怎么进行Spark Streaming 原理剖析

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一：初始化与接收数据。

Spark Streaming 通过分布在各个节点上的接收器，缓存接收到的数据，并将数据包装成Spark能够处理的RDD的格式，输入到Spark Streaming，之后由Spark Streaming将作业提交到Spark集群进行执行，如下图：

初始化的过程主要可以概括为两点。即：

1. 调度器的初始化。

调度器调度Spark Streaming的运行，用户可以通过配置相关参数进行调优。

2. 将输入流的接收器转化为Spark能够处理的RDD格式，并在集群进行分布式分配，然后启动接收器集合中的每个接收器。

针对不同的数据源，Spark Streaming提供了不同的数据接收器，分布在各个节点上的每个接收器可以认为是一个特定的进程，接收一部分流数据作为输入。

首先，先看看JavaStreamingContext的部分源码，说明能够接收一个Socket文本数据，也可以把文件当做输入流作为数据源，如下：

class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {  def this(master: String, appName: String, batchDuration: Duration) =    this(new StreamingContext(master, appName, batchDuration, null, Nil, Map()))  def socketTextStream(hostname: String, port: Int): JavaReceiverInputDStream[String] = {    ssc.socketTextStream(hostname, port)  }    def textFileStream(directory: String): JavaDStream[String] = {    ssc.textFileStream(directory)  }}

eg：完成以下代码的时候：

val lines = ssc.socketTextStream("master", 9999)

这样可以从socket接收文本数据，而其返回的是JavaReceiverInputDStream，它是ReceiverInputDStream的一个实现，内含Receiver，可接收数据，并转化为Spark能处理的RDD数据。

在来看看JavaReceiverInputDStream中的部分源码：

class JavaReceiverInputDStream[T](val receiverInputDStream: ReceiverInputDStream[T])  (implicit overrideval classTag: ClassTag[T]) extends JavaInputDStream[T](receiverInputDStream) {}object JavaReceiverInputDStream {  implicit def fromReceiverInputDStream[T: ClassTag](      receiverInputDStream: ReceiverInputDStream[T]): JavaReceiverInputDStream[T] = {    new JavaReceiverInputDStream[T](receiverInputDStream)  }}

通过源码了解JavaReceiverInputDStream是ReceiverInputDStream的一个实现，内含Receiver，可接收数据，并转化为RDD 数据，其部分源码如下：（注意英文注释）

abstract class ReceiverInputDStream[T: ClassTag](ssc_ : StreamingContext)  extends InputDStream[T](ssc_) {  /**   * Gets the receiver object that will be sent to the worker nodes   * to receive data. This method needs to defined by any specific implementation   * of a ReceiverInputDStream.   */  def getReceiver(): Receiver[T]  // Nothing to start or stop as both taken care of by the ReceiverTracker.  def start() {}  /**   * Generates RDDs with blocks received by the receiver of this stream. */  override def compute(validTime: Time): Option[RDD[T]] = {    val blockRDD = {      if (validTime < graph.startTime) {        new BlockRDD[T](ssc.sc, Array.empty)      } else {        // Otherwise, ask the tracker for all the blocks that have been allocated to this stream        // for this batch        val receiverTracker = ssc.scheduler.receiverTracker        val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty)        // Register the input blocks information into InputInfoTracker        val inputInfo = StreamInputInfo(id, blockInfos.flatMap(_.numRecords).sum)        ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)        // Create the BlockRDD        createBlockRDD(validTime, blockInfos)      }    }    Some(blockRDD)  }}

当调用 getReceiver()时候，过程如下：（SocketInputDStream的部分源码）

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)  }}

而其实际上是 new 了一个SocketReceiver对象，并将之前的参数给传递进来，实现如下：

private[streaming]class SocketReceiver[T: ClassTag](    host: String,    port: Int,    bytesToObjects: InputStream => Iterator[T],    storageLevel: StorageLevel  ) extends Receiver[T](storageLevel) with Logging {  def onStart() {    // Start the thread that receives data over a connection    new Thread("Socket Receiver") {      setDaemon(true)      override def run() { receive() }    }.start()  }  /** 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)      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 {      //...    }   }}

对于子类实现这个方法，worker节点调用后能得到Receiver，使得数据接收的工作能分布到worker上。

接收器分布在各个节点上，如下：

二：数据接收与转化

在上述"初始化与接收数据"步骤中，简单介绍了receiver集合转化为RDD，在集群上分布式地接收数据流，那么接下来将简单了解receiver是怎样接收并处理数据流。大致流程如下图：

