| <!doctype html> |
| <html lang="zh-CN" data-theme="light"> |
| <head> |
| <meta charset="utf-8" /> |
| <meta name="viewport" content="width=device-width,initial-scale=1" /> |
| <meta name="generator" content="VuePress 2.0.0-rc.9" /> |
| <meta name="theme" content="VuePress Theme Hope 2.0.0-rc.34" /> |
| <style> |
| html { |
| background: var(--bg-color, #fff); |
| } |
| |
| html[data-theme="dark"] { |
| background: var(--bg-color, #1d1e1f); |
| } |
| |
| body { |
| background: var(--bg-color); |
| } |
| </style> |
| <script> |
| const userMode = localStorage.getItem("vuepress-theme-hope-scheme"); |
| const systemDarkMode = |
| window.matchMedia && |
| window.matchMedia("(prefers-color-scheme: dark)").matches; |
| |
| if (userMode === "dark" || (userMode !== "light" && systemDarkMode)) { |
| document.documentElement.setAttribute("data-theme", "dark"); |
| } |
| </script> |
| <link rel="alternate" hreflang="en-us" href="https://iotdb.apache.org/UserGuide/V1.1.x/Query-Data/Group-By.html"><meta property="og:url" content="https://iotdb.apache.org/zh/UserGuide/V1.1.x/Query-Data/Group-By.html"><meta property="og:site_name" content="IoTDB Website"><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":"","image":[""],"dateModified":"2023-07-10T03:11:17.000Z","author":[]}</script><link rel="icon" href="/favicon.ico"><meta name="Description" content="Apache IoTDB: Time Series Database for IoT"><meta name="Keywords" content="TSDB, time series, time series database, IoTDB, IoT database, IoT data management,时序数据库, 时间序列管理, IoTDB, 物联网数据库, 实时数据库, 物联网数据管理, 物联网数据"><meta name="baidu-site-verification" content="wfKETzB3OT"><meta name="google-site-verification" content="mZWAoRY0yj_HAr-s47zHCGHzx5Ju-RVm5wDbPnwQYFo"><script type="text/javascript"> |
| var _paq = window._paq = window._paq || []; |
| /* tracker methods like "setCustomDimension" should be called before "trackPageView" */ |
| _paq.push(["setDoNotTrack", true]); |
| _paq.push(["disableCookies"]); |
| _paq.push(['trackPageView']); |
| _paq.push(['enableLinkTracking']); |
| (function() { |
| var u="https://analytics.apache.org/"; |
| _paq.push(['setTrackerUrl', u+'matomo.php']); |
| _paq.push(['setSiteId', '56']); |
| var d=document, g=d.createElement('script'), s=d.getElementsByTagName('script')[0]; |
| g.async=true; g.src=u+'matomo.js'; s.parentNode.insertBefore(g,s); |
| })(); |
| </script><title>IoTDB Website</title><meta name="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..."> |
| <link rel="preload" href="/assets/style-DnEHAOmf.css" as="style"><link rel="stylesheet" href="/assets/style-DnEHAOmf.css"> |
| <link rel="modulepreload" href="/assets/app-DrPcRZG6.js"><link rel="modulepreload" href="/assets/Group-By.html-DPZM77HS.js"> |
| |
| </head> |
| <body> |
| <div id="app"><!--[--><!--[--><!--[--><span tabindex="-1"></span><a href="#main-content" class="vp-skip-link sr-only">跳至主要內容</a><!--]--><!--[--><div class="theme-container has-toc"><!--[--><header id="navbar" class="vp-navbar hide-icon"><div class="vp-navbar-start"><button type="button" class="vp-toggle-sidebar-button" title="Toggle Sidebar"><span class="icon"></span></button><!--[--><!----><!--]--><!--[--><a class="route-link vp-brand" href="/zh/"><img class="vp-nav-logo" src="/logo.png" alt><!----><span class="vp-site-name hide-in-pad">IoTDB Website</span></a><!--]--><!--[--><!----><!--]--></div><div class="vp-navbar-center"><!--[--><!----><!--]--><!--[--><!--]--><!--[--><!----><!--]--></div><div class="vp-navbar-end"><!--[--><!----><!--]--><!--[--><!--[--><div id="docsearch-container" style="display:none;"></div><div><button type="button" class="DocSearch DocSearch-Button" aria-label="搜索文档"><span class="DocSearch-Button-Container"><svg width="20" height="20" class="DocSearch-Search-Icon" viewBox="0 0 20 20"><path d="M14.386 14.386l4.0877 4.0877-4.0877-4.0877c-2.9418 2.9419-7.7115 2.9419-10.6533 0-2.9419-2.9418-2.9419-7.7115 0-10.6533 2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">搜索文档</span></span><span class="DocSearch-Button-Keys"><kbd class="DocSearch-Button-Key"><svg width="15" height="15" class="DocSearch-Control-Key-Icon"><path d="M4.505 4.496h2M5.505 5.496v5M8.216 4.496l.055 5.993M10 7.5c.333.333.5.667.5 1v2M12.326 4.5v5.996M8.384 4.496c1.674 0 2.116 0 2.116 1.5s-.442 1.5-2.116 1.5M3.205 9.303c-.09.448-.277 1.21-1.241 1.203C1 10.5.5 9.513.5 8V7c0-1.57.5-2.5 1.464-2.494.964.006 1.134.598 1.24 1.342M12.553 10.5h1.953" stroke-width="1.2" stroke="currentColor" fill="none" stroke-linecap="square"></path></svg></kbd><kbd class="DocSearch-Button-Key">K</kbd></span></button></div><!--]--><nav class="vp-nav-links"><div class="vp-nav-item hide-in-mobile"><div class="dropdown-wrapper"><button type="button" class="dropdown-title" aria-label="文档"><span class="title"><!---->文档</span><span class="arrow"></span><ul class="nav-dropdown"><li class="dropdown-item"><a class="route-link nav-link" href="/zh/UserGuide/latest/QuickStart/QuickStart.html" aria-label="v1.3.x"><!---->v1.3.x<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/UserGuide/V1.2.x/QuickStart/QuickStart.html" aria-label="v1.2.x"><!---->v1.2.x<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/UserGuide/V1.1.x/QuickStart/QuickStart.html" aria-label="v1.1.x"><!---->v1.1.x<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/UserGuide/V1.0.x/QuickStart/QuickStart.html" aria-label="v1.0.x"><!---->v1.0.x<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/UserGuide/V0.13.x/QuickStart/QuickStart.html" aria-label="v0.13.x"><!