2. spark-2.4.6源码分析（基于yarn cluster模式）-YARN client启动，提交ApplicationMaster

2. spark-2.4.6源码分析（基于yarn cluster模式）-YARN client启动，提交ApplicationMaster

通过上一节的研究，我们发现，spark提交任务后，启动了org.apache.spark.deploy.yarn.Client.run()方法，这里实际上就是spark利用yanr的客户端API在向YARN提交任务了，接下来我们一起来分析这个过程：

def run(): Unit = {
    this.appId = submitApplication()
    if (!launcherBackend.isConnected() && fireAndForget) {
      val report = getApplicationReport(appId)
      val state = report.getYarnApplicationState
      logInfo(s"Application report for $appId (state: $state)")
      logInfo(formatReportDetails(report))
      if (state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) {
        throw new SparkException(s"Application $appId finished with status: $state")
      }
    } else {
      val YarnAppReport(appState, finalState, diags) = monitorApplication(appId)
      if (appState == YarnApplicationState.FAILED || finalState == FinalApplicationStatus.FAILED) {
        diags.foreach { err =>
          logError(s"Application diagnostics message: $err")
        }
		...
      }
      
    }
  }

可以看到，首先是利用YARNClient，向YARN提交应用，获取到了应用ID，过程如下：

def submitApplication(): ApplicationId = {
    var appId: ApplicationId = null
    try {
      launcherBackend.connect()
      yarnClient.init(hadoopConf)
      yarnClient.start()

      logInfo("Requesting a new application from cluster with %d NodeManagers"
        .format(yarnClient.getYarnClusterMetrics.getNumNodeManagers))

      // Get a new application from our RM
      val newApp = yarnClient.createApplication()
      val newAppResponse = newApp.getNewApplicationResponse()
      appId = newAppResponse.getApplicationId()

      new CallerContext("CLIENT", sparkConf.get(APP_CALLER_CONTEXT),
        Option(appId.toString)).setCurrentContext()

      // Verify whether the cluster has enough resources for our AM
      verifyClusterResources(newAppResponse)

      // Set up the appropriate contexts to launch our AM
      val containerContext = createContainerLaunchContext(newAppResponse)
      val appContext = createApplicationSubmissionContext(newApp, containerContext)

      // Finally, submit and monitor the application
      logInfo(s"Submitting application $appId to ResourceManager")
      yarnClient.submitApplication(appContext)
      launcherBackend.setAppId(appId.toString)
      reportLauncherState(SparkAppHandle.State.SUBMITTED)

      appId
    } catch {...
  }

这里首先会启动LauncherBackend线程，该线程会和spark集群建立TCP连接，建立连接之后，接下来会创建YARN应用，然后创建ContainerLaunchContext提交给YARN，在
createContainerLaunchContext方法中：

val amClass =
      if (isClusterMode) {
        Utils.classForName("org.apache.spark.deploy.yarn.ApplicationMaster").getName
      } else {
        Utils.classForName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName
      }
      val amClass =
      if (isClusterMode) {
        Utils.classForName("org.apache.spark.deploy.yarn.ApplicationMaster").getName
      } else {
        Utils.classForName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName
      }
    if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) {
      args.userArgs = ArrayBuffer(args.primaryRFile) ++ args.userArgs
    }
    val userArgs = args.userArgs.flatMap { arg =>
      Seq("--arg", YarnSparkHadoopUtil.escapeForShell(arg))
    }
    val amArgs =
      Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ primaryRFile ++ userArgs ++
      Seq("--properties-file", buildPath(Environment.PWD.$$(), LOCALIZED_CONF_DIR, SPARK_CONF_FILE))

    // Command for the ApplicationMaster
    val commands = prefixEnv ++
      Seq(Environment.JAVA_HOME.$$() + "/bin/java", "-server") ++
      javaOpts ++ amArgs ++
      Seq(
        "1>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout",
        "2>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr")

    // TODO: it would be nicer to just make sure there are no null commands here
    val printableCommands = commands.map(s => if (s == null) "null" else s).toList
    amContainer.setCommands(printableCommands.asJava)

可以发现，我们通过YARN，发送给RresourManager执行的类是org.apache.spark.deploy.yarn.ApplicationMaster
另外在createApplicationSubmissionContext，获取到ApplicationSubmissionContext将会设置好需要的Container的内存和CPU容量，但是需要注意的是在cluster模式下，这里申请的内存和CPU是Driver的，并不是executor的，drvier的cpu不指定的话，默认就是1.

再回到run方法中，由于已经启动了launcherBackend,这时候进入else分支：

 val YarnAppReport(appState, finalState, diags) = monitorApplication(appId)
      if (appState == YarnApplicationState.FAILED || finalState == FinalApplicationStatus.FAILED) {
        diags.foreach { err =>
          logError(s"Application diagnostics message: $err")
        }
        throw new SparkException(s"Application $appId finished with failed status")
      }
      if (appState == YarnApplicationState.KILLED || finalState == FinalApplicationStatus.KILLED) {
        throw new SparkException(s"Application $appId is killed")
      }
      if (finalState == FinalApplicationStatus.UNDEFINED) {
        throw new SparkException(s"The final status of application $appId is undefined")
      }

而monitorApplication则是以while死循环，一直在获取YarnAppReport监控应用，到这里YARN client端就已经启动了，下一节我们分析ApplicationMaster启动。