spark 版本定制 第5课：基于案例一节课贯通Spark Streaming流计算框架运行源码11

上文已经从源码分析了Receiver接收的数据交由BlockManager管理，整个数据接收流都已经运转起来了，那么让我们回到分析JobScheduler的博客中。

// JobScheduler.scala line 62
  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 {
      inputDStream <- ssc.graph.getInputStreams
      rateController <- inputDStream.rateController
    } ssc.addStreamingListener(rateController)

    listenerBus.start(ssc.sparkContext)
    receiverTracker = new ReceiverTracker(ssc)
    inputInfoTracker = new InputInfoTracker(ssc)
    receiverTracker.start()
    jobGenerator.start()
    logInfo("Started JobScheduler")
  }

前面好几篇博客都是 由 receiverTracker.start() 延展开。延展完毕后，继续下一步。

// JobScheduler.scala line 83
jobGenerator.start()

jobGenerator的实例化过程，前面已经分析过。深入下源码了解到。

1. 实例化eventLoop，此处的eventLoop与JobScheduler中的eventLoop不一样，对应的是不同的泛型。
2. EventLoop.start
3. 首次启动，startFirstTime

  // JobGenerator.scala line 78
  /** 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 = 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()
    }
  }

// JobGenerator.scala line 189
  /** Starts the generator for the first time */
  private def startFirstTime() {
    val startTime = new Time(timer.getStartTime())
    graph.start(startTime - graph.batchDuration)
    timer.start(startTime.milliseconds)
    logInfo("Started JobGenerator at " + startTime)
  }

将DStreamGraph.start

1. 将所有的outputStreams都initialize，初始化首次执行时间，依赖的DStream一并设置。
2. 如果设置了duration，将所有的outputStreams都remember，依赖的DStream一并设置
3. 启动前验证，主要是验证chechpoint设置是否冲突以及各种Duration
4. 将所有的inputStreams启动；读者扫描了下目前版本1.6.0InputDStraem及其所有的子类。start方法啥都没做。结合之前的博客，inputStreams都已经交由ReceiverTracker管理了。

// DStreamGraph.scala line 39
  def start(time: Time) {
    this.synchronized {
      require(zeroTime == null, "DStream graph computation already started")
      zeroTime = time
      startTime = time
      outputStreams.foreach(_.initialize(zeroTime))
      outputStreams.foreach(_.remember(rememberDuration))
      outputStreams.foreach(_.validateAtStart)
      inputStreams.par.foreach(_.start())
    }
  }

至此，只是做了一些简单的初始化，并没有让数据处理起来。

再回到JobGenerator。此时，将循环定时器启动，

// JobGenerator.scala line 193
    timer.start(startTime.milliseconds)

循环定时器启动；读者是不是很熟悉，是不是在哪见过这个循环定时器？

没错，就是BlockGenerator.scala line 105 、109 ，两个线程，其中一个是循环定时器，定时将数据放入待push队列中。

// RecurringTimer.scala line 59
  def start(startTime: Long): Long = synchronized {
    nextTime = startTime
    thread.start()
    logInfo("Started timer for " + name + " at time " + nextTime)
    nextTime
  }

具体的逻辑是在构造是传入的方法：longTime => eventLoop.post(GenerateJobs(new Time(longTime)))；

输入是Long，

方法体是eventLoop.post(GenerateJobs(new Time(longTime)))

// JobGenerator.scala line 58
  private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
    longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator")

只要线程状态不是stopped，一直循环。

1. 初始化的时候将上面的方法传进来，  callback: (Long) => Unit 对应的就是  longTime => eventLoop.post(GenerateJobs(new Time(longTime)))
2. start的时候 thread.run启动，里面的loop方法被执行。
3. loop中调用的是 triggerActionForNextInterval。
4. triggerActionForNextInterval调用构造传入的callback，也就是上面的 longTime => eventLoop.post(GenerateJobs(new Time(longTime))) 

private[streaming]
class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String)
  extends Logging {
// RecurringTimer.scala line 27
  private val thread = new Thread("RecurringTimer - " + name) {
    setDaemon(true)
    override def run() { loop }
  }
// RecurringTimer.scala line 56
  /**
   * Start at the given start time.
   */
  def start(startTime: Long): Long = synchronized {
    nextTime = startTime
    thread.start()
    logInfo("Started timer for " + name + " at time " + nextTime)
    nextTime
  }
// RecurringTimer.scala line 92
  private def triggerActionForNextInterval(): Unit = {
    clock.waitTillTime(nextTime)
    callback(nextTime)
    prevTime = nextTime
    nextTime += period
    logDebug("Callback for " + name + " called at time " + prevTime)
  }

