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  if (hasCachedTimeInterval) {
    return true;
  }

  curStartTime = usedIndex * slidingStep + startTime;
  usedIndex++;
  if (curStartTime &lt; endTime) {
    hasCachedTimeInterval = true;
    curEndTime = Math.min(curStartTime + interval, endTime);
    return true;
  } else {
    return false;
  }
}
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br></div></div><h2 id="不带值过滤条件的降采样查询"><a href="#不带值过滤条件的降采样查询" class="header-anchor">#</a> 不带值过滤条件的降采样查询</h2> <p>不带值过滤条件的降采样查询逻辑主要在 <code>GroupByWithoutValueFilterDataSet</code> 类中，该类继承了 <code>GroupByEngineDataSet</code>。</p> <p>该类有如下关键字段：</p> <ul><li><p>private Map&lt;Path, GroupByExecutor&gt; pathExecutors 针对于相同 <code>Path</code> 的聚合函数进行归类，并封装成 <code>GroupByExecutor</code> ,
<code>GroupByExecutor</code> 封装了每个 <code>Path</code> 的数据计算逻辑和方法，在后面介绍</p></li> <li><p>private TimeRange timeRange 将每次计算的时间区间封装成对象，用于判断 <code>Statistics</code> 是否可以直接参与计算</p></li> <li><p>private Filter timeFilter   将用户定义的查询区间生成为 <code>Filter</code> 对象，用来过滤可用的<code>文件</code>、<code>chunk</code>、<code>page</code></p></li></ul> <p>首先，在初始化 <code>initGroupBy()</code> 方法中，根据表达式计算出 <code>timeFilter</code>，并为每个 <code>path</code> 生成 <code>GroupByExecutor</code> 。</p> <p><code>nextWithoutConstraint()</code> 方法通过调用 <code>GroupByExecutor.calcResult()</code> 方法计算出每个 <code>Path</code> 内的所有聚合方法的聚合值 <code>aggregateResults</code>。
以下方法用于将结果列表转化为 RowRecord，需要注意列表中没有结果时， RowRecord 中添加 <code>null</code>：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>for (AggregateResult res : fields) {
  if (res == null) {
    record.addField(null);
    continue;
  }
  record.addField(res.getResult(), res.getResultDataType());
}
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br></div></div><h3 id="groupbyexecutor"><a href="#groupbyexecutor" class="header-anchor">#</a> GroupByExecutor</h3> <p>封装了相同 path 下的所有聚合函数的计算方法，该类有如下关键字段：</p> <ul><li>private IAggregateReader reader 读取当前 <code>Path</code> 数据用到的 <code>SeriesAggregateReader</code></li> <li>private BatchData preCachedData 每次从 <code>Reader</code> 读取的数据是一批，很有可能会超过当前的时间段，那么这个 <code>BatchData</code> 就会被缓存留给下一次使用</li> <li>private List&lt;Pair&lt;AggregateResult, Integer&gt;&gt; results 存储了当前 <code>Path</code> 里所有的聚合方法，
例如：<code>select count(a),sum(a),avg(b)</code> ，<code>count</code> 和 <code>sum</code> 方法就被存到一起。
右侧的 <code>Integer</code> 用于结果集转化为RowRecord之前，需要将其顺序还原为用户查询的顺序。</li></ul> <h4 id="主要方法"><a href="#主要方法" class="header-anchor">#</a> 主要方法</h4> <div class="language- line-numbers-mode"><pre class="language-text"><code>//从 reader 中读取数据，并计算，该类的主方法。
private List&lt;Pair&lt;AggregateResult, Integer&gt;&gt; calcResult() throws IOException, QueryProcessException;

//添加当前 path 的聚合操作
private void addAggregateResult(AggregateResult aggrResult, int index);

//判断当前 path 是否已经完成了所有的聚合计算
private boolean isEndCalc();

//从上次计算没有使用完缓存的 BatchData 中计算结果
private boolean calcFromCacheData() throws IOException;

//使用 BatchData 计算
private void calcFromBatch(BatchData batchData) throws IOException;

//使用 Page 或 Chunk 的 Statistics 信息直接计算结果
private void calcFromStatistics(Statistics statistics) throws QueryProcessException;

//清空所有计算结果
private void resetAggregateResults();

//遍历并计算 page 中的数据
private boolean readAndCalcFromPage() throws IOException, QueryProcessException;

