(defn setup-ticks! [worker executor-data]
 (let [storm-conf (:storm-conf executor-data)
    tick-time-secs (storm-conf TOPOLOGY-TICK-TUPLE-FREQ-SECS)
    receive-queue (:receive-queue executor-data)
    context (:worker-context executor-data)]
  (when tick-time-secs
   (if (or (system-id? (:component-id executor-data))
       (and (= false (storm-conf TOPOLOGY-ENABLE-MESSAGE-TIMEOUTS))
          (= :spout (:type executor-data))))
    (log-message \"Timeouts disabled for executor \" (:component-id executor-data) \":\" (:executor-id executor-data))
    (schedule-recurring
     (:user-timer worker)
     tick-time-secs
     tick-time-secs
     (fn []
      (disruptor/publish
       receive-queue
       [[nil (TupleImpl. context [tick-time-secs] Constants/SYSTEM_TASK_ID Constants/SYSTEM_TICK_STREAM_ID)]]
       )))))))

public static boolean isTick(Tuple tuple) {
  return tuple != null
      && Constants.SYSTEM_COMPONENT_ID .equals(tuple.getSourceComponent())
      && Constants.SYSTEM_TICK_STREAM_ID.equals(tuple.getSourceStreamId());
}

  public String getComponentId(int taskId) {
    if(taskId==Constants.SYSTEM_TASK_ID) {
      return Constants.SYSTEM_COMPONENT_ID;
    } else {
      return _taskToComponent.get(taskId);
    }
  }

String spoutId = \"wordGenerator\";
  String counterId = \"counter\";
  String intermediateRankerId = \"intermediateRanker\";
  String totalRankerId = \"finalRanker\";
  // 这里，假设TestWordSpout就是我们发送话题tuple的源
  builder.setSpout(spoutId, new TestWordSpout(), 5);
  // RollingCountBolt的时间窗口为9秒钟，每3秒发送一次统计结果到下游
  builder.setBolt(counterId, new RollingCountBolt(9, 3), 4).fieldsGrouping(spoutId, new Fields(\"word\"));
  // IntermediateRankingsBolt，将完成部分聚合，统计出top-n的话题
  builder.setBolt(intermediateRankerId, new IntermediateRankingsBolt(TOP_N), 4).fieldsGrouping(counterId, new Fields(
    \"obj\"));
    // TotalRankingsBolt， 将完成完整聚合，统计出top-n的话题
  builder.setBolt(totalRankerId, new TotalRankingsBolt(TOP_N)).globalGrouping(intermediateRankerId);

 @Override
 public void execute(Tuple tuple) {
  if (TupleUtils.isTick(tuple)) {
   LOG.debug(\"Received tick tuple, triggering emit of current window counts\");
   // tick来了，将时间窗口内的统计结果发送，并让窗口滚动
   emitCurrentWindowCounts();
  }
  else {
   // 常规tuple，对话题计数即可
   countObjAndAck(tuple);
  }
 }

// obj即为话题，增加一个计数 count++
 // 注意，这里的速度基本取决于流的速度，可能每秒百万，也可能每秒几十.
 // 内存不足？ bolt可以scale-out.
 private void countObjAndAck(Tuple tuple) {
  Object obj = tuple.getValue(0);
  counter.incrementCount(obj);
  collector.ack(tuple);
 }
 
 // 将统计结果发送到下游
 private void emitCurrentWindowCounts() {
  Map<Object, Long> counts = counter.getCountsThenAdvanceWindow();
  int actualWindowLengthInSeconds = lastModifiedTracker.secondsSinceOldestModification();
  lastModifiedTracker.markAsModified();
  if (actualWindowLengthInSeconds != windowLengthInSeconds) {
   LOG.warn(String.format(WINDOW_LENGTH_WARNING_TEMPLATE, actualWindowLengthInSeconds, windowLengthInSeconds));
  }
  emit(counts, actualWindowLengthInSeconds);
 }

 public final void execute(Tuple tuple, BasicOutputCollector collector) {
  if (TupleUtils.isTick(tuple)) {
   getLogger().debug(\"Received tick tuple, triggering emit of current rankings\");
   // 将聚合并排序的结果发送到下游
   emitRankings(collector);
  }
  else {
   // 聚合并排序
   updateRankingsWithTuple(tuple);
  }
 }

// IntermediateRankingsBolt的聚合排序方法：
 @Override
 void updateRankingsWithTuple(Tuple tuple) {
  // 这一步，将话题、话题出现的次数提取出来
  Rankable rankable = RankableObjectWithFields.from(tuple);
  // 这一步，将话题出现的次数进行聚合，然后重排序所有话题
  super.getRankings().updateWith(rankable);
 }

// TotalRankingsBolt的聚合排序方法
 @Override
 void updateRankingsWithTuple(Tuple tuple) {
 // 提出来自IntermediateRankingsBolt的中间结果
  Rankings rankingsToBeMerged = (Rankings) tuple.getValue(0);
 // 聚合并排序
  super.getRankings().updateWith(rankingsToBeMerged);
 // 去0，节约内存
  super.getRankings().pruneZeroCounts();
 }

 private void rerank() {
  Collections.sort(rankedItems);
  Collections.reverse(rankedItems);
 }