| import{_ as s,r as t,o as a,c as r,a as o,d,e,b as l,w as c,f as i}from"./app-0b7998a5.js";const m={},u=i('<h1 id="deadband-process" tabindex="-1"><a class="header-anchor" href="#deadband-process" aria-hidden="true">#</a> Deadband Process</h1><h2 id="sdt" tabindex="-1"><a class="header-anchor" href="#sdt" aria-hidden="true">#</a> SDT</h2><p>The Swinging Door Trending (SDT) algorithm is a deadband process algorithm.<br> SDT has low computational complexity and uses a linear trend to represent a quantity of data.</p><p>In IoTDB SDT compresses and discards data when flushing into the disk.</p><p>IoTDB allows you to specify the properties of SDT when creating a time series, and supports three properties:</p><ul><li>CompDev (Compression Deviation)</li></ul><p>CompDev is the most important parameter in SDT that represents the maximum difference between the<br> current sample and the current linear trend. CompDev needs to be greater than 0 to perform compression.</p><ul><li>CompMinTime (Compression Minimum Time Interval)</li></ul><p>CompMinTime is a parameter measures the time distance between two stored data points, which is used for noisy reduction.<br> If the time interval between the current point and the last stored point is less than or equal to its value,<br> current point will NOT be stored regardless of compression deviation.<br> The default value is 0 with time unit ms.</p><ul><li>CompMaxTime (Compression Maximum Time Interval)</li></ul><p>CompMaxTime is a parameter measure the time distance between two stored data points.<br> If the time interval between the current point and the last stored point is greater than or equal to its value,<br> current point will be stored regardless of compression deviation.<br> The default value is 9,223,372,036,854,775,807 with time unit ms.</p>',11),p=i(`<p>Supported datatypes:</p><ul><li>INT32 (Integer)</li><li>INT64 (Long Integer)</li><li>FLOAT (Single Precision Floating Point)</li><li>DOUBLE (Double Precision Floating Point)</li></ul><p>The following is an example of using SDT compression.</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>IoTDB> 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>Prior to flushing and SDT compression, the results are shown below:</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>IoTDB> 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>After flushing and SDT compression, the results are shown below:</p><div class="language-text line-numbers-mode" data-ext="text"><pre class="language-text"><code>IoTDB> FLUSH |
| IoTDB> 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 takes effect when flushing to the disk. The SDT algorithm always stores the first point and does not store the last point.</p><p>The data in [2017-11-01T00:06:00.001, 2017-11-01T00:06:00.007] is within the compression deviation thus discarded.<br> The data point at time 2017-11-01T00:06:00.007 is stored because the next data point at time 2017-11-01T00:06:00.015<br> exceeds compression deviation. When a data point exceeds the compression deviation, SDT stores the last read<br> point and updates the upper and lower boundaries. The last point at time 2017-11-01T00:06:00.018 is not stored.</p>`,10);function h(v,T){const n=t("RouterLink");return a(),r("div",null,[o(` |
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| |
| `),u,d("p",null,[e("The specified syntax for SDT is detailed in "),l(n,{to:"/UserGuide/Master/Reference/SQL-Reference.html"},{default:c(()=>[e("Create Timeseries Statement")]),_:1}),e(".")]),p])}const g=s(m,[["render",h],["__file","Deadband-Process.html.vue"]]);export{g as default}; |