Receiver提供了一系列store()接口，eg：（更多请查看源码）

abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable {  def store(dataItem: T) {    supervisor.pushSingle(dataItem)  }  /** Store an ArrayBuffer of received data as a data block into Spark's memory. */  def store(dataBuffer: ArrayBuffer[T]) {    supervisor.pushArrayBuffer(dataBuffer, None, None)  }}

对于这些接口，已经是实现好了的，会由worker节点上初始化的ReceiverSupervisor来完成这些存储功能。ReceiverSupervisor还会对Receiver做监控，如监控是否启动了、是否停止了、是否要重启、汇报error等等。部分ReceiverSupervisor如下：

def createBlockGenerator(blockGeneratorListener: BlockGeneratorListener): BlockGenerator

private[streaming] abstract class ReceiverSupervisor(    receiver: Receiver[_],    conf: SparkConf  ) extends Logging {  /** Push a single data item to backend data store. */  def pushSingle(data: Any)  /** Store an ArrayBuffer of received data as a data block into Spark's memory. */  def pushArrayBuffer(      arrayBuffer: ArrayBuffer[_],      optionalMetadata: Option[Any],      optionalBlockId: Option[StreamBlockId]    )  /**   * Create a custom [[BlockGenerator]] that the receiver implementation can directly control   * using their provided [[BlockGeneratorListener]].   *   * Note: Do not explicitly start or stop the `BlockGenerator`, the `ReceiverSupervisorImpl`   * will take care of it.   */  def createBlockGenerator(blockGeneratorListener: BlockGeneratorListener): BlockGenerator  /** Start receiver */  def startReceiver(): Unit = synchronized {    //...  }}

ReceiverSupervisor的存储接口的实现，借助的是BlockManager，数据会以RDD的形式被存放，根据StorageLevel选择不同存放策略。默认是序列化后存内存，放不下的话写磁盘(executor)。被计算出来的RDD中间结果，默认存放策略是序列化后只存内存。

最根本原因是store函数内部的实现是调用了BlockGenerator的addData方法，最终是将数据存储在currentBuffer中，而currentBuffer其实就是一个ArrayBuffer[Any]。

BlockGenerator的 addData方法 源码如下： currentBuffer += data

/**   * Push a single data item into the buffer.   */  def addData(data: Any): Unit = {    if (state == Active) {      waitToPush()      synchronized {        if (state == Active) {          currentBuffer += data        } else {          throw new SparkException(            "Cannot add data as BlockGenerator has not been started or has been stopped")        }      }    } else {      throw new SparkException(        "Cannot add data as BlockGenerator has not been started or has been stopped")    }  }

而如何将缓冲数据转化为数据块呢？

其实在BlockGenerator内部存在两个线程：

（1）、定期地生成新的batch，然后再将之前生成的batch封装成block。这里的定期其实就是spark.streaming.blockInterval参数配置的，默认是200ms。源码如下：

privateval blockIntervalMs = conf.getTimeAsMs("spark.streaming.blockInterval", "200ms")

（2）、将生成的block发送到Block Manager中。

第一个线程：

第一个线程定期地调用updateCurrentBuffer函数将存储在currentBuffer中的数据封装成Block，至于块是如何产生的，即在 BlockGenerator中有一个定时器（RecurringTimer），将当前缓冲区中的数据以用户定义的时间间隔封装为一个数据块Block。源码如下：

 privateval blockIntervalTimer =    new RecurringTimer(clock, blockIntervalMs, updateCurrentBuffer, "BlockGenerator")

然后将一批记录转化为的一个数据块放在blocksForPushing中，blocksForPushing是ArrayBlockingQueue[Block]类型的队列。源码如下：

  privateval blockQueueSize = conf.getInt("spark.streaming.blockQueueSize", 10)  privateval blocksForPushing = new ArrayBlockingQueue[Block](blockQueueSize)

其大小默认是10，我们可以通过spark.streaming.blockQueueSize参数配置（当然，在很多情况下这个值不需要我们去配置）。当blocksForPushing没有多余的空间，那么该线程就会阻塞，直到有剩余的空间可用于存储新生成的Block。如果你的数据量真的很大，大到blocksForPushing无法及时存储那些block，这时候你就得考虑加大spark.streaming.blockQueueSize的大小了。

updateCurrentBuffer函数实现如下源码:

 /** Change the buffer to which single records are added to. */  private def updateCurrentBuffer(time: Long): Unit = {    try {      var newBlock: Block = null      synchronized {        if (currentBuffer.nonEmpty) {          val newBlockBuffer = currentBuffer          currentBuffer = new ArrayBuffer[Any]          val blockId = StreamBlockId(receiverId, time - blockIntervalMs)          listener.onGenerateBlock(blockId)          newBlock = new Block(blockId, newBlockBuffer)        }      }      if (newBlock != null) {        blocksForPushing.put(newBlock)  // put is blocking when queue is full      }    } catch {      case ie: InterruptedException =>        logInfo("Block updating timer thread was interrupted")      case e: Exception =>        reportError("Error in block updating thread", e)    }  }

