[大数据]Spark源码——注册机制

worker向master注册

• worker在启动之后，就会向master进行注册

• 对于worker状态为DEAD的，过滤掉。在compeleRecover()中，master在一定时间期限已经完成了恢复，但是发现其中的worker为UNKNOWN的情况下，对worker进行remove，将worker的状态设置为DEAD，如果过了很长时间worker又莫名其妙的向master进行注册的情况下，直接过滤掉。

• 对于worker状态为UNKNOWN，master会将旧的worker信息给清理掉，替换成新的worker信息。比如说master刚启动的时候，会向worker发送新的地址的时候，master会将该worker状态设置为unknown，worker向master返回注册信息的时候，master会将旧的worker信息给清理掉，替换成新的worker信息

• 将worker加入内存缓存中（HashMap），用持久化引擎将worker信息持久化，可能是文件系统，可能是zookeeper

• 调用schedule()方法进行调度

进去源码看看

找到register相关的代码

  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
    } //过滤状态为DEAD的worker

    val workerAddress = worker.endpoint.address
    if (addressToWorker.contains(workerAddress)) {
    //如果worker地址存在，则说明该worker是oldworker
      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.
        //如果worker状态为UNKNOWN，说明在恢复过程中被重启了，则该worker要被杀死
        removeWorker(oldWorker)
      } else {
        logInfo("Attempted to re-register worker at same address: " + workerAddress)
        return false
      }
    }

    workers += worker //将新的worker添加到hashset中
    idToWorker(worker.id) = worker
    addressToWorker(workerAddress) = worker//地址也添加
    if (reverseProxy) {
       webUi.addProxyTargets(worker.id, worker.webUiAddress)
    }
    true
  }

driver向master注册

• 用spark-submit提交sparkApplication的时候，dirver首先就会向master进行注册，将driver信息放入到内存缓存中，也就是hashmap中
• 加入等待调度队列，也就是ArrayBuffer
• 用持久化引擎将driver信息持久化，可能是文件系统，可能是zookeeper
• 调用schedule()方法进行调度

case RequestSubmitDriver(description) =>
      if (state != RecoveryState.ALIVE) {
      //如果状态不为Alive，则提交失败，只能提交到Alive的Master
        val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " +
          "Can only accept driver submissions in ALIVE state."
        context.reply(SubmitDriverResponse(self, false, None, msg))
      } else {
        logInfo("Driver submitted " + description.command.mainClass)
        //调用createDriver方法，创建Driver
        val driver = createDriver(description)
        //使用持久化引擎将Driver持久化
       
        persistenceEngine.addDriver(driver)
       //加入内存结构
        waitingDrivers += driver
        drivers.add(driver)
		//调度
        schedule()

        // TODO: It might be good to instead have the submission client poll the master to determine
        //       the current status of the driver. For now it's simply "fire and forget".

		//成功提交
        context.reply(SubmitDriverResponse(self, true, Some(driver.id),
          s"Driver successfully submitted as ${driver.id}"))
      }

 private def createDriver(desc: DriverDescription): DriverInfo = {
    val now = System.currentTimeMillis()
    val date = new Date(now)
    new DriverInfo(now, newDriverId(date), desc, date)
  }

application向master进行注册（registerApplication()方法）

• Driver启动好之后，会执行我们的application代码，执行sparkContext的初始化，底层的SparkDeploySchedulerBackend，会通过AppClient内部的线程，ClientEndPoint发送RegisterAppliction，到master进行Application进行注册。
• 

 /**
     *  Register with all masters asynchronously and returns an array `Future`s for cancellation.
     */
    private def tryRegisterAllMasters(): Array[JFuture[_]] = {
      for (masterAddress <- masterRpcAddresses) yield {
        registerMasterThreadPool.submit(new Runnable {
          override def run(): Unit = try {
            if (registered.get) {
              return
            }
            logInfo("Connecting to master " + masterAddress.toSparkURL + "...")
            val masterRef = rpcEnv.setupEndpointRef(masterAddress, Master.ENDPOINT_NAME)
            masterRef.send(RegisterApplication(appDescription, self)) 
            //这里调用了RegisterApplication
          } catch {
            case ie: InterruptedException => // Cancelled
            case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
          }
        })
      }
    }

在Master中找到RegisterApplication这个东西

 case RegisterApplication(description, driver) =>
      // TODO Prevent repeated registrations from some driver
      if (state == RecoveryState.STANDBY) {
      //如果master不是alive，则不理这个application
        // ignore, don't send response
      } else {
        logInfo("Registering app " + description.name)
        val app = createApplication(description, driver)
        //用ApplicationDescription信息，创建ApplicationInfo
        registerApplication(app)//注册application
        //将Application加入缓存，将Application加入等待调度的队列
        logInfo("Registered app " + description.name + " with ID " + app.id)
        //持久化
        persistenceEngine.addApplication(app)
        //反向，向SparkDeploySchedulerBackend的AppClient的ClientEndPoint发送消息，也就是registeredApplication，而不是registerApplication

        driver.send(RegisteredApplication(app.id, self))
        schedule()
      }

• 将application信息放入到内存缓存中，也就是hashmap中
• 将application加入等待的调度队列，也就是ArrayBuffer
• 用持久化引擎将application信息持久化，可能是文件系统，可能是zookeeper

进到createApplication

private def createApplication(desc: ApplicationDescription, driver: RpcEndpointRef):
      ApplicationInfo = {
    val now = System.currentTimeMillis()
    val date = new Date(now)//创建一个时间戳
    val appId = newApplicationId(date)//创建ApplicaitonId,传入时间戳
    new ApplicationInfo(now, appId, desc, date, driver, defaultCores)
  }

看看registerApplication

private def registerApplication(app: ApplicationInfo): Unit = {
    val appAddress = app.driver.address
    if (addressToApp.contains(appAddress)) {
    // 如果driver的地址存在的情况下，就直接返回，就相当于对driver进行重复注册
      logInfo("Attempted to re-register application at same address: " + appAddress)
      return
    }
	//将Application的信息加入到内存缓存中
    applicationMetricsSystem.registerSource(app.appSource)
    apps += app
    idToApp(app.id) = app
    endpointToApp(app.driver) = app
    addressToApp(appAddress) = app
     //将Application的信息加入到等待调度的队列中，调度的算法为FIFO
    waitingApps += app
    //private val waitingApps = new ArrayBuffer[ApplicationInfo]
    if (reverseProxy) {
      webUi.addProxyTargets(app.id, app.desc.appUiUrl)
    }
  }