《深入理解SPARK：核心思想与源码分析》——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章第二部分的内容：

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 = 10000
private val eventQueue = new LinkedBlockingQueue[SparkListenerEvent](EVENT_QUEUE_CAPACITY)
private var queueFullErrorMessageLogged = false
private var started = false
// A counter that represents the number of events produced and consumed in the queue
private 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 event
LiveListenerBus.this.synchronized {
val event = eventQueue.poll
if (event == SparkListenerShutdown) {
// Get out of the while loop and shutdown the daemon thread
return
}
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 = Int
type StageId = Int
type StageAttemptId = Int
type PoolName = String
type 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.toSeq

var 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 EnvironmentListener
val storageStatusListener = new StorageStatusListener
val 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.sc
val killEnabled = parent.killEnabled
def isFairScheduler = listener.schedulingMode.exists(_ == SchedulingMode.FAIR)
val listener = parent.jobProgressListener

attachPage(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.toSeq
val completedJobs = listener.completedJobs.reverse.toSeq
val failedJobs = listener.failedJobs.reverse.toSeq
val now = System.currentTimeMillis

val 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.SUCCEEDED
val 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.getTabs

def 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] = None
protected val localHostName = Utils.localHostName()
protected val publicHostName = Option(System.getenv("SPARK_PUBLIC_DNS")).getOrElse(localHostName)
private val className = Utils.getFormattedClassName(this)

def getBasePath: String = basePath
def getTabs: Seq[WebUITab] = tabs.toSeq
def getHandlers: Seq[ServletContextHandler] = handlers.toSeq
def 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.prefix
attachHandler(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 += handler
serverInfo.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

　　parkUI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 = null
var backend: SchedulerBackend = null
val mapOutputTracker = SparkEnv.get.mapOutputTracker
var schedulableBuilder: SchedulableBuilder = null
var rootPool: Pool = null
// default scheduler is FIFO
private 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.currentTimeMillis
var localActor: ActorRef = null

override 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 = backend
rootPool = 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 done
private[scheduler] val waitingStages = new HashSet[Stage]
// Stages we are running right now
private[scheduler] val runningStages = new HashSet[Stage]
// Stages that must be resubmitted due to fetch failures
private[scheduler] val failedStages = new HashSet[Stage]

private[scheduler] val activeJobs = new HashSet[ActiveJob]

// Contains the locations that each RDD's partitions are cached on
private 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 submitted
implicit 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 hook
dagScheduler.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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