SparkContext的初始化（仲篇）——SparkUI、环境变量及调度

《深入理解Spark：核心思想与源码分析》一书前言的内容请看链接《深入理解SPARK：核心思想与源码分析》一书正式出版上市

《深入理解Spark：核心思想与源码分析》一书第一章的内容请看链接《第1章环境准备》

《深入理解Spark：核心思想与源码分析》一书第二章的内容请看链接《第2章 Spark设计理念与基本架构》

由于本书的第3章内容较多，所以打算分别开辟四篇随笔分别展现。

《深入理解Spark：核心思想与源码分析》一书第三章第一部分的内容请看链接《深入理解Spark：核心思想与源码分析》——SparkContext的初始化（伯篇）》

本文展现第3章第一部分的内容：

3.4 SPARKUI详解

　　任何系统都需要提供监控功能，用浏览器能访问具有样式及布局，并提供丰富监控数据的页面无疑是一种简单、高效的方式。SparkUI就是这样的服务，它的构成如图3-1所示。在大型分布式系统中，采用事件监听机制是最常见的。为什么要使用事件监听机制？假如SparkUI采用Scala的函数调用方式，那么随着整个集群规模的增加，对函数的调用会越来越多，最终会受到Driver所在JVM的线程数量限制而影响监控数据的更新，甚至出现监控数据无法及时显示给用户的情况。由于函数调用多数情况下是同步调用，这就导致线程被阻塞，在分布式环境中，还可能因为网络问题，导致线程被长时间占用。将函数调用更换为发送事件，事件的处理是异步的，当前线程可以继续执行后续逻辑，线程池中的线程还可以被重用，这样整个系统的并发度会大大增加。发送的事件会存入缓存，由定时调度器取出后，分配给监听此事件的监听器对监控数据进行更新。

图3-1 SparkUI架构

　　我们先将图3-1中的各个组件作简单介绍：DAGScheduler是主要的产生各类SparkListenerEvent的源头，它将各种SparkListenerEvent发送到listenerBus的事件队列中，listenerBus通过定时器将SparkListenerEvent事件匹配到具体的SparkListener，改变SparkListener中的统计监控数据，最终由SparkUI的界面展示。从图3-1中还可以看到Spark里定义了很多监听器SparkListener的实现，包括JobProgressListener、EnviromentListener、StorageListener、ExecutorsListener几种，它们的类继承体系如图3-2所示。

图3-2 SparkListener继承体系

3.4.1 listenerBus详解

　　listenerBus的类型是LiveListenerBus，LiveListenerBus实现了监听器模型，通过监听事件触发对各种监听器监听状态信息的修改，达到UI界面的数据刷新效果。LiveListenerBus由以下部分组成：

q 事件阻塞队列：类型为LinkedBlockingQueue[SparkListenerEvent]，固定大小是10000；

q 监听器数组：类型为ArrayBuffer[SparkListener]，存放各类监听器SparkListener。SparkListener是；

q 事件匹配监听器的线程：此Thread不断拉取LinkedBlockingQueue中的事情，遍历监听器，调用监听器的方法。任何事件都会在LinkedBlockingQueue中存在一段时间，然后Thread处理了此事件后，会将其清除。因此使用listener bus这个名字再合适不过了，到站就下车。listenerBus的实现，见代码清单3-15。

代码清单3-15 LiveListenerBus的事件处理实现

private val EVENT_QUEUE_CAPACITY = 10000private val eventQueue = new LinkedBlockingQueue[SparkListenerEvent](EVENT_QUEUE_CAPACITY)private var queueFullErrorMessageLogged = falseprivate var started = false// A counter that represents the number of events produced and consumed in the queueprivate val eventLock = new Semaphore(0)private val listenerThread = new Thread("SparkListenerBus") {setDaemon(true)override def run(): Unit = Utils.logUncaughtExceptions {while (true) {eventLock.acquire()// Atomically remove and process this eventLiveListenerBus.this.synchronized {val event = eventQueue.pollif (event == SparkListenerShutdown) {// Get out of the while loop and shutdown the daemon threadreturn}Option(event).foreach(postToAll)}}}}def start() {if (started) {throw new IllegalStateException("Listener bus already started!")}listenerThread.start()started = true}
def post(event: SparkListenerEvent) {val eventAdded = eventQueue.offer(event)if (eventAdded) {eventLock.release()} else {logQueueFullErrorMessage()}}def listenerThreadIsAlive: Boolean = synchronized { listenerThread.isAlive }def queueIsEmpty: Boolean = synchronized { eventQueue.isEmpty }def stop() {if (!started) {throw new IllegalStateException("Attempted to stop a listener bus that has not yet started!")}post(SparkListenerShutdown)listenerThread.join()}

