Spark技术内幕：Client，Master和Worker 通信源码解析

http://blog.csdn.net/anzhsoft/article/details/30802603

Spark的Cluster Manager可以有几种部署模式：

1. Standlone
2. Mesos
3. YARN
4. EC2
5. Local
   

在向集群提交计算任务后，系统的运算模型就是Driver Program定义的SparkContext向APP Master提交，有APP Master进行计算资源的调度并最终完成计算。具体阐述可以阅读《Spark：大数据的电花火石！ 》。

那么Standalone模式下，Client，Master和Worker是如何进行通信，注册并开启服务的呢？

1. node之间的RPC - akka

模块间通信有很多成熟的实现，现在很多成熟的Framework已经早已经让我们摆脱原始的Socket编程了。简单归类，可以归纳为基于消息的传递和基于资源共享的同步机制。

基于消息的传递的机制应用比较广泛的有Message Queue。Message Queue, 是一种应用程序对应用程序的通信方法。应用程序通过读写出入队列的消息（针对应用程序的数据）来通信，而无需专用连接来链接它们。消 息传递指的是程序之间通过在消息中发送数据进行通信，而不是通过直接调用彼此来通信，直接调用通常是用于诸如远程过程调用的技术。排队指的是应用程序通过 队列来通信。队列的使用除去了接收和发送应用程序同时执行的要求。其中较为成熟的MQ产品有IBM WEBSPHERE MQ和RabbitMQ（AMQP的开源实现，现在由Pivotal维护）。

还有不得不提的是ZeroMQ，一个致力于进入Linux内核的基于Socket的编程框架。官方的说法： “ZeroMQ是一个简单好用的传输层，像框架一样的一个socket library，它使得Socket编程更加简单、简洁和性能更高。是一个消息处理队列库，可在多个线程、内核和主机盒之间弹性伸缩。ZMQ的明确目标是“成为标准网络协议栈的一部分，之后进入Linux内核”。

Spark在很多模块之间的通信选择是Scala原生支持的akka，一个用 Scala 编写的库，用于简化编写容错的、高可伸缩性的 Java 和 Scala 的 Actor 模型应用。akka有以下5个特性：

1. 易于构建并行和分布式应用 （Simple Concurrency & Distribution）:  Akka在设计时采用了异步通讯和分布式架构，并对上层进行抽象，如Actors、Futures ，STM等。
2. 可靠性（Resilient by Design）: 系统具备自愈能力，在本地/远程都有监护。
3. 高性能（High Performance）：在单机中每秒可发送50,000,000个消息。内存占用小，1GB内存中可保存2,500,000个actors。
4. 弹性，无中心（Elastic — Decentralized）：自适应的负责均衡，路由，分区，配置
5. 可扩展（Extensible）：可以使用Akka 扩展包进行扩展。
   

在Spark中的Client，Master和Worker实际上都是一个actor，拿Client来说：

 import akka.actor._
 import akka.pattern.ask
 import akka.remote.{AssociationErrorEvent, DisassociatedEvent, RemotingLifecycleEvent}

 private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends Actor with Logging {
   var masterActor: ActorSelection = _
   val timeout = AkkaUtils.askTimeout(conf)

   override def preStart() = {
     masterActor = context.actorSelection(Master.toAkkaUrl(driverArgs.master))

     context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])

     println(s"Sending ${driverArgs.cmd} command to ${driverArgs.master}")

     driverArgs.cmd match {
       case "launch" =>
         ...
         masterActor ! RequestSubmitDriver(driverDescription)

       case "kill" =>
         val driverId = driverArgs.driverId
         val killFuture = masterActor ! RequestKillDriver(driverId)
     }
   }

   override def receive = {

     case SubmitDriverResponse(success, driverId, message) =>
       println(message)
       if (success) pollAndReportStatus(driverId.get) else System.exit(-1)

     case KillDriverResponse(driverId, success, message) =>
       println(message)
       if (success) pollAndReportStatus(driverId) else System.exit(-1)

     case DisassociatedEvent(_, remoteAddress, _) =>
       println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.")
       System.exit(-1)

     case AssociationErrorEvent(cause, _, remoteAddress, _) =>
       println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.")
       println(s"Cause was: $cause")
       System.exit(-1)
   }
 }

 /**
  * Executable utility for starting and terminating drivers inside of a standalone cluster.
  */
 object Client {
   def main(args: Array[String]) {
     println("WARNING: This client is deprecated and will be removed in a future version of Spark.")
     println("Use ./bin/spark-submit with \"--master spark://host:port\"")

     val conf = new SparkConf()
     val driverArgs = new ClientArguments(args)

     if (!driverArgs.logLevel.isGreaterOrEqual(Level.WARN)) {
       conf.set("spark.akka.logLifecycleEvents", "true")
     }
     conf.set("spark.akka.askTimeout", "10")
     conf.set("akka.loglevel", driverArgs.logLevel.toString.replace("WARN", "WARNING"))
     Logger.getRootLogger.setLevel(driverArgs.logLevel)

