第5课：基于案例一节课贯通Spark Streaming流计算框架的运行源码

一：案例实战演示：

 

package com.dt.spark.sparkstreaming
import org.apache.spark.SparkConf
import org.apache.spark.sql.Row
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
  * 使用Spark Streaming+Spark SQL来在线动态计算电商中不同类别中最热门的商品排名，例如手机这个类别下面最热门的三种手机、电视这个类别
  * 下最热门的三种电视，该实例在实际生产环境下具有非常重大的意义；
  *
  * @author DT大数据梦工厂
  * 新浪微博：http://weibo.com/ilovepains/
  *
  *
  *   实现技术：Spark Streaming+Spark SQL，之所以Spark Streaming能够使用ML、sql、graphx等功能是因为有foreachRDD和Transform
  * 等接口，这些接口中其实是基于RDD进行操作，所以以RDD为基石，就可以直接使用Spark其它所有的功能，就像直接调用API一样简单。
  *  假设说这里的数据的格式：user item category，例如Rocky Samsung Android
  */
object OnlineTheTop3ItemForEachCategory2DB {
  def main(args: Array[String]){
    /**
      * 第1步：创建Spark的配置对象SparkConf，设置Spark程序的运行时的配置信息，
      * 例如说通过setMaster来设置程序要链接的Spark集群的Master的URL,如果设置
      * 为local，则代表Spark程序在本地运行，特别适合于机器配置条件非常差（例如
      * 只有1G的内存）的初学者       *
      */
    val conf = new SparkConf() //创建SparkConf对象
    conf.setAppName("OnlineTheTop3ItemForEachCategory2DB") //设置应用程序的名称，在程序运行的监控界面可以看到名称
    //    conf.setMaster("spark://Master:7077") //此时，程序在Spark集群
    conf.setMaster("local[6]")
    //设置batchDuration时间间隔来控制Job生成的频率并且创建Spark Streaming执行的入口
    val ssc = new StreamingContext(conf, Seconds(5))

    ssc.checkpoint("/root/Documents/SparkApps/checkpoint")


    val userClickLogsDStream = ssc.socketTextStream("Master", 9999)

    val formattedUserClickLogsDStream = userClickLogsDStream.map(clickLog =>
      (clickLog.split(" ")(2) + "_" + clickLog.split(" ")(1), 1))

    //    val categoryUserClickLogsDStream = formattedUserClickLogsDStream.reduceByKeyAndWindow((v1:Int, v2: Int) => v1 + v2,
    //      (v1:Int, v2: Int) => v1 - v2, Seconds(60), Seconds(20))

    val categoryUserClickLogsDStream = formattedUserClickLogsDStream.reduceByKeyAndWindow(_+_,
      _-_, Seconds(60), Seconds(20))

    categoryUserClickLogsDStream.foreachRDD { rdd => {
      if (rdd.isEmpty()) {
        println("No data inputted!!!")
      } else {
        val categoryItemRow = rdd.map(reducedItem => {
          val category = reducedItem._1.split("_")(0)
          val item = reducedItem._1.split("_")(1)
          val click_count = reducedItem._2
          Row(category, item, click_count)
        })

        val structType = StructType(Array(
          StructField("category", StringType, true),
          StructField("item", StringType, true),
          StructField("click_count", IntegerType, true)
        ))

        val hiveContext = new HiveContext(rdd.context)
        val categoryItemDF = hiveContext.createDataFrame(categoryItemRow, structType)

        categoryItemDF.registerTempTable("categoryItemTable")

        val reseltDataFram = hiveContext.sql("SELECT category,item,click_count FROM (SELECT category,item,click_count,row_number()" +
          " OVER (PARTITION BY category ORDER BY click_count DESC) rank FROM categoryItemTable) subquery " +
          " WHERE rank <= 3")
        reseltDataFram.show()

        val resultRowRDD = reseltDataFram.rdd

        resultRowRDD.foreachPartition { partitionOfRecords => {

          if (partitionOfRecords.isEmpty){
            println("This RDD is not null but partition is null")
          } else {
            // ConnectionPool is a static, lazily initialized pool of connections
            val connection = ConnectionPool.getConnection()
            partitionOfRecords.foreach(record => {
              val sql = "insert into categorytop3(category,item,client_count) values('" + record.getAs("category") + "','" +
                record.getAs("item") + "'," + record.getAs("click_count") + ")"
              val stmt = connection.createStatement();
              stmt.executeUpdate(sql);

