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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
import java.io.NotSerializableException
import java.nio.ByteBuffer
import java.util.concurrent.ConcurrentLinkedQueue
import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
import scala.math.max
import scala.util.control.NonFatal
import org.apache.spark._
import org.apache.spark.TaskState.TaskState
import org.apache.spark.internal.{config, Logging}
import org.apache.spark.scheduler.SchedulingMode._
import org.apache.spark.util.{AccumulatorV2, Clock, SystemClock, Utils}
import org.apache.spark.util.collection.MedianHeap
/**
* Schedules the tasks within a single TaskSet in the TaskSchedulerImpl. This class keeps track of
* each task, retries tasks if they fail (up to a limited number of times), and
* handles locality-aware scheduling for this TaskSet via delay scheduling. The main interfaces
* to it are resourceOffer, which asks the TaskSet whether it wants to run a task on one node,
* and statusUpdate, which tells it that one of its tasks changed state (e.g. finished).
*
* THREADING: This class is designed to only be called from code with a lock on the
* TaskScheduler (e.g. its event handlers). It should not be called from other threads.
*
* @param sched the TaskSchedulerImpl associated with the TaskSetManager
* @param taskSet the TaskSet to manage scheduling for
* @param maxTaskFailures if any particular task fails this number of times, the entire
* task set will be aborted
*/
private[spark] class TaskSetManager(
sched: TaskSchedulerImpl,
val taskSet: TaskSet,
val maxTaskFailures: Int,
blacklistTracker: Option[BlacklistTracker] = None,
clock: Clock = new SystemClock()) extends Schedulable with Logging {
private val conf = sched.sc.conf
// SPARK-21563 make a copy of the jars/files so they are consistent across the TaskSet
private val addedJars = HashMap[String, Long](sched.sc.addedJars.toSeq: _*)
private val addedFiles = HashMap[String, Long](sched.sc.addedFiles.toSeq: _*)
// Quantile of tasks at which to start speculation
val SPECULATION_QUANTILE = conf.getDouble("spark.speculation.quantile", 0.75)
val SPECULATION_MULTIPLIER = conf.getDouble("spark.speculation.multiplier", 1.5)
val maxResultSize = conf.get(config.MAX_RESULT_SIZE)
val speculationEnabled = conf.getBoolean("spark.speculation", false)
// Serializer for closures and tasks.
val env = SparkEnv.get
val ser = env.closureSerializer.newInstance()
val tasks = taskSet.tasks
private[scheduler] val partitionToIndex = tasks.zipWithIndex
.map { case (t, idx) => t.partitionId -> idx }.toMap
val numTasks = tasks.length
val copiesRunning = new Array[Int](numTasks)
// For each task, tracks whether a copy of the task has succeeded. A task will also be
// marked as "succeeded" if it failed with a fetch failure, in which case it should not
// be re-run because the missing map data needs to be regenerated first.
val successful = new Array[Boolean](numTasks)
private val numFailures = new Array[Int](numTasks)
// Add the tid of task into this HashSet when the task is killed by other attempt tasks.
// This happened while we set the `spark.speculation` to true. The task killed by others
// should not resubmit while executor lost.
private val killedByOtherAttempt = new HashSet[Long]
val taskAttempts = Array.fill[List[TaskInfo]](numTasks)(Nil)
private[scheduler] var tasksSuccessful = 0
val weight = 1
val minShare = 0
var priority = taskSet.priority
var stageId = taskSet.stageId
val name = "TaskSet_" + taskSet.id
var parent: Pool = null
private var totalResultSize = 0L
private var calculatedTasks = 0
private[scheduler] val taskSetBlacklistHelperOpt: Option[TaskSetBlacklist] = {
blacklistTracker.map { _ =>
new TaskSetBlacklist(sched.sc.listenerBus, conf, stageId, taskSet.stageAttemptId, clock)
}
}
private[scheduler] val runningTasksSet = new HashSet[Long]
override def runningTasks: Int = runningTasksSet.size
def someAttemptSucceeded(tid: Long): Boolean = {
successful(taskInfos(tid).index)
}
// True once no more tasks should be launched for this task set manager. TaskSetManagers enter
// the zombie state once at least one attempt of each task has completed successfully, or if the
// task set is aborted (for example, because it was killed). TaskSetManagers remain in the zombie
// state until all tasks have finished running; we keep TaskSetManagers that are in the zombie
// state in order to continue to track and account for the running tasks.
// TODO: We should kill any running task attempts when the task set manager becomes a zombie.
private[scheduler] var isZombie = false
// Set of pending tasks for each executor. These collections are actually
// treated as stacks, in which new tasks are added to the end of the
// ArrayBuffer and removed from the end. This makes it faster to detect
// tasks that repeatedly fail because whenever a task failed, it is put
// back at the head of the stack. These collections may contain duplicates
// for two reasons:
// (1): Tasks are only removed lazily; when a task is launched, it remains
// in all the pending lists except the one that it was launched from.
// (2): Tasks may be re-added to these lists multiple times as a result
// of failures.