LiveListenerBus中调用的postToAll方法实际定义在父类SparkListenerBus中，如代码清单3-16所示。

代码清单3-16 SparkListenerBus中的监听器调用

  protected val sparkListeners = new ArrayBuffer[SparkListener]with mutable.SynchronizedBuffer[SparkListener]def addListener(listener: SparkListener) {sparkListeners += listener}def postToAll(event: SparkListenerEvent) {event match {case stageSubmitted: SparkListenerStageSubmitted =>foreachListener(_.onStageSubmitted(stageSubmitted))case stageCompleted: SparkListenerStageCompleted =>foreachListener(_.onStageCompleted(stageCompleted))case jobStart: SparkListenerJobStart =>foreachListener(_.onJobStart(jobStart))case jobEnd: SparkListenerJobEnd =>foreachListener(_.onJobEnd(jobEnd))case taskStart: SparkListenerTaskStart =>foreachListener(_.onTaskStart(taskStart))case taskGettingResult: SparkListenerTaskGettingResult =>foreachListener(_.onTaskGettingResult(taskGettingResult))case taskEnd: SparkListenerTaskEnd =>foreachListener(_.onTaskEnd(taskEnd))case environmentUpdate: SparkListenerEnvironmentUpdate =>foreachListener(_.onEnvironmentUpdate(environmentUpdate))case blockManagerAdded: SparkListenerBlockManagerAdded =>foreachListener(_.onBlockManagerAdded(blockManagerAdded))case blockManagerRemoved: SparkListenerBlockManagerRemoved =>foreachListener(_.onBlockManagerRemoved(blockManagerRemoved))case unpersistRDD: SparkListenerUnpersistRDD =>foreachListener(_.onUnpersistRDD(unpersistRDD))case applicationStart: SparkListenerApplicationStart =>foreachListener(_.onApplicationStart(applicationStart))case applicationEnd: SparkListenerApplicationEnd =>foreachListener(_.onApplicationEnd(applicationEnd))case metricsUpdate: SparkListenerExecutorMetricsUpdate =>foreachListener(_.onExecutorMetricsUpdate(metricsUpdate))case SparkListenerShutdown =>}}private def foreachListener(f: SparkListener => Unit): Unit = {sparkListeners.foreach { listener =>try {f(listener)} catch {case e: Exception =>logError(s"Listener ${Utils.getFormattedClassName(listener)} threw an exception", e)}}}

3.4.2 构造JobProgressListener

　　我们以JobProgressListener为例来讲解SparkListener。JobProgressListener是SparkContext中一个重要的组成部分，通过监听listenerBus中的事件更新任务进度。SparkStatusTracker和SparkUI实际上也是通过JobProgressListener来实现任务状态跟踪的。创建JobProgressListener的代码如下。

  private[spark] val jobProgressListener = new JobProgressListener(conf)listenerBus.addListener(jobProgressListener)val statusTracker = new SparkStatusTracker(this)

JobProgressListener的作用是通过HashMap、ListBuffer等数据结构存储JobId及对应的JobUIData信息，并按照激活、完成、失败等job状态统计。对于StageId、StageInfo等信息按照激活、完成、忽略、失败等stage状态统计。并且存储StageId与JobId的一对多关系。这些统计信息最终会被JobPage和StagePage等页面访问和渲染。JobProgressListener的数据结构见代码清单3-17。

