Spark源码系列（七）Spark on yarn具体实现

  def runApp(): ApplicationId = {
    // 校验参数，内存不能小于384Mb，Executor的数量不能少于1个。
    validateArgs()
    // 这两个是父类的方法，初始化并且启动Client
    init(yarnConf)
    start()

    // 记录集群的信息(e.g, NodeManagers的数量，队列的信息).
    logClusterResourceDetails()

    // 准备提交请求到ResourcManager (specifically its ApplicationsManager (ASM)// Get a new client application.
    val newApp = super.createApplication()
    val newAppResponse = newApp.getNewApplicationResponse()
    val appId = newAppResponse.getApplicationId()
    // 检查集群的内存是否满足当前的作业需求
    verifyClusterResources(newAppResponse)

    // 准备资源和环境变量.
    //1.获得工作目录的具体地址: /.sparkStaging/appId/
    val appStagingDir = getAppStagingDir(appId)
　　//2.创建工作目录，设置工作目录权限，上传运行时所需要的jar包
    val localResources = prepareLocalResources(appStagingDir)
    //3.设置运行时需要的环境变量
    val launchEnv = setupLaunchEnv(localResources, appStagingDir)
　　//4.设置运行时JVM参数，设置SPARK_USE_CONC_INCR_GC为true的话，就使用CMS的垃圾回收机制
    val amContainer = createContainerLaunchContext(newAppResponse, localResources, launchEnv)

    // 设置application submission context. 
    val appContext = newApp.getApplicationSubmissionContext()
    appContext.setApplicationName(args.appName)
    appContext.setQueue(args.amQueue)
    appContext.setAMContainerSpec(amContainer)
    appContext.setApplicationType("SPARK")

    // 设置ApplicationMaster的内存，Resource是表示资源的类，目前有CPU和内存两种.
    val memoryResource = Records.newRecord(classOf[Resource]).asInstanceOf[Resource]
    memoryResource.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
    appContext.setResource(memoryResource)

    // 提交Application.
    submitApp(appContext)
    appId
  }

 while (!args.isEmpty) { args match { case ("--jar") :: value :: tail => userJar = value args = tail case ("--class") :: value :: tail => userClass = value args = tail case ("--args" | "--arg") :: value :: tail => if (args(0) == "--args") { println("--args is deprecated. Use --arg instead.") } userArgsBuffer += value args = tail case ("--master-class" | "--am-class") :: value :: tail => if (args(0) == "--master-class") { println("--master-class is deprecated. Use --am-class instead.") } amClass = value args = tail case ("--master-memory" | "--driver-memory") :: MemoryParam(value) :: tail => if (args(0) == "--master-memory") { println("--master-memory is deprecated. Use --driver-memory instead.") } amMemory = value args = tail case ("--num-workers" | "--num-executors") :: IntParam(value) :: tail => if (args(0) == "--num-workers") { println("--num-workers is deprecated. Use --num-executors instead.") } numExecutors = value args = tail case ("--worker-memory" | "--executor-memory") :: MemoryParam(value) :: tail => if (args(0) == "--worker-memory") { println("--worker-memory is deprecated. Use --executor-memory instead.") } executorMemory = value args = tail case ("--worker-cores" | "--executor-cores") :: IntParam(value) :: tail => if (args(0) == "--worker-cores") { println("--worker-cores is deprecated. Use --executor-cores instead.") } executorCores = value args = tail case ("--queue") :: value :: tail => amQueue = value args = tail case ("--name") :: value :: tail => appName = value args = tail case ("--addJars") :: value :: tail => addJars = value args = tail case ("--files") :: value :: tail => files = value args = tail case ("--archives") :: value :: tail => archives = value args = tail case Nil => if (userClass == null) { printUsageAndExit(1) } case _ => printUsageAndExit(1, args) } }