</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br><span class="line-number">16</span><br><span class="line-number">17</span><br><span class="line-number">18</span><br><span class="line-number">19</span><br><span class="line-number">20</span><br><span class="line-number">21</span><br><span class="line-number">22</span><br><span class="line-number">23</span><br><span class="line-number">24</span><br></div></div><p><code>GroupByExecutor</code> 中因为相同 <code>path</code> 的不同聚合函数使用的数据是相同的，所以在入口方法 <code>calcResult</code> 中负责读取该 <code>Path</code> 的所有数据,
取出来的数据再调用 <code>calcFromBatch</code> 方法完成遍历所有聚合函数对 <code>BatchData</code> 的计算。</p> <p><code>calcResult</code> 方法返回当前 Path 下的所有AggregateResult，以及当前聚合值在用户查询顺序里的位置，其主要逻辑：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>//把上次遗留的数据先做计算，如果能直接获得结果就结束计算
if (calcFromCacheData()) {
    return results;
}

//因为一个chunk是包含多个page的，那么必须先使用完当前chunk的page，再打开下一个chunk
if (readAndCalcFromPage()) {
    return results;
}

//遗留的数据如果计算完了就要打开新的chunk继续计算
while (reader.hasNextChunk()) {
    Statistics chunkStatistics = reader.currentChunkStatistics();
      // 判断能否使用 Statistics，并执行计算
       ....
      // 跳过当前chunk
      reader.skipCurrentChunk();
      // 如果已经获取到了所有结果就结束计算
      if (isEndCalc()) {
        return true;
      }
      continue;
    }
    //如果不能使用 chunkStatistics 就需要使用page数据计算
    if (readAndCalcFromPage()) {
      return results;
    }
}
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br><span class="line-number">16</span><br><span class="line-number">17</span><br><span class="line-number">18</span><br><span class="line-number">19</span><br><span class="line-number">20</span><br><span class="line-number">21</span><br><span class="line-number">22</span><br><span class="line-number">23</span><br><span class="line-number">24</span><br><span class="line-number">25</span><br><span class="line-number">26</span><br><span class="line-number">27</span><br><span class="line-number">28</span><br></div></div><p><code>readAndCalcFromPage</code> 方法是从当前打开的chunk中获取page的数据，并计算聚合结果。当完成所有计算时返回 true，否则返回 false。主要逻辑：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>while (reader.hasNextPage()) {
    Statistics pageStatistics = reader.currentPageStatistics();
    //只有page与其它page不相交时，才能使用 pageStatistics
    if (pageStatistics != null) {
        // 判断能否使用 Statistics，并执行计算
        ....
        // 跳过当前page
        reader.skipCurrentPage();
        // 如果已经获取到了所有结果就结束计算
        if (isEndCalc()) {
          return true;
        }
        continue;
      }
    }
    // 不能使用 Statistics 时，只能取出所有数据进行计算
    BatchData batchData = reader.nextPage();
    if (batchData == null || !batchData.hasCurrent()) {
      continue;
    }
    // 如果刚打开的page就超过时间范围，缓存取出来的数据并直接结束计算
    if (batchData.currentTime() &gt;= curEndTime) {
      preCachedData = batchData;
      return true;
    }
    //执行计算
    calcFromBatch(batchData);
    ...
}

