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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.scheduler.cluster
import java.util.concurrent.TimeUnit
import java.util.concurrent.atomic.AtomicInteger
import javax.annotation.concurrent.GuardedBy
import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
import scala.concurrent.Future
import org.apache.spark.{ExecutorAllocationClient, SparkEnv, SparkException, TaskState}
import org.apache.spark.internal.Logging
import org.apache.spark.rpc._
import org.apache.spark.scheduler._
import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.ENDPOINT_NAME
import org.apache.spark.util.{RpcUtils, SerializableBuffer, ThreadUtils, Utils}
/**
* A scheduler backend that waits for coarse-grained executors to connect.
* This backend holds onto each executor for the duration of the Spark job rather than relinquishing
* executors whenever a task is done and asking the scheduler to launch a new executor for
* each new task. Executors may be launched in a variety of ways, such as Mesos tasks for the
* coarse-grained Mesos mode or standalone processes for Spark's standalone deploy mode
* (spark.deploy.*).
*/
private[spark]
class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: RpcEnv)
extends ExecutorAllocationClient with SchedulerBackend with Logging {
// Use an atomic variable to track total number of cores in the cluster for simplicity and speed
protected val totalCoreCount = new AtomicInteger(0)
// Total number of executors that are currently registered
protected val totalRegisteredExecutors = new AtomicInteger(0)
protected val conf = scheduler.sc.conf
private val maxRpcMessageSize = RpcUtils.maxMessageSizeBytes(conf)
private val defaultAskTimeout = RpcUtils.askRpcTimeout(conf)
// Submit tasks only after (registered resources / total expected resources)
// is equal to at least this value, that is double between 0 and 1.
private val _minRegisteredRatio =
math.min(1, conf.getDouble("spark.scheduler.minRegisteredResourcesRatio", 0))
// Submit tasks after maxRegisteredWaitingTime milliseconds
// if minRegisteredRatio has not yet been reached
private val maxRegisteredWaitingTimeMs =
conf.getTimeAsMs("spark.scheduler.maxRegisteredResourcesWaitingTime", "30s")
private val createTime = System.currentTimeMillis()
// Accessing `executorDataMap` in `DriverEndpoint.receive/receiveAndReply` doesn't need any
// protection. But accessing `executorDataMap` out of `DriverEndpoint.receive/receiveAndReply`
// must be protected by `CoarseGrainedSchedulerBackend.this`. Besides, `executorDataMap` should
// only be modified in `DriverEndpoint.receive/receiveAndReply` with protection by
// `CoarseGrainedSchedulerBackend.this`.
private val executorDataMap = new HashMap[String, ExecutorData]
// Number of executors requested by the cluster manager, [[ExecutorAllocationManager]]
@GuardedBy("CoarseGrainedSchedulerBackend.this")
private var requestedTotalExecutors = 0
// Number of executors requested from the cluster manager that have not registered yet
@GuardedBy("CoarseGrainedSchedulerBackend.this")
private var numPendingExecutors = 0
private val listenerBus = scheduler.sc.listenerBus
// Executors we have requested the cluster manager to kill that have not died yet; maps
// the executor ID to whether it was explicitly killed by the driver (and thus shouldn't
// be considered an app-related failure).
@GuardedBy("CoarseGrainedSchedulerBackend.this")
private val executorsPendingToRemove = new HashMap[String, Boolean]
// A map to store hostname with its possible task number running on it
@GuardedBy("CoarseGrainedSchedulerBackend.this")
protected var hostToLocalTaskCount: Map[String, Int] = Map.empty
// The number of pending tasks which is locality required
@GuardedBy("CoarseGrainedSchedulerBackend.this")
protected var localityAwareTasks = 0
// The num of current max ExecutorId used to re-register appMaster
@volatile protected var currentExecutorIdCounter = 0
private val reviveThread =
ThreadUtils.newDaemonSingleThreadScheduledExecutor("driver-revive-thread")
class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)])
extends ThreadSafeRpcEndpoint with Logging {
// Executors that have been lost, but for which we don't yet know the real exit reason.