 def run() { // 设置本地目录，默认是先使用yarn的YARN_LOCAL_DIRS目录，再到LOCAL_DIRS System.setProperty("spark.local.dir", getLocalDirs()) // set the web ui port to be ephemeral for yarn so we don't conflict with // other spark processes running on the same box System.setProperty("spark.ui.port", "0") // when running the AM, the Spark master is always "yarn-cluster" System.setProperty("spark.master", "yarn-cluster") 　　// 设置优先级为30，和mapreduce的优先级一样。它比HDFS的优先级高，因为它的操作是清理该作业在hdfs上面的Staging目录 ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30) appAttemptId = getApplicationAttemptId() 　　// 通过yarn.resourcemanager.am.max-attempts来设置，默认是2 　　// 目前发现它只在清理Staging目录的时候用 isLastAMRetry = appAttemptId.getAttemptId() >= maxAppAttempts amClient = AMRMClient.createAMRMClient() amClient.init(yarnConf) amClient.start() // setup AmIpFilter for the SparkUI - do this before we start the UI 　　// 方法的介绍说是yarn用来保护ui界面的，我感觉是设置ip代理的  addAmIpFilter() 　　// 注册ApplicationMaster到内部的列表里 ApplicationMaster.register(this) // 安全认证相关的东西，默认是不开启的，省得给自己找事 val securityMgr = new SecurityManager(sparkConf) // 启动driver程序  userThread = startUserClass() // 等待SparkContext被实例化，主要是等待spark.driver.port property被使用 　　// 等待结束之后，实例化一个YarnAllocationHandler  waitForSparkContextInitialized() // Do this after Spark master is up and SparkContext is created so that we can register UI Url. 　　// 向yarn注册当前的ApplicationMaster, 这个时候isFinished不能为true，是true就说明程序失败了  synchronized { if (!isFinished) { registerApplicationMaster() registered = true } } // 申请Container来启动Executor  allocateExecutors() // 等待程序运行结束  userThread.join() System.exit(0) }

 private def allocateExecutors() { try { logInfo("Allocating " + args.numExecutors + " executors.") // 分host、rack、任意机器三种类型向ResourceManager提交ContainerRequest 　　　 // 请求的Container数量可能大于需要的数量  yarnAllocator.addResourceRequests(args.numExecutors) // Exits the loop if the user thread exits. while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive) { if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) { finishApplicationMaster(FinalApplicationStatus.FAILED, "max number of executor failures reached") } 　　　　 // 把请求回来的资源进行分配，并释放掉多余的资源  yarnAllocator.allocateResources() ApplicationMaster.incrementAllocatorLoop(1) Thread.sleep(100) } } finally { // In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT, // so that the loop in ApplicationMaster#sparkContextInitialized() breaks.  ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT) } logInfo("All executors have launched.") // 启动一个线程来状态报告 if (userThread.isAlive) { // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses. val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000) // we want to be reasonably responsive without causing too many requests to RM. val schedulerInterval = sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000) // must be <= timeoutInterval / 2. val interval = math.min(timeoutInterval / 2, schedulerInterval) launchReporterThread(interval) } }