代码清单3-17 JobProgressListener维护的信息

class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {import JobProgressListener._type JobId = Inttype StageId = Inttype StageAttemptId = Inttype PoolName = Stringtype ExecutorId = String// Jobs:val activeJobs = new HashMap[JobId, JobUIData]val completedJobs = ListBuffer[JobUIData]()val failedJobs = ListBuffer[JobUIData]()val jobIdToData = new HashMap[JobId, JobUIData]// Stages:val activeStages = new HashMap[StageId, StageInfo]val completedStages = ListBuffer[StageInfo]()val skippedStages = ListBuffer[StageInfo]()val failedStages = ListBuffer[StageInfo]()val stageIdToData = new HashMap[(StageId, StageAttemptId), StageUIData]val stageIdToInfo = new HashMap[StageId, StageInfo]val stageIdToActiveJobIds = new HashMap[StageId, HashSet[JobId]]val poolToActiveStages = HashMap[PoolName, HashMap[StageId, StageInfo]]()var numCompletedStages = 0 // 总共完成的Stage数量var numFailedStages = 0 / 总共失败的Stage数量// Misc:val executorIdToBlockManagerId = HashMap[ExecutorId, BlockManagerId]()def blockManagerIds = executorIdToBlockManagerId.values.toSeqvar schedulingMode: Option[SchedulingMode] = None// number of non-active jobs and stages (there is no limit for active jobs and stages):val retainedStages = conf.getInt("spark.ui.retainedStages", DEFAULT_RETAINED_STAGES)val retainedJobs = conf.getInt("spark.ui.retainedJobs", DEFAULT_RETAINED_JOBS)

JobProgressListener 实现了onJobStart、onJobEnd、onStageCompleted、onStageSubmitted、onTaskStart、onTaskEnd等方法，这些方法正是在listenerBus的驱动下，改变JobProgressListener中的各种Job、Stage相关的数据。

3.4.3 SparkUI的创建与初始化

创建SparkUI的实现，见代码清单3-18。

代码清单3-18 SparkUI的声明

  private[spark] val ui: Option[SparkUI] =if (conf.getBoolean("spark.ui.enabled", true)) {Some(SparkUI.createLiveUI(this, conf, listenerBus, jobProgressListener,env.securityManager,appName))} else {None}ui.foreach(_.bind())

可以看到如果不需要提供SparkUI服务，可以将属性spark.ui.enabled修改为false。其中createLiveUI实际是调用了create方法，见代码清单3-19。

代码清单3-19 SparkUI的创建

  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))}

在create方法里，除了JobProgressListener是外部传入的之外，又增加了一些SparkListener。例如，用于对JVM参数、Spark属性、Java系统属性、classpath等进行监控的EnvironmentListener；用于维护executor的存储状态的StorageStatusListener；用于准备将executor的信息展示在ExecutorsTab的ExecutorsListener；用于准备将executor相关存储信息展示在BlockManagerUI的StorageListener等。最后创建SparkUI，参见代码清单3-20。

代码清单3-20 create方法的实现

  private def create(sc: Option[SparkContext],conf: SparkConf,listenerBus: SparkListenerBus,securityManager: SecurityManager,appName: String,basePath: String = "",jobProgressListener: Option[JobProgressListener] = None): SparkUI = {val _jobProgressListener: JobProgressListener = jobProgressListener.getOrElse {val listener = new JobProgressListener(conf)listenerBus.addListener(listener)listener}val environmentListener = new EnvironmentListenerval storageStatusListener = new StorageStatusListenerval executorsListener = new ExecutorsListener(storageStatusListener)val storageListener = new StorageListener(storageStatusListener)listenerBus.addListener(environmentListener)listenerBus.addListener(storageStatusListener)listenerBus.addListener(executorsListener)listenerBus.addListener(storageListener)new SparkUI(sc, conf, securityManager, environmentListener, storageStatusListener,executorsListener, _jobProgressListener, storageListener, appName, basePath)}

SparkUI服务默认是可以被杀掉的，通过修改属性spark.ui.killEnabled为false可以保证不被杀死。initialize方法，会组织前端页面各个Tab和Page的展示及布局，参见代码清单3-21。

代码清单3-21 SparkUI的初始化

private[spark] class SparkUI private (val sc: Option[SparkContext],val conf: SparkConf,val securityManager: SecurityManager,val environmentListener: EnvironmentListener,val storageStatusListener: StorageStatusListener,val executorsListener: ExecutorsListener,val jobProgressListener: JobProgressListener,val storageListener: StorageListener,var appName: String,val basePath: String)extends WebUI(securityManager, SparkUI.getUIPort(conf), conf, basePath, "SparkUI")with Logging {val killEnabled = sc.map(_.conf.getBoolean("spark.ui.killEnabled", true)).getOrElse(false)/** Initialize all components of the server. */def initialize() {attachTab(new JobsTab(this))val stagesTab = new StagesTab(this)attachTab(stagesTab)attachTab(new StorageTab(this))attachTab(new EnvironmentTab(this))attachTab(new ExecutorsTab(this))attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath))attachHandler(createRedirectHandler("/stages/stage/kill", "/stages", stagesTab.handleKillRequest))}initialize()