---->v0.13.x<!----></a></li></ul></button></div></div><div class="vp-nav-item hide-in-mobile"><a href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=177051872" rel="noopener noreferrer" target="_blank" aria-label="系统设计" class="nav-link"><!---->系统设计<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></div><div class="vp-nav-item hide-in-mobile"><a class="route-link nav-link" href="/zh/Download/" aria-label="下载"><!---->下载<!----></a></div><div class="vp-nav-item hide-in-mobile"><div class="dropdown-wrapper"><button type="button" class="dropdown-title" aria-label="社区"><span class="title"><!---->社区</span><span class="arrow"></span><ul class="nav-dropdown"><li class="dropdown-item"><a class="route-link nav-link" href="/zh/Community/About.html" aria-label="关于社区"><!---->关于社区<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/Community/Development-Guide.html" aria-label="贡献指南"><!---->贡献指南<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/Community/Powered-By.html" aria-label="社区伙伴"><!---->社区伙伴<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/Community/Feedback.html" aria-label="交流与反馈"><!---->交流与反馈<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/Community/Materials.html" aria-label="活动与报告"><!---->活动与报告<!----></a></li><li class="dropdown-item"><a class="route-link nav-link" href="/zh/Community/Community-Project-Committers.html" aria-label="Commiters"><!---->Commiters<!----></a></li></ul></button></div></div><div class="vp-nav-item hide-in-mobile"><div class="dropdown-wrapper"><button type="button" class="dropdown-title" aria-label="ASF"><span class="title"><!---->ASF</span><span class="arrow"></span><ul class="nav-dropdown"><li class="dropdown-item"><a href="https://www.apache.org/" rel="noopener noreferrer" target="_blank" aria-label="基金会" class="nav-link"><!---->基金会<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></li><li class="dropdown-item"><a href="https://www.apache.org/licenses/" rel="noopener noreferrer" target="_blank" aria-label="许可证" class="nav-link"><!---->许可证<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></li><li class="dropdown-item"><a href="https://www.apache.org/security/" rel="noopener noreferrer" target="_blank" aria-label="安全" class="nav-link"><!---->安全<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></li><li class="dropdown-item"><a href="https://www.apache.org/foundation/sponsorship.html" rel="noopener noreferrer" target="_blank" aria-label="赞助" class="nav-link"><!---->赞助<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></li><li class="dropdown-item"><a href="https://www.apache.org/foundation/thanks.html" rel="noopener noreferrer" target="_blank" aria-label="致谢" class="nav-link"><!---->致谢<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></li><li class="dropdown-item"><a href="https://www.apache.org/events/current-event" rel="noopener noreferrer" target="_blank" aria-label="活动" class="nav-link"><!---->活动<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></li><li class="dropdown-item"><a href="https://privacy.apache.org/policies/privacy-policy-public.html" rel="noopener noreferrer" target="_blank" aria-label="隐私" class="nav-link"><!---->隐私<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></li></ul></button></div></div></nav><div class="vp-nav-item"><div class="dropdown-wrapper"><button type="button" class="dropdown-title" aria-label="选择语言"><!--[--><svg xmlns="http://www.w3.org/2000/svg" class="icon i18n-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="i18n icon" style="width:1rem;height:1rem;vertical-align:middle;"><path d="M379.392 460.8 494.08 575.488l-42.496 102.4L307.2 532.48 138.24 701.44l-71.68-72.704L234.496 460.8l-45.056-45.056c-27.136-27.136-51.2-66.56-66.56-108.544h112.64c7.68 14.336 16.896 27.136 26.112 35.84l45.568 46.08 45.056-45.056C382.976 312.32 409.6 247.808 409.6 204.8H0V102.4h256V0h102.4v102.4h256v102.4H512c0 70.144-37.888 161.28-87.04 210.944L378.88 460.8zM576 870.4 512 1024H409.6l256-614.4H768l256 614.4H921.6l-64-153.6H576zM618.496 768h196.608L716.8 532.48 618.496 768z"></path></svg><!--]--><span class="arrow"></span><ul class="nav-dropdown"><li class="dropdown-item"><a class="route-link nav-link" href="/UserGuide/V1.1.x/Query-Data/Group-By.html" aria-label="English"><!---->English<!----></a></li><li class="dropdown-item"><a class="route-link nav-link active" href="/zh/UserGuide/V1.1.x/Query-Data/Group-By.html" aria-label="简体中文"><!---->简体中文<!----></a></li></ul></button></div></div><div class="vp-nav-item hide-in-mobile"><button type="button" id="appearance-switch"><svg xmlns="http://www.w3.org/2000/svg" class="icon auto-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="auto icon" style="display:none;"><path d="M512 992C246.92 992 32 777.08 32 512S246.92 32 512 32s480 214.92 480 480-214.92 480-480 480zm0-840c-198.78 0-360 161.22-360 360 0 198.84 161.22 360 360 360s360-161.16 360-360c0-198.78-161.22-360-360-360zm0 660V212c165.72 0 300 134.34 300 300 0 165.72-134.28 300-300 300z"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="icon dark-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="dark icon" style="display:none;"><path d="M524.8 938.667h-4.267a439.893 439.893 0 0 1-313.173-134.4 446.293 446.293 0 0 1-11.093-597.334A432.213 432.213 0 0 1 366.933 90.027a42.667 42.667 0 0 1 45.227 9.386 42.667 42.667 0 0 1 10.24 42.667 358.4 358.4 0 0 0 82.773 375.893 361.387 361.387 0 0 0 376.747 82.774 42.667 42.667 0 0 1 54.187 55.04 433.493 433.493 0 0 1-99.84 154.88 438.613 438.613 