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import{_ as i,c as l,b as s,d as t,e as n,a as p,w as r,r as d,o}from"./app-Bhq43HqP.js";const c={};function m(v,e){const a=d("RouteLink");return o(),l("div",null,[e[3]||(e[3]=s('<h1 id="死区处理" tabindex="-1"><a class="header-anchor" href="#死区处理"><span>死区处理</span></a></h1><h2 id="旋转门压缩" tabindex="-1"><a class="header-anchor" href="#旋转门压缩"><span>旋转门压缩</span></a></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)),t("p",null,[e[1]||(e[1]=n("SDT 的指定语法详见本文 ")),p(a,{to:"/zh/UserGuide/V1.0.x/Reference/SQL-Reference.html"},{default:r(()=>e[0]||(e[0]=[n("SQL 参考文档")])),_:1}),e[2]||(e[2]=n("。"))]),e[4]||(e[4]=s(`<p>以下是使用 SDT 压缩的示例。</p><div class="language-text line-numbers-mode" data-highlighter="prismjs" data-ext="text" data-title="text"><pre><code><span class="line">IoTDB&gt; CREATE TIMESERIES root.sg1.d0.s0 WITH DATATYPE=INT32,ENCODING=PLAIN,DEADBAND=SDT,COMPDEV=2</span>
<span class="line"></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><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-highlighter="prismjs" data-ext="text" data-title="text"><pre><code><span class="line">IoTDB&gt; SELECT s0 FROM root.sg1.d0</span>
<span class="line">+-----------------------------+--------------+</span>
<span class="line">| Time|root.sg1.d0.s0|</span>
<span class="line">+-----------------------------+--------------+</span>
<span class="line">|2017-11-01T00:06:00.001+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.002+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.003+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.004+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.005+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.006+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.007+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.015+08:00| 10|</span>
<span class="line">|2017-11-01T00:06:00.016+08:00| 20|</span>
<span class="line">|2017-11-01T00:06:00.017+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.018+08:00| 30|</span>
<span class="line">+-----------------------------+--------------+</span>
<span class="line">Total line number = 11</span>
<span class="line">It costs 0.008s</span>
<span class="line"></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><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-highlighter="prismjs" data-ext="text" data-title="text"><pre><code><span class="line">IoTDB&gt; FLUSH</span>
<span class="line">IoTDB&gt; SELECT s0 FROM root.sg1.d0</span>
<span class="line">+-----------------------------+--------------+</span>
<span class="line">| Time|root.sg1.d0.s0|</span>
<span class="line">+-----------------------------+--------------+</span>
<span class="line">|2017-11-01T00:06:00.001+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.007+08:00| 1|</span>
<span class="line">|2017-11-01T00:06:00.015+08:00| 10|</span>
<span class="line">|2017-11-01T00:06:00.016+08:00| 20|</span>
<span class="line">|2017-11-01T00:06:00.017+08:00| 1|</span>
<span class="line">+-----------------------------+--------------+</span>
<span class="line">Total line number = 5</span>
<span class="line">It costs 0.044s</span>
<span class="line"></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><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))])}const T=i(c,[["render",m],["__file","Deadband-Process.html.vue"]]),b=JSON.parse('{"path":"/zh/UserGuide/V1.0.x/Data-Concept/Deadband-Process.html","title":"死区处理","lang":"zh-CN","frontmatter":{"description":"死区处理 旋转门压缩 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。 在 IoTDB 中，SDT 在刷新到磁盘时会压缩并丢弃数据。 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： CompDev （Compression Deviation，压缩偏差） CompDev ...","head":[["link",{"rel":"alternate","hreflang":"en-us","href":"https://iotdb.apache.org/UserGuide/V1.0.x/Data-Concept/Deadband-Process.html"}],["meta",{"property":"og:url","content":"https://iotdb.apache.org/zh/UserGuide/V1.0.x/Data-Concept/Deadband-Process.html"}],["meta",{"property":"og:site_name","content":"IoTDB Website"}],["meta",{"property":"og:title","content":"死区处理"}],["meta",{"property":"og:description","content":"死区处理 旋转门压缩 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。 在 IoTDB 中，SDT 在刷新到磁盘时会压缩并丢弃数据。 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： CompDev （Compression Deviation，压缩偏差） CompDev ..."}],["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\\":\\"死区处理\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2023-07-10T03:11:17.000Z\\",\\"author\\":[]}"]]},"headers":[{"level":2,"title":"旋转门压缩","slug":"旋转门压缩","link":"#旋转门压缩","children":[]}],"git":{"createdTime":1688958677000,"updatedTime":1688958677000,"contributors":[{"name":"CritasWang","email":"critas@outlook.com","commits":1}]},"readingTime":{"minutes":2.88,"words":863},"filePathRelative":"zh/UserGuide/V1.0.x/Data-Concept/Deadband-Process.md","localizedDate":"2023年7月10日","autoDesc":true}');export{T as comp,b as data};