import{_ as s,r as d,o as a,c as l,a as r,d as t,e,b as o,w as c,f as i}from"./app-dNeAgOFp.js";const u={},v=i('<h1 id="死区处理" tabindex="-1"><a class="header-anchor" href="#死区处理" aria-hidden="true">#</a> 死区处理</h1><h2 id="旋转门压缩" tabindex="-1"><a class="header-anchor" href="#旋转门压缩" aria-hidden="true">#</a> 旋转门压缩</h2><p>旋转门压缩（SDT）算法是一种死区处理算法。SDT 的计算复杂度较低，并使用线性趋势来表示大量数据。</p><p>在 IoTDB 中，SDT 在刷新到磁盘时会压缩并丢弃数据。</p><p>IoTDB 允许您在创建时间序列时指定 SDT 的属性，并支持以下三个属性：</p><ul><li>CompDev （Compression Deviation，压缩偏差）</li></ul><p>CompDev 是 SDT 中最重要的参数，它表示当前样本与当前线性趋势之间的最大差值。CompDev 设置的值需要大于 0。</p><ul><li>CompMinTime （Compression Minimum Time Interval，最小压缩时间间隔）</li></ul><p>CompMinTime 是测量两个存储的数据点之间的时间距离的参数，用于减少噪声。<br> 如果当前点和最后存储的点之间的时间间隔小于或等于其值，则无论压缩偏差如何，都不会存储当前点。<br> 默认值为 0，单位为毫秒。</p><ul><li>CompMaxTime （Compression Maximum Time Interval，最大压缩时间间隔）</li></ul><p>CompMaxTime 是测量两个存储的数据点之间的时间距离的参数。<br> 如果当前点和最后一个存储点之间的时间间隔大于或等于其值，<br> 无论压缩偏差如何，都将存储当前点。<br> 默认值为 9,223,372,036,854,775,807，单位为毫秒。</p><p>支持的数据类型：</p><ul><li>INT32（整型）</li><li>INT64（长整型）</li><li>FLOAT（单精度浮点数）</li><li>DOUBLE（双精度浮点数）</li></ul>',13),m=i(`<p>以下是使用 SDT 压缩的示例。</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>IoTDB&gt; CREATE TIMESERIES root.sg1.d0.s0 WITH DATATYPE=INT32,ENCODING=PLAIN,DEADBAND=SDT,COMPDEV=2
</code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>刷入磁盘和 SDT 压缩之前，结果如下所示：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>IoTDB&gt; SELECT s0 FROM root.sg1.d0
+-----------------------------+--------------+
|                         Time|root.sg1.d0.s0|
+-----------------------------+--------------+
|2017-11-01T00:06:00.001+08:00|             1|
|2017-11-01T00:06:00.002+08:00|             1|
|2017-11-01T00:06:00.003+08:00|             1|
|2017-11-01T00:06:00.004+08:00|             1|
|2017-11-01T00:06:00.005+08:00|             1|
|2017-11-01T00:06:00.006+08:00|             1|
|2017-11-01T00:06:00.007+08:00|             1|
|2017-11-01T00:06:00.015+08:00|            10|
|2017-11-01T00:06:00.016+08:00|            20|
|2017-11-01T00:06:00.017+08:00|             1|
|2017-11-01T00:06:00.018+08:00|            30|
+-----------------------------+--------------+
Total line number = 11
It costs 0.008s
</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></div><p>刷入磁盘和 SDT 压缩之后，结果如下所示：</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>IoTDB&gt; FLUSH
IoTDB&gt; SELECT s0 FROM root.sg1.d0
+-----------------------------+--------------+
|                         Time|root.sg1.d0.s0|
+-----------------------------+--------------+
|2017-11-01T00:06:00.001+08:00|             1|
|2017-11-01T00:06:00.007+08:00|             1|
|2017-11-01T00:06:00.015+08:00|            10|
|2017-11-01T00:06:00.016+08:00|            20|
|2017-11-01T00:06:00.017+08:00|             1|
+-----------------------------+--------------+
Total line number = 5
It costs 0.044s
</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>SDT 在刷新到磁盘时进行压缩。 SDT 算法始终存储第一个点，并且不存储最后一个点。</p><p>时间范围在 [2017-11-01T00:06:00.001, 2017-11-01T00:06:00.007] 的数据在压缩偏差内，因此被压缩和丢弃。<br> 之所以存储时间为 2017-11-01T00:06:00.007 的数据点，是因为下一个数据点 2017-11-01T00:06:00.015 的值超过压缩偏差。<br> 当一个数据点超过压缩偏差时，SDT 将存储上一个读取的数据点，并重新计算上下压缩边界。作为最后一个数据点，不存储时间 2017-11-01T00:06:00.018。</p>`,8);function T(p,b){const n=d("RouterLink");return a(),l("div",null,[r(`

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`),v,t("p",null,[e("SDT 的指定语法详见本文 "),o(n,{to:"/zh/UserGuide/V1.2.x/Reference/SQL-Reference.html"},{default:c(()=>[e("SQL 参考文档")]),_:1}),e("。")]),m])}const D=s(u,[["render",T],["__file","Deadband-Process.html.vue"]]);export{D as default};