spark ExecutorMonitor 源码
spark ExecutorMonitor 代码
文件路径：/core/src/main/scala/org/apache/spark/scheduler/dynalloc/ExecutorMonitor.scala
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* 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
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*
* http://www.apache.org/licenses/LICENSE-2.0
*
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* distributed under the License is distributed on an "AS IS" BASIS,
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package org.apache.spark.scheduler.dynalloc
import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
import java.util.concurrent.atomic.AtomicLong
import scala.collection.JavaConverters._
import scala.collection.mutable
import org.apache.spark._
import org.apache.spark.errors.SparkCoreErrors
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config._
import org.apache.spark.resource.ResourceProfile.UNKNOWN_RESOURCE_PROFILE_ID
import org.apache.spark.scheduler._
import org.apache.spark.storage.{RDDBlockId, ShuffleDataBlockId}
import org.apache.spark.util.Clock
/**
* A monitor for executor activity, used by ExecutorAllocationManager to detect idle executors.
*/
private[spark] class ExecutorMonitor(
conf: SparkConf,
client: ExecutorAllocationClient,
listenerBus: LiveListenerBus,
clock: Clock,
metrics: ExecutorAllocationManagerSource = null)
extends SparkListener with CleanerListener with Logging {
private val idleTimeoutNs = TimeUnit.SECONDS.toNanos(
conf.get(DYN_ALLOCATION_EXECUTOR_IDLE_TIMEOUT))
private val storageTimeoutNs = TimeUnit.SECONDS.toNanos(
conf.get(DYN_ALLOCATION_CACHED_EXECUTOR_IDLE_TIMEOUT))
private val shuffleTimeoutNs = TimeUnit.MILLISECONDS.toNanos(
conf.get(DYN_ALLOCATION_SHUFFLE_TRACKING_TIMEOUT))
private val fetchFromShuffleSvcEnabled = conf.get(SHUFFLE_SERVICE_ENABLED) &&
conf.get(SHUFFLE_SERVICE_FETCH_RDD_ENABLED)
private val shuffleTrackingEnabled = !conf.get(SHUFFLE_SERVICE_ENABLED) &&
conf.get(DYN_ALLOCATION_SHUFFLE_TRACKING_ENABLED)
private val executors = new ConcurrentHashMap[String, Tracker]()
private val execResourceProfileCount = new ConcurrentHashMap[Int, Int]()
// The following fields are an optimization to avoid having to scan all executors on every EAM
// schedule interval to find out which ones are timed out. They keep track of when the next
// executor timeout is expected to happen, and the current list of timed out executors. There's
// also a flag that forces the EAM task to recompute the timed out executors, in case some event
// arrives on the listener bus that may cause the current list of timed out executors to change.
//
// There's also per-executor state used for this purpose, so that recomputations can be triggered
// only when really necessary.
//
// Note that this isn't meant to, and cannot, always make the right decision about which executors
// are indeed timed out. For example, the EAM thread may detect a timed out executor while a new
// "task start" event has just been posted to the listener bus and hasn't yet been delivered to
// this listener. There are safeguards in other parts of the code that would prevent that executor
// from being removed.
private val nextTimeout = new AtomicLong(Long.MaxValue)
private var timedOutExecs = Seq.empty[(String, Int)]
// Active job tracking.
//
// The following state is used when an external shuffle service is not in use, and allows Spark
// to scale down based on whether the shuffle data stored in executors is in use.
//
// The algorithm works as following: when jobs start, some state is kept that tracks which stages
// are part of that job, and which shuffle ID is attached to those stages. As tasks finish, the
// executor tracking code is updated to include the list of shuffles for which it's storing
// shuffle data.
//
// If executors hold shuffle data that is related to an active job, then the executor is
// considered to be in "shuffle busy" state; meaning that the executor is not allowed to be
// removed. If the executor has shuffle data but it doesn't relate to any active job, then it
// may be removed when idle, following the shuffle-specific timeout configuration.
