大家好，又见面了，我是你们的朋友全栈君。如果您正在找激活码,请点击查看最新教程,关注关注公众号 “全栈程序员社区” 获取激活教程,可能之前旧版本教程已经失效.最新Idea2022.1教程亲测有效,一键激活。

Jetbrains全系列IDE稳定放心使用

TaskScheduler负责提交任务，并且请求集群管理器调度任务。

1. 提交任务
2. 请求集群管理器调度任务
   Spark的集群管理器有三种：独立集群管理器、Hadoop Yarn、Apache Mesos。可以参考Spark集群管理器介绍-博客园了解一下。

1 创建TaskScheduler：createTaskScheduler

创建TaskScheduler的源代码为SparkContext.createTaskScheduler，如下所示。该方法会根据master的配置匹配部署模式，每种部署模式中都会创建两个类（TaskSchedulerImpl、SchedulerBackend）的实例，只是TaskSchedulerImpl都相同，SchedulerBackend不同。

/**
   * 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)
        }
    }
  }

2 TaskScheduler的实现类：TaskSchedulerImpl

TaskSchedulerImpl的源代码如下：

private[spark] class TaskSchedulerImpl(
    val sc: SparkContext,
    val maxTaskFailures: Int,
    isLocal: Boolean = false)
  extends TaskScheduler with Logging {
  ...
  }

TaskSchedulerImpl的构造过程：

1. 从SparkConf中读取配置信息，包括每个任务分配的CPU数、调度模式（调度模式又FAIR和FIFO两种，默认为FIFO，可以修改属性spark.scheduler.mode来改变）等。源代码为：

  val conf = sc.conf

  // How often to check for speculative tasks
  val SPECULATION_INTERVAL_MS = conf.getTimeAsMs("spark.speculation.interval", "100ms")

  // Duplicate copies of a task will only be launched if the original copy has been running for
  // at least this amount of time. This is to avoid the overhead of launching speculative copies
  // of tasks that are very short.
  val MIN_TIME_TO_SPECULATION = 100

  private val speculationScheduler =
    ThreadUtils.newDaemonSingleThreadScheduledExecutor("task-scheduler-speculation")

  // Threshold above which we warn user initial TaskSet may be starved
  val STARVATION_TIMEOUT_MS = conf.getTimeAsMs("spark.starvation.timeout", "15s")

  // CPUs to request per task
  val CPUS_PER_TASK = conf.getInt("spark.task.cpus", 1)

  // default scheduler is FIFO
  private val schedulingModeConf = conf.get(SCHEDULER_MODE_PROPERTY, SchedulingMode.FIFO.toString)

private lazy val barrierSyncTimeout = conf.get(config.BARRIER_SYNC_TIMEOUT)

2. 创建TaskResultGetter，它的作用是通过线程池对Worker上的Executor发送的Task的执行结果进行处理。

  //源码：TaskSchedulerImpl的部分源码
  // This is a var so that we can reset it for testing purposes.
  private[spark] var taskResultGetter = new TaskResultGetter(sc.env, this)

其中，该线程池由Executors.newFixedThreadPool创建，默认4个线程，线程名字以task-result-getter开头，线程工厂默认是Executors.defaultThreadFactory。根据下面两部分源码逐个解释：

