任务提交流程
概述
在阐明了Spark的Master的启动流程与Worker启动流程。接下继续执行的就是Worker上的Executor进程了，本文继续分析整个Executor的启动与任务提交流程
Spark-submit
提交一个任务到集群通过的是Spark-submit
通过启动脚本的方式启动它的主类，这里以WordCount为例子
`spark-submit --class cn.itcast.spark.WordCount``
bin/spark-clas -> org.apache.spark.deploy.SparkSubmit 调用这个类的main方法
doRunMain方法中传进来一个自定义spark应用程序的main方法
class cn.kinge.spark.WordCount通过反射拿到类的实例的引用
mainClass = Utils.classForName(childMainClass)在通过反射调用
class cn.kinge.spark.WordCount的main方法
我们来看SparkSubmit的main方法
def main(args: Array[String]): Unit = { val appArgs = new SparkSubmitArguments(args) if (appArgs.verbose) {
printStream.println(appArgs)
} //匹配任务类型
appArgs.action match { case SparkSubmitAction.SUBMIT => submit(appArgs) case SparkSubmitAction.KILL => kill(appArgs) case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)
}
}这里的类型是submit，调用submit方法
private[spark] def submit(args: SparkSubmitArguments): Unit = { val (childArgs, childClasspath, sysProps, childMainClass) = prepareSubmitEnvironment(args)
def doRunMain(): Unit = {
。。。。。。 try {
proxyUser.doAs(new PrivilegedExceptionAction[Unit]() { override def run(): Unit = { //childMainClass这个你自己定义的App的main所在的全类名
runMain(childArgs, childClasspath, sysProps, childMainClass, args.verbose)
}
})
} catch {
。。。。。。
}
}
。。。。。。。 //掉用上面的doRunMain
doRunMain()
}submit里调用了doRunMain()，然后调用了runMain，来看runMain
private def runMain(
。。。。。。 try { //通过反射
mainClass = Class.forName(childMainClass, true, loader)
} catch {
。。。。。。
} //反射拿到面方法实例
val mainMethod = mainClass.getMethod("main", new Array[String](0).getClass) if (!Modifier.isStatic(mainMethod.getModifiers)) { throw new IllegalStateException("The main method in the given main class must be static")
}
。。。。。。 try { //调用App的main方法
mainMethod.invoke(null, childArgs.toArray)
} catch { case t: Throwable => throw findCause(t)
}
}最主要的流程就在这里了，上面的代码注释很清楚，通过反射调用我们写的类的main方法，大体的流程到此
SparkSubmit时序图
Executor启动流程
SparkSubmit通过反射调用了我们程序的main方法后，就开始执行我们的代码
，一个Spark程序中需要创建SparkContext对象，我们就从这个对象开始
SparkContext的构造方法代码很长，主要关注的地方如下
class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationClient {
。。。。。。
private[spark] def createSparkEnv(
conf: SparkConf,
isLocal: Boolean,
listenerBus: LiveListenerBus): SparkEnv = { //通过SparkEnv来创建createDriverEnv
SparkEnv.createDriverEnv(conf, isLocal, listenerBus)
} //在这里调用了createSparkEnv，返回一个SparkEnv对象，这个对象里面有很多重要属性，最重要的ActorSystem
private[spark] val env = createSparkEnv(conf, isLocal, listenerBus) SparkEnv.set(env) //创建taskScheduler
// Create and start the scheduler
private[spark] var (schedulerBackend, taskScheduler) = SparkContext.createTaskScheduler(this, master) //创建DAGScheduler
dagScheduler = new DAGScheduler(this) //启动TaksScheduler
taskScheduler.start()
。。。。。
}Spark的构造方法主要干三件事，创建了一个SparkEnv，taskScheduler，dagScheduler，我们先来看createTaskScheduler里干了什么
//通过给定的URL创建TaskScheduler
private def createTaskScheduler(
..... //匹配URL选择不同的方式
master match {
。。。。。。 //这个是Spark的Standalone模式
case SPARK_REGEX(sparkUrl) => //首先创建TaskScheduler
val scheduler = new TaskSchedulerImpl(sc) val masterUrls = sparkUrl.split(",").map("spark://" + _) //很重要
val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls) //初始化了一个调度器，默认是FIFO
scheduler.initialize(backend)
(backend, scheduler)
。。。。。
}
}通过master的url来匹配到Standalone模式：然后初始化了SparkDeploySchedulerBackend和TaskSchedulerImpl，这两个对象很重要，是启动任务调度的核心，然后调用了scheduler.initialize(backend)进行初始化
启动TaksScheduler初始化完成，回到我们的SparkContext构造方法后面继续调用了taskScheduler.start() 启动TaksScheduler
来看start方法
override def start() { //调用backend的实现的start方法
backend.start() if (!isLocal && conf.getBoolean("spark.speculation", false)) {
logInfo("Starting speculative execution thread") import sc.env.actorSystem.dispatcher
sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, SPECULATION_INTERVAL milliseconds) { Utils.tryOrExit { checkSpeculatableTasks() }
