1 start-slave.sh

# NOTE: This exact class name is matched downstream by SparkSubmit.
# Any changes need to be reflected there.
CLASS="org.apache.spark.deploy.worker.Worker"


# Start up the appropriate number of workers on this machine.
# quick local function to start a worker
function start_instance {
  WORKER_NUM=$1
  shift

  if [ "$SPARK_WORKER_PORT" = "" ]; then
    PORT_FLAG=
    PORT_NUM=
  else
    PORT_FLAG="--port"
    PORT_NUM=$(( $SPARK_WORKER_PORT + $WORKER_NUM - 1 ))
  fi
  WEBUI_PORT=$(( $SPARK_WORKER_WEBUI_PORT + $WORKER_NUM - 1 ))

  "${SPARK_HOME}/sbin"/spark-daemon.sh start $CLASS $WORKER_NUM \
     --webui-port "$WEBUI_PORT" $PORT_FLAG $PORT_NUM $MASTER "$@"
}

if [ "$SPARK_WORKER_INSTANCES" = "" ]; then
  start_instance 1 "$@"
else
  for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do
    start_instance $(( 1 + $i )) "$@"
  done
fi

2 调用主函数

进入org.apache.spark.deploy.worker.Worker.scala

private[deploy] object Worker extends Logging {
  val SYSTEM_NAME = "sparkWorker"
  val ENDPOINT_NAME = "Worker"
  private val SSL_NODE_LOCAL_CONFIG_PATTERN = """\-Dspark\.ssl\.useNodeLocalConf\=(.+)""".r

  def main(argStrings: Array[String]) {
    Thread.setDefaultUncaughtExceptionHandler(new SparkUncaughtExceptionHandler(
      exitOnUncaughtException = false))
    Utils.initDaemon(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)
    // With external shuffle service enabled, if we request to launch multiple workers on one host,
    // we can only successfully launch the first worker and the rest fails, because with the port
    // bound, we may launch no more than one external shuffle service on each host.
    // When this happens, we should give explicit reason of failure instead of fail silently. For
    // more detail see SPARK-20989.
    val externalShuffleServiceEnabled = conf.get(config.SHUFFLE_SERVICE_ENABLED)
    val sparkWorkerInstances = scala.sys.env.getOrElse("SPARK_WORKER_INSTANCES", "1").toInt
    require(externalShuffleServiceEnabled == false || sparkWorkerInstances <= 1,
      "Starting multiple workers on one host is failed because we may launch no more than one " +
        "external shuffle service on each host, please set spark.shuffle.service.enabled to " +
        "false or set SPARK_WORKER_INSTANCES to 1 to resolve the conflict.")
    rpcEnv.awaitTermination()
  }

}

看看startRpcEnvAndEndpoint方法

  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, ENDPOINT_NAME, workDir, conf, securityMgr))
    rpcEnv
  }

创建SecurityManager；
创建RpcEnv；
创建Worker(RpcEndpoint)；
以"Worker"为服务名，注册Worker到RpcEnv。

其过程和Master启动类似，可参见Spark源码：启动Master。

3 创建RpcEnv

调用 NettyRpcEnvFactory 创建 RpcEnv。

进入org.apache.spark.rpc.netty.NettyRpcEnvFactory.scala

  def create(config: RpcEnvConfig): RpcEnv = {
    val sparkConf = config.conf
    // Use JavaSerializerInstance in multiple threads is safe. However, if we plan to support
    // KryoSerializer in future, we have to use ThreadLocal to store SerializerInstance
    val javaSerializerInstance =
      new JavaSerializer(sparkConf).newInstance().asInstanceOf[JavaSerializerInstance]
    val nettyEnv =
      new NettyRpcEnv(sparkConf, javaSerializerInstance, config.advertiseAddress,
        config.securityManager, config.numUsableCores)
    if (!config.clientMode) {
      val startNettyRpcEnv: Int => (NettyRpcEnv, Int) = { actualPort =>
        nettyEnv.startServer(config.bindAddress, actualPort)
        (nettyEnv, nettyEnv.address.port)
      }
      try {
        Utils.startServiceOnPort(config.port, startNettyRpcEnv, sparkConf, config.name)._1
      } catch {
        case NonFatal(e) =>
          nettyEnv.shutdown()
          throw e
      }
    }
    nettyEnv
  }

