spark源码分析之Executor启动与任务提交篇

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.apache.spark.WordCount

1. bin/spark-clas -> org.apache.spark.deploy.SparkSubmit 调用这个类的main方法

2. doRunMain方法中传进来一个自定义spark应用程序的main方法
   class cn.apache.spark.WordCount

3. 通过反射拿到类的实例的引用mainClass = Utils.classForName(childMainClass)

4. 在通过反射调用class cn.apache.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

1. spark源码分析Master与Worker启动流程篇
2. spark源码分析之Executor启动与任务提交篇
3. spark源码分析之任务调度篇