> Spark > Spark源码：启动TaskScheduler

Spark源码：启动TaskScheduler

Spark SenLin 5年前 (2020-03-09) 569次浏览 已收录 0个评论

源码目录

初始化 SparkContext 时，会创建TaskScheduler，现在来看看TaskScheduler 启动过程。

1 启动TaskScheduler

调用_taskScheduler.start()启动TaskScheduler。

  • 进入org.apache.spark.scheduler.TaskSchedulerImpl.scala
  private val speculationScheduler =
    ThreadUtils.newDaemonSingleThreadScheduledExecutor("task-scheduler-speculation")

  override def start() {
    backend.start()

    if (!isLocal && conf.getBoolean("spark.speculation", false)) {
      logInfo("Starting speculative execution thread")
      speculationScheduler.scheduleWithFixedDelay(new Runnable {
        override def run(): Unit = Utils.tryOrStopSparkContext(sc) {
          checkSpeculatableTasks()
        }
      }, SPECULATION_INTERVAL_MS, SPECULATION_INTERVAL_MS, TimeUnit.MILLISECONDS)
    }
  }
  1. backend.start()启动SchedulerBackend；
  2. 如果是 非Local模式 且 spark.speculation = true，即开启了推测机制，则定时启新线程执行checkSpeculatableTasks，检查可推测的Tasks。

2 启动SchedulerBackend

  • 进入org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend.scala
private[spark] class StandaloneSchedulerBackend(
    scheduler: TaskSchedulerImpl,
    sc: SparkContext,
    masters: Array[String])
  extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv)
  with StandaloneAppClientListener
  with Logging {

  override def start() {
    super.start()

    // SPARK-21159. The scheduler backend should only try to connect to the launcher when in client
    // mode. In cluster mode, the code that submits the application to the Master needs to connect
    // to the launcher instead.
    if (sc.deployMode == "client") {
      launcherBackend.connect()
    }

    // The endpoint for executors to talk to us
    val driverUrl = RpcEndpointAddress(
      sc.conf.get("spark.driver.host"),
      sc.conf.get("spark.driver.port").toInt,
      CoarseGrainedSchedulerBackend.ENDPOINT_NAME).toString
    val args = Seq(
      "--driver-url", driverUrl,
      "--executor-id", "{{EXECUTOR_ID}}",
      "--hostname", "{{HOSTNAME}}",
      "--cores", "{{CORES}}",
      "--app-id", "{{APP_ID}}",
      "--worker-url", "{{WORKER_URL}}")
    val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
      .map(Utils.splitCommandString).getOrElse(Seq.empty)
    val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath")
      .map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil)
    val libraryPathEntries = sc.conf.getOption("spark.executor.extraLibraryPath")
      .map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil)

    // When testing, expose the parent class path to the child. This is processed by
    // compute-classpath.{cmd,sh} and makes all needed jars available to child processes
    // when the assembly is built with the "*-provided" profiles enabled.
    val testingClassPath =
      if (sys.props.contains("spark.testing")) {
        sys.props("java.class.path").split(java.io.File.pathSeparator).toSeq
      } else {
        Nil
      }

    // 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 webUrl = sc.ui.map(_.webUrl).getOrElse("")
    val coresPerExecutor = conf.getOption("spark.executor.cores").map(_.toInt)
    // If we're using dynamic allocation, set our initial executor limit to 0 for now.
    // ExecutorAllocationManager will send the real initial limit to the Master later.
    val initialExecutorLimit =
      if (Utils.isDynamicAllocationEnabled(conf)) {
        Some(0)
      } else {
        None
      }
    val appDesc = ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
      webUrl, sc.eventLogDir, sc.eventLogCodec, coresPerExecutor, initialExecutorLimit)
    client = new StandaloneAppClient(sc.env.rpcEnv, masters, appDesc, this, conf)
    client.start()
    launcherBackend.setState(SparkAppHandle.State.SUBMITTED)
    waitForRegistration()
    launcherBackend.setState(SparkAppHandle.State.RUNNING)
  }

}

StandaloneSchedulerBackend 继承了 CoarseGrainedSchedulerBackend。

  1. super.start() 调用父类 CoarseGrainedSchedulerBackend 的 start 方法；
  2. 配置各种参数：driverUrl、args、extraJavaOpts、classPathEntries、libraryPathEntries、javaOpts等，构建Command("org.apache.spark.executor.CoarseGrainedExecutorBackend", ......)；
  3. 生成 ApplicationDescription，将 Command 加入到 ApplicationDescription 中，后面会使用到；
  4. 创建 StandaloneAppClient 并启动；
  5. 更新 app 状态为 SUBMITTED；
  6. 等待 app 注册并启动；
  7. 更新 app 状态为 RUNNING。

注：这边的 2、3两步和提交 Application 时启动 Driver 的过程很相似：

  • 在启动 Driver 时，配置各种参数构建Command("org.apache.spark.deploy.worker.DriverWrapper",......)，然后创建 DriverDescription；
  • 此处，配置各种参数构建Command("org.apache.spark.executor.CoarseGrainedExecutorBackend", ......)，然后创建 ApplicationDescription。

