Spark2.4.0 SparkContext 源碼分析
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時序圖
前置條件
- Hadoop版本: hadoop-2.9.2
- Spark版本: spark-2.4.0-bin-hadoop2.7
- JDK.1.8.0_191
- scala2.11.12
主要内容描述
- createSparkEnv
- Started SparkUI
- 注冊端點HeartbeatReceiver
- createTaskScheduler
- 啟動任務排程器，指定預設任務排程模式FIFO,建構排程池
- new DAGScheduler
- 注冊DriverEndpoint端點:CoarseGrainedScheduler
- new StandaloneAppClient
- 注冊端點:AppClient, ClientEndpoint
- 回調ClientEndpoint.onStart()方法,該方法向所有master注冊
- 發送消息:RegisterApplication
- ClientEndpoint.receive()函數接收master回複的消息： RegisteredApplication
SparkContext
類函造方法
- 完成對SparkContext的構造
- new new DAGScheduler
try {
_conf = config.clone()
_conf.validateSettings()
if (!_conf.contains("spark.master")) {
throw new SparkException("A master URL must be set in your configuration")
}
if (!_conf.contains("spark.app.name")) {
throw new SparkException("An application name must be set in your configuration")
}
// log out spark.app.name in the Spark driver logs
logInfo(s"Submitted application: $appName")
// System property spark.yarn.app.id must be set if user code ran by AM on a YARN cluster
if (master == "yarn" && deployMode == "cluster" && !_conf.contains("spark.yarn.app.id")) {
throw new SparkException("Detected yarn cluster mode, but isn't running on a cluster. " +
"Deployment to YARN is not supported directly by SparkContext. Please use spark-submit.")
}
if (_conf.getBoolean("spark.logConf", false)) {
logInfo("Spark configuration:\n" + _conf.toDebugString)
}
// Set Spark driver host and port system properties. This explicitly sets the configuration
// instead of relying on the default value of the config constant.
_conf.set(DRIVER_HOST_ADDRESS, _conf.get(DRIVER_HOST_ADDRESS))
_conf.setIfMissing("spark.driver.port", "0")
_conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER)
_jars = Utils.getUserJars(_conf)
_files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.nonEmpty))
.toSeq.flatten
_eventLogDir =
if (isEventLogEnabled) {
val unresolvedDir = conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR)
.stripSuffix("/")
Some(Utils.resolveURI(unresolvedDir))
} else {
None
}
_eventLogCodec = {
val compress = _conf.getBoolean("spark.eventLog.compress", false)
if (compress && isEventLogEnabled) {
Some(CompressionCodec.getCodecName(_conf)).map(CompressionCodec.getShortName)
} else {
None
}
}
_listenerBus = new LiveListenerBus(_conf)
// Initialize the app status store and listener before SparkEnv is created so that it gets
// all events.
_statusStore = AppStatusStore.createLiveStore(conf)
listenerBus.addToStatusQueue(_statusStore.listener.get)
// Create the Spark execution environment (cache, map output tracker, etc)
_env = createSparkEnv(_conf, isLocal, listenerBus)
SparkEnv.set(_env)
// If running the REPL, register the repl's output dir with the file server.
_conf.getOption("spark.repl.class.outputDir").foreach { path =>
val replUri = _env.rpcEnv.fileServer.addDirectory("/classes", new File(path))
_conf.set("spark.repl.class.uri", replUri)
}
_statusTracker = new SparkStatusTracker(this, _statusStore)
_progressBar =
if (_conf.get(UI_SHOW_CONSOLE_PROGRESS) && !log.isInfoEnabled) {
Some(new ConsoleProgressBar(this))
} else {
None
}
_ui =
if (conf.getBoolean("spark.ui.enabled", true)) {
Some(SparkUI.create(Some(this), _statusStore, _conf, _env.securityManager, appName, "",
startTime))
} else {
// For tests, do not enable the UI
None
}
// Bind the UI before starting the task scheduler to communicate
// the bound port to the cluster manager properly
_ui.foreach(_.bind())
_hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(_conf)
// Add each JAR given through the constructor
if (jars != null) {
jars.foreach(addJar)
}
if (files != null) {
files.foreach(addFile)
}
_executorMemory = _conf.getOption("spark.executor.memory")
.orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY")))
.orElse(Option(System.getenv("SPARK_MEM"))
.map(warnSparkMem))
.map(Utils.memoryStringToMb)
.getOrElse(1024)
// Convert java options to env vars as a work around
// since we can't set env vars directly in sbt.
