《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）》
《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（仲篇）》
本文展现第3章第三部分的内容：
3.8 TaskScheduler的启动
3.7节介绍了任务调度器TaskScheduler的创建，要想TaskScheduler发挥作用，必须要启动它，代码如下。
taskScheduler.start()
TaskScheduler在启动的时候，实际调用了backend的start方法。
override def start() {
backend.start()
}
以LocalBackend为例，启动LocalBackend时向actorSystem注册了LocalActor，见代码清单3-30所示（在《深入理解Spark：核心思想与源码分析》——SparkContext的初始化（仲篇）》一文中）。
3.8.1 创建LocalActor
创建LocalActor的过程主要是构建本地的Executor，见代码清单3-36。
代码清单3-36 LocalActor的实现
private[spark] class LocalActor(scheduler: TaskSchedulerImpl, executorBackend: LocalBackend,
private val totalCores: Int) extends Actor with ActorLogReceive with Logging {
import context.dispatcher // to use Akka's scheduler.scheduleOnce()
private var freeCores = totalCores
private val localExecutorId = SparkContext.DRIVER_IDENTIFIER
private val localExecutorHostname = "localhost"
val executor = new Executor(
localExecutorId, localExecutorHostname, scheduler.conf.getAll, totalCores, isLocal = true)
override def receiveWithLogging = {
case ReviveOffers =>
reviveOffers()
case StatusUpdate(taskId, state, serializedData) =>
scheduler.statusUpdate(taskId, state, serializedData)
if (TaskState.isFinished(state)) {
freeCores += scheduler.CPUS_PER_TASK
reviveOffers()
}
case KillTask(taskId, interruptThread) =>
executor.killTask(taskId, interruptThread)
case StopExecutor =>
executor.stop()
}
}
Executor的构建，见代码清单3-37，主要包括以下步骤：
1) 创建并注册ExecutorSource。ExecutorSource是做什么的呢？笔者将在3.10.2节详细介绍。
2) 获取SparkEnv。如果是非local模式，Worker上的CoarseGrainedExecutorBackend向Driver上的CoarseGrainedExecutorBackend注册Executor时，则需要新建SparkEnv。可以修改属性spark.executor.port（默认为0，表示随机生成）来配置Executor中的ActorSystem的端口号。
3) 创建并注册ExecutorActor。ExecutorActor负责接受发送给Executor的消息。
4) urlClassLoader的创建。为什么需要创建这个ClassLoader？在非local模式中，Driver或者Worker上都会有多个Executor，每个Executor都设置自身的urlClassLoader，用于加载任务上传的jar包中的类，有效对任务的类加载环境进行隔离。
5) 创建Executor执行TaskRunner任务（TaskRunner将在5.5节介绍）的线程池。此线程池是通过调用Utils.newDaemonCachedThreadPool创建的，具体实现请参阅附录A。
6) 启动Executor的心跳线程。此线程用于向Driver发送心跳。
此外，还包括Akka发送消息的帧大小（10485760字节）、结果总大小的字节限制（1073741824字节）、正在运行的task的列表、设置serializer的默认ClassLoader为创建的ClassLoader等。
代码清单3-37 Executor的构建
val executorSource = new ExecutorSource(this, executorId)
private val env = {
if (!isLocal) {
val port = conf.getInt("spark.executor.port", 0)
val _env = SparkEnv.createExecutorEnv(
conf, executorId, executorHostname, port, numCores, isLocal, actorSystem)
SparkEnv.set(_env)
_env.metricsSystem.registerSource(executorSource)
_env.blockManager.initialize(conf.getAppId)
_env
} else {
SparkEnv.get
}
}
private val executorActor = env.actorSystem.actorOf(
Props(new ExecutorActor(executorId)), "ExecutorActor")
private val urlClassLoader = createClassLoader()
private val replClassLoader = addReplClassLoaderIfNeeded(urlClassLoader)
env.serializer.setDefaultClassLoader(urlClassLoader)
private val akkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf)
private val maxResultSize = Utils.getMaxResultSize(conf)
val threadPool = Utils.newDaemonCachedThreadPool("Executor task launch worker")
private val runningTasks = new ConcurrentHashMap[Long, TaskRunner]
startDriverHeartbeater()
3.8.2 ExecutorSource的创建与注册
