spark/storage/BlockManagerUI.scala b/core/src/main/scala/spark/storage/BlockManagerUI.scala deleted file mode 100644 index 631455abcd..0000000000 --- a/core/src/main/scala/spark/storage/BlockManagerUI.scala +++ /dev/null @@ -1,82 +0,0 @@ -package spark.storage - -import akka.actor.{ActorRef, ActorSystem} - -import akka.util.Timeout -import scala.concurrent.duration._ -import spray.httpx.TwirlSupport._ -import spray.routing.Directives - -import spark.{Logging, SparkContext} -import spark.util.AkkaUtils -import spark.Utils - - -/** - * Web UI server for the BlockManager inside each SparkContext. - */ -private[spark] -class BlockManagerUI(val actorSystem: ActorSystem, blockManagerMaster: ActorRef, sc: SparkContext) - extends Directives with Logging { - - implicit val implicitActorSystem = actorSystem - val STATIC_RESOURCE_DIR = "spark/deploy/static" - - implicit val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") - val host = Utils.localHostName() - val port = if (System.getProperty("spark.ui.port") != null) { - System.getProperty("spark.ui.port").toInt - } else { - // TODO: Unfortunately, it's not possible to pass port 0 to spray and figure out which - // random port it bound to, so we have to try to find a local one by creating a socket. - Utils.findFreePort() - } - - /** Start a HTTP server to run the Web interface */ - def start() { - try { - AkkaUtils.startSprayServer(actorSystem, "0.0.0.0", port, handler, "BlockManagerHTTPServer") - logInfo("Started BlockManager web UI at http://%s:%d".format(host, port)) - } catch { - case e: Exception => - logError("Failed to create BlockManager WebUI", e) - System.exit(1) - } - } - - val handler = { - get { - path("") { - complete { - // Request the current storage status from the Master - val storageStatusList = sc.getExecutorStorageStatus - // Calculate macro-level statistics - val maxMem = storageStatusList.map(_.maxMem).reduce(_+_) - val remainingMem = storageStatusList.map(_.memRemaining).reduce(_+_) - val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize)) - .reduceOption(_+_).getOrElse(0L) - val rdds = StorageUtils.rddInfoFromStorageStatus(storageStatusList, sc) - spark.storage.html.index. - render(maxMem, remainingMem, diskSpaceUsed, rdds, storageStatusList) - } - } ~ - path("rdd") { - parameter("id") { id => - complete { - val prefix = "rdd_" + id.toString - val storageStatusList = sc.getExecutorStorageStatus - val filteredStorageStatusList = StorageUtils. - filterStorageStatusByPrefix(storageStatusList, prefix) - val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head - spark.storage.html.rdd.render(rddInfo, filteredStorageStatusList) - } - } - } ~ - pathPrefix("static") { - getFromResourceDirectory(STATIC_RESOURCE_DIR) - } - } - } - - private[spark] def appUIAddress = "http://" + host + ":" + port -} diff --git a/core/src/main/scala/spark/ui/JettyUtils.scala b/core/src/main/scala/spark/ui/JettyUtils.scala new file mode 100644 index 0000000000..bc6f9c10d5 --- /dev/null +++ b/core/src/main/scala/spark/ui/JettyUtils.scala @@ -0,0 +1,115 @@ +package spark.ui + +import annotation.tailrec + +import javax.servlet.http.{HttpServletResponse, HttpServletRequest} + +import net.liftweb.json.{JValue, pretty, render} + +import org.eclipse.jetty.server.{Server, Request, Handler} +import org.eclipse.jetty.server.handler.{ResourceHandler, HandlerList, ContextHandler, AbstractHandler} +import org.eclipse.jetty.util.thread.QueuedThreadPool + +import scala.util.{Try, Success, Failure} +import scala.xml.Node + +import spark.Logging + +/** Utilities for launching a web server using Jetty's HTTP Server class */ +private[spark] object JettyUtils extends Logging { + // Base type for a function that returns something based on an HTTP request. Allows for + // implicit conversion from many types of functions to jetty Handlers. + type Responder[T] = HttpServletRequest => T + + // Conversions from various types of Responder's to jetty Handlers + implicit def jsonResponderToHandler(responder: Responder[JValue]): Handler = + createHandler(responder, "text/json", (in: JValue) => pretty(render(in))) + + implicit def htmlResponderToHandler(responder: Responder[Seq[Node]]): Handler = + createHandler(responder, "text/html", (in: Seq[Node]) => "<!DOCTYPE html>" + in.toString) + + implicit def textResponderToHandler(responder: Responder[String]): Handler = + createHandler(responder, "text/plain") + + private def createHandler[T <% AnyRef](responder: Responder[T], contentType: String, + extractFn: T => String = (in: Any) => in.toString): Handler = { + new AbstractHandler { + def handle(target: String, + baseRequest: Request, + request: HttpServletRequest, + response: HttpServletResponse) { + response.setContentType("%s;charset=utf-8".format(contentType)) + response.setStatus(HttpServletResponse.SC_OK) + baseRequest.setHandled(true) + val result = responder(request) + response.setHeader("Cache-Control", "no-cache, no-store, must-revalidate") + response.getWriter().println(extractFn(result)) + } + } + } + + /** Creates a handler that always redirects the user to a given path */ + def createRedirectHandler(newPath: String): Handler = { + new AbstractHandler { + def handle(target: String, + baseRequest: Request, + request: HttpServletRequest, + response: HttpServletResponse) { + response.setStatus(302) + response.setHeader("Location", baseRequest.getRootURL + newPath) + baseRequest.setHandled(true) + } + } + } + + /** Creates a handler for serving files from a static directory */ + def createStaticHandler(resourceBase: String): ResourceHandler = { + val staticHandler = new ResourceHandler + Option(getClass.getClassLoader.getResource(resourceBase)) match { + case Some(res) => + staticHandler.setResourceBase(res.toString) + case None => + throw new Exception("Could not find resource path for Web UI: " + resourceBase) + } + staticHandler + } + + /** + * Attempts to start a Jetty server at the supplied ip:port which uses the supplied handlers. + * + * If the desired port number is contented, continues incrementing ports until a free port is + * found. Returns the chosen port and the jetty Server object. + */ + def startJettyServer(ip: String, port: Int, handlers: Seq[(String, Handler)]): (Server, Int) = { + val handlersToRegister = handlers.map { case(path, handler) => + val contextHandler = new ContextHandler(path) + contextHandler.setHandler(handler) + contextHandler.asInstanceOf[org.eclipse.jetty.server.Handler] + } + + val handlerList = new HandlerList + handlerList.setHandlers(handlersToRegister.toArray) + + @tailrec + def connect(currentPort: Int): (Server, Int) = { + val server = new Server(currentPort) + val pool = new QueuedThreadPool + pool.setDaemon(true) + server.setThreadPool(pool) + server.setHandler(handlerList) + + Try { server.start() } match { + case s: Success[_] => + sys.addShutdownHook(server.stop()) // Be kind, un-bind + (server, server.getConnectors.head.getLocalPort) + case f: Failure[_] => + server.stop() + logInfo("Failed to create UI at port, %s. Trying again.".format(currentPort)) + logInfo("Error was: " + f.toString) + connect((currentPort + 1) % 65536) + } + } + + connect(port) + } +} diff --git a/core/src/main/scala/spark/ui/Page.scala b/core/src/main/scala/spark/ui/Page.scala new file mode 100644 index 0000000000..c853b44b76 --- /dev/null +++ b/core/src/main/scala/spark/ui/Page.scala @@ -0,0 +1,3 @@ +package spark.ui + +private[spark] object Page extends Enumeration { val Storage, Jobs = Value }
\ No newline at end of file diff --git a/core/src/main/scala/spark/ui/SparkUI.scala b/core/src/main/scala/spark/ui/SparkUI.scala new file mode 100644 index 0000000000..b3bdc2c490 --- /dev/null +++ b/core/src/main/scala/spark/ui/SparkUI.scala @@ -0,0 +1,63 @@ +package spark.ui + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.{Handler, Server} + +import spark.{Logging, SparkContext, Utils} +import spark.ui.storage.BlockManagerUI +import spark.ui.jobs.JobProgressUI +import spark.ui.UIUtils._ +import spark.ui.JettyUtils._ + +/** Top level user interface for Spark */ +private[spark] class SparkUI(sc: SparkContext) extends Logging { + // TODO(pwendell): It would be nice to add a view that prints out environment information + + val host = Utils.localHostName() + val port = Option(System.getProperty("spark.ui.port")).getOrElse(SparkUI.DEFAULT_PORT).toInt + var boundPort: Option[Int] = None + var server: Option[Server] = None + + val handlers = Seq[(String, Handler)]( + ("/static", createStaticHandler(SparkUI.STATIC_RESOURCE_DIR)), + ("/", createRedirectHandler("/stages")) + ) + val storage = new BlockManagerUI(sc) + val jobs = new JobProgressUI(sc) + val allHandlers = storage.getHandlers ++ jobs.getHandlers ++ handlers + + /** Bind the HTTP server which backs this web interface */ + def bind() { + try { + val (srv, usedPort) = JettyUtils.startJettyServer("0.0.0.0", port, allHandlers) + logInfo("Started Spark Web UI at http://%s:%d".format(host, usedPort)) + server = Some(srv) + boundPort = Some(usedPort) + } catch { + case e: Exception => + logError("Failed to create Spark JettyUtils", e) + System.exit(1) + } + } + + /** Initialize all components of the server */ + def start() { + // NOTE: This is decoupled from bind() because of the following dependency cycle: + // DAGScheduler() requires that the port of this server is known + // This server must register all handlers, including JobProgressUI, before binding + // JobProgressUI registers a listener with SparkContext, which requires sc to initialize + jobs.start() + } + + def stop() { + server.foreach(_.stop()) + } + + private[spark] def appUIAddress = "http://" + host + ":" + boundPort.getOrElse("-1") +} + +private[spark] object SparkUI { + val DEFAULT_PORT = "33000" + val STATIC_RESOURCE_DIR = "spark/ui/static" +} diff --git a/core/src/main/scala/spark/ui/UIUtils.scala b/core/src/main/scala/spark/ui/UIUtils.scala new file mode 100644 index 0000000000..7b79290d1b --- /dev/null +++ b/core/src/main/scala/spark/ui/UIUtils.scala @@ -0,0 +1,113 @@ +package spark.ui + +import scala.xml.Node + +import spark.SparkContext + +/** Utility functions for generating XML pages with spark content. */ +private[spark] object UIUtils { + import Page._ + + /** Returns a spark page with correctly formatted headers */ + def headerSparkPage(content: => Seq[Node], sc: SparkContext, title: String, page: Page.Value) + : Seq[Node] = { + val storage = page match { + case Storage => <li class="active"><a href="/storage">Storage</a></li> + case _ => <li><a href="/storage">Storage</a></li> + } + val jobs = page match { + case Jobs => <li class="active"><a href="/stages">Jobs</a></li> + case _ => <li><a href="/stages">Jobs</a></li> + } + + <html> + <head> + <meta http-equiv="Content-type" content="text/html; charset=utf-8" /> + <link rel="stylesheet" href="/static/bootstrap.min.css" type="text/css" /> + <link rel="stylesheet" href="/static/webui.css" type="text/css" /> + <link rel="stylesheet" href="/static/bootstrap-responsive.min.css" type="text/css" /> + <script src="/static/sorttable.js"></script> + <title>{title}</title> + <style type="text/css"> + table.sortable thead {{ cursor: pointer; }} + </style> + </head> + <body> + <div class="container"> + + <div class="row"> + <div class="span12"> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <div class="brand"><img src="/static/spark-logo-77x50px-hd.png" /></div> + <ul class="nav"> + {storage} + {jobs} + </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> + </div> + </div> + </div> + </div> + </div> + + <div class="row" style="padding-top: 5px;"> + <div class="span12"> + <h1 style="vertical-align: bottom; display: inline-block;"> + {title} + </h1> + </div> + </div> + <hr/> + {content} + </div> + </body> + </html> + } + + /** Returns a page with the spark css/js and a simple format. Used for scheduler UI. */ + def basicSparkPage(content: => Seq[Node], title: String): Seq[Node] = { + <html> + <head> + <meta http-equiv="Content-type" content="text/html; charset=utf-8" /> + <link rel="stylesheet" href="/static/bootstrap.min.css" type="text/css" /> + <link rel="stylesheet" href="/static/bootstrap-responsive.min.css" type="text/css" /> + <script src="/static/sorttable.js"></script> + <title>{title}</title> + <style type="text/css"> + table.sortable thead {{ cursor: pointer; }} + </style> + </head> + <body> + <div class="container"> + <div class="row"> + <div class="span2"> + <img src="/static/spark_logo.png" /> + </div> + <div class="span10"> + <h1 style="vertical-align: bottom; margin-top: 40px; display: inline-block;"> + {title} + </h1> + </div> + </div> + {content} + </div> + </body> + </html> + } + + /** Returns an HTML table constructed by generating a row for each object in a sequence. */ + def listingTable[T](headers: Seq[String], makeRow: T => Seq[Node], rows: Seq[T]): Seq[Node] = { + <table class="table table-bordered table-striped table-condensed sortable"> + <thead>{headers.map(h => <th>{h}</th>)}</thead> + <tbody> + {rows.map(r => makeRow(r))} + </tbody> + </table> + } +}
\ No newline at end of file diff --git a/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala new file mode 100644 index 0000000000..8bbc6ce88e --- /dev/null +++ b/core/src/main/scala/spark/ui/UIWorkloadGenerator.scala @@ -0,0 +1,71 @@ +package spark.ui + +import scala.util.Random + +import spark.SparkContext +import spark.SparkContext._ + +/** + * Continuously generates jobs that expose various features of the WebUI (internal testing tool). + * + * Usage: ./run spark.ui.UIWorkloadGenerator [master] + */ +private[spark] object UIWorkloadGenerator { + val NUM_PARTITIONS = 100 + val INTER_JOB_WAIT_MS = 500 + + def main(args: Array[String]) { + val master = args(0) + val appName = "Spark UI Tester" + 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) + } + val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS) + def nextFloat() = (new Random()).nextFloat() + + val jobs = Seq[(String, () => Long)]( + ("Count", baseData.count), + ("Cache and Count", baseData.map(x => x).cache.count), + ("Single Shuffle", baseData.map(x => (x % 10, x)).reduceByKey(_ + _).count), + ("Entirely failed phase", baseData.map(x => throw new Exception).count), + ("Partially failed phase", { + baseData.map{x => + val probFailure = (4.0 / NUM_PARTITIONS) + if (nextFloat() < probFailure) { + throw new Exception("This is a task failure") + } + 1 + }.count + }), + ("Partially failed phase (longer tasks)", { + baseData.map{x => + val probFailure = (4.0 / NUM_PARTITIONS) + if (nextFloat() < probFailure) { + Thread.sleep(100) + throw new Exception("This is a task failure") + } + 1 + }.count + }), + ("Job with delays", baseData.map(x => Thread.sleep(100)).count) + ) + + while (true) { + for ((desc, job) <- jobs) { + try { + setName(desc) + job() + println("Job funished: " + desc) + } catch { + case e: Exception => + println("Job Failed: " + desc) + } + Thread.sleep(INTER_JOB_WAIT_MS) + } + } + } +} diff --git a/core/src/main/scala/spark/ui/jobs/IndexPage.scala b/core/src/main/scala/spark/ui/jobs/IndexPage.scala new file mode 100644 index 0000000000..1e675ab2cb --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/IndexPage.scala @@ -0,0 +1,112 @@ +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.ui.Page._ +import spark.storage.StorageLevel + +/** Page showing list of all ongoing and recently finished stages */ +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> + <td>Duration</td> + <td colspan="2">Tasks: Complete/Total</td> + <td>Shuffle Activity</td> + <td>Stored RDD</td> + </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" + } + } + + 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.origin}</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/JobProgressUI.scala b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala new file mode 100644 index 0000000000..c5d2aa5d60 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala @@ -0,0 +1,127 @@ +package spark.ui.jobs + +import scala.concurrent.duration._ + +import java.text.SimpleDateFormat + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.Handler + +import scala.Seq +import scala.collection.mutable.