for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing"))
value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} {
executorEnvs(envKey) = value
}
Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v =>
executorEnvs("SPARK_PREPEND_CLASSES") = v
}
// The Mesos scheduler backend relies on this environment variable to set executor memory.
// TODO: Set this only in the Mesos scheduler.
executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m"
executorEnvs ++= _conf.getExecutorEnv
executorEnvs("SPARK_USER") = sparkUser
// We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will
// retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640)
_heartbeatReceiver = env.rpcEnv.setupEndpoint(
HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this))
// Create and start the scheduler
val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)
_schedulerBackend = sched
_taskScheduler = ts
_dagScheduler = new DAGScheduler(this)
_heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)
// start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
// constructor
_taskScheduler.start()
_applicationId = _taskScheduler.applicationId()
_applicationAttemptId = taskScheduler.applicationAttemptId()
_conf.set("spark.app.id", _applicationId)
if (_conf.getBoolean("spark.ui.reverseProxy", false)) {
System.setProperty("spark.ui.proxyBase", "/proxy/" + _applicationId)
}
_ui.foreach(_.setAppId(_applicationId))
_env.blockManager.initialize(_applicationId)
// The metrics system for Driver need to be set spark.app.id to app ID.
// So it should start after we get app ID from the task scheduler and set spark.app.id.
_env.metricsSystem.start()
// Attach the driver metrics servlet handler to the web ui after the metrics system is started.
_env.metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler)))
_eventLogger =
if (isEventLogEnabled) {
val logger =
new EventLoggingListener(_applicationId, _applicationAttemptId, _eventLogDir.get,
_conf, _hadoopConfiguration)
logger.start()
listenerBus.addToEventLogQueue(logger)
Some(logger)
} else {
None
}
// Optionally scale number of executors dynamically based on workload. Exposed for testing.
val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)
_executorAllocationManager =
if (dynamicAllocationEnabled) {
schedulerBackend match {
case b: ExecutorAllocationClient =>
Some(new ExecutorAllocationManager(
schedulerBackend.asInstanceOf[ExecutorAllocationClient], listenerBus, _conf,
_env.blockManager.master))
case _ =>
None
}
} else {
None
}
_executorAllocationManager.foreach(_.start())
_cleaner =
if (_conf.getBoolean("spark.cleaner.referenceTracking", true)) {
Some(new ContextCleaner(this))
} else {
None
}
_cleaner.foreach(_.start())
setupAndStartListenerBus()
postEnvironmentUpdate()
postApplicationStart()
// Post init
_taskScheduler.postStartHook()
_env.metricsSystem.registerSource(_dagScheduler.metricsSource)
_env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))
_executorAllocationManager.foreach { e =>
_env.metricsSystem.registerSource(e.executorAllocationManagerSource)
}
// Make sure the context is stopped if the user forgets about it. This avoids leaving
// unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM
// is killed, though.
