注意看表红的地方，那是代码的执行流程，有些通信的地方没有标注，因为通信是在太多了
DAG：
<span style="font-size:14px;"><span style="font-size:18px;"> private[scheduler] def handleJobSubmitted(jobId: Int,
finalRDD: RDD[_],
func: (TaskContext, Iterator[_]) => _,
partitions: Array[Int],
allowLocal: Boolean,
callSite: CallSite,
listener: JobListener,
properties: Properties = null)
{
var finalStage: Stage = 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 = newStage(finalRDD, partitions.size, None, jobId, callSite)
} catch {
case e: Exception =>
logWarning("Creating new stage failed due to exception - job: " + jobId, e)
listener.jobFailed(e)
return
}
if (finalStage != null) {
<span style="color:#ff0000;"> val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)</span>
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
if (shouldRunLocally) {
// Compute very short actions like first() or take() with no parent stages locally.
listenerBus.post(SparkListenerJobStart(job.jobId, Array[Int](), properties))
runLocally(job)
} else {
jobIdToActiveJob(jobId) = job
activeJobs += job
finalStage.resultOfJob = Some(job)
listenerBus.post(SparkListenerJobStart(job.jobId, jobIdToStageIds(jobId).toArray,
properties))
<span style="color:#ff0000;"> submitStage(finalStage)</span>
}
}
submitWaitingStages()
}</span></span>
再来：
<span style="font-size:14px;"><span style="font-size:18px;">/** 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）<span style="color:#ff0000;">//这个方法可以和submitStage对比一下</span>
logDebug("missing: " + missing)
if (missing == Nil) {<span style="color:#ff0000;">//如果这个条件满足，那么就是第一个stage，这个不用解释吧</span>
logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents")
<span style="color:#ff0000;"> submitMissingTasks(stage, jobId.get)</span>
} else {
for (parent <- missing) {
submitStage(parent)
}
waitingStages += stage
}
}
} else {
abortStage(stage, "No active job for stage " + stage.id)
}
}
</span></span>
再来：这个方法是将stage转化为tasks的，task是并行运行的且task是根据stage的partitions的个数来确定的
<span style="font-size:14px;"><span style="font-size:18px;"> private def submitMissingTasks(stage: Stage, jobId: Int) {
logDebug("submitMissingTasks(" + stage + ")")
// Get our pending tasks and remember them in our pendingTasks entry
stage.pendingTasks.clear()//这边为什么要清楚tasks呢，因为这次submittask可能是因为有些partition运行失败，但不是所有的都失败，所以只需要运行相关的partitions就行
// First figure out the indexes of partition ids to compute.
<span style="color:#ff0000;"> val partitionsToCompute: Seq[Int] = {//shuffle是要写到不同文件中的
if (stage.isShuffleMap) {
(0 until stage.numPartitions).filter(id => stage.outputLocs(id) == Nil)//灭有没处理的partition
} else {
val job = stage.resultOfJob.get
(0 until job.numPartitions).filter(id => !job.finished(id))
}
}</span>
val properties = if (jobIdToActiveJob.contains(jobId)) {
jobIdToActiveJob(stage.jobId).properties
} else {
// this stage will be assigned to "default" pool
null
}
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.latestInfo = StageInfo.fromStage(stage, Some(partitionsToCompute.size))
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.
<span style="color:#ff0000;"> var taskBinary: Broadcast[Array[Byte]] = null//序列化stage
try {
// For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
// For ResultTask, serialize and broadcast (rdd, func).
val taskBinaryBytes: Array[Byte] =
if (stage.isShuffleMap) {
closureSerializer.serialize((stage.rdd, stage.shuffleDep.get) : AnyRef).array()//每进行一次shhuffle就会进行一次广播,但这个不是真正运行的任务
} else {
closureSerializer.serialize((stage.rdd, stage.resultOfJob.get.func) : AnyRef).array()
}
taskBinary = sc.broadcast(taskBinaryBytes)</span>
} catch {
// In the case of a failure during serialization, abort the stage.
