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    gitHub
    <span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a></nav>  <ul class="sidebar-links"><li><section class="sidebar-group depth-0"><p class="sidebar-heading"><span>系统设计</span> <!----></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>应用概览</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>TsFile</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>查询引擎</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>元数据管理</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>存储引擎</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading open"><span>数据查询</span> <span class="arrow down"></span></p> <ul class="sidebar-links sidebar-group-items"><li><a href="/zh/SystemDesign/DataQuery/DataQuery.html" class="sidebar-link">数据查询</a></li><li><a href="/zh/SystemDesign/DataQuery/QueryFundamentals.html" class="sidebar-link">查询基础介绍</a></li><li><a href="/zh/SystemDesign/DataQuery/SeriesReader.html" class="sidebar-link">查询基础组件</a></li><li><a href="/zh/SystemDesign/DataQuery/RawDataQuery.html" class="sidebar-link">原始数据查询</a></li><li><a href="/zh/SystemDesign/DataQuery/AggregationQuery.html" aria-current="page" class="active sidebar-link">聚合查询</a></li><li><a href="/zh/SystemDesign/DataQuery/GroupByQuery.html" class="sidebar-link">降采样查询</a></li><li><a href="/zh/SystemDesign/DataQuery/LastQuery.html" class="sidebar-link">最近时间戳 Last 查询</a></li><li><a href="/zh/SystemDesign/DataQuery/AlignByDeviceQuery.html" class="sidebar-link">按设备对齐查询</a></li><li><a href="/zh/SystemDesign/DataQuery/FillFunction.html" class="sidebar-link">空值填充</a></li><li><a href="/zh/SystemDesign/DataQuery/GroupByFillQuery.html" class="sidebar-link">降采样补空值查询</a></li><li><a href="/zh/SystemDesign/DataQuery/OrderByTimeQuery.html" class="sidebar-link">按时间倒序查询</a></li></ul></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>工具</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>连接器</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>客户端</span> <span class="arrow right"></span></p> <!----></section></li></ul> </aside> <main class="page"> <div class="content content__default"><h1 id="聚合查询"><a href="#聚合查询" class="header-anchor">#</a> 聚合查询</h1> <p>聚合查询的主要逻辑在 AggregateExecutor</p> <ul><li>org.apache.iotdb.db.query.executor.AggregationExecutor</li></ul> <h2 id="不带值过滤条件的聚合查询"><a href="#不带值过滤条件的聚合查询" class="header-anchor">#</a> 不带值过滤条件的聚合查询</h2> <p>对于不带值过滤条件的聚合查询，通过 <code>executeWithoutValueFilter()</code> 方法获得结果并构建 dataSet。首先使用 <code>mergeSameSeries()</code> 方法将对于相同时间序列的聚合查询合并，例如：如果需要计算count(s1), sum(s2), count(s3), sum(s1)，即需要计算s1的两个聚合值，那么将会得到pathToAggrIndexesMap结果为：s1 -&gt; 0, 3; s2 -&gt; 1; s3 -&gt; 2。</p> <p>那么将会得到 <code>pathToAggrIndexesMap</code>，其中每一个 entry 都是一个 series 的聚合查询，因此可以通过调用 <code>groupAggregationsBySeries()</code> 方法计算出其聚合值 <code>aggregateResults</code>。在最后创建结果集之前，需要将其顺序还原为用户查询的顺序。最后使用 <code>constructDataSet()</code> 方法创建结果集并返回。</p> <p>下面详细讲解 <code>groupAggregationsBySeries()</code> 方法。首先创建一个 <code>IAggregateReader</code>：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>IAggregateReader seriesReader = new SeriesAggregateReader(
        pathToAggrIndexes.getKey(), tsDataType, context, QueryResourceManager.getInstance()
        .getQueryDataSource(seriesPath, context, timeFilter), timeFilter, null);
</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></div></div><p>对于每一个 entry（即series），首先为其每一种聚合查询创建一个聚合结果 <code>AggregateResult</code>，同时维护一个布尔值列表 <code>isCalculatedList</code>，对应每一个 <code>AggregateResult</code>是否已经计算完成，并记录需要剩余计算的聚合函数数目 <code>remainingToCalculate</code>。布尔值列表和这个计数值将会使得某些聚合函数（如 <code>FIRST_VALUE</code>）在获得结果后，不需要再继续进行整个循环过程。</p> <p>接下来，按照5.2节所介绍的 <code>aggregateReader</code> 使用方法，更新 <code>AggregateResult</code>：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>while (aggregateReader.hasNextChunk()) {
  if (aggregateReader.canUseCurrentChunkStatistics()) {
    Statistics chunkStatistics = aggregateReader.currentChunkStatistics();
    
