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MapReduce剖析笔记之四:TaskTracker通过心跳机制获取任务的流程

上一节分析到了JobTracker把作业从队列里取出来并进行了初始化,所谓的初始化,主要是获取了Map、Reduce任务的数量,并统计了哪些DataNode所在的服务器可以处理哪些Split等等,将这些信息缓存起来,但还没有进行实质的分配。等待TaskTracker跟自己通信。

TaskTracker一般运行于DataNode之上,下面是它的声明,可见,是一个线程类:

/*******************************************************
 * TaskTracker is a process that starts and tracks MR Tasks
 * in a networked environment.  It contacts the JobTracker
 * for Task assignments and reporting results.
 *
 *******************************************************/
public class TaskTracker implements MRConstants, TaskUmbilicalProtocol,
    Runnable, TaskTrackerMXBean
。。。。。

TaskTracker里面存在一个main入口,当启动TaskTracker时进入该方法:

  /**
   * Start the TaskTracker, point toward the indicated JobTracker
   */
  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 {
      JobConf conf=new JobConf();
      // enable the server to track time spent waiting on locks
      ReflectionUtils.setContentionTracing
        (conf.getBoolean("tasktracker.contention.tracking", false));
      DefaultMetricsSystem.initialize("TaskTracker");
      TaskTracker tt = new TaskTracker(conf);
      MBeans.register("TaskTracker", "TaskTrackerInfo", tt);
      tt.run();
    } catch (Throwable e) {
      LOG.error("Can not start task tracker because "+
                StringUtils.stringifyException(e));
      System.exit(-1);
    }
  }
  

可见,在里面会执行TaskTracker tt = new TaskTracker(conf);创建一个TaskTracker对象,并利用tt.run()进入其run方法:

  /**
   * 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();
      startCleanupThreads();
      boolean denied = false;
      while (running && !shuttingDown) {
        boolean staleState = false;
        try {
          // This while-loop attempts reconnects if we get network errors
          while (running && !staleState && !shuttingDown && !denied) {
            try {
              State osState = offerService();
              ............

前面是获得写日志管理器,开启清理线程等操作。之后则进入offerService这个方法,这个方法比较长,offerService基本就是一个永远的循环,这从开始的代码可以看出:

  /**
   * Main service loop.  Will stay in this loop forever.
   */
  State offerService() throws Exception {
    long lastHeartbeat = System.currentTimeMillis();

    while (running && !shuttingDown) {
。。。。
        // Send the heartbeat and process the jobtracker's directives
        HeartbeatResponse heartbeatResponse = transmitHeartBeat(now);
。。。。

在这个方法里,只要TaskTracker还没有停止,就会一直循环,在循环里面,如果超过了心跳间隔,就会执行transmitHeartBeat这个心跳方法。我们逐步来分析。

首先TaskTracker会往JobTracker请求,查看版本信息和系统目录等。这里采用的也是RPC机制,并且与前面各节分析的客户端的机制相同,TaskTracker里面也包含一个jobClient,其类型是InterTrackerProtocol,这个协议其实和客户端那个JobSubmissionProtocol类似,都是一个继承于VersionedProtocol的接口,其定义为:

interface InterTrackerProtocol extends VersionedProtocol {
  HeartbeatResponse heartbeat(TaskTrackerStatus status, 
                              boolean restarted, 
                              boolean initialContact,
                              boolean acceptNewTasks,
                              short responseId)
    throws IOException;
  
  public String getFilesystemName() throws IOException;

  public void reportTaskTrackerError(String taskTracker,
                                     String errorClass,
                                     String errorMessage) throws IOException;

  TaskCompletionEvent[] getTaskCompletionEvents(JobID jobid, int fromEventId , int maxEvents) throws IOException;

  public String getSystemDir();

  public String getVIVersion() throws IOException;
}

这里面的方法实际上都由JobTracker实现,这可以从JobTracker的定义看出来,它实现了InterTrackerProtocol、JobSubmissionProtocol等接口,同时为客户端、TaskTracker等提供RPC服务:

public class JobTracker implements MRConstants, InterTrackerProtocol,
    JobSubmissionProtocol, TaskTrackerManager, RefreshUserMappingsProtocol,
    RefreshAuthorizationPolicyProtocol, AdminOperationsProtocol,
    JobTrackerMXBean
。。。。。

