Spark源码分析：SparkContext初始化

1.Spark怎么运行？

spark一般从spark-shell开始，

具体调用层次关系：

Spark-shell->spark submit->real.main->sparkLoop—>createSparkcontext

2.Spark初始化综述

Spark初始化主要涉及一下内容：

sparkenv. taskscheduler DAGScheduler webui

• No1

  通过sparkconf来构建sparkenv， sparkenv主要包含blockmanager mapoutputtracker shufflefetcher connectionmanager

  SparkEnv构造函数如下：

  class SparkEnv (
      val executorId: String,
      private[spark] val rpcEnv: RpcEnv,
      val serializer: Serializer,
      val closureSerializer: Serializer,
      val serializerManager: SerializerManager,
      val mapOutputTracker: MapOutputTracker,//用来缓存mapstatus信息
      val shuffleManager: ShuffleManager,//路由维护表
      val broadcastManager: BroadcastManager,//广播
      val blockManager: BlockManager,//块管理
      val securityManager: SecurityManager,//安全管理
      val metricsSystem: MetricsSystem,//测量
      val memoryManager: MemoryManager,
      val outputCommitCoordinator: OutputCommitCoordinator,
      val conf: SparkConf) extends Logging 
  

• No2

  创建taskscheduler。根据运行模式选择schedulerbackend，同时启动taskscheduler。

  /**
   * Create a task scheduler based on a given master URL.
   * Return a 2-tuple of the scheduler backend and the task scheduler.
   */
  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)
        }
    }
  }
  
  def initialize(backend: SchedulerBackend) {
      this.backend = backend
      schedulableBuilder = {
        schedulingMode match {
          case SchedulingMode.FIFO =>
            new FIFOSchedulableBuilder(rootPool)
          case SchedulingMode.FAIR =>
            new FairSchedulableBuilder(rootPool, conf)
          case _ =>
            throw new IllegalArgumentException(s"Unsupported $SCHEDULER_MODE_PROPERTY: " +
            s"$schedulingMode")
        }
      }
      schedulableBuilder.buildPools()
    }
  
  

• No3

  根据taskscheduler实例创建dagscheduler。

  class DAGScheduler(
      private[scheduler] val sc: SparkContext,
      private[scheduler] val taskScheduler: TaskScheduler,
      listenerBus: LiveListenerBus,
      mapOutputTracker: MapOutputTrackerMaster,
      blockManagerMaster: BlockManagerMaster,
      env: SparkEnv,
      clock: Clock = new SystemClock())
    extends Logging {
  
    def this(sc: SparkContext, taskScheduler: TaskScheduler) = {
      this(
        sc,
        taskScheduler,
        sc.listenerBus,
        sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster],
        sc.env.blockManager.master,
        sc.env)
    }
  

• No4

  启动webUI。