spark源码之SparkContext

SparkContext可以说是Spark应用的发动机引擎，Spark Drive的初始化围绕这SparkContext的初始化。

SparkContext总览

sparkcontxt的主要组成部分

• sparkEnv：spark运行环境，Executor是处理任务的执行器，依赖于SparkEnv的环境。Driver中也包含SparkEnv，为了保证Local模式下任务执行。此外，SparkEnv还包含serializerManager、RpcEnv、BlockManager、mapOutputTracker等组件。
• LiveListenerBus：SparkContext的事件总线。接受各个使用方的事件，通过异步方式进行匹配后调用SparkListener的不同方法。
• SparkUI：间接依赖于计算引擎、调度引擎、存储引擎体系，Job、stage、存储、executor等的监控都会以SparkListener的形式投递到LiveListenerBus，SparkUI从各个SparkListener中读取数据并显示到Web。
• SparkStatusTracker：提供job、Stage等的监控信息，是低级API，只能提供一致性机制。
• ConsoleProgressBar：利用SparkStatusTracker的API，在控制台展示Stage的进度。因SparkStatusTracker的一致性，显示一般有延时。
• DAGScheduler（非常重要）：DAG调度器，负责创建job、提供划分算法划分stage、提交stage等。
• TaskScheduler（非常重要）：任务调度器，按照调度算法对集群管理器已经分配给应用程序的资源进行二次调度后分配给任务。TaskScheduler的Task由DAGScheduler创建。
• HeartbeatReceiver：心跳接收器，所有Executor都向HeartbeatReceiver发送心跳信息，HeartbeatReceiver接收到以后更新Executor的最后可见时间，然后将信息给TaskScheduler做处理。
• ContextCleaner：使用异步方式清理应用作用域的RDD、ShuffleDependcy和Broadcast。
• JobProgressListener：作业进度监听器。
• EventLoggingListener（可选）：将事件持久化到存储的监听器，当spark.eventLog.enabled为true时使用
• ExecutorAllocationManager：Exexcutor动态分配管理器。
• ShutdownHokManager：用于关闭钩子函的管理器，在JVM退出时，执行清理 工作。

下面学习SparkContext的初始化过程

创建SparkEnv

// This function allows components created by SparkEnv to be mocked in unit tests:
private[spark] def createSparkEnv(
      conf: SparkConf,
      isLocal: Boolean,
      listenerBus: LiveListenerBus): SparkEnv = {
    SparkEnv.createDriverEnv(conf, isLocal, listenerBus, SparkContext.numDriverCores(master, conf))
  }

private[spark] def env: SparkEnv = _env

先是创建createSparkEnv()方法，调用了createDriverEnv()

/* ------------------------------------------------------------------------------------- *
 | Initialization. This code initializes the context in a manner that is exception-safe. |
 | All internal fields holding state are initialized here, and any error prompts the     |
 | stop() method to be called.                                                           |
 * ------------------------------------------------------------------------------------- */

private def warnSparkMem(value: String): String = {
  logWarning("Using SPARK_MEM to set amount of memory to use per executor process is " +
    "deprecated, please use spark.executor.memory instead.")
  value
}

/** Control our logLevel. This overrides any user-defined log settings.
 * @param logLevel The desired log level as a string.
 * Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN
 */
def setLogLevel(logLevel: String) {
  // let's allow lowercase or mixed case too
  val upperCased = logLevel.toUpperCase(Locale.ROOT)
  require(SparkContext.VALID_LOG_LEVELS.contains(upperCased),
    s"Supplied level $logLevel did not match one of:" +
      s" ${SparkContext.VALID_LOG_LEVELS.mkString(",")}")
  Utils.setLogLevel(org.apache.log4j.Level.toLevel(upperCased))
}

try {
  _conf = config.clone()
  _conf.validateSettings()

  if (!_conf.contains("spark.master")) {
    throw new SparkException("A master URL must be set in your configuration")
  }
  if (!_conf.contains("spark.app.name")) {
    throw new SparkException("An application name must be set in your configuration")
  }

  // log out spark.app.name in the Spark driver logs
  logInfo(s"Submitted application: $appName")

  // System property spark.yarn.app.id must be set if user code ran by AM on a YARN cluster
  if (master == "yarn" && deployMode == "cluster" && !_conf.contains("spark.yarn.app.id")) {
    throw new SparkException("Detected yarn cluster mode, but isn't running on a cluster. " +
      "Deployment to YARN is not supported directly by SparkContext. Please use spark-submit.")
  }

  if (_conf.getBoolean("spark.logConf", false)) {
    logInfo("Spark configuration:
" + _conf.toDebugString)
  }