新建NettyRpcEnv时，在其内部创建Dispatcher、NettyStreamManager、TransportContext等；
NettyRpcEnv.startServer 时，调用其内部的TransportContext创建TransportServer；
在TransportContext创建TransportServer时，会调用 init 初始化TransportServer；
在初始化TransportServer时，基于Netty API初始化ServerBootstrap，设置管道初始化器ChannelInitializer到ServerBootstrap内部的ChannelHandler，为ServerBootstrap绑定InetSocketAddress等。

4 创建并注册Worker(RpcEndpoint)

进入org.apache.spark.rpc.netty.Dispatcher.scala

  def registerRpcEndpoint(name: String, endpoint: RpcEndpoint): NettyRpcEndpointRef = {
    val addr = RpcEndpointAddress(nettyEnv.address, name)
    val endpointRef = new NettyRpcEndpointRef(nettyEnv.conf, addr, nettyEnv)
    synchronized {
      if (stopped) {
        throw new IllegalStateException("RpcEnv has been stopped")
      }
      if (endpoints.putIfAbsent(name, new EndpointData(name, endpoint, endpointRef)) != null) {
        throw new IllegalArgumentException(s"There is already an RpcEndpoint called $name")
      }
      val data = endpoints.get(name)
      endpointRefs.put(data.endpoint, data.ref)
      receivers.offer(data)  // for the OnStart message
    }
    endpointRef
  }

创建NettyRpcEndpointRef；
创建EndpointData(name, endpoint, endpointRef)，加入Dispatcher的内部队列；
创建EndpointData时会创建Inbox，创建Inbox时会加入OnStart到Inbox的内部队列；

5. 取出OnStart处理

放入到Inbox内部队列的OnStart，会被Dispatcher.MessageLoop取出来，然后调用Inbox.process()处理，最终会调用endpoint.onStart()。

进入org.apache.spark.deploy.worker.Worker.scala

  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)
    createWorkDir()
    startExternalShuffleService()
    webUi = new WorkerWebUI(this, workDir, webUiPort)
    webUi.bind()

    workerWebUiUrl = s"http://$publicAddress:${webUi.boundPort}"
    registerWithMaster()

    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)
  }

创建Worker工作目录；
绑定WorkerWebUI；
向Master进行注册；
启动MetricsSystem。

进入org.apache.spark.deploy.worker.Worker.scala

  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
        registerMasterFutures = tryRegisterAllMasters()
        connectionAttemptCount = 0
        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.")
    }
  }

5.1 尝试注册到所有Master上

进入org.apache.spark.deploy.worker.Worker.scala

  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(masterAddress, Master.ENDPOINT_NAME)
            sendRegisterMessageToMaster(masterEndpoint)
          } catch {
            case ie: InterruptedException => // Cancelled
            case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
          }
        }
      })
    }
  }

遍历 masterRpcAddresses: Array[RpcAddress]，每个 masterRpcAddress 都启一个新线程提交到线程池中；
新线程中利用之前创建的 RpcEnv，基于 masterRpcAddress 和 masterEndpointName 获取对应的 masterRpcEndpointRef；
利用 masterRpcEndpointRef 发送注册消息 RegisterWorker 给 masterRpcEndpoint，用于注册 Worker 到 Master。

  private def sendRegisterMessageToMaster(masterEndpoint: RpcEndpointRef): Unit = {
    masterEndpoint.send(RegisterWorker(
      workerId,
      host,
      port,
      self,
      cores,
      memory,
      workerWebUiUrl,
      masterEndpoint.address))
  }

消息的发送接收流程是通用的。

5.2 通用消息发送流程

进入org.apache.spark.rpc.netty.NettyRpcEndpointRef.scala

  override def send(message: Any): Unit = {
    require(message != null, "Message is null")
    nettyEnv.send(new RequestMessage(nettyEnv.address, this, message))
  }

进入org.apache.spark.rpc.netty.NettyRpcEnv.scala

  private[netty] def send(message: RequestMessage): Unit = {
    val remoteAddr = message.receiver.address
    if (remoteAddr == address) {
      // Message to a local RPC endpoint.
      try {
        dispatcher.postOneWayMessage(message)
      } catch {
        case e: RpcEnvStoppedException => logDebug(e.getMessage)
      }
    } else {
      // Message to a remote RPC endpoint.
      postToOutbox(message.receiver, OneWayOutboxMessage(message.serialize(this)))
    }
  }

