Spark Job执行流程大体如下：用户提交Job后会生成SparkContext对象，SparkContext向Cluster Manager（在Standalone模式下是Spark Master）申请Executor资源，并将Job分解成一系列可并行处理的task，然后将task分发到不同的Executor上运行，Executor在task执行完后将结果返回到SparkContext。
注：上文介绍Spark申请Executor资源的过程。本文介绍Job从拆分成一系列task到task分发到Executor上运行的过程。如下图所示！
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1.DAGScheduler接收用户提交的job
用户提交Job后，SparkContext通过runJob()调用DAGScheduler的runJob()。在runJob()中，调用submitJob来提交Job，然后等待Job的运行结果。
def runJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
allowLocal: Boolean,
resultHandler: (Int, U) => Unit,
properties: Properties): Unit = {
val start = System.nanoTime
val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties)
waiter.awaitResult() match {
case JobSucceeded =>
logInfo("Job %d finished: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
case JobFailed(exception: Exception) =>
logInfo("Job %d failed: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
throw exception
}
}
submitJob()通过eventProcessLoop把Job交给handleJobSubmitted()处理。
def submitJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
allowLocal: Boolean,
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 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, allowLocal, callSite, waiter,
SerializationUtils.clone(properties)))
waiter
}
- DAGScheduler将job拆分为不同的stage
首先每个job自动产生一个finalStage，然后递归地得到整个stage DAG。
private[scheduler] def handleJobSubmitted(jobId: Int,
finalRDD: RDD[_],
func: (TaskContext, Iterator[_]) => _,
partitions: Array[Int],
allowLocal: Boolean,
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 = newResultStage(finalRDD, partitions.size, jobId, callSite)
} catch {
case e: Exception =>
logWarning("Creating new stage failed due to exception - job: " + jobId, e)
listener.jobFailed(e)
return
}
if (finalStage != null) {
val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
clearCacheLocs()
logInfo("Got job %s (%s) with %d output partitions (allowLocal=%s)".format(
job.jobId, callSite.shortForm, partitions.length, allowLocal))
logInfo("Final stage: " + finalStage + "(" + finalStage.name + ")")
logInfo("Parents of final stage: " + finalStage.parents)
logInfo("Missing parents: " + getMissingParentStages(finalStage))
val shouldRunLocally =
localExecutionEnabled && allowLocal && finalStage.parents.isEmpty && partitions.length == 1
val jobSubmissionTime = clock.getTimeMillis()
if (shouldRunLocally) {
// Compute very short actions like first() or take() with no parent stages locally.
listenerBus.post(
SparkListenerJobStart(job.jobId, jobSubmissionTime, Seq.empty, properties))
runLocally(job)
} else {
jobIdToActiveJob(jobId) = job
activeJobs += job
finalStage.resultOfJob = Some(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)
}
}
submitWaitingStages()
}
submitStage负责得到整个stage DAG，并调用submitMissingTasks(()把每个stage拆分成可运行的task。
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)
}
}
注意stage之间有依赖关系，所以Spark是一个一个stage地运行。正在运行的stage保存在runningStages，等待运行的stage保存在waitingStages。当一个stage运行成功后，DAGScheduler在handleTaskCompletion()里运行下一个stage。
- DAGScheduler把每个stage拆分为可并行计算的task， 并将所有task提交到TaskSchedulerImpl
submitMissingTasks产生出与partition数量相等的task，并封装成TaskSet，提交给TaskSchedulerImpl。
private def submitMissingTasks(stage: Stage, jobId: Int) {
......
