20200619 当执行spark-submit提交一个任务时都干了啥呢？

当执行spark-submit提交一个任务时都干了啥呢？（☆☆☆☆）

cat spark-submit

#!/usr/bin/env bash

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if [ -z "${SPARK_HOME}" ]; then
  source "$(dirname "$0")"/find-spark-home
fi

# disable randomized hash for string in Python 3.3+
export PYTHONHASHSEED=0

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

从上边可以看出其实是执行了org.apache.spark.deploy.SparkSubmit这个类，去源码中看看这个类，直接看main函数

def main(args: Array[String]): Unit = {
   val submit = new SparkSubmit()
   submit.doSubmit(args)
 }

执行了doSubmit 再去看看这里边干了点啥，一个模式匹配，执行submit方法

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)

    val appArgs = parseArguments(args)
    if (appArgs.verbose) {
      logInfo(appArgs.toString)
    }
    appArgs.action match {
      case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog)
      case SparkSubmitAction.KILL => kill(appArgs)
      case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)
      case SparkSubmitAction.PRINT_VERSION => printVersion()
    }
  }

看看submit都干了啥。提交任务都要先进行反序列化，所以submit()方法会先获取到反向代理，之后开启线程，解析到入口类的main方法并执行。

private def submit(args: SparkSubmitArguments, uninitLog: Boolean): Unit = {

    def doRunMain(): Unit = {
      //拿到反向代理对象，在这个方法中，首先会检查参数中是否指定了proxyUser，这个参数是指定spark 程序运行的用户，并且检查了用户是否有提交spark 应用程序的权限。一般在使用了安全管理的集群中配合使用。我们前面的提交脚本中没有指定proxy User，所以这里会直接执行runMain方法。我们跟进去看看runMain 方法
      if (args.proxyUser != null) {
        val proxyUser = UserGroupInformation.createProxyUser(args.proxyUser,
          UserGroupInformation.getCurrentUser())
        try {
          proxyUser.doAs(new PrivilegedExceptionAction[Unit]() {
            //解析到入口类的main方法去runMain里再看看
            override def run(): Unit = {
              runMain(args, uninitLog)
            }
          })
        } 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(args, uninitLog)
      }
    }

    // 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
    } else {
      doRunMain()
    }
  }

看看runMain 里干了点啥

private def runMain(args: SparkSubmitArguments, uninitLog: Boolean): Unit = {
    //首先是拿到各种参数解析校验
    val (childArgs, childClasspath, sparkConf, childMainClass) = prepareSubmitEnvironment(args)
    // Let the main class re-initialize the logging system once it starts.
    if (uninitLog) {
      Logging.uninitialize()
    }

    if (args.verbose) {
      logInfo(s"Main class:\n$childMainClass")
      logInfo(s"Arguments:\n${childArgs.mkString("\n")}")
      // sysProps may contain sensitive information, so redact before printing
      logInfo(s"Spark config:\n${Utils.redact(sparkConf.getAll.toMap).mkString("\n")}")
      logInfo(s"Classpath elements:\n${childClasspath.mkString("\n")}")
      logInfo("\n")
    }
    val loader = getSubmitClassLoader(sparkConf)
    for (jar <- childClasspath) {
      addJarToClasspath(jar, loader)
    }

    var mainClass: Class[_] = null

    try {
      //获取到入口类的main方法，也就是你传入的你要执行的类,通过类名加载这个类
      mainClass = Utils.classForName(childMainClass)
    } catch {
      case e: ClassNotFoundException =>
        logError(s"Failed to load class $childMainClass.")
        if (childMainClass.contains("thriftserver")) {
          logInfo(s"Failed to load main class $childMainClass.")
          logInfo("You need to build Spark with -Phive and -Phive-thriftserver.")
        }
        throw new SparkUserAppException(CLASS_NOT_FOUND_EXIT_STATUS)
      case e: NoClassDefFoundError =>
        logError(s"Failed to load $childMainClass: ${e.getMessage()}")
        if (e.getMessage.contains("org/apache/hadoop/hive")) {
          logInfo(s"Failed to load hive class.")
          logInfo("You need to build Spark with -Phive and -Phive-thriftserver.")
        }
        throw new SparkUserAppException(CLASS_NOT_FOUND_EXIT_STATUS)
    }
    //反射创建类的对象并进行类型转换
    val app: SparkApplication = if (classOf[SparkApplication].isAssignableFrom(mainClass)) {
      mainClass.getConstructor().newInstance().asInstanceOf[SparkApplication]
    } else {
      new JavaMainApplication(mainClass)
    }

    @tailrec
    def findCause(t: Throwable): Throwable = t match {
      case e: UndeclaredThrowableException =>
        if (e.getCause() != null) findCause(e.getCause()) else e
      case e: InvocationTargetException =>
        if (e.getCause() != null) findCause(e.getCause()) else e
      case e: Throwable =>
        e
    }

    try {
      //运行childMainClass的start方法
      app.start(childArgs.toArray, sparkConf)
    } catch {
      case t: Throwable =>
        throw findCause(t)
    }
  }

  /** Throw a SparkException with the given error message. */
  private def error(msg: String): Unit = throw new SparkException(msg)

}