Hadoop学习总结：Map-Reduce的过程解析
原文地址：http://www.cnblogs.com/end/archive/2011/04/26/2029496.html
一、客户端
Map-Reduce的过程首先是由客户端提交一个任务开始的。
提交任务主要是通过JobClient.runJob(JobConf)静态函数实现的：
| public static RunningJob runJob(JobConf job) throws IOException { //首先生成一个JobClient对象 JobClient jc = new JobClient(job); …… //调用submitJob来提交一个任务 running = jc.submitJob(job); JobID jobId = running.getID(); …… while (true) { //while循环中不断得到此任务的状态，并打印到客户端console中 } return running; } |
其中JobClient的submitJob函数实现如下：
| public RunningJob submitJob(JobConf job) throws FileNotFoundException, InvalidJobConfException, IOException { //从JobTracker得到当前任务的id JobID jobId = jobSubmitClient.getNewJobId(); //准备将任务运行所需要的要素写入HDFS： //任务运行程序所在的jar封装成job.jar //任务所要处理的input split信息写入job.split //任务运行的配置项汇总写入job.xml Path submitJobDir = new Path(getSystemDir(), jobId.toString()); Path submitJarFile = new Path(submitJobDir, "job.jar"); Path submitSplitFile = new Path(submitJobDir, "job.split"); //此处将-libjars命令行指定的jar上传至HDFS configureCommandLineOptions(job, submitJobDir, submitJarFile); Path submitJobFile = new Path(submitJobDir, "job.xml"); …… //通过input format的格式获得相应的input split，默认类型为FileSplit InputSplit[] splits = job.getInputFormat().getSplits(job, job.getNumMapTasks());
// 生成一个写入流，将input split得信息写入job.split文件 FSDataOutputStream out = FileSystem.create(fs, submitSplitFile, new FsPermission(JOB_FILE_PERMISSION)); try { //写入job.split文件的信息包括：split文件头，split文件版本号，split的个数，接着依次写入每一个input split的信息。 //对于每一个input split写入：split类型名(默认FileSplit)，split的大小，split的内容(对于FileSplit，写入文件名，此split 在文件中的起始位置)，split的location信息(即在那个DataNode上)。 writeSplitsFile(splits, out); } finally { out.close(); } job.set("mapred.job.split.file", submitSplitFile.toString()); //根据split的个数设定map task的个数 job.setNumMapTasks(splits.length); // 写入job的配置信息入job.xml文件 out = FileSystem.create(fs, submitJobFile, new FsPermission(JOB_FILE_PERMISSION)); try { job.writeXml(out); } finally { out.close(); } //真正的调用JobTracker来提交任务 JobStatus status = jobSubmitClient.submitJob(jobId); …… } |
二、JobTracker
JobTracker作为一个单独的JVM运行，其运行的main函数主要调用有下面两部分：
- 调用静态函数startTracker(new JobConf())创建一个JobTracker对象
- 调用JobTracker.offerService()函数提供服务
在JobTracker的构造函数中，会生成一个taskScheduler成员变量，来进行Job的调度，默认为JobQueueTaskScheduler，也即按照FIFO的方式调度任务。
在offerService函数中，则调用taskScheduler.start()，在这个函数中，为JobTracker(也即taskScheduler的taskTrackerManager)注册了两个Listener：
- JobQueueJobInProgressListener jobQueueJobInProgressListener用于监控job的运行状态
- EagerTaskInitializationListener eagerTaskInitializationListener用于对Job进行初始化
EagerTaskInitializationListener中有一个线程JobInitThread，不断得到jobInitQueue中的JobInProgress对象，调用JobInProgress对象的initTasks函数对任务进行初始化操作。
在上一节中，客户端调用了JobTracker.submitJob函数，此函数首先生成一个JobInProgress对象，然后调用addJob函数，其中有如下的逻辑：
| synchronized (jobs) { synchronized (taskScheduler) { jobs.put(job.getProfile().getJobID(), job); //对JobTracker的每一个listener都调用jobAdded函数 for (JobInProgressListener listener : jobInProgressListeners) { listener.jobAdded(job); } } } |
EagerTaskInitializationListener的jobAdded函数就是向jobInitQueue中添加一个JobInProgress对象，于是自然触发了此Job的初始化操作，由JobInProgress得initTasks函数完成：
|
public synchronized void initTasks() throws IOException { …… //从HDFS中读取job.split文件从而生成input splits String jobFile = profile.getJobFile(); Path sysDir = new Path(this.jobtracker.getSystemDir()); FileSystem fs = sysDir.getFileSystem(conf); DataInputStream splitFile = fs.open(new Path(conf.get("mapred.job.split.file"))); JobClient.RawSplit[] splits; try { splits = JobClient.readSplitFile(splitFile); } finally { splitFile.close(); } //map task的个数就是input split的个数 numMapTasks = splits.length; //为每个map tasks生成一个TaskInProgress来处理一个input split maps = new TaskInProgress[numMapTasks]; for(int i=0; i < numMapTasks; ++i) { inputLength += splits[i].getDataLength(); maps[i] = new TaskInProgress(jobId, jobFile, splits[i], jobtracker, conf, this, i); } //对于map task，将其放入nonRunningMapCache，是一个Map<Node, List<TaskInProgress>>，也即对于map task来讲，其将会被分配到其input split所在的Node上。nonRunningMapCache将在JobTracker向TaskTracker分配map task的时候使用。 if (numMapTasks > 0) {