0 0 1-311.467 128z"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="icon light-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="light icon" style="display:block;"><path d="M952 552h-80a40 40 0 0 1 0-80h80a40 40 0 0 1 0 80zM801.88 280.08a41 41 0 0 1-57.96-57.96l57.96-58a41.04 41.04 0 0 1 58 58l-58 57.96zM512 752a240 240 0 1 1 0-480 240 240 0 0 1 0 480zm0-560a40 40 0 0 1-40-40V72a40 40 0 0 1 80 0v80a40 40 0 0 1-40 40zm-289.88 88.08-58-57.96a41.04 41.04 0 0 1 58-58l57.96 58a41 41 0 0 1-57.96 57.96zM192 512a40 40 0 0 1-40 40H72a40 40 0 0 1 0-80h80a40 40 0 0 1 40 40zm30.12 231.92a41 41 0 0 1 57.96 57.96l-57.96 58a41.04 41.04 0 0 1-58-58l58-57.96zM512 832a40 40 0 0 1 40 40v80a40 40 0 0 1-80 0v-80a40 40 0 0 1 40-40zm289.88-88.08 58 57.96a41.04 41.04 0 0 1-58 58l-57.96-58a41 41 0 0 1 57.96-57.96z"></path></svg></button></div><div class="vp-nav-item vp-action"><a class="vp-action-link" href="https://github.com/apache/iotdb" target="_blank" rel="noopener noreferrer" aria-label="GitHub"><svg xmlns="http://www.w3.org/2000/svg" class="icon github-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="github icon" style="width:1.25rem;height:1.25rem;vertical-align:middle;"><path d="M511.957 21.333C241.024 21.333 21.333 240.981 21.333 512c0 216.832 140.544 400.725 335.574 465.664 24.49 4.395 32.256-10.07 32.256-23.083 0-11.69.256-44.245 0-85.205-136.448 29.61-164.736-64.64-164.736-64.64-22.315-56.704-54.4-71.765-54.4-71.765-44.587-30.464 3.285-29.824 3.285-29.824 49.195 3.413 75.179 50.517 75.179 50.517 43.776 75.008 114.816 53.333 142.762 40.79 4.523-31.66 17.152-53.377 31.19-65.537-108.971-12.458-223.488-54.485-223.488-242.602 0-53.547 19.114-97.323 50.517-131.67-5.035-12.33-21.93-62.293 4.779-129.834 0 0 41.258-13.184 134.912 50.346a469.803 469.803 0 0 1 122.88-16.554c41.642.213 83.626 5.632 122.88 16.554 93.653-63.488 134.784-50.346 134.784-50.346 26.752 67.541 9.898 117.504 4.864 129.834 31.402 34.347 50.474 78.123 50.474 131.67 0 188.586-114.73 230.016-224.042 242.09 17.578 15.232 33.578 44.672 33.578 90.454v135.85c0 13.142 7.936 27.606 32.854 22.87C862.25 912.597 1002.667 728.747 1002.667 512c0-271.019-219.648-490.667-490.71-490.667z"></path></svg></a></div><!--]--><!--[--><!----><!--]--><button type="button" class="vp-toggle-navbar-button" aria-label="Toggle Navbar" aria-expanded="false" aria-controls="nav-screen"><span><span class="vp-top"></span><span class="vp-middle"></span><span class="vp-bottom"></span></span></button></div></header><!----><!--]--><!----><div class="toggle-sidebar-wrapper"><span class="arrow start"></span></div><aside id="sidebar" class="vp-sidebar"><!--[--><!----><!--]--><ul class="vp-sidebar-links"><li><section class="vp-sidebar-group"><p class="vp-sidebar-header"><!----><span class="vp-sidebar-title">IoTDB用户手册 (V1.1.x)</span><!----></p><ul class="vp-sidebar-links"></ul></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">关于IoTDB</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">快速上手</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">数据模式与概念</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">语法约定</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">应用编程接口</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">元数据操作</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">数据写入（数据更新）</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">数据删除</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable active" type="button"><!----><span class="vp-sidebar-title">数据查询</span><span class="vp-arrow down"></span></button><ul class="vp-sidebar-links"><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Overview.html" aria-label="概述"><!---->概述<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Select-Expression.html" aria-label="选择表达式"><!---->选择表达式<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Last-Query.html" aria-label="最新点查询"><!---->最新点查询<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Align-By.html" aria-label="查询对齐模式"><!---->查询对齐模式<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Where-Condition.html" aria-label="查询过滤条件"><!---->查询过滤条件<!----></a></li><li><a class="route-link nav-link active vp-sidebar-link vp-sidebar-page active" href="/zh/UserGuide/V1.1.x/Query-Data/Group-By.html" aria-label="分段分组聚合"><!---->分段分组聚合<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Having-Condition.html" aria-label="聚合结果过滤"><!---->聚合结果过滤<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Order-By.html" aria-label="结果集排序"><!---->结果集排序<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Fill.html" aria-label="结果集补空值"><!---->结果集补空值<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Pagination.html" aria-label="结果集分页"><!---->结果集分页<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Select-Into.html" aria-label="查询写回"><!---->查询写回<!----></a></li><li><a class="route-link nav-link vp-sidebar-link vp-sidebar-page" href="/zh/UserGuide/V1.1.x/Query-Data/Continuous-Query.html" aria-label="连续查询"><!---->连续查询<!----></a></li></ul></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">运算符和函数</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">触发器</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">监控告警</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">权限管理</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">运维工具</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">端云协同</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">系统集成</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">分布式</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">FAQ</span><span class="vp-arrow end"></span></button><!