// RecurringTimer.scala line 100
  /**
   * Repeatedly call the callback every interval.
   */
  private def loop() {
    try {
      while (!stopped) {
        triggerActionForNextInterval()
      }
      triggerActionForNextInterval()
    } catch {
      case e: InterruptedException =>
    }
  }
// ...一些代码
}

定时发送GenerateJobs 类型的事件消息，eventLoop.post中将事件消息加入到eventQueue中

// EventLoop.scala line 102
  def post(event: E): Unit = {
    eventQueue.put(event)
  }

同时，此EventLoop中的另一个成员变量 eventThread。会一直从队列中取事件消息，将此事件作为参数调用onReceive。而此onReceive在实例化时被override了。

// JobGenerator.scala line 86
    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()

onReceive调用的是

// JobGenerator.scala line 177
  /** Processes all events */
  private def processEvent(event: JobGeneratorEvent) {
    logDebug("Got event " + event)
    event match {
      case GenerateJobs(time) => generateJobs(time)
      // 其他case class
    }
  }

GenerateJobs case class 是匹配到 generateJobs(time:Time) 来处理

1. 获取当前时间批次ReceiverTracker收集到的所有的Blocks，若开启WAL会执行WAL
2. DStreamGraph生产任务
3. 提交任务
4. 若设置checkpoint，则checkpoint

// JobGenerator.scala line 240
  /** 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
      graph.generateJobs(time) // generate jobs using allocated block
    } match {
      case Success(jobs) =>
        val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)
        jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
      case Failure(e) =>
        jobScheduler.reportError("Error generating jobs for time " + time, e)
    }
    eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
  }

上述代码不是特别容易理解。细细拆分：咋一看以为是try{} catch{case ... }，仔细一看，是Try{}match{}

追踪下代码，原来Try是大写的，是一个伴生对象，apply接收的参数是一个方法，返回Try的实例。在scala.util.Try.scala 代码如下：

// scala.util.Try.scala line 155
object Try {
  /** Constructs a `Try` using the by-name parameter.  This
   * method will ensure any non-fatal exception is caught and a
   * `Failure` object is returned.
   */
  def apply[T](r: => T): Try[T] =
    try Success(r) catch {
      case NonFatal(e) => Failure(e)
    }

}

Try有两个子类，都是case class 。分别是Success和Failure。如图。

再返回调用处，Try中的代码块最后执行的是 graph.generateJobs(time) 。跟踪下：

返回的是outputStream.generateJob(time)。

// DStreamGraph.scala line 111
  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
  }

从前文可知，outputStream其实都是ForEachDStream。进入ForEachDStream，override了generateJob。

1. parent.getOrCompute(time) 返回一个Option[Job]。
2. 若有rdd，则返回可能是new Job(time,jobFunc)

// ForEachDStream.scala line 46
  override def generateJob(time: Time): Option[Job] = {
    parent.getOrCompute(time) match {
      case Some(rdd) =>
        val jobFunc = () => createRDDWithLocalProperties(time, displayInnerRDDOps) {
          foreachFunc(rdd, time)
        }
        Some(new Job(time, jobFunc))
      case None => None
    }
  }