protected val executorsPendingLossReason = new HashSet[String]
protected val addressToExecutorId = new HashMap[RpcAddress, String]
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)
}
override def receive: PartialFunction[Any, Unit] = {
case StatusUpdate(executorId, taskId, state, data) =>
scheduler.statusUpdate(taskId, state, data.value)
if (TaskState.isFinished(state)) {
executorDataMap.get(executorId) match {
case Some(executorInfo) =>
executorInfo.freeCores += scheduler.CPUS_PER_TASK
makeOffers(executorId)
case None =>
// Ignoring the update since we don't know about the executor.
logWarning(s"Ignored task status update ($taskId state $state) " +
s"from unknown executor with ID $executorId")
}
}
case ReviveOffers =>
makeOffers()
case KillTask(taskId, executorId, interruptThread, reason) =>
executorDataMap.get(executorId) match {
case Some(executorInfo) =>
executorInfo.executorEndpoint.send(
KillTask(taskId, executorId, interruptThread, reason))
case None =>
// Ignoring the task kill since the executor is not registered.
logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.")
}
case KillExecutorsOnHost(host) =>
scheduler.getExecutorsAliveOnHost(host).foreach { exec =>
killExecutors(exec.toSeq, adjustTargetNumExecutors = false, countFailures = false,
force = true)
}
case UpdateDelegationTokens(newDelegationTokens) =>
executorDataMap.values.foreach { ed =>
ed.executorEndpoint.send(UpdateDelegationTokens(newDelegationTokens))
}
case RemoveExecutor(executorId, reason) =>
// We will remove the executor's state and cannot restore it. However, the connection
// between the driver and the executor may be still alive so that the executor won't exit
// automatically, so try to tell the executor to stop itself. See SPARK-13519.
executorDataMap.get(executorId).foreach(_.executorEndpoint.send(StopExecutor))
removeExecutor(executorId, reason)
}
override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
case RegisterExecutor(executorId, executorRef, hostname, cores, logUrls) =>
if (executorDataMap.contains(executorId)) {
executorRef.send(RegisterExecutorFailed("Duplicate executor ID: " + executorId))
context.reply(true)
} else if (scheduler.nodeBlacklist.contains(hostname)) {
// If the cluster manager gives us an executor on a blacklisted node (because it
// already started allocating those resources before we informed it of our blacklist,
// or if it ignored our blacklist), then we reject that executor immediately.
logInfo(s"Rejecting $executorId as it has been blacklisted.")
executorRef.send(RegisterExecutorFailed(s"Executor is blacklisted: $executorId"))
context.reply(true)
} else {
// If the executor's rpc env is not listening for incoming connections, `hostPort`
// will be null, and the client connection should be used to contact the executor.
val executorAddress = if (executorRef.address != null) {
executorRef.address
} else {
context.senderAddress
}
logInfo(s"Registered executor $executorRef ($executorAddress) with ID $executorId")
addressToExecutorId(executorAddress) = executorId
totalCoreCount.addAndGet(cores)
totalRegisteredExecutors.addAndGet(1)
val data = new ExecutorData(executorRef, executorAddress, hostname,
cores, cores, logUrls)
// This must be synchronized because variables mutated
// in this block are read when requesting executors
CoarseGrainedSchedulerBackend.this.synchronized {
executorDataMap.put(executorId, data)
if (currentExecutorIdCounter < executorId.toInt) {
currentExecutorIdCounter = executorId.toInt
}
if (numPendingExecutors > 0) {
numPendingExecutors -= 1
logDebug(s"Decremented number of pending executors ($numPendingExecutors left)")
}
}
executorRef.send(RegisteredExecutor)
// Note: some tests expect the reply to come after we put the executor in the map
context.reply(true)
listenerBus.post(
SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data))
makeOffers()
}
case StopDriver =>
context.reply(true)
stop()
case StopExecutors =>
logInfo("Asking each executor to shut down")
for ((_, executorData) <- executorDataMap) {
executorData.executorEndpoint.send(StopExecutor)
}
context.reply(true)
case RemoveWorker(workerId, host, message) =>
removeWorker(workerId, host, message)
context.reply(true)
case RetrieveSparkAppConfig =>
val reply = SparkAppConfig(
sparkProperties,
SparkEnv.get.securityManager.getIOEncryptionKey(),
fetchHadoopDelegationTokens())
context.reply(reply)
}
// Make fake resource offers on all executors
private def makeOffers() {
// Make sure no executor is killed while some task is launching on it
val taskDescs = withLock {
// Filter out executors under killing
val activeExecutors = executorDataMap.filterKeys(executorIsAlive)
val workOffers = activeExecutors.map {
case (id, executorData) =>
new WorkerOffer(id, executorData.executorHost, executorData.freeCores,
Some(executorData.executorAddress.hostPort))
}.toIndexedSeq
scheduler.resourceOffers(workOffers)
}
if (!taskDescs.isEmpty) {
launchTasks(taskDescs)
}
}
override def onDisconnected(remoteAddress: RpcAddress): Unit = {
addressToExecutorId
.get(remoteAddress)
.foreach(removeExecutor(_, SlaveLost("Remote RPC client disassociated. Likely due to " +
"containers exceeding thresholds, or network issues. Check driver logs for WARN " +
"messages.")))