case e: NotSerializableException =>
abortStage(stage, "Task not serializable: " + e.toString)
runningStages -= stage
return
case NonFatal(e) =>
abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}")
runningStages -= stage
return
}
<span style="color:#ff0000;"> val tasks: Seq[Task[_]] = if (stage.isShuffleMap) {
partitionsToCompute.map { id =>
val locs = getPreferredLocs(stage.rdd, id)
val part = stage.rdd.partitions(id)
new ShuffleMapTask(stage.id, taskBinary, part, locs)//因为有那么多的partitions，所以要更具不同的配置信息创建多个ShuffleMapTask，task只有来年各种，resulttask，shuffletask
}
} else {
val job = stage.resultOfJob.get
partitionsToCompute.map { id =>
val p: Int = job.partitions(id)
val part = stage.rdd.partitions(p)
val locs = getPreferredLocs(stage.rdd, p)
new ResultTask(stage.id, taskBinary, part, locs, id)
}
}</span>
if (tasks.size > 0) {
// Preemptively serialize a task to make sure it can be serialized. We are catching this
// exception here because it would be fairly hard to catch the non-serializable exception
// down the road, where we have several different implementations for local scheduler and
// cluster schedulers.
//
// We've already serialized RDDs and closures in taskBinary, but here we check for all other
// objects such as Partition.
try {
closureSerializer.serialize(tasks.head)
} catch {
case e: NotSerializableException =>
abortStage(stage, "Task not serializable: " + e.toString)
runningStages -= stage
return
case NonFatal(e) => // Other exceptions, such as IllegalArgumentException from Kryo.
abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}")
runningStages -= stage
return
}
logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
<span style="color:#ff0000;"> stage.pendingTasks ++= tasks//这才是要真正要运行的，这些task是并行运行的</span>
logDebug("New pending tasks: " + stage.pendingTasks)
<span style="background-color: rgb(102, 102, 102);"> <span style="color:#ff0000;">taskScheduler</span>.submitTasks(
new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties))//<span style="color:#ff0000;">现在就到了TaskSchedulerImpl中</span></span>
stage.latestInfo.submissionTime = Some(clock.getTime())
} else {
// Because we posted SparkListenerStageSubmitted earlier, we should post
// SparkListenerStageCompleted here in case there are no tasks to run.
listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
logDebug("Stage " + stage + " is actually done; %b %d %d".format(
stage.isAvailable, stage.numAvailableOutputs, stage.numPartitions))
runningStages -= stage
}
}</span></span>
再来：
<span style="font-size:14px;"> override def submitTasks(taskSet: TaskSet) {
val tasks = taskSet.tasks//这是一个task数组，数组中任务的执行逻辑是一样的，只是数据的location不一样
logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
this.synchronized {
val manager = new TaskSetManager(this, taskSet, maxTaskFailures)
activeTaskSets(taskSet.id) = manager
schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
}
<span style="color:#ff0000;"> backend.reviveOffers()</span>
}</span>
再来：
<span style="font-size:14px;"> // Make fake resource offers on all executors
def makeOffers() {
launchTasks(<span style="color:#ff0000;">scheduler</span>.resourceOffers(executorDataMap.map { case (id, executorData) =>//划红线的部分是：TaskSchedulerImpl
new WorkerOffer(id, executorData.executorHost, executorData.freeCores)
}.toSeq))
}</span>
再来：
<span style="font-size:14px;"> /**
* <span style="color:#ff0000;">Called by cluster manager to offer resources on slaves</span>. We respond by asking our active task
* sets for tasks in order of priority. We fill each node with tasks in a round-robin manner so
* that tasks are balanced across the cluster.