此处，remoteAddr != address，走postToOutbox分支，发送OneWayOutboxMessage。

进入org.apache.spark.rpc.netty.NettyRpcEnv.scala

  private def postToOutbox(receiver: NettyRpcEndpointRef, message: OutboxMessage): Unit = {
    if (receiver.client != null) {
      message.sendWith(receiver.client)
    } else {
      require(receiver.address != null,
        "Cannot send message to client endpoint with no listen address.")
      val targetOutbox = {
        val outbox = outboxes.get(receiver.address)
        if (outbox == null) {
          val newOutbox = new Outbox(this, receiver.address)
          val oldOutbox = outboxes.putIfAbsent(receiver.address, newOutbox)
          if (oldOutbox == null) {
            newOutbox
          } else {
            oldOutbox
          }
        } else {
          outbox
        }
      }
      if (stopped.get) {
        // It's possible that we put `targetOutbox` after stopping. So we need to clean it.
        outboxes.remove(receiver.address)
        targetOutbox.stop()
      } else {
        targetOutbox.send(message)
      }
    }
  }

从ConcurrentHashMap[RpcAddress, Outbox]中，根据receiver.address获取Outbox，如果未获取到，则新创建Outbox；
targetOutbox.send(message) 发送消息。

进入org.apache.spark.rpc.netty.Outbox.scala

  /**
   * Send a message. If there is no active connection, cache it and launch a new connection. If
   * [[Outbox]] is stopped, the sender will be notified with a [[SparkException]].
   */
  def send(message: OutboxMessage): Unit = {
    val dropped = synchronized {
      if (stopped) {
        true
      } else {
        messages.add(message)
        false
      }
    }
    if (dropped) {
      message.onFailure(new SparkException("Message is dropped because Outbox is stopped"))
    } else {
      drainOutbox()
    }
  }

将消息加入队列LinkedList[OutboxMessage]中；
drainOutbox取出队列中的消息。

进入org.apache.spark.rpc.netty.Outbox.scala

  /**
   * Drain the message queue. If there is other draining thread, just exit. If the connection has
   * not been established, launch a task in the `nettyEnv.clientConnectionExecutor` to setup the
   * connection.
   */
  private def drainOutbox(): Unit = {
    var message: OutboxMessage = null
    synchronized {
      if (stopped) {
        return
      }
      if (connectFuture != null) {
        // We are connecting to the remote address, so just exit
        return
      }
      if (client == null) {
        // There is no connect task but client is null, so we need to launch the connect task.
        launchConnectTask()
        return
      }
      if (draining) {
        // There is some thread draining, so just exit
        return
      }
      message = messages.poll()
      if (message == null) {
        return
      }
      draining = true
    }
    while (true) {
      try {
        val _client = synchronized { client }
        if (_client != null) {
          message.sendWith(_client)
        } else {
          assert(stopped == true)
        }
      } catch {
        case NonFatal(e) =>
          handleNetworkFailure(e)
          return
      }
      synchronized {
        if (stopped) {
          return
        }
        message = messages.poll()
        if (message == null) {
          draining = false
          return
        }
      }
    }
  }

调用 launchConnectTask 获取 TransportClient；
messages.poll 取出队列中的消息；
利用 TransportClient 发送消息。

进入org.apache.spark.rpc.netty.OneWayOutboxMessage.scala

  override def sendWith(client: TransportClient): Unit = {
    client.send(content)
  }

进入org.apache.spark.network.client.TransportClient.java

  /**
   * Sends an opaque message to the RpcHandler on the server-side. No reply is expected for the
   * message, and no delivery guarantees are made.
   *
   * @param message The message to send.
   */
  public void send(ByteBuffer message) {
    channel.writeAndFlush(new OneWayMessage(new NioManagedBuffer(message)));
  }

调用 Netty 的 Channel.writeAndFlush 发送 OneWayMessage。

5.3 通用消息处理流程

消息发送出去后，经由Netty发送给Server端的TransportChannelHandler去处理。

在 NettyRpcEnv.startServer() 时，会利用 TransportContext 创建 TransportServer，在创建始化 TransportServer 时会初始化管道，加入TransportChannelHandler。