// Duplicates are handled in dequeueTaskFromList, which ensures that a
// task hasn't already started running before launching it.
private val pendingTasksForExecutor = new HashMap[String, ArrayBuffer[Int]]
// Set of pending tasks for each host. Similar to pendingTasksForExecutor,
// but at host level.
private val pendingTasksForHost = new HashMap[String, ArrayBuffer[Int]]
// Set of pending tasks for each rack -- similar to the above.
private val pendingTasksForRack = new HashMap[String, ArrayBuffer[Int]]
// Set containing pending tasks with no locality preferences.
private[scheduler] var pendingTasksWithNoPrefs = new ArrayBuffer[Int]
// Set containing all pending tasks (also used as a stack, as above).
private val allPendingTasks = new ArrayBuffer[Int]
// Tasks that can be speculated. Since these will be a small fraction of total
// tasks, we'll just hold them in a HashSet.
private[scheduler] val speculatableTasks = new HashSet[Int]
// Task index, start and finish time for each task attempt (indexed by task ID)
private[scheduler] val taskInfos = new HashMap[Long, TaskInfo]
// Use a MedianHeap to record durations of successful tasks so we know when to launch
// speculative tasks. This is only used when speculation is enabled, to avoid the overhead
// of inserting into the heap when the heap won't be used.
val successfulTaskDurations = new MedianHeap()
// How frequently to reprint duplicate exceptions in full, in milliseconds
val EXCEPTION_PRINT_INTERVAL =
conf.getLong("spark.logging.exceptionPrintInterval", 10000)
// Map of recent exceptions (identified by string representation and top stack frame) to
// duplicate count (how many times the same exception has appeared) and time the full exception
// was printed. This should ideally be an LRU map that can drop old exceptions automatically.
private val recentExceptions = HashMap[String, (Int, Long)]()
// Figure out the current map output tracker epoch and set it on all tasks
val epoch = sched.mapOutputTracker.getEpoch
logDebug("Epoch for " + taskSet + ": " + epoch)
for (t <- tasks) {
t.epoch = epoch
}
// Add all our tasks to the pending lists. We do this in reverse order
// of task index so that tasks with low indices get launched first.
for (i <- (0 until numTasks).reverse) {
addPendingTask(i)
}
/**
* Track the set of locality levels which are valid given the tasks locality preferences and
* the set of currently available executors. This is updated as executors are added and removed.
* This allows a performance optimization, of skipping levels that aren't relevant (eg., skip
* PROCESS_LOCAL if no tasks could be run PROCESS_LOCAL for the current set of executors).
*/
private[scheduler] var myLocalityLevels = computeValidLocalityLevels()
// Time to wait at each level
private[scheduler] var localityWaits = myLocalityLevels.map(getLocalityWait)
// Delay scheduling variables: we keep track of our current locality level and the time we
// last launched a task at that level, and move up a level when localityWaits[curLevel] expires.
// We then move down if we manage to launch a "more local" task.
private var currentLocalityIndex = 0 // Index of our current locality level in validLocalityLevels
private var lastLaunchTime = clock.getTimeMillis() // Time we last launched a task at this level
override def schedulableQueue: ConcurrentLinkedQueue[Schedulable] = null
override def schedulingMode: SchedulingMode = SchedulingMode.NONE
private[scheduler] var emittedTaskSizeWarning = false
/** Add a task to all the pending-task lists that it should be on. */
private[spark] def addPendingTask(index: Int) {
for (loc <- tasks(index).preferredLocations) {
loc match {
case e: ExecutorCacheTaskLocation =>
pendingTasksForExecutor.getOrElseUpdate(e.executorId, new ArrayBuffer) += index
case e: HDFSCacheTaskLocation =>
val exe = sched.getExecutorsAliveOnHost(loc.host)
exe match {
case Some(set) =>
for (e <- set) {
pendingTasksForExecutor.getOrElseUpdate(e, new ArrayBuffer) += index
}
logInfo(s"Pending task $index has a cached location at ${e.host} " +
", where there are executors " + set.mkString(","))
case None => logDebug(s"Pending task $index has a cached location at ${e.host} " +
", but there are no executors alive there.")
}
case _ =>
}
pendingTasksForHost.getOrElseUpdate(loc.host, new ArrayBuffer) += index
for (rack <- sched.getRackForHost(loc.host)) {
pendingTasksForRack.getOrElseUpdate(rack, new ArrayBuffer) += index
}
}
if (tasks(index).preferredLocations == Nil) {
pendingTasksWithNoPrefs += index
}
allPendingTasks += index // No point scanning this whole list to find the old task there
}
/**
* Return the pending tasks list for a given executor ID, or an empty list if
* there is no map entry for that host
*/
private def getPendingTasksForExecutor(executorId: String): ArrayBuffer[Int] = {
pendingTasksForExecutor.getOrElse(executorId, ArrayBuffer())
}
/**
* Return the pending tasks list for a given host, or an empty list if
* there is no map entry for that host
*/
private def getPendingTasksForHost(host: String): ArrayBuffer[Int] = {
pendingTasksForHost.getOrElse(host, ArrayBuffer())
}
/**
* Return the pending rack-local task list for a given rack, or an empty list if
* there is no map entry for that rack
*/
private def getPendingTasksForRack(rack: String): ArrayBuffer[Int] = {
pendingTasksForRack.getOrElse(rack, ArrayBuffer())
}
/**
* Dequeue a pending task from the given list and return its index.