*/
def resourceOffers(offers: Seq[WorkerOffer]):<span style="color:#ff0000;"> Seq[Seq[TaskDescription]]</span> = synchronized {//他返回的是TaskDescription，而这个又是什么呢，看下面
// Mark each slave as alive and remember its hostname
// Also track if new executor is added
var newExecAvail = false
for (o <- offers) {
executorIdToHost(o.executorId) = o.host
if (!executorsByHost.contains(o.host)) {
executorsByHost(o.host) = new HashSet[String]()
executorAdded(o.executorId, o.host)
newExecAvail = true
}
for (rack <- getRackForHost(o.host)) {
hostsByRack.getOrElseUpdate(rack, new HashSet[String]()) += o.host
}
}</span>
再来:
<span style="font-size:14px;">private[spark] class TaskDescription(
val taskId: Long,
val executorId: String,
val name: String,
val index: Int, // Index within this task's TaskSet
<span style="color:#ff0000;"> _serializedTask: ByteBuffer</span>)//他是一个bytebuffer，也就是说他是一个序列化之后的对象
extends Serializable {
// Because ByteBuffers are not serializable, wrap the task in a SerializableBuffer
private val buffer = new SerializableBuffer(_serializedTask)
def serializedTask: ByteBuffer = buffer.value
override def toString: String = "TaskDescription(TID=%d, index=%d)".format(taskId, index)
}</span>
再来：
<span style="font-size:14px;">// Launch tasks returned by a set of resource offers
def launchTasks(tasks: Seq[Seq[TaskDescription]]) {
for (task <- tasks.flatten) {
val ser = SparkEnv.get.closureSerializer.newInstance()
val serializedTask = ser.serialize(task)
if (serializedTask.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
val taskSetId = scheduler.taskIdToTaskSetId(task.taskId)
scheduler.activeTaskSets.get(taskSetId).foreach { taskSet =>
try {
var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
"spark.akka.frameSize (%d bytes) - reserved (%d bytes). Consider increasing " +
"spark.akka.frameSize or using broadcast variables for large values."
msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize,
AkkaUtils.reservedSizeBytes)
taskSet.abort(msg)
} catch {
case e: Exception => logError("Exception in error callback", e)
}
}
}
else {
val executorData = executorDataMap(task.executorId)
executorData.freeCores -= scheduler.CPUS_PER_TASK
<span style="color:#ff0000;"> executorData.executorActor ! LaunchTask(new SerializableBuffer(serializedTask))//发布任务 </span>
}
}
}</span>
至于在executor中是怎么运行的，一句话就是反序列化rdd，进行计算
当一个task运行完成的时候：
Task执行是通过TaskRunner来运行，它需要通过ExecutorBackend和Driver通信，通信消息是StatusUpdate：
1、Task运行之前，告诉Driver当前Task的状态为TaskState.RUNNING。
2、Task运行之后，告诉Driver当前Task的状态为TaskState.FINISHED，并返回计算结果。
3、如果Task运行过程中发生错误，告诉Driver当前Task的状态为TaskState.FAILED，并返回错误原因。
4、如果Task在中途被Kill掉了，告诉Driver当前Task的状态为TaskState.FAILED。
下面讲的是运行成功的状态，图太大了，所以插到了最后面。
1、Task运行结束之后，调用ExecutorBackend的statusUpdate方法，把结果返回。结果超过10M，就把结果保存在blockManager处，返回blockId，需要的时候通过blockId到blockManager认领。
2、ExecutorBackend直接向Driver发送StatusUpdate返回Task的信息。
3、Driver（这里具体指的是SchedulerBackend）接收到StatusUpdate消息之后，调用TaskScheduler的statusUpdate方法，然后准备给ExecutorBackend发送下一批Task。
4、TaskScheduler通过TaskId找到管理这个Task的TaskSetManager（负责管理一批Task的类），从TaskSetManager里面删掉这个Task，并把Task插入到TaskResultGetter（负责获取Task结果的类）的成功队列里。
5、TaskResultGetter获取到结果之后，调用TaskScheduler的handleSuccessfulTask方法把结果返回。
6、TaskScheduler调用TaskSetManager的handleSuccessfulTask方法，处理成功的Task。
7、TaskSetManager调用DAGScheduler的taskEnded方法，告诉DAGScheduler这个Task运行结束了，如果这个时候Task全部成功了，就会结束TaskSetManager。
8、DAGScheduler在taskEnded方法里触发CompletionEvent事件，CompletionEvent分ResultTask和ShuffleMapTask来处理。
再来：
<span style="font-size:14px;"> private[scheduler] def handleTaskCompletion(event: CompletionEvent) {
val task = event.task
val stageId = task.stageId
val taskType = Utils.getFormattedClassName(task)
// The success case is dealt with separately below, since we need to compute accumulator
// updates before posting.
