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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.executor
import java.io.{File, NotSerializableException}
import java.lang.Thread.UncaughtExceptionHandler
import java.lang.management.ManagementFactory
import java.net.{URI, URL}
import java.nio.ByteBuffer
import java.util.Properties
import java.util.concurrent._
import javax.annotation.concurrent.GuardedBy
import scala.collection.JavaConverters._
import scala.collection.mutable.{ArrayBuffer, HashMap, Map}
import scala.concurrent.duration._
import scala.util.control.NonFatal
import com.google.common.util.concurrent.ThreadFactoryBuilder
import org.apache.spark._
import org.apache.spark.deploy.SparkHadoopUtil
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config._
import org.apache.spark.memory.{SparkOutOfMemoryError, TaskMemoryManager}
import org.apache.spark.rpc.RpcTimeout
import org.apache.spark.scheduler.{DirectTaskResult, IndirectTaskResult, Task, TaskDescription}
import org.apache.spark.shuffle.FetchFailedException
import org.apache.spark.storage.{StorageLevel, TaskResultBlockId}
import org.apache.spark.util._
import org.apache.spark.util.io.ChunkedByteBuffer
/**
* Spark executor, backed by a threadpool to run tasks.
*
* This can be used with Mesos, YARN, and the standalone scheduler.
* An internal RPC interface is used for communication with the driver,
* except in the case of Mesos fine-grained mode.
*/
private[spark] class Executor(
executorId: String,
executorHostname: String,
env: SparkEnv,
userClassPath: Seq[URL] = Nil,
isLocal: Boolean = false,
uncaughtExceptionHandler: UncaughtExceptionHandler = new SparkUncaughtExceptionHandler)
extends Logging {
logInfo(s"Starting executor ID $executorId on host $executorHostname")
// Application dependencies (added through SparkContext) that we've fetched so far on this node.
// Each map holds the master's timestamp for the version of that file or JAR we got.
private val currentFiles: HashMap[String, Long] = new HashMap[String, Long]()
private val currentJars: HashMap[String, Long] = new HashMap[String, Long]()
private val EMPTY_BYTE_BUFFER = ByteBuffer.wrap(new Array[Byte](0))
private val conf = env.conf
// No ip or host:port - just hostname
Utils.checkHost(executorHostname)
// must not have port specified.
assert (0 == Utils.parseHostPort(executorHostname)._2)
// Make sure the local hostname we report matches the cluster scheduler's name for this host
Utils.setCustomHostname(executorHostname)
if (!isLocal) {
// Setup an uncaught exception handler for non-local mode.
// Make any thread terminations due to uncaught exceptions kill the entire
// executor process to avoid surprising stalls.
Thread.setDefaultUncaughtExceptionHandler(uncaughtExceptionHandler)
}
// Start worker thread pool
private val threadPool = {
val threadFactory = new ThreadFactoryBuilder()
.setDaemon(true)
.setNameFormat("Executor task launch worker-%d")
.setThreadFactory(new ThreadFactory {
override def newThread(r: Runnable): Thread =
// Use UninterruptibleThread to run tasks so that we can allow running codes without being
// interrupted by `Thread.interrupt()`. Some issues, such as KAFKA-1894, HADOOP-10622,
// will hang forever if some methods are interrupted.
new UninterruptibleThread(r, "unused") // thread name will be set by ThreadFactoryBuilder
})
.build()
Executors.newCachedThreadPool(threadFactory).asInstanceOf[ThreadPoolExecutor]
}
private val executorSource = new ExecutorSource(threadPool, executorId)
// Pool used for threads that supervise task killing / cancellation
private val taskReaperPool = ThreadUtils.newDaemonCachedThreadPool("Task reaper")
// For tasks which are in the process of being killed, this map holds the most recently created
// TaskReaper. All accesses to this map should be synchronized on the map itself (this isn't
// a ConcurrentHashMap because we use the synchronization for purposes other than simply guarding
// the integrity of the map's internal state). The purpose of this map is to prevent the creation
// of a separate TaskReaper for every killTask() of a given task. Instead, this map allows us to
// track whether an existing TaskReaper fulfills the role of a TaskReaper that we would otherwise
// create. The map key is a task id.