进入org.apache.spark.network.server.TransportChannelHandler.java

  @Override
  public void channelRead(ChannelHandlerContext ctx, Object request) throws Exception {
    if (request instanceof RequestMessage) {
      requestHandler.handle((RequestMessage) request);
    } else if (request instanceof ResponseMessage) {
      responseHandler.handle((ResponseMessage) request);
    } else {
      ctx.fireChannelRead(request);
    }
  }

判断消息类型，调用不同的Handler处理。

进入org.apache.spark.network.server.TransportRequestHandler.java

  @Override
  public void handle(RequestMessage request) {
    if (request instanceof ChunkFetchRequest) {
      processFetchRequest((ChunkFetchRequest) request);
    } else if (request instanceof RpcRequest) {
      processRpcRequest((RpcRequest) request);
    } else if (request instanceof OneWayMessage) {
      processOneWayMessage((OneWayMessage) request);
    } else if (request instanceof StreamRequest) {
      processStreamRequest((StreamRequest) request);
    } else if (request instanceof UploadStream) {
      processStreamUpload((UploadStream) request);
    } else {
      throw new IllegalArgumentException("Unknown request type: " + request);
    }
  }

在Worker端，发送的是OneWayMessage。

进入org.apache.spark.network.server.TransportRequestHandler.java

  private void processOneWayMessage(OneWayMessage req) {
    try {
      rpcHandler.receive(reverseClient, req.body().nioByteBuffer());
    } catch (Exception e) {
      logger.error("Error while invoking RpcHandler#receive() for one-way message.", e);
    } finally {
      req.body().release();
    }
  }

调用 RpcHandler 来真正处理消息。

进入org.apache.spark.rpc.netty.NettyRpcHandler.scala

  override def receive(
      client: TransportClient,
      message: ByteBuffer): Unit = {
    val messageToDispatch = internalReceive(client, message)
    dispatcher.postOneWayMessage(messageToDispatch)
  }

创建用于内部接收处理的消息；
调用Dispatcher.postOneWayMessage发送内部消息。

进入org.apache.spark.rpc.netty.Dispatcher.scala

  /** Posts a one-way message. */
  def postOneWayMessage(message: RequestMessage): Unit = {
    postMessage(message.receiver.name, OneWayMessage(message.senderAddress, message.content),
      (e) => throw e)
  }


  /**
   * Posts a message to a specific endpoint.
   *
   * @param endpointName name of the endpoint.
   * @param message the message to post
   * @param callbackIfStopped callback function if the endpoint is stopped.
   */
  private def postMessage(
      endpointName: String,
      message: InboxMessage,
      callbackIfStopped: (Exception) => Unit): Unit = {
    val error = synchronized {
      val data = endpoints.get(endpointName)
      if (stopped) {
        Some(new RpcEnvStoppedException())
      } else if (data == null) {
        Some(new SparkException(s"Could not find $endpointName."))
      } else {
        data.inbox.post(message)
        receivers.offer(data)
        None
      }
    }
    // We don't need to call `onStop` in the `synchronized` block
    error.foreach(callbackIfStopped)
  }

这里就是向Dispatcher内部队列中加入待处理消息的地方，Dispatcher.MessageLoop 循环取出队列中的消息调用 EndpointData.inbox.process 处理。

具体过程可见 Spark源码：启动Master 中的 Dispatcher 里消息处理过程。

进入org.apache.spark.rpc.netty.Inbox.scala

  /**
   * Process stored messages.
   */
  def process(dispatcher: Dispatcher): Unit = {
    var message: InboxMessage = null
    inbox.synchronized {
      if (!enableConcurrent && numActiveThreads != 0) {
        return
      }
      message = messages.poll()
      if (message != null) {
        numActiveThreads += 1
      } else {
        return
      }
    }
    while (true) {
      safelyCall(endpoint) {
        message match {
          case RpcMessage(_sender, content, context) =>
            try {
              endpoint.receiveAndReply(context).applyOrElse[Any, Unit](content, { msg =>
                throw new SparkException(s"Unsupported message $message from ${_sender}")
              })
            } catch {
              case e: Throwable =>
                context.sendFailure(e)
                // Throw the exception -- this exception will be caught by the safelyCall function.
                // The endpoint's onError function will be called.
                throw e
            }