ExecutorSource用于测量系统。通过metricRegistry的register方法注册计量，这些计量信息包括threadpool.activeTasks、threadpool.completeTasks、threadpool.currentPool_size、threadpool.maxPool_size、filesystem.hdfs.write_bytes、filesystem.hdfs.read_ops、filesystem.file.write_bytes、filesystem.hdfs.largeRead_ops、filesystem.hdfs.write_ops等，ExecutorSource的实现见代码清单3-38。Metric接口的具体实现，参考附录D。
代码清单3-38 ExecutorSource的实现
private[spark] class ExecutorSource(val executor: Executor, executorId: String) extends Source {
private def fileStats(scheme: String) : Option[FileSystem.Statistics] =
FileSystem.getAllStatistics().filter(s => s.getScheme.equals(scheme)).headOption
private def registerFileSystemStat[T](
scheme: String, name: String, f: FileSystem.Statistics => T, defaultValue: T) = {
metricRegistry.register(MetricRegistry.name("filesystem", scheme, name), new Gauge[T] {
override def getValue: T = fileStats(scheme).map(f).getOrElse(defaultValue)
})
}
override val metricRegistry = new MetricRegistry()
override val sourceName = "executor"
metricRegistry.register(MetricRegistry.name("threadpool", "activeTasks"), new Gauge[Int] {
override def getValue: Int = executor.threadPool.getActiveCount()
})
metricRegistry.register(MetricRegistry.name("threadpool", "completeTasks"), new Gauge[Long] {
override def getValue: Long = executor.threadPool.getCompletedTaskCount()
})
metricRegistry.register(MetricRegistry.name("threadpool", "currentPool_size"), new Gauge[Int] {
override def getValue: Int = executor.threadPool.getPoolSize()
})
metricRegistry.register(MetricRegistry.name("threadpool", "maxPool_size"), new Gauge[Int] {
override def getValue: Int = executor.threadPool.getMaximumPoolSize()
})
// Gauge for file system stats of this executor
for (scheme <- Array("hdfs", "file")) {
registerFileSystemStat(scheme, "read_bytes", _.getBytesRead(), 0L)
registerFileSystemStat(scheme, "write_bytes", _.getBytesWritten(), 0L)
registerFileSystemStat(scheme, "read_ops", _.getReadOps(), 0)
registerFileSystemStat(scheme, "largeRead_ops", _.getLargeReadOps(), 0)
registerFileSystemStat(scheme, "write_ops", _.getWriteOps(), 0)
}
}
创建完ExecutorSource后，调用MetricsSystem的registerSource方法将ExecutorSource注册到MetricsSystem。registerSource方法使用MetricRegistry的register方法，将Source注册到MetricRegistry，见代码清单3-39。关于MetricRegistry，具体参阅附录D。
代码清单3-39 MetricsSystem注册Source的实现
def registerSource(source: Source) {
sources += source
try {
val regName = buildRegistryName(source)
registry.register(regName, source.metricRegistry)
} catch {
case e: IllegalArgumentException => logInfo("Metrics already registered", e)
}
}
3.8.3 ExecutorActor的构建与注册
ExecutorActor很简单，当接收到SparkUI发来的消息时，将所有线程的栈信息发送回去，代码实现如下。
override def receiveWithLogging = {
case TriggerThreadDump =>
sender ! Utils.getThreadDump()
}
3.8.4 Spark自身ClassLoader的创建
获取要创建的ClassLoader的父加载器currentLoader，然后根据currentJars生成URL数组，spark.files.userClassPathFirst属性指定加载类时是否先从用户的classpath下加载，最后创建ExecutorURLClassLoader或者ChildExecutorURLClassLoader，见代码清单3-40。
代码清单3-40 Spark自身ClassLoader的创建
private def createClassLoader(): MutableURLClassLoader = {
val currentLoader = Utils.getContextOrSparkClassLoader
val urls = currentJars.keySet.map { uri =>
new File(uri.split("/").last).toURI.toURL
}.toArray
val userClassPathFirst = conf.getBoolean("spark.files.userClassPathFirst", false)
userClassPathFirst match {
case true => new ChildExecutorURLClassLoader(urls, currentLoader)
