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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.util.Properties
import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
import java.util.concurrent.atomic.AtomicInteger
import java.util.function.BiFunction
import scala.annotation.tailrec
import scala.collection.Map
import scala.collection.mutable.{ArrayStack, HashMap, HashSet}
import scala.concurrent.duration._
import scala.language.existentials
import scala.language.postfixOps
import scala.util.control.NonFatal
import org.apache.commons.lang3.SerializationUtils
import org.apache.spark._
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.executor.TaskMetrics
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config
import org.apache.spark.network.util.JavaUtils
import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult}
import org.apache.spark.rdd.{DeterministicLevel, RDD, RDDCheckpointData}
import org.apache.spark.rpc.RpcTimeout
import org.apache.spark.storage._
import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat
import org.apache.spark.util._
/**
* The high-level scheduling layer that implements stage-oriented scheduling. It computes a DAG of
* stages for each job, keeps track of which RDDs and stage outputs are materialized, and finds a
* minimal schedule to run the job. It then submits stages as TaskSets to an underlying
* TaskScheduler implementation that runs them on the cluster. A TaskSet contains fully independent
* tasks that can run right away based on the data that's already on the cluster (e.g. map output
* files from previous stages), though it may fail if this data becomes unavailable.
*
* Spark stages are created by breaking the RDD graph at shuffle boundaries. RDD operations with
* "narrow" dependencies, like map() and filter(), are pipelined together into one set of tasks
* in each stage, but operations with shuffle dependencies require multiple stages (one to write a
* set of map output files, and another to read those files after a barrier). In the end, every
* stage will have only shuffle dependencies on other stages, and may compute multiple operations
* inside it. The actual pipelining of these operations happens in the RDD.compute() functions of
* various RDDs
*
* In addition to coming up with a DAG of stages, the DAGScheduler also determines the preferred
* locations to run each task on, based on the current cache status, and passes these to the
* low-level TaskScheduler. Furthermore, it handles failures due to shuffle output files being
* lost, in which case old stages may need to be resubmitted. Failures *within* a stage that are
* not caused by shuffle file loss are handled by the TaskScheduler, which will retry each task
* a small number of times before cancelling the whole stage.
*
* When looking through this code, there are several key concepts:
*
* - Jobs (represented by [[ActiveJob]]) are the top-level work items submitted to the scheduler.
* For example, when the user calls an action, like count(), a job will be submitted through
* submitJob. Each Job may require the execution of multiple stages to build intermediate data.
*
* - Stages ([[Stage]]) are sets of tasks that compute intermediate results in jobs, where each
* task computes the same function on partitions of the same RDD. Stages are separated at shuffle
* boundaries, which introduce a barrier (where we must wait for the previous stage to finish to
* fetch outputs). There are two types of stages: [[ResultStage]], for the final stage that
* executes an action, and [[ShuffleMapStage]], which writes map output files for a shuffle.
* Stages are often shared across multiple jobs, if these jobs reuse the same RDDs.
*
* - Tasks are individual units of work, each sent to one machine.
*
* - Cache tracking: the DAGScheduler figures out which RDDs are cached to avoid recomputing them
* and likewise remembers which shuffle map stages have already produced output files to avoid
* redoing the map side of a shuffle.
*
* - Preferred locations: the DAGScheduler also computes where to run each task in a stage based
* on the preferred locations of its underlying RDDs, or the location of cached or shuffle data.
*
* - Cleanup: all data structures are cleared when the running jobs that depend on them finish,
* to prevent memory leaks in a long-running application.
*
* To recover from failures, the same stage might need to run multiple times, which are called
* "attempts". If the TaskScheduler reports that a task failed because a map output file from a
* previous stage was lost, the DAGScheduler resubmits that lost stage. This is detected through a
* CompletionEvent with FetchFailed, or an ExecutorLost event. The DAGScheduler will wait a small
* amount of time to see whether other nodes or tasks fail, then resubmit TaskSets for any lost
* stage(s) that compute the missing tasks. As part of this process, we might also have to create
* Stage objects for old (finished) stages where we previously cleaned up the Stage object. Since
* tasks from the old attempt of a stage could still be running, care must be taken to map any
* events received in the correct Stage object.
*
* Here's a checklist to use when making or reviewing changes to this class:
*
* - All data structures should be cleared when the jobs involving them end to avoid indefinite
* accumulation of state in long-running programs.
*
* - When adding a new data structure, update `DAGSchedulerSuite.assertDataStructuresEmpty` to
* include the new structure. This will help to catch memory leaks.
*/
private[spark] class DAGScheduler(
private[scheduler] val sc: SparkContext,
private[scheduler] val taskScheduler: TaskScheduler,
listenerBus: LiveListenerBus,
mapOutputTracker: MapOutputTrackerMaster,
blockManagerMaster: BlockManagerMaster,
env: SparkEnv,
clock: Clock = new SystemClock())
extends Logging {
def this(sc: SparkContext, taskScheduler: TaskScheduler) = {
this(
sc,
taskScheduler,
sc.listenerBus,
sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster],
sc.env.blockManager.master,
sc.env)
}
def this(sc: SparkContext) = this(sc, sc.taskScheduler)
private[spark] val metricsSource: DAGSchedulerSource = new DAGSchedulerSource(this)
private[scheduler] val nextJobId = new AtomicInteger(0)
private[scheduler] def numTotalJobs: Int = nextJobId.get()
private val nextStageId = new AtomicInteger(0)
private[scheduler] val jobIdToStageIds = new HashMap[Int, HashSet[Int]]
private[scheduler] val stageIdToStage = new HashMap[Int, Stage]
/**
* Mapping from shuffle dependency ID to the ShuffleMapStage that will generate the data for
* that dependency. Only includes stages that are part of currently running job (when the job(s)
* that require the shuffle stage complete, the mapping will be removed, and the only record of
* the shuffle data will be in the MapOutputTracker).
*/
private[scheduler] val shuffleIdToMapStage = new HashMap[Int, ShuffleMapStage]
private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob]
// Stages we need to run whose parents aren't done
private[scheduler] val waitingStages = new HashSet[Stage]
// Stages we are running right now
private[scheduler] val runningStages = new HashSet[Stage]
// Stages that must be resubmitted due to fetch failures
private[scheduler] val failedStages = new HashSet[Stage]
private[scheduler] val activeJobs = new HashSet[ActiveJob]
/**
* Contains the locations that each RDD's partitions are cached on. This map's keys are RDD ids
* and its values are arrays indexed by partition numbers. Each array value is the set of
* locations where that RDD partition is cached.
*
* All accesses to this map should be guarded by synchronizing on it (see SPARK-4454).
*/
private val cacheLocs = new HashMap[Int, IndexedSeq[Seq[TaskLocation]]]
// For tracking failed nodes, we use the MapOutputTracker's epoch number, which is sent with
// every task. When we detect a node failing, we note the current epoch number and failed
// executor, increment it for new tasks, and use this to ignore stray ShuffleMapTask results.
//
// TODO: Garbage collect information about failure epochs when we know there are no more
// stray messages to detect.
private val failedEpoch = new HashMap[String, Long]
private [scheduler] val outputCommitCoordinator = env.outputCommitCoordinator
// A closure serializer that we reuse.
// This is only safe because DAGScheduler runs in a single thread.
private val closureSerializer = SparkEnv.get.closureSerializer.newInstance()
/** If enabled, FetchFailed will not cause stage retry, in order to surface the problem. */
private val disallowStageRetryForTest = sc.getConf.getBoolean("spark.test.noStageRetry", false)
/**
* Whether to unregister all the outputs on the host in condition that we receive a FetchFailure,
* this is set default to false, which means, we only unregister the outputs related to the exact
* executor(instead of the host) on a FetchFailure.
*/
private[scheduler] val unRegisterOutputOnHostOnFetchFailure =
sc.getConf.get(config.UNREGISTER_OUTPUT_ON_HOST_ON_FETCH_FAILURE)
/**
* Number of consecutive stage attempts allowed before a stage is aborted.
*/
private[scheduler] val maxConsecutiveStageAttempts =
sc.getConf.getInt("spark.stage.maxConsecutiveAttempts",
DAGScheduler.DEFAULT_MAX_CONSECUTIVE_STAGE_ATTEMPTS)
/**
* Number of max concurrent tasks check failures for each barrier job.
*/
private[scheduler] val barrierJobIdToNumTasksCheckFailures = new ConcurrentHashMap[Int, Int]
/**
* Time in seconds to wait between a max concurrent tasks check failure and the next check.
*/
private val timeIntervalNumTasksCheck = sc.getConf
.get(config.BARRIER_MAX_CONCURRENT_TASKS_CHECK_INTERVAL)
/**
* Max number of max concurrent tasks check failures allowed for a job before fail the job
* submission.
*/
private val maxFailureNumTasksCheck = sc.getConf
.get(config.BARRIER_MAX_CONCURRENT_TASKS_CHECK_MAX_FAILURES)
private val messageScheduler =
ThreadUtils.newDaemonSingleThreadScheduledExecutor("dag-scheduler-message")
private[spark] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this)
taskScheduler.setDAGScheduler(this)
/**
* Called by the TaskSetManager to report task's starting.
*/
def taskStarted(task: Task[_], taskInfo: TaskInfo) {
eventProcessLoop.post(BeginEvent(task, taskInfo))
}
/**
* Called by the TaskSetManager to report that a task has completed
* and results are being fetched remotely.
*/
def taskGettingResult(taskInfo: TaskInfo) {
eventProcessLoop.post(GettingResultEvent(taskInfo))
}
/**
* Called by the TaskSetManager to report task completions or failures.