//
// The following fields are not thread-safe and should be only used from the event thread.
private val shuffleToActiveJobs = new mutable.HashMap[Int, mutable.ArrayBuffer[Int]]()
private val stageToShuffleID = new mutable.HashMap[Int, Int]()
private val jobToStageIDs = new mutable.HashMap[Int, Seq[Int]]()
def reset(): Unit = {
executors.clear()
execResourceProfileCount.clear()
nextTimeout.set(Long.MaxValue)
timedOutExecs = Nil
}
/**
* Returns the list of executors and their ResourceProfile id that are currently considered to
* be timed out. Should only be called from the EAM thread.
*/
def timedOutExecutors(): Seq[(String, Int)] = {
val now = clock.nanoTime()
if (now >= nextTimeout.get()) {
// Temporarily set the next timeout at Long.MaxValue. This ensures that after
// scanning all executors below, we know when the next timeout for non-timed out
// executors is (whether that update came from the scan, or from a new event
// arriving in a different thread).
nextTimeout.set(Long.MaxValue)
var newNextTimeout = Long.MaxValue
timedOutExecs = executors.asScala
.filter { case (_, exec) =>
!exec.pendingRemoval && !exec.hasActiveShuffle && !exec.decommissioning}
.filter { case (_, exec) =>
val deadline = exec.timeoutAt
if (deadline > now) {
newNextTimeout = math.min(newNextTimeout, deadline)
exec.timedOut = false
false
} else {
exec.timedOut = true
true
}
}
.map { case (name, exec) => (name, exec.resourceProfileId)}
.toSeq
updateNextTimeout(newNextTimeout)
}
timedOutExecs.sortBy(_._1)
}
/**
* Mark the given executors as pending to be removed. Should only be called in the EAM thread.
* This covers both kills and decommissions.
*/
def executorsKilled(ids: Seq[String]): Unit = {
ids.foreach { id =>
val tracker = executors.get(id)
if (tracker != null) {
tracker.pendingRemoval = true
}
}
// Recompute timed out executors in the next EAM callback, since this call invalidates
// the current list.
nextTimeout.set(Long.MinValue)
}
private[spark] def executorsDecommissioned(ids: Seq[String]): Unit = {
ids.foreach { id =>
val tracker = executors.get(id)
if (tracker != null) {
tracker.decommissioning = true
}
}
// Recompute timed out executors in the next EAM callback, since this call invalidates
// the current list.
nextTimeout.set(Long.MinValue)
}
def executorCount: Int = executors.size()
def executorCountWithResourceProfile(id: Int): Int = {
execResourceProfileCount.getOrDefault(id, 0)
}
// for testing
def getResourceProfileId(executorId: String): Int = {
val execTrackingInfo = executors.get(executorId)
if (execTrackingInfo != null) {
execTrackingInfo.resourceProfileId
} else {
UNKNOWN_RESOURCE_PROFILE_ID
}
}
def pendingRemovalCount: Int = executors.asScala.count { case (_, exec) => exec.pendingRemoval }
def pendingRemovalCountPerResourceProfileId(id: Int): Int = {
executors.asScala.count { case (k, v) => v.resourceProfileId == id && v.pendingRemoval }
}
def decommissioningCount: Int = executors.asScala.count { case (_, exec) =>
exec.decommissioning
}
def decommissioningPerResourceProfileId(id: Int): Int = {
executors.asScala.count { case (k, v) =>
v.resourceProfileId == id && v.decommissioning
}
}
override def onJobStart(event: SparkListenerJobStart): Unit = {
if (!shuffleTrackingEnabled) {
return
}
val shuffleStages = event.stageInfos.flatMap { s =>
s.shuffleDepId.toSeq.map { shuffleId =>
s.stageId -> shuffleId
}
}
var updateExecutors = false
shuffleStages.foreach { case (stageId, shuffle) =>
val jobIDs = shuffleToActiveJobs.get(shuffle) match {
case Some(jobs) =>
// If a shuffle is being re-used, we need to re-scan the executors and update their
// tracker with the information that the shuffle data they're storing is in use.