{HashSet, ListBuffer, HashMap, ArrayBuffer} + +import spark.ui.JettyUtils._ +import spark.{ExceptionFailure, SparkContext, Success, Utils} +import spark.scheduler._ +import spark.scheduler.cluster.TaskInfo +import spark.executor.TaskMetrics +import collection.mutable + +/** Web UI showing progress status of all jobs in the given SparkContext. */ +private[spark] class JobProgressUI(val sc: SparkContext) { + private var _listener: Option[JobProgressListener] = None + def listener = _listener.get + val dateFmt = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") + + private val indexPage = new IndexPage(this) + private val stagePage = new StagePage(this) + + def start() { + _listener = Some(new JobProgressListener) + sc.addSparkListener(listener) + } + + def formatDuration(ms: Long) = Utils.msDurationToString(ms) + + def getHandlers = Seq[(String, Handler)]( + ("/stages/stage", (request: HttpServletRequest) => stagePage.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/StagePage.scala b/core/src/main/scala/spark/ui/jobs/StagePage.scala new file mode 100644 index 0000000000..51b82b6a8c --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/StagePage.scala @@ -0,0 +1,114 @@ +package spark.ui.jobs + +import java.util.Date + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import spark.ui.UIUtils._ +import spark.ui.Page._ +import spark.util.Distribution +import spark.{ExceptionFailure, Utils} +import spark.scheduler.cluster.TaskInfo +import spark.executor.TaskMetrics + +/** Page showing statistics and task list for a given stage */ +private[spark] class StagePage(parent: JobProgressUI) { + def listener = parent.listener + val dateFmt = parent.dateFmt + + def render(request: HttpServletRequest): Seq[Node] = { + val stageId = request.getParameter("id").toInt + + 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) + + val shuffleRead = listener.hasShuffleRead(stageId) + val shuffleWrite = listener.hasShuffleWrite(stageId) + + 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 taskTable = listingTable(taskHeaders, taskRow, tasks) + + // Excludes tasks which failed and have incomplete metrics + val validTasks = tasks.filter(t => Option(t._2).isDefined) + + val summaryTable: Option[Seq[Node]] = + if (validTasks.size == 0) { + None + } + else { + val serviceTimes = validTasks.map{case (info, metrics, exception) => + metrics.get.executorRunTime.toDouble} + val serviceQuantiles = "Duration" +: Distribution(serviceTimes).get.getQuantiles().map( + ms => parent.formatDuration(ms.toLong)) + + def getQuantileCols(data: Seq[Double]) = + Distribution(data).get.getQuantiles().map(d => Utils.memoryBytesToString(d.toLong)) + + val shuffleReadSizes = validTasks.map { + case(info, metrics, exception) => + metrics.get.shuffleReadMetrics.map(_.remoteBytesRead).getOrElse(0L).toDouble + } + val shuffleReadQuantiles = "Shuffle Read (Remote)" +: getQuantileCols(shuffleReadSizes) + + val shuffleWriteSizes = validTasks.map { + case(info, metrics, exception) => + metrics.get.shuffleWriteMetrics.map(_.shuffleBytesWritten).getOrElse(0L).toDouble + } + val shuffleWriteQuantiles = "Shuffle Write" +: getQuantileCols(shuffleWriteSizes) + + val listings: Seq[Seq[String]] = Seq(serviceQuantiles, + if (shuffleRead) shuffleReadQuantiles else Nil, + if (shuffleWrite) shuffleWriteQuantiles else Nil) + + val quantileHeaders = Seq("Metric", "Min", "25%", "50%", "75%", "Max") + def quantileRow(data: Seq[String]): Seq[Node] = <tr> {data.map(d => <td>{d}</td>)} </tr> + Some(listingTable(quantileHeaders, quantileRow, listings)) + } + + val content = + <h2>Summary Metrics</h2> ++ summaryTable.getOrElse(Nil) ++ <h2>Tasks</h2> ++ taskTable; + + headerSparkPage(content, parent.sc, "Stage Details: %s".format(stageId), Jobs) + } + + + def taskRow(taskData: (TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])): Seq[Node] = { + def fmtStackTrace(trace: Seq[StackTraceElement]): Seq[Node] = + trace.map(e => <span style="display:block;">{e.toString}</span>) + val (info, metrics, exception) = taskData + <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> + <td>{info.taskLocality}</td> + <td>{info.hostPort}</td> + <td>{dateFmt.format(new Date(info.launchTime))}</td> + {metrics.flatMap{m => m.shuffleReadMetrics}.map{s => + <td>{Utils.memoryBytesToString(s.remoteBytesRead)}</td>}.getOrElse("")} + {metrics.flatMap{m => m.shuffleWriteMetrics}.map{s => + <td>{Utils.memoryBytesToString(s.shuffleBytesWritten)}</td>}.getOrElse("")} + <td>{exception.map(e => + <span> + {e.className} ({e.description})<br/> + {fmtStackTrace(e.stackTrace)} + </span>).getOrElse("")} + </td> + </tr> + } +} diff --git a/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala b/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala new file mode 100644 index 0000000000..252600aad4 --- /dev/null +++ b/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala @@ -0,0 +1,24 @@ +package spark.ui.storage + +import scala.concurrent.duration._ + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.Handler + +import spark.{Logging, SparkContext} +import spark.ui.JettyUtils._ + +/** Web UI showing storage status of all RDD's in the given SparkContext. */ +private[spark] class BlockManagerUI(val sc: SparkContext) extends Logging { + implicit val timeout = Duration.create( + System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + + val indexPage = new IndexPage(this) + val rddPage = new RDDPage(this) + + def getHandlers = Seq[(String, Handler)]( + ("/storage/rdd", (request: HttpServletRequest) => rddPage.render(request)), + ("/storage", (request: HttpServletRequest) => indexPage.render(request)) + ) +} diff --git a/core/src/main/scala/spark/ui/storage/IndexPage.scala b/core/src/main/scala/spark/ui/storage/IndexPage.scala new file mode 100644 index 0000000000..d284134391 --- /dev/null +++ b/core/src/main/scala/spark/ui/storage/IndexPage.scala @@ -0,0 +1,64 @@ +package spark.ui.storage + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import spark.storage.{RDDInfo, StorageUtils} +import spark.Utils +import spark.ui.UIUtils._ +import spark.ui.Page._ + +/** Page showing list of RDD's currently stored in the cluster */ +private[spark] class IndexPage(parent: BlockManagerUI) { + val sc = parent.sc + + def render(request: HttpServletRequest): Seq[Node] = { + val storageStatusList = sc.getExecutorStorageStatus + // Calculate macro-level statistics + val 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", + "Storage Level", + "Cached Partitions", + "Fraction Partitions Cached", + "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}; + + headerSparkPage(content, parent.sc, "Spark Storage ", Storage) + } + + def rddRow(rdd: RDDInfo): Seq[Node] = { + <tr> + <td> + <a href={"/storage/rdd?id=%s".format(rdd.id)}> + {rdd.name} + </a> + </td> + <td>{rdd.storageLevel.description} + </td> + <td>{rdd.numCachedPartitions}</td> + <td>{rdd.numCachedPartitions / rdd.numPartitions.toDouble}</td> + <td>{Utils.memoryBytesToString(rdd.memSize)}</td> + <td>{Utils.memoryBytesToString(rdd.diskSize)}</td> + </tr> + } +} diff --git a/core/src/main/scala/spark/ui/storage/RDDPage.scala b/core/src/main/scala/spark/ui/storage/RDDPage.scala new file mode 100644 index 0000000000..0cb1e47ea5 --- /dev/null +++ b/core/src/main/scala/spark/ui/storage/RDDPage.scala @@ -0,0 +1,104 @@ +package spark.ui.storage + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import spark.storage.{StorageStatus, StorageUtils} +import spark.ui.UIUtils._ +import spark.Utils +import spark.storage.BlockManagerMasterActor.BlockStatus +import spark.ui.Page._ + +/** Page showing storage details for a given RDD */ +private[spark] class RDDPage(parent: BlockManagerUI) { + val sc = parent.sc + + def render(request: HttpServletRequest): Seq[Node] = { + val id = request.getParameter("id") + val prefix = "rdd_" + id.toString + val storageStatusList = sc.getExecutorStorageStatus + val filteredStorageStatusList = StorageUtils. + filterStorageStatusByPrefix(storageStatusList, prefix) + val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head + + val workerHeaders = Seq("Host", "Memory Usage", "Disk Usage") + val workers = filteredStorageStatusList.map((prefix, _)) + val workerTable = listingTable(workerHeaders, workerRow, workers) + + val blockHeaders = Seq("Block Name", "Storage Level", "Size in Memory", "Size on Disk") + val blocks = filteredStorageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1) + val blockTable = listingTable(blockHeaders, blockRow, blocks) + + val content = + <div class="row"> + <div class="span12"> + <ul class="unstyled"> + <li> + <strong>Storage Level:</strong> + {rddInfo.storageLevel.description} + </li> + <li> + <strong>Cached Partitions:</strong> + {rddInfo.numCachedPartitions} + </li> + <li> + <strong>Total Partitions:</strong> + {rddInfo.numPartitions} + </li> + <li> + <strong>Memory Size:</strong> + {Utils.memoryBytesToString(rddInfo.memSize)} + </li> + <li> + <strong>Disk Size:</strong> + {Utils.memoryBytesToString(rddInfo.diskSize)} + </li> + </ul> + </div> + </div> + <hr/> + <div class="row"> + <div class="span12"> + {workerTable} + </div> + </div> + <hr/> + <div class="row"> + <div class="span12"> + <h3> RDD Summary </h3> + {blockTable} + </div> + </div>; + + headerSparkPage(content, parent.sc, "RDD Info: " + rddInfo.name, Jobs) + } + + def blockRow(blk: (String, BlockStatus)): Seq[Node] = { + val (id, block) = blk + <tr> + <td>{id}</td> + <td> + {block.storageLevel.description} + </td> + <td sorttable_customkey={block.memSize.toString}> + {Utils.memoryBytesToString(block.memSize)} + </td> + <td sorttable_customkey={block.diskSize.toString}> + {Utils.memoryBytesToString(block.diskSize)} + </td> + </tr> + } + + def workerRow(worker: (String, StorageStatus)): Seq[Node] = { + val (prefix, status) = worker + <tr> + <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td> + <td> + {Utils.memoryBytesToString(status.memUsed(prefix))} + ({Utils.memoryBytesToString(status.memRemaining)} Total Available) + </td> + <td>{Utils.memoryBytesToString(status.diskUsed(prefix))}</td> + </tr> + } +} diff --git a/core/src/main/scala/spark/util/AkkaUtils.scala b/core/src/main/scala/spark/util/AkkaUtils.scala index ea39888c21..c381a0510b 100644 --- a/core/src/main/scala/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/spark/util/AkkaUtils.scala @@ -1,20 +1,10 @@ package spark.util -import akka.actor.{Props, ActorSystem, ExtendedActorSystem} +import akka.actor.{ActorSystem, ExtendedActorSystem} import com.typesafe.config.ConfigFactory import scala.concurrent.duration._ -import akka.pattern.ask -import akka.remote.RemoteActorRefProvider - -import spray.routing.Route -import spray.io.IOExtension -import spray.routing.HttpServiceActor -import spray.can.server.{HttpServer, ServerSettings} -import spray.io.SingletonHandler import scala.concurrent.Await -import spark.{Utils, SparkException} - -import java.util.concurrent.TimeoutException +import akka.remote.RemoteActorRefProvider /** * Various utility classes for working with Akka. @@ -65,29 +55,4 @@ private[spark] object AkkaUtils { return (actorSystem, boundPort) } - /** - * Creates a Spray HTTP server bound to a given IP and port with a given Spray Route object to - * handle requests. Returns the bound port or throws a SparkException on failure. - * TODO: Not changing ip to host here - is it required ? - */ - def startSprayServer(actorSystem: ActorSystem, ip: String, port: Int, route: Route, name: String = "HttpServer") = { - val ioWorker = IOExtension(actorSystem).ioBridge() - val httpService = actorSystem.actorOf(Props(HttpServiceActor(route))) - val server = actorSystem.actorOf( - Props(new HttpServer(ioWorker, SingletonHandler(httpService), ServerSettings())), name = name) - actorSystem.registerOnTermination { actorSystem.stop(ioWorker) } - val timeout = 3.seconds - val future = server.ask(HttpServer.Bind(ip, port))(timeout) - try { - Await.result(future, timeout) match { - case bound: HttpServer.Bound => - server - case other: Any => - throw new SparkException("Failed to bind web UI to port " + port + ": " + other) - } - } catch { - case e: TimeoutException => - throw new SparkException("Failed to bind web UI to port " + port) - } - } } diff --git a/core/src/main/twirl/spark/common/layout.scala.html b/core/src/main/twirl/spark/common/layout.scala.html deleted file mode 100644 index b9192060aa..0000000000 --- a/core/src/main/twirl/spark/common/layout.scala.html +++ /dev/null @@ -1,35 +0,0 @@ -@(title: String)(content: Html) - -<!DOCTYPE html> -<html> - - <head> - <meta http-equiv="Content-type" content="text/html; charset=utf-8"> - <link rel="stylesheet" href="static/bootstrap.min.css" type="text/css"> - <link rel="stylesheet" href="static/bootstrap-responsive.min.css" type="text/css"> - <script src="static/sorttable.js"></script> - <title>@title</title> - <style type="text/css"> - table.sortable thead { cursor: pointer; } - </style> - </head> - - <body> - <div class="container"> - - <!-- HEADER --> - <div class="row"> - <div class="span12"> - <img src="static/spark_logo.png"> - <h1 style="vertical-align: bottom; margin-bottom: 10px; margin-left: 30px; display: inline-block;"> @title </h1> - </div> - </div> - - <hr/> - - @content - - </div> - </body> - -</html>
