本篇博文的目标是：
Driver的ReceiverTracker接收到数据之后，下一步对数据是如何进行管理

一：ReceiverTracker的架构设计
1. Driver在Executor启动Receiver方式，每个Receiver都封装成一个Task，此时一个Job中就一个Task,而Task中就一条数据，也就是Receiver数据。由此，多少个Job也就可以启动多少个Receiver.
2. ReceiverTracker在启动Receiver的时候他有ReceiverSupervisor,其实现是ReceiverSupervisorImpl, ReceiverSupervisor本身启动的时候会启动Receiver，Receiver不断的接收数据，通过BlockGenerator将数据转换成Block。定时器会不断的把Block数据通过BlockManager或者WAL进行存储，数据存储之后ReceiverSupervisorImpl会把存储后的数据的元数据Metadate汇报给ReceiverTracker,其实是汇报给ReceiverTracker中的RPC实体ReceiverTrackerEndpoint.
ReceiverSupervisorImpl会将元数据汇报给ReceiverTracker,那么接收到之后,下一步就对数据进行管理。

1. 通过receivedBlockHandler写数据

private val receivedBlockHandler: ReceivedBlockHandler = {
  if (WriteAheadLogUtils.enableReceiverLog(env.conf)) {
    if (checkpointDirOption.isEmpty) {
      throw new SparkException(
        "Cannot enable receiver write-ahead log without checkpoint directory set. " +
          "Please use streamingContext.checkpoint() to set the checkpoint directory. " +
          "See documentation for more details.")
    }
//WAL
    new WriteAheadLogBasedBlockHandler(env.blockManager, receiver.streamId,
      receiver.storageLevel, env.conf, hadoopConf, checkpointDirOption.get)
  } else {
//BlockManager
    new BlockManagerBasedBlockHandler(env.blockManager, receiver.storageLevel)
  }
}

2.  PushAndReportBlock存储Block数据，且把信息汇报给Driver。

/** Store block and report it to driver */
def pushAndReportBlock(
    receivedBlock: ReceivedBlock,
    metadataOption: Option[Any],
    blockIdOption: Option[StreamBlockId]
  ) {
  val blockId = blockIdOption.getOrElse(nextBlockId)
  val time = System.currentTimeMillis
  val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock)
  logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms")
  val numRecords = blockStoreResult.numRecords
  val blockInfo = ReceivedBlockInfo(streamId, numRecords, metadataOption, blockStoreResult)
  trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo))
  logDebug(s"Reported block $blockId")
}

3.  此时trackerEndpoint是ReceiverTrackerEndpoint

/** Remote RpcEndpointRef for the ReceiverTracker */
private val trackerEndpoint = RpcUtils.makeDriverRef("ReceiverTracker", env.conf, env.rpcEnv)

4.  ReceivedBlockInfo:封装Block的存储信息。

/** Information about blocks received by the receiver */
private[streaming] case class ReceivedBlockInfo(
    streamId: Int, //block属于哪个接收的流
    numRecords: Option[Long],//多少条记录
    metadataOption: Option[Any],//元数据信息
    blockStoreResult: ReceivedBlockStoreResult
  ) {

  require(numRecords.isEmpty || numRecords.get >= 0, "numRecords must not be negative")

  @volatile private var _isBlockIdValid = true

  def blockId: StreamBlockId = blockStoreResult.blockId

  def walRecordHandleOption: Option[WriteAheadLogRecordHandle] = {
    blockStoreResult match {
      case walStoreResult: WriteAheadLogBasedStoreResult => Some(walStoreResult.walRecordHandle)
      case _ => None
    }
  }

  /** Is the block ID valid, that is, is the block present in the Spark executors. */
  def isBlockIdValid(): Boolean = _isBlockIdValid

  /**
   * Set the block ID as invalid. This is useful when it is known that the block is not present
   * in the Spark executors.
   */
  def setBlockIdInvalid(): Unit = {
    _isBlockIdValid = false
  }
}