logDebug(s"Reusing shuffle $shuffle in job ${event.jobId}.")
updateExecutors = true
jobs
case _ =>
logDebug(s"Registered new shuffle $shuffle (from stage $stageId).")
val jobs = new mutable.ArrayBuffer[Int]()
shuffleToActiveJobs(shuffle) = jobs
jobs
}
jobIDs += event.jobId
}
if (updateExecutors) {
val activeShuffleIds = shuffleStages.map(_._2)
var needTimeoutUpdate = false
val activatedExecs = new ExecutorIdCollector()
executors.asScala.foreach { case (id, exec) =>
if (!exec.hasActiveShuffle) {
exec.updateActiveShuffles(activeShuffleIds)
if (exec.hasActiveShuffle) {
needTimeoutUpdate = true
activatedExecs.add(id)
}
}
}
if (activatedExecs.nonEmpty) {
logDebug(s"Activated executors $activatedExecs due to shuffle data needed by new job" +
s"${event.jobId}.")
}
if (needTimeoutUpdate) {
nextTimeout.set(Long.MinValue)
}
}
stageToShuffleID ++= shuffleStages
jobToStageIDs(event.jobId) = shuffleStages.map(_._1)
}
override def onJobEnd(event: SparkListenerJobEnd): Unit = {
if (!shuffleTrackingEnabled) {
return
}
var updateExecutors = false
val activeShuffles = new mutable.ArrayBuffer[Int]()
shuffleToActiveJobs.foreach { case (shuffleId, jobs) =>
jobs -= event.jobId
if (jobs.nonEmpty) {
activeShuffles += shuffleId
} else {
// If a shuffle went idle we need to update all executors to make sure they're correctly
// tracking active shuffles.
updateExecutors = true
}
}
if (updateExecutors) {
if (log.isDebugEnabled()) {
if (activeShuffles.nonEmpty) {
logDebug(
s"Job ${event.jobId} ended, shuffles ${activeShuffles.mkString(",")} still active.")
} else {
logDebug(s"Job ${event.jobId} ended, no active shuffles remain.")
}
}
val deactivatedExecs = new ExecutorIdCollector()
executors.asScala.foreach { case (id, exec) =>
if (exec.hasActiveShuffle) {
exec.updateActiveShuffles(activeShuffles)
if (!exec.hasActiveShuffle) {
deactivatedExecs.add(id)
}
}
}
if (deactivatedExecs.nonEmpty) {
logDebug(s"Executors $deactivatedExecs do not have active shuffle data after job " +
s"${event.jobId} finished.")
}
}
jobToStageIDs.remove(event.jobId).foreach { stages =>
stages.foreach { id => stageToShuffleID -= id }
}
}
override def onTaskStart(event: SparkListenerTaskStart): Unit = {
val executorId = event.taskInfo.executorId
// Guard against a late arriving task start event (SPARK-26927).
if (client.isExecutorActive(executorId)) {
val exec = ensureExecutorIsTracked(executorId, UNKNOWN_RESOURCE_PROFILE_ID)
exec.updateRunningTasks(1)
}
}
override def onTaskEnd(event: SparkListenerTaskEnd): Unit = {
val executorId = event.taskInfo.executorId
val exec = executors.get(executorId)
if (exec != null) {
// If the task succeeded and the stage generates shuffle data, record that this executor
// holds data for the shuffle. This code will track all executors that generate shuffle
// for the stage, even if speculative tasks generate duplicate shuffle data and end up
// being ignored by the map output tracker.
//
// This means that an executor may be marked as having shuffle data, and thus prevented
// from being removed, even though the data may not be used.
