—————☼—————☼—————☼—————☼—————☼—————
Spark Streaming概述
Spark Streaming 初始化过程
Spark Streaming Receiver启动过程分析
Spark Streaming 数据准备阶段分析（Receiver方式）
Spark Streaming 数据计算阶段分析
SparkStreaming Backpressure分析
Spark Streaming Executor DynamicAllocation 机制分析

—————☼—————☼—————☼—————☼—————☼—————

1、引入Backpressure的原因

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架构模型

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 Spark Streaming架构

Spark Streaming架构如下图所示（对其详细解析，参见"Spark Streaming 数据准备阶段分析"和"Spark Streaming 数据计算阶段分析"

2.2 BackPressure执行过程

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）.

BackPressure执行流程

3、BackPressure 源码解析

3.1 RateController类体系结构

RateController继承自StreamingListener.用于处理BatchCompleted事件。
其实类继承结构如下代码所示：

/**
 * 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 {
}

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注册并开启监听。

 // attach rate controllers of input streams to receive batch completion updates
    for {
      inputDStream <- ssc.graph.getInputStreams
      rateController <- inputDStream.rateController
    } ssc.addStreamingListener(rateController)
 listenerBus.start()

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：

• BatchCompleted触发过程
• BatchCompleted事件处理过程

3.3.1 BatchCompleted触发过程

对BatchedCompleted的分析，应该从JobScheduler入手，因为BatchedCompleted是批次处理结束的标志，也就是JobScheduler调度的作业执行完成时触发的，因此进行作业调度执行分析。
JobGenerater在调用generateJobs（）方法生成Job后，会使用JobScheduler的submitJobSet方法对Job进行提交. submitJobSet的具体实现如下：

  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，jobSet中的Job将通过jobExecutor进行处理，对Job进行处理的处理器为JobHandler。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() {
      val oldProps = ssc.sparkContext.getLocalProperties
      try {
        logInfo("Handler job at " + job.time)
        ssc.sparkContext.setLocalProperties(SerializationUtils.clone(ssc.savedProperties.get()))
        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 = eventLoop
        if (_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.
          SparkHadoopWriterUtils.disableOutputSpecValidation.withValue(true) {
            job.run()
          }
          _eventLoop = eventLoop
          if (_eventLoop != null) {
            _eventLoop.post(JobCompleted(job, clock.getTimeMillis()))
          }
        } else {
          // JobScheduler has been stopped.
        }
      } finally {
        ssc.sparkContext.setLocalProperties(oldProps)
      }
    }
  }
}

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事件的处理过程

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.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事件并发送给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.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：

 /**
   * 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 records 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)
      }
    }

其中rateLimiter的setRate的实现如下：

  public final void setRate(double permitsPerSecond) {
        Preconditions.checkArgument(permitsPerSecond > 0.0D && !Double.isNaN(permitsPerSecond), "rate must be positive");
        Object var3 = this.mutex;
        synchronized(this.mutex) {
            this.resync(this.readSafeMicros());
            double stableIntervalMicros = (double)TimeUnit.SECONDS.toMicros(1L) / permitsPerSecond;
            this.stableIntervalMicros = stableIntervalMicros;
            this.doSetRate(permitsPerSecond, stableIntervalMicros);
        }
    }

到此，backpressure反压机制调整rate结束。

4、流量控制点（生效位置）

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()
      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。

本文最初是本人发在博客园（http://www.cnblogs.com/barrenlake/p/5349949.html）