----></section></li><li><section class="vp-sidebar-group"><button class="vp-sidebar-header clickable" type="button"><!----><span class="vp-sidebar-title">参考</span><span class="vp-arrow end"></span></button><!----></section></li></ul><!--[--><!----><!--]--></aside><!--[--><main id="main-content" class="vp-page"><!--[--><!--[--><!----><!--]--><!----><nav class="vp-breadcrumb disable"></nav><div class="vp-page-title"><h1><!----></h1><div class="page-info"><!----><!----><span class="page-date-info" aria-label="写作日期"><svg xmlns="http://www.w3.org/2000/svg" class="icon calendar-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="calendar icon"><path d="M716.4 110.137c0-18.753-14.72-33.473-33.472-33.473-18.753 0-33.473 14.72-33.473 33.473v33.473h66.993v-33.473zm-334.87 0c0-18.753-14.72-33.473-33.473-33.473s-33.52 14.72-33.52 33.473v33.473h66.993v-33.473zm468.81 33.52H716.4v100.465c0 18.753-14.72 33.473-33.472 33.473a33.145 33.145 0 01-33.473-33.473V143.657H381.53v100.465c0 18.753-14.72 33.473-33.473 33.473a33.145 33.145 0 01-33.473-33.473V143.657H180.6A134.314 134.314 0 0046.66 277.595v535.756A134.314 134.314 0 00180.6 947.289h669.74a134.36 134.36 0 00133.94-133.938V277.595a134.314 134.314 0 00-133.94-133.938zm33.473 267.877H147.126a33.145 33.145 0 01-33.473-33.473c0-18.752 14.72-33.473 33.473-33.473h736.687c18.752 0 33.472 14.72 33.472 33.473a33.145 33.145 0 01-33.472 33.473z"></path></svg><span><!----></span><meta property="datePublished" content="2023-07-10T03:11:17.000Z"></span><span class="page-pageview-info" aria-label="访问量"><svg xmlns="http://www.w3.org/2000/svg" class="icon eye-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="eye icon"><path d="M992 512.096c0-5.76-.992-10.592-1.28-11.136-.192-2.88-1.152-8.064-2.08-10.816-.256-.672-.544-1.376-.832-2.08-.48-1.568-1.024-3.104-1.6-4.32C897.664 290.112 707.104 160 512 160c-195.072 0-385.632 130.016-473.76 322.592-1.056 2.112-1.792 4.096-2.272 5.856a55.512 55.512 0 00-.64 1.6c-1.76 5.088-1.792 8.64-1.632 7.744-.832 3.744-1.568 11.168-1.568 11.168-.224 2.272-.224 4.032.032 6.304 0 0 .736 6.464 1.088 7.808.128 1.824.576 4.512 1.12 6.976h-.032c.448 2.08 1.12 4.096 1.984 6.08.48 1.536.992 2.976 1.472 4.032C126.432 733.856 316.992 864 512 864c195.136 0 385.696-130.048 473.216-321.696 1.376-2.496 2.24-4.832 2.848-6.912.256-.608.48-1.184.672-1.728 1.536-4.48 1.856-8.32 1.728-8.32l-.032.032c.608-3.104 1.568-7.744 1.568-13.28zM512 672c-88.224 0-160-71.776-160-160s71.776-160 160-160 160 71.776 160 160-71.776 160-160 160z"></path></svg><span id="ArtalkPV" class="vp-pageview waline-pageview-count" data-path="/zh/UserGuide/V1.1.x/Query-Data/Group-By.html" data-page-key="/zh/UserGuide/V1.1.x/Query-Data/Group-By.html">...</span></span><span class="page-reading-time-info" aria-label="阅读时间"><svg xmlns="http://www.w3.org/2000/svg" class="icon timer-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="timer icon"><path d="M799.387 122.15c4.402-2.978 7.38-7.897 7.38-13.463v-1.165c0-8.933-7.38-16.312-16.312-16.312H256.33c-8.933 0-16.311 7.38-16.311 16.312v1.165c0 5.825 2.977 10.874 7.637 13.592 4.143 194.44 97.22 354.963 220.201 392.763-122.204 37.542-214.893 196.511-220.2 389.397-4.661 5.049-7.638 11.651-7.638 19.03v5.825h566.49v-5.825c0-7.379-2.849-13.981-7.509-18.9-5.049-193.016-97.867-351.985-220.2-389.527 123.24-37.67 216.446-198.453 220.588-392.892zM531.16 450.445v352.632c117.674 1.553 211.787 40.778 211.787 88.676H304.097c0-48.286 95.149-87.382 213.728-88.676V450.445c-93.077-3.107-167.901-81.297-167.901-177.093 0-8.803 6.99-15.793 15.793-15.793 8.803 0 15.794 6.99 15.794 15.793 0 80.261 63.69 145.635 142.01 145.635s142.011-65.374 142.011-145.635c0-8.803 6.99-15.793 15.794-15.793s15.793 6.99 15.793 15.793c0 95.019-73.789 172.82-165.96 177.093z"></path></svg><span>大约 27 分钟</span><meta property="timeRequired" content="PT27M"></span><!----><!----></div><hr></div><div class="vp-toc-placeholder"><aside id="toc"><!--[--><!----><!--]--><div class="vp-toc-header">此页内容<button type="button" class="print-button" title="打印"><svg xmlns="http://www.w3.org/2000/svg" class="icon print-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="print icon"><path d="M819.2 364.8h-44.8V128c0-17.067-14.933-32-32-32H281.6c-17.067 0-32 14.933-32 32v236.8h-44.8C145.067 364.8 96 413.867 96 473.6v192c0 59.733 49.067 108.8 108.8 108.8h44.8V896c0 17.067 14.933 32 32 32h460.8c17.067 0 32-14.933 32-32V774.4h44.8c59.733 0 108.8-49.067 108.8-108.8v-192c0-59.733-49.067-108.8-108.8-108.8zM313.6 160h396.8v204.8H313.6V160zm396.8 704H313.6V620.8h396.8V864zM864 665.6c0 25.6-19.2 44.8-44.8 44.8h-44.8V588.8c0-17.067-14.933-32-32-32H281.6c-17.067 0-32 14.933-32 32v121.6h-44.8c-25.6 0-44.8-19.2-44.8-44.8v-192c0-25.6 19.2-44.8 44.8-44.8h614.4c25.6 0 44.8 19.2 44.8 44.8v192z"></path></svg></button><div class="arrow end"></div></div><div class="vp-toc-wrapper"><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level2" href="#分段分组聚合">分段分组聚合</a></li><li><ul class="vp-toc-list"><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#时间区间分段聚合">时间区间分段聚合</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#路径层级分组聚合">路径层级分组聚合</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#标签分组聚合">标签分组聚合</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#差值分段聚合">差值分段聚合</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#条件分段聚合">条件分段聚合</a></li><!----><!--]--><!--[--><li class="vp-toc-item"><a class="route-link vp-toc-link level3" href="#会话分段聚合">会话分段聚合</a></li><!