if (tasks.size > 0) {
logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
stage.pendingTasks ++= tasks
logDebug("New pending tasks: " + stage.pendingTasks)
taskScheduler.submitTasks(
new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties))
stage.latestInfo.submissionTime = Some(clock.getTimeMillis())
} 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)
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)
}
}
TaskSchedulerImpl的submitTasks将TaskSet封装成TaskSetManager，放入调度器（schedulableBuilder）等待调度（Spark有两种调度方式：FIFO和Fair。注意只调度同一SparkContext下的任务）。之后调用SparkDeploySchedulerBackend的reviveOffers()。TaskSetManager主要用来调度一个TaskSet内的task，比如，为给定的executor分配一个task。
val tasks = taskSet.tasks
logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
this.synchronized {
val manager = createTaskSetManager(taskSet, maxTaskFailures)
activeTaskSets(taskSet.id) = manager
schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
if (!isLocal && !hasReceivedTask) {
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
}
backend.reviveOffers()
}
SparkDeploySchedulerBackend的reviveOffers()向driver发送ReviveOffers，driver收到ReviveOffers后调用makeOffers()。
case ReviveOffers =>
makeOffers()
- SparkDeploySchedulerBackend调用Executor执行task
首先通过resourceOffers得到在哪个Executor运行哪个task的信息，然后调用launchTasks向Executor发送task。
// Filter out executors under killing
val activeExecutors = executorDataMap.filterKeys(!executorsPendingToRemove.contains(_))
val workOffers = activeExecutors.map { case (id, executorData) =>
new WorkerOffer(id, executorData.executorHost, executorData.freeCores)
}.toSeq
launchTasks(scheduler.resourceOffers(workOffers))
}
- Executor执行task
CoarseGrainedExecutorBackend在接收到LaunchTask后，调用Executor的launchTask运行task。
case LaunchTask(data) =>
if (executor == null) {
logError("Received LaunchTask command but executor was null")
System.exit(1)
} else {
val taskDesc = ser.deserialize[TaskDescription](data.value)
logInfo("Got assigned task " + taskDesc.taskId)
executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber,
taskDesc.name, taskDesc.serializedTask)
}
Executor的内部是一个线程池，每一个提交的task都会包装为TaskRunner交由threadpool执行。
def launchTask(
context: ExecutorBackend,
taskId: Long,
attemptNumber: Int,
taskName: String,
serializedTask: ByteBuffer): Unit = {
val tr = new TaskRunner(context, taskId = taskId, attemptNumber = attemptNumber, taskName,
serializedTask)
runningTasks.put(taskId, tr)
threadPool.execute(tr)
}
在TaskRunner中，task.run()真正运行每个task的任务。
execBackend: ExecutorBackend,
val taskId: Long,
val attemptNumber: Int,
taskName: String,
serializedTask: ByteBuffer)
extends Runnable {
......
override def run(): Unit = {
val taskMemoryManager = new TaskMemoryManager(env.executorMemoryManager)
val deserializeStartTime = System.currentTimeMillis()
Thread.currentThread.setContextClassLoader(replClassLoader)
val ser = env.closureSerializer.newInstance()
logInfo(s"Running $taskName (TID $taskId)")
execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
var taskStart: Long = 0
startGCTime = computeTotalGcTime()
try {
val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask)
updateDependencies(taskFiles, taskJars)
task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader)
task.setTaskMemoryManager(taskMemoryManager)
......
env.mapOutputTracker.updateEpoch(task.epoch)
// Run the actual task and measure its runtime.
taskStart = System.currentTimeMillis()
val value = try {
task.run(taskAttemptId = taskId, attemptNumber = attemptNumber)
} finally {
......
}
......
}
}
最终，每个task的运行都会调用iterator()来递归计算RDD。下面是以ShufflerMapTask为例，rdd.iterator(partition, context)会从根partition来计算这个task的输出partition。
private[spark] class ShuffleMapTask(
stageId: Int,
taskBinary: Broadcast[Array[Byte]],
partition: Partition,
@transient private var locs: Seq[TaskLocation])
extends Task[MapStatus](stageId, partition.index) with Logging {
override def runTask(context: TaskContext): MapStatus = {
// Deserialize the RDD using the broadcast variable.
val deserializeStartTime = System.currentTimeMillis()
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
_executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime
metrics = Some(context.taskMetrics)
var writer: ShuffleWriter[Any, Any] = null
try {
val manager = SparkEnv.get.shuffleManager
writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
return writer.stop(success = true).get
} catch {
case e: Exception =>
try {
if (writer != null) {
writer.stop(success = false)
}
} catch {
case e: Exception =>
log.debug("Could not stop writer", e)
}
throw e
}
}
}
至此，一个stage的TaskSet的执行流程结束，等此TaskSet中的所有task结束后会回到第三步继续执行下一个stage，直到finalStage结束。：）