  // Set Spark driver host and port system properties. This explicitly sets the configuration
  // instead of relying on the default value of the config constant.
  _conf.set(DRIVER_HOST_ADDRESS, _conf.get(DRIVER_HOST_ADDRESS))
  _conf.setIfMissing("spark.driver.port", "0")

  _conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER)

  _jars = Utils.getUserJars(_conf)
  _files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.nonEmpty))
    .toSeq.flatten

  _eventLogDir =
    if (isEventLogEnabled) {
      val unresolvedDir = conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR)
        .stripSuffix("/")
      Some(Utils.resolveURI(unresolvedDir))
    } else {
      None
    }

  _eventLogCodec = {
    val compress = _conf.getBoolean("spark.eventLog.compress", false)
    if (compress && isEventLogEnabled) {
      Some(CompressionCodec.getCodecName(_conf)).map(CompressionCodec.getShortName)
    } else {
      None
    }
  }

  _listenerBus = new LiveListenerBus(_conf)

  // Initialize the app status store and listener before SparkEnv is created so that it gets
  // all events.
  _statusStore = AppStatusStore.createLiveStore(conf)
  listenerBus.addToStatusQueue(_statusStore.listener.get)

  // Create the Spark execution environment (cache, map output tracker, etc)
  _env = createSparkEnv(_conf, isLocal, listenerBus)
  SparkEnv.set(_env)

　　因为SparkEnv的很多组件都向LiveListenerBus的事件总线队列中投递事件，所以首先创建的LiveListenerBus，这个类主要功能如下

• 保存有消息队列,负责消息的缓存
• 保存有注册过的listener,负责消息的分发
  这是一个简单的监听器模型。

SparkUI实现

SparkUI涉及太多组件，这里暂时不深入剖析，后续单独剖析。下面是创建SparkUI的代码

_statusTracker = new SparkStatusTracker(this, _statusStore)
　
　_progressBar =
      if (_conf.get(UI_SHOW_CONSOLE_PROGRESS) && !log.isInfoEnabled) {
        Some(new ConsoleProgressBar(this))
      } else {
        None
      }

    _ui =
      if (conf.getBoolean("spark.ui.enabled", true)) {
        Some(SparkUI.create(Some(this), _statusStore, _conf, _env.securityManager, appName, "",
          startTime))
      } else {
        // For tests, do not enable the UI
        None
      }
    // Bind the UI before starting the task scheduler to communicate
    // the bound port to the cluster manager properly
    _ui.foreach(_.bind())

创建心跳接收器

如果是local模式，Driver和executor再同一节点，可以直接使用本地交互。出现异常可以方便知道。

当再生产环境中时，往往Executor和Driver是在不同节点上启动的，因此，Driver为了能够掌控Executor，在Driver中创建了心跳接收器。

// We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will
    // retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640)
    _heartbeatReceiver = env.rpcEnv.setupEndpoint(
      HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this))

代码用了SparkEnv的子组件NettyRpcEnv的setupEndpoint()方法，

该方法的作用：是向RpcEnv的Dispatcher注册HeartbeatReceiver，并返回HeartbeatReceiver的NettyRpcEndPointRef的引用。

创建和启动调度系统

TaskScheduler负责请求集群管理器给应用程序分配并运行Executor（一级调度）和给任务分配Executor并运行任务（二级调度），可以看作是任务调度的客户端。

DAGScheduler主要在任务正式交给TaskSchedulerImp提交前的准备工作，包括创建Job、将DAG的RDD划分到不同的stage、提交Stage等。

// Create and start the scheduler
    val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)
    _schedulerBackend = sched
    _taskScheduler = ts
    _dagScheduler = new DAGScheduler(this)
    _heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)

    // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
    // constructor
    _taskScheduler.start()

createTaskScheduler()方法返回Scheduler和TaskScheduler的对偶（此处补scala的知识），表示SparkContext的_taskScheduler已经有了TAskScheduler的引用，HeartbeatReceiver接收到TaskSchedulerIsSet消息后将获取sparkContext的

_taskScheduler属性设置到自身的scheduler属性中。

/**
   * 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)
        }
    }
  }

初始化块管理器BlockManager

BlockManager是SparkEnv的组件之一，囊括了spark存储体系的所有组件和功能，是存储体系最重要的组件。spark的存储体系后续学习。

_applicationId = _taskScheduler.applicationId()

_env.blockManager.initialize(_applicationId)

　　