----><!--]--></ul></li><!--]--></ul><div class="vp-toc-marker" style="top:-1.7rem;"></div></div><!--[--><!----><!--]--></aside></div><!--[--><!----><!--]--><div class="theme-hope-content"><h2 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></h2><h3 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></h3><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><h4 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></h4><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><h4 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></h4><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><h4 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></h4><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><h4 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></h4><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><h4 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></h4><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><h3 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></h3><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><h3 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></h3><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>create database root.factory1; |
| create timeseries root.factory1.d1.temperature with datatype=FLOAT tags(city=Beijing, workshop=w1); |
| create timeseries root.factory1.d2.temperature with datatype=FLOAT tags(city=Beijing, workshop=w1); |
| create timeseries root.factory1.d3.temperature with datatype=FLOAT tags(city=Beijing, workshop=w2); |
| create timeseries root.factory1.d4.temperature with datatype=FLOAT tags(city=Beijing, workshop=w2); |
| create timeseries root.factory1.d5.temperature with datatype=FLOAT tags(city=Shanghai, workshop=w1); |
| create timeseries root.factory1.d6.temperature with datatype=FLOAT tags(city=Shanghai, workshop=w1); |
| create timeseries root.factory1.d7.temperature with datatype=FLOAT tags(city=Shanghai, workshop=w2); |
| create timeseries root.factory1.d8.temperature with datatype=FLOAT; |
| create timeseries root.factory1.d9.temperature with datatype=FLOAT; |
| |
| insert into root.factory1.d1(time, temperature) values(1000, 104.0); |
| insert into root.factory1.d1(time, temperature) values(3000, 104.2); |
| insert into root.factory1.d1(time, temperature) values(5000, 103.3); |
| insert into root.factory1.d1(time, temperature) values(7000, 104.1); |
| |
| insert into root.factory1.d2(time, temperature) values(1000, 104.4); |
| insert into root.factory1.d2(time, temperature) values(3000, 103.7); |
| insert into root.factory1.d2(time, temperature) values(5000, 103.3); |
| insert into root.factory1.d2(time, temperature) values(7000, 102.9); |
| |
| insert into root.factory1.d3(time, temperature) values(1000, 103.9); |
| insert into root.factory1.d3(time, temperature) values(3000, 103.8); |
| insert into root.factory1.d3(time, temperature) values(5000, 102.7); |
| insert into root.factory1.d3(time, temperature) values(7000, 106.9); |
| |
| insert into root.factory1.d4(time, temperature) values(1000, 103.9); |
| insert into root.factory1.d4(time, temperature) values(5000, 102.7); |
| insert into root.factory1.d4(time, temperature) values(7000, 106.9); |
| |
| insert into root.factory1.d5(time, temperature) values(1000, 112.9); |
| insert into root.factory1.d5(time, temperature) values(7000, 113.0); |
| |
| insert into root.factory1.d6(time, temperature) values(1000, 113.9); |
| insert into root.factory1.d6(time, temperature) values(3000, 113.3); |
| insert into root.factory1.d6(time, temperature) values(5000, 112.7); |
| insert into root.factory1.d6(time, temperature) values(7000, 112.3); |
| |
| insert into root.factory1.d7(time, temperature) values(1000, 101.2); |
| insert into root.factory1.d7(time, temperature) values(3000, 99.3); |
| insert into root.factory1.d7(time, temperature) values(5000, 100.1); |
| insert into root.factory1.d7(time, temperature) values(7000, 99.8); |
| |
| insert into root.factory1.d8(time, temperature) values(1000, 50.0); |
| insert into root.factory1.d8(time, temperature) values(3000, 52.1); |
| insert into root.factory1.d8(time, temperature) values(5000, 50.1); |
| insert into root.factory1.d8(time, temperature) values(7000, 50.5); |
| |
| insert into root.factory1.d9(time, temperature) values(1000, 50.3); |
| insert into root.factory1.d9(time, temperature) values(3000, 52.1); |
| </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><h4 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></h4><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>SELECT AVG(temperature) FROM root.factory1.** GROUP BY TAGS(city); |
| </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><h4 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></h4><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>SELECT avg(temperature) FROM root.factory1.** GROUP BY TAGS(city, workshop); |
| </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><h4 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></h4><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>SELECT AVG(temperature) FROM root.factory1.** GROUP BY ([1000, 10000), 5s), TAGS(city, workshop); |