}
// Make fake resource offers on just one executor
private def makeOffers(executorId: String) {
// Make sure no executor is killed while some task is launching on it
val taskDescs = withLock {
// Filter out executors under killing
if (executorIsAlive(executorId)) {
val executorData = executorDataMap(executorId)
val workOffers = IndexedSeq(
new WorkerOffer(executorId, executorData.executorHost, executorData.freeCores,
Some(executorData.executorAddress.hostPort)))
scheduler.resourceOffers(workOffers)
} else {
Seq.empty
}
}
if (!taskDescs.isEmpty) {
launchTasks(taskDescs)
}
}
private def executorIsAlive(executorId: String): Boolean = synchronized {
!executorsPendingToRemove.contains(executorId) &&
!executorsPendingLossReason.contains(executorId)
}
// Launch tasks returned by a set of resource offers
private def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
for (task <- tasks.flatten) {
val serializedTask = TaskDescription.encode(task)
if (serializedTask.limit() >= maxRpcMessageSize) {
Option(scheduler.taskIdToTaskSetManager.get(task.taskId)).foreach { taskSetMgr =>
try {
var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
"spark.rpc.message.maxSize (%d bytes). Consider increasing " +
"spark.rpc.message.maxSize or using broadcast variables for large values."
msg = msg.format(task.taskId, task.index, serializedTask.limit(), maxRpcMessageSize)
taskSetMgr.abort(msg)
} catch {
case e: Exception => logError("Exception in error callback", e)
}
}
}
else {
val executorData = executorDataMap(task.executorId)
executorData.freeCores -= scheduler.CPUS_PER_TASK
logDebug(s"Launching task ${task.taskId} on executor id: ${task.executorId} hostname: " +
s"${executorData.executorHost}.")
executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask)))
}
}
}
// Remove a disconnected slave from the cluster
private def removeExecutor(executorId: String, reason: ExecutorLossReason): Unit = {
logDebug(s"Asked to remove executor $executorId with reason $reason")
executorDataMap.get(executorId) match {
case Some(executorInfo) =>
// This must be synchronized because variables mutated
// in this block are read when requesting executors
val killed = CoarseGrainedSchedulerBackend.this.synchronized {
addressToExecutorId -= executorInfo.executorAddress
executorDataMap -= executorId
executorsPendingLossReason -= executorId
executorsPendingToRemove.remove(executorId).getOrElse(false)
}
totalCoreCount.addAndGet(-executorInfo.totalCores)
totalRegisteredExecutors.addAndGet(-1)
scheduler.executorLost(executorId, if (killed) ExecutorKilled else reason)
listenerBus.post(
SparkListenerExecutorRemoved(System.currentTimeMillis(), executorId, reason.toString))
case None =>
// SPARK-15262: If an executor is still alive even after the scheduler has removed
// its metadata, we may receive a heartbeat from that executor and tell its block
// manager to reregister itself. If that happens, the block manager master will know
// about the executor, but the scheduler will not. Therefore, we should remove the
// executor from the block manager when we hit this case.
scheduler.sc.env.blockManager.master.removeExecutorAsync(executorId)
logInfo(s"Asked to remove non-existent executor $executorId")
}
}
// Remove a lost worker from the cluster
private def removeWorker(workerId: String, host: String, message: String): Unit = {
logDebug(s"Asked to remove worker $workerId with reason $message")
scheduler.workerRemoved(workerId, host, message)
}
/**
* Stop making resource offers for the given executor. The executor is marked as lost with
* the loss reason still pending.
