deploy目录下的SparkSubmit类

以前说的各类脚本：spark-submit,spark-class也好，仍是launcher工程也好，主要工做是准备各类环境、依赖包、JVM参数等运行环境。实际的提交主要仍是Spark Code中的deploy下的SparkSubmit类来负责的。

deploy目录下的SparkSubmit类，前面提到过，主要入口方法是runMain。

咱们先看看其余方法吧。

一、prepareSubmitEnvironment

这个方法准备提交的环境和参数。

先判断集群管理方式（cluster manager）：yarn、meros、k8s，standalone。部署方式（deploy mode )： client仍是cluster。

后面要根据这些信息设置不一样的Backend和Wapper类等。

提交模式这一段真很差讲，由于它包含了太多种类的部署环境了，个性化较强，要慢慢看了。

cluster方式只看两种：yarn cluster和standalone cluster。把yarn和standalone两个搞懂了，其余的也就很好理解了。

这个方法返回一个四元组：

@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

核心代码

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 }
    }

 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
      }
    }

// 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) }
      }
    }

上面这段代码很是核心，很是重要。它定义了不一样的集群模式不一样的部署方式下，应用使用什么类来包装咱们的spark程序，好适应不一样的集群环境下的提交流程。

咱们就多花点时间来分析一下这段代码。

先看看ChildMainClass：

standaloneCluster下：REST_CLUSTER_SUBMIT_CLASS=classOf[RestSubmissionClientApp].getName()

yarnCluster下：YARN_CLUSTER_SUBMIT_CLASS=org.apache.spark.deploy.yarn.YarnClusterApplication

standalone client模式下：STANDALONE_CLUSTER_SUBMIT_CLASS = classOf[ClientApp].getName()

二、runMain

上一步得到四元组以后，就是runMain的流程了。

核心代码先上：

private def runMain(args: SparkSubmitArguments, uninitLog: Boolean): Unit = {
    val (childArgs, childClasspath, sparkConf, childMainClass) = prepareSubmitEnvironment(args)
    val loader = getSubmitClassLoader(sparkConf)
    for (jar <- childClasspath) {
      addJarToClasspath(jar, loader)
    }
    var mainClass: Class[_] = null
    try {
      mainClass = Utils.classForName(childMainClass)
    } catch {
      
    }
    val app: SparkApplication = if (classOf[SparkApplication].isAssignableFrom(mainClass)) {
      mainClass.getConstructor().newInstance().asInstanceOf[SparkApplication]
    } else {
      new JavaMainApplication(mainClass)
    }

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

搞清了prepareSubmitEnvironment的流程，runMain也就很简单了，它就是启动ChildMainClass（是SparkApplication的子类），而后执行start方法。

若是不是cluster模式而是client模式，那么ChildMainClass就是args.mainClass。这点须要注意下，这时候ChildMainClass就会用JavaMainApplication来包装了：

new JavaMainApplication(mainClass)；

后面的内容就是看看RestSubmissionClientApp和org.apache.spark.deploy.yarn.YarnClusterApplication的实现逻辑了。