65、Spark Streaming：数据接收原理剖析与源码分析

一、数据接收原理

二、源码分析

入口包org.apache.spark.streaming.receiver下ReceiverSupervisorImpl类的onStart()方法

###

 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()
  }

ReceiverSupervisorImpl类的onStart()方法，调用了blockGenerator.start()方法，跟进去看看

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

  def start() {
    // BlockGenerator.start()方法，其实就是启动内部两个关键的后台线程，
    // 一个是blockIntervalTimer，负责将currentBuffer中的原始数据，打包成一个个的block
    // 另一个是blockPushingThread，负责将blocksForPushing中的block，调用pushArrayBuffer()方法
    blockIntervalTimer.start()
    blockPushingThread.start()
    logInfo("Started BlockGenerator")
  }

blockGenerator.start()方法，调用了blockIntervalTimer.start()和blockPushingThread.start()方法
先看看有关变量的定义

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

private val blockInterval = conf.getLong("spark.streaming.blockInterval", 200)
  // blockInterval,是有一个默认值的，spark.streaming.blockInterval，默认是200ms，每隔200ms，就会调用updateCurrentBuffer函数
  private val blockIntervalTimer =
    new RecurringTimer(clock, blockInterval, updateCurrentBuffer, "BlockGenerator")
  // blocksForPushing队列的长度，可以调节的，spark.streaming.blockQueueSize，默认10个，可大可小
  private val blockQueueSize = conf.getInt("spark.streaming.blockQueueSize", 10)
  // blocksForPushing队列，
  private val blocksForPushing = new ArrayBlockingQueue[Block](blockQueueSize)
  // blockPushingThread，后台线程，启动之后，就会调用keepPushingBlocks()方法，这个方法中，就会每隔一段时间，去blocksForPushing队列中取block
  private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
 
  // 这个currentBuffer，就是用于存放原始的数据
  @volatile private var currentBuffer = new ArrayBuffer[Any]

blockIntervalTimer.start()就是一个线程，这个方法就不看了
重点看下blockPushingThread.start()方法，这个线程开始运行，会调用keepPushingBlocks()方法，代码如下

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

  private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }

看keepPushingBlocks()方法

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

private def keepPushingBlocks() {
    logInfo("Started block pushing thread")
    try {
      while(!stopped) {
        // 从blocksForPushing这个队列中，poll出来当前队列队首的block，对于阻塞队列，默认设置100ms的超时
        Option(blocksForPushing.poll(100, TimeUnit.MILLISECONDS)) match {
            // 如果拿到了block，调用pushBlock去推送block
          case Some(block) => pushBlock(block)
          case None =>
        }
      }
      // Push out the blocks that are still left
      logInfo("Pushing out the last " + blocksForPushing.size() + " blocks")
      while (!blocksForPushing.isEmpty) {
        logDebug("Getting block ")
        val block = blocksForPushing.take()
        pushBlock(block)
        logInfo("Blocks left to push " + blocksForPushing.size())
      }
      logInfo("Stopped block pushing thread")
    } catch {
      case ie: InterruptedException =>
        logInfo("Block pushing thread was interrupted")
      case e: Exception =>
        reportError("Error in block pushing thread", e)
    }
  }

可以看到keepPushingBlocks()方法，如果拿到了block，调用pushBlock()方法
看看pushBlock()方法

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

  private def pushBlock(block: Block) {
    listener.onPushBlock(block.id, block.buffer)
    logInfo("Pushed block " + block.id)
  }

pushBlock()方法会调用listener.onPushBlock()方法，这个listener是BlockGeneratorListener，onPushBlock()在ReceiverSupervisorImpl类中,
看ReceiverSupervisorImpl类的onPushBlock()方法:

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

    // onPushBlock就会去调用pushArrayBuffer去推送block
    def onPushBlock(blockId: StreamBlockId, arrayBuffer: ArrayBuffer[_]) {
      pushArrayBuffer(arrayBuffer, None, Some(blockId))
    }

onPushBlock就会去调用pushArrayBuffer()方法
看pushArrayBuffer()方法:

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

  def pushArrayBuffer(
      arrayBuffer: ArrayBuffer[_],
      metadataOption: Option[Any],
      blockIdOption: Option[StreamBlockId]
    ) {
    pushAndReportBlock(ArrayBufferBlock(arrayBuffer), metadataOption, blockIdOption)
  }

接着看pushAndReportBlock()方法:

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

  def pushAndReportBlock(
      receivedBlock: ReceivedBlock,
      metadataOption: Option[Any],
      blockIdOption: Option[StreamBlockId]
    ) {
    val blockId = blockIdOption.getOrElse(nextBlockId)
    val numRecords = receivedBlock match {
      case ArrayBufferBlock(arrayBuffer) => arrayBuffer.size
      case _ => -1
    }
 
    val time = System.currentTimeMillis
    // 还用receivedBlockHandler，去调用storeBlock方法，存储block到BlockManager中，这里，也可以看出预写日志的机制
    val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock)
    logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms")
 
    // 封装一个ReceiverBlockInfo对象，里面有一个streamId
    val blockInfo = ReceivedBlockInfo(streamId, numRecords, blockStoreResult)
    // 调用了ReceiverTracker的Acrot的ask方法，发送AddBlock消息
    val future = trackerActor.ask(AddBlock(blockInfo))(askTimeout)
    Await.result(future, askTimeout)
    logDebug(s"Reported block $blockId")
  }

