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Task的执行过程分析
Task的执行过程分析
Task的执行通过Worker启动时生成的Executor实例进行，
caseRegisteredExecutor(sparkProperties)=>
logInfo("Successfullyregistered with driver")
//Make this host instead of hostPort ?
executor=newExecutor(executorId, Utils.parseHostPort(hostPort)._1,sparkProperties)
通过executor实例的launchTask启动task的执行操作。
deflaunchTask(context: ExecutorBackend, taskId: Long, serializedTask:ByteBuffer) {
valtr = newTaskRunner(context, taskId, serializedTask)
runningTasks.put(taskId,tr)
threadPool.execute(tr)
}
生成TaskRunner线程，把task与当前的Wroker的启动的executorBackend传入，
onyarn模式为CoarseGrainedExecutorBackend.
通过threadPool线程池执行生成TaskRunner线程。
TaskRunner.run函数:
用于执行task任务的线程
overridedefrun(): Unit = SparkHadoopUtil.get.runAsUser(sparkUser){ () =>
valstartTime =System.currentTimeMillis()
SparkEvn后面在进行分析。
SparkEnv.set(env)
Thread.currentThread.setContextClassLoader(replClassLoader)
valser =SparkEnv.get.closureSerializer.newInstance()
logInfo("Runningtask ID " + taskId)
通过execBackend更新此task的状态。设置task的状态为RUNNING.向master发送StatusUpdate事件。
execBackend.statusUpdate(taskId,TaskState.RUNNING,EMPTY_BYTE_BUFFER)
varattemptedTask:Option[Task[Any]] = None
vartaskStart:Long = 0
defgcTime =ManagementFactory.getGarbageCollectorMXBeans.map(_.getCollectionTime).sum
valstartGCTime= gcTime
try{
SparkEnv.set(env)
Accumulators.clear()
解析出task的资源信息。包括要执行的jar,其它资源，task定义信息
val(taskFiles,taskJars,taskBytes)= Task.deserializeWithDependencies(serializedTask)
更新资源信息，并把task执行的jar更新到当前Thread的ClassLoader中。
updateDependencies(taskFiles,taskJars)
通过SparkEnv中配置的Serialize实现对task定义进行反serialize,得到Task实例。
Task的具体实现为ShuffleMapTask或者ResultTask
task=ser.deserialize[Task[Any]](taskBytes,Thread.currentThread.getContextClassLoader)
如果killed的值为true,不执行当前task任务，进入catch处理。
//If this task has been killed before wedeserializedit, let‘s quit now. Otherwise,
//continue executing the task.
if(killed) {
//Throw an exception rather than returning, because returning within atry{} block
//causes a NonLocalReturnControl exception to be thrown. TheNonLocalReturnControl
//exception will be caught by the catch block, leading to an incorrectExceptionFailure
//for the task.
throwTaskKilledException
}
attemptedTask= Some(task)
logDebug("Task" + taskId +"‘sepoch is " + task.epoch)
env.mapOutputTracker.updateEpoch(task.epoch)
生成TaskContext实例，通过Task.runTask执行task的任务，等待task执行完成。
//Run the actual task and measure its runtime.
taskStart= System.currentTimeMillis()
valvalue =task.run(taskId.toInt)
valtaskFinish= System.currentTimeMillis()
此时task执行结束，检查如果task是被killed的结果，进入catch处理。
//If the task has been killed, let‘s fail it.
