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大数据：Spark Shuffle（二）Executor、Driver之间Shuffle结果消息传递、追踪
1. 前言
在博客里介绍了ShuffleWrite关于shuffleMapTask如何运行，输出Shuffle结果到文件Shuffle_shuffleId_mapId_0.data文件中，每个executor需要向Driver汇报当前节点的Shuffle结果状态，Driver保存结果信息进行下个Task的调度。
2. StatusUpdate消息
当Executor运行完Task的时候需要向Driver汇报StatusUpdate的消息
override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) {
val msg = StatusUpdate(executorId, taskId, state, data)
driver match {
case Some(driverRef) => driverRef.send(msg)
case None => logWarning(s"Drop $msg because has not yet connected to driver")
}
}
整个结构体中包含了
- ExecutorId: Executor自己的ID
- TaskId: task分配的ID
- State: Task的运行状态
LAUNCHING, RUNNING, FINISHED, FAILED, KILLED, LOST
- Data: 保存序列化的Result
2.1 Executor端发送
在Task运行后的结果，Executor会将结果首先序列化成ByteBuffer封装成DirectTaskResult，再次序列化DirectTaskResult成ByteBuffer，很显然序列化的结果的大小会决定不同的传递策略。在这里会有两个筏值来控制
- 最大的返回结果大小，如果超过设定的最大返回结果时，返回的结果内容会被丢弃，只是返回序列化的InDirectTaskResult，里面包含着BlockID和序列化后的结果大小
spark.driver.maxResultSize
- 最大的直接返回结果大小：如果返回的结果大于最大的直接返回结果大小，小于最大的返回结果大小，采用了保存的折中的策略，尝试将BlockID保存到BlockManager中，关于BlockManager可以参考前面写的BlockManager系列，返回InDirectTaskResult，里面包含着BlockID和序列化后的结果大小
spark.task.maxDirectResultSize
- 直接返回：如果返回的结果小于等于最大的直接返回结果大小，将直接将序列化的DirectTaskResult返回给Driver端
val serializedResult: ByteBuffer = {
if (maxResultSize > 0 && resultSize > maxResultSize) {
logWarning(s"Finished $taskName (TID $taskId). Result is larger than maxResultSize " +
s"(${Utils.bytesToString(resultSize)} > ${Utils.bytesToString(maxResultSize)}), " +
s"dropping it.")
ser.serialize(new IndirectTaskResult[Any](TaskResultBlockId(taskId), resultSize))
} else if (resultSize > maxDirectResultSize) {
val blockId = TaskResultBlockId(taskId)
env.blockManager.putBytes(
blockId,
new ChunkedByteBuffer(serializedDirectResult.duplicate()),
StorageLevel.MEMORY_AND_DISK_SER)
logInfo(
s"Finished $taskName (TID $taskId). $resultSize bytes result sent via BlockManager)")
ser.serialize(new IndirectTaskResult[Any](blockId, resultSize))
} else {
logInfo(s"Finished $taskName (TID $taskId). $resultSize bytes result sent to driver")
serializedDirectResult
}
}2.2 Driver端接收
Driver端处理StatusUpdate的消息的代码如下：case StatusUpdate(executorId, taskId, state, data) =>
scheduler.statusUpdate(taskId, state, data.value)
if (TaskState.isFinished(state)) {
executorDataMap.get(executorId) match {
case Some(executorInfo) =>
executorInfo.freeCores += scheduler.CPUS_PER_TASK
makeOffers(executorId)
case None =>
// Ignoring the update since we don‘t know about the executor.
logWarning(s"Ignored task status update ($taskId state $state) " +
s"from unknown executor with ID $executorId")
}
}此处的scheduler是TaskSchedulerImpl.scala
if (TaskState.isFinished(state)) {
cleanupTaskState(tid)
taskSet.removeRunningTask(tid)
if (state == TaskState.FINISHED) {
taskResultGetter.enqueueSuccessfulTask(taskSet, tid, serializedData)
} else if (Set(TaskState.FAILED, TaskState.KILLED, TaskState.LOST).contains(state)) {
taskResultGetter.enqueueFailedTask(taskSet, tid, state, serializedData)
}
}代码statusUpdate调用了enqueueSuccessfulTask方法 def enqueueSuccessfulTask(
taskSetManager: TaskSetManager,
tid: Long,
serializedData: ByteBuffer): Unit = {
getTaskResultExecutor.execute(new Runnable {
override def run(): Unit = Utils.logUncaughtExceptions {
try {
val (result, size) = serializer.get().deserialize[TaskResult[_]](serializedData) match {
case directResult: DirectTaskResult[_] =>
if (!taskSetManager.canFetchMoreResults(serializedData.limit())) {
return
}
// deserialize "value" without holding any lock so that it won‘t block other threads.
