【Spark Core】任务运行机制和Task源代码浅析1

引言

上一小节《TaskScheduler源代码与任务提交原理浅析2》介绍了Driver側将Stage进行划分。依据Executor闲置情况分发任务，终于通过DriverActor向executorActor发送任务消息。

我们要了解Executor的运行机制首先要了解Executor在Driver側的注冊过程。这篇文章先了解一下Application和Executor的注冊过程。

1. Task类及其相关

1.1 Task类

Spark将由Executor运行的Task分为ShuffleMapTask和ResultTask两种。其源代码存在scheduler package中。
Task是介于DAGScheduler和TaskScheduler中间的接口，在DAGScheduler，须要把DAG中的每一个stage的每一个partitions封装成task。终于把taskset提交给TaskScheduler。

/**
 * A unit of execution. We have two kinds of Task‘s in Spark:
 * - [[org.apache.spark.scheduler.ShuffleMapTask]]
 * - [[org.apache.spark.scheduler.ResultTask]]
 *
 * A Spark job consists of one or more stages. The very last stage in a job consists of multiple
 * ResultTasks, while earlier stages consist of ShuffleMapTasks. A ResultTask executes the task
 * and sends the task output back to the driver application. A ShuffleMapTask executes the task
 * and divides the task output to multiple buckets (based on the task‘s partitioner).
 *
 * @param stageId id of the stage this task belongs to
 * @param partitionId index of the number in the RDD
 */
private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) extends Serializable 

Task相应一个stageId和partitionId。
提供runTask()接口、kill()接口等。
提供killed变量、TaskMetrics变量、TaskContext变量等。

除了上述基本接口和变量，Task的伴生对象提供了序列化和反序列化应用依赖的jar包的方法。原因是Task须要保证工作节点具备本次Task须要的其它依赖。注冊到SparkContext下，所以提供了把依赖转成流写入写出的方法。

1.2 ShuffleMapTask

相应于ShuffleMap Stage, 产生的结果作为其它stage的输入。
ShuffleMapTask复写了MapStatus向外读写的方法，由于向外读写的内容包含：stageId，rdd，dep，partitionId，epoch和split(某个partition)。对于当中的stageId，rdd。dep有统一的序列化和反序列化操作并会cache在内存里，再放到ObjectOutput里写出去。序列化操作使用的是Gzip，序列化信息会维护在serializedInfoCache = newHashMap[Int, Array[Byte]]。这部分须要序列化并保存的原因是：stageId，rdd。dep真正代表了本次Shuffle Task的信息，为了减轻master节点负担，把这部分序列化结果cache了起来。

/**
* A ShuffleMapTask divides the elements of an RDD into multiple buckets (based on a partitioner
* specified in the ShuffleDependency).
*
* See [[org.apache.spark.scheduler.Task]] for more information.
*
 * @param stageId id of the stage this task belongs to
 * @param taskBinary broadcast version of of the RDD and the ShuffleDependency. Once deserialized,
 *                   the type should be (RDD[_], ShuffleDependency[_, _, _]).
 * @param partition partition of the RDD this task is associated with
 * @param locs preferred task execution locations for locality scheduling
 */
private[spark] class ShuffleMapTask(
    stageId: Int,
    taskBinary: Broadcast[Array[Byte]],
    partition: Partition,
    @transient private var locs: Seq[TaskLocation])
  extends Task[MapStatus](stageId, partition.index) with Logging {

1.3 ResultTask

相应于Result Stage直接产生结果。

/**
 * A task that sends back the output to the driver application.
 *
 * See [[Task]] for more information.
 *
 * @param stageId id of the stage this task belongs to
 * @param taskBinary broadcasted version of the serialized RDD and the function to apply on each
 *                   partition of the given RDD. Once deserialized, the type should be
 *                   (RDD[T], (TaskContext, Iterator[T]) => U).
 * @param partition partition of the RDD this task is associated with
 * @param locs preferred task execution locations for locality scheduling
 * @param outputId index of the task in this job (a job can launch tasks on only a subset of the
 *                 input RDD‘s partitions).
 */
private[spark] class ResultTask[T, U](
    stageId: Int,
    taskBinary: Broadcast[Array[Byte]],
    partition: Partition,
    @transient locs: Seq[TaskLocation],
    val outputId: Int)
  extends Task[U](stageId, partition.index) with Serializable {

1.4 TaskSet

TaskSet是一个数据结构，用于封装一个stage的全部的tasks, 以提交给TaskScheduler。

TaskSet就是能够做pipeline的一组全然同样的task，每一个task的处理逻辑全然同样。不同的是处理数据，每一个task负责处理一个partition。pipeline，能够称为大数据处理的基石。仅仅有数据进行pipeline处理，才干将其放到集群中去运行。对于一个task来说，它从数据源获得逻辑。然后依照拓扑顺序，顺序运行（实际上是调用rdd的compute）。