| </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><h4 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></h4><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><h3 id="差值分段聚合" tabindex="-1"><a class="header-anchor" href="#差值分段聚合"><span>差值分段聚合</span></a></h3><p>IoTDB支持通过<code>GROUP BY VARIATION</code>语句来根据差值进行分组。<code>GROUP BY VARIATION</code>会将第一个点作为一个组的<strong>基准点</strong>，每个新的数据在按照给定规则与基准点进行差值运算后，<br> 如果差值小于给定的阈值则将该新点归于同一组，否则结束当前分组，以这个新的数据为新的基准点开启新的分组。<br> 该分组方式不会重叠，且没有固定的开始结束时间。其子句语法如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">group</span> <span class="token keyword">by</span> variation<span class="token punctuation">(</span>controlExpression<span class="token punctuation">[</span><span class="token punctuation">,</span>delta<span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token punctuation">,</span>ignoreNull<span class="token operator">=</span><span class="token boolean">true</span><span class="token operator">/</span><span class="token boolean">false</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></div><p>不同的参数含义如下</p><ul><li>controlExpression</li></ul><p>分组所参照的值，<strong>可以是查询数据中的某一列或是多列的表达式<br> （多列表达式计算后仍为一个值，使用多列表达式时指定的列必须都为数值列）</strong>， 差值便是根据数据的controlExpression的差值运算。</p><ul><li>delta</li></ul><p>分组所使用的阈值，同一分组中<strong>每个点的controlExpression对应的值与该组中基准点对应值的差值都小于<code>delta</code></strong>。当<code>delta=0</code>时，相当于一个等值分组，所有连续且expression值相同的数据将被分到一组。</p><ul><li>ignoreNull</li></ul><p>用于指定<code>controlExpression</code>的值为null时对数据的处理方式，当<code>ignoreNull</code>为false时，该null值会被视为新的值，<code>ignoreNull</code>为true时，则直接跳过对应的点。</p><p>在<code>delta</code>取不同值时，<code>controlExpression</code>支持的返回数据类型以及当<code>ignoreNull</code>为false时对于null值的处理方式可以见下表：</p><table><thead><tr><th>delta</th><th>controlExpression支持的返回类型</th><th>ignoreNull=false时对于Null值的处理</th></tr></thead><tbody><tr><td>delta!=0</td><td>INT32、INT64、FLOAT、DOUBLE</td><td>若正在维护分组的值不为null,null视为无穷大/无穷小，结束当前分组。连续的null视为差值相等的值，会被分配在同一个分组</td></tr><tr><td>delta=0</td><td>TEXT、BINARY、INT32、INT64、FLOAT、DOUBLE</td><td>null被视为新分组中的新值，连续的null属于相同的分组</td></tr></tbody></table><p>下图为差值分段的一个分段方式示意图，与组中第一个数据的控制列值的差值在delta内的控制列对应的点属于相同的分组。</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/UserGuide/Process-Data/GroupBy/groupByVariation.jpeg" alt="groupByVariation"><h4 id="使用注意事项" tabindex="-1"><a class="header-anchor" href="#使用注意事项"><span>使用注意事项</span></a></h4><ol><li><code>controlExpression</code>的结果应该为唯一值，如果使用通配符拼接后出现多列，则报错。</li><li>对于一个分组，默认Time列输出分组的开始时间，查询时可以使用select <code>__endTime</code>的方式来使得结果输出分组的结束时间。</li><li>与<code>ALIGN BY DEVICE</code>搭配使用时会对每个device进行单独的分组操作。</li><li>当没有指定<code>delta</code>和<code>ignoreNull</code>时，<code>delta</code>默认为0，<code>ignoreNull</code>默认为true。</li><li>当前暂不支持与<code>GROUP BY LEVEL</code>搭配使用。</li></ol><p>使用如下的原始数据，接下来会给出几个事件分段查询的使用样例</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------+-------+-------+--------+-------+-------+ |
| | Time| s1| s2| s3| s4| s5| s6| |
| +-----------------------------+-------+-------+-------+--------+-------+-------+ |
| |1970-01-01T08:00:00.000+08:00| 4.5| 9.0| 0.0| 45.0| 9.0| 8.25| |
| |1970-01-01T08:00:00.010+08:00| null| 19.0| 10.0| 145.0| 19.0| 8.25| |
| |1970-01-01T08:00:00.020+08:00| 24.5| 29.0| null| 245.0| 29.0| null| |
| |1970-01-01T08:00:00.030+08:00| 34.5| null| 30.0| 345.0| null| null| |
| |1970-01-01T08:00:00.040+08:00| 44.5| 49.0| 40.0| 445.0| 49.0| 8.25| |
| |1970-01-01T08:00:00.050+08:00| null| 59.0| 50.0| 545.0| 59.0| 6.25| |
| |1970-01-01T08:00:00.060+08:00| 64.5| 69.0| 60.0| 645.0| 69.0| null| |
| |1970-01-01T08:00:00.070+08:00| 74.5| 79.0| null| null| 79.0| 3.25| |
| |1970-01-01T08:00:00.080+08:00| 84.5| 89.0| 80.0| 845.0| 89.0| 3.25| |
| |1970-01-01T08:00:00.090+08:00| 94.5| 99.0| 90.0| 945.0| 99.0| 3.25| |
| |1970-01-01T08:00:00.150+08:00| 66.5| 77.0| 90.0| 945.0| 99.0| 9.25| |
| +-----------------------------+-------+-------+-------+--------+-------+-------+ |
| </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></div><h4 id="delta-0时的等值事件分段" tabindex="-1"><a class="header-anchor" href="#delta-0时的等值事件分段"><span>delta=0时的等值事件分段</span></a></h4><p>使用如下sql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> __endTime<span class="token punctuation">,</span> <span class="token function">avg</span><span class="token punctuation">(</span>s1<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token function">count</span><span class="token punctuation">(</span>s2<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token function">sum</span><span class="token punctuation">(</span>s3<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>sg<span class="token punctuation">.</span>d <span class="token keyword">group</span> <span class="token keyword">by</span> variation<span class="token punctuation">(</span>s6<span class="token punctuation">)</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>得到如下的查询结果，这里忽略了s6为null的行</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| | Time| __endTime|avg(root.sg.d.s1)|count(root.sg.d.s2)|sum(root.sg.d.s3)| |
| +-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| |1970-01-01T08:00:00.000+08:00|1970-01-01T08:00:00.040+08:00| 24.5| 3| 50.0| |
| |1970-01-01T08:00:00.050+08:00|1970-01-01T08:00:00.050+08:00| null| 1| 50.0| |
| |1970-01-01T08:00:00.070+08:00|1970-01-01T08:00:00.090+08:00| 84.5| 3| 170.0| |
| |1970-01-01T08:00:00.150+08:00|1970-01-01T08:00:00.150+08:00| 66.5| 1| 90.0| |