5.  ReceivedBlockStoreResult:

/** Trait that represents the metadata related to storage of blocks */
private[streaming] trait ReceivedBlockStoreResult {
  // Any implementation of this trait will store a block id
  def blockId: StreamBlockId
  // Any implementation of this trait will have to return the number of records
  def numRecords: Option[Long]
}

ReceiverTracker的源码源码遍历
1. 下面的消息是完成Receiver和ReceiverTracker之间通信的。

/**
 * Messages used by the NetworkReceiver and the ReceiverTracker to communicate
 * with each other.
 */
//这里使用sealed意思是ReceiverTrackerMessage包含所有的消息。
private[streaming] sealed trait ReceiverTrackerMessage
private[streaming] case class RegisterReceiver(
    streamId: Int,
    typ: String,
    host: String,
    executorId: String,
    receiverEndpoint: RpcEndpointRef
  ) extends ReceiverTrackerMessage
private[streaming] case class AddBlock(receivedBlockInfo: ReceivedBlockInfo)
  extends ReceiverTrackerMessage
private[streaming] case class ReportError(streamId: Int, message: String, error: String)
private[streaming] case class DeregisterReceiver(streamId: Int, msg: String, error: String)
  extends ReceiverTrackerMessage

2.  Driver和ReceiverTrackerEndpoint之间的交流通过ReceiverTrackerLocalMessage。

/**
 * Messages used by the driver and ReceiverTrackerEndpoint to communicate locally.
 */
private[streaming] sealed trait ReceiverTrackerLocalMessage

3.  ReceiverTrackerLocalMessage中的子类

/**
 * This message will trigger ReceiverTrackerEndpoint to restart a Spark job for the receiver.
 */
//从起Receiver
private[streaming] case class RestartReceiver(receiver: Receiver[_])
  extends ReceiverTrackerLocalMessage

/**
 * This message is sent to ReceiverTrackerEndpoint when we start to launch Spark jobs for receivers
 * at the first time.
 */
//启动Receiver的集合
private[streaming] case class StartAllReceivers(receiver: Seq[Receiver[_]])
  extends ReceiverTrackerLocalMessage

/**
 * This message will trigger ReceiverTrackerEndpoint to send stop signals to all registered
 * receivers.
 */
//程序结束的时候会发出停止所有Receiver的信息。
private[streaming] case object StopAllReceivers extends ReceiverTrackerLocalMessage

/**
 * A message used by ReceiverTracker to ask all receiver's ids still stored in
 * ReceiverTrackerEndpoint.
 */
//正在存信息的是ReceiverTrackerEndpoint
private[streaming] case object AllReceiverIds extends ReceiverTrackerLocalMessage

// UpdateReceiverRateLimit实例可能会有几个，因此在程序运行的时候需要限流。
private[streaming] case class UpdateReceiverRateLimit(streamUID: Int, newRate: Long)
  extends ReceiverTrackerLocalMessage

4.  ReceiverTracker：管理Receiver的启动，Receiver的执行，回收，执行过程中接收数据的管理。DStreamGraph中会有成员记录所有的数据流来源，免得每次都去检索。

/**
 * This class manages the execution of the receivers of ReceiverInputDStreams. Instance of
 * this class must be created after all input streams have been added and StreamingContext.start()
 * has been called because it needs the final set of input streams at the time of instantiation.
 *
 * @param skipReceiverLaunch Do not launch the receiver. This is useful for testing.
 */
private[streaming]

class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false) extends Logging {
//所有的InputStream都会交给graph
private val receiverInputStreams = ssc.graph.getReceiverInputStreams()
private val receiverInputStreamIds = receiverInputStreams.map { _.id }
private val receivedBlockTracker = new ReceivedBlockTracker(
  ssc.sparkContext.conf,
  ssc.sparkContext.hadoopConfiguration,
  receiverInputStreamIds,
  ssc.scheduler.clock,
  ssc.isCheckpointPresent,
  Option(ssc.checkpointDir)
)
private val listenerBus = ssc.scheduler.listenerBus

ReceiverTracker中的receiverAndReply:

1. ReceiverTrackerEndpoint接收消息，并回复addBlock消息。

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.")
          }
        }
      })
    } else {
      context.reply(addBlock(receivedBlockInfo))
    }
  case DeregisterReceiver(streamId, message, error) =>
    deregisterReceiver(streamId, message, error)
    context.reply(true)
  // Local messages
//查看是否有活跃的Receiver
  case AllReceiverIds =>
    context.reply(receiverTrackingInfos.filter(_._2.state != ReceiverState.INACTIVE).keys.toSeq)
//停止所有Receivers
  case StopAllReceivers =>
    assert(isTrackerStopping || isTrackerStopped)
    stopReceivers()
    context.reply(true)
}

2.  addBlock源码如下：

/** Add new blocks for the given stream */
private def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
  receivedBlockTracker.addBlock(receivedBlockInfo)
}

3.  ReceiverBlockTracker的addBlock源码如下：把具体的一个Receiver汇报上来的数据的元数据信息写入streamIdToUnallocatedBlockQueues中。

/** Add received block. This event will get written to the write ahead log (if enabled). */
def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
  try {
    val writeResult = writeToLog(BlockAdditionEvent(receivedBlockInfo))
    if (writeResult) {
      synchronized {
        getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo
      }
      logDebug(s"Stream ${receivedBlockInfo.streamId} received " +
        s"block ${receivedBlockInfo.blockStoreResult.blockId}")
    } else {
      logDebug(s"Failed to acknowledge stream ${receivedBlockInfo.streamId} receiving " +
        s"block ${receivedBlockInfo.blockStoreResult.blockId} in the Write Ahead Log.")
    }
    writeResult
  } catch {
    case NonFatal(e) =>
      logError(s"Error adding block $receivedBlockInfo", e)
      false
  }
}

其中getReceivedBlockQueue是ReceivedBlockQueue类型。

/** Get the queue of received blocks belonging to a particular stream */
private def getReceivedBlockQueue(streamId: Int): ReceivedBlockQueue = {
  streamIdToUnallocatedBlockQueues.getOrElseUpdate(streamId, new ReceivedBlockQueue)
}

4.  其中HashMap中第一个参数是StreamId,第二个参数ReceivedBlockQueue是StreamId对应接收到的Receiver.

private val streamIdToUnallocatedBlockQueues = new mutable.HashMap[Int, ReceivedBlockQueue]

5.  WritetToLog源码如下：

/** Write an update to the tracker to the write ahead log */
private def writeToLog(record: ReceivedBlockTrackerLogEvent): Boolean = {
  if (isWriteAheadLogEnabled) { //先判断是否可以写入到log中。
    logTrace(s"Writing record: $record")
    try {
//write方法将数据写入      writeAheadLogOption.get.write(ByteBuffer.wrap(Utils.serialize(record)),
        clock.getTimeMillis())
      true
    } catch {
      case NonFatal(e) =>
        logWarning(s"Exception thrown while writing record: $record to the WriteAheadLog.", e)
        false
    }
  } else {
    true
  }
}

ReceiverBlockTracker源码分析：
1. 保持跟踪所有接收到的Block。并且根据需要把他们分配给batches.
假设提供checkpoint的话,ReceiverBlockTracker中的信息包括receiver接收到的block数据和分配的信息。Driver如果失败的话，就读取checkpoint中的信息。

/**
 * Class that keep track of all the received blocks, and allocate them to batches
 * when required. All actions taken by this class can be saved to a write ahead log
 * (if a checkpoint directory has been provided), so that the state of the tracker
 * (received blocks and block-to-batch allocations) can be recovered after driver failure.
 *
 * Note that when any instance of this class is created with a checkpoint directory,
 * it will try reading events from logs in the directory.
 */
private[streaming] class ReceivedBlockTracker(