logDebug("Adding shutdown hook") // force eager creation of logger
_shutdownHookRef = ShutdownHookManager.addShutdownHook(
ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>
logInfo("Invoking stop() from shutdown hook")
try {
stop()
} catch {
case e: Throwable =>
logWarning("Ignoring Exception while stopping SparkContext from shutdown hook", e)
}
}
} catch {
case NonFatal(e) =>
logError("Error initializing SparkContext.", e)
try {
stop()
} catch {
case NonFatal(inner) =>
logError("Error stopping SparkContext after init error.", inner)
} finally {
throw e
}
}
建立SparkEnv
- ).SparkEnv對象是在這個方法中構造的
- ).new SecurityManager()
- ).new NettyRpcEnvFactory()
- ).建立NettyRpcEnv
- ).Utils.startServiceOnPort(啟動sparkDriver)
- ). new BroadcastManager
- ).注冊端點MapOutputTracker
- ).ShuffleManager:SortShuffleManager
- ).預設記憶體管理器：UnifiedMemoryManager
- ).SortShuffleManager
- ).UnifiedMemoryManager
- ).注冊端點BlockManagerMaster
- ).new BlockManager
- ).注冊端點OutputCommitCoordinator
// Create the Spark execution environment (cache, map output tracker, etc)
_env = createSparkEnv(_conf, isLocal, listenerBus)
SparkEnv.set(_env) 建立SparkUI
_ui =
if (conf.getBoolean("spark.ui.enabled", true)) {
Some(SparkUI.create(Some(this), _statusStore, _conf, _env.securityManager, appName, "",
startTime))
} else {
// For tests, do not enable the UI
None
}
// Bind the UI before starting the task scheduler to communicate
// the bound port to the cluster manager properly
_ui.foreach(_.bind()) 建立任務排程器
// Create and start the scheduler
val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode) SparkContext.createTaskScheduler
- 根據master配置比對對應的任務排程器
- 本地模式 local
- 本地模式n個線程 LOCAL_N_REGEX
- standalone模式 SPARK_REGEX
- 本文分析standalone模式的處理方式
/**
* Create a task scheduler based on a given master URL.
* Return a 2-tuple of the scheduler backend and the task scheduler.
*/
private def createTaskScheduler(
sc: SparkContext,
master: String,
deployMode: String): (SchedulerBackend, TaskScheduler) = {
import SparkMasterRegex._
// When running locally, don't try to re-execute tasks on failure.
val MAX_LOCAL_TASK_FAILURES = 1
master match {
case "local" =>
val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
val backend = new LocalSchedulerBackend(sc.getConf, scheduler, 1)
scheduler.initialize(backend)
(backend, scheduler)
case LOCAL_N_REGEX(threads) =>
def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
// local[*] estimates the number of cores on the machine; local[N] uses exactly N threads.
val threadCount = if (threads == "*") localCpuCount else threads.toInt
if (threadCount <= 0) {
throw new SparkException(s"Asked to run locally with $threadCount threads")
}
val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
scheduler.initialize(backend)
(backend, scheduler)
case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
// local[*, M] means the number of cores on the computer with M failures
// local[N, M] means exactly N threads with M failures
val threadCount = if (threads == "*") localCpuCount else threads.toInt
val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true)
val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
scheduler.initialize(backend)
(backend, scheduler)
case SPARK_REGEX(sparkUrl) =>
val scheduler = new TaskSchedulerImpl(sc)
val masterUrls = sparkUrl.split(",").map("spark://" + _)
val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
scheduler.initialize(backend)
(backend, scheduler)
case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
// Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang.