*
* @return Whether executor should be disabled
*/
protected def disableExecutor(executorId: String): Boolean = {
val shouldDisable = CoarseGrainedSchedulerBackend.this.synchronized {
if (executorIsAlive(executorId)) {
executorsPendingLossReason += executorId
true
} else {
// Returns true for explicitly killed executors, we also need to get pending loss reasons;
// For others return false.
executorsPendingToRemove.contains(executorId)
}
}
if (shouldDisable) {
logInfo(s"Disabling executor $executorId.")
scheduler.executorLost(executorId, LossReasonPending)
}
shouldDisable
}
}
var driverEndpoint: RpcEndpointRef = null
protected def minRegisteredRatio: Double = _minRegisteredRatio
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)
}
protected def createDriverEndpointRef(
properties: ArrayBuffer[(String, String)]): RpcEndpointRef = {
rpcEnv.setupEndpoint(ENDPOINT_NAME, createDriverEndpoint(properties))
}
protected def createDriverEndpoint(properties: Seq[(String, String)]): DriverEndpoint = {
new DriverEndpoint(rpcEnv, properties)
}
def stopExecutors() {
try {
if (driverEndpoint != null) {
logInfo("Shutting down all executors")
driverEndpoint.askSync[Boolean](StopExecutors)
}
} catch {
case e: Exception =>
throw new SparkException("Error asking standalone scheduler to shut down executors", e)
}
}
override def stop() {
reviveThread.shutdownNow()
stopExecutors()
try {
if (driverEndpoint != null) {
driverEndpoint.askSync[Boolean](StopDriver)
}
} catch {
case e: Exception =>
throw new SparkException("Error stopping standalone scheduler's driver endpoint", e)
}
}
/**
* Reset the state of CoarseGrainedSchedulerBackend to the initial state. Currently it will only
* be called in the yarn-client mode when AM re-registers after a failure.
* */
protected def reset(): Unit = {
val executors: Set[String] = synchronized {
requestedTotalExecutors = 0
numPendingExecutors = 0
executorsPendingToRemove.clear()
executorDataMap.keys.toSet
}
// Remove all the lingering executors that should be removed but not yet. The reason might be
// because (1) disconnected event is not yet received; (2) executors die silently.
executors.foreach { eid =>
removeExecutor(eid, SlaveLost("Stale executor after cluster manager re-registered."))
}
}
override def reviveOffers() {
driverEndpoint.send(ReviveOffers)
}
override def killTask(
taskId: Long, executorId: String, interruptThread: Boolean, reason: String) {
driverEndpoint.send(KillTask(taskId, executorId, interruptThread, reason))
}
override def defaultParallelism(): Int = {
conf.getInt("spark.default.parallelism", math.max(totalCoreCount.get(), 2))
}
/**
* Called by subclasses when notified of a lost worker. It just fires the message and returns
* at once.
*/
protected def removeExecutor(executorId: String, reason: ExecutorLossReason): Unit = {
driverEndpoint.send(RemoveExecutor(executorId, reason))
}
protected def removeWorker(workerId: String, host: String, message: String): Unit = {
driverEndpoint.ask[Boolean](RemoveWorker(workerId, host, message)).failed.foreach(t =>
logError(t.getMessage, t))(ThreadUtils.sameThread)
}
def sufficientResourcesRegistered(): Boolean = true
override def isReady(): Boolean = {
if (sufficientResourcesRegistered) {
logInfo("SchedulerBackend is ready for scheduling beginning after " +
s"reached minRegisteredResourcesRatio: $minRegisteredRatio")
return true
}
if ((System.currentTimeMillis() - createTime) >= maxRegisteredWaitingTimeMs) {
logInfo("SchedulerBackend is ready for scheduling beginning after waiting " +
s"maxRegisteredResourcesWaitingTime: $maxRegisteredWaitingTimeMs(ms)")
return true
}
false
}
/**
* Return the number of executors currently registered with this backend.
