diff options
145 files changed, 5457 insertions, 1279 deletions
@@ -12,7 +12,7 @@ This README file only contains basic setup instructions. ## Building -Spark requires Scala 2.9.2 (Scala 2.10 is not yet supported). The project is +Spark requires Scala 2.9.3 (Scala 2.10 is not yet supported). The project is built using Simple Build Tool (SBT), which is packaged with it. To build Spark and its example programs, run: diff --git a/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 47937a0322..bd48b43da3 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="$FWDIR/python" @@ -51,6 +52,7 @@ function dev_classpath { CLASSPATH+=":$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/conf/spark-env.sh.template b/conf/spark-env.sh.template index 37565ca827..b8936314ec 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -3,8 +3,10 @@ # This file contains environment variables required to run Spark. Copy it as # spark-env.sh and edit that to configure Spark for your site. At a minimum, # the following two variables should be set: -# - MESOS_NATIVE_LIBRARY, to point to your Mesos native library (libmesos.so) -# - SCALA_HOME, to point to your Scala installation +# - SCALA_HOME, to point to your Scala installation, or SCALA_LIBRARY_PATH to +# point to the directory for Scala library JARs (if you install Scala as a +# Debian or RPM package, these are in a separate path, often /usr/share/java) +# - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos # # If using the standalone deploy mode, you can also set variables for it: # - SPARK_MASTER_IP, to bind the master to a different IP address @@ -12,14 +14,6 @@ # - SPARK_WORKER_CORES, to set the number of cores to use on this machine # - SPARK_WORKER_MEMORY, to set how much memory to use (e.g. 1000m, 2g) # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT -# - SPARK_WORKER_INSTANCES, to set the number of worker instances/processes to be spawned on every slave machine -# -# Finally, Spark also relies on the following variables, but these can be set -# on just the *master* (i.e. in your driver program), and will automatically -# be propagated to workers: -# - SPARK_MEM, to change the amount of memory used per node (this should -# be in the same format as the JVM's -Xmx option, e.g. 300m or 1g) -# - SPARK_CLASSPATH, to add elements to Spark's classpath -# - SPARK_JAVA_OPTS, to add JVM options -# - SPARK_LIBRARY_PATH, to add extra search paths for native libraries. +# - SPARK_WORKER_INSTANCES, to set the number of worker instances/processes +# to be spawned on every slave machine diff --git a/core/pom.xml b/core/pom.xml index 88f0ed70f3..dbb2da9a9c 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -56,28 +56,24 @@ <artifactId>akka-slf4j</artifactId> </dependency> <dependency> - <groupId>it.unimi.dsi</groupId> - <artifactId>fastutil</artifactId> - </dependency> - <dependency> - <groupId>colt</groupId> - <artifactId>colt</artifactId> + <groupId>org.scala-lang</groupId> + <artifactId>scalap</artifactId> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-can</artifactId> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-server</artifactId> + <groupId>net.liftweb</groupId> + <artifactId>lift-json_2.9.2</artifactId> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-json_2.9.2</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> @@ -171,10 +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 7630fe7803..8b313c645f 100644 --- a/core/src/main/scala/spark/PairRDDFunctions.scala +++ b/core/src/main/scala/spark/PairRDDFunctions.scala @@ -21,6 +21,7 @@ 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 f336c2ea1e..8aa77266bc 100644 --- a/core/src/main/scala/spark/RDD.scala +++ b/core/src/main/scala/spark/RDD.scala @@ -105,6 +105,9 @@ abstract class RDD[T: ClassManifest]( // Methods and fields available on all RDDs // ======================================================================= + /** The SparkContext that created this RDD. */ + def sparkContext: SparkContext = sc + /** A unique ID for this RDD (within its SparkContext). */ val id: Int = sc.newRddId() @@ -119,7 +122,7 @@ abstract class RDD[T: ClassManifest]( /** User-defined generator of this RDD*/ var generator = Utils.getCallSiteInfo.firstUserClass - + /** Reset generator*/ def setGenerator(_generator: String) = { generator = _generator @@ -281,31 +284,35 @@ abstract class RDD[T: ClassManifest]( 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) @@ -363,7 +370,7 @@ abstract class RDD[T: ClassManifest]( /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: String, env: Map[String, String]): RDD[String] = + def pipe(command: String, env: Map[String, String]): RDD[String] = new PipedRDD(this, command, env) @@ -374,24 +381,24 @@ abstract class RDD[T: ClassManifest]( * @param command command to run in forked process. * @param env environment variables to set. * @param printPipeContext Before piping elements, this function is called as an oppotunity - * to pipe context data. Print line function (like out.println) will be + * to pipe context data. Print line function (like out.println) will be * passed as printPipeContext's parameter. - * @param printRDDElement Use this function to customize how to pipe elements. This function - * will be called with each RDD element as the 1st parameter, and the + * @param printRDDElement Use this function to customize how to pipe elements. This function + * will be called with each RDD element as the 1st parameter, and the * print line function (like out.println()) as the 2nd parameter. * An example of pipe the RDD data of groupBy() in a streaming way, * instead of constructing a huge String to concat all the elements: - * def printRDDElement(record:(String, Seq[String]), f:String=>Unit) = + * def printRDDElement(record:(String, Seq[String]), f:String=>Unit) = * for (e <- record._2){f(e)} * @return the result RDD */ def pipe( - command: Seq[String], - env: Map[String, String] = Map(), + command: Seq[String], + env: Map[String, String] = Map(), printPipeContext: (String => Unit) => Unit = null, - printRDDElement: (T, String => Unit) => Unit = null): RDD[String] = - new PipedRDD(this, command, env, - if (printPipeContext ne null) sc.clean(printPipeContext) else null, + printRDDElement: (T, String => Unit) => Unit = null): RDD[String] = + new PipedRDD(this, command, env, + if (printPipeContext ne null) sc.clean(printPipeContext) else null, if (printRDDElement ne null) sc.clean(printRDDElement) else null) /** @@ -771,10 +778,20 @@ abstract class RDD[T: ClassManifest]( }.reduce { (queue1, queue2) => queue1 ++= queue2 queue1 - }.toArray + }.toArray.sorted(ord.reverse) } /** + * Returns the first K elements from this RDD as defined by + * the specified implicit Ordering[T] and maintains the + * ordering. + * @param num the number of top elements to return + * @param ord the implicit ordering for T + * @return an array of top elements + */ + def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = top(num)(ord.reverse) + + /** * Save this RDD as a text file, using string representations of elements. */ def saveAsTextFile(path: String) { diff --git a/core/src/main/scala/spark/SparkContext.scala b/core/src/main/scala/spark/SparkContext.scala index 70a9d7698c..43e6af2351 100644 --- a/core/src/main/scala/spark/SparkContext.scala +++ b/core/src/main/scala/spark/SparkContext.scala @@ -36,6 +36,7 @@ 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 @@ -46,8 +47,9 @@ 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 @@ -93,11 +95,6 @@ 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]() @@ -106,6 +103,9 @@ 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) { @@ -115,13 +115,14 @@ class SparkContext( // Environment variables to pass to our executors private[spark] val executorEnvs = HashMap[String, String]() // Note: SPARK_MEM is included for Mesos, but overwritten for standalone mode in ExecutorRunner - for (key <- Seq("SPARK_MEM", "SPARK_CLASSPATH", "SPARK_LIBRARY_PATH", "SPARK_JAVA_OPTS", - "SPARK_TESTING")) { + for (key <- Seq("SPARK_CLASSPATH", "SPARK_LIBRARY_PATH", "SPARK_JAVA_OPTS", "SPARK_TESTING")) { val value = System.getenv(key) if (value != null) { executorEnvs(key) = value } } + // Since memory can be set with a system property too, use that + executorEnvs("SPARK_MEM") = SparkContext.executorMemoryRequested + "m" if (environment != null) { executorEnvs ++= environment } @@ -156,14 +157,12 @@ class SparkContext( scheduler case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) => - // Check to make sure SPARK_MEM <= memoryPerSlave. Otherwise Spark will just hang. + // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang. val memoryPerSlaveInt = memoryPerSlave.toInt - val sparkMemEnv = System.getenv("SPARK_MEM") - val sparkMemEnvInt = if (sparkMemEnv != null) Utils.memoryStringToMb(sparkMemEnv) else 512 - if (sparkMemEnvInt > memoryPerSlaveInt) { + if (SparkContext.executorMemoryRequested > memoryPerSlaveInt) { throw new SparkException( - "Slave memory (%d MB) cannot be smaller than SPARK_MEM (%d MB)".format( - memoryPerSlaveInt, sparkMemEnvInt)) + "Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format( + memoryPerSlaveInt, SparkContext.executorMemoryRequested)) } val scheduler = new ClusterScheduler(this) @@ -215,6 +214,8 @@ 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() @@ -577,6 +578,7 @@ 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 @@ -881,6 +883,15 @@ object SparkContext { /** Find the JAR that contains the class of a particular object */ def jarOfObject(obj: AnyRef): Seq[String] = jarOfClass(obj.getClass) + + /** Get the amount of memory per executor requested through system properties or SPARK_MEM */ + private[spark] val executorMemoryRequested = { + // TODO: Might need to add some extra memory for the non-heap parts of the JVM + Option(System.getProperty("spark.executor.memory")) + .orElse(Option(System.getenv("SPARK_MEM"))) + .map(Utils.memoryStringToMb) + .getOrElse(512) + } } diff --git a/core/src/main/scala/spark/SparkEnv.scala b/core/src/main/scala/spark/SparkEnv.scala index 7ccde2e818..ec59b4f48f 100644 --- a/core/src/main/scala/spark/SparkEnv.scala +++ b/core/src/main/scala/spark/SparkEnv.scala @@ -59,7 +59,8 @@ class SparkEnv ( def createPythonWorker(pythonExec: String, envVars: Map[String, String]): java.net.Socket = { synchronized { - pythonWorkers.getOrElseUpdate((pythonExec, envVars), new PythonWorkerFactory(pythonExec, envVars)).create() + val key = (pythonExec, envVars) + pythonWorkers.getOrElseUpdate(key, new PythonWorkerFactory(pythonExec, envVars)).create() } } 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 f41efa9d29..c6a3f97872 100644 --- a/core/src/main/scala/spark/Utils.scala +++ b/core/src/main/scala/spark/Utils.scala @@ -6,6 +6,7 @@ import java.util.{Locale, Random, UUID} import java.util.concurrent.{ConcurrentHashMap, Executors, ThreadFactory, ThreadPoolExecutor} import java.util.regex.Pattern +import scala.collection.Map import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.collection.JavaConversions._ import scala.io.Source @@ -486,6 +487,26 @@ 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 = { @@ -525,10 +546,15 @@ private object Utils extends Logging { /** * Execute a command and get its output, throwing an exception if it yields a code other than 0. */ - def executeAndGetOutput(command: Seq[String], workingDir: File = new File(".")): String = { - val process = new ProcessBuilder(command: _*) + def executeAndGetOutput(command: Seq[String], workingDir: File = new File("."), + extraEnvironment: Map[String, String] = Map.empty): String = { + val builder = new ProcessBuilder(command: _*) .directory(workingDir) - .start() + val environment = builder.environment() + for ((key, value) <- extraEnvironment) { + environment.put(key, value) + } + val process = builder.start() new Thread("read stderr for " + command(0)) { override def run() { for (line <- Source.fromInputStream(process.getErrorStream).getLines) { @@ -600,18 +626,20 @@ 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 part of a file from byte 'start' to 'end'. */ + def offsetBytes(path: String, start: Long, end: Long): String = { + val file = new File(path) + val length = file.length() + val effectiveEnd = math.min(length, end) + val effectiveStart = math.max(0, start) + val buff = new Array[Byte]((effectiveEnd-effectiveStart).toInt) + val stream = new FileInputStream(file) + + stream.skip(effectiveStart) + stream.read(buff) + stream.close() + Source.fromBytes(buff).mkString } /** diff --git a/core/src/main/scala/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/spark/api/java/JavaRDDLike.scala index b555f2030a..27f40ecdfd 100644 --- a/core/src/main/scala/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/spark/api/java/JavaRDDLike.scala @@ -384,4 +384,29 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[T]] top(num, comp) } + + /** + * Returns the first K elements from this RDD as defined by + * the specified Comparator[T] and maintains the order. + * @param num the number of top elements to return + * @param comp the comparator that defines the order + * @return an array of top elements + */ + def takeOrdered(num: Int, comp: Comparator[T]): JList[T] = { + import scala.collection.JavaConversions._ + val topElems = rdd.takeOrdered(num)(Ordering.comparatorToOrdering(comp)) + val arr: java.util.Collection[T] = topElems.toSeq + new java.util.ArrayList(arr) + } + + /** + * Returns the first K elements from this RDD using the + * natural ordering for T while maintain the order. + * @param num the