// TODO: Only track used files (SPARK-31974)
if (shuffleTrackingEnabled && event.reason == Success) {
stageToShuffleID.get(event.stageId).foreach { shuffleId =>
exec.addShuffle(shuffleId)
}
}
// Update the number of running tasks after checking for shuffle data, so that the shuffle
// information is up-to-date in case the executor is going idle.
exec.updateRunningTasks(-1)
}
}
override def onExecutorAdded(event: SparkListenerExecutorAdded): Unit = {
val exec = ensureExecutorIsTracked(event.executorId, event.executorInfo.resourceProfileId)
exec.updateRunningTasks(0)
logInfo(s"New executor ${event.executorId} has registered (new total is ${executors.size()})")
}
private def decrementExecResourceProfileCount(rpId: Int): Unit = {
val count = execResourceProfileCount.getOrDefault(rpId, 0)
execResourceProfileCount.replace(rpId, count, count - 1)
execResourceProfileCount.remove(rpId, 0)
}
override def onExecutorRemoved(event: SparkListenerExecutorRemoved): Unit = {
val removed = executors.remove(event.executorId)
if (removed != null) {
decrementExecResourceProfileCount(removed.resourceProfileId)
if (event.reason == ExecutorLossMessage.decommissionFinished ||
(event.reason != null && event.reason.startsWith(ExecutorDecommission.msgPrefix))) {
metrics.gracefullyDecommissioned.inc()
} else if (removed.decommissioning) {
metrics.decommissionUnfinished.inc()
} else if (removed.pendingRemoval) {
metrics.driverKilled.inc()
} else {
metrics.exitedUnexpectedly.inc()
}
logInfo(s"Executor ${event.executorId} is removed. Remove reason statistics: (" +
s"gracefully decommissioned: ${metrics.gracefullyDecommissioned.getCount()}, " +
s"decommision unfinished: ${metrics.decommissionUnfinished.getCount()}, " +
s"driver killed: ${metrics.driverKilled.getCount()}, " +
s"unexpectedly exited: ${metrics.exitedUnexpectedly.getCount()}).")
if (!removed.pendingRemoval || !removed.decommissioning) {
nextTimeout.set(Long.MinValue)
}
}
}
override def onBlockUpdated(event: SparkListenerBlockUpdated): Unit = {
if (!client.isExecutorActive(event.blockUpdatedInfo.blockManagerId.executorId)) {
return
}
val exec = ensureExecutorIsTracked(event.blockUpdatedInfo.blockManagerId.executorId,
UNKNOWN_RESOURCE_PROFILE_ID)
// Check if it is a shuffle file, or RDD to pick the correct codepath for update
if (!event.blockUpdatedInfo.blockId.isInstanceOf[RDDBlockId]) {
if (event.blockUpdatedInfo.blockId.isInstanceOf[ShuffleDataBlockId] &&
shuffleTrackingEnabled) {
/**
* The executor monitor keeps track of locations of cache and shuffle blocks and this can
* be used to decide which executor(s) Spark should shutdown first. Since we move shuffle
* blocks around now this wires it up so that it keeps track of it. We only do this for
* data blocks as index and other blocks blocks do not necessarily mean the entire block
* has been committed.
*/
event.blockUpdatedInfo.blockId match {
case ShuffleDataBlockId(shuffleId, _, _) => exec.addShuffle(shuffleId)
case _ => // For now we only update on data blocks
}
}
return
}
val storageLevel = event.blockUpdatedInfo.storageLevel
val blockId = event.blockUpdatedInfo.blockId.asInstanceOf[RDDBlockId]
// SPARK-27677. When a block can be fetched from the external shuffle service, the executor can
// be removed without hurting the application too much, since the cached data is still
// available. So don't count blocks that can be served by the external service.