private val taskReaperForTask: HashMap[Long, TaskReaper] = HashMap[Long, TaskReaper]()
if (!isLocal) {
env.blockManager.initialize(conf.getAppId)
env.metricsSystem.registerSource(executorSource)
env.metricsSystem.registerSource(env.blockManager.shuffleMetricsSource)
}
// Whether to load classes in user jars before those in Spark jars
private val userClassPathFirst = conf.getBoolean("spark.executor.userClassPathFirst", false)
// Whether to monitor killed / interrupted tasks
private val taskReaperEnabled = conf.getBoolean("spark.task.reaper.enabled", false)
// Create our ClassLoader
// do this after SparkEnv creation so can access the SecurityManager
private val urlClassLoader = createClassLoader()
private val replClassLoader = addReplClassLoaderIfNeeded(urlClassLoader)
// Set the classloader for serializer
env.serializer.setDefaultClassLoader(replClassLoader)
// SPARK-21928. SerializerManager's internal instance of Kryo might get used in netty threads
// for fetching remote cached RDD blocks, so need to make sure it uses the right classloader too.
env.serializerManager.setDefaultClassLoader(replClassLoader)
private val executorPlugins: Seq[ExecutorPlugin] = {
val pluginNames = conf.get(EXECUTOR_PLUGINS)
if (pluginNames.nonEmpty) {
logDebug(s"Initializing the following plugins: ${pluginNames.mkString(", ")}")
// Plugins need to load using a class loader that includes the executor's user classpath
val pluginList: Seq[ExecutorPlugin] =
Utils.withContextClassLoader(replClassLoader) {
val plugins = Utils.loadExtensions(classOf[ExecutorPlugin], pluginNames, conf)
plugins.foreach { plugin =>
plugin.init()
logDebug(s"Successfully loaded plugin " + plugin.getClass().getCanonicalName())
}
plugins
}
logDebug("Finished initializing plugins")
pluginList
} else {
Nil
}
}
// Max size of direct result. If task result is bigger than this, we use the block manager
// to send the result back.
private val maxDirectResultSize = Math.min(
conf.getSizeAsBytes("spark.task.maxDirectResultSize", 1L << 20),
RpcUtils.maxMessageSizeBytes(conf))
private val maxResultSize = conf.get(MAX_RESULT_SIZE)
// Maintains the list of running tasks.
private val runningTasks = new ConcurrentHashMap[Long, TaskRunner]
// Executor for the heartbeat task.
private val heartbeater = ThreadUtils.newDaemonSingleThreadScheduledExecutor("driver-heartbeater")
// must be initialized before running startDriverHeartbeat()
private val heartbeatReceiverRef =
RpcUtils.makeDriverRef(HeartbeatReceiver.ENDPOINT_NAME, conf, env.rpcEnv)
/**
* When an executor is unable to send heartbeats to the driver more than `HEARTBEAT_MAX_FAILURES`
* times, it should kill itself. The default value is 60. It means we will retry to send
* heartbeats about 10 minutes because the heartbeat interval is 10s.
*/
private val HEARTBEAT_MAX_FAILURES = conf.getInt("spark.executor.heartbeat.maxFailures", 60)
/**
* Count the failure times of heartbeat. It should only be accessed in the heartbeat thread. Each
* successful heartbeat will reset it to 0.
*/
private var heartbeatFailures = 0
startDriverHeartbeater()
private[executor] def numRunningTasks: Int = runningTasks.size()
def launchTask(context: ExecutorBackend, taskDescription: TaskDescription): Unit = {
val tr = new TaskRunner(context, taskDescription)
runningTasks.put(taskDescription.taskId, tr)
threadPool.execute(tr)
}
def killTask(taskId: Long, interruptThread: Boolean, reason: String): Unit = {
val taskRunner = runningTasks.get(taskId)
if (taskRunner != null) {
if (taskReaperEnabled) {
val maybeNewTaskReaper: Option[TaskReaper] = taskReaperForTask.synchronized {
val shouldCreateReaper = taskReaperForTask.get(taskId) match {
case None => true
case Some(existingReaper) => interruptThread && !existingReaper.interruptThread
}
if (shouldCreateReaper) {
val taskReaper = new TaskReaper(
taskRunner, interruptThread = interruptThread, reason = reason)
taskReaperForTask(taskId) = taskReaper
Some(taskReaper)
} else {
None
}
}
// Execute the TaskReaper from outside of the synchronized block.
maybeNewTaskReaper.foreach(taskReaperPool.execute)
} else {
taskRunner.kill(interruptThread = interruptThread, reason = reason)
}
}
}
/**
* Function to kill the running tasks in an executor.
* This can be called by executor back-ends to kill the
* tasks instead of taking the JVM down.
