SparkSteaming运行流程分析以及CheckPoint操作

本文主要通过源码来了解SparkStreaming程序从任务生成到任务完成整个执行流程以及中间伴随的checkpoint操作

注：下面源码只贴出跟分析内容有关的代码，其他省略

1 分析流程

应用程序入口：

val sparkConf = new SparkConf().setAppName("SparkStreaming")
val sc = new SparkContext(sparkConf)
val ssc = new StreamingContext(sc, Seconds(batchDuration.toLong))
ssc.start()
ssc.awaitTermination()

一旦ssc.start()调用之后，程序便真正开始运行

第一步：

SparkStreamingContext.start()进行如下主要工作：

  • 调用JobScheduler.start()
  • 发送StreamingListenerStreamingStarted消息
JobScheduler.start()

def start(): Unit = synchronized {
    state match {
      case INITIALIZED =>
        StreamingContext.ACTIVATION_LOCK.synchronized {
          StreamingContext.assertNoOtherContextIsActive()
          try｛
              ...
              scheduler.start()
            }
            state = StreamingContextState.ACTIVE
            scheduler.listenerBus.post(
              StreamingListenerStreamingStarted(System.currentTimeMillis()))
          } catch {
            ...
          }
          StreamingContext.setActiveContext(this)
        }
        ...
      case ACTIVE =>
        logWarning("StreamingContext has already been started")
      case STOPPED =>
        throw new IllegalStateException("StreamingContext has already been stopped")
    }
  }

第二步：

调用JobScheduler.start()执行以下主要操作：

  • 创建EventLoop用于处理接收到的JobSchedulerEvent，processEvent就是实际的处理逻辑
  • 调用jobGenerator.start()
JobScheduler.start()：

def start(): Unit = synchronized {
    if (eventLoop != null) return // scheduler has already been started

    logDebug("Starting JobScheduler")
    //创建一个Event监听器并启动
    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()
    ...
    //启动JobGenerator
    jobGenerator.start()
    ...
  }

第三步：

JobGenerator.start()执行以下主要操作：

  • 创建EventLoop[JobGeneratorEvent]用于处理JobGeneratorEvent事件
  • 开始执行job的生成工作
    • 创建一个timer周期地执行eventLoop.post(GenerateJobs(new Time(longTime)))
    • JobGenerator.start()中的EventLoop收到GenerateJobs事件后，去执行generateJobs(time)
    • generateJobs()中生成JobSet并调用jobScheduler.submitJobSet()进行提交，然后发送一个DoCheckpointEvent进行checkpoint
JobGenerator.start()

def start(): Unit = synchronized {
    if (eventLoop != null) return // generator has already been started
    //创建checkpointWriter用于将checkpoint信息持久化
    checkpointWriter
    //创建了Event监听器，用于监听JobGeneratorEvent并处理
    eventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") {
      override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event)

      override protected def onError(e: Throwable): Unit = {
        jobScheduler.reportError("Error in job generator", e)
      }
    }
    eventLoop.start()

    if (ssc.isCheckpointPresent) {
      //从checkpoint中恢复
      restart()
    } else {
      //首次创建
      startFirstTime()
    }
}

首次启动会调用startFirstTime()，在该方法中主要是调用已经初始化好的RecurringTimer.start()进行周期性的发送GenerateJobs事件，这个周期是ssc.graph.batchDuration.milliseconds也就是你所设置的batchTime，JobGenerate.start()中所创建好的EventLoop收到GenerateJobs事件，就会执行processEvent(),从而执行generateJobs(time)来进行Job的生成工作

  private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
    longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")

  private def startFirstTime() {
    val startTime = new Time(timer.getStartTime())
    graph.start(startTime - graph.batchDuration)
    timer.start(startTime.milliseconds)
    logInfo("Started JobGenerator at " + startTime)
  }

  private def processEvent(event: JobGeneratorEvent) {
    logDebug("Got event " + event)
    event match {
      case GenerateJobs(time) => generateJobs(time)
      case ClearMetadata(time) => clearMetadata(time)
      case DoCheckpoint(time, clearCheckpointDataLater) =>
        doCheckpoint(time, clearCheckpointDataLater)
      case ClearCheckpointData(time) => clearCheckpointData(time)
    }
  }

generateJobs的主要工作：

  • 生成JobSet并调用jobScheduler.submitJobSet()进行提交
  • 发送一个DoCheckpointEvent进行checkpoint
  private def generateJobs(time: Time) {
    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)
        PythonDStream.stopStreamingContextIfPythonProcessIsDead(e)
    }
    eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
  }