2.  ReceivedBlockTracker通过调用allocateBlocksToBatch方法把接收到的数据分配给当前执行的Batch Duractions作业。

allocateBlocksToBatch被JobGenerator调用的。

/**
 * Allocate all unallocated blocks to the given batch.
 * This event will get written to the write ahead log (if enabled).
 */
def allocateBlocksToBatch(batchTime: Time): Unit = synchronized {
  if (lastAllocatedBatchTime == null || batchTime > lastAllocatedBatchTime) {
    val streamIdToBlocks = streamIds.map { streamId =>
        (streamId, getReceivedBlockQueue(streamId).dequeueAll(x => true))
    }.toMap
    val allocatedBlocks = AllocatedBlocks(streamIdToBlocks)
    if (writeToLog(BatchAllocationEvent(batchTime, allocatedBlocks))) {
// allocatedBlocks是接收到数据
// batchTime 是时间
      timeToAllocatedBlocks.put(batchTime, allocatedBlocks)
//每次分配的时候都会更新时间
      lastAllocatedBatchTime = batchTime
    } else {
      logInfo(s"Possibly processed batch $batchTime need to be processed again in WAL recovery")
    }
  } else {
    // This situation occurs when:
    // 1. WAL is ended with BatchAllocationEvent, but without BatchCleanupEvent,
    // possibly processed batch job or half-processed batch job need to be processed again,
    // so the batchTime will be equal to lastAllocatedBatchTime.
    // 2. Slow checkpointing makes recovered batch time older than WAL recovered
    // lastAllocatedBatchTime.
    // This situation will only occurs in recovery time.
    logInfo(s"Possibly processed batch $batchTime need to be processed again in WAL recovery")
  }
}

JobGenerator中的generateJob

/** 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

3.  AllocatedBlocks源码如下：

/** Class representing the blocks of all the streams allocated to a batch */
private[streaming]
case class AllocatedBlocks(streamIdToAllocatedBlocks: Map[Int, Seq[ReceivedBlockInfo]]) {
  def getBlocksOfStream(streamId: Int): Seq[ReceivedBlockInfo] = {
    streamIdToAllocatedBlocks.getOrElse(streamId, Seq.empty)
  }
}

ReceiverTracker的receive方法架构如下：

4. ReceiverTracker中receive源码如下：

override def receive: PartialFunction[Any, Unit] = {
  // Local messages
//启动所有的receivers，在ReceiverTracker刚启动的时候会给自己发消息，通过//schedulingPolicy来触发消息。
  case StartAllReceivers(receivers) =>
    val scheduledLocations = schedulingPolicy.scheduleReceivers(receivers, getExecutors)
    for (receiver <- receivers) {
      val executors = scheduledLocations(receiver.streamId)
      updateReceiverScheduledExecutors(receiver.streamId, executors)
      receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation
      startReceiver(receiver, executors)
    }
//当Executor帮我们分配Receiver或者Receiver失效，然后给自己发消息触发Receiver重新分发。
  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)
//当我们快要完成数据计算的时候，会发送此消息，将所有的Receiver交给我们
  case c: CleanupOldBlocks => 
    receiverTrackingInfos.values.flatMap(_.endpoint).foreach(_.send(c))
// ReceiverTracker可以动态的调整Receiver接收的RateLimit
  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)
}

5.  在ReceiverSupervisorImpl的receive方法中就接收到了ReceiverTracker的CleanupOldBlocks消息。

/** RpcEndpointRef for receiving messages from the ReceiverTracker in the driver */
private val endpoint = env.rpcEnv.setupEndpoint(
  "Receiver-" + streamId + "-" + System.currentTimeMillis(), new ThreadSafeRpcEndpoint {
    override val rpcEnv: RpcEnv = env.rpcEnv

    override def receive: PartialFunction[Any, Unit] = {
      case StopReceiver =>
        logInfo("Received stop signal")
        ReceiverSupervisorImpl.this.stop("Stopped by driver", None)
      case CleanupOldBlocks(threshTime) =>
        logDebug("Received delete old batch signal")
//根据时间就clean Old Block
        cleanupOldBlocks(threshTime)
//
      case UpdateRateLimit(eps) =>
        logInfo(s"Received a new rate limit: $eps.")
        registeredBlockGenerators.foreach { bg =>
          bg.updateRate(eps)
        }
    }
  })