* @param interruptThread whether to interrupt the task thread
*/
def killAllTasks(interruptThread: Boolean, reason: String) : Unit = {
runningTasks.keys().asScala.foreach(t =>
killTask(t, interruptThread = interruptThread, reason = reason))
}
def stop(): Unit = {
env.metricsSystem.report()
heartbeater.shutdown()
heartbeater.awaitTermination(10, TimeUnit.SECONDS)
threadPool.shutdown()
// Notify plugins that executor is shutting down so they can terminate cleanly
Utils.withContextClassLoader(replClassLoader) {
executorPlugins.foreach { plugin =>
try {
plugin.shutdown()
} catch {
case e: Exception =>
logWarning("Plugin " + plugin.getClass().getCanonicalName() + " shutdown failed", e)
}
}
}
if (!isLocal) {
env.stop()
}
}
/** Returns the total amount of time this JVM process has spent in garbage collection. */
private def computeTotalGcTime(): Long = {
ManagementFactory.getGarbageCollectorMXBeans.asScala.map(_.getCollectionTime).sum
}
class TaskRunner(
execBackend: ExecutorBackend,
private val taskDescription: TaskDescription)
extends Runnable {
val taskId = taskDescription.taskId
val threadName = s"Executor task launch worker for task $taskId"
private val taskName = taskDescription.name
/** If specified, this task has been killed and this option contains the reason. */
@volatile private var reasonIfKilled: Option[String] = None
@volatile private var threadId: Long = -1
def getThreadId: Long = threadId
/** Whether this task has been finished. */
@GuardedBy("TaskRunner.this")
private var finished = false
def isFinished: Boolean = synchronized { finished }
/** How much the JVM process has spent in GC when the task starts to run. */
@volatile var startGCTime: Long = _
/**
* The task to run. This will be set in run() by deserializing the task binary coming
* from the driver. Once it is set, it will never be changed.
*/
@volatile var task: Task[Any] = _
def kill(interruptThread: Boolean, reason: String): Unit = {
logInfo(s"Executor is trying to kill $taskName (TID $taskId), reason: $reason")
reasonIfKilled = Some(reason)
if (task != null) {
synchronized {
if (!finished) {
task.kill(interruptThread, reason)
}
}
}
}
/**
* Set the finished flag to true and clear the current thread's interrupt status
*/
private def setTaskFinishedAndClearInterruptStatus(): Unit = synchronized {
this.finished = true
// SPARK-14234 - Reset the interrupted status of the thread to avoid the
// ClosedByInterruptException during execBackend.statusUpdate which causes
// Executor to crash
Thread.interrupted()
// Notify any waiting TaskReapers. Generally there will only be one reaper per task but there
// is a rare corner-case where one task can have two reapers in case cancel(interrupt=False)
// is followed by cancel(interrupt=True). Thus we use notifyAll() to avoid a lost wakeup:
notifyAll()
}
/**
* Utility function to:
* 1. Report executor runtime and JVM gc time if possible
* 2. Collect accumulator updates
* 3. Set the finished flag to true and clear current thread's interrupt status
*/
private def collectAccumulatorsAndResetStatusOnFailure(taskStartTime: Long) = {
// Report executor runtime and JVM gc time
Option(task).foreach(t => {
t.metrics.setExecutorRunTime(System.currentTimeMillis() - taskStartTime)
t.metrics.setJvmGCTime(computeTotalGcTime() - startGCTime)
})
// Collect latest accumulator values to report back to the driver
val accums: Seq[AccumulatorV2[_, _]] =
Option(task).map(_.collectAccumulatorUpdates(taskFailed = true)).getOrElse(Seq.empty)
val accUpdates = accums.map(acc => acc.toInfo(Some(acc.value), None))
setTaskFinishedAndClearInterruptStatus()
(accums, accUpdates)
}
override def run(): Unit = {
threadId = Thread.currentThread.getId
Thread.currentThread.setName(threadName)
val threadMXBean = ManagementFactory.getThreadMXBean
val taskMemoryManager = new TaskMemoryManager(env.memoryManager, taskId)
val deserializeStartTime = System.currentTimeMillis()
val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
Thread.currentThread.setContextClassLoader(replClassLoader)
val ser = env.closureSerializer.newInstance()
logInfo(s"Running $taskName (TID $taskId)")
execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
var taskStartTime: Long = 0
var taskStartCpu: Long = 0
startGCTime = computeTotalGcTime()
try {
// Must be set before updateDependencies() is called, in case fetching dependencies
// requires access to properties contained within (e.g. for access control).