* Return None if the list is empty.
* This method also cleans up any tasks in the list that have already
* been launched, since we want that to happen lazily.
*/
private def dequeueTaskFromList(
execId: String,
host: String,
list: ArrayBuffer[Int]): Option[Int] = {
var indexOffset = list.size
while (indexOffset > 0) {
indexOffset -= 1
val index = list(indexOffset)
if (!isTaskBlacklistedOnExecOrNode(index, execId, host)) {
// This should almost always be list.trimEnd(1) to remove tail
list.remove(indexOffset)
if (copiesRunning(index) == 0 && !successful(index)) {
return Some(index)
}
}
}
None
}
/** Check whether a task once ran an attempt on a given host */
private def hasAttemptOnHost(taskIndex: Int, host: String): Boolean = {
taskAttempts(taskIndex).exists(_.host == host)
}
private def isTaskBlacklistedOnExecOrNode(index: Int, execId: String, host: String): Boolean = {
taskSetBlacklistHelperOpt.exists { blacklist =>
blacklist.isNodeBlacklistedForTask(host, index) ||
blacklist.isExecutorBlacklistedForTask(execId, index)
}
}
/**
* Return a speculative task for a given executor if any are available. The task should not have
* an attempt running on this host, in case the host is slow. In addition, the task should meet
* the given locality constraint.
*/
// Labeled as protected to allow tests to override providing speculative tasks if necessary
protected def dequeueSpeculativeTask(execId: String, host: String, locality: TaskLocality.Value)
: Option[(Int, TaskLocality.Value)] =
{
speculatableTasks.retain(index => !successful(index)) // Remove finished tasks from set
def canRunOnHost(index: Int): Boolean = {
!hasAttemptOnHost(index, host) &&
!isTaskBlacklistedOnExecOrNode(index, execId, host)
}
if (!speculatableTasks.isEmpty) {
// Check for process-local tasks; note that tasks can be process-local
// on multiple nodes when we replicate cached blocks, as in Spark Streaming
for (index <- speculatableTasks if canRunOnHost(index)) {
val prefs = tasks(index).preferredLocations
val executors = prefs.flatMap(_ match {
case e: ExecutorCacheTaskLocation => Some(e.executorId)
case _ => None
});
if (executors.contains(execId)) {
speculatableTasks -= index
return Some((index, TaskLocality.PROCESS_LOCAL))
}
}
// Check for node-local tasks
if (TaskLocality.isAllowed(locality, TaskLocality.NODE_LOCAL)) {
for (index <- speculatableTasks if canRunOnHost(index)) {
val locations = tasks(index).preferredLocations.map(_.host)
if (locations.contains(host)) {
speculatableTasks -= index
return Some((index, TaskLocality.NODE_LOCAL))
}
}
}
// Check for no-preference tasks
if (TaskLocality.isAllowed(locality, TaskLocality.NO_PREF)) {
for (index <- speculatableTasks if canRunOnHost(index)) {
val locations = tasks(index).preferredLocations
if (locations.size == 0) {
speculatableTasks -= index
return Some((index, TaskLocality.PROCESS_LOCAL))
}
}
}
// Check for rack-local tasks
if (TaskLocality.isAllowed(locality, TaskLocality.RACK_LOCAL)) {
for (rack <- sched.getRackForHost(host)) {
for (index <- speculatableTasks if canRunOnHost(index)) {
val racks = tasks(index).preferredLocations.map(_.host).flatMap(sched.getRackForHost)
if (racks.contains(rack)) {
speculatableTasks -= index
return Some((index, TaskLocality.RACK_LOCAL))
}
}
}
}
// Check for non-local tasks
if (TaskLocality.isAllowed(locality, TaskLocality.ANY)) {
for (index <- speculatableTasks if canRunOnHost(index)) {
speculatableTasks -= index
return Some((index, TaskLocality.ANY))
}
}
}
None
}
/**
* Dequeue a pending task for a given node and return its index and locality level.
* Only search for tasks matching the given locality constraint.
*
* @return An option containing (task index within the task set, locality, is speculative?)