具体见Spark源码：提交Application到Spark集群。

2.1 启动CoarseGrainedSchedulerBackend

  • 进入org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.scala
  override def start() {
    val properties = new ArrayBuffer[(String, String)]
    for ((key, value) <- scheduler.sc.conf.getAll) {
      if (key.startsWith("spark.")) {
        properties += ((key, value))
      }
    }

    // TODO (prashant) send conf instead of properties
    driverEndpoint = createDriverEndpointRef(properties)
  }


  protected def createDriverEndpointRef(
      properties: ArrayBuffer[(String, String)]): RpcEndpointRef = {
    rpcEnv.setupEndpoint(ENDPOINT_NAME, createDriverEndpoint(properties))
  }


  protected def createDriverEndpoint(properties: Seq[(String, String)]): DriverEndpoint = {
    new DriverEndpoint(rpcEnv, properties)
  }

创建 DriverEndpoint，以 “CoarseGrainedScheduler” 为名注册到 SparkContext.DriverSparkEnv.RpcEnv 上（详见 Spark源码：初始化SparkContext）。

注：每次注册 RpcEndpoint 到 RpcEnv 上时，都会加入OnStart 到 Inbox 的队列中，因此必然要执行 RpcEndpoint.onStart() 方法。

来看看 DriverEndpoint.onStart 方法。

  • 进入org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.DriverEndpoint
  class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)])
    extends ThreadSafeRpcEndpoint with Logging {

    override def onStart() {
      // Periodically revive offers to allow delay scheduling to work
      val reviveIntervalMs = conf.getTimeAsMs("spark.scheduler.revive.interval", "1s")

      reviveThread.scheduleAtFixedRate(new Runnable {
        override def run(): Unit = Utils.tryLogNonFatalError {
          Option(self).foreach(_.send(ReviveOffers))
        }
      }, 0, reviveIntervalMs, TimeUnit.MILLISECONDS)
    }

  }

该方法中会启一个新线程定时给自己发送 ReviveOffers 消息。

  • 进入org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.DriverEndpoint.scala
    override def receive: PartialFunction[Any, Unit] = {

      case ReviveOffers =>
        makeOffers()
    }


    // Make fake resource offers on all executors
    private def makeOffers() {
      // Make sure no executor is killed while some task is launching on it
      val taskDescs = withLock {
        // Filter out executors under killing
        val activeExecutors = executorDataMap.filterKeys(executorIsAlive)
        val workOffers = activeExecutors.map {
          case (id, executorData) =>
            new WorkerOffer(id, executorData.executorHost, executorData.freeCores,
              Some(executorData.executorAddress.hostPort))
        }.toIndexedSeq
        scheduler.resourceOffers(workOffers)
      }
      if (!taskDescs.isEmpty) {
        launchTasks(taskDescs)
      }
    }

此方法会遍历 CoarseGrainedSchedulerBackend.executorDataMap，而此时 executorDataMap 中还没有任何东西，因此该方法等于啥也没干，等后面分析。

2.2 创建 StandaloneAppClient 并启动

  • 进入org.apache.spark.deploy.client.StandaloneAppClient.scala
private[spark] class StandaloneAppClient(
    rpcEnv: RpcEnv,
    masterUrls: Array[String],
    appDescription: ApplicationDescription,
    listener: StandaloneAppClientListener,
    conf: SparkConf)
  extends Logging {

  def start() {
    // Just launch an rpcEndpoint; it will call back into the listener.
    endpoint.set(rpcEnv.setupEndpoint("AppClient", new ClientEndpoint(rpcEnv)))
  }

  // 省略部分内容
}

创建ClientEndpoint，并以 “AppClient” 为名注册到 SparkContext.DriverSparkEnv.RpcEnv 上（详见 Spark源码：初始化SparkContext）。

  • 进入org.apache.spark.deploy.client.StandaloneAppClient.ClientEndpoint.scala
    override def onStart(): Unit = {
      try {
        registerWithMaster(1)
      } catch {
        case e: Exception =>
          logWarning("Failed to connect to master", e)
          markDisconnected()
          stop()
      }
    }


    private def registerWithMaster(nthRetry: Int) {
      registerMasterFutures.set(tryRegisterAllMasters())
      registrationRetryTimer.set(registrationRetryThread.schedule(new Runnable {
        override def run(): Unit = {
          if (registered.get) {
            registerMasterFutures.get.foreach(_.cancel(true))
            registerMasterThreadPool.shutdownNow()
          } else if (nthRetry >= REGISTRATION_RETRIES) {
            markDead("All masters are unresponsive! Giving up.")
          } else {
            registerMasterFutures.get.foreach(_.cancel(true))
            registerWithMaster(nthRetry + 1)
          }
        }
      }, REGISTRATION_TIMEOUT_SECONDS, TimeUnit.SECONDS))
    }