if (storageLevel.isValid && (!fetchFromShuffleSvcEnabled || !storageLevel.useDisk)) {
val hadCachedBlocks = exec.cachedBlocks.nonEmpty
val blocks = exec.cachedBlocks.getOrElseUpdate(blockId.rddId,
new mutable.BitSet(blockId.splitIndex))
blocks += blockId.splitIndex
if (!hadCachedBlocks) {
exec.updateTimeout()
}
} else {
exec.cachedBlocks.get(blockId.rddId).foreach { blocks =>
blocks -= blockId.splitIndex
if (blocks.isEmpty) {
exec.cachedBlocks -= blockId.rddId
if (exec.cachedBlocks.isEmpty) {
exec.updateTimeout()
}
}
}
}
}
override def onUnpersistRDD(event: SparkListenerUnpersistRDD): Unit = {
executors.values().asScala.foreach { exec =>
exec.cachedBlocks -= event.rddId
if (exec.cachedBlocks.isEmpty) {
exec.updateTimeout()
}
}
}
override def onOtherEvent(event: SparkListenerEvent): Unit = event match {
case ShuffleCleanedEvent(id) => cleanupShuffle(id)
case _ =>
}
override def rddCleaned(rddId: Int): Unit = { }
override def shuffleCleaned(shuffleId: Int): Unit = {
// Only post the event if tracking is enabled
if (shuffleTrackingEnabled) {
// Because this is called in a completely separate thread, we post a custom event to the
// listener bus so that the internal state is safely updated.
listenerBus.post(ShuffleCleanedEvent(shuffleId))
}
}
override def broadcastCleaned(broadcastId: Long): Unit = { }
override def accumCleaned(accId: Long): Unit = { }
override def checkpointCleaned(rddId: Long): Unit = { }
// Visible for testing.
private[scheduler] def isExecutorIdle(id: String): Boolean = {
Option(executors.get(id)).map(_.isIdle).getOrElse(throw SparkCoreErrors.noExecutorIdleError(id))
}
// Visible for testing
private[dynalloc] def timedOutExecutors(when: Long): Seq[String] = {
executors.asScala.flatMap { case (id, tracker) =>
if (tracker.isIdle && tracker.timeoutAt <= when) Some(id) else None
}.toSeq
}
// Visible for testing
private[spark] def executorsPendingToRemove(): Set[String] = {
executors.asScala.filter { case (_, exec) => exec.pendingRemoval }.keys.toSet
}
// Visible for testing
private[spark] def executorsDecommissioning(): Set[String] = {
executors.asScala.filter { case (_, exec) => exec.decommissioning }.keys.toSet
}
/**
* This method should be used when updating executor state. It guards against a race condition in
* which the `SparkListenerTaskStart` event is posted before the `SparkListenerBlockManagerAdded`
* event, which is possible because these events are posted in different threads. (see SPARK-4951)
*/
// Visible for testing.
private[scheduler] def ensureExecutorIsTracked(
id: String, resourceProfileId: Int) : Tracker = {
val numExecsWithRpId = execResourceProfileCount.computeIfAbsent(resourceProfileId, _ => 0)
val execTracker = executors.computeIfAbsent(id, _ => {
val newcount = numExecsWithRpId + 1
execResourceProfileCount.put(resourceProfileId, newcount)
logDebug(s"Executor added with ResourceProfile id: $resourceProfileId " +
s"count is now $newcount")
new Tracker(resourceProfileId)
})
// if we had added executor before without knowing the resource profile id, fix it up
if (execTracker.resourceProfileId == UNKNOWN_RESOURCE_PROFILE_ID &&
resourceProfileId != UNKNOWN_RESOURCE_PROFILE_ID) {
logDebug(s"Executor: $id, resource profile id was unknown, setting " +
s"it to $resourceProfileId")
execTracker.resourceProfileId = resourceProfileId
// fix up the counts for each resource profile id
execResourceProfileCount.put(resourceProfileId, numExecsWithRpId + 1)
decrementExecResourceProfileCount(UNKNOWN_RESOURCE_PROFILE_ID)
}
execTracker
}
private def updateNextTimeout(newValue: Long): Unit = {
while (true) {
val current = nextTimeout.get()
if (newValue >= current || nextTimeout.compareAndSet(current, newValue)) {
return
}
}
}
private def cleanupShuffle(id: Int): Unit = {
logDebug(s"Cleaning up state related to shuffle $id.")