number of top elements to return + * @return an array of top elements + */ + def takeOrdered(num: Int): JList[T] = { + val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[T]] + takeOrdered(num, comp) + } } diff --git a/core/src/main/scala/spark/api/python/PythonRDD.scala b/core/src/main/scala/spark/api/python/PythonRDD.scala index 63140cf37f..31d8ea89d4 100644 --- a/core/src/main/scala/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/spark/api/python/PythonRDD.scala @@ -22,6 +22,8 @@ private[spark] class PythonRDD[T: ClassManifest]( accumulator: Accumulator[JList[Array[Byte]]]) extends RDD[Array[Byte]](parent) { + val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + // Similar to Runtime.exec(), if we are given a single string, split it into words // using a standard StringTokenizer (i.e. by spaces) def this(parent: RDD[T], command: String, envVars: JMap[String, String], @@ -45,37 +47,38 @@ private[spark] class PythonRDD[T: ClassManifest]( new Thread("stdin writer for " + pythonExec) { override def run() { SparkEnv.set(env) - val out = new PrintWriter(worker.getOutputStream) - val dOut = new DataOutputStream(worker.getOutputStream) + val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize) + val dataOut = new DataOutputStream(stream) + val printOut = new PrintWriter(stream) // Partition index - dOut.writeInt(split.index) + dataOut.writeInt(split.index) // sparkFilesDir - PythonRDD.writeAsPickle(SparkFiles.getRootDirectory, dOut) + PythonRDD.writeAsPickle(SparkFiles.getRootDirectory, dataOut) // Broadcast variables - dOut.writeInt(broadcastVars.length) + dataOut.writeInt(broadcastVars.length) for (broadcast <- broadcastVars) { - dOut.writeLong(broadcast.id) - dOut.writeInt(broadcast.value.length) - dOut.write(broadcast.value) - dOut.flush() + dataOut.writeLong(broadcast.id) + dataOut.writeInt(broadcast.value.length) + dataOut.write(broadcast.value) } + dataOut.flush() // Serialized user code for (elem <- command) { - out.println(elem) + printOut.println(elem) } - out.flush() + printOut.flush() // Data values for (elem <- parent.iterator(split, context)) { - PythonRDD.writeAsPickle(elem, dOut) + PythonRDD.writeAsPickle(elem, dataOut) } - dOut.flush() - out.flush() + dataOut.flush() + printOut.flush() worker.shutdownOutput() } }.start() // Return an iterator that read lines from the process's stdout - val stream = new DataInputStream(worker.getInputStream) + val stream = new DataInputStream(new BufferedInputStream(worker.getInputStream, bufferSize)) return new Iterator[Array[Byte]] { def next(): Array[Byte] = { val obj = _nextObj @@ -275,6 +278,8 @@ class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int) extends AccumulatorParam[JList[Array[Byte]]] { Utils.checkHost(serverHost, "Expected hostname") + + val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new JArrayList @@ -288,7 +293,7 @@ class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int) // This happens on the master, where we pass the updates to Python through a socket val socket = new Socket(serverHost, serverPort) val in = socket.getInputStream - val out = new DataOutputStream(socket.getOutputStream) + val out = new DataOutputStream(new BufferedOutputStream(socket.getOutputStream, bufferSize)) out.writeInt(val2.size) for (array <- val2) { out.writeInt(array.length) diff --git a/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala index 8844411d73..85d1dfeac8 100644 --- a/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala +++ b/core/src/main/scala/spark/api/python/PythonWorkerFactory.scala @@ -51,7 +51,6 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String val workerEnv = pb.environment() workerEnv.putAll(envVars) daemon = pb.start() - daemonPort = new DataInputStream(daemon.getInputStream).readInt() // Redirect the stderr to ours new Thread("stderr reader for " + pythonExec) { @@ -69,6 +68,25 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String } } }.start() + + val in = new DataInputStream(daemon.getInputStream) + daemonPort = in.readInt() + + // Redirect further stdout output to our stderr + new Thread("stdout reader for " + pythonExec) { + override def run() { + scala.util.control.Exception.ignoring(classOf[IOException]) { + // FIXME HACK: We copy the stream on the level of bytes to + // attempt to dodge encoding problems. + var buf = new Array[Byte](1024) + var len = in.read(buf) + while (len != -1) { + System.err.write(buf, 0, len) + len = in.read(buf) + } + } + } + }.start() } catch { case e => { stopDaemon() diff --git a/core/src/main/scala/spark/deploy/JsonProtocol.scala b/core/src/main/scala/spark/deploy/JsonProtocol.scala index ea832101d2..335aaeb769 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 cc.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)) } -} +}
\ No newline at end of file diff --git a/core/src/main/scala/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/spark/deploy/LocalSparkCluster.scala index 55bb61b0cc..939f26b6f4 100644 --- a/core/src/main/scala/spark/deploy/LocalSparkCluster.scala +++ b/core/src/main/scala/spark/deploy/LocalSparkCluster.scala @@ -45,6 +45,7 @@ class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: I // Stop the workers before the master so they don't get upset that it disconnected workerActorSystems.foreach(_.shutdown()) workerActorSystems.foreach(_.awaitTermination()) + masterActorSystems.foreach(_.shutdown()) masterActorSystems.foreach(_.awaitTermination()) } diff --git a/core/src/main/scala/spark/deploy/WebUI.scala b/core/src/main/scala/spark/deploy/WebUI.scala index ad1a1092b2..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 3e965e82ac..4dd6c448a9 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 = { @@ -51,19 +54,12 @@ 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() context.system.scheduler.schedule(0 millis, WORKER_TIMEOUT millis)(timeOutDeadWorkers()) } - def startWebUi() { - val webUi = new MasterWebUI(context.system, self) - try { - AkkaUtils.startSprayServer(context.system, "0.0.0.0", webUiPort, webUi.handler) - } catch { - case e: Exception => - logError("Failed to create web UI", e) - System.exit(1) - } + override def postStop() { + webUi.stop() } override def receive = { @@ -75,7 +71,7 @@ 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.get) schedule() } } @@ -278,7 +274,9 @@ 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 a4e21c8130..0000000000 --- a/core/src/main/scala/spark/deploy/master/MasterWebUI.scala +++ /dev/null @@ -1,74 +0,0 @@ -package spark.deploy.master - -import akka.actor.{ActorRef, ActorSystem} -import akka.dispatch.Await -import akka.pattern.ask -import akka.util.{Duration, Timeout} -import akka.util.duration._ -import cc.spray.Directives -import cc.spray.directives._ -import cc.spray.typeconversion.TwirlSupport._ -import cc.spray.http.MediaTypes -import cc.spray.typeconversion.SprayJsonSupport._ - -import spark.deploy._ -import spark.deploy.JsonProtocol._ - -/** - * Web UI server for the standalone master. - */ -private[spark] -class MasterWebUI(val actorSystem: ActorSystem, master: ActorRef) extends Directives { - val RESOURCE_DIR = "spark/deploy/master/webui" - val STATIC_RESOURCE_DIR = "spark/deploy/static" - - implicit val timeout = Timeout(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 - respondWithMediaType(MediaTypes.`application/json`) { ctx => - ctx.complete(future.mapTo[MasterState]) - } - case _ => - completeWith { - val future = master ? RequestMasterState - future.map { - masterState => spark.deploy.master.html.index.render(masterState.asInstanceOf[MasterState]) - } - } - } ~ - path("app") { - parameters("appId", 'format ?) { - case (appId, Some(js)) if (js.equalsIgnoreCase("json")) => - val future = master ? RequestMasterState - val appInfo = for (masterState <- future.mapTo[MasterState]) yield { - masterState.activeApps.find(_.id == appId).getOrElse({ - masterState.completedApps.find(_.id == appId).getOrElse(null) - }) - } - respondWithMediaType(MediaTypes.`application/json`) { ctx => - ctx.complete(appInfo.mapTo[ApplicationInfo]) - } - case (appId, _) => - completeWith { - val future = master ? RequestMasterState - future.map { state => - val masterState = state.asInstanceOf[MasterState] - val app = masterState.activeApps.find(_.id == appId).getOrElse({ - masterState.completedApps.find(_.id == appId).getOrElse(null) - }) - spark.deploy.master.html.app_details.render(app) - } - } - } - } ~ - pathPrefix("static") { - getFromResourceDirectory(STATIC_RESOURCE_DIR) - } ~ - getFromResourceDirectory(RESOURCE_DIR) - } - } -} diff --git a/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala new file mode 100644 index 0000000000..8553377d8f --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ui/ApplicationPage.scala @@ -0,0 +1,100 @@ +package spark.deploy.master.ui + +import akka.dispatch.Await +import akka.pattern.ask +import akka.util.duration._ + +import javax.servlet.http.HttpServletRequest + +import net.liftweb.json.JsonAST.JValue + +import scala.xml.Node + +import spark.deploy.{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/logPage?appId=%s&executorId=%s&logType=stdout" + .format(executor.worker.webUiAddress, executor.application.id, executor.id)}>stdout</a> + <a href={"%s/logPage?appId=%s&executorId=%s&logType=stderr" + .format(executor.worker.webUiAddress, executor.application.id, executor.id)}>stderr</a> + </td> + </tr> + } +} diff --git a/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala new file mode 100644 index 0000000000..c6de2bafa3 --- /dev/null +++ b/core/src/main/scala/spark/deploy/master/ui/IndexPage.scala @@ -0,0 +1,119 @@ +package spark.deploy.master.ui + +import akka.dispatch.Await +import akka.pattern.ask +import akka.util.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..82bc6961e2 --- /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 akka.util.Duration + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.{Handler, Server} + +import spark.{Logging, Utils} +import spark.ui.JettyUtils +import spark.ui.JettyUtils._ + +/** + * Web UI server for the standalone master. + */ +private[spark] +class MasterWebUI(val master: ActorRef, requestedPort: 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" +}
\ No newline at end of file diff --git a/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala index d7f58b2cb1..5d3d54c65e 100644 --- a/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/spark/deploy/worker/ExecutorRunner.scala @@ -98,7 +98,9 @@ private[spark] class ExecutorRunner( // Figure out our classpath with the external compute-classpath script val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh" - val classPath = Utils.executeAndGetOutput(Seq(sparkHome + "/bin/compute-classpath" + ext)) + val classPath = Utils.executeAndGetOutput( + Seq(sparkHome + "/bin/compute-classpath" + ext), + extraEnvironment=appDesc.command.environment) Seq("-cp", classPath) ++ libraryOpts ++ userOpts ++ memoryOpts } diff --git a/core/src/main/scala/spark/deploy/worker/Worker.scala b/core/src/main/scala/spark/deploy/worker/Worker.scala index 3dc2207170..f20ea42d7f 100644 --- a/core/src/main/scala/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/spark/deploy/worker/Worker.scala @@ -14,6 +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, @@ -44,6 +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 @@ -75,29 +77,19 @@ private[spark] class Worker( sparkHome = new File(Option(System.getenv("SPARK_HOME")).getOrElse(".")) logInfo("Spark home: " + sparkHome) createWorkDir() + webUi = new WorkerWebUI(this, workDir, Some(webUiPort)) + webUi.start() connectToMaster() - startWebUi() } 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(context.system, 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) - } - } - override def receive = { case RegisteredWorker(url) => masterWebUiUrl = url @@ -168,6 +160,7 @@ 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 3235c50d1b..0000000000 --- a/core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala +++ /dev/null @@ -1,57 +0,0 @@ -package spark.deploy.worker - -import akka.actor.{ActorRef, ActorSystem} -import akka.dispatch.Await -import akka.pattern.ask -import akka.util.{Duration, Timeout} -import akka.util.duration._ -import cc.spray.Directives -import cc.spray.typeconversion.TwirlSupport._ -import cc.spray.http.MediaTypes -import cc.spray.typeconversion.SprayJsonSupport._ - -import spark.deploy.{WorkerState, RequestWorkerState} -import spark.deploy.JsonProtocol._ -import java.io.File - -/** - * Web UI server for the standalone worker. - */ -private[spark] -class WorkerWebUI(val actorSystem: ActorSystem, worker: ActorRef, workDir: File) extends Directives { - 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 - respondWithMediaType(MediaTypes.`application/json`) { ctx => - ctx.complete(future.mapTo[WorkerState]) - } - } - case _ => - completeWith{ - val future = worker ? RequestWorkerState - future.map { workerState => - spark.deploy.worker.html.index(workerState.asInstanceOf[WorkerState]) - } - } - } ~ - path("log") { - parameters("appId", "executorId", "logType") { (appId, executorId, logType) => - respondWithMediaType(cc.spray.http.MediaTypes.`text/plain`) { - getFromFileName(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..c515f2e238 --- /dev/null +++ b/core/src/main/scala/spark/deploy/worker/ui/IndexPage.scala @@ -0,0 +1,100 @@ +package spark.deploy.worker.ui + +import akka.dispatch.Await +import akka.pattern.ask +import akka.util.duration._ + +import javax.servlet.http.HttpServletRequest + +import net.liftweb.json.JsonAST.JValue + +import scala.xml.Node + +import spark.deploy.