            })
            ConnectionPool.returnConnection(connection) // return to the pool for future reuse

          }
        }
        }
      }


    }
    }



    /**
      * 在StreamingContext调用start方法的内部其实是会启动JobScheduler的Start方法，进行消息循环，在JobScheduler
      * 的start内部会构造JobGenerator和ReceiverTacker，并且调用JobGenerator和ReceiverTacker的start方法：
      *   1，JobGenerator启动后会不断的根据batchDuration生成一个个的Job
      *   2，ReceiverTracker启动后首先在Spark Cluster中启动Receiver（其实是在Executor中先启动ReceiverSupervisor），在Receiver收到
      *   数据后会通过ReceiverSupervisor存储到Executor并且把数据的Metadata信息发送给Driver中的ReceiverTracker，在ReceiverTracker
      *   内部会通过ReceivedBlockTracker来管理接收到的元数据信息
      * 每个BatchInterval会产生一个具体的Job，其实这里的Job不是Spark Core中所指的Job，它只是基于DStreamGraph而生成的RDD
      * 的DAG而已，从Java角度讲，相当于Runnable接口实例，此时要想运行Job需要提交给JobScheduler，在JobScheduler中通过线程池的方式找到一个
      * 单独的线程来提交Job到集群运行（其实是在线程中基于RDD的Action触发真正的作业的运行），为什么使用线程池呢？
      *   1，作业不断生成，所以为了提升效率，我们需要线程池；这和在Executor中通过线程池执行Task有异曲同工之妙；
      *   2，有可能设置了Job的FAIR公平调度的方式，这个时候也需要多线程的支持；
      *
      */
    ssc.start()
    ssc.awaitTermination()

  }
}

 二：源码分析

 

 

 

 

第一步：创建StreamingContext

 

val ssc = new StreamingContext(conf, Seconds(5))

 1，StreamingContext源码如下：

 

 

/**
 * Create a StreamingContext by providing the configuration necessary for a new SparkContext.
 * @param conf a org.apache.spark.SparkConf object specifying Spark parameters
 * @param batchDuration the time interval at which streaming data will be divided into batches
 */
def this(conf: SparkConf, batchDuration: Duration) = {
  this(StreamingContext.createNewSparkContext(conf), null, batchDuration)
}

/**
 * Create a StreamingContext by providing the details necessary for creating a new SparkContext.
 * @param master cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).
 * @param appName a name for your job, to display on the cluster web UI
 * @param batchDuration the time interval at which streaming data will be divided into batches
 */
def this(
    master: String,
    appName: String,
    batchDuration: Duration,
    sparkHome: String = null,
    jars: Seq[String] = Nil,
    environment: Map[String, String] = Map()) = {
  this(StreamingContext.createNewSparkContext(master, appName, sparkHome, jars, environment),
       null, batchDuration)
}

 

   2. 其中this里面的第一个参数创建SparkContext，Spark Streaming就是Spark Core上面的一个应用程序。

 

   private[streaming] def createNewSparkContext(conf: SparkConf): SparkContext = {
  new SparkContext(conf)
}

 第二步：获取输入数据源

 

 

    

   val userClickLogsDStream = ssc.socketTextStream("Master", 9999)

   1.       socketTextStream接收socket数据流。

 

 

   /**
 * Create a input stream from TCP source hostname:port. Data is received using
 * a TCP socket and the receive bytes is interpreted as UTF8 encoded `\n` delimited
 * lines.
 * @param hostname      Hostname to connect to for receiving data
 * @param port          Port to connect to for receiving data
 * @param storageLevel  Storage level to use for storing the received objects
 *                      (default: StorageLevel.MEMORY_AND_DISK_SER_2)
 */
def socketTextStream(
    hostname: String,
    port: Int,
    storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
  ): ReceiverInputDStream[String] = withNamedScope("socket text stream") {
  socketStream[String](hostname, port, SocketReceiver.bytesToLines, storageLevel)
}

   2.      创建SocketInputDStream实例。

 

    

