Hadoop之推测执行

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原创最新推荐文章于 2026-04-16 11:22:02 发布 1.1w 阅读 AI 写同款· GEO 优化 ›

讲一下对 Speculative Execution 的理解。是现代微处理器采用的一种关键性能优化技术，它允许处理器在不确定指令是否会被实际执行的情况下，提前执行指令，以充分利用处理器资源并减少等待时间。这一技术是当代计算机性能提升的核心驱动力之一，几乎存在于所有高性能CPU架构中。阅读详情

最近在测试环境跑任务，有一部分任务出现如下情况：

推测执行(Speculative Execution)是指在集群环境下运行MapReduce，可能是程序Bug，负载不均或者其他的一些问题，导致在一个JOB下的多个TASK速度不一致，比如有的任务已经完成，但是有些任务可能只跑了10%，根据木桶原理，这些任务将成为整个JOB的短板，如果集群启动了推测执行，这时为了最大限度的提高短板，Hadoop会为该task启动备份任务，让speculative task与原始task同时处理一份数据，哪个先运行完，则将谁的结果作为最终结果，并且在运行完成后Kill掉另外一个任务。

推测执行(Speculative Execution)是通过利用更多的资源来换取时间的一种优化策略，但是在资源很紧张的情况下，推测执行也不一定能带来时间上的优化，假设在测试环境中，DataNode总的内存空间是40G，每个Task可申请的内存设置为1G，现在有一个任务的输入数据为5G，HDFS分片为128M，这样Map Task的个数就40个，基本占满了所有的DataNode节点，如果还因为每些Map Task运行过慢，启动了Speculative Task，这样就可能会影响到Reduce Task的执行了，影响了Reduce的执行，自然而然就使整个JOB的执行时间延长。所以是否启用推测执行，如果能根据资源情况来决定，如果在资源本身就不够的情况下，还要跑推测执行的任务，这样会导致后续启动的任务无法获取到资源，以导致无法执行。

默认的推测执行器是：org.apache.hadoop.mapreduce.v2.app.speculate.DefaultSpeculator，如果要改变推测执行的策略，可以按照这个类重写，继承org.apache.hadoop.service.AbstractService，实现org.apache.hadoop.mapreduce.v2.app.speculate.Speculator接口。

DefaultSpeculator构造方法：

  public DefaultSpeculator
      (Configuration conf, AppContext context,
       TaskRuntimeEstimator estimator, Clock clock) {
    super(DefaultSpeculator.class.getName());

    this.conf = conf;
    this.context = context;
    this.estimator = estimator;
    this.clock = clock;
    this.eventHandler = context.getEventHandler();
    this.soonestRetryAfterNoSpeculate =
        conf.getLong(MRJobConfig.SPECULATIVE_RETRY_AFTER_NO_SPECULATE,
                MRJobConfig.DEFAULT_SPECULATIVE_RETRY_AFTER_NO_SPECULATE);
    this.soonestRetryAfterSpeculate =
        conf.getLong(MRJobConfig.SPECULATIVE_RETRY_AFTER_SPECULATE,
                MRJobConfig.DEFAULT_SPECULATIVE_RETRY_AFTER_SPECULATE);
    this.proportionRunningTasksSpeculatable =
        conf.getDouble(MRJobConfig.SPECULATIVECAP_RUNNING_TASKS,
                MRJobConfig.DEFAULT_SPECULATIVECAP_RUNNING_TASKS);
    this.proportionTotalTasksSpeculatable =
        conf.getDouble(MRJobConfig.SPECULATIVECAP_TOTAL_TASKS,
                MRJobConfig.DEFAULT_SPECULATIVECAP_TOTAL_TASKS);
    this.minimumAllowedSpeculativeTasks =
        conf.getInt(MRJobConfig.SPECULATIVE_MINIMUM_ALLOWED_TASKS,
                MRJobConfig.DEFAULT_SPECULATIVE_MINIMUM_ALLOWED_TASKS);
  }

mapreduce.map.speculative：如果为true则Map Task可以推测执行，即一个Map Task可以启动Speculative Task运行并行执行，该Speculative Task与原始Task同时处理同一份数据，谁先处理完，则将谁的结果作为最终结果。默认为true。

mapreduce.reduce.speculative：同上，默认值为true。

mapreduce.job.speculative.speculative-cap-running-tasks：能够推测重跑正在运行任务（单个JOB）的百分之几，默认是：0.1

mapreduce.job.speculative.speculative-cap-total-tasks：能够推测重跑全部任务（单个JOB）的百分之几，默认是：0.01

mapreduce.job.speculative.minimum-allowed-tasks：可以推测重新执行允许的最小任务数。默认是：10

首先，mapreduce.job.speculative.minimum-allowed-tasks和mapreduce.job.speculative.speculative-cap-total-tasks * 总任务数，取最大值。