*/
private def dequeueTask(execId: String, host: String, maxLocality: TaskLocality.Value)
: Option[(Int, TaskLocality.Value, Boolean)] =
{
for (index <- dequeueTaskFromList(execId, host, getPendingTasksForExecutor(execId))) {
return Some((index, TaskLocality.PROCESS_LOCAL, false))
}
if (TaskLocality.isAllowed(maxLocality, TaskLocality.NODE_LOCAL)) {
for (index <- dequeueTaskFromList(execId, host, getPendingTasksForHost(host))) {
return Some((index, TaskLocality.NODE_LOCAL, false))
}
}
if (TaskLocality.isAllowed(maxLocality, TaskLocality.NO_PREF)) {
// Look for noPref tasks after NODE_LOCAL for minimize cross-rack traffic
for (index <- dequeueTaskFromList(execId, host, pendingTasksWithNoPrefs)) {
return Some((index, TaskLocality.PROCESS_LOCAL, false))
}
}
if (TaskLocality.isAllowed(maxLocality, TaskLocality.RACK_LOCAL)) {
for {
rack <- sched.getRackForHost(host)
index <- dequeueTaskFromList(execId, host, getPendingTasksForRack(rack))
} {
return Some((index, TaskLocality.RACK_LOCAL, false))
}
}
if (TaskLocality.isAllowed(maxLocality, TaskLocality.ANY)) {
for (index <- dequeueTaskFromList(execId, host, allPendingTasks)) {
return Some((index, TaskLocality.ANY, false))
}
}
// find a speculative task if all others tasks have been scheduled
dequeueSpeculativeTask(execId, host, maxLocality).map {
case (taskIndex, allowedLocality) => (taskIndex, allowedLocality, true)}
}
/**
* Respond to an offer of a single executor from the scheduler by finding a task
*
* NOTE: this function is either called with a maxLocality which
* would be adjusted by delay scheduling algorithm or it will be with a special
* NO_PREF locality which will be not modified
*
* @param execId the executor Id of the offered resource
* @param host the host Id of the offered resource
* @param maxLocality the maximum locality we want to schedule the tasks at
*/
@throws[TaskNotSerializableException]
def resourceOffer(
execId: String,
host: String,
maxLocality: TaskLocality.TaskLocality)
: Option[TaskDescription] =
{
val offerBlacklisted = taskSetBlacklistHelperOpt.exists { blacklist =>
blacklist.isNodeBlacklistedForTaskSet(host) ||
blacklist.isExecutorBlacklistedForTaskSet(execId)
}
if (!isZombie && !offerBlacklisted) {
val curTime = clock.getTimeMillis()
var allowedLocality = maxLocality
if (maxLocality != TaskLocality.NO_PREF) {
allowedLocality = getAllowedLocalityLevel(curTime)
if (allowedLocality > maxLocality) {
// We're not allowed to search for farther-away tasks
allowedLocality = maxLocality
}
}
dequeueTask(execId, host, allowedLocality).map { case ((index, taskLocality, speculative)) =>
// Found a task; do some bookkeeping and return a task description
val task = tasks(index)
val taskId = sched.newTaskId()
// Do various bookkeeping
copiesRunning(index) += 1
val attemptNum = taskAttempts(index).size
val info = new TaskInfo(taskId, index, attemptNum, curTime,
execId, host, taskLocality, speculative)
taskInfos(taskId) = info
taskAttempts(index) = info :: taskAttempts(index)
// Update our locality level for delay scheduling
// NO_PREF will not affect the variables related to delay scheduling
if (maxLocality != TaskLocality.NO_PREF) {
currentLocalityIndex = getLocalityIndex(taskLocality)
lastLaunchTime = curTime
}
// Serialize and return the task
val serializedTask: ByteBuffer = try {
ser.serialize(task)
} catch {
// If the task cannot be serialized, then there's no point to re-attempt the task,
// as it will always fail. So just abort the whole task-set.
case NonFatal(e) =>
val msg = s"Failed to serialize task $taskId, not attempting to retry it."
logError(msg, e)
abort(s"$msg Exception during serialization: $e")
throw new TaskNotSerializableException(e)
}
if (serializedTask.limit() > TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024 &&
!emittedTaskSizeWarning) {
emittedTaskSizeWarning = true
logWarning(s"Stage ${task.stageId} contains a task of very large size " +
s"(${serializedTask.limit() / 1024} KB). The maximum recommended task size is " +
s"${TaskSetManager.TASK_SIZE_TO_WARN_KB} KB.")
}
addRunningTask(taskId)
// We used to log the time it takes to serialize the task, but task size is already
// a good proxy to task serialization time.
// val timeTaken = clock.getTime() - startTime
val taskName = s"task ${info.id} in stage ${taskSet.id}"
logInfo(s"Starting $taskName (TID $taskId, $host, executor ${info.executorId}, " +
s"partition ${task.partitionId}, $taskLocality, ${serializedTask.limit()} bytes)")
sched.dagScheduler.taskStarted(task, info)
new TaskDescription(
taskId,
attemptNum,
execId,
taskName,
index,
addedFiles,
addedJars,
task.localProperties,
serializedTask)
}
} else {
None
}
}
private def maybeFinishTaskSet() {
if (isZombie && runningTasks == 0) {
sched.taskSetFinished(this)
if (tasksSuccessful == numTasks) {
blacklistTracker.foreach(_.updateBlacklistForSuccessfulTaskSet(
taskSet.stageId,
taskSet.stageAttemptId,
taskSetBlacklistHelperOpt.get.execToFailures))
}
}
}
/**
* Get the level we can launch tasks according to delay scheduling, based on current wait time.