3.4.4 SparkUI的页面布局及展示

　　SparkUI究竟是如何实现页面布局及展示的？JobsTab展示所有Job的进度、状态信息，这里我们以它为例来说明。JobsTab会复用SparkUI的killEnabled、SparkContext、jobProgressListener，包括AllJobsPage和JobPage两个页面，见代码清单3-22。

代码清单3-22 JobsTab的实现

private[ui] class JobsTab(parent: SparkUI) extends SparkUITab(parent, "jobs") {val sc = parent.scval killEnabled = parent.killEnableddef isFairScheduler = listener.schedulingMode.exists(_ == SchedulingMode.FAIR)val listener = parent.jobProgressListenerattachPage(new AllJobsPage(this))attachPage(new JobPage(this))
}

AllJobsPage由render方法渲染，利用jobProgressListener中的统计监控数据生成激活、完成、失败等状态的Job摘要信息，并调用jobsTable方法生成表格等html元素，最终使用UIUtils的headerSparkPage封装好css、js、header及页面布局等，见代码清单3-23。

代码清单3-23 AllJobsPage的实现

def render(request: HttpServletRequest): Seq[Node] = {listener.synchronized {val activeJobs = listener.activeJobs.values.toSeqval completedJobs = listener.completedJobs.reverse.toSeqval failedJobs = listener.failedJobs.reverse.toSeqval now = System.currentTimeMillisval activeJobsTable =jobsTable(activeJobs.sortBy(_.startTime.getOrElse(-1L)).reverse)val completedJobsTable =jobsTable(completedJobs.sortBy(_.endTime.getOrElse(-1L)).reverse)val failedJobsTable =jobsTable(failedJobs.sortBy(_.endTime.getOrElse(-1L)).reverse)val summary: NodeSeq =<div><ul class="unstyled">{if (startTime.isDefined) {// Total duration is not meaningful unless the UI is live<li><strong>Total Duration: </strong>{UIUtils.formatDuration(now - startTime.get)}</li>}}<li><strong>Scheduling Mode: </strong>{listener.schedulingMode.map(_.toString).getOrElse("Unknown")}</li><li><a href="#active"><strong>Active Jobs:</strong></a>{activeJobs.size}</li><li><a href="#completed"><strong>Completed Jobs:</strong></a>{completedJobs.size}</li><li><a href="#failed"><strong>Failed Jobs:</strong></a>{failedJobs.size}</li></ul></div>

jobsTable用来生成表格数据，见代码清单3-24。

代码清单3-24 jobsTable处理表格的实现

private def jobsTable(jobs: Seq[JobUIData]): Seq[Node] = {val someJobHasJobGroup = jobs.exists(_.jobGroup.isDefined)val columns: Seq[Node] = {<th>{if (someJobHasJobGroup) "Job Id (Job Group)" else "Job Id"}</th><th>Description</th><th>Submitted</th><th>Duration</th><th class="sorttable_nosort">Stages: Succeeded/Total</th><th class="sorttable_nosort">Tasks (for all stages): Succeeded/Total</th>}<table class="table table-bordered table-striped table-condensed sortable"><thead>{columns}</thead><tbody>{jobs.map(makeRow)}</tbody></table>}

