Spark源码系列（七）Spark on yarn具体实现
本来不打算写的了，但是真的是闲来无事，整天看美剧也没啥意思。这一章打算讲一下Spark on yarn的实现，1.0.0里面已经是一个stable的版本了，可是1.0.1也出来了，离1.0.0发布才一个月的时间，更新太快了，节奏跟不上啊，这里仍旧是讲1.0.0的代码，所以各位朋友也不要再问我讲的是哪个版本，目前为止发布的文章都是基于1.0.0的代码。
在第一章《spark-submit提交作业过程》的时候，我们讲过Spark on yarn的在cluster模式下它的main class是org.apache.spark.deploy.yarn.Client。okay，这个就是我们的头号目标。
提交作业
找到main函数，里面调用了run方法，我们直接看run方法。
val appId = runApp() monitorApplication(appId) System.exit(0)
运行App，跟踪App，最后退出。我们先看runApp吧。
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 }
monitorApplication就不说了，不停的调用getApplicationReport方法获得最新的Report，然后调用getYarnApplicationState获取当前状态，如果状态为FINISHED、FAILED、KILLED就退出。
说到这里，顺便把跟yarn相关的参数也贴出来一下，大家一看就清楚了。
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) } }
ApplicationMaster
直接看run方法就可以了，main函数就干了那么一件事...
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) }
run方法里面主要干了5项工作：
1、初始化工作
2、启动driver程序
3、注册ApplicationMaster
4、分配Executors
5、等待程序运行结束
我们重点看分配Executor方法。
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) } }
这里面我们只需要看addResourceRequests和allocateResources方法即可。
先说addResourceRequests方法，代码就不贴了。
Client向ResourceManager提交Container的请求，分三种类型：优先选择机器、同一个rack的机器、任意机器。
优先选择机器是在RDD里面的getPreferredLocations获得的机器位置，如果没有优先选择机器，也就没有同一个rack之说了，可以是任意机器。
下面我们接着看allocateResources方法。
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() } } }
1、把从ResourceManager中获得的Container进行选择，选择顺序是按照前面的介绍的三种类别依次进行，优先选择机器 > 同一个rack的机器 > 任意机器。
2、选择了Container之后，给每一个Container都启动一个ExecutorRunner一对一贴身服务，给它发送运行CoarseGrainedExecutorBackend的命令。
3、ExecutorRunner通过NMClient来向NodeManager发送请求。
总结：
把作业发布到yarn上面去执行这块涉及到的类不多，主要是涉及到Client、ApplicationMaster、YarnAllocationHandler、ExecutorRunner这四个类。
1、Client作为Yarn的客户端，负责向Yarn发送启动ApplicationMaster的命令。
2、ApplicationMaster就像项目经理一样负责整个项目所需要的工作，包括请求资源，分配资源，启动Driver和Executor，Executor启动失败的错误处理。
3、ApplicationMaster的请求、分配资源是通过YarnAllocationHandler来进行的。
4、Container选择的顺序是：优先选择机器 > 同一个rack的机器 > 任意机器。
5、ExecutorRunner只负责向Container发送启动CoarseGrainedExecutorBackend的命令。
6、Executor的错误处理是在ApplicationMaster的launchReporterThread方法里面，它启动的线程除了报告运行状态，还会监控Executor的运行，一旦发现有丢失的Executor就重新请求。
7、在yarn目录下看到的名称里面带有YarnClient的是属于yarn-client模式的类，实现和前面的也差不多。
其它的内容更多是Yarn的客户端api使用，我也不太会，只是看到了能懂个意思，哈哈。
岑玉海
转载请注明出处，谢谢！