*/
private def numExistingExecutors: Int = executorDataMap.size
override def getExecutorIds(): Seq[String] = {
executorDataMap.keySet.toSeq
}
override def maxNumConcurrentTasks(): Int = {
executorDataMap.values.map { executor =>
executor.totalCores / scheduler.CPUS_PER_TASK
}.sum
}
/**
* Request an additional number of executors from the cluster manager.
* @return whether the request is acknowledged.
*/
final override def requestExecutors(numAdditionalExecutors: Int): Boolean = {
if (numAdditionalExecutors < 0) {
throw new IllegalArgumentException(
"Attempted to request a negative number of additional executor(s) " +
s"$numAdditionalExecutors from the cluster manager. Please specify a positive number!")
}
logInfo(s"Requesting $numAdditionalExecutors additional executor(s) from the cluster manager")
val response = synchronized {
requestedTotalExecutors += numAdditionalExecutors
numPendingExecutors += numAdditionalExecutors
logDebug(s"Number of pending executors is now $numPendingExecutors")
if (requestedTotalExecutors !=
(numExistingExecutors + numPendingExecutors - executorsPendingToRemove.size)) {
logDebug(
s"""requestExecutors($numAdditionalExecutors): Executor request doesn't match:
|requestedTotalExecutors = $requestedTotalExecutors
|numExistingExecutors = $numExistingExecutors
|numPendingExecutors = $numPendingExecutors
|executorsPendingToRemove = ${executorsPendingToRemove.size}""".stripMargin)
}
// Account for executors pending to be added or removed
doRequestTotalExecutors(requestedTotalExecutors)
}
defaultAskTimeout.awaitResult(response)
}
/**
* Update the cluster manager on our scheduling needs. Three bits of information are included
* to help it make decisions.
* @param numExecutors The total number of executors we'd like to have. The cluster manager
* shouldn't kill any running executor to reach this number, but,
* if all existing executors were to die, this is the number of executors
* we'd want to be allocated.
* @param localityAwareTasks The number of tasks in all active stages that have a locality
* preferences. This includes running, pending, and completed tasks.
* @param hostToLocalTaskCount A map of hosts to the number of tasks from all active stages
* that would like to like to run on that host.
* This includes running, pending, and completed tasks.
* @return whether the request is acknowledged by the cluster manager.
*/
final override def requestTotalExecutors(
numExecutors: Int,
localityAwareTasks: Int,
hostToLocalTaskCount: Map[String, Int]
): Boolean = {
if (numExecutors < 0) {
throw new IllegalArgumentException(
"Attempted to request a negative number of executor(s) " +
s"$numExecutors from the cluster manager. Please specify a positive number!")
}
val response = synchronized {
this.requestedTotalExecutors = numExecutors
this.localityAwareTasks = localityAwareTasks
this.hostToLocalTaskCount = hostToLocalTaskCount
numPendingExecutors =
math.max(numExecutors - numExistingExecutors + executorsPendingToRemove.size, 0)
doRequestTotalExecutors(numExecutors)
}
defaultAskTimeout.awaitResult(response)
}
/**
* Request executors from the cluster manager by specifying the total number desired,
* including existing pending and running executors.
*
* The semantics here guarantee that we do not over-allocate executors for this application,
* since a later request overrides the value of any prior request. The alternative interface
* of requesting a delta of executors risks double counting new executors when there are
* insufficient resources to satisfy the first request. We make the assumption here that the
* cluster manager will eventually fulfill all requests when resources free up.
*
* @return a future whose evaluation indicates whether the request is acknowledged.
*/
protected def doRequestTotalExecutors(requestedTotal: Int): Future[Boolean] =
Future.successful(false)
/**
* Request that the cluster manager kill the specified executors.
*
* @param executorIds identifiers of executors to kill
* @param adjustTargetNumExecutors whether the target number of executors be adjusted down
* after these executors have been killed
* @param countFailures if there are tasks running on the executors when they are killed, whether
* those failures be counted to task failure limits?