*/
def taskEnded(
task: Task[_],
reason: TaskEndReason,
result: Any,
accumUpdates: Seq[AccumulatorV2[_, _]],
taskInfo: TaskInfo): Unit = {
eventProcessLoop.post(
CompletionEvent(task, reason, result, accumUpdates, taskInfo))
}
/**
* Update metrics for in-progress tasks and let the master know that the BlockManager is still
* alive. Return true if the driver knows about the given block manager. Otherwise, return false,
* indicating that the block manager should re-register.
*/
def executorHeartbeatReceived(
execId: String,
// (taskId, stageId, stageAttemptId, accumUpdates)
accumUpdates: Array[(Long, Int, Int, Seq[AccumulableInfo])],
blockManagerId: BlockManagerId): Boolean = {
listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, accumUpdates))
blockManagerMaster.driverEndpoint.askSync[Boolean](
BlockManagerHeartbeat(blockManagerId), new RpcTimeout(600 seconds, "BlockManagerHeartbeat"))
}
/**
* Called by TaskScheduler implementation when an executor fails.
*/
def executorLost(execId: String, reason: ExecutorLossReason): Unit = {
eventProcessLoop.post(ExecutorLost(execId, reason))
}
/**
* Called by TaskScheduler implementation when a worker is removed.
*/
def workerRemoved(workerId: String, host: String, message: String): Unit = {
eventProcessLoop.post(WorkerRemoved(workerId, host, message))
}
/**
* Called by TaskScheduler implementation when a host is added.
*/
def executorAdded(execId: String, host: String): Unit = {
eventProcessLoop.post(ExecutorAdded(execId, host))
}
/**
* Called by the TaskSetManager to cancel an entire TaskSet due to either repeated failures or
* cancellation of the job itself.
*/
def taskSetFailed(taskSet: TaskSet, reason: String, exception: Option[Throwable]): Unit = {
eventProcessLoop.post(TaskSetFailed(taskSet, reason, exception))
}
/**
* Called by the TaskSetManager when it decides a speculative task is needed.
*/
def speculativeTaskSubmitted(task: Task[_]): Unit = {
eventProcessLoop.post(SpeculativeTaskSubmitted(task))
}
private[scheduler]
def getCacheLocs(rdd: RDD[_]): IndexedSeq[Seq[TaskLocation]] = cacheLocs.synchronized {
// Note: this doesn't use `getOrElse()` because this method is called O(num tasks) times
if (!cacheLocs.contains(rdd.id)) {
// Note: if the storage level is NONE, we don't need to get locations from block manager.
val locs: IndexedSeq[Seq[TaskLocation]] = if (rdd.getStorageLevel == StorageLevel.NONE) {
IndexedSeq.fill(rdd.partitions.length)(Nil)
} else {
val blockIds =
rdd.partitions.indices.map(index => RDDBlockId(rdd.id, index)).toArray[BlockId]
blockManagerMaster.getLocations(blockIds).map { bms =>
bms.map(bm => TaskLocation(bm.host, bm.executorId))
}
}
cacheLocs(rdd.id) = locs
}
cacheLocs(rdd.id)
}
private def clearCacheLocs(): Unit = cacheLocs.synchronized {
cacheLocs.clear()
}
/**
* Gets a shuffle map stage if one exists in shuffleIdToMapStage. Otherwise, if the
* shuffle map stage doesn't already exist, this method will create the shuffle map stage in
* addition to any missing ancestor shuffle map stages.
*/
private def getOrCreateShuffleMapStage(
shuffleDep: ShuffleDependency[_, _, _],
firstJobId: Int): ShuffleMapStage = {
shuffleIdToMapStage.get(shuffleDep.shuffleId) match {
case Some(stage) =>
stage
case None =>
// Create stages for all missing ancestor shuffle dependencies.
getMissingAncestorShuffleDependencies(shuffleDep.rdd).foreach { dep =>
// Even though getMissingAncestorShuffleDependencies only returns shuffle dependencies
// that were not already in shuffleIdToMapStage, it's possible that by the time we
// get to a particular dependency in the foreach loop, it's been added to
// shuffleIdToMapStage by the stage creation process for an earlier dependency. See
// SPARK-13902 for more information.
if (!shuffleIdToMapStage.contains(dep.shuffleId)) {
createShuffleMapStage(dep, firstJobId)
}
}
// Finally, create a stage for the given shuffle dependency.
createShuffleMapStage(shuffleDep, firstJobId)
}
}
/**
* Check to make sure we don't launch a barrier stage with unsupported RDD chain pattern. The
* following patterns are not supported:
* 1. Ancestor RDDs that have different number of partitions from the resulting RDD (eg.
* union()/coalesce()/first()/take()/PartitionPruningRDD);
* 2. An RDD that depends on multiple barrier RDDs (eg. barrierRdd1.zip(barrierRdd2)).
*/
private def checkBarrierStageWithRDDChainPattern(rdd: RDD[_], numTasksInStage: Int): Unit = {
val predicate: RDD[_] => Boolean = (r =>
r.getNumPartitions == numTasksInStage && r.dependencies.filter(_.rdd.isBarrier()).size <= 1)
if (rdd.isBarrier() && !traverseParentRDDsWithinStage(rdd, predicate)) {
throw new BarrierJobUnsupportedRDDChainException
}
}
/**
* Creates a ShuffleMapStage that generates the given shuffle dependency's partitions. If a
* previously run stage generated the same shuffle data, this function will copy the output
* locations that are still available from the previous shuffle to avoid unnecessarily
* regenerating data.
*/
def createShuffleMapStage(shuffleDep: ShuffleDependency[_, _, _], jobId: Int): ShuffleMapStage = {
val rdd = shuffleDep.rdd
checkBarrierStageWithDynamicAllocation(rdd)
checkBarrierStageWithNumSlots(rdd)
checkBarrierStageWithRDDChainPattern(rdd, rdd.getNumPartitions)
val numTasks = rdd.partitions.length
val parents = getOrCreateParentStages(rdd, jobId)
val id = nextStageId.getAndIncrement()
val stage = new ShuffleMapStage(
id, rdd, numTasks, parents, jobId, rdd.creationSite, shuffleDep, mapOutputTracker)
stageIdToStage(id) = stage
shuffleIdToMapStage(shuffleDep.shuffleId) = stage
updateJobIdStageIdMaps(jobId, stage)
if (!mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) {
// Kind of ugly: need to register RDDs with the cache and map output tracker here
// since we can't do it in the RDD constructor because # of partitions is unknown
logInfo("Registering RDD " + rdd.id + " (" + rdd.getCreationSite + ")")
mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.length)
}
stage
}
/**
* We don't support run a barrier stage with dynamic resource allocation enabled, it shall lead
* to some confusing behaviors (eg. with dynamic resource allocation enabled, it may happen that
* we acquire some executors (but not enough to launch all the tasks in a barrier stage) and
* later release them due to executor idle time expire, and then acquire again).
*
* We perform the check on job submit and fail fast if running a barrier stage with dynamic
* resource allocation enabled.
*
* TODO SPARK-24942 Improve cluster resource management with jobs containing barrier stage
*/
private def checkBarrierStageWithDynamicAllocation(rdd: RDD[_]): Unit = {
if (rdd.isBarrier() && Utils.isDynamicAllocationEnabled(sc.getConf)) {
throw new BarrierJobRunWithDynamicAllocationException
}
}
/**
* Check whether the barrier stage requires more slots (to be able to launch all tasks in the
* barrier stage together) than the total number of active slots currently. Fail current check
* if trying to submit a barrier stage that requires more slots than current total number. If
* the check fails consecutively beyond a configured number for a job, then fail current job
* submission.
*/
private def checkBarrierStageWithNumSlots(rdd: RDD[_]): Unit = {
if (rdd.isBarrier() && rdd.getNumPartitions > sc.maxNumConcurrentTasks) {
throw new BarrierJobSlotsNumberCheckFailed
}
}
/**
* Create a ResultStage associated with the provided jobId.
*/
private def createResultStage(
rdd: RDD[_],
func: (TaskContext, Iterator[_]) => _,
partitions: Array[Int],
jobId: Int,
callSite: CallSite): ResultStage = {
checkBarrierStageWithDynamicAllocation(rdd)
checkBarrierStageWithNumSlots(rdd)
checkBarrierStageWithRDDChainPattern(rdd, partitions.toSet.size)
val parents = getOrCreateParentStages(rdd, jobId)
val id = nextStageId.getAndIncrement()
val stage = new ResultStage(id, rdd, func, partitions, parents, jobId, callSite)
stageIdToStage(id) = stage
updateJobIdStageIdMaps(jobId, stage)
stage
}
/**
* Get or create the list of parent stages for a given RDD. The new Stages will be created with
* the provided firstJobId.
*/
private def getOrCreateParentStages(rdd: RDD[_], firstJobId: Int): List[Stage] = {
getShuffleDependencies(rdd).map { shuffleDep =>
getOrCreateShuffleMapStage(shuffleDep, firstJobId)
}.toList
}
/** Find ancestor shuffle dependencies that are not registered in shuffleToMapStage yet */
private def getMissingAncestorShuffleDependencies(
rdd: RDD[_]): ArrayStack[ShuffleDependency[_, _, _]] = {
val ancestors = new ArrayStack[ShuffleDependency[_, _, _]]
val visited = new HashSet[RDD[_]]
// We are manually maintaining a stack here to prevent StackOverflowError
// caused by recursively visiting
val waitingForVisit = new ArrayStack[RDD[_]]
waitingForVisit.push(rdd)
while (waitingForVisit.nonEmpty) {
val toVisit = waitingForVisit.pop()
if (!visited(toVisit)) {
visited += toVisit
getShuffleDependencies(toVisit).foreach { shuffleDep =>
if (!shuffleIdToMapStage.contains(shuffleDep.shuffleId)) {
ancestors.push(shuffleDep)
waitingForVisit.push(shuffleDep.rdd)
} // Otherwise, the dependency and its ancestors have already been registered.
}
}
}
ancestors
}
/**
* Returns shuffle dependencies that are immediate parents of the given RDD.
