Spark版本定制七：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

 

摘要：JobScheduler是Spark Streaming整个调度的核心，其地位相当于Spark Core上的调度中心中的DAGScheduler！

         

一、JobScheduler内幕实现

问：JobScheduler是在什么地方生成的？

答：JobScheduler是在StreamingContext实例化时产生的，从StreamingContext的源码第183行中可以看出：

      private[streaming] val scheduler = new JobScheduler(this)

 

问：Spark Streaming为啥要设置两条线程？ 
答：setMaster指定的两条线程是指程序运行的时候至少需要两条线程。一条线程用于接收数据，需要不断的循环。另一条是处理线程，是我们自己指定的线程数用于作业处理。如StreamingContext的start()方法所示：

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.
            //Spark Streaming内部启动的线程，用于整个作业的调度
            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")
    }
  }

进入JobScheduler源码：

/**
   JobScheduler负责逻辑层面的Job，并将其物理级别的运行在Spark之上
 * This class schedules jobs to be run on Spark. It uses the JobGenerator to generate
 * the jobs and runs them using a thread pool.

 */
private[streaming]
class JobScheduler(val ssc: StreamingContext) extends Logging {

  //通过JobSet集合，不断地存放接收到的Job
  private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]
  //设置并行度，默认为1，想要修改作业运行的并行度在spark-conf或者应用程序中修改此值就中
  为什么要修改并发度呢？
  答：有时候应用程序中有多个输出，会导致多个job的执行，都是在一个batchDurations里面，job之间执行无需互相等待，所以可以通过设置此值并发执行！
     不同的Batch，线程池中有很多的线程，也可以并发运行！
  private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)
  //将逻辑级别的Job转化为物理级别的job就是通过newDaemonFixedThreadPool线程实现的
  private val jobExecutor =
    ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs, "streaming-job-executor")
  //实例化JobGenerator
  private val jobGenerator = new JobGenerator(this)
  val clock = jobGenerator.clock
  val listenerBus = new StreamingListenerBus()
   
  //下面三个是说在JobScheduler启动时实例化
  // These two are created only when scheduler starts.
  // eventLoop not being null means the scheduler has been started and not stopped
  var receiverTracker: ReceiverTracker = null
  // A tracker to track all the input stream information as well as processed record number
  var inputInfoTracker: InputInfoTracker = null

  private var eventLoop: 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()

    // 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")
  }

二、JobScheduler深度思考

下面从应用程序的输出方法print()入手，反推Job的生成过程：

1.点击应用程序中的print()方法后，跳入DStream的print():

/**
 * Print the first ten elements of each RDD generated in this DStream. This is an output
 * operator, so this DStream will be registered as an output stream and there materialized.
 */
def print(): Unit = ssc.withScope {
  print(10)
}

2.再次点击上面红线标记的print()方法：

   

/**
 * Print the first num elements of each RDD generated in this DStream. This is an output
 * operator, so this DStream will be registered as an output stream and there materialized.
 */
def print(num: Int): Unit = ssc.withScope {
  def foreachFunc: (RDD[T], Time) => Unit = {
    (rdd: RDD[T], time: Time) => {
      val firstNum = rdd.take(num + 1)
      // scalastyle:off println
      println("-------------------------------------------")
      println("Time: " + time)
      println("-------------------------------------------")
      firstNum.take(num).foreach(println)
      if (firstNum.length > num) println("...")
      println()
      // scalastyle:on println
    }
  }
  foreachRDD(context.sparkContext.clean(foreachFunc), displayInnerRDDOps = false)
}

从图中红色标记的代码可以得出：SparkStreaming最终执行的时候还是对RDD进行各种逻辑级别的操作！

3.再次点击图上的foreachRDD进入foreachRDD方法：

  

/**
 * Apply a function to each RDD in this DStream. This is an output operator, so
 * 'this' DStream will be registered as an output stream and therefore materialized.
 * @param foreachFunc foreachRDD function
 * @param displayInnerRDDOps Whether the detailed callsites and scopes of the RDDs generated
 *                           in the `foreachFunc` to be displayed in the UI. If `false`, then
 *                           only the scopes and callsites of `foreachRDD` will override those
 *                           of the RDDs on the display.
 */
private def foreachRDD(
    foreachFunc: (RDD[T], Time) => Unit,
    displayInnerRDDOps: Boolean): Unit = {
  new ForEachDStream(this,
    context.sparkContext.clean(foreachFunc, false), displayInnerRDDOps).register()
}

4.点击上图的ForEachDStream进入ForEachDStream类并找到了generateJob方法：

   

/**
 * An internal DStream used to represent output operations like DStream.foreachRDD.
 * @param parent        Parent DStream
 * @param foreachFunc   Function to apply on each RDD generated by the parent DStream
 * @param displayInnerRDDOps Whether the detailed callsites and scopes of the RDDs generated
 *                           by `foreachFunc` will be displayed in the UI; only the scope and
 *                           callsite of `DStream.foreachRDD` will be displayed.
 */
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
  //根据时间间隔不断的产生Job
  override def generateJob(time: Time): Option[Job] = {
    parent.getOrCompute(time) match {
      case Some(rdd) =>
        val jobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {
          //基于时间生成的RDD，由于是输出，所以是最后一个RDD，接下来我们只要找出哪儿调用ForEachDStream的generateJob方法，就能知道job最终的生成
          foreachFunc(rdd, time)
        }
        Some(new Job(time, jobFunc))
      case None => None
    }
  }
}

5.上一讲中我们得出了如下的流程：

    streamingcontext.start-->jobscheduler.start-->receiverTracker.start()-->JobGenterator.start()-->EventLoop-->processEvent()-->generateJobs()-->jobScheduler.receiverTracker.allocateBlocksToBatch(time)-->graph.generateJobs(time)　　

　其中最后的graph.generateJobs是DSTreamGraph的方法，进入之：

   

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

private val outputStreams = new ArrayBuffer[DStream[_]]()

通过查看DStream的子类继承结构和上面的ForEachDStream的generateJob方法，得出DStream的子类中只有ForEachDStream override了DStream的generateJob!
最终得出结论：

真正Job的生成是通过ForeachDStream的generateJob来生成的，此时的job是逻辑级别的，真正被物理级别的调用是在JobGenerator中generateJob方法中：

/** 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))
  }

进入jobScheduler.submitJobSet方法：

//将逻辑级别的Job转化为物理级别的job就是通过newDaemonFixedThreadPool线程实现的
  private val jobExecutor =
    ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs, "streaming-job-executor")

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)
    }
  }

至此，整个job的生成、执行就非常清晰了，最后总结如下：

从上一讲中，我们得知JobScheduler包含两个核心组件JobGenerator和ReceiverTracker，它们分别负责Job的生成和源数据的接收，

ReceiverTracker启动后会导致运行在Executor端的Receiver启动并且接收数据，ReceiverTracker会记录Receiver接收到的数据meta信息，  

JobGenerator的启动导致每隔BatchDuration，就调用DStreamGraph生成RDD Graph，并生成Job，

JobScheduler中的线程池来提交封装的JobSet对象(时间值，Job，数据源的meta)。Job中封装了业务逻辑，导致最后一个RDD的action被触发，

被DAGScheduler真正调度在Spark集群上执行该Job。

 

特别感谢王家林老师的独具一格的讲解：

王家林老师名片：

中国Spark第一人

新浪微博：http://weibo.com/ilovepains

微信公众号：DT_Spark

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