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| <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" class="sidebar-link">聚合查询</a></li><li><a href="/zh/SystemDesign/DataQuery/GroupByQuery.html" aria-current="page" class="active 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="降采样查询"><a href="#降采样查询" class="header-anchor">#</a> 降采样查询</h1> <ul><li>org.apache.iotdb.db.query.dataset.groupby.GroupByEngineDataSet</li></ul> <p>降采样查询的结果集都会继承 <code>GroupByEngineDataSet</code>，该类包含如下字段：</p> <ul><li>protected long queryId</li> <li>private long interval</li> <li>private long slidingStep</li></ul> <p>以下两个字段针对整个查询，时间段为左闭右开，即 <code>[startTime, endTime)</code>：</p> <ul><li>private long startTime</li> <li>private long endTime</li></ul> <p>以下字段针对当前分段，时间段为左闭右开，即 <code>[curStartTime, curEndTime)</code></p> <ul><li>protected long curStartTime;</li> <li>protected long curEndTime;</li> <li>private int usedIndex;</li> <li>protected boolean hasCachedTimeInterval;</li></ul> <p><code>GroupByEngineDataSet</code> 的核心方法很容易，首先根据是否有缓存的时间段判断是否有下一分段，有则返回 <code>true</code>；如果没有就计算分段开始时间，将 <code>usedIndex</code> 增加1。如果分段开始时间已经超过了查询结束时间，返回 <code>false</code>，否则计算查询结束时间，将 <code>hasCachedTimeInterval</code> 置为<code>true</code>，并返回 <code>true</code>：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>protected boolean hasNextWithoutConstraint() { |
| if (hasCachedTimeInterval) { |
| return true; |
| } |
| |
| curStartTime = usedIndex * slidingStep + startTime; |
| usedIndex++; |
| if (curStartTime < 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<Path, GroupByExecutor> 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<Pair<AggregateResult, Integer>> 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<Pair<AggregateResult, Integer>> 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() >= 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<AggregateResult, Integer> 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() >= 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<IReaderByTimestamp> 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 < paths.size(); i++) { |
| aggregateResultList.get(i).updateResultUsingTimestamps( |
| timestampArray, timeArrayLength, allDataReaderList.get(i)); |
| } |
| |
| timeArrayLength = 0; |
| // 判断是否到结束 |
| if (timestamp >= 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 < timeStampFetchSize && timestampGenerator.hasNext(); cnt++) { |
| timestamp = timestampGenerator.next(); |
| if (timestamp < 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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