\ No newline at end of file diff --git a/core/src/main/twirl/spark/deploy/master/app_details.scala.html b/core/src/main/twirl/spark/deploy/master/app_details.scala.html deleted file mode 100644 index 5e5e5de551..0000000000 --- a/core/src/main/twirl/spark/deploy/master/app_details.scala.html +++ /dev/null @@ -1,38 +0,0 @@ -@(app: spark.deploy.master.ApplicationInfo) - -@spark.common.html.layout(title = "Application Details") { - - <!-- Application Details --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li><strong>ID:</strong> @app.id</li> - <li><strong>Description:</strong> @app.desc.name</li> - <li><strong>User:</strong> @app.desc.user</li> - <li><strong>Cores:</strong> - @if(app.desc.maxCores == Integer.MAX_VALUE) { - Unlimited (@app.coresGranted granted) - } else { - @app.desc.maxCores (@app.coresGranted granted, @app.coresLeft left) - } - </li> - <li><strong>Memory per Slave:</strong> @app.desc.memoryPerSlave</li> - <li><strong>Submit Date:</strong> @app.submitDate</li> - <li><strong>State:</strong> @app.state</li> - <li><strong><a href="@app.appUiUrl">Application Detail UI</a></strong></li> - </ul> - </div> - </div> - - <hr/> - - <!-- Executors --> - <div class="row"> - <div class="span12"> - <h3> Executor Summary </h3> - <br/> - @executors_table(app.executors.values.toList) - </div> - </div> - -} diff --git a/core/src/main/twirl/spark/deploy/master/app_row.scala.html b/core/src/main/twirl/spark/deploy/master/app_row.scala.html deleted file mode 100644 index feb306f35c..0000000000 --- a/core/src/main/twirl/spark/deploy/master/app_row.scala.html +++ /dev/null @@ -1,20 +0,0 @@ -@(app: spark.deploy.master.ApplicationInfo) - -@import spark.Utils -@import spark.deploy.WebUI.formatDate -@import spark.deploy.WebUI.formatDuration - -<tr> - <td> - <a href="app?appId=@(app.id)">@app.id</a> - </td> - <td>@app.desc.name</td> - <td> - @app.coresGranted - </td> - <td>@Utils.memoryMegabytesToString(app.desc.memoryPerSlave)</td> - <td>@formatDate(app.submitDate)</td> - <td>@app.desc.user</td> - <td>@app.state.toString()</td> - <td>@formatDuration(app.duration)</td> -</tr> diff --git a/core/src/main/twirl/spark/deploy/master/app_table.scala.html b/core/src/main/twirl/spark/deploy/master/app_table.scala.html deleted file mode 100644 index f789cee0f1..0000000000 --- a/core/src/main/twirl/spark/deploy/master/app_table.scala.html +++ /dev/null @@ -1,21 +0,0 @@ -@(apps: Array[spark.deploy.master.ApplicationInfo]) - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>ID</th> - <th>Description</th> - <th>Cores</th> - <th>Memory per Node</th> - <th>Submit Time</th> - <th>User</th> - <th>State</th> - <th>Duration</th> - </tr> - </thead> - <tbody> - @for(j <- apps) { - @app_row(j) - } - </tbody> -</table> diff --git a/core/src/main/twirl/spark/deploy/master/executor_row.scala.html b/core/src/main/twirl/spark/deploy/master/executor_row.scala.html deleted file mode 100644 index 21e72c7aab..0000000000 --- a/core/src/main/twirl/spark/deploy/master/executor_row.scala.html +++ /dev/null @@ -1,15 +0,0 @@ -@(executor: spark.deploy.master.ExecutorInfo) - -<tr> - <td>@executor.id</td> - <td> - <a href="@executor.worker.webUiAddress">@executor.worker.id</a> - </td> - <td>@executor.cores</td> - <td>@executor.memory</td> - <td>@executor.state</td> - <td> - <a href="@(executor.worker.webUiAddress)/log?appId=@(executor.application.id)&executorId=@(executor.id)&logType=stdout">stdout</a> - <a href="@(executor.worker.webUiAddress)/log?appId=@(executor.application.id)&executorId=@(executor.id)&logType=stderr">stderr</a> - </td> -</tr> diff --git a/core/src/main/twirl/spark/deploy/master/executors_table.scala.html b/core/src/main/twirl/spark/deploy/master/executors_table.scala.html deleted file mode 100644 index cafc42c80e..0000000000 --- a/core/src/main/twirl/spark/deploy/master/executors_table.scala.html +++ /dev/null @@ -1,19 +0,0 @@ -@(executors: List[spark.deploy.master.ExecutorInfo]) - -<table class="table table-bordered table-striped table-condensed"> - <thead> - <tr> - <th>ExecutorID</th> - <th>Worker</th> - <th>Cores</th> - <th>Memory</th> - <th>State</th> - <th>Logs</th> - </tr> - </thead> - <tbody> - @for(e <- executors) { - @executor_row(e) - } - </tbody> -</table>
\ No newline at end of file diff --git a/core/src/main/twirl/spark/deploy/master/index.scala.html b/core/src/main/twirl/spark/deploy/master/index.scala.html deleted file mode 100644 index b9b9f08810..0000000000 --- a/core/src/main/twirl/spark/deploy/master/index.scala.html +++ /dev/null @@ -1,53 +0,0 @@ -@(state: spark.deploy.MasterState) -@import spark.deploy.master._ -@import spark.Utils - -@spark.common.html.layout(title = "Spark Master on " + state.host + ":" + state.port) { - - <!-- Cluster Details --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li><strong>URL:</strong> @(state.uri)</li> - <li><strong>Workers:</strong> @state.workers.size </li> - <li><strong>Cores:</strong> @{state.workers.map(_.cores).sum} Total, - @{state.workers.map(_.coresUsed).sum} Used</li> - <li><strong>Memory:</strong> @{Utils.memoryMegabytesToString(state.workers.map(_.memory).sum)} Total, - @{Utils.memoryMegabytesToString(state.workers.map(_.memoryUsed).sum)} Used</li> - <li><strong>Applications:</strong> @state.activeApps.size Running, @state.completedApps.size Completed </li> - </ul> - </div> - </div> - - <!-- Worker Summary --> - <div class="row"> - <div class="span12"> - <h3> Workers </h3> - <br/> - @worker_table(state.workers.sortBy(_.id)) - </div> - </div> - - <hr/> - - <!-- App Summary (Running) --> - <div class="row"> - <div class="span12"> - <h3> Running Applications </h3> - <br/> - @app_table(state.activeApps.sortBy(_.startTime).reverse) - </div> - </div> - - <hr/> - - <!-- App Summary (Completed) --> - <div class="row"> - <div class="span12"> - <h3> Completed Applications </h3> - <br/> - @app_table(state.completedApps.sortBy(_.endTime).reverse) - </div> - </div> - -} diff --git a/core/src/main/twirl/spark/deploy/master/worker_row.scala.html b/core/src/main/twirl/spark/deploy/master/worker_row.scala.html deleted file mode 100644 index 46277ca421..0000000000 --- a/core/src/main/twirl/spark/deploy/master/worker_row.scala.html +++ /dev/null @@ -1,14 +0,0 @@ -@(worker: spark.deploy.master.WorkerInfo) - -@import spark.Utils - -<tr> - <td> - <a href="@worker.webUiAddress">@worker.id</a> - </td> - <td>@{worker.host}:@{worker.port}</td> - <td>@worker.state</td> - <td>@worker.cores (@worker.coresUsed Used)</td> - <td>@{Utils.memoryMegabytesToString(worker.memory)} - (@{Utils.memoryMegabytesToString(worker.memoryUsed)} Used)</td> -</tr> diff --git a/core/src/main/twirl/spark/deploy/master/worker_table.scala.html b/core/src/main/twirl/spark/deploy/master/worker_table.scala.html deleted file mode 100644 index b249411a62..0000000000 --- a/core/src/main/twirl/spark/deploy/master/worker_table.scala.html +++ /dev/null @@ -1,18 +0,0 @@ -@(workers: Array[spark.deploy.master.WorkerInfo]) - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>ID</th> - <th>Address</th> - <th>State</th> - <th>Cores</th> - <th>Memory</th> - </tr> - </thead> - <tbody> - @for(w <- workers) { - @worker_row(w) - } - </tbody> -</table> diff --git a/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html b/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html deleted file mode 100644 index dad0a89080..0000000000 --- a/core/src/main/twirl/spark/deploy/worker/executor_row.scala.html +++ /dev/null @@ -1,20 +0,0 @@ -@(executor: spark.deploy.worker.ExecutorRunner) - -@import spark.Utils - -<tr> - <td>@executor.execId</td> - <td>@executor.cores</td> - <td>@Utils.memoryMegabytesToString(executor.memory)</td> - <td> - <ul class="unstyled"> - <li><strong>ID:</strong> @executor.appId</li> - <li><strong>Name:</strong> @executor.appDesc.name</li> - <li><strong>User:</strong> @executor.appDesc.user</li> - </ul> - </td> - <td> - <a href="log?appId=@(executor.appId)&executorId=@(executor.execId)&logType=stdout">stdout</a> - <a href="log?appId=@(executor.appId)&executorId=@(executor.execId)&logType=stderr">stderr</a> - </td> -</tr> diff --git a/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html b/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html deleted file mode 100644 index 327a2399c7..0000000000 --- a/core/src/main/twirl/spark/deploy/worker/executors_table.scala.html +++ /dev/null @@ -1,18 +0,0 @@ -@(executors: List[spark.deploy.worker.ExecutorRunner]) - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>ExecutorID</th> - <th>Cores</th> - <th>Memory</th> - <th>Job Details</th> - <th>Logs</th> - </tr> - </thead> - <tbody> - @for(e <- executors) { - @executor_row(e) - } - </tbody> -</table>
\ No newline at end of file diff --git a/core/src/main/twirl/spark/deploy/worker/index.scala.html b/core/src/main/twirl/spark/deploy/worker/index.scala.html deleted file mode 100644 index 0e66af9284..0000000000 --- a/core/src/main/twirl/spark/deploy/worker/index.scala.html +++ /dev/null @@ -1,44 +0,0 @@ -@(worker: spark.deploy.WorkerState) -@import spark.Utils - -@spark.common.html.layout(title = "Spark Worker on " + worker.host + ":" + worker.port) { - - <!-- Worker Details --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li><strong>ID:</strong> @worker.workerId</li> - <li><strong> - Master URL:</strong> @worker.masterUrl - </li> - <li><strong>Cores:</strong> @worker.cores (@worker.coresUsed Used)</li> - <li><strong>Memory:</strong> @{Utils.memoryMegabytesToString(worker.memory)} - (@{Utils.memoryMegabytesToString(worker.memoryUsed)} Used)</li> - </ul> - <p><a href="@worker.masterWebUiUrl">Back to Master</a></p> - </div> - </div> - - <hr/> - - <!-- Running Executors --> - <div class="row"> - <div class="span12"> - <h3> Running Executors </h3> - <br/> - @executors_table(worker.executors) - </div> - </div> - - <hr/> - - <!-- Finished Executors --> - <div class="row"> - <div class="span12"> - <h3> Finished Executors </h3> - <br/> - @executors_table(worker.finishedExecutors) - </div> - </div> - -} diff --git a/core/src/main/twirl/spark/storage/index.scala.html b/core/src/main/twirl/spark/storage/index.scala.html deleted file mode 100644 index 2b337f6133..0000000000 --- a/core/src/main/twirl/spark/storage/index.scala.html +++ /dev/null @@ -1,40 +0,0 @@ -@(maxMem: Long, remainingMem: Long, diskSpaceUsed: Long, rdds: Array[spark.storage.RDDInfo], storageStatusList: Array[spark.storage.StorageStatus]) -@import spark.Utils - -@spark.common.html.layout(title = "Storage Dashboard") { - - <!-- High-Level Information --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li><strong>Memory:</strong> - @{Utils.memoryBytesToString(maxMem - remainingMem)} Used - (@{Utils.memoryBytesToString(remainingMem)} Available) </li> - <li><strong>Disk:</strong> @{Utils.memoryBytesToString(diskSpaceUsed)} Used </li> - </ul> - </div> - </div> - - <hr/> - - <!-- RDD Summary --> - <div class="row"> - <div class="span12"> - <h3> RDD Summary </h3> - <br/> - @rdd_table(rdds) - </div> - </div> - - <hr/> - - <!-- Worker Summary --> - <div class="row"> - <div class="span12"> - <h3> Worker Summary </h3> - <br/> - @worker_table(storageStatusList) - </div> - </div> - -}
\ No newline at end of file diff --git a/core/src/main/twirl/spark/storage/rdd.scala.html b/core/src/main/twirl/spark/storage/rdd.scala.html deleted file mode 100644 index d85addeb17..0000000000 --- a/core/src/main/twirl/spark/storage/rdd.scala.html +++ /dev/null @@ -1,81 +0,0 @@ -@(rddInfo: spark.storage.RDDInfo, storageStatusList: Array[spark.storage.StorageStatus]) -@import spark.Utils - -@spark.common.html.layout(title = "RDD Info ") { - - <!-- High-Level Information --> - <div class="row"> - <div class="span12"> - <ul class="unstyled"> - <li> - <strong>Storage Level:</strong> - @(rddInfo.storageLevel.description) - <li> - <strong>Cached Partitions:</strong> - @(rddInfo.numCachedPartitions) - </li> - <li> - <strong>Total Partitions:</strong> - @(rddInfo.numPartitions) - </li> - <li> - <strong>Memory Size:</strong> - @{Utils.memoryBytesToString(rddInfo.memSize)} - </li> - <li> - <strong>Disk Size:</strong> - @{Utils.memoryBytesToString(rddInfo.diskSize)} - </li> - </ul> - </div> - </div> - - <hr/> - - <!-- RDD Summary --> - <div class="row"> - <div class="span12"> - <h3> RDD Summary </h3> - <br/> - - - <!-- Block Table Summary --> - <table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>Block Name</th> - <th>Storage Level</th> - <th>Size in Memory</th> - <th>Size on Disk</th> - </tr> - </thead> - <tbody> - @storageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1).map { case (k,v) => - <tr> - <td>@k</td> - <td> - @(v.storageLevel.description) - </td> - <td>@{Utils.memoryBytesToString(v.memSize)}</td> - <td>@{Utils.memoryBytesToString(v.diskSize)}</td> - </tr> - } - </tbody> - </table> - - - </div> - </div> - - <hr/> - - <!-- Worker Table --> - <div class="row"> - <div class="span12"> - <h3> Worker Summary </h3> - <br/> - @worker_table(storageStatusList, "rdd_" + rddInfo.id ) - </div> - </div> - -}
\ No newline at end of file diff --git a/core/src/main/twirl/spark/storage/rdd_table.scala.html b/core/src/main/twirl/spark/storage/rdd_table.scala.html deleted file mode 100644 index a51e64aed0..0000000000 --- a/core/src/main/twirl/spark/storage/rdd_table.scala.html +++ /dev/null @@ -1,32 +0,0 @@ -@(rdds: Array[spark.storage.RDDInfo]) -@import spark.Utils - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>RDD Name</th> - <th>Storage Level</th> - <th>Cached Partitions</th> - <th>Fraction Partitions Cached</th> - <th>Size in Memory</th> - <th>Size on Disk</th> - </tr> - </thead> - <tbody> - @for(rdd <- rdds) { - <tr> - <td> - <a href="rdd?id=@(rdd.id)"> - @rdd.name - </a> - </td> - <td>@(rdd.storageLevel.description) - </td> - <td>@rdd.numCachedPartitions</td> - <td>@(rdd.numCachedPartitions / rdd.numPartitions.toDouble)</td> - <td>@{Utils.memoryBytesToString(rdd.memSize)}</td> - <td>@{Utils.memoryBytesToString(rdd.diskSize)}</td> - </tr> - } - </tbody> -</table>
\ No newline at end of file diff --git a/core/src/main/twirl/spark/storage/worker_table.scala.html b/core/src/main/twirl/spark/storage/worker_table.scala.html deleted file mode 100644 index cd72a688c1..0000000000 --- a/core/src/main/twirl/spark/storage/worker_table.scala.html +++ /dev/null @@ -1,24 +0,0 @@ -@(workersStatusList: Array[spark.storage.StorageStatus], prefix: String = "") -@import spark.Utils - -<table class="table table-bordered table-striped table-condensed sortable"> - <thead> - <tr> - <th>Host</th> - <th>Memory Usage</th> - <th>Disk Usage</th> - </tr> - </thead> - <tbody> - @for(status <- workersStatusList) { - <tr> - <td>@(status.blockManagerId.host + ":" + status.blockManagerId.port)</td> - <td> - @(Utils.memoryBytesToString(status.memUsed(prefix))) - (@(Utils.memoryBytesToString(status.memRemaining)) Total Available) - </td> - <td>@(Utils.memoryBytesToString(status.diskUsed(prefix)))</td> - </tr> - } - </tbody> -</table>
