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复习内容：
Spark中Stage的提交 http://www.jianshu.com/p/75751908329a

Spark中Task的提交

1.在复习内容部分我们介绍了在方法onStageSubmitted中，Stage的提交，那么在该方法中还有Task的提交，如下所示：
<code>
override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = synchronized {
//（1）Stage的提交，详见文章-Spark中Task的提交
//（2）Task的提交
//broadcasted task的二进制，用来分发tasks给executors。
//注意：我们broadcast RDD的拷贝并且对于每一个task我们将要反序列化，这意味着每个task得到一个不同的RDD 拷贝
var taskBinary: Broadcast[Array[Byte]] = null
try {
// For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
// For ResultTask, serialize and broadcast (rdd, func).
val taskBinaryBytes: Array[Byte] = stage match {
case stage: ShuffleMapStage =>
closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef).array()
case stage: ResultStage =>
closureSerializer.serialize((stage.rdd, stage.func): AnyRef).array()
}
//将序列化后的task广播出去
taskBinary = sc.broadcast(taskBinaryBytes)
} catch {
case e: NotSerializableException =>
abortStage(stage, "Task not serializable: " + e.toString, Some(e))
runningStages -= stage
return
case NonFatal(e) =>
abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}", Some(e))
runningStages -= stage
return
}
//根据stage生成tasks
val tasks: Seq[Task[]] = try {
stage match {
//对于ShuffleMapStages生成ShuffleMapTask
case stage: ShuffleMapStage =>
partitionsToCompute.map { id =>
val locs = taskIdToLocations(id)
val part = stage.rdd.partitions(id)
//可见一个partition，一个task，一个位置信息
new ShuffleMapTask(stage.id, stage.latestInfo.attemptId,
taskBinary, part, locs, stage.internalAccumulators)
}
//对于ResultStage生成ResultTask
case stage: ResultStage =>
val job = stage.resultOfJob.get
partitionsToCompute.map { id =>
val p: Int = stage.partitions(id)
val part = stage.rdd.partitions(p)
val locs = taskIdToLocations(id)
new ResultTask(stage.id, stage.latestInfo.attemptId,
taskBinary, part, locs, id, stage.internalAccumulators)
}
}
} catch {
case NonFatal(e) =>
abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}", Some(e))
runningStages -= stage
return
}
//如果tasks的num大于0
if (tasks.size > 0) {
logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
stage.pendingPartitions ++= tasks.map(.partitionId)
logDebug("New pending partitions: " + stage.pendingPartitions)
//调用taskScheduler提交TaskSet，详见2
taskScheduler.submitTasks(new TaskSet(
tasks.toArray, stage.id, stage.latestInfo.attemptId, stage.firstJobId, properties))
stage.latestInfo.submissionTime = Some(clock.getTimeMillis())
} else {
//因为我们之前就已经发送了事件SparkListenerStageSubmitted，所以我们标记Stage为completed防止没有任务提交
markStageAsFinished(stage, None)
//将debugString记录到日志中
val debugString = stage match {
case stage: ShuffleMapStage =>
s"Stage ${stage} is actually done; " +
s"(available: ${stage.isAvailable}," +
s"available outputs: ${stage.numAvailableOutputs}," +
s"partitions: ${stage.numPartitions})"
case stage : ResultStage =>
s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})"
}
logDebug(debugString)
}
}
</code>
2.Task的提交会调用taskScheduler的submitTasks方法进行，TaskScheduler是trait，它的唯一的具体实现是TaskSchedulerImpl，submitTasks方法如下所示：
<code>
override def submitTasks(taskSet: TaskSet) {
val tasks = taskSet.tasks
logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
this.synchronized {
//为一个taskSet创建一个TaskSetManager
val manager = createTaskSetManager(taskSet, maxTaskFailures)
val stage = taskSet.stageId
val stageTaskSets =
taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
stageTaskSets(taskSet.stageAttemptId) = manager
val conflictingTaskSet = stageTaskSets.exists { case (, ts) =>
ts.taskSet != taskSet && !ts.isZombie
}
if (conflictingTaskSet) {
throw new IllegalStateException(s"more than one active taskSet for stage $stage:" +
s" ${stageTaskSets.toSeq.map{._2.taskSet.id}.mkString(",")}")
}
//将taskSetManager和taskSet添加到两种可调度的tree中，FIFO or FAIR
schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
if (!isLocal && !hasReceivedTask) {
//一个定时器
starvationTimer.scheduleAtFixedRate(new TimerTask() {
override def run() {
if (!hasLaunchedTask) {
logWarning("Initial job has not accepted any resources; " +
"check your cluster UI to ensure that workers are registered " +
"and have sufficient resources")
} else {
this.cancel()
}
}
}, STARVATION_TIMEOUT_MS, STARVATION_TIMEOUT_MS)
}
hasReceivedTask = true
}
//不同（集群）模式进行资源的分配
backend.reviveOffers()
}
</code>
这样我们就完成了Task的提交，那么不同模式对于Task的资源又是如何分配的呢，我们后面介绍。