Spark On YARN启动流程源码分析（一）

本文主要参考：

a. https://www.cnblogs.com/yy3b2007com/p/10934090.html

0. 说明

a. 关于spark源码会不定期的更新与补充

b. 对于spark源码的历史博文，也会不定期修改、增加、优化

c. spark源码对应的spark版本为2.4.1

1. 引导

该篇主要讲解执行spark-submit.sh脚本时将任务提交给Yarn阶段代码分析。其中spark的代码版本为2.4.1.

(1) spark-submit的入口函数

一般提交一个spark作业的方式采用spark-submit来提交

# Run on a Spark standalone cluster
./bin/spark-submit \
  --class org.apache.spark.examples.SparkPi \
  --master spark://207.184.161.138:7077 \
  --executor-memory 20G \
  --total-executor-cores  \
  /path/to/examples.jar \
  

这个是提交到standalone集群的方式，其中脚本spark-submit从spark2.4的安装bin目录下找到

#!/usr/bin/env bash

if [ -z "${SPARK_HOME}" ]; then
  source "$(dirname "$")"/find-spark-home
fi

# disable randomized hash for string in Python 3.3+
export PYTHONHASHSEED=

exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"

从如上脚本内容上来看，可以发现：

a. spark-submit提交任务时，实际上最终是调用了SparkSubmit类。

b. 调用bin目录下的spark-class脚本，实际上执行的是java进程命令。

从SparkSubmit的伴生类上可以看到入口main函数：

object SparkSubmit extends CommandLineUtils with Logging {

  // Cluster managers -------- Spark集群管理的模式
  private val YARN =
  private val STANDALONE =
  private val MESOS =
  private val LOCAL =
  private val KUBERNETES =
  private val ALL_CLUSTER_MGRS = YARN | STANDALONE | MESOS | LOCAL | KUBERNETES

  // Deploy modes  ---------- 部署模式
  private val CLIENT =
  private val CLUSTER =
  private val ALL_DEPLOY_MODES = CLIENT | CLUSTER

  // Special primary resource names that represent shells rather than application jars.
  private val SPARK_SHELL = "spark-shell"
  private val PYSPARK_SHELL = "pyspark-shell"
  private val SPARKR_SHELL = "sparkr-shell"
  private val SPARKR_PACKAGE_ARCHIVE = "sparkr.zip"
  private val R_PACKAGE_ARCHIVE = "rpkg.zip"

  private val CLASS_NOT_FOUND_EXIT_STATUS = 

  // Following constants are visible for testing.
  private[deploy] val YARN_CLUSTER_SUBMIT_CLASS =
    "org.apache.spark.deploy.yarn.YarnClusterApplication"  // Yarn集群的提交类
  private[deploy] val REST_CLUSTER_SUBMIT_CLASS = classOf[RestSubmissionClientApp].getName() // 集群模式提交的其他类名
  private[deploy] val STANDALONE_CLUSTER_SUBMIT_CLASS = classOf[ClientApp].getName() // 独立部署模式提交类
  private[deploy] val KUBERNETES_CLUSTER_SUBMIT_CLASS =
    "org.apache.spark.deploy.k8s.submit.KubernetesClientApplication"  // K8s集群提交的类

  override def main(args: Array[String]): Unit = {
    // 构建SparkSubmit实例
    val submit = new SparkSubmit() {
      self =>

      // 重写SparkSubmit的解析参数方法
      override protected def parseArguments(args: Array[String]): SparkSubmitArguments = {
        // 构建SparkSubmitArguments对象
        new SparkSubmitArguments(args) {
      // 重写logInfo和logWarning，调用该类中如下定义的2个方法
          override protected def logInfo(msg: => String): Unit = self.logInfo(msg)

          override protected def logWarning(msg: => String): Unit = self.logWarning(msg)
        }
      }

      override protected def logInfo(msg: => String): Unit = printMessage(msg)

      override protected def logWarning(msg: => String): Unit = printMessage(s"Warning: $msg")

      override def doSubmit(args: Array[String]): Unit = {
        try {
          super.doSubmit(args)
        } catch {
          case e: SparkUserAppException =>
            exitFn(e.exitCode)
        }
      }

    }

    // 执行提交代码
    submit.doSubmit(args)
  }

  /**
   * Return whether the given primary resource represents a user jar.
   */
  private[deploy] def isUserJar(res: String): Boolean = {
    !isShell(res) && !isPython(res) && !isInternal(res) && !isR(res)
  }

  /**
   * Return whether the given primary resource represents a shell.
   */
  private[deploy] def isShell(res: String): Boolean = {
    (res == SPARK_SHELL || res == PYSPARK_SHELL || res == SPARKR_SHELL)
  }

  /**
   * Return whether the given main class represents a sql shell.
   */
  private[deploy] def isSqlShell(mainClass: String): Boolean = {
    mainClass == "org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
  }

  /**
   * Return whether the given main class represents a thrift server.
   */
  private def isThriftServer(mainClass: String): Boolean = {
    mainClass == "org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
  }

  /**
   * Return whether the given primary resource requires running python.
   */
  private[deploy] def isPython(res: String): Boolean = {
    res != null && res.endsWith(".py") || res == PYSPARK_SHELL
  }

  /**
   * Return whether the given primary resource requires running R.
   */
  private[deploy] def isR(res: String): Boolean = {
    res != null && res.endsWith(".R") || res == SPARKR_SHELL
  }

  private[deploy] def isInternal(res: String): Boolean = {
    res == SparkLauncher.NO_RESOURCE
  }

}

在SparkSubmit类中doSubmit函数实现十分简单：

  def doSubmit(args: Array[String]): Unit = {
    // Initialize logging if it hasn't been done yet. Keep track of whether logging needs to
    // be reset before the application starts.
    val uninitLog = initializeLogIfNecessary(true, silent = true)

    // 解析参数信息
    val appArgs = parseArguments(args)
    if (appArgs.verbose) {
      logInfo(appArgs.toString)
    }
    appArgs.action match {
      case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog) // 应用提交
      case SparkSubmitAction.KILL => kill(appArgs) // 应用删除(只适用于standalone和memos集群)
      case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs) // 查询应用状态(只适用于standalone和memos集群)
      case SparkSubmitAction.PRINT_VERSION => printVersion()  // 打印应用版本信息
    }
  }

