Spark源码阅读：Master接收到ClientActor后，进行worker的资源分配

看一下appActor的preStart方法

override def preStart() {
  context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
  try {
    registerWithMaster()
  } catch {
    case e: Exception =>
      logWarning("Failed to connect to master", e)
      markDisconnected()
      context.stop(self)
  }
}

调用registerWithMaster向Master注册信息：

def registerWithMaster() {
  tryRegisterAllMasters()
  import context.dispatcher
  var retries = 0
  registrationRetryTimer = Some {
    context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) {
      Utils.tryOrExit {
        retries += 1
        if (registered) {
          registrationRetryTimer.foreach(_.cancel())
        } else if (retries >= REGISTRATION_RETRIES) {
          markDead("All masters are unresponsive! Giving up.")
        } else {
          tryRegisterAllMasters()
        }
      }
    }
  }
}

看一下tryRegisterAllMaster:

 

def tryRegisterAllMasters() {
  for (masterAkkaUrl <- masterAkkaUrls) {
    logInfo("Connecting to master " + masterAkkaUrl + "...")
    val actor = context.actorSelection(masterAkkaUrl)
    actor ! RegisterApplication(appDescription)
  }
}

上面的代码中，遍历出所有的masterAkkaUrl 获取master的Actor连接，向master发送一个注册消息，我们去master中找到匹配的消息。

case RegisterApplication(description) => {
  if (state == RecoveryState.STANDBY) {
    // ignore, don't send response
  } else {
    logInfo("Registering app " + description.name)
    val app = createApplication(description, sender)
    registerApplication(app)
    logInfo("Registered app " + description.name + " with ID " + app.id)
    persistenceEngine.addApplication(app)
    sender ! RegisteredApplication(app.id, masterUrl)
    schedule()
  }
}

createApplictaion主要是创建app的描述，放到内存，sender指的是clientActor。