spark storm 反压
因特殊业务场景，如大促、秒杀活动与突发热点事情等业务流量在短时间内剧增，形成巨大的流量毛刺，数据流入的速度远高于数据处理的速度，对流处理系统构成巨大的负载压力，如果不能正确处理，可能导致集群资源耗尽最终集群崩溃，因此有效的反压机制(backpressure)对保障流处理系统的稳定至关重要。
Storm和Spark Streaming都提供了反压机制，实现各不相同
对于开启了acker机制的storm程序，可以通过设置conf.setMaxSpoutPending参数来实现反压效果，如果下游组件(bolt)处理速度跟不上导致spout发送的tuple没有及时确认的数超过了参数设定的值，spout会停止发送数据，这种方式的缺点是很难调优conf.setMaxSpoutPending参数的设置以达到最好的反压效果，设小了会导致吞吐上不去，设大了会导致worker OOM；有震荡，数据流会处于一个颠簸状态，效果不如逐级反压；另外对于关闭acker机制的程序无效；
新的storm自动反压机制(Automatic Back Pressure)通过监控bolt中的接收队列的情况，当超过高水位值时专门的线程会将反压信息写到 Zookeeper ，Zookeeper上的watch会通知该拓扑的所有Worker都进入反压状态，最后Spout降低tuple发送的速度。具体实现：
Spark Streaming程序中当计算过程中出现batch processing time > batch interval的情况时，(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),意味着处理数据的速度小于接收数据的速度，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),可以通过设置参数spark.streaming.receiver.maxRate来限制Receiver的数据接收速率，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
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
Streaming架构如下图所示:
BackPressure执行过程如下图所示:
本文转至：https://www.cnblogs.com/barrenlake/p/5349949.html
3.1 RateController类体系
RatenController 继承自StreamingListener. 用于处理BatchCompleted事件。核心代码为：
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** * 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.streamIdToInputInfo for { processingEnd <- batchCompleted.batchInfo.processingEndTime workDelay <- batchCompleted.batchInfo.processingDelay waitDelay <- batchCompleted.batchInfo.schedulingDelay elems <- 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：
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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 updates for { inputDStream <- ssc.graph.getInputStreams rateController <- 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
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/** 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 batch graph.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：
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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：
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：
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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。
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override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) { val elements = batchCompleted.batchInfo.streamIdToInputInfo for { processingEnd <- batchCompleted.batchInfo.processingEndTime workDelay <- batchCompleted.batchInfo.processingDelay waitDelay <- batchCompleted.batchInfo.schedulingDelay elems <- 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
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/** 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。
其中RateLimiter的updateRate实现如下：
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/** * 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：
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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。
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/** * 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。
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