| import{_ as n,o as s,c as a,e}from"./app-Bx8hKGcu.js";const t={},p=e(`<h1 id="分段分组聚合" tabindex="-1"><a class="header-anchor" href="#分段分组聚合"><span>分段分组聚合</span></a></h1><h2 id="时间区间分段聚合" tabindex="-1"><a class="header-anchor" href="#时间区间分段聚合"><span>时间区间分段聚合</span></a></h2><p>分段聚合是一种时序数据典型的查询方式，数据以高频进行采集，需要按照一定的时间间隔进行聚合计算，如计算每天的平均气温，需要将气温的序列按天进行分段，然后计算平均值。</p><p>在 IoTDB 中，聚合查询可以通过 <code>GROUP BY</code> 子句指定按照时间区间分段聚合。用户可以指定聚合的时间间隔和滑动步长，相关参数如下：</p><ul><li>参数 1：时间轴显示时间窗口大小</li><li>参数 2：聚合窗口的大小（必须为正数）</li><li>参数 3：聚合窗口的滑动步长（可选，默认与聚合窗口大小相同）</li></ul><p>下图中指出了这三个参数的含义：</p><img style="width:100%;max-width:800px;max-height:600px;margin-left:auto;margin-right:auto;display:block;" src="https://alioss.timecho.com/docs/img/github/69109512-f808bc80-0ab2-11ea-9e4d-b2b2f58fb474.png"><p>接下来，我们给出几个典型例子：</p><h3 id="未指定滑动步长的时间区间分组聚合查询" tabindex="-1"><a class="header-anchor" href="#未指定滑动步长的时间区间分组聚合查询"><span>未指定滑动步长的时间区间分组聚合查询</span></a></h3><p>对应的 SQL 语句是：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span><span class="token punctuation">,</span> max_value<span class="token punctuation">(</span>temperature<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf01<span class="token punctuation">.</span>wt01 <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span>T00:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">,</span> <span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">07</span>T23:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">)</span><span class="token punctuation">,</span><span class="token number">1</span>d<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>这条查询的含义是：</p><p>由于用户没有指定滑动步长，滑动步长将会被默认设置为跟时间间隔参数相同，也就是<code>1d</code>。</p><p>上面这个例子的第一个参数是显示窗口参数，决定了最终的显示范围是 [2017-11-01T00:00:00, 2017-11-07T23:00:00)。</p><p>上面这个例子的第二个参数是划分时间轴的时间间隔参数，将<code>1d</code>当作划分间隔，显示窗口参数的起始时间当作分割原点，时间轴即被划分为连续的时间间隔：[0,1d), [1d, 2d), [2d, 3d) 等等。</p><p>然后系统将会用 WHERE 子句中的时间和值过滤条件以及 GROUP BY 语句中的第一个参数作为数据的联合过滤条件，获得满足所有过滤条件的数据（在这个例子里是在 [2017-11-01T00:00:00, 2017-11-07 T23:00:00) 这个时间范围的数据），并把这些数据映射到之前分割好的时间轴中（这个例子里是从 2017-11-01T00:00:00 到 2017-11-07T23:00:00:00 的每一天）</p><p>每个时间间隔窗口内都有数据，SQL 执行后的结果集如下所示：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------------+----------------------------------------+ |
| | Time|count(root.ln.wf01.wt01.status)|max_value(root.ln.wf01.wt01.temperature)| |
| +-----------------------------+-------------------------------+----------------------------------------+ |
| |2017-11-01T00:00:00.000+08:00| 1440| 26.0| |
| |2017-11-02T00:00:00.000+08:00| 1440| 26.0| |
| |2017-11-03T00:00:00.000+08:00| 1440| 25.99| |
| |2017-11-04T00:00:00.000+08:00| 1440| 26.0| |
| |2017-11-05T00:00:00.000+08:00| 1440| 26.0| |
| |2017-11-06T00:00:00.000+08:00| 1440| 25.99| |
| |2017-11-07T00:00:00.000+08:00| 1380| 26.0| |
| +-----------------------------+-------------------------------+----------------------------------------+ |
| Total line number = 7 |
| It costs 0.024s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="指定滑动步长的时间区间分组聚合查询" tabindex="-1"><a class="header-anchor" href="#指定滑动步长的时间区间分组聚合查询"><span>指定滑动步长的时间区间分组聚合查询</span></a></h3><p>对应的 SQL 语句是：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span><span class="token punctuation">,</span> max_value<span class="token punctuation">(</span>temperature<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf01<span class="token punctuation">.</span>wt01 <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span> <span class="token number">00</span>:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">,</span> <span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">07</span> <span class="token number">23</span>:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token number">3</span>h<span class="token punctuation">,</span> <span class="token number">1</span>d<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>这条查询的含义是：</p><p>由于用户指定了滑动步长为<code>1d</code>，GROUP BY 语句执行时将会每次把时间间隔往后移动一天的步长，而不是默认的 3 小时。</p><p>也就意味着，我们想要取从 2017-11-01 到 2017-11-07 每一天的凌晨 0 点到凌晨 3 点的数据。</p><p>上面这个例子的第一个参数是显示窗口参数，决定了最终的显示范围是 [2017-11-01T00:00:00, 2017-11-07T23:00:00)。</p><p>上面这个例子的第二个参数是划分时间轴的时间间隔参数，将<code>3h</code>当作划分间隔，显示窗口参数的起始时间当作分割原点，时间轴即被划分为连续的时间间隔：[2017-11-01T00:00:00, 2017-11-01T03:00:00), [2017-11-02T00:00:00, 2017-11-02T03:00:00), [2017-11-03T00:00:00, 2017-11-03T03:00:00) 等等。</p><p>上面这个例子的第三个参数是每次时间间隔的滑动步长。</p><p>然后系统将会用 WHERE 子句中的时间和值过滤条件以及 GROUP BY 语句中的第一个参数作为数据的联合过滤条件，获得满足所有过滤条件的数据（在这个例子里是在 [2017-11-01T00:00:00, 2017-11-07 T23:00:00) 这个时间范围的数据），并把这些数据映射到之前分割好的时间轴中（这个例子里是从 2017-11-01T00:00:00 到 2017-11-07T23:00:00:00 的每一天的凌晨 0 点到凌晨 3 点）</p><p>每个时间间隔窗口内都有数据，SQL 执行后的结果集如下所示：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------------+----------------------------------------+ |
| | Time|count(root.ln.wf01.wt01.status)|max_value(root.ln.wf01.wt01.temperature)| |
| +-----------------------------+-------------------------------+----------------------------------------+ |