| +-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| </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></div><p>当指定ignoreNull为false时，会将s6为null的数据也考虑进来</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> __endTime<span class="token punctuation">,</span> <span class="token function">avg</span><span class="token punctuation">(</span>s1<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token function">count</span><span class="token punctuation">(</span>s2<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token function">sum</span><span class="token punctuation">(</span>s3<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>sg<span class="token punctuation">.</span>d <span class="token keyword">group</span> <span class="token keyword">by</span> variation<span class="token punctuation">(</span>s6<span class="token punctuation">,</span> ignoreNull<span class="token operator">=</span><span class="token boolean">false</span><span class="token punctuation">)</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>得到如下的结果</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| | Time| __endTime|avg(root.sg.d.s1)|count(root.sg.d.s2)|sum(root.sg.d.s3)| |
| +-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| |1970-01-01T08:00:00.000+08:00|1970-01-01T08:00:00.010+08:00| 4.5| 2| 10.0| |
| |1970-01-01T08:00:00.020+08:00|1970-01-01T08:00:00.030+08:00| 29.5| 1| 30.0| |
| |1970-01-01T08:00:00.040+08:00|1970-01-01T08:00:00.040+08:00| 44.5| 1| 40.0| |
| |1970-01-01T08:00:00.050+08:00|1970-01-01T08:00:00.050+08:00| null| 1| 50.0| |
| |1970-01-01T08:00:00.060+08:00|1970-01-01T08:00:00.060+08:00| 64.5| 1| 60.0| |
| |1970-01-01T08:00:00.070+08:00|1970-01-01T08:00:00.090+08:00| 84.5| 3| 170.0| |
| |1970-01-01T08:00:00.150+08:00|1970-01-01T08:00:00.150+08:00| 66.5| 1| 90.0| |
| +-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| </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><h4 id="delta-0时的差值事件分段" tabindex="-1"><a class="header-anchor" href="#delta-0时的差值事件分段"><span>delta!=0时的差值事件分段</span></a></h4><p>使用如下sql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> __endTime<span class="token punctuation">,</span> <span class="token function">avg</span><span class="token punctuation">(</span>s1<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token function">count</span><span class="token punctuation">(</span>s2<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token function">sum</span><span class="token punctuation">(</span>s3<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>sg<span class="token punctuation">.</span>d <span class="token keyword">group</span> <span class="token keyword">by</span> variation<span class="token punctuation">(</span>s6<span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">)</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>得到如下的查询结果</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| | Time| __endTime|avg(root.sg.d.s1)|count(root.sg.d.s2)|sum(root.sg.d.s3)| |
| +-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| |1970-01-01T08:00:00.000+08:00|1970-01-01T08:00:00.050+08:00| 24.5| 4| 100.0| |
| |1970-01-01T08:00:00.070+08:00|1970-01-01T08:00:00.090+08:00| 84.5| 3| 170.0| |
| |1970-01-01T08:00:00.150+08:00|1970-01-01T08:00:00.150+08:00| 66.5| 1| 90.0| |
| +-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| </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>group by子句中的controlExpression同样支持列的表达式</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> __endTime<span class="token punctuation">,</span> <span class="token function">avg</span><span class="token punctuation">(</span>s1<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token function">count</span><span class="token punctuation">(</span>s2<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token function">sum</span><span class="token punctuation">(</span>s3<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>sg<span class="token punctuation">.</span>d <span class="token keyword">group</span> <span class="token keyword">by</span> variation<span class="token punctuation">(</span>s6<span class="token operator">+</span>s5<span class="token punctuation">,</span> <span class="token number">10</span><span class="token punctuation">)</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>得到如下的查询结果</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| | Time| __endTime|avg(root.sg.d.s1)|count(root.sg.d.s2)|sum(root.sg.d.s3)| |
| +-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| |1970-01-01T08:00:00.000+08:00|1970-01-01T08:00:00.010+08:00| 4.5| 2| 10.0| |
| |1970-01-01T08:00:00.040+08:00|1970-01-01T08:00:00.050+08:00| 44.5| 2| 90.0| |
| |1970-01-01T08:00:00.070+08:00|1970-01-01T08:00:00.080+08:00| 79.5| 2| 80.0| |
| |1970-01-01T08:00:00.090+08:00|1970-01-01T08:00:00.150+08:00| 80.5| 2| 180.0| |
| +-----------------------------+-----------------------------+-----------------+-------------------+-----------------+ |
| </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></div><h3 id="条件分段聚合" tabindex="-1"><a class="header-anchor" href="#条件分段聚合"><span>条件分段聚合</span></a></h3><p>当需要根据指定条件对数据进行筛选，并将连续的符合条件的行分为一组进行聚合运算时，可以使用<code>GROUP BY CONDITION</code>的分段方式；不满足给定条件的行因为不属于任何分组会被直接简单忽略。<br> 其语法定义如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">group</span> <span class="token keyword">by</span> condition<span class="token punctuation">(</span>predict<span class="token punctuation">,</span><span class="token punctuation">[</span>keep<span class="token operator">></span><span class="token operator">/</span><span class="token operator">>=</span><span class="token operator">/</span><span class="token operator">=</span><span class="token operator">/</span><span class="token operator"><=</span><span class="token operator">/</span><span class="token operator"><</span><span class="token punctuation">]</span>threshold<span class="token punctuation">,</span><span class="token punctuation">[</span><span class="token punctuation">,</span>ignoreNull<span class="token operator">=</span><span class="token boolean">true</span><span class="token operator">/</span><span class="token boolean">false</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></div><ul><li>predict</li></ul><p>返回boolean数据类型的合法表达式，用于分组的筛选。