// We should call it here, so that when it‘s called again in
// "TaskSetManager.handleSuccessfulTask", it does not need to deserialize the value.
directResult.value(taskResultSerializer.get())
(directResult, serializedData.limit())
case IndirectTaskResult(blockId, size) =>
if (!taskSetManager.canFetchMoreResults(size)) {
// dropped by executor if size is larger than maxResultSize
sparkEnv.blockManager.master.removeBlock(blockId)
return
}
logDebug("Fetching indirect task result for TID %s".format(tid))
scheduler.handleTaskGettingResult(taskSetManager, tid)
val serializedTaskResult = sparkEnv.blockManager.getRemoteBytes(blockId)
if (!serializedTaskResult.isDefined) {
/* We won‘t be able to get the task result if the machine that ran the task failed
* between when the task ended and when we tried to fetch the result, or if the
* block manager had to flush the result. */
scheduler.handleFailedTask(
taskSetManager, tid, TaskState.FINISHED, TaskResultLost)
return
}
val deserializedResult = serializer.get().deserialize[DirectTaskResult[_]](
serializedTaskResult.get.toByteBuffer)
// force deserialization of referenced value
deserializedResult.value(taskResultSerializer.get())
sparkEnv.blockManager.master.removeBlock(blockId)
(deserializedResult, size)
}
// Set the task result size in the accumulator updates received from the executors.
// We need to do this here on the driver because if we did this on the executors then
// we would have to serialize the result again after updating the size.
result.accumUpdates = result.accumUpdates.map { a =>
if (a.name == Some(InternalAccumulator.RESULT_SIZE)) {
val acc = a.asInstanceOf[LongAccumulator]
assert(acc.sum == 0L, "task result size should not have been set on the executors")
acc.setValue(size.toLong)
acc
} else {
a
}
}
scheduler.handleSuccessfulTask(taskSetManager, tid, result)
} catch {
case cnf: ClassNotFoundException =>
val loader = Thread.currentThread.getContextClassLoader
taskSetManager.abort("ClassNotFound with classloader: " + loader)
// Matching NonFatal so we don‘t catch the ControlThrowable from the "return" above.
case NonFatal(ex) =>
logError("Exception while getting task result", ex)
taskSetManager.abort("Exception while getting task result: %s".format(ex))
}
}
})
}在函数中，反序列化的过程是通过线程池调用单独线程来运行的，Netty的接收数据线程是不能被堵塞的同时还接受着别的消息的，反序列化是耗时的任务显然不能使用Netty的消息处理线程。2.2.1 DirectTaskResult处理过程
- 直接反序列化传递过来的ByteBuffer就可以了，反序列化后，同时进行了整体返回内容的大小的判断，在前面的2.1中介绍参数：spark.driver.maxResultSize，这个参数是Driver端的参数控制的，在Spark中会启动多个Task，参数的控制是一个整体的控制所有的Tasks的返回结果的数量大小，当然单个task使用该筏值的控制是没有问题，只要有一个任务返回的结果超过maxResultSize，自然整体也会超过maxResultSize。
- 在反序列化DirectTaskResult
- 对DirectTaskResult里的result进行了反序列化
2.2.2 InDirectTaskResult处理过程
- 通过size判断大小是否超过spark.driver.maxResultSize筏值控制
- 通过BlockManager来获取BlockID的内容，反序列化成DirectTaskResult
- 对DirectTaskResult里的result进行了反序列化
sched.dagScheduler.taskEnded(tasks(index), Success, result.value(), result.accumUpdates, info)
回到了Dag的调度，往eventProcessLoop的队列里放入了一个CompletionEvent
def taskEnded(
task: Task[_],
reason: TaskEndReason,
result: Any,
accumUpdates: Seq[AccumulatorV2[_, _]],
taskInfo: TaskInfo): Unit = {
eventProcessLoop.post(
CompletionEvent(task, reason, result, accumUpdates, taskInfo))
}处理eventProcessLoop队列的event是在DAG的线程处理的，在这里我们就不讨论DAG的任务调度了。
2.3 MapOutputTracker
MapOutputTracker是当运行完ShuffleMapTask的时候，ShuffleWrite会生成Shuffle_shuffleId_mapId_reduceId.data、index文件，而executor需要将具体的信息返回给Driver，当Driver进行下一步的Task运算的时候，Executor也需要获取具体Shuffle数据文件的信息进行下一步的action算子的运算，整个结构的管理就是通过MapOutputTracker跟踪器进行追踪的。
2.3.1 RegisterMapOutput
Execute端