| |2017-11-01T00:00:00.000+08:00| 180| 25.98| |
| |2017-11-02T00:00:00.000+08:00| 180| 25.98| |
| |2017-11-03T00:00:00.000+08:00| 180| 25.96| |
| |2017-11-04T00:00:00.000+08:00| 180| 25.96| |
| |2017-11-05T00:00:00.000+08:00| 180| 26.0| |
| |2017-11-06T00:00:00.000+08:00| 180| 25.85| |
| |2017-11-07T00:00:00.000+08:00| 180| 25.99| |
| +-----------------------------+-------------------------------+----------------------------------------+ |
| Total line number = 7 |
| It costs 0.006s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>滑动步长可以小于聚合窗口，此时聚合窗口之间有重叠时间（类似于一个滑动窗口）。</p><p>例如 SQL：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span><span class="token punctuation">,</span> max_value<span class="token punctuation">(</span>temperature<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf01<span class="token punctuation">.</span>wt01 <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span> <span class="token number">00</span>:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">,</span> <span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span> <span class="token number">10</span>:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token number">4</span>h<span class="token punctuation">,</span> <span class="token number">2</span>h<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>SQL 执行后的结果集如下所示：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------------+----------------------------------------+ |
| | Time|count(root.ln.wf01.wt01.status)|max_value(root.ln.wf01.wt01.temperature)| |
| +-----------------------------+-------------------------------+----------------------------------------+ |
| |2017-11-01T00:00:00.000+08:00| 180| 25.98| |
| |2017-11-01T02:00:00.000+08:00| 180| 25.98| |
| |2017-11-01T04:00:00.000+08:00| 180| 25.96| |
| |2017-11-01T06:00:00.000+08:00| 180| 25.96| |
| |2017-11-01T08:00:00.000+08:00| 180| 26.0| |
| +-----------------------------+-------------------------------+----------------------------------------+ |
| Total line number = 5 |
| It costs 0.006s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="按照自然月份的时间区间分组聚合查询" tabindex="-1"><a class="header-anchor" href="#按照自然月份的时间区间分组聚合查询"><span>按照自然月份的时间区间分组聚合查询</span></a></h3><p>对应的 SQL 语句是：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf01<span class="token punctuation">.</span>wt01 <span class="token keyword">where</span> <span class="token keyword">time</span> <span class="token operator">></span> <span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span>T01:<span class="token number">00</span>:<span class="token number">00</span> <span class="token keyword">group</span> <span class="token keyword">by</span><span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span>T00:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">,</span> <span class="token number">2019</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">07</span>T23:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token number">1</span>mo<span class="token punctuation">,</span> <span class="token number">2</span>mo<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>这条查询的含义是：</p><p>由于用户指定了滑动步长为<code>2mo</code>，GROUP BY 语句执行时将会每次把时间间隔往后移动 2 个自然月的步长，而不是默认的 1 个自然月。</p><p>也就意味着，我们想要取从 2017-11-01 到 2019-11-07 每 2 个自然月的第一个月的数据。</p><p>上面这个例子的第一个参数是显示窗口参数，决定了最终的显示范围是 [2017-11-01T00:00:00, 2019-11-07T23:00:00)。</p><p>起始时间为 2017-11-01T00:00:00，滑动步长将会以起始时间作为标准按月递增，取当月的 1 号作为时间间隔的起始时间。</p><p>上面这个例子的第二个参数是划分时间轴的时间间隔参数，将<code>1mo</code>当作划分间隔，显示窗口参数的起始时间当作分割原点，时间轴即被划分为连续的时间间隔：[2017-11-01T00:00:00, 2017-12-01T00:00:00), [2018-02-01T00:00:00, 2018-03-01T00:00:00), [2018-05-03T00:00:00, 2018-06-01T00:00:00) 等等。</p><p>上面这个例子的第三个参数是每次时间间隔的滑动步长。</p><p>然后系统将会用 WHERE 子句中的时间和值过滤条件以及 GROUP BY 语句中的第一个参数作为数据的联合过滤条件，获得满足所有过滤条件的数据（在这个例子里是在 [2017-11-01T00:00:00, 2019-11-07T23:00:00) 这个时间范围的数据），并把这些数据映射到之前分割好的时间轴中（这个例子里是从 2017-11-01T00:00:00 到 2019-11-07T23:00:00:00 的每两个自然月的第一个月）</p><p>每个时间间隔窗口内都有数据，SQL 执行后的结果集如下所示：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------------+ |
| | Time|count(root.ln.wf01.wt01.status)| |
| +-----------------------------+-------------------------------+ |
| |2017-11-01T00:00:00.000+08:00| 259| |
| |2018-01-01T00:00:00.000+08:00| 250| |
| |2018-03-01T00:00:00.000+08:00| 259| |
| |2018-05-01T00:00:00.000+08:00| 251| |
| |2018-07-01T00:00:00.000+08:00| 242| |
| |2018-09-01T00:00:00.000+08:00| 225| |
| |2018-11-01T00:00:00.000+08:00| 216| |
| |2019-01-01T00:00:00.000+08:00| 207| |
| |2019-03-01T00:00:00.000+08:00| 216| |
| |2019-05-01T00:00:00.000+08:00| 207| |
| |2019-07-01T00:00:00.000+08:00| 199| |
| |2019-09-01T00:00:00.000+08:00| 181| |
| |2019-11-01T00:00:00.000+08:00| 60| |