     // TODO: See if we can initialize akka so return messages are sent back using the same TCP
     //       flow. Else, this (sadly) requires the DriverClient be routable from the Master.
     val (actorSystem, _) = AkkaUtils.createActorSystem(
       "driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf))

     actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf))

     actorSystem.awaitTermination()
   }
 }

其中第19行的含义就是向Master提交Driver的请求，

 masterActor ! RequestSubmitDriver(driverDescription)

而Master将在receive里处理这个请求。当然了27行到44行的是处理Client Actor收到的消息。

可以看出，通过akka，可以非常简单高效的处理模块间的通信，这可以说是Spark RPC的一大特色。

2. Client，Master和Workerq启动通信详解

源码位置：spark-1.0.0\core\src\main\scala\org\apache\spark\deploy。主要涉及的类：Client.scala, Master.scala和Worker.scala。这三大模块之间的通信框架如下图。

Standalone模式下存在的角色：

1. Client：负责提交作业到Master。

2. Master：接收Client提交的作业，管理Worker，并命令Worker启动Driver和Executor。

3. Worker：负责管理本节点的资源，定期向Master汇报心跳，接收Master的命令，比如启动Driver和Executor。

实际上，Master和Worker要处理的消息要比这多得多，本图只是反映了集群启动和向集群提交运算时候的主要消息处理。

接下来将分别走读这三大角色的源码。

2.1 Client源码解析

Client启动：

 object Client {
   def main(args: Array[String]) {
     println("WARNING: This client is deprecated and will be removed in a future version of Spark.")
     println("Use ./bin/spark-submit with \"--master spark://host:port\"")

     val conf = new SparkConf()
     val driverArgs = new ClientArguments(args)

     if (!driverArgs.logLevel.isGreaterOrEqual(Level.WARN)) {
       conf.set("spark.akka.logLifecycleEvents", "true")
     }
     conf.set("spark.akka.askTimeout", "10")
     conf.set("akka.loglevel", driverArgs.logLevel.toString.replace("WARN", "WARNING"))
     Logger.getRootLogger.setLevel(driverArgs.logLevel)

     // TODO: See if we can initialize akka so return messages are sent back using the same TCP
     //       flow. Else, this (sadly) requires the DriverClient be routable from the Master.
     val (actorSystem, _) = AkkaUtils.createActorSystem(
       "driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf))
     // 使用ClientActor初始化actorSystem
     actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf))
     //启动并等待actorSystem的结束
     actorSystem.awaitTermination()
   }
 }

从行21可以看出，核心实现是由ClientActor实现的。Client的Actor是akka.Actor的一个扩展。对于Actor，从它对recevie的override就可以看出它需要处理的消息。

 override def receive = {

   case SubmitDriverResponse(success, driverId, message) =>
     println(message)
     if (success) pollAndReportStatus(driverId.get) else System.exit(-1)

   case KillDriverResponse(driverId, success, message) =>
     println(message)
     if (success) pollAndReportStatus(driverId) else System.exit(-1)

   case DisassociatedEvent(_, remoteAddress, _) =>
     println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.")
     System.exit(-1)

   case AssociationErrorEvent(cause, _, remoteAddress, _) =>
     println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.")
     println(s"Cause was: $cause")
     System.exit(-1)
 }