表格中每行数据又是通过makeRow方法渲染的，参见代码清单3-25。

代码清单3-25 生成表格中的行

def makeRow(job: JobUIData): Seq[Node] = {val lastStageInfo = Option(job.stageIds).filter(_.nonEmpty).flatMap { ids => listener.stageIdToInfo.get(ids.max) }val lastStageData = lastStageInfo.flatMap { s =>listener.stageIdToData.get((s.stageId, s.attemptId))}val isComplete = job.status == JobExecutionStatus.SUCCEEDEDval lastStageName = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)")val lastStageDescription = lastStageData.flatMap(_.description).getOrElse("")val duration: Option[Long] = {job.startTime.map { start =>val end = job.endTime.getOrElse(System.currentTimeMillis())end - start}}val formattedDuration = duration.map(d => UIUtils.formatDuration(d)).getOrElse("Unknown")val formattedSubmissionTime = job.startTime.map(UIUtils.formatDate).getOrElse("Unknown")val detailUrl ="%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), job.jobId)<tr><td sorttable_customkey={job.jobId.toString}>{job.jobId} {job.jobGroup.map(id => s"($id)").getOrElse("")}</td><td><div><em>{lastStageDescription}</em></div><a href={detailUrl}>{lastStageName}</a></td><td sorttable_customkey={job.startTime.getOrElse(-1).toString}>{formattedSubmissionTime}</td><td sorttable_customkey={duration.getOrElse(-1).toString}>{formattedDuration}</td><td class="stage-progress-cell">{job.completedStageIndices.size}/{job.stageIds.size - job.numSkippedStages}{if (job.numFailedStages > 0) s"(${job.numFailedStages} failed)"}{if (job.numSkippedStages > 0) s"(${job.numSkippedStages} skipped)"}</td><td class="progress-cell">{UIUtils.makeProgressBar(started = job.numActiveTasks, completed = job.numCompletedTasks,failed = job.numFailedTasks, skipped = job.numSkippedTasks,total = job.numTasks - job.numSkippedTasks)}</td></tr>}

代码清单3-22中的attachPage方法存在于JobsTab的父类WebUITab中，WebUITab维护有ArrayBuffer[WebUIPage]的数据结构，AllJobsPage和JobPage将被放入此ArrayBuffer中，参见代码清单3-26。

代码清单3-26 WebUITab的实现

private[spark] abstract class WebUITab(parent: WebUI, val prefix: String) {val pages = ArrayBuffer[WebUIPage]()val name = prefix.capitalize/** Attach a page to this tab. This prepends the page's prefix with the tab's own prefix. */def attachPage(page: WebUIPage) {page.prefix = (prefix + "/" + page.prefix).stripSuffix("/")pages += page}/** Get a list of header tabs from the parent UI. */def headerTabs: Seq[WebUITab] = parent.getTabsdef basePath: String = parent.getBasePath
}

JobsTab创建之后，将被attachTab方法加入SparkUI的ArrayBuffer[WebUITab]中，并且通过attachPage方法，给每一个page生成org.eclipse.jetty.servlet.ServletContextHandler，最后调用attachHandler方法将ServletContextHandler绑定到SparkUI，即加入到handlers ：ArrayBuffer[ServletContextHandler]和样例类ServerInfo样例类的rootHandler（ContextHandlerCollection）中。SparkUI继承自WebUI，attachTab方法在WebUI中实现，参见代码清单3-27。

代码清单3-27 WebUI的实现

private[spark] abstract class WebUI( securityManager: SecurityManager, port: Int,conf: SparkConf, basePath: String = "", name: String = "") extends Logging {protected val tabs = ArrayBuffer[WebUITab]()protected val handlers = ArrayBuffer[ServletContextHandler]()protected var serverInfo: Option[ServerInfo] = Noneprotected val localHostName = Utils.localHostName()protected val publicHostName = Option(System.getenv("SPARK_PUBLIC_DNS")).getOrElse(localHostName)private val className = Utils.getFormattedClassName(this)def getBasePath: String = basePathdef getTabs: Seq[WebUITab] = tabs.toSeqdef getHandlers: Seq[ServletContextHandler] = handlers.toSeqdef getSecurityManager: SecurityManager = securityManager/** Attach a tab to this UI, along with all of its attached pages. */def attachTab(tab: WebUITab) {tab.pages.foreach(attachPage)tabs += tab}/** Attach a page to this UI. */def attachPage(page: WebUIPage) {val pagePath = "/" + page.prefixattachHandler(createServletHandler(pagePath,(request: HttpServletRequest) => page.render(request), securityManager, basePath))attachHandler(createServletHandler(pagePath.stripSuffix("/") + "/json",(request: HttpServletRequest) => page.renderJson(request), securityManager, basePath))}/** Attach a handler to this UI. */def attachHandler(handler: ServletContextHandler) {handlers += handlerserverInfo.foreach { info =>info.rootHandler.addHandler(handler)if (!handler.isStarted) {handler.start()}}}

