Spark Streaming Backpressure分析

转载自：http://www.cnblogs.com/barrenlake/p/5349949.html#

1、为什么引入Backpressure

                默认情况下，Spark Streaming通过Receiver以生产者生产数据的速率接收数据，计算过程中会出现 batch processing time > batch interval 的情况，其中 batch processing time 为实际计算一个批次花费时间， batch interval为Streaming应用设置的批处理间隔。这意味着Spark Streaming的数据接收速率高于Spark从队列中移除数据的速率，也就是数据处理能力低，在设置间隔内不能完全处理当前接收速率接收的数据。如果这种情况持续过长的时间，会造成数据在内存中堆积，导致Receiver所在Executor内存溢出等问题（如果设置StorageLevel包含disk, 则内存存放不下的数据会溢写至disk, 加大延迟）。Spark 1.5以前版本，用户如果要限制Receiver的数据接收速率，可以通过设置静态配制参数“spark.streaming.receiver.maxRate”的值来实现，此举虽然可以通过限制接收速率，来适配当前的处理能力，防止内存溢出，但也会引入其它问题。比如：producer数据生产高于maxRate，当前集群处理能力也高于maxRate，这就会造成资源利用率下降等问题。为了更好的协调数据接收速率与资源处理能力，Spark Streaming 从v1.5开始引入反压机制（back-pressure）,通过动态控制数据接收速率来适配集群数据处理能力。

2、Backpressure

                Spark Streaming Backpressure:  根据JobScheduler反馈作业的执行信息来动态调整Receiver数据接收率。通过属性“spark.streaming.backpressure.enabled”来控制是否启用backpressure机制，默认值false，即不启用。

2.1 Streaming架构如下图所示（详见Streaming数据接收过程文档和Streaming 源码解析）

2.2 BackPressure执行过程如下图所示:

　　在原架构的基础上加上一个新的组件RateController,这个组件负责监听“OnBatchCompleted”事件，然后从中抽取processingDelay 及schedulingDelay信息.  Estimator依据这些信息估算出最大处理速度（rate），最后由基于Receiver的Input Stream将rate通过ReceiverTracker与ReceiverSupervisorImpl转发给BlockGenerator（继承自RateLimiter）.

  

3、BackPressure 源码解析

3.1 RateController类体系

                RatenController 继承自StreamingListener. 用于处理BatchCompleted事件。核心代码为：

*** A StreamingListener that receives batch completion updates, and maintains* an estimate of the speed at which this stream should ingest messages,* given an estimate computation from a `RateEstimator`*/ private[streaming] abstract class RateController(val streamUID: Int, rateEstimator: RateEstimator) extends StreamingListener with Serializable { …… …… /*** Compute the new rate limit and publish it asynchronously.*/private def computeAndPublish(time: Long, elems: Long, workDelay: Long, waitDelay: Long): Unit =Future[Unit] {val newRate = rateEstimator.compute(time, elems, workDelay, waitDelay)newRate.foreach { s =>rateLimit.set(s.toLong)publish(getLatestRate())}}def getLatestRate(): Long = rateLimit.get()override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) {val elements = batchCompleted.batchInfo.streamIdToInputInfofor {processingEnd <- batchCompleted.batchInfo.processingEndTimeworkDelay <- batchCompleted.batchInfo.processingDelaywaitDelay <- batchCompleted.batchInfo.schedulingDelayelems <- elements.get(streamUID).map(_.numRecords)} computeAndPublish(processingEnd, elems, workDelay, waitDelay)} }

3.2 RateController的注册

                JobScheduler启动时会抽取在DStreamGraph中注册的所有InputDstream中的rateController，并向ListenerBus注册监听. 此部分代码如下：

def start(): Unit = synchronized {if (eventLoop != null) return // scheduler has already been started logDebug("Starting JobScheduler")eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") {override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event)override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e)}eventLoop.start()// attach rate controllers of input streams to receive batch completion updatesfor {inputDStream <- ssc.graph.getInputStreamsrateController <- inputDStream.rateController} ssc.addStreamingListener(rateController)listenerBus.start()receiverTracker = new ReceiverTracker(ssc)inputInfoTracker = new InputInfoTracker(ssc)receiverTracker.start()jobGenerator.start()logInfo("Started JobScheduler")}

3.3 BackPressure执行过程分析

                BackPressure 执行过程分为BatchCompleted事件触发时机和事件处理两个过程

3.3.1 BatchCompleted触发过程

                对BatchedCompleted的分析，应该从JobGenerator入手，因为BatchedCompleted是批次处理结束的标志，也就是JobGenerator产生的作业执行完成时触发的，因此进行作业执行分析。

                Streaming 应用中JobGenerator每个Batch Interval都会为应用中的每个Output Stream建立一个Job, 该批次中的所有Job组成一个Job Set.使用JobScheduler的submitJobSet进行批量Job提交。此部分代码结构如下所示

