Spark 源码分析之ShuffleMapTask处理

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2018/12/07 20:25
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Spark 源码分析之ShuffleMapTask处理

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<iframe width="800" height="500" src="//player.bilibili.com/player.html?aid=37442139&cid=66008946&page=22" scrolling="no" border="0" frameborder="no" framespacing="0" allowfullscreen="true"> </iframe>

<iframe width="800" height="500" src="//player.bilibili.com/player.html?aid=37442139&cid=66008946&page=23" scrolling="no" border="0" frameborder="no" framespacing="0" allowfullscreen="true"> </iframe>

图解

输入数据

a b k l j
c a n m o

排序后的数据

((0,b),1)
((0,j),1)
((0,l),1)
((0,n),1)

---------
((1,a),2)
((1,c),1)
((1,k),1)
((1,m),1)
((1,o),1)



输出数据

(b,1)
(j,1)
(l,1)
(n,1)

---------
(a,2)
(c,1)
(k,1)
(m,1)
(o,1)



粗粒度执行器处理LaunchTask消息

  • CoarseGrainedExecutorBackend的receive()方法收到任务调度器发送过来的启动任务的消息,并进行消息处理: LaunchTask()
  • 该方法中调用 Executor.launchTask()方法
    case LaunchTask(data) =>
      if (executor == null) {
        exitExecutor(1, "Received LaunchTask command but executor was null")
      } else {
        val taskDesc = ser.deserialize[TaskDescription](data.value)
        logInfo("Got assigned task " + taskDesc.taskId)
        executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber,
          taskDesc.name, taskDesc.serializedTask)
      }
  • Executor.launchTask()方法
  • 用线程池来启动Task,这样保证任务可以排队等候
  • 当线程池中的任务被执行时调用 TaskRunner.run()方法

  // Maintains the list of running tasks.
  private val runningTasks = new ConcurrentHashMap[Long, TaskRunner
   // Start worker thread pool
  private val threadPool = ThreadUtils.newDaemonCachedThreadPool("Executor task launch worker")
  
 def launchTask(
      context: ExecutorBackend,
      taskId: Long,
      attemptNumber: Int,
      taskName: String,
      serializedTask: ByteBuffer): Unit = {
    val tr = new TaskRunner(context, taskId = taskId, attemptNumber = attemptNumber, taskName,
      serializedTask)
    runningTasks.put(taskId, tr)
    threadPool.execute(tr)
  }

  • TaskRunner.run() 方法
  • 调用Task的实现类,进行任务处理
  • 实现类(ShuffleMapTask或ResutlTask)处理任务完成后,发送任务状态为TaskState.FINISHED 的消息
override def run(): Unit = {
      val taskMemoryManager = new TaskMemoryManager(env.memoryManager, taskId)
      val deserializeStartTime = System.currentTimeMillis()
      Thread.currentThread.setContextClassLoader(replClassLoader)
      val ser = env.closureSerializer.newInstance()
      logInfo(s"Running $taskName (TID $taskId)")
      execBackend.statusUpdate(taskId, TaskState.RUNNING, EMPTY_BYTE_BUFFER)
      var taskStart: Long = 0
      startGCTime = computeTotalGcTime()

      try {
        val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask)
        updateDependencies(taskFiles, taskJars)
        task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader)
        task.setTaskMemoryManager(taskMemoryManager)

        // If this task has been killed before we deserialized it, let's quit now. Otherwise,
        // continue executing the task.
        if (killed) {
          // Throw an exception rather than returning, because returning within a try{} block
          // causes a NonLocalReturnControl exception to be thrown. The NonLocalReturnControl
          // exception will be caught by the catch block, leading to an incorrect ExceptionFailure
          // for the task.
          throw new TaskKilledException
        }

        logDebug("Task " + taskId + "'s epoch is " + task.epoch)
        env.mapOutputTracker.updateEpoch(task.epoch)