 利用这些RPC服务,TaskTracker如果是第一次启动,会查看版本信息和系统目录信息(后面再执行offerService时则跳过这些步骤):

        // If the TaskTracker is just starting up:
        // 1. Verify the versions matches with the JobTracker
        // 2. Get the system directory & filesystem
        if(justInited) {
          String jtBuildVersion = jobClient.getBuildVersion();
          String jtVersion = jobClient.getVIVersion();
          。。。。。。。。。
          String dir = jobClient.getSystemDir();
          。。。。。。。。。
          systemDirectory = new Path(dir);
          systemFS = systemDirectory.getFileSystem(fConf);
        }

之后,会进行心跳方法transmitHeartBeat,这个方法也比较长,也分为几步,首先会判断是否存在任务状态的对象,如果不存在就创建一个:

    if (status == null) {
      synchronized (this) {
        status = new TaskTrackerStatus(taskTrackerName, localHostname, 
                                       httpPort, 
                                       cloneAndResetRunningTaskStatuses(
                                         sendCounters), 
                                       taskFailures,
                                       localStorage.numFailures(),
                                       maxMapSlots,
                                       maxReduceSlots); 
      }
    } else {
      LOG.info("Resending 'status' to '" + jobTrackAddr.getHostName() +
               "' with reponseId '" + heartbeatResponseId);
    }

TaskTrackerStatus记录了TaskTracker当前的任务状态,可以看出,里面包含两个参数:maxMapSlots、maxReduceSlots,分别代表了本机可以最多执行的Map任务和Reduce任务数量,这两个数量是配置的,从代码里看出,默认都是2:

    maxMapSlots = conf.getInt( "mapred.tasktracker.map.tasks.maximum", 2);
    maxReduceSlots = conf.getInt( "mapred.tasktracker.reduce.tasks.maximum", 2);

那么,在初始化TaskTrackerStatus的时候,就把这两个参数记录下来。另外,从TaskTracker与JobTracker之间的心跳RPC服务来看,TaskTrackerStatus对象会被传递至JobTracker,供其分配任务作为参考:

  HeartbeatResponse heartbeat(TaskTrackerStatus status, 
                              boolean restarted, 
                              boolean initialContact,
                              boolean acceptNewTasks,
                              short responseId)
    throws IOException;

接下来,TaskTracker准备给TaskTrackerStatus对象赋值,然后传递至JobTracker。在赋值时,首先判断下是否能接收新任务,通过判断当前的任务数量是否超过了配置的最大数量得到,如果可以接收新任务,则把本机的内存信息等写入到TaskTrackerStatus相关的变量中:

    //
    // Check if we should ask for a new Task
    //
    boolean askForNewTask;
    long localMinSpaceStart;
    synchronized (this) {
      askForNewTask = 
        ((status.countOccupiedMapSlots() < maxMapSlots || 
          status.countOccupiedReduceSlots() < maxReduceSlots) && 
         acceptNewTasks); 
      localMinSpaceStart = minSpaceStart;
    }
    if (askForNewTask) {
      askForNewTask = enoughFreeSpace(localMinSpaceStart);
      long freeDiskSpace = getFreeSpace();
      long totVmem = getTotalVirtualMemoryOnTT();
      long totPmem = getTotalPhysicalMemoryOnTT();
      long availableVmem = getAvailableVirtualMemoryOnTT();
      long availablePmem = getAvailablePhysicalMemoryOnTT();
      long cumuCpuTime = getCumulativeCpuTimeOnTT();
      long cpuFreq = getCpuFrequencyOnTT();
      int numCpu = getNumProcessorsOnTT();
      float cpuUsage = getCpuUsageOnTT();

      status.getResourceStatus().setAvailableSpace(freeDiskSpace);
      status.getResourceStatus().setTotalVirtualMemory(totVmem);
      status.getResourceStatus().setTotalPhysicalMemory(totPmem);
      status.getResourceStatus().setMapSlotMemorySizeOnTT(
          mapSlotMemorySizeOnTT);
      status.getResourceStatus().setReduceSlotMemorySizeOnTT(
          reduceSlotSizeMemoryOnTT);
      status.getResourceStatus().setAvailableVirtualMemory(availableVmem); 
      status.getResourceStatus().setAvailablePhysicalMemory(availablePmem);
      status.getResourceStatus().setCumulativeCpuTime(cumuCpuTime);
      status.getResourceStatus().setCpuFrequency(cpuFreq);
      status.getResourceStatus().setNumProcessors(numCpu);
      status.getResourceStatus().setCpuUsage(cpuUsage);
    }