不难明白这是一个主控函数，根据接受的action类型，调用对应的处理：

a. case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog)---提交spark任务

b.case SparkSubmitAction.KILL => kill(appArgs)---杀掉spark任务

c. case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)---获取任务状态

d. case SparkSubmitAction.PRINT_VERSION => printVersion()---打印版本信息

我们想明白spark任务提交的具体实现类，需要进入submit函数查看具体的业务：

   /**
   * 使用提供的参数信息来提交application
   * Submit the application using the provided parameters.
   *
   * 运行包含两步：
   * 第一步，我们通过设置适当的类路径，系统属性和应用程序参数来准备启动环境，以便基于集群管理和部署模式运行子主类。
   * 第二步，我们使用这个启动环境来调用子主类的主方法。
   * This runs in two steps. First, we prepare the launch environment by setting up
   * the appropriate classpath, system properties, and application arguments for
   * running the child main class based on the cluster manager and the deploy mode.
   * Second, we use this launch environment to invoke the main method of the child
   * main class.
   */
  @tailrec
  private def submit(args: SparkSubmitArguments, uninitLog: Boolean): Unit = {
    // 通过设置适当的类路径，系统属性和应用程序参数来准备启动环境，以便基于集群管理和部署模式运行子主类。
    val (childArgs, childClasspath, sparkConf, childMainClass) = prepareSubmitEnvironment(args)

    def doRunMain(): Unit = {
      if (args.proxyUser != null) {
        val proxyUser = UserGroupInformation.createProxyUser(args.proxyUser,
          UserGroupInformation.getCurrentUser())
        try {
          proxyUser.doAs(new PrivilegedExceptionAction[Unit]() {
            override def run(): Unit = {
              runMain(childArgs, childClasspath, sparkConf, childMainClass, args.verbose)
            }
          })
        } catch {
          case e: Exception =>
            // Hadoop's AuthorizationException suppresses the exception's stack trace, which
            // makes the message printed to the output by the JVM not very helpful. Instead,
            // detect exceptions with empty stack traces here, and treat them differently.
            if (e.getStackTrace().length == 0) {
              error(s"ERROR: ${e.getClass().getName()}: ${e.getMessage()}")
            } else {
              throw e
            }
        }
      } else {
        runMain(childArgs, childClasspath, sparkConf, childMainClass, args.verbose)
      }
    }

    // Let the main class re-initialize the logging system once it starts.
    if (uninitLog) {
      Logging.uninitialize()
    }

    //在独立集群模式下，有两个提交网关：
    //（1）使用o.a.s.deploy.Client作为包装器的传统RPC网关
    //（2）Spark 1.3中引入了新的基于REST的网关
    //后者是Spark 1.3的默认行为，但如果主端点不是REST服务器，则Spark Submit将故障转移到使用旧网关。
    // In standalone cluster mode, there are two submission gateways:
    //   (1) The traditional RPC gateway using o.a.s.deploy.Client as a wrapper
    //   (2) The new REST-based gateway introduced in Spark 1.3
    // The latter is the default behavior as of Spark 1.3, but Spark submit will fail over
    // to use the legacy gateway if the master endpoint turns out to be not a REST server.
    if (args.isStandaloneCluster && args.useRest) {
      try {
        logInfo("Running Spark using the REST application submission protocol.")
        doRunMain()
      } catch {
        // Fail over to use the legacy submission gateway
        case e: SubmitRestConnectionException =>
          logWarning(s"Master endpoint ${args.master} was not a REST server. " +
            "Falling back to legacy submission gateway instead.")
          args.useRest = false
          submit(args, false)
      }
    // In all other modes, just run the main class as prepared
    // 其他模式，只需直接运行主类
    } else {
      doRunMain()
    }
  }

其中：

prepareSubmitEnvironment方法通过设置适当的类路径，系统属性和应用程序参数来准备启动环境，以便基于集群管理和部署模式运行子主类

submit(…)函数最后一行会调用该函数内部自定义函数doRunMain()，该函数会根据应用程序参数（args.proxyUser）做一次判断处理：

a. 如果是代理用户，则使用proxyUser 对runMain()函数包装调用；

b.  如果非代理用户，则直接调用runMain()函数。

(2) 任务运行环境准备

通过设置适当的类路径，系统属性和应用程序参数来准备启动环境，以便基于集群管理和部署模式运行子主类。

val (childArgs, childClasspath, sparkConf, childMainClass) = prepareSubmitEnvironment(args)

/**
   * 未提交的应用程序准备环境
   * Prepare the environment for submitting an application.
   *
   * @param args the parsed SparkSubmitArguments used for environment preparation.
   * @param conf the Hadoop Configuration, this argument will only be set in unit test.
   * 返回一个4元组(childArgs, childClasspath, sparkConf, childMainClass)
   *        childArgs：子进程的参数
   *        childClasspath：子级的类路径条目列表
   *        sparkConf：系统参数map集合
   *        childMainClass：子级的主类
   * @return a 4-tuple:
   *        (1) the arguments for the child process,
   *        (2) a list of classpath entries for the child,
   *        (3) a map of system properties, and
   *        (4) the main class for the child
   *
   * Exposed for testing.
   */
  private[deploy] def prepareSubmitEnvironment(
      args: SparkSubmitArguments,
      conf: Option[HadoopConfiguration] = None)
      : (Seq[String], Seq[String], SparkConf, String) = {
    // Return values
    val childArgs = new ArrayBuffer[String]()
    val childClasspath = new ArrayBuffer[String]()
    val sparkConf = new SparkConf()
    var childMainClass = ""

    // 设置集群管理器，
    // 从这个列表中可以得到信息：spark目前支持的集群管理器包含：YARN,STANDLONE,MESOS,KUBERNETES,LOCAL，
    // 在spark-submit参数的--master中指定。
    // Set the cluster manager
    val clusterManager: Int = args.master match {
      case "yarn" => YARN
      case "yarn-client" | "yarn-cluster" =>
        // spark2.0之前可以使用yarn-cleint,yarn-cluster作为--master参数，从spark2.0起，不再支持，这里默认自动转化为yarn，并给出警告信息。
        logWarning(s"Master ${args.master} is deprecated since 2.0." +
          " Please use master \"yarn\" with specified deploy mode instead.")
        YARN
      case m if m.startsWith("spark") => STANDALONE
      case m if m.startsWith("mesos") => MESOS
      case m if m.startsWith("k8s") => KUBERNETES
      case m if m.startsWith("local") => LOCAL
      case _ =>
        error("Master must either be yarn or start with spark, mesos, k8s, or local")
        -1
    }

    // 设置部署模式--deploy-mode，默认为client模式。
    // Set the deploy mode; default is client mode
    var deployMode: Int = args.deployMode match {
      case "client" | null => CLIENT
      case "cluster" => CLUSTER
      case _ =>
        error("Deploy mode must be either client or cluster")
        -1
    }

    // 由于”yarn-cluster“和”yarn-client“方式已被弃用，因此封装了--master和--deploy-mode。
    // 如果只指定了一个--master和--deploy-mode，我们有一些逻辑来推断它们之间的关系；如果它们不一致，我们可以提前退出。
    // Because the deprecated way of specifying "yarn-cluster" and "yarn-client" encapsulate both
    // the master and deploy mode, we have some logic to infer the master and deploy mode
    // from each other if only one is specified, or exit early if they are at odds.
    if (clusterManager == YARN) {
      (args.master, args.deployMode) match {
        case ("yarn-cluster", null) =>
          deployMode = CLUSTER
          args.master = "yarn"
        case ("yarn-cluster", "client") =>
          error("Client deploy mode is not compatible with master \"yarn-cluster\"")
        case ("yarn-client", "cluster") =>
          error("Cluster deploy mode is not compatible with master \"yarn-client\"")
        case (_, mode) =>
          args.master = "yarn"
      }