val memoryPerSlaveInt = memoryPerSlave.toInt
if (sc.executorMemory > memoryPerSlaveInt) {
throw new SparkException(
"Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format(
memoryPerSlaveInt, sc.executorMemory))
}
val scheduler = new TaskSchedulerImpl(sc)
val localCluster = new LocalSparkCluster(
numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt, sc.conf)
val masterUrls = localCluster.start()
val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
scheduler.initialize(backend)
backend.shutdownCallback = (backend: StandaloneSchedulerBackend) => {
localCluster.stop()
}
(backend, scheduler)
case masterUrl =>
val cm = getClusterManager(masterUrl) match {
case Some(clusterMgr) => clusterMgr
case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
}
try {
val scheduler = cm.createTaskScheduler(sc, masterUrl)
val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
cm.initialize(scheduler, backend)
(backend, scheduler)
} catch {
case se: SparkException => throw se
case NonFatal(e) =>
throw new SparkException("External scheduler cannot be instantiated", e)
}
}
} SparkContext.createTaskScheduler standalone模式
- 任務排程器 val scheduler = new TaskSchedulerImpl(sc)
- standalone後端排程器 val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
- 調用任務排程器的 initialize(backend) 方法
- 傳回(backend, scheduler)
case SPARK_REGEX(sparkUrl) =>
val scheduler = new TaskSchedulerImpl(sc)
val masterUrls = sparkUrl.split(",").map("spark://" + _)
val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
scheduler.initialize(backend)
(backend, scheduler) TaskSchedulerImpl.initialize
- 給變量backend 指派: StandaloneSchedulerBackend
- 比對排程模式，用schedulableBuilder建構排程池
var backend: SchedulerBackend = null
def initialize(backend: SchedulerBackend) {
this.backend = backend
schedulableBuilder = {
schedulingMode match {
case SchedulingMode.FIFO =>
new FIFOSchedulableBuilder(rootPool)
case SchedulingMode.FAIR =>
new FairSchedulableBuilder(rootPool, conf)
case _ =>
throw new IllegalArgumentException(s"Unsupported $SCHEDULER_MODE_PROPERTY: " +
s"$schedulingMode")
}
}
schedulableBuilder.buildPools()
}
- 預設任務排程器排程方式FIFO
private val schedulingModeConf = conf.get(SCHEDULER_MODE_PROPERTY, SchedulingMode.FIFO.toString)
val schedulingMode: SchedulingMode =
try {
SchedulingMode.withName(schedulingModeConf.toUpperCase(Locale.ROOT))
} catch {
case e: java.util.NoSuchElementException =>
throw new SparkException(s"Unrecognized $SCHEDULER_MODE_PROPERTY: $schedulingModeConf")
}
val rootPool: Pool = new Pool("", schedulingMode, 0, 0)
SparkContext.DAGScheduler
_dagScheduler = new DAGScheduler(this)
SparkContext 起動任務排程器
- 調用TaskSchedulerImpl.start()函數
_taskScheduler.start() TaskSchedulerImpl.start()
- 調用StandaloneSchedulerBackend.start()函數
override def start() {
backend.start()
if (!isLocal && conf.getBoolean("spark.speculation", false)) {
logInfo("Starting speculative execution thread")
speculationScheduler.scheduleWithFixedDelay(new Runnable {
override def run(): Unit = Utils.tryOrStopSparkContext(sc) {
checkSpeculatableTasks()
}
}, SPECULATION_INTERVAL_MS, SPECULATION_INTERVAL_MS, TimeUnit.MILLISECONDS)
}
} StandaloneSchedulerBackend.start()
- StandaloneSchedulerBackend extends CoarseGrainedSchedulerBackend
- super.start()調用CoarseGrainedSchedulerBackend.start()函數
- client = new StandaloneAppClient() 執行個體化StandaloneAppClinet
- client.start() 函數排程
override def start() {
super.start()
// SPARK-21159. The scheduler backend should only try to connect to the launcher when in client
// mode. In cluster mode, the code that submits the application to the Master needs to connect
// to the launcher instead.