6.  RateLimiter中的updateRate源码如下：

  /**
   * Set the rate limit to `newRate`. The new rate will not exceed the maximum rate configured by
   * {{{spark.streaming.receiver.maxRate}}}, even if `newRate` is higher than that.
   *
   * @param newRate A new rate in events per second. It has no effect if it's 0 or negative.
   */
  private[receiver] def updateRate(newRate: Long): Unit =
    if (newRate > 0) {
      if (maxRateLimit > 0) {
        rateLimiter.setRate(newRate.min(maxRateLimit))
      } else {
        rateLimiter.setRate(newRate)
      }
    }
}

7.  其中setRate源码如下：

/**
 * Updates the stable rate of this {@code RateLimiter}, that is, the
 * {@code permitsPerSecond} argument provided in the factory method that
 * constructed the {@code RateLimiter}. Currently throttled threads will <b>not</b>
 * be awakened as a result of this invocation, thus they do not observe the new rate;
 * only subsequent requests will.
 *
 * <p>Note though that, since each request repays (by waiting, if necessary) the cost
 * of the <i>previous</i> request, this means that the very next request
 * after an invocation to {@code setRate} will not be affected by the new rate;
 * it will pay the cost of the previous request, which is in terms of the previous rate.
 *
 * <p>The behavior of the {@code RateLimiter} is not modified in any other way,
 * e.g. if the {@code RateLimiter} was configured with a warmup period of 20 seconds,
 * it still has a warmup period of 20 seconds after this method invocation.
 *
 * @param permitsPerSecond the new stable rate of this {@code RateLimiter}.
 */
public final void setRate(double permitsPerSecond) {
  Preconditions.checkArgument(permitsPerSecond > 0.0
      && !Double.isNaN(permitsPerSecond), "rate must be positive");
  synchronized (mutex) {
    resync(readSafeMicros());
    double stableIntervalMicros = TimeUnit.SECONDS.toMicros(1L) / permitsPerSecond;
    this.stableIntervalMicros = stableIntervalMicros;
    doSetRate(permitsPerSecond, stableIntervalMicros);
  }
}

ReceiverTracker中receiveAndReply中StopAllReceivers流程如下：
1. stopReceivers源码如下：

  /** Send stop signal to the receivers. */
  private def stopReceivers() {
    receiverTrackingInfos.values.flatMap(_.endpoint).foreach 
//给ReceiverSupervisorImpl发送消息。
{ _.send(StopReceiver) }
    logInfo("Sent stop signal to all " + receiverTrackingInfos.size + " receivers")
  }
}

2.  在ReceiverSupervisorImpl中receive接收到了此消息。

/** RpcEndpointRef for receiving messages from the ReceiverTracker in the driver */
private val endpoint = env.rpcEnv.setupEndpoint(
  "Receiver-" + streamId + "-" + System.currentTimeMillis(), new ThreadSafeRpcEndpoint {
    override val rpcEnv: RpcEnv = env.rpcEnv

    override def receive: PartialFunction[Any, Unit] = {
      case StopReceiver =>
        logInfo("Received stop signal")
        ReceiverSupervisorImpl.this.stop("Stopped by driver", None)
      case CleanupOldBlocks(threshTime) =>
        logDebug("Received delete old batch signal")
        cleanupOldBlocks(threshTime)
      case UpdateRateLimit(eps) =>
        logInfo(s"Received a new rate limit: $eps.")
        registeredBlockGenerators.foreach { bg =>
          bg.updateRate(eps)
        }
    }
  })

3.  stop函数在ReceiverSupervisor中实现的。

/** Mark the supervisor and the receiver for stopping */
def stop(message: String, error: Option[Throwable]) {
  stoppingError = error.orNull
  stopReceiver(message, error)
  onStop(message, error)
  futureExecutionContext.shutdownNow()
  stopLatch.countDown()
}

4.  stopReceiver源码如下：

/** Stop receiver */
def stopReceiver(message: String, error: Option[Throwable]): Unit = synchronized {
  try {
    logInfo("Stopping receiver with message: " + message + ": " + error.getOrElse(""))
    receiverState match {
      case Initialized =>
        logWarning("Skip stopping receiver because it has not yet stared")
      case Started =>
        receiverState = Stopped
        receiver.onStop()
        logInfo("Called receiver onStop")
        onReceiverStop(message, error)
      case Stopped =>
        logWarning("Receiver has been stopped")
    }
  } catch {
    case NonFatal(t) =>
      logError("Error stopping receiver " + streamId + t.getStackTraceString)
  }
}