• 默认4个线程
  TaskResultGetter类中的THREADS常量，通过字符串”spark.resultGetter.threads”得到值4。后面将THREADS作为参数，传入进ThreadUtils.newDaemonFixedThreadPool方法，再传入进Executors.newFixedThreadPool方法，依次向下传入，最终传入进ThreadPoolExecutor的构造方法，作为参数corePoolSize，通过this.corePoolSize = corePoolSize;设置线程池的默认线程数为4。
• 线程名字以task-result-getter开头
  在方法newDaemonFixedThreadPool方法的英文注释中说得很清楚。“Thread names are formatted as prefix-ID, where ID is a unique, sequentially assigned integer.”即线程名的格式是prefix-ID，其中prefix即TaskResultGetter类中通过语句ThreadUtils.newDaemonFixedThreadPool(THREADS, "task-result-getter")传入的字符串”task-result-getter”，ID是惟一的、按顺序分配的整数。
• 线程工厂默认是Executors.defaultThreadFactory
  说实话，没找到哎
  TaskResultGetter
  —>
  protected val getTaskResultExecutor: ExecutorService = ThreadUtils.newDaemonFixedThreadPool(THREADS, "task-result-getter")
  —>
  def newDaemonFixedThreadPool(nThreads: Int, prefix: String): ThreadPoolExecutor = { //使用到的线程工厂 val threadFactory = namedThreadFactory(prefix) ... }
  —>
  def namedThreadFactory(prefix: String): ThreadFactory = { new ThreadFactoryBuilder().setDaemon(true).setNameFormat(prefix + "-%d").build() }
  —>
  public ThreadFactory build() {return build(this);}
  —>
  private static ThreadFactory build(ThreadFactoryBuilder builder) { ... final ThreadFactory backingThreadFactory = builder.backingThreadFactory != null ? builder.backingThreadFactory : Executors.defaultThreadFactory(); Thread thread = backingThreadFactory.newThread(runnable); ... }
  —>
  //源码来自：Executors.java static class DefaultThreadFactory implements ThreadFactory {...}
  追根溯源到最后，最初的线程工厂实例即backingThreadFactory，它是类Executors.defaultThreadFactory的实例。

//源码：TaskResultGetter的部分源码
/**
  Runs a thread pool that deserializes and remotely fetches (if necessary) task results.
 */
private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedulerImpl)
  extends Logging {
//默认创建4个线程。
  private val THREADS = sparkEnv.conf.getInt("spark.resultGetter.threads", 4)

  // Exposed for testing.
  protected val getTaskResultExecutor: ExecutorService =
    ThreadUtils.newDaemonFixedThreadPool(THREADS, "task-result-getter")
    ...
    }

//源码：newDaemonFixedThreadPool方法的源码，来自ThreadUtils.scala文件。
  /**
   * Wrapper over newFixedThreadPool. Thread names are formatted as prefix-ID, where ID is a
   * unique, sequentially assigned integer.
   */
  def newDaemonFixedThreadPool(nThreads: Int, prefix: String): ThreadPoolExecutor = {
    val threadFactory = namedThreadFactory(prefix)
    Executors.newFixedThreadPool(nThreads, threadFactory).asInstanceOf[ThreadPoolExecutor]
  }

//源码：ThreadPoolExecutor
    /**
     * Creates a new {@code ThreadPoolExecutor} with the given initial
     * parameters.
     *
     * @param corePoolSize the number of threads to keep in the pool, even
     *        if they are idle, unless {@code allowCoreThreadTimeOut} is set
     * @param maximumPoolSize the maximum number of threads to allow in the
     *        pool
     * @param keepAliveTime when the number of threads is greater than
     *        the core, this is the maximum time that excess idle threads
     *        will wait for new tasks before terminating.
     * @param unit the time unit for the {@code keepAliveTime} argument
     * @param workQueue the queue to use for holding tasks before they are
     *        executed.  This queue will hold only the {@code Runnable}
     *        tasks submitted by the {@code execute} method.
     * @param threadFactory the factory to use when the executor
     *        creates a new thread
     * @param handler the handler to use when execution is blocked
     *        because the thread bounds and queue capacities are reached
     * @throws IllegalArgumentException if one of the following holds:<br>
     *         {@code corePoolSize < 0}<br>
     *         {@code keepAliveTime < 0}<br>
     *         {@code maximumPoolSize <= 0}<br>
     *         {@code maximumPoolSize < corePoolSize}
     * @throws NullPointerException if {@code workQueue}
     *         or {@code threadFactory} or {@code handler} is null
     */
    public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue,
                              ThreadFactory threadFactory,
                              RejectedExecutionHandler handler) {
        if (corePoolSize < 0 ||
            maximumPoolSize <= 0 ||
            maximumPoolSize < corePoolSize ||
            keepAliveTime < 0)
            throw new IllegalArgumentException();
        if (workQueue == null || threadFactory == null || handler == null)
            throw new NullPointerException();
        this.acc = System.getSecurityManager() == null ?
                null :
                AccessController.getContext();
        this.corePoolSize = corePoolSize;
        this.maximumPoolSize = maximumPoolSize;
        this.workQueue = workQueue;
        this.keepAliveTime = unit.toNanos(keepAliveTime);
        this.threadFactory = threadFactory;
        this.handler = handler;
    }

