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。

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/** 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.rpcEnv       override 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：

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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。

本文转自

https://www.cnblogs.com/barrenlake/p/5349949.html

https://blog.csdn.net/zengxiaosen/article/details/72822869