\ No newline at end of file diff --git a/core/src/test/resources/fairscheduler.xml b/core/src/test/resources/fairscheduler.xml index 5a688b0ebb..6e573b1883 100644 --- a/core/src/test/resources/fairscheduler.xml +++ b/core/src/test/resources/fairscheduler.xml @@ -1,3 +1,4 @@ +<?xml version="1.0"?> <allocations> <pool name="1"> <minShare>2</minShare> diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties index 6ec89c0184..d05cf3dec1 100644 --- a/core/src/test/resources/log4j.properties +++ b/core/src/test/resources/log4j.properties @@ -8,3 +8,4 @@ log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: # Ignore messages below warning level from Jetty, because it's a bit verbose log4j.logger.org.eclipse.jetty=WARN +org.eclipse.jetty.LEVEL=WARN diff --git a/core/src/test/scala/spark/DistributedSuite.scala b/core/src/test/scala/spark/DistributedSuite.scala index 0866fb47b3..0024ede828 100644 --- a/core/src/test/scala/spark/DistributedSuite.scala +++ b/core/src/test/scala/spark/DistributedSuite.scala @@ -10,6 +10,7 @@ import org.scalatest.time.{Span, Millis} import org.scalacheck.Arbitrary._ import org.scalacheck.Gen import org.scalacheck.Prop._ +import org.eclipse.jetty.server.{Server, Request, Handler} import com.google.common.io.Files @@ -17,6 +18,7 @@ import scala.collection.mutable.ArrayBuffer import SparkContext._ import storage.{GetBlock, BlockManagerWorker, StorageLevel} +import ui.JettyUtils class NotSerializableClass diff --git a/core/src/test/scala/spark/DriverSuite.scala b/core/src/test/scala/spark/DriverSuite.scala index 5e84b3a66a..31c3dd75fb 100644 --- a/core/src/test/scala/spark/DriverSuite.scala +++ b/core/src/test/scala/spark/DriverSuite.scala @@ -2,6 +2,9 @@ package spark import java.io.File +import org.apache.log4j.Logger +import org.apache.log4j.Level + import org.scalatest.FunSuite import org.scalatest.concurrent.Timeouts import org.scalatest.prop.TableDrivenPropertyChecks._ @@ -27,6 +30,7 @@ class DriverSuite extends FunSuite with Timeouts { */ object DriverWithoutCleanup { def main(args: Array[String]) { + Logger.getRootLogger().setLevel(Level.WARN) val sc = new SparkContext(args(0), "DriverWithoutCleanup") sc.parallelize(1 to 100, 4).count() } diff --git a/core/src/test/scala/spark/FileServerSuite.scala b/core/src/test/scala/spark/FileServerSuite.scala index f1a35bced3..9c24ca430d 100644 --- a/core/src/test/scala/spark/FileServerSuite.scala +++ b/core/src/test/scala/spark/FileServerSuite.scala @@ -85,7 +85,6 @@ class FileServerSuite extends FunSuite with LocalSparkContext { in.close() _ * fileVal + _ * fileVal }.collect - println(result) assert(result.toSet === Set((1,200), (2,300), (3,500))) } diff --git a/core/src/test/scala/spark/LocalSparkContext.scala b/core/src/test/scala/spark/LocalSparkContext.scala index 76d5258b02..bd184222ed 100644 --- a/core/src/test/scala/spark/LocalSparkContext.scala +++ b/core/src/test/scala/spark/LocalSparkContext.scala @@ -2,12 +2,21 @@ package spark import org.scalatest.Suite import org.scalatest.BeforeAndAfterEach +import org.scalatest.BeforeAndAfterAll + +import org.jboss.netty.logging.InternalLoggerFactory +import org.jboss.netty.logging.Slf4JLoggerFactory /** Manages a local `sc` {@link SparkContext} variable, correctly stopping it after each test. */ -trait LocalSparkContext extends BeforeAndAfterEach { self: Suite => +trait LocalSparkContext extends BeforeAndAfterEach with BeforeAndAfterAll { self: Suite => @transient var sc: SparkContext = _ + override def beforeAll() { + InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory()); + super.beforeAll() + } + override def afterEach() { resetSparkContext() super.afterEach() diff --git a/core/src/test/scala/spark/PipedRDDSuite.scala b/core/src/test/scala/spark/PipedRDDSuite.scala index 1c9ca50811..d263bb00e9 100644 --- a/core/src/test/scala/spark/PipedRDDSuite.scala +++ b/core/src/test/scala/spark/PipedRDDSuite.scala @@ -67,7 +67,7 @@ class PipedRDDSuite extends FunSuite with SharedSparkContext { test("pipe with non-zero exit status") { val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) - val piped = nums.pipe("cat nonexistent_file") + val piped = nums.pipe(Seq("cat nonexistent_file", "2>", "/dev/null")) intercept[SparkException] { piped.collect() } diff --git a/core/src/test/scala/spark/RDDSuite.scala b/core/src/test/scala/spark/RDDSuite.scala index d8db69b1c9..e41ae385c0 100644 --- a/core/src/test/scala/spark/RDDSuite.scala +++ b/core/src/test/scala/spark/RDDSuite.scala @@ -251,4 +251,37 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(topK.size === 2) assert(topK.sorted === Array("b", "a")) } + + test("takeSample") { + val data = sc.parallelize(1 to 100, 2) + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=false, 20, seed) + assert(sample.size === 20) // Got exactly 20 elements + assert(sample.toSet.size === 20) // Elements are distinct + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=false, 200, seed) + assert(sample.size === 100) // Got only 100 elements + assert(sample.toSet.size === 100) // Elements are distinct + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 20, seed) + assert(sample.size === 20) // Got exactly 20 elements + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 100, seed) + assert(sample.size === 100) // Got exactly 100 elements + // Chance of getting all distinct elements is astronomically low, so test we got < 100 + assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 200, seed) + assert(sample.size === 200) // Got exactly 200 elements + // Chance of getting all distinct elements is still quite low, so test we got < 100 + assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements") + } + } } diff --git a/core/src/test/scala/spark/ui/UISuite.scala b/core/src/test/scala/spark/ui/UISuite.scala new file mode 100644 index 0000000000..b40c29bede --- /dev/null +++ b/core/src/test/scala/spark/ui/UISuite.scala @@ -0,0 +1,75 @@ +package spark.ui + +import org.scalatest.FunSuite +import org.eclipse.jetty.server.Server +import java.net.ServerSocket +import scala.util.{Failure, Success, Try} +import spark.Utils +import com.google.common.io.Files +import java.io.{FileOutputStream, File} +import com.google.common.base.Charsets + +class UISuite extends FunSuite { + test("jetty port increases under contention") { + val startPort = 33333 + val server = new Server(startPort) + server.start() + val (jettyServer1, boundPort1) = JettyUtils.startJettyServer("localhost", startPort, Seq()) + val (jettyServer2, boundPort2) = JettyUtils.startJettyServer("localhost", startPort, Seq()) + + assert(boundPort1 === startPort + 1) + assert(boundPort2 === startPort + 2) + } + + test("jetty binds to port 0 correctly") { + val (jettyServer, boundPort) = JettyUtils.startJettyServer("localhost", 0, Seq()) + assert(jettyServer.getState === "STARTED") + assert(boundPort != 0) + Try {new ServerSocket(boundPort)} match { + case Success(s) => fail("Port %s doesn't seem used by jetty server".format(boundPort)) + case Failure (e) => + } + } + + test("string formatting of time durations") { + val second = 1000 + val minute = second * 60 + val hour = minute * 60 + def str = Utils.msDurationToString(_) + + assert(str(123) === "123 ms") + assert(str(second) === "1.0 s") + assert(str(second + 462) === "1.5 s") + assert(str(hour) === "1.00 h") + assert(str(minute) === "1.0 m") + assert(str(minute + 4 * second + 34) === "1.1 m") + assert(str(10 * hour + minute + 4 * second) === "10.02 h") + assert(str(10 * hour + 59 * minute + 59 * second + 999) === "11.00 h") + } + + test("reading last n bytes of a file") { + val tmpDir = Files.createTempDir() + + // File smaller than limit + val f1Path = tmpDir + "/f1" + val f1 = new FileOutputStream(f1Path) + f1.write("a\nb\nc\nd".getBytes(Charsets.UTF_8)) + f1.close() + assert(Utils.lastNBytes(f1Path, 1024) === "a\nb\nc\nd") + + // File larger than limit + val f2Path = tmpDir + "/f2" + val f2 = new FileOutputStream(f2Path) + f2.write("1\n2\n3\n4\n5\n6\n7\n8\n".getBytes(Charsets.UTF_8)) + f2.close() + assert(Utils.lastNBytes(f2Path, 8) === "5\n6\n7\n8\n") + + // Request limit too + val f3Path = tmpDir + "/f2" + val f3 = new FileOutputStream(f3Path) + f3.write("1\n2\n3\n4\n5\n6\n7\n8\n".getBytes(Charsets.UTF_8)) + f3.close() + assert(Utils.lastNBytes(f3Path, 8) === "5\n6\n7\n8\n") + + } +} diff --git a/docs/configuration.md b/docs/configuration.md index 3266db7af1..5a80510959 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -146,9 +146,16 @@ Apart from these, the following properties are also available, and may be useful </tr> <tr> <td>spark.ui.port</td> - <td>(random)</td> + <td>33000</td> + <td> + Port for your application's dashboard, which shows memory and workload data + </td> +</tr> +<tr> + <td>spark.ui.retained_stages</td> + <td>1000</td> <td> - Port for your application's dashboard, which shows memory usage of each RDD. + How many stages the Spark UI remembers before garbage collecting. </td> </tr> <tr> diff --git a/examples/pom.xml b/examples/pom.xml index 064f1179e5..64e68711bd 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -16,6 +16,10 @@ <dependencies> <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> + </dependency> + <dependency> <groupId>org.eclipse.jetty</groupId> <artifactId>jetty-server</artifactId> </dependency> diff --git a/mllib/data/als/test.data 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spark.mllib.clustering + +import scala.collection.mutable.ArrayBuffer +import scala.util.Random + +import spark.{SparkContext, RDD} +import spark.SparkContext._ +import spark.Logging +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix + + +/** + * K-means clustering with support for multiple parallel runs and a k-means++ like initialization + * mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested, + * they are executed together with joint passes over the data for efficiency. + * + * This is an iterative algorithm that will make multiple passes over the data, so any RDDs given + * to it should be cached by the user. + */ +class KMeans private ( + var k: Int, + var maxIterations: Int, + var runs: Int, + var initializationMode: String, + var initializationSteps: Int, + var epsilon: Double) + extends Serializable with Logging +{ + private type ClusterCenters = Array[Array[Double]] + + def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4) + + /** Set the number of clusters to create (k). Default: 2. */ + def setK(k: Int): KMeans = { + this.k = k + this + } + + /** Set maximum number of iterations to run. Default: 20. */ + def setMaxIterations(maxIterations: Int): KMeans = { + this.maxIterations = maxIterations + this + } + + /** + * Set the initialization algorithm. This can be either "random" to choose random points as + * initial cluster centers, or "k-means||" to use a parallel variant of k-means++ + * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||. + */ + def setInitializationMode(initializationMode: String): KMeans = { + if (initializationMode != KMeans.RANDOM && initializationMode != KMeans.K_MEANS_PARALLEL) { + throw new IllegalArgumentException("Invalid initialization mode: " + initializationMode) + } + this.initializationMode = initializationMode + this + } + + /** + * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm + * this many times with random starting conditions (configured by the initialization mode), then + * return the best clustering found over any run. Default: 1. + */ + def setRuns(runs: Int): KMeans = { + if (runs <= 0) { + throw new IllegalArgumentException("Number of runs must be positive") + } + this.runs = runs + this + } + + /** + * Set the number of steps for the k-means|| initialization mode. This is an advanced + * setting -- the default of 5 is almost always enough. Default: 5. + */ + def setInitializationSteps(initializationSteps: Int): KMeans = { + if (initializationSteps <= 0) { + throw new IllegalArgumentException("Number of initialization steps must be positive") + } + this.initializationSteps = initializationSteps + this + } + + /** + * Set the distance threshold within which we've consider centers to have converged. + * If all centers move less than this Euclidean distance, we stop iterating one run. + */ + def setEpsilon(epsilon: Double): KMeans = { + this.epsilon = epsilon + this + } + + /** + * Train a K-means model on the given set of points; `data` should be cached for high + * performance, because this is an iterative algorithm. + */ + def train(data: RDD[Array[Double]]): KMeansModel = { + // TODO: check whether data is persistent; this needs RDD.storageLevel to be publicly readable + + val sc = data.sparkContext + + val centers = if (initializationMode == KMeans.RANDOM) { + initRandom(data) + } else { + initKMeansParallel(data) + } + + val active = Array.fill(runs)(true) + val costs = Array.fill(runs)(0.0) + + var activeRuns = new ArrayBuffer[Int] ++ (0 until runs) + var iteration = 0 + + // Execute iterations of Lloyd's algorithm until all runs have converged + while (iteration < maxIterations && !activeRuns.isEmpty) { + type WeightedPoint = (DoubleMatrix, Long) + def mergeContribs(p1: WeightedPoint, p2: WeightedPoint): WeightedPoint = { + (p1._1.addi(p2._1), p1._2 + p2._2) + } + + val activeCenters = activeRuns.map(r => centers(r)).toArray + val costAccums = activeRuns.map(_ => sc.accumulator(0.0)) + + // Find the sum and count of points mapping to each center + val totalContribs = data.mapPartitions { points => + val runs = activeCenters.length + val k = activeCenters(0).length + val dims = activeCenters(0)(0).length + + val sums = Array.fill(runs, k)(new DoubleMatrix(dims)) + val counts = Array.fill(runs, k)(0L) + + for (point <- points; (centers, runIndex) <- activeCenters.zipWithIndex) { + val (bestCenter, cost) = KMeans.findClosest(centers, point) + costAccums(runIndex) += cost + sums(runIndex)(bestCenter).addi(new DoubleMatrix(point)) + counts(runIndex)(bestCenter) += 1 + } + + val contribs = for (i <- 0 until runs; j <- 0 until k) yield { + ((i, j), (sums(i)(j), counts(i)(j))) + } + contribs.iterator + }.reduceByKey(mergeContribs).collectAsMap() + + // Update the cluster centers and costs for each active run + for ((run, i) <- activeRuns.zipWithIndex) { + var changed = false + for (j <- 0 until k) { + val (sum, count) = totalContribs((i, j)) + if (count != 0) { + val newCenter = sum.divi(count).data + if (MLUtils.squaredDistance(newCenter, centers(run)(j)) > epsilon * epsilon) { + changed = true + } + centers(run)(j) = newCenter + } + } + if (!changed) { + active(run) = false + logInfo("Run " + run + " finished in " + (iteration + 1) + " iterations") + } + costs(run) = costAccums(i).value + } + + activeRuns = activeRuns.filter(active(_)) + iteration += 1 + } + + val bestRun = costs.zipWithIndex.min._2 + new KMeansModel(centers(bestRun)) + } + + /** + * Initialize `runs` sets of cluster centers at random. + */ + private def initRandom(data: RDD[Array[Double]]): Array[ClusterCenters] = { + // Sample all the cluster centers in one pass to avoid repeated scans + val sample = data.takeSample(true, runs * k, new Random().nextInt()) + Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k)) + } + + /** + * Initialize `runs` sets of cluster centers using the k-means|| algorithm by Bahmani et al. + * (Bahmani et al., Scalable K-Means++, VLDB 2012). This is a variant of k-means++ that tries + * to find with dissimilar cluster centers by starting with a random center and then doing + * passes where more centers are chosen with probability proportional to their squared distance + * to the current cluster set. It results in a provable approximation to an optimal clustering. + * + * The original paper can be found at http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf. + */ + private def initKMeansParallel(data: RDD[Array[Double]]): Array[ClusterCenters] = { + // Initialize each run's center to a random point + val seed = new Random().nextInt() + val sample = data.takeSample(true, runs, seed) + val centers = Array.tabulate(runs)(r => ArrayBuffer(sample(r))) + + // On each step, sample 2 * k points on average for each run with probability proportional + // to their squared distance from that run's current centers + for (step <- 0 until initializationSteps) { + val centerArrays = centers.map(_.toArray) + val sumCosts = data.flatMap { point => + for (r <- 0 until runs) yield (r, KMeans.pointCost(centerArrays(r), point)) + }.reduceByKey(_ + _).collectAsMap() + val chosen = data.mapPartitionsWithIndex { (index, points) => + val rand = new Random(seed ^ (step << 16) ^ index) + for { + p <- points + r <- 0 until runs + if rand.nextDouble() < KMeans.pointCost(centerArrays(r), p) * 2 * k / sumCosts(r) + } yield (r, p) + }.collect() + for ((r, p) <- chosen) { + centers(r) += p + } + } + + // Finally, we might have a set of more than k candidate centers for each run; weigh each + // candidate by the number of points in the dataset mapping to it and run a local k-means++ + // on the weighted centers to pick just k of them + val centerArrays = centers.map(_.toArray) + val weightMap = data.flatMap { p => + for (r <- 0 until runs) yield ((r, KMeans.findClosest(centerArrays(r), p)._1), 1.0) + }.reduceByKey(_ + _).collectAsMap() + val finalCenters = (0 until runs).map { r => + val myCenters = centers(r).toArray + val myWeights = (0 until myCenters.length).map(i => weightMap.getOrElse((r, i), 0.0)).toArray + LocalKMeans.kMeansPlusPlus(r, myCenters, myWeights, k, 