      // 如果我们想去使用YARN的话，必须确保它包含在我们构建中。
      // Make sure YARN is included in our build if we're trying to use it
      if (!Utils.classIsLoadable(YARN_CLUSTER_SUBMIT_CLASS) && !Utils.isTesting) {
        error(
          "Could not load YARN classes. " +
          "This copy of Spark may not have been compiled with YARN support.")
      }
    }

    if (clusterManager == KUBERNETES) {
      args.master = Utils.checkAndGetK8sMasterUrl(args.master)
      // Make sure KUBERNETES is included in our build if we're trying to use it
      if (!Utils.classIsLoadable(KUBERNETES_CLUSTER_SUBMIT_CLASS) && !Utils.isTesting) {
        error(
          "Could not load KUBERNETES classes. " +
            "This copy of Spark may not have been compiled with KUBERNETES support.")
      }
    }

    // 下边的一些模式是不支持，尽早让它们失败。
    // Fail fast, the following modes are not supported or applicable
    (clusterManager, deployMode) match {
      case (STANDALONE, CLUSTER) if args.isPython =>
        error("Cluster deploy mode is currently not supported for python " +
          "applications on standalone clusters.")
      case (STANDALONE, CLUSTER) if args.isR =>
        error("Cluster deploy mode is currently not supported for R " +
          "applications on standalone clusters.")
      case (LOCAL, CLUSTER) =>
        error("Cluster deploy mode is not compatible with master \"local\"")
      case (_, CLUSTER) if isShell(args.primaryResource) =>
        error("Cluster deploy mode is not applicable to Spark shells.")
      case (_, CLUSTER) if isSqlShell(args.mainClass) =>
        error("Cluster deploy mode is not applicable to Spark SQL shell.")
      case (_, CLUSTER) if isThriftServer(args.mainClass) =>
        error("Cluster deploy mode is not applicable to Spark Thrift server.")
      case _ =>
    }

    // 如果args.deployMode为null的话，给它赋值更新。稍后它将作为Spark的属性向下传递
    // Update args.deployMode if it is null. It will be passed down as a Spark property later.
    (args.deployMode, deployMode) match {
      case (null, CLIENT) => args.deployMode = "client"
      case (null, CLUSTER) => args.deployMode = "cluster"
      case _ =>
    }

    // 根据资源管理器和部署模式，进行逻辑判断出几种特殊运行方式。
    val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER
    val isMesosCluster = clusterManager == MESOS && deployMode == CLUSTER
    val isStandAloneCluster = clusterManager == STANDALONE && deployMode == CLUSTER
    val isKubernetesCluster = clusterManager == KUBERNETES && deployMode == CLUSTER
    val isMesosClient = clusterManager == MESOS && deployMode == CLIENT

    if (!isMesosCluster && !isStandAloneCluster) {
      // Resolve maven dependencies if there are any and add classpath to jars. Add them to py-files
      // too for packages that include Python code
      val resolvedMavenCoordinates = DependencyUtils.resolveMavenDependencies(
        args.packagesExclusions, args.packages, args.repositories, args.ivyRepoPath,
        args.ivySettingsPath)

      if (!StringUtils.isBlank(resolvedMavenCoordinates)) {
        args.jars = mergeFileLists(args.jars, resolvedMavenCoordinates)
        if (args.isPython || isInternal(args.primaryResource)) {
          args.pyFiles = mergeFileLists(args.pyFiles, resolvedMavenCoordinates)
        }
      }

      // install any R packages that may have been passed through --jars or --packages.
      // Spark Packages may contain R source code inside the jar.
      if (args.isR && !StringUtils.isBlank(args.jars)) {
        RPackageUtils.checkAndBuildRPackage(args.jars, printStream, args.verbose)
      }
    }

    args.sparkProperties.foreach { case (k, v) => sparkConf.set(k, v) }
    val hadoopConf = conf.getOrElse(SparkHadoopUtil.newConfiguration(sparkConf))
    val targetDir = Utils.createTempDir()

    // assure a keytab is available from any place in a JVM
    if (clusterManager == YARN || clusterManager == LOCAL || isMesosClient) {
      if (args.principal != null) {
        if (args.keytab != null) {
          require(new File(args.keytab).exists(), s"Keytab file: ${args.keytab} does not exist")
          // Add keytab and principal configurations in sysProps to make them available
          // for later use; e.g. in spark sql, the isolated class loader used to talk
          // to HiveMetastore will use these settings. They will be set as Java system
          // properties and then loaded by SparkConf
          sparkConf.set(KEYTAB, args.keytab)
          sparkConf.set(PRINCIPAL, args.principal)
          UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab)
        }
      }
    }

    // Resolve glob path for different resources.
    args.jars = Option(args.jars).map(resolveGlobPaths(_, hadoopConf)).orNull
    args.files = Option(args.files).map(resolveGlobPaths(_, hadoopConf)).orNull
    args.pyFiles = Option(args.pyFiles).map(resolveGlobPaths(_, hadoopConf)).orNull
    args.archives = Option(args.archives).map(resolveGlobPaths(_, hadoopConf)).orNull

    lazy val secMgr = new SecurityManager(sparkConf)

    // In client mode, download remote files.
    var localPrimaryResource: String = null
    var localJars: String = null
    var localPyFiles: String = null
    if (deployMode == CLIENT) {
      localPrimaryResource = Option(args.primaryResource).map {
        downloadFile(_, targetDir, sparkConf, hadoopConf, secMgr)
      }.orNull
      localJars = Option(args.jars).map {
        downloadFileList(_, targetDir, sparkConf, hadoopConf, secMgr)
      }.orNull
      localPyFiles = Option(args.pyFiles).map {
        downloadFileList(_, targetDir, sparkConf, hadoopConf, secMgr)
      }.orNull
    }

    // When running in YARN, for some remote resources with scheme:
    //   1. Hadoop FileSystem doesn't support them.
    //   2. We explicitly bypass Hadoop FileSystem with "spark.yarn.dist.forceDownloadSchemes".
    // We will download them to local disk prior to add to YARN's distributed cache.
    // For yarn client mode, since we already download them with above code, so we only need to
    // figure out the local path and replace the remote one.
    if (clusterManager == YARN) {
      val forceDownloadSchemes = sparkConf.get(FORCE_DOWNLOAD_SCHEMES)

      def shouldDownload(scheme: String): Boolean = {
        forceDownloadSchemes.contains("*") || forceDownloadSchemes.contains(scheme) ||
          Try { FileSystem.getFileSystemClass(scheme, hadoopConf) }.isFailure
      }

      def downloadResource(resource: String): String = {
        val uri = Utils.resolveURI(resource)
        uri.getScheme match {
          case "local" | "file" => resource
          case e if shouldDownload(e) =>
            val file = new File(targetDir, new Path(uri).getName)
            if (file.exists()) {
              file.toURI.toString
            } else {
              downloadFile(resource, targetDir, sparkConf, hadoopConf, secMgr)
            }
          case _ => uri.toString
        }
      }

      args.primaryResource = Option(args.primaryResource).map { downloadResource }.orNull
      args.files = Option(args.files).map { files =>
        Utils.stringToSeq(files).map(downloadResource).mkString(",")
      }.orNull
      args.pyFiles = Option(args.pyFiles).map { pyFiles =>
        Utils.stringToSeq(pyFiles).map(downloadResource).mkString(",")
      }.orNull
      args.jars = Option(args.jars).map { jars =>
        Utils.stringToSeq(jars).map(downloadResource).mkString(",")
      }.orNull
      args.archives = Option(args.archives).map { archives =>
        Utils.stringToSeq(archives).map(downloadResource).mkString(",")
      }.orNull
    }