*
* This function will not return more distant ancestors. For example, if C has a shuffle
* dependency on B which has a shuffle dependency on A:
*
* A <-- B <-- C
*
* calling this function with rdd C will only return the B <-- C dependency.
*
* This function is scheduler-visible for the purpose of unit testing.
*/
private[scheduler] def getShuffleDependencies(
rdd: RDD[_]): HashSet[ShuffleDependency[_, _, _]] = {
val parents = new HashSet[ShuffleDependency[_, _, _]]
val visited = new HashSet[RDD[_]]
val waitingForVisit = new ArrayStack[RDD[_]]
waitingForVisit.push(rdd)
while (waitingForVisit.nonEmpty) {
val toVisit = waitingForVisit.pop()
if (!visited(toVisit)) {
visited += toVisit
toVisit.dependencies.foreach {
case shuffleDep: ShuffleDependency[_, _, _] =>
parents += shuffleDep
case dependency =>
waitingForVisit.push(dependency.rdd)
}
}
}
parents
}
/**
* Traverses the given RDD and its ancestors within the same stage and checks whether all of the
* RDDs satisfy a given predicate.
*/
private def traverseParentRDDsWithinStage(rdd: RDD[_], predicate: RDD[_] => Boolean): Boolean = {
val visited = new HashSet[RDD[_]]
val waitingForVisit = new ArrayStack[RDD[_]]
waitingForVisit.push(rdd)
while (waitingForVisit.nonEmpty) {
val toVisit = waitingForVisit.pop()
if (!visited(toVisit)) {
if (!predicate(toVisit)) {
return false
}
visited += toVisit
toVisit.dependencies.foreach {
case _: ShuffleDependency[_, _, _] =>
// Not within the same stage with current rdd, do nothing.
case dependency =>
waitingForVisit.push(dependency.rdd)
}
}
}
true
}
private def getMissingParentStages(stage: Stage): List[Stage] = {
val missing = new HashSet[Stage]
val visited = new HashSet[RDD[_]]
// We are manually maintaining a stack here to prevent StackOverflowError
// caused by recursively visiting
val waitingForVisit = new ArrayStack[RDD[_]]
def visit(rdd: RDD[_]) {
if (!visited(rdd)) {
visited += rdd
val rddHasUncachedPartitions = getCacheLocs(rdd).contains(Nil)
if (rddHasUncachedPartitions) {
for (dep <- rdd.dependencies) {
dep match {
case shufDep: ShuffleDependency[_, _, _] =>
val mapStage = getOrCreateShuffleMapStage(shufDep, stage.firstJobId)
if (!mapStage.isAvailable) {
missing += mapStage
}
case narrowDep: NarrowDependency[_] =>
waitingForVisit.push(narrowDep.rdd)
}
}
}
}
}
waitingForVisit.push(stage.rdd)
while (waitingForVisit.nonEmpty) {
visit(waitingForVisit.pop())
}
missing.toList
}
/**
* Registers the given jobId among the jobs that need the given stage and
* all of that stage's ancestors.
*/
private def updateJobIdStageIdMaps(jobId: Int, stage: Stage): Unit = {
@tailrec
def updateJobIdStageIdMapsList(stages: List[Stage]) {
if (stages.nonEmpty) {
val s = stages.head
s.jobIds += jobId
jobIdToStageIds.getOrElseUpdate(jobId, new HashSet[Int]()) += s.id
val parentsWithoutThisJobId = s.parents.filter { ! _.jobIds.contains(jobId) }
updateJobIdStageIdMapsList(parentsWithoutThisJobId ++ stages.tail)
}
}
updateJobIdStageIdMapsList(List(stage))
}
/**
* Removes state for job and any stages that are not needed by any other job. Does not
* handle cancelling tasks or notifying the SparkListener about finished jobs/stages/tasks.
*
* @param job The job whose state to cleanup.
*/
private def cleanupStateForJobAndIndependentStages(job: ActiveJob): Unit = {
val registeredStages = jobIdToStageIds.get(job.jobId)
if (registeredStages.isEmpty || registeredStages.get.isEmpty) {
logError("No stages registered for job " + job.jobId)
} else {
stageIdToStage.filterKeys(stageId => registeredStages.get.contains(stageId)).foreach {
case (stageId, stage) =>
val jobSet = stage.jobIds
if (!jobSet.contains(job.jobId)) {
logError(
"Job %d not registered for stage %d even though that stage was registered for the job"
.format(job.jobId, stageId))
} else {
def removeStage(stageId: Int) {
// data structures based on Stage
for (stage <- stageIdToStage.get(stageId)) {
if (runningStages.contains(stage)) {
logDebug("Removing running stage %d".format(stageId))
runningStages -= stage
}
for ((k, v) <- shuffleIdToMapStage.find(_._2 == stage)) {
shuffleIdToMapStage.remove(k)
}
if (waitingStages.contains(stage)) {
logDebug("Removing stage %d from waiting set.".format(stageId))
waitingStages -= stage
}
if (failedStages.contains(stage)) {
logDebug("Removing stage %d from failed set.".format(stageId))
failedStages -= stage
}
}
// data structures based on StageId
stageIdToStage -= stageId
logDebug("After removal of stage %d, remaining stages = %d"
.format(stageId, stageIdToStage.size))
}
jobSet -= job.jobId
if (jobSet.isEmpty) { // no other job needs this stage
removeStage(stageId)
}
}
}
}
jobIdToStageIds -= job.jobId
jobIdToActiveJob -= job.jobId
activeJobs -= job
job.finalStage match {
case r: ResultStage => r.removeActiveJob()
case m: ShuffleMapStage => m.removeActiveJob(job)
}
}
/**
* Submit an action job to the scheduler.
*
* @param rdd target RDD to run tasks on
* @param func a function to run on each partition of the RDD
* @param partitions set of partitions to run on; some jobs may not want to compute on all
* partitions of the target RDD, e.g. for operations like first()
* @param callSite where in the user program this job was called
* @param resultHandler callback to pass each result to
* @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
*
* @return a JobWaiter object that can be used to block until the job finishes executing
* or can be used to cancel the job.
*
* @throws IllegalArgumentException when partitions ids are illegal
*/
def submitJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): JobWaiter[U] = {
// Check to make sure we are not launching a task on a partition that does not exist.
val maxPartitions = rdd.partitions.length
partitions.find(p => p >= maxPartitions || p < 0).foreach { p =>
throw new IllegalArgumentException(
"Attempting to access a non-existent partition: " + p + ". " +
"Total number of partitions: " + maxPartitions)
}
val jobId = nextJobId.getAndIncrement()
if (partitions.size == 0) {
// Return immediately if the job is running 0 tasks
return new JobWaiter[U](this, jobId, 0, resultHandler)
}
assert(partitions.size > 0)
val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
eventProcessLoop.post(JobSubmitted(
jobId, rdd, func2, partitions.toArray, callSite, waiter,
SerializationUtils.clone(properties)))
waiter
}
/**
* Run an action job on the given RDD and pass all the results to the resultHandler function as
* they arrive.
*
* @param rdd target RDD to run tasks on
* @param func a function to run on each partition of the RDD
* @param partitions set of partitions to run on; some jobs may not want to compute on all
* partitions of the target RDD, e.g. for operations like first()
* @param callSite where in the user program this job was called
* @param resultHandler callback to pass each result to
* @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
*
* @note Throws `Exception` when the job fails
*/
def runJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): Unit = {
val start = System.nanoTime
val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
ThreadUtils.awaitReady(waiter.completionFuture, Duration.Inf)
waiter.completionFuture.value.get match {
case scala.util.Success(_) =>
logInfo("Job %d finished: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
case scala.util.Failure(exception) =>
logInfo("Job %d failed: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
// SPARK-8644: Include user stack trace in exceptions coming from DAGScheduler.
val callerStackTrace = Thread.currentThread().getStackTrace.tail
exception.setStackTrace(exception.getStackTrace ++ callerStackTrace)
throw exception
}
}
/**
* Run an approximate job on the given RDD and pass all the results to an ApproximateEvaluator
* as they arrive. Returns a partial result object from the evaluator.
*
* @param rdd target RDD to run tasks on
* @param func a function to run on each partition of the RDD
* @param evaluator `ApproximateEvaluator` to receive the partial results
* @param callSite where in the user program this job was called
* @param timeout maximum time to wait for the job, in milliseconds
* @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
*/
def runApproximateJob[T, U, R](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
evaluator: ApproximateEvaluator[U, R],
callSite: CallSite,
timeout: Long,
properties: Properties): PartialResult[R] = {
val listener = new ApproximateActionListener(rdd, func, evaluator, timeout)
val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
val partitions = (0 until rdd.partitions.length).toArray
val jobId = nextJobId.getAndIncrement()
eventProcessLoop.post(JobSubmitted(
jobId, rdd, func2, partitions, callSite, listener, SerializationUtils.clone(properties)))
listener.awaitResult() // Will throw an exception if the job fails
}
/**
* Submit a shuffle map stage to run independently and get a JobWaiter object back. The waiter
* can be used to block until the job finishes executing or can be used to cancel the job.
* This method is used for adaptive query planning, to run map stages and look at statistics
* about their outputs before submitting downstream stages.
*
* @param dependency the ShuffleDependency to run a map stage for
* @param callback function called with the result of the job, which in this case will be a
* single MapOutputStatistics object showing how much data was produced for each partition
* @param callSite where in the user program this job was submitted
* @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
*/
def submitMapStage[K, V, C](
dependency: ShuffleDependency[K, V, C],
callback: MapOutputStatistics => Unit,
callSite: CallSite,
properties: Properties): JobWaiter[MapOutputStatistics] = {
val rdd = dependency.rdd
val jobId = nextJobId.getAndIncrement()
if (rdd.partitions.length == 0) {
throw new SparkException("Can't run submitMapStage on RDD with 0 partitions")
}
// We create a JobWaiter with only one "task", which will be marked as complete when the whole
// map stage has completed, and will be passed the MapOutputStatistics for that stage.