30) + } + + finalCenters.toArray + } +} + + +/** + * Top-level methods for calling K-means clustering. + */ +object KMeans { + // Initialization mode names + val RANDOM = "random" + val K_MEANS_PARALLEL = "k-means||" + + def train( + data: RDD[Array[Double]], + k: Int, + maxIterations: Int, + runs: Int, + initializationMode: String) + : KMeansModel = + { + new KMeans().setK(k) + .setMaxIterations(maxIterations) + .setRuns(runs) + .setInitializationMode(initializationMode) + .train(data) + } + + def train(data: RDD[Array[Double]], k: Int, maxIterations: Int, runs: Int): KMeansModel = { + train(data, k, maxIterations, runs, K_MEANS_PARALLEL) + } + + def train(data: RDD[Array[Double]], k: Int, maxIterations: Int): KMeansModel = { + train(data, k, maxIterations, 1, K_MEANS_PARALLEL) + } + + /** + * Return the index of the closest point in `centers` to `point`, as well as its distance. + */ + private[mllib] def findClosest(centers: Array[Array[Double]], point: Array[Double]) + : (Int, Double) = + { + var bestDistance = Double.PositiveInfinity + var bestIndex = 0 + for (i <- 0 until centers.length) { + val distance = MLUtils.squaredDistance(point, centers(i)) + if (distance < bestDistance) { + bestDistance = distance + bestIndex = i + } + } + (bestIndex, bestDistance) + } + + /** + * Return the K-means cost of a given point against the given cluster centers. + */ + private[mllib] def pointCost(centers: Array[Array[Double]], point: Array[Double]): Double = { + var bestDistance = Double.PositiveInfinity + for (i <- 0 until centers.length) { + val distance = MLUtils.squaredDistance(point, centers(i)) + if (distance < bestDistance) { + bestDistance = distance + } + } + bestDistance + } + + def main(args: Array[String]) { + if (args.length != 4) { + println("Usage: KMeans <master> <input_file> <k> <max_iterations>") + System.exit(1) + } + val (master, inputFile, k, iters) = (args(0), args(1), args(2).toInt, args(3).toInt) + 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 cost = model.computeCost(data) + println("Cluster centers:") + for (c <- model.clusterCenters) { + println(" " + c.mkString(" ")) + } + println("Cost: " + cost) + System.exit(0) + } +} diff --git a/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala new file mode 100644 index 0000000000..4fd0646160 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/clustering/KMeansModel.scala @@ -0,0 +1,27 @@ +package spark.mllib.clustering + +import spark.RDD +import spark.SparkContext._ +import spark.mllib.util.MLUtils + + +/** + * A clustering model for K-means. Each point belongs to the cluster with the closest center. + */ +class KMeansModel(val clusterCenters: Array[Array[Double]]) extends Serializable { + /** Total number of clusters. */ + def k: Int = clusterCenters.length + + /** Return the cluster index that a given point belongs to. */ + def predict(point: Array[Double]): Int = { + KMeans.findClosest(clusterCenters, point)._1 + } + + /** + * Return the K-means cost (sum of squared distances of points to their nearest center) for this + * model on the given data. + */ + def computeCost(data: RDD[Array[Double]]): Double = { + data.map(p => KMeans.pointCost(clusterCenters, p)).sum + } +} diff --git a/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala b/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala new file mode 100644 index 0000000000..e12b3be251 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/clustering/LocalKMeans.scala @@ -0,0 +1,88 @@ +package spark.mllib.clustering + +import scala.util.Random + +import org.jblas.{DoubleMatrix, SimpleBlas} + +/** + * An utility object to run K-means locally. This is private to the ML package because it's used + * in the initialization of KMeans but not meant to be publicly exposed. + */ +private[mllib] object LocalKMeans { + /** + * Run K-means++ on the weighted point set `points`. This first does the K-means++ + * initialization procedure and then roudns of Lloyd's algorithm. + */ + def kMeansPlusPlus( + seed: Int, + points: Array[Array[Double]], + weights: Array[Double], + k: Int, + maxIterations: Int) + : Array[Array[Double]] = + { + val rand = new Random(seed) + val dimensions = points(0).length + val centers = new Array[Array[Double]](k) + + // Initialize centers by sampling using the k-means++ procedure + centers(0) = pickWeighted(rand, points, weights) + for (i <- 1 until k) { + // Pick the next center with a probability proportional to cost under current centers + val curCenters = centers.slice(0, i) + val sum = points.zip(weights).map { case (p, w) => + w * KMeans.pointCost(curCenters, p) + }.sum + val r = rand.nextDouble() * sum + var cumulativeScore = 0.0 + var j = 0 + while (j < points.length && cumulativeScore < r) { + cumulativeScore += weights(j) * KMeans.pointCost(curCenters, points(j)) + j += 1 + } + centers(i) = points(j-1) + } + + // Run up to maxIterations iterations of Lloyd's algorithm + val oldClosest = Array.fill(points.length)(-1) + var iteration = 0 + var moved = true + while (moved && iteration < maxIterations) { + moved = false + val sums = Array.fill(k)(new DoubleMatrix(dimensions)) + val counts = Array.fill(k)(0.0) + for ((p, i) <- points.zipWithIndex) { + val index = KMeans.findClosest(centers, p)._1 + SimpleBlas.axpy(weights(i), new DoubleMatrix(p), sums(index)) + counts(index) += weights(i) + if (index != oldClosest(i)) { + moved = true + oldClosest(i) = index + } + } + // Update centers + for (i <- 0 until k) { + if (counts(i) == 0.0) { + // Assign center to a random point + centers(i) = points(rand.nextInt(points.length)) + } else { + centers(i) = sums(i).divi(counts(i)).data + } + } + iteration += 1 + } + + centers + } + + private def pickWeighted[T](rand: Random, data: Array[T], weights: Array[Double]): T = { + val r = rand.nextDouble() * weights.sum + var i = 0 + var curWeight = 0.0 + while (i < data.length && curWeight < r) { + curWeight += weights(i) + i += 1 + } + data(i - 1) + } +} diff --git a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala new file mode 100644 index 0000000000..90b0999a5e --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala @@ -0,0 +1,33 @@ +package spark.mllib.optimization + +import org.jblas.DoubleMatrix + +abstract class Gradient extends Serializable { + /** + * Compute the gradient for a given row of data. + * + * @param data - One row of data. Row matrix of size 1xn where n is the number of features. + * @param label - Label for this data item. + * @param weights - Column matrix containing weights for every feature. + */ + def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): + (DoubleMatrix, Double) +} + +class LogisticGradient extends Gradient { + override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): + (DoubleMatrix, Double) = { + val margin: Double = -1.0 * data.dot(weights) + val gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label + + val gradient = data.mul(gradientMultiplier) + val loss = + if (margin > 0) { + math.log(1 + math.exp(0 - margin)) + } else { + math.log(1 + math.exp(margin)) - margin + } + + (gradient, loss) + } +} diff --git a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala new file mode 100644 index 0000000000..eff853f379 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala @@ -0,0 +1,62 @@ +package spark.mllib.optimization + +import spark.{Logging, RDD, SparkContext} +import spark.SparkContext._ + +import org.jblas.DoubleMatrix + +import scala.collection.mutable.ArrayBuffer + + +object GradientDescent { + + /** + * Run gradient descent in parallel using mini batches. + * Based on Matlab code written by John Duchi. + * + * @param data - Input data for SGD. RDD of form (label, [feature values]). + * @param gradient - Gradient object that will be used to compute the gradient. + * @param updater - Updater object that will be used to update the model. + * @param stepSize - stepSize to be used during update. + * @param numIters - number of iterations that SGD should be run. + * @param 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. + */ + def runMiniBatchSGD( + data: RDD[(Double, Array[Double])], + gradient: Gradient, + updater: Updater, + stepSize: Double, + numIters: Int, + miniBatchFraction: Double=1.0) : (DoubleMatrix, Array[Double]) = { + + val lossHistory = new ArrayBuffer[Double](numIters) + + 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 + + for (i <- 1 to numIters) { + val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42+i).map { + case (y, features) => + val featuresRow = new DoubleMatrix(features.length, 1, features:_*) + val (grad, loss) = gradient.compute(featuresRow, y, weights) + (grad, loss) + }.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2)) + + lossHistory.append(lossSum / miniBatchSize + reg_val) + val update = updater.compute(weights, gradientSum.div(miniBatchSize), stepSize, i) + weights = update._1 + reg_val = update._2 + } + + (weights, lossHistory.toArray) + } +} diff --git a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala new file mode 100644 index 0000000000..ea80bfcbfd --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala @@ -0,0 +1,27 @@ +package spark.mllib.optimization + +import org.jblas.DoubleMatrix + +abstract class Updater extends Serializable { + /** + * Compute an updated value for weights given the gradient, stepSize and iteration number. + * + * @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 + * + * @return weightsNew - Column matrix containing updated weights + * @return reg_val - regularization value + */ + def compute(weightsOlds: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int): + (DoubleMatrix, Double) +} + +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)) + (weightsOld.sub(normGradient), 0) + } +} diff --git a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala new file mode 100644 index 0000000000..6c9fb2359c --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala @@ -0,0 +1,389 @@ +package spark.mllib.recommendation + +import scala.collection.mutable.{ArrayBuffer, BitSet} +import scala.util.Random + +import spark.{HashPartitioner, Partitioner, SparkContext, RDD} +import spark.storage.StorageLevel +import spark.SparkContext._ + +import org.jblas.{DoubleMatrix, SimpleBlas, Solve} + + +/** + * Out-link information for a user or product block. This includes the original user/product IDs + * of the elements within this block, and the list of destination blocks that each user or + * product will need to send its feature vector to. + */ +private[recommendation] case class OutLinkBlock( + elementIds: Array[Int], shouldSend: Array[BitSet]) + + +/** + * In-link information for a user (or product) block. This includes the original user/product IDs + * of the elements within this block, as well as an array of indices and ratings that specify + * which user in the block will be rated by which products from each product block (or vice-versa). + * Specifically, if this InLinkBlock is for users, ratingsForBlock(b)(i) will contain two arrays, + * indices and ratings, for the i'th product that will be sent to us by product block b (call this + * P). These arrays represent the users that product P had ratings for (by their index in this + * block), as well as the corresponding rating for each one. We can thus use this information when + * we get product block b's message to update the corresponding users. + */ +private[recommendation] case class InLinkBlock( + elementIds: Array[Int], ratingsForBlock: Array[Array[(Array[Int], Array[Double])]]) + + +/** + * Alternating Least Squares matrix factorization. + * + * This is a blocked implementation of the ALS factorization algorithm that groups the two sets + * of factors (referred to as "users" and "products") into blocks and reduces communication by only + * sending one copy of each user vector to each product block on each iteration, and only for the + * product blocks that need that user's feature vector. This is achieved by precomputing some + * information about the ratings matrix to determine the "out-links" of each user (which blocks of + * products it will contribute to) and "in-link" information for each product (which of the feature + * vectors it receives from each user block it will depend on). This allows us to send only an + * array of feature vectors between each user block and product block, and have the product block + * find the users' ratings and update the products based on these messages. + */ +class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var lambda: Double) + extends Serializable +{ + def this() = this(-1, 10, 10, 0.01) + + /** + * Set the number of blocks to parallelize the computation into; pass -1 for an auto-configured + * number of blocks. Default: -1. + */ + def setBlocks(numBlocks: Int): ALS = { + this.numBlocks = numBlocks + this + } + + /** Set the rank of the feature matrices computed (number of features). Default: 10. */ + def setRank(rank: Int): ALS = { + this.rank = rank + this + } + + /** Set the number of iterations to run. Default: 10. */ + def setIterations(iterations: Int): ALS = { + this.iterations = iterations + this + } + + /** Set the regularization parameter, lambda. Default: 0.01. */ + def setLambda(lambda: Double): ALS = { + this.lambda = lambda + this + } + + /** + * Run ALS with the configured parmeters on an input RDD of (user, product, rating) triples. + * Returns a MatrixFactorizationModel with feature vectors for each user and product. + */ + def train(ratings: RDD[(Int, Int, Double)]): MatrixFactorizationModel = { + val numBlocks = if (this.numBlocks == -1) { + math.max(ratings.context.defaultParallelism, ratings.partitions.size) + } else { + this.numBlocks + } + + val partitioner = new HashPartitioner(numBlocks) + + val ratingsByUserBlock = ratings.map{ case (u, p, r) => (u % numBlocks, (u, p, r)) } + val ratingsByProductBlock = ratings.map{ case (u, p, r) => (p % numBlocks, (p, u, r)) } + + val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock) + val (productInLinks, productOutLinks) = makeLinkRDDs(numBlocks, ratingsByProductBlock) + + // Initialize user and product factors randomly + val seed = new Random().nextInt() + var users = userOutLinks.mapValues(_.elementIds.map(u => randomFactor(rank, seed ^ u))) + var products = productOutLinks.mapValues(_.elementIds.map(p => randomFactor(rank, seed ^ ~p))) + + for (iter <- 0 until iterations) { + // perform ALS update + products = updateFeatures(users, userOutLinks, productInLinks, partitioner, rank, lambda) + users = updateFeatures(products, productOutLinks, userInLinks, partitioner, rank, lambda) + } + + // Flatten and cache the two final RDDs to un-block them + val usersOut = users.join(userOutLinks).flatMap { case (b, (factors, outLinkBlock)) => + for (i <- 0 until factors.length) yield (outLinkBlock.elementIds(i), factors(i)) + } + val productsOut = products.join(productOutLinks).flatMap { case (b, (factors, outLinkBlock)) => + for (i <- 0 until factors.length) yield (outLinkBlock.elementIds(i), factors(i)) + } + + usersOut.persist() + productsOut.persist() + + new MatrixFactorizationModel(rank, usersOut, productsOut) + } + + /** + * Make the out-links table for a block of the users (or products) dataset given the list of + * (user, product, rating) values for the users in that block (or the opposite for products). + */ + private def makeOutLinkBlock(numBlocks: Int, ratings: Array[(Int, Int, Double)]): OutLinkBlock = { + val