    // do some aggregate calculation using chunk statistics
    ...
    
    aggregateReader.skipCurrentChunk();
    continue;
  }
	  
  while (aggregateReader.hasNextPage()) {
	 if (aggregateReader.canUseCurrentPageStatistics()) {
	   Statistics pageStatistic = aggregateReader.currentPageStatistics();
	   
	   // do some aggregate calculation using page statistics
      ...
	   
	   aggregateReader.skipCurrentPage();
	   continue;
	 } else {
	 	BatchData batchData = aggregateReader.nextPage();
	 	// do some aggregate calculation using batch data
      ...
	 }	 
  }
}
</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></div></div><p>需要注意的是，在对于每一个result进行更新之前，需要首先判断其是否已经被计算完（利用 <code>isCalculatedList</code> 列表）；每一次更新后，调用 <code>isCalculatedAggregationResult()</code> 方法同时更新列表中的布尔值。如果列表中所有值均为true，即 <code>remainingToCalculate</code> 值为0，证明所有聚合函数结果均已计算完，可以返回。</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>if (Boolean.FALSE.equals(isCalculatedList.get(i))) {
  AggregateResult aggregateResult = aggregateResultList.get(i);
  ... // 更新
  if (aggregateResult.isCalculatedAggregationResult()) {
    isCalculatedList.set(i, true);
    remainingToCalculate--;
    if (remainingToCalculate == 0) {
      return aggregateResultList;
    }
  }
}
</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></div></div><p>在使用 <code>overlapedPageData</code> 进行更新时，由于获得每一个聚合函数结果都会遍历这个 batchData，因此需要调用 <code>resetBatchData()</code> 方法将指针指向其开始位置，使得下一个函数可以遍历。</p> <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>对于带值过滤条件的聚合查询，通过 <code>executeWithoutValueFilter()</code> 方法获得结果并构建 dataSet。首先根据表达式创建 <code>timestampGenerator</code>，然后为每一个时间序列创建一个 <code>SeriesReaderByTimestamp</code>，放到 <code>readersOfSelectedSeries</code>列表中；为每一个查询创建一个创建一个聚合结果 <code>AggregateResult</code>，放到 <code>aggregateResults</code>列表中。</p> <p>初始化完成后，调用 <code>aggregateWithValueFilter()</code> 方法更新结果：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>while (timestampGenerator.hasNext()) {
  // 生成timestamps
  long[] timeArray = new long[aggregateFetchSize];
  int timeArrayLength = 0;
  for (int cnt = 0; cnt &lt; aggregateFetchSize; cnt++) {
    if (!timestampGenerator.hasNext()) {
      break;
    }
    timeArray[timeArrayLength++] = timestampGenerator.next();
  }

  // 利用timestamps计算聚合结果
  for (int i = 0; i &lt; readersOfSelectedSeries.size(); i++) {
    aggregateResults.get(i).updateResultUsingTimestamps(timeArray, timeArrayLength,
      readersOfSelectedSeries.get(i));
    }
  }
</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><h2 id="使用level来统计点数"><a href="#使用level来统计点数" class="header-anchor">#</a> 使用Level来统计点数</h2> <p>对于count聚合查询，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>AggregationExecutor</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>然后调用上述的聚合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，把聚合查询返回的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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