Sparn On Yarn启动流程源码分析

Spark SenLin 5年前 (2019-11-03) 422次浏览 已收录 0个评论

YARN模式下启动流程

1.YarnschedulerBackend启动入口

YARN的启动是在SparkContext初始化scheduler时启动的,通过ClassLoader初始化YarnschedulerBackend和YARTaskscheduler。

    //scheduler的初始化, 调用createTaskScheduler()方法
    // Create and start the scheduler
    val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)
    _schedulerBackend = sched
    _taskScheduler = ts
    _dagScheduler = new DAGScheduler(this)
    _heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)
    
    // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
    // constructor
    _taskScheduler.start()
    
    /**
   * Create a task scheduler based on a given master URL.
   * Return a 2-tuple of the scheduler backend and the task scheduler.
   */
   // 该方法根据master字符串进行匹配,如果是local/standalone模式,匹配响应的schedulerBackend和taskscheduler,
   // 如果是yarn,则走默认形式
  private def createTaskScheduler(
      sc: SparkContext,
      master: String,
      deployMode: String): (SchedulerBackend, TaskScheduler) = {
    import SparkMasterRegex._

    // When running locally, don't try to re-execute tasks on failure.
    val MAX_LOCAL_TASK_FAILURES = 1

    master match {
      case "local" =>
        val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, 1)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_N_REGEX(threads) =>
       ...
      case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
        ...
      case SPARK_REGEX(sparkUrl) =>
        ...
      case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
       ...
      case masterUrl =>
         // 这个方法如何实现基于classLoader调用YarnClusterManager.class的(scala语法不熟,待考证)
        val cm = getClusterManager(masterUrl) match {
          case Some(clusterMgr) => clusterMgr
          case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
        }
        try {
          val scheduler = cm.createTaskScheduler(sc, masterUrl)
          val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
          cm.initialize(scheduler, backend)
          (backend, scheduler)
        } catch {
          case se: SparkException => throw se
          case NonFatal(e) =>
            throw new SparkException("External scheduler cannot be instantiated", e)
        }
    }
  }
  
  //getClusterManager()通过类加载,加载ExternalClusterManager类,同时过滤出可以构造出yarn类型的schedulerBackend和taskscheduler
   private def getClusterManager(url: String): Option[ExternalClusterManager] = {
    val loader = Utils.getContextOrSparkClassLoader
    val serviceLoaders =
      ServiceLoader.load(classOf[ExternalClusterManager], loader).asScala.filter(_.canCreate(url))
    if (serviceLoaders.size > 1) {
      throw new SparkException(
        s"Multiple external cluster managers registered for the url $url: $serviceLoaders")
    }
    serviceLoaders.headOption
  }
  
  
  // createTaskScheduler()函数真正返回的schedulerBackend和taskscheduler是通过下面这个class
  private[spark] class YarnClusterManager extends ExternalClusterManager{
  }

创建ApplicationMaster

SparkContext初始化过程中,会向YARN集群初始化Application(Master),流程如下:

 /**
   * Submit an application running our ApplicationMaster to the ResourceManager.
   *
   * The stable Yarn API provides a convenience method (YarnClient#createApplication) for
   * creating applications and setting up the application submission context. This was not
   * available in the alpha API.
   */
  def submitApplication(user: Option[String] = None): ApplicationId = {
    var appId: ApplicationId = null
    try {
      launcherBackend.connect()
      // Setup the credentials before doing anything else,
      // so we have don't have issues at any point.
      setupCredentials(user)
      yarnClient.init(yarnConf)
      yarnClient.start()
      sparkUser = user

      logInfo(s"[DEVELOP] [sparkUser:${sparkUser}] Requesting a new application " +
        s"from cluster with %d NodeManagers"
        .format(yarnClient.getYarnClusterMetrics.getNumNodeManagers))

      // Get a new application from our RM
      val newApp = yarnClient.createApplication()
      val newAppResponse = newApp.getNewApplicationResponse()
      appId = newAppResponse.getApplicationId()
      reportLauncherState(SparkAppHandle.State.SUBMITTED)
      launcherBackend.setAppId(appId.toString)

      new CallerContext("CLIENT", Option(appId.toString)).setCurrentContext()