// This makes it easier to avoid race conditions between the user code and the map output
// tracker that might result if we told the user the stage had finished, but then they queries
// the map output tracker and some node failures had caused the output statistics to be lost.
val waiter = new JobWaiter(this, jobId, 1, (i: Int, r: MapOutputStatistics) => callback(r))
eventProcessLoop.post(MapStageSubmitted(
jobId, dependency, callSite, waiter, SerializationUtils.clone(properties)))
waiter
}
/**
* Cancel a job that is running or waiting in the queue.
*/
def cancelJob(jobId: Int, reason: Option[String]): Unit = {
logInfo("Asked to cancel job " + jobId)
eventProcessLoop.post(JobCancelled(jobId, reason))
}
/**
* Cancel all jobs in the given job group ID.
*/
def cancelJobGroup(groupId: String): Unit = {
logInfo("Asked to cancel job group " + groupId)
eventProcessLoop.post(JobGroupCancelled(groupId))
}
/**
* Cancel all jobs that are running or waiting in the queue.
*/
def cancelAllJobs(): Unit = {
eventProcessLoop.post(AllJobsCancelled)
}
private[scheduler] def doCancelAllJobs() {
// Cancel all running jobs.
runningStages.map(_.firstJobId).foreach(handleJobCancellation(_,
Option("as part of cancellation of all jobs")))
activeJobs.clear() // These should already be empty by this point,
jobIdToActiveJob.clear() // but just in case we lost track of some jobs...
}
/**
* Cancel all jobs associated with a running or scheduled stage.
*/
def cancelStage(stageId: Int, reason: Option[String]) {
eventProcessLoop.post(StageCancelled(stageId, reason))
}
/**
* Kill a given task. It will be retried.
*
* @return Whether the task was successfully killed.
*/
def killTaskAttempt(taskId: Long, interruptThread: Boolean, reason: String): Boolean = {
taskScheduler.killTaskAttempt(taskId, interruptThread, reason)
}
/**
* Resubmit any failed stages. Ordinarily called after a small amount of time has passed since
* the last fetch failure.
*/
private[scheduler] def resubmitFailedStages() {
if (failedStages.size > 0) {
// Failed stages may be removed by job cancellation, so failed might be empty even if
// the ResubmitFailedStages event has been scheduled.
logInfo("Resubmitting failed stages")
clearCacheLocs()
val failedStagesCopy = failedStages.toArray
failedStages.clear()
for (stage <- failedStagesCopy.sortBy(_.firstJobId)) {
submitStage(stage)
}
}
}
/**
* Check for waiting stages which are now eligible for resubmission.
* Submits stages that depend on the given parent stage. Called when the parent stage completes
* successfully.
*/
private def submitWaitingChildStages(parent: Stage) {
logTrace(s"Checking if any dependencies of $parent are now runnable")
logTrace("running: " + runningStages)
logTrace("waiting: " + waitingStages)
logTrace("failed: " + failedStages)
val childStages = waitingStages.filter(_.parents.contains(parent)).toArray
waitingStages --= childStages
for (stage <- childStages.sortBy(_.firstJobId)) {
submitStage(stage)
}
}
/** Finds the earliest-created active job that needs the stage */
// TODO: Probably should actually find among the active jobs that need this
// stage the one with the highest priority (highest-priority pool, earliest created).
// That should take care of at least part of the priority inversion problem with
// cross-job dependencies.
private def activeJobForStage(stage: Stage): Option[Int] = {
val jobsThatUseStage: Array[Int] = stage.jobIds.toArray.sorted
jobsThatUseStage.find(jobIdToActiveJob.contains)
}
private[scheduler] def handleJobGroupCancelled(groupId: String) {
// Cancel all jobs belonging to this job group.
// First finds all active jobs with this group id, and then kill stages for them.
val activeInGroup = activeJobs.filter { activeJob =>
Option(activeJob.properties).exists {
_.getProperty(SparkContext.SPARK_JOB_GROUP_ID) == groupId
}
}
val jobIds = activeInGroup.map(_.jobId)
jobIds.foreach(handleJobCancellation(_,
Option("part of cancelled job group %s".format(groupId))))
}
private[scheduler] def handleBeginEvent(task: Task[_], taskInfo: TaskInfo) {
// Note that there is a chance that this task is launched after the stage is cancelled.
// In that case, we wouldn't have the stage anymore in stageIdToStage.
val stageAttemptId =
stageIdToStage.get(task.stageId).map(_.latestInfo.attemptNumber).getOrElse(-1)
listenerBus.post(SparkListenerTaskStart(task.stageId, stageAttemptId, taskInfo))
}
private[scheduler] def handleSpeculativeTaskSubmitted(task: Task[_]): Unit = {
listenerBus.post(SparkListenerSpeculativeTaskSubmitted(task.stageId))
}
private[scheduler] def handleTaskSetFailed(
taskSet: TaskSet,
reason: String,
exception: Option[Throwable]): Unit = {
stageIdToStage.get(taskSet.stageId).foreach { abortStage(_, reason, exception) }
}
private[scheduler] def cleanUpAfterSchedulerStop() {
for (job <- activeJobs) {
val error =
new SparkException(s"Job ${job.jobId} cancelled because SparkContext was shut down")
job.listener.jobFailed(error)
// Tell the listeners that all of the running stages have ended. Don't bother
// cancelling the stages because if the DAG scheduler is stopped, the entire application
// is in the process of getting stopped.
val stageFailedMessage = "Stage cancelled because SparkContext was shut down"
// The `toArray` here is necessary so that we don't iterate over `runningStages` while
// mutating it.
runningStages.toArray.foreach { stage =>
markStageAsFinished(stage, Some(stageFailedMessage))
}
listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobFailed(error)))
}
}
private[scheduler] def handleGetTaskResult(taskInfo: TaskInfo) {
listenerBus.post(SparkListenerTaskGettingResult(taskInfo))
}
private[scheduler] def handleJobSubmitted(jobId: Int,
finalRDD: RDD[_],
func: (TaskContext, Iterator[_]) => _,
partitions: Array[Int],
callSite: CallSite,
listener: JobListener,
properties: Properties) {
var finalStage: ResultStage = null
try {
// New stage creation may throw an exception if, for example, jobs are run on a
// HadoopRDD whose underlying HDFS files have been deleted.
finalStage = createResultStage(finalRDD, func, partitions, jobId, callSite)
} catch {
case e: BarrierJobSlotsNumberCheckFailed =>
logWarning(s"The job $jobId requires to run a barrier stage that requires more slots " +
"than the total number of slots in the cluster currently.")
// If jobId doesn't exist in the map, Scala coverts its value null to 0: Int automatically.
val numCheckFailures = barrierJobIdToNumTasksCheckFailures.compute(jobId,
new BiFunction[Int, Int, Int] {
override def apply(key: Int, value: Int): Int = value + 1
})
if (numCheckFailures <= maxFailureNumTasksCheck) {
messageScheduler.schedule(
new Runnable {
override def run(): Unit = eventProcessLoop.post(JobSubmitted(jobId, finalRDD, func,
partitions, callSite, listener, properties))
},
timeIntervalNumTasksCheck,
TimeUnit.SECONDS
)
return
} else {
// Job failed, clear internal data.
barrierJobIdToNumTasksCheckFailures.remove(jobId)
listener.jobFailed(e)
return
}
case e: Exception =>
logWarning("Creating new stage failed due to exception - job: " + jobId, e)
listener.jobFailed(e)
return
}
// Job submitted, clear internal data.
barrierJobIdToNumTasksCheckFailures.remove(jobId)
val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
clearCacheLocs()
logInfo("Got job %s (%s) with %d output partitions".format(
job.jobId, callSite.shortForm, partitions.length))
logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
logInfo("Parents of final stage: " + finalStage.parents)
logInfo("Missing parents: " + getMissingParentStages(finalStage))
val jobSubmissionTime = clock.getTimeMillis()
jobIdToActiveJob(jobId) = job
activeJobs += job
finalStage.setActiveJob(job)
val stageIds = jobIdToStageIds(jobId).toArray
val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
listenerBus.post(
SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
submitStage(finalStage)
}
private[scheduler] def handleMapStageSubmitted(jobId: Int,
dependency: ShuffleDependency[_, _, _],
callSite: CallSite,
listener: JobListener,
properties: Properties) {
// Submitting this map stage might still require the creation of some parent stages, so make
// sure that happens.
var finalStage: ShuffleMapStage = null
try {
// New stage creation may throw an exception if, for example, jobs are run on a
// HadoopRDD whose underlying HDFS files have been deleted.
finalStage = getOrCreateShuffleMapStage(dependency, jobId)
} catch {
case e: Exception =>
logWarning("Creating new stage failed due to exception - job: " + jobId, e)
listener.jobFailed(e)
return
}
val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
clearCacheLocs()
logInfo("Got map stage job %s (%s) with %d output partitions".format(
jobId, callSite.shortForm, dependency.rdd.partitions.length))
logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
logInfo("Parents of final stage: " + finalStage.parents)
logInfo("Missing parents: " + getMissingParentStages(finalStage))
val jobSubmissionTime = clock.getTimeMillis()
jobIdToActiveJob(jobId) = job
activeJobs += job
finalStage.addActiveJob(job)
val stageIds = jobIdToStageIds(jobId).toArray
val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
listenerBus.post(
SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
submitStage(finalStage)
// If the whole stage has already finished, tell the listener and remove it
if (finalStage.isAvailable) {
markMapStageJobAsFinished(job, mapOutputTracker.getStatistics(dependency))
}
}
/** Submits stage, but first recursively submits any missing parents. */
private def submitStage(stage: Stage) {
val jobId = activeJobForStage(stage)
if (jobId.isDefined) {
logDebug("submitStage(" + stage + ")")
if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) {
val missing = getMissingParentStages(stage).sortBy(_.id)
logDebug("missing: " + missing)
if (missing.isEmpty) {
logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents")
submitMissingTasks(stage, jobId.get)
} else {
for (parent <- missing) {
submitStage(parent)
}
waitingStages += stage
}
}
} else {
abortStage(stage, "No active job for stage " + stage.id, None)
}
}
/** Called when stage's parents are available and we can now do its task. */
private def submitMissingTasks(stage: Stage, jobId: Int) {
logDebug("submitMissingTasks(" + stage + ")")
// First figure out the indexes of partition ids to compute.
