第7课：Spark Streaming源码解读之JobScheduler内幕实现和深度思考

本期内容：

1. JobScheduler内幕实现

2. JobScheduler深度思考

 

所有工作的关键都是jobScheduler

SparkStreaming至少要设置两条线程是因为一条用于接收数据，一条用于计算，调度和执行分离开。

源码

StreamingContext.scala

Start()

/**
 * Start the execution of the streams.
 *
 * @throws IllegalStateException if the StreamingContext is already stopped.
 */
def start(): Unit = synchronized {
  state match{
    case INITIALIZED=>
      startSite.set(DStream.getCreationSite())
      StreamingContext.ACTIVATION_LOCK.synchronized {
        StreamingContext.assertNoOtherContextIsActive()
        try {
          validate()

          // Start the streaming scheduler in a new thread, so that thread local properties
          // like call sites and job groups can be reset without affecting those of the
          // current thread.

//调度层面启动的新线程
          ThreadUtils.runInNewThread("streaming-start") {
            sparkContext.setCallSite(startSite.get)
            sparkContext.clearJobGroup()
            sparkContext.setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL,"false")
            scheduler.start()
          }
          state = StreamingContextState.ACTIVE
        } catch{
          case NonFatal(e) =>
            logError("Error starting the context, marking it as stopped", e)
            scheduler.stop(false)
            state = StreamingContextState.STOPPED
            throw e
        }
        StreamingContext.setActiveContext(this)
      }
      shutdownHookRef = ShutdownHookManager.addShutdownHook(
        StreamingContext.SHUTDOWN_HOOK_PRIORITY)(stopOnShutdown)
      // Registering Streaming Metrics at the start of the StreamingContext
      assert(env.metricsSystem !=null)
      env.metricsSystem.registerSource(streamingSource)
      uiTab.foreach(_.attach())
      logInfo("StreamingContext started")
    case ACTIVE=>
      logWarning("StreamingContext has already been started")
    case STOPPED=>
      throw new IllegalStateException("StreamingContext has already been stopped")
  }
}

runInNewThread

defrunInNewThread[T](
    threadName: String,
    isDaemon: Boolean = true)(body: =>T): T= {
  @volatile varexception: Option[Throwable] = None
  @volatile varresult: T= null.asInstanceOf[T]

  val thread =new Thread(threadName) {
    override def run(): Unit = {
      try {
        result = body
      } catch {
        case NonFatal(e) =>
          exception = Some(e)
      }
    }
  }
  thread.setDaemon(isDaemon)
  thread.start()
  thread.join()

  exception match {
    case Some(realException) =>
      // Remove the part of the stack that shows method calls into this helper method
      // This means drop everything from the top until the stack element
      // ThreadUtils.runInNewThread(), and then drop that as well (hence the `drop(1)`).
      val baseStackTrace = Thread.currentThread().getStackTrace().dropWhile(
        ! _.getClassName.contains(this.getClass.getSimpleName)).drop(1)

      // Remove the part of the new thread stack that shows methods call from this helper method
      val extraStackTrace = realException.getStackTrace.takeWhile(
        ! _.getClassName.contains(this.getClass.getSimpleName))

      // Combine the two stack traces, with a place holder just specifying that there
      // was a helper method used, without any further details of the helper
      val placeHolderStackElem =new StackTraceElement(
        s"... run in separate thread using${ThreadUtils.getClass.getName.stripSuffix("$")} ..",
        " ","", -1)
      val finalStackTrace = extraStackTrace ++Seq(placeHolderStackElem) ++ baseStackTrace

      // Update the stack trace and rethrow the exception in the caller thread
      realException.setStackTrace(finalStackTrace)
      throw realException
    case None =>
      result
  }
}

private[streaming]val scheduler= newJobScheduler(this)

jobScheduler.scala下面

private valjobExecutor =
  ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs,"streaming-job-executor")

newDaemonFixedThreadPool

/**
 * Wrapper over newFixedThreadPool. Thread names are formatted as prefix-ID, where ID is a
 * unique, sequentially assigned integer.
 */
def newDaemonFixedThreadPool(nThreads: Int, prefix:String): ThreadPoolExecutor = {
  val threadFactory =namedThreadFactory(prefix)
  Executors.newFixedThreadPool(nThreads, threadFactory).asInstanceOf[ThreadPoolExecutor]
}

 

scheduler.start()

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")
}

 

JobGenerator.start

 

/** 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 = newEventLoop[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()
  }
}

 

ForEachDStream

private[streaming]
class ForEachDStream[T: ClassTag] (
    parent: DStream[T],
    foreachFunc: (RDD[T], Time) => Unit,
    displayInnerRDDOps: Boolean
  ) extends DStream[Unit](parent.ssc) {

  override def dependencies:List[DStream[_]] =List(parent)

  override def slideDuration: Duration = parent.slideDuration

  override def compute(validTime: Time): Option[RDD[Unit]] = None

  override def generateJob(time: Time): Option[Job] = {
    parent.getOrCompute(time) match {
      case Some(rdd) =>
        val jobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {
          foreachFunc(rdd, time)
        }
        Some(new Job(time, jobFunc))
      case None => None
    }
  }
}

DStreamGraph.scala

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
}

private valoutputStreams =new ArrayBuffer[DStream[_]]()

JobGenerator. generateJobs

JobSet基于时间生成的job, streamIdToInputInfos是job要处理的数据

/** 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)
  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))
}

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(newJobHandler(job)))
    logInfo("Added jobs for time " + jobSet.time)
  }
}

JobScheduler

private classJobHandler(job: Job) extendsRunnable withLogging {
  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)
    }
  }
}

private vareventLoop: EventLoop[JobSchedulerEvent] =null

 

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()

 

private defprocessEvent(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)
  }
}

private defhandleJobStart(job: Job, startTime: Long) {
  val jobSet =jobSets.get(job.time)
  val isFirstJobOfJobSet = !jobSet.hasStarted
  jobSet.handleJobStart(job)
  if (isFirstJobOfJobSet) {
    // "StreamingListenerBatchStarted" should be posted after calling "handleJobStart" to get the
    // correct "jobSet.processingStartTime".
    listenerBus.post(StreamingListenerBatchStarted(jobSet.toBatchInfo))
  }
  job.setStartTime(startTime)
  listenerBus.post(StreamingListenerOutputOperationStarted(job.toOutputOperationInfo))
  logInfo("Starting job " + job.id +" from job set of time " + jobSet.time)
}

def handleJobStart(job: Job) {
  if (processingStartTime< 0)processingStartTime = System.currentTimeMillis()
}

回到JobScheduler.JobHandler内部类，run方法中job.run

把业务逻辑封装到function里面

class Job(valtime: 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())
  }

  def result: Try[_] = {
    if (_result== null) {
      throw new IllegalStateException("Cannot access result before job finishes")
    }
    _result
  }

ForEachDStream.scala

override defgenerateJob(time: Time): Option[Job] = {
  parent.getOrCompute(time) match {
    case Some(rdd) =>
      val jobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {
        foreachFunc(rdd, time)
      }
      Some(new Job(time, jobFunc))
    case None => None
  }
}

想改变作业并发的执行，可改变其并发度（JobScheduler）

private valnumConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs",1)

 

 

 

 

 

 

 

 

博主：罗白莲
资料来源于：王家林（Spark版本定制班课程）
新浪微博：http://www.weibo.com/ilovepains