SparkStreaming 动态生成 Job 并提交执行

前言

Spark Streaming Job的生成是通过JobGenerator每隔 batchDuration 长时间动态生成的，每个batch 对应提交一个JobSet，因为针对一个batch可能有多个输出操作。

概述流程：

定时器定时向 eventLoop 发送生成job的请求
通过receiverTracker 为当前batch分配block
为当前batch生成对应的 Jobs
将Jobs封装成JobSet 提交执行

入口

在 JobGenerator 初始化的时候就创建了一个定时器：

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

每隔 batchDuration 就会向 eventLoop 发送 GenerateJobs(new Time(longTime))消息，eventLoop的事件处理方法中会调用generateJobs(time)方法：

      case GenerateJobs(time) => generateJobs(time)

private def generateJobs(time: Time) {
    // Checkpoint all RDDs marked for checkpointing to ensure their lineages are
    // truncated periodically. Otherwise, we may run into stack overflows (SPARK-6847).
    ssc.sparkContext.setLocalProperty(RDD.CHECKPOINT_ALL_MARKED_ANCESTORS, "true")
    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)
        PythonDStream.stopStreamingContextIfPythonProcessIsDead(e)
    }
    eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
  }

为当前batchTime分配Block

首先调用receiverTracker.allocateBlocksToBatch(time)方法为当前batchTime分配对应的Block，最终会调用receiverTracker的Block管理者receivedBlockTracker的allocateBlocksToBatch方法：

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))) {
        timeToAllocatedBlocks.put(batchTime, allocatedBlocks)
        lastAllocatedBatchTime = batchTime
      } else {
        logInfo(s"Possibly processed batch $batchTime needs to be processed again in WAL recovery")
      }
    } else {
      logInfo(s"Possibly processed batch $batchTime needs to be processed again in WAL recovery")
    }
  }

private def getReceivedBlockQueue(streamId: Int): ReceivedBlockQueue = {
    streamIdToUnallocatedBlockQueues.getOrElseUpdate(streamId, new ReceivedBlockQueue)
  }

可以看到是从streamIdToUnallocatedBlockQueues中获取到所有streamId对应的未分配的blocks，该队列的信息是supervisor 存储好Block后向receiverTracker上报的Block信息，详情可见 ReceiverTracker 数据产生与存储。

获取到所有streamId对应的未分配的blockInfos后，将其放入了timeToAllocatedBlocks:Map[Time, AllocatedBlocks]中，后面生成RDD的时候会用到。

为当前batchTime生成Jobs

调用DStreamGraph的generateJobs方法为当前batchTime生成job：

 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
  }

一个outputStream就对应一个job，遍历所有的outputStreams，为其生成job：

# ForEachDStream
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
    }
  }

先获取到time对应的RDD，然后将其作为参数再调用foreachFunc方法，foreachFunc方法是通过构造器传过来的，我们来看看print()输出的情况：

def print(num: Int): Unit = ssc.withScope {
    def foreachFunc: (RDD[T], Time) => Unit = {
      (rdd: RDD[T], time: Time) => {
        val firstNum = rdd.take(num + 1)
        // scalastyle:off println
        println("-------------------------------------------")
        println(s"Time: $time")
        println("-------------------------------------------")
        firstNum.take(num).foreach(println)
        if (firstNum.length > num) println("...")
        println()
        // scalastyle:on println
      }
    }
    foreachRDD(context.sparkContext.clean(foreachFunc), displayInnerRDDOps = false)
  }

这里的构造的foreachFunc方法就是最终和rdd一起提交job的执行方法，也即对rdd调用take()后并打印，真正触发action操作的是在这个func函数里，现在再来看看是怎么拿到rdd的，每个DStream都有一个generatedRDDs:Map[Time, RDD[T]]变量，来保存time对应的RDD，若获取不到则会通过compute()方法来计算，对于需要在executor上启动Receiver来接收数据的ReceiverInputDStream来说：

 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)
  }

会通过receiverTracker来获取该batch对应的blocks，前面已经分析过为所有streamId分配了对应的未分配的block，并且放在了timeToAllocatedBlocks:Map[Time, AllocatedBlocks]中，这里底层就是从这个timeToAllocatedBlocks获取到的blocksInfo，然后调用了createBlockRDD(validTime, blockInfos)通过blockId创建了RDD。

最后，将通过此RDD和foreachFun构建jobFunc，并创建Job返回。

封装jobs成JobSet并提交执行

每个outputStream对应一个Job，最终就会生成一个jobs，为这个jobs创建JobSet，并通过jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))来提交这个JobSet：

jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job)))

然后通过jobExecutor来执行，jobExecutor是一个线程池，并行度默认为1，可通过spark.streaming.concurrentJobs配置，即同时可执行几个批次的数据。

处理类JobHandler中调用的是Job.run()，执行的是前面构建的 jobFunc 方法。

teeyog / blog