   /**
 * Create a input stream from TCP source hostname:port. Data is received using
 * a TCP socket and the receive bytes it interepreted as object using the given
 * converter.
 * @param hostname      Hostname to connect to for receiving data
 * @param port          to connect to for receiving data
 * @param converter     Function to convert the byte stream to objects
 * @param storageLevel  Storage level to use for storing the received objects
 * @tparam T            Type of the objects received (after converting bytes to objects)
 */
def socketStream[T: ClassTag](
    hostname: String,
    port: Int,
    converter: (InputStream) => Iterator[T],
    storageLevel: StorageLevel
  ): ReceiverInputDStream[T] = {
  new SocketInputDStream[T](this, hostname, port, converter, storageLevel)
}

   3.       通过SocketReceiver接收数据。

 

 

   private[streaming]
class SocketInputDStream[T: ClassTag](
    ssc_ : StreamingContext,
    host: String,
    port: Int,
    bytesToObjects: InputStream => Iterator[T],
    storageLevel: StorageLevel
  ) extends ReceiverInputDStream[T](ssc_) {

  def getReceiver(): Receiver[T] = {
    new SocketReceiver(host, port, bytesToObjects, storageLevel)
  }
}

 4，SocketReceiver中通过onstart方法调用receiver方法。

 

   

   def onStart() {
  // Start the thread that receives data over a connection
  new Thread("Socket Receiver") {
    setDaemon(true)
    override def run() { receive() }
  }.start()
}

  5，Receive方法通过网络连接，接收来自网络的数据。

 

 

   /** Create a socket connection and receive data until receiver is stopped */
def receive() {
  var socket: Socket = null
  try {
    logInfo("Connecting to " + host + ":" + port)
    socket = new Socket(host, port)
    logInfo("Connected to " + host + ":" + port)
//根据IP和端口
    val iterator = bytesToObjects(socket.getInputStream())
    while(!isStopped && iterator.hasNext) {
      store(iterator.next)
    }
    if (!isStopped()) {
      restart("Socket data stream had no more data")
    } else {
      logInfo("Stopped receiving")
    }
  } catch {
    case e: java.net.ConnectException =>
      restart("Error connecting to " + host + ":" + port, e)
    case NonFatal(e) =>
      logWarning("Error receiving data", e)
      restart("Error receiving data", e)
  } finally {
    if (socket != null) {
      socket.close()
      logInfo("Closed socket to " + host + ":" + port)
    }
  }

    6.      Receive接收到数据产生DStream，而DStream内部是以RDD的方式封装数据。

// RDDs generated, marked as private[streaming] so that testsuites can access it
@transient
private[streaming] var generatedRDDs = new HashMap[Time, RDD[T]] ()

   socketTextStream读取数据的调用过程如下：

 

 第三步：根据自己的业务进行transformation操作。

 

 第四步：调用start方法。

 

/**
  * 在StreamingContext调用start方法的内部其实是会启动JobScheduler的Start方法，进行消息循环，在JobScheduler
  * 的start内部会构造JobGenerator和ReceiverTacker，并且调用JobGenerator和ReceiverTacker的start方法：
  *   1，JobGenerator启动后会不断的根据batchDuration生成一个个的Job
  *   2，ReceiverTracker启动后首先在Spark Cluster中启动Receiver（其实是在Executor中先启动ReceiverSupervisor），在Receiver收到
  *   数据后会通过ReceiverSupervisor存储到Executor并且把数据的Metadata信息发送给Driver中的ReceiverTracker，在ReceiverTracker
  *   内部会通过ReceivedBlockTracker来管理接受到的元数据信息
  * 每个BatchInterval会产生一个具体的Job，其实这里的Job不是Spark Core中所指的Job，它只是基于DStreamGraph而生成的RDD
  * 的DAG而已，从Java角度讲，相当于Runnable接口实例，此时要想运行Job需要提交给JobScheduler，在JobScheduler中通过线程池的方式找到一个
  * 单独的线程来提交Job到集群运行（其实是在线程中基于RDD的Action触发真正的作业的运行），为什么使用线程池呢？
  *   1，作业不断生成，所以为了提升效率，我们需要线程池；这和在Executor中通过线程池执行Task有异曲同工之妙；
  *   2，有可能设置了Job的FAIR公平调度的方式，这个时候也需要多线程的支持；
  *
  */

ssc.start()

 1.       Start源码如下：

 

 