由于代码清单3-27所在的类中使用import org.apache.spark.ui.JettyUtils._导入了JettyUtils的静态方法，所以createServletHandler方法实际是JettyUtils 的静态方法createServletHandler。createServletHandler实际创建了javax.servlet.http.HttpServlet的匿名内部类实例，此实例实际使用(request: HttpServletRequest) => page.render(request)这个函数参数来处理请求，进而渲染页面呈现给用户。有关createServletHandler的实现，及Jetty的相关信息，请参阅附录C。

3.4.5 SparkUI启动

　　SparkUI创建好后，需要调用父类WebUI的bind方法，绑定服务和端口，bind方法中主要的代码实现如下。

      serverInfo = Some(startJettyServer("0.0.0.0", port, handlers, conf, name))

JettyUtils的静态方法startJettyServer的实现请参阅附录C。最终启动了Jetty提供的服务，默认端口是4040。

3.5 HADOOP相关配置及EXECUTOR环境变量

3.5.1 Hadoop相关配置信息

　　默认情况下，Spark使用HDFS作为分布式文件系统，所以需要获取Hadoop相关配置信息的代码如下。

  val hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(conf)

　　获取的配置信息包括：

q Amazon S3文件系统AccessKeyId和SecretAccessKey加载到Hadoop的Configuration；

q 将SparkConf中所有spark.hadoop.开头的属性都复制到Hadoop的Configuration；

q 将SparkConf的属性spark.buffer.size复制为Hadoop的Configuration的配置io.file.buffer.size。

注意：如果指定了SPARK_YARN_MODE属性，则会使用YarnSparkHadoopUtil，否则默认为SparkHadoopUtil。

3.5.2 Executor环境变量

　　对Executor的环境变量的处理，参见代码清单3-28。executorEnvs 包含的环境变量将会在7.2.2节中介绍的注册应用的过程中发送给Master，Master给Worker发送调度后，Worker最终使用executorEnvs提供的信息启动Executor。可以通过配置spark.executor.memory指定Executor占用的内存大小，也可以配置系统变量SPARK_EXECUTOR_MEMORY或者SPARK_MEM对其大小进行设置。

代码清单3-28 Executor 环境变量的处理

private[spark] val executorMemory = conf.getOption("spark.executor.memory").orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY"))).orElse(Option(System.getenv("SPARK_MEM")).map(warnSparkMem)).map(Utils.memoryStringToMb).getOrElse(512)// Environment variables to pass to our executors.private[spark] val executorEnvs = HashMap[String, String]()for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing"))value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} {executorEnvs(envKey) = value}Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v =>executorEnvs("SPARK_PREPEND_CLASSES") = v}// The Mesos scheduler backend relies on this environment variable to set executor memory.executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m"executorEnvs ++= conf.getExecutorEnv// Set SPARK_USER for user who is running SparkContext.val sparkUser = Option {Option(System.getenv("SPARK_USER")).getOrElse(System.getProperty("user.name"))}.getOrElse {SparkContext.SPARK_UNKNOWN_USER}executorEnvs("SPARK_USER") = sparkUser

3.6 创建任务调度器TASKSCHEDULER

　　TaskScheduler也是SparkContext的重要组成部分，负责任务的提交，并且请求集群管理器对任务调度。TaskScheduler也可以看做任务调度的客户端。创建TaskScheduler的代码如下。

  private[spark] var (schedulerBackend, taskScheduler) =SparkContext.createTaskScheduler(this, master)

createTaskScheduler方法会根据master的配置匹配部署模式，创建TaskSchedulerImpl，并生成不同的SchedulerBackend。本章为了使读者更容易理解Spark的初始化流程，故以local模式为例，其余模式将在第6章详解。master匹配local模式的代码如下。

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

3.6.1 创建TaskSchedulerImpl

　　TaskSchedulerImpl的构造过程如下：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（调度模式有FAIR和FIFO两种，默认为FIFO，可以修改属性spark.scheduler.mode来改变）等。2) 创建TaskResultGetter，它的作用是通过线程池（Executors.newFixedThreadPool创建的，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），对slave发送的task的执行结果进行处理。TaskSchedulerImpl的主要组成，见代码清单3-29。

