spark 1.6.0 core源码分析4 worker启动流程

worker的main方法，与master类似，创建sparkConf，参数解析，以及构造worker对象并创建rpcEnv用于对外或者本身的信息交互。

private[deploy] object Worker extends Logging {  val SYSTEM_NAME = "sparkWorker"  val ENDPOINT_NAME = "Worker"  def main(argStrings: Array[String]) {    SignalLogger.register(log)    val conf = new SparkConf    val args = new WorkerArguments(argStrings, conf)    val rpcEnv = startRpcEnvAndEndpoint(args.host, args.port, args.webUiPort, args.cores,      args.memory, args.masters, args.workDir, conf = conf)    rpcEnv.awaitTermination()  }  def startRpcEnvAndEndpoint(      host: String,      port: Int,      webUiPort: Int,      cores: Int,      memory: Int,      masterUrls: Array[String],      workDir: String,      workerNumber: Option[Int] = None,      conf: SparkConf = new SparkConf): RpcEnv = {    // The LocalSparkCluster runs multiple local sparkWorkerX RPC Environments    val systemName = SYSTEM_NAME + workerNumber.map(_.toString).getOrElse("")    val securityMgr = new SecurityManager(conf)    val rpcEnv = RpcEnv.create(systemName, host, port, conf, securityMgr)    val masterAddresses = masterUrls.map(RpcAddress.fromSparkURL(_))    rpcEnv.setupEndpoint(ENDPOINT_NAME, new Worker(rpcEnv, webUiPort, cores, memory,      masterAddresses, systemName, ENDPOINT_NAME, workDir, conf, securityMgr))    rpcEnv  }

同样的执行onstart方法想master注册

override def onStart() {    assert(!registered)    logInfo("Starting Spark worker %s:%d with %d cores, %s RAM".format(      host, port, cores, Utils.megabytesToString(memory)))    logInfo(s"Running Spark version ${org.apache.spark.SPARK_VERSION}")    logInfo("Spark home: " + sparkHome)    <strong>createWorkDir() //创建工作目录</strong>    shuffleService.startIfEnabled()//是否额外的启动一个shuffle服务，确保被executor所读写的shuffle文件在executor退出后被保存，可配    webUi = new WorkerWebUI(this, workDir, webUiPort)    webUi.bind()    <strong>registerWithMaster() //向master注册</strong>    metricsSystem.registerSource(workerSource)    metricsSystem.start()    // Attach the worker metrics servlet handler to the web ui after the metrics system is started.    metricsSystem.getServletHandlers.foreach(webUi.attachHandler)  }

private def registerWithMaster() {    // onDisconnected may be triggered multiple times, so don't attempt registration    // if there are outstanding registration attempts scheduled.    registrationRetryTimer match {      case None =>        registered = false //这里向所有的master rpcEnv发送RegisterWorker消息，上几节有讲master收到该消息后，如果成功处理会反馈RegisteredWorker消息，不成功会发送RegisterWorkerFailed消息        registerMasterFutures = tryRegisterAllMasters()        connectionAttemptCount = 0 //这里在一定时间之后会进入ReregisterWithMaster，里面会判断是否已注册，如果没有会再次发送注册信息。这个是否注册的状态是由master反馈回来的        registrationRetryTimer = Some(forwordMessageScheduler.scheduleAtFixedRate(          new Runnable {            override def run(): Unit = Utils.tryLogNonFatalError {              Option(self).foreach(_.send(ReregisterWithMaster))            }          },          INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS,          INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS,          TimeUnit.SECONDS))      case Some(_) =>        logInfo("Not spawning another attempt to register with the master, since there is an" +          " attempt scheduled already.")    }  }

看worker收到master的RegisteredWorker消息，要注册时并不知道哪台是主，哪台是备，所以向所有配置的master都发送注册信息。主备都收到worker的注册信息之后，只有主才会反馈，并带上自己的masterUrl信息，worker以此来认定主master的rpcEnv用于真正的信息交互
worker要通过心跳来保持与master的时刻连通，所以注册成功之后，有一个connected标记是否连接正常，在changeMaster方法内部设置connected ＝ true

private def tryRegisterAllMasters(): Array[JFuture[_]] = {    masterRpcAddresses.map { masterAddress =>      registerMasterThreadPool.submit(new Runnable {        override def run(): Unit = {          try {            logInfo("Connecting to master " + masterAddress + "...")            val masterEndpoint =              rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, masterAddress, Master.ENDPOINT_NAME)           <strong> registerWithMaster(masterEndpoint)</strong>          } catch {            case ie: InterruptedException => // Cancelled            case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)          }        }      })    }  }<pre name="code" class="java">  private def registerWithMaster(masterEndpoint: RpcEndpointRef): Unit = {    masterEndpoint.ask[RegisterWorkerResponse](RegisterWorker(      workerId, host, port, self, cores, memory, webUi.boundPort, publicAddress))      .onComplete {        // This is a very fast action so we can use "ThreadUtils.sameThread"        case Success(msg) =>          Utils.tryLogNonFatalError {            <strong>handleRegisterResponse(msg)</strong>          }        case Failure(e) =>          logError(s"Cannot register with master: ${masterEndpoint.address}", e)          System.exit(1)      }(ThreadUtils.sameThread)  }

case RegisteredWorker(masterRef, masterWebUiUrl) =>        logInfo("Successfully registered with master " + masterRef.address.toSparkURL)        registered = true <strong>//注册成功</strong>        changeMaster(masterRef, masterWebUiUrl) //这里是将主master的信息保存        forwordMessageScheduler.scheduleAtFixedRate(new Runnable { //在注册成功之后，才开启定时器向master发送心跳          override def run(): Unit = Utils.tryLogNonFatalError {            self.send(SendHeartbeat) //每4分钟发送一次心跳到master   Send a heartbeat every (heartbeat timeout) / 4 milliseconds</strong>          }        }, 0, HEARTBEAT_MILLIS, TimeUnit.MILLISECONDS)        if (CLEANUP_ENABLED) {          logInfo(            s"Worker cleanup enabled; old application directories will be deleted in: $workDir")          forwordMessageScheduler.scheduleAtFixedRate(new Runnable {//定时器清理workDir下很久都没有更新的且app也不在执行状态的目录            override def run(): Unit = Utils.tryLogNonFatalError {              self.send(WorkDirCleanup)            }          }, CLEANUP_INTERVAL_MILLIS, CLEANUP_INTERVAL_MILLIS, TimeUnit.MILLISECONDS)        }

如果收到RegisterWorkerFailed消息，则退出

下面看master接受到worker的心跳之后如何处理

由于worker注册时，master已经将workerId存入idToWorker中，所以这里走Some分支。很简单，只是更新该worker的一个时间戳。这里有必要说明一下None分支，在注册消息到达后，在master 的idToWorker和workers中都会保存，但是当master检测到worker超时时，将worker从idToWorker中删除，这样新的任务就选不了该worker了，但不删除workers中的。workers中的只会在间隔很长一段时间之后仍然没有心跳上来，才说明该worker真正无法再工作了，再从workers中删除。这里的None分支就是应对超时过后，心跳又继续上来了，就向worker发送重新注册的消息ReconnectWorker