/**
 * A set of tasks submitted together to the low-level TaskScheduler, usually representing
 * missing partitions of a particular stage.
 */
private[spark] class TaskSet(
    val tasks: Array[Task[_]],
    val stageId: Int,
    val attempt: Int,
    val priority: Int,
    val properties: Properties) {
    val id: String = stageId + "." + attempt

  override def toString: String = "TaskSet " + id
}

2. Executor注冊到Driver

Driver发送LaunchTask消息被Executor接收，Executor会使用launchTask对消息进行处理。

只是在这个过程之前。我们要知道，假设Executor没有注冊到Driver，即便接收到LaunchTask指令，也不会做任务处理。所以我们要先搞清楚。Executor是怎样在Driver側进行注冊的。

2.1 Application注冊

Executor的注冊是发生在Application的注冊过程中的，我们以Standalone模式为例：

SparkContext创建schedulerBackend和taskScheduler，schedulerBackend作为TaskScheduler对象的一个成员存在 –> 在TaskScheduler对象调用start函数时，事实上调用了backend.start()函数 –> backend.start()函数中启动了AppClient，AppClient的当中一个參数ApplicationDescription就是封装的运行CoarseGrainedExecutorBackend的命令 –> AppClient内部启动了一个ClientActor。这个ClientActor启动之后，会尝试向Master发送一个指令actor ! RegisterApplication(appDescription) 注冊一个Application

以下是SparkDeploySchedulerBackend的start函数中的部分注冊Application的代码：

    // Start executors with a few necessary configs for registering with the scheduler
    val sparkJavaOpts = Utils.sparkJavaOpts(conf, SparkConf.isExecutorStartupConf)
    val javaOpts = sparkJavaOpts ++ extraJavaOpts
    val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
      args, sc.executorEnvs, classPathEntries ++ testingClassPath, libraryPathEntries, javaOpts)
    val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("")
    val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
      appUIAddress, sc.eventLogDir, sc.eventLogCodec)

    client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
    client.start()

AppClient向Master提交Application
AppClient是Application和Master交互的接口。它的包含一个类型为org.apache.spark.deploy.client.AppClient.ClientActor的成员变量actor。它负责了全部的与Master的交互。当中提交Application过程涉及的函数调用为：
ClientActor的preStart() –> 调用registerWithMaster() –> 调用tryRegisterAllMasters –> actor ! RegisterApplication(appDescription) –> Master的receiveWithLogging函数处理RegisterApplication消息。

以下是RegisterApplication(appDescription)消息的相关处理代码（在Master.scala中的receiveWithLogging部分代码）：

    case RegisterApplication(description) => {
      if (state == RecoveryState.STANDBY) {
        // ignore, don‘t send response
      } else {
        logInfo("Registering app " + description.name)
        val app = createApplication(description, sender)
        registerApplication(app)
        logInfo("Registered app " + description.name + " with ID " + app.id)
        persistenceEngine.addApplication(app)
        sender ! RegisteredApplication(app.id, masterUrl)
        schedule()//为处于待分配资源的Application分配资源。
在每次有新的Application增加或者新的资源增加时都会调用schedule进行调度  
      }
    }

这段代码做了以下几件事：

1. createApplication为这个app构建一个描写叙述App数据结构的ApplicationInfo
2. 注冊该Application，更新相应的映射关系。增加到等待队列里面
3. 用persistenceEngine持久化Application信息，默认是不保存的。另外还有两种方式，保存在文件或者Zookeeper当中
4. 通过发送方注冊成功
5. 開始作业调度（为处于待分配资源的Application分配资源。在每次有新的Application增加或者新的资源增加时都会调用schedule进行调度）

2.2 Master中的schedule函数

schedule()为处于待分配资源的Application分配资源。在每次有新的Application增加或者新的资源增加时都会调用schedule进行调度。为Application分配资源选择worker（executor），如今有两种策略：

1. 尽量的打散。即一个Application尽可能多的分配到不同的节点。

   这个能够通过设置spark.deploy.spreadOut来实现。

   默认值为true，即尽量的打散。

2. 尽量的集中，即一个Application尽量分配到尽可能少的节点。

对于同一个Application，它在一个worker上仅仅能拥有一个executor；当然了。这个executor可能拥有多于1个core。

对于策略1，任务的部署会慢于策略2，可是GC的时间会更快。

schedule函数的源代码，解释在中文凝视中：

  /*
   * Schedule the currently available resources among waiting apps. This method will be called
   * every time a new app joins or resource availability changes.
   */
  private def schedule() {
    if (state != RecoveryState.ALIVE) { return }