这里主要看receivedBlockHandler.storeBlock()方法和trackerActor.ask(AddBlock(blockInfo))(askTimeout)
首先看receivedBlockHandler.storeBlock()，看看receivedBlockHandler是什么

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

private val receivedBlockHandler: ReceivedBlockHandler = {
    // 如果开启了预写日志机制，spark.streaming.receiver.writeAheadLog.enable，默认false
    // 如果为true，那么receivedBlockHandler就是WriteAheadLogBasedBlockHandler
    // 如果没有开启预写日志机制，那么receivedBlockHandler就是BlockManagerBasedBlockHandler
    if (env.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) {
      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.")
      }
      new WriteAheadLogBasedBlockHandler(env.blockManager, receiver.streamId,
        receiver.storageLevel, env.conf, hadoopConf, checkpointDirOption.get)
    } else {
      new BlockManagerBasedBlockHandler(env.blockManager, receiver.storageLevel)
    }

接着分别看BlockManagerBasedBlockHandler和WriteAheadLogBasedBlockHandler的storeBlock()方法
先看WriteAheadLogBasedBlockHandler

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

def storeBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = {
 
    // Serialize the block so that it can be inserted into both
    // 先用BlockManager序列化数据
    val serializedBlock = block match {
      case ArrayBufferBlock(arrayBuffer) =>
        blockManager.dataSerialize(blockId, arrayBuffer.iterator)
      case IteratorBlock(iterator) =>
        blockManager.dataSerialize(blockId, iterator)
      case ByteBufferBlock(byteBuffer) =>
        byteBuffer
      case _ =>
        throw new Exception(s"Could not push $blockId to block manager, unexpected block type")
    }
 
    // Store the block in block manager
    // 将数据保存到BlockManager中去，默认的持久化策略，StorageLevel，是带_SER,_2的，会序列化，复制一份副本到其他Executor的BlockManager，以供容错
    val storeInBlockManagerFuture = Future {
      val putResult =
        blockManager.putBytes(blockId, serializedBlock, effectiveStorageLevel, tellMaster = true)
      if (!putResult.map { _._1 }.contains(blockId)) {
        throw new SparkException(
          s"Could not store $blockId to block manager with storage level $storageLevel")
      }
    }
 
    // Store the block in write ahead log
    // 将block存入预写日志，通过logManager的writeToLog()方法
    val storeInWriteAheadLogFuture = Future {
      logManager.writeToLog(serializedBlock)
    }
 
    // Combine the futures, wait for both to complete, and return the write ahead log segment
    val combinedFuture = storeInBlockManagerFuture.zip(storeInWriteAheadLogFuture).map(_._2)
    val segment = Await.result(combinedFuture, blockStoreTimeout)
    WriteAheadLogBasedStoreResult(blockId, segment)
  }

再看BlockManagerBasedBlockHandler

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

 // 直接将数据保存到BlockManager中，就可以了
  def storeBlock(blockId: StreamBlockId, block: ReceivedBlock): ReceivedBlockStoreResult = {
    val putResult: Seq[(BlockId, BlockStatus)] = block match {
      case ArrayBufferBlock(arrayBuffer) =>
        blockManager.putIterator(blockId, arrayBuffer.iterator, storageLevel, tellMaster = true)
      case IteratorBlock(iterator) =>
        blockManager.putIterator(blockId, iterator, storageLevel, tellMaster = true)
      case ByteBufferBlock(byteBuffer) =>
        blockManager.putBytes(blockId, byteBuffer, storageLevel, tellMaster = true)
      case o =>
        throw new SparkException(
          s"Could not store $blockId to block manager, unexpected block type ${o.getClass.getName}")
    }
    if (!putResult.map { _._1 }.contains(blockId)) {
      throw new SparkException(
        s"Could not store $blockId to block manager with storage level $storageLevel")
    }
    BlockManagerBasedStoreResult(blockId)
  }

接着看trackerActor.ask(AddBlock(blockInfo))(askTimeout)，会发一个AddBlock消息到ReceiverTracker，进入看一下：

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

  private def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = {
    receivedBlockTracker.addBlock(receivedBlockInfo)
  }

接着看receivedBlockTracker的addBlock方法,除了这个方法之外，还看receivedBlockTracker的几个重要变量,
先看方法:

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

  def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = synchronized {
    try {
      writeToLog(BlockAdditionEvent(receivedBlockInfo))
      getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo
      logDebug(s"Stream ${receivedBlockInfo.streamId} received " +
        s"block ${receivedBlockInfo.blockStoreResult.blockId}")
      true
    } catch {
      case e: Exception =>
        logError(s"Error adding block $receivedBlockInfo", e)
        false
    }
  }

再看变量

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

// 封装了streamId到block的映射
  private val streamIdToUnallocatedBlockQueues = new mutable.HashMap[Int, ReceivedBlockQueue]
  // 封装了time到block的映射
  private val timeToAllocatedBlocks = new mutable.HashMap[Time, AllocatedBlocks]
  // 如果开启了预写机制机制，这还有LogManager，ReceiverTracker接收到数据时，也会判断，
  // 如果开启了预写日志机制，写一份到预写日志中
  private val logManagerOption = createLogManager()