if(task.killed){
throwTaskKilledException
}
对task执行的返回结果进行serialize操作。
valresultSer =SparkEnv.get.serializer.newInstance()
valbeforeSerialization= System.currentTimeMillis()
valvalueBytes=resultSer.serialize(value)
valafterSerialization= System.currentTimeMillis()
发送监控指标
for(m <-task.metrics){
m.hostname= Utils.localHostName()
m.executorDeserializeTime= (taskStart-startTime).toInt
m.executorRunTime= (taskFinish-taskStart).toInt
m.jvmGCTime= gcTime - startGCTime
m.resultSerializationTime= (afterSerialization-beforeSerialization).toInt
}
valaccumUpdates= Accumulators.values
把Task的返回结果生成DirectTaskResult实例。并对其进行serialize操作。
valdirectResult=newDirectTaskResult(valueBytes,accumUpdates,task.metrics.getOrElse(null))
valserializedDirectResult=ser.serialize(directResult)
logInfo("Serializedsize of result for " + taskId +"is " +serializedDirectResult.limit)
检查taskresult的大小是否超过了akka的发送消息大小，
如果是通过BlockManager来管理结果，设置RDD的存储级别为MEMORY与DISK，否则表示没有达到actor消息大小，
直接使用TaskResult,此部分信息主要是需要通过状态更新向Scheduler向送StatusUpdate事件调用。
valserializedResult= {
if(serializedDirectResult.limit>=akkaFrameSize-1024) {
logInfo("Storingresult for " + taskId +"in local BlockManager")
valblockId =TaskResultBlockId(taskId)
env.blockManager.putBytes(
blockId,serializedDirectResult,StorageLevel.MEMORY_AND_DISK_SER)
ser.serialize(newIndirectTaskResult[Any](blockId))
}else{
logInfo("Sendingresult for " + taskId +"directly to driver")
serializedDirectResult
}
}
通过execBackend更新此task的状态。设置task的状态为FINISHED.向master发送StatusUpdate事件。
execBackend.statusUpdate(taskId,TaskState.FINISHED,serializedResult)
logInfo("Finishedtask ID " + taskId)
}catch{
出现异常，发送FAILED事件。
caseffe:FetchFailedException => {
valreason =ffe.toTaskEndReason
execBackend.statusUpdate(taskId,TaskState.FAILED,ser.serialize(reason))
}
caseTaskKilledException => {
logInfo("Executorkilled task " + taskId)
execBackend.statusUpdate(taskId,TaskState.KILLED,ser.serialize(TaskKilled))
}
caset:Throwable => {
valserviceTime= (System.currentTimeMillis() - taskStart).toInt
valmetrics =attemptedTask.flatMap(t=> t.metrics)
for(m <-metrics) {
m.executorRunTime=serviceTime
m.jvmGCTime= gcTime - startGCTime
}
valreason =ExceptionFailure(t.getClass.getName,t.toString,t.getStackTrace,metrics)
execBackend.statusUpdate(taskId,TaskState.FAILED,ser.serialize(reason))
//TODO: Should we exit the whole executor here? On the one hand, thefailed task may
//have left some weird state around depending on when the exception wasthrown, but on
//the other hand, maybe we could detect that when future tasks fail andexit then.
logError("Exceptionin task ID " + taskId,t)
//System.exit(1)
}
}finally{
从shuffleMemoryMap中移出此线程对应的shuffle的内存空间
//TODO: Unregister shuffle memory only for ResultTask
valshuffleMemoryMap=env.shuffleMemoryMap
shuffleMemoryMap.synchronized{
shuffleMemoryMap.remove(Thread.currentThread().getId)
}
从runningTasks中移出此task
runningTasks.remove(taskId)
}
}
}
Task执行过程的状态更新
ExecutorBackend.statusUpdate
onyarn模式实现类CoarseGrainedExecutorBackend,通过master的actor发送StatusUpdate事件。
overridedefstatusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) {
driver! StatusUpdate(executorId, taskId, state, data)
}
master中的ExecutorBackend处理状态更新操作：
实现类：CoarseGrainedSchedulerBackend.DriverActor
caseStatusUpdate(executorId,taskId,state,data) =>
通过TaskSchedulerImpl的statusUpdate处理状态更新。
scheduler.statusUpdate(taskId,state,data.value)
如果Task状态为完成状态，完成状态包含(FINISHED,FAILED,KILLED,LOST)
if(TaskState.isFinished(state)){
if(executorActor.contains(executorId)){
每一个task占用一个cpucore,此时task完成，把可用的core值加一
freeCores(executorId) +=1
在此executor上接着执行其于的task任务，此部分可参见scheduler调度过程分析中的部分说明。
makeOffers(executorId)
}else{
//Ignoring the update since we don‘t know about the executor.
valmsg ="Ignored task status update (%dstate %s) from unknown executor %s with ID %s"
logWarning(msg.format(taskId,state,sender,executorId))
}
}
TaskSchedulerImpl.statusUpdate函数处理流程
defstatusUpdate(tid: Long, state: TaskState, serializedData: ByteBuffer){
varfailedExecutor:Option[String] = None
synchronized {
try{
如果Task的状态传入为Task的执行丢失，同时task在executor列表中存在
if(state == TaskState.LOST&&taskIdToExecutorId.contains(tid)){
得到此task执行的worker所属的executorID，
//We lost this entire executor, so remember that it‘s gone
valexecId =taskIdToExecutorId(tid)
如果此executor是active的Executor，执行scheduler的executorLost操作。
包含TaskSetManager，会执行TaskSetManager.executorLost操作.