启动度量系统

spark在监控方面有自己的一套体系，一个系统的监控功能可丰富可测试性、性能优化、运维评估、数据统计等。spark的度量系统使用的是codahale提供的第三方仓库Metrics。

spark的度量系统的三个重要概念：

• Instance：指定度量系统的实例名，分为Master、Worker、Application、Driver和Executor
• Source：数据来源，分为应用度量来源（ApplicationSource）、Worker度量来源（WorkerSource）、DAGSceduler度量来源（DAGScedulerSource）、BlockManager度量来源（BlockManagerSource）
• Sink：度量数据输出。默认Servlet，还提供了ConsoleSink、CsvSink、JmxSink、MetricsServlet、GraphiteSink等。

metricsSystem对Source和Sink进行封装，将Source的数据输出到不同的Sink。

metricsSystem是SparkEnv内部组件之一，是整个spark应用程序的度量系统。

// The metrics system for Driver need to be set spark.app.id to app ID.
    // So it should start after we get app ID from the task scheduler and set spark.app.id.
    _env.metricsSystem.start()
    // Attach the driver metrics servlet handler to the web ui after the metrics system is started.
    _env.metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler)))

将系统的ServletContextHandler添加到SparkUI中。

创建事件日志监听器（可选）

 _eventLogger =
      if (isEventLogEnabled) {
        val logger =
          new EventLoggingListener(_applicationId, _applicationAttemptId, _eventLogDir.get,
            _conf, _hadoopConfiguration)
        logger.start()
        listenerBus.addToEventLogQueue(logger)
        Some(logger)
      } else {
        None
      }

创建和启动ExecutorAllocationManager

ExecutorAllocationManager是基于工作负载动态分配和删除Executor的代理。

它内部会定时根据工作负载计算所需的Executor数量，

如果对Executor需求大于集群管理器申请的数量，那么向集群管理器添加Executor。反之，向集群管理器申请取消部分Executor。

此外它内部还会定时向集群管理器申请一出（杀死）过期了的Executor。

// Optionally scale number of executors dynamically based on workload. Exposed for testing.
    val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)
    _executorAllocationManager =
      if (dynamicAllocationEnabled) {
        schedulerBackend match {
          case b: ExecutorAllocationClient =>
            Some(new ExecutorAllocationManager(
              schedulerBackend.asInstanceOf[ExecutorAllocationClient], listenerBus, _conf,
              _env.blockManager.master))
          case _ =>
            None
        }
      } else {
        None
      }
    _executorAllocationManager.foreach(_.start())

ContextCleaner的创建和启动

用于清理超出应用范围的RDD、shuffle对应的map任务状态、Shuffle元数据、Broadcast对象及RDD的checkpoint数据

• 创建ContexCleaner

_cleaner =
      if (_conf.getBoolean("spark.cleaner.referenceTracking", true)) {
        Some(new ContextCleaner(this))
      } else {
        None
      }
    _cleaner.foreach(_.start())

• 启动ContexCleaner

/** Start the cleaner. */
  def start(): Unit = {
    cleaningThread.setDaemon(true)
    cleaningThread.setName("Spark Context Cleaner")
    cleaningThread.start()
    periodicGCService.scheduleAtFixedRate(new Runnable {
      override def run(): Unit = System.gc()
    }, periodicGCInterval, periodicGCInterval, TimeUnit.SECONDS)
  }

除了GC的定时器，ContextCleaner的其余工作原理和listenerBus一样（采用监听器模式，由异步线程来处理）。

Spark环境更新

•  用户提交任务时添加的额外的jar包或者其他文件，该如何指定他们呢？

SparkContext初始化的时候会读取用户指定的Jar文件或者其他文件

_jars = Utils.getUserJars(_conf)
    _files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.nonEmpty))
      .toSeq.flatten

首先读取的时Jar文件，然后读取用户设置的其他文件。

当用Yarn模式时，_jars是spark.jars和spark.yarn.dist.jars的Jar文件的并集。

其他模式时，只采用spark.jars指定的Jar文件。

• 任务如何获取这些jar和文件呢？

def jars: Seq[String] = _jars
def files: Seq[String] = _files

// Add each JAR given through the constructor
if (jars != null) {
  jars.foreach(addJar)
}

if (files != null) {
  files.foreach(addFile)
}

addJar将Jar文件添加到Driver的RPC环境中。

由于addJar和addFile可能会对应用的环境产生影响，因此在SparkContext初始化的最后对更新环境

postEnvironmentUpdate()

SparkContext的收尾工作

postEnvironmentUpdate()
  postApplicationStart()