*/
private def getAllowedLocalityLevel(curTime: Long): TaskLocality.TaskLocality = {
// Remove the scheduled or finished tasks lazily
def tasksNeedToBeScheduledFrom(pendingTaskIds: ArrayBuffer[Int]): Boolean = {
var indexOffset = pendingTaskIds.size
while (indexOffset > 0) {
indexOffset -= 1
val index = pendingTaskIds(indexOffset)
if (copiesRunning(index) == 0 && !successful(index)) {
return true
} else {
pendingTaskIds.remove(indexOffset)
}
}
false
}
// Walk through the list of tasks that can be scheduled at each location and returns true
// if there are any tasks that still need to be scheduled. Lazily cleans up tasks that have
// already been scheduled.
def moreTasksToRunIn(pendingTasks: HashMap[String, ArrayBuffer[Int]]): Boolean = {
val emptyKeys = new ArrayBuffer[String]
val hasTasks = pendingTasks.exists {
case (id: String, tasks: ArrayBuffer[Int]) =>
if (tasksNeedToBeScheduledFrom(tasks)) {
true
} else {
emptyKeys += id
false
}
}
// The key could be executorId, host or rackId
emptyKeys.foreach(id => pendingTasks.remove(id))
hasTasks
}
while (currentLocalityIndex < myLocalityLevels.length - 1) {
val moreTasks = myLocalityLevels(currentLocalityIndex) match {
case TaskLocality.PROCESS_LOCAL => moreTasksToRunIn(pendingTasksForExecutor)
case TaskLocality.NODE_LOCAL => moreTasksToRunIn(pendingTasksForHost)
case TaskLocality.NO_PREF => pendingTasksWithNoPrefs.nonEmpty
case TaskLocality.RACK_LOCAL => moreTasksToRunIn(pendingTasksForRack)
}
if (!moreTasks) {
// This is a performance optimization: if there are no more tasks that can
// be scheduled at a particular locality level, there is no point in waiting
// for the locality wait timeout (SPARK-4939).
lastLaunchTime = curTime
logDebug(s"No tasks for locality level ${myLocalityLevels(currentLocalityIndex)}, " +
s"so moving to locality level ${myLocalityLevels(currentLocalityIndex + 1)}")
currentLocalityIndex += 1
} else if (curTime - lastLaunchTime >= localityWaits(currentLocalityIndex)) {
// Jump to the next locality level, and reset lastLaunchTime so that the next locality
// wait timer doesn't immediately expire
lastLaunchTime += localityWaits(currentLocalityIndex)
logDebug(s"Moving to ${myLocalityLevels(currentLocalityIndex + 1)} after waiting for " +
s"${localityWaits(currentLocalityIndex)}ms")
currentLocalityIndex += 1
} else {
return myLocalityLevels(currentLocalityIndex)
}
}
myLocalityLevels(currentLocalityIndex)
}
/**
* Find the index in myLocalityLevels for a given locality. This is also designed to work with
* localities that are not in myLocalityLevels (in case we somehow get those) by returning the
* next-biggest level we have. Uses the fact that the last value in myLocalityLevels is ANY.
*/
def getLocalityIndex(locality: TaskLocality.TaskLocality): Int = {
var index = 0
while (locality > myLocalityLevels(index)) {
index += 1
}
index
}
/**
* Check whether the given task set has been blacklisted to the point that it can't run anywhere.
*
* It is possible that this taskset has become impossible to schedule *anywhere* due to the
* blacklist. The most common scenario would be if there are fewer executors than
* spark.task.maxFailures. We need to detect this so we can fail the task set, otherwise the job
* will hang.
*
* There's a tradeoff here: we could make sure all tasks in the task set are schedulable, but that
* would add extra time to each iteration of the scheduling loop. Here, we take the approach of
* making sure at least one of the unscheduled tasks is schedulable. This means we may not detect
* the hang as quickly as we could have, but we'll always detect the hang eventually, and the
* method is faster in the typical case. In the worst case, this method can take
* O(maxTaskFailures + numTasks) time, but it will be faster when there haven't been any task
* failures (this is because the method picks one unscheduled task, and then iterates through each
* executor until it finds one that the task isn't blacklisted on).
*/
private[scheduler] def abortIfCompletelyBlacklisted(
hostToExecutors: HashMap[String, HashSet[String]]): Unit = {
taskSetBlacklistHelperOpt.foreach { taskSetBlacklist =>
val appBlacklist = blacklistTracker.get
// Only look for unschedulable tasks when at least one executor has registered. Otherwise,
// task sets will be (unnecessarily) aborted in cases when no executors have registered yet.
if (hostToExecutors.nonEmpty) {
// find any task that needs to be scheduled
val pendingTask: Option[Int] = {
// usually this will just take the last pending task, but because of the lazy removal
// from each list, we may need to go deeper in the list. We poll from the end because
// failed tasks are put back at the end of allPendingTasks, so we're more likely to find
// an unschedulable task this way.
val indexOffset = allPendingTasks.lastIndexWhere { indexInTaskSet =>
copiesRunning(indexInTaskSet) == 0 && !successful(indexInTaskSet)
}
if (indexOffset == -1) {
None
} else {
Some(allPendingTasks(indexOffset))
}
}
pendingTask.foreach { indexInTaskSet =>
// try to find some executor this task can run on. Its possible that some *other*
// task isn't schedulable anywhere, but we will discover that in some later call,
// when that unschedulable task is the last task remaining.