| +-----------------------------+-------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>对应的 SQL 语句是：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf01<span class="token punctuation">.</span>wt01 <span class="token keyword">group</span> <span class="token keyword">by</span><span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2017</span><span class="token operator">-</span><span class="token number">10</span><span class="token operator">-</span><span class="token number">31</span>T00:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">,</span> <span class="token number">2019</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">07</span>T23:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token number">1</span>mo<span class="token punctuation">,</span> <span class="token number">2</span>mo<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>这条查询的含义是：</p><p>由于用户指定了滑动步长为<code>2mo</code>，GROUP BY 语句执行时将会每次把时间间隔往后移动 2 个自然月的步长，而不是默认的 1 个自然月。</p><p>也就意味着，我们想要取从 2017-10-31 到 2019-11-07 每 2 个自然月的第一个月的数据。</p><p>与上述示例不同的是起始时间为 2017-10-31T00:00:00，滑动步长将会以起始时间作为标准按月递增，取当月的 31 号（即最后一天）作为时间间隔的起始时间。若起始时间设置为 30 号，滑动步长会将时间间隔的起始时间设置为当月 30 号，若不存在则为最后一天。</p><p>上面这个例子的第一个参数是显示窗口参数，决定了最终的显示范围是 [2017-10-31T00:00:00, 2019-11-07T23:00:00)。</p><p>上面这个例子的第二个参数是划分时间轴的时间间隔参数，将<code>1mo</code>当作划分间隔，显示窗口参数的起始时间当作分割原点，时间轴即被划分为连续的时间间隔：[2017-10-31T00:00:00, 2017-11-31T00:00:00), [2018-02-31T00:00:00, 2018-03-31T00:00:00), [2018-05-31T00:00:00, 2018-06-31T00:00:00) 等等。</p><p>上面这个例子的第三个参数是每次时间间隔的滑动步长。</p><p>然后系统将会用 WHERE 子句中的时间和值过滤条件以及 GROUP BY 语句中的第一个参数作为数据的联合过滤条件，获得满足所有过滤条件的数据（在这个例子里是在 [2017-10-31T00:00:00, 2019-11-07T23:00:00) 这个时间范围的数据），并把这些数据映射到之前分割好的时间轴中（这个例子里是从 2017-10-31T00:00:00 到 2019-11-07T23:00:00:00 的每两个自然月的第一个月）</p><p>每个时间间隔窗口内都有数据，SQL 执行后的结果集如下所示：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------------+ |
| | Time|count(root.ln.wf01.wt01.status)| |
| +-----------------------------+-------------------------------+ |
| |2017-10-31T00:00:00.000+08:00| 251| |
| |2017-12-31T00:00:00.000+08:00| 250| |
| |2018-02-28T00:00:00.000+08:00| 259| |
| |2018-04-30T00:00:00.000+08:00| 250| |
| |2018-06-30T00:00:00.000+08:00| 242| |
| |2018-08-31T00:00:00.000+08:00| 225| |
| |2018-10-31T00:00:00.000+08:00| 216| |
| |2018-12-31T00:00:00.000+08:00| 208| |
| |2019-02-28T00:00:00.000+08:00| 216| |
| |2019-04-30T00:00:00.000+08:00| 208| |
| |2019-06-30T00:00:00.000+08:00| 199| |
| |2019-08-31T00:00:00.000+08:00| 181| |
| |2019-10-31T00:00:00.000+08:00| 69| |
| +-----------------------------+-------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="左开右闭区间" tabindex="-1"><a class="header-anchor" href="#左开右闭区间"><span>左开右闭区间</span></a></h3><p>每个区间的结果时间戳为区间右端点，对应的 SQL 语句是：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf01<span class="token punctuation">.</span>wt01 <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token punctuation">(</span><span class="token punctuation">(</span><span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span>T00:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">,</span> <span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">07</span>T23:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token number">1</span>d<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>这条查询语句的时间区间是左开右闭的，结果中不会包含时间点 2017-11-01 的数据，但是会包含时间点 2017-11-07 的数据。</p><p>SQL 执行后的结果集如下所示：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------------+ |
| | Time|count(root.ln.wf01.wt01.status)| |
| +-----------------------------+-------------------------------+ |
| |2017-11-02T00:00:00.000+08:00| 1440| |
| |2017-11-03T00:00:00.000+08:00| 1440| |
| |2017-11-04T00:00:00.000+08:00| 1440| |
| |2017-11-05T00:00:00.000+08:00| 1440| |
| |2017-11-06T00:00:00.000+08:00| 1440| |
| |2017-11-07T00:00:00.000+08:00| 1440| |
| |2017-11-07T23:00:00.000+08:00| 1380| |
| +-----------------------------+-------------------------------+ |
| Total line number = 7 |
| It costs 0.004s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="与分组聚合混合使用" tabindex="-1"><a class="header-anchor" href="#与分组聚合混合使用"><span>与分组聚合混合使用</span></a></h3><p>通过定义 LEVEL 来统计指定层级下的数据点个数。</p><p>例如：</p><p>统计降采样后的数据点个数</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf01<span class="token punctuation">.</span>wt01 <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token punctuation">(</span><span class="token punctuation">(</span><span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span>T00:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">,</span> <span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">07</span>T23:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token number">1</span>d<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token keyword">level</span><span class="token operator">=</span><span class="token number">1</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>结果：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------+ |
| | Time|COUNT(root.ln.*.*.status)| |
| +-----------------------------+-------------------------+ |
| |2017-11-02T00:00:00.000+08:00| 1440| |
| |2017-11-03T00:00:00.000+08:00| 1440| |
| |2017-11-04T00:00:00.000+08:00| 1440| |
| |2017-11-05T00:00:00.000+08:00| 1440| |