设置当前的executor为failedExecutor,共函数最后使用。
if(activeExecutorIds.contains(execId)){
removeExecutor(execId)
failedExecutor= Some(execId)
}
}
taskIdToTaskSetId.get(tid)match{
caseSome(taskSetId)=>
如果task状态是完成状态，非RUNNING状态。移出对应的容器中的值
if(TaskState.isFinished(state)) {
taskIdToTaskSetId.remove(tid)
if(taskSetTaskIds.contains(taskSetId)){
taskSetTaskIds(taskSetId)-= tid
}
taskIdToExecutorId.remove(tid)
}
activeTaskSets.get(taskSetId).foreach{ taskSet =>
如果task是成功完成，从TaskSet中移出此task，同时通过TaskResultGetter获取数据。
if(state == TaskState.FINISHED){
taskSet.removeRunningTask(tid)
taskResultGetter.enqueueSuccessfulTask(taskSet,tid, serializedData)
}elseif(Set(TaskState.FAILED,TaskState.KILLED,TaskState.LOST).contains(state)){
task任务执行失败的处理部分：
taskSet.removeRunningTask(tid)
taskResultGetter.enqueueFailedTask(taskSet,tid, state, serializedData)
}
}
caseNone =>
logInfo("Ignoringupdate with state %s from TID %s because its task set is gone"
.format(state, tid))
}
}catch{
casee:Exception => logError("Exceptionin statusUpdate",e)
}
}
如果有failed的worker,通过dagScheduler处理此executor.
//Update the DAGScheduler without holding a lock on this, since thatcan deadlock
if(failedExecutor!= None) {
dagScheduler.executorLost(failedExecutor.get)
发起task执行的分配与任务执行操作。
backend.reviveOffers()
}
}
TaskStatus.LOST状态,同时executor在activeExecutorIds中
TaskStatus的状态为LOST时，同时executor是活动的executor(也就是有过执行task的情况)
privatedefremoveExecutor(executorId: String) {
从activeExecutorIds中移出此executor
activeExecutorIds-= executorId
得到此executor对应的worker的host
valhost =executorIdToHost(executorId)
取出host对应的所有executor,并移出当前的executor
valexecs =executorsByHost.getOrElse(host,newHashSet)
execs-=executorId
if(execs.isEmpty){
executorsByHost-=host
}
从executor对应的host容器中移出此executor
executorIdToHost-= executorId
此处主要是去执行TaskSetManager.executorLost函数。
rootPool.executorLost(executorId,host)
}
TaskSetManager.executorLost函数:
此函数主要处理executor导致task丢失的情况，把executor上的task重新添加到pending的tasks列表中
overridedefexecutorLost(execId: String, host: String) {
logInfo("Re-queueingtasks for " + execId +"from TaskSet " +taskSet.id)
//Re-enqueue pending tasks for this host based on the status of thecluster -- for example, a
//task that used to have locations on only this host might now go tothe no-prefslist. Note
//that it‘s okay if we add a task to the same queue twice (if it hadmultiple preferred
//locations), because findTaskFromList will skip already-running tasks.
重新生成此TaskSet中的pending队列，因为当前executor的实例被移出，需要重新生成。
for(index <-getPendingTasksForExecutor(execId)) {
addPendingTask(index,readding=true)
}
for(index <-getPendingTasksForHost(host)) {
addPendingTask(index,readding=true)
}
//Re-enqueue any tasks that ran on the failed executor if this is ashuffle map stage
如果当前的RDD是shuffle的rdd,
if(tasks(0).isInstanceOf[ShuffleMapTask]){
for((tid,info) <-taskInfosifinfo.executorId== execId) {
valindex =taskInfos(tid).index
if(successful(index)){
successful(index)=false
copiesRunning(index) -=1
tasksSuccessful-=1
addPendingTask(index)
//Tell the DAGScheduler that this task was resubmitted so that itdoesn‘t think our
//stage finishes when a total of tasks.size tasks finish.