          case OneWayMessage(_sender, content) =>
            endpoint.receive.applyOrElse[Any, Unit](content, { msg =>
              throw new SparkException(s"Unsupported message $message from ${_sender}")
            })

          case OnStart =>
            endpoint.onStart()
            if (!endpoint.isInstanceOf[ThreadSafeRpcEndpoint]) {
              inbox.synchronized {
                if (!stopped) {
                  enableConcurrent = true
                }
              }
            }

          case OnStop =>
            val activeThreads = inbox.synchronized { inbox.numActiveThreads }
            assert(activeThreads == 1,
              s"There should be only a single active thread but found $activeThreads threads.")
            dispatcher.removeRpcEndpointRef(endpoint)
            endpoint.onStop()
            assert(isEmpty, "OnStop should be the last message")

          case RemoteProcessConnected(remoteAddress) =>
            endpoint.onConnected(remoteAddress)

          case RemoteProcessDisconnected(remoteAddress) =>
            endpoint.onDisconnected(remoteAddress)

          case RemoteProcessConnectionError(cause, remoteAddress) =>
            endpoint.onNetworkError(cause, remoteAddress)
        }
      }

      inbox.synchronized {
        // "enableConcurrent" will be set to false after `onStop` is called, so we should check it
        // every time.
        if (!enableConcurrent && numActiveThreads != 1) {
          // If we are not the only one worker, exit
          numActiveThreads -= 1
          return
        }
        message = messages.poll()
        if (message == null) {
          numActiveThreads -= 1
          return
        }
      }
    }
  }

匹配OneWayMessage，进入对应分支，此处的 endpoint 是 Master，因此进入Master.receive方法。

进入org.apache.spark.deploy.master.Master.scala

  override def receive: PartialFunction[Any, Unit] = {
    case ElectedLeader =>
      val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData(rpcEnv)
      state = if (storedApps.isEmpty && storedDrivers.isEmpty && storedWorkers.isEmpty) {
        RecoveryState.ALIVE
      } else {
        RecoveryState.RECOVERING
      }
      logInfo("I have been elected leader! New state: " + state)
      if (state == RecoveryState.RECOVERING) {
        beginRecovery(storedApps, storedDrivers, storedWorkers)
        recoveryCompletionTask = forwardMessageThread.schedule(new Runnable {
          override def run(): Unit = Utils.tryLogNonFatalError {
            self.send(CompleteRecovery)
          }
        }, WORKER_TIMEOUT_MS, TimeUnit.MILLISECONDS)
      }

    case RegisterWorker(
      id, workerHost, workerPort, workerRef, cores, memory, workerWebUiUrl, masterAddress) =>
      logInfo("Registering worker %s:%d with %d cores, %s RAM".format(
        workerHost, workerPort, cores, Utils.megabytesToString(memory)))
      if (state == RecoveryState.STANDBY) {
        workerRef.send(MasterInStandby)
      } else if (idToWorker.contains(id)) {
        workerRef.send(RegisterWorkerFailed("Duplicate worker ID"))
      } else {
        val worker = new WorkerInfo(id, workerHost, workerPort, cores, memory,
          workerRef, workerWebUiUrl)
        if (registerWorker(worker)) {
          persistenceEngine.addWorker(worker)
          workerRef.send(RegisteredWorker(self, masterWebUiUrl, masterAddress))
          schedule()
        } else {
          val workerAddress = worker.endpoint.address
          logWarning("Worker registration failed. Attempted to re-register worker at same " +
            "address: " + workerAddress)
          workerRef.send(RegisterWorkerFailed("Attempted to re-register worker at same address: "
            + workerAddress))
        }
      }

    case RegisterApplication(description, driver) =>
      // TODO Prevent repeated registrations from some driver
      if (state == RecoveryState.STANDBY) {
        // ignore, don't send response
      } else {
        logInfo("Registering app " + description.name)
        val app = createApplication(description, driver)
        registerApplication(app)
        logInfo("Registered app " + description.name + " with ID " + app.id)
        persistenceEngine.addApplication(app)
        driver.send(RegisteredApplication(app.id, self))
        schedule()
      }

    case Heartbeat(workerId, worker) =>
      idToWorker.get(workerId) match {
        case Some(workerInfo) =>
          workerInfo.lastHeartbeat = System.currentTimeMillis()
        case None =>
          if (workers.map(_.id).contains(workerId)) {
            logWarning(s"Got heartbeat from unregistered worker $workerId." +
              " Asking it to re-register.")
            worker.send(ReconnectWorker(masterUrl))
          } else {
            logWarning(s"Got heartbeat from unregistered worker $workerId." +
              " This worker was never registered, so ignoring the heartbeat.")
          }
      }