   /**
 * 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.
//线程本地存储，线程有自己的私有属性，设置这些线程的时候不会影响其他线程，
          ThreadUtils.runInNewThread("streaming-start") {
            sparkContext.setCallSite(startSite.get)
            sparkContext.clearJobGroup()
            sparkContext.setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, "false")
//调用JobScheduler的start方法。
            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 =>
//当有StreamingContext运行的时候就不许新的StreamingContext运行了，因为，//目前Spark还不支持多个SparkContext同时运行。
      logWarning("StreamingContext has already been started")
    case STOPPED =>
      throw new IllegalStateException("StreamingContext has already been stopped")
  }
}

 2.       追踪JobScheduler的start方法源码如下：

 

 

  JoScheduler的启动主要实现以下步骤：

 

<!--[if !supportLists]-->1.       <!--[endif]-->创建eventLoop的匿名类实现，主要是处理各类JobScheduler的事件。

 

 

   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 {
//获得inputDStream
    inputDStream <- ssc.graph.getInputStreams
// rateController可以控制输入速度
    rateController <- inputDStream.rateController
  } ssc.addStreamingListener(rateController)
//启动StreamingListenerBus,主要是用于更新Spark UI中的StreamTab的内容。
  listenerBus.start(ssc.sparkContext)
  receiverTracker = new ReceiverTracker(ssc)
  inputInfoTracker = new InputInfoTracker(ssc)
  receiverTracker.start()
  jobGenerator.start()
  logInfo("Started JobScheduler")
}

 3.       JobScheduler负责动态作业调度的具体类。

 

 

JobScheduler是整个Job的调度器，本身用了一条线程循环去监听不同的Job启动，Job完成或失败等：

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)
  }
}

 

其中receiverTracker的start方法源码如下：

ReceiverTracker的作用是: 处理数据接收，数据缓存，Block生成等工作。

ReceiverTracker是以发送Job的方式到集群中的Executor去启动receiver。

/** Start the endpoint and receiver execution thread. */
def start(): Unit = synchronized {
  if (isTrackerStarted) {
    throw new SparkException("ReceiverTracker already started")
  }

  if (!receiverInputStreams.isEmpty) {
    endpoint = ssc.env.rpcEnv.setupEndpoint(
      "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv))
    if (!skipReceiverLaunch) launchReceivers()
    logInfo("ReceiverTracker started")
    trackerState = Started
  }
}

ReceiverTrackEndpoint用于接收来自Receiver的消息。

Receive接收消息：启动一个Job接收消息。

/** RpcEndpoint to receive messages from the receivers. */
private class ReceiverTrackerEndpoint(override val rpcEnv: RpcEnv) extends ThreadSafeRpcEndpoint {

  // TODO Remove this thread pool after https://github.com/apache/spark/issues/7385 is merged
  private val submitJobThreadPool = ExecutionContext.fromExecutorService(
    ThreadUtils.newDaemonCachedThreadPool("submit-job-thread-pool"))

  private val walBatchingThreadPool = ExecutionContext.fromExecutorService(
    ThreadUtils.newDaemonCachedThreadPool("wal-batching-thread-pool"))

  @volatile private var active: Boolean = true

  override def receive: PartialFunction[Any, Unit] = {
    // Local messages
    case StartAllReceivers(receivers) =>

      val scheduledLocations = schedulingPolicy.scheduleReceivers(receivers, getExecutors)
      for (receiver <- receivers) {

//在那些机器上启动executors
        val executors = scheduledLocations(receiver.streamId)
        updateReceiverScheduledExecutors(receiver.streamId, executors)
        receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation
        startReceiver(receiver, executors)
      }
    case RestartReceiver(receiver) =>
      // Old scheduled executors minus the ones that are not active any more
      val oldScheduledExecutors = getStoredScheduledExecutors(receiver.streamId)
      val scheduledLocations = if (oldScheduledExecutors.nonEmpty) {
          // Try global scheduling again
          oldScheduledExecutors
        } else {
          val oldReceiverInfo = receiverTrackingInfos(receiver.streamId)
          // Clear "scheduledLocations" to indicate we are going to do local scheduling
          val newReceiverInfo = oldReceiverInfo.copy(
            state = ReceiverState.INACTIVE, scheduledLocations = None)
          receiverTrackingInfos(receiver.streamId) = newReceiverInfo
          schedulingPolicy.rescheduleReceiver(
            receiver.streamId,
            receiver.preferredLocation,
            receiverTrackingInfos,
            getExecutors)
        }
      // Assume there is one receiver restarting at one time, so we don't need to update
      // receiverTrackingInfos
      startReceiver(receiver, scheduledLocations)
    case c: CleanupOldBlocks =>
      receiverTrackingInfos.values.flatMap(_.endpoint).foreach(_.send(c))
    case UpdateReceiverRateLimit(streamUID, newRate) =>
      for (info <- receiverTrackingInfos.get(streamUID); eP <- info.endpoint) {
        eP.send(UpdateRateLimit(newRate))
      }
    // Remote messages
    case ReportError(streamId, message, error) =>
      reportError(streamId, message, error)
  }

调用startReceiver方法在Executors上启动receiver.其中以封装函数startReceiverFunc的方式启动receiver.