    // If we're running a python app, set the main class to our specific python runner
    if (args.isPython && deployMode == CLIENT) {
      if (args.primaryResource == PYSPARK_SHELL) {
        args.mainClass = "org.apache.spark.api.python.PythonGatewayServer"
      } else {
        // If a python file is provided, add it to the child arguments and list of files to deploy.
        // Usage: PythonAppRunner <main python file> <extra python files> [app arguments]
        args.mainClass = "org.apache.spark.deploy.PythonRunner"
        args.childArgs = ArrayBuffer(localPrimaryResource, localPyFiles) ++ args.childArgs
      }
      if (clusterManager != YARN) {
        // The YARN backend handles python files differently, so don't merge the lists.
        args.files = mergeFileLists(args.files, args.pyFiles)
      }
    }

    if (localPyFiles != null) {
      sparkConf.set("spark.submit.pyFiles", localPyFiles)
    }

    // In YARN mode for an R app, add the SparkR package archive and the R package
    // archive containing all of the built R libraries to archives so that they can
    // be distributed with the job
    if (args.isR && clusterManager == YARN) {
      val sparkRPackagePath = RUtils.localSparkRPackagePath
      if (sparkRPackagePath.isEmpty) {
        error("SPARK_HOME does not exist for R application in YARN mode.")
      }
      val sparkRPackageFile = new File(sparkRPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
      if (!sparkRPackageFile.exists()) {
        error(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
      }
      val sparkRPackageURI = Utils.resolveURI(sparkRPackageFile.getAbsolutePath).toString

      // Distribute the SparkR package.
      // Assigns a symbol link name "sparkr" to the shipped package.
      args.archives = mergeFileLists(args.archives, sparkRPackageURI + "#sparkr")

      // Distribute the R package archive containing all the built R packages.
      if (!RUtils.rPackages.isEmpty) {
        val rPackageFile =
          RPackageUtils.zipRLibraries(new File(RUtils.rPackages.get), R_PACKAGE_ARCHIVE)
        if (!rPackageFile.exists()) {
          error("Failed to zip all the built R packages.")
        }

        val rPackageURI = Utils.resolveURI(rPackageFile.getAbsolutePath).toString
        // Assigns a symbol link name "rpkg" to the shipped package.
        args.archives = mergeFileLists(args.archives, rPackageURI + "#rpkg")
      }
    }

    // TODO: Support distributing R packages with standalone cluster
    if (args.isR && clusterManager == STANDALONE && !RUtils.rPackages.isEmpty) {
      error("Distributing R packages with standalone cluster is not supported.")
    }

    // TODO: Support distributing R packages with mesos cluster
    if (args.isR && clusterManager == MESOS && !RUtils.rPackages.isEmpty) {
      error("Distributing R packages with mesos cluster is not supported.")
    }

    // If we're running an R app, set the main class to our specific R runner
    if (args.isR && deployMode == CLIENT) {
      if (args.primaryResource == SPARKR_SHELL) {
        args.mainClass = "org.apache.spark.api.r.RBackend"
      } else {
        // If an R file is provided, add it to the child arguments and list of files to deploy.
        // Usage: RRunner <main R file> [app arguments]
        args.mainClass = "org.apache.spark.deploy.RRunner"
        args.childArgs = ArrayBuffer(localPrimaryResource) ++ args.childArgs
        args.files = mergeFileLists(args.files, args.primaryResource)
      }
    }

    if (isYarnCluster && args.isR) {
      // In yarn-cluster mode for an R app, add primary resource to files
      // that can be distributed with the job
      args.files = mergeFileLists(args.files, args.primaryResource)
    }

    // Special flag to avoid deprecation warnings at the client
    sys.props("SPARK_SUBMIT") = "true"

    // A list of rules to map each argument to system properties or command-line options in
    // each deploy mode; we iterate through these below
    val options = List[OptionAssigner](

      // All cluster managers
      OptionAssigner(args.master, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, confKey = "spark.master"),
      OptionAssigner(args.deployMode, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
        confKey = "spark.submit.deployMode"),
      OptionAssigner(args.name, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, confKey = "spark.app.name"),
      OptionAssigner(args.ivyRepoPath, ALL_CLUSTER_MGRS, CLIENT, confKey = "spark.jars.ivy"),
      OptionAssigner(args.driverMemory, ALL_CLUSTER_MGRS, CLIENT,
        confKey = "spark.driver.memory"),
      OptionAssigner(args.driverExtraClassPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
        confKey = "spark.driver.extraClassPath"),
      OptionAssigner(args.driverExtraJavaOptions, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
        confKey = "spark.driver.extraJavaOptions"),
      OptionAssigner(args.driverExtraLibraryPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
        confKey = "spark.driver.extraLibraryPath"),

      // Propagate attributes for dependency resolution at the driver side
      OptionAssigner(args.packages, STANDALONE | MESOS, CLUSTER, confKey = "spark.jars.packages"),
      OptionAssigner(args.repositories, STANDALONE | MESOS, CLUSTER,
        confKey = "spark.jars.repositories"),
      OptionAssigner(args.ivyRepoPath, STANDALONE | MESOS, CLUSTER, confKey = "spark.jars.ivy"),
      OptionAssigner(args.packagesExclusions, STANDALONE | MESOS,
        CLUSTER, confKey = "spark.jars.excludes"),

      // Yarn only
      OptionAssigner(args.queue, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.queue"),
      OptionAssigner(args.numExecutors, YARN, ALL_DEPLOY_MODES,
        confKey = "spark.executor.instances"),
      OptionAssigner(args.pyFiles, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.pyFiles"),
      OptionAssigner(args.jars, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.jars"),
      OptionAssigner(args.files, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.files"),
      OptionAssigner(args.archives, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.archives"),
      OptionAssigner(args.principal, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.principal"),
      OptionAssigner(args.keytab, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.keytab"),

      // Other options
      OptionAssigner(args.executorCores, STANDALONE | YARN | KUBERNETES, ALL_DEPLOY_MODES,
        confKey = "spark.executor.cores"),
      OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN | KUBERNETES, ALL_DEPLOY_MODES,
        confKey = "spark.executor.memory"),
      OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,
        confKey = "spark.cores.max"),
      OptionAssigner(args.files, LOCAL | STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,
        confKey = "spark.files"),
      OptionAssigner(args.jars, LOCAL, CLIENT, confKey = "spark.jars"),
      OptionAssigner(args.jars, STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,
        confKey = "spark.jars"),
      OptionAssigner(args.driverMemory, STANDALONE | MESOS | YARN | KUBERNETES, CLUSTER,
        confKey = "spark.driver.memory"),
      OptionAssigner(args.driverCores, STANDALONE | MESOS | YARN | KUBERNETES, CLUSTER,
        confKey = "spark.driver.cores"),
      OptionAssigner(args.supervise.toString, STANDALONE | MESOS, CLUSTER,
        confKey = "spark.driver.supervise"),
      OptionAssigner(args.ivyRepoPath, STANDALONE, CLUSTER, confKey = "spark.jars.ivy"),

