Hadoop Map/Reduce 执行流程关键代码JobClient.runJob(conf) | 运行job|-->JobClient jc = new JobClient(job);|-->RunningJob rj = jc.submitJob(job);|-->submitJobInternal(job);|-->int reduces = job.getNumReduceTasks();|-->JobContext context = new JobContext(job, jobId);|-->maps = writeOldSplits(job, submitSplitFile);|-->job.setNumMapTasks(maps);|-->job.writeXml(out);|-->JobStatus status = jobSubmitClient.submitJob(jobId);JobTracker.submitJob(JobId) |提交job|-->JobInProgress job = new JobInProgress(jobId, this, this.conf);|-->checkAccess(job, QueueManager.QueueOperation.SUBMIT_JOB); |检查权限|-->checkMemoryRequirements(job); |检查内存需求|-->addJob(jobId, job); |添加至job队列|-->jobs.put(job.getProfile().getJobID(), job);|--> for (JobInProgressListener listener : jobInProgressListeners) ｜添加至监听器，供调度使用|-->listener.jobAdded(job);JobTracker.heartbeat() |JobTracker启动后供TaskTracker以RPC方式来调用，返回Response集合|-->List<TaskTrackerAction> actions = new ArrayList<TaskTrackerAction>();|-->tasks = taskScheduler.assignTasks(taskTrackerStatus); |通过调度器选择合适的tasks|-->for (Task task : tasks)|-->expireLaunchingTasks.addNewTask(task.getTaskID());|-->actions.add(new LaunchTaskAction(task)); |实际actions还会添加commmitTask等|-->response.setHeartbeatInterval(nextInterval);|-->response.setActions(actions.toArray(new TaskTrackerAction[actions.size()]));|-->return response;TaskTracker.offerService |TaskTracker启动后通过offerservice()不断发心跳至JobTracker中|-->transmitHeartBeat()|-->HeartbeatResponse heartbeatResponse = jobClient.heartbeat(status, justStarted, justInited,askForNewTask, heartbeatResponseId);|-->TaskTrackerAction[] actions = heartbeatResponse.getActions();|-->for(TaskTrackerAction action: actions)|-->if (action instanceof LaunchTaskAction)|-->addToTaskQueue((LaunchTaskAction)action); |添加至执行Queue，根据map/reduce task分别添加|-->if (action.getTask().isMapTask()) {|-->mapLauncher.addToTaskQueue(action);|-->TaskInProgress tip = registerTask(action, this);|-->tasksToLaunch.add(tip);|-->tasksToLaunch.notifyAll(); |唤醒阻塞进程|-->else |-->reduceLauncher.addToTaskQueue(action);TaskLauncher.run()|--> while (tasksToLaunch.isEmpty()) |-->tasksToLaunch.wait();|-->tip = tasksToLaunch.remove(0);|-->startNewTask(tip);|-->localizeJob(tip);|-->launchTaskForJob(tip, new JobConf(rjob.jobConf)); |-->tip.setJobConf(jobConf);|-->tip.launchTask(); |TaskInProgress.launchTask()|-->this.runner = task.createRunner(TaskTracker.this, this); |区分map/reduce|-->this.runner.start();MapTaskRunner.run() |执行MapTask|-->File workDir = new File(lDirAlloc.getLocalPathToRead() |准备执行路径|-->String jar = conf.getJar(); |准备jar包|-->File jvm = new File(new File(System.getProperty("java.home"), "bin"), "java"); |获取jvm|-->vargs.add(Child.class.getName()); ｜添加参数，Child类作为main主函数启动|-->tracker.addToMemoryManager(t.getTaskID(), t.isMapTask(), conf, pidFile); |添加至内存管理|-->jvmManager.launchJvm(this, jvmManager.constructJvmEnv(setup,vargs,stdout,stderr,logSize, |统一纳入jvm管理器当中并启动workDir, env, pidFile, conf));|-->mapJvmManager.reapJvm(t, env); |区分map/reduce操作JvmManager.reapJvm() ||--> while (jvmIter.hasNext())|-->JvmRunner jvmRunner = jvmIter.next().getValue();|-->JobID jId = jvmRunner.jvmId.getJobId();|-->setRunningTaskForJvm(jvmRunner.jvmId, t);|-->spawnNewJvm(jobId, env, t);|-->JvmRunner jvmRunner = new JvmRunner(env,jobId); |-->jvmIdToRunner.put(jvmRunner.jvmId, jvmRunner);|-->jvmRunner.start(); |执行JvmRunner的run()方法|-->jvmRunner.run()|-->runChild(env);|-->List<String> wrappedCommand = TaskLog.captureOutAndError(env.setup, env.vargs, env.stdout, env.stderr, env.logSize, env.pidFile); |选取main函数|-->shexec.execute(); |执行|-->int exitCode = shexec.getExitCode(); ｜获取执行状态值|--> updateOnJvmExit(jvmId, exitCode, killed); ｜更新Jvm状态Child.main() 执行Task(map/reduce)|-->JVMId jvmId = new JVMId(firstTaskid.getJobID(),firstTaskid.isMap(),jvmIdInt);|-->TaskUmbilicalProtocol umbilical = (TaskUmbilicalProtocol)RPC.getProxy(TaskUmbilicalProtocol.class,TaskUmbilicalProtocol.versionID, address, defaultConf);|--> while (true) |-->JvmTask myTask = umbilical.getTask(jvmId);|-->task = myTask.getTask();|-->taskid = task.getTaskID();|-->TaskRunner.setupWorkDir(job);|-->task.run(job, umbilical); |以maptask为例|-->TaskReporter reporter = new TaskReporter(getProgress(), umbilical);|-->if (useNewApi)|-->runNewMapper(job, split, umbilical, reporter);|-->else|-->runOldMapper(job, split, umbilical, reporter);|-->inputSplit = (InputSplit) ReflectionUtils.newInstance(job.getClassByName(splitClass), job);|-->MapRunnable<INKEY,INVALUE,OUTKEY,OUTVALUE> runner = ReflectionUtils.newInstance(job.getMapRunnerClass(), job);|-->runner.run(in, new OldOutputCollector(collector, conf), reporter);MapRunner.run()|--> K1 key = input.createKey();|-->V1 value = input.createValue();|-->while (input.next(key, value)) |-->mapper.map(key, value, output, reporter);|--> if(incrProcCount) |-->reporter.incrCounter(SkipBadRecords.COUNTER_GROUP, |-->SkipBadRecords.COUNTER_MAP_PROCESSED_RECORDS, 1);|-->mapper.close();