这些资源信息包括物理内存、虚拟内存、Map/Reduce任务槽的内存占用、累积的CPU时间、CPU频率、CPU数量、使用率等各种资源状态。

另外,还进行了健康检查,把信息也记录在里面,这里用一个TaskTrackerHealthStatus记录健康状态。这里进行健康检查实际上让系统运行一个脚本:

    TaskTrackerHealthStatus healthStatus = status.getHealthStatus();
    synchronized (this) {
      if (healthChecker != null) {
        healthChecker.setHealthStatus(healthStatus);
      } else {
        healthStatus.setNodeHealthy(true);
        healthStatus.setLastReported(0L);
        healthStatus.setHealthReport("");
      }
    }

记录了这些信息后,就执行心跳方法:

    HeartbeatResponse heartbeatResponse = jobClient.heartbeat(status, 
                                                              justStarted,
                                                              justInited,
                                                              askForNewTask, 
                                                              heartbeatResponseId);

前面看到,jobClient实际上是一个接口,当调用该方法的时候,利用动态代理,会将该方法交由JobTracker端的同名方法执行。

这个方法较为复杂,我们分几个步骤分别分析。

 

1、检查TaskTracker的合法性

JobTracker会首先检查该TaskTracker的合法性:

    // Make sure heartbeat is from a tasktracker allowed by the jobtracker.
    if (!acceptTaskTracker(status)) {
      throw new DisallowedTaskTrackerException(status);
    }

  /**
   * Returns true if the tasktracker is in the hosts list and 
   * not in the exclude list. 
   */
  private boolean acceptTaskTracker(TaskTrackerStatus status) {
    return (inHostsList(status) && !inExcludedHostsList(status));
  }  

inHostsList和inExcludedHostsList的代码为:

  /**
   * Return if the specified tasktracker is in the hosts list, 
   * if one was configured.  If none was configured, then this 
   * returns true.
   */
  private boolean inHostsList(TaskTrackerStatus status) {
    Set<String> hostsList = hostsReader.getHosts();
    return (hostsList.isEmpty() || hostsList.contains(status.getHost()));
  }

  /**
   * Return if the specified tasktracker is in the exclude list.
   */
  private boolean inExcludedHostsList(TaskTrackerStatus status) {
    Set<String> excludeList = hostsReader.getExcludedHosts();
    return excludeList.contains(status.getHost());
  }

JobTracker里面存在一个HostsFileReader对象hostsReader,用于记录是否合法的TaskTracker,该对象在JobTracker创建时会被创建出来,该对象存在两个队列,其定义为:

// Keeps track of which datanodes/tasktrackers are allowed to connect to the 
// namenode/jobtracker.
public class HostsFileReader {
  private Set<String> includes;
  private Set<String> excludes;
  private String includesFile;
  private String excludesFile;
  。。。。。。

在这个对象里,includes表示可以访问的host列表,excludes则表示不可访问的host列表,最初,这两个列表的内容是根据两个配置文件生成的:

    // Read the hosts/exclude files to restrict access to the jobtracker.
    this.hostsReader = new HostsFileReader(conf.get("mapred.hosts", ""),
                                           conf.get("mapred.hosts.exclude", ""));

 

2.更新TaskTracker的健康状态

如果是一个合法的TaskTracker,接下来还会检查是否在灰名单里,对于JobTracker而言,不是每个TaskTracker都适合分配任务,比如有的TaskTracker心跳间隔超时了, 出现过故障等等,JobTracker使用一个FaultyTrackersInfo对象记录这些TaskTracker:

  // statistics about TaskTrackers with faults; may lead to graylisting
  private FaultyTrackersInfo faultyTrackers = new FaultyTrackersInfo();

其检查代码为:

    // First check if the last heartbeat response got through
    String trackerName = status.getTrackerName();
    long now = clock.getTime();
    if (restarted) {
      faultyTrackers.markTrackerHealthy(status.getHost());
    } else {
      faultyTrackers.checkTrackerFaultTimeout(status.getHost(), now);
    }

restarted是TaskTracker通过heartbeat方法传递过来的,这说明如果TaskTracker是重启了的,则认为该TaskTracker处于健康状态,进行标记:

    /**
     * Removes the tracker from the blacklist, graylist, and
     * potentially-faulty list, when it is restarted.
     * 
     * Assumes JobTracker is locked on the entry.
     * 
     * @param hostName
     */
    void markTrackerHealthy(String hostName) {
      synchronized (potentiallyFaultyTrackers) {
        FaultInfo fi = potentiallyFaultyTrackers.remove(hostName);
        if (fi != null) {
          // a tracker can be both blacklisted and graylisted, so check both
          if (fi.isGraylisted()) {
            LOG.info("Marking " + hostName + " healthy from graylist");
            decrGraylistedTrackers(getNumTaskTrackersOnHost(hostName));
          }
          if (fi.isBlacklisted()) {
            LOG.info("Marking " + hostName + " healthy from blacklist");
            addHostCapacity(hostName);
          }
          // no need for fi.unBlacklist() for either one:  fi is already gone
        }
      }
    }

从上面的代码看出,对于刚刚重启的主机,把它从出错的TaskTracker集合potentiallyFaultyTrackers里面删除。并更新numGraylistedTrackers、numBlacklistedTrackers等数量以及totalMapTaskCapacity、totalReduceTaskCapacity的数量。

对于不是重启的主机,检查是否应该放入黑名单:

    void checkTrackerFaultTimeout(String hostName, long now) {
      synchronized (potentiallyFaultyTrackers) {
        FaultInfo fi = potentiallyFaultyTrackers.get(hostName);
        // getFaultCount() auto-rotates the buckets, clearing out the oldest
        // as needed, before summing the faults:
        if (fi != null && fi.getFaultCount(now) < TRACKER_FAULT_THRESHOLD) {
          unBlacklistTracker(hostName, ReasonForBlackListing.EXCEEDING_FAILURES,
                             true, now);
        }
      }
    }

所谓黑名单,其判断标准是当前TaskTracker所在的主机是否发生了超过了TRACKER_FAULT_THRESHOLD=4次失败的情况。按照Hadoop代码,应该是以滑窗方式检测一个主机是否应该放入黑名单,从下面的说明可以看出,如果在3小时范围内,出现了大于4次失败,则应该放入黑名单。

  // Fault threshold (number occurring within TRACKER_FAULT_TIMEOUT_WINDOW)
  // to consider a task tracker bad enough to blacklist heuristically.  This
  // is functionally the same as the older "MAX_BLACKLISTS_PER_TRACKER" value.
  private int TRACKER_FAULT_THRESHOLD; // = 4;

  // Width of overall fault-tracking sliding window (in minutes). (Default
  // of 24 hours matches previous "UPDATE_FAULTY_TRACKER_INTERVAL" value that
  // was used to forgive a single fault if no others occurred in the interval.)
  private int TRACKER_FAULT_TIMEOUT_WINDOW; // = 180 (3 hours)

 

3.判断JobTracker自身的故障、心跳是否重复等

JobTracker会保存一个映射表,记录了各个TaskTracker上一次的心跳响应:

  // (trackerID --> last sent HeartBeatResponse)
  Map<String, HeartbeatResponse> trackerToHeartbeatResponseMap = 
    new TreeMap<String, HeartbeatResponse>();

从该表中取出对应于该TaskTracker的上一次心跳响应:

    HeartbeatResponse prevHeartbeatResponse =
      trackerToHeartbeatResponseMap.get(trackerName);

如果TaskTracker不是第一次联系JobTracker(这个信息TaskTracker通过心跳传递过来),且JobTracker没有保存着对应于该TaskTracker的上一次响应,一种可能是JobTracker刚刚重启了,重启与否可以判断出来;如果也不是重启,那表明出现了严重问题。出现这种情况时,JobTracker也不进行任务分配了,直接给TaskTracker返回一个响应。如下面代码所示:

      // If this isn't the 'initial contact' from the tasktracker,
      // there is something seriously wrong if the JobTracker has
      // no record of the 'previous heartbeat'; if so, ask the 
      // tasktracker to re-initialize itself.
      if (prevHeartbeatResponse == null) {
        // This is the first heartbeat from the old tracker to the newly 
        // started JobTracker
        if (hasRestarted()) {
          addRestartInfo = true;
          // inform the recovery manager about this tracker joining back
          recoveryManager.unMarkTracker(trackerName);
        } else {
          // Jobtracker might have restarted but no recovery is needed
          // otherwise this code should not be reached
          LOG.warn("Serious problem, cannot find record of 'previous' " +
                   "heartbeat for '" + trackerName + 
                   "'; reinitializing the tasktracker");
          return new HeartbeatResponse(responseId, 
              new TaskTrackerAction[] {new ReinitTrackerAction()});
        }

如果JobTracker确实也保存了对应于该TaskTracker的上一次响应,那么检查一下两者是否是重复,JobTracker理论上会为每次心跳响应赋一个递增的值:

        // It is completely safe to not process a 'duplicate' heartbeat from a 
        // {@link TaskTracker} since it resends the heartbeat when rpcs are 
        // lost see {@link TaskTracker.transmitHeartbeat()};
        // acknowledge it by re-sending the previous response to let the 
        // {@link TaskTracker} go forward. 
        if (prevHeartbeatResponse.getResponseId() != responseId) {
          LOG.info("Ignoring 'duplicate' heartbeat from '" + 
              trackerName + "'; resending the previous 'lost' response");
          return prevHeartbeatResponse;
        }

 

4、处理心跳,更新TaskTracker状态等:

当合法性检查通过后,JobTracker会更新自己记录的关于TaskTracker的状态:

    // Process this heartbeat 
    short newResponseId = (short)(responseId + 1);
    status.setLastSeen(now);
    if (!processHeartbeat(status, initialContact, now)) {
      if (prevHeartbeatResponse != null) {
        trackerToHeartbeatResponseMap.remove(trackerName);
      }
      return new HeartbeatResponse(newResponseId, 
                   new TaskTrackerAction[] {new ReinitTrackerAction()});
    }

首先递增响应ID,调用processHeartbeat更新TaskTracker的状态,如果不成功,则将其从trackerToHeartbeatResponseMap中移除;但仍然返回一个响应给TaskTracker。

在processHeartbeat方法里,主要是更新JobTracker自己的数据结构:

  /**
   * Process incoming heartbeat messages from the task trackers.
   */
  private synchronized boolean processHeartbeat(
                                 TaskTrackerStatus trackerStatus, 
                                 boolean initialContact,
                                 long timeStamp) throws UnknownHostException {
。。。。。。。。。。。。
    updateTaskStatuses(trackerStatus);
    updateNodeHealthStatus(trackerStatus, timeStamp);
    
    return true;
  }

JobTracker里存在要执行的Map和Reduce Task,每个Task的实例不止一个,一方面是考虑到容错,当一个机器挂掉的时候,让其它机器来执行这个任务,同时,在多个机器上分配,让他们执行一样的任务,这样谁先执行完,这个任务也就执行完,避免某些机器出了点问题,速度太慢,影响了整体的进度,所以,一个任务Task实际上有多个实例,称为TaskAttemptID,TaskAttemptID的格式类似这样的格式:

attempt_200707121733_0003_m_000005_0

这代表任务2007年7月12日17时33分启动的JobTracker中第0003号作业的第000005号map task的第0号task attempt。

JobTracker会去查看TaskTracker传递过来的TaskAttemptID,如果该任务对应的Job都不存在了,表名执行完了等等,就会把它放入一个清空队列里面:

  void updateTaskStatuses(TaskTrackerStatus status) {
    String trackerName = status.getTrackerName();
    for (TaskStatus report : status.getTaskReports()) {
      report.setTaskTracker(trackerName);
      TaskAttemptID taskId = report.getTaskID();

      JobInProgress job = getJob(taskId.getJobID());
      if (job == null) {
        // if job is not there in the cleanup list ... add it
        synchronized (trackerToJobsToCleanup) {
          Set<JobID> jobs = trackerToJobsToCleanup.get(trackerName);
          if (jobs == null) {
            jobs = new HashSet<JobID>();
            trackerToJobsToCleanup.put(trackerName, jobs);
          }
          jobs.add(taskId.getJobID());
        }
        continue;
      }
      。。。。。。