通过DAGScheduler发送CompletionEvent处理事件，事件类型为Resubmitted,
sched.dagScheduler.taskEnded(tasks(index),Resubmitted,null,null,info,null)
}
}
}
如果task还处于running状态，同时此task在lost的executor上运行，
//Also re-enqueue any tasks that were running on the node
for((tid,info) <-taskInfosifinfo.running&&info.executorId== execId) {
设置task的failed值为true,移出此task的running列表中的值，重新添加task到pendingtasks队列中。
handleFailedTask(tid,TaskState.FAILED,None)
}
}
DAGScheduler处理CompletionEvent事件。
...........................
casecompletion@ CompletionEvent(task,reason,_, _, taskInfo,taskMetrics)=>
listenerBus.post(SparkListenerTaskEnd(task,reason,taskInfo,taskMetrics))
handleTaskCompletion(completion)
.........................
caseResubmitted =>
logInfo("Resubmitted" +task+ ", so marking it as stillrunning")
pendingTasks(stage)+=task
(TaskState.FAILED,TaskState.KILLED,TaskState.LOST)状态
.........................
}elseif(Set(TaskState.FAILED,TaskState.KILLED,TaskState.LOST).contains(state)){
把task从running容器中移出
taskSet.removeRunningTask(tid)
此函数主要是解析出出错的信息。并通过TaskSchedulerImpl.handleFailedTask处理exception
taskResultGetter.enqueueFailedTask(taskSet,tid, state, serializedData)
}
TaskSchedulerImpl.handleFailedTask函数：
defhandleFailedTask(
taskSetManager: TaskSetManager,
tid: Long,
taskState: TaskState,
reason: Option[TaskEndReason]) =synchronized {
taskSetManager.handleFailedTask(tid,taskState, reason)
如果task不是被KILLED掉的task,重新发起task的分配与执行操作。
if(taskState != TaskState.KILLED){
//Need to revive offers again now that the task set manager state hasbeen updated to
//reflect failed tasks that need to be re-run.
backend.reviveOffers()
}
}
TaskSetManager.handleFailedTask函数流程
TaskSetManager.handleFailedTask,函数，处理task执行的exception信息。
defhandleFailedTask(tid: Long, state: TaskState, reason:Option[TaskEndReason]) {
valinfo =taskInfos(tid)
if(info.failed){
return
}
removeRunningTask(tid)
valindex =info.index
info.markFailed()
varfailureReason="unknown"
if(!successful(index)){
logWarning("LostTID %s (task %s:%d)".format(tid,taskSet.id,index))
copiesRunning(index) -=1
如果是通过TaskSetManager.executorLost函数发起的此函数调用(Task.LOST)，下面的case部分不会执行，
否则是task的执行exception情况，也就是状态更新中非Task.LOST状态时。
//Check if the problem is a map output fetch failure. In that case,this
//task will never succeed on any node, so tell the scheduler about it.
reason.foreach {
casefetchFailed:FetchFailed =>
读取失败，移出所有此taskset的task执行。并从scheduler中移出此taskset的调度,不再执行下面流程
logWarning("Losswas due to fetch failure from " +fetchFailed.bmAddress)
sched.dagScheduler.taskEnded(tasks(index),fetchFailed,null,null,info,null)
successful(index)=true
tasksSuccessful+=1
sched.taskSetFinished(this)
removeAllRunningTasks()
return
caseTaskKilled =>
task被kill掉，移出此task,同时不再执行下面流程
logWarning("Task%d was killed.".format(tid))
sched.dagScheduler.taskEnded(tasks(index),reason.get,null,null,info,null)
return
caseef:ExceptionFailure =>
sched.dagScheduler.taskEnded(
tasks(index),ef,null,null,info,ef.metrics.getOrElse(null))
if(ef.className== classOf[NotSerializableException].getName()) {
//If the task result wasn‘trerializable,there‘s no point in trying to re-execute it.