</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br><span class="line-number">16</span><br><span class="line-number">17</span><br><span class="line-number">18</span><br><span class="line-number">19</span><br><span class="line-number">20</span><br><span class="line-number">21</span><br><span class="line-number">22</span><br><span class="line-number">23</span><br><span class="line-number">24</span><br><span class="line-number">25</span><br><span class="line-number">26</span><br><span class="line-number">27</span><br><span class="line-number">28</span><br><span class="line-number">29</span><br><span class="line-number">30</span><br></div></div><p><code>calcFromBatch</code> 方法是对于取出的BatchData数据，遍历所有聚合函数进行计算，主要逻辑为：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>for (Pair&lt;AggregateResult, Integer&gt; result : results) {
    //如果某个函数已经完成了计算，就不在进行计算了，比如最小值这种计算
    if (result.left.isCalculatedAggregationResult()) {
      continue;
    }
    // 执行计算
    ....
}
//判断当前的 batchdata 里的数据是否还能被下次使用，如果能则加到缓存中
if (batchData.getMaxTimestamp() &gt;= curEndTime) {
    preCachedData = batchData;
}
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br></div></div><h2 id="带值过滤条件的聚合查询"><a href="#带值过滤条件的聚合查询" class="header-anchor">#</a> 带值过滤条件的聚合查询</h2> <p>带值过滤条件的降采样查询逻辑主要在 <code>GroupByWithValueFilterDataSet</code> 类中，该类继承了 <code>GroupByEngineDataSet</code>。</p> <p>该类有如下关键字段：</p> <ul><li><div class="language- line-numbers-mode"><pre class="language-text"><code>private List&lt;IReaderByTimestamp&gt; allDataReaderList
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br></div></div></li> <li><p>private GroupByPlan groupByTimePlan</p></li> <li><p>private TimeGenerator timestampGenerator</p></li> <li><p>private long timestamp 用于为下一个 group by 分区缓存 timestamp</p></li> <li><p>private boolean hasCachedTimestamp 用于判断是否有为下一个 group by 分区缓存 timestamp</p></li> <li><p>private int timeStampFetchSize 是 group by 计算 batch 的大小</p></li></ul> <p>首先，在初始化 <code>initGroupBy()</code> 方法中，根据表达式创建 <code>timestampGenerator</code>；然后为每一个时间序列创建一个 <code>SeriesReaderByTimestamp</code>，放到 <code>allDataReaderList</code>列表中</p> <p>初始化完成后，调用 <code>nextWithoutConstraint()</code> 方法更新结果。如果有为下一个 group by 分区缓存 timestamp，且时间符合要求，则将其加入 <code>timestampArray</code> 中，否则直接返回 <code>aggregateResultList</code> 结果；在没有为下一个 group by 分区缓存 timestamp 的情况下，使用 <code>timestampGenerator</code> 进行遍历：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>while (timestampGenerator.hasNext()) {
  // 调用 constructTimeArrayForOneCal() 方法，得到 timestamp 列表
  timeArrayLength = constructTimeArrayForOneCal(timestampArray, timeArrayLength);

  // 调用 updateResultUsingTimestamps() 方法，使用 timestamp 列表计算聚合结果
  for (int i = 0; i &lt; paths.size(); i++) {
    aggregateResultList.get(i).updateResultUsingTimestamps(
        timestampArray, timeArrayLength, allDataReaderList.get(i));
  }

  timeArrayLength = 0;
  // 判断是否到结束
  if (timestamp &gt;= curEndTime) {
    hasCachedTimestamp = true;
    break;
  }
}
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br><span class="line-number">16</span><br><span class="line-number">17</span><br></div></div><p>其中的 <code>constructTimeArrayForOneCal()</code> 方法遍历 timestampGenerator 构建 timestamp 列表：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>for (int cnt = 1; cnt &lt; timeStampFetchSize &amp;&amp; timestampGenerator.hasNext(); cnt++) {
  timestamp = timestampGenerator.next();
  if (timestamp &lt; curEndTime) {
    timestampArray[timeArrayLength++] = timestamp;
  } else {
    hasCachedTimestamp = true;
    break;
  }
}
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br></div></div><h2 id="使用level来汇总降采样的总点数"><a href="#使用level来汇总降采样的总点数" class="header-anchor">#</a> 使用Level来汇总降采样的总点数</h2> <p>降采样后，我们也可以使用level关键字来进一步汇总点数。</p> <p>这个逻辑在 <code>GroupByTimeDataSet</code>类里。</p> <ol><li><p>首先，把所有涉及到的时序按level来进行汇集，最后的路径。</p> <blockquote><p>例如把root.sg1.d1.s0,root.sg1.d2.s1按level=1汇集成root.sg1。</p></blockquote></li> <li><p>然后调用上述的降采样逻辑求出所有时序的总点数信息，这个会返回RowRecord数据结构。</p></li> <li><p>最后，把降采样返回的RowRecord按上述的final paths，进行累加，组合成新的RowRecord。</p> <blockquote><p>例如，把《root.sg1.d1.s0,3》，《root.sg1.d2.s1，4》聚合成《root.sg1，7》</p></blockquote></li></ol> <blockquote><p>注意:</p> <ol><li>这里只支持count操作</li> <li>root的层级level=0</li></ol></blockquote></div> <div class="page-nav"><p class="inner"><span class="prev">
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