//创建reduce task this.reduces = new TaskInProgress[numReduceTasks]; for (int i = 0; i < numReduceTasks; i++) { reduces[i] = new TaskInProgress(jobId, jobFile, numMapTasks, i, jobtracker, conf, this); //reduce task放入nonRunningReduces，其将在JobTracker向TaskTracker分配reduce task的时候使用。 nonRunningReduces.add(reduces[i]); }
//创建两个cleanup task，一个用来清理map，一个用来清理reduce. cleanup = new TaskInProgress[2]; cleanup[0] = new TaskInProgress(jobId, jobFile, splits[0], jobtracker, conf, this, numMapTasks); cleanup[0].setJobCleanupTask(); cleanup[1] = new TaskInProgress(jobId, jobFile, numMapTasks, numReduceTasks, jobtracker, conf, this); cleanup[1].setJobCleanupTask(); //创建两个初始化 task，一个初始化map，一个初始化reduce. setup = new TaskInProgress[2]; setup[0] = new TaskInProgress(jobId, jobFile, splits[0], jobtracker, conf, this, numMapTasks + 1 ); setup[0].setJobSetupTask(); setup[1] = new TaskInProgress(jobId, jobFile, numMapTasks, numReduceTasks + 1, jobtracker, conf, this); setup[1].setJobSetupTask(); tasksInited.set(true);//初始化完毕 …… } |
三、TaskTracker
TaskTracker也是作为一个单独的JVM来运行的，在其main函数中，主要是调用了new TaskTracker(conf).run()，其中run函数主要调用了：
|
State offerService() throws Exception { long lastHeartbeat = 0; //TaskTracker进行是一直存在的 while (running && !shuttingDown) { …… long now = System.currentTimeMillis(); //每隔一段时间就向JobTracker发送heartbeat long waitTime = heartbeatInterval - (now - lastHeartbeat); if (waitTime > 0) { synchronized(finishedCount) { if (finishedCount[0] == 0) { finishedCount.wait(waitTime); } finishedCount[0] = 0; } } …… //发送Heartbeat到JobTracker，得到response HeartbeatResponse heartbeatResponse = transmitHeartBeat(now); …… //从Response中得到此TaskTracker需要做的事情 TaskTrackerAction[] actions = heartbeatResponse.getActions(); …… if (actions != null){ for(TaskTrackerAction action: actions) { if (action instanceof LaunchTaskAction) { //如果是运行一个新的Task，则将Action添加到任务队列中 addToTaskQueue((LaunchTaskAction)action); } else if (action instanceof CommitTaskAction) { CommitTaskAction commitAction = (CommitTaskAction)action; if (!commitResponses.contains(commitAction.getTaskID())) { commitResponses.add(commitAction.getTaskID()); } } else { tasksToCleanup.put(action); } } } } return State.NORMAL; } |
其中transmitHeartBeat主要逻辑如下：
|
private HeartbeatResponse transmitHeartBeat(long now) throws IOException { //每隔一段时间，在heartbeat中要返回给JobTracker一些统计信息 boolean sendCounters; if (now > (previousUpdate + COUNTER_UPDATE_INTERVAL)) { sendCounters = true; previousUpdate = now; } else { sendCounters = false; } …… //报告给JobTracker，此TaskTracker的当前状态 if (status == null) { synchronized (this) { status = new TaskTrackerStatus(taskTrackerName, localHostname, httpPort, cloneAndResetRunningTaskStatuses( sendCounters), failures, maxCurrentMapTasks, maxCurrentReduceTasks); } } …… //当满足下面的条件的时候，此TaskTracker请求JobTracker为其分配一个新的Task来运行： //当前TaskTracker正在运行的map task的个数小于可以运行的map task的最大个数 //当前TaskTracker正在运行的reduce task的个数小于可以运行的reduce task的最大个数 boolean askForNewTask; long localMinSpaceStart; synchronized (this) { askForNewTask = (status.countMapTasks() < maxCurrentMapTasks || status.countReduceTasks() < maxCurrentReduceTasks) && acceptNewTasks; localMinSpaceStart = minSpaceStart; } …… //向JobTracker发送heartbeat，这是一个RPC调用 HeartbeatResponse heartbeatResponse = jobClient.heartbeat(status, justStarted, askForNewTask, heartbeatResponseId); …… return heartbeatResponse; } |
四、JobTracker
当 JobTracker被RPC调用来发送heartbeat的时候，JobTracker的heartbeat(TaskTrackerStatus status,boolean initialContact, boolean acceptNewTasks, short responseId)函数被调用：
|