| |2017-11-06T00:00:00.000+08:00| 1440| |
| |2017-11-07T00:00:00.000+08:00| 1440| |
| |2017-11-07T23:00:00.000+08:00| 1380| |
| +-----------------------------+-------------------------+ |
| Total line number = 7 |
| It costs 0.006s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>加上滑动 Step 的降采样后的结果也可以汇总</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf01<span class="token punctuation">.</span>wt01 <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">01</span> <span class="token number">00</span>:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">,</span> <span class="token number">2017</span><span class="token operator">-</span><span class="token number">11</span><span class="token operator">-</span><span class="token number">07</span> <span class="token number">23</span>:<span class="token number">00</span>:<span class="token number">00</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token number">3</span>h<span class="token punctuation">,</span> <span class="token number">1</span>d<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token keyword">level</span><span class="token operator">=</span><span class="token number">1</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------+ |
| | Time|COUNT(root.ln.*.*.status)| |
| +-----------------------------+-------------------------+ |
| |2017-11-01T00:00:00.000+08:00| 180| |
| |2017-11-02T00:00:00.000+08:00| 180| |
| |2017-11-03T00:00:00.000+08:00| 180| |
| |2017-11-04T00:00:00.000+08:00| 180| |
| |2017-11-05T00:00:00.000+08:00| 180| |
| |2017-11-06T00:00:00.000+08:00| 180| |
| |2017-11-07T00:00:00.000+08:00| 180| |
| +-----------------------------+-------------------------+ |
| Total line number = 7 |
| It costs 0.004s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h2 id="路径层级分组聚合" tabindex="-1"><a class="header-anchor" href="#路径层级分组聚合"><span>路径层级分组聚合</span></a></h2><p>在时间序列层级结构中，分层聚合查询用于<strong>对某一层级下同名的序列进行聚合查询</strong>。</p><ul><li>使用 <code>GROUP BY LEVEL = INT</code> 来指定需要聚合的层级，并约定 <code>ROOT</code> 为第 0 层。若统计 "root.ln" 下所有序列则需指定 level 为 1。</li><li>分层聚合查询支持使用所有内置聚合函数。对于 <code>sum</code>，<code>avg</code>，<code>min_value</code>， <code>max_value</code>， <code>extreme</code> 五种聚合函数，需保证所有聚合的时间序列数据类型相同。其他聚合函数没有此限制。</li></ul><p><strong>示例1：</strong> 不同 database 下均存在名为 status 的序列， 如 "root.ln.wf01.wt01.status", "root.ln.wf02.wt02.status", 以及 "root.sgcc.wf03.wt01.status", 如果需要统计不同 database 下 status 序列的数据点个数，使用以下查询：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span><span class="token operator">*</span><span class="token operator">*</span> <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token keyword">level</span> <span class="token operator">=</span> <span class="token number">1</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>运行结果为：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-------------------------+---------------------------+ |
| |count(root.ln.*.*.status)|count(root.sgcc.*.*.status)| |
| +-------------------------+---------------------------+ |
| | 20160| 10080| |
| +-------------------------+---------------------------+ |
| Total line number = 1 |
| It costs 0.003s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p><strong>示例2：</strong> 统计不同设备下 status 序列的数据点个数，可以规定 level = 3，</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span><span class="token operator">*</span><span class="token operator">*</span> <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token keyword">level</span> <span class="token operator">=</span> <span class="token number">3</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>运行结果为：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+---------------------------+---------------------------+ |
| |count(root.*.*.wt01.status)|count(root.*.*.wt02.status)| |
| +---------------------------+---------------------------+ |
| | 20160| 10080| |
| +---------------------------+---------------------------+ |
| Total line number = 1 |
| It costs 0.003s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>注意，这时会将 database <code>ln</code> 和 <code>sgcc</code> 下名为 <code>wt01</code> 的设备视为同名设备聚合在一起。</p><p><strong>示例3：</strong> 统计不同 database 下的不同设备中 status 序列的数据点个数，可以使用以下查询：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token keyword">status</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span><span class="token operator">*</span><span class="token operator">*</span> <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token keyword">level</span> <span class="token operator">=</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">3</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>运行结果为：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+----------------------------+----------------------------+------------------------------+ |
| |count(root.ln.*.wt01.status)|count(root.ln.*.wt02.status)|count(root.sgcc.*.wt01.status)| |
| +----------------------------+----------------------------+------------------------------+ |