val partitionsToCompute: Seq[Int] = stage.findMissingPartitions()
// Use the scheduling pool, job group, description, etc. from an ActiveJob associated
// with this Stage
val properties = jobIdToActiveJob(jobId).properties
runningStages += stage
// SparkListenerStageSubmitted should be posted before testing whether tasks are
// serializable. If tasks are not serializable, a SparkListenerStageCompleted event
// will be posted, which should always come after a corresponding SparkListenerStageSubmitted
// event.
stage match {
case s: ShuffleMapStage =>
outputCommitCoordinator.stageStart(stage = s.id, maxPartitionId = s.numPartitions - 1)
case s: ResultStage =>
outputCommitCoordinator.stageStart(
stage = s.id, maxPartitionId = s.rdd.partitions.length - 1)
}
val taskIdToLocations: Map[Int, Seq[TaskLocation]] = try {
stage match {
case s: ShuffleMapStage =>
partitionsToCompute.map { id => (id, getPreferredLocs(stage.rdd, id))}.toMap
case s: ResultStage =>
partitionsToCompute.map { id =>
val p = s.partitions(id)
(id, getPreferredLocs(stage.rdd, p))
}.toMap
}
} catch {
case NonFatal(e) =>
stage.makeNewStageAttempt(partitionsToCompute.size)
listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
abortStage(stage, s"Task creation failed: $e\n${Utils.exceptionString(e)}", Some(e))
runningStages -= stage
return
}
stage.makeNewStageAttempt(partitionsToCompute.size, taskIdToLocations.values.toSeq)
// If there are tasks to execute, record the submission time of the stage. Otherwise,
// post the even without the submission time, which indicates that this stage was
// skipped.
if (partitionsToCompute.nonEmpty) {
stage.latestInfo.submissionTime = Some(clock.getTimeMillis())
}
listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
// TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times.
// Broadcasted binary for the task, used to dispatch tasks to executors. Note that we broadcast
// the serialized copy of the RDD and for each task we will deserialize it, which means each
// task gets a different copy of the RDD. This provides stronger isolation between tasks that
// might modify state of objects referenced in their closures. This is necessary in Hadoop
// where the JobConf/Configuration object is not thread-safe.
var taskBinary: Broadcast[Array[Byte]] = null
var partitions: Array[Partition] = null
try {
// For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
// For ResultTask, serialize and broadcast (rdd, func).
var taskBinaryBytes: Array[Byte] = null
// taskBinaryBytes and partitions are both effected by the checkpoint status. We need
// this synchronization in case another concurrent job is checkpointing this RDD, so we get a
// consistent view of both variables.
RDDCheckpointData.synchronized {
taskBinaryBytes = stage match {
case stage: ShuffleMapStage =>
JavaUtils.bufferToArray(
closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef))
case stage: ResultStage =>
JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef))
}
partitions = stage.rdd.partitions
}
taskBinary = sc.broadcast(taskBinaryBytes)
} catch {
// In the case of a failure during serialization, abort the stage.
case e: NotSerializableException =>
abortStage(stage, "Task not serializable: " + e.toString, Some(e))
runningStages -= stage
// Abort execution
return
case NonFatal(e) =>
abortStage(stage, s"Task serialization failed: $e\n${Utils.exceptionString(e)}", Some(e))
runningStages -= stage
return
}
val tasks: Seq[Task[_]] = try {
val serializedTaskMetrics = closureSerializer.serialize(stage.latestInfo.taskMetrics).array()
stage match {
case stage: ShuffleMapStage =>
stage.pendingPartitions.clear()
partitionsToCompute.map { id =>
val locs = taskIdToLocations(id)
val part = partitions(id)
stage.pendingPartitions += id
new ShuffleMapTask(stage.id, stage.latestInfo.attemptNumber,
taskBinary, part, locs, properties, serializedTaskMetrics, Option(jobId),
Option(sc.applicationId), sc.applicationAttemptId, stage.rdd.isBarrier())
}
case stage: ResultStage =>
partitionsToCompute.map { id =>
val p: Int = stage.partitions(id)
val part = partitions(p)
val locs = taskIdToLocations(id)
new ResultTask(stage.id, stage.latestInfo.attemptNumber,
taskBinary, part, locs, id, properties, serializedTaskMetrics,
Option(jobId), Option(sc.applicationId), sc.applicationAttemptId,
stage.rdd.isBarrier())
}
}
} catch {
case NonFatal(e) =>
abortStage(stage, s"Task creation failed: $e\n${Utils.exceptionString(e)}", Some(e))
runningStages -= stage
return
}
if (tasks.size > 0) {
logInfo(s"Submitting ${tasks.size} missing tasks from $stage (${stage.rdd}) (first 15 " +
s"tasks are for partitions ${tasks.take(15).map(_.partitionId)})")
taskScheduler.submitTasks(new TaskSet(
tasks.toArray, stage.id, stage.latestInfo.attemptNumber, jobId, properties))
} else {
// Because we posted SparkListenerStageSubmitted earlier, we should mark
// the stage as completed here in case there are no tasks to run
markStageAsFinished(stage, None)
stage match {
case stage: ShuffleMapStage =>
logDebug(s"Stage ${stage} is actually done; " +
s"(available: ${stage.isAvailable}," +
s"available outputs: ${stage.numAvailableOutputs}," +
s"partitions: ${stage.numPartitions})")
markMapStageJobsAsFinished(stage)
case stage : ResultStage =>
logDebug(s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})")
}
submitWaitingChildStages(stage)
}
}
/**
* Merge local values from a task into the corresponding accumulators previously registered
* here on the driver.
*
* Although accumulators themselves are not thread-safe, this method is called only from one
* thread, the one that runs the scheduling loop. This means we only handle one task
* completion event at a time so we don't need to worry about locking the accumulators.
* This still doesn't stop the caller from updating the accumulator outside the scheduler,
* but that's not our problem since there's nothing we can do about that.
*/
private def updateAccumulators(event: CompletionEvent): Unit = {
val task = event.task
val stage = stageIdToStage(task.stageId)
event.accumUpdates.foreach { updates =>
val id = updates.id
try {
// Find the corresponding accumulator on the driver and update it
val acc: AccumulatorV2[Any, Any] = AccumulatorContext.get(id) match {
case Some(accum) => accum.asInstanceOf[AccumulatorV2[Any, Any]]
case None =>
throw new SparkException(s"attempted to access non-existent accumulator $id")
}
acc.merge(updates.asInstanceOf[AccumulatorV2[Any, Any]])
// To avoid UI cruft, ignore cases where value wasn't updated
if (acc.name.isDefined && !updates.isZero) {
stage.latestInfo.accumulables(id) = acc.toInfo(None, Some(acc.value))
event.taskInfo.setAccumulables(
acc.toInfo(Some(updates.value), Some(acc.value)) +: event.taskInfo.accumulables)
}
} catch {
case NonFatal(e) =>
// Log the class name to make it easy to find the bad implementation
val accumClassName = AccumulatorContext.get(id) match {
case Some(accum) => accum.getClass.getName
case None => "Unknown class"
}
logError(
s"Failed to update accumulator $id ($accumClassName) for task ${task.partitionId}",
e)
}
}
}
private def postTaskEnd(event: CompletionEvent): Unit = {
val taskMetrics: TaskMetrics =
if (event.accumUpdates.nonEmpty) {
try {
TaskMetrics.fromAccumulators(event.accumUpdates)
} catch {
case NonFatal(e) =>
val taskId = event.taskInfo.taskId
logError(s"Error when attempting to reconstruct metrics for task $taskId", e)
null
}
} else {
null
}
listenerBus.post(SparkListenerTaskEnd(event.task.stageId, event.task.stageAttemptId,
Utils.getFormattedClassName(event.task), event.reason, event.taskInfo, taskMetrics))
}
/**
* Responds to a task finishing. This is called inside the event loop so it assumes that it can
* modify the scheduler's internal state. Use taskEnded() to post a task end event from outside.
*/
private[scheduler] def handleTaskCompletion(event: CompletionEvent) {
val task = event.task
val stageId = task.stageId
outputCommitCoordinator.taskCompleted(
stageId,
task.stageAttemptId,
task.partitionId,
event.taskInfo.attemptNumber, // this is a task attempt number
event.reason)
if (!stageIdToStage.contains(task.stageId)) {
// The stage may have already finished when we get this event -- eg. maybe it was a
// speculative task. It is important that we send the TaskEnd event in any case, so listeners
// are properly notified and can chose to handle it. For instance, some listeners are
// doing their own accounting and if they don't get the task end event they think
// tasks are still running when they really aren't.
postTaskEnd(event)
// Skip all the actions if the stage has been cancelled.
return
}
val stage = stageIdToStage(task.stageId)
// Make sure the task's accumulators are updated before any other processing happens, so that
// we can post a task end event before any jobs or stages are updated. The accumulators are
// only updated in certain cases.
event.reason match {
case Success =>
task match {
case rt: ResultTask[_, _] =>
val resultStage = stage.asInstanceOf[ResultStage]
resultStage.activeJob match {
case Some(job) =>
// Only update the accumulator once for each result task.
if (!job.finished(rt.outputId)) {
updateAccumulators(event)
}
case None => // Ignore update if task's job has finished.
