转自:http://blog.****.net/androidlushangderen/article/details/41477061
上次分析完JobTracker通过TaskScheduler如何把作业分配给TaskTracker,这次把目光 移动到TaskTracker上面。TaskTracker在这里其实是一个slave的从属关系。我在后面的分析会通过TaskTracker的执行流程,主要讲他的2个过程的分析1.作业启动执行2.与JobTracker的heatbeat的过程。2个过程都是非常的典型。
与JobTracker一样,TaskTracker也是作为一项服务所运行的,他也有自己的main函数入口。下面是一张全局的TaskTracker执行过程流程图:
jvmManager负责为每个Task分配一个java虚拟机环境让其执行,避免任务之间的干扰,TaskMemoryManager负责任务内存的监控,对于某些任务恶意消耗资源内存,会给予杀死此任务的处理。
1.TaskTracker任务启动
下面从main函数的入口开始分析一下TaskTracker的执行流程:
- /**
- * Start the TaskTracker, point toward the indicated JobTracker
- * taskTracker同样也是一个服务程序,main函数开始执行
- */
- public static void main(String argv[]) throws Exception {
- StringUtils.startupShutdownMessage(TaskTracker.class, argv, LOG);
- if (argv.length != 0) {
- System.out.println("usage: TaskTracker");
- System.exit(-1);
- }
- try {
- //初始化作业配置
- JobConf conf=new JobConf();
- // enable the server to track time spent waiting on locks
- ReflectionUtils.setContentionTracing
- (conf.getBoolean("tasktracker.contention.tracking", false));
- //初始化度量统计系统
- DefaultMetricsSystem.initialize("TaskTracker");
- //根据作业配置初始化TaskTracker
- TaskTracker tt = new TaskTracker(conf);
- //注册MBean,方便外界工具检测TaskTracker的状态
- MBeans.register("TaskTracker", "TaskTrackerInfo", tt);
- //执行TaskTracker服务主程序
- tt.run();
- } catch (Throwable e) {
- LOG.error("Can not start task tracker because "+
- StringUtils.stringifyException(e));
- System.exit(-1);
- }
- }
让后我们进入其中的执行主程序tt.run():
- /**
- * The server retry loop.
- * This while-loop attempts to connect to the JobTracker. It only
- * loops when the old TaskTracker has gone bad (its state is
- * stale somehow) and we need to reinitialize everything.
- */
- public void run() {
- try {
- getUserLogManager().start();
- //开启CleanUp清理线程
- startCleanupThreads();
- boolean denied = false;
- while (running && !shuttingDown && !denied) {
- boolean staleState = false;
- try {
- // This while-loop attempts reconnects if we get network errors
- while (running && !staleState && !shuttingDown && !denied) {
- try {
- //offerService()执行了核心的启动操作
- State osState = offerService();
- if (osState == State.STALE) {
- staleState = true;
- } else if (osState == State.DENIED) {
- denied = true;
- }
- ......
我们可以看到,这里通过while操作,循环进行服务操作,如果拒绝服务,则会shutdown中断服务,服务的主要操作又在offerService方法中:
- /**
- * Main service loop. Will stay in this loop forever.
- * 主要的循环服务操作
- */
- State offerService() throws Exception {
- .....
- // Send the heartbeat and process the jobtracker's directives
- //发送给JobTracker心跳包
- HeartbeatResponse heartbeatResponse = transmitHeartBeat(now);
- // Note the time when the heartbeat returned, use this to decide when to send the
- // next heartbeat
- lastHeartbeat = System.currentTimeMillis();
- ....
- //在这里获取了心跳回应中的action命令
- TaskTrackerAction[] actions = heartbeatResponse.getActions();
- if(LOG.isDebugEnabled()) {
- LOG.debug("Got heartbeatResponse from JobTracker with responseId: " +
- heartbeatResponse.getResponseId() + " and " +
- ((actions != null) ? actions.length : 0) + " actions");
- }
- if (reinitTaskTracker(actions)) {
- return State.STALE;
- }
- // resetting heartbeat interval from the response.