logError("Task%s:%s had a not serializable result: %s; not retrying".format(
taskSet.id,index,ef.description))
abort("Task%s:%s had a not serializable result: %s".format(
taskSet.id,index,ef.description))
return
}
valkey =ef.description
failureReason="Exception failure:%s".format(ef.description)
valnow =clock.getTime()
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){
vallocs =ef.stackTrace.map(loc=>"\tat%s".format(loc.toString))
logWarning("Losswas due to %s\n%s\n%s".format(
ef.className,ef.description,locs.mkString("\n")))
}else{
logInfo("Losswas due to %s [duplicate %d]".format(ef.description,dupCount))
}
caseTaskResultLost =>
failureReason="Lost result for TID %s onhost %s".format(tid,info.host)
logWarning(failureReason)
sched.dagScheduler.taskEnded(tasks(index),TaskResultLost,null,null,info,null)
case_ => {}
}
重新把task添加到pending的执行队列中,同时如果状态非KILLED的状态，设置并检查是否达到重试的最大次数
//On non-fetch failures, re-enqueue the task as pending for a maxnumber of retries
addPendingTask(index)
if(state != TaskState.KILLED){
numFailures(index) +=1
if(numFailures(index)>=maxTaskFailures){
logError("Task%s:%d failed %d times; aborting job".format(
taskSet.id,index,maxTaskFailures))
abort("Task%s:%d failed %d times (most recent failure: %s)".format(
taskSet.id,index,maxTaskFailures,failureReason))
}
}
}else{
logInfo("Ignoringtask-lost event for TID " + tid +
"because task " +index+ " is already finished")
}
}
DAGScheduler处理taskEnded流程：
deftaskEnded(
task: Task[_],
reason: TaskEndReason,
result: Any,
accumUpdates: Map[Long, Any],
taskInfo: TaskInfo,
taskMetrics: TaskMetrics) {
eventProcessActor! CompletionEvent(task, reason, result, accumUpdates, taskInfo,taskMetrics)
}
处理CompletionEvent事件：
casecompletion@ CompletionEvent(task,reason, _,_,taskInfo,taskMetrics)=>
listenerBus.post(SparkListenerTaskEnd(task,reason,taskInfo,taskMetrics))
handleTaskCompletion(completion)
DAGScheduler.handleTaskCompletion
读取失败的case,
caseFetchFailed(bmAddress,shuffleId,mapId,reduceId)=>
//Mark the stage that the reducer was in asunrunnable
valfailedStage=stageIdToStage(task.stageId)
running-=failedStage
failed+=failedStage
..............................
//Mark the map whose fetch failed as broken in the map stage
valmapStage =shuffleToMapStage(shuffleId)
if(mapId !=-1) {
mapStage.removeOutputLoc(mapId,bmAddress)
mapOutputTracker.unregisterMapOutput(shuffleId,mapId,bmAddress)
}
...........................
failed+=mapStage
//Remember that a fetch failed now; this is used to resubmit the broken
//stages later, after a small wait (to give other tasks the chance tofail)
lastFetchFailureTime= System.currentTimeMillis()// TODO:Use pluggableclock
//TODO: mark the executor as failed only if there were lots of fetchfailures on it
if(bmAddress!=null){
把stage中可执行的partition中对应的executorid的location全部移出。
handleExecutorLost(bmAddress.executorId,Some(task.epoch))
}
caseExceptionFailure(className,description,stackTrace,metrics) =>
//Do nothing here, left up to the TaskScheduler to decide how to handleuser failures
caseTaskResultLost =>
//Do nothing here; the TaskScheduler handles these failures andresubmits the task.
