本期内容：
1、数据接收架构设计模式
2、数据接收源码彻底研究
1、Receiver接受数据的过程类似于MVC模式：
Receiver，ReceiverSupervisor和Driver的关系相当于Model，Control，View，也就是MVC。
Model就是Receiver，存储数据Control，就是ReceiverSupervisor，Driver是获得元数据，也就是View。
2、数据的位置信息会被封装到RDD里面。
3、Receiver接受数据，交给ReceiverSupervisor去存储数据。
4、ReceiverTracker是通过发送一个又一个的Job，每个Job只有一个Task，每个Task里面就只有一个ReceiverSupervisor，用这个函数启动每一个Receiver。
下面我们简单的看下Receiver启动流程，应用程序首先通过JobScheduler的start方法来启动receiverTracker的start方法：
def start(): Unit = synchronized {if (eventLoop != null) return // scheduler has already been started
logDebug("Starting JobScheduler")eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") {override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event)
override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e) }eventLoop.start()
// attach rate controllers of input streams to receive batch completion updatesfor { inputDStream <- ssc.graph.getInputStreams rateController <- inputDStream.rateController} ssc.addStreamingListener(rateController)
listenerBus.start(ssc.sparkContext)receiverTracker = new ReceiverTracker(ssc)inputInfoTracker = new InputInfoTracker(ssc)receiverTracker.start() //receiver启动jobGenerator.start() logInfo("Started JobScheduler")}
通过调用receiverTracker.start()方法来进行一系列的操作：
/** Start the endpoint and receiver execution thread. */def start(): Unit = synchronized {if (isTrackerStarted) {throw new SparkException("ReceiverTracker already started") }
if (!receiverInputStreams.isEmpty) {endpoint = ssc.env.rpcEnv.setupEndpoint("ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv)) //Rpc消息通信，获取receiver的状态if (!skipReceiverLaunch) launchReceivers() //启动receiver logInfo("ReceiverTracker started")trackerState = Started}}
下面通过画图简单的描述下Receiver启动的内部机制：
参考博客：http://blog.csdn.net/hanburgud/article/details/51471047
http://lqding.blog.51cto.com/9123978/1774426
备注：
资料来源于：DT_大数据梦工厂（Spark发行版本定制）
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时间： 2024-10-11 12:26:23