Spark 源码系列（七）Spark on yarn 具体实现

原本不打算写的了，可是真的是闲来无事，成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 里面已是一个 stable 的版本了，但是 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，因此各位朋友也不要再问我讲的是哪一个版本，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 提交做业过程》的时候，咱们讲过 Spark on yarn 的在 cluster 模式下它的 main class 是 org.apache.spark.deploy.yarn.Client。okay，这个就是咱们的头号目标。

提交做业

找到 main 函数，里面调用了 run 方法，咱们直接看 run 方法。

val appId = runApp()
    monitorApplication(appId)
    System.exit(0)
复制代码

运行 App，跟踪 App，最后退出。咱们先看 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，初始化而且启动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知足当前的做业需求
    verifyClusterResources(newAppResponse)

    // 准备资源和环境变量.
    //1.得到工做目录的具体地址: /.sparkStaging/appId/
    val appStagingDir = getAppStagingDir(appId)
  //2.建立工做目录，设置工做目录权限，上传运行时所须要的jar包
    val localResources = prepareLocalResources(appStagingDir)
    //3.设置运行时须要的环境变量
    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
  }
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monitorApplication 就不说了，不停的调用 getApplicationReport 方法得到最新的 Report，而后调用 getYarnApplicationState 获取当前状态，若是状态为 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，你们一看就清楚了。

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)
      }
    }
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ApplicationMaster

直接看 run 方法就能够了，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的优先级同样。它比HDFS的优先级高，由于它的操做是清理该做业在hdfs上面的Staging目录
    ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30)

    appAttemptId = getApplicationAttemptId()
  // 经过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，我感受是设置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，免得给本身找事
    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
  // 等待结束以后，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)
  }
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run 方法里面主要干了 5 项工做：

一、初始化工做

二、启动 driver 程序

三、注册 ApplicationMaster

四、分配 Executors

五、等待程序运行结束

咱们重点看分配 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数量可能大于须要的数量
      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")
        }
&emsp;&emsp;&emsp;&emsp; // 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)
    }
  }
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这里面咱们只须要看 addResourceRequests 和 allocateResources 方法便可。

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 得到的机器位置，若是没有优先选择机器，也就没有同一个 rack 之说了，能够是任意机器。

下面咱们接着看 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.
  // 以前已经提交过Container请求了，如今只须要获取response便可 
    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) {
&emsp;&emsp;&emsp;&emsp; // 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]]()
&emsp;&emsp;&emsp; // 遍历全部的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
&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;
        if (requiredHostCount >= remainingContainers.size) {
          // 须要的比现有的多，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) {
          // 得到的container比所须要的多，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) {
          // 仍是不够，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，为每一个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
&emsp;&emsp;&emsp;&emsp;&emsp;// 内存须要大于Executor的内存 + 384
        val executorMemoryOverhead = (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)

        if (numExecutorsRunningNow > maxExecutors) {
          // 正在运行的比须要的多了，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)
            }
          }
&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;// 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()
        }
      }
      
  }
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一、把从 ResourceManager 中得到的 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。

二、选择了 Container 以后，给每个 Container 都启动一个 ExecutorRunner 一对一贴身服务，给它发送运行 CoarseGrainedExecutorBackend 的命令。

三、ExecutorRunner 经过 NMClient 来向 NodeManager 发送请求。

总结：

把做业发布到 yarn 上面去执行这块涉及到的类很少，主要是涉及到 Client、ApplicationMaster、YarnAllocationHandler、ExecutorRunner 这四个类。

一、Client 做为 Yarn 的客户端，负责向 Yarn 发送启动 ApplicationMaster 的命令。

二、ApplicationMaster 就像项目经理同样负责整个项目所须要的工做，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。

三、ApplicationMaster 的请求、分配资源是经过 YarnAllocationHandler 来进行的。

四、Container 选择的顺序是：优先选择机器 > 同一个 rack 的机器 > 任意机器。

五、ExecutorRunner 只负责向 Container 发送启动 CoarseGrainedExecutorBackend 的命令。

六、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 就从新请求。

七、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，实现和前面的也差很少。

其它的内容更可能是 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。

参考文献

提交做业ApplicationMaster