/** Generate jobs and perform checkpoint for the given `time`. */ private def generateJobs(time: Time) {// Set the SparkEnv in this thread, so that job generation code can access the environment// Example: BlockRDDs are created in this thread, and it needs to access BlockManager// Update: This is probably redundant after threadlocal stuff in SparkEnv has been removed.SparkEnv.set(ssc.env)// Checkpoint all RDDs marked for checkpointing to ensure their lineages are// truncated periodically. Otherwise, we may run into stack overflows (SPARK-6847).ssc.sparkContext.setLocalProperty(RDD.CHECKPOINT_ALL_MARKED_ANCESTORS, "true")Try {jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batchgraph.generateJobs(time) // generate jobs using allocated block } match {case Success(jobs) =>val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))case Failure(e) =>jobScheduler.reportError("Error generating jobs for time " + time, e)}eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false)) }

 其中，sumitJobSet会创建固定数量的后台线程（具体由“spark.streaming.concurrentJobs”指定），去处理Job Set中的Job. 具体实现逻辑为：

def submitJobSet(jobSet: JobSet) {if (jobSet.jobs.isEmpty) {logInfo("No jobs added for time " + jobSet.time)} else {listenerBus.post(StreamingListenerBatchSubmitted(jobSet.toBatchInfo))jobSets.put(jobSet.time, jobSet)jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job)))logInfo("Added jobs for time " + jobSet.time)} }

其中JobHandler用于执行Job及处理Job执行结果信息。当Job执行完成时会产生JobCompleted事件. JobHandler的具体逻辑如下面代码所示：

private class JobHandler(job: Job) extends Runnable with Logging {import JobScheduler._def run() {try {val formattedTime = UIUtils.formatBatchTime(job.time.milliseconds, ssc.graph.batchDuration.milliseconds, showYYYYMMSS = false)val batchUrl = s"/streaming/batch/?id=${job.time.milliseconds}"val batchLinkText = s"[output operation ${job.outputOpId}, batch time ${formattedTime}]"ssc.sc.setJobDescription(s"""Streaming job from <a href="$batchUrl">$batchLinkText</a>""")ssc.sc.setLocalProperty(BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString)ssc.sc.setLocalProperty(OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString)// Checkpoint all RDDs marked for checkpointing to ensure their lineages are// truncated periodically. Otherwise, we may run into stack overflows (SPARK-6847).ssc.sparkContext.setLocalProperty(RDD.CHECKPOINT_ALL_MARKED_ANCESTORS, "true")// We need to assign `eventLoop` to a temp variable. Otherwise, because// `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then// it's possible that when `post` is called, `eventLoop` happens to null.var _eventLoop = eventLoopif (_eventLoop != null) {_eventLoop.post(JobStarted(job, clock.getTimeMillis()))// Disable checks for existing output directories in jobs launched by the streaming// scheduler, since we may need to write output to an existing directory during checkpoint// recovery; see SPARK-4835 for more details.PairRDDFunctions.disableOutputSpecValidation.withValue(true) {job.run()}_eventLoop = eventLoopif (_eventLoop != null) {_eventLoop.post(JobCompleted(job, clock.getTimeMillis()))}} else {// JobScheduler has been stopped. }} finally {ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null)ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null)}}} }

　　当Job执行完成时，向eventLoop发送JobCompleted事件。EventLoop事件处理器接到JobCompleted事件后将调用handleJobCompletion 来处理Job完成事件。handleJobCompletion使用Job执行信息创建StreamingListenerBatchCompleted事件并通过StreamingListenerBus向监听器发送。实现如下：

private def handleJobCompletion(job: Job, completedTime: Long) {val jobSet = jobSets.get(job.time)jobSet.handleJobCompletion(job)job.setEndTime(completedTime)listenerBus.post(StreamingListenerOutputOperationCompleted(job.toOutputOperationInfo))logInfo("Finished job " + job.id + " from job set of time " + jobSet.time)if (jobSet.hasCompleted) {jobSets.remove(jobSet.time)jobGenerator.onBatchCompletion(jobSet.time)logInfo("Total delay: %.3f s for time %s (execution: %.3f s)".format(jobSet.totalDelay / 1000.0, jobSet.time.toString,jobSet.processingDelay / 1000.0))listenerBus.post(StreamingListenerBatchCompleted(jobSet.toBatchInfo))}job.result match {case Failure(e) =>reportError("Error running job " + job, e)case _ =>}}

3.3.2、BatchCompleted事件处理过程

                StreamingListenerBus将事件转交给具体的StreamingListener，因此BatchCompleted将交由RateController进行处理。RateController接到BatchCompleted事件后将调用onBatchCompleted对事件进行处理。

override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) {val elements = batchCompleted.batchInfo.streamIdToInputInfofor {processingEnd <- batchCompleted.batchInfo.processingEndTimeworkDelay <- batchCompleted.batchInfo.processingDelaywaitDelay <- batchCompleted.batchInfo.schedulingDelayelems <- elements.get(streamUID).map(_.numRecords)} computeAndPublish(processingEnd, elems, workDelay, waitDelay) }

　　onBatchCompleted会从完成的任务中抽取任务的执行延迟和调度延迟，然后用这两个参数用RateEstimator（目前存在唯一实现PIDRateEstimator，proportional-integral-derivative (PID) controller， PID控制器）估算出新的rate并发布。代码如下：