val blacklistedEverywhere = hostToExecutors.forall { case (host, execsOnHost) =>
// Check if the task can run on the node
val nodeBlacklisted =
appBlacklist.isNodeBlacklisted(host) ||
taskSetBlacklist.isNodeBlacklistedForTaskSet(host) ||
taskSetBlacklist.isNodeBlacklistedForTask(host, indexInTaskSet)
if (nodeBlacklisted) {
true
} else {
// Check if the task can run on any of the executors
execsOnHost.forall { exec =>
appBlacklist.isExecutorBlacklisted(exec) ||
taskSetBlacklist.isExecutorBlacklistedForTaskSet(exec) ||
taskSetBlacklist.isExecutorBlacklistedForTask(exec, indexInTaskSet)
}
}
}
if (blacklistedEverywhere) {
val partition = tasks(indexInTaskSet).partitionId
abort(s"""
|Aborting $taskSet because task $indexInTaskSet (partition $partition)
|cannot run anywhere due to node and executor blacklist.
|Most recent failure:
|${taskSetBlacklist.getLatestFailureReason}
|
|Blacklisting behavior can be configured via spark.blacklist.*.
|""".stripMargin)
}
}
}
}
}
/**
* Marks the task as getting result and notifies the DAG Scheduler
*/
def handleTaskGettingResult(tid: Long): Unit = {
val info = taskInfos(tid)
info.markGettingResult(clock.getTimeMillis())
sched.dagScheduler.taskGettingResult(info)
}
/**
* Check whether has enough quota to fetch the result with `size` bytes
*/
def canFetchMoreResults(size: Long): Boolean = sched.synchronized {
totalResultSize += size
calculatedTasks += 1
if (maxResultSize > 0 && totalResultSize > maxResultSize) {
val msg = s"Total size of serialized results of ${calculatedTasks} tasks " +
s"(${Utils.bytesToString(totalResultSize)}) is bigger than spark.driver.maxResultSize " +
s"(${Utils.bytesToString(maxResultSize)})"
logError(msg)
abort(msg)
false
} else {
true
}
}
/**
* Marks a task as successful and notifies the DAGScheduler that the task has ended.
*/
def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]): Unit = {
val info = taskInfos(tid)
val index = info.index
info.markFinished(TaskState.FINISHED, clock.getTimeMillis())
if (speculationEnabled) {
successfulTaskDurations.insert(info.duration)
}
removeRunningTask(tid)
// Kill any other attempts for the same task (since those are unnecessary now that one
// attempt completed successfully).
for (attemptInfo <- taskAttempts(index) if attemptInfo.running) {
logInfo(s"Killing attempt ${attemptInfo.attemptNumber} for task ${attemptInfo.id} " +
s"in stage ${taskSet.id} (TID ${attemptInfo.taskId}) on ${attemptInfo.host} " +
s"as the attempt ${info.attemptNumber} succeeded on ${info.host}")
killedByOtherAttempt += attemptInfo.taskId
sched.backend.killTask(
attemptInfo.taskId,
attemptInfo.executorId,
interruptThread = true,
reason = "another attempt succeeded")
}
if (!successful(index)) {
tasksSuccessful += 1
logInfo(s"Finished task ${info.id} in stage ${taskSet.id} (TID ${info.taskId}) in" +
s" ${info.duration} ms on ${info.host} (executor ${info.executorId})" +
s" ($tasksSuccessful/$numTasks)")
// Mark successful and stop if all the tasks have succeeded.
successful(index) = true
if (tasksSuccessful == numTasks) {
isZombie = true
}
} else {
logInfo("Ignoring task-finished event for " + info.id + " in stage " + taskSet.id +
" because task " + index + " has already completed successfully")
}
// There may be multiple tasksets for this stage -- we let all of them know that the partition
// was completed. This may result in some of the tasksets getting completed.
sched.markPartitionCompletedInAllTaskSets(stageId, tasks(index).partitionId, info)
// This method is called by "TaskSchedulerImpl.handleSuccessfulTask" which holds the
// "TaskSchedulerImpl" lock until exiting. To avoid the SPARK-7655 issue, we should not
// "deserialize" the value when holding a lock to avoid blocking other threads. So we call
// "result.value()" in "TaskResultGetter.enqueueSuccessfulTask" before reaching here.
// Note: "result.value()" only deserializes the value when it's called at the first time, so
// here "result.value()" just returns the value and won't block other threads.
sched.dagScheduler.taskEnded(tasks(index), Success, result.value(), result.accumUpdates, info)
maybeFinishTaskSet()
}
private[scheduler] def markPartitionCompleted(partitionId: Int, taskInfo: TaskInfo): Unit = {
partitionToIndex.get(partitionId).foreach { index =>
if (!successful(index)) {
if (speculationEnabled && !isZombie) {
successfulTaskDurations.insert(taskInfo.duration)
}
tasksSuccessful += 1
successful(index) = true
if (tasksSuccessful == numTasks) {
isZombie = true
}
maybeFinishTaskSet()
}
}
}
/**
* Marks the task as failed, re-adds it to the list of pending tasks, and notifies the
* DAG Scheduler.