代码清单3-29 TaskSchedulerImpl的实现

  var dagScheduler: DAGScheduler = nullvar backend: SchedulerBackend = nullval mapOutputTracker = SparkEnv.get.mapOutputTrackervar schedulableBuilder: SchedulableBuilder = nullvar rootPool: Pool = null// default scheduler is FIFOprivate val schedulingModeConf = conf.get("spark.scheduler.mode", "FIFO")val schedulingMode: SchedulingMode = try {SchedulingMode.withName(schedulingModeConf.toUpperCase)} catch {case e: java.util.NoSuchElementException =>throw new SparkException(s"Unrecognized spark.scheduler.mode: $schedulingModeConf")}// This is a var so that we can reset it for testing purposes.private[spark] var taskResultGetter = new TaskResultGetter(sc.env, this)

TaskSchedulerImpl的调度模式有FAIR和FIFO两种。任务的最终调度实际都是落实到接口SchedulerBackend的具体实现上的。为方便分析，我们先来看看local模式中SchedulerBackend的实现LocalBackend。LocalBackend依赖于LocalActor与ActorSystem进行消息通信。LocalBackend参见代码清单3-30。

代码清单3-30 LocalBackend的实现

private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores: Int)extends SchedulerBackend with ExecutorBackend {private val appId = "local-" + System.currentTimeMillisvar localActor: ActorRef = nulloverride def start() {localActor = SparkEnv.get.actorSystem.actorOf(Props(new LocalActor(scheduler, this, totalCores)),"LocalBackendActor")}override def stop() {localActor ! StopExecutor}override def reviveOffers() {localActor ! ReviveOffers}override def defaultParallelism() =scheduler.conf.getInt("spark.default.parallelism", totalCores)override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) {localActor ! KillTask(taskId, interruptThread)}override def statusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) {localActor ! StatusUpdate(taskId, state, serializedData)}override def applicationId(): String = appId
}

3.6.2 TaskSchedulerImpl的初始化

　　创建完TaskSchedulerImpl和LocalBackend后，对TaskSchedulerImpl调用方法initialize进行初始化。初始化过程如下：

1) 使TaskSchedulerImpl持有LocalBackend的引用。

2) 创建Pool，Pool中缓存了调度队列、调度算法及TaskSetManager集合等信息。

3) 创建FIFOSchedulableBuilder，FIFOSchedulableBuilder用来操作Pool中的调度队列。

Initialize方法的实现见代码清单3-31。

代码清单3-31 TaskSchedulerImpl的初始化

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

3.7 创建和启动DAGSCHEDULER

　　DAGScheduler主要用于在任务正式交给TaskSchedulerImpl提交之前做一些准备工作，包括：创建Job，将DAG中的RDD划分到不同的Stage、提交Stage，等等。创建DAGScheduler的代码如下。

@volatile private[spark] var dagScheduler: DAGScheduler = _dagScheduler = new DAGScheduler(this)

DAGScheduler的数据结构主要维护jobId和stageId的关系、Stage、ActiveJob，以及缓存的RDD的partitions的位置信息，见代码清单3-32。

代码清单3-32 DAGScheduler维护的数据结构

  private[scheduler] val nextJobId = new AtomicInteger(0)private[scheduler] def numTotalJobs: Int = nextJobId.get()private val nextStageId = new AtomicInteger(0)private[scheduler] val jobIdToStageIds = new HashMap[Int, HashSet[Int]]private[scheduler] val stageIdToStage = new HashMap[Int, Stage]private[scheduler] val shuffleToMapStage = new HashMap[Int, Stage]private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob]// Stages we need to run whose parents aren't doneprivate[scheduler] val waitingStages = new HashSet[Stage]// Stages we are running right nowprivate[scheduler] val runningStages = new HashSet[Stage]// Stages that must be resubmitted due to fetch failuresprivate[scheduler] val failedStages = new HashSet[Stage]private[scheduler] val activeJobs = new HashSet[ActiveJob]// Contains the locations that each RDD's partitions are cached onprivate val cacheLocs = new HashMap[Int, Array[Seq[TaskLocation]]]private val failedEpoch = new HashMap[String, Long]private val dagSchedulerActorSupervisor =env.actorSystem.actorOf(Props(new DAGSchedulerActorSupervisor(this)))private val closureSerializer = SparkEnv.get.closureSerializer.newInstance()

在构造DAGScheduler的时候会调用initializeEventProcessActor方法创建DAGSchedulerEventProcessActor，见代码清单3-33。