        // Run the actual task and measure its runtime.
        taskStart = System.currentTimeMillis()
        var threwException = true
        val (value, accumUpdates) = try {
          val res = task.run(
            taskAttemptId = taskId,
            attemptNumber = attemptNumber,
            metricsSystem = env.metricsSystem)
          threwException = false
          res
        } finally {
          val releasedLocks = env.blockManager.releaseAllLocksForTask(taskId)
          val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory()
          if (freedMemory > 0) {
            val errMsg = s"Managed memory leak detected; size = $freedMemory bytes, TID = $taskId"
            if (conf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false) && !threwException) {
              throw new SparkException(errMsg)
            } else {
              logError(errMsg)
            }
          }

          if (releasedLocks.nonEmpty) {
            val errMsg =
              s"${releasedLocks.size} block locks were not released by TID = $taskId:\n" +
              releasedLocks.mkString("[", ", ", "]")
            if (conf.getBoolean("spark.storage.exceptionOnPinLeak", false) && !threwException) {
              throw new SparkException(errMsg)
            } else {
              logError(errMsg)
            }
          }
        }
        val taskFinish = System.currentTimeMillis()

        // If the task has been killed, let's fail it.
        if (task.killed) {
          throw new TaskKilledException
        }

        val resultSer = env.serializer.newInstance()
        val beforeSerialization = System.currentTimeMillis()
        val valueBytes = resultSer.serialize(value)
        val afterSerialization = System.currentTimeMillis()

        for (m <- task.metrics) {
          // Deserialization happens in two parts: first, we deserialize a Task object, which
          // includes the Partition. Second, Task.run() deserializes the RDD and function to be run.
          m.setExecutorDeserializeTime(
            (taskStart - deserializeStartTime) + task.executorDeserializeTime)
          // We need to subtract Task.run()'s deserialization time to avoid double-counting
          m.setExecutorRunTime((taskFinish - taskStart) - task.executorDeserializeTime)
          m.setJvmGCTime(computeTotalGcTime() - startGCTime)
          m.setResultSerializationTime(afterSerialization - beforeSerialization)
          m.updateAccumulators()
        }

        val directResult = new DirectTaskResult(valueBytes, accumUpdates, task.metrics.orNull)
        val serializedDirectResult = ser.serialize(directResult)
        val resultSize = serializedDirectResult.limit

        // directSend = sending directly back to the driver
        val serializedResult: ByteBuffer = {
          if (maxResultSize > 0 && resultSize > maxResultSize) {
            logWarning(s"Finished $taskName (TID $taskId). Result is larger than maxResultSize " +
              s"(${Utils.bytesToString(resultSize)} > ${Utils.bytesToString(maxResultSize)}), " +
              s"dropping it.")
            ser.serialize(new IndirectTaskResult[Any](TaskResultBlockId(taskId), resultSize))
          } else if (resultSize >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
            val blockId = TaskResultBlockId(taskId)
            env.blockManager.putBytes(
              blockId, serializedDirectResult, StorageLevel.MEMORY_AND_DISK_SER)
            logInfo(
              s"Finished $taskName (TID $taskId). $resultSize bytes result sent via BlockManager)")
            ser.serialize(new IndirectTaskResult[Any](blockId, resultSize))
          } else {
            logInfo(s"Finished $taskName (TID $taskId). $resultSize bytes result sent to driver")
            serializedDirectResult
          }
        }

        execBackend.statusUpdate(taskId, TaskState.FINISHED, serializedResult)

      } catch {
        case ffe: FetchFailedException =>
          val reason = ffe.toTaskFailedReason
          execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))

        case _: TaskKilledException | _: InterruptedException if task.killed =>
          logInfo(s"Executor killed $taskName (TID $taskId)")
          execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled))

        case CausedBy(cDE: CommitDeniedException) =>
          val reason = cDE.toTaskFailedReason
          execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason))

        case t: Throwable =>
          // Attempt to exit cleanly by informing the driver of our failure.
          // If anything goes wrong (or this was a fatal exception), we will delegate to
          // the default uncaught exception handler, which will terminate the Executor.
          logError(s"Exception in $taskName (TID $taskId)", t)

