Spark源码分析

Spark on Yarn
client
Cluster
本质区别，driver位置不同
1)有哪些不同得进程？
2)分别有什么作用？
3)Spark作业执行流程是什么样的

跑yarn有

--master yarn

CoarseGrainedExecutorBackend 默认executor有两个
CoarseGrainedExecutorBackend 
SparkSubmit
ApplicationMaster

跑Client有

--master client

CoarseGrainedExecutorBackend 默认executor有两个
CoarseGrainedExecutorBackend 
SparkSubmit
ExecutorLauncher

在起来之后，client不能杀死sparksubmit， cluster可以杀死sparksubmit

SparkSubmit.main{
	调用： doSubmit
	val submit = new SparkSubmit()
	submit.doSubmit(args) {
		val appArgs = parseArguments(args) {
			mergeDefalutSparkProperties()   //  使用default得参数
			ignoreNonSparkProperties()
			loadEnvironmentArguments(){
				action = Option(action).getOrElse(SUBMIT)
			}
		}
		submit(appArgs, uninitLog){
				val (childArgs, childClasspath, sparkConf, childMainClass) = prepareSubmitElements{
							if(deployMode == CLIENT){ // 如果选用client模式
								childMainClass = args.mainClass  // 用户指定 类 
							}
							if(deployMode == CLUSTER){ // 如果选用cluster模式
								childMainClass = org.apache.spark.deploy.yarn.YarnClusterApplication  // 固定类
							}
		}
		
		var  mainClass == Utils.classForName(childMainClass) // 使用主main
		val app : SparkApplication
		app.start(childArgs.toArray, SparkConf){
			new Client(new ClientArguments(args),conf).run(){
				this.appId = submitApplication() // 提交应用程序， log(： requesting a new application from cluster with 1 nodeManagers)   ==== 》  获取yarn得链接状态，

			val containerContext = createContainerLaunchContext(newAppResponse)  // client.scala
			{
				val amClass = 
					if (isClusterMode) {
						Utils.classForName("org.apache.spark,deploy.yarn.applicationMaster").getName  // cluset模式是applicationmaster
						}
						else {
							Utils.classForName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName  // client是executorLauncher
							}
			
			}
			val appcontext = createApplicationSubmissionContext(newApp, containerContext)  // client.scala
			
			}
		}

		
		
	}
}
SparkSubmitArgument ==> 就Submit提交解析得参数包装
参数转Java

ApplicationMaster.main{
	master = new Applicationmaster(am)
	master.run()
		runImpl(){
			if(isClusterMode){
				runDriver(){
					userClassTread = strartUserApplication(){
						val mainMethod = userClassLoader.loadClass(args.userClass).getMethod("main",classOf[Array[String]])
						mainMethod.invoke(null, userArgs.toArray)
					}
					val sc = ThreadUtils.awaitResult(SparkContextPromis.function)
					registerAm(host,port,userConf,sc.ui.map(_.webUrl))
					createAllocator(driverRef, userConf){
						allocator.allocateResources(){
							handleAllocatoedContainers(allocatedContainers.asScala){
								runAllocatedContainers(containersToUse){
									//启动container
									for(container <- containersToUse){  // 两个container 启动
										launcherPool.execute(Thread).run(){
											val commands = prepareCommand(){
												org.apache.spark.executor.CoarseGrainedExecutorBackend									
									}
									nmClient.startContainer(container.get, ctx)
								}
							}
					}
					
				}	
			}else{
					runExecutorLauncher()
			}
			
		}
}

SparkCongtext{
private var SchedulerBackend
private var _taskSchedule}