  // Post init
  _taskScheduler.postStartHook() // 等待SchedulerBackend准备完成
  // 向度量系统注册Source
  _env.metricsSystem.registerSource(_dagScheduler.metricsSource)
  _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))
  _executorAllocationManager.foreach { e =>
    _env.metricsSystem.registerSource(e.executorAllocationManagerSource)
  }

  // Make sure the context is stopped if the user forgets about it. This avoids leaving
  // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM
  // is killed, though.
  // 添加SparkContext的关闭钩子
  logDebug("Adding shutdown hook") // force eager creation of logger
  _shutdownHookRef = ShutdownHookManager.addShutdownHook(
    ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>
    logInfo("Invoking stop() from shutdown hook")
    try {
      stop()
    } catch {
      case e: Throwable =>
        logWarning("Ignoring Exception while stopping SparkContext from shutdown hook", e)
    }
  }
} catch {
  case NonFatal(e) =>
    logError("Error initializing SparkContext.", e)
    try {
      stop()
    } catch {
      case NonFatal(inner) =>
        logError("Error stopping SparkContext after init error.", inner)
    } finally {
      throw e
    }
}


// In order to prevent multiple SparkContexts from being active at the same time, mark this
// context as having finished construction. 
// NOTE: this must be placed at the end of the SparkContext constructor.
SparkContext.setActiveContext(this, allowMultipleContexts)

　　

SparkContext提供的常用方法

broadcast

/**
   * Broadcast a read-only variable to the cluster, returning a
   * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions.
   * The variable will be sent to each cluster only once.
   *
   * @param value value to broadcast to the Spark nodes
   * @return `Broadcast` object, a read-only variable cached on each machine
   */
  def broadcast[T: ClassTag](value: T): Broadcast[T] = {
    assertNotStopped()
    require(!classOf[RDD[_]].isAssignableFrom(classTag[T].runtimeClass),
      "Can not directly broadcast RDDs; instead, call collect() and broadcast the result.")
    val bc = env.broadcastManager.newBroadcast[T](value, isLocal)
    val callSite = getCallSite
    logInfo("Created broadcast " + bc.id + " from " + callSite.shortForm)
    cleaner.foreach(_.registerBroadcastForCleanup(bc))
    bc
  }

 实质上是调用了SparkEnv的BroadcastManager的newBroadcast()方法生成广播对象。

addSparkListener

用于向LiveListenerBus中提娜佳实现了特质SparkListenerInterface的监听器

/**
   * :: DeveloperApi ::
   * Register a listener to receive up-calls from events that happen during execution.
   */
  @DeveloperApi
  def addSparkListener(listener: SparkListenerInterface) {
    listenerBus.addToSharedQueue(listener)
  }

 runjob

SparkContext重载了runjob方法。最终都调用下面这个runjob。

/**
   * Run a function on a given set of partitions in an RDD and pass the results to the given
   * handler function. This is the main entry point for all actions in Spark.
   *
   * @param rdd target RDD to run tasks on
   * @param func a function to run on each partition of the RDD
   * @param partitions set of partitions to run on; some jobs may not want to compute on all
   * partitions of the target RDD, e.g. for operations like `first()`
   * @param resultHandler callback to pass each result to
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      resultHandler: (Int, U) => Unit): Unit = {
    if (stopped.get()) {
      throw new IllegalStateException("SparkContext has been shutdown")
    }
    val callSite = getCallSite
    val cleanedFunc = clean(func)
    logInfo("Starting job: " + callSite.shortForm)
    if (conf.getBoolean("spark.logLineage", false)) {
      logInfo("RDD's recursive dependencies:
" + rdd.toDebugString)
    }
    //调用sparkContext之前初始化时创建的DAGScheduler的runJob()方法
    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
    progressBar.foreach(_.finishAll())
    rdd.doCheckpoint() // 保存检查点
  }

setCheckPoint

给作业中的RDD指定保存检查点的目录，是启用检查点机制的前提。

/**
   * Set the directory under which RDDs are going to be checkpointed.
   * @param directory path to the directory where checkpoint files will be stored
   * (must be HDFS path if running in cluster)
   */
  def setCheckpointDir(directory: String) {

    // If we are running on a cluster, log a warning if the directory is local.
    // Otherwise, the driver may attempt to reconstruct the checkpointed RDD from
    // its own local file system, which is incorrect because the checkpoint files
    // are actually on the executor machines.
    if (!isLocal && Utils.nonLocalPaths(directory).isEmpty) {
      logWarning("Spark is not running in local mode, therefore the checkpoint directory " +
        s"must not be on the local filesystem. Directory '$directory' " +
        "appears to be on the local filesystem.")
    }

　　

参考

1.《Spark内核设计的艺术架构设计与实现》

 2.Spark2.4.3源码