Spark 源码阅读（6）——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。