如果Job都没有完成初始化,那么也要将其清理掉:

      if (!job.inited()) {
        // if job is not yet initialized ... kill the attempt
        synchronized (trackerToTasksToCleanup) {
          Set<TaskAttemptID> tasks = trackerToTasksToCleanup.get(trackerName);
          if (tasks == null) {
            tasks = new HashSet<TaskAttemptID>();
            trackerToTasksToCleanup.put(trackerName, tasks);
          }
          tasks.add(taskId);
        }
        continue;
      }

另外,如果JobTracker是重启过的,那么还会把TaskTracker报告的任务信息加进来:

      TaskInProgress tip = taskidToTIPMap.get(taskId);
      // Check if the tip is known to the jobtracker. In case of a restarted
      // jt, some tasks might join in later
      if (tip != null || hasRestarted()) {
        if (tip == null) {
          tip = job.getTaskInProgress(taskId.getTaskID());
          job.addRunningTaskToTIP(tip, taskId, status, false);
        }
        
        // Update the job and inform the listeners if necessary
        JobStatus prevStatus = (JobStatus)job.getStatus().clone();
        // Clone TaskStatus object here, because JobInProgress
        // or TaskInProgress can modify this object and
        // the changes should not get reflected in TaskTrackerStatus.
        // An old TaskTrackerStatus is used later in countMapTasks, etc.
        job.updateTaskStatus(tip, (TaskStatus)report.clone());
        JobStatus newStatus = (JobStatus)job.getStatus().clone();
        
        // Update the listeners if an incomplete job completes
        if (prevStatus.getRunState() != newStatus.getRunState()) {
          JobStatusChangeEvent event = 
            new JobStatusChangeEvent(job, EventType.RUN_STATE_CHANGED, 
                                     prevStatus, newStatus);
          updateJobInProgressListeners(event);
        }
      } else {
        LOG.info("Serious problem.  While updating status, cannot find taskid " 
                 + report.getTaskID());
      }

 

5、分配任务:

在上面的操作都做完了以后,JobTracker开始分配任务,首先创建一个心跳响应,以及关于这个心跳响应的任务分配队列List<TaskTrackerAction>:

    // Initialize the response to be sent for the heartbeat
    HeartbeatResponse response = new HeartbeatResponse(newResponseId, null);
    List<TaskTrackerAction> actions = new ArrayList<TaskTrackerAction>();
    boolean isBlacklisted = faultyTrackers.isBlacklisted(status.getHost());
    // Check for new tasks to be executed on the tasktracker
    if (recoveryManager.shouldSchedule() && acceptNewTasks && !isBlacklisted) {
      TaskTrackerStatus taskTrackerStatus = getTaskTrackerStatus(trackerName);
      if (taskTrackerStatus == null) {
        LOG.warn("Unknown task tracker polling; ignoring: " + trackerName);
      } else {
        List<Task> tasks = getSetupAndCleanupTasks(taskTrackerStatus);
        if (tasks == null ) {
          tasks = taskScheduler.assignTasks(taskTrackers.get(trackerName));
        }
        if (tasks != null) {
          for (Task task : tasks) {
            expireLaunchingTasks.addNewTask(task.getTaskID());
            if(LOG.isDebugEnabled()) {
              LOG.debug(trackerName + " -> LaunchTask: " + task.getTaskID());
            }
            actions.add(new LaunchTaskAction(task));
          }
        }
      }
    }

如果该TaskTracker支持任务调度,且不在黑名单里面,则可以进行分配,核心的代码是:

          tasks = taskScheduler.assignTasks(taskTrackers.get(trackerName));

这个方法用于任务分配。taskScheduler是一个TaskScheduler对象,而TaskScheduler是一个抽象类,有几种实现,默认的是JobQueueTaskScheduler。关于任务分配过程,也比较复杂,留作后续分析,这里先假定已经分配了任务,并被加入到队列List<TaskTrackerAction>中。

 

6、将其他任务加入到队列中:

除了分配的任务,还需要把以下一些任务加入队列actions:

需要被kill的Task:

    // Check for tasks to be killed
    List<TaskTrackerAction> killTasksList = getTasksToKill(trackerName);
    if (killTasksList != null) {
      actions.addAll(killTasksList);
    }

需要被kill或清理的任务:

    // Check for jobs to be killed/cleanedup
    List<TaskTrackerAction> killJobsList = getJobsForCleanup(trackerName);
    if (killJobsList != null) {
      actions.addAll(killJobsList);
    }

需要保存输出的任务:

    // Check for tasks whose outputs can be saved
    List<TaskTrackerAction> commitTasksList = getTasksToSave(status);
    if (commitTasksList != null) {
      actions.addAll(commitTasksList);
    }

 

7、获得心跳响应,返回:

将任务队列actions加入到心跳响应:

    // calculate next heartbeat interval and put in heartbeat response
    int nextInterval = getNextHeartbeatInterval();
    response.setHeartbeatInterval(nextInterval);
    response.setActions(actions.toArray(new TaskTrackerAction[actions.size()]));
    
    // check if the restart info is req
    if (addRestartInfo) {
      response.setRecoveredJobs(recoveryManager.getJobsToRecover());
    }
        
    // Update the trackerToHeartbeatResponseMap
    trackerToHeartbeatResponseMap.put(trackerName, response);

 

至此,TaskTracker这个通过RPC访问的heartbeat服务方法执行结束了,JobTracker向TaskTracker返回了心跳响应,将要执行的任务信息返回给TaskTracker。

回到TaskTracker,上一部分分析到发出心跳,接下来就对响应进行解析了,继续offerService方法的后续分析:

首先要看一下,是否有任务需要清理:

        // Check if the map-event list needs purging
        Set<JobID> jobs = heartbeatResponse.getRecoveredJobs();
        if (jobs.size() > 0) {
          synchronized (this) {
            // purge the local map events list
            for (JobID job : jobs) {
              RunningJob rjob;
              synchronized (runningJobs) {
                rjob = runningJobs.get(job);          
                if (rjob != null) {
                  synchronized (rjob) {
                    FetchStatus f = rjob.getFetchStatus();
                    if (f != null) {
                      f.reset();
                    }
                  }
                }
              }
            }

            // Mark the reducers in shuffle for rollback
            synchronized (shouldReset) {
              for (Map.Entry<TaskAttemptID, TaskInProgress> entry 
                   : runningTasks.entrySet()) {
                if (entry.getValue().getStatus().getPhase() == Phase.SHUFFLE) {
                  this.shouldReset.add(entry.getKey());
                }
              }
            }
          }
        }

接下来获得所有任务信息:

        TaskTrackerAction[] actions = heartbeatResponse.getActions();

对于每个任务,如果需要启动,就加入到队列addToTaskQueue中:

        if (actions != null){ 
          for(TaskTrackerAction action: actions) {
            if (action instanceof LaunchTaskAction) {
              addToTaskQueue((LaunchTaskAction)action);
            } else if (action instanceof CommitTaskAction) {
              CommitTaskAction commitAction = (CommitTaskAction)action;
              if (!commitResponses.contains(commitAction.getTaskID())) {
                LOG.info("Received commit task action for " + 
                          commitAction.getTaskID());
                commitResponses.add(commitAction.getTaskID());
              }
            } else {
              addActionToCleanup(action);
            }
          }
        }

如果需要提交的,就进行要提交的队列中,否则加入到要清理的队列中。

之后,杀死那些很久没有反馈进度的任务:

        markUnresponsiveTasks();

当磁盘空间不够时,杀死某些任务以腾出空间:

        killOverflowingTasks();

此时,TaskTracker利用心跳从JobTracker获得了任务,并加入了自己的各个队列,有的是待启动的队列,有的是要提交的队列,有的是要清理的队列,这些队列里面的任务,会有其他线程来取了以后去执行。这一部分暂时告一段落。

 

上面我们遗留了一个问题,就是JobTracker在调用JobQueueTaskScheduler的任务分配方法时,是如何按照什么策略分配的还没有涉及。这个在下一博文里面进行分析。

另外,关于TaskTracker怎么从队列里取出任务继续启动JAVA虚拟机执行等过程,我们也留作后续博文分析研究。

 

posted on 2014-08-17 22:38  esingchan  阅读(1376)  评论(0编辑  收藏