Executor.taskDeserializationProps.set(taskDescription.properties)
updateDependencies(taskDescription.addedFiles, taskDescription.addedJars)
task = ser.deserialize[Task[Any]](
taskDescription.serializedTask, Thread.currentThread.getContextClassLoader)
task.localProperties = taskDescription.properties
task.setTaskMemoryManager(taskMemoryManager)
// If this task has been killed before we deserialized it, let's quit now. Otherwise,
// continue executing the task.
val killReason = reasonIfKilled
if (killReason.isDefined) {
// Throw an exception rather than returning, because returning within a try{} block
// causes a NonLocalReturnControl exception to be thrown. The NonLocalReturnControl
// exception will be caught by the catch block, leading to an incorrect ExceptionFailure
// for the task.
throw new TaskKilledException(killReason.get)
}
// The purpose of updating the epoch here is to invalidate executor map output status cache
// in case FetchFailures have occurred. In local mode `env.mapOutputTracker` will be
// MapOutputTrackerMaster and its cache invalidation is not based on epoch numbers so
// we don't need to make any special calls here.
if (!isLocal) {
logDebug("Task " + taskId + "'s epoch is " + task.epoch)
env.mapOutputTracker.asInstanceOf[MapOutputTrackerWorker].updateEpoch(task.epoch)
}
// Run the actual task and measure its runtime.
taskStartTime = System.currentTimeMillis()
taskStartCpu = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
var threwException = true
val value = Utils.tryWithSafeFinally {
val res = task.run(
taskAttemptId = taskId,
attemptNumber = taskDescription.attemptNumber,
metricsSystem = env.metricsSystem)
threwException = false
res
} {
val releasedLocks = env.blockManager.releaseAllLocksForTask(taskId)
val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory()
if (freedMemory > 0 && !threwException) {
val errMsg = s"Managed memory leak detected; size = $freedMemory bytes, TID = $taskId"
if (conf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) {
throw new SparkException(errMsg)
} else {
logWarning(errMsg)
}
}
if (releasedLocks.nonEmpty && !threwException) {
val errMsg =
s"${releasedLocks.size} block locks were not released by TID = $taskId:\n" +
releasedLocks.mkString("[", ", ", "]")
if (conf.getBoolean("spark.storage.exceptionOnPinLeak", false)) {
throw new SparkException(errMsg)
} else {
logInfo(errMsg)
}
}
}
task.context.fetchFailed.foreach { fetchFailure =>
// uh-oh. it appears the user code has caught the fetch-failure without throwing any
// other exceptions. Its *possible* this is what the user meant to do (though highly
// unlikely). So we will log an error and keep going.
logError(s"TID ${taskId} completed successfully though internally it encountered " +
s"unrecoverable fetch failures! Most likely this means user code is incorrectly " +
s"swallowing Spark's internal ${classOf[FetchFailedException]}", fetchFailure)
}
val taskFinish = System.currentTimeMillis()
val taskFinishCpu = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
// If the task has been killed, let's fail it.
task.context.killTaskIfInterrupted()
val resultSer = env.serializer.newInstance()
val beforeSerialization = System.currentTimeMillis()
val valueBytes = resultSer.serialize(value)
val afterSerialization = System.currentTimeMillis()
// Deserialization happens in two parts: first, we deserialize a Task object, which
// includes the Partition. Second, Task.run() deserializes the RDD and function to be run.
task.metrics.setExecutorDeserializeTime(
(taskStartTime - deserializeStartTime) + task.executorDeserializeTime)
task.metrics.setExecutorDeserializeCpuTime(
(taskStartCpu - deserializeStartCpuTime) + task.executorDeserializeCpuTime)
// We need to subtract Task.run()'s deserialization time to avoid double-counting
task.metrics.setExecutorRunTime((taskFinish - taskStartTime) - task.executorDeserializeTime)
task.metrics.setExecutorCpuTime(
(taskFinishCpu - taskStartCpu) - task.executorDeserializeCpuTime)
task.metrics.setJvmGCTime(computeTotalGcTime() - startGCTime)
task.metrics.setResultSerializationTime(afterSerialization - beforeSerialization)
// Expose task metrics using the Dropwizard metrics system.