      // An internal option used only for spark-shell to add user jars to repl's classloader,
      // previously it uses "spark.jars" or "spark.yarn.dist.jars" which now may be pointed to
      // remote jars, so adding a new option to only specify local jars for spark-shell internally.
      OptionAssigner(localJars, ALL_CLUSTER_MGRS, CLIENT, confKey = "spark.repl.local.jars")
    )

    // 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(",") }
    }
    // Add the main application jar and any added jars to classpath in case YARN client
    // requires these jars.
    // This assumes both primaryResource and user jars are local jars, or already downloaded
    // to local by configuring "spark.yarn.dist.forceDownloadSchemes", otherwise it will not be
    // added to the classpath of YARN client.
    if (isYarnCluster) {
      if (isUserJar(args.primaryResource)) {
        childClasspath += args.primaryResource
      }
      if (args.jars != null) { childClasspath ++= args.jars.split(",") }
    }

    if (deployMode == CLIENT) {
      if (args.childArgs != null) { childArgs ++= args.childArgs }
    }

    // Map all arguments to command-line options or system properties for our chosen mode
    for (opt <- options) {
      if (opt.value != null &&
          (deployMode & opt.deployMode) != 0 &&
          (clusterManager & opt.clusterManager) != 0) {
        if (opt.clOption != null) { childArgs += (opt.clOption, opt.value) }
        if (opt.confKey != null) { sparkConf.set(opt.confKey, opt.value) }
      }
    }

    // In case of shells, spark.ui.showConsoleProgress can be true by default or by user.
    if (isShell(args.primaryResource) && !sparkConf.contains(UI_SHOW_CONSOLE_PROGRESS)) {
      sparkConf.set(UI_SHOW_CONSOLE_PROGRESS, true)
    }

    // Add the application jar automatically so the user doesn't have to call sc.addJar
    // For YARN cluster mode, the jar is already distributed on each node as "app.jar"
    // For python and R files, the primary resource is already distributed as a regular file
    if (!isYarnCluster && !args.isPython && !args.isR) {
      var jars = sparkConf.getOption("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq.empty)
      if (isUserJar(args.primaryResource)) {
        jars = jars ++ Seq(args.primaryResource)
      }
      sparkConf.set("spark.jars", jars.mkString(","))
    }

    // In standalone cluster mode, use the REST client to submit the application (Spark 1.3+).
    // All Spark parameters are expected to be passed to the client through system properties.
    if (args.isStandaloneCluster) {
      if (args.useRest) {
        childMainClass = REST_CLUSTER_SUBMIT_CLASS
        childArgs += (args.primaryResource, args.mainClass)
      } else {
        // In legacy standalone cluster mode, use Client as a wrapper around the user class
        childMainClass = STANDALONE_CLUSTER_SUBMIT_CLASS
        if (args.supervise) { childArgs += "--supervise" }
        Option(args.driverMemory).foreach { m => childArgs += ("--memory", m) }
        Option(args.driverCores).foreach { c => childArgs += ("--cores", c) }
        childArgs += "launch"
        childArgs += (args.master, args.primaryResource, args.mainClass)
      }
      if (args.childArgs != null) {
        childArgs ++= args.childArgs
      }
    }

    // Let YARN know it's a pyspark app, so it distributes needed libraries.
    if (clusterManager == YARN) {
      if (args.isPython) {
        sparkConf.set("spark.yarn.isPython", "true")
      }
    }

    if (clusterManager == MESOS && UserGroupInformation.isSecurityEnabled) {
      setRMPrincipal(sparkConf)
    }

    // 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) }
      }
    }

    if (isMesosCluster) {
      assert(args.useRest, "Mesos cluster mode is only supported through the REST submission API")
      childMainClass = REST_CLUSTER_SUBMIT_CLASS
      if (args.isPython) {
        // Second argument is main class
        childArgs += (args.primaryResource, "")
        if (args.pyFiles != null) {
          sparkConf.set("spark.submit.pyFiles", args.pyFiles)
        }
      } else if (args.isR) {
        // Second argument is main class
        childArgs += (args.primaryResource, "")
      } else {
        childArgs += (args.primaryResource, args.mainClass)
      }
      if (args.childArgs != null) {
        childArgs ++= args.childArgs
      }
    }

    if (isKubernetesCluster) {
      childMainClass = KUBERNETES_CLUSTER_SUBMIT_CLASS
      if (args.primaryResource != SparkLauncher.NO_RESOURCE) {
        if (args.isPython) {
          childArgs ++= Array("--primary-py-file", args.primaryResource)
          childArgs ++= Array("--main-class", "org.apache.spark.deploy.PythonRunner")
          if (args.pyFiles != null) {
            childArgs ++= Array("--other-py-files", args.pyFiles)
          }
        } else if (args.isR) {
          childArgs ++= Array("--primary-r-file", args.primaryResource)
          childArgs ++= Array("--main-class", "org.apache.spark.deploy.RRunner")
        }
        else {
          childArgs ++= Array("--primary-java-resource", args.primaryResource)
          childArgs ++= Array("--main-class", args.mainClass)
        }
      } else {
        childArgs ++= Array("--main-class", args.mainClass)
      }
      if (args.childArgs != null) {
        args.childArgs.foreach { arg =>
          childArgs += ("--arg", arg)
        }
      }
    }

    // Load any properties specified through --conf and the default properties file
    for ((k, v) <- args.sparkProperties) {
      sparkConf.setIfMissing(k, v)
    }

    // Ignore invalid spark.driver.host in cluster modes.
    if (deployMode == CLUSTER) {
      sparkConf.remove("spark.driver.host")
    }

    // Resolve paths in certain spark properties
    val pathConfigs = Seq(
      "spark.jars",
      "spark.files",
      "spark.yarn.dist.files",
      "spark.yarn.dist.archives",
      "spark.yarn.dist.jars")
    pathConfigs.foreach { config =>
      // Replace old URIs with resolved URIs, if they exist
      sparkConf.getOption(config).foreach { oldValue =>
        sparkConf.set(config, Utils.resolveURIs(oldValue))
      }
    }

    // Resolve and format python file paths properly before adding them to the PYTHONPATH.
    // The resolving part is redundant in the case of --py-files, but necessary if the user
    // explicitly sets `spark.submit.pyFiles` in his/her default properties file.
    sparkConf.getOption("spark.submit.pyFiles").foreach { pyFiles =>
      val resolvedPyFiles = Utils.resolveURIs(pyFiles)
      val formattedPyFiles = if (!isYarnCluster && !isMesosCluster) {
        PythonRunner.formatPaths(resolvedPyFiles).mkString(",")
      } else {
        // Ignoring formatting python path in yarn and mesos cluster mode, these two modes
        // support dealing with remote python files, they could distribute and add python files
        // locally.
        resolvedPyFiles
      }
      sparkConf.set("spark.submit.pyFiles", formattedPyFiles)
    }

    (childArgs, childClasspath, sparkConf, childMainClass)
  }

其中：

a. 当部署模式为client，则子进程的主类为用户通过spark-submit提交的类，即代码中的：childMainClass = args.mainClass

b. 当master为Yarn且部署模式为cluster时，子进程的主类为：org.apache.spark.deploy.yarn.YarnClusterApplication

1) 准备Yarn(Cluster Manager)的执行类：

使用spark-submit启动时，实际上执行的是exec "SPARKHOME"/bin/spark−class  org.apache.spark.deploy.SparkSubmit   "@"