userIds = ratings.map(_._1).distinct.sorted + val numUsers = userIds.length + val userIdToPos = userIds.zipWithIndex.toMap + val shouldSend = Array.fill(numUsers)(new BitSet(numBlocks)) + for ((u, p, r) <- ratings) { + shouldSend(userIdToPos(u))(p % numBlocks) = true + } + OutLinkBlock(userIds, shouldSend) + } + + /** + * Make the in-links table for a block of the users (or products) dataset given a list of + * (user, product, rating) values for the users in that block (or the opposite for products). + */ + private def makeInLinkBlock(numBlocks: Int, ratings: Array[(Int, Int, Double)]): InLinkBlock = { + val userIds = ratings.map(_._1).distinct.sorted + val numUsers = userIds.length + val userIdToPos = userIds.zipWithIndex.toMap + val ratingsForBlock = new Array[Array[(Array[Int], Array[Double])]](numBlocks) + for (productBlock <- 0 until numBlocks) { + val ratingsInBlock = ratings.filter(t => t._2 % numBlocks == productBlock) + val ratingsByProduct = ratingsInBlock.groupBy(_._2) // (p, Seq[(u, p, r)]) + .toArray + .sortBy(_._1) + .map{case (p, rs) => (rs.map(t => userIdToPos(t._1)), rs.map(_._3))} + ratingsForBlock(productBlock) = ratingsByProduct + } + InLinkBlock(userIds, ratingsForBlock) + } + + /** + * Make RDDs of InLinkBlocks and OutLinkBlocks given an RDD of (blockId, (u, p, r)) values for + * the users (or (blockId, (p, u, r)) for the products). We create these simultaneously to avoid + * having to shuffle the (blockId, (u, p, r)) RDD twice, or to cache it. + */ + private def makeLinkRDDs(numBlocks: Int, ratings: RDD[(Int, (Int, Int, Double))]) + : (RDD[(Int, InLinkBlock)], RDD[(Int, OutLinkBlock)]) = + { + val grouped = ratings.partitionBy(new HashPartitioner(numBlocks)) + val links = grouped.mapPartitionsWithIndex((blockId, elements) => { + val ratings = elements.map(_._2).toArray + val inLinkBlock = makeInLinkBlock(numBlocks, ratings) + val outLinkBlock = makeOutLinkBlock(numBlocks, ratings) + Iterator.single((blockId, (inLinkBlock, outLinkBlock))) + }, true) + links.persist(StorageLevel.MEMORY_AND_DISK) + (links.mapValues(_._1), links.mapValues(_._2)) + } + + /** + * Make a random factor vector with the given seed. + * TODO: Initialize things using mapPartitionsWithIndex to make it faster? + */ + private def randomFactor(rank: Int, seed: Int): Array[Double] = { + val rand = new Random(seed) + Array.fill(rank)(rand.nextDouble) + } + + /** + * Compute the user feature vectors given the current products (or vice-versa). This first joins + * the products with their out-links to generate a set of messages to each destination block + * (specifically, the features for the products that user block cares about), then groups these + * by destination and joins them with the in-link info to figure out how to update each user. + * It returns an RDD of new feature vectors for each user block. + */ + private def updateFeatures( + products: RDD[(Int, Array[Array[Double]])], + productOutLinks: RDD[(Int, OutLinkBlock)], + userInLinks: RDD[(Int, InLinkBlock)], + partitioner: Partitioner, + rank: Int, + lambda: Double) + : RDD[(Int, Array[Array[Double]])] = + { + val numBlocks = products.partitions.size + productOutLinks.join(products).flatMap { case (bid, (outLinkBlock, factors)) => + val toSend = Array.fill(numBlocks)(new ArrayBuffer[Array[Double]]) + for (p <- 0 until outLinkBlock.elementIds.length; userBlock <- 0 until numBlocks) { + if (outLinkBlock.shouldSend(p)(userBlock)) { + toSend(userBlock) += factors(p) + } + } + toSend.zipWithIndex.map{ case (buf, idx) => (idx, (bid, buf.toArray)) } + }.groupByKey(partitioner) + .join(userInLinks) + .mapValues{ case (messages, inLinkBlock) => updateBlock(messages, inLinkBlock, rank, lambda) } + } + + /** + * Compute the new feature vectors for a block of the users matrix given the list of factors + * it received from each product and its InLinkBlock. + */ + def updateBlock(messages: Seq[(Int, Array[Array[Double]])], inLinkBlock: InLinkBlock, + rank: Int, lambda: Double) + : Array[Array[Double]] = + { + // Sort the incoming block factor messages by block ID and make them an array + val blockFactors = messages.sortBy(_._1).map(_._2).toArray // Array[Array[Double]] + val numBlocks = blockFactors.length + val numUsers = inLinkBlock.elementIds.length + + // We'll sum up the XtXes using vectors that represent only the lower-triangular part, since + // the matrices are symmetric + val triangleSize = rank * (rank + 1) / 2 + val userXtX = Array.fill(numUsers)(DoubleMatrix.zeros(triangleSize)) + val userXy = Array.fill(numUsers)(DoubleMatrix.zeros(rank)) + + // Some temp variables to avoid memory allocation + val tempXtX = DoubleMatrix.zeros(triangleSize) + val fullXtX = DoubleMatrix.zeros(rank, rank) + + // Compute the XtX and Xy values for each user by adding products it rated in each product block + for (productBlock <- 0 until numBlocks) { + for (p <- 0 until blockFactors(productBlock).length) { + val x = new DoubleMatrix(blockFactors(productBlock)(p)) + fillXtX(x, tempXtX) + val (us, rs) = inLinkBlock.ratingsForBlock(productBlock)(p) + for (i <- 0 until us.length) { + userXtX(us(i)).addi(tempXtX) + SimpleBlas.axpy(rs(i), x, userXy(us(i))) + } + } + } + + // Solve the least-squares problem for each user and return the new feature vectors + userXtX.zipWithIndex.map{ case (triangularXtX, index) => + // Compute the full XtX matrix from the lower-triangular part we got above + fillFullMatrix(triangularXtX, fullXtX) + // Add regularization + (0 until rank).foreach(i => fullXtX.data(i*rank + i) += lambda) + // Solve the resulting matrix, which is symmetric and positive-definite + Solve.solvePositive(fullXtX, userXy(index)).data + } + } + + /** + * Set xtxDest to the lower-triangular part of x transpose * x. For efficiency in summing + * these matrices, we store xtxDest as only rank * (rank+1) / 2 values, namely the values + * at (0,0), (1,0), (1,1), (2,0), (2,1), (2,2), etc in that order. + */ + private def fillXtX(x: DoubleMatrix, xtxDest: DoubleMatrix) { + var i = 0 + var pos = 0 + while (i < x.length) { + var j = 0 + while (j <= i) { + xtxDest.data(pos) = x.data(i) * x.data(j) + pos += 1 + j += 1 + } + i += 1 + } + } + + /** + * Given a triangular matrix in the order of fillXtX above, compute the full symmetric square + * matrix that it represents, storing it into destMatrix. + */ + private def fillFullMatrix(triangularMatrix: DoubleMatrix, destMatrix: DoubleMatrix) { + val rank = destMatrix.rows + var i = 0 + var pos = 0 + while (i < rank) { + var j = 0 + while (j <= i) { + destMatrix.data(i*rank + j) = triangularMatrix.data(pos) + destMatrix.data(j*rank + i) = triangularMatrix.data(pos) + pos += 1 + j += 1 + } + i += 1 + } + } +} + + +/** + * Top-level methods for calling Alternating Least Squares (ALS) matrix factorizaton. + */ +object ALS { + /** + * Train a matrix factorization model given an RDD of ratings given by users to some products, + * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the + * product of two lower-rank matrices of a given rank (number of features). To solve for these + * features, we run a given number of iterations of ALS. This is done using a level of + * parallelism given by `blocks`. + * + * @param ratings RDD of (userID, productID, rating) pairs + * @param rank number of features to use + * @param iterations number of iterations of ALS (recommended: 10-20) + * @param lambda regularization factor (recommended: 0.01) + * @param blocks level of parallelism to split computation into + */ + def train( + ratings: RDD[(Int, Int, Double)], + rank: Int, + iterations: Int, + lambda: Double, + blocks: Int) + : MatrixFactorizationModel = + { + new ALS(blocks, rank, iterations, lambda).train(ratings) + } + + /** + * Train a matrix factorization model given an RDD of ratings given by users to some products, + * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the + * product of two lower-rank matrices of a given rank (number of features). To solve for these + * features, we run a given number of iterations of ALS. The level of parallelism is determined + * automatically based on the number of partitions in `ratings`. + * + * @param ratings RDD of (userID, productID, rating) pairs + * @param rank number of features to use + * @param iterations number of iterations of ALS (recommended: 10-20) + * @param lambda regularization factor (recommended: 0.01) + */ + def train(ratings: RDD[(Int, Int, Double)], rank: Int, iterations: Int, lambda: Double) + : MatrixFactorizationModel = + { + train(ratings, rank, iterations, lambda, -1) + } + + /** + * Train a matrix factorization model given an RDD of ratings given by users to some products, + * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the + * product of two lower-rank matrices of a given rank (number of features). To solve for these + * features, we run a given number of iterations of ALS. The level of parallelism is determined + * automatically based on the number of partitions in `ratings`. + * + * @param ratings RDD of (userID, productID, rating) pairs + * @param rank number of features to use + * @param iterations number of iterations of ALS (recommended: 10-20) + */ + def train(ratings: RDD[(Int, Int, Double)], rank: Int, iterations: Int) + : MatrixFactorizationModel = + { + train(ratings, rank, iterations, 0.01, -1) + } + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: ALS <master> <ratings_file> <rank> <iterations> <output_dir>") + System.exit(1) + } + val (master, ratingsFile, rank, iters, outputDir) = + (args(0), args(1), args(2).toInt, args(3).toInt, args(4)) + val sc = new SparkContext(master, "ALS") + val ratings = sc.textFile(ratingsFile).map { line => + val fields = line.split(',') + (fields(0).toInt, fields(1).toInt, fields(2).toDouble) + } + val model = ALS.train(ratings, rank, iters) + model.userFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") } + .saveAsTextFile(outputDir + "/userFeatures") + model.productFeatures.map{ case (id, vec) => id + "," + vec.mkString(" ") } + .saveAsTextFile(outputDir + "/productFeatures") + println("Final user/product features written to " + outputDir) + System.exit(0) + } +} diff --git a/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala new file mode 100644 index 0000000000..fb812a6dbe --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -0,0 +1,23 @@ +package spark.mllib.recommendation + +import spark.RDD +import spark.SparkContext._ + +import org.jblas._ + +class MatrixFactorizationModel( + val rank: Int, + val userFeatures: RDD[(Int, Array[Double])], + val productFeatures: RDD[(Int, Array[Double])]) + extends Serializable +{ + /** Predict the rating of one user for one product. */ + def predict(user: Int, product: Int): Double = { + val userVector = new DoubleMatrix(userFeatures.lookup(user).head) + val productVector = new DoubleMatrix(productFeatures.lookup(product).head) + userVector.dot(productVector) + } + + // TODO: Figure out what good bulk prediction methods would look like. + // Probably want a way to get the top users for a product or vice-versa. +} diff --git a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala new file mode 100644 index 0000000000..e4db7bb9b7 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala @@ -0,0 +1,158 @@ +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/LogisticRegressionGenerator.scala b/mllib/src/main/scala/spark/mllib/regression/LogisticRegressionGenerator.scala new file mode 100644 index 0000000000..6e7c023bac --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/LogisticRegressionGenerator.scala @@ -0,0 +1,41 @@ +package spark.mllib.regression + +import scala.util.Random + +import org.jblas.DoubleMatrix + +import spark.{RDD, SparkContext} +import spark.mllib.util.MLUtils + +object LogisticRegressionGenerator { + + def main(args: Array[String]) { + if (args.length != 5) { + println("Usage: LogisticRegressionGenerator " + + "<master> <output_dir> <num_examples> <num_features> <num_partitions>") + System.exit(1) + } + + val sparkMaster: String = args(0) + val outputPath: String = args(1) + val nexamples: Int = if (args.length > 2) args(2).toInt else 1000 + val nfeatures: Int = if (args.length > 3) args(3).toInt else 2 + val parts: Int = if (args.length > 4) args(4).toInt else 2 + val eps = 3 + + val sc = new SparkContext(sparkMaster, "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) + } + + MLUtils.saveLabeledData(data, outputPath) + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/regression/Regression.scala b/mllib/src/main/scala/spark/mllib/regression/Regression.scala new file mode 100644 index 0000000000..f79974c191 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/Regression.scala @@ -0,0 +1,21 @@ +package spark.mllib.regression + +import spark.RDD + +trait RegressionModel { + /** + * Predict values for the given data set using the model trained. + * + * @param testData RDD representing data points to be predicted + * @return RDD[Double] where each entry contains the corresponding prediction + */ + def predict(testData: RDD[Array[Double]]): RDD[Double] + + /** + * Predict values for a single data point using the model trained. + * + * @param testData array representing a single data point + * @return Double prediction from the trained model + */ + def predict(testData: Array[Double]): Double +} diff --git a/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala new file mode 100644 index 0000000000..5f813df402 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala @@ -0,0 +1,194 @@ +package spark.mllib.regression + +import spark.