// Update task metrics counters
executorSource.METRIC_CPU_TIME.inc(task.metrics.executorCpuTime)
executorSource.METRIC_RUN_TIME.inc(task.metrics.executorRunTime)
executorSource.METRIC_JVM_GC_TIME.inc(task.metrics.jvmGCTime)
executorSource.METRIC_DESERIALIZE_TIME.inc(task.metrics.executorDeserializeTime)
executorSource.METRIC_DESERIALIZE_CPU_TIME.inc(task.metrics.executorDeserializeCpuTime)
executorSource.METRIC_RESULT_SERIALIZE_TIME.inc(task.metrics.resultSerializationTime)
executorSource.METRIC_SHUFFLE_FETCH_WAIT_TIME
.inc(task.metrics.shuffleReadMetrics.fetchWaitTime)
executorSource.METRIC_SHUFFLE_WRITE_TIME.inc(task.metrics.shuffleWriteMetrics.writeTime)
executorSource.METRIC_SHUFFLE_TOTAL_BYTES_READ
.inc(task.metrics.shuffleReadMetrics.totalBytesRead)
executorSource.METRIC_SHUFFLE_REMOTE_BYTES_READ
.inc(task.metrics.shuffleReadMetrics.remoteBytesRead)
executorSource.METRIC_SHUFFLE_REMOTE_BYTES_READ_TO_DISK
.inc(task.metrics.shuffleReadMetrics.remoteBytesReadToDisk)
executorSource.METRIC_SHUFFLE_LOCAL_BYTES_READ
.inc(task.metrics.shuffleReadMetrics.localBytesRead)
executorSource.METRIC_SHUFFLE_RECORDS_READ
.inc(task.metrics.shuffleReadMetrics.recordsRead)
executorSource.METRIC_SHUFFLE_REMOTE_BLOCKS_FETCHED
.inc(task.metrics.shuffleReadMetrics.remoteBlocksFetched)
executorSource.METRIC_SHUFFLE_LOCAL_BLOCKS_FETCHED
.inc(task.metrics.shuffleReadMetrics.localBlocksFetched)
executorSource.METRIC_SHUFFLE_BYTES_WRITTEN
.inc(task.metrics.shuffleWriteMetrics.bytesWritten)
executorSource.METRIC_SHUFFLE_RECORDS_WRITTEN
.inc(task.metrics.shuffleWriteMetrics.recordsWritten)
executorSource.METRIC_INPUT_BYTES_READ
.inc(task.metrics.inputMetrics.bytesRead)
executorSource.METRIC_INPUT_RECORDS_READ
.inc(task.metrics.inputMetrics.recordsRead)
executorSource.METRIC_OUTPUT_BYTES_WRITTEN
.inc(task.metrics.outputMetrics.bytesWritten)
executorSource.METRIC_OUTPUT_RECORDS_WRITTEN
.inc(task.metrics.outputMetrics.recordsWritten)
executorSource.METRIC_RESULT_SIZE.inc(task.metrics.resultSize)
executorSource.METRIC_DISK_BYTES_SPILLED.inc(task.metrics.diskBytesSpilled)
executorSource.METRIC_MEMORY_BYTES_SPILLED.inc(task.metrics.memoryBytesSpilled)
// Note: accumulator updates must be collected after TaskMetrics is updated
val accumUpdates = task.collectAccumulatorUpdates()
// TODO: do not serialize value twice
val directResult = new DirectTaskResult(valueBytes, accumUpdates)
val serializedDirectResult = ser.serialize(directResult)
val resultSize = serializedDirectResult.limit()
// directSend = sending directly back to the driver
val serializedResult: ByteBuffer = {
if (maxResultSize > 0 && resultSize > maxResultSize) {
logWarning(s"Finished $taskName (TID $taskId). Result is larger than maxResultSize " +
s"(${Utils.bytesToString(resultSize)} > ${Utils.bytesToString(maxResultSize)}), " +
s"dropping it.")
ser.serialize(new IndirectTaskResult[Any](TaskResultBlockId(taskId), resultSize))
} else if (resultSize > maxDirectResultSize) {
val blockId = TaskResultBlockId(taskId)
env.blockManager.putBytes(
blockId,
new ChunkedByteBuffer(serializedDirectResult.duplicate()),
StorageLevel.MEMORY_AND_DISK_SER)
logInfo(
s"Finished $taskName (TID $taskId). $resultSize bytes result sent via BlockManager)")
ser.serialize(new IndirectTaskResult[Any](blockId, resultSize))
} else {
logInfo(s"Finished $taskName (TID $taskId). $resultSize bytes result sent to driver")
serializedDirectResult
}
}
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult)
} catch {
case t: TaskKilledException =>
logInfo(s"Executor killed $taskName (TID $taskId), reason: ${t.reason}")
val (accums, accUpdates) = collectAccumulatorsAndResetStatusOnFailure(taskStartTime)
val serializedTK = ser.serialize(TaskKilled(t.reason, accUpdates, accums))
execBackend.statusUpdate(taskId, TaskState.KILLED, serializedTK)
case _: InterruptedException | NonFatal(_) if
task != null && task.reasonIfKilled.isDefined =>
val killReason = task.reasonIfKilled.getOrElse("unknown reason")
logInfo(s"Executor interrupted and killed $taskName (TID $taskId), reason: $killReason")
val (accums, accUpdates) = collectAccumulatorsAndResetStatusOnFailure(taskStartTime)
val serializedTK = ser.serialize(TaskKilled(killReason, accUpdates, accums))
execBackend.statusUpdate(taskId, TaskState.KILLED, serializedTK)
case t: Throwable if hasFetchFailure && !Utils.isFatalError(t) =>
val reason = task.context.fetchFailed.get.toTaskFailedReason
if (!t.isInstanceOf[FetchFailedException]) {
// there was a fetch failure in the task, but some user code wrapped that exception
// and threw something else. Regardless, we treat it as a fetch failure.