在SparkSubmit中，prepareSubmitEnvironment方法中会为spark提交做准备，准备好运行环境相关。

private[deploy] def prepareSubmitEnvironment(args: SparkSubmitArguments,conf: Option[HadoopConfiguration] = None): (Seq[String], Seq[String], SparkConf, String)

其中该方法内部代码中，发现当cluster manager为yarn时：

a. 当--deploy-mode:cluster时

会调用YarnClusterApplication进行提交。YarnClusterApplication是org.apache.spark.deploy.yarn.Client中的一个内部类，在YarnClusterApplication中new了一个Client对象，并调用了run方法

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("spark.jars")
    conf.remove("spark.files")

    new Client(new ClientArguments(args), conf).run()
  }

b. 当--deploy-mode:client时

调用application-jar.jar自身main函数，执行的是JavaMainApplication

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.deploy

import java.lang.reflect.Modifier

import org.apache.spark.SparkConf

/**
 * Entry point for a Spark application. Implementations must provide a no-argument constructor.
 */
private[spark] trait SparkApplication {

  def start(args: Array[String], conf: SparkConf): Unit

}

/**
 * Implementation of SparkApplication that wraps a standard Java class with a "main" method.
 *
 * Configuration is propagated to the application via system properties, so running multiple
 * of these in the same JVM may lead to undefined behavior due to configuration leaks.
 */
private[deploy] class JavaMainApplication(klass: Class[_]) extends SparkApplication {

  override def start(args: Array[String], conf: SparkConf): Unit = {
    val mainMethod = klass.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")
    }

    val sysProps = conf.getAll.toMap
    sysProps.foreach { case (k, v) =>
      sys.props(k) = v
    }

    mainMethod.invoke(null, args)
  }

}

从JavaMainApplication实现可以发现，JavaMainApplication中调用start方法时，只是通过反射执行application-jar.jar的main函数。

(3) 提交到Yarn

1) yarn-cluster运行流程：

当yarn-custer模式中，YarnClusterApplication类中运行的是Client中run方法，Client#run()中实现了任务提交流程：

/**
   * Submit an application to the ResourceManager.
   * If set spark.yarn.submit.waitAppCompletion to true, it will stay alive
   * reporting the application's status until the application has exited for any reason.
   * Otherwise, the client process will exit after submission.
   * If the application finishes with a failed, killed, or undefined status,
   * throw an appropriate SparkException.
   */
  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")
        }
        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")
      }
    }
  }

在Client类的run()方法中会调用submitApplication()方法，该方法实现：

/**
   * Submit an application running our ApplicationMaster to the ResourceManager.
   *
   * The stable Yarn API provides a convenience method (YarnClient#createApplication) for
   * creating applications and setting up the application submission context. This was not
   * available in the alpha API.
   */
  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 {
      case e: Throwable =>
        if (appId != null) {
          cleanupStagingDir(appId)
        }
        throw e
    }
  }

run()方法则是实现向yarn中的ResourceManager（后文全部简称RM）提交运行任务，并运行我们的ApplicationMaster(后文简称AM)。

稳定的Yarn API提供了一种方便的方法（YarnClient#createApplication），用于创建应用程序和设置应用程序提交上下文。

submitApplication()方法具体操作步骤：

l  初始化并启动YarnClient，后边将使用yarnClient提供的各种API

l  通过调用yarnClient#createApplication()方法，从RM获取一个newApp(application)，该newApp用于运行AM。通过newApp#getNewApplicationResponse()返回newApp需要资源情况(newAppResponse)。

l  通过newAppResponse验证集群是否有足够的资源来运行AM。

l  设置适当的上下文来以启动AM。

l  调用yarnClient#submitApplication(appContext)向yarn提交任务启动的请求，并监控application。

yarn-client运行流程：

• 对于部署方式是Client的情况，SparkSubmit的main函数中通过反射执行应用程序的main方法
• 在应用程序的main方法中，创建SparkContext实例
• 在创建SparkContext的实例过程中，通过如下语句创建Scheduler和Backend实例

  private var _schedulerBackend: SchedulerBackend = _
  private var _taskScheduler: TaskScheduler = _

  private[spark] def schedulerBackend: SchedulerBackend = _schedulerBackend

  private[spark] def taskScheduler: TaskScheduler = _taskScheduler
  private[spark] def taskScheduler_=(ts: TaskScheduler): Unit = {
    _taskScheduler = ts
  }

  // 构造函数中初始化赋值
    // Create and start the scheduler
    val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)
    _schedulerBackend = sched
_taskScheduler = ts

https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/SparkContext.scala

SparkContext初始化过程

在Yarn模式下，SparkContext初始化位置因--deploy-mode不同而不同：

yarn-cluster模式下：client会先申请向RM(Yarn Resource Manager)一个Container，来启动AM（ApplicationMaster）进程，而SparkContext运行在AM（ApplicationMaster）进程中；

yarn-client模式下  ：在提交节点上执行SparkContext初始化，由client类（JavaMainApplication）调用。

/**
   * Create a task scheduler based on a given master URL.
   * Return a 2-tuple of the scheduler backend and the task scheduler.
   */
  private def createTaskScheduler(。。。): (SchedulerBackend, TaskScheduler) = {
    。。。
    master match {
      case "local" =>
        。。。
      case LOCAL_N_REGEX(threads) =>
        。。。
      case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
        。。。。
      case SPARK_REGEX(sparkUrl) =>
        。。。。
      case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
        。。。。
      case masterUrl =>
        val cm = getClusterManager(masterUrl) match {
          case Some(clusterMgr) => clusterMgr
          case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
        }
        try {
          val scheduler = cm.createTaskScheduler(sc, masterUrl)
          val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
          cm.initialize(scheduler, backend)
          (backend, scheduler)
        } catch {
          case se: SparkException => throw se
          case NonFatal(e) =>
            throw new SparkException("External scheduler cannot be instantiated", e)
        }
    }
  }

  private def getClusterManager(url: String): Option[ExternalClusterManager] = {
    val loader = Utils.getContextOrSparkClassLoader
    val serviceLoaders =
      ServiceLoader.load(classOf[ExternalClusterManager], loader).asScala.filter(_.canCreate(url))
    if (serviceLoaders.size > 1) {
      throw new SparkException(
        s"Multiple external cluster managers registered for the url $url: $serviceLoaders")
    }
    serviceLoaders.headOption
  }  

https://github.com/apache/spark/blob/branch-2.4/core/src/main/scala/org/apache/spark/SparkContext.scala

1）SparkContext#createTaskScheduler(。。。)

根据不同的资源管理方式cluster manager来创建不同的TaskScheduler，SchedulerBackend。

1.1）SchedulerBackend与cluster manager资源管理器交互取得应用被分配的资源。

1.2）TaskSheduler在不同的job之间调度，同时接收被分配的资源，之后由他来给每一个Task分配资源。

2）SparkContext#createTaskScheduler(。。。)

最后一个match case是对其他资源管理方式（除了local和standelone{spark://}外的mesos，yarn，kubernetes【外部资源管理器】的资源管理方式）的处理。