- heartbeatInterval = heartbeatResponse.getHeartbeatInterval();
- justStarted = false;
- justInited = false;
- if (actions != null){
- for(TaskTrackerAction action: actions) {
- if (action instanceof LaunchTaskAction) {
- //如果是执行Task任务指令,执行添加任务到任务队列中
- addToTaskQueue((LaunchTaskAction)action);
- } else if (action instanceof CommitTaskAction) {
- //如果是提交任务的指令,则执行后面的操作
- CommitTaskAction commitAction = (CommitTaskAction)action;
- if (!commitResponses.contains(commitAction.getTaskID())) {
- LOG.info("Received commit task action for " +
- commitAction.getTaskID());
- commitResponses.add(commitAction.getTaskID());
- }
- } else {
- //其他的指令一律添加到tasksToCleanup队列中等待被处理
- tasksToCleanup.put(action);
- }
- }
- }
- .....
在这里我省略了比较多的代码,把执行任务相关的核心操作保留了,在这里就开始执行了后面的和Task相关的很多操作了,当然这些任务都是通过收到JobTracker的心跳包Response来获得的,在通过获取里面的TaskTrackerAction命令来判断执行的。TaskTrackerAction里面包含了1枚举类,包括了以下的相关指令:
具体什么意思,看上面的英文解释就能理解了吧,上面代表了6种命令操作,我们侧重看第一个launch_task的命令执行,在上面的判断执行方法是addToTaskQueue();方法:
- private void addToTaskQueue(LaunchTaskAction action) {
- //任务类型加入到任务待执行的容器中
- if (action.getTask().isMapTask()) {
- mapLauncher.addToTaskQueue(action);
- } else {
- reduceLauncher.addToTaskQueue(action);
- }
- }
这里的mapLauncher,reduceLauncher的类型是TaskLauncher,他是一个线程类:
- class TaskLauncher extends Thread {
- private IntWritable numFreeSlots;
- private final int maxSlots;
- private List<TaskInProgress> tasksToLaunch;
- ....
也就是说,待执行的map,Reduce任务都是添加到taskToLauch中的,
- public void addToTaskQueue(LaunchTaskAction action) {
- //新建1个TIP,并加入tasksToLaunch列表
- synchronized (tasksToLaunch) {
- TaskInProgress tip = registerTask(action, this);
- tasksToLaunch.add(tip);
- //唤醒所有被tasksToLaunch wait的操作,说明此时有新的任务了
- tasksToLaunch.notifyAll();
- }
- }
加入之后唤醒相应的操作,这个就很好理解了,一定是在empty的时候被阻塞住了,
- public void run() {
- while (!Thread.interrupted()) {
- try {
- TaskInProgress tip;
- Task task;
- synchronized (tasksToLaunch) {
- while (tasksToLaunch.isEmpty()) {
- tasksToLaunch.wait();
- }
- //get the TIP
- tip = tasksToLaunch.remove(0);
- task = tip.getTask();
- LOG.info("Trying to launch : " + tip.getTask().getTaskID() +
- " which needs " + task.getNumSlotsRequired() + " slots");
- }
- //wait for free slots to run
- .....
- //got a free slot. launch the task
- startNewTask(tip);
到了startNewTask就是开始所谓的任务了。到此为止,TaskTracker的任务执行这条路,我们算彻底打通了,相关时序图如下:
2.Heateat过程
下面我们看另外一个流程,心跳机制。此过程的实现同样的主要是在offerService的循环操作中。首先第一步,判断是否到了发送心跳包的时间,因为心跳包是隔周期性的时间发送的,所以这里必须会进行判读:
- /**
- * Main service loop. Will stay in this loop forever.
- * 主要的循环服务操作
- */
- State offerService() throws Exception {
- long lastHeartbeat = System.currentTimeMillis();
- while (running && !shuttingDown) {
- try {
- long now = System.currentTimeMillis();
- // accelerate to account for multiple finished tasks up-front
- //判断上次心跳的时间+心跳等待时间是否已经到了当前时间,如果到了可以发送新的心跳包
- long remaining =
- (lastHeartbeat + getHeartbeatInterval(finishedCount.get())) - now;
- //如果还没到,时间有剩余,则要强行等待剩余的时间
- while (remaining > 0) {
- // sleeps for the wait time or
- // until there are *enough* empty slots to schedule tasks
- synchronized (finishedCount) {
- finishedCount.wait(remaining);
- // Recompute
- now = System.currentTimeMillis();
- remaining =
- (lastHeartbeat + getHeartbeatInterval(finishedCount.get())) - now;
- if (remaining <= 0) {
- // Reset count
- finishedCount.set(0);
- break;
- }
- }
- }
- .....
假设已经到达了发送时间了,会执行后面的操作,检测版本号,TaskTracker和JobTracker的版本号必须一致:
- .....