public synchronized HeartbeatResponse heartbeat(TaskTrackerStatus status, boolean initialContact, boolean acceptNewTasks, short responseId) throws IOException { …… String trackerName = status.getTrackerName(); …… short newResponseId = (short)(responseId + 1); …… HeartbeatResponse response = new HeartbeatResponse(newResponseId, null); List<TaskTrackerAction> actions = new ArrayList<TaskTrackerAction>(); //如果TaskTracker向JobTracker请求一个task运行 if (acceptNewTasks) { TaskTrackerStatus taskTrackerStatus = getTaskTracker(trackerName); if (taskTrackerStatus == null) { LOG.warn("Unknown task tracker polling; ignoring: " + trackerName); } else { //setup和cleanup的task优先级最高 List<Task> tasks = getSetupAndCleanupTasks(taskTrackerStatus); if (tasks == null ) { //任务调度器分配任务 tasks = taskScheduler.assignTasks(taskTrackerStatus); } if (tasks != null) { for (Task task : tasks) { //将任务放入actions列表，返回给TaskTracker expireLaunchingTasks.addNewTask(task.getTaskID()); actions.add(new LaunchTaskAction(task)); } } } } …… int nextInterval = getNextHeartbeatInterval(); response.setHeartbeatInterval(nextInterval); response.setActions( actions.toArray(new TaskTrackerAction[actions.size()])); …… return response; } |
默认的任务调度器为JobQueueTaskScheduler，其assignTasks如下：
|
public synchronized List<Task> assignTasks(TaskTrackerStatus taskTracker) throws IOException { ClusterStatus clusterStatus = taskTrackerManager.getClusterStatus(); int numTaskTrackers = clusterStatus.getTaskTrackers(); Collection<JobInProgress> jobQueue = jobQueueJobInProgressListener.getJobQueue(); int maxCurrentMapTasks = taskTracker.getMaxMapTasks(); int maxCurrentReduceTasks = taskTracker.getMaxReduceTasks(); int numMaps = taskTracker.countMapTasks(); int numReduces = taskTracker.countReduceTasks(); //计算剩余的map和reduce的工作量：remaining int remainingReduceLoad = 0; int remainingMapLoad = 0; synchronized (jobQueue) { for (JobInProgress job : jobQueue) { if (job.getStatus().getRunState() == JobStatus.RUNNING) { int totalMapTasks = job.desiredMaps(); int totalReduceTasks = job.desiredReduces(); remainingMapLoad += (totalMapTasks - job.finishedMaps()); remainingReduceLoad += (totalReduceTasks - job.finishedReduces()); } } } //计算平均每个TaskTracker应有的工作量，remaining/numTaskTrackers是剩余的工作量除以TaskTracker的个数。 int maxMapLoad = 0; int maxReduceLoad = 0; if (numTaskTrackers > 0) { maxMapLoad = Math.min(maxCurrentMapTasks, (int) Math.ceil((double) remainingMapLoad / numTaskTrackers)); maxReduceLoad = Math.min(maxCurrentReduceTasks, (int) Math.ceil((double) remainingReduceLoad / numTaskTrackers)); } ……
//map优先于reduce，当TaskTracker上运行的map task数目小于平均的工作量，则向其分配map task if (numMaps < maxMapLoad) { int totalNeededMaps = 0; synchronized (jobQueue) { for (JobInProgress job : jobQueue) { if (job.getStatus().getRunState() != JobStatus.RUNNING) { continue; } Task t = job.obtainNewMapTask(taskTracker, numTaskTrackers, taskTrackerManager.getNumberOfUniqueHosts()); if (t != null) { return Collections.singletonList(t); } …… } } } //分配完map task，再分配reduce task if (numReduces < maxReduceLoad) { int totalNeededReduces = 0; synchronized (jobQueue) { for (JobInProgress job : jobQueue) { if (job.getStatus().getRunState() != JobStatus.RUNNING || job.numReduceTasks == 0) { continue; } Task t = job.obtainNewReduceTask(taskTracker, numTaskTrackers, taskTrackerManager.getNumberOfUniqueHosts()); if (t != null) { return Collections.singletonList(t); } …… } } } return null; } |
从 上面的代码中我们可以知道，JobInProgress的obtainNewMapTask是用来分配map task的，其主要调用findNewMapTask，根据TaskTracker所在的Node从nonRunningMapCache中查找 TaskInProgress。JobInProgress的obtainNewReduceTask是用来分配reduce task的，其主要调用findNewReduceTask，从nonRunningReduces查找TaskInProgress。
五、TaskTracker
在 向JobTracker发送heartbeat后，返回的reponse中有分配好的任务LaunchTaskAction，将其加入队列，调用 addToTaskQueue，如果是map task则放入mapLancher(类型为TaskLauncher)，如果是reduce task则放入reduceLancher(类型为TaskLauncher)：
| private void addToTaskQueue(LaunchTaskAction action) { if (action.getTask().isMapTask()) { font-size: 13px; margin-top: 5px; margin-right: auto; margin-bottom: 5px; margin-left: auto; text-indent:
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