SparkContext#createTaskScheduler(。。。)#master match#case masterUrl下边调用了getClusterManager(masterUrl)方法，该方法返回对象是实现了ExternalClusterManager接口的YarnClusterManager类对象。

备注：实现了ExternalClusterManager接口的类还包含：

MesosClusterManager （https://github.com/apache/spark/blob/branch-2.4/resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterManager.scala）

KubernetesClusterManager （https://github.com/apache/spark/blob/branch-2.4/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/KubernetesClusterManager.scala）

ExternalClusterManager接口定义：

private[spark] trait ExternalClusterManager {
  def canCreate(masterURL: String): Boolean

  def createTaskScheduler(sc: SparkContext, masterURL: String): TaskScheduler

  def createSchedulerBackend(sc: SparkContext,
      masterURL: String,
      scheduler: TaskScheduler): SchedulerBackend

  def initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit
}

https://github.com/apache/spark/blob/branch-2.4/core/src/main/scala/org/apache/spark/scheduler/ExternalClusterManager.scala

ExternalClusterManager接口提供了4个方法：

-canCreate(masterURL: String):Boolean  检查此群集管理器实例是否可以为某个masterURL创建scheduler组件。

-createTaskScheduler(sc: SparkContext, masterURL: String):TaskScheduler  为给定的SparkContext创建TaskScheduler实例

-createSchedulerBackend(sc: SparkContext,masterURL: String,scheduler: TaskScheduler): SchedulerBackend  为给定的SparkContext和调度程序创建SchedulerBackend 。这是在使用“ExternalClusterManager.createTaskScheduler（）”创建TaskScheduler后调用的。

-initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit  初始化TaskScheduler和SchedulerBackend，在创建调度程序组件之后调用。

YarnClusterManager类定义：

private[spark] class YarnClusterManager extends ExternalClusterManager {

  override def canCreate(masterURL: String): Boolean = {
    masterURL == "yarn"
  }

  override def createTaskScheduler(sc: SparkContext, masterURL: String): TaskScheduler = {
    sc.deployMode match {
      case "cluster" => new YarnClusterScheduler(sc)
      case "client" => new YarnScheduler(sc)
      case _ => throw new SparkException(s"Unknown deploy mode '${sc.deployMode}' for Yarn")
    }
  }

  override def createSchedulerBackend(sc: SparkContext,
      masterURL: String,
      scheduler: TaskScheduler): SchedulerBackend = {
    sc.deployMode match {
      case "cluster" =>
        new YarnClusterSchedulerBackend(scheduler.asInstanceOf[TaskSchedulerImpl], sc)
      case "client" =>
        new YarnClientSchedulerBackend(scheduler.asInstanceOf[TaskSchedulerImpl], sc)
      case  _ =>
        throw new SparkException(s"Unknown deploy mode '${sc.deployMode}' for Yarn")
    }
  }

  override def initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit = {
    scheduler.asInstanceOf[TaskSchedulerImpl].initialize(backend)
  }
}

https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterManager.scala

YarnClusterManager#createTaskScheduler(...)：

在该方法中会根据SparkContext对象的deployMode属性来进行分支判断：

client时，返回YarnScheduler(https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnScheduler.scala)实例对象；

cluster时，返回YarnClusterScheduler(https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala)实例对象。

YarnClusterManager#createSchedulerBackend(...)：

在该方法中会根据SparkContext对象的deployMode属性来进行分支判断：

client时，返回YarnClientSchedulerBackend(https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala)实例对象；

cluster时，返回YarnClusterSchedulerBackend(https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala)实例对象。

Yarn作业运行运行架构原理解析：

1、分析Spark on YARN的Cluster模式，从用户提交作业到作业运行结束整个运行期间的过程分析。

客户端进行操作

• 　　1、根据yarnConf来初始化yarnClient，并启动yarnClient
• 　　2、创建客户端Application，并获取Application的ID，进一步判断集群中的资源是否满足executor和ApplicationMaster申请的资源，如果不满足则抛出IllegalArgumentException；
• 　　3、设置资源、环境变量：其中包括了设置Application的Staging目录、准备本地资源（jar文件、log4j.properties）、设置Application其中的环境变量、创建Container启动的Context等；
• 　　4、设置Application提交的Context，包括设置应用的名字、队列、AM的申请的Container、标记该作业的类型为Spark；
• 　　5、申请Memory，并最终通过yarnClient.submitApplication向ResourceManager提交该Application。

　　当作业提交到YARN上之后，客户端就没事了，甚至在终端关掉那个进程也没事，因为整个作业运行在YARN集群上进行，运行的结果将会保存到HDFS或者日志中。

提交到YARN集群，YARN操作

• 　　1、运行ApplicationMaster的run方法；
• 　　2、设置好相关的环境变量。
• 　　3、创建amClient，并启动；
• 　　4、在Spark UI启动之前设置Spark UI的AmIpFilter；
• 　　5、在startUserClass函数专门启动了一个线程（名称为Driver的线程）来启动用户提交的Application，也就是启动了Driver。在Driver中将会初始化SparkContext；
• 　　6、等待SparkContext初始化完成，最多等待spark.yarn.applicationMaster.waitTries次数（默认为10），如果等待了的次数超过了配置的，程序将会退出；否则用SparkContext初始化yarnAllocator；

　　怎么知道SparkContext初始化完成？
　　其实在5步骤中启动Application的过程中会初始化SparkContext，在初始化SparkContext的时候将会创建YarnClusterScheduler，在SparkContext初始化完成的时候，会调用YarnClusterScheduler类中的postStartHook方法，而该方法会通知ApplicationMaster已经初始化好了SparkContext

• 　　7、当SparkContext、Driver初始化完成的时候，通过amClient向ResourceManager注册ApplicationMaster
• 　　8、分配并启动Executeors。在启动Executeors之前，先要通过yarnAllocator获取到numExecutors个Container，然后在Container中启动Executeors。如果在启动Executeors的过程中失败的次数达到了maxNumExecutorFailures的次数，maxNumExecutorFailures的计算规则如下：

// Default to numExecutors * 2, with minimum of 3
private val maxNumExecutorFailures =sparkConf.getInt("spark.yarn.max.executor.failures",
sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors *2,3)))

　　那么这个Application将失败，将Application Status标明为FAILED，并将关闭SparkContext。其实，启动Executeors是通过ExecutorRunnable实现的，而ExecutorRunnable内部是启动CoarseGrainedExecutorBackend的。

• 　　9、最后，Task将在CoarseGrainedExecutorBackend里面运行，然后运行状况会通过Akka通知CoarseGrainedScheduler，直到作业运行完成。

2、Spark on YARN client 模式作业运行全过程分析

我们知道Spark on yarn有两种模式：yarn-cluster和yarn-client。这两种模式作业虽然都是在yarn上面运行，但是其中的运行方式很不一样，今天我就来谈谈Spark on YARN yarn-client模式作业从提交到运行的过程剖析。
　　和yarn-cluster模式一样，整个程序也是通过spark-submit脚本提交的。但是yarn-client作业程序的运行不需要通过Client类来封装启动，而是直接通过反射机制调用作业的main函数。下面就来分析：