| | 10080| 10080| 10080| |
| +----------------------------+----------------------------+------------------------------+ |
| Total line number = 1 |
| It costs 0.003s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p><strong>示例4：</strong> 查询所有序列下温度传感器 temperature 的最大值，可以使用下列查询语句：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> max_value<span class="token punctuation">(</span>temperature<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span><span class="token operator">*</span><span class="token operator">*</span> <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token keyword">level</span> <span class="token operator">=</span> <span class="token number">0</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>运行结果：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+---------------------------------+ |
| |max_value(root.*.*.*.temperature)| |
| +---------------------------------+ |
| | 26.0| |
| +---------------------------------+ |
| Total line number = 1 |
| It costs 0.013s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p><strong>示例5：</strong> 上面的查询都是针对某一个传感器，特别地，<strong>如果想要查询某一层级下所有传感器拥有的总数据点数，则需要显式规定测点为 <code>*</code></strong></p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token operator">*</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span><span class="token operator">*</span><span class="token operator">*</span> <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token keyword">level</span> <span class="token operator">=</span> <span class="token number">2</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>运行结果：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+----------------------+----------------------+ |
| |count(root.*.wf01.*.*)|count(root.*.wf02.*.*)| |
| +----------------------+----------------------+ |
| | 20160| 20160| |
| +----------------------+----------------------+ |
| Total line number = 1 |
| It costs 0.013s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h2 id="标签分组聚合" tabindex="-1"><a class="header-anchor" href="#标签分组聚合"><span>标签分组聚合</span></a></h2><p>IoTDB 支持通过 <code>GROUP BY TAGS</code> 语句根据时间序列中定义的标签的键值做聚合查询。</p><p>我们先在 IoTDB 中写入如下示例数据，稍后会以这些数据为例介绍标签聚合查询。</p><p>这些是某工厂 <code>factory1</code> 在多个城市的多个车间的设备温度数据， 时间范围为 [1000, 10000)。</p><p>时间序列路径中的设备一级是设备唯一标识。城市信息 <code>city</code> 和车间信息 <code>workshop</code> 则被建模在该设备时间序列的标签中。<br> 其中，设备 <code>d1</code>、<code>d2</code> 在 <code>Beijing</code> 的 <code>w1</code> 车间， <code>d3</code>、<code>d4</code> 在 <code>Beijing</code> 的 <code>w2</code> 车间，<code>d5</code>、<code>d6</code> 在 <code>Shanghai</code> 的 <code>w1</code> 车间，<code>d7</code> 在 <code>Shanghai</code> 的 <code>w2</code> 车间。<br><code>d8</code> 和 <code>d9</code> 设备目前处于调试阶段，还未被分配到具体的城市和车间，所以其相应的标签值为空值。</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">create</span> <span class="token keyword">database</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d1<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span> tags<span class="token punctuation">(</span>city<span class="token operator">=</span>Beijing<span class="token punctuation">,</span> workshop<span class="token operator">=</span>w1<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d2<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span> tags<span class="token punctuation">(</span>city<span class="token operator">=</span>Beijing<span class="token punctuation">,</span> workshop<span class="token operator">=</span>w1<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d3<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span> tags<span class="token punctuation">(</span>city<span class="token operator">=</span>Beijing<span class="token punctuation">,</span> workshop<span class="token operator">=</span>w2<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d4<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span> tags<span class="token punctuation">(</span>city<span class="token operator">=</span>Beijing<span class="token punctuation">,</span> workshop<span class="token operator">=</span>w2<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d5<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span> tags<span class="token punctuation">(</span>city<span class="token operator">=</span>Shanghai<span class="token punctuation">,</span> workshop<span class="token operator">=</span>w1<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d6<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span> tags<span class="token punctuation">(</span>city<span class="token operator">=</span>Shanghai<span class="token punctuation">,</span> workshop<span class="token operator">=</span>w1<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d7<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span> tags<span class="token punctuation">(</span>city<span class="token operator">=</span>Shanghai<span class="token punctuation">,</span> workshop<span class="token operator">=</span>w2<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d8<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span><span class="token punctuation">;</span> |
