Spark Streaming源码解读之Job动态生成和深度思考

JobGenerator和ReceiverTracker的类对象是JobSchedule的类成员。从SparkStreaming应用程序valssc=StreamingContext(conf)入口开始，直到ssc.start()启动了SparkStreaming框架的执行后，一直到JobSchedule调用start()，schedule.start()调用了ReceiverTracker和JobGenerator类对象：

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(ssc.sparkContext)
  receiverTracker = new ReceiverTracker(ssc)
  inputInfoTracker = new InputInfoTracker(ssc)
  receiverTracker.start()
  jobGenerator.start()
  logInfo("Started JobScheduler")
}

JobScheduler有两个非常重要的成员：

·        JobGenerator 

·        ReceiverTracker

JobScheduler 将每个batch的RDD DAG的具体生成工作委托给JobGenerator，将源头数据输入的记录工作委托给ReceiverTracker 。

在JobGenerator中有两个至关重要的成员就是RecurringTimer和EventLoop；RecurringTimer它控制了job的触发。每到batchInterval时间，就往EventLoop的队列中放入一个消息。而EventLoop则不断的查看消息队列，一旦有消息就处理。JobGenerator会根据BatchDuration时间间隔，随着时间的推移，会不断的产生作业，驱使checkpoint操作和清理之前DStream的数据。

先看下JobGenerator的start方法，checkpoint的初始化操作，实例化并启动消息循环体EventLoop，开启定时生成Job的定时器:

/** Start generation of jobs */ def start(): Unit = synchronized {
  if (eventLoop != null) return // generator has already been started   // Call checkpointWriter here to initialize it before eventLoop uses it to avoid a deadlock.  // See SPARK-10125  checkpointWriter   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) {
    restart()
  } else {
    startFirstTime()
  }
}

EvenLoop类中有存储消息的LinkedBlockingDeque类对象和后台线程，后台线程从队列中获取消息，然后调用onReceive方法对该消息进行处理，这里的onReceive方法即匿名内部类中重写onReceive方法的processEvent方法。

processEvent方法是对消息类型进行模式匹配，然后路由到对应处理该消息的方法中。消息的处理一般是发给另外一个线程来处理的，消息循环器不处理耗时的业务逻辑： 

/** Processes all events */ 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在获取到数据后调用DStreamGraph的generateJobs方法来生成Job：

def generateJobs(time: Time): Seq[Job] = {
  logDebug("Generating jobs for time " + time)
  val jobs = this.synchronized {
    outputStreams.flatMap { outputStream =>
      val jobOption = outputStream.generateJob(time)
      jobOption.foreach(_.setCallSite(outputStream.creationSite))
      jobOption
    }
  }
  logDebug("Generated " + jobs.length + " jobs for time " + time)
  jobs
}

streamIdToInputInfos是基于时间的数据，获得了这个数据后，jobScheduler.submitJobSet这个方法就产生了jobset，以这个JobSet交给JobSchedule进行调度执行Job。

generateJobs方法中outputStreams是整个DStream中的最后一个DStream。这里outputStream.generateJob(time)类似于RDD中从后往前推：

def generateJobs(time: Time): Seq[Job] = {
  logDebug("Generating jobs for time " + time)
  val jobs = this.synchronized {
    outputStreams.flatMap { outputStream =>
      val jobOption = outputStream.generateJob(time)
      jobOption.foreach(_.setCallSite(outputStream.creationSite))
      jobOption
    }
  }
  logDebug("Generated " + jobs.length + " jobs for time " + time)
  jobs
}

generateJob方法中jobFunc 封装了context.sparkContext.runJob(rdd, emptyFunc)：

/**  * Generate a SparkStreaming job for the given time. This is an internal method that  * should not be called directly. This default implementation creates a job  * that materializes the corresponding RDD. Subclasses of DStream may override this  * to generate their own jobs.  */ private[streaming] def generateJob(time: Time): Option[Job] = {
  getOrCompute(time) match {
    case Some(rdd) => {
      val jobFunc = () => {
        val emptyFunc = { (iterator: Iterator[T]) => {} }
        context.sparkContext.runJob(rdd, emptyFunc)
      }
      Some(new Job(time, jobFunc))
    }
    case None => None
  }
}