{RequestWorkerState, JsonProtocol, WorkerState} +import spark.deploy.worker.ExecutorRunner +import spark.Utils +import spark.ui.UIUtils + +private[spark] class IndexPage(parent: WorkerWebUI) { + val workerActor = parent.worker.self + val worker = parent.worker + val timeout = parent.timeout + + def renderJson(request: HttpServletRequest): JValue = { + val stateFuture = (workerActor ? RequestWorkerState)(timeout).mapTo[WorkerState] + val workerState = Await.result(stateFuture, 30 seconds) + JsonProtocol.writeWorkerState(workerState) + } + + def render(request: HttpServletRequest): Seq[Node] = { + val stateFuture = (workerActor ? 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={"logPage?appId=%s&executorId=%s&logType=stdout" + .format(executor.appId, executor.execId)}>stdout</a> + <a href={"logPage?appId=%s&executorId=%s&logType=stderr" + .format(executor.appId, executor.execId)}>stderr</a> + </td> + </tr> + } + +} diff --git a/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala new file mode 100644 index 0000000000..ccd55c1ce4 --- /dev/null +++ b/core/src/main/scala/spark/deploy/worker/ui/WorkerWebUI.scala @@ -0,0 +1,165 @@ +package spark.deploy.worker.ui + +import akka.actor.ActorRef +import akka.util.{Duration, Timeout} + +import java.io.{FileInputStream, File} + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.{Handler, Server} + +import spark.deploy.worker.Worker +import spark.{Utils, Logging} +import spark.ui.JettyUtils +import spark.ui.JettyUtils._ +import spark.ui.UIUtils + +/** + * Web UI server for the standalone worker. + */ +private[spark] +class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[Int] = None) + extends Logging { + implicit val timeout = Timeout( + Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")) + val host = Utils.localHostName() + val port = requestedPort.getOrElse( + System.getProperty("worker.ui.port", WorkerWebUI.DEFAULT_PORT).toInt) + + var server: Option[Server] = None + var boundPort: Option[Int] = None + + val indexPage = new IndexPage(this) + + val handlers = Array[(String, Handler)]( + ("/static", createStaticHandler(WorkerWebUI.STATIC_RESOURCE_DIR)), + ("/log", (request: HttpServletRequest) => log(request)), + ("/logPage", (request: HttpServletRequest) => logPage(request)), + ("/json", (request: HttpServletRequest) => indexPage.renderJson(request)), + ("*", (request: HttpServletRequest) => indexPage.render(request)) + ) + + def start() { + try { + val (srv, bPort) = JettyUtils.startJettyServer("0.0.0.0", port, handlers) + server = Some(srv) + boundPort = Some(bPort) + logInfo("Started Worker web UI at http://%s:%d".format(host, bPort)) + } catch { + case e: Exception => + logError("Failed to create Worker JettyUtils", e) + System.exit(1) + } + } + + def log(request: HttpServletRequest): String = { + val defaultBytes = 100 * 1024 + val appId = request.getParameter("appId") + val executorId = request.getParameter("executorId") + val logType = request.getParameter("logType") + val offset = Option(request.getParameter("offset")).map(_.toLong) + val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes) + val path = "%s/%s/%s/%s".format(workDir.getPath, appId, executorId, logType) + + val (startByte, endByte) = getByteRange(path, offset, byteLength) + val file = new File(path) + val logLength = file.length + + val pre = "==== Bytes %s-%s of %s of %s/%s/%s ====\n" + .format(startByte, endByte, logLength, appId, executorId, logType) + pre + Utils.offsetBytes(path, startByte, endByte) + } + + def logPage(request: HttpServletRequest): Seq[scala.xml.Node] = { + val defaultBytes = 100 * 1024 + val appId = request.getParameter("appId") + val executorId = request.getParameter("executorId") + val logType = request.getParameter("logType") + val offset = Option(request.getParameter("offset")).map(_.toLong) + val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes) + val path = "%s/%s/%s/%s".format(workDir.getPath, appId, executorId, logType) + + val (startByte, endByte) = getByteRange(path, offset, byteLength) + val file = new File(path) + val logLength = file.length + + val logText = <node>{Utils.offsetBytes(path, startByte, endByte)}</node> + + val linkToMaster = <p><a href={worker.masterWebUiUrl}>Back to Master</a></p> + + val range = <span>Bytes {startByte.toString} - {endByte.toString} of {logLength}</span> + + val backButton = + if (startByte > 0) { + <a href={"?appId=%s&executorId=%s&logType=%s&offset=%s&byteLength=%s" + .format(appId, executorId, logType, math.max(startByte-byteLength, 0), + byteLength)}> + <button>Previous {Utils.memoryBytesToString(math.min(byteLength, startByte))}</button> + </a> + } + else { + <button disabled="disabled">Previous 0 B</button> + } + + val nextButton = + if (endByte < logLength) { + <a href={"?appId=%s&executorId=%s&logType=%s&offset=%s&byteLength=%s". + format(appId, executorId, logType, endByte, byteLength)}> + <button>Next {Utils.memoryBytesToString(math.min(byteLength, logLength-endByte))}</button> + </a> + } + else { + <button disabled="disabled">Next 0 B</button> + } + + val content = + <html> + <body> + {linkToMaster} + <hr /> + <div> + <div style="float:left;width:40%">{backButton}</div> + <div style="float:left;">{range}</div> + <div style="float:right;">{nextButton}</div> + </div> + <br /> + <div style="height:500px;overflow:auto;padding:5px;"> + <pre>{logText}</pre> + </div> + </body> + </html> + UIUtils.basicSparkPage(content, logType + " log page for " + appId) + } + + /** Determine the byte range for a log or log page. */ + def getByteRange(path: String, offset: Option[Long], byteLength: Int) + : (Long, Long) = { + val defaultBytes = 100 * 1024 + val maxBytes = 1024 * 1024 + + val file = new File(path) + val logLength = file.length() + val getOffset = offset.getOrElse(logLength-defaultBytes) + + val startByte = + if (getOffset < 0) 0L + else if (getOffset > logLength) logLength + else getOffset + + val logPageLength = math.min(byteLength, maxBytes) + + val endByte = math.min(startByte+logPageLength, logLength) + + (startByte, endByte) + } + + def stop() { + server.foreach(_.stop()) + } +} + +private[spark] object WorkerWebUI { + val STATIC_RESOURCE_DIR = "spark/ui/static" + val DEFAULT_PORT="8081" +} diff --git a/core/src/main/scala/spark/executor/Executor.scala b/core/src/main/scala/spark/executor/Executor.scala index 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 f7d60be5db..3d3b9ea011 100644 --- a/core/src/main/scala/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/spark/scheduler/DAGScheduler.scala @@ -256,7 +256,7 @@ 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 } @@ -312,7 +312,7 @@ 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) @@ -324,7 +324,7 @@ 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 } @@ -503,6 +503,7 @@ 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 @@ -524,6 +525,7 @@ 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) @@ -617,8 +619,11 @@ 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) } } @@ -651,7 +656,7 @@ class DAGScheduler( "(generation " + currentGeneration + ")") } } - + private def handleExecutorGained(execId: String, hostPort: String) { // remove from failedGeneration(execId) ? if (failedGeneration.contains(execId)) { @@ -666,11 +671,13 @@ 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 } @@ -747,6 +754,10 @@ 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/cluster/SchedulerBackend.scala b/core/src/main/scala/spark/scheduler/cluster/SchedulerBackend.scala index 9ac875de3a..8844057a5c 100644 --- a/core/src/main/scala/spark/scheduler/cluster/SchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/cluster/SchedulerBackend.scala @@ -1,6 +1,6 @@ package spark.scheduler.cluster -import spark.Utils +import spark.{SparkContext, Utils} /** * A backend interface for cluster scheduling systems that allows plugging in different ones under @@ -14,14 +14,7 @@ private[spark] trait SchedulerBackend { def defaultParallelism(): Int // Memory used by each executor (in megabytes) - protected val executorMemory = { - // TODO: Might need to add some extra memory for the non-heap parts of the JVM - Option(System.getProperty("spark.executor.memory")) - .orElse(Option(System.getenv("SPARK_MEM"))) - .map(Utils.memoryStringToMb) - .getOrElse(512) - } - + protected val executorMemory: Int = SparkContext.executorMemoryRequested // TODO: Probably want to add a killTask too } diff --git a/core/src/main/scala/spark/scheduler/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/scheduler/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala index ca7fab4cc5..e83368b98d 100644 --- a/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/spark/scheduler/mesos/MesosSchedulerBackend.scala @@ -43,8 +43,12 @@ private[spark] class MesosSchedulerBackend( // An ExecutorInfo for our tasks var execArgs: Array[Byte] = null + var classLoader: ClassLoader = null + override def start() { synchronized { + classLoader = Thread.currentThread.getContextClassLoader + new Thread("MesosSchedulerBackend driver") { setDaemon(true) override def run() { @@ -114,13 +118,28 @@ private[spark] class MesosSchedulerBackend( return execArgs } + private def setClassLoader(): ClassLoader = { + val oldClassLoader = Thread.currentThread.getContextClassLoader + Thread.currentThread.setContextClassLoader(classLoader) + return oldClassLoader + } + + private def restoreClassLoader(oldClassLoader: ClassLoader) { + Thread.currentThread.setContextClassLoader(oldClassLoader) + } + override def offerRescinded(d: SchedulerDriver, o: OfferID) {} override def registered(d: SchedulerDriver, frameworkId: FrameworkID, masterInfo: MasterInfo) { - logInfo("Registered as framework ID " + frameworkId.getValue) - registeredLock.synchronized { - isRegistered = true - registeredLock.notifyAll() + val oldClassLoader = setClassLoader() + try { + logInfo("Registered as framework ID " + frameworkId.getValue) + registeredLock.synchronized { + isRegistered = true + registeredLock.notifyAll() + } + } finally { + restoreClassLoader(oldClassLoader) } } @@ -142,49 +161,54 @@ private[spark] class MesosSchedulerBackend( * tasks are balanced across the cluster. */ override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) { - synchronized { - // Build a big list of the offerable workers, and remember their indices so that we can - // figure out which Offer to reply to for each worker - val offerableIndices = new ArrayBuffer[Int] - val offerableWorkers = new ArrayBuffer[WorkerOffer] - - def enoughMemory(o: Offer) = { - val mem = getResource(o.getResourcesList, "mem") - val slaveId = o.getSlaveId.getValue - mem >= executorMemory || slaveIdsWithExecutors.contains(slaveId) - } + val oldClassLoader = setClassLoader() + try { + synchronized { + // Build a big list of the offerable workers, and remember their indices so that we can + // figure out which Offer to reply to for each worker + val offerableIndices = new ArrayBuffer[Int] + val offerableWorkers = new ArrayBuffer[WorkerOffer] + + def enoughMemory(o: Offer) = { + val mem = getResource(o.getResourcesList, "mem") + val slaveId = o.getSlaveId.getValue + mem >= executorMemory || slaveIdsWithExecutors.contains(slaveId) + } - for ((offer, index) <- offers.zipWithIndex if enoughMemory(offer)) { - offerableIndices += index - offerableWorkers += new WorkerOffer( - offer.getSlaveId.getValue, - offer.getHostname, - getResource(offer.getResourcesList, "cpus").toInt) - } + for ((offer, index) <- offers.zipWithIndex if enoughMemory(offer)) { + offerableIndices += index + offerableWorkers += new WorkerOffer( + offer.getSlaveId.getValue, + offer.getHostname, + getResource(offer.getResourcesList, "cpus").toInt) + } - // Call into the ClusterScheduler - val taskLists = scheduler.resourceOffers(offerableWorkers) - - // Build a list of Mesos tasks for each slave - val mesosTasks = offers.map(o => Collections.emptyList[MesosTaskInfo]()) - for ((taskList, index) <- taskLists.zipWithIndex) { - if (!taskList.isEmpty) { - val offerNum = offerableIndices(index) - val slaveId = offers(offerNum).getSlaveId.getValue - slaveIdsWithExecutors += slaveId - mesosTasks(offerNum) = new JArrayList[MesosTaskInfo](taskList.size) - for (taskDesc <- taskList) { - taskIdToSlaveId(taskDesc.taskId) = slaveId - mesosTasks(offerNum).add(createMesosTask(taskDesc, slaveId)) + // Call into the ClusterScheduler + val taskLists = scheduler.resourceOffers(offerableWorkers) + + // Build a list of Mesos tasks for each slave + val mesosTasks = offers.map(o => Collections.emptyList[MesosTaskInfo]()) + for ((taskList, index) <- taskLists.zipWithIndex) { + if (!taskList.isEmpty) { + val offerNum = offerableIndices(index) + val slaveId = offers(offerNum).getSlaveId.getValue + slaveIdsWithExecutors += slaveId + mesosTasks(offerNum) = new JArrayList[MesosTaskInfo](taskList.size) + for (taskDesc <- taskList) { + taskIdToSlaveId(taskDesc.taskId) = slaveId + mesosTasks(offerNum).add(createMesosTask(taskDesc, slaveId)) + } } } - } - // Reply to the offers - val filters = Filters.newBuilder().setRefuseSeconds(1).build() // TODO: lower timeout? - for (i <- 0 until offers.size) { - d.launchTasks(offers(i).getId, mesosTasks(i), filters) + // Reply to the offers + val filters = Filters.newBuilder().setRefuseSeconds(1).build() // TODO: lower timeout? + for (i <- 0 until offers.size) { + d.launchTasks(offers(i).getId, mesosTasks(i), filters) + } } + } finally { + restoreClassLoader(oldClassLoader) } } @@ -224,23 +248,33 @@ private[spark] class