第二个线程：

第二个线程不断地调用keepPushingBlocks函数从blocksForPushing阻塞队列中获取生成的Block，然后调用pushBlock方法将Block存储到BlockManager中。

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

keepPushingBlocks实现源码如下：

 /** Keep pushing blocks to the BlockManager. */  private def keepPushingBlocks() {    logInfo("Started block pushing thread")    def areBlocksBeingGenerated: Boolean = synchronized {      state != StoppedGeneratingBlocks    }    try {      // While blocks are being generated, keep polling for to-be-pushed blocks and push them.      while (areBlocksBeingGenerated) {        Option(blocksForPushing.poll(10, TimeUnit.MILLISECONDS)) match {          case Some(block) => pushBlock(block)          case None =>        }      }      // At this point, state is StoppedGeneratingBlock. So drain the queue of to-be-pushed blocks.      logInfo("Pushing out the last " + blocksForPushing.size() + " blocks")      while (!blocksForPushing.isEmpty) {        val block = blocksForPushing.take()        logDebug(s"Pushing block $block")        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)    }  }

pushBlock实现源码如下：

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

当存储到BlockManager中后，会返回一个BlcokStoreResult结果，这就是成功存储到BlockManager的StreamBlcokId。

然后下一步就是将BlcokStoreResult封装成ReceivedBlockInfo，这也就是最新的未处理过的数据，然后通过Akka告诉ReceiverTracker有新的块加入，ReceiverTracker 会调用addBlock方法将ReceivedBlockInfo存储到streamIdToUnallocatedBlockQueues队列中。

ReceiverTracker会将存储的blockId放到对应StreamId的队列中。（HashMap）

 privateval receiverTrackingInfos = new HashMap[Int, ReceiverTrackingInfo]

receiverTrackingInfos.put(streamId, receiverTrackingInfo)

ReceiverTracker中addBlock方法源码的实现如下：

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

ReceivedBlockInfo源码实现如下：

def blockId: StreamBlockId = blockStoreResult.blockId

/** Information about blocks received by the receiver */private[streaming] case class ReceivedBlockInfo(    streamId: Int,    numRecords: Option[Long],    metadataOption: Option[Any],    blockStoreResult: ReceivedBlockStoreResult  ) {  require(numRecords.isEmpty || numRecords.get >= 0, "numRecords must not be negative")  @volatile private var _isBlockIdValid = true  def blockId: StreamBlockId = blockStoreResult.blockId}

最后看看 ReceivedBlockStoreResult 的部分源码：

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]}

三：生成RDD与提交Spark Job

Spark Streaming 根据时间段，将数据切分为 RDD，然后触发 RDD 的 Action 提
交 Job， Job 被 提 交 到 Job Manager 中 的 Job Queue 中 由 JobScheduler 调 度， 之 后
Job Scheduler 将 Job 提交到 Spark 的 Job 调度器，然后将 Job 转换为大量的任务分
发给 Spark 集群执行，

然后接下来了解下JobScheduler的源码：

JobScheduler是整个Spark Streaming调度的核心组件。

部分源码：

 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)    }  }

进入Graph生成job的方法，Graph本质是DStreamGraph类生成的对象,部分源码如下：

final private[streaming] class DStreamGraph extends Serializable with Logging {  privateval inputStreams = new ArrayBuffer[InputDStream[_]]()  privateval outputStreams = new ArrayBuffer[DStream[_]]()  def generateJobs(time: Time): Seq[Job] = {    logDebug("Generating jobs for time " + time)    val jobs = this.synchronized {      outputStreams.flatMap { outputStream =>        val jobOption = outputStream.generateJob(time)        jobOption.foreach(_.setCallSite(outputStream.creationSite))        jobOption      }    }    logDebug("Generated " + jobs.length + " jobs for time " + time)    jobs  }}

outputStreaming中的对象是DStream，看下DStream的generateJob：

此处相当于针对每个时间段生成一个RDD，会调用SparkContext的方法runJob提交Spark的一个Job。

private[streaming] def generateJob(time: Time): Option[Job] = {    getOrCompute(time) match {      case Some(rdd) => {        val jobFunc = () => {          val emptyFunc = { (iterator: Iterator[T]) => {} }          context.sparkContext.runJob(rdd, emptyFunc)        }        Some(new Job(time, jobFunc))      }      case None => None    }  }

以上就是怎么进行Spark Streaming 原理剖析，小编相信有部分知识点可能是我们日常工作会见到或用到的。希望你能通过这篇文章学到更多知识。更多详情敬请关注行业资讯频道。