然后，拿到上一步的值和mapreduce.job.speculative.speculative-cap-running-tasks * 正在运行的任务数，取最大值，该值就是猜测执行的运行的任务数

mapreduce.job.speculative.retry-after-no-speculate：等待时间（毫秒）做下一轮的猜测，如果没有任务，推测在这一轮。默认：1000（ms）

mapreduce.job.speculative.retry-after-speculate：等待时间（毫秒）做下一轮的猜测，如果有任务推测在这一轮。默认：15000（ms）

mapreduce.job.speculative.slowtaskthreshold：标准差，任务的平均进展率必须低于所有正在运行任务的平均值才会被认为是太慢的任务，默认值：1.0

启动服务：

  @Override
  protected void serviceStart() throws Exception {
    Runnable speculationBackgroundCore
        = new Runnable() {
            @Override
            public void run() {
              while (!stopped && !Thread.currentThread().isInterrupted()) {
                long backgroundRunStartTime = clock.getTime();
                try {
                  //计算推测，会根据Map和Reduce的任务类型，遍历mapContainerNeeds和reduceContainerNeeds，满足条件则启动推测任务。
                  int speculations = computeSpeculations(); 
                  long mininumRecomp
                      = speculations > 0 ? soonestRetryAfterSpeculate
                                         : soonestRetryAfterNoSpeculate;

                  long wait = Math.max(mininumRecomp,
                        clock.getTime() - backgroundRunStartTime);

                  if (speculations > 0) {
                    LOG.info("We launched " + speculations
                        + " speculations.  Sleeping " + wait + " milliseconds.");
                  }

                  Object pollResult
                      = scanControl.poll(wait, TimeUnit.MILLISECONDS);
                } catch (InterruptedException e) {
                  if (!stopped) {
                    LOG.error("Background thread returning, interrupted", e);
                  }
                  return;
                }
              }
            }
          };
    speculationBackgroundThread = new Thread
        (speculationBackgroundCore, "DefaultSpeculator background processing");
    speculationBackgroundThread.start();

    super.serviceStart();
  }

最后我们看看源码，是如何启动一个推测任务的：

private int maybeScheduleASpeculation(TaskType type) {
    int successes = 0;

    long now = clock.getTime();

    ConcurrentMap<JobId, AtomicInteger> containerNeeds
        = type == TaskType.MAP ? mapContainerNeeds : reduceContainerNeeds;

    for (ConcurrentMap.Entry<JobId, AtomicInteger> jobEntry : containerNeeds.entrySet()) {//遍历所有的JOB
      // This race conditon is okay.  If we skip a speculation attempt we
      //  should have tried because the event that lowers the number of
      //  containers needed to zero hasn't come through, it will next time.
      // Also, if we miss the fact that the number of containers needed was
      //  zero but increased due to a failure it's not too bad to launch one
      //  container prematurely.
      if (jobEntry.getValue().get() > 0) {
        continue;
      }

      int numberSpeculationsAlready = 0;
      int numberRunningTasks = 0;

      // loop through the tasks of the kind
      Job job = context.getJob(jobEntry.getKey());

      Map<TaskId, Task> tasks = job.getTasks(type);//获取JOB的task

      int numberAllowedSpeculativeTasks
          = (int) Math.max(MINIMUM_ALLOWED_SPECULATIVE_TASKS,
                           PROPORTION_TOTAL_TASKS_SPECULATABLE * tasks.size());//上面有介绍

      TaskId bestTaskID = null;
      long bestSpeculationValue = -1L;

      // this loop is potentially pricey.
      // TODO track the tasks that are potentially worth looking at
      for (Map.Entry<TaskId, Task> taskEntry : tasks.entrySet()) {//遍历所有任务
        long mySpeculationValue = speculationValue(taskEntry.getKey(), now);//获取推测值

        if (mySpeculationValue == ALREADY_SPECULATING) {
          ++numberSpeculationsAlready;
        }

        if (mySpeculationValue != NOT_RUNNING) {
          ++numberRunningTasks;
        }

        if (mySpeculationValue > bestSpeculationValue) {
          bestTaskID = taskEntry.getKey();
          bestSpeculationValue = mySpeculationValue;
        }
      }
      numberAllowedSpeculativeTasks
          = (int) Math.max(numberAllowedSpeculativeTasks,
                           PROPORTION_RUNNING_TASKS_SPECULATABLE * numberRunningTasks);