• 　　1、通过SparkSubmit类的launch的函数直接调用作业的main函数（通过反射机制实现），如果是集群模式就会调用Client的main函数。
• 　　2、而应用程序的main函数一定都有个SparkContent，并对其进行初始化；
• 　　3、在SparkContent初始化中将会依次做如下的事情：设置相关的配置、注册MapOutputTracker、BlockManagerMaster、BlockManager，创建taskScheduler和dagScheduler；其中比较重要的是创建taskScheduler和dagScheduler。在创建taskScheduler的时候会根据我们传进来的master来选择Scheduler和SchedulerBackend。由于我们选择的是yarn-client模式，程序会选择YarnClientClusterScheduler和YarnClientSchedulerBackend，并将YarnClientSchedulerBackend的实例初始化YarnClientClusterScheduler，上面两个实例的获取都是通过反射机制实现的，YarnClientSchedulerBackend类是CoarseGrainedSchedulerBackend类的子类，YarnClientClusterScheduler是TaskSchedulerImpl的子类，仅仅重写了TaskSchedulerImpl中的getRackForHost方法。
• 　　4、初始化完taskScheduler后，将创建dagScheduler，然后通过taskScheduler.start()启动taskScheduler，而在taskScheduler启动的过程中也会调用SchedulerBackend的start方法。在SchedulerBackend启动的过程中将会初始化一些参数，封装在ClientArguments中，并将封装好的ClientArguments传进Client类中，并client.runApp()方法获取Application ID。
• 　　5、client.runApp里面的做是和前面客户端进行操作那节类似，不同的是在里面启动是ExecutorLauncher（yarn-cluster模式启动的是ApplicationMaster）。
• 　　6、在ExecutorLauncher里面会初始化并启动amClient，然后向ApplicationMaster注册该Application。注册完之后将会等待driver的启动，当driver启动完之后，会创建一个MonitorActor对象用于和CoarseGrainedSchedulerBackend进行通信（只有事件AddWebUIFilter他们之间才通信，Task的运行状况不是通过它和CoarseGrainedSchedulerBackend通信的）。然后就是设置addAmIpFilter，当作业完成的时候，ExecutorLauncher将通过amClient设置Application的状态为FinalApplicationStatus.SUCCEEDED。
• 　　7、分配Executors，这里面的分配逻辑和yarn-cluster里面类似，就不再说了。
• 　　8、最后，Task将在CoarseGrainedExecutorBackend里面运行，然后运行状况会通过Akka通知CoarseGrainedScheduler，直到作业运行完成。
• 　　9、在作业运行的时候，YarnClientSchedulerBackend会每隔1秒通过client获取到作业的运行状况，并打印出相应的运行信息，当Application的状态是FINISHED、FAILED和KILLED中的一种，那么程序将退出等待。
• 　　10、最后有个线程会再次确认Application的状态，当Application的状态是FINISHED、FAILED和KILLED中的一种，程序就运行完成，并停止SparkContext。整个过程就结束了。

YARN-Cluster运行架构原理

在YARN-Cluster模式中，当用户向YARN中提交一个应用程序后，YARN将分两个阶段运行该应用程序：

• 1.第一个阶段是把Spark的Driver作为一个ApplicationMaster在YARN集群中先启动；
• 2.第二个阶段是由ApplicationMaster创建应用程序，然后为它向ResourceManager申请资源，并启动Executor来运行Task，同时监控它的整个运行过程，直到运行完成

说明如下：

• Spark Yarn Client向YARN中提交应用程序，包括ApplicationMaster程序、启动ApplicationMaster的命令、需要在Executor中运行的程序等；
• ResourceManager收到请求后，在集群中选择一个NodeManager，为该应用程序分配第一个Container，要求它在这个Container中启动应用程序的ApplicationMaster，其中ApplicationMaster进行SparkContext等的初始化；
• ApplicationMaster向ResourceManager注册，这样用户可以直接通过ResourceManage查看应用程序的运行状态，然后它将采用轮询的方式通过RPC协议为各个任务申请资源，并监控它们的运行状态直到运行结束；
• 一旦ApplicationMaster申请到资源（也就是Container）后，便与对应的NodeManager通信，要求它在获得的Container中启动CoarseGrainedExecutorBackend，CoarseGrainedExecutorBackend启动后会向ApplicationMaster中的SparkContext注册并申请Task。这一点和Standalone模式一样，只不过SparkContext在Spark Application中初始化时，使用CoarseGrainedSchedulerBackend配合YarnClusterScheduler进行任务的调度，其中YarnClusterScheduler只是对TaskSchedulerImpl的一个简单包装，增加了对Executor的等待逻辑等；
• ApplicationMaster中的SparkContext分配Task给CoarseGrainedExecutorBackend执行，CoarseGrainedExecutorBackend运行Task并向ApplicationMaster汇报运行的状态和进度，以让ApplicationMaster随时掌握各个任务的运行状态，从而可以在任务失败时重新启动任务；
• 应用程序运行完成后，ApplicationMaster向ResourceManager申请注销并关闭自己；

跟踪CoarseGrainedExecutorBackend启动脚本：

 

YARN-Client运行架构原理

说明如下：

• Spark Yarn Client向YARN的ResourceManager申请启动Application Master。同时在SparkContent初始化中将创建DAGScheduler和TASKScheduler等，由于我们选择的是Yarn-Client模式，程序会选择YarnClientClusterSchedulerYarnScheduler和YarnClientSchedulerBackend；
• ResourceManager收到请求后，在集群中选择一个NodeManager，为该应用程序分配第一个Container，要求它在这个Container中启动应用程序的ApplicationMaster，与YARN-Cluster区别的是在该ApplicationMaster不运行SparkContext，只与SparkContext进行联系进行资源的分派；
• Client中的SparkContext初始化完毕后，与ApplicationMaster建立通讯，向ResourceManager注册，根据任务信息向ResourceManager申请资源（Container）；
• 一旦ApplicationMaster申请到资源（也就是Container）后，便与对应的NodeManager通信，要求它在获得的Container中启动CoarseGrainedExecutorBackend，CoarseGrainedExecutorBackend启动后会向Client中的SparkContext注册并申请Task；
• client中的SparkContext分配Task给CoarseGrainedExecutorBackend执行，CoarseGrainedExecutorBackend运行Task并向Driver汇报运行的状态和进度，以让Client随时掌握各个任务的运行状态，从而可以在任务失败时重新启动任务；
• 应用程序运行完成后，Client的SparkContext向ResourceManager申请注销并关闭自己。

Client模式 vs Cluster模式

• 理解YARN-Client和YARN-Cluster深层次的区别之前先清楚一个概念：Application Master。在YARN中，每个Application实例都有一个ApplicationMaster进程，它是Application启动的第一个容器。它负责和ResourceManager打交道并请求资源，获取资源之后告诉NodeManager为其启动Container。从深层次的含义讲YARN-Cluster和YARN-Client模式的区别其实就是ApplicationMaster进程的区别；
• YARN-Cluster模式下，Driver运行在AM(Application Master)中，它负责向YARN申请资源，并监督作业的运行状况。当用户提交了作业之后，就可以关掉Client，作业会继续在YARN上运行，因而YARN-Cluster模式不适合运行交互类型的作业；
• YARN-Client模式下，Application Master仅仅向YARN请求Executor，Client会和请求的Container通信来调度他们工作，也就是说Client不能离开；