}
case _ =>
updateAccumulators(event)
}
case _: ExceptionFailure | _: TaskKilled => updateAccumulators(event)
case _ =>
}
postTaskEnd(event)
event.reason match {
case Success =>
task match {
case rt: ResultTask[_, _] =>
// Cast to ResultStage here because it's part of the ResultTask
// TODO Refactor this out to a function that accepts a ResultStage
val resultStage = stage.asInstanceOf[ResultStage]
resultStage.activeJob match {
case Some(job) =>
if (!job.finished(rt.outputId)) {
job.finished(rt.outputId) = true
job.numFinished += 1
// If the whole job has finished, remove it
if (job.numFinished == job.numPartitions) {
markStageAsFinished(resultStage)
cleanupStateForJobAndIndependentStages(job)
listenerBus.post(
SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobSucceeded))
}
// taskSucceeded runs some user code that might throw an exception. Make sure
// we are resilient against that.
try {
job.listener.taskSucceeded(rt.outputId, event.result)
} catch {
case e: Exception =>
// TODO: Perhaps we want to mark the resultStage as failed?
job.listener.jobFailed(new SparkDriverExecutionException(e))
}
}
case None =>
logInfo("Ignoring result from " + rt + " because its job has finished")
}
case smt: ShuffleMapTask =>
val shuffleStage = stage.asInstanceOf[ShuffleMapStage]
shuffleStage.pendingPartitions -= task.partitionId
val status = event.result.asInstanceOf[MapStatus]
val execId = status.location.executorId
logDebug("ShuffleMapTask finished on " + execId)
if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) {
logInfo(s"Ignoring possibly bogus $smt completion from executor $execId")
} else {
// The epoch of the task is acceptable (i.e., the task was launched after the most
// recent failure we're aware of for the executor), so mark the task's output as
// available.
mapOutputTracker.registerMapOutput(
shuffleStage.shuffleDep.shuffleId, smt.partitionId, status)
}
if (runningStages.contains(shuffleStage) && shuffleStage.pendingPartitions.isEmpty) {
markStageAsFinished(shuffleStage)
logInfo("looking for newly runnable stages")
logInfo("running: " + runningStages)
logInfo("waiting: " + waitingStages)
logInfo("failed: " + failedStages)
// This call to increment the epoch may not be strictly necessary, but it is retained
// for now in order to minimize the changes in behavior from an earlier version of the
// code. This existing behavior of always incrementing the epoch following any
// successful shuffle map stage completion may have benefits by causing unneeded
// cached map outputs to be cleaned up earlier on executors. In the future we can
// consider removing this call, but this will require some extra investigation.
// See https://github.com/apache/spark/pull/17955/files#r117385673 for more details.
mapOutputTracker.incrementEpoch()
clearCacheLocs()
if (!shuffleStage.isAvailable) {
// Some tasks had failed; let's resubmit this shuffleStage.
// TODO: Lower-level scheduler should also deal with this
logInfo("Resubmitting " + shuffleStage + " (" + shuffleStage.name +
") because some of its tasks had failed: " +
shuffleStage.findMissingPartitions().mkString(", "))
submitStage(shuffleStage)
} else {
markMapStageJobsAsFinished(shuffleStage)
submitWaitingChildStages(shuffleStage)
}
}
}
case FetchFailed(bmAddress, shuffleId, mapId, _, failureMessage) =>
val failedStage = stageIdToStage(task.stageId)
val mapStage = shuffleIdToMapStage(shuffleId)
if (failedStage.latestInfo.attemptNumber != task.stageAttemptId) {
logInfo(s"Ignoring fetch failure from $task as it's from $failedStage attempt" +
s" ${task.stageAttemptId} and there is a more recent attempt for that stage " +
s"(attempt ${failedStage.latestInfo.attemptNumber}) running")
} else {
failedStage.failedAttemptIds.add(task.stageAttemptId)
val shouldAbortStage =
failedStage.failedAttemptIds.size >= maxConsecutiveStageAttempts ||
disallowStageRetryForTest
// It is likely that we receive multiple FetchFailed for a single stage (because we have
// multiple tasks running concurrently on different executors). In that case, it is
// possible the fetch failure has already been handled by the scheduler.
if (runningStages.contains(failedStage)) {
logInfo(s"Marking $failedStage (${failedStage.name}) as failed " +
s"due to a fetch failure from $mapStage (${mapStage.name})")
markStageAsFinished(failedStage, errorMessage = Some(failureMessage),
willRetry = !shouldAbortStage)
} else {
logDebug(s"Received fetch failure from $task, but its from $failedStage which is no " +
s"longer running")
}
if (mapStage.rdd.isBarrier()) {
// Mark all the map as broken in the map stage, to ensure retry all the tasks on
// resubmitted stage attempt.
mapOutputTracker.unregisterAllMapOutput(shuffleId)
} else if (mapId != -1) {
// Mark the map whose fetch failed as broken in the map stage
mapOutputTracker.unregisterMapOutput(shuffleId, mapId, bmAddress)
}
if (failedStage.rdd.isBarrier()) {
failedStage match {
case failedMapStage: ShuffleMapStage =>
// Mark all the map as broken in the map stage, to ensure retry all the tasks on
// resubmitted stage attempt.
mapOutputTracker.unregisterAllMapOutput(failedMapStage.shuffleDep.shuffleId)
case failedResultStage: ResultStage =>
// Abort the failed result stage since we may have committed output for some
// partitions.
val reason = "Could not recover from a failed barrier ResultStage. Most recent " +
s"failure reason: $failureMessage"
abortStage(failedResultStage, reason, None)
}
}
if (shouldAbortStage) {
val abortMessage = if (disallowStageRetryForTest) {
"Fetch failure will not retry stage due to testing config"
} else {
s"""$failedStage (${failedStage.name})
|has failed the maximum allowable number of
|times: $maxConsecutiveStageAttempts.
|Most recent failure reason: $failureMessage""".stripMargin.replaceAll("\n", " ")
}
abortStage(failedStage, abortMessage, None)
} else { // update failedStages and make sure a ResubmitFailedStages event is enqueued
// TODO: Cancel running tasks in the failed stage -- cf. SPARK-17064
val noResubmitEnqueued = !failedStages.contains(failedStage)
failedStages += failedStage
failedStages += mapStage
if (noResubmitEnqueued) {
// If the map stage is INDETERMINATE, which means the map tasks may return
// different result when re-try, we need to re-try all the tasks of the failed
// stage and its succeeding stages, because the input data will be changed after the
// map tasks are re-tried.
// Note that, if map stage is UNORDERED, we are fine. The shuffle partitioner is
// guaranteed to be determinate, so the input data of the reducers will not change
// even if the map tasks are re-tried.
if (mapStage.rdd.outputDeterministicLevel == DeterministicLevel.INDETERMINATE) {
// It's a little tricky to find all the succeeding stages of `failedStage`, because
// each stage only know its parents not children. Here we traverse the stages from
// the leaf nodes (the result stages of active jobs), and rollback all the stages
// in the stage chains that connect to the `failedStage`. To speed up the stage
// traversing, we collect the stages to rollback first. If a stage needs to
// rollback, all its succeeding stages need to rollback to.
val stagesToRollback = scala.collection.mutable.HashSet(failedStage)
def collectStagesToRollback(stageChain: List[Stage]): Unit = {
if (stagesToRollback.contains(stageChain.head)) {
stageChain.drop(1).foreach(s => stagesToRollback += s)
} else {
stageChain.head.parents.foreach { s =>
collectStagesToRollback(s :: stageChain)
}
}
}
def generateErrorMessage(stage: Stage): String = {
"A shuffle map stage with indeterminate output was failed and retried. " +
s"However, Spark cannot rollback the $stage to re-process the input data, " +
"and has to fail this job. Please eliminate the indeterminacy by " +
"checkpointing the RDD before repartition and try again."
}
activeJobs.foreach(job => collectStagesToRollback(job.finalStage :: Nil))
stagesToRollback.foreach {
case mapStage: ShuffleMapStage =>
val numMissingPartitions = mapStage.findMissingPartitions().length
if (numMissingPartitions < mapStage.numTasks) {
// TODO: support to rollback shuffle files.
// Currently the shuffle writing is "first write wins", so we can't re-run a
// shuffle map stage and overwrite existing shuffle files. We have to finish
// SPARK-8029 first.
abortStage(mapStage, generateErrorMessage(mapStage), None)
}
case resultStage: ResultStage if resultStage.activeJob.isDefined =>
val numMissingPartitions = resultStage.findMissingPartitions().length
if (numMissingPartitions < resultStage.numTasks) {
// TODO: support to rollback result tasks.
abortStage(resultStage, generateErrorMessage(resultStage), None)
}
case _ =>
}
}
// We expect one executor failure to trigger many FetchFailures in rapid succession,
// but all of those task failures can typically be handled by a single resubmission of
// the failed stage. We avoid flooding the scheduler's event queue with resubmit
// messages by checking whether a resubmit is already in the event queue for the
// failed stage. If there is already a resubmit enqueued for a different failed
// stage, that event would also be sufficient to handle the current failed stage, but
// producing a resubmit for each failed stage makes debugging and logging a little
// simpler while not producing an overwhelming number of scheduler events.
logInfo(
s"Resubmitting $mapStage (${mapStage.name}) and " +
s"$failedStage (${failedStage.name}) due to fetch failure"
)
messageScheduler.schedule(
new Runnable {
override def run(): Unit = eventProcessLoop.post(ResubmitFailedStages)
},
DAGScheduler.RESUBMIT_TIMEOUT,
TimeUnit.MILLISECONDS
)
}
}
// TODO: mark the executor as failed only if there were lots of fetch failures on it
if (bmAddress != null) {
val hostToUnregisterOutputs = if (env.blockManager.externalShuffleServiceEnabled &&
unRegisterOutputOnHostOnFetchFailure) {
// We had a fetch failure with the external shuffle service, so we
// assume all shuffle data on the node is bad.