MesosSchedulerBackend( } override def statusUpdate(d: SchedulerDriver, status: TaskStatus) { - val tid = status.getTaskId.getValue.toLong - val state = TaskState.fromMesos(status.getState) - synchronized { - if (status.getState == MesosTaskState.TASK_LOST && taskIdToSlaveId.contains(tid)) { - // We lost the executor on this slave, so remember that it's gone - slaveIdsWithExecutors -= taskIdToSlaveId(tid) - } - if (isFinished(status.getState)) { - taskIdToSlaveId.remove(tid) + val oldClassLoader = setClassLoader() + try { + val tid = status.getTaskId.getValue.toLong + val state = TaskState.fromMesos(status.getState) + synchronized { + if (status.getState == MesosTaskState.TASK_LOST && taskIdToSlaveId.contains(tid)) { + // We lost the executor on this slave, so remember that it's gone + slaveIdsWithExecutors -= taskIdToSlaveId(tid) + } + if (isFinished(status.getState)) { + taskIdToSlaveId.remove(tid) + } } + scheduler.statusUpdate(tid, state, status.getData.asReadOnlyByteBuffer) + } finally { + restoreClassLoader(oldClassLoader) } - scheduler.statusUpdate(tid, state, status.getData.asReadOnlyByteBuffer) } override def error(d: SchedulerDriver, message: String) { - logError("Mesos error: " + message) - scheduler.error(message) + val oldClassLoader = setClassLoader() + try { + logError("Mesos error: " + message) + scheduler.error(message) + } finally { + restoreClassLoader(oldClassLoader) + } } override def stop() { @@ -256,11 +290,16 @@ private[spark] class MesosSchedulerBackend( override def frameworkMessage(d: SchedulerDriver, e: ExecutorID, s: SlaveID, b: Array[Byte]) {} private def recordSlaveLost(d: SchedulerDriver, slaveId: SlaveID, reason: ExecutorLossReason) { - logInfo("Mesos slave lost: " + slaveId.getValue) - synchronized { - slaveIdsWithExecutors -= slaveId.getValue + val oldClassLoader = setClassLoader() + try { + logInfo("Mesos slave lost: " + slaveId.getValue) + synchronized { + slaveIdsWithExecutors -= slaveId.getValue + } + scheduler.executorLost(slaveId.getValue, reason) + } finally { + restoreClassLoader(oldClassLoader) } - scheduler.executorLost(slaveId.getValue, reason) } override def slaveLost(d: SchedulerDriver, slaveId: SlaveID) { diff --git a/core/src/main/scala/spark/storage/BlockManagerUI.scala b/core/src/main/scala/spark/storage/BlockManagerUI.scala deleted file mode 100644 index e02281344a..0000000000 --- a/core/src/main/scala/spark/storage/BlockManagerUI.scala +++ /dev/null @@ -1,79 +0,0 @@ -package spark.storage - -import akka.actor.{ActorRef, ActorSystem} -import akka.util.Duration -import akka.util.duration._ -import cc.spray.typeconversion.TwirlSupport._ -import cc.spray.Directives -import spark.{Logging, SparkContext} -import spark.util.AkkaUtils -import spark.Utils - - -/** - * Web UI server for the BlockManager inside each SparkContext. - */ -private[spark] -class BlockManagerUI(val actorSystem: ActorSystem, blockManagerMaster: ActorRef, sc: SparkContext) - extends Directives with Logging { - - val STATIC_RESOURCE_DIR = "spark/deploy/static" - - implicit val timeout = 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("") { - completeWith { - // Request the current storage status from the Master - val storageStatusList = sc.getExecutorStorageStatus - // Calculate macro-level statistics - val maxMem = storageStatusList.map(_.maxMem).reduce(_+_) - val remainingMem = storageStatusList.map(_.memRemaining).reduce(_+_) - val diskSpaceUsed = storageStatusList.flatMap(_.blocks.values.map(_.diskSize)) - .reduceOption(_+_).getOrElse(0L) - val rdds = StorageUtils.rddInfoFromStorageStatus(storageStatusList, sc) - spark.storage.html.index. - render(maxMem, remainingMem, diskSpaceUsed, rdds, storageStatusList) - } - } ~ - path("rdd") { - parameter("id") { id => - completeWith { - val prefix = "rdd_" + id.toString - val storageStatusList = sc.getExecutorStorageStatus - val filteredStorageStatusList = StorageUtils. - filterStorageStatusByPrefix(storageStatusList, prefix) - val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head - spark.storage.html.rdd.render(rddInfo, filteredStorageStatusList) - } - } - } ~ - pathPrefix("static") { - getFromResourceDirectory(STATIC_RESOURCE_DIR) - } - } - } - - private[spark] def appUIAddress = "http://" + host + ":" + port -} diff --git a/core/src/main/scala/spark/storage/StorageUtils.scala b/core/src/main/scala/spark/storage/StorageUtils.scala index 950c0cdf35..aca16f533a 100644 --- a/core/src/main/scala/spark/storage/StorageUtils.scala +++ b/core/src/main/scala/spark/storage/StorageUtils.scala @@ -39,12 +39,19 @@ case class RDDInfo(id: Int, name: String, storageLevel: StorageLevel, private[spark] object StorageUtils { - /* Given the current storage status of the BlockManager, returns information for each RDD */ - def rddInfoFromStorageStatus(storageStatusList: Array[StorageStatus], + /* Returns RDD-level information, compiled from a list of StorageStatus objects */ + def rddInfoFromStorageStatus(storageStatusList: Seq[StorageStatus], sc: SparkContext) : Array[RDDInfo] = { rddInfoFromBlockStatusList(storageStatusList.flatMap(_.blocks).toMap, sc) } + /* Returns a map of blocks to their locations, compiled from a list of StorageStatus objects */ + def blockLocationsFromStorageStatus(storageStatusList: Seq[StorageStatus]) = { + val blockLocationPairs = storageStatusList + .flatMap(s => s.blocks.map(b => (b._1, s.blockManagerId.hostPort))) + blockLocationPairs.groupBy(_._1).map{case (k, v) => (k, v.unzip._2)}.toMap + } + /* Given a list of BlockStatus objets, returns information for each RDD */ def rddInfoFromBlockStatusList(infos: Map[String, BlockStatus], sc: SparkContext) : Array[RDDInfo] = { 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..84730cc091 --- /dev/null +++ b/core/src/main/scala/spark/ui/jobs/JobProgressUI.scala @@ -0,0 +1,127 @@ +package spark.ui.jobs + +import akka.util.Duration + +import java.text.SimpleDateFormat + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.Handler + +import scala.Seq +import scala.collection.mutable.{HashSet, ListBuffer, HashMap, ArrayBuffer} + +import spark.ui.JettyUtils._ +import spark.{ExceptionFailure, SparkContext, Success, Utils} +import spark.scheduler._ +import 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 + } +}
\ No newline at end of file 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..3d67888e2c --- /dev/null +++ b/core/src/main/scala/spark/ui/storage/BlockManagerUI.scala @@ -0,0 +1,24 @@ +package spark.ui.storage + +import akka.util.Duration + +import javax.servlet.http.HttpServletRequest + +import org.eclipse.jetty.server.Handler + +import spark.{Logging, SparkContext} +import spark.ui.JettyUtils._ + +/** Web UI showing storage status of all RDD's in the given SparkContext. */ +private[spark] class BlockManagerUI(val sc: SparkContext) extends Logging { + implicit val timeout = Duration.create( + System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds") + + val indexPage = new IndexPage(this) + val rddPage = new RDDPage(this) + + def getHandlers = Seq[(String, Handler)]( + ("/storage/rdd", (request: HttpServletRequest) => rddPage.render(request)), + ("/storage", (request: HttpServletRequest) => indexPage.render(request)) + ) +} diff --git a/core/src/main/scala/spark/ui/storage/IndexPage.scala b/core/src/main/scala/spark/ui/storage/IndexPage.scala new file mode 100644 index 0000000000..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..428db6fa95 --- /dev/null +++ b/core/src/main/scala/spark/ui/storage/RDDPage.scala @@ -0,0 +1,113 @@ +package spark.ui.storage + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import spark.storage.{StorageStatus, StorageUtils} +import spark.ui.UIUtils._ +import spark.Utils +import spark.storage.BlockManagerMasterActor.BlockStatus +import spark.ui.Page._ + +/** Page showing storage details for a given RDD */ +private[spark] class RDDPage(parent: BlockManagerUI) { + val sc = parent.sc + + def render(request: HttpServletRequest): Seq[Node] = { + val id = request.getParameter("id") + val prefix = "rdd_" + id.toString + val storageStatusList = sc.getExecutorStorageStatus + val filteredStorageStatusList = StorageUtils. + filterStorageStatusByPrefix(storageStatusList, prefix) + val rddInfo = StorageUtils.rddInfoFromStorageStatus(filteredStorageStatusList, sc).head + + val workerHeaders = Seq("Host", "Memory Usage", "Disk Usage") + val workers = filteredStorageStatusList.map((prefix, _)) + val workerTable = listingTable(workerHeaders, workerRow, workers) + + val blockHeaders = Seq("Block Name", "Storage Level", "Size in Memory", "Size on Disk", + "Locations") + + val blockStatuses = filteredStorageStatusList.flatMap(_.blocks).toArray.sortWith(_._1 < _._1) + val blockLocations = StorageUtils.blockLocationsFromStorageStatus(filteredStorageStatusList) + val blocks = blockStatuses.map { + case(id, status) => (id, status, blockLocations.get(id).getOrElse(Seq("UNKNOWN"))) + } + val blockTable = listingTable(blockHeaders, blockRow, blocks) + + val content = + <div class="row"> + <div class="span12"> + <ul class="unstyled"> + <li> + <strong>Storage Level:</strong> + {rddInfo.storageLevel.description} + </li> + <li> + <strong>Cached Partitions:</strong> + {rddInfo.numCachedPartitions} + </li> + <li> + <strong>Total Partitions:</strong> + {rddInfo.numPartitions} + </li> + <li> + <strong>Memory Size:</strong> + {Utils.memoryBytesToString(rddInfo.memSize)} + </li> + <li> + <strong>Disk Size:</strong> + {Utils.memoryBytesToString(rddInfo.diskSize)} + </li> + </ul> + </div> + </div> + <hr/> + <div class="row"> + <div class="span12"> + {workerTable} + </div> + </div> + <hr/> + <div class="row"> + <div class="span12"> + <h3> RDD Summary </h3> + {blockTable} + </div> + </div>; + + headerSparkPage(content, parent.sc, "RDD Info: " + rddInfo.name, Jobs) + } + + def blockRow(row: (String, BlockStatus, Seq[String])): Seq[Node] = { + val (id, block, locations) = row + <tr> + <td>{id}</td> + <td> + {block.storageLevel.description} + </td> + <td sorttable_customkey={block.memSize.toString}> + {Utils.memoryBytesToString(block.memSize)} + </td> + <td sorttable_customkey={block.diskSize.toString}> + {Utils.memoryBytesToString(block.diskSize)} + </td> + <td> + {locations.map(l => <span>{l}<br/></span>)} + </td> + </tr> + } + + def workerRow(worker: (String, StorageStatus)): Seq[Node] = { + val (prefix, status) = worker + <tr> + <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td> + <td> + {Utils.memoryBytesToString(status.memUsed(prefix))} + ({Utils.memoryBytesToString(status.memRemaining)} Total Available) + </td> + <td>{Utils.memoryBytesToString(status.diskUsed(prefix))}</td> + </tr> + } +} diff --git a/core/src/main/scala/spark/util/AkkaUtils.scala b/core/src/main/scala/spark/util/AkkaUtils.scala index e93cc3b485..18fc9673bf 100644 --- a/core/src/main/scala/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/spark/util/AkkaUtils.scala @@ -1,18 +1,10 @@ package spark.util -import akka.actor.{ActorRef, Props, ActorSystemImpl, ActorSystem} +import akka.actor.{ActorSystem, ExtendedActorSystem} import com.typesafe.config.ConfigFactory import akka.util.duration._ -import akka.pattern.ask import akka.remote.RemoteActorRefProvider -import cc.spray.Route -import cc.spray.io.IoWorker -import cc.spray.{SprayCanRootService, HttpService} -import cc.spray.can.server.HttpServer -import cc.spray.io.pipelines.MessageHandlerDispatch.SingletonHandler -import akka.dispatch.Await -import spark.{Utils, SparkException} -import java.util.concurrent.TimeoutException + /** * Various utility classes for working with Akka. @@ -56,36 +48,8 @@ private[spark] object AkkaUtils { // Figure out the port number we bound to, in case port was passed as 0. This is a bit of a // hack because Akka doesn't let you figure out the port through the public API yet. - val provider = actorSystem.asInstanceOf[ActorSystemImpl].provider + val provider = actorSystem.asInstanceOf[ExtendedActorSystem].provider val boundPort = provider.asInstanceOf[RemoteActorRefProvider].transport.address.port.get return (actorSystem, boundPort) } - - /** - * Creates a Spray HTTP server bound to a given IP and port with a given Spray Route object to - * handle requests. Returns the bound port or throws a SparkException on failure. - * TODO: Not changing ip to host here - is it required ? - */ - def startSprayServer(actorSystem: ActorSystem, ip: String, port: Int, route: Route, - name: String = "HttpServer"): ActorRef = { - val ioWorker = new IoWorker(actorSystem).start() - val httpService = actorSystem.actorOf(Props(new HttpService(route))) - val rootService = actorSystem.actorOf(Props(new SprayCanRootService(httpService))) - val server = actorSystem.actorOf( - Props(new HttpServer(ioWorker, SingletonHandler(rootService))), name = name) - actorSystem.registerOnTermination { ioWorker.stop() } - val timeout = 3.seconds - val future = server.ask(HttpServer.Bind(ip, port))(timeout) - try { - Await.result(future, timeout) match { - case bound: HttpServer.Bound => - return server - case other: Any => - throw new SparkException("Failed to bind web UI to port " + port + ": " + other) - } - } catch { - case e: TimeoutException => - throw new SparkException("Failed to bind web UI to port " + port) - } - } } diff --git a/core/src/main/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..aa3ee5f5ee 100644 --- a/core/src/test/scala/spark/RDDSuite.scala +++ b/core/src/test/scala/spark/RDDSuite.scala @@ -240,7 +240,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { val ints = sc.makeRDD(scala.util.Random.shuffle(nums), 2) val topK = ints.top(5) assert(topK.size === 5) - assert(topK.sorted === nums.sorted.takeRight(5)) + assert(topK === nums.reverse.take(5)) } test("top with custom ordering") { @@ -251,4 +251,55 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(topK.size === 2) assert(topK.sorted === Array("b", "a")) } + + test("takeOrdered with predefined ordering") { + val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + val rdd = sc.makeRDD(nums, 2) + val sortedLowerK = rdd.takeOrdered(5) + assert(sortedLowerK.size === 5) + assert(sortedLowerK === Array(1, 2, 3, 4, 5)) + } + + test("takeOrdered with custom ordering") { + val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + implicit val ord = implicitly[Ordering[Int]].reverse + val rdd = sc.makeRDD(nums, 2) + val sortedTopK = rdd.takeOrdered(5) + assert(sortedTopK.size === 5) + assert(sortedTopK === Array(10, 9, 8, 7, 6)) + assert(sortedTopK === nums.sorted(ord).take(5)) + } + + test("takeSample") { + val data = sc.parallelize(1 to 100, 2) + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=false, 20, seed) + assert(sample.size === 20) // Got exactly 20 elements + assert(sample.toSet.size === 20) // Elements are distinct + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=false, 200, seed) + assert(sample.size === 100) // Got only 100 elements + assert(sample.toSet.size === 100) // Elements are distinct + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 20, seed) + assert(sample.size === 20) // Got exactly 20 elements + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 100, seed) + assert(sample.size === 100) // Got exactly 100 elements + // Chance of getting all distinct elements is astronomically low, so test we got < 100 + assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 200, seed) + assert(sample.size === 200) // Got exactly 200 elements + // Chance of getting all distinct elements is still quite low, so test we got < 100 + assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements") + } + } } diff --git a/core/src/test/scala/spark/UtilsSuite.scala b/core/src/test/scala/spark/UtilsSuite.scala index 4a113e16bf..1e1260f606 100644 --- a/core/src/test/scala/spark/UtilsSuite.scala +++ b/core/src/test/scala/spark/UtilsSuite.scala @@ -1,7 +1,10 @@ package spark +import com.google.common.base.Charsets +import com.google.common.io.Files +import java.io.{ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream, File} import org.scalatest.FunSuite -import java.io.{ByteArrayOutputStream, ByteArrayInputStream} +import org.apache.commons.io.FileUtils import scala.util.Random class UtilsSuite extends FunSuite { @@ -71,5 +74,49 @@ class UtilsSuite extends FunSuite { assert(Utils.splitCommandString("''") === Seq("")) assert(Utils.splitCommandString("\"\"") === Seq("")) } + + test("string formatting of time durations") { + val second = 1000 + val minute = second * 60 + val hour = minute * 60 + def str = Utils.msDurationToString(_) + + assert(str(123) === "123 ms") + assert(str(second) === "1.0 s") + assert(str(second + 462) === "1.5 s") + assert(str(hour) === "1.00 h") + assert(str(minute) === "1.0 m") + assert(str(minute + 4 * second + 34) === "1.1 m") + assert(str(10 * hour + minute + 4 * second) === "10.02 h") + assert(str(10 * hour + 59 * minute + 59 * second + 999) === "11.00 h") + } + + test("reading offset bytes of a file") { + val tmpDir2 = Files.createTempDir() + val f1Path = tmpDir2 + "/f1" + val f1 = new FileOutputStream(f1Path) + f1.write("1\n2\n3\n4\n5\n6\n7\n8\n9\n".getBytes(Charsets.UTF_8)) + f1.close() + + // Read first few bytes + assert(Utils.offsetBytes(f1Path, 0, 5) === "1\n2\n3") + + // Read some middle bytes + assert(Utils.offsetBytes(f1Path, 4, 11) === "3\n4\n5\n6") + + // Read last few bytes + assert(Utils.offsetBytes(f1Path, 12, 18) === "7\n8\n9\n") + + // Read some nonexistent bytes in the beginning + assert(Utils.offsetBytes(f1Path, -5, 5) === "1\n2\n3") + + // Read some nonexistent bytes at the end + assert(Utils.offsetBytes(f1Path, 12, 22) === "7\n8\n9\n") + + // Read some nonexistent bytes on both ends + assert(Utils.offsetBytes(f1Path, -3, 25) === "1\n2\n3\n4\n5\n6\n7\n8\n9\n") + + FileUtils.deleteDirectory(tmpDir2) + } } 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..fc0c160720 --- /dev/null +++ b/core/src/test/scala/spark/ui/UISuite.scala @@ -0,0 +1,29 @@ +package spark.ui + +import scala.util.{Failure, Success, Try} +import java.net.ServerSocket +import org.scalatest.FunSuite +import org.eclipse.jetty.server.Server + +class UISuite extends FunSuite { + test("jetty port increases under contention") { + val startPort = 33333 + val server = new Server(startPort) + server.start() + val (jettyServer1, boundPort1) = JettyUtils.startJettyServer("localhost", startPort, Seq()) + val (jettyServer2, boundPort2) = JettyUtils.startJettyServer("localhost", startPort, Seq()) + + assert(boundPort1 === startPort + 1) + assert(boundPort2 === startPort + 2) + } + + test("jetty binds to port 0 correctly") { + val (jettyServer, boundPort) = JettyUtils.startJettyServer("localhost", 0, Seq()) + assert(jettyServer.getState === "STARTED") + assert(boundPort != 0) + Try {new ServerSocket(boundPort)} match { + case Success(s) => fail("Port %s doesn't seem used by jetty server".format(boundPort)) + case Failure (e) => + } + } +} diff --git a/docs/configuration.md b/docs/configuration.md index 2de512f896..5a80510959 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -22,26 +22,30 @@ the copy executable. Inside `spark-env.sh`, you *must* set at least the following two variables: -* `SCALA_HOME`, to point to your Scala installation. +* `SCALA_HOME`, to point to your Scala installation, or `SCALA_LIBRARY_PATH` to point to the directory for Scala + library JARs (if you install Scala as a Debian or RPM package, there is no `SCALA_HOME`, but these libraries + are in a separate path, typically /usr/share/java; look for `scala-library.jar`). * `MESOS_NATIVE_LIBRARY`, if you are [running on a Mesos cluster](running-on-mesos.html). -In addition, there are four other variables that control execution. These can be set *either in `spark-env.sh` -or in each job's driver program*, because they will automatically be propagated to workers from the driver. -For a multi-user environment, we recommend setting the in the driver program instead of `spark-env.sh`, so -that different user jobs can use different amounts of memory, JVM options, etc. +In addition, there are four other variables that control execution. These should be set *in the environment that +launches the job's driver program* instead of `spark-env.sh`, because they will be automatically propagated to +workers. Setting these per-job instead of in `spark-env.sh` ensures that different jobs can have different settings +for these variables. -* `SPARK_MEM`, to set the amount of memory used per node (this should be in the same format as the - JVM's -Xmx option, e.g. `300m` or `1g`) * `SPARK_JAVA_OPTS`, to add JVM options. This includes any system properties that you'd like to pass with `-D`. * `SPARK_CLASSPATH`, to add elements to Spark's classpath. * `SPARK_LIBRARY_PATH`, to add search directories for native libraries. +* `SPARK_MEM`, to set the amount of memory used per node. This should be in the same format as the + JVM's -Xmx option, e.g. `300m` or `1g`. Note that this option will soon be deprecated in favor of + the `spark.executor.memory` system property, so we recommend using that in new code. -Note that if you do set these in `spark-env.sh`, they will override the values set by user programs, which -is undesirable; you can choose to have `spark-env.sh` set them only if the user program hasn't, as follows: +Beware that if you do set these variables in `spark-env.sh`, they will override the values set by user programs, +which is undesirable; if you prefer, you can choose to have `spark-env.sh` set them only if the user program +hasn't, as follows: {% highlight bash %} -if [ -z "$SPARK_MEM" ] ; then - SPARK_MEM="1g" +if [ -z "$SPARK_JAVA_OPTS" ] ; then + SPARK_JAVA_OPTS="-verbose:gc" fi {% endhighlight %} @@ -55,11 +59,18 @@ val sc = new SparkContext(...) {% endhighlight %} Most of the configurable system properties control internal settings that have reasonable default values. However, -there are at least four properties that you will commonly want to control: +there are at least five properties that you will commonly want to control: <table class="table"> <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr> <tr> + <td>spark.executor.memory</td> + <td>512m</td> + <td> + Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. `512m`, `2g`). + </td> +</tr> +<tr> <td>spark.serializer</td> <td>spark.JavaSerializer</td> <td> @@ -135,9 +146,16 @@ Apart from these, the following properties are also available, and may be useful </tr> <tr> <td>spark.ui.port</td> - <td>(random)</td> + <td>33000</td> + <td> + Port for your application's dashboard, which shows memory and workload data + </td> +</tr> +<tr> + <td>spark.ui.retained_stages</td> + <td>1000</td> <td> - Port for your application's dashboard, which shows memory usage of each RDD. + How many stages the Spark UI remembers before garbage collecting. </td> </tr> <tr> diff --git a/docs/ec2-scripts.md b/docs/ec2-scripts.md index dc57035eba..eab8a0ff20 100644 --- a/docs/ec2-scripts.md +++ b/docs/ec2-scripts.md @@ -106,9 +106,8 @@ permissions on your private key file, you can run `launch` with the # Configuration You can edit `/root/spark/conf/spark-env.sh` on each machine to set Spark configuration options, such -as JVM options and, most crucially, the amount of memory to use per machine (`SPARK_MEM`). -This file needs to be copied to **every machine** to reflect the change. The easiest way to do this -is to use a script we provide called `copy-dir`. First edit your `spark-env.sh` file on the master, +as JVM options. This file needs to be copied to **every machine** to reflect the change. The easiest way to +do this is to use a script we provide called `copy-dir`. First edit your `spark-env.sh` file on the master, then run `~/spark-ec2/copy-dir /root/spark/conf` to RSYNC it to all the workers. The [configuration guide](configuration.html) describes the available configuration options. diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index 7f1e7cf93d..e8aaac74d0 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -17,10 +17,9 @@ There are a few key differences between the Python and Scala APIs: * Python is dynamically typed, so RDDs can hold objects of different types. * PySpark does not currently support the following Spark features: - Special functions on RDDs of doubles, such as `mean` and `stdev` - - `lookup` + - `lookup`, `sample` and `sort` - `persist` at storage levels other than `MEMORY_ONLY` - - `sample` - - `sort` + - Execution on Windows -- this is slated for a future release In PySpark, RDDs support the same methods as their Scala counterparts but take Python functions and return Python collection types. Short functions can be passed to RDD methods using Python's [`lambda`](http://www.diveintopython.net/power_of_introspection/lambda_functions.html) syntax: diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index f5788dc467..8cd1b0cd66 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -7,7 +7,7 @@ title: Spark Streaming Programming Guide {:toc} # Overview -A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collections of elements) representing a continuous stream of data. DStreams can be created from live incoming data (such as data from a socket, Kafka, etc.) or can be generated by transformong existing DStreams using parallel operators like `map`, `reduce`, and `window`. The basic processing model is as follows: +A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collections of elements) representing a continuous stream of data. DStreams can be created from live incoming data (such as data from a socket, Kafka, etc.) or can be generated by transforming existing DStreams using parallel operators like `map`, `reduce`, and `window`. The basic processing model is as follows: (i) While a Spark Streaming driver program is running, the system receives data from various sources and and divides it into batches. Each batch of data is treated as an RDD, that is, an immutable parallel collection of data. These input RDDs are saved in memory and replicated to two nodes for fault-tolerance. This sequence of RDDs is collectively called an InputDStream. (ii) Data received by InputDStreams are processed using DStream operations. Since all data is represented as RDDs and all DStream operations as RDD operations, data is automatically recovered in the event of node failures. @@ -20,7 +20,7 @@ The first thing a Spark Streaming program must do is create a `StreamingContext` new StreamingContext(master, appName, batchDuration, [sparkHome], [jars]) {% endhighlight %} -The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls) and can be "local" for local testing. The `appName` is a name of your program, which will be shown on your cluster's web UI. The `batchDuration` is the size of the batches (as explained earlier). This must be set carefully such the cluster can keep up with the processing of the data streams. Start with something conservative like 5 seconds. See the [Performance Tuning](#setting-the-right-batch-size) section for a detailed discussion. Finally, `sparkHome` and `jars` are necessary when running on a cluster to specify the location of your code, as described in the [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster). +The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls) and can be "local" for local testing. The `appName` is a name of your program, which will be shown on your cluster's web UI. The `batchDuration` is the size of the batches (as explained earlier). This must be set carefully such that the cluster can keep up with the processing of the data streams. Start with something conservative like 5 seconds. See the [Performance Tuning](#setting-the-right-batch-size) section for a detailed discussion. Finally, `sparkHome` and `jars` are necessary when running on a cluster to specify the location of your code, as described in the [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster). This constructor creates a SparkContext for your job as well, which can be accessed with `streamingContext.sparkContext`. diff --git a/docs/tuning.md b/docs/tuning.md index 32c7ab86e9..5ffca54481 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -157,9 +157,9 @@ their work directories), *not* on your driver program. **Cache Size Tuning** -One important configuration parameter for GC is the amount of memory that should be used for -caching RDDs. By default, Spark uses 66% of the configured memory (`SPARK_MEM`) to cache RDDs. This means that - 33% of memory is available for any objects created during task execution. +One important configuration parameter for GC is the amount of memory that should be used for caching RDDs. +By default, Spark uses 66% of the configured executor memory (`spark.executor.memory` or `SPARK_MEM`) to +cache RDDs. This means that 33% of memory is available for any objects created during task execution. In case your tasks slow down and you find that your JVM is garbage-collecting frequently or running out of memory, lowering this value will help reduce the memory consumption. To change this to say 50%, you can call diff --git a/examples/pom.xml b/examples/pom.xml index 3e5271ec2f..1976765c3d 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> @@ -193,6 +197,11 @@ <artifactId>hadoop-yarn-common</artifactId> <scope>provided</scope> </dependency> + <dependency> + <groupId>org.apache.hbase</groupId> + <artifactId>hbase</artifactId> + <version>0.94.6</version> + </dependency> </dependencies> <build> <plugins> diff --git a/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala b/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala new file mode 100644 index 0000000000..51c3c9f9b4 --- /dev/null +++ b/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala @@ -0,0 +1,50 @@ +package spark.streaming.examples + +import spark.streaming._ +import spark.streaming.StreamingContext._ + +/** + * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every second. + * Usage: StatefulNetworkWordCount <master> <hostname> <port> + * <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1. + * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data. + * + * To run this on your local machine, you need to first run a Netcat server + * `$ nc -lk 9999` + * and then run the example + * `$ ./run spark.streaming.examples.StatefulNetworkWordCount local[2] localhost 9999` + */ +object StatefulNetworkWordCount { + def main(args: Array[String]) { + if (args.length < 3) { + System.err.println("Usage: StatefulNetworkWordCount <master> <hostname> <port>\n" + + "In local mode, <master> should be 'local[n]' with n > 1") + System.exit(1) + } + + val updateFunc = (values: Seq[Int], state: Option[Int]) => { + val currentCount = values.foldLeft(0)(_ + _) + + val previousCount = state.getOrElse(0) + + Some(currentCount + previousCount) + } + + // Create the context with a 1 second batch size + val ssc = new StreamingContext(args(0), "NetworkWordCumulativeCountUpdateStateByKey", Seconds(1), + System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) + ssc.checkpoint(".") + + // Create a NetworkInputDStream on target ip:port and count the + // words in input stream of \n delimited test (eg. generated by 'nc') + val lines = ssc.socketTextStream(args(1), args(2).toInt) + val words = lines.flatMap(_.split(" ")) + val wordDstream = words.map(x => (x, 1)) + + // Update the cumulative count using updateStateByKey + // This will give a Dstream made of state (which is the cumulative count of the words) + val stateDstream = wordDstream.updateStateByKey[Int](updateFunc) + stateDstream.print() + ssc.start() + } +} diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala index a9642100e3..528778ed72 100644 --- a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala +++ b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala @@ -26,8 +26,8 @@ import spark.SparkContext._ */ object TwitterAlgebirdCMS { def main(args: Array[String]) { - if (args.length < 3) { - System.err.println("Usage: TwitterAlgebirdCMS <master> <twitter_username> <twitter_password>" + + if (args.length < 1) { + System.err.println("Usage: TwitterAlgebirdCMS <master>" + " [filter1] [filter2] ... [filter n]") System.exit(1) } @@ -40,12 +40,11 @@ object TwitterAlgebirdCMS { // K highest frequency elements to take val TOPK = 10 - val Array(master, username, password) = args.slice(0, 3) - val filters = args.slice(3, args.length) + val (master, filters) = (args.head, args.tail) val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10), System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - val stream = ssc.twitterStream(username, password, filters, StorageLevel.MEMORY_ONLY_SER) + val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER) val users = stream.map(status => status.getUser.getId) diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala index f3288bfb85..896e9fd8af 100644 --- a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala +++ b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala @@ -21,20 +21,19 @@ import spark.streaming.dstream.TwitterInputDStream */ object TwitterAlgebirdHLL { def main(args: Array[String]) { - if (args.length < 3) { - System.err.println("Usage: TwitterAlgebirdHLL <master> <twitter_username> <twitter_password>" + + if (args.length < 1) { + System.err.println("Usage: TwitterAlgebirdHLL <master>" + " [filter1] [filter2] ... [filter n]") System.exit(1) } /** Bit size parameter for HyperLogLog, trades off accuracy vs size */ val BIT_SIZE = 12 - val Array(master, username, password) = args.slice(0, 3) - val filters = args.slice(3, args.length) + val (master, filters) = (args.head, args.tail) val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5), System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - val stream = ssc.twitterStream(username, password, filters, StorageLevel.MEMORY_ONLY_SER) + val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER) val users = stream.map(status => status.getUser.getId) diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala b/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala index 9d4494c6f2..65f0b6d352 100644 --- a/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala +++ b/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala @@ -12,18 +12,17 @@ import spark.SparkContext._ */ object TwitterPopularTags { def main(args: Array[String]) { - if (args.length < 3) { - System.err.println("Usage: TwitterPopularTags <master> <twitter_username> <twitter_password>" + + if (args.length < 1) { + System.err.println("Usage: TwitterPopularTags <master>" + " [filter1] [filter2] ... [filter n]") System.exit(1) } - val Array(master, username, password) = args.slice(0, 3) - val filters = args.slice(3, args.length) + val (master, filters) = (args.head, args.tail) val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2), System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR"))) - val stream = ssc.twitterStream(username, password, filters) + val stream = ssc.twitterStream(None, filters) val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#"))) diff --git a/make-distribution.sh b/make-distribution.sh index 855475864d..feb13d52f9 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -18,6 +18,7 @@ FWDIR="$(cd `dirname $0`; pwd)" DISTDIR="$FWDIR/dist" # Get version from SBT +export TERM=dumb # Prevents color codes in SBT output VERSION=$($FWDIR/sbt/sbt "show version" | tail -1 | cut -f 2) echo "Making distribution for Spark $VERSION in $DISTDIR..." @@ -35,4 +36,4 @@ cp $FWDIR/repl/target/*.jar "$DISTDIR/jars/" # Copy other things cp -r "$FWDIR/bin" "$DISTDIR" cp -r "$FWDIR/conf" "$DISTDIR" -cp "$FWDIR/run" "$FWDIR/spark-shell" "$DISTDIR"
\ No newline at end of file +cp "$FWDIR/run" "$FWDIR/spark-shell" "$DISTDIR" diff --git a/mllib/data/als/test.data b/mllib/data/als/test.data new file mode 100644 index 0000000000..e476cc23e0 --- /dev/null +++ b/mllib/data/als/test.data @@ -0,0 +1,16 @@ +1,1,5.0 +1,2,1.0 +1,3,5.0 +1,4,1.0 +2,1,5.0 +2,2,1.0 +2,3,5.0 +2,4,1.0 +3,1,1.0 +3,2,5.0 +3,3,1.0 +3,4,5.0 +4,1,1.0 +4,2,5.0 +4,3,1.0 +4,4,5.0 diff --git a/mllib/data/lr-data/random.data b/mllib/data/lr-data/random.data new file mode 100755 index 0000000000..29bcb8acba --- /dev/null +++ b/mllib/data/lr-data/random.data @@ -0,0 +1,1000 @@ +0.0,-0.19138793197590276 0.7834675900121327 +1.0,3.712420417753061 3.55967640829891 +0.0,-0.3173743619974614 0.9034702789806682 +1.0,4.759494447180777 3.407011867344781 +0.0,-0.7078607074437426 -0.7866705652344417 +1.0,2.6708084832010215 2.5322909406378016 +0.0,-0.07553885038446313 -0.1297104483563081 +1.0,2.759487072285262 2.474689814713741 +0.0,-2.2199161547238107 0.7543109438660762 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index 0000000000..b0e141ff32 --- /dev/null +++ b/mllib/src/main/scala/spark/mllib/clustering/KMeans.scala @@ -0,0 +1,317 @@ +package spark.mllib.clustering + +import scala.collection.mutable.ArrayBuffer +import scala.util.Random + +import spark.{SparkContext, RDD} +import spark.SparkContext._ +import spark.Logging +import spark.mllib.util.MLUtils + +import org.jblas.DoubleMatrix + + +/** + * K-means clustering with support for multiple parallel runs and a k-means++ like initialization + * mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested, + * they are executed together with joint passes over the data for efficiency. + * + * This is an iterative algorithm that will make multiple passes over the data, so any RDDs given + * to it should be cached by the user. + */ +class KMeans private ( + var k: Int, + var maxIterations: Int, + var runs: Int, + var initializationMode: String, + var initializationSteps: Int, + var epsilon: Double) + extends Serializable with Logging +{ + private type ClusterCenters = Array[Array[Double]] + + def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4) + + /** Set the number of clusters to create (k). Default: 2. */ + def setK(k: Int): KMeans = { + this.k = k + this + } + + /** Set maximum number of iterations to run. Default: 20. */ + def setMaxIterations(maxIterations: Int): KMeans = { + this.maxIterations = maxIterations + this + } + + /** + * Set the initialization algorithm. This can be either "random" to choose random points as + * initial cluster centers, or "k-means||" to use a parallel variant of k-means++ + * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||. + */ + def setInitializationMode(initializationMode: String): KMeans = { + if (initializationMode != KMeans.RANDOM && initializationMode != KMeans.K_MEANS_PARALLEL) { + throw new IllegalArgumentException("Invalid initialization mode: " + initializationMode) + } + this.initializationMode = initializationMode + this + } + + /** + * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm + * this many times with random starting conditions (configured by the initialization mode), then + * return the best clustering found over any run. Default: 1. + */ + def setRuns(runs: Int): KMeans = { + if (runs <= 0) { + throw new IllegalArgumentException("Number of runs must be positive") + } + this.runs = runs + this + } + + /** + * Set the number of steps for the k-means|| initialization mode. This is an advanced + * setting -- the default of 5 is almost always enough. Default: 5. + */ + def setInitializationSteps(initializationSteps: Int): KMeans = { + if (initializationSteps <= 0) { + throw new IllegalArgumentException("Number of initialization steps must be positive") + } + this.initializationSteps = initializationSteps + this + } + + /** + * Set the distance threshold within which we've consider centers to have converged. + * If all centers move less than this Euclidean distance, we stop iterating one run. + */ + def setEpsilon(epsilon: Double): KMeans = { + this.epsilon = epsilon + this + } + + /** + * Train a K-means model on the given set of points; `data` should be cached for high + * performance, because this is an iterative algorithm. + */ + def train(data: RDD[Array[Double]]): KMeansModel = { + // TODO: check whether data is persistent; this needs RDD.storageLevel to be publicly readable + + val sc = data.sparkContext + + val centers = if (initializationMode == KMeans.RANDOM) { + initRandom(data) + } else { + initKMeansParallel(data) + } + + val active = Array.fill(runs)(true) + val costs = Array.fill(runs)(0.0) + + var activeRuns = new ArrayBuffer[Int] ++ (0 until runs) + var iteration = 0 + + // Execute iterations of Lloyd's algorithm until all runs have converged + while (iteration < maxIterations && !activeRuns.isEmpty) { + type WeightedPoint = (DoubleMatrix, Long) + def mergeContribs(p1: WeightedPoint, p2: WeightedPoint): WeightedPoint = { + (p1._1.addi(p2._1), p1._2 + p2._2) + } + + val activeCenters = activeRuns.map(r => centers(r)).toArray + val costAccums = activeRuns.map(_ => sc.accumulator(0.0)) + + // Find