* @param force whether to force kill busy executors, default false
* @return the ids of the executors acknowledged by the cluster manager to be removed.
*/
final override def killExecutors(
executorIds: Seq[String],
adjustTargetNumExecutors: Boolean,
countFailures: Boolean,
force: Boolean): Seq[String] = {
logInfo(s"Requesting to kill executor(s) ${executorIds.mkString(", ")}")
val response = withLock {
val (knownExecutors, unknownExecutors) = executorIds.partition(executorDataMap.contains)
unknownExecutors.foreach { id =>
logWarning(s"Executor to kill $id does not exist!")
}
// If an executor is already pending to be removed, do not kill it again (SPARK-9795)
// If this executor is busy, do not kill it unless we are told to force kill it (SPARK-9552)
val executorsToKill = knownExecutors
.filter { id => !executorsPendingToRemove.contains(id) }
.filter { id => force || !scheduler.isExecutorBusy(id) }
executorsToKill.foreach { id => executorsPendingToRemove(id) = !countFailures }
logInfo(s"Actual list of executor(s) to be killed is ${executorsToKill.mkString(", ")}")
// If we do not wish to replace the executors we kill, sync the target number of executors
// with the cluster manager to avoid allocating new ones. When computing the new target,
// take into account executors that are pending to be added or removed.
val adjustTotalExecutors =
if (adjustTargetNumExecutors) {
requestedTotalExecutors = math.max(requestedTotalExecutors - executorsToKill.size, 0)
if (requestedTotalExecutors !=
(numExistingExecutors + numPendingExecutors - executorsPendingToRemove.size)) {
logDebug(
s"""killExecutors($executorIds, $adjustTargetNumExecutors, $countFailures, $force):
|Executor counts do not match:
|requestedTotalExecutors = $requestedTotalExecutors
|numExistingExecutors = $numExistingExecutors
|numPendingExecutors = $numPendingExecutors
|executorsPendingToRemove = ${executorsPendingToRemove.size}""".stripMargin)
}
doRequestTotalExecutors(requestedTotalExecutors)
} else {
numPendingExecutors += executorsToKill.size
Future.successful(true)
}
val killExecutors: Boolean => Future[Boolean] =
if (!executorsToKill.isEmpty) {
_ => doKillExecutors(executorsToKill)
} else {
_ => Future.successful(false)
}
val killResponse = adjustTotalExecutors.flatMap(killExecutors)(ThreadUtils.sameThread)
killResponse.flatMap(killSuccessful =>
Future.successful (if (killSuccessful) executorsToKill else Seq.empty[String])
)(ThreadUtils.sameThread)
}
defaultAskTimeout.awaitResult(response)
}
/**
* Kill the given list of executors through the cluster manager.
* @return whether the kill request is acknowledged.
*/
protected def doKillExecutors(executorIds: Seq[String]): Future[Boolean] =
Future.successful(false)
/**
* Request that the cluster manager kill all executors on a given host.
* @return whether the kill request is acknowledged.
*/
final override def killExecutorsOnHost(host: String): Boolean = {
logInfo(s"Requesting to kill any and all executors on host ${host}")
// A potential race exists if a new executor attempts to register on a host
// that is on the blacklist and is no no longer valid. To avoid this race,
// all executor registration and killing happens in the event loop. This way, either
// an executor will fail to register, or will be killed when all executors on a host
// are killed.
// Kill all the executors on this host in an event loop to ensure serialization.
driverEndpoint.send(KillExecutorsOnHost(host))
true
}
protected def fetchHadoopDelegationTokens(): Option[Array[Byte]] = { None }
// SPARK-27112: We need to ensure that there is ordering of lock acquisition
// between TaskSchedulerImpl and CoarseGrainedSchedulerBackend objects in order to fix
// the deadlock issue exposed in SPARK-27112
private def withLock[T](fn: => T): T = scheduler.synchronized {
CoarseGrainedSchedulerBackend.this.synchronized { fn }
}
}
private[spark] object CoarseGrainedSchedulerBackend {
val ENDPOINT_NAME = "CoarseGrainedScheduler"
}
