Spark内核源码深度剖析：SparkContext原理剖析与源码分析

1.SparkContex原理剖析

1.图解：

2.SparkContext源码分析

1.TaskScheduler创建：
SparkContext.scala

 // Create and start the scheduler
  private[spark] var (schedulerBackend, taskScheduler) =
    SparkContext.createTaskScheduler(this, master)

//不同的提交模式，会创建不同的TaskScheduler
private def createTaskScheduler(
      sc: SparkContext,
      master: String): (SchedulerBackend, TaskScheduler) = {
 master match {
        //standalone模式
        case SPARK_REGEX(sparkUrl) =>
        //TaskSchedulerImpl（）底层通过操作一个SchedulerBackend,针对不同的种类的cluster（standalone、yarn和mesos），调度task。
    //他也可以通过使用一个LocalBackend，并且将isLocal参数设置为true，来在本地模式下工作。
    //它负责处理一些童工的资源，比如决定多个job的调度顺序，启动检查任务执行
    //客户端首先应用调度initialize()方法和start（）方法，然后通过runTasks()方法提交task sets
        val scheduler = new TaskSchedulerImpl(sc)
        val masterUrls = sparkUrl.split(",").map("spark://" + _)
        val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls)
        scheduler.initialize(backend)
        (backend, scheduler)

}

TaskSchedulerImpl.scala

def initialize(backend: SchedulerBackend) {
    this.backend = backend
    // temporarily set rootPool name to empty
    rootPool = new Pool("", schedulingMode, 0, 0)
    schedulableBuilder = {
      schedulingMode match {
        case SchedulingMode.FIFO =>
          new FIFOSchedulableBuilder(rootPool)
        case SchedulingMode.FAIR =>
          new FairSchedulableBuilder(rootPool, conf)
      }
    }
    schedulableBuilder.buildPools()
  }

1.TaskScheduler启动：
TaskSchedulerImpl.scala

override def start() {
//重点是调用了SparkDeploySchedulerBackend类的start
    backend.start()

    if (!isLocal && conf.getBoolean("spark.speculation", false)) {
      logInfo("Starting speculative execution thread")
      import sc.env.actorSystem.dispatcher
      sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds,
            SPECULATION_INTERVAL milliseconds) {
        Utils.tryOrExit { checkSpeculatableTasks() }
      }
    }
  }

SparkDeploySchedulerBackend.scala

override def start() {
    super.start()

    // The endpoint for executors to talk to us
    val driverUrl = AkkaUtils.address(
      AkkaUtils.protocol(actorSystem),
      SparkEnv.driverActorSystemName,
      conf.get("spark.driver.host"),
      conf.get("spark.driver.port"),
      CoarseGrainedSchedulerBackend.ACTOR_NAME)
    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 appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("")
    //ApplicationDescription非常重要，它代表了当前的这个
    //application的一切情况
    //包括application最大需要多少CPU core，每个slave上需要多少内存
    val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
      appUIAddress, sc.eventLogDir, sc.eventLogCodec)
    //创建APPClient
    //APPClient是一个接口，它负责为application与Spark集群进行通  
   //信。它会接收一个Spark Master的URL，以及一个application，和 
   //一个集群事件的监听器，以及各种事件发生时监听器的回调函数             
    client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
    client.start()

    waitForRegistration()
  }

2.DAGScheduler创建：
SparkContext.scala

 @volatile private[spark] var dagScheduler: DAGScheduler = _
  try {
  //DAGScheduler类实现了面向stage的调度机制的高层次的调度层，他会为每个job计算一个stage的DAG（有向无环图）,
//追踪RDD和stage的输出是否被物化了（物化就是说，写入了磁盘或者内存等地方），并且寻找一个最少
//消耗(最优、最小）调度机制来运行job，它会将stage作为tasksets提交到底层的TaskSchedulerImple上，
//来在集群上运行它们（task）
//除了处理stage的DAG，它还负责决定运行每个task的最佳位置，基于当前的缓存状态，将这些最佳位置提交底层的
//TaskSchedulerImpl。此外，它会处理理由于shuffle输出文件丢失导致的失败，在这种情况下，旧的stage可能就会
//被重新提交，一个stage内部的失败，如果不是由于shuffle文件丢失所导致的，会被TAskSchedule处理，它会多次重试
//每一个task，直到最后，实在是不行了，才会去取消整个stage
    dagScheduler = new DAGScheduler(this)
  } catch {
    case e: Exception => {
      try {
        stop()
      } finally {
        throw new SparkException("Error while constructing DAGScheduler", e)
      }
    }
  }

3.SparkUI的创建：
SparkContext.scala

// Initialize the Spark UI
  private[spark] val ui: Option[SparkUI] =
    if (conf.getBoolean("spark.ui.enabled", true)) {
      Some(SparkUI.createLiveUI(this, conf, listenerBus, jobProgressListener,
        env.securityManager,appName))
    } else {
      // For tests, do not enable the UI
      None
    }

SparkUI.scala

//默认端口
val DEFAULT_PORT = 4040
def createLiveUI(
      sc: SparkContext,
      conf: SparkConf,
      listenerBus: SparkListenerBus,
      jobProgressListener: JobProgressListener,
      securityManager: SecurityManager,
      appName: String): SparkUI =  {
    create(Some(sc), conf, listenerBus, securityManager, appName,
      jobProgressListener = Some(jobProgressListener))
  }