LiveListenerBus
首先,它定义了 4 个 消息堵塞队列,队列的名字分别为shared、appStatus、executorManagement、eventLog。队列的类型是 org.apache.spark.scheduler.AsyncEventQueue#AsyncEventQueue,保存在 queues 变量中。每一个队列上都可以注册监听器,如果队列没有监听器,则会被移除。
它有启动和stop和start两个标志位来指示 监听总线的的启动停止状态。 如果总线没有启动,有事件过来,先放到 一个待添加的可变数组中,否则直接将事件 post 到每一个队列中。
其直接依赖类是 AsyncEventQueue, 相当于 LiveListenerBus 的多事件队列是对 AsyncEventQueue 进一步的封装。
AsyncEventQueue
其继承关系如下:
它有启动和stop和start两个标志位来指示 监听总线的的启动停止状态。
其内部维护了listenersPlusTimers 主要就是用来保存注册到这个总线上的监听器对象的。
post 操作将事件放入内部的 LinkedBlockingQueue中,默认大小是 10000。
有一个事件分发器,它不停地从 LinkedBlockingQueue 执行 take 操作,获取事件,并将事件进一步分发给所有的监听器,由org.apache.spark.scheduler.SparkListenerBus#doPostEvent 方法实现事件转发,具体代码如下:
1 protected override def doPostEvent(
2 listener: SparkListenerInterface,
3 event: SparkListenerEvent): Unit = {
4 event match {
5 case stageSubmitted: SparkListenerStageSubmitted =>
6 listener.onStageSubmitted(stageSubmitted)
7 case stageCompleted: SparkListenerStageCompleted =>
8 listener.onStageCompleted(stageCompleted) 9 case jobStart: SparkListenerJobStart => 10 listener.onJobStart(jobStart) 11 case jobEnd: SparkListenerJobEnd => 12 listener.onJobEnd(jobEnd) 13 case taskStart: SparkListenerTaskStart => 14 listener.onTaskStart(taskStart) 15 case taskGettingResult: SparkListenerTaskGettingResult => 16 listener.onTaskGettingResult(taskGettingResult) 17 case taskEnd: SparkListenerTaskEnd => 18 listener.onTaskEnd(taskEnd) 19 case environmentUpdate: SparkListenerEnvironmentUpdate => 20 listener.onEnvironmentUpdate(environmentUpdate) 21 case blockManagerAdded: SparkListenerBlockManagerAdded => 22 listener.onBlockManagerAdded(blockManagerAdded) 23 case blockManagerRemoved: SparkListenerBlockManagerRemoved => 24 listener.onBlockManagerRemoved(blockManagerRemoved) 25 case unpersistRDD: SparkListenerUnpersistRDD => 26 listener.onUnpersistRDD(unpersistRDD) 27 case applicationStart: SparkListenerApplicationStart => 28 listener.onApplicationStart(applicationStart) 29 case applicationEnd: SparkListenerApplicationEnd => 30 listener.onApplicationEnd(applicationEnd) 31 case metricsUpdate: SparkListenerExecutorMetricsUpdate => 32 listener.onExecutorMetricsUpdate(metricsUpdate) 33 case executorAdded: SparkListenerExecutorAdded => 34 listener.onExecutorAdded(executorAdded) 35 case executorRemoved: SparkListenerExecutorRemoved => 36 listener.onExecutorRemoved(executorRemoved) 37 case executorBlacklistedForStage: SparkListenerExecutorBlacklistedForStage => 38 listener.onExecutorBlacklistedForStage(executorBlacklistedForStage) 39 case nodeBlacklistedForStage: SparkListenerNodeBlacklistedForStage => 40 listener.onNodeBlacklistedForStage(nodeBlacklistedForStage) 41 case executorBlacklisted: SparkListenerExecutorBlacklisted => 42 listener.onExecutorBlacklisted(executorBlacklisted) 43 case executorUnblacklisted: SparkListenerExecutorUnblacklisted => 44 listener.onExecutorUnblacklisted(executorUnblacklisted) 45 case nodeBlacklisted: SparkListenerNodeBlacklisted => 46 listener.onNodeBlacklisted(nodeBlacklisted) 47 case nodeUnblacklisted: SparkListenerNodeUnblacklisted => 48 listener.onNodeUnblacklisted(nodeUnblacklisted) 49 case blockUpdated: SparkListenerBlockUpdated => 50 listener.onBlockUpdated(blockUpdated) 51 case speculativeTaskSubmitted: SparkListenerSpeculativeTaskSubmitted => 52 listener.onSpeculativeTaskSubmitted(speculativeTaskSubmitted) 53 case _ => listener.onOtherEvent(event) 54 } 55 }
然后去调用 listener 的相对应的方法。
就这样,事件总线上的消息事件被监听器消费了。