Some(bmAddress.host)
} else {
// Unregister shuffle data just for one executor (we don't have any
// reason to believe shuffle data has been lost for the entire host).
None
}
removeExecutorAndUnregisterOutputs(
execId = bmAddress.executorId,
fileLost = true,
hostToUnregisterOutputs = hostToUnregisterOutputs,
maybeEpoch = Some(task.epoch))
}
}
case failure: TaskFailedReason if task.isBarrier =>
// Also handle the task failed reasons here.
failure match {
case Resubmitted =>
handleResubmittedFailure(task, stage)
case _ => // Do nothing.
}
// Always fail the current stage and retry all the tasks when a barrier task fail.
val failedStage = stageIdToStage(task.stageId)
if (failedStage.latestInfo.attemptNumber != task.stageAttemptId) {
logInfo(s"Ignoring task failure from $task as it's from $failedStage attempt" +
s" ${task.stageAttemptId} and there is a more recent attempt for that stage " +
s"(attempt ${failedStage.latestInfo.attemptNumber}) running")
} else {
logInfo(s"Marking $failedStage (${failedStage.name}) as failed due to a barrier task " +
"failed.")
val message = s"Stage failed because barrier task $task finished unsuccessfully.\n" +
failure.toErrorString
try {
// killAllTaskAttempts will fail if a SchedulerBackend does not implement killTask.
val reason = s"Task $task from barrier stage $failedStage (${failedStage.name}) " +
"failed."
taskScheduler.killAllTaskAttempts(stageId, interruptThread = false, reason)
} catch {
case e: UnsupportedOperationException =>
// Cannot continue with barrier stage if failed to cancel zombie barrier tasks.
// TODO SPARK-24877 leave the zombie tasks and ignore their completion events.
logWarning(s"Could not kill all tasks for stage $stageId", e)
abortStage(failedStage, "Could not kill zombie barrier tasks for stage " +
s"$failedStage (${failedStage.name})", Some(e))
}
markStageAsFinished(failedStage, Some(message))
failedStage.failedAttemptIds.add(task.stageAttemptId)
// TODO Refactor the failure handling logic to combine similar code with that of
// FetchFailed.
val shouldAbortStage =
failedStage.failedAttemptIds.size >= maxConsecutiveStageAttempts ||
disallowStageRetryForTest
if (shouldAbortStage) {
val abortMessage = if (disallowStageRetryForTest) {
"Barrier stage will not retry stage due to testing config. Most recent failure " +
s"reason: $message"
} else {
s"""$failedStage (${failedStage.name})
|has failed the maximum allowable number of
|times: $maxConsecutiveStageAttempts.
|Most recent failure reason: $message
""".stripMargin.replaceAll("\n", " ")
}
abortStage(failedStage, abortMessage, None)
} else {
failedStage match {
case failedMapStage: ShuffleMapStage =>
// Mark all the map as broken in the map stage, to ensure retry all the tasks on
// resubmitted stage attempt.
mapOutputTracker.unregisterAllMapOutput(failedMapStage.shuffleDep.shuffleId)
case failedResultStage: ResultStage =>
// Abort the failed result stage since we may have committed output for some
// partitions.
val reason = "Could not recover from a failed barrier ResultStage. Most recent " +
s"failure reason: $message"
abortStage(failedResultStage, reason, None)
}
// In case multiple task failures triggered for a single stage attempt, ensure we only
// resubmit the failed stage once.
val noResubmitEnqueued = !failedStages.contains(failedStage)
failedStages += failedStage
if (noResubmitEnqueued) {
logInfo(s"Resubmitting $failedStage (${failedStage.name}) due to barrier stage " +
"failure.")
messageScheduler.schedule(new Runnable {
override def run(): Unit = eventProcessLoop.post(ResubmitFailedStages)
}, DAGScheduler.RESUBMIT_TIMEOUT, TimeUnit.MILLISECONDS)
}
}
}
case Resubmitted =>
handleResubmittedFailure(task, stage)
case _: TaskCommitDenied =>
// Do nothing here, left up to the TaskScheduler to decide how to handle denied commits
case _: ExceptionFailure | _: TaskKilled =>
// Nothing left to do, already handled above for accumulator updates.
case TaskResultLost =>
// Do nothing here; the TaskScheduler handles these failures and resubmits the task.
case _: ExecutorLostFailure | UnknownReason =>
// Unrecognized failure - also do nothing. If the task fails repeatedly, the TaskScheduler
// will abort the job.
}
}
private def handleResubmittedFailure(task: Task[_], stage: Stage): Unit = {
logInfo(s"Resubmitted $task, so marking it as still running.")
stage match {
case sms: ShuffleMapStage =>
sms.pendingPartitions += task.partitionId
case _ =>
throw new SparkException("TaskSetManagers should only send Resubmitted task " +
"statuses for tasks in ShuffleMapStages.")
}
}
private[scheduler] def markMapStageJobsAsFinished(shuffleStage: ShuffleMapStage): Unit = {
// Mark any map-stage jobs waiting on this stage as finished
if (shuffleStage.isAvailable && shuffleStage.mapStageJobs.nonEmpty) {
val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep)
for (job <- shuffleStage.mapStageJobs) {
markMapStageJobAsFinished(job, stats)
}
}
}
/**
* Responds to an executor being lost. This is called inside the event loop, so it assumes it can
* modify the scheduler's internal state. Use executorLost() to post a loss event from outside.
*
* We will also assume that we've lost all shuffle blocks associated with the executor if the
* executor serves its own blocks (i.e., we're not using external shuffle), the entire slave
* is lost (likely including the shuffle service), or a FetchFailed occurred, in which case we
* presume all shuffle data related to this executor to be lost.
*
* Optionally the epoch during which the failure was caught can be passed to avoid allowing
* stray fetch failures from possibly retriggering the detection of a node as lost.
*/
private[scheduler] def handleExecutorLost(
execId: String,
workerLost: Boolean): Unit = {
// if the cluster manager explicitly tells us that the entire worker was lost, then
// we know to unregister shuffle output. (Note that "worker" specifically refers to the process
// from a Standalone cluster, where the shuffle service lives in the Worker.)
val fileLost = workerLost || !env.blockManager.externalShuffleServiceEnabled
removeExecutorAndUnregisterOutputs(
execId = execId,
fileLost = fileLost,
hostToUnregisterOutputs = None,
maybeEpoch = None)
}
private def removeExecutorAndUnregisterOutputs(
execId: String,
fileLost: Boolean,
hostToUnregisterOutputs: Option[String],
maybeEpoch: Option[Long] = None): Unit = {
val currentEpoch = maybeEpoch.getOrElse(mapOutputTracker.getEpoch)
if (!failedEpoch.contains(execId) || failedEpoch(execId) < currentEpoch) {
failedEpoch(execId) = currentEpoch
logInfo("Executor lost: %s (epoch %d)".format(execId, currentEpoch))
blockManagerMaster.removeExecutor(execId)
if (fileLost) {
hostToUnregisterOutputs match {
case Some(host) =>
logInfo("Shuffle files lost for host: %s (epoch %d)".format(host, currentEpoch))
mapOutputTracker.removeOutputsOnHost(host)
case None =>
logInfo("Shuffle files lost for executor: %s (epoch %d)".format(execId, currentEpoch))
mapOutputTracker.removeOutputsOnExecutor(execId)
}
clearCacheLocs()
} else {
logDebug("Additional executor lost message for %s (epoch %d)".format(execId, currentEpoch))
}
}
}
/**
* Responds to a worker being removed. This is called inside the event loop, so it assumes it can
* modify the scheduler's internal state. Use workerRemoved() to post a loss event from outside.
*
* We will assume that we've lost all shuffle blocks associated with the host if a worker is
* removed, so we will remove them all from MapStatus.
*
* @param workerId identifier of the worker that is removed.
* @param host host of the worker that is removed.
* @param message the reason why the worker is removed.
*/
private[scheduler] def handleWorkerRemoved(
workerId: String,
host: String,
message: String): Unit = {
logInfo("Shuffle files lost for worker %s on host %s".format(workerId, host))
mapOutputTracker.removeOutputsOnHost(host)
clearCacheLocs()
}
private[scheduler] def handleExecutorAdded(execId: String, host: String) {
// remove from failedEpoch(execId) ?
if (failedEpoch.contains(execId)) {
logInfo("Host added was in lost list earlier: " + host)
failedEpoch -= execId
}
}
private[scheduler] def handleStageCancellation(stageId: Int, reason: Option[String]) {
stageIdToStage.get(stageId) match {
case Some(stage) =>
val jobsThatUseStage: Array[Int] = stage.jobIds.toArray
jobsThatUseStage.foreach { jobId =>
val reasonStr = reason match {
case Some(originalReason) =>
s"because $originalReason"
case None =>
s"because Stage $stageId was cancelled"
}
handleJobCancellation(jobId, Option(reasonStr))
}
case None =>
logInfo("No active jobs to kill for Stage " + stageId)
}
}
private[scheduler] def handleJobCancellation(jobId: Int, reason: Option[String]) {
if (!jobIdToStageIds.contains(jobId)) {
logDebug("Trying to cancel unregistered job " + jobId)
} else {
failJobAndIndependentStages(
jobIdToActiveJob(jobId), "Job %d cancelled %s".format(jobId, reason.getOrElse("")))
}
}
/**
* Marks a stage as finished and removes it from the list of running stages.
*/
private def markStageAsFinished(
stage: Stage,
errorMessage: Option[String] = None,
willRetry: Boolean = false): Unit = {
val serviceTime = stage.latestInfo.submissionTime match {
case Some(t) => "%.03f".format((clock.getTimeMillis() - t) / 1000.0)
case _ => "Unknown"
}
if (errorMessage.isEmpty) {
logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime))
stage.latestInfo.completionTime = Some(clock.getTimeMillis())
// Clear failure count for this stage, now that it's succeeded.