    // 省略部分内容

  }

匹配RegisterWorker，走对应分支。

  private def registerWorker(worker: WorkerInfo): Boolean = {
    // There may be one or more refs to dead workers on this same node (w/ different ID's),
    // remove them.
    workers.filter { w =>
      (w.host == worker.host && w.port == worker.port) && (w.state == WorkerState.DEAD)
    }.foreach { w =>
      workers -= w
    }

    val workerAddress = worker.endpoint.address
    if (addressToWorker.contains(workerAddress)) {
      val oldWorker = addressToWorker(workerAddress)
      if (oldWorker.state == WorkerState.UNKNOWN) {
        // A worker registering from UNKNOWN implies that the worker was restarted during recovery.
        // The old worker must thus be dead, so we will remove it and accept the new worker.
        removeWorker(oldWorker, "Worker replaced by a new worker with same address")
      } else {
        logInfo("Attempted to re-register worker at same address: " + workerAddress)
        return false
      }
    }

    workers += worker
    idToWorker(worker.id) = worker
    addressToWorker(workerAddress) = worker
    true
  }

正常情况下，创建WorkerInfo，尝试注册Worker到Master上，判断Master中有没有与当前注册Worker地址相同且状态为DEAD或UNKNOWN的Worker，如果有则将其移除，然后将 WorkerInfo 加入 Master 内部的 HashSet[WorkerInfo] 中；
发送 RegisteredWorker(实现接口RegisterWorkerResponse) 消息给当前注册的Worker对应的RpcEndpointRef，用于告知Worker注册成功；
调用 schedule() 为正在等待的 apps 规划可用资源，该方法在有新app加入或可用资源改变时都会被调用。新提交app时需要调用schedule()方法来规划一下，看是否有足够的资源启动运行该app；同时资源变化时也要调用schedule()方法规划一下，看正在等待的app是否可以获得足够资源启动运行了。

消息发送接收流程和之前一样。

5.4 返回消息发送给Worker

回到org.apache.spark.deploy.worker.Worker.scala

  override def receive: PartialFunction[Any, Unit] = synchronized {
    case msg: RegisterWorkerResponse =>
      handleRegisterResponse(msg)

    case SendHeartbeat =>
      if (connected) { sendToMaster(Heartbeat(workerId, self)) }

    // 省略部分内容

  }

发送的消息是RegisterWorkerResponse类型，进入对应分支。

  private def handleRegisterResponse(msg: RegisterWorkerResponse): Unit = synchronized {
    msg match {
      case RegisteredWorker(masterRef, masterWebUiUrl, masterAddress) =>
        if (preferConfiguredMasterAddress) {
          logInfo("Successfully registered with master " + masterAddress.toSparkURL)
        } else {
          logInfo("Successfully registered with master " + masterRef.address.toSparkURL)
        }
        registered = true
        changeMaster(masterRef, masterWebUiUrl, masterAddress)
        forwordMessageScheduler.scheduleAtFixedRate(new Runnable {
          override def run(): Unit = Utils.tryLogNonFatalError {
            self.send(SendHeartbeat)
          }
        }, 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 {
            override def run(): Unit = Utils.tryLogNonFatalError {
              self.send(WorkDirCleanup)
            }
          }, CLEANUP_INTERVAL_MILLIS, CLEANUP_INTERVAL_MILLIS, TimeUnit.MILLISECONDS)
        }

        val execs = executors.values.map { e =>
          new ExecutorDescription(e.appId, e.execId, e.cores, e.state)
        }
        masterRef.send(WorkerLatestState(workerId, execs.toList, drivers.keys.toSeq))

      case RegisterWorkerFailed(message) =>
        if (!registered) {
          logError("Worker registration failed: " + message)
          System.exit(1)
        }

      case MasterInStandby =>
        // Ignore. Master not yet ready.
    }
  }

在此处，会启一个新线程，定时调用 self.send(SendHeartbeat) 发送心跳。

5.5 心跳

调用 self.send(SendHeartbeat) 发送心跳时，self 表示自身对应的RpcEndpointRef，因此 Worker 会给自己发一个 SendHeartbeat 消息。

