Spark提交Yarn的過程

spark-submit.sh-> spark-class.sh，而後調用SparkSubmit.scala。

根據client或者cluster模式處理方式不同。

client：直接在spark-class.sh運行的地方包裝要給進程來執行driver。

cluster：將driver提交到集羣去執行。

核心在SparkSubmit.scala的prepareSubmitEnvironment方法中，截取一段處理Yarn集羣環境的看一下。

// In client mode, launch the application main class directly
    // In addition, add the main application jar and any added jars (if any) to the classpath
    if (deployMode == CLIENT) {
      childMainClass = args.mainClass
      if (localPrimaryResource != null && isUserJar(localPrimaryResource)) {
        childClasspath += localPrimaryResource
      }
      if (localJars != null) { childClasspath ++= localJars.split(",") }
    }

client模式，childMainClass就是driver的main方法。

接下來看看Yarn cluster模式：

// In yarn-cluster mode, use yarn.Client as a wrapper around the user class
    if (isYarnCluster) {
      childMainClass = YARN_CLUSTER_SUBMIT_CLASS
      if (args.isPython) {
        childArgs += ("--primary-py-file", args.primaryResource)
        childArgs += ("--class", "org.apache.spark.deploy.PythonRunner")
      } else if (args.isR) {
        val mainFile = new Path(args.primaryResource).getName
        childArgs += ("--primary-r-file", mainFile)
        childArgs += ("--class", "org.apache.spark.deploy.RRunner")
      } else {
        if (args.primaryResource != SparkLauncher.NO_RESOURCE) {
          childArgs += ("--jar", args.primaryResource)
        }
        childArgs += ("--class", args.mainClass)
      }
      if (args.childArgs != null) {
        args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
      }
    }

這時候childMainClass變成了

YARN_CLUSTER_SUBMIT_CLASS =    "org.apache.spark.deploy.yarn.YarnClusterApplication"

private[spark] class YarnClusterApplication extends SparkApplication {
  override def start(args: Array[String], conf: SparkConf): Unit = {
    // SparkSubmit would use yarn cache to distribute files & jars in yarn mode,
    // so remove them from sparkConf here for yarn mode.
    conf.remove(JARS)
    conf.remove(FILES)
    new Client(new ClientArguments(args), conf, null).run()
  }
}

看源碼能夠看到，YarnClusterApplication最終是用到了deploy/yarn/Client.scala

client.run調用client.submitApplication方法提交到Yarn集羣。

def submitApplication(): ApplicationId = {
     // Set up the appropriate contexts to launch our AM
      val containerContext = createContainerLaunchContext(newAppResponse)
      val appContext = createApplicationSubmissionContext(newApp, containerContext)
}

主要是createContainerLaunchContext方法：

/**
   * Set up a ContainerLaunchContext to launch our ApplicationMaster container.
   * This sets up the launch environment, java options, and the command for launching the AM.
   */
private def createContainerLaunchContext(newAppResponse: GetNewApplicationResponse){

val userClass =
      if (isClusterMode) {
        Seq("--class", YarnSparkHadoopUtil.escapeForShell(args.userClass))
      } else {
        Nil
      }    
 val amClass =
      if (isClusterMode) {
        Utils.classForName("org.apache.spark.deploy.yarn.ApplicationMaster").getName
      } else {
        Utils.classForName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName
      }
 val amArgs =
      Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ primaryRFile ++ userArgs ++
      Seq("--properties-file",
        buildPath(Environment.PWD.$$(), LOCALIZED_CONF_DIR, SPARK_CONF_FILE)) ++
      Seq("--dist-cache-conf",
        buildPath(Environment.PWD.$$(), LOCALIZED_CONF_DIR, DIST_CACHE_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")
}

這樣就生成要執行的命令了，就是Command。上面這句話啥意思呢：

（1）cluster模式

用ApplicationMaster啓動userClass。

（2）client模式

啓動Executor

這裏咱們要看的是cluster模式，至此就清楚了，在cluster模式下，在Yarn集羣中用ApplicationMaster包裝了userClass並啓動。userClass就是driver的意思。