第一个操作：jobScheduler.submitJobSet()中的主要操作是遍历jobSet中的job，并将其作为参数传入JobHandler对象中，并将JobHandler丢到jobExecutor中去执行。JobHandler是实现了Runnable，它的run()主要做了以下三件事

  • 发送JobStarted事件
  • 执行Job.run()
  • 发送JobCompleted事件
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)
    }
}

private class JobHandler(job: Job) extends Runnable with Logging {
    import JobScheduler._
    def run() {
      try {
        var _eventLoop = eventLoop
        if (_eventLoop != null) {
          _eventLoop.post(JobStarted(job, clock.getTimeMillis()))//发送JobStarted事件
          SparkHadoopWriterUtils.disableOutputSpecValidation.withValue(true) {
            job.run()
          }
          _eventLoop = eventLoop
          if (_eventLoop != null) {
            _eventLoop.post(JobCompleted(job, clock.getTimeMillis()))//发送JobCompleted事件
          }
        } else {
        }
      } finally {
        ssc.sparkContext.setLocalProperties(oldProps)
      }
    }
  }

第二个操作：发送DoCheckpoint事件

JobScheduler.start()中创建的EventLoop的核心内容是processEvent(event)方法，Event的类型有三种，分别是JobStarted、JobCompleted和ErrorReported，EventLoop收到DoCheckpoint事件后会执行doCheckpoint()：

  //JobGenerator.processEvent()
  private def processEvent(event: JobGeneratorEvent) {
    logDebug("Got event " + event)
    event match {
      ...
      case DoCheckpoint(time, clearCheckpointDataLater) =>
        doCheckpoint(time, clearCheckpointDataLater)
      ...
    }
  }

doCheckpoint()调用graph.updateCheckpointData进行checkpoint信息的更新，调用checkpointWriter.write对checkpoint信息进行持久化

  private def doCheckpoint(time: Time, clearCheckpointDataLater: Boolean) {
    if (shouldCheckpoint && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) {
      logInfo("Checkpointing graph for time " + time)
      //将新的checkpoint写到
      ssc.graph.updateCheckpointData(time)
      //将checkpoint写到文件系统中
      checkpointWriter.write(new Checkpoint(ssc, time), clearCheckpointDataLater)
    } else if (clearCheckpointDataLater) {
      markBatchFullyProcessed(time)
    }
  }

checkpoint的update中主要是调用DStreamGraph.updateCheckpointData：

def updateCheckpointData(time: Time) {
    logInfo("Updating checkpoint data for time " + time)
    this.synchronized {
      outputStreams.foreach(_.updateCheckpointData(time))
    }
    logInfo("Updated checkpoint data for time " + time)
  }

outputStreams.foreach(_.updateCheckpointData(time))则是调用了DStream中的updateCheckpointData方法，而该方法主要是调用checkpointData.update(currentTime)来进行更新，并且调用该DStream所依赖的DStream进行更新。

private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]]()

private[streaming] def updateCheckpointData(currentTime: Time) {
    logDebug(s"Updating checkpoint data for time $currentTime")
    checkpointData.update(currentTime)
    dependencies.foreach(_.updateCheckpointData(currentTime))
    logDebug(s"Updated checkpoint data for time $currentTime: $checkpointData")
  }

我们接下来来看看checkpointData.update(currentTime)：我们可以在DStream中看到以下的实现：

private[streaming] val checkpointData = new DStreamCheckpointData(this)

我们接着找到了：DStreamCheckpointData.update，DStreamCheckpointData有其他子类用于自定义保存的内容和逻辑

  //key为指定时间，value为checkpoint file内容
  @transient private var timeToCheckpointFile = new HashMap[Time, String]
  // key为batchtime，value该batch中最先checkpointed RDD的time
  @transient private var timeToOldestCheckpointFileTime = new HashMap[Time, Time]
  protected[streaming] def currentCheckpointFiles = data.asInstanceOf[HashMap[Time, String]]

def update(time: Time) {
    //从dsteam中获得要checkpoint的RDDs,generatedRDDs就是一个HashMap[Time, RDD[T]]
    val checkpointFiles = dstream.generatedRDDs.filter(_._2.getCheckpointFile.isDefined)
                                       .map(x => (x._1, x._2.getCheckpointFile.get))
    logDebug("Current checkpoint files:\n" + checkpointFiles.toSeq.mkString("\n"))

