64、Spark Streaming：StreamingContext初始化与Receiver启动原理剖析与源码分析

一、StreamingContext源码分析

###入口 org.apache.spark.streaming/StreamingContext.scala

/**
  * 在创建和完成StreamContext的初始化之后，创建了DStreamGraph、JobScheduler等关联组件之后，就会调用StreamContext的socketTextStream等方法，
  * 来创建输入DStream，然后针对输入DStream执行一系列的transformation转换操作，最后，会执行一个output输出操作，来触发针对一个一个的batch的job触发和执行
  *
  * 上述初始化操作完成之后，start()方法是必须要调用的，不调用的话，相当于整个Spark Streaming应用程序不会执行
  * StreamingContext.start(),启动一个Spark Streaming应用程序，这个start()方法，会创建StreamingContext的另外两个重要组件
  * ReceiverTracker、JobGenerator，另外，最重要的的，启动整个Spark Streaming应用程序输入的DStream对应的Receiver，在Spark
  * 集群的某个worker节点上的Executor中启动Receiver
  */
class StreamingContext private[streaming] (
    sc_ : SparkContext,
    cp_ : Checkpoint,
    batchDur_ : Duration
  ) extends Logging {


##DStreamGraph

// 重要组件，DStreamGraph，里面保存了，我们定义的Spark Streaming Application中，一系列的DStream的依赖关系以及互相之间的算子的应用
  private[streaming] val graph: DStreamGraph = {
    if (isCheckpointPresent) {
      cp_.graph.setContext(this)
      cp_.graph.restoreCheckpointData()
      cp_.graph
    } else {
      assert(batchDur_ != null, "Batch duration for streaming context cannot be null")
      val newGraph = new DStreamGraph()
      newGraph.setBatchDuration(batchDur_)
      newGraph
    }
  }


##JobScheduler

 // JobScheduler，涉及到job的调度，JobGenerator会负责每隔batch interval，生成一个job，然后通过JobScheduler来调度和提交job
  // 底层，其实还是基于Spark的核心计算引擎，底层DAGScheduler、TaskScheduler、Worker、Executor、Task，如果定义了reduceByKey，
  // 还是会走shuffle，底层的数据存取组件，还是Executor关联的BlockManager，负责持久化数据存储的组件，还是CacheManager
  private[streaming] val scheduler = new JobScheduler(this)



##StreamingContext 的start()方法

/**
    * 这个，就是Streaming应用程序启动的入口
    */
  def start(): Unit = synchronized {
    if (state == Started) {
      throw new SparkException("StreamingContext has already been started")
    }
    if (state == Stopped) {
      throw new SparkException("StreamingContext has already been stopped")
    }
    validate()
    sparkContext.setCallSite(DStream.getCreationSite())
    // 调用JobScheduler的start()方法
    scheduler.start()
    state = Started
  }

调用JobScheduler的start()方法,看看这个方法

###org.apache.spark.streaming.scheduler/JobScheduler.scala
/**
    * StreamingContext的start()方法，其实是比较简单的，真正重要的是，调用JobScheduler的start()方法
    */
  def start(): Unit = synchronized {
    if (eventActor != null) return // scheduler has already been started
 
    logDebug("Starting JobScheduler")
    eventActor = ssc.env.actorSystem.actorOf(Props(new Actor {
      def receive = {
        case event: JobSchedulerEvent => processEvent(event)
      }
    }), "JobScheduler")
 
    listenerBus.start()
    // 创建了ReceiverTracker组件，数据接收相关
    receiverTracker = new ReceiverTracker(ssc)
    // 并启动
    // 至此，我们说的StreamingContext相关几个重要组件，都创建出来了，
    // 然后，启动DStream关联的Receiver，逻辑都在ReceiverTracker的start()方法中
    receiverTracker.start()
    // 这是JobGenerator，创建JobScheduler的时候，直接就把JobGenerator给创建出来了，启动
    jobGenerator.start()
    logInfo("Started JobScheduler")
  }

看ReceiverTracker的start()方法

###org.apache.spark.streaming.scheduler/ReceiverTracker.scala

  def start() = synchronized {
    if (actor != null) {
      throw new SparkException("ReceiverTracker already started")
    }
 
    if (!receiverInputStreams.isEmpty) {
      actor = ssc.env.actorSystem.actorOf(Props(new ReceiverTrackerActor),
        "ReceiverTracker")
      // 这个start()方法中，主要就是调用了内部的ReceiverLauncher的start()方法，这个ReceiverTracker
      // 的主要作用，就是启动Receiver
      if (!skipReceiverLaunch) receiverExecutor.start()
      logInfo("ReceiverTracker started")
    }
  }