shuffleToActiveJobs -= id
executors.asScala.foreach { case (_, exec) =>
exec.removeShuffle(id)
}
}
private[scheduler] class Tracker(var resourceProfileId: Int) {
@volatile var timeoutAt: Long = Long.MaxValue
// Tracks whether this executor is thought to be timed out. It's used to detect when the list
// of timed out executors needs to be updated due to the executor's state changing.
@volatile var timedOut: Boolean = false
var pendingRemoval: Boolean = false
var decommissioning: Boolean = false
var hasActiveShuffle: Boolean = false
private var idleStart: Long = -1
private var runningTasks: Int = 0
// Maps RDD IDs to the partition IDs stored in the executor.
// This should only be used in the event thread.
val cachedBlocks = new mutable.HashMap[Int, mutable.BitSet]()
// The set of shuffles for which shuffle data is held by the executor.
// This should only be used in the event thread.
private val shuffleIds = if (shuffleTrackingEnabled) new mutable.HashSet[Int]() else null
def isIdle: Boolean = idleStart >= 0 && !hasActiveShuffle
def updateRunningTasks(delta: Int): Unit = {
runningTasks = math.max(0, runningTasks + delta)
idleStart = if (runningTasks == 0) clock.nanoTime() else -1L
updateTimeout()
}
def updateTimeout(): Unit = {
val oldDeadline = timeoutAt
val newDeadline = if (idleStart >= 0) {
val timeout = if (cachedBlocks.nonEmpty || (shuffleIds != null && shuffleIds.nonEmpty)) {
val _cacheTimeout = if (cachedBlocks.nonEmpty) storageTimeoutNs else Long.MaxValue
val _shuffleTimeout = if (shuffleIds != null && shuffleIds.nonEmpty) {
shuffleTimeoutNs
} else {
Long.MaxValue
}
math.min(_cacheTimeout, _shuffleTimeout)
} else {
idleTimeoutNs
}
val deadline = idleStart + timeout
if (deadline >= 0) deadline else Long.MaxValue
} else {
Long.MaxValue
}
timeoutAt = newDeadline
// If the executor was thought to be timed out, but the new deadline is later than the
// old one, ask the EAM thread to update the list of timed out executors.
if (newDeadline > oldDeadline && timedOut) {
nextTimeout.set(Long.MinValue)
} else {
updateNextTimeout(newDeadline)
}
}
def addShuffle(id: Int): Unit = {
if (shuffleIds.add(id)) {
hasActiveShuffle = true
}
}
def removeShuffle(id: Int): Unit = {
if (shuffleIds.remove(id) && shuffleIds.isEmpty) {
hasActiveShuffle = false
if (isIdle) {
updateTimeout()
}
}
}
def updateActiveShuffles(ids: Iterable[Int]): Unit = {
val hadActiveShuffle = hasActiveShuffle
hasActiveShuffle = ids.exists(shuffleIds.contains)
if (hadActiveShuffle && isIdle) {
updateTimeout()
}
}
}
private case class ShuffleCleanedEvent(id: Int) extends SparkListenerEvent {
override protected[spark] def logEvent: Boolean = false
}
/** Used to collect executor IDs for debug messages (and avoid too long messages). */
private class ExecutorIdCollector {
private val ids = if (log.isDebugEnabled) new mutable.ArrayBuffer[String]() else null
private var excess = 0
def add(id: String): Unit = if (log.isDebugEnabled) {
if (ids.size < 10) {
ids += id
} else {
excess += 1
}
}
def nonEmpty: Boolean = ids != null && ids.nonEmpty
override def toString(): String = {
ids.mkString(",") + (if (excess > 0) s" (and $excess more)" else "")
}
}
}
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