val fetchFailedCls = classOf[FetchFailedException].getName
logWarning(s"TID ${taskId} encountered a ${fetchFailedCls} and " +
s"failed, but the ${fetchFailedCls} was hidden by another " +
s"exception. Spark is handling this like a fetch failure and ignoring the " +
s"other exception: $t")
}
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))
case CausedBy(cDE: CommitDeniedException) =>
val reason = cDE.toTaskCommitDeniedReason
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(reason))
case t: Throwable =>
// Attempt to exit cleanly by informing the driver of our failure.
// If anything goes wrong (or this was a fatal exception), we will delegate to
// the default uncaught exception handler, which will terminate the Executor.
logError(s"Exception in $taskName (TID $taskId)", t)
// SPARK-20904: Do not report failure to driver if if happened during shut down. Because
// libraries may set up shutdown hooks that race with running tasks during shutdown,
// spurious failures may occur and can result in improper accounting in the driver (e.g.
// the task failure would not be ignored if the shutdown happened because of premption,
// instead of an app issue).
if (!ShutdownHookManager.inShutdown()) {
val (accums, accUpdates) = collectAccumulatorsAndResetStatusOnFailure(taskStartTime)
val serializedTaskEndReason = {
try {
ser.serialize(new ExceptionFailure(t, accUpdates).withAccums(accums))
} catch {
case _: NotSerializableException =>
// t is not serializable so just send the stacktrace
ser.serialize(new ExceptionFailure(t, accUpdates, false).withAccums(accums))
}
}
setTaskFinishedAndClearInterruptStatus()
execBackend.statusUpdate(taskId, TaskState.FAILED, serializedTaskEndReason)
} else {
logInfo("Not reporting error to driver during JVM shutdown.")
}
// Don't forcibly exit unless the exception was inherently fatal, to avoid
// stopping other tasks unnecessarily.
if (!t.isInstanceOf[SparkOutOfMemoryError] && Utils.isFatalError(t)) {
uncaughtExceptionHandler.uncaughtException(Thread.currentThread(), t)
}
} finally {
runningTasks.remove(taskId)
}
}
private def hasFetchFailure: Boolean = {
task != null && task.context != null && task.context.fetchFailed.isDefined
}
}
/**
* Supervises the killing / cancellation of a task by sending the interrupted flag, optionally
* sending a Thread.interrupt(), and monitoring the task until it finishes.
*
* Spark's current task cancellation / task killing mechanism is "best effort" because some tasks
* may not be interruptable or may not respond to their "killed" flags being set. If a significant
* fraction of a cluster's task slots are occupied by tasks that have been marked as killed but
* remain running then this can lead to a situation where new jobs and tasks are starved of
* resources that are being used by these zombie tasks.
*
* The TaskReaper was introduced in SPARK-18761 as a mechanism to monitor and clean up zombie
* tasks. For backwards-compatibility / backportability this component is disabled by default
* and must be explicitly enabled by setting `spark.task.reaper.enabled=true`.
*
* A TaskReaper is created for a particular task when that task is killed / cancelled. Typically
* a task will have only one TaskReaper, but it's possible for a task to have up to two reapers
* in case kill is called twice with different values for the `interrupt` parameter.
*
* Once created, a TaskReaper will run until its supervised task has finished running. If the
* TaskReaper has not been configured to kill the JVM after a timeout (i.e. if
* `spark.task.reaper.killTimeout < 0`) then this implies that the TaskReaper may run indefinitely
* if the supervised task never exits.