/*** Compute the new rate limit and publish it asynchronously.*/private def computeAndPublish(time: Long, elems: Long, workDelay: Long, waitDelay: Long): Unit =Future[Unit] {val newRate = rateEstimator.compute(time, elems, workDelay, waitDelay)newRate.foreach { s =>rateLimit.set(s.toLong)publish(getLatestRate())}}

其中publish（）由RateController的子类ReceiverRateController来定义。具体逻辑如下（ReceiverInputDStream中定义）：

/*** A RateController that sends the new rate to receivers, via the receiver tracker.*/private[streaming] class ReceiverRateController(id: Int, estimator: RateEstimator)extends RateController(id, estimator) {override def publish(rate: Long): Unit =ssc.scheduler.receiverTracker.sendRateUpdate(id, rate)}

publish的功能为新生成的rate 借助ReceiverTracker进行转发。ReceiverTracker将rate包装成UpdateReceiverRateLimit事交ReceiverTrackerEndpoint

/** Update a receiver's maximum ingestion rate */ def sendRateUpdate(streamUID: Int, newRate: Long): Unit = synchronized {if (isTrackerStarted) {endpoint.send(UpdateReceiverRateLimit(streamUID, newRate))} }

ReceiverTrackerEndpoint接到消息后，其将会从receiverTrackingInfos列表中获取Receiver注册时使用的endpoint(实为ReceiverSupervisorImpl)，再将rate包装成UpdateLimit发送至endpoint.其接到信息后，使用updateRate更新BlockGenerators(RateLimiter子类),来计算出一个固定的令牌间隔。

/** RpcEndpointRef for receiving messages from the ReceiverTracker in the driver */ private val endpoint = env.rpcEnv.setupEndpoint("Receiver-" + streamId + "-" + System.currentTimeMillis(), new ThreadSafeRpcEndpoint {override val rpcEnv: RpcEnv = env.rpcEnvoverride def receive: PartialFunction[Any, Unit] = {case StopReceiver =>logInfo("Received stop signal")ReceiverSupervisorImpl.this.stop("Stopped by driver", None)case CleanupOldBlocks(threshTime) =>logDebug("Received delete old batch signal")cleanupOldBlocks(threshTime)case UpdateRateLimit(eps) =>logInfo(s"Received a new rate limit: $eps.")registeredBlockGenerators.asScala.foreach { bg =>bg.updateRate(eps)}}})

其中RateLimiter的updateRate实现如下：

/*** Set the rate limit to `newRate`. The new rate will not exceed the maximum rate configured by* {{{spark.streaming.receiver.maxRate}}}, even if `newRate` is higher than that.** @param newRate A new rate in events per second. It has no effect if it's 0 or negative.*/private[receiver] def updateRate(newRate: Long): Unit =if (newRate > 0) {if (maxRateLimit > 0) {rateLimiter.setRate(newRate.min(maxRateLimit))} else {rateLimiter.setRate(newRate)}}

 setRate的实现 如下：

public final void setRate(double permitsPerSecond) {Preconditions.checkArgument(permitsPerSecond > 0.0&& !Double.isNaN(permitsPerSecond), "rate must be positive");synchronized (mutex) {resync(readSafeMicros());double stableIntervalMicros = TimeUnit.SECONDS.toMicros(1L) / permitsPerSecond; //固定间隔this.stableIntervalMicros = stableIntervalMicros;doSetRate(permitsPerSecond, stableIntervalMicros);}}

到此，backpressure反压机制调整rate结束。

4．流量控制点

　　当Receiver开始接收数据时，会通过supervisor.pushSingle()方法将接收的数据存入currentBuffer等待BlockGenerator定时将数据取走，包装成block. 在将数据存放入currentBuffer之时，要获取许可（令牌）。如果获取到许可就可以将数据存入buffer, 否则将被阻塞，进而阻塞Receiver从数据源拉取数据。

/*** Push a single data item into the buffer.*/ def addData(data: Any): Unit = {if (state == Active) {waitToPush() //获取令牌 synchronized {if (state == Active) {currentBuffer += data} else {throw new SparkException("Cannot add data as BlockGenerator has not been started or has been stopped")}}} else {throw new SparkException("Cannot add data as BlockGenerator has not been started or has been stopped")} }

      其令牌投放采用令牌桶机制进行， 原理如下图所示:

　　令牌桶机制： 大小固定的令牌桶可自行以恒定的速率源源不断地产生令牌。如果令牌不被消耗，或者被消耗的速度小于产生的速度，令牌就会不断地增多，直到把桶填满。后面再产生的令牌就会从桶中溢出。最后桶中可以保存的最大令牌数永远不会超过桶的大小。当进行某操作时需要令牌时会从令牌桶中取出相应的令牌数，如果获取到则继续操作，否则阻塞。用完之后不用放回。

　　Streaming 数据流被Receiver接收后，按行解析后存入iterator中。然后逐个存入Buffer，在存入buffer时会先获取token，如果没有token存在，则阻塞；如果获取到则将数据存入buffer.  然后等价后续生成block操作。

 

转载于:https://www.cnblogs.com/itboys/p/7797516.html

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