</p><ul><li>keep[>/>=/=/<=/<]threshold</li></ul><p>keep表达式用来指定形成分组所需要连续满足<code>predict</code>条件的数据行数，只有行数满足keep表达式的分组才会被输出。keep表达式由一个'keep'字符串和<code>long</code>类型的threshold组合或者是单独的<code>long</code>类型数据构成。</p><ul><li>ignoreNull=true/false</li></ul><p>用于指定遇到predict为null的数据行时的处理方式，为true则跳过该行，为false则结束当前分组。</p><h4 id="使用注意事项-1" tabindex="-1"><a class="header-anchor" href="#使用注意事项-1"><span>使用注意事项</span></a></h4><ol><li>keep条件在查询中是必需的，但可以省略掉keep字符串给出一个<code>long</code>类型常数，默认为<code>keep=该long型常数</code>的等于条件。</li><li><code>ignoreNull</code>默认为true。</li><li>对于一个分组，默认Time列输出分组的开始时间，查询时可以使用select <code>__endTime</code>的方式来使得结果输出分组的结束时间。</li><li>与<code>ALIGN BY DEVICE</code>搭配使用时会对每个device进行单独的分组操作。</li><li>当前暂不支持与<code>GROUP BY LEVEL</code>搭配使用。</li></ol><p>对于如下原始数据,下面会给出几个查询样例:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------+-------------------------------------+------------------------------------+ |
| | Time|root.sg.beijing.car01.soc|root.sg.beijing.car01.charging_status|root.sg.beijing.car01.vehicle_status| |
| +-----------------------------+-------------------------+-------------------------------------+------------------------------------+ |
| |1970-01-01T08:00:00.001+08:00| 14.0| 1| 1| |
| |1970-01-01T08:00:00.002+08:00| 16.0| 1| 1| |
| |1970-01-01T08:00:00.003+08:00| 16.0| 0| 1| |
| |1970-01-01T08:00:00.004+08:00| 16.0| 0| 1| |
| |1970-01-01T08:00:00.005+08:00| 18.0| 1| 1| |
| |1970-01-01T08:00:00.006+08:00| 24.0| 1| 1| |
| |1970-01-01T08:00:00.007+08:00| 36.0| 1| 1| |
| |1970-01-01T08:00:00.008+08:00| 36.0| null| 1| |
| |1970-01-01T08:00:00.009+08:00| 45.0| 1| 1| |
| |1970-01-01T08:00:00.010+08:00| 60.0| 1| 1| |
| +-----------------------------+-------------------------+-------------------------------------+------------------------------------+ |
| </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>查询至少连续两行以上的charging_status=1的数据，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> max_time<span class="token punctuation">(</span>charging_status<span class="token punctuation">)</span><span class="token punctuation">,</span><span class="token function">count</span><span class="token punctuation">(</span>vehicle_status<span class="token punctuation">)</span><span class="token punctuation">,</span>last_value<span class="token punctuation">(</span>soc<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> condition<span class="token punctuation">(</span>charging_status<span class="token operator">=</span><span class="token number">1</span><span class="token punctuation">,</span>KEEP<span class="token operator">>=</span><span class="token number">2</span><span class="token punctuation">,</span>ignoreNull<span class="token operator">=</span><span class="token boolean">true</span><span class="token punctuation">)</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>得到结果如下：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------------------------------------+-------------------------------------------+-------------------------------------+ |
| | Time|max_time(root.sg.beijing.car01.charging_status)|count(root.sg.beijing.car01.vehicle_status)|last_value(root.sg.beijing.car01.soc)| |
| +-----------------------------+-----------------------------------------------+-------------------------------------------+-------------------------------------+ |
| |1970-01-01T08:00:00.001+08:00| 2| 2| 16.0| |
| |1970-01-01T08:00:00.005+08:00| 10| 5| 60.0| |
| +-----------------------------+-----------------------------------------------+-------------------------------------------+-------------------------------------+ |
| </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></div><p>当设置<code>ignoreNull</code>为false时，遇到null值为将其视为一个不满足条件的行，会结束正在计算的分组。</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_time<span class="token punctuation">(</span>charging_status<span class="token punctuation">)</span><span class="token punctuation">,</span><span class="token function">count</span><span class="token punctuation">(</span>vehicle_status<span class="token punctuation">)</span><span class="token punctuation">,</span>last_value<span class="token punctuation">(</span>soc<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> condition<span class="token punctuation">(</span>charging_status<span class="token operator">=</span><span class="token number">1</span><span class="token punctuation">,</span>KEEP<span class="token operator">>=</span><span class="token number">2</span><span class="token punctuation">,</span>ignoreNull<span class="token operator">=</span><span class="token boolean">false</span><span class="token punctuation">)</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>得到如下结果，原先的分组被含null的行拆分:</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------------------------------------+-------------------------------------------+-------------------------------------+ |
| | Time|max_time(root.sg.beijing.car01.charging_status)|count(root.sg.beijing.car01.vehicle_status)|last_value(root.sg.beijing.car01.soc)| |
| +-----------------------------+-----------------------------------------------+-------------------------------------