      // Verify whether the cluster has enough resources for our AM
      verifyClusterResources(newAppResponse)

      // Set up the appropriate contexts to launch our AM
      
      // 关键是这两个方法:
      // 1. 创建ApplicationMaster ContainerLaunch上下文,将ContainerLaunch命令、jar包、java变量等环境准备完毕;
      // 2. 创建Application提交至YARN的上下文,主要读取配置文件设置调用YARN接口前的上下文变量。
      
      val containerContext = createContainerLaunchContext(newAppResponse)
      val appContext = createApplicationSubmissionContext(newApp, containerContext)

      // Finally, submit and monitor the application
      logInfo(s"Submitting application $appId to ResourceManager")
      yarnClient.submitApplication(appContext)
      appId
    } catch {
      case e: Throwable =>
        if (appId != null) {
          cleanupStagingDir(appId)
        }
        throw e
    }
  }

真正Application启动是调用如下方法:

    val amClass =
      if (isClusterMode) {
        Utils.classForName("org.apache.spark.deploy.yarn.ApplicationMaster").getName
      } else {
        Utils.classForName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName
      }

启动ApplicationMaster

基于YARN-client的模式启动,所以直接跳转至org.apache.spark.deploy.yarn.ExecutorLauncher, 该类也是封装在ApplicationMaseter中,顺着main()函数往下走,调用ApplicationMaster.run()函数-> runExecutorLauncher(securityMgr)

  private def runExecutorLauncher(securityMgr: SecurityManager): Unit = {
    val port = sparkConf.getInt("spark.yarn.am.port", 0)

    // 创建RPCEndpoint同driver交互
    rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, port, sparkConf, securityMgr,
      clientMode = true)
    val driverRef = waitForSparkDriver()
    // WHY?
    addAmIpFilter()
    
    // 关键函数,向Driver注册AM
    registerAM(sparkConf, rpcEnv, driverRef, sparkConf.get("spark.driver.appUIAddress", ""),
      securityMgr)

    // In client mode the actor will stop the reporter thread.
    reporterThread.join()
  }
  
  
  
   private def registerAM(
      _sparkConf: SparkConf,
      _rpcEnv: RpcEnv,
      driverRef: RpcEndpointRef,
      uiAddress: String,
      securityMgr: SecurityManager) = {
    val appId = client.getAttemptId().getApplicationId().toString()
    val attemptId = client.getAttemptId().getAttemptId().toString()
    val historyAddress =
      _sparkConf.get(HISTORY_SERVER_ADDRESS)
        .map { text => SparkHadoopUtil.get.substituteHadoopVariables(text, yarnConf) }
        .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}/${attemptId}" }
        .getOrElse("")

    val driverUrl = RpcEndpointAddress(
      _sparkConf.get("spark.driver.host"),
      _sparkConf.get("spark.driver.port").toInt,
      CoarseGrainedSchedulerBackend.ENDPOINT_NAME).toString

    // Before we initialize the allocator, let's log the information about how executors will
    // be run up front, to avoid printing this out for every single executor being launched.
    // Use placeholders for information that changes such as executor IDs.
    logInfo {
      val executorMemory = sparkConf.get(EXECUTOR_MEMORY).toInt
      val executorCores = sparkConf.get(EXECUTOR_CORES)

      //  申请Executor资源(debug log)
      val dummyRunner = new ExecutorRunnable(None, yarnConf, sparkConf, driverUrl, "<executorId>",
        "<hostname>", executorMemory, executorCores, appId, securityMgr, localResources)
      dummyRunner.launchContextDebugInfo()
    }

    //向RM注册driver地址
    allocator = client.register(driverUrl,
      driverRef,
      yarnConf,
      _sparkConf,
      uiAddress,
      historyAddress,
      securityMgr,
      localResources)

    //申请Executor资源
    allocator.allocateResources()
    reporterThread = launchReporterThread()
  }

调用yarn RM接口完成资源申请,同时初始化ApplicationMaster容器:

 /**
   * Request resources such that, if YARN gives us all we ask for, we'll have a number of containers
   * equal to maxExecutors.
   *
   * Deal with any containers YARN has granted to us by possibly launching executors in them.
   *
   * This must be synchronized because variables read in this method are mutated by other methods.
   */
  def allocateResources(): Unit = synchronized {
    updateResourceRequests()

    val progressIndicator = 0.1f
    // Poll the ResourceManager. This doubles as a heartbeat if there are no pending container
    // requests.
    // 调用YARN接口,分配container
    val allocateResponse = amClient.allocate(progressIndicator)
    
     // 获取分配container资源状态
    val allocatedContainers = allocateResponse.getAllocatedContainers()

    if (allocatedContainers.size > 0) {
      logInfo("Allocated containers: %d. Current executor count: %d. Cluster resources: %s."
        .format(
          allocatedContainers.size,
          numExecutorsRunning,
          allocateResponse.getAvailableResources))
        
        // 当申请完毕资源后,处理函数:会初始化该executor环境,等待分配task       
       handleAllocatedContainers(allocatedContainers.asScala)
    }

    val completedContainers = allocateResponse.getCompletedContainersStatuses()
    if (completedContainers.size > 0) {
      logInfo("Completed %d containers".format(completedContainers.size))
      processCompletedContainers(completedContainers.asScala)
      logInfo("Finished processing %d completed containers. Current running executor count: %d."
        .format(completedContainers.size, numExecutorsRunning))
    }
  }

继续往下走,当想RM申请完资源后,会调用ExecutorLaunch初始化Executor环境,具体如下:

/**
   * Handle containers granted by the RM by launching executors on them.
   *
   * Due to the way the YARN allocation protocol works, certain healthy race conditions can result
   * in YARN granting containers that we no longer need. In this case, we release them.
   *
   * Visible for testing.
   */
  def handleAllocatedContainers(allocatedContainers: Seq[Container]): Unit = {
    val containersToUse = new ArrayBuffer[Container](allocatedContainers.size)

    // Match incoming requests by host
    val remainingAfterHostMatches = new ArrayBuffer[Container]
    for (allocatedContainer <- allocatedContainers) {
      matchContainerToRequest(allocatedContainer, allocatedContainer.getNodeId.getHost,
        containersToUse, remainingAfterHostMatches)
    }

    // Match remaining by rack
    val remainingAfterRackMatches = new ArrayBuffer[Container]
    for (allocatedContainer <- remainingAfterHostMatches) {
      val rack = RackResolver.resolve(conf, allocatedContainer.getNodeId.getHost).getNetworkLocation
      matchContainerToRequest(allocatedContainer, rack, containersToUse,
        remainingAfterRackMatches)
    }

    // Assign remaining that are neither node-local nor rack-local
    val remainingAfterOffRackMatches = new ArrayBuffer[Container]
    for (allocatedContainer <- remainingAfterRackMatches) {
      matchContainerToRequest(allocatedContainer, ANY_HOST, containersToUse,
        remainingAfterOffRackMatches)
    }

    if (!remainingAfterOffRackMatches.isEmpty) {
      logDebug(s"Releasing ${remainingAfterOffRackMatches.size} unneeded containers that were " +
        s"allocated to us")
      for (container <- remainingAfterOffRackMatches) {
        internalReleaseContainer(container)
      }
    }
     
     // 以上执行为剔除不可用的container之后最终执行可以使用的Container
    runAllocatedContainers(containersToUse)

    logInfo("Received %d containers from YARN, launching executors on %d of them."
      .format(allocatedContainers.size, containersToUse.size))
  }
  
  
  /**
   * Launches executors in the allocated containers.
   */
  private def runAllocatedContainers(containersToUse: ArrayBuffer[Container]): Unit = {
    for (container <- containersToUse) {
      executorIdCounter += 1
      val executorHostname = container.getNodeId.getHost
      val containerId = container.getId
      val executorId = executorIdCounter.toString
      
      assert(container.getResource.getMemory >= resource.getMemory)
      logInfo(s"Launching container $containerId on host $executorHostname")

      def updateInternalState(): Unit = synchronized {
        numExecutorsRunning += 1
        executorIdToContainer(executorId) = container
        containerIdToExecutorId(container.getId) = executorId

        val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
          new HashSet[ContainerId])
        containerSet += containerId
        allocatedContainerToHostMap.put(containerId, executorHostname)
      }

      if (numExecutorsRunning < targetNumExecutors) {
        if (launchContainers) {
            // 将创建exector任务提交至线程池
          launcherPool.execute(new Runnable {
          