/**
 * Start a receiver along with its scheduled executors
 */
private def startReceiver(
    receiver: Receiver[_],
    scheduledLocations: Seq[TaskLocation]): Unit = {
  def shouldStartReceiver: Boolean = {
    // It's okay to start when trackerState is Initialized or Started
    !(isTrackerStopping || isTrackerStopped)
  }

  val receiverId = receiver.streamId
  if (!shouldStartReceiver) {
    onReceiverJobFinish(receiverId)
    return
  }

  val checkpointDirOption = Option(ssc.checkpointDir)
  val serializableHadoopConf =
    new SerializableConfiguration(ssc.sparkContext.hadoopConfiguration)

  // Function to start the receiver on the worker node
  val startReceiverFunc: Iterator[Receiver[_]] => Unit =
    (iterator: Iterator[Receiver[_]]) => {
      if (!iterator.hasNext) {
        throw new SparkException(
          "Could not start receiver as object not found.")
      }
      if (TaskContext.get().attemptNumber() == 0) {
        val receiver = iterator.next()
        assert(iterator.hasNext == false)
        val supervisor = new ReceiverSupervisorImpl(
          receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
        supervisor.start()
        supervisor.awaitTermination()
      } else {
        // It's restarted by TaskScheduler, but we want to reschedule it again. So exit it.
      }
    }

  // Create the RDD using the scheduledLocations to run the receiver in a Spark job
  val receiverRDD: RDD[Receiver[_]] =
    if (scheduledLocations.isEmpty) {
      ssc.sc.makeRDD(Seq(receiver), 1)
    } else {
      val preferredLocations = scheduledLocations.map(_.toString).distinct
      ssc.sc.makeRDD(Seq(receiver -> preferredLocations))
    }
  receiverRDD.setName(s"Receiver $receiverId")
  ssc.sparkContext.setJobDescription(s"Streaming job running receiver $receiverId")
  ssc.sparkContext.setCallSite(Option(ssc.getStartSite()).getOrElse(Utils.getCallSite()))

  val future = ssc.sparkContext.submitJob[Receiver[_], Unit, Unit](
    receiverRDD, startReceiverFunc, Seq(0), (_, _) => Unit, ())
  // We will keep restarting the receiver job until ReceiverTracker is stopped
  future.onComplete {
    case Success(_) =>
      if (!shouldStartReceiver) {
        onReceiverJobFinish(receiverId)
      } else {
        logInfo(s"Restarting Receiver $receiverId")
        self.send(RestartReceiver(receiver))
      }
    case Failure(e) =>
      if (!shouldStartReceiver) {
        onReceiverJobFinish(receiverId)
      } else {
        logError("Receiver has been stopped. Try to restart it.", e)
        logInfo(s"Restarting Receiver $receiverId")
        self.send(RestartReceiver(receiver))
      }
  }(submitJobThreadPool)
  logInfo(s"Receiver ${receiver.streamId} started")
}

在startReceiver方法内部会启动supervisor.

/** Start the supervisor */
def start() {
  onStart()
  startReceiver()
}

首先调用了onStart()方法，其实调用的是子类的onstart方法。

/**
 * Called when supervisor is started.
 * Note that this must be called before the receiver.onStart() is called to ensure
 * things like [[BlockGenerator]]s are started before the receiver starts sending data.
 */
protected def onStart() { }

也就是ReceiverSupervisorImpl的onStart方法。

override protected def onStart() {
  registeredBlockGenerators.foreach { _.start() }
}

BlockGenerator的start方法启动了BlockIntervalTimer和BlockPushingThread.

