spark源码分析, 任务反序列化及执行

1 ==> 接受消息,org.apache.spark.executor.CoarseGrainedExecutorBackend#receive

    case LaunchTask(data) =>
      if (executor == null) {
        exitExecutor(1, "Received LaunchTask command but executor was null")
      } else {
        val taskDesc = TaskDescription.decode(data.value)
        logInfo("Got assigned task " + taskDesc.taskId)
        executor.launchTask(this, taskDesc)
      }

2. ==> org.apache.spark.executor.Executor#launchTask

  // Maintains the list of running tasks.
  private val runningTasks = new ConcurrentHashMap[Long, TaskRunner]

 def launchTask(context: ExecutorBackend, taskDescription: TaskDescription): Unit = {
    val tr = new TaskRunner(context, taskDescription)
    runningTasks.put(taskDescription.taskId, tr)
    threadPool.execute(tr)
  }

3. ==>org.apache.spark.executor.Executor.TaskRunner#run

override def run(): Unit = {
      threadId = Thread.currentThread.getId
      Thread.currentThread.setName(threadName)
      val threadMXBean = ManagementFactory.getThreadMXBean
      val taskMemoryManager = new TaskMemoryManager(env.memoryManager, taskId)
  
//下载依赖
        updateDependencies(taskDescription.addedFiles, taskDescription.addedJars)
//反序列化得到真正的 task
        task = ser.deserialize[Task[Any]](taskDescription.serializedTask, Thread.currentThread.getContextClassLoader)
        task.localProperties = taskDescription.properties
        task.setTaskMemoryManager(taskMemoryManager)

      
    val value = Utils.tryWithSafeFinally {
              val res = task.run(
                taskAttemptId = taskId,
                attemptNumber = taskDescription.attemptNumber,
                metricsSystem = env.metricsSystem)
              threwException = false
              res
            } {
              val releasedLocks = env.blockManager.releaseAllLocksForTask(taskId)
              val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory()
            }
    //处理执行结果
    val resultSer = env.serializer.newInstance()
    val beforeSerialization = System.currentTimeMillis()
    val valueBytes = resultSer.serialize(value)
    val afterSerialization = System.currentTimeMillis()       

    // Note: accumulator updates must be collected after TaskMetrics is updated
    val accumUpdates = task.collectAccumulatorUpdates()
    // TODO: do not serialize value twice
    val directResult = new DirectTaskResult(valueBytes, accumUpdates)
    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 > maxDirectResultSize) {
        val blockId = TaskResultBlockId(taskId)
        env.blockManager.putBytes(
          blockId,
          new ChunkedByteBuffer(serializedDirectResult.duplicate()),
          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
      }
    }

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

==> org.apache.spark.executor.Executor#updateDependencies

  /**
   * Download any missing dependencies if we receive a new set of files and JARs from the
   * SparkContext. Also adds any new JARs we fetched to the class loader.
   */
  private def updateDependencies(newFiles: Map[String, Long], newJars: Map[String, Long]) {
    lazy val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf)
    synchronized {
      // Fetch missing dependencies
      for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) {
        logInfo("Fetching " + name + " with timestamp " + timestamp)
        // Fetch file with useCache mode, close cache for local mode.
        Utils.fetchFile(name, new File(SparkFiles.getRootDirectory()), conf,
          env.securityManager, hadoopConf, timestamp, useCache = !isLocal)
        currentFiles(name) = timestamp
      }
      for ((name, timestamp) <- newJars) {
        val localName = new URI(name).getPath.split("/").last
        val currentTimeStamp = currentJars.get(name)
          .orElse(currentJars.get(localName))
          .getOrElse(-1L)
        if (currentTimeStamp < timestamp) {
          logInfo("Fetching " + name + " with timestamp " + timestamp)
          // Fetch file with useCache mode, close cache for local mode.
          Utils.fetchFile(name, new File(SparkFiles.getRootDirectory()), conf,
            env.securityManager, hadoopConf, timestamp, useCache = !isLocal)
          currentJars(name) = timestamp
          // Add it to our class loader
          val url = new File(SparkFiles.getRootDirectory(), localName).toURI.toURL
          if (!urlClassLoader.getURLs().contains(url)) {
            logInfo("Adding " + url + " to class loader")
            urlClassLoader.addURL(url)
          }
        }
      }
    }
  }

==> org.apache.spark.scheduler.Task#run

 final def run(
      taskAttemptId: Long,
      attemptNumber: Int,
      metricsSystem: MetricsSystem): T = {
    SparkEnv.get.blockManager.registerTask(taskAttemptId)


    val taskContext = new TaskContextImpl(
      stageId,
      stageAttemptId, // stageAttemptId and stageAttemptNumber are semantically equal
      partitionId,
      taskAttemptId,
      attemptNumber,
      taskMemoryManager,
      localProperties,
      metricsSystem,
      metrics)

    context = if (isBarrier) {
      new BarrierTaskContext(taskContext)
    } else {
      taskContext
    }

    TaskContext.setTaskContext(context)
    taskThread = Thread.currentThread()

    if (_reasonIfKilled != null) {
      kill(interruptThread = false, _reasonIfKilled)
    }

    new CallerContext(
      "TASK",
      SparkEnv.get.conf.get(APP_CALLER_CONTEXT),
      appId,
      appAttemptId,
      jobId,
      Option(stageId),
      Option(stageAttemptId),
      Option(taskAttemptId),
      Option(attemptNumber)).setCurrentContext()

    try {
    //这个类只是一个模板类或者抽象类, 具体实现类分为ResultTask, ShuffleMapTask 两种
      runTask(context)
    } 
  }