代码清单3-33 DAGSchedulerEventProcessActor的初始化

  private[scheduler] var eventProcessActor: ActorRef = _private def initializeEventProcessActor() {// blocking the thread until supervisor is started, which ensures eventProcessActor is// not null before any job is submittedimplicit val timeout = Timeout(30 seconds)val initEventActorReply =dagSchedulerActorSupervisor ? Props(new DAGSchedulerEventProcessActor(this))eventProcessActor = Await.result(initEventActorReply, timeout.duration).asInstanceOf[ActorRef]}initializeEventProcessActor()

这里的DAGSchedulerActorSupervisor主要作为DAGSchedulerEventProcessActor的监管者，负责生成DAGSchedulerEventProcessActor。从代码清单3-34可以看出，DAGSchedulerActorSupervisor对于DAGSchedulerEventProcessActor采用了Akka的一对一监管策略。DAGSchedulerActorSupervisor一旦生成DAGSchedulerEventProcessActor，并注册到ActorSystem，ActorSystem就会调用DAGSchedulerEventProcessActor的preStart，taskScheduler于是就持有了dagScheduler，见代码清单3-35。从代码清单3-35我们还看到DAGSchedulerEventProcessActor所能处理的消息类型，比如handleJobSubmitted、handleBeginEvent、handleTaskCompletion等。DAGSchedulerEventProcessActor接受这些消息后会有不同的处理动作，在本章，读者只需要理解到这里即可，后面章节用到时会详细分析。

代码清单3-34 DAGSchedulerActorSupervisor的监管策略

private[scheduler] class DAGSchedulerActorSupervisor(dagScheduler: DAGScheduler)extends Actor with Logging {override val supervisorStrategy =OneForOneStrategy() {case x: Exception =>logError("eventProcesserActor failed; shutting down SparkContext", x)try {dagScheduler.doCancelAllJobs()} catch {case t: Throwable => logError("DAGScheduler failed to cancel all jobs.", t)}dagScheduler.sc.stop()Stop}def receive = {case p: Props => sender ! context.actorOf(p)case _ => logWarning("received unknown message in DAGSchedulerActorSupervisor")}
}

代码清单3-35 DAGSchedulerEventProcessActor的实现

private[scheduler] class DAGSchedulerEventProcessActor(dagScheduler: DAGScheduler)extends Actor with Logging {override def preStart() {dagScheduler.taskScheduler.setDAGScheduler(dagScheduler)}/*** The main event loop of the DAG scheduler.*/def receive = {case JobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite, listener, properties) =>dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, allowLocal, callSite,listener, properties)case StageCancelled(stageId) =>dagScheduler.handleStageCancellation(stageId)case JobCancelled(jobId) =>dagScheduler.handleJobCancellation(jobId)case JobGroupCancelled(groupId) =>dagScheduler.handleJobGroupCancelled(groupId)case AllJobsCancelled =>dagScheduler.doCancelAllJobs()case ExecutorAdded(execId, host) =>dagScheduler.handleExecutorAdded(execId, host)case ExecutorLost(execId) =>dagScheduler.handleExecutorLost(execId, fetchFailed = false)case BeginEvent(task, taskInfo) =>dagScheduler.handleBeginEvent(task, taskInfo)case GettingResultEvent(taskInfo) =>dagScheduler.handleGetTaskResult(taskInfo)case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>dagScheduler.handleTaskCompletion(completion)case TaskSetFailed(taskSet, reason) =>dagScheduler.handleTaskSetFailed(taskSet, reason)case ResubmitFailedStages =>dagScheduler.resubmitFailedStages()}override def postStop() {// Cancel any active jobs in postStop hookdagScheduler.cleanUpAfterSchedulerStop()}

未完待续。。。

后记：自己牺牲了7个月的周末和下班空闲时间，通过研究Spark源码和原理，总结整理的《深入理解Spark：核心思想与源码分析》一书现在已经正式出版上市，目前亚马逊、京东、当当、天猫等网站均有销售，欢迎感兴趣的同学购买。我开始研究源码时的Spark版本是1.2.0，经过7个多月的研究和出版社近4个月的流程，Spark自身的版本迭代也很快，如今最新已经是1.6.0。目前市面上另外2本源码研究的Spark书籍的版本分别是0.9.0版本和1.2.0版本，看来这些书的作者都与我一样，遇到了这种问题。由于研究和出版都需要时间，所以不能及时跟上Spark的脚步，还请大家见谅。但是Spark核心部分的变化相对还是很少的，如果对版本不是过于追求，依然可以选择本书。

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