调用了receiverExecutor.start()方法，receiverExecutor是ReceiverTracker内部的ReceiverLauncher类

###org.apache.spark.streaming.scheduler/ReceiverTracker.scala

  class ReceiverLauncher {
    @transient val env = ssc.env
    @volatile @transient private var running = false
    @transient val thread  = new Thread() {
      override def run() {
        try {
          SparkEnv.set(env)
          // 开始启动所有的DStream对应的Receiver
          startReceivers()
        } catch {
          case ie: InterruptedException => logInfo("ReceiverLauncher interrupted")
        }
      }
    }
 
    // ReceiverLauncher的start()方法，其实启动了内部的一个线程，相当于使用异步的方式来启动Receiver
    def start() {
      thread.start()
    }

receiverExecutor.start()，通过 startReceivers()，开始启动所有的DStream对应的Receiver
看这个方法

###org.apache.spark.streaming.scheduler/ReceiverTracker.scala


 /**
      * 一直到这里，ReceiverTracker的startReceivers()都是在Driver上执行的
      */
    private def startReceivers() {
 
      // 将程序中创建的所有的DStream，调用其getReceiver()方法，拿到一个Receiver集合
      val receivers = receiverInputStreams.map(nis => {
        //
        val rcvr = nis.getReceiver()
        rcvr.setReceiverId(nis.id)
        rcvr
      })
 
      // 拿到这些Receiver的一些最佳位置
      // Right now, we only honor preferences if all receivers have them
      val hasLocationPreferences = receivers.map(_.preferredLocation.isDefined).reduce(_ && _)
 
      // Create the parallel collection of receivers to distributed them on the worker nodes
      val tempRDD =
        if (hasLocationPreferences) {
          val receiversWithPreferences = receivers.map(r => (r, Seq(r.preferredLocation.get)))
          ssc.sc.makeRDD[Receiver[_]](receiversWithPreferences)
        } else {
          ssc.sc.makeRDD(receivers, receivers.size)
        }
 
      val checkpointDirOption = Option(ssc.checkpointDir)
      val serializableHadoopConf = new SerializableWritable(ssc.sparkContext.hadoopConfiguration)
 
      // Function to start the receiver on the worker node
      // 这里，定义了启动Receiver的核心逻辑
      // 只是定义而已，不是在这里执行的，定义了一个startReceiver函数
      // 这个函数的执行，以及后面的过程，都是在executor上执行的，Receiver的启动，是在executor上的，而不是driver
      val startReceiver = (iterator: Iterator[Receiver[_]]) => {
        if (!iterator.hasNext) {
          throw new SparkException(
            "Could not start receiver as object not found.")
        }
        val receiver = iterator.next()
        // 将每一个Receiver封装在ReceiverSupervisorImpl中，并调用其start()方法,启动
        val supervisor = new ReceiverSupervisorImpl(
          receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
        supervisor.start()
        supervisor.awaitTermination()
      }
      // Run the dummy Spark job to ensure that all slaves have registered.
      // This avoids all the receivers to be scheduled on the same node.
      if (!ssc.sparkContext.isLocal) {
        ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
      }
 
      // Distribute the receivers and start them
      logInfo("Starting " + receivers.length + " receivers")
      running = true
      // 调用StreamingContext的SparkContext的runJob()方法，真正的，将启动Receiver的startReceiver函数
      // 分布到各个worker节点的executor上去执行
      ssc.sparkContext.runJob(tempRDD, ssc.sparkContext.clean(startReceiver))
      running = false
      logInfo("All of the receivers have been terminated")
    }

二、Receiver源码

看下receiverInputStreams是什么

###org.apache.spark.streaming.scheduler/ReceiverTracker.scala

// 这个receiverInputStreams，就是从StreamingContext的graph中，取出的，就是说，每次调用StreamingContext创建一个输入DStream时，都会
  // 放入DStreamGraph的ReceiverInputStreams
  private val receiverInputStreams = ssc.graph.getReceiverInputStreams()