消息发送接收流程类似，最终由 Worker.receive 方法处理消息 SendHeartbeat，走 SendHeartbeat 对应分支（）。

进入org.apache.spark.deploy.worker.Worker.scala

  override def receive: PartialFunction[Any, Unit] = synchronized {
    case msg: RegisterWorkerResponse =>
      handleRegisterResponse(msg)

    case SendHeartbeat =>
      if (connected) { sendToMaster(Heartbeat(workerId, self)) }

    // 省略部分内容

  }

发送心跳信息给Master。

  /**
   * Send a message to the current master. If we have not yet registered successfully with any
   * master, the message will be dropped.
   */
  private def sendToMaster(message: Any): Unit = {
    master match {
      case Some(masterRef) => masterRef.send(message)
      case None =>
        logWarning(
          s"Dropping $message because the connection to master has not yet been established")
    }
  }

消息发送接收流程类似。

消息 Heartbeat 最后发送给 Master 去处理。

再次回到org.apache.spark.deploy.master.Master.scala

  override def receive: PartialFunction[Any, Unit] = {

    case Heartbeat(workerId, worker) =>
      idToWorker.get(workerId) match {
        case Some(workerInfo) =>
          workerInfo.lastHeartbeat = System.currentTimeMillis()
        case None =>
          if (workers.map(_.id).contains(workerId)) {
            logWarning(s"Got heartbeat from unregistered worker $workerId." +
              " Asking it to re-register.")
            worker.send(ReconnectWorker(masterUrl))
          } else {
            logWarning(s"Got heartbeat from unregistered worker $workerId." +
              " This worker was never registered, so ignoring the heartbeat.")
          }
      }

    // 省略部分信息

  }

此处会更新注册在 Master 上的 WorkerInfo.lastHeartbeat 为当前时间。

5.6 心跳超时检测

啥时候检测心跳超时呢？

在Master启动时，最后会调用 onStart() 方法，详见 Spark源码：启动Master，Worker启动时也是类似。

进入org.apache.spark.deploy.master.Master.scala

  override def onStart(): Unit = {

    checkForWorkerTimeOutTask = forwardMessageThread.scheduleAtFixedRate(new Runnable {
      override def run(): Unit = Utils.tryLogNonFatalError {
        self.send(CheckForWorkerTimeOut)
      }
    }, 0, WORKER_TIMEOUT_MS, TimeUnit.MILLISECONDS)

    // 省略部分内容
  }

启新线程定时调用 self.send(CheckForWorkerTimeOut)。

和上面类似，self 表示自身对应的 RpcEndpointRef，即 Master，消息发给自己，自己在 receive 方法中接收处理。

进入org.apache.spark.deploy.master.Master.scala

  override def receive: PartialFunction[Any, Unit] = {

    case CheckForWorkerTimeOut =>
      timeOutDeadWorkers()

    // 省略部分内容
  }

  /** Check for, and remove, any timed-out workers */
  private def timeOutDeadWorkers() {
    // Copy the workers into an array so we don't modify the hashset while iterating through it
    val currentTime = System.currentTimeMillis()
    val toRemove = workers.filter(_.lastHeartbeat < currentTime - WORKER_TIMEOUT_MS).toArray
    for (worker <- toRemove) {
      if (worker.state != WorkerState.DEAD) {
        logWarning("Removing %s because we got no heartbeat in %d seconds".format(
          worker.id, WORKER_TIMEOUT_MS / 1000))
        removeWorker(worker, s"Not receiving heartbeat for ${WORKER_TIMEOUT_MS / 1000} seconds")
      } else {
        if (worker.lastHeartbeat < currentTime - ((REAPER_ITERATIONS + 1) * WORKER_TIMEOUT_MS)) {
          workers -= worker // we've seen this DEAD worker in the UI, etc. for long enough; cull it
        }
      }
    }
  }

找出心跳超时的 Worker，从 Master 的注册信息中移除。

Spark源码：启动Worker