{Logging, RDD, SparkContext} +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix +import org.jblas.Solve + +import scala.annotation.tailrec +import scala.collection.mutable + +/** + * Ridge Regression from Joseph Gonzalez's implementation in MLBase + */ +class RidgeRegressionModel( + val weights: DoubleMatrix, + val intercept: Double, + val lambdaOpt: Double, + val lambdas: Seq[(Double, Double, DoubleMatrix)]) + extends RegressionModel { + + override def predict(testData: RDD[Array[Double]]): RDD[Double] = { + testData.map { x => + (new DoubleMatrix(1, x.length, x:_*).mmul(this.weights)).get(0) + this.intercept + } + } + + override def predict(testData: Array[Double]): Double = { + (new DoubleMatrix(1, testData.length, testData:_*).mmul(this.weights)).get(0) + this.intercept + } +} + +class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double) + extends Logging { + + def this() = this(0.0, 100.0) + + /** + * Set the lower bound on binary search for lambda's. Default is 0. + */ + def setLowLambda(low: Double) = { + this.lambdaLow = low + this + } + + /** + * Set the upper bound on binary search for lambda's. Default is 100.0. + */ + def setHighLambda(hi: Double) = { + this.lambdaHigh = hi + this + } + + def train(input: RDD[(Double, Array[Double])]): RidgeRegressionModel = { + val nfeatures: Int = input.take(1)(0)._2.length + val nexamples: Long = input.count() + + val (yMean, xColMean, xColSd) = MLUtils.computeStats(input, nfeatures, nexamples) + + val data = input.map { case(y, features) => + val yNormalized = y - yMean + val featuresMat = new DoubleMatrix(nfeatures, 1, features:_*) + val featuresNormalized = featuresMat.sub(xColMean).divi(xColSd) + (yNormalized, featuresNormalized.toArray) + } + + // Compute XtX - Size of XtX is nfeatures by nfeatures + val XtX: DoubleMatrix = data.map { case (y, features) => + val x = new DoubleMatrix(1, features.length, features:_*) + x.transpose().mmul(x) + }.reduce(_.addi(_)) + + // Compute Xt*y - Size of Xty is nfeatures by 1 + val Xty: DoubleMatrix = data.map { case (y, features) => + new DoubleMatrix(features.length, 1, features:_*).mul(y) + }.reduce(_.addi(_)) + + // Define a function to compute the leave one out cross validation error + // for a single example + def crossValidate(lambda: Double): (Double, Double, DoubleMatrix) = { + // Compute the MLE ridge regression parameter value + + // Ridge Regression parameter = inv(XtX + \lambda*I) * Xty + val XtXlambda = DoubleMatrix.eye(nfeatures).muli(lambda).addi(XtX) + val w = Solve.solveSymmetric(XtXlambda, Xty) + + val invXtX = Solve.solveSymmetric(XtXlambda, DoubleMatrix.eye(nfeatures)) + + // compute the generalized cross validation score + val cverror = data.map { + case (y, features) => + val x = new DoubleMatrix(features.length, 1, features:_*) + val yhat = w.transpose().mmul(x).get(0) + val H_ii = x.transpose().mmul(invXtX).mmul(x).get(0) + val residual = (y - yhat) / (1.0 - H_ii) + residual * residual + }.reduce(_ + _) / nexamples + + (lambda, cverror, w) + } + + // Binary search for the best assignment to lambda. + def binSearch(low: Double, high: Double): Seq[(Double, Double, DoubleMatrix)] = { + val buffer = mutable.ListBuffer.empty[(Double, Double, DoubleMatrix)] + + @tailrec + def loop(low: Double, high: Double): Seq[(Double, Double, DoubleMatrix)] = { + val mid = (high - low) / 2 + low + val lowValue = crossValidate((mid - low) / 2 + low) + val highValue = crossValidate((high - mid) / 2 + mid) + val (newLow, newHigh) = if (lowValue._2 < highValue._2) { + (low, mid + (high-low)/4) + } else { + (mid - (high-low)/4, high) + } + if (newHigh - newLow > 1.0E-7) { + buffer += lowValue += highValue + loop(newLow, newHigh) + } else { + buffer += lowValue += highValue + buffer.result() + } + } + + loop(low, high) + } + + // Actually compute the best lambda + val lambdas = binSearch(lambdaLow, lambdaHigh).sortBy(_._1) + + // Find the best parameter set by taking the lowest cverror. + val (lambdaOpt, cverror, weights) = lambdas.reduce((a, b) => if (a._2 < b._2) a else b) + + // Return the model which contains the solution + val weightsScaled = weights.div(xColSd) + val intercept = yMean - (weights.transpose().mmul(xColMean.div(xColSd)).get(0)) + val model = new RidgeRegressionModel(weightsScaled, intercept, lambdaOpt, lambdas) + + logInfo("RidgeRegression: optimal lambda " + model.lambdaOpt) + logInfo("RidgeRegression: optimal weights " + model.weights) + logInfo("RidgeRegression: optimal intercept " + model.intercept) + logInfo("RidgeRegression: cross-validation error " + cverror) + + model + } +} + +/** + * Top-level methods for calling Ridge Regression. + */ +object RidgeRegression { + + /** + * Train a ridge regression model given an RDD of (response, features) pairs. + * We use the closed form solution to compute the cross-validation score for + * a given lambda. The optimal lambda is computed by performing binary search + * between the provided bounds of lambda. + * + * @param input RDD of (response, array of features) pairs. + * @param lambdaLow lower bound used in binary search for lambda + * @param lambdaHigh upper bound used in binary search for lambda + */ + def train( + input: RDD[(Double, Array[Double])], + lambdaLow: Double, + lambdaHigh: Double) + : RidgeRegressionModel = + { + new RidgeRegression(lambdaLow, lambdaHigh).train(input) + } + + /** + * Train a ridge regression model given an RDD of (response, features) pairs. + * We use the closed form solution to compute the cross-validation score for + * a given lambda. The optimal lambda is computed by performing binary search + * between lambda values of 0 and 100. + * + * @param input RDD of (response, array of features) pairs. + */ + def train(input: RDD[(Double, Array[Double])]) : RidgeRegressionModel = { + train(input, 0.0, 100.0) + } + + def main(args: Array[String]) { + if (args.length != 2) { + println("Usage: RidgeRegression <master> <input_dir>") + System.exit(1) + } + val sc = new SparkContext(args(0), "RidgeRegression") + val data = MLUtils.loadLabeledData(sc, args(1)) + val model = RidgeRegression.train(data, 0, 1000) + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala b/mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala new file mode 100644 index 0000000000..b83f505d8e --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/regression/RidgeRegressionGenerator.scala @@ -0,0 +1,55 @@ +package spark.mllib.regression + +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 + + 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 => + org.jblas.util.Random.seed(42 + p) + val examplesInPartition = nexamples / parts + + val X = DoubleMatrix.rand(examplesInPartition, nfeatures) + val y = X.mmul(w) + + val rnd = new Random(42 + p) + + val normalValues = Array.fill[Double](examplesInPartition)(rnd.nextGaussian() * eps) + val yObs = new DoubleMatrix(normalValues).addi(y) + + Iterator.tabulate(examplesInPartition) { i => + (yObs.get(i, 0), X.getRow(i).toArray) + } + } + + MLUtils.saveLabeledData(data, outputPath) + sc.stop() + } +} diff --git a/mllib/src/main/scala/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala new file mode 100644 index 0000000000..08a031dded --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/util/MLUtils.scala @@ -0,0 +1,95 @@ +package spark.mllib.util + +import spark.{RDD, SparkContext} +import spark.SparkContext._ + +import org.jblas.DoubleMatrix + +/** + * Helper methods to load and save data + * Data format: + * <l>, <f1> <f2> ... + * where <f1>, <f2> are feature values in Double and <l> is the corresponding label as Double. + */ +object MLUtils { + + /** + * @param sc SparkContext + * @param dir Directory to the input data files. + * @return An RDD of tuples. For each tuple, the first element is the label, and the second + * element represents the feature values (an array of Double). + */ + def loadLabeledData(sc: SparkContext, dir: String): RDD[(Double, Array[Double])] = { + sc.textFile(dir).map { line => + val parts = line.split(",") + val label = parts(0).toDouble + val features = parts(1).trim().split(" ").map(_.toDouble) + (label, features) + } + } + + def saveLabeledData(data: RDD[(Double, Array[Double])], dir: String) { + val dataStr = data.map(x => x._1 + "," + x._2.mkString(" ")) + dataStr.saveAsTextFile(dir) + } + + /** + * Utility function to compute mean and standard deviation on a given dataset. + * + * @param data - input data set whose statistics are computed + * @param nfeatures - number of features + * @param nexamples - number of examples in input dataset + * + * @return (yMean, xColMean, xColSd) - Tuple consisting of + * yMean - mean of the labels + * xColMean - Row vector with mean for every column (or feature) of the input data + * xColSd - Row vector standard deviation for every column (or feature) of the input data. + */ + def computeStats(data: RDD[(Double, Array[Double])], nfeatures: Int, nexamples: Long): + (Double, DoubleMatrix, DoubleMatrix) = { + val yMean: Double = data.map { case (y, features) => y }.reduce(_ + _) / nexamples + + // NOTE: We shuffle X by column here to compute column sum and sum of squares. + val xColSumSq: RDD[(Int, (Double, Double))] = data.flatMap { case(y, features) => + val nCols = features.length + // Traverse over every column and emit (col, value, value^2) + Iterator.tabulate(nCols) { i => + (i, (features(i), features(i)*features(i))) + } + }.reduceByKey { case(x1, x2) => + (x1._1 + x2._1, x1._2 + x2._2) + } + val xColSumsMap = xColSumSq.collectAsMap() + + val xColMean = DoubleMatrix.zeros(nfeatures, 1) + val xColSd = DoubleMatrix.zeros(nfeatures, 1) + + // Compute mean and unbiased variance using column sums + var col = 0 + while (col < nfeatures) { + xColMean.put(col, xColSumsMap(col)._1 / nexamples) + val variance = + (xColSumsMap(col)._2 - (math.pow(xColSumsMap(col)._1, 2) / nexamples)) / (nexamples) + xColSd.put(col, math.sqrt(variance)) + col += 1 + } + + (yMean, xColMean, xColSd) + } + + /** + * Return the squared Euclidean distance between two vectors. + */ + def squaredDistance(v1: Array[Double], v2: Array[Double]): Double = { + if (v1.length != v2.length) { + throw new IllegalArgumentException("Vector sizes don't match") + } + var i = 0 + var sum = 0.0 + while (i < v1.length) { + sum += (v1(i) - v2(i)) * (v1(i) - v2(i)) + i += 1 + } + sum + } +} diff --git a/mllib/src/test/resources/log4j.properties b/mllib/src/test/resources/log4j.properties new file mode 100644 index 0000000000..390c92763c --- /dev/null +++ b/mllib/src/test/resources/log4j.properties @@ -0,0 +1,11 @@ +# Set everything to be logged to the file core/target/unit-tests.log +log4j.rootCategory=INFO, file +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=false +log4j.appender.file.file=ml/target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN + diff --git a/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala new file mode 100644 index 0000000000..cb096f39a9 --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/clustering/KMeansSuite.scala @@ -0,0 +1,153 @@ +package spark.mllib.clustering + +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext +import spark.SparkContext._ + +import org.jblas._ + + +class KMeansSuite extends FunSuite with BeforeAndAfterAll { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + val EPSILON = 1e-4 + + import KMeans.{RANDOM, K_MEANS_PARALLEL} + + def prettyPrint(point: Array[Double]): String = point.mkString("(", ", ", ")") + + def prettyPrint(points: Array[Array[Double]]): String = { + points.map(prettyPrint).mkString("(", "; ", ")") + } + + // L1 distance between two points + def distance1(v1: Array[Double], v2: Array[Double]): Double = { + v1.zip(v2).map{ case (a, b) => math.abs(a-b) }.max + } + + // Assert that two vectors are equal within tolerance EPSILON + def assertEqual(v1: Array[Double], v2: Array[Double]) { + def errorMessage = prettyPrint(v1) + " did not equal " + prettyPrint(v2) + assert(v1.length == v2.length, errorMessage) + assert(distance1(v1, v2) <= EPSILON, errorMessage) + } + + // Assert that two sets of points are equal, within EPSILON tolerance + def assertSetsEqual(set1: Array[Array[Double]], set2: Array[Array[Double]]) { + def errorMessage = prettyPrint(set1) + " did not equal " + prettyPrint(set2) + assert(set1.length == set2.length, errorMessage) + for (v <- set1) { + val closestDistance = set2.map(w => distance1(v, w)).min + if (closestDistance > EPSILON) { + fail(errorMessage) + } + } + for (v <- set2) { + val closestDistance = set1.map(w => distance1(v, w)).min + if (closestDistance > EPSILON) { + fail(errorMessage) + } + } + } + + test("single cluster") { + val data = sc.parallelize(Array( + Array(1.0, 2.0, 6.0), + Array(1.0, 3.0, 0.0), + Array(1.0, 4.0, 6.0) + )) + + // No matter how many runs or iterations we use, we should get one cluster, + // centered at the mean of the points + + var model = KMeans.train(data, k=1, maxIterations=1) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=2) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train( + data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + } + + test("single cluster with big dataset") { + val smallData = Array( + Array(1.0, 2.0, 6.0), + Array(1.0, 3.0, 0.0), + Array(1.0, 4.0, 6.0) + ) + val data = sc.parallelize((1 to 100).flatMap(_ => smallData), 4) + + // No matter how many runs or iterations we use, we should get one cluster, + // centered at the mean of the points + + var model = KMeans.train(data, k=1, maxIterations=1) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=2) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=5) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=RANDOM) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + + model = KMeans.train(data, k=1, maxIterations=1, runs=1, initializationMode=K_MEANS_PARALLEL) + assertSetsEqual(model.clusterCenters, Array(Array(1.0, 3.0, 4.0))) + } + + test("k-means|| initialization") { + val points = Array( + Array(1.0, 2.0, 6.0), + Array(1.0, 3.0, 0.0), + Array(1.0, 4.0, 6.0), + Array(1.0, 0.0, 1.0), + Array(1.0, 1.0, 1.0) + ) + val rdd = sc.parallelize(points) + + // K-means|| initialization should place all clusters into distinct centers because + // it will make at least five passes, and it will give non-zero probability to each + // unselected point as long as it hasn't yet selected all of them + + var model = KMeans.train(rdd, k=5, maxIterations=1) + assertSetsEqual(model.clusterCenters, points) + + // Iterations of Lloyd's should not change the answer either + model = KMeans.train(rdd, k=5, maxIterations=10) + assertSetsEqual(model.clusterCenters, points) + + // Neither should more runs + model = KMeans.train(rdd, k=5, maxIterations=10, runs=5) + assertSetsEqual(model.clusterCenters, points) + } +} diff --git a/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala new file mode 100644 index 0000000000..2ada9ae76b --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/recommendation/ALSSuite.scala @@ -0,0 +1,80 @@ +package spark.mllib.recommendation + +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext +import spark.SparkContext._ + +import org.jblas._ + + +class ALSSuite extends FunSuite with BeforeAndAfterAll { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + test("rank-1 matrices") { + testALS(10, 20, 1, 15, 0.7, 0.3) + } + + test("rank-2 matrices") { + testALS(20, 30, 2, 15, 0.7, 0.3) + } + + /** + * Test if we can correctly factorize R = U * P where U and P are of known rank. + * + * @param users number of users + * @param products number of products + * @param features number of features (rank of problem) + * @param iterations number of iterations to run + * @param samplingRate what fraction of the user-product pairs are known + * @param matchThreshold max difference allowed to consider a predicted rating correct + */ + def testALS(users: Int, products: Int, features: Int, iterations: Int, + samplingRate: Double, matchThreshold: Double) + { + val rand = new Random(42) + + // Create a random matrix with uniform values from -1 to 1 + def randomMatrix(m: Int, n: Int) = + new DoubleMatrix(m, n, Array.fill(m * n)(rand.nextDouble() * 2 - 1): _*) + + val userMatrix = randomMatrix(users, features) + val productMatrix = randomMatrix(features, products) + val trueRatings = userMatrix.mmul(productMatrix) + + val sampledRatings = { + for (u <- 0 until users; p <- 0 until products if rand.nextDouble() < samplingRate) + yield (u, p, trueRatings.get(u, p)) + } + + val model = ALS.train(sc.parallelize(sampledRatings), features, iterations) + + val predictedU = new DoubleMatrix(users, features) + for ((u, vec) <- model.userFeatures.collect(); i <- 0 until features) { + predictedU.put(u, i, vec(i)) + } + val predictedP = new DoubleMatrix(products, features) + for ((p, vec) <- model.productFeatures.collect(); i <- 0 until features) { + predictedP.put(p, i, vec(i)) + } + val predictedRatings = predictedU.mmul(predictedP.transpose) + + for (u <- 0 until users; p <- 0 until products) { + val prediction = predictedRatings.get(u, p) + val correct = trueRatings.get(u, p) + if (math.abs(prediction - correct) > matchThreshold) { + fail("Model failed to predict (%d, %d): %f vs %f\ncorr: %s\npred: %s\nU: %s\n P: %s".format( + u, p, correct, prediction, trueRatings, predictedRatings, predictedU, predictedP)) + } + } + } +} + diff --git