the sum and count of points mapping to each center + val totalContribs = data.mapPartitions { points => + val runs = activeCenters.length + val k = activeCenters(0).length + val dims = activeCenters(0)(0).length + + val sums = Array.fill(runs, k)(new DoubleMatrix(dims)) + val counts = Array.fill(runs, k)(0L) + + for (point <- points; (centers, runIndex) <- activeCenters.zipWithIndex) { + val (bestCenter, cost) = KMeans.findClosest(centers, point) + costAccums(runIndex) += cost + sums(runIndex)(bestCenter).addi(new DoubleMatrix(point)) + counts(runIndex)(bestCenter) += 1 + } + + val contribs = for (i <- 0 until runs; j <- 0 until k) yield { + ((i, j), (sums(i)(j), counts(i)(j))) + } + contribs.iterator + }.reduceByKey(mergeContribs).collectAsMap() + + // Update the cluster centers and costs for each active run + for ((run, i) <- activeRuns.zipWithIndex) { + var changed = false + for (j <- 0 until k) { + val (sum, count) = totalContribs((i, j)) + if (count != 0) { + val newCenter = sum.divi(count).data + if (MLUtils.squaredDistance(newCenter, centers(run)(j)) > epsilon * epsilon) { + changed = true + } + centers(run)(j) = newCenter + } + } + if (!changed) { + active(run) = false + logInfo("Run " + run + " finished in " + (iteration + 1) + " iterations") + } + costs(run) = costAccums(i).value + } + + activeRuns = activeRuns.filter(active(_)) + iteration += 1 + } + + val bestRun = costs.zipWithIndex.min._2 + new KMeansModel(centers(bestRun)) + } + + /** + * Initialize `runs` sets of cluster centers at random. + */ + private def initRandom(data: RDD[Array[Double]]): Array[ClusterCenters] = { + // Sample all the cluster centers in one pass to avoid repeated scans + val sample = data.takeSample(true, runs * k, new Random().nextInt()) + Array.tabulate(runs)(r => sample.slice(r * k, (r + 1) * k)) + } + + /** + * Initialize `runs` sets of cluster centers using the k-means|| algorithm by Bahmani et al. + * (Bahmani et al., Scalable K-Means++, VLDB 2012). This is a variant of k-means++ that tries + * to find with dissimilar cluster centers by starting with a random center and then doing + * passes where more centers are chosen with probability proportional to their squared distance + * to the current cluster set. It results in a provable approximation to an optimal clustering. + * + * The original paper can be found at http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf. + */ + private def initKMeansParallel(data: RDD[Array[Double]]): Array[ClusterCenters] = { + // Initialize each run's center to a random point + val seed = new Random().nextInt() + val sample = data.takeSample(true, runs, seed) + val centers = Array.tabulate(runs)(r => ArrayBuffer(sample(r))) + + // On each step, sample 2 * k points on average for each run with probability proportional + // to their squared distance from that run's current centers + for (step <- 0 until initializationSteps) { + val centerArrays = centers.map(_.toArray) + val sumCosts = data.flatMap { point => + for (r <- 0 until runs) yield (r, KMeans.pointCost(centerArrays(r), point)) + }.reduceByKey(_ + _).collectAsMap() + val chosen = data.mapPartitionsWithIndex { (index, points) => + val rand = new Random(seed ^ (step << 16) ^ index) + for { + p <- points + r <- 0 until runs + if rand.nextDouble() < KMeans.pointCost(centerArrays(r), p) * 2 * k / sumCosts(r) + } yield (r, p) + }.collect() + for ((r, p) <- chosen) { + centers(r) += p + } + } + + // Finally, we might have a set of more than k candidate centers for each run; weigh each + // candidate by the number of points in the dataset mapping to it and run a local k-means++ + // on the weighted centers to pick just k of them + val centerArrays = centers.map(_.toArray) + val weightMap = data.flatMap { p => + for (r <- 0 until runs) yield ((r, KMeans.findClosest(centerArrays(r), p)._1), 1.0) + }.reduceByKey(_ + _).collectAsMap() + val finalCenters = (0 until runs).map { r => + val myCenters = centers(r).toArray + val myWeights = (0 until myCenters.length).map(i => weightMap.getOrElse((r, i), 0.0)).toArray + LocalKMeans.kMeansPlusPlus(r, myCenters, myWeights, k, 30) + } + + finalCenters.toArray + } +} + + +/** + * Top-level methods for calling K-means clustering. + */ +object KMeans { + // Initialization mode names + val RANDOM = "random" + val K_MEANS_PARALLEL = "k-means||" + + def train( + data: RDD[Array[Double]], + k: Int, + maxIterations: Int, + runs: Int, + initializationMode: String) + : KMeansModel = + { + new KMeans().setK(k) + .setMaxIterations(maxIterations) + .setRuns(runs) + .setInitializationMode(initializationMode) + .train(data) + } + + def train(data: RDD[Array[Double]], k: Int, maxIterations: Int, runs: Int): KMeansModel = { + train(data, k, maxIterations, runs, K_MEANS_PARALLEL) + } + + def train(data: RDD[Array[Double]], k: Int, maxIterations: Int): KMeansModel = { + train(data, k, maxIterations, 1, K_MEANS_PARALLEL) + } + + /** + * Return the index of the closest point in `centers` to `point`, as well as its distance. + */ + private[mllib] def findClosest(centers: Array[Array[Double]], point: Array[Double]) + : (Int, Double) = + { + var bestDistance = Double.PositiveInfinity + var bestIndex = 0 + for (i <- 0 until centers.length) { + val distance = MLUtils.squaredDistance(point, centers(i)) + if (distance < bestDistance) { + bestDistance = distance + bestIndex = i + } + } + (bestIndex, bestDistance) + } + + /** + * Return the K-means cost of a given point against the given cluster centers. + */ + private[mllib] def pointCost(centers: Array[Array[Double]], point: Array[Double]): Double = { + var bestDistance = Double.PositiveInfinity + for (i <- 0 until centers.length) { + val distance = MLUtils.squaredDistance(point, centers(i)) + if (distance < bestDistance) { + bestDistance = distance + } + } + bestDistance + } + + def main(args: Array[String]) { + if (args.length != 4) { + println("Usage: KMeans <master> <input_file> <k> <max_iterations>") + 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) + } +} @@ -54,9 +54,7 @@ <scala.version>2.9.3</scala.version> <mesos.version>0.9.0-incubating</mesos.version> <akka.version>2.0.3</akka.version> - <spray.version>1.0-M2.1</spray.version> - <spray.json.version>1.1.1</spray.json.version> - <slf4j.version>1.6.1</slf4j.version> + <slf4j.version>1.7.2</slf4j.version> <cdh.version>4.1.2</cdh.version> <log4j.version>1.2.17</log4j.version> @@ -98,28 +96,6 @@ <enabled>false</enabled> </snapshots> </repository> - <repository> - <id>spray-repo</id> - <name>Spray Repository</name> - <url>http://repo.spray.cc/</url> - <releases> - <enabled>true</enabled> - </releases> - <snapshots> - <enabled>false</enabled> - </snapshots> - </repository> - <repository> - <id>twitter4j-repo</id> - <name>Twitter4J Repository</name> - <url>http://twitter4j.org/maven2/</url> - <releases> - <enabled>true</enabled> - </releases> - <snapshots> - <enabled>false</enabled> - </snapshots> - </repository> </repositories> <pluginRepositories> <pluginRepository> @@ -230,26 +206,6 @@ <version>1.2.0</version> </dependency> <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-can</artifactId> - <version>${spray.version}</version> - </dependency> - <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-server</artifactId> - <version>${spray.version}</version> - </dependency> - <dependency> - <groupId>cc.spray</groupId> - <artifactId>spray-json_2.9.2</artifactId> - <version>${spray.json.version}</version> - </dependency> - <dependency> - <groupId>org.tomdz.twirl</groupId> - <artifactId>twirl-api</artifactId> - <version>1.0.2</version> - </dependency> - <dependency> <groupId>com.github.scala-incubator.io</groupId> <artifactId>scala-io-file_2.9.2</artifactId> <version>0.4.1</version> @@ -270,7 +226,11 @@ <version>10.4.2.0</version> <scope>test</scope> </dependency> - + <dependency> + <groupId>net.liftweb</groupId> + <artifactId>lift-json_2.9.2</artifactId> + <version>2.5</version> + </dependency> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-compiler</artifactId> @@ -281,6 +241,16 @@ <artifactId>jline</artifactId> <version>${scala.version}</version> </dependency> + <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scala-library</artifactId> + <version>${scala.version}</version> + </dependency> + <dependency> + <groupId>org.scala-lang</groupId> + <artifactId>scalap</artifactId> + <version>${scala.version}</version> + </dependency> <dependency> <groupId>log4j</groupId> @@ -347,18 +317,6 @@ <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> @@ -564,12 +522,12 @@ <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> @@ -631,12 +589,12 @@ <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 6058fb185e..bc9214d17d 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") @@ -138,12 +139,12 @@ object SparkBuild extends Build { resolvers ++= Seq( "JBoss Repository" at "http://repository.jboss.org/nexus/content/repositories/releases/", "Spray Repository" at "http://repo.spray.cc/", - "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/", - "Twitter4J Repository" at "http://twitter4j.org/maven2/" + "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/" ), libraryDependencies ++= Seq( - "com.google.guava" % "guava" % "11.0.1", + "com.google.guava" % "guava" % "14.0.1", + "com.google.code.findbugs" % "jsr305" % "1.3.+", "log4j" % "log4j" % "1.2.16", "org.slf4j" % "slf4j-api" % slf4jVersion, "org.slf4j" % "slf4j-log4j12" % slf4jVersion, @@ -157,9 +158,7 @@ object SparkBuild extends Build { "com.typesafe.akka" % "akka-slf4j" % "2.0.3" excludeAll(excludeNetty), "it.unimi.dsi" % "fastutil" % "6.4.4", "colt" % "colt" % "1.2.0", - "cc.spray" % "spray-can" % "1.0-M2.1" excludeAll(excludeNetty), - "cc.spray" % "spray-server" % "1.0-M2.1" excludeAll(excludeNetty), - "cc.spray" % "spray-json_2.9.2" % "1.1.1" excludeAll(excludeNetty), + "net.liftweb" % "lift-json_2.9.2" % "2.5", "org.apache.mesos" % "mesos" % "0.9.0-incubating", "io.netty" % "netty-all" % "4.0.0.Beta2", "org.apache.derby" % "derby" % "10.4.2.0" % "test" @@ -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 := {} @@ -198,7 +197,7 @@ object SparkBuild extends Build { def replSettings = sharedSettings ++ Seq( name := "spark-repl", libraryDependencies <+= scalaVersion("org.scala-lang" % "scala-compiler" % _) - ) ++ assemblySettings ++ extraAssemblySettings ++ Twirl.settings + ) ++ assemblySettings ++ extraAssemblySettings def examplesSettings = sharedSettings ++ Seq( name := "spark-examples", @@ -220,6 +219,13 @@ 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", resolvers ++= 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/python/pyspark/daemon.py b/python/pyspark/daemon.py index 78a2da1e18..78c9457b84 100644 --- a/python/pyspark/daemon.py +++ b/python/pyspark/daemon.py @@ -1,10 +1,13 @@ import os +import signal +import socket import sys +import traceback import multiprocessing from ctypes import c_bool from errno import EINTR, ECHILD -from socket import socket, AF_INET, SOCK_STREAM, SOMAXCONN -from signal import signal, SIGHUP, SIGTERM, SIGCHLD, SIG_DFL, SIG_IGN +from socket import AF_INET, SOCK_STREAM, SOMAXCONN +from signal import SIGHUP, SIGTERM, SIGCHLD, SIG_DFL, SIG_IGN from pyspark.worker import main as worker_main from pyspark.serializers import write_int @@ -33,11 +36,12 @@ def compute_real_exit_code(exit_code): def worker(listen_sock): # Redirect stdout to stderr os.dup2(2, 1) + sys.stdout = sys.stderr # The sys.stdout object is different from file descriptor 1 # Manager sends SIGHUP to request termination of workers in the pool def handle_sighup(*args): assert should_exit() - signal(SIGHUP, handle_sighup) + signal.signal(SIGHUP, handle_sighup) # Cleanup zombie children def handle_sigchld(*args): @@ -51,7 +55,7 @@ def worker(listen_sock): handle_sigchld() elif err.errno != ECHILD: raise - signal(SIGCHLD, handle_sigchld) + signal.signal(SIGCHLD, handle_sigchld) # Handle clients while not should_exit(): @@ -70,19 +74,22 @@ def worker(listen_sock): # never receives SIGCHLD unless a worker crashes. if os.fork() == 0: # Leave the worker pool - signal(SIGHUP, SIG_DFL) + signal.signal(SIGHUP, SIG_DFL) listen_sock.close() - # Handle the client then exit - sockfile = sock.makefile() + # Read the socket using fdopen instead of socket.makefile() because the latter + # seems to be very slow; note that we need to dup() the file descriptor because + # otherwise writes also cause a seek that makes us miss data on the read side. + infile = os.fdopen(os.dup(sock.fileno()), "a+", 65536) + outfile = os.fdopen(os.dup(sock.fileno()), "a+", 65536) exit_code = 0 try: - worker_main(sockfile, sockfile) + worker_main(infile, outfile) except SystemExit as exc: - exit_code = exc.code + exit_code = exc.code finally: - sockfile.close() - sock.close() - os._exit(compute_real_exit_code(exit_code)) + outfile.flush() + sock.close() + os._exit(compute_real_exit_code(exit_code)) else: sock.close() @@ -92,7 +99,6 @@ def launch_worker(listen_sock): try: worker(listen_sock) except Exception as err: - import traceback traceback.print_exc() os._exit(1) else: @@ -105,7 +111,7 @@ def manager(): os.setpgid(0, 0) # Create a listening socket on the AF_INET loopback interface - listen_sock = socket(AF_INET, SOCK_STREAM) + listen_sock = socket.socket(AF_INET, SOCK_STREAM) listen_sock.bind(('127.0.0.1', 0)) listen_sock.listen(max(1024, 2 * POOLSIZE, SOMAXCONN)) listen_host, listen_port = listen_sock.getsockname() @@ -121,8 +127,8 @@ def manager(): exit_flag.value = True # Gracefully exit on SIGTERM, don't die on SIGHUP - signal(SIGTERM, lambda signum, frame: shutdown()) - signal(SIGHUP, SIG_IGN) + signal.signal(SIGTERM, lambda signum, frame: shutdown()) + signal.signal(SIGHUP, SIG_IGN) # Cleanup zombie children def handle_sigchld(*args): @@ -133,7 +139,7 @@ def manager(): except EnvironmentError as err: if err.errno not in (ECHILD, EINTR): raise - signal(SIGCHLD, handle_sigchld) + signal.signal(SIGCHLD, handle_sigchld) # Initialization complete sys.stdout.close() @@ -148,7 +154,7 @@ def manager(): shutdown() raise finally: - signal(SIGTERM, SIG_DFL) + signal.signal(SIGTERM, SIG_DFL) exit_flag.value = True # Send SIGHUP to notify workers of shutdown os.kill(0, SIGHUP) @@ -5,4 +5,4 @@ if [ "$MESOS_HOME" != "" ]; then fi export SPARK_HOME=$(cd "$(dirname $0)/.."; pwd) export SPARK_TESTING=1 # To put test classes on classpath -java -Xmx1200m -XX:MaxPermSize=250m -XX:ReservedCodeCacheSize=128m $EXTRA_ARGS -jar $SPARK_HOME/sbt/sbt-launch-*.jar "$@" +java -Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=128m $EXTRA_ARGS -jar $SPARK_HOME/sbt/sbt-launch-*.jar "$@" diff --git a/streaming/pom.xml b/streaming/pom.xml index 4dc9a19d51..2fb5bbdeb5 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</artifactId> <version>2.0.3</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 3ec922957d..8d0a83d439 100644 --- a/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala +++ b/streaming/src/main/scala/spark/streaming/PairDStreamFunctions.scala @@ -14,6 +14,7 @@ import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.mapred.{JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} import org.apache.hadoop.mapred.OutputFormat +import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.conf.Configuration class PairDStreamFunctions[K: ClassManifest, V: ClassManifest](self: DStream[(K,V)]) @@ -470,7 +471,7 @@ 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/StreamingContext.scala b/streaming/src/main/scala/spark/streaming/StreamingContext.scala index f2c4073f22..36b841af8f 100644 --- a/streaming/src/main/scala/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/spark/streaming/StreamingContext.scala @@ -27,6 +27,7 @@ import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import org.apache.hadoop.mapreduce.lib.input.TextInputFormat import org.apache.hadoop.fs.Path import twitter4j.Status +import twitter4j.auth.Authorization /** @@ -380,18 +381,18 @@ class StreamingContext private ( /** * Create a input stream that returns tweets received from Twitter. - * @param username Twitter username - * @param password Twitter password + * @param twitterAuth Twitter4J authentication, or None to use Twitter4J's default OAuth + * authorization; this uses the system properties twitter4j.oauth.consumerKey, + * .consumerSecret, .accessToken and .accessTokenSecret. * @param filters Set of filter strings to get only those tweets that match them * @param storageLevel Storage level to use for storing the received objects */ def twitterStream( - username: String, - password: String, + twitterAuth: Option[Authorization] = None, filters: Seq[String] = Nil, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 ): DStream[Status] = { - val inputStream = new TwitterInputDStream(this, username, password, filters, storageLevel) + val inputStream = new TwitterInputDStream(this, twitterAuth, filters, storageLevel) registerInputStream(inputStream) inputStream } diff --git a/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala index 4259d4891c..ed7b789d98 100644 --- a/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala @@ -4,23 +4,18 @@ import spark.streaming._ import receivers.{ActorReceiver, ReceiverSupervisorStrategy} import spark.streaming.dstream._ import spark.storage.StorageLevel - import spark.api.java.function.{Function => JFunction, Function2 => JFunction2} import spark.api.java.{JavaSparkContext, JavaRDD} - import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} - import twitter4j.Status - import akka.actor.Props import akka.actor.SupervisorStrategy import akka.zeromq.Subscribe - import scala.collection.JavaConversions._ - import java.lang.{Long => JLong, Integer => JInt} import java.io.InputStream import java.util.{Map => JMap} +import twitter4j.auth.Authorization /** * A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic @@ -312,47 +307,76 @@ class JavaStreamingContext(val ssc: StreamingContext) { /** * Create a input stream that returns tweets received from Twitter. - * @param username Twitter username - * @param password Twitter password + * @param twitterAuth Twitter4J Authorization object * @param filters Set of filter strings to get only those tweets that match them * @param storageLevel Storage level to use for storing the received objects */ def twitterStream( - username: String, - password: String, + twitterAuth: Authorization, filters: Array[String], storageLevel: StorageLevel ): JavaDStream[Status] = { - ssc.twitterStream(username, password, filters, storageLevel) + ssc.twitterStream(Some(twitterAuth), filters, storageLevel) + } + + /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * .consumerSecret, .accessToken and .accessTokenSecret to be set. + * @param filters Set of filter strings to get only those tweets that match them + * @param storageLevel Storage level to use for storing the received objects + */ + def twitterStream( + filters: Array[String], + storageLevel: StorageLevel + ): JavaDStream[Status] = { + ssc.twitterStream(None, filters, storageLevel) } /** * Create a input stream that returns tweets received from Twitter. - * @param username Twitter username - * @param password Twitter password + * @param twitterAuth Twitter4J Authorization * @param filters Set of filter strings to get only those tweets that match them */ def twitterStream( - username: String, - password: String, + twitterAuth: Authorization, filters: Array[String] ): JavaDStream[Status] = { - ssc.twitterStream(username, password, filters) + ssc.twitterStream(Some(twitterAuth), filters) + } + + /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * .consumerSecret, .accessToken and .accessTokenSecret to be set. + * @param filters Set of filter strings to get only those tweets that match them + */ + def twitterStream( + filters: Array[String] + ): JavaDStream[Status] = { + ssc.twitterStream(None, filters) } /** * Create a input stream that returns tweets received from Twitter. - * @param username Twitter username - * @param password Twitter password + * @param twitterAuth Twitter4J Authorization */ def twitterStream( - username: String, - password: String + twitterAuth: Authorization ): JavaDStream[Status] = { - ssc.twitterStream(username, password) + ssc.twitterStream(Some(twitterAuth)) } /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * .consumerSecret, .accessToken and .accessTokenSecret to be set. + */ + def twitterStream(): JavaDStream[Status] = { + ssc.twitterStream() + } + + /** * Create an input stream with any arbitrary user implemented actor receiver. * @param props Props object defining creation of the actor * @param name Name of the actor diff --git a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala index 26805e9621..122a529bb7 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/NetworkInputDStream.scala @@ -140,12 +140,10 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log /** - * Pushes a block (as iterator of values) into the block manager. + * Pushes a block (as an ArrayBuffer filled with data) into the block manager. */ - def pushBlock(blockId: String, iterator: Iterator[T], metadata: Any, level: StorageLevel) { - val buffer = new ArrayBuffer[T] ++ iterator - env.blockManager.put(blockId, buffer.asInstanceOf[ArrayBuffer[Any]], level) - + def pushBlock(blockId: String, arrayBuffer: ArrayBuffer[T], metadata: Any, level: StorageLevel) { + env.blockManager.put(blockId, arrayBuffer.asInstanceOf[ArrayBuffer[Any]], level) actor ! ReportBlock(blockId, metadata) } @@ -195,7 +193,7 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log class BlockGenerator(storageLevel: StorageLevel) extends Serializable with Logging { - case class Block(id: String, iterator: Iterator[T], metadata: Any = null) + case class Block(id: String, buffer: ArrayBuffer[T], metadata: Any = null) val clock = new SystemClock() val blockInterval = System.getProperty("spark.streaming.blockInterval", "200").toLong @@ -222,17 +220,13 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log currentBuffer += obj } - private def createBlock(blockId: String, iterator: Iterator[T]) : Block = { - new Block(blockId, iterator) - } - private def updateCurrentBuffer(time: Long) { try { val newBlockBuffer = currentBuffer currentBuffer = new ArrayBuffer[T] if (newBlockBuffer.size > 0) { val blockId = "input-" + NetworkReceiver.this.streamId + "-" + (time - blockInterval) - val newBlock = createBlock(blockId, newBlockBuffer.toIterator) + val newBlock = new Block(blockId, newBlockBuffer) blocksForPushing.add(newBlock) } } catch { @@ -248,7 +242,7 @@ abstract class NetworkReceiver[T: ClassManifest]() extends Serializable with Log try { while(true) { val block = blocksForPushing.take() - NetworkReceiver.this.pushBlock(block.id, block.iterator, block.metadata, storageLevel) + NetworkReceiver.this.pushBlock(block.id, block.buffer, block.metadata, storageLevel) } } catch { case ie: InterruptedException => diff --git a/streaming/src/main/scala/spark/streaming/dstream/TwitterInputDStream.scala b/streaming/src/main/scala/spark/streaming/dstream/TwitterInputDStream.scala index c697498862..ff7a58be45 100644 --- a/streaming/src/main/scala/spark/streaming/dstream/TwitterInputDStream.scala +++ b/streaming/src/main/scala/spark/streaming/dstream/TwitterInputDStream.scala @@ -3,34 +3,45 @@ package spark.streaming.dstream import spark._ import spark.streaming._ import storage.StorageLevel - import twitter4j._ -import twitter4j.auth.BasicAuthorization +import twitter4j.auth.Authorization +import java.util.prefs.Preferences +import twitter4j.conf.ConfigurationBuilder +import twitter4j.conf.PropertyConfiguration +import twitter4j.auth.OAuthAuthorization +import twitter4j.auth.AccessToken /* A stream of Twitter statuses, potentially filtered by one or more keywords. * -* @constructor create a new Twitter stream using the supplied username and password to authenticate. +* @constructor create a new Twitter stream using the supplied Twitter4J authentication credentials. * An optional set of string filters can be used to restrict the set of tweets. The Twitter API is * such that this may return a sampled subset of all tweets during each interval. +* +* If no Authorization object is provided, initializes OAuth authorization using the system +* properties twitter4j.oauth.consumerKey, .consumerSecret, .accessToken and .accessTokenSecret. */ private[streaming] class TwitterInputDStream( @transient ssc_ : StreamingContext, - username: String, - password: String, + twitterAuth: Option[Authorization], filters: Seq[String], storageLevel: StorageLevel ) extends NetworkInputDStream[Status](ssc_) { + + private def createOAuthAuthorization(): Authorization = { + new OAuthAuthorization(new ConfigurationBuilder().build()) + } + private val authorization = twitterAuth.getOrElse(createOAuthAuthorization()) + override def getReceiver(): NetworkReceiver[Status] = { - new TwitterReceiver(username, password, filters, storageLevel) + new TwitterReceiver(authorization, filters, storageLevel) } } private[streaming] class TwitterReceiver( - username: String, - password: String, + twitterAuth: Authorization, filters: Seq[String], storageLevel: StorageLevel ) extends NetworkReceiver[Status] { @@ -40,8 +51,7 @@ class TwitterReceiver( protected override def onStart() { blockGenerator.start() - twitterStream = new TwitterStreamFactory() - .getInstance(new BasicAuthorization(username, password)) + twitterStream = new TwitterStreamFactory().getInstance(twitterAuth) twitterStream.addListener(new StatusListener { def onStatus(status: Status) = { blockGenerator += status diff --git a/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala b/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala index b3201d0b28..036c95a860 100644 --- a/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala +++ b/streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala @@ -9,6 +9,8 @@ import spark.streaming.dstream.NetworkReceiver import java.util.concurrent.atomic.AtomicInteger +import scala.collection.mutable.ArrayBuffer + /** A helper with set of defaults for supervisor strategy **/ object ReceiverSupervisorStrategy { @@ -136,8 +138,9 @@ private[streaming] class ActorReceiver[T: ClassManifest]( } protected def pushBlock(iter: Iterator[T]) { - pushBlock("block-" + streamId + "-" + System.nanoTime(), - iter, null, storageLevel) + val buffer = new ArrayBuffer[T] + buffer ++= iter + pushBlock("block-" + streamId + "-" + System.nanoTime(), buffer, null, storageLevel) } protected def onStart() = { diff --git a/streaming/src/test/java/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/spark/streaming/JavaAPISuite.java index e5fdbe1b7a..4cf10582a9 100644 --- a/streaming/src/test/java/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/spark/streaming/JavaAPISuite.java @@ -1267,7 +1267,7 @@ public class JavaAPISuite implements Serializable { @Test public void testTwitterStream() { String[] filters = new String[] { "good", "bad", "ugly" }; - JavaDStream test = ssc.twitterStream("username", "password", filters, StorageLevel.MEMORY_ONLY()); + JavaDStream test = ssc.twitterStream(filters, StorageLevel.MEMORY_ONLY()); } @Test |