==>org.apache.spark.scheduler.ShuffleMapTask#runTask

ShuffleMapTask将rdd的元素，切分为多个bucket, 基于ShuffleDependency指定的partitioner，默认就是HashPartitioner

ShuffleMapTask 核心方法是 RDD.iterator[底层调用 compute 方法(fn(context,index,partition))],

执行完成rdd之后，rdd或返回处理过后的partition数据，这些数据通过shuffleWriter在经过HashPartitioner写入对应的分区中

// ShuffleMapTask将rdd的元素，切分为多个bucket
// 基于ShuffleDependency指定的partitioner，默认就是HashPartitioner
private[spark] class ShuffleMapTask(
   ...
   // ShuffleMapTask的 runTask 有 MapStatus返回值
  override def runTask(context: TaskContext): MapStatus = {
    // Deserialize the RDD using the broadcast variable.
    val threadMXBean = ManagementFactory.getThreadMXBean
    val deserializeStartTime = System.currentTimeMillis()
    val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
      threadMXBean.getCurrentThreadCpuTime
    } else 0L

    // 对task要处理的数据，做反序列化操作
 
    val ser = SparkEnv.get.closureSerializer.newInstance()
    //获得 RDD 
    val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])](
      ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
    _executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime
    _executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
      threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
    } else 0L

    var writer: ShuffleWriter[Any, Any] = null
    try {
      // 拿到shuffleManager
      val manager = SparkEnv.get.shuffleManager
      // 拿到shuffleWriter
      writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)

      // 核心逻辑，调用rdd的iterator方法，并且传入了当前要处理的partition
      // 执行完成rdd之后，rdd或返回处理过后的partition数据，这些数据通过shuffleWriter
      // 在经过HashPartitioner写入对应的分区中
      
      writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])

      // 返回结果 MapStatus ，里面封装了ShuffleMapTask存储在哪里，其实就是BlockManager相关信息
      writer.stop(success = true).get
    } 
  }
  ...
}

==> org.apache.spark.scheduler.ResultTask#runTask

  override def runTask(context: TaskContext): U = {
    // Deserialize the RDD and the func using the broadcast variables.
    val threadMXBean = ManagementFactory.getThreadMXBean
    val deserializeStartTime = System.currentTimeMillis()
    val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
      threadMXBean.getCurrentThreadCpuTime
    } else 0L
    val ser = SparkEnv.get.closureSerializer.newInstance()
    val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)](
      ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader)
    _executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime
    _executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) {
      threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
    } else 0L

   //直接调用用户自定义函数
    func(context, rdd.iterator(partition, context))
  }

==> org.apache.spark.rdd.RDD#iterator

 final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
　　 //结果不需要存储
    if (storageLevel != StorageLevel.NONE) {
      getOrCompute(split, context)
    } else {
      computeOrReadCheckpoint(split, context)
    }
  }

==> org.apache.spark.rdd.RDD#computeOrReadCheckpoint

  /**
   * Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing.
   */
  private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] =
  {
    if (isCheckpointedAndMaterialized) {
      firstParent[T].iterator(split, context)
    } else {
     //核心方法, 此方法为虚方法,具体实现由具体 RDD 子类实现,如 MapPartitionsRDD,JdbcRDD等
      compute(split, context)
    }
  }    

demo: 

class MapPartitionsRDD[U: ClassTag, T: ClassTag](
    var prev: RDD[T],
    f: (TaskContext, Int, Iterator[T]) => Iterator[U],  // (TaskContext, partition index, iterator)
    preservesPartitioning: Boolean = false,
    isFromBarrier: Boolean = false,
    isOrderSensitive: Boolean = false)
  extends RDD[U](prev) {


  override def compute(split: Partition, context: TaskContext): Iterator[U] =
    f(context, split.index, firstParent[T].iterator(split, context))

}


class JdbcRDD[T: ClassTag](
    sc: SparkContext,
    getConnection: () => Connection,
    sql: String,
    lowerBound: Long,
    upperBound: Long,
    numPartitions: Int,
    mapRow: (ResultSet) => T = JdbcRDD.resultSetToObjectArray _)
  extends RDD[T](sc, Nil) with Logging {
  override def getPartitions: Array[Partition] = {
    // bounds are inclusive, hence the + 1 here and - 1 on end
    val length = BigInt(1) + upperBound - lowerBound
    (0 until numPartitions).map { i =>
      val start = lowerBound + ((i * length) / numPartitions)
      val end = lowerBound + (((i + 1) * length) / numPartitions) - 1
      new JdbcPartition(i, start.toLong, end.toLong)
    }.toArray
  }

  override def compute(thePart: Partition, context: TaskContext): Iterator[T] = new NextIterator[T]
  {
    context.addTaskCompletionListener[Unit]{ context => closeIfNeeded() }
    val part = thePart.asInstanceOf[JdbcPartition]
    val conn = getConnection()
    val stmt = conn.prepareStatement(sql, ResultSet.TYPE_FORWARD_ONLY, ResultSet.CONCUR_READ_ONLY)

    val url = conn.getMetaData.getURL
    
    val rs = stmt.executeQuery()

    override def getNext(): T = {
      if (rs.next()) {
        mapRow(rs)
      } else {
        finished = true
        null.asInstanceOf[T]
      }
    }

    override def close() {
     
    }
  }
}