          // SPARK-20904: Do not report failure to driver if if happened during shut down. Because
          // libraries may set up shutdown hooks that race with running tasks during shutdown,
          // spurious failures may occur and can result in improper accounting in the driver (e.g.
          // the task failure would not be ignored if the shutdown happened because of premption,
          // instead of an app issue).
          if (!ShutdownHookManager.inShutdown()) {
            val metrics: Option[TaskMetrics] = Option(task).flatMap { task =>
              task.metrics.map { m =>
                m.setExecutorRunTime(System.currentTimeMillis() - taskStart)
                m.setJvmGCTime(computeTotalGcTime() - startGCTime)
                m.updateAccumulators()
                m
              }
            }
            val serializedTaskEndReason = {
              try {
                ser.serialize(new ExceptionFailure(t, metrics))
              } catch {
                case _: NotSerializableException =>
                  // t is not serializable so just send the stacktrace
                  ser.serialize(new ExceptionFailure(t, metrics, false))
              }
            }
            execBackend.statusUpdate(taskId, TaskState.FAILED, serializedTaskEndReason)
          } else {
            logInfo("Not reporting error to driver during JVM shutdown.")
          }

          // Don't forcibly exit unless the exception was inherently fatal, to avoid
          // stopping other tasks unnecessarily.
          if (Utils.isFatalError(t)) {
            SparkUncaughtExceptionHandler.uncaughtException(t)
          }

      } finally {
        runningTasks.remove(taskId)
      }
    }
  }
  • 先调用抽象类Task.run()方法,访方法中调用实现类的 runTask()方法
  • 调用Task的实现类runTask()方法进行任务处理
 val (value, accumUpdates) = try {
          val res = task.run(
            taskAttemptId = taskId,
            attemptNumber = attemptNumber,
            metricsSystem = env.metricsSystem)
          threwException = false
          res
        } 

ShuflleMapTask的处理进程

  • ShuffleMapTask.runTask()方法
  • 首先拿到参数
  • 参数(rdd,dep) DAGScheduler对stage(ShhuffleMapStage)中引用的rdd和shuffleDep 进行了变量广播,所以这时可以直接取到,进行反序列化就可以用
  • SuffileManager没有配参数,所以取SparkEnv中配置的默认org.apache.spark.shuffle.sort.SortShuffleManager
override def runTask(context: TaskContext): MapStatus = {
    // Deserialize the RDD using the broadcast variable.
    val deserializeStartTime = System.currentTimeMillis()
    val ser = SparkEnv.get.closureSerializer.newInstance()
    val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
      ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
    _executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime

    metrics = Some(context.taskMetrics)
    var writer: ShuffleWriter[Any, Any] = null
    try {
      val manager = SparkEnv.get.shuffleManager
      writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
      writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
      writer.stop(success = true).get
    } catch {
      case e: Exception =>
        try {
          if (writer != null) {
            writer.stop(success = false)
          }
        } catch {
          case e: Exception =>
            log.debug("Could not stop writer", e)
        }
        throw e
    }
  }

  • DAGScheduller.scal 对stage中的数据进行序列化,保存到参数taskBinary中
 // TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times.
    // Broadcasted binary for the task, used to dispatch tasks to executors. Note that we broadcast
    // the serialized copy of the RDD and for each task we will deserialize it, which means each
    // task gets a different copy of the RDD. This provides stronger isolation between tasks that
    // might modify state of objects referenced in their closures. This is necessary in Hadoop
    // where the JobConf/Configuration object is not thread-safe.
    var taskBinary: Broadcast[Array[Byte]] = null
    try {
      // For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
      // For ResultTask, serialize and broadcast (rdd, func).
      val taskBinaryBytes: Array[Byte] = stage match {
        case stage: ShuffleMapStage =>
          closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef).array()
        case stage: ResultStage =>
          closureSerializer.serialize((stage.rdd, stage.func): AnyRef).array()
      }