在ShuffleMapTask中运行后会生成一个MapStatus，也就是上图的Map0结构，ComressedMapStatus、HighlyCompressedMapStatus这里的两个区别主要是增对Partition1...的size long的压缩，但这里的压缩算法并不准确比，如CompressedMapStatus的算法：
def compressSize(size: Long): Byte = {
if (size == 0) {
0
} else if (size <= 1L) {
1
} else {
math.min(255, math.ceil(math.log(size) / math.log(LOG_BASE)).toInt).toByte
}
}求Log1.1(size)的整数转为byte，也就是支持最大1.1^255=35G左右为何不需要计算精准的尺寸？
还记得前面博客里提到的Shuffle_shuffleId_mapId_reduceId.index文件么，这里才是精准的位置，当读取本地文件的时候，并不使用MapStatus里的Size
Size有何用？
有存在别的Execute获取别的Execute的Shuffle结果文件，此时的size是获取文件的大概位置。
MapStatus就是Task运行的结果，被序列化成前面说的DirectTaskResult中的value，通过StatusUpdate消息传递
Driver端
在前面一直介绍到DAG线程调度处理CompletionEvent
private[scheduler] def handleTaskCompletion(event: CompletionEvent) {
............
case smt: ShuffleMapTask =>
val shuffleStage = stage.asInstanceOf[ShuffleMapStage]
updateAccumulators(event)
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 {
shuffleStage.addOutputLoc(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)
// 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(
shuffleStage.shuffleDep.shuffleId,
shuffleStage.outputLocInMapOutputTrackerFormat(),
changeEpoch = true)
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 {
// Mark any map-stage jobs waiting on this stage as finished
if (shuffleStage.mapStageJobs.nonEmpty) {
val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep)
for (job <- shuffleStage.mapStageJobs) {
markMapStageJobAsFinished(job, stats)
}
}
submitWaitingChildStages(shuffleStage)
}
}处理shuffleMapTask的结果的时候，mapOutputTracker.registerMapOutputs进行了MapOutputs的注册
protected val mapStatuses = new ConcurrentHashMap[Int, Array[MapStatus]]().asScala
def registerMapOutputs(shuffleId: Int, statuses: Array[MapStatus], changeEpoch: Boolean = false) {
mapStatuses.put(shuffleId, statuses.clone())
if (changeEpoch) {
incrementEpoch()
}
}在Driver端保存了一个Map，以ShuffldId为Key，MapStatus的数组2.3.2 获取MapStatus
在ResultTask中，通过获取反序列化的ShuffledRDD，在Fetcher shuffle Block的时候
val blockFetcherItr = new ShuffleBlockFetcherIterator(
context,
blockManager.shuffleClient,
blockManager,
mapOutputTracker.getMapSizesByExecutorId(handle.shuffleId, startPartition, endPartition),
// Note: we use getSizeAsMb when no suffix is provided for backwards compatibility
SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024,
SparkEnv.get.conf.getInt("spark.reducer.maxReqsInFlight", Int.MaxValue))通过getMapSizesByExecutorId获取shuffledId所对应的MapStatus
def getMapSizesByExecutorId(shuffleId: Int, startPartition: Int, endPartition: Int)
: Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
logDebug(s"Fetching outputs for shuffle $shuffleId, partitions $startPartition-$endPartition")
val statuses = getStatuses(shuffleId)
// Synchronize on the returned array because, on the driver, it gets mutated in place
statuses.synchronized {
return MapOutputTracker.convertMapStatuses(shuffleId, startPartition, endPartition, statuses)
}
}在getStatuses中
private def getStatuses(shuffleId: Int): Array[MapStatus] = {
val statuses = mapStatuses.get(shuffleId).orNull
if (statuses == null) {
logInfo("Don‘t have map outputs for shuffle " + shuffleId + ", fetching them")
val startTime = System.currentTimeMillis
var fetchedStatuses: Array[MapStatus] = null
fetching.synchronized {
// Someone else is fetching it; wait for them to be done
while (fetching.contains(shuffleId)) {
try {
fetching.wait()
} catch {
case e: InterruptedException =>
}
}
// Either while we waited the fetch happened successfully, or
// someone fetched it in between the get and the fetching.synchronized.