case false => new ExecutorURLClassLoader(urls, currentLoader)
}
}
Utils.getContextOrSparkClassLoader的实现见附录A。ExecutorURLClassLoader或者ChildExecutorURLClassLoader实际上都继承了URLClassLoader，见代码清单3-41。
代码清单3-41 ChildExecutorURLClassLoader与ExecutorURLClassLoader的实现
private[spark] class ChildExecutorURLClassLoader(urls: Array[URL], parent: ClassLoader)
extends MutableURLClassLoader {
private object userClassLoader extends URLClassLoader(urls, null){
override def addURL(url: URL) {
super.addURL(url)
}
override def findClass(name: String): Class[_] = {
super.findClass(name)
}
}
private val parentClassLoader = new ParentClassLoader(parent)
override def findClass(name: String): Class[_] = {
try {
userClassLoader.findClass(name)
} catch {
case e: ClassNotFoundException => {
parentClassLoader.loadClass(name)
}
}
}
def addURL(url: URL) {
userClassLoader.addURL(url)
}
def getURLs() = {
userClassLoader.getURLs()
}
}
private[spark] class ExecutorURLClassLoader(urls: Array[URL], parent: ClassLoader)
extends URLClassLoader(urls, parent) with MutableURLClassLoader {
override def addURL(url: URL) {
super.addURL(url)
}
}
如果需要REPL交互，还会调用addReplClassLoaderIfNeeded创建replClassLoader，见代码清单3-42。
代码清单3-42 addReplClassLoaderIfNeeded的实现
private def addReplClassLoaderIfNeeded(parent: ClassLoader): ClassLoader = {
val classUri = conf.get("spark.repl.class.uri", null)
if (classUri != null) {
logInfo("Using REPL class URI: " + classUri)
val userClassPathFirst: java.lang.Boolean =
conf.getBoolean("spark.files.userClassPathFirst", false)
try {
val klass = Class.forName("org.apache.spark.repl.ExecutorClassLoader")
.asInstanceOf[Class[_ <: ClassLoader]]
val constructor = klass.getConstructor(classOf[SparkConf], classOf[String],
classOf[ClassLoader], classOf[Boolean])
constructor.newInstance(conf, classUri, parent, userClassPathFirst)
} catch {
case _: ClassNotFoundException =>
logError("Could not find org.apache.spark.repl.ExecutorClassLoader on classpath!")
System.exit(1)
null
}
} else {
parent
}
}
3.8.5 启动Executor的心跳线程
Executor的心跳由startDriverHeartbeater启动，见代码清单3-43。Executor心跳线程的间隔由属性spark.executor.heartbeatInterval配置，默认是10000毫秒。此外，超时时间是30秒，超时重试次数是3次，重试间隔是3000毫秒，使用actorSystem.actorSelection (url)方法查找到匹配的Actor引用， url是akka.tcp://sparkDriver@ $driverHost:$driverPort/user/HeartbeatReceiver，最终创建一个运行过程中，每次会休眠10000到20000毫秒的线程。此线程从runningTasks获取最新的有关Task的测量信息，将其与executorId、blockManagerId封装为Heartbeat消息，向HeartbeatReceiver发送Heartbeat消息。
代码清单3-43 启动Executor的心跳线程
def startDriverHeartbeater() {
val interval = conf.getInt("spark.executor.heartbeatInterval", 10000)
val timeout = AkkaUtils.lookupTimeout(conf)
val retryAttempts = AkkaUtils.numRetries(conf)
val retryIntervalMs = AkkaUtils.retryWaitMs(conf)
val heartbeatReceiverRef = AkkaUtils.makeDriverRef("HeartbeatReceiver", conf,env.actorSystem)
val t = new Thread() {
override def run() {
// Sleep a random interval so the heartbeats don't end up in sync
Thread.sleep(interval + (math.random * interval).asInstanceOf[Int])
while (!isStopped) {
val tasksMetrics = new ArrayBuffer[(Long, TaskMetrics)]()
val curGCTime = gcTime
for (taskRunner <- runningTasks.values()) {
if (!taskRunner.attemptedTask.isEmpty) {
Option(taskRunner.task).flatMap(_.metrics).foreach { metrics =>
metrics.updateShuffleReadMetrics
metrics.jvmGCTime = curGCTime - taskRunner.startGCTime