*/
def handleFailedTask(tid: Long, state: TaskState, reason: TaskFailedReason) {
val info = taskInfos(tid)
if (info.failed || info.killed) {
return
}
removeRunningTask(tid)
info.markFinished(state, clock.getTimeMillis())
val index = info.index
copiesRunning(index) -= 1
var accumUpdates: Seq[AccumulatorV2[_, _]] = Seq.empty
val failureReason = s"Lost task ${info.id} in stage ${taskSet.id} (TID $tid, ${info.host}," +
s" executor ${info.executorId}): ${reason.toErrorString}"
val failureException: Option[Throwable] = reason match {
case fetchFailed: FetchFailed =>
logWarning(failureReason)
if (!successful(index)) {
successful(index) = true
tasksSuccessful += 1
}
isZombie = true
if (fetchFailed.bmAddress != null) {
blacklistTracker.foreach(_.updateBlacklistForFetchFailure(
fetchFailed.bmAddress.host, fetchFailed.bmAddress.executorId))
}
None
case ef: ExceptionFailure =>
// ExceptionFailure's might have accumulator updates
accumUpdates = ef.accums
if (ef.className == classOf[NotSerializableException].getName) {
// If the task result wasn't serializable, there's no point in trying to re-execute it.
logError("Task %s in stage %s (TID %d) had a not serializable result: %s; not retrying"
.format(info.id, taskSet.id, tid, ef.description))
abort("Task %s in stage %s (TID %d) had a not serializable result: %s".format(
info.id, taskSet.id, tid, ef.description))
return
}
val key = ef.description
val now = clock.getTimeMillis()
val (printFull, dupCount) = {
if (recentExceptions.contains(key)) {
val (dupCount, printTime) = recentExceptions(key)
if (now - printTime > EXCEPTION_PRINT_INTERVAL) {
recentExceptions(key) = (0, now)
(true, 0)
} else {
recentExceptions(key) = (dupCount + 1, printTime)
(false, dupCount + 1)
}
} else {
recentExceptions(key) = (0, now)
(true, 0)
}
}
if (printFull) {
logWarning(failureReason)
} else {
logInfo(
s"Lost task ${info.id} in stage ${taskSet.id} (TID $tid) on ${info.host}, executor" +
s" ${info.executorId}: ${ef.className} (${ef.description}) [duplicate $dupCount]")
}
ef.exception
case tk: TaskKilled =>
// TaskKilled might have accumulator updates
accumUpdates = tk.accums
logWarning(failureReason)
None
case e: ExecutorLostFailure if !e.exitCausedByApp =>
logInfo(s"Task $tid failed because while it was being computed, its executor " +
"exited for a reason unrelated to the task. Not counting this failure towards the " +
"maximum number of failures for the task.")
None
case e: TaskFailedReason => // TaskResultLost and others
logWarning(failureReason)
None
}
sched.dagScheduler.taskEnded(tasks(index), reason, null, accumUpdates, info)
if (!isZombie && reason.countTowardsTaskFailures) {
assert (null != failureReason)
taskSetBlacklistHelperOpt.foreach(_.updateBlacklistForFailedTask(
info.host, info.executorId, index, failureReason))
numFailures(index) += 1
if (numFailures(index) >= maxTaskFailures) {
logError("Task %d in stage %s failed %d times; aborting job".format(
index, taskSet.id, maxTaskFailures))
abort("Task %d in stage %s failed %d times, most recent failure: %s\nDriver stacktrace:"
.format(index, taskSet.id, maxTaskFailures, failureReason), failureException)
return
}
}
if (successful(index)) {
logInfo(s"Task ${info.id} in stage ${taskSet.id} (TID $tid) failed, but the task will not" +
s" be re-executed (either because the task failed with a shuffle data fetch failure," +
s" so the previous stage needs to be re-run, or because a different copy of the task" +
s" has already succeeded).")
} else {
addPendingTask(index)
}
maybeFinishTaskSet()
}
def abort(message: String, exception: Option[Throwable] = None): Unit = sched.synchronized {
// TODO: Kill running tasks if we were not terminated due to a Mesos error
sched.dagScheduler.taskSetFailed(taskSet, message, exception)
isZombie = true
maybeFinishTaskSet()
}
/** If the given task ID is not in the set of running tasks, adds it.
*
* Used to keep track of the number of running tasks, for enforcing scheduling policies.
*/
def addRunningTask(tid: Long) {
if (runningTasksSet.add(tid) && parent != null) {
parent.increaseRunningTasks(1)
}
}
/** If the given task ID is in the set of running tasks, removes it. */
def removeRunningTask(tid: Long) {
if (runningTasksSet.remove(tid) && parent != null) {
parent.decreaseRunningTasks(1)
}
}
override def getSchedulableByName(name: String): Schedulable = {
null
}
override def addSchedulable(schedulable: Schedulable) {}
override def removeSchedulable(schedulable: Schedulable) {}
override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = {
val sortedTaskSetQueue = new ArrayBuffer[TaskSetManager]()
sortedTaskSetQueue += this
sortedTaskSetQueue
}
/** Called by TaskScheduler when an executor is lost so we can re-enqueue our tasks */
override def executorLost(execId: String, host: String, reason: ExecutorLossReason) {
// Re-enqueue any tasks that ran on the failed executor if this is a shuffle map stage,
// and we are not using an external shuffle server which could serve the shuffle outputs.