a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala new file mode 100644 index 0000000000..04d3400cb4 --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala @@ -0,0 +1,57 @@ +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 new file mode 100644 index 0000000000..df41dbbdff --- /dev/null +++ b/mllib/src/test/scala/spark/mllib/regression/RidgeRegressionSuite.scala @@ -0,0 +1,47 @@ +package spark.mllib.regression + +import scala.util.Random + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import spark.SparkContext +import spark.SparkContext._ + + +class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll { + val sc = new SparkContext("local", "test") + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + // Test if we can correctly learn Y = 3 + X1 + X2 when + // X1 and X2 are collinear. + test("multi-collinear variables") { + val rnd = new Random(43) + val x1 = Array.fill[Double](20)(rnd.nextGaussian()) + + // Pick a mean close to mean of x1 + val rnd1 = new Random(42) //new NormalDistribution(0.1, 0.01) + val x2 = Array.fill[Double](20)(0.1 + rnd1.nextGaussian() * 0.01) + + val xMat = (0 until 20).map(i => Array(x1(i), x2(i))).toArray + + val y = xMat.map(i => 3 + i(0) + i(1)) + val testData = (0 until 20).map(i => (y(i), xMat(i))).toArray + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + val ridgeReg = new RidgeRegression().setLowLambda(0) + .setHighLambda(10) + + val model = ridgeReg.train(testRDD) + + assert(model.intercept >= 2.9 && model.intercept <= 3.1) + assert(model.weights.length === 2) + assert(model.weights.get(0) >= 0.9 && model.weights.get(0) <= 1.1) + assert(model.weights.get(1) >= 0.9 && model.weights.get(1) <= 1.1) + } +} @@ -98,17 +98,6 @@ <enabled>false</enabled> </snapshots> </repository> - <repository> - <id>spray-repo</id> - <name>Spray Repository</name> - <url>http://repo.spray.cc/</url> - <releases> - <enabled>true</enabled> - </releases> - <snapshots> - <enabled>false</enabled> - </snapshots> - </repository> </repositories> <pluginRepositories> <pluginRepository> @@ -219,31 +208,6 @@ <version>1.2.0</version> </dependency> <dependency> - <groupId>io.spray</groupId> - <artifactId>spray-can</artifactId> - <version>${spray.version}</version> - </dependency> - <dependency> - <groupId>io.spray</groupId> - <artifactId>spray-routing</artifactId> - <version>${spray.version}</version> - </dependency> - <dependency> - <groupId>io.spray</groupId> - <artifactId>spray-io</artifactId> - <version>${spray.version}</version> - </dependency> - <dependency> - <groupId>io.spray</groupId> - <artifactId>spray-json_${scala.version}</artifactId> - <version>${spray.json.version}</version> - </dependency> - <dependency> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-api</artifactId> - <version>1.0.2</version> - </dependency> - <dependency> <groupId>com.github.scala-incubator.io</groupId> <artifactId>scala-io-file_2.10</artifactId> <version>0.4.1</version> @@ -264,7 +228,11 @@ <version>10.4.2.0</version> <scope>test</scope> </dependency> - + <dependency> + <groupId>net.liftweb</groupId> + <artifactId>lift-json_2.10</artifactId> + <version>2.5.1</version> + </dependency> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-compiler</artifactId> @@ -275,6 +243,16 @@ <artifactId>jline</artifactId> <version>2.10.0</version> </dependency> + <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> + <version>${scala.version}</version> + </dependency> + <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scalap</artifactId> + <version>${scala.version}</version> + </dependency> <dependency> <groupId>log4j</groupId> @@ -341,18 +319,6 @@ <version>1.7</version> </plugin> <plugin> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-maven-plugin</artifactId> - <version>1.0.1</version> - <executions> - <execution> - <goals> - <goal>generate</goal> - </goals> - </execution> - </executions> - </plugin> - <plugin> <groupId>net.alchim31.maven</groupId> <artifactId>scala-maven-plugin</artifactId> <version>3.1.0</version> @@ -558,12 +524,12 @@ <dependency> <groupId>org.apache.avro</groupId> <artifactId>avro</artifactId> - <version>1.7.1.cloudera.2</version> + <version>1.7.4</version> </dependency> <dependency> <groupId>org.apache.avro</groupId> <artifactId>avro-ipc</artifactId> - <version>1.7.1.cloudera.2</version> + <version>1.7.4</version> <exclusions> <exclusion> <groupId>org.jboss.netty</groupId> @@ -625,12 +591,12 @@ <dependency> <groupId>org.apache.avro</groupId> <artifactId>avro</artifactId> - <version>1.7.1.cloudera.2</version> + <version>1.7.4</version> </dependency> <dependency> <groupId>org.apache.avro</groupId> <artifactId>avro-ipc</artifactId> - <version>1.7.1.cloudera.2</version> + <version>1.7.4</version> </dependency> </dependencies> </dependencyManagement> diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index d35b39fcf3..bb2a97837c 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -4,7 +4,6 @@ import sbt.Classpaths.publishTask import Keys._ import sbtassembly.Plugin._ import AssemblyKeys._ -import twirl.sbt.TwirlPlugin._ // For Sonatype publishing //import com.jsuereth.pgp.sbtplugin.PgpKeys._ @@ -25,7 +24,7 @@ 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) + lazy val root = Project("root", file("."), settings = rootSettings) aggregate(core, repl, examples, bagel, streaming, mllib) lazy val core = Project("core", file("core"), settings = coreSettings) @@ -37,6 +36,8 @@ object SparkBuild extends Build { lazy val streaming = Project("streaming", file("streaming"), settings = streamingSettings) dependsOn (core) + lazy val mllib = Project("mllib", file("mllib"), settings = mllibSettings) dependsOn (core) + // A configuration to set an alternative publishLocalConfiguration lazy val MavenCompile = config("m2r") extend(Compile) lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy") @@ -141,28 +142,26 @@ object SparkBuild extends Build { ), libraryDependencies ++= Seq( - "com.google.guava" % "guava" % "11.0.1", - "log4j" % "log4j" % "1.2.16", - "org.slf4j" % "slf4j-api" % slf4jVersion, - "org.slf4j" % "slf4j-log4j12" % slf4jVersion, - "com.ning" % "compress-lzf" % "0.8.4", - "commons-daemon" % "commons-daemon" % "1.0.10", - "org.apache.hadoop" % "hadoop-core" % HADOOP_VERSION excludeAll(excludeNetty, excludeJackson), - "org.ow2.asm" % "asm" % "4.0", - "com.google.protobuf" % "protobuf-java" % "2.4.1", - "de.javakaffee" % "kryo-serializers" % "0.22", - "com.typesafe.akka" %% "akka-remote" % "2.1.4" excludeAll(excludeNetty), - "com.typesafe.akka" %% "akka-slf4j" % "2.1.4" excludeAll(excludeNetty), - "it.unimi.dsi" % "fastutil" % "6.4.4", - "io.spray" % "spray-can" % "1.1-M7" excludeAll(excludeNetty), - "io.spray" % "spray-io" % "1.1-M7" excludeAll(excludeNetty), - "io.spray" % "spray-routing" % "1.1-M7" excludeAll(excludeNetty), - "io.spray" %% "spray-json" % "1.2.3" excludeAll(excludeNetty), - "colt" % "colt" % "1.2.0", - "org.apache.mesos" % "mesos" % "0.9.0-incubating", - "org.apache.derby" % "derby" % "10.4.2.0" % "test", - "org.scala-lang" % "jline" % "2.10.1", - "org.scala-lang" % "scala-reflect" % "2.10.1" + "com.google.guava" % "guava" % "14.0.1", + "com.google.code.findbugs" % "jsr305" % "1.3.+", + "log4j" % "log4j" % "1.2.16", + "org.slf4j" % "slf4j-api" % slf4jVersion, + "org.slf4j" % "slf4j-log4j12" % slf4jVersion, + "com.ning" % "compress-lzf" % "0.8.4", + "commons-daemon" % "commons-daemon" % "1.0.10", + "org.apache.hadoop" % "hadoop-core" % HADOOP_VERSION excludeAll(excludeNetty, excludeJackson), + "org.ow2.asm" % "asm" % "4.0", + "com.google.protobuf" % "protobuf-java" % "2.4.1", + "de.javakaffee" % "kryo-serializers" % "0.22", + "com.typesafe.akka" %% "akka-remote" % "2.1.4" excludeAll(excludeNetty), + "com.typesafe.akka" %% "akka-slf4j" % "2.1.4" excludeAll(excludeNetty), + "net.liftweb" %% "lift-json" % "2.5.1", + "it.unimi.dsi" % "fastutil" % "6.4.4", + "colt" % "colt" % "1.2.0", + "org.apache.mesos" % "mesos" % "0.9.0-incubating", + "org.apache.derby" % "derby" % "10.4.2.0" % "test", + "org.scala-lang" % "jline" % "2.10.1", + "org.scala-lang" % "scala-reflect" % "2.10.1" ) ++ ( if (HADOOP_MAJOR_VERSION == "2") { if (HADOOP_YARN) { @@ -189,7 +188,7 @@ object SparkBuild extends Build { "src/hadoop" + HADOOP_MAJOR_VERSION + "/scala" } ) } - ) ++ assemblySettings ++ extraAssemblySettings ++ Twirl.settings + ) ++ assemblySettings ++ extraAssemblySettings def rootSettings = sharedSettings ++ Seq( publish := {} @@ -219,6 +218,13 @@ object SparkBuild extends Build { def bagelSettings = sharedSettings ++ Seq(name := "spark-bagel") + def mllibSettings = sharedSettings ++ Seq( + name := "spark-mllib", + libraryDependencies ++= Seq( + "org.jblas" % "jblas" % "1.2.3" + ) + ) + def streamingSettings = sharedSettings ++ Seq( name := "spark-streaming", libraryDependencies ++= Seq( diff --git a/project/plugins.sbt b/project/plugins.sbt index f806e66481..1b0f879b94 100644 --- a/project/plugins.sbt +++ b/project/plugins.sbt @@ -10,8 +10,6 @@ addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.1.1") addSbtPlugin("com.github.mpeltonen" % "sbt-idea" % "1.2.0") -addSbtPlugin("io.spray" %% "sbt-twirl" % "0.6.1") - // For Sonatype publishing //resolvers += Resolver.url("sbt-plugin-releases", new URL("http://scalasbt.artifactoryonline.com/scalasbt/sbt-plugin-releases/"))(Resolver.ivyStylePatterns) diff --git a/streaming/pom.xml b/streaming/pom.xml index 1589e3202f..7b621c1239 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -59,6 +59,10 @@ <version>3.0.3</version> </dependency> <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> + </dependency> + <dependency> <groupId>com.typesafe.akka</groupId> <artifactId>akka-zeromq_${scala.version}</artifactId> <version>${akka.version}</version> diff --git a/streaming/src/main/scala/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/spark/streaming/Checkpoint.scala index 66e67cbfa1..450e48d66e 100644 --- a/streaming/src/main/scala/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/spark/streaming/Checkpoint.scala @@ -8,7 +8,7 @@ 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 private[streaming] class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) @@ -91,7 +91,12 @@ class CheckpointWriter(checkpointDir: String) extends Logging { oos.writeObject(checkpoint) oos.close() bos.close() - executor.execute(new CheckpointWriteHandler(checkpoint.checkpointTime, bos.toByteArray)) + try { + executor.execute(new CheckpointWriteHandler(checkpoint.checkpointTime, bos.toByteArray)) + } catch { + case rej: RejectedExecutionException => + logError("Could not submit checkpoint task to the thread pool executor", rej) + } } def stop() { diff --git a/streaming/src/main/scala/spark/streaming/Duration.scala b/streaming/src/main/scala/spark/streaming/Duration.scala index ee26206e24..c2135195d8 100644 --- a/streaming/src/main/scala/spark/streaming/Duration.scala +++ b/streaming/src/main/scala/spark/streaming/Duration.scala @@ -1,5 +1,7 @@ package spark.streaming +import spark.Utils + case class Duration (private val millis: Long) { def < (that: Duration): Boolean = (this.millis < that.millis) @@ -32,8 +34,10 @@ case class Duration (private val millis: Long) { def toFormattedString: String = millis.toString def milliseconds: Long = millis -} + def prettyPrint = Utils.msDurationToString(millis) + +} /** * Helper object that creates instance of [[spark.streaming.Duration]] representing diff --git a/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala b/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala index aa1a8b6ba2..b0ebdea01c 100644 --- a/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala +++ b/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala @@ -15,6 +15,7 @@ import scala.reflect.{ClassTag, classTag} import org.apache.hadoop.mapred.{JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} import org.apache.hadoop.mapred.OutputFormat +import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.conf.Configuration class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) @@ -471,7 +472,7 @@ extends Serializable { valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: JobConf = new JobConf - ) { + ) { val saveFunc = (rdd: RDD[(K, V)], time: Time) => { val file = rddToFileName(prefix, suffix, time) rdd.saveAsHadoopFile(file, keyClass, valueClass, outputFormatClass, conf) diff --git a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala index 3b9cd0e89b..71d2b9715c 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala @@ -143,12 +143,10 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging /** - * Pushes a block (as iterator of values) into the block manager. + * Pushes a block (as an ArrayBuffer filled with data) into the block manager. */ - def pushBlock(blockId: String, iterator: Iterator[T], metadata: Any, level: StorageLevel) { - val buffer = new ArrayBuffer[T] ++ iterator - env.blockManager.put(blockId, buffer.asInstanceOf[ArrayBuffer[Any]], level) - + def pushBlock(blockId: String, arrayBuffer: ArrayBuffer[T], metadata: Any, level: StorageLevel) { + env.blockManager.put(blockId, arrayBuffer.asInstanceOf[ArrayBuffer[Any]], level) actor ! ReportBlock(blockId, metadata) } @@ -198,7 +196,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging class BlockGenerator(storageLevel: StorageLevel) extends Serializable with Logging { - case class Block(id: String, iterator: Iterator[T], metadata: Any = null) + case class Block(id: String, buffer: ArrayBuffer[T], metadata: Any = null) val clock = new SystemClock() val blockInterval = System.getProperty("spark.streaming.blockInterval", "200").toLong @@ -225,17 +223,13 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging currentBuffer += obj } - private def createBlock(blockId: String, iterator: Iterator[T]) : Block = { - new Block(blockId, iterator) - } - private def updateCurrentBuffer(time: Long) { try { val newBlockBuffer = currentBuffer currentBuffer = new ArrayBuffer[T] if (newBlockBuffer.size > 0) { val blockId = "input-" + NetworkReceiver.this.streamId + "-" + (time - blockInterval) - val newBlock = createBlock(blockId, newBlockBuffer.toIterator) + val newBlock = new Block(blockId, newBlockBuffer) blocksForPushing.add(newBlock) } } catch { @@ -251,7 +245,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging try { while(true) { val block = blocksForPushing.take() - NetworkReceiver.this.pushBlock(block.id, block.iterator, block.metadata, storageLevel) + NetworkReceiver.this.pushBlock(block.id, block.buffer, block.metadata, storageLevel) } } catch { case ie: InterruptedException => diff --git a/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala b/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala index 42e885af5c..279e39d6d6 100644 --- a/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala +++ b/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala @@ -13,6 +13,8 @@ import spark.streaming.dstream.NetworkReceiver import java.util.concurrent.atomic.AtomicInteger +import scala.collection.mutable.ArrayBuffer + /** A helper with set of defaults for supervisor strategy */ object ReceiverSupervisorStrategy { @@ -137,8 +139,9 @@ private[streaming] class ActorReceiver[T: ClassTag]( } protected def pushBlock(iter: Iterator[T]) { - pushBlock("block-" + streamId + "-" + System.nanoTime(), - iter, null, storageLevel) + val buffer = new ArrayBuffer[T] + buffer ++= iter + pushBlock("block-" + streamId + "-" + System.nanoTime(), buffer, null, storageLevel) } protected def onStart() = { |
