标签:spark spark core 源码
源码位置:org.apache.spark.deploy.worker.Worker.scala
首先查看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ActorRef用于对外或者本身的信息交互。这里masters参数可以设置多个
def main(argStrings: Array[String]) { SignalLogger.register(log) val conf = new SparkConf val args = new WorkerArguments(argStrings, conf) val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort, args.cores, args.memory, args.masters, args.workDir) actorSystem.awaitTermination() }
程序起来后,同样是先执行akka 的preStart方法
override def preStart() { 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()//创建worker内部工作目录 //订阅akka生命周期事件 context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) //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 shuffleService.startIfEnabled() webUi = new WorkerWebUI(this, workDir, webUiPort) webUi.bind() registerWithMaster()//最重要的动作了,见下面 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) }向Master注册自己
private def registerWithMaster() { // DisassociatedEvent 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 actorRef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 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 { context.system.scheduler.schedule(INITIAL_REGISTRATION_RETRY_INTERVAL, INITIAL_REGISTRATION_RETRY_INTERVAL, self, ReregisterWithMaster) } 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消息会怎么做?这里要说一点,worker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actorRef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
<pre name="code" class="java">case RegisteredWorker(masterUrl, masterWebUiUrl) => logInfo("Successfully registered with master " + masterUrl) registered = true //状态设置为已注册,不然的话,一定时间过后,会发起ReregisterWithMaster而重复注册 changeMaster(masterUrl, masterWebUiUrl)//这里是将主master的信息保存 //在注册成功之后,才开启定时器向master发送心跳 context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis, self, SendHeartbeat) //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 if (CLEANUP_ENABLED) { logInfo(s"Worker cleanup enabled; old application directories will be deleted in: $workDir") context.system.scheduler.schedule(CLEANUP_INTERVAL_MILLIS millis, CLEANUP_INTERVAL_MILLIS millis, self, WorkDirCleanup) }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
case Heartbeat(workerId) => { 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.") sender ! ReconnectWorker(masterUrl) } else { logWarning(s"Got heartbeat from unregistered worker $workerId." + " This worker was never registered, so ignoring the heartbeat.") } } }
至此,worker启动流程以及主动发送的消息介绍完了,剩下的都是被动接收并处理的流程,在之后结合具体job介绍。。。
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标签:spark spark core 源码
原文地址:http://blog.csdn.net/yueqian_zhu/article/details/47976127