fetchedStatuses = mapStatuses.get(shuffleId).orNull
if (fetchedStatuses == null) {
// We have to do the fetch, get others to wait for us.
fetching += shuffleId
}
}
if (fetchedStatuses == null) {
// We won the race to fetch the statuses; do so
logInfo("Doing the fetch; tracker endpoint = " + trackerEndpoint)
// This try-finally prevents hangs due to timeouts:
try {
val fetchedBytes = askTracker[Array[Byte]](GetMapOutputStatuses(shuffleId))
fetchedStatuses = MapOutputTracker.deserializeMapStatuses(fetchedBytes)
logInfo("Got the output locations")
mapStatuses.put(shuffleId, fetchedStatuses)
} finally {
fetching.synchronized {
fetching -= shuffleId
fetching.notifyAll()
}
}
}
logDebug(s"Fetching map output statuses for shuffle $shuffleId took " +
s"${System.currentTimeMillis - startTime} ms")
if (fetchedStatuses != null) {
return fetchedStatuses
} else {
logError("Missing all output locations for shuffle " + shuffleId)
throw new MetadataFetchFailedException(
shuffleId, -1, "Missing all output locations for shuffle " + shuffleId)
}
} else {
return statuses
}
}- 做了一层缓存mapStatus，对同一个Executor来说，里面的线程都是运行同一个Driver的提交的任务，相同的shuffeID，MapStatus是一样的
- 对同一个Executor、ShuffeID来说，通过Driver获取信息只需要一次，Driver里保存的Shuffle的结果是单点的，对同一个Executor来说获取同一个ShuffleID只需要请求一次，在Traker里面保存了一个队列fetching，里面保存的ShuffeID代表的是有线程正在从Driver端获取ShuffleID的MapStatus，如果发现有值，当前线程会等待，直到其他的线程获取ShuffleID状态并保存到缓存结束，当前线程直接从缓存中获取当前状态
- Executor 向Driver发送GetMapOutputStatuses(shuffleId)消息
- Driver收到GetMapOutputStatuses消息后保存到消息队列mapOutputRequests，Map-Output-Dispatcher-x多线程处理消息队列，返回序列化的MapStatus
- Executor反序列化成MapStatus
2.2.3 以BlockManagerId为key的Shuffle的序列
在前面的博客里提到过Driver分配Task的数量的策略是依赖于Partition，在单个任务ShuffledMapTask对Data进行分片也是依赖于Partition
前面一个的Partition 是MapId，后面一个Partition 指的是ReduceId
在ResultTask里所取的Shuffle数据文件中的Partition是ReduceId，而不是MapId
也就是每个ResultTask会去获取所有不同的MapId中相同的PartitionID部分Shuffle文件，而不是继续按前面的Map进行分配，那意味着ResultTask将会去获取所有Shuffle文件
Shuffle_shuffleId_mapId_0.data中的Partition那部分进行Action操作，这样可以适当避免在ShuffledMapTask中分配的数据不均衡，导致单个Shuffle_shuffleId_mapId_0.data文件数据过大的问题。
具体的代码实现如下：
private def convertMapStatuses(
shuffleId: Int,
startPartition: Int,
endPartition: Int,
statuses: Array[MapStatus]): Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
assert (statuses != null)
val splitsByAddress = new HashMap[BlockManagerId, ArrayBuffer[(BlockId, Long)]]
for ((status, mapId) <- statuses.zipWithIndex) {
if (status == null) {
val errorMessage = s"Missing an output location for shuffle $shuffleId"
logError(errorMessage)
throw new MetadataFetchFailedException(shuffleId, startPartition, errorMessage)
} else {
for (part <- startPartition until endPartition) {
splitsByAddress.getOrElseUpdate(status.location, ArrayBuffer()) +=
((ShuffleBlockId(shuffleId, mapId, part), status.getSizeForBlock(part)))
}
}
}
splitsByAddress.toSeq
}大数据：Spark Shuffle（二）Executor、Driver之间Shuffle结果消息传递、追踪
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