5.  最终调用onStop方法

/**
 * This method is called by the system when the receiver is stopped. All resources
 * (threads, buffers, etc.) setup in `onStart()` must be cleaned up in this method.
 */
def onStop()

6.  onReceiverStop方法在子类ReceiverSupervisorImpl中会有具体实现。

override protected def onReceiverStop(message: String, error: Option[Throwable]) {
  logInfo("Deregistering receiver " + streamId)
  val errorString = error.map(Throwables.getStackTraceAsString).getOrElse("")
//告诉Driver端也就是ReceiverTracker调用DeregisterReceiver
  trackerEndpoint.askWithRetry[Boolean](DeregisterReceiver(streamId, message, errorString))
  logInfo("Stopped receiver " + streamId)
}

7.  onStop方法在ReceiverSupervisorImpl中实现如下：

override protected def onStop(message: String, error: Option[Throwable]) {
  registeredBlockGenerators.foreach { _.stop() }
//停止消息循环
  env.rpcEnv.stop(endpoint)
}

StopAllReceivers全流程如下：

总结：
Receiver接收到数据之后合并存储数据后，ReceiverSupervisorImpl会把数据汇报给ReceiverTracker, ReceiverTracker接收到元数据，其内部汇报的是RPC通信体，接收到数据之后，内部有ReceivedBlockTracker会管理数据的分配，JobGenerator会将每个Batch,每次工作的时候会根据元数据信息从ReceiverTracker中获取相应的元数据信息生成RDD。
ReceiverBlockTracker中 allocateBlocksToBatch专门管理Block元数据信息，作为一个内部的管理对象。

门面设计模式：
ReceiverTracker和ReceivedBlockTracker的关系是：具体干活的是ReceivedBlockTracker，但是外部代表是ReceiverTracker。

private type ReceivedBlockQueue = mutable.Queue[ReceivedBlockInfo]

//为每个Receiver单独维护一个Queue
// streamIdToUnallocatedBlockQueues里面封装的是所有汇报上来的数据，但是没有被分配的数据。
private val streamIdToUnallocatedBlockQueues = new mutable.HashMap[Int, ReceivedBlockQueue]
//维护的是已经分配到Batch的元数据信息。
private val timeToAllocatedBlocks = new mutable.HashMap[Time, AllocatedBlocks]
private val writeAheadLogOption = createWriteAheadLog()

private var lastAllocatedBatchTime: Time = null

JobGenerator在计算基于Batch的Job的时候,我们的DStreamGraph生成RDD的DAG的时候会调用此方法。

/** Get the blocks allocated to the given batch. */
//此方法就会生成RDD。
def getBlocksOfBatch(batchTime: Time): Map[Int, Seq[ReceivedBlockInfo]] = synchronized {
  timeToAllocatedBlocks.get(batchTime).map { _.streamIdToAllocatedBlocks }.getOrElse(Map.empty)
}

当一个Batch计算完的时候，他会把已经使用的数据块的数据信息清理掉。

/**
 * Clean up block information of old batches. If waitForCompletion is true, this method
 * returns only after the files are cleaned up.
 */
def cleanupOldBatches(cleanupThreshTime: Time, waitForCompletion: Boolean): Unit = synchronized {
  require(cleanupThreshTime.milliseconds < clock.getTimeMillis())
  val timesToCleanup = timeToAllocatedBlocks.keys.filter { _ < cleanupThreshTime }.toSeq
  logInfo("Deleting batches " + timesToCleanup)
  if (writeToLog(BatchCleanupEvent(timesToCleanup))) {
    timeToAllocatedBlocks --= timesToCleanup
    writeAheadLogOption.foreach(_.clean(cleanupThreshTime.milliseconds, waitForCompletion))
  } else {
    logWarning("Failed to acknowledge batch clean up in the Write Ahead Log.")
  }
}

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