    // checkpoint文件添加到最后要进行序列化的HashMap中
    if (!checkpointFiles.isEmpty) {
      currentCheckpointFiles.clear()
      currentCheckpointFiles ++= checkpointFiles
      //更新checkpointfile
      timeToCheckpointFile ++= currentCheckpointFiles
      // key为传入的time，value为最先进行checkpoint的rdd的time
      timeToOldestCheckpointFileTime(time) = currentCheckpointFiles.keys.min(Time.ordering)
    }
  }

第四步：任务完成

在上面generateJobs中所调用的jobScheduler.submitJobSet()中提到每个Job都会作为参数传入JobHandler,而JobHandler会丢到JobExecutor中去执行，而JobHandler的主要工作是发送JobStarted事件，执行完任务后会发送JobCompleted事件，而JobScheduler.EventLoop收到事件后会执行handleJobCompletion方法

 //JobScheduler.processEvent()
 private def processEvent(event: JobSchedulerEvent) {
    try {
      event match {
        case JobStarted(job, startTime) => handleJobStart(job, startTime)
        case JobCompleted(job, completedTime) => handleJobCompletion(job, completedTime)
        case ErrorReported(m, e) => handleError(m, e)
      }
    } catch {
      case e: Throwable =>
        reportError("Error in job scheduler", e)
    }
  }

handleJobCompletion方法会调用jobSet.handleJobCompletion(job)，并且在最后会判断jobSet是否已经全部完成，如果是的话会执行jobGenerator.onBatchCompletion(jobSet.time)

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) {
      listenerBus.post(StreamingListenerBatchCompleted(jobSet.toBatchInfo))
    }
    job.result match {
      case Failure(e) =>
        reportError("Error running job " + job, e)
      case _ => //如果所有事件完成则会执行以下操作
        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
          ))
        }
    }
  }

此时到JobGenerator中找到onBatchCompletion():

def onBatchCompletion(time: Time) {
    eventLoop.post(ClearMetadata(time))
}

JobGenerator.processEvent()执行clearMetadata(time)

private def processEvent(event: JobGeneratorEvent) {
    logDebug("Got event " + event)
    event match {
      case GenerateJobs(time) => generateJobs(time)
      case ClearMetadata(time) => clearMetadata(time)
      case DoCheckpoint(time, clearCheckpointDataLater) =>
        doCheckpoint(time, clearCheckpointDataLater)
      case ClearCheckpointData(time) => clearCheckpointData(time)
    }
}

clearMetadata()对原数据进行checkpoint或者删除

private def clearMetadata(time: Time) {
    ssc.graph.clearMetadata(time)

    // If checkpointing is enabled, then checkpoint,
    // else mark batch to be fully processed
    if (shouldCheckpoint) {
      //如果需要进行checkpoint，发送DoCheckpoint事件
      eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = true))
    } else {
      //将meta数据进行删除
    }
}

2 总结

到这里SparkStreaming的启动、任务生成、任务结束、Checkpoint操作基本就了解完毕了，最后我们来做一个总结，SparkStreming程序的运行流程如下：

  • SparkStreamingContext.start() 启动 JobScheduler
  • JobScheduler的启动操作
    • JobScheduler 创建了 EventLoop[JobSchedulerEvent] 来处理 JobStarted 和 JobCompleted 事件
    • 启动 JobGenerator
  • JobGenerator 的启动操作
    • JobGenerator 创建了 EventLoop[JobGeneratorEvent] 来处理 GenerateJobs、ClearMetaData、DoCheckPoint和ClearCheckpointData 事件
    • 创建RecurringTimer周期性地发送 GenerateJobs 事件用于任务的周期性创建和执行
  • JobGenerator的任务生成工作
    • 调用 geneateJobs() 来生成 JobSet 并通过 JobScheduler.submitJobset 进行任务的提交

      • submitJobset 将 Job 作为参数传入 JobHandler ，并将 JobHandler 丢进线程池 JobExecutor 中执行
    • 发送 DoCheckPoint 事件进行元数据的 checkpoint
  • 任务完成
    • JobHandler 中任务完成之后会发送 JobCompleted 事件，JobScheduler.EventLoop 接收到该事件后会进行处理，并且判断 JobSet 全部完成之后，发送 ClearMetaData 事件，进行数据的 checkpoint 或者删除