if (sc.deployMode == "client") {
launcherBackend.connect()
}
// The endpoint for executors to talk to us
val driverUrl = RpcEndpointAddress(
sc.conf.get("spark.driver.host"),
sc.conf.get("spark.driver.port").toInt,
CoarseGrainedSchedulerBackend.ENDPOINT_NAME).toString
val args = Seq(
"--driver-url", driverUrl,
"--executor-id", "{{EXECUTOR_ID}}",
"--hostname", "{{HOSTNAME}}",
"--cores", "{{CORES}}",
"--app-id", "{{APP_ID}}",
"--worker-url", "{{WORKER_URL}}")
val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
.map(Utils.splitCommandString).getOrElse(Seq.empty)
val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath")
.map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil)
val libraryPathEntries = sc.conf.getOption("spark.executor.extraLibraryPath")
.map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil)
// When testing, expose the parent class path to the child. This is processed by
// compute-classpath.{cmd,sh} and makes all needed jars available to child processes
// when the assembly is built with the "*-provided" profiles enabled.
val testingClassPath =
if (sys.props.contains("spark.testing")) {
sys.props("java.class.path").split(java.io.File.pathSeparator).toSeq
} else {
Nil
}
// Start executors with a few necessary configs for registering with the scheduler
val sparkJavaOpts = Utils.sparkJavaOpts(conf, SparkConf.isExecutorStartupConf)
val javaOpts = sparkJavaOpts ++ extraJavaOpts
val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
args, sc.executorEnvs, classPathEntries ++ testingClassPath, libraryPathEntries, javaOpts)
val webUrl = sc.ui.map(_.webUrl).getOrElse("")
val coresPerExecutor = conf.getOption("spark.executor.cores").map(_.toInt)
// If we're using dynamic allocation, set our initial executor limit to 0 for now.
// ExecutorAllocationManager will send the real initial limit to the Master later.
val initialExecutorLimit =
if (Utils.isDynamicAllocationEnabled(conf)) {
Some(0)
} else {
None
}
val appDesc = ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
webUrl, sc.eventLogDir, sc.eventLogCodec, coresPerExecutor, initialExecutorLimit)
client = new StandaloneAppClient(sc.env.rpcEnv, masters, appDesc, this, conf)
client.start()
launcherBackend.setState(SparkAppHandle.State.SUBMITTED)
waitForRegistration()
launcherBackend.setState(SparkAppHandle.State.RUNNING)
} CoarseGrainedSchedulerBackend.start()
- 注冊DriverEndpoint: CoarseGrainedScheduler
- 首先調用DriverEndpoint.OnStart()函數
override def start() {
val properties = new ArrayBuffer[(String, String)]
for ((key, value) <- scheduler.sc.conf.getAll) {
if (key.startsWith("spark.")) {
properties += ((key, value))
}
}
// TODO (prashant) send conf instead of properties
driverEndpoint = createDriverEndpointRef(properties)
}
DriverEndpoint.OnStart()函數
- 調用線程池定時任務，預設每隔一秒發送消息:ReviveOffers
- DriverEndpoint.receive()函數對ReviveOffers消息進行處理
- 調用CoarseGrainedSchedulerBackend.DriverEndpoint.makeOffers()函數,為所有的executor配置設定資源
override def onStart() {
// Periodically revive offers to allow delay scheduling to work
val reviveIntervalMs = conf.getTimeAsMs("spark.scheduler.revive.interval", "1s")
reviveThread.scheduleAtFixedRate(new Runnable {
override def run(): Unit = Utils.tryLogNonFatalError {
Option(self).foreach(_.send(ReviveOffers))
}
}, 0, reviveIntervalMs, TimeUnit.MILLISECONDS)
}
StandaloneAppClient.start()
- 注冊通信端點: AppClient
- 注意，通信端點首先調用OnStart()函數，即調用ClientEndpoint.OnStart()函數,該函數會向master注冊應用程式
def start() {
// Just launch an rpcEndpoint; it will call back into the listener.
endpoint.set(rpcEnv.setupEndpoint("AppClient", new ClientEndpoint(rpcEnv)))
} ClientEndpoint.OnStart()
- 調用函數 registerWithMaster(1)向master注冊應用程式
override def onStart(): Unit = {
try {
registerWithMaster(1)
} catch {
case e: Exception =>
logWarning("Failed to connect to master", e)
markDisconnected()
stop()
}
} ClientEndpoint.registerWithMaster()
- 調用函數 tryRegisterAllMasters() 向所有master注冊應用程式
/**
* Register with all masters asynchronously. It will call `registerWithMaster` every
* REGISTRATION_TIMEOUT_SECONDS seconds until exceeding REGISTRATION_RETRIES times.