| <span class="token keyword">create</span> timeseries root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d9<span class="token punctuation">.</span>temperature <span class="token keyword">with</span> datatype<span class="token operator">=</span><span class="token keyword">FLOAT</span><span class="token punctuation">;</span> |
| |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d1<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">104.0</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d1<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">3000</span><span class="token punctuation">,</span> <span class="token number">104.2</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d1<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">5000</span><span class="token punctuation">,</span> <span class="token number">103.3</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d1<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">7000</span><span class="token punctuation">,</span> <span class="token number">104.1</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d2<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">104.4</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d2<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">3000</span><span class="token punctuation">,</span> <span class="token number">103.7</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d2<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">5000</span><span class="token punctuation">,</span> <span class="token number">103.3</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d2<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">7000</span><span class="token punctuation">,</span> <span class="token number">102.9</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d3<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">103.9</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d3<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">3000</span><span class="token punctuation">,</span> <span class="token number">103.8</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d3<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">5000</span><span class="token punctuation">,</span> <span class="token number">102.7</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d3<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">7000</span><span class="token punctuation">,</span> <span class="token number">106.9</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d4<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">103.9</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d4<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">5000</span><span class="token punctuation">,</span> <span class="token number">102.7</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d4<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">7000</span><span class="token punctuation">,</span> <span class="token number">106.9</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d5<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">112.9</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d5<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">7000</span><span class="token punctuation">,</span> <span class="token number">113.0</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d6<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">113.9</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d6<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">3000</span><span class="token punctuation">,</span> <span class="token number">113.3</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d6<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">5000</span><span class="token punctuation">,</span> <span class="token number">112.7</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d6<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">7000</span><span class="token punctuation">,</span> <span class="token number">112.3</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d7<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">101.2</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d7<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">3000</span><span class="token punctuation">,</span> <span class="token number">99.3</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d7<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">5000</span><span class="token punctuation">,</span> <span class="token number">100.1</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d7<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">7000</span><span class="token punctuation">,</span> <span class="token number">99.8</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d8<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">50.0</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d8<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">3000</span><span class="token punctuation">,</span> <span class="token number">52.1</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d8<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">5000</span><span class="token punctuation">,</span> <span class="token number">50.1</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d8<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">7000</span><span class="token punctuation">,</span> <span class="token number">50.5</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