*/
private class TaskReaper(
taskRunner: TaskRunner,
val interruptThread: Boolean,
val reason: String)
extends Runnable {
private[this] val taskId: Long = taskRunner.taskId
private[this] val killPollingIntervalMs: Long =
conf.getTimeAsMs("spark.task.reaper.pollingInterval", "10s")
private[this] val killTimeoutMs: Long = conf.getTimeAsMs("spark.task.reaper.killTimeout", "-1")
private[this] val takeThreadDump: Boolean =
conf.getBoolean("spark.task.reaper.threadDump", true)
override def run(): Unit = {
val startTimeMs = System.currentTimeMillis()
def elapsedTimeMs = System.currentTimeMillis() - startTimeMs
def timeoutExceeded(): Boolean = killTimeoutMs > 0 && elapsedTimeMs > killTimeoutMs
try {
// Only attempt to kill the task once. If interruptThread = false then a second kill
// attempt would be a no-op and if interruptThread = true then it may not be safe or
// effective to interrupt multiple times:
taskRunner.kill(interruptThread = interruptThread, reason = reason)
// Monitor the killed task until it exits. The synchronization logic here is complicated
// because we don't want to synchronize on the taskRunner while possibly taking a thread
// dump, but we also need to be careful to avoid races between checking whether the task
// has finished and wait()ing for it to finish.
var finished: Boolean = false
while (!finished && !timeoutExceeded()) {
taskRunner.synchronized {
// We need to synchronize on the TaskRunner while checking whether the task has
// finished in order to avoid a race where the task is marked as finished right after
// we check and before we call wait().
if (taskRunner.isFinished) {
finished = true
} else {
taskRunner.wait(killPollingIntervalMs)
}
}
if (taskRunner.isFinished) {
finished = true
} else {
logWarning(s"Killed task $taskId is still running after $elapsedTimeMs ms")
if (takeThreadDump) {
try {
Utils.getThreadDumpForThread(taskRunner.getThreadId).foreach { thread =>
if (thread.threadName == taskRunner.threadName) {
logWarning(s"Thread dump from task $taskId:\n${thread.stackTrace}")
}
}
} catch {
case NonFatal(e) =>
logWarning("Exception thrown while obtaining thread dump: ", e)
}
}
}
}
if (!taskRunner.isFinished && timeoutExceeded()) {
if (isLocal) {
logError(s"Killed task $taskId could not be stopped within $killTimeoutMs ms; " +
"not killing JVM because we are running in local mode.")
} else {
// In non-local-mode, the exception thrown here will bubble up to the uncaught exception
// handler and cause the executor JVM to exit.
throw new SparkException(
s"Killing executor JVM because killed task $taskId could not be stopped within " +
s"$killTimeoutMs ms.")
}
}
} finally {
// Clean up entries in the taskReaperForTask map.
taskReaperForTask.synchronized {
taskReaperForTask.get(taskId).foreach { taskReaperInMap =>
if (taskReaperInMap eq this) {
taskReaperForTask.remove(taskId)
} else {
// This must have been a TaskReaper where interruptThread == false where a subsequent
// killTask() call for the same task had interruptThread == true and overwrote the
// map entry.
}
}
}
}
}
}
/**
* Create a ClassLoader for use in tasks, adding any JARs specified by the user or any classes
* created by the interpreter to the search path
*/
private def createClassLoader(): MutableURLClassLoader = {
// Bootstrap the list of jars with the user class path.
val now = System.currentTimeMillis()
userClassPath.foreach { url =>
currentJars(url.getPath().split("/").last) = now
}
val currentLoader = Utils.getContextOrSparkClassLoader
// For each of the jars in the jarSet, add them to the class loader.
// We assume each of the files has already been fetched.
val urls = userClassPath.toArray ++ currentJars.keySet.map { uri =>
new File(uri.split("/").last).toURI.toURL
}
if (userClassPathFirst) {
new ChildFirstURLClassLoader(urls, currentLoader)
} else {
new MutableURLClassLoader(urls, currentLoader)
}
}
/**
* If the REPL is in use, add another ClassLoader that will read
* new classes defined by the REPL as the user types code
*/
private def addReplClassLoaderIfNeeded(parent: ClassLoader): ClassLoader = {
val classUri = conf.get("spark.repl.class.uri", null)
if (classUri != null) {
logInfo("Using REPL class URI: " + classUri)
try {
val _userClassPathFirst: java.lang.Boolean = userClassPathFirst
val klass = Utils.classForName("org.apache.spark.repl.ExecutorClassLoader")
.asInstanceOf[Class[_ <: ClassLoader]]
val constructor = klass.getConstructor(classOf[SparkConf], classOf[SparkEnv],
classOf[String], classOf[ClassLoader], classOf[Boolean])
constructor.newInstance(conf, env, classUri, parent, _userClassPathFirst)
} catch {
case _: ClassNotFoundException =>
logError("Could not find org.apache.spark.repl.ExecutorClassLoader on classpath!")