// The reason is the next stage wouldn't be able to fetch the data from this dead executor
// so we would need to rerun these tasks on other executors.
if (tasks(0).isInstanceOf[ShuffleMapTask] && !env.blockManager.externalShuffleServiceEnabled
&& !isZombie) {
for ((tid, info) <- taskInfos if info.executorId == execId) {
val index = taskInfos(tid).index
if (successful(index) && !killedByOtherAttempt.contains(tid)) {
successful(index) = false
copiesRunning(index) -= 1
tasksSuccessful -= 1
addPendingTask(index)
// Tell the DAGScheduler that this task was resubmitted so that it doesn't think our
// stage finishes when a total of tasks.size tasks finish.
sched.dagScheduler.taskEnded(
tasks(index), Resubmitted, null, Seq.empty, info)
}
}
}
for ((tid, info) <- taskInfos if info.running && info.executorId == execId) {
val exitCausedByApp: Boolean = reason match {
case exited: ExecutorExited => exited.exitCausedByApp
case ExecutorKilled => false
case _ => true
}
handleFailedTask(tid, TaskState.FAILED, ExecutorLostFailure(info.executorId, exitCausedByApp,
Some(reason.toString)))
}
// recalculate valid locality levels and waits when executor is lost
recomputeLocality()
}
/**
* Check for tasks to be speculated and return true if there are any. This is called periodically
* by the TaskScheduler.
*
*/
override def checkSpeculatableTasks(minTimeToSpeculation: Int): Boolean = {
// Can't speculate if we only have one task, and no need to speculate if the task set is a
// zombie.
if (isZombie || numTasks == 1) {
return false
}
var foundTasks = false
val minFinishedForSpeculation = (SPECULATION_QUANTILE * numTasks).floor.toInt
logDebug("Checking for speculative tasks: minFinished = " + minFinishedForSpeculation)
if (tasksSuccessful >= minFinishedForSpeculation && tasksSuccessful > 0) {
val time = clock.getTimeMillis()
val medianDuration = successfulTaskDurations.median
val threshold = max(SPECULATION_MULTIPLIER * medianDuration, minTimeToSpeculation)
// TODO: Threshold should also look at standard deviation of task durations and have a lower
// bound based on that.
logDebug("Task length threshold for speculation: " + threshold)
for (tid <- runningTasksSet) {
val info = taskInfos(tid)
val index = info.index
if (!successful(index) && copiesRunning(index) == 1 && info.timeRunning(time) > threshold &&
!speculatableTasks.contains(index)) {
logInfo(
"Marking task %d in stage %s (on %s) as speculatable because it ran more than %.0f ms"
.format(index, taskSet.id, info.host, threshold))
speculatableTasks += index
sched.dagScheduler.speculativeTaskSubmitted(tasks(index))
foundTasks = true
}
}
}
foundTasks
}
private def getLocalityWait(level: TaskLocality.TaskLocality): Long = {
val defaultWait = conf.get(config.LOCALITY_WAIT)
val localityWaitKey = level match {
case TaskLocality.PROCESS_LOCAL => "spark.locality.wait.process"
case TaskLocality.NODE_LOCAL => "spark.locality.wait.node"
case TaskLocality.RACK_LOCAL => "spark.locality.wait.rack"
case _ => null
}
if (localityWaitKey != null) {
conf.getTimeAsMs(localityWaitKey, defaultWait.toString)
} else {
0L
}
}
/**
* Compute the locality levels used in this TaskSet. Assumes that all tasks have already been
* added to queues using addPendingTask.
*
*/
private def computeValidLocalityLevels(): Array[TaskLocality.TaskLocality] = {
import TaskLocality.{PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY}
val levels = new ArrayBuffer[TaskLocality.TaskLocality]
if (!pendingTasksForExecutor.isEmpty &&
pendingTasksForExecutor.keySet.exists(sched.isExecutorAlive(_))) {
levels += PROCESS_LOCAL
}
if (!pendingTasksForHost.isEmpty &&
pendingTasksForHost.keySet.exists(sched.hasExecutorsAliveOnHost(_))) {
levels += NODE_LOCAL
}
if (!pendingTasksWithNoPrefs.isEmpty) {
levels += NO_PREF
}
if (!pendingTasksForRack.isEmpty &&
pendingTasksForRack.keySet.exists(sched.hasHostAliveOnRack(_))) {
levels += RACK_LOCAL
}
levels += ANY
logDebug("Valid locality levels for " + taskSet + ": " + levels.mkString(", "))
levels.toArray
}
def recomputeLocality() {
val previousLocalityLevel = myLocalityLevels(currentLocalityIndex)
myLocalityLevels = computeValidLocalityLevels()
localityWaits = myLocalityLevels.map(getLocalityWait)
currentLocalityIndex = getLocalityIndex(previousLocalityLevel)
}
def executorAdded() {
recomputeLocality()
}
}
private[spark] object TaskSetManager {
// The user will be warned if any stages contain a task that has a serialized size greater than
// this.
val TASK_SIZE_TO_WARN_KB = 100
}