JobTracker作业启动过程分析
转自:http://blog.csdn.net/androidlushangderen/article/details/41356521
在Hadoop中,启动作业运行的方式有很多,可以用命令行格式把打包好后的作业提交还可以,用Hadoop的插件进行应用开发,在这么多的方式中,都会必经过一个流程,作业会以JobInProgress的形式提交到JobTracker中。什么叫JobTracker呢,也许有些人了解Hadoop只知道他的MapReduce计算模型,那个过程只是其中的Task执行的一个具体过程,比较微观上的流程,而JobTrack是一个比较宏观上的东西。涉及到作业的提交的过程。Hadoop遵循的是Master/Slave的架构,也就是主从关系,对应的就是JobTracker/TaskTracker,前者负责资源管理和作业调度,后者主要负责执行由前者分配过来的作业。这样说的话,简单明了。JobTracker里面的执行的过程很多,那就得从开头开始分析,也就是作业最最开始的提交流程开始。后面的分析我会结合MapReduce的代码穿插式的分析,便于大家理解。
其实在作业的提交状态之前,还不会到达JobTacker阶段的,首先是到了MapReduce中一个叫JobClient的类中。也就是说,比如用户通过bin/hadoop jar xxx.jar把打包的jar包上传到系统中时,首先会触发的就是JobClient.。
- public RunningJob submitJob(String jobFile) throws FileNotFoundException,
 - InvalidJobConfException,
 - IOException {
 - // Load in the submitted job details
 - JobConf job = new JobConf(jobFile);
 - return submitJob(job);
 - }
 
之后人家根据配置文件接着调用submitJob()方法
- public RunningJob submitJob(JobConf job) throws FileNotFoundException,
 - IOException {
 - try {
 - //又继续调用的是submitJobInternal方法
 - return submitJobInternal(job);
 - } catch (InterruptedException ie) {
 - throw new IOException("interrupted", ie);
 - } catch (ClassNotFoundException cnfe) {
 - throw new IOException("class not found", cnfe);
 - }
 - }
 
来到了submitJobInternal的主要方法了
- ...
 - jobCopy = (JobConf)context.getConfiguration();
 - // Create the splits for the job 为作业创建输入信息
 - FileSystem fs = submitJobDir.getFileSystem(jobCopy);
 - LOG.debug("Creating splits at " + fs.makeQualified(submitJobDir));
 - int maps = writeSplits(context, submitJobDir);
 - jobCopy.setNumMapTasks(maps);
 - // write "queue admins of the queue to which job is being submitted"
 - // to job file.
 - String queue = jobCopy.getQueueName();
 - AccessControlList acl = jobSubmitClient.getQueueAdmins(queue);
 - jobCopy.set(QueueManager.toFullPropertyName(queue,
 - QueueACL.ADMINISTER_JOBS.getAclName()), acl.getACLString());
 - // Write job file to JobTracker's fs
 - FSDataOutputStream out =
 - FileSystem.create(fs, submitJobFile,
 - new FsPermission(JobSubmissionFiles.JOB_FILE_PERMISSION));
 - try {
 - jobCopy.writeXml(out);
 - } finally {
 - out.close();
 - }
 - //
 - // Now, actually submit the job (using the submit name)
 - //
 - printTokens(jobId, jobCopy.getCredentials());
 - //所有信息配置完毕,作业的初始化工作完成,最后将通过RPC方式正式提交作业
 - status = jobSubmitClient.submitJob(
 - jobId, submitJobDir.toString(), jobCopy.getCredentials());
 - JobProfile prof = jobSubmitClient.getJobProfile(jobId);
 
在这里他会执行一些作业提交之前需要进行的初始化工作,最后会RPC调用远程的提交方法。下面是一个时序图
至此我们知道,我们作业已经从本地提交出去了,后面的事情就是JobTracker的事情了,这个时候我们直接会触发的是JobTacker的addJob()方法。
- private synchronized JobStatus addJob(JobID jobId, JobInProgress job)
 - throws IOException {
 - totalSubmissions++;
 - synchronized (jobs) {
 - synchronized (taskScheduler) {
 - jobs.put(job.getProfile().getJobID(), job);
 - //观察者模式,会触发每个监听器的方法
 - for (JobInProgressListener listener : jobInProgressListeners) {
 - listener.jobAdded(job);
 - }
 - }
 - }
 - myInstrumentation.submitJob(job.getJobConf(), jobId);
 - job.getQueueMetrics().submitJob(job.getJobConf(), jobId);
 - LOG.info("Job " + jobId + " added successfully for user '"
 - + job.getJobConf().getUser() + "' to queue '"
 - + job.getJobConf().getQueueName() + "'");
 - AuditLogger.logSuccess(job.getUser(),
 - Operation.SUBMIT_JOB.name(), jobId.toString());
 - return job.getStatus();
 - }
 