      taskBinary = sc.broadcast(taskBinaryBytes)
  • taskBinary 序列化stage信息作为参数传输,由于是Broadcast 类型,所以在所有worker上会进行广播,这样就可以在执行task时,直接取
  val tasks: Seq[Task[_]] = try {
      stage match {
        case stage: ShuffleMapStage =>
          stage.pendingPartitions.clear()
          partitionsToCompute.map { id =>
            val locs = taskIdToLocations(id)
            val part = stage.rdd.partitions(id)
            stage.pendingPartitions += id
            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId,
              taskBinary, part, locs, stage.internalAccumulators)
          }

        case stage: ResultStage =>
          val job = stage.activeJob.get
          partitionsToCompute.map { id =>
            val p: Int = stage.partitions(id)
            val part = stage.rdd.partitions(p)
            val locs = taskIdToLocations(id)
            new ResultTask(stage.id, stage.latestInfo.attemptId,
              taskBinary, part, locs, id, stage.internalAccumulators)
          }
      }
  • SuffileManager没有配参数,所以取SparkEnv中配置的默认org.apache.spark.shuffle.sort.SortShuffleManager
// Let the user specify short names for shuffle managers
   val shortShuffleMgrNames = Map(
     "hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager",
     "sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager",
     "tungsten-sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager")
   val shuffleMgrName = conf.get("spark.shuffle.manager", "sort")
   val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
   val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)
  • RDD中的某个partition的迭代器作为参数,进行写入操作(最终的输出文件是ShuffleMapTask的输出)
 writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
  • SortShuffleManager.write()方法
  • 首先判断依赖是否在map进行合并(mapSideCombine),reduceByKey算子写死为true
  • 会实例化对象来存放数据(所以此时输出的数据是有序的)org.apache.spark.util.collection
  • 实例ExternalSorter来进行排序
  • 并把当前分区Iterator中的数据插入 ExternalSorter
  • 写入输出文件val partitionLengths = sorter.writePartitionedFile(blockId, tmp)
 /** Write a bunch of records to this task's output */
  override def write(records: Iterator[Product2[K, V]]): Unit = {
    sorter = if (dep.mapSideCombine) {
      require(dep.aggregator.isDefined, "Map-side combine without Aggregator specified!")
      new ExternalSorter[K, V, C](
        context, dep.aggregator, Some(dep.partitioner), dep.keyOrdering, dep.serializer)
    } else {
      // In this case we pass neither an aggregator nor an ordering to the sorter, because we don't
      // care whether the keys get sorted in each partition; that will be done on the reduce side
      // if the operation being run is sortByKey.
      new ExternalSorter[K, V, V](
        context, aggregator = None, Some(dep.partitioner), ordering = None, dep.serializer)
    }
    sorter.insertAll(records)

    // Don't bother including the time to open the merged output file in the shuffle write time,
    // because it just opens a single file, so is typically too fast to measure accurately
    // (see SPARK-3570).
    val output = shuffleBlockResolver.getDataFile(dep.shuffleId, mapId)
    val tmp = Utils.tempFileWith(output)
    try {
      val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockResolver.NOOP_REDUCE_ID)
      val partitionLengths = sorter.writePartitionedFile(blockId, tmp)
      shuffleBlockResolver.writeIndexFileAndCommit(dep.shuffleId, mapId, partitionLengths, tmp)
      mapStatus = MapStatus(blockManager.shuffleServerId, partitionLengths)
    } finally {
      if (tmp.exists() && !tmp.delete()) {
        logError(s"Error while deleting temp file ${tmp.getAbsolutePath}")
      }
    }
  }
  • ExternalSorter.insertAll
  • 将分区中的数据插入PartitionedAppendOnlyMap对象map中
  • reduceByKey()算子中 shouldCombine = true是写死的
  • map中元素的数据格式为 ( (partition,key) ,value ) = ((分区编号,key),value)
  • 默认在map端进行合并,所以此时对相同的Key,会执行reduceByKey()自定义的函数,也就是对相同的key的数据进行合并操作
  • 如果当前分区的数据量太大,溢出部分数据到文件中
private var map = new PartitionedAppendOnlyMap[K, C]