Job对象，方法run会导致传入的func被调用:

private[streaming]
class Job(val time: Time, func: () => _) {
  private var _id: String = _
  private var _outputOpId: Int = _
  private var isSet = false  private var _result: Try[_] = null  private var _callSite: CallSite = null  private var _startTime: Option[Long] = None
  private var _endTime: Option[Long] = None

  def run() {
    _result = Try(func())
  }

getOrCompute方法，先根据传入的时间在HashMap中查找下RDD是否存在，如果不存在则调用compute方法计算获取RDD，再根据storageLevel 是否需要persist，是否到了checkpoint时间点进行checkpoint操作，最后把该RDD放入到HashMap中：

private[streaming] final def getOrCompute(time: Time): Option[RDD[T]] = {   // If RDD was already generated, then retrieve it from HashMap,   // or else compute the RDD   generatedRDDs.get(time).orElse {     // Compute the RDD if time is valid (e.g. correct time in a sliding window)     // of RDD generation, else generate nothing.     if (isTimeValid(time)) {       val rddOption = createRDDWithLocalProperties(time, displayInnerRDDOps = false) {         // 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. We need to have this call here because         // compute() might cause Spark jobs to be launched.         PairRDDFunctions.disableOutputSpecValidation.withValue(true) {           compute(time)         }       }       rddOption.foreach { case newRDD =>         // Register the generated RDD for caching and checkpointing         if (storageLevel != StorageLevel.NONE) {           newRDD.persist(storageLevel)           logDebug(s"Persisting RDD ${newRDD.id} for time $time to $storageLevel")         }         if (checkpointDuration != null && (time - zeroTime).isMultipleOf(checkpointDuration)) {           newRDD.checkpoint()           logInfo(s"Marking RDD ${newRDD.id} for time $time for checkpointing")         }         generatedRDDs.put(time, newRDD)       }       rddOption     } else {       None     }   } }

再次回到JobGenerator类中，看下start方法中在消息循环体启动后，先判断之前是否进行checkpoint操作，如果是从checkpoint目录中读取然后再调用restart重启JobGenerator，如果是第一次则调用startFirstTime方法：

JobGenerator类中的startFirstTime方法，启动定时生成Job的Timer：

timer对象为RecurringTimer，其start方法内部启动一个线程，在线程中不断调用triggerActionForNextInterval方法：

private[streaming] class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String)   extends Logging {   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   /**    * Get the time when this timer will fire if it is started right now.    * The time will be a multiple of this timer's period and more than    * current system time.    */   def getStartTime(): Long = {     (math.floor(clock.getTimeMillis().toDouble / period) + 1).toLong * period   }   /**    * Get the time when the timer will fire if it is restarted right now.    * This time depends on when the timer was started the first time, and was stopped    * for whatever reason. The time must be a multiple of this timer's period and    * more than current time.    */   def getRestartTime(originalStartTime: Long): Long = {     val gap = clock.getTimeMillis() - originalStartTime     (math.floor(gap.toDouble / period).toLong + 1) * period + originalStartTime   }   /**    * Start at the given start time.    */   def start(startTime: Long): Long = synchronized {     nextTime = startTime     thread.start()     logInfo("Started timer for " + name + " at time " + nextTime)     nextTime   }   /**    * Start at the earliest time it can start based on the period.    */   def start(): Long = {     start(getStartTime())   }   /**    * Stop the timer, and return the last time the callback was made.    *    * @param interruptTimer True will interrupt the callback if it is in progress (not guaranteed to    *                       give correct time in this case). False guarantees that there will be at    *                       least one callback after `stop` has been called.    */   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)   }   /**    * Repeatedly call the callback every interval.    */   private def loop() {     try {       while (!stopped) {         triggerActionForNextInterval()       }       triggerActionForNextInterval()     } catch {       case e: InterruptedException =>     }   } }

triggerActionForNextInterval方法，等待BatchDuration后回调callback这个方法，这里的callback方法是构造RecurringTimer对象时传入的方法，即longTime => eventLoop.post(GenerateJobs(new Time(longTime)))，不断向消息循环体发送GenerateJobs消息。

再次聚焦generateJobs这个方法生成Job的步骤：

第一步：获取当前时间段内的数据。

第二步：生成Job，RDD之间的依赖关系。

第三步：获取生成Job对应的StreamId的信息。

第四步：封装成JobSet交给JobScheduler。

第五步：进行checkpoint操作。

其中submitJobSet方法，只是把JobSet放到ConcurrentHashMap中，把Job封装为JobHandler提交到jobExecutor线程池中：

JobHandler对象为实现Runnable 接口，job的run方法导致了func的调用，即基于DStream的业务逻辑：

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)       // 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.         PairRDDFunctions.disableOutputSpecValidation.withValue(true) {           job.run()         }         _eventLoop = eventLoop         if (_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)     }   } }