           // 真正完成executer初始化的是ExecutorRunnable()类
            override def run(): Unit = {
              try {
                new ExecutorRunnable(
                  Some(container),
                  conf,
                  sparkConf,
                  driverUrl,
                  executorId,
                  executorHostname,
                  executorMemory,
                  executorCores,
                  appAttemptId.getApplicationId.toString,
                  securityMgr,
                  localResources
                ).run()
                updateInternalState()
              } catch {
                case NonFatal(e) =>
                  logError(s"Failed to launch executor $executorId on container $containerId", e)
                  // Assigned container should be released immediately to avoid unnecessary resource
                  // occupation.
                  amClient.releaseAssignedContainer(containerId)
              }
            }
          })
        } else {
          // For test only
          updateInternalState()
        }
      } else {
        logInfo(("Skip launching executorRunnable as runnning Excecutors count: %d " +
          "reached target Executors count: %d.").format(numExecutorsRunning, targetNumExecutors))
      }
    }
  }

Executor的启动

在ExecutorRunnable.run()方法中,会启动executor的执行命令,具体如下:

private def prepareCommand(): List[String] = {
    // Extra options for the JVM
    val javaOpts = ListBuffer[String]()

    // java/spark  运行时环境变量
    ....
    
    YarnSparkHadoopUtil.addOutOfMemoryErrorArgument(javaOpts)
    
    // executor真正的启动命令,真正调用的是`org.apache.spark.executor.CoarseGrainedExecutorBackend`
    
    val commands = prefixEnv ++ Seq(
      YarnSparkHadoopUtil.expandEnvironment(Environment.JAVA_HOME) + "/bin/java",
      "-server") ++
      javaOpts ++
      Seq("org.apache.spark.executor.CoarseGrainedExecutorBackend",
        "--driver-url", masterAddress,
        "--executor-id", executorId,
        "--hostname", hostname,
        "--cores", executorCores.toString,
        "--app-id", appId) ++
      userClassPath ++
      Seq(
        s"1>${ApplicationConstants.LOG_DIR_EXPANSION_VAR}/stdout",
        s"2>${ApplicationConstants.LOG_DIR_EXPANSION_VAR}/stderr")

    // TODO: it would be nicer to just make sure there are no null commands here
    commands.map(s => if (s == null) "null" else s).toList
  }

org.apache.spark.executor.CoarseGrainedExecutorBackend的实现逻辑比较简单,在run()函数中创建了一个RPCEndPoint,等待LaunchTask(data)消息接受,接受之后,调用exector.launchTask()执行任务,执行任务的流程则是将task加入runningTasks,并调用threadPool进行execute。

运行结果

YARN集群的日志由于分散在多台机器上,比较分散,所以想通过日志来跟踪启动流程比较困难,但是如果集群小的话,通过这个方式来验证整个流程还是挺不错的方式。

ApplicationMaster的执行日志,可以看到最终调用的org.apache.spark.executor.CoarseGrainedExecutorBackend 来启动executor。