// We only limit consecutive failures of stage attempts,so that if a stage is
// re-used many times in a long-running job, unrelated failures don't eventually cause the
// stage to be aborted.
stage.clearFailures()
} else {
stage.latestInfo.stageFailed(errorMessage.get)
logInfo(s"$stage (${stage.name}) failed in $serviceTime s due to ${errorMessage.get}")
}
if (!willRetry) {
outputCommitCoordinator.stageEnd(stage.id)
}
listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
runningStages -= stage
}
/**
* Aborts all jobs depending on a particular Stage. This is called in response to a task set
* being canceled by the TaskScheduler. Use taskSetFailed() to inject this event from outside.
*/
private[scheduler] def abortStage(
failedStage: Stage,
reason: String,
exception: Option[Throwable]): Unit = {
if (!stageIdToStage.contains(failedStage.id)) {
// Skip all the actions if the stage has been removed.
return
}
val dependentJobs: Seq[ActiveJob] =
activeJobs.filter(job => stageDependsOn(job.finalStage, failedStage)).toSeq
failedStage.latestInfo.completionTime = Some(clock.getTimeMillis())
for (job <- dependentJobs) {
failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason", exception)
}
if (dependentJobs.isEmpty) {
logInfo("Ignoring failure of " + failedStage + " because all jobs depending on it are done")
}
}
/** Fails a job and all stages that are only used by that job, and cleans up relevant state. */
private def failJobAndIndependentStages(
job: ActiveJob,
failureReason: String,
exception: Option[Throwable] = None): Unit = {
val error = new SparkException(failureReason, exception.getOrElse(null))
var ableToCancelStages = true
val shouldInterruptThread =
if (job.properties == null) false
else job.properties.getProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, "false").toBoolean
// Cancel all independent, running stages.
val stages = jobIdToStageIds(job.jobId)
if (stages.isEmpty) {
logError("No stages registered for job " + job.jobId)
}
stages.foreach { stageId =>
val jobsForStage: Option[HashSet[Int]] = stageIdToStage.get(stageId).map(_.jobIds)
if (jobsForStage.isEmpty || !jobsForStage.get.contains(job.jobId)) {
logError(
"Job %d not registered for stage %d even though that stage was registered for the job"
.format(job.jobId, stageId))
} else if (jobsForStage.get.size == 1) {
if (!stageIdToStage.contains(stageId)) {
logError(s"Missing Stage for stage with id $stageId")
} else {
// This is the only job that uses this stage, so fail the stage if it is running.
val stage = stageIdToStage(stageId)
if (runningStages.contains(stage)) {
try { // cancelTasks will fail if a SchedulerBackend does not implement killTask
taskScheduler.cancelTasks(stageId, shouldInterruptThread)
markStageAsFinished(stage, Some(failureReason))
} catch {
case e: UnsupportedOperationException =>
logInfo(s"Could not cancel tasks for stage $stageId", e)
ableToCancelStages = false
}
}
}
}
}
if (ableToCancelStages) {
// SPARK-15783 important to cleanup state first, just for tests where we have some asserts
// against the state. Otherwise we have a *little* bit of flakiness in the tests.
cleanupStateForJobAndIndependentStages(job)
job.listener.jobFailed(error)
listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobFailed(error)))
}
}
/** Return true if one of stage's ancestors is target. */
private def stageDependsOn(stage: Stage, target: Stage): Boolean = {
if (stage == target) {
return true
}
val visitedRdds = new HashSet[RDD[_]]
// We are manually maintaining a stack here to prevent StackOverflowError
// caused by recursively visiting
val waitingForVisit = new ArrayStack[RDD[_]]
def visit(rdd: RDD[_]) {
if (!visitedRdds(rdd)) {
visitedRdds += rdd
for (dep <- rdd.dependencies) {
dep match {
case shufDep: ShuffleDependency[_, _, _] =>
val mapStage = getOrCreateShuffleMapStage(shufDep, stage.firstJobId)
if (!mapStage.isAvailable) {
waitingForVisit.push(mapStage.rdd)
} // Otherwise there's no need to follow the dependency back
case narrowDep: NarrowDependency[_] =>
waitingForVisit.push(narrowDep.rdd)
}
}
}
}
waitingForVisit.push(stage.rdd)
while (waitingForVisit.nonEmpty) {
visit(waitingForVisit.pop())
}
visitedRdds.contains(target.rdd)
}
/**
* Gets the locality information associated with a partition of a particular RDD.
*
* This method is thread-safe and is called from both DAGScheduler and SparkContext.
*
* @param rdd whose partitions are to be looked at
* @param partition to lookup locality information for
* @return list of machines that are preferred by the partition
*/
private[spark]
def getPreferredLocs(rdd: RDD[_], partition: Int): Seq[TaskLocation] = {
getPreferredLocsInternal(rdd, partition, new HashSet)
}
/**
* Recursive implementation for getPreferredLocs.
*
* This method is thread-safe because it only accesses DAGScheduler state through thread-safe
* methods (getCacheLocs()); please be careful when modifying this method, because any new
* DAGScheduler state accessed by it may require additional synchronization.
*/
private def getPreferredLocsInternal(
rdd: RDD[_],
partition: Int,
visited: HashSet[(RDD[_], Int)]): Seq[TaskLocation] = {
// If the partition has already been visited, no need to re-visit.
// This avoids exponential path exploration. SPARK-695
if (!visited.add((rdd, partition))) {
// Nil has already been returned for previously visited partitions.
return Nil
}
// If the partition is cached, return the cache locations
val cached = getCacheLocs(rdd)(partition)
if (cached.nonEmpty) {
return cached
}
// If the RDD has some placement preferences (as is the case for input RDDs), get those
val rddPrefs = rdd.preferredLocations(rdd.partitions(partition)).toList
if (rddPrefs.nonEmpty) {
return rddPrefs.map(TaskLocation(_))
}
// If the RDD has narrow dependencies, pick the first partition of the first narrow dependency
// that has any placement preferences. Ideally we would choose based on transfer sizes,
// but this will do for now.
rdd.dependencies.foreach {
case n: NarrowDependency[_] =>
for (inPart <- n.getParents(partition)) {
val locs = getPreferredLocsInternal(n.rdd, inPart, visited)
if (locs != Nil) {
return locs
}
}
case _ =>
}
Nil
}
/** Mark a map stage job as finished with the given output stats, and report to its listener. */
def markMapStageJobAsFinished(job: ActiveJob, stats: MapOutputStatistics): Unit = {
// In map stage jobs, we only create a single "task", which is to finish all of the stage
// (including reusing any previous map outputs, etc); so we just mark task 0 as done
job.finished(0) = true
job.numFinished += 1
job.listener.taskSucceeded(0, stats)
cleanupStateForJobAndIndependentStages(job)
listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobSucceeded))
}
def stop() {
messageScheduler.shutdownNow()
eventProcessLoop.stop()
taskScheduler.stop()
}
eventProcessLoop.start()
}
private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler)
extends EventLoop[DAGSchedulerEvent]("dag-scheduler-event-loop") with Logging {
private[this] val timer = dagScheduler.metricsSource.messageProcessingTimer
/**
* The main event loop of the DAG scheduler.
*/
override def onReceive(event: DAGSchedulerEvent): Unit = {
val timerContext = timer.time()
try {
doOnReceive(event)
} finally {
timerContext.stop()
}
}
private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)
case StageCancelled(stageId, reason) =>
dagScheduler.handleStageCancellation(stageId, reason)
case JobCancelled(jobId, reason) =>
dagScheduler.handleJobCancellation(jobId, reason)
case JobGroupCancelled(groupId) =>
dagScheduler.handleJobGroupCancelled(groupId)
case AllJobsCancelled =>
dagScheduler.doCancelAllJobs()
case ExecutorAdded(execId, host) =>
dagScheduler.handleExecutorAdded(execId, host)
case ExecutorLost(execId, reason) =>
val workerLost = reason match {
case SlaveLost(_, true) => true
case _ => false
}
dagScheduler.handleExecutorLost(execId, workerLost)
case WorkerRemoved(workerId, host, message) =>
dagScheduler.handleWorkerRemoved(workerId, host, message)
case BeginEvent(task, taskInfo) =>
dagScheduler.handleBeginEvent(task, taskInfo)
case SpeculativeTaskSubmitted(task) =>
dagScheduler.handleSpeculativeTaskSubmitted(task)
case GettingResultEvent(taskInfo) =>
dagScheduler.handleGetTaskResult(taskInfo)
case completion: CompletionEvent =>
dagScheduler.handleTaskCompletion(completion)
case TaskSetFailed(taskSet, reason, exception) =>
dagScheduler.handleTaskSetFailed(taskSet, reason, exception)
case ResubmitFailedStages =>
dagScheduler.resubmitFailedStages()
}
override def onError(e: Throwable): Unit = {
logError("DAGSchedulerEventProcessLoop failed; shutting down SparkContext", e)
try {
dagScheduler.doCancelAllJobs()
} catch {
case t: Throwable => logError("DAGScheduler failed to cancel all jobs.", t)
}
dagScheduler.sc.stopInNewThread()
}
override def onStop(): Unit = {
// Cancel any active jobs in postStop hook
dagScheduler.cleanUpAfterSchedulerStop()
}
}
private[spark] object DAGScheduler {
// The time, in millis, to wait for fetch failure events to stop coming in after one is detected;
// this is a simplistic way to avoid resubmitting tasks in the non-fetchable map stage one by one
// as more failure events come in
val RESUBMIT_TIMEOUT = 200
// Number of consecutive stage attempts allowed before a stage is aborted
val DEFAULT_MAX_CONSECUTIVE_STAGE_ATTEMPTS = 4
}