那么ForEachDStream的parent是什么呢？看下我们的案例：

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Durations, StreamingContext}
/**
  * 感谢王家林老师的知识分享
  * 王家林老师名片：
  * 中国Spark第一人
  * 新浪微博：http://weibo.com/ilovepains
  * 微信公众号：DT_Spark
  * 博客：http://blog.sina.com.cn/ilovepains
  * 手机：18610086859
  * QQ：1740415547
  * 邮箱：[email protected]
  * YY课堂：每天20:00免费现场授课频道68917580
  * 王家林：DT大数据梦工厂创始人、Spark亚太研究院院长和首席专家、大数据培训专家、大数据架构师。
  */
object StreamingWordCountSelfScala {
  def main(args: Array[String]) {
    val sparkConf = new SparkConf().setMaster("spark://master:7077").setAppName("StreamingWordCountSelfScala")
    val ssc = new StreamingContext(sparkConf, Durations.seconds(5)) // 每5秒收割一次数据
    val lines = ssc.socketTextStream("localhost", 9999) // 监听 本地9999 socket 端口
    val words = lines.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _) // flat map 后 reduce
    words.print() // 打印结果
    ssc.start() // 启动
    ssc.awaitTermination()
    ssc.stop(true)
  }
}

按照前文的描述：本例中 DStream的依赖是 SocketInputDStream << FlatMappedDStream << MappedDStream << ShuffledDStream << ForEachDStream

笔者扫描了下DStream及其所有子类，发现只有DStream有 getOrCompute，没有一个子类override了此方法。如此一来，是ShuffledDStream.getorCompute

在一般情况下，是RDD不存在，执行orElse代码快，

// DStream.scala line 338
  /**
   * Get the RDD corresponding to the given time; either retrieve it from cache
   * or compute-and-cache it.
   */
  private[streaming] final def getOrCompute(time: Time): Option[RDD[T]] = {
    // If RDD was already generated, then retrieve it from HashMap,
    // or else compute the RDD
    generatedRDDs.get(time).orElse {
      // Compute the RDD if time is valid (e.g. correct time in a sliding window)
      // of RDD generation, else generate nothing.
      if (isTimeValid(time)) {

        val rddOption = createRDDWithLocalProperties(time, displayInnerRDDOps = false) {
          // 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. We need to have this call here because
          // compute() might cause Spark jobs to be launched.
          PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
            compute(time)  // line 352
          }
        }

        rddOption.foreach { case newRDD =>
          // Register the generated RDD for caching and checkpointing
          if (storageLevel != StorageLevel.NONE) {
            newRDD.persist(storageLevel)
            logDebug(s"Persisting RDD ${newRDD.id} for time $time to $storageLevel")
          }
          if (checkpointDuration != null && (time - zeroTime).isMultipleOf(checkpointDuration)) {
            newRDD.checkpoint()
            logInfo(s"Marking RDD ${newRDD.id} for time $time for checkpointing")
          }
          generatedRDDs.put(time, newRDD)
        }
        rddOption
      } else {
        None
      }
    }
  }

ShuffledDStream.compute 

又调用parent.getOrCompute

// ShuffledDStream.scala line 40
  override def compute(validTime: Time): Option[RDD[(K, C)]] = {
    parent.getOrCompute(validTime) match {
      case Some(rdd) => Some(rdd.combineByKey[C](
          createCombiner, mergeValue, mergeCombiner, partitioner, mapSideCombine))
      case None => None
    }
  }

MappedDStream的compute，又是父类的getOrCompute，结果又调用compute，如此循环。

// MappedDStream.scala line 34
  override def compute(validTime: Time): Option[RDD[U]] = {
    parent.getOrCompute(validTime).map(_.map[U](mapFunc))
  }

FlatMappedDStream的compute，又是父类的getOrCompute。结果又调用compute，如此循环。

// FlatMappedDStream.scala line 34
  override def compute(validTime: Time): Option[RDD[U]] = {
    parent.getOrCompute(validTime).map(_.flatMap(flatMapFunc))
  }

直到DStreamshi SocketInputDStream，也就是inputStream时，compute是继承自父类。

先不考虑if中的逻辑，直接else代码块。

进入createBlockRDD

// ReceiverInputDStream.scala line 69
  override def compute(validTime: Time): Option[RDD[T]] = {
    val blockRDD = {

      if (validTime < graph.startTime) {
        // If this is called for any time before the start time of the context,
        // then this returns an empty RDD. This may happen when recovering from a
        // driver failure without any write ahead log to recover pre-failure data.
        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)
  }

new BlockRDD[T](ssc.sc, validBlockIds) line 127，RDD实例化成功

// ReceiverInputDStream.scala line 94
  private[streaming] def createBlockRDD(time: Time, blockInfos: Seq[ReceivedBlockInfo]): RDD[T] = {

    if (blockInfos.nonEmpty) {
      val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray

      // Are WAL record handles present with all the blocks
      val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }

      if (areWALRecordHandlesPresent) {
        // If all the blocks have WAL record handle, then create a WALBackedBlockRDD
        val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray
        val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray
        new WriteAheadLogBackedBlockRDD[T](
          ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid)
      } else {
        // Else, create a BlockRDD. However, if there are some blocks with WAL info but not
        // others then that is unexpected and log a warning accordingly.
        if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) {
          if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
            logError("Some blocks do not have Write Ahead Log information; " +
              "this is unexpected and data may not be recoverable after driver failures")
          } else {
            logWarning("Some blocks have Write Ahead Log information; this is unexpected")
          }
        }
        val validBlockIds = blockIds.filter { id =>
          ssc.sparkContext.env.blockManager.master.contains(id)
        }
        if (validBlockIds.size != blockIds.size) {
          logWarning("Some blocks could not be recovered as they were not found in memory. " +
            "To prevent such data loss, enabled Write Ahead Log (see programming guide " +
            "for more details.")
        }
        new BlockRDD[T](ssc.sc, validBlockIds) // line 127
      }
    } else {
      // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD
      // according to the configuration
      if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
        new WriteAheadLogBackedBlockRDD[T](
          ssc.sparkContext, Array.empty, Array.empty, Array.empty)
      } else {
        new BlockRDD[T](ssc.sc, Array.empty)
      }
    }
  }

此BlockRDD是Spark Core的RDD的子类，且没有依赖的RDD。至此，RDD的实例化已经完成。

// BlockRDD.scala line 30
private[spark]
class BlockRDD[T: ClassTag](sc: SparkContext, @transient val blockIds: Array[BlockId])
  extends RDD[T](sc, Nil) 

// RDd.scala line 74
abstract class RDD[T: ClassTag](
    @transient private var _sc: SparkContext,
    @transient private var deps: Seq[Dependency[_]]
  ) extends Serializable with Logging

至此，最终还原回来的RDD：

new BlockRDD[T](ssc.sc, validBlockIds).map(_.flatMap(flatMapFunc)).map(_.map[U](mapFunc)).combineByKey[C](createCombiner, mergeValue, mergeCombiner, partitioner, mapSideCombine)。

在本例中则为

new BlockRDD[T](ssc.sc, validBlockIds).map(_.flatMap(t=>t.split(" "))).map(_.map[U](t=>(t,1))).combineByKey[C](t=>t, (t1,t2)=>t1+t2, (t1,t2)=>t1+t2,partitioner, true)

而最终的print为

() => foreachFunc(new BlockRDD[T](ssc.sc, validBlockIds).map(_.flatMap(t=>t.split(" "))).map(_.map[U](t=>(t,1))).combineByKey[C](t=>t, (t1,t2)=>t1+t2, (t1,t2)=>t1+t2,partitioner, true),time)

其中foreachFunc为 DStrean.scala line 766

至此，RDD已经通过DStream实例化完成，现在再回顾下，是否可以理解DStream是RDD的模版。

不过别急，回到ForEachDStream.scala line 46 ，将上述函数作为构造参数，传入Job。

 

下节内容从源码分析Job提交，敬请期待。

 

感谢王家林老师的知识分享

 

王家林老师名片：

中国Spark第一人

新浪微博：http://weibo.com/ilovepains

微信公众号：DT_Spark

博客：http://blog.sina.com.cn/ilovepains

手机：18610086859

QQ：1740415547

邮箱：[email protected]

YY课堂：每天20:00免费现场授课频道68917580

王家林：DT大数据梦工厂创始人、Spark亚太研究院院长和首席专家、大数据培训专家、大数据架构师。