附：RecurringTimer和EventLoop的源码，并作简单的注释

RecurringTimer的代码如下：

private[streaming]
class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String)
  extends Logging {
  //创建一个thread，用来执行loop
  private val thread = new Thread("RecurringTimer - " + name) {
    setDaemon(true)
    override def run() { loop }
  }

  @volatile private var prevTime = -1L
  @volatile private var nextTime = -1L
  @volatile private var stopped = false

  def getStartTime(): Long = {
    (math.floor(clock.getTimeMillis().toDouble / period) + 1).toLong * period
  }

  def getRestartTime(originalStartTime: Long): Long = {
    val gap = clock.getTimeMillis() - originalStartTime
    (math.floor(gap.toDouble / period).toLong + 1) * period + originalStartTime
  }

  //start方法中主要是启动thread，用于执行thread中的loop
  def start(startTime: Long): Long = synchronized {
    nextTime = startTime
    thread.start()
    logInfo("Started timer for " + name + " at time " + nextTime)
    nextTime
  }

  def start(): Long = {
    start(getStartTime())
  }

  def stop(interruptTimer: Boolean): Long = synchronized {
    if (!stopped) {
      stopped = true
      if (interruptTimer) {
        thread.interrupt()
      }
      thread.join()
      logInfo("Stopped timer for " + name + " after time " + prevTime)
    }
    prevTime
  }

  private def triggerActionForNextInterval(): Unit = {
    clock.waitTillTime(nextTime)
    callback(nextTime)
    prevTime = nextTime
    nextTime += period
    logDebug("Callback for " + name + " called at time " + prevTime)
  }

  //周期性地执行callback的内容，也就是
  private def loop() {
    try {
      while (!stopped) {
        triggerActionForNextInterval()
      }
      triggerActionForNextInterval()
    } catch {
      case e: InterruptedException =>
    }
  }
}

EventLoop的源码：主要功能就是创建一个线程在后台判断是否Event进来，有的话则进行相应的处理

private[spark] abstract class EventLoop[E](name: String) extends Logging {

  private val eventQueue: BlockingQueue[E] = new LinkedBlockingDeque[E]()

  private val stopped = new AtomicBoolean(false)

  private val eventThread = new Thread(name) {
    setDaemon(true)

    override def run(): Unit = {
      try {
        while (!stopped.get) {
          val event = eventQueue.take()
          try {
            onReceive(event)
          } catch {
            case NonFatal(e) =>
              try {
                onError(e)
              } catch {
                case NonFatal(e) => logError("Unexpected error in " + name, e)
              }
          }
        }
      } catch {
        case ie: InterruptedException => // exit even if eventQueue is not empty
        case NonFatal(e) => logError("Unexpected error in " + name, e)
      }
    }

  }

  def start(): Unit = {
    if (stopped.get) {
      throw new IllegalStateException(name + " has already been stopped")
    }
    // Call onStart before starting the event thread to make sure it happens before onReceive
    onStart()
    eventThread.start()
  }

  def stop(): Unit = {
    if (stopped.compareAndSet(false, true)) {
      eventThread.interrupt()
      var onStopCalled = false
      try {
        eventThread.join()
        // Call onStop after the event thread exits to make sure onReceive happens before onStop
        onStopCalled = true
        onStop()
      } catch {
        case ie: InterruptedException =>
          Thread.currentThread().interrupt()
          if (!onStopCalled) {
            // ie is thrown from `eventThread.join()`. Otherwise, we should not call `onStop` since
            // it‘s already called.
            onStop()
          }
      }
    } else {
      // Keep quiet to allow calling `stop` multiple times.
    }
  }

  //将event放进eventQueue中，在eventThread进行相应的处理
  def post(event: E): Unit = {
    eventQueue.put(event)
  }