      // If we found a speculation target, fire it off
      if (bestTaskID != null
          && numberAllowedSpeculativeTasks > numberSpeculationsAlready) {//允许的个数大于准备推测执行的个数，就开始创建推测运行任务
        addSpeculativeAttempt(bestTaskID);//发送一个T_ADD_SPEC_ATTEMPT事件，启动另外一个任务。
        ++successes;
      }
    }

    return successes;
  }

private long speculationValue(TaskId taskID, long now) {
    Job job = context.getJob(taskID.getJobId());
    Task task = job.getTask(taskID);
    Map<TaskAttemptId, TaskAttempt> attempts = task.getAttempts();
    long acceptableRuntime = Long.MIN_VALUE;
    long result = Long.MIN_VALUE;

    if (!mayHaveSpeculated.contains(taskID)) {//是否包含在推测运行的SET中
      acceptableRuntime = estimator.thresholdRuntime(taskID);//运行的阀值
      if (acceptableRuntime == Long.MAX_VALUE) {
        return ON_SCHEDULE;
      }
    }

    TaskAttemptId runningTaskAttemptID = null;

    int numberRunningAttempts = 0;

    for (TaskAttempt taskAttempt : attempts.values()) {
      if (taskAttempt.getState() == TaskAttemptState.RUNNING
          || taskAttempt.getState() == TaskAttemptState.STARTING) {//任务在运行状态下，或开始状态下
        if (++numberRunningAttempts > 1) {//重试超过一次的，直接返回，则numberSpeculationsAlready的值加1
          return ALREADY_SPECULATING;
        }
        runningTaskAttemptID = taskAttempt.getID();

        long estimatedRunTime = estimator.estimatedRuntime(runningTaskAttemptID);//估算的运行时间

        long taskAttemptStartTime
            = estimator.attemptEnrolledTime(runningTaskAttemptID);//任务的开始时间
        if (taskAttemptStartTime > now) {
          // This background process ran before we could process the task
          //  attempt status change that chronicles the attempt start
          return TOO_NEW;
        }

        long estimatedEndTime = estimatedRunTime + taskAttemptStartTime;//估算的运行时间+任务的开始时间，等于完成时间

        long estimatedReplacementEndTime
            = now + estimator.estimatedNewAttemptRuntime(taskID);//新开启一个任务的完成时间

        float progress = taskAttempt.getProgress();
        TaskAttemptHistoryStatistics data =
            runningTaskAttemptStatistics.get(runningTaskAttemptID);
        if (data == null) {
          runningTaskAttemptStatistics.put(runningTaskAttemptID,
            new TaskAttemptHistoryStatistics(estimatedRunTime, progress, now));
        } else {
          if (estimatedRunTime == data.getEstimatedRunTime()
              && progress == data.getProgress()) {
            // Previous stats are same as same stats
            if (data.notHeartbeatedInAWhile(now)) {
              // Stats have stagnated for a while, simulate heart-beat.
              TaskAttemptStatus taskAttemptStatus = new TaskAttemptStatus();
              taskAttemptStatus.id = runningTaskAttemptID;
              taskAttemptStatus.progress = progress;
              taskAttemptStatus.taskState = taskAttempt.getState();
              // Now simulate the heart-beat
              handleAttempt(taskAttemptStatus);
            }
          } else {
            // Stats have changed - update our data structure
            data.setEstimatedRunTime(estimatedRunTime);
            data.setProgress(progress);
            data.resetHeartBeatTime(now);
          }
        }

        if (estimatedEndTime < now) {//完成时间小于当前时间
          return PROGRESS_IS_GOOD;
        }

        if (estimatedReplacementEndTime >= estimatedEndTime) {//新开任务的完成时间小于或等于当前时间
          return TOO_LATE_TO_SPECULATE;
        }

        result = estimatedEndTime - estimatedReplacementEndTime;
      }
    }

    // If we are here, there's at most one task attempt.
    if (numberRunningAttempts == 0) {//任务没有运行
      return NOT_RUNNING;
    }



    if (acceptableRuntime == Long.MIN_VALUE) {
      acceptableRuntime = estimator.thresholdRuntime(taskID);
      if (acceptableRuntime == Long.MAX_VALUE) {
        return ON_SCHEDULE;
      }
    }

    return result;
  }

DefaultSpeculator依赖于一个执行时间估算器，默认采用了LegacyTaskRuntimeEstimator，此外，MRv2还提供了另外一个实现：ExponentiallySmoothedTaskRuntimeEstimator，该实现采用了平滑算法对结果进行平滑处理。