* Once we connect to a master successfully, all scheduling work and Futures will be cancelled.
*
* nthRetry means this is the nth attempt to register with master.
*/
private def registerWithMaster(nthRetry: Int) {
registerMasterFutures.set(tryRegisterAllMasters())
registrationRetryTimer.set(registrationRetryThread.schedule(new Runnable {
override def run(): Unit = {
if (registered.get) {
registerMasterFutures.get.foreach(_.cancel(true))
registerMasterThreadPool.shutdownNow()
} else if (nthRetry >= REGISTRATION_RETRIES) {
markDead("All masters are unresponsive! Giving up.")
} else {
registerMasterFutures.get.foreach(_.cancel(true))
registerWithMaster(nthRetry + 1)
}
}
}, REGISTRATION_TIMEOUT_SECONDS, TimeUnit.SECONDS))
} ClientEndpoint.tryRegisterAllMasters()
- 向所有master發送消息: RegisterApplication()
- 注意 ClientEndpoint 是一個通信端點，函數receive*()也可以接收消息,如接收master發過來的消息：RegisteredApplication
/**
* Register with all masters asynchronously and returns an array `Future`s for cancellation.
*/
private def tryRegisterAllMasters(): Array[JFuture[_]] = {
for (masterAddress <- masterRpcAddresses) yield {
registerMasterThreadPool.submit(new Runnable {
override def run(): Unit = try {
if (registered.get) {
return
}
logInfo("Connecting to master " + masterAddress.toSparkURL + "...")
val masterRef = rpcEnv.setupEndpointRef(masterAddress, Master.ENDPOINT_NAME)
masterRef.send(RegisterApplication(appDescription, self))
} catch {
case ie: InterruptedException => // Cancelled
case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
}
})
}
} ClientEndpoint.receive()
- 接收master發過來的注冊應用程式完成消息: RegisteredApplication
override def receive: PartialFunction[Any, Unit] = {
case RegisteredApplication(appId_, masterRef) =>
// FIXME How to handle the following cases?
// 1. A master receives multiple registrations and sends back multiple
// RegisteredApplications due to an unstable network.
// 2. Receive multiple RegisteredApplication from different masters because the master is
// changing.
appId.set(appId_)
registered.set(true)
master = Some(masterRef)
listener.connected(appId.get)
case ApplicationRemoved(message) =>
markDead("Master removed our application: %s".format(message))
stop()
case ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) =>
val fullId = appId + "/" + id
logInfo("Executor added: %s on %s (%s) with %d core(s)".format(fullId, workerId, hostPort,
cores))
listener.executorAdded(fullId, workerId, hostPort, cores, memory)
case ExecutorUpdated(id, state, message, exitStatus, workerLost) =>
val fullId = appId + "/" + id
val messageText = message.map(s => " (" + s + ")").getOrElse("")
logInfo("Executor updated: %s is now %s%s".format(fullId, state, messageText))
if (ExecutorState.isFinished(state)) {
listener.executorRemoved(fullId, message.getOrElse(""), exitStatus, workerLost)
}
case WorkerRemoved(id, host, message) =>
logInfo("Master removed worker %s: %s".format(id, message))
listener.workerRemoved(id, host, message)
case MasterChanged(masterRef, masterWebUiUrl) =>
logInfo("Master has changed, new master is at " + masterRef.address.toSparkURL)
master = Some(masterRef)
alreadyDisconnected = false
masterRef.send(MasterChangeAcknowledged(appId.get))
} end
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