在这里设置了很多监听器,监听作业的一个情况。那么分析到这里,我们当然也也要顺便学习一下JobTracker的是怎么运行开始的呢。其实JobTracker是一个后台服务程序,他有自己的main方法入口执行地址。上面的英文是这么对此进行描述的:
- /**
 - * Start the JobTracker process. This is used only for debugging. As a rule,
 - * JobTracker should be run as part of the DFS Namenode process.
 - * JobTracker也是一个后台进程,伴随NameNode进程启动进行,main方法是他的执行入口地址
 - */
 - public static void main(String argv[]
 - ) throws IOException, InterruptedException
 
上面说的很明白,作为NameNode的附属进程操作,NameNode跟JonTracker一样,全局只有一个,也是Master/Slave的关系对应的是DataNode数据结点。这些是HDFS相关的东西了。
- public static void main(String argv[]
 - ) throws IOException, InterruptedException {
 - StringUtils.startupShutdownMessage(JobTracker.class, argv, LOG);
 - try {
 - if(argv.length == 0) {
 - //调用startTracker方法开始启动JobTracker
 - JobTracker tracker = startTracker(new JobConf());
 - //JobTracker初始化完毕,开启里面的各项线程服务
 - tracker.offerService();
 - }
 - else {
 - if ("-dumpConfiguration".equals(argv[0]) && argv.length == 1) {
 - dumpConfiguration(new PrintWriter(System.out));
 - }
 - else {
 - System.out.println("usage: JobTracker [-dumpConfiguration]");
 - System.exit(-1);
 - }
 - }
 - } catch (Throwable e) {
 - LOG.fatal(StringUtils.stringifyException(e));
 - System.exit(-1);
 - }
 - }
 
里面2个主要方法,初始化JobTracker,第二个开启服务方法。首先看startTracker(),最后会执行到new JobTracker()构造函数里面去了:
- JobTracker(final JobConf conf, String identifier, Clock clock, QueueManager qm)
 - throws IOException, InterruptedException {
 - .....
 - //初始化安全相关操作
 - secretManager =
 - new DelegationTokenSecretManager(secretKeyInterval,
 - tokenMaxLifetime,
 - tokenRenewInterval,
 - DELEGATION_TOKEN_GC_INTERVAL);
 - secretManager.startThreads();
 - ......
 - // Read the hosts/exclude files to restrict access to the jobtracker.
 - this.hostsReader = new HostsFileReader(conf.get("mapred.hosts", ""),
 - conf.get("mapred.hosts.exclude", ""));
 - //初始化ACL访问控制列表
 - aclsManager = new ACLsManager(conf, new JobACLsManager(conf), queueManager);
 - LOG.info("Starting jobtracker with owner as " +
 - getMROwner().getShortUserName());
 - // Create the scheduler
 - Class<? extends TaskScheduler> schedulerClass
 - = conf.getClass("mapred.jobtracker.taskScheduler",
 - JobQueueTaskScheduler.class, TaskScheduler.class);
 - //初始化Task任务调度器
 - taskScheduler = (TaskScheduler) ReflectionUtils.newInstance(schedulerClass, conf);
 - // Set service-level authorization security policy
 - if (conf.getBoolean(
 - ServiceAuthorizationManager.SERVICE_AUTHORIZATION_CONFIG, false)) {
 - ServiceAuthorizationManager.refresh(conf, new MapReducePolicyProvider());
 - }
 - int handlerCount = conf.getInt("mapred.job.tracker.handler.count", 10);
 - this.interTrackerServer =
 - RPC.getServer(this, addr.getHostName(), addr.getPort(), handlerCount,
 - false, conf, secretManager);
 - if (LOG.isDebugEnabled()) {
 - Properties p = System.getProperties();
 - for (Iterator it = p.keySet().iterator(); it.hasNext();) {
 - String key = (String) it.next();
 - String val = p.getProperty(key);
 - LOG.debug("Property '" + key + "' is " + val);
 - }
 - }
 