def insertAll(records: Iterator[Product2[K, V]]): Unit = {
    // TODO: stop combining if we find that the reduction factor isn't high
    val shouldCombine = aggregator.isDefined

    if (shouldCombine) {
      // Combine values in-memory first using our AppendOnlyMap
      val mergeValue = aggregator.get.mergeValue
      val createCombiner = aggregator.get.createCombiner
      var kv: Product2[K, V] = null
      val update = (hadValue: Boolean, oldValue: C) => {
        if (hadValue) mergeValue(oldValue, kv._2) else createCombiner(kv._2)
      }
      while (records.hasNext) {
        addElementsRead()
        kv = records.next()
        map.changeValue((getPartition(kv._1), kv._1), update)
        maybeSpillCollection(usingMap = true)
      }
    } else {
      // Stick values into our buffer
      while (records.hasNext) {
        addElementsRead()
        val kv = records.next()
        buffer.insert(getPartition(kv._1), kv._1, kv._2.asInstanceOf[C])
        maybeSpillCollection(usingMap = false)
      }
    }
  }
  • ExternalSorter.writePartitionedFile()
  • 对 ExternalSorter中的数据进行排序,排序的规则为,(partition,key),先按partition进行升序排序,parition相等的再按key进行升序排序
  • 每个任务单独建一个输出数据文件和索引文件(数据是先按parition升序排序,再按Key升序排序)
  • 索引文件依次保存每个partition索引对应的文件长度
 /**
   * Write all the data added into this ExternalSorter into a file in the disk store. This is
   * called by the SortShuffleWriter.
   *
   * @param blockId block ID to write to. The index file will be blockId.name + ".index".
   * @return array of lengths, in bytes, of each partition of the file (used by map output tracker)
   */
  def writePartitionedFile(
      blockId: BlockId,
      outputFile: File): Array[Long] = {

    // Track location of each range in the output file
    val lengths = new Array[Long](numPartitions)

    if (spills.isEmpty) {
      // Case where we only have in-memory data
      val collection = if (aggregator.isDefined) map else buffer
      val it = collection.destructiveSortedWritablePartitionedIterator(comparator)
      while (it.hasNext) {
        val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize,
          context.taskMetrics.shuffleWriteMetrics.get)
        val partitionId = it.nextPartition()
        while (it.hasNext && it.nextPartition() == partitionId) {
          it.writeNext(writer)
        }
        writer.commitAndClose()
        val segment = writer.fileSegment()
        lengths(partitionId) = segment.length
      }
    } else {
      // We must perform merge-sort; get an iterator by partition and write everything directly.
      for ((id, elements) <- this.partitionedIterator) {
        if (elements.hasNext) {
          val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize,
            context.taskMetrics.shuffleWriteMetrics.get)
          for (elem <- elements) {
            writer.write(elem._1, elem._2)
          }
          writer.commitAndClose()
          val segment = writer.fileSegment()
          lengths(id) = segment.length
        }
      }
    }

    context.taskMetrics().incMemoryBytesSpilled(memoryBytesSpilled)
    context.taskMetrics().incDiskBytesSpilled(diskBytesSpilled)
    context.internalMetricsToAccumulators(
      InternalAccumulator.PEAK_EXECUTION_MEMORY).add(peakMemoryUsedBytes)

    lengths
  }
  • WritablePartitionedPairCollection.partitionKeyComparator.
  • 排序规则
  • 对 ExternalSorter中的数据进行排序,排序的规则为,(partition,key),先按partition进行升序排序,parition相等的再按key进行升序排序
  /**
   * A comparator for (Int, K) pairs that orders them both by their partition ID and a key ordering.
   */
  def partitionKeyComparator[K](keyComparator: Comparator[K]): Comparator[(Int, K)] = {
    new Comparator[(Int, K)] {
      override def compare(a: (Int, K), b: (Int, K)): Int = {
        val partitionDiff = a._1 - b._1
        if (partitionDiff != 0) {
          partitionDiff
        } else {
          keyComparator.compare(a._2, b._2)
        }
      }
    }
  }
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