    // First schedule drivers, they take strict precedence over applications
    // Randomization helps balance drivers
    val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE))
    val numWorkersAlive = shuffledAliveWorkers.size
    var curPos = 0

    for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers
      // We assign workers to each waiting driver in a round-robin fashion. For each driver, we
      // start from the last worker that was assigned a driver, and continue onwards until we have
      // explored all alive workers.
      var launched = false
      var numWorkersVisited = 0
      while (numWorkersVisited < numWorkersAlive && !launched) {
        val worker = shuffledAliveWorkers(curPos)
        numWorkersVisited += 1
        if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
          launchDriver(worker, driver)
          waitingDrivers -= driver
          launched = true
        }
        curPos = (curPos + 1) % numWorkersAlive
      }
    }

    // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app
    // in the queue, then the second app, etc.
    if (spreadOutApps) {//尽量的打散负载，如有可能。每一个executor分配一个core  
      // Try to spread out each app among all the nodes, until it has all its cores
      for (app <- waitingApps if app.coresLeft > 0) {
        // 可用的worker的标准：State是Alive，其上并没有该Application的executor，可用内存满足要求。
  
        // 在可用的worker中，优先选择可用core数多的。  
        val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
          .filter(canUse(app, _)).sortBy(_.coresFree).reverse
        val numUsable = usableWorkers.length
        val assigned = new Array[Int](numUsable) // Number of cores to give on each node
        var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
        var pos = 0
        while (toAssign > 0) {
          if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
            toAssign -= 1
            assigned(pos) += 1
          }
          pos = (pos + 1) % numUsable
        }
        // Now that we‘ve decided how many cores to give on each node, let‘s actually give them
        for (pos <- 0 until numUsable) {
          if (assigned(pos) > 0) {
            val exec = app.addExecutor(usableWorkers(pos), assigned(pos))
            launchExecutor(usableWorkers(pos), exec)
            app.state = ApplicationState.RUNNING
          }
        }
      }
    } else { //尽可能多的利用worker的core
      // Pack each app into as few nodes as possible until we‘ve assigned all its cores
      for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) {
        for (app <- waitingApps if app.coresLeft > 0) {
          if (canUse(app, worker)) {
            val coresToUse = math.min(worker.coresFree, app.coresLeft)
            if (coresToUse > 0) {
              val exec = app.addExecutor(worker, coresToUse)
              launchExecutor(worker, exec)
              app.state = ApplicationState.RUNNING
            }
          }
        }
      }
    }
  }

2.3 launchExecutor函数

在选择了worker和确定了worker上得executor须要的CPU core数后。Master会调用 launchExecutor(worker: WorkerInfo, exec: ExecutorInfo)向Worker发送请求，向AppClient发送executor已经增加的消息。

同一时候会更新master保存的worker的信息。包含增加executor，降低可用的CPU core数和memory数。Master不会等到真正在worker上成功启动executor后再更新worker的信息。假设worker启动executor失败。那么它会发送FAILED的消息给Master，Master收到该消息时再次更新worker的信息就可以。

  def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc) {
    logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
    worker.addExecutor(exec)
    worker.actor ! LaunchExecutor(masterUrl,
      exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory)
    exec.application.driver ! ExecutorAdded(
      exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
  }

2.4 Executor的创建

以下的调用关系链是Worker接收到来自Master的LaunchExecutor消息后的调用过程：
LaunchExecutor的消息处理中创建ExecutorRunner –> ExecutorRunner会将在SparkDeploySchedulerBackend中准备好的ApplicationDescription以进程的形式启动起来 –> 启动ApplicationDescription中携带的CoarseGrainedExecutorBackend –> CoarseGrainedExecutorBackend启动后，会首先通过传入的driverUrl这个參数向在CoarseGrainedSchedulerBackend::DriverActor发送RegisterExecutor消息 –> DriverActor会回复RegisteredExecutor –> CoarseGrainedExecutorBackend会创建一个Executor –> Executor创建完成。

CoarseGrainedExecutorBackend启动后。preStart函数运行的相关操作：

  override def preStart() {
    logInfo("Connecting to driver: " + driverUrl)
    driver = context.actorSelection(driverUrl)
    driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
    context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
  }

CoarseGrainedExecutorBackend接收RegisteredExecutor消息后，创建Executor的操作：

  override def receiveWithLogging = {
    case RegisteredExecutor =>
      logInfo("Successfully registered with driver")
      val (hostname, _) = Utils.parseHostPort(hostPort)
      executor = new Executor(executorId, hostname, env, userClassPath, isLocal = false)

    ......

參考资料

Spark Core源代码分析: Spark任务模型
Spark技术内幕：Executor分配具体解释 —— 强烈推荐该博文，当中博主结合Spark源代码对Executor的分配解说的很具体

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