 def allocateResources() { // We have already set the container request. Poll the ResourceManager for a response. // This doubles as a heartbeat if there are no pending container requests. 　　// 之前已经提交过Container请求了，现在只需要获取response即可  val progressIndicator = 0.1f val allocateResponse = amClient.allocate(progressIndicator) val allocatedContainers = allocateResponse.getAllocatedContainers() if (allocatedContainers.size > 0) { var numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * allocatedContainers.size) if (numPendingAllocateNow < 0) { numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * numPendingAllocateNow) } val hostToContainers = new HashMap[String, ArrayBuffer[Container]]() for (container <- allocatedContainers) { 　　　　 // 内存 > Executor所需内存 + 384 if (isResourceConstraintSatisfied(container)) { // 把container收入名册当中，等待发落 val host = container.getNodeId.getHost val containersForHost = hostToContainers.getOrElseUpdate(host, new ArrayBuffer[Container]()) containersForHost += container } else { // 内存不够，释放掉它  releaseContainer(container) } } // 找到合适的container来使用. val dataLocalContainers = new HashMap[String, ArrayBuffer[Container]]() val rackLocalContainers = new HashMap[String, ArrayBuffer[Container]]() val offRackContainers = new HashMap[String, ArrayBuffer[Container]]() 　　　 // 遍历所有的host for (candidateHost <- hostToContainers.keySet) { val maxExpectedHostCount = preferredHostToCount.getOrElse(candidateHost, 0) val requiredHostCount = maxExpectedHostCount - allocatedContainersOnHost(candidateHost) val remainingContainersOpt = hostToContainers.get(candidateHost) var remainingContainers = remainingContainersOpt.get 　　　　　　 if (requiredHostCount >= remainingContainers.size) { // 需要的比现有的多，把符合数据本地性的添加到dataLocalContainers映射关系里  dataLocalContainers.put(candidateHost, remainingContainers) // 没有containner剩下的. remainingContainers = null } else if (requiredHostCount > 0) { // 获得的container比所需要的多，把多余的释放掉 val (dataLocal, remaining) = remainingContainers.splitAt(remainingContainers.size - requiredHostCount) dataLocalContainers.put(candidateHost, dataLocal) for (container <- remaining) releaseContainer(container) remainingContainers = null } // 数据所在机器已经分配满任务了，只能在同一个rack里面挑选了 if (remainingContainers != null) { val rack = YarnAllocationHandler.lookupRack(conf, candidateHost) if (rack != null) { val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0) val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) - rackLocalContainers.getOrElse(rack, List()).size if (requiredRackCount >= remainingContainers.size) { // Add all remaining containers to to `dataLocalContainers`.  dataLocalContainers.put(rack, remainingContainers) remainingContainers = null } else if (requiredRackCount > 0) { // Container list has more containers that we need for data locality. val (rackLocal, remaining) = remainingContainers.splitAt(remainingContainers.size - requiredRackCount) val existingRackLocal = rackLocalContainers.getOrElseUpdate(rack, new ArrayBuffer[Container]()) existingRackLocal ++= rackLocal remainingContainers = remaining } } } if (remainingContainers != null) { // 还是不够，只能放到别的rack的机器上运行了  offRackContainers.put(candidateHost, remainingContainers) } } // 按照数据所在机器、同一个rack、任意机器来排序 val allocatedContainersToProcess = new ArrayBuffer[Container](allocatedContainers.size) allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(dataLocalContainers) allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(rackLocalContainers) allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(offRackContainers) // 遍历选择了的Container，为每个Container启动一个ExecutorRunnable线程专门负责给它发送命令 for (container <- allocatedContainersToProcess) { val numExecutorsRunningNow = numExecutorsRunning.incrementAndGet() val executorHostname = container.getNodeId.getHost val containerId = container.getId 　　　　　// 内存需要大于Executor的内存 + 384 val executorMemoryOverhead = (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD) if (numExecutorsRunningNow > maxExecutors) { // 正在运行的比需要的多了，释放掉多余的Container  releaseContainer(container) numExecutorsRunning.decrementAndGet() } else { val executorId = executorIdCounter.incrementAndGet().toString val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( sparkConf.get("spark.driver.host"), sparkConf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ACTOR_NAME) // To be safe, remove the container from `pendingReleaseContainers`.  pendingReleaseContainers.remove(containerId) // 把container记录到已分配的rack的映射关系当中 val rack = YarnAllocationHandler.lookupRack(conf, executorHostname) allocatedHostToContainersMap.synchronized { val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname, new HashSet[ContainerId]()) containerSet += containerId allocatedContainerToHostMap.put(containerId, executorHostname) if (rack != null) { allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1) } } 　　　　　　// 启动一个线程给它进行跟踪服务，给它发送运行Executor的命令 val executorRunnable = new ExecutorRunnable( container, conf, sparkConf, driverUrl, executorId, executorHostname, executorMemory, executorCores) new Thread(executorRunnable).start() } } }