2.2 Master的源码分析

源码分析详见注释。

 override def receive = {
    case ElectedLeader => {
      // 被选为Master，首先判断是否该Master原来为active，如果是那么进行Recovery。
    }
    case CompleteRecovery => completeRecovery() // 删除没有响应的worker和app，并且将所有没有worker的Driver分配worker
    case RevokedLeadership => {
      // Master将关闭。
    }
    case RegisterWorker(id, workerHost, workerPort, cores, memory, workerUiPort, publicAddress) =>
    {
      // 如果该Master不是active，不做任何操作，返回
      // 如果注册过该worker id，向sender返回错误
      sender ! RegisterWorkerFailed("Duplicate worker ID")
      // 注册worker，如果worker注册成功则返回成功的消息并且进行调度
      sender ! RegisteredWorker(masterUrl, masterWebUiUrl)
      schedule()
      // 如果worker注册失败，发送消息到sender
      sender ! RegisterWorkerFailed("Attempted to re-register worker at same address: " + workerAddress)
    }
    case RequestSubmitDriver(description) => {
        // 如果master不是active，返回错误
        sender ! SubmitDriverResponse(false, None, msg)
        // 否则创建driver，返回成功的消息
        sender ! SubmitDriverResponse(true, Some(driver.id), s"Driver successfully submitted as ${driver.id}")
      }
    }
    case RequestKillDriver(driverId) => {
      if (state != RecoveryState.ALIVE) {
        // 如果master不是active，返回错误
        val msg = s"Can only kill drivers in ALIVE state. Current state: $state."
        sender ! KillDriverResponse(driverId, success = false, msg)
      } else {
        logInfo("Asked to kill driver " + driverId)
        val driver = drivers.find(_.id == driverId)
        driver match {
          case Some(d) =>
              //如果driver仍然在等待队列，从等待队列删除并且更新driver状态为KILLED
            } else {
              // 通知worker kill driver id的driver。结果会由workder发消息给master ! DriverStateChanged
              d.worker.foreach { w => w.actor ! KillDriver(driverId) }
            }
            // 注意，此时driver不一定被kill，master只是通知了worker去kill driver。
            sender ! KillDriverResponse(driverId, success = true, msg)
          case None =>
            // driver已经被kill，直接返回结果
            sender ! KillDriverResponse(driverId, success = false, msg)
        }
      }
    }
    case RequestDriverStatus(driverId) => {
      // 查找请求的driver，如果找到则返回driver的状态
      (drivers ++ completedDrivers).find(_.id == driverId) match {
        case Some(driver) =>
          sender ! DriverStatusResponse(found = true, Some(driver.state),
            driver.worker.map(_.id), driver.worker.map(_.hostPort), driver.exception)
        case None =>
          sender ! DriverStatusResponse(found = false, None, None, None, None)
      }
    }
    case RegisterApplication(description) => {
        //如果是standby，那么忽略这个消息
        //否则注册application；返回结果并且开始调度
    }
    case ExecutorStateChanged(appId, execId, state, message, exitStatus) => {
      // 通过idToApp获得app，然后通过app获得executors，从而通过execId获得executor
      val execOption = idToApp.get(appId).flatMap(app => app.executors.get(execId))
      execOption match {
        case Some(exec) => {
          exec.state = state
          exec.application.driver ! ExecutorUpdated(execId, state, message, exitStatus)
          if (ExecutorState.isFinished(state)) {
            val appInfo = idToApp(appId)
            // Remove this executor from the worker and app
            logInfo("Removing executor " + exec.fullId + " because it is " + state)
            appInfo.removeExecutor(exec)
            exec.worker.removeExecutor(exec)
           }
      ｝
    }
    case DriverStateChanged(driverId, state, exception) => {
      //  如果Driver的state为ERROR | FINISHED | KILLED | FAILED， 删除它。
    }
    case Heartbeat(workerId) => {
      // 更新worker的时间戳 workerInfo.lastHeartbeat = System.currentTimeMillis()
    }
    case MasterChangeAcknowledged(appId) => {
      //  将appId的app的状态置为WAITING，为切换Master做准备。
      }
    case WorkerSchedulerStateResponse(workerId, executors, driverIds) => {
      // 通过workerId查找到worker，那么worker的state置为ALIVE，
      // 并且查找状态为idDefined的executors，并且将这些executors都加入到app中，
      // 然后保存这些app到worker中。可以理解为Worker在Master端的Recovery
      idToWorker.get(workerId) match {
        case Some(worker) =>
          logInfo("Worker has been re-registered: " + workerId)
          worker.state = WorkerState.ALIVE

          val validExecutors = executors.filter(exec => idToApp.get(exec.appId).isDefined)
          for (exec <- validExecutors) {
            val app = idToApp.get(exec.appId).get
            val execInfo = app.addExecutor(worker, exec.cores, Some(exec.execId))
            worker.addExecutor(execInfo)
            execInfo.copyState(exec)
          }
          // 将所有的driver设置为RUNNING然后加入到worker中。
          for (driverId <- driverIds) {
            drivers.find(_.id == driverId).foreach { driver =>
              driver.worker = Some(worker)
              driver.state = DriverState.RUNNING
              worker.drivers(driverId) = driver
            }
          }
      }
    }
    case DisassociatedEvent(_, address, _) => {
      // 这个请求是Worker或者是App发送的。删除address对应的Worker和App
      // 如果Recovery可以结束，那么结束Recovery
    }
    case RequestMasterState => {
      //向sender返回master的状态
      sender ! MasterStateResponse(host, port, workers.toArray, apps.toArray, completedApps.toArray, drivers.toArray, completedDrivers.toArray, state)
    }
    case CheckForWorkerTimeOut => {
      //删除超时的Worker
    }
    case RequestWebUIPort => {
      //向sender返回web ui的端口号
      sender ! WebUIPortResponse(webUi.boundPort)
    }
  }

2.3 Worker 源码解析

通过对Client和Master的源码解析，相信你也知道如何去分析Worker是如何和Master进行通信的了，没错，答案就在下面：

 override def receive