Spark的任务调度模式有两种FAIR（公平调度）、FIFO（先入先出），任务的最终调度实际都是落实到接口SchedulerBackend的具体实现上。
在SchedulerBackEnd中到底是怎么调度的呢？？？，看过几篇文章太简略，笔者需要再学习一下，后面继续补充。关于此有一篇不错的文章，即Spark理论学习笔记（一）-一笑之奈何-博客园

3 TaskScheduler的启动

TaskScheduler的启动源码：

SparkContext.scala中启动TaskScheduler的代码：

    //源码来自SparkContext.scala
    // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
    // constructor
    _taskScheduler.start()

_taskScheduler是TaskSchedulerImpl的实例，TaskSchedulerImpl是TaskScheduler的实现类，并且TaskSchedulerImpl重写了TaskScheduler中的start()方法，所以_taskScheduler.start()实际启动了TaskSchedulerImpl中的start()方法。由下面的源码可知，TaskScheduler在启动的时候，实际调用了backend的start()方法。

//源码来自TaskSchedulerImpl.scala
  override def start() {
    backend.start()
  }

**那么backend启动后，又做了什么呢？**接着看第四章

4 TaskScheduler提交任务

在下面的源码中，backend.reviveOffers()这一句用于提交任务。调用taskScheduler的submitTasks方法，最终会转到调用backend的reviveOffers方法。所谓的taskScheduler调度任务，实际是由schedulerBackend调度任务。

  //源码来自：TaskSchedulerImpl.scala
  override def submitTasks(taskSet: TaskSet) {
    ...
  }

4 SchedulerBackend

/**
 * Used when running a local version of Spark where the executor, backend, and master all run in
 * the same JVM. It sits behind a [[TaskSchedulerImpl]] and handles launching tasks on a single
 * Executor (created by the [[LocalSchedulerBackend]]) running locally.
 */
private[spark] class LocalSchedulerBackend(
    conf: SparkConf,
    scheduler: TaskSchedulerImpl,
    val totalCores: Int)
  extends SchedulerBackend with ExecutorBackend with Logging {
  ...
	  override def start() {
	    val rpcEnv = SparkEnv.get.rpcEnv
	    val executorEndpoint = new LocalEndpoint(rpcEnv, userClassPath, scheduler, this, totalCores)
	    localEndpoint = rpcEnv.setupEndpoint("LocalSchedulerBackendEndpoint", executorEndpoint)
	    listenerBus.post(SparkListenerExecutorAdded(
	      System.currentTimeMillis,
	      executorEndpoint.localExecutorId,
	      new ExecutorInfo(executorEndpoint.localExecutorHostname, totalCores, Map.empty)))
	    launcherBackend.setAppId(appId)
	    launcherBackend.setState(SparkAppHandle.State.RUNNING)
	  }
  ...
  }

/**
 * A backend interface for scheduling systems that allows plugging in different ones under
 * TaskSchedulerImpl. We assume a Mesos-like model where the application gets resource offers as
 * machines become available and can launch tasks on them.
 */
private[spark] trait SchedulerBackend {
  private val appId = "spark-application-" + System.currentTimeMillis

  def start(): Unit
  def stop(): Unit
  def reviveOffers(): Unit
  def defaultParallelism(): Int
  ...
}