输入DStram，一定都会有一个重要的方法，getReceiver()，如SocketInputDStream

###org.apache.spark.streaming.dstream/SocketInputDStream.scala

private[streaming]
class SocketInputDStream[T: ClassTag](
    @transient ssc_ : StreamingContext,
    host: String,
    port: Int,
    bytesToObjects: InputStream => Iterator[T],
    storageLevel: StorageLevel
  ) extends ReceiverInputDStream[T](ssc_) {
 
  // 输入DStram，一定都会有一个重要的方法，getReceiver(),这个方法就负责返回DStream的Receiver
  def getReceiver(): Receiver[T] = {
    new SocketReceiver(host, port, bytesToObjects, storageLevel)
  }
}

接下来supervisor.start()方法，supervisor是ReceiverSupervisorImpl类的对象，ReceiverSupervisorImpl类并没有start()方法，
该start()在ReceiverSupervisorImpl的父类ReceiverSupervisor里面
看看ReceiverSupervisor的start()方法

###org.apache.spark.streaming.receiver/ReceiverSupervisor.scala

  /** Called when supervisor is started */
  protected def onStart() { }
  /** Called when receiver is stopped */
  protected def onReceiverStop(message: String, error: Option[Throwable]) { }
 
  /** Start the supervisor */
  def start() {
    onStart()
    startReceiver()
  }

ReceiverSupervisor的start()调用了onStart()方法，而ReceiverSupervisor是抽象方法，
所以应该看实现类的的onStart()，看ReceiverSupervisorImpl的onStart()方法

###org.apache.spark.streaming.receiver/ReceiverSupervisorImpl.scala

  override protected def onStart() {
    // 这里的blockGenerator很重要，和数据接收有关，其运行在worker的executor端负责数据接收后的一些存取工作，以及配合ReceiverTracker
    // 在Executor上，启动Receiver之前，就会先启动这个Receiver相关的一个blockGenerator，该组件，在数据接收中，极其重要
    blockGenerator.start()
  }

再次回到JobScheduler的start()方法，调用了jobGenerator.start()方法，

###org.apache.spark.streaming.scheduler/JobGenerator.scala

  def start(): Unit = synchronized {
    if (eventActor != null) return // generator has already been started
 
    eventActor = ssc.env.actorSystem.actorOf(Props(new Actor {
      def receive = {
        case event: JobGeneratorEvent =>  processEvent(event)
      }
    }), "JobGenerator")
    if (ssc.isCheckpointPresent) {
      restart()
    } else {
      startFirstTime()
    }
  }

看startFirstTime()方法

###org.apache.spark.streaming.scheduler/JobGenerator.scala

/**
    * 只要JobGenerator一启动，这里就初始化一个开始时间，后面，根据我们自己的batch interval，每到一个batch interval
    * 都会从上一个time，也就是这里的startTime，开始将batch interval内的数据封装成一个batch
    */
  private def startFirstTime() {
    val startTime = new Time(timer.getStartTime())
    graph.start(startTime - graph.batchDuration)
    timer.start(startTime.milliseconds)
    logInfo("Started JobGenerator at " + startTime)
  }

看DStream的output方法，比如print

###org.apache.spark.streaming.dstream/DStream.scala

def print() {
    print(10)
  }
 
  /**
   * 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) {
    def foreachFunc = (rdd: RDD[T], time: Time) => {
      val firstNum = rdd.take(num + 1)
      println ("-------------------------------------------")
      println ("Time: " + time)
      println ("-------------------------------------------")
      firstNum.take(num).foreach(println)
      if (firstNum.size > num) println("...")
      println()
    }
    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
  }

ForEachDStream类

###org.apache.spark.streaming.dstream/ForEachDStream.scala

class ForEachDStream[T: ClassTag] (
    parent: DStream[T],
    foreachFunc: (RDD[T], Time) => Unit
  ) extends DStream[Unit](parent.ssc) {
 
  override def dependencies = List(parent)
 
  override def slideDuration: Duration = parent.slideDuration
 
  override def compute(validTime: Time): Option[RDD[Unit]] = None
 
 
  // 所有的output操作，其实都会来调用ForEachDStream的generateJob()方法，所以，每次执行DStreamGraph的
  // 时候，到最后，都会调用到这里，底层会触发job的提交
  override def generateJob(time: Time): Option[Job] = {
    parent.getOrCompute(time) match {
      case Some(rdd) =>
        val jobFunc = () => {
          ssc.sparkContext.setCallSite(creationSite)
          foreachFunc(rdd, time)
        }
        Some(new Job(time, jobFunc))
      case None => None
    }
  }
}