TaskStatus.FINISHED状态
此状态表示task正常完成，
if(state == TaskState.FINISHED){
移出taskSet中的running队列中移出此task
taskSet.removeRunningTask(tid)
获取task的响应数据。
taskResultGetter.enqueueSuccessfulTask(taskSet,tid, serializedData)
TaskResultGetter.enqueueSuccessfulTask函数：
defenqueueSuccessfulTask(
taskSetManager: TaskSetManager,tid: Long, serializedData: ByteBuffer) {
getTaskResultExecutor.execute(newRunnable {
overridedefrun() {
try{
从响应的结果中得到数据，需要先执行deserialize操作。
valresult =serializer.get().deserialize[TaskResult[_]](serializedData)match{
如果result的结果小于akka的actor传输的大小，直接返回task的执行结果
casedirectResult:DirectTaskResult[_] => directResult
caseIndirectTaskResult(blockId)=>
否则，result结果太大，通过BlockManager管理，通过blockManager拿到result的数据
logDebug("Fetchingindirect task result for TID %s".format(tid))
给DAGScheduler发送GettingResultEvent事件处理，
见下面TaskSchedulerImpl.handleTaskGettingResult函数
scheduler.handleTaskGettingResult(taskSetManager,tid)
得到task的执行结果
valserializedTaskResult= sparkEnv.blockManager.getRemoteBytes(blockId)
task执行完成，并拿结果失败，见上面的错误处理中的TaskResultLost部分。
if(!serializedTaskResult.isDefined){
/*We won‘t be able to get the task result if the machine that ran thetask failed
* between when the taskended and when we tried to fetch the result, or if the
* block manager had toflush the result. */
scheduler.handleFailedTask(
taskSetManager, tid,TaskState.FINISHED,Some(TaskResultLost))
return
}
对task的执行结果进行deserialized操作。
valdeserializedResult=serializer.get().deserialize[DirectTaskResult[_]](
serializedTaskResult.get)
拿到执行结果，移出对应的blockid
sparkEnv.blockManager.master.removeBlock(blockId)
deserializedResult
}
result.metrics.resultSize= serializedData.limit()
见下面的TaskSchedulerImpl.handleSuccessfulTask处理函数。
scheduler.handleSuccessfulTask(taskSetManager,tid,result)
}catch{
casecnf:ClassNotFoundException =>
valloader =Thread.currentThread.getContextClassLoader
taskSetManager.abort("ClassNotFoundwith classloader: " + loader)
caseex:Throwable =>
taskSetManager.abort("Exceptionwhile deserializing and fetching task: %s".format(ex))
}
}
})
}
TaskSchedulerImpl.handleTaskGettingResult函数：
defhandleTaskGettingResult(taskSetManager: TaskSetManager, tid: Long) {
taskSetManager.handleTaskGettingResult(tid)
}
taskSetManager中
defhandleTaskGettingResult(tid: Long) = {
valinfo =taskInfos(tid)
info.markGettingResult()
sched.dagScheduler.taskGettingResult(tasks(info.index),info)
}
通过DAGScheduler发起GettingResultEvent事件。
deftaskGettingResult(task: Task[_], taskInfo: TaskInfo) {
eventProcessActor! GettingResultEvent(task, taskInfo)
}
对GettingResultEvent事件的处理：其实就是打个酱油，无实际处理操作。
caseGettingResultEvent(task,taskInfo)=>
listenerBus.post(SparkListenerTaskGettingResult(task,taskInfo))
TaskSchedulerImpl.handleSuccessfulTask处理函数：
defhandleSuccessfulTask(
taskSetManager: TaskSetManager,
tid: Long,
taskResult: DirectTaskResult[_])= synchronized {
taskSetManager.handleSuccessfulTask(tid,taskResult)
}
TastSetManager中
defhandleSuccessfulTask(tid: Long, result: DirectTaskResult[_]) = {
valinfo =taskInfos(tid)
valindex =info.index
info.markSuccessful()
从running队列中移出此task
removeRunningTask(tid)
if(!successful(index)){
logInfo("FinishedTID %s in %d ms on %s (progress: %d/%d)".format(
tid,info.duration,info.host,tasksSuccessful,numTasks))
向dagscheduler发送success消息，
sched.dagScheduler.taskEnded(
tasks(index),Success,result.value,result.accumUpdates,info,result.metrics)
设置成功完成的task个数加一,同时在successful容器中设置task对应的运行状态为true,表示成功。
//Mark successful and stop if all the tasks have succeeded.