System.exit(1)
null
}
} else {
parent
}
}
/**
* Download any missing dependencies if we receive a new set of files and JARs from the
* SparkContext. Also adds any new JARs we fetched to the class loader.
*/
private def updateDependencies(newFiles: Map[String, Long], newJars: Map[String, Long]) {
lazy val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf)
synchronized {
// Fetch missing dependencies
for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) {
logInfo("Fetching " + name + " with timestamp " + timestamp)
// Fetch file with useCache mode, close cache for local mode.
Utils.fetchFile(name, new File(SparkFiles.getRootDirectory()), conf,
env.securityManager, hadoopConf, timestamp, useCache = !isLocal)
currentFiles(name) = timestamp
}
for ((name, timestamp) <- newJars) {
val localName = new URI(name).getPath.split("/").last
val currentTimeStamp = currentJars.get(name)
.orElse(currentJars.get(localName))
.getOrElse(-1L)
if (currentTimeStamp < timestamp) {
logInfo("Fetching " + name + " with timestamp " + timestamp)
// Fetch file with useCache mode, close cache for local mode.
Utils.fetchFile(name, new File(SparkFiles.getRootDirectory()), conf,
env.securityManager, hadoopConf, timestamp, useCache = !isLocal)
currentJars(name) = timestamp
// Add it to our class loader
val url = new File(SparkFiles.getRootDirectory(), localName).toURI.toURL
if (!urlClassLoader.getURLs().contains(url)) {
logInfo("Adding " + url + " to class loader")
urlClassLoader.addURL(url)
}
}
}
}
}
/** Reports heartbeat and metrics for active tasks to the driver. */
private def reportHeartBeat(): Unit = {
// list of (task id, accumUpdates) to send back to the driver
val accumUpdates = new ArrayBuffer[(Long, Seq[AccumulatorV2[_, _]])]()
val curGCTime = computeTotalGcTime()
for (taskRunner <- runningTasks.values().asScala) {
if (taskRunner.task != null) {
taskRunner.task.metrics.mergeShuffleReadMetrics()
taskRunner.task.metrics.setJvmGCTime(curGCTime - taskRunner.startGCTime)
accumUpdates += ((taskRunner.taskId, taskRunner.task.metrics.accumulators()))
}
}
val message = Heartbeat(executorId, accumUpdates.toArray, env.blockManager.blockManagerId)
val heartbeatIntervalInSec =
conf.getTimeAsMs("spark.executor.heartbeatInterval", "10s").millis.toSeconds.seconds
try {
val response = heartbeatReceiverRef.askSync[HeartbeatResponse](
message, new RpcTimeout(heartbeatIntervalInSec, "spark.executor.heartbeatInterval"))
if (response.reregisterBlockManager) {
logInfo("Told to re-register on heartbeat")
env.blockManager.reregister()
}
heartbeatFailures = 0
} catch {
case NonFatal(e) =>
logWarning("Issue communicating with driver in heartbeater", e)
heartbeatFailures += 1
if (heartbeatFailures >= HEARTBEAT_MAX_FAILURES) {
logError(s"Exit as unable to send heartbeats to driver " +
s"more than $HEARTBEAT_MAX_FAILURES times")
System.exit(ExecutorExitCode.HEARTBEAT_FAILURE)
}
}
}
/**
* Schedules a task to report heartbeat and partial metrics for active tasks to driver.
*/
private def startDriverHeartbeater(): Unit = {
val intervalMs = conf.getTimeAsMs("spark.executor.heartbeatInterval", "10s")
// Wait a random interval so the heartbeats don't end up in sync
val initialDelay = intervalMs + (math.random * intervalMs).asInstanceOf[Int]
val heartbeatTask = new Runnable() {
override def run(): Unit = Utils.logUncaughtExceptions(reportHeartBeat())
}
heartbeater.scheduleAtFixedRate(heartbeatTask, initialDelay, intervalMs, TimeUnit.MILLISECONDS)
}
}
private[spark] object Executor {
// This is reserved for internal use by components that need to read task properties before a
// task is fully deserialized. When possible, the TaskContext.getLocalProperty call should be
// used instead.
val taskDeserializationProps: ThreadLocal[Properties] = new ThreadLocal[Properties]
}