if (event.reason != Success) {
val attemptId = stageIdToStage.get(task.stageId).map(_.latestInfo.attemptId).getOrElse(-1)
listenerBus.post(SparkListenerTaskEnd(stageId, attemptId, taskType, event.reason,event.taskInfo, event.taskMetrics))
}
if (!stageIdToStage.contains(task.stageId)) {
// Skip all the actions if the stage has been cancelled.
return
}
val stage = stageIdToStage(task.stageId)
def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None) = {
val serviceTime = stage.latestInfo.submissionTime match {
case Some(t) => "%.03f".format((clock.getTime() - 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.getTime())
} else {
stage.latestInfo.stageFailed(errorMessage.get)
logInfo("%s (%s) failed in %s s".format(stage, stage.name, serviceTime))
}
listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
runningStages -= stage
}
event.reason match {
case Success =>
if (event.accumUpdates != null) {
try {
Accumulators.add(event.accumUpdates)
event.accumUpdates.foreach { case (id, partialValue) =>
val acc = Accumulators.originals(id).asInstanceOf[Accumulable[Any, Any]]
// To avoid UI cruft, ignore cases where value wasn't updated
if (acc.name.isDefined && partialValue != acc.zero) {
val name = acc.name.get
val stringPartialValue = Accumulators.stringifyPartialValue(partialValue)
val stringValue = Accumulators.stringifyValue(acc.value)
stage.latestInfo.accumulables(id) = AccumulableInfo(id, name, stringValue)
event.taskInfo.accumulables +=
AccumulableInfo(id, name, Some(stringPartialValue), stringValue)
}
}
} catch {
// If we see an exception during accumulator update, just log the error and move on.
case e: Exception =>
logError(s"Failed to update accumulators for $task", e)
}
}
listenerBus.post(SparkListenerTaskEnd(stageId, stage.latestInfo.attemptId, taskType,
event.reason, event.taskInfo, event.taskMetrics))
stage.pendingTasks -= task
task match {
case rt: ResultTask[_, _] =>
stage.resultOfJob 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(stage)
cleanupStateForJobAndIndependentStages(job)
listenerBus.post(SparkListenerJobEnd(job.jobId, 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 stage as failed?
job.listener.jobFailed(new SparkDriverExecutionException(e))
}
}
case None =>
logInfo("Ignoring result from " + rt + " because its job has finished")
}
case smt: ShuffleMapTask =>
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("Ignoring possibly bogus ShuffleMapTask completion from " + execId)
} else {
stage.addOutputLoc(smt.partitionId, status)
}
if (runningStages.contains(stage) && stage.pendingTasks.isEmpty) {
markStageAsFinished(stage)
logInfo("looking for newly runnable stages")
logInfo("running: " + runningStages)
logInfo("waiting: " + waitingStages)
logInfo("failed: " + failedStages)
if (stage.shuffleDep.isDefined) {
// We supply true to increment the epoch number here in case this is a
// recomputation of the map outputs. In that case, some nodes may have cached
// locations with holes (from when we detected the error) and will need the
// epoch incremented to refetch them.
// TODO: Only increment the epoch number if this is not the first time
// we registered these map outputs.
mapOutputTracker.registerMapOutputs(
stage.shuffleDep.get.shuffleId,
stage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray,
changeEpoch = true)
}
clearCacheLocs()
if (stage.outputLocs.exists(_ == Nil)) {
// Some tasks had failed; let's resubmit this stage
// TODO: Lower-level scheduler should also deal with this
logInfo("Resubmitting " + stage + " (" + stage.name +
") because some of its tasks had failed: " +
stage.outputLocs.zipWithIndex.filter(_._1 == Nil).map(_._2).mkString(", "))
submitStage(stage)
} else {
val newlyRunnable = new ArrayBuffer[Stage]
for (stage <- waitingStages) {
logInfo("Missing parents for " + stage + ": " + getMissingParentStages(stage))
}
for (stage <- waitingStages if getMissingParentStages(stage) == Nil) {
newlyRunnable += stage
}
waitingStages --= newlyRunnable
runningStages ++= newlyRunnable
for {
stage <- newlyRunnable.sortBy(_.id)
jobId <- activeJobForStage(stage)
} {
<span style="color:#ff0000;"> logInfo("Submitting " + stage + " (" + stage.rdd + "), which is now runnable")
submitMissingTasks(stage, jobId)//</span>
}
}
}
}</span>