}
}
}这里的backend是SparkDeploySchedulerBackend调用了它的start
override def start() { //CoarseGrainedSchedulerBackend的start方法，在这个方法里面创建了一个DriverActor
super.start() // The endpoint for executors to talk to us
//下面是为了启动java子进程做准备，准备一下参数
val driverUrl = AkkaUtils.address( AkkaUtils.protocol(actorSystem), SparkEnv.driverActorSystemName,
conf.get("spark.driver.host"),
conf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ACTOR_NAME) val args = Seq( "--driver-url", driverUrl, "--executor-id", "{{EXECUTOR_ID}}", "--hostname", "{{HOSTNAME}}", "--cores", "{{CORES}}", "--app-id", "{{APP_ID}}", "--worker-url", "{{WORKER_URL}}") val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
.map(Utils.splitCommandString).getOrElse(Seq.empty) val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath")
.map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil) val libraryPathEntries = sc.conf.getOption("spark.executor.extraLibraryPath")
.map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil) // When testing, expose the parent class path to the child. This is processed by
// compute-classpath.{cmd,sh} and makes all needed jars available to child processes
// when the assembly is built with the "*-provided" profiles enabled.
val testingClassPath = if (sys.props.contains("spark.testing")) {
sys.props("java.class.path").split(java.io.File.pathSeparator).toSeq
} else { Nil
} // Start executors with a few necessary configs for registering with the scheduler
val sparkJavaOpts = Utils.sparkJavaOpts(conf, SparkConf.isExecutorStartupConf) val javaOpts = sparkJavaOpts ++ extraJavaOpts //用command拼接参数，最终会启动org.apache.spark.executor.CoarseGrainedExecutorBackend子进程
val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
args, sc.executorEnvs, classPathEntries ++ testingClassPath, libraryPathEntries, javaOpts) val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("") //用ApplicationDescription封装了一些重要的参数
val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
appUIAddress, sc.eventLogDir, sc.eventLogCodec) //在这里面创建ClientActor
client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf) //启动ClientActor
client.start()
waitForRegistration()
}这里是拼装了启动Executor的一些参数，类名+参数 封装成ApplicationDescription。最后传给并创建AppClient并调用它的start方法
AppClient创建时序图
AppClient的start方法
接来下关注start方法
def start() { // Just launch an actor; it will call back into the listener.
actor = actorSystem.actorOf(Props(new ClientActor))
}在start方法里创建了与Master通信的ClientActor,然后会调用它的preStart方法向Master注册,接下来看它的preStart
override def preStart() {
context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) try { //ClientActor向Master注册
registerWithMaster()
} catch { case e: Exception =>
logWarning("Failed to connect to master", e)
markDisconnected()
context.stop(self)
}
}最后会调用该方法向所有Master注册
def tryRegisterAllMasters() { for (masterAkkaUrl <- masterAkkaUrls) {
logInfo("Connecting to master " + masterAkkaUrl + "...") //t通过actorSelection拿到了Master的引用
val actor = context.actorSelection(masterAkkaUrl) //向Master发送异步的注册App的消息
actor ! RegisterApplication(appDescription)
}
}ClientActor发送来的注册App的消息，ApplicationDescription，他包含了需求的资源，要求启动的Executor类名和一些参数
Master的Receiver
case RegisterApplication(description) => { if (state == RecoveryState.STANDBY) { // ignore, don't send response
} else {
logInfo("Registering app " + description.name) //创建App sender：ClientActor
val app = createApplication(description, sender) //注册App
registerApplication(app)
logInfo("Registered app " + description.name + " with ID " + app.id) //持久化App
persistenceEngine.addApplication(app) //向ClientActor反馈信息，告诉他app注册成功了
sender ! RegisteredApplication(app.id, masterUrl) //TODO 调度任务
schedule()
}
}registerApplication（app）