- // If the TaskTracker is just starting up:
- // 1. Verify the buildVersion
- // 2. Get the system directory & filesystem
- if(justInited) {
- //验证版本号,如果版本号不对,则返回拒绝状态
- String jobTrackerBV = jobClient.getBuildVersion();
- if(!VersionInfo.getBuildVersion().equals(jobTrackerBV)) {
- String msg = "Shutting down. Incompatible buildVersion." +
- "\nJobTracker's: " + jobTrackerBV +
- "\nTaskTracker's: "+ VersionInfo.getBuildVersion();
- LOG.error(msg);
- try {
- jobClient.reportTaskTrackerError(taskTrackerName, null, msg);
- } catch(Exception e ) {
- LOG.info("Problem reporting to jobtracker: " + e);
- }
- return State.DENIED;
- }
如果通过上述2个验证,基本上就达到了发送的条件了,下面就准备发送操作了:
- // Send the heartbeat and process the jobtracker's directives
- //发送给JobTracker心跳包
- HeartbeatResponse heartbeatResponse = transmitHeartBeat(now);
就是在上面这个方法中实现了发送的操作,此方法的返回值是JobTracker的心跳回复包,里面就包含着刚刚的TaskTrackerAction命令信息。我们进入transmitHeartBeat。之前分析过,心跳机制的有1个主要的作用就是汇报TaskTracker的资源使用情况和作业执行情况给JobTracker节点。以此可以让主节点可以进行资源调配。所以在上面的这个方法必不可少的操作是构建TaskTracker的Status状态信息。这个类包含的信息还比较多。下面是主要的此类的关系结构:
里面2大包含类ResourceStatus(TaskTracker资源使用情况),TaskTrackerHealthStatus(TaskTracker节点健康状况)。首先当然是新建一个Status了:
- /**
- * Build and transmit the heart beat to the JobTracker
- * 将TaskTracker自身的状态信息发送给JobTracker,并获得一个心跳包的回应
- * @param now current time
- * @return false if the tracker was unknown
- * @throws IOException
- */
- HeartbeatResponse transmitHeartBeat(long now) throws IOException {
- ....
- //
- // Check if the last heartbeat got through...
- // if so then build the heartbeat information for the JobTracker;
- // else resend the previous status information.
- //
- if (status == null) {
- synchronized (this) {
- status = new TaskTrackerStatus(taskTrackerName, localHostname,
- httpPort,
- cloneAndResetRunningTaskStatuses(
- sendCounters),
- failures,
- maxMapSlots,
- maxReduceSlots);
- }
后面就是各种获取节点CPU,内存等基本信息,这里就不列举了,不过这里提一点,对于TaskTracker是否还能运行任务,在这里是通过TaskTracker是否达到了它的maxSlot上限作为1个标准。一般1个Reduce Task占据1个slot单元,1个Map Task同样占据1个Slot单元,如果1个TaskTracker结点拥有好多slot单元,那么他就可以运行很多Task。
- //
- // Check if we should ask for a new Task
- // 检测TaskTracker是否需要一个新 Task任务
- //
- boolean askForNewTask;
- long localMinSpaceStart;
- synchronized (this) {
- //通过判断当前所占据的slots数量是否已经达到最大slot的数量作为标准
- askForNewTask =
- ((status.countOccupiedMapSlots() < maxMapSlots ||
- status.countOccupiedReduceSlots() < maxReduceSlots) &&
- acceptNewTasks);
- localMinSpaceStart = minSpaceStart;
- }
askForNewTask布尔类型就代表TaskTracker是否还能运行新的任务,封装好了这些Status信息之后,就要执行关键的发送步骤了:
- //
- // Xmit the heartbeat
- // 通过JobClient发送给JobTracker,并获得1个回复
- //
- HeartbeatResponse heartbeatResponse = jobClient.heartbeat(status,
- justStarted,
- justInited,
- askForNewTask,
- heartbeatResponseId);
是通过JobClient的方法发送的。得到的heartbeatResponse返回结果就是JobTracker结果了。至于里面JobClient具体怎么发送就不是本次分析的重点了,HeartBeat也分析完毕。同样看一下流程图:
总结
2个过程都是在offerService核心服务程序中执行的。了解完JobTracker和TaskTracker的工作原理,在聊了具体Task任务的执行的5个阶段,从微观Task细节的执行到宏观上作业调度的原理分析理解,的确对MapReduce计算模型的理解上升了许多的层次。