  //返回eventThread是否存活
  def isActive: Boolean = eventThread.isAlive

  //eventThread启动前调用
  protected def onStart(): Unit = {}

  //在eventThred结束后调用
  protected def onStop(): Unit = {}

  //实现类实现Event的处理逻辑
  protected def onReceive(event: E): Unit

  //出错时的处理逻辑
  protected def onError(e: Throwable): Unit

}

原文地址：https://www.cnblogs.com/simple-focus/p/8463259.html

时间： 2024-10-05 22:13:04

SparkSteaming运行流程分析以及CheckPoint操作的相关文章

HBase1.0.0源码分析之请求处理流程分析以Put操作为例（二）

HBase1.0.0源码分析之请求处理流程分析以Put操作为例(二) 1.通过mutate(put)操作,将单个put操作添加到缓冲操作中,这些缓冲操作其实就是Put的父类的一个List的集合.如下: private List<Row> writeAsyncBuffer = new LinkedList<>(); writeAsyncBuffer.add(m); 当writeAsyncBuffer满了之后或者是人为的调用backgroundFlushCommits操作促使缓冲池中的

Struts运行流程分析与声明式验证

strust2运行流程分析 1.发送一个HttpServletRequest给StrutsPrepareAndExecuteFilter 2.StrutsPrepareAndExecuteFilter询问ActionMapper:该请求是否是一个Struts2请求(即是否返回一个非空的ActionMapping对象) 3.若ActionMapper认为该请求是一个Struts2请求,则StrutsPrepareAndExecuteFilter把请求的处理交给ActionProxy 4.Actio

thttpd和cgilua安装与运行流程分析

安装 参考如下博文安装thttpd软件 http://blog.csdn.net/21aspnet/article/details/7045845 http://blog.csdn.net/dragoncheng/article/details/5614559 thttpd配置文件: [email protected]:/usr/local/bin# cat /usr/local/thttpd/conf/ etc/  logs/ man/  sbin/ www/  [email protecte

springmvc的运行流程分析

前几篇文章对springmvc讲解的很清楚,大家看下,有问题,我们再一起讨论. 其实springmyuxvc最为重要是它的运行流程,接着,我们来分析一下,其运行过程,废话不多说,看图说话: 分析如下: 1,用户发起请求到前端控制器(DispatchService) 2,前端控制器通过HandlerMapping找到Handler(即是Action) 3,HandlerMapping返回HandlerExecutionChain(执行链),该执行链包含两部分内容,(Handler对象,拦截器数组)

MapReduce运行流程分析

研究MapReduce已经有一段时间了.起初是从分析WordCount程序开始,后来开始阅读Hadoop源码,自认为已经看清MapReduce的运行流程.现在把自己的理解贴出来,与大家分享,欢迎纠错. 还是以最经典的WordCount程序作为基础,来分析map阶段.reduce阶段和最复杂的shuffle阶段. 文本1:hello world                                      文本2:map reduce hello hadoop            

HBase1.0.0源码分析之请求处理流程分析以Put操作为例（一）

如下面的代码所示,是HBase Put操作的简单代码实例,关于代码中的Connection connection = ConnectionFactory.createConnection(conf),已近在前一篇博 HBase1.0.0源码分析之Client启动连接流程,中介绍了链接的相关流程以及所启动的服务信息. TableName tn = TableName.valueOf("test010"); try (Connection connection = ConnectionFa

yii框架详解 之 CWebApplication 运行流程分析

在 程序入口处,index.php 用一句 Yii::createWebApplication($config)->run();  开始了app的运行. 那么,首先查看 CWebApplication 的 构造函数,如下: public function __construct($config=null) { Yii::setApplication($this); // set basePath at early as possible to avoid trouble if(is_string

[原创]java WEB学习笔记70：Struts2 学习之路-- struts2拦截器源码分析，运行流程

本博客的目的:①总结自己的学习过程,相当于学习笔记 ②将自己的经验分享给大家,相互学习,互相交流,不可商用 内容难免出现问题,欢迎指正,交流,探讨,可以留言,也可以通过以下方式联系. 本人互联网技术爱好者,互联网技术发烧友 微博:伊直都在0221 QQ:951226918 -----------------------------------------------------------------------------------------------------------------

转：[gevent源码分析] 深度分析gevent运行流程

[gevent源码分析] 深度分析gevent运行流程 http://blog.csdn.net/yueguanghaidao/article/details/24281751 一直对gevent运行流程比较模糊,最近看源码略有所得,不敢独享,故分享之. gevent是一个高性能网络库,底层是libevent,1.0版本之后是libev,核心是greenlet.gevent和eventlet是亲近,唯一不同的是eventlet是自己实现的事件驱动,而gevent是使用libev.两者都有广泛的应