def registerApplication(app: ApplicationInfo): Unit = { val appAddress = app.driver.path.address if (addressToApp.contains(appAddress)) {
logInfo("Attempted to re-register application at same address: " + appAddress) return
} //把App放到集合里面
applicationMetricsSystem.registerSource(app.appSource)
apps += app
idToApp(app.id) = app
actorToApp(app.driver) = app
addressToApp(appAddress) = app
waitingApps += app
}Master将接受的信息保存到集合并序列化后发送一个RegisteredApplication消息通知反馈给ClientActor，接着执行schedule()方法,该方法中会遍历workers集合，并执行launchExecutor
def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc) {
logInfo("Launching executor " + exec.fullId + " on worker " + worker.id) //记录该worker上使用了多少资源
worker.addExecutor(exec) //Master向Worker发送启动Executor的消息
worker.actor ! LaunchExecutor(masterUrl,
exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory) //Master向ClientActor发送消息，告诉ClientActor executor已经启动了
exec.application.driver ! ExecutorAdded(
exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
}这里Master向Worker发送启动Executor的消息worker.actor ! LaunchExecutor(masterUrl, exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory)
application.desc里包含了Executor类的启动信息
case LaunchExecutor(masterUrl, appId, execId, appDesc, cores_, memory_) => 。。。。。 appDirectories(appId) = appLocalDirs //创建一个ExecutorRunner，这个很重要，保存了Executor的执行配置和参数 val manager = new ExecutorRunner( appId, execId, appDesc.copy(command = Worker.maybeUpdateSSLSettings(appDesc.command, conf)), cores_, memory_, self, workerId, host, webUi.boundPort, publicAddress, sparkHome, executorDir, akkaUrl, conf, appLocalDirs, ExecutorState.LOADING) executors(appId + "/" + execId) = manager //TODO 开始启动ExecutorRunner manager.start() 。。。。。。 } } }
Worker的Receiver接受到了启动Executor的消息，appDesc对象保存了Command命令、Executor的实现类和参数
manager.start()里会创建一个线程
def start() { //启动一个线程
workerThread = new Thread("ExecutorRunner for " + fullId) { //用一个子线程来帮助Worker启动Executor子进程
override def run() { fetchAndRunExecutor() }
}
workerThread.start() // Shutdown hook that kills actors on shutdown.
shutdownHook = new Thread() { override def run() {
killProcess(Some("Worker shutting down"))
}
} Runtime.getRuntime.addShutdownHook(shutdownHook)
}在线程中调用了fetchAndRunExecutor()方法，我们来看该方法
def fetchAndRunExecutor() { try { // Launch the process
val builder = CommandUtils.buildProcessBuilder(appDesc.command, memory,
sparkHome.getAbsolutePath, substituteVariables) //构建命令
val command = builder.command()
logInfo("Launch command: " + command.mkString("\"", "\" \"", "\""))
builder.directory(executorDir)
builder.environment.put("SPARK_LOCAL_DIRS", appLocalDirs.mkString(",")) // In case we are running this from within the Spark Shell, avoid creating a "scala"
// parent process for the executor command
builder.environment.put("SPARK_LAUNCH_WITH_SCALA", "0") // Add webUI log urls
val baseUrl = s"http://$publicAddress:$webUiPort/logPage/?appId=$appId&executorId=$execId&logType="
builder.environment.put("SPARK_LOG_URL_STDERR", s"${baseUrl}stderr")
builder.environment.put("SPARK_LOG_URL_STDOUT", s"${baseUrl}stdout") //启动子进程
process = builder.start() val header = "Spark Executor Command: %s\n%s\n\n".format(
command.mkString("\"", "\" \"", "\""), "=" * 40) // Redirect its stdout and stderr to files
val stdout = new File(executorDir, "stdout")
stdoutAppender = FileAppender(process.getInputStream, stdout, conf) val stderr = new File(executorDir, "stderr") Files.write(header, stderr, UTF_8)
stderrAppender = FileAppender(process.getErrorStream, stderr, conf) // Wait for it to exit; executor may exit with code 0 (when driver instructs it to shutdown)
// or with nonzero exit code
//开始执行，等待结束信号
val exitCode = process.waitFor()
。。。。
}
}这里面进行了类名和参数的拼装，具体拼装过程不用关心，最终builder.start()会以SystemRuntime的方式启动一个子进程，这个是进程的类名是CoarseGrainedExecutorBackend