/** Start block generating and pushing threads. */
def start(): Unit = synchronized {
  if (state == Initialized) {
    state = Active
    blockIntervalTimer.start()
    blockPushingThread.start()
    logInfo("Started BlockGenerator")
  } else {
    throw new SparkException(
      s"Cannot start BlockGenerator as its not in the Initialized state [state = $state]")
  }
}

   回到上面，我们现在看ReceiverSupervisor.startReceiver方法的调用。

/** Start receiver */
def startReceiver(): Unit = synchronized {
  try {
    if (onReceiverStart()) {
      logInfo("Starting receiver")
      receiverState = Started
      receiver.onStart()
      logInfo("Called receiver onStart")
    } else {
      // The driver refused us
      stop("Registered unsuccessfully because Driver refused to start receiver " + streamId, None)
    }
  } catch {
    case NonFatal(t) =>
      stop("Error starting receiver " + streamId, Some(t))
  }
}

其中onReceiverStart方法在子类ReceiverSupervisorImpl的onReceiverStart，启用给ReciverTrackEndpoint发送registerReceiver消息。

override protected def onReceiverStart(): Boolean = {
  val msg = RegisterReceiver(
    streamId, receiver.getClass.getSimpleName, host, executorId, endpoint)
  trackerEndpoint.askWithRetry[Boolean](msg)
}

此时，ReceiverTrackEndpoint接收到消息后会调用registerReceiver方法。

override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
  // Remote messages
  case RegisterReceiver(streamId, typ, host, executorId, receiverEndpoint) =>
    val successful =
      registerReceiver(streamId, typ, host, executorId, receiverEndpoint, context.senderAddress)
    context.reply(successful)
  case AddBlock(receivedBlockInfo) =>
    if (WriteAheadLogUtils.isBatchingEnabled(ssc.conf, isDriver = true)) {
      walBatchingThreadPool.execute(new Runnable {
        override def run(): Unit = Utils.tryLogNonFatalError {
          if (active) {
            context.reply(addBlock(receivedBlockInfo))
          } else {
            throw new IllegalStateException("ReceiverTracker RpcEndpoint shut down.")
          }
        }
      })

 至此，ReceiverTrack的启动就完成了。下面就回到我们最初的代码。

   obScheduler的start方法：

receiverTracker.start()
jobGenerator.start()

   启动JobGenerator，JobGenerator负责对DstreamGraph的初始化，DStream与RDD的转换，生成Job,提交执行等工作。

/** 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用于接收JobGeneratorEvent消息的通信体。
  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()
  }

   调用processEvent，以时间间隔发消息。

/** 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中发time就是我们指点的batch Duractions

/** 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 {

// batch时间间隔获得Block数据。
    jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch

// generateJobs生成Job
    graph.generateJobs(time) // generate jobs using allocated block
  } match {
    case Success(jobs) =>
      val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)

//如果作业成功生成，那么就提交这个作业。将作业提交给JobScheduler.
      jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
    case Failure(e) =>
      jobScheduler.reportError("Error generating jobs for time " + time, e)
  }
  eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}

    submitJobSet提交Job.

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)
  }
}

    而我们提交的Job，是被JobHandle封装的。

 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)
      }
    }
  }
}

 

总体流程如下图所示：

 InputDStream继承关系图如下：

 补充：

Spark运行的时候会启动作业，runDummySparkJob函数是为了确保Receiver不会集中在一个节点上。

/**
 * Run the dummy Spark job to ensure that all slaves have registered. This avoids all the
 * receivers to be scheduled on the same node.
 *
 * TODO Should poll the executor number and wait for executors according to
 * "spark.scheduler.minRegisteredResourcesRatio" and
 * "spark.scheduler.maxRegisteredResourcesWaitingTime" rather than running a dummy job.
 */
private def runDummySparkJob(): Unit = {
  if (!ssc.sparkContext.isLocal) {
    ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect()
  }
  assert(getExecutors.nonEmpty)
}

 

 

/**
 * Get the receivers from the ReceiverInputDStreams, distributes them to the
 * worker nodes as a parallel collection, and runs them.
 */
private def launchReceivers(): Unit = {
  val receivers = receiverInputStreams.map(nis => {
    val rcvr = nis.getReceiver()
    rcvr.setReceiverId(nis.id)
    rcvr
  })

  runDummySparkJob()

  logInfo("Starting " + receivers.length + " receivers")

//在资源没有问题的前提下

//ReceiverTrackEndpoint => endpoint
  endpoint.send(StartAllReceivers(receivers))
}

 

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