17/05/05 16:54:58 INFO ApplicationMaster: Preparing Local resources
17/05/05 16:54:59 WARN DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
17/05/05 16:54:59 WARN Client: Exception encountered while connecting to the server : org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.ipc.StandbyException): Operation category READ is not supported in state standby
17/05/05 16:54:59 INFO ApplicationMaster: ApplicationAttemptId: appattempt_1493803865684_0180_000002
17/05/05 16:54:59 INFO SecurityManager: Changing view acls to: hzlishuming
17/05/05 16:54:59 INFO SecurityManager: Changing modify acls to: hzlishuming
17/05/05 16:54:59 INFO SecurityManager: Changing view acls groups to: 
17/05/05 16:54:59 INFO SecurityManager: Changing modify acls groups to: 
17/05/05 16:54:59 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(hzlishuming); groups with view permissions: Set(); users  with modify permissions: Set(hzlishuming); groups with modify permissions: Set()
17/05/05 16:54:59 INFO AMCredentialRenewer: Scheduling login from keytab in 61745357 millis.
17/05/05 16:54:59 INFO ApplicationMaster: Waiting for Spark driver to be reachable.
17/05/05 16:54:59 INFO ApplicationMaster: Driver now available: xxxx:47065
17/05/05 16:54:59 INFO TransportClientFactory: Successfully created connection to /xxxx:47065 after 110 ms (0 ms spent in bootstraps)
17/05/05 16:54:59 INFO ApplicationMaster$AMEndpoint: Add WebUI Filter. AddWebUIFilter(org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter,Map(PROXY_HOSTS -> ....)
17/05/05 16:55:00 INFO ApplicationMaster: 
===============================================================================
YARN executor launch context:
  env:
    CLASSPATH -> {{PWD}}<CPS>{{PWD}}/__spark_conf__<CPS>{{PWD}}/__spark_libs__/*<CPS>$HADOOP_CONF_DIR<CPS>$HADOOP_COMMON_HOME/share/hadoop/common/*<CPS>$HADOOP_COMMON_HOME/share/hadoop/common/lib/*<CPS>$HADOOP_HDFS_HOME/share/hadoop/hdfs/*<CPS>$HADOOP_HDFS_HOME/share/hadoop/hdfs/lib/*<CPS>$HADOOP_YARN_HOME/share/hadoop/yarn/*<CPS>$HADOOP_YARN_HOME/share/hadoop/yarn/lib/*<CPS>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*<CPS>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*
    SPARK_YARN_STAGING_DIR -> hdfs://hz-test01/user/hzlishuming/.sparkStaging/application_1493803865684_0180
    SPARK_USER -> hzlishuming
    SPARK_YARN_MODE -> true

  command:
    {{JAVA_HOME}}/bin/java  
      -server  
      -Xmx4096m  
      '-XX:PermSize=1024m'  
      '-XX:MaxPermSize=1024m'  
      '-verbose:gc'  
      '-XX:+PrintGCDetails'  
      '-XX:+PrintGCDateStamps'  
      '-XX:+PrintTenuringDistribution'  
      -Djava.io.tmpdir={{PWD}}/tmp  
      '-Dspark.driver.port=47065'  
      -Dspark.yarn.app.container.log.dir=<LOG_DIR>  
      -XX:OnOutOfMemoryError='kill %p'  
      org.apache.spark.executor.CoarseGrainedExecutorBackend  
      --driver-url  
      spark://CoarseGrainedScheduler@....:47065  
      --executor-id  
      <executorId>  
      --hostname  
      <hostname>  
      --cores 

在Driver端,注册完executor之后留下日志如下:

 433 17/05/05 16:04:59 INFO YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(null) () with ID 1
 434 17/05/05 16:04:59 INFO YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(null) () with ID 2
 435 17/05/05 16:04:59 INFO BlockManagerMasterEndpoint: Registering block manager xxxx with 2004.6 MB RAM, BlockManagerId(1, h, 54063, None)
 436 17/05/05 16:04:59 INFO BlockManagerMasterEndpoint: Registering block manager xxxx with 2004.6 MB RAM, BlockManagerId(2, xxx, 42904, None)

executor的启动日志,可以通过SparkUI上查看,处理流程上面已经交代,执行的为 org.apache.spark.executor.CoarseGrainedExecutorBackend逻辑。

17/05/05 16:55:15 INFO MemoryStore: MemoryStore started with capacity 2004.6 MB
17/05/05 16:55:16 INFO CoarseGrainedExecutorBackend: Connecting to driver: spark://CoarseGrainedScheduler@xxx.35:47065
17/05/05 16:55:16 INFO CoarseGrainedExecutorBackend: Successfully registered with driver
17/05/05 16:55:16 INFO Executor: Starting executor ID 4 on host hadoop694.lt.163.org
17/05/05 16:55:16 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 40418.
17/05/05 16:55:16 INFO NettyBlockTransferService: Server created on xxx:40418

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