到此Executor进程就启动起来了
Executor创建时序图
Executor任务调度对象启动
Executor进程后，就首先要执行main方法，main的代码如下
//Executor进程启动的入口
def main(args: Array[String]) {
。。。。
//拼装参数
while (!argv.isEmpty) {
argv match { case ("--driver-url") :: value :: tail =>
driverUrl = value
argv = tail case ("--executor-id") :: value :: tail =>
executorId = value
argv = tail case ("--hostname") :: value :: tail =>
hostname = value
argv = tail case ("--cores") :: value :: tail =>
cores = value.toInt
argv = tail case ("--app-id") :: value :: tail =>
appId = value
argv = tail case ("--worker-url") :: value :: tail => // Worker url is used in spark standalone mode to enforce fate-sharing with worker
workerUrl = Some(value)
argv = tail case ("--user-class-path") :: value :: tail =>
userClassPath += new URL(value)
argv = tail case Nil => case tail => System.err.println(s"Unrecognized options: ${tail.mkString(" ")}")
printUsageAndExit()
}
} if (driverUrl == null || executorId == null || hostname == null || cores <= 0 ||
appId == null) {
printUsageAndExit()
} //开始执行Executor
run(driverUrl, executorId, hostname, cores, appId, workerUrl, userClassPath)
}执行了run方法
private def run( driverUrl: String, executorId: String, hostname: String, cores: Int, appId: String, workerUrl: Option[String], userClassPath: Seq[URL]) 。。。。。 //通过actorSystem创建CoarseGrainedExecutorBackend -> Actor //CoarseGrainedExecutorBackend -> DriverActor通信 env.actorSystem.actorOf( Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId, sparkHostPort, cores, userClassPath, env), name = "Executor") 。。。。。。 } env.actorSystem.awaitTermination() } }
run方法中创建了CoarseGrainedExecutorBackend的Actor对象用于准备和DriverActor通信，接着会继续调用preStart生命周期方法
override def preStart() {
logInfo("Connecting to driver: " + driverUrl) //Executor跟DriverActor建立连接
driver = context.actorSelection(driverUrl) //Executor向DriverActor发送消息
driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
}Executor向DriverActor发送注册的消息driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
DriverActor的receiver收到消息后
def receiveWithLogging = { //Executor发送给DriverActor的注册消息
case RegisterExecutor(executorId, hostPort, cores, logUrls) => Utils.checkHostPort(hostPort, "Host port expected " + hostPort) if (executorDataMap.contains(executorId)) {
sender ! RegisterExecutorFailed("Duplicate executor ID: " + executorId)
} else {
logInfo("Registered executor: " + sender + " with ID " + executorId) //DriverActor向Executor发送注册成功的消息
sender ! RegisteredExecutor
addressToExecutorId(sender.path.address) = executorId
totalCoreCount.addAndGet(cores)
totalRegisteredExecutors.addAndGet(1) val (host, _) = Utils.parseHostPort(hostPort)
//将Executor的信息封装起来
val data = new ExecutorData(sender, sender.path.address, host, cores, cores, logUrls) // This must be synchronized because variables mutated
// in this block are read when requesting executors
CoarseGrainedSchedulerBackend.this.synchronized { //往集合添加Executor的信息对象
executorDataMap.put(executorId, data) if (numPendingExecutors > 0) {
numPendingExecutors -= 1
logDebug(s"Decremented number of pending executors ($numPendingExecutors left)")
}
}
listenerBus.post( SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data)) //将来用来执行真正的业务逻辑
makeOffers()
}DriverActor的receiver里将Executor信息封装到Map中保存起来，并发送反馈消息 sender ! RegisteredExecutor
给CoarseGrainedExecutorBackend
override def receiveWithLogging = { case RegisteredExecutor =>
logInfo("Successfully registered with driver") val (hostname, _) = Utils.parseHostPort(hostPort)
executor = new Executor(executorId, hostname, env, userClassPath, isLocal = false)CoarseGrainedExecutorBackend收到消息后创建一个Executor对象用于准备任务的执行，到此Executor 就已经成功启动了，接下来就是等待任务的调度与执行。
作者：那年的坏人
链接：https://www.jianshu.com/p/0b19b2b954b6