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MapReduce job在JobTracker初始化源码级分析
mapreduce job提交流程源码级分析(三)中已经说明用户最终调用JobTracker.submitJob方法来向JobTracker提交作业。而这个方法的核心提交方法是JobTracker.addJob(JobID jobId, JobInProgress job)方法,这个addJob方法会把Job提交到调度器(默认是JobQueueTaskScheduler)的监听器JobQueueJobInProgressListener和EagerTaskInitializationListener(本文只讨论默认调度器)中,使用方法jobAdded(JobInProgress job),JobQueueJobInProgressListener任务是监控各个JobInProcess生命周期中的变化;EagerTaskInitializationListener是发现有新Job后对其初始化的。
一、JobQueueJobInProgressListener.jobAdded(JobInProgress job)方法。就一句代码jobQueue.put(new JobSchedulingInfo(job.getStatus()), job),先构建一个JobSchedulingInfo对象,然后和JobInProgress对应起来放入jobQueue中。JobSchedulingInfo类维护这调度这个job必备的一些信息,比如优先级(默认是NORMAL)、JobID以及开始时间startTime。
二、EagerTaskInitializationListener.jobAdded(JobInProgress job)方法。
1 /** 2 * We add the JIP to the jobInitQueue, which is processed 3 * asynchronously to handle split-computation and build up 4 * the right TaskTracker/Block mapping. 5 */ 6 @Override 7 public void jobAdded(JobInProgress job) { 8 synchronized (jobInitQueue) { 9 jobInitQueue.add(job); //添加进List<JobInProgress> jobInitQueue 10 resortInitQueue(); 11 jobInitQueue.notifyAll(); //唤醒阻塞的进程 12 } 13 14 }
上面方法中resortInitQueue()方法主要是对jobInitQueue中JobInProcess进行排序,先按照优先级排序,相同的再按开始时间。EagerTaskInitializationListener.start()在调度器初始化时JobQueueTaskScheduler.start()就调用了,所以先于jobAdded方法调用。EagerTaskInitializationListener.start()代码如下:
1 public void start() throws IOException { 2 this.jobInitManagerThread = new Thread(jobInitManager, "jobInitManager"); 3 jobInitManagerThread.setDaemon(true); 4 this.jobInitManagerThread.start(); 5 }
start()方法会启动一个线程:JobInitManager。
1 ///////////////////////////////////////////////////////////////// 2 // Used to init new jobs that have just been created 3 ///////////////////////////////////////////////////////////////// 4 class JobInitManager implements Runnable { 5 6 public void run() { 7 JobInProgress job = null; 8 while (true) { 9 try { 10 synchronized (jobInitQueue) { 11 while (jobInitQueue.isEmpty()) { 12 jobInitQueue.wait(); 13 } 14 job = jobInitQueue.remove(0); 15 } 16 threadPool.execute(new InitJob(job)); 17 } catch (InterruptedException t) { 18 LOG.info("JobInitManagerThread interrupted."); 19 break; 20 } 21 } 22 LOG.info("Shutting down thread pool"); 23 threadPool.shutdownNow(); 24 } 25 } 26 27 class InitJob implements Runnable { 28 29 private JobInProgress job; 30 31 public InitJob(JobInProgress job) { 32 this.job = job; 33 } 34 35 public void run() { 36 ttm.initJob(job);//对应JobTracker的对应方法 37 } 38 }
JobInitManager线程的run方法是一个死循环始终监控jobInitQueue是否为空,不为空的话就取出0位置的JobInProgress,在InitJob线程中初始化:TaskTrackerManager.initJob(job)对应JobTracker的initJob方法。这里为什么会另起线程来初始化Job呢?原因很简单,就是可能jobInitQueue中同时会有很多JobInProgress,一个一个的初始化会比较慢,所以采用多线程的方式初始化。来看initJob方法的代码:
1 public void initJob(JobInProgress job) { 2 if (null == job) { 3 LOG.info("Init on null job is not valid"); 4 return; 5 } 6 7 try { 8 JobStatus prevStatus = (JobStatus)job.getStatus().clone(); 9 LOG.info("Initializing " + job.getJobID()); 10 job.initTasks(); //调用该实例的initTasks方 法,对job进行初始化 11 // Inform the listeners if the job state has changed 12 // Note : that the job will be in PREP state. 13 JobStatus newStatus = (JobStatus)job.getStatus().clone(); 14 if (prevStatus.getRunState() != newStatus.getRunState()) { 15 JobStatusChangeEvent event = 16 new JobStatusChangeEvent(job, EventType.RUN_STATE_CHANGED, prevStatus, 17 newStatus); 18 synchronized (JobTracker.this) { 19 updateJobInProgressListeners(event); 20 } 21 } 22 } catch (KillInterruptedException kie) { 23 // If job was killed during initialization, job state will be KILLED 24 LOG.error("Job initialization interrupted:\n" + 25 StringUtils.stringifyException(kie)); 26 killJob(job); 27 } catch (Throwable t) { 28 String failureInfo = 29 "Job initialization failed:\n" + StringUtils.stringifyException(t); 30 // If the job initialization is failed, job state will be FAILED 31 LOG.error(failureInfo); 32 job.getStatus().setFailureInfo(failureInfo); 33 failJob(job); 34 } 35 }
首先是获取初始化前的状态prevStatus;然后是job.initTasks()初始化;在获取初始化的后的状态newStatus;
job.initTasks()方法代码比较多,主要的工作是检查之后获取输入数据的分片信息TaskSplitMetaInfo[] splits = createSplits(jobId)这是去读的上传到HDFS中的文件job.splitmetainfo和job.split,要确保numMapTasks == splits.length,然后构建numMapTasks个TaskInProgress作为MapTask,