Spark調度模式

當一個Spark App提交運行的時候，採用何種集羣調度模式是在SParkCOntext中設置的。代碼以下：

private def createTaskScheduler(
      sc: SparkContext,
      master: String,
      deployMode: String): (SchedulerBackend, TaskScheduler) = {
    import SparkMasterRegex._

    // When running locally, don't try to re-execute tasks on failure.
    val MAX_LOCAL_TASK_FAILURES = 1

    master match {
      case "local" =>
        val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, 1)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_N_REGEX(threads) =>
        def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
        // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads.
        val threadCount = if (threads == "*") localCpuCount else threads.toInt
        if (threadCount <= 0) {
          throw new SparkException(s"Asked to run locally with $threadCount threads")
        }
        val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
        def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
        // local[*, M] means the number of cores on the computer with M failures
        // local[N, M] means exactly N threads with M failures
        val threadCount = if (threads == "*") localCpuCount else threads.toInt
        val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
        scheduler.initialize(backend)
        (backend, scheduler)

      case SPARK_REGEX(sparkUrl) =>
        val scheduler = new TaskSchedulerImpl(sc)
        val masterUrls = sparkUrl.split(",").map("spark://" + _)
        val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
        // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang.
        val memoryPerSlaveInt = memoryPerSlave.toInt
        if (sc.executorMemory > memoryPerSlaveInt) {
          throw new SparkException(
            "Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format(
              memoryPerSlaveInt, sc.executorMemory))
        }

        val scheduler = new TaskSchedulerImpl(sc)
        val localCluster = new LocalSparkCluster(
          numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt, sc.conf)
        val masterUrls = localCluster.start()
        val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
        scheduler.initialize(backend)
        backend.shutdownCallback = (backend: StandaloneSchedulerBackend) => {
          localCluster.stop()
        }
        (backend, scheduler)

      case masterUrl =>
        val cm = getClusterManager(masterUrl) match {
          case Some(clusterMgr) => clusterMgr
          case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
        }
        try {
          val scheduler = cm.createTaskScheduler(sc, masterUrl)
          val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
          cm.initialize(scheduler, backend)
          (backend, scheduler)
        } catch {
          case se: SparkException => throw se
          case NonFatal(e) =>
            throw new SparkException("External scheduler cannot be instantiated", e)
        }
    }
  }

1）本地模式

採用TaskSchedulerImpl和LocalSchedulerBackend

2）master="spark://xxx"方式

採用TaskSchedulerImpl和StandaloneSchedulerBackend

3）local-cluster模式

本地集羣模式，TaskSchedulerImpl和StandaloneSchedulerBackend，額外增長一個LocalSparkCluster組件。

4）其餘集羣模式（包括yarn）

採用getClusterManager(masterUrl)方法來獲取集羣管理器，而後調用集羣管理器的方法來建立調度器和調度BackEndpoint。

val cm=getClusterManager(masterUrl)

val scheduler = cm.createTaskScheduler(sc, masterUrl)
val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)

Yarn模式下的調度器實現要看Spark-yarn相關的代碼了。