这篇文章主要讲解了“Spark提交Yarn的详细过程”，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习“Spark提交Yarn的详细过程”吧！
这篇文章主要讲解了“Spark提交Yarn的详细过程”，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习“Spark提交Yarn的详细过程”吧！
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spark-submit.sh-> spark-class.sh，然后调用SparkSubmit.scala。
根据client或者cluster模式处理方式不一样。
client：直接在spark-class.sh运行的地方包装要给进程来执行driver。
cluster：将driver提交到集群去执行。
核心在SparkSubmit.scala的prepareSubmitEnvironment方法中，截取一段处理Yarn集群环境的看一下。
// 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。
接下来看看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) } } }
这时候childMainClass变成了
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() }}
看源码可以看到，YarnClusterApplication最终是用到了deploy/yarn/Client.scala
client.run调用client.submitApplication方法提交到Yarn集群。
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")}
这样就生成要执行的命令了，就是Command。上面这句话啥意思呢：
（1）cluster模式
用ApplicationMaster启动userClass。
（2）client模式
启动Executor
这里我们要看的是cluster模式，至此就清楚了，在cluster模式下，在Yarn集群中用ApplicationMaster包装了userClass并启动。userClass就是driver的意思。
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