    private def tryRegisterAllMasters(): Array[JFuture[_]] = {
      for (masterAddress <- masterRpcAddresses) yield {
        registerMasterThreadPool.submit(new Runnable {
          override def run(): Unit = try {
            if (registered.get) {
              return
            }
            logInfo("Connecting to master " + masterAddress.toSparkURL + "...")
            val masterRef = rpcEnv.setupEndpointRef(masterAddress, Master.ENDPOINT_NAME)
            masterRef.send(RegisterApplication(appDescription, self))
          } catch {
            case ie: InterruptedException => // Cancelled
            case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
          }
        })
      }
    }
  1. 遍历所有masterRpcAddresses；
  2. 根据 masterAddress 和 masterEndpointName 获取masterRpcEndpointRef；
  3. 利用 masterRpcEndpointRef 发送 RegisterApplication(ApplicationDescription, DriverRpcEndpointRef) 消息。

创建 StandaloneAppClient 并启动其实就是为了给 Master 发消息，准备注册 Application。

2.3 注册Application

  • 进入org.apache.spark.deploy.master.Master.scala
  override def receive: PartialFunction[Any, Unit] = {

    case RegisterApplication(description, driver) =>
      // TODO Prevent repeated registrations from some driver
      if (state == RecoveryState.STANDBY) {
        // ignore, don't send response
      } else {
        logInfo("Registering app " + description.name)
        val app = createApplication(description, driver)
        registerApplication(app)
        logInfo("Registered app " + description.name + " with ID " + app.id)
        persistenceEngine.addApplication(app)
        driver.send(RegisteredApplication(app.id, self))
        schedule()
      }

  }
  1. 如果是 STANDBY Master，不回响应；
  2. 调用 createApplication(appDescription, driverRpcEndpointRef) 方法创建 ApplicationInfo；
  3. 调用 registerApplication 注册 app，即将上面创建的 ApplicationInfo 加入到 Master.waitingApps 中；
  4. 利用 driverRpcEndpointRef 发送 RegisteredApplication 消息，即发送消息 RegisteredApplication 给 Driver，告诉 Driver application已经注册完成；
  5. 调用 schedule() 方法。

2.4 启动Application

  • 进入org.apache.spark.deploy.master.Master.scala
  private def schedule(): Unit = {
    if (state != RecoveryState.ALIVE) {
      return
    }
    // Drivers take strict precedence over executors
    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
      }
    }
    startExecutorsOnWorkers()
  }

在 Spark源码：提交Application到Spark集群 中，注册完 Driver 后也是调用该方法启动 Driver 的。

那里由于还没有往 Master.waitingApps 中加入app，因此调用 startExecutorsOnWorkers 方法啥也不干，但是这里，已经有 app 加入到 Master.waitingApps 中了，因此调用 startExecutorsOnWorkers 方法会为 app 启动 Executors 了。

说明几点：

  1. 这里注册 Application 时创建的 ApplicationInfo 加入到 Master.waitingApps 中，在 Spark源码：提交Application到Spark集群 中，注册 Driver 时创建的 DriverInfo 加入到了 Master.waitingDrivers 中；
  2. schedule() 方法中做了两件事：
    1）遍历 Master.waitingDrivers 启动各 Driver；
    2）遍历 Master.waitingApps 为各 App 启动 Executors。

调用 startExecutorsOnWorkers 方法为 app 启动 Executors 的具体过程，后面文章分析。

3 总结

  1. 调用 TaskSchedulerImpl.start 方法启动 TaskScheduler 时会调用 SchedulerBackend.start 方法启动 SchedulerBackend；
  2. SchedulerBackend 是 TaskScheduler 的后台线程，用于接收处理一些发给 TaskScheduler 的消息；
  3. StandaloneSchedulerBackend 启动时调用其父类 CoarseGrainedSchedulerBackend 的 start 方法用于启动 CoarseGrainedSchedulerBackend；
  4. 启动 CoarseGrainedSchedulerBackend 时会创建 DriverEndpoint 并注册到 SparkContext.DriverSparkEnv.RpcEnv 上（详见 Spark源码：初始化SparkContext）；
  5. DriverEndpoint.onStart 方法被调用，该方法中启一个新线程定时给自己发 ReviveOffers 消息，自己处理 ReviveOffers 消息时调用 makeOffers 方法，这一过程其实就是定时调度提交 Tasks 的过程；
  6. 创建 StandaloneAppClient 并调用其 start 方法，给所有 Master 发送消息 RegisterApplication，准备注册 Application；
  7. Master 收到 RegisterApplication 消息后创建 ApplicationInfo 并放到 Master.waitingApps 中，表示 Application 已注册完，回响应给 Driver；
  8. 调用 schedule 方法启动 Application，schedule 方法内做两件事：
    1）遍历 Master.waitingDrivers 启动各 Driver
    2）遍历 Master.waitingApps 为各 App 启动 Executors
  9. 为 App 启动 Executors 过程后面文章分析。
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