if (isLocal) {
val copiedMetrics = Utils.deserialize[TaskMetrics](Utils.serialize(metrics))
tasksMetrics += ((taskRunner.taskId, copiedMetrics))
} else {
// It will be copied by serialization
tasksMetrics += ((taskRunner.taskId, metrics))
}
}
}
}
val message = Heartbeat(executorId, tasksMetrics.toArray, env.blockManager.blockManagerId)
try {
val response = AkkaUtils.askWithReply[HeartbeatResponse](message, heartbeatReceiverRef,
retryAttempts, retryIntervalMs, timeout)
if (response.reregisterBlockManager) {
logWarning("Told to re-register on heartbeat")
env.blockManager.reregister()
}
} catch {
case NonFatal(t) => logWarning("Issue communicating with driver in heartbeater", t)
}
Thread.sleep(interval)
}
}
}
t.setDaemon(true)
t.setName("Driver Heartbeater")
t.start()
}
这个心跳线程的作用是什么呢？其作用有两个：
更新正在处理的任务的测量信息；
通知BlockManagerMaster，此Executor上的BlockManager依然活着。
下面对心跳线程的实现详细分析下，读者可以自行选择是否需要阅读。
初始化TaskSchedulerImpl后会创建心跳接收器HeartbeatReceiver。HeartbeatReceiver接受所有分配给当前Driver Application的Executor的心跳，并将Task、Task计量信息、心跳等交给TaskSchedulerImpl和DAGScheduler作进一步处理。创建心跳接收器的代码如下。
private val heartbeatReceiver = env.actorSystem.actorOf(
Props(new HeartbeatReceiver(taskScheduler)), "HeartbeatReceiver")
HeartbeatReceiver在收到心跳消息后，会调用TaskScheduler的executorHeartbeatReceived方法，代码如下。
override def receiveWithLogging = {
case Heartbeat(executorId, taskMetrics, blockManagerId) =>
val response = HeartbeatResponse(
!scheduler.executorHeartbeatReceived(executorId, taskMetrics, blockManagerId))
sender ! response
}
executorHeartbeatReceived的实现代码如下。
val metricsWithStageIds: Array[(Long, Int, Int, TaskMetrics)] = synchronized {
taskMetrics.flatMap { case (id, metrics) =>
taskIdToTaskSetId.get(id)
.flatMap(activeTaskSets.get)
.map(taskSetMgr => (id, taskSetMgr.stageId, taskSetMgr.taskSet.attempt, metrics))
}
}
dagScheduler.executorHeartbeatReceived(execId, metricsWithStageIds, blockManagerId)
这段程序通过遍历taskMetrics，依据taskIdToTaskSetId和activeTaskSets找到TaskSetManager。然后将taskId、TaskSetManager.stageId、TaskSetManager .taskSet.attempt、TaskMetrics封装到Array[(Long, Int, Int, TaskMetrics)]的数组metricsWithStageIds中。最后调用了dagScheduler的executorHeartbeatReceived方法，其实现如下。
listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, taskMetrics))
implicit val timeout = Timeout(600 seconds)
Await.result(
blockManagerMaster.driverActor ? BlockManagerHeartbeat(blockManagerId),
timeout.duration).asInstanceOf[Boolean]
dagScheduler将executorId、metricsWithStageIds封装为SparkListenerExecutorMetricsUpdate事件，并post到listenerBus中，此事件用于更新Stage的各种测量数据。最后给BlockManagerMaster持有的BlockManagerMasterActor发送BlockManagerHeartbeat消息。BlockManagerMasterActor在收到消息后会匹配执行heartbeatReceived方法（会在4.3.1节介绍）。heartbeatReceived最终更新BlockManagerMaster对BlockManager最后可见时间（即更新BlockManagerId对应的BlockManagerInfo的_lastSeenMs，见代码清单3-44）。
代码清单3-44 BlockManagerMasterActor的心跳处理
private def heartbeatReceived(blockManagerId: BlockManagerId): Boolean = {
if (!blockManagerInfo.contains(blockManagerId)) {
blockManagerId.isDriver && !isLocal
} else {
blockManagerInfo(blockManagerId).updateLastSeenMs()
true
}
}
local模式下Executor的心跳通信过程，可以用图3-3来表示。
图3-3 Executor的心跳通信过程
注意：在非local模式中Executor发送心跳的过程是一样的，主要的区别是Executor进程与Driver不在同一个进程，甚至不在同一个节点上。
接下来会初始化块管理器BlockManager，代码如下。
env.blockManager.initialize(applicationId)
具体的初始化过程，请参阅第4章。