里面主要干了这么几件事:
1.初始化ACL访问控制列表数据
2.创建TaskSchedule任务调度器
3.得到DPC Server。
4.还有其他一些零零碎碎的操作....
然后第2个方法offService(),主要开启了各项服务;
- public void offerService() throws InterruptedException, IOException {
 - // Prepare for recovery. This is done irrespective of the status of restart
 - // flag.
 - while (true) {
 - try {
 - recoveryManager.updateRestartCount();
 - break;
 - } catch (IOException ioe) {
 - LOG.warn("Failed to initialize recovery manager. ", ioe);
 - // wait for some time
 - Thread.sleep(FS_ACCESS_RETRY_PERIOD);
 - LOG.warn("Retrying...");
 - }
 - }
 - taskScheduler.start();
 - .....
 - this.expireTrackersThread = new Thread(this.expireTrackers,
 - "expireTrackers");
 - //启动该线程的主要作用是发现和清理死掉的任务
 - this.expireTrackersThread.start();
 - this.retireJobsThread = new Thread(this.retireJobs, "retireJobs");
 - //启动该线程的作用是清理长时间驻留在内存中且已经执行完的任务
 - this.retireJobsThread.start();
 - expireLaunchingTaskThread.start();
 - if (completedJobStatusStore.isActive()) {
 - completedJobsStoreThread = new Thread(completedJobStatusStore,
 - "completedjobsStore-housekeeper");
 - //该线程的作用是把已经运行完成的任务的信息保存到HDFS中,以便后续的查询
 - completedJobsStoreThread.start();
 - }
 - // start the inter-tracker server once the jt is ready
 - this.interTrackerServer.start();
 - synchronized (this) {
 - state = State.RUNNING;
 - }
 - LOG.info("Starting RUNNING");
 - this.interTrackerServer.join();
 - LOG.info("Stopped interTrackerServer");
 - }
 
主要3大线程在这个方法里被开开启了,expireTrackersThread,retireJobsThread,completedJobsStoreThread,还有1个RPC服务的开启,interTrackerServer.start(),还有细节的操作就不列举出来了。好了JobTraker的close方法的流程刚刚好和以上的操作相反,之前启动过的线程统统关掉。
- void close() throws IOException {
 - //服务停止
 - if (this.infoServer != null) {
 - LOG.info("Stopping infoServer");
 - try {
 - this.infoServer.stop();
 - } catch (Exception ex) {
 - LOG.warn("Exception shutting down JobTracker", ex);
 - }
 - }
 - if (this.interTrackerServer != null) {
 - LOG.info("Stopping interTrackerServer");
 - this.interTrackerServer.stop();
 - }
 - if (this.expireTrackersThread != null && this.expireTrackersThread.isAlive()) {
 - LOG.info("Stopping expireTrackers");
 - //执行线程中断操作
 - this.expireTrackersThread.interrupt();
 - try {
 - //等待线程执行完毕再执行后面的操作
 - this.expireTrackersThread.join();
 - } catch (InterruptedException ex) {
 - ex.printStackTrace();
 - }
 - }
 - if (this.retireJobsThread != null && this.retireJobsThread.isAlive()) {
 - LOG.info("Stopping retirer");
 - this.retireJobsThread.interrupt();
 - try {
 - this.retireJobsThread.join();
 - } catch (InterruptedException ex) {
 - ex.printStackTrace();
 - }
 - }
 - if (taskScheduler != null) {
 - //调度器的方法终止
 - taskScheduler.terminate();
 - }
 - if (this.expireLaunchingTaskThread != null && this.expireLaunchingTaskThread.isAlive()) {
 - LOG.info("Stopping expireLaunchingTasks");
 - this.expireLaunchingTaskThread.interrupt();
 - try {
 - this.expireLaunchingTaskThread.join();
 - } catch (InterruptedException ex) {
 - ex.printStackTrace();
 - }
 - }
 - if (this.completedJobsStoreThread != null &&
 - this.completedJobsStoreThread.isAlive()) {
 - LOG.info("Stopping completedJobsStore thread");
 - this.completedJobsStoreThread.interrupt();
 - try {
 - this.completedJobsStoreThread.join();
 - } catch (InterruptedException ex) {
 - ex.printStackTrace();
 - }
 - }
 - if (jobHistoryServer != null) {
 - LOG.info("Stopping job history server");
 - try {
 - jobHistoryServer.shutdown();
 - } catch (Exception ex) {
 - LOG.warn("Exception shutting down Job History server", ex);
 - }
 - }
 - DelegationTokenRenewal.close();
 - LOG.info("stopped all jobtracker services");
 - return;
 - }
 
至此,JobTracker的执行过程总算有了一个了解了吧,不算太难。后面的过程分析。JobTracker是如何把任务进行分解和分配的,从宏观上去理解Hadoop的工作原理。下面是以上过程的一个时序图
                    
                
                
            
        
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