tasksSuccessful+=1
successful(index)=true
如果完成的task个数，达到task的总个数，完成此taskset,也就相当于完成了一个rdd
if(tasksSuccessful==numTasks){
sched.taskSetFinished(this)
}
}else{
logInfo("Ignorningtask-finished event for TID " + tid+" because task "+
index+" has already completedsuccessfully")
}
}
DAGScheduler处理CompletionEvent的Success,,,,
caseSuccess =>
logInfo("Completed" +task)
if(event.accumUpdates!=null){
Accumulators.add(event.accumUpdates)// TODO: do this only if task wasn‘tresubmitted
}
把等待执行队列中移出此task
pendingTasks(stage)-=task
stageToInfos(stage).taskInfos+=event.taskInfo-> event.taskMetrics
根据task的执行类型，处理两个类型的Task
taskmatch{
如果task是ResultTask,表示不需要shuffle操作
casert:ResultTask[_, _] =>
resultStageToJob.get(stage)match{
caseSome(job)=>
如果此执行的stage的ActiveJob中对应此task的partition存储的finished标志为false,
if(!job.finished(rt.outputId)){
设置task的完成标志为true
job.finished(rt.outputId)=true
把job中完成的task个数加一，同时检查是否所有的task都完成,如果所有task都完成，
从相关的容器中移出此job与对应的stage.
job.numFinished+=1
//If the whole job has finished, remove it
if(job.numFinished==job.numPartitions){
idToActiveJob-=stage.jobId
activeJobs-=job
resultStageToJob-=stage
markStageAsFinished(stage)
jobIdToStageIdsRemove(job.jobId)
listenerBus.post(SparkListenerJobEnd(job,JobSucceeded))
}
调用ActiveJob内的JobWaiter.taskSucceeded函数，更新此task为完成，同时把result传入进行输出处理。
job.listener.taskSucceeded(rt.outputId,event.result)
}
caseNone =>
logInfo("Ignoringresult from " +rt+ " because its job has finished")
}
针对shuffle的task的执行完成，处理流程：
casesmt:ShuffleMapTask =>
valstatus =event.result.asInstanceOf[MapStatus]
valexecId =status.location.executorId
logDebug("ShuffleMapTaskfinished on " +execId)
if(failedEpoch.contains(execId)&&smt.epoch<=failedEpoch(execId)){
logInfo("Ignoringpossibly bogus ShuffleMapTask completion from "+execId)
}else{
把shuffle的result(MapStatus)写入到stage的outputLoc中。每添加一个会把numAvailableOutputs的值加一，
当numAvailableOutputs的值==numPartitions的值时，表示shuffle的map执行完成。
stage.addOutputLoc(smt.partitionId,status)
}
如果此stage还处在running状态，同时pendingTasks中所有的task已经处理完成
if(running.contains(stage)&&pendingTasks(stage).isEmpty){
更新stage的状态
markStageAsFinished(stage)
.......................................
此处表示shuffle的stage处理完成，把shuffleid与stage的outputLocs注册到mapOutputTracker中。
把每一个shuffletaks执行的executor与host等信息，每一个task执行完成的大小。注册到mapoutput中。
每一个task的shuffle的writer都会有shuffleid的信息，注册成功后，
下一个stage会根据mapoutputtracker中此shuffleid的信息读取数据。
mapOutputTracker.registerMapOutputs(
stage.shuffleDep.get.shuffleId,
stage.outputLocs.map(list=>if(list.isEmpty)nullelselist.head).toArray,
changeEpoch =true)
}
clearCacheLocs()
stage中每一个partition的outputLoc默认值为Nil，如果发现有partition的值为Nil,表示有task处理失败，
重新提交此stage.此时会把没有成功的task重新执行。
if(stage.outputLocs.exists(_== Nil)) {
.........................................
submitStage(stage)
}else{
valnewlyRunnable=newArrayBuffer[Stage]
for(stage <-waiting) {
logInfo("Missingparents for " +stage+ ": "+ getMissingParentStages(stage))
}
此处检查下面未执行的所有的stage,如果stage(RDD)的上级shuffle依赖完成,
或者后面所有的stage不再有shuffle的stage的所有stage,拿到这些个stage.
for(stage <-waiting ifgetMissingParentStages(stage) == Nil) {
newlyRunnable+=stage
}
执行此stage后面的所有可执行的stage,把waiting中移出要执行的stage,
waiting--=newlyRunnable
在running队列中添加要执行的新的stage.
running++=newlyRunnable
for{
stage<-newlyRunnable.sortBy(_.id)
jobId<- activeJobForStage(stage)
} {
提交下一个stage的task分配与执行。
logInfo("Submitting" +stage+ " ("+ stage.rdd+"), which is now runnable")
submitMissingTasks(stage,jobId)
}
}
}
}
JobWaiter.taskSucceeded函数，
task完成后的处理函数。
overridedef taskSucceeded(index: Int,result: Any): Unit = synchronized {
if(_jobFinished){
thrownewUnsupportedOperationException("taskSucceeded()called on a finished JobWaiter")
}
通过resultHandler函数把结果进行处理。此函数是生成JobWaiter时传入
resultHandler(index,result.asInstanceOf[T])
把完成的task值加一
finishedTasks+=1
if(finishedTasks== totalTasks) {
如果完成的task个数等于所有的task的个数时，设置job的完成状态为true,并设置状态为JobSucceeded
如果设置为true,表示job执行完成，前面的等待执行完成结束等待。
_jobFinished=true
jobResult= JobSucceeded
this.notifyAll()
}
}
Task.runTask函数实现
Task的实现分为两类，
需要进行shuffle操作的ShuffleMapTask,
不需要进行shuffle操作的ResultTask.
ResulitTask.runTask
overridedef runTask(context:TaskContext): U = {
metrics= Some(context.taskMetrics)
try{
此处通过生成task实例时也就是DAGScheduler的runJob时传入的function进行处理
比如在PairRDDFunction.saveAsHadoopDataset中定义的writeToFile函数
rdd.iterator中会根据不现的RDD的实现，执行其compute函数，
而compute函数具体执行通过业务代码中定义的如map函数传入的定义的function进行执行，
func(context,rdd.iterator(split,context))
}finally{
context.executeOnCompleteCallbacks()
}
}
ShuffleMapTask.runTask
overridedef runTask(context:TaskContext): MapStatus = {
valnumOutputSplits=dep.partitioner.numPartitions
metrics= Some(context.taskMetrics)
valblockManager= SparkEnv.get.blockManager
valshuffleBlockManager=blockManager.shuffleBlockManager
varshuffle:ShuffleWriterGroup = null
varsuccess =false
try{
通过shuffleId拿到一个shuffle的写入实例
//Obtain all the block writers for shuffle blocks.
valser =SparkEnv.get.serializerManager.get(dep.serializerClass,SparkEnv.get.conf)
shuffle=shuffleBlockManager.forMapTask(dep.shuffleId,partitionId,numOutputSplits,ser)
执行rdd.iterator操作，生成Pair,也就是Product2,根据key重新shuffle到不同的文件中。
当所有的shuffle的task完成后，会把此stage注册到mapOutputTracker中，
等待下一个stage从中读取数据并执行其它操作，每一个shuffle的task完成后会生成一个MapStatus实例，
此实例主要包含有shuffle执行的executor与host等信息，每一个task执行完成的大小。
具体的shuffle数据读取可参见后面的shufle分析.
//Write the map output to its associated buckets.
for(elem <-rdd.iterator(split,context)) {
valpair =elem.asInstanceOf[Product2[Any,Any]]
valbucketId =dep.partitioner.getPartition(pair._1)
shuffle.writers(bucketId).write(pair)
}
//Commit the writes. Get the size of each bucket block (total blocksize).
vartotalBytes=0L
vartotalTime =0L
valcompressedSizes:Array[Byte] = shuffle.writers.map{ writer: BlockObjectWriter =>
writer.commit()
writer.close()
valsize =writer.fileSegment().length
totalBytes+=size
totalTime+= writer.timeWriting()
MapOutputTracker.compressSize(size)
}
//Update shuffle metrics.
valshuffleMetrics=newShuffleWriteMetrics
shuffleMetrics.shuffleBytesWritten=totalBytes
shuffleMetrics.shuffleWriteTime=totalTime
metrics.get.shuffleWriteMetrics= Some(shuffleMetrics)
success=true
newMapStatus(blockManager.blockManagerId,compressedSizes)
}catch{casee:Exception =>
//If there is an exception from running the task, revert the partialwrites
//and throw the exception upstream to Spark.
if(shuffle !=null&&shuffle.writers!=null){
for(writer <-shuffle.writers){
writer.revertPartialWrites()
writer.close()
}
}
throwe
}finally{
//Release the writers back to the shuffle block manager.
if(shuffle !=null&&shuffle.writers!=null){
shuffle.releaseWriters(success)
}
//Execute thecallbackson task completion.
context.executeOnCompleteCallbacks()
}
}