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| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d9<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">50.3</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| <span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span>d9<span class="token punctuation">(</span><span class="token keyword">time</span><span class="token punctuation">,</span> temperature<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">3000</span><span class="token punctuation">,</span> <span class="token number">52.1</span><span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="单标签聚合查询" tabindex="-1"><a class="header-anchor" href="#单标签聚合查询"><span>单标签聚合查询</span></a></h3><p>用户想统计该工厂每个地区的设备的温度的平均值，可以使用如下查询语句</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">SELECT</span> <span class="token function">AVG</span><span class="token punctuation">(</span>temperature<span class="token punctuation">)</span> <span class="token keyword">FROM</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span><span class="token operator">*</span><span class="token operator">*</span> <span class="token keyword">GROUP</span> <span class="token keyword">BY</span> TAGS<span class="token punctuation">(</span>city<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>该查询会将具有同一个 <code>city</code> 标签值的时间序列的所有满足查询条件的点做平均值计算，计算结果如下</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+--------+------------------+ |
| | city| avg(temperature)| |
| +--------+------------------+ |
| | Beijing|104.04666697184244| |
| |Shanghai|107.85000076293946| |
| | NULL| 50.84999910990397| |
| +--------+------------------+ |
| Total line number = 3 |
| It costs 0.231s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>从结果集中可以看到，和时间区间聚合、按层次聚合相比，标签聚合的查询结果的不同点是：</p><ol><li>标签聚合查询的聚合结果不会再做去星号展开，而是将多个时间序列的数据作为一个整体进行聚合计算。</li><li>标签聚合查询除了输出聚合结果列，还会输出聚合标签的键值列。该列的列名为聚合指定的标签键，列的值则为所有查询的时间序列中出现的该标签的值。<br> 如果某些时间序列未设置该标签，则在键值列中有一行单独的 <code>NULL</code> ，代表未设置标签的所有时间序列数据的聚合结果。</li></ol><h3 id="多标签聚合查询" tabindex="-1"><a class="header-anchor" href="#多标签聚合查询"><span>多标签聚合查询</span></a></h3><p>除了基本的单标签聚合查询外，还可以按顺序指定多个标签进行聚合计算。</p><p>例如，用户想统计每个城市的每个车间内设备的平均温度。但因为各个城市的车间名称有可能相同，所以不能直接按照 <code>workshop</code> 做标签聚合。必须要先按照城市，再按照车间处理。</p><p>SQL 语句如下</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">SELECT</span> <span class="token function">avg</span><span class="token punctuation">(</span>temperature<span class="token punctuation">)</span> <span class="token keyword">FROM</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span><span class="token operator">*</span><span class="token operator">*</span> <span class="token keyword">GROUP</span> <span class="token keyword">BY</span> TAGS<span class="token punctuation">(</span>city<span class="token punctuation">,</span> workshop<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>查询结果如下</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+--------+--------+------------------+ |
| | city|workshop| avg(temperature)| |
| +--------+--------+------------------+ |
| | NULL| NULL| 50.84999910990397| |
| |Shanghai| w1|113.01666768391927| |
| | Beijing| w2| 104.4000004359654| |
| |Shanghai| w2|100.10000038146973| |
| | Beijing| w1|103.73750019073486| |
| +--------+--------+------------------+ |
| Total line number = 5 |
| It costs 0.027s |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>从结果集中可以看到，和单标签聚合相比，多标签聚合的查询结果会根据指定的标签顺序，输出相应标签的键值列。</p><h3 id="基于时间区间的标签聚合查询" tabindex="-1"><a class="header-anchor" href="#基于时间区间的标签聚合查询"><span>基于时间区间的标签聚合查询</span></a></h3><p>按照时间区间聚合是时序数据库中最常用的查询需求之一。IoTDB 在基于时间区间的聚合基础上，支持进一步按照标签进行聚合查询。</p><p>例如，用户想统计时间 <code>[1000, 10000)</code> 范围内，每个城市每个车间中的设备每 5 秒内的平均温度。</p><p>SQL 语句如下</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">SELECT</span> <span class="token function">AVG</span><span class="token punctuation">(</span>temperature<span class="token punctuation">)</span> <span class="token keyword">FROM</span> root<span class="token punctuation">.</span>factory1<span class="token punctuation">.</span><span class="token operator">*</span><span class="token operator">*</span> <span class="token keyword">GROUP</span> <span class="token keyword">BY</span> <span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1000</span><span class="token punctuation">,</span> <span class="token number">10000</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token number">5</span>s<span class="token punctuation">)</span><span class="token punctuation">,</span> TAGS<span class="token punctuation">(</span>city<span class="token punctuation">,</span> workshop<span class="token punctuation">)</span><span class="token punctuation">;</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>查询结果如下</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+--------+--------+------------------+ |
| | Time| city|workshop| avg(temperature)| |
| +-----------------------------+--------+--------+------------------+ |
| |1970-01-01T08:00:01.000+08:00| NULL| NULL| 50.91999893188476| |
| |1970-01-01T08:00:01.000+08:00|Shanghai| w1|113.20000076293945| |
| |1970-01-01T08:00:01.000+08:00| Beijing| w2| 103.4| |
| |1970-01-01T08:00:01.000+08:00|Shanghai| w2| 100.1999994913737| |
| |1970-01-01T08:00:01.000+08:00| Beijing| w1|103.81666692097981| |
| |1970-01-01T08:00:06.000+08:00| NULL| NULL| 50.5| |
| |1970-01-01T08:00:06.000+08:00|Shanghai| w1| 112.6500015258789| |
| |1970-01-01T08:00:06.000+08:00| Beijing| w2| 106.9000015258789| |
| |1970-01-01T08:00:06.000+08:00|Shanghai| w2| 99.80000305175781| |
| |1970-01-01T08:00:06.000+08:00| Beijing| w1| 103.5| |
| +-----------------------------+--------+--------+------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>和标签聚合相比，基于时间区间的标签聚合的查询会首先按照时间区间划定聚合范围，在时间区间内部再根据指定的标签顺序，进行相应数据的聚合计算。在输出的结果集中，会包含一列时间列，该时间列值的含义和时间区间聚合查询的相同。</p><h3 id="标签聚合查询的限制" tabindex="-1"><a class="header-anchor" href="#标签聚合查询的限制"><span>标签聚合查询的限制</span></a></h3><p>由于标签聚合功能仍然处于开发阶段，目前有如下未实现功能。</p><blockquote><ol><li>暂不支持 <code>HAVING</code> 子句过滤查询结果。</li><li>暂不支持结果按照标签值排序。</li><li>暂不支持 <code>LIMIT</code>，<code>OFFSET</code>，<code>SLIMIT</code>，<code>SOFFSET</code>。</li><li>暂不支持 <code>ALIGN BY DEVICE</code>。</li><li>暂不支持聚合函数内部包含表达式，例如 <code>count(s+1)</code>。</li><li>不支持值过滤条件聚合，和分层聚合查询行为保持一致。</li></ol></blockquote>`,132),o=[p];function c(l,i){return s(),a("div",null,o)}const r=n(t,[["render",c],["__file","Group-By.html.vue"]]),d=JSON.parse('{"path":"/zh/UserGuide/V1.0.x/Query-Data/Group-By.html","title":"分段分组聚合","lang":"zh-CN","frontmatter":{"description":"分段分组聚合 时间区间分段聚合 分段聚合是一种时序数据典型的查询方式，数据以高频进行采集，需要按照一定的时间间隔进行聚合计算，如计算每天的平均气温，需要将气温的序列按天进行分段，然后计算平均值。 在 IoTDB 中，聚合查询可以通过 GROUP BY 子句指定按照时间区间分段聚合。用户可以指定聚合的时间间隔和滑动步长，相关参数如下： 参数 1：时间轴显...","head":[["link",{"rel":"alternate","hreflang":"en-us","href":"https://iotdb.apache.org/UserGuide/V1.0.x/Query-Data/Group-By.html"}],["meta",{"property":"og:url","content":"https://iotdb.apache.org/zh/UserGuide/V1.0.x/Query-Data/Group-By.html"}],["meta",{"property":"og:site_name","content":"IoTDB Website"}],["meta",{"property":"og:title","content":"分段分组聚合"}],["meta",{"property":"og:description","content":"分段分组聚合 时间区间分段聚合 分段聚合是一种时序数据典型的查询方式，数据以高频进行采集，需要按照一定的时间间隔进行聚合计算，如计算每天的平均气温，需要将气温的序列按天进行分段，然后计算平均值。 在 IoTDB 中，聚合查询可以通过 GROUP BY 子句指定按照时间区间分段聚合。用户可以指定聚合的时间间隔和滑动步长，相关参数如下： 参数 1：时间轴显..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"zh-CN"}],["meta",{"property":"og:locale:alternate","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2023-07-10T03:11:17.000Z"}],["meta",{"property":"article:modified_time","content":"2023-07-10T03:11:17.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"分段分组聚合\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2023-07-10T03:11:17.000Z\\",\\"author\\":[]}"]]},"headers":[{"level":2,"title":"时间区间分段聚合","slug":"时间区间分段聚合","link":"#时间区间分段聚合","children":[{"level":3,"title":"未指定滑动步长的时间区间分组聚合查询","slug":"未指定滑动步长的时间区间分组聚合查询","link":"#未指定滑动步长的时间区间分组聚合查询","children":[]},{"level":3,"title":"指定滑动步长的时间区间分组聚合查询","slug":"指定滑动步长的时间区间分组聚合查询","link":"#指定滑动步长的时间区间分组聚合查询","children":[]},{"level":3,"title":"按照自然月份的时间区间分组聚合查询","slug":"按照自然月份的时间区间分组聚合查询","link":"#按照自然月份的时间区间分组聚合查询","children":[]},{"level":3,"title":"左开右闭区间","slug":"左开右闭区间","link":"#左开右闭区间","children":[]},{"level":3,"title":"与分组聚合混合使用","slug":"与分组聚合混合使用","link":"#与分组聚合混合使用","children":[]}]},{"level":2,"title":"路径层级分组聚合","slug":"路径层级分组聚合","link":"#路径层级分组聚合","children":[]},{"level":2,"title":"标签分组聚合","slug":"标签分组聚合","link":"#标签分组聚合","children":[{"level":3,"title":"单标签聚合查询","slug":"单标签聚合查询","link":"#单标签聚合查询","children":[]},{"level":3,"title":"多标签聚合查询","slug":"多标签聚合查询","link":"#多标签聚合查询","children":[]},{"level":3,"title":"基于时间区间的标签聚合查询","slug":"基于时间区间的标签聚合查询","link":"#基于时间区间的标签聚合查询","children":[]},{"level":3,"title":"标签聚合查询的限制","slug":"标签聚合查询的限制","link":"#标签聚合查询的限制","children":[]}]}],"git":{"createdTime":1688958677000,"updatedTime":1688958677000,"contributors":[{"name":"CritasWang","email":"critas@outlook.com","commits":1}]},"readingTime":{"minutes":17.25,"words":5175},"filePathRelative":"zh/UserGuide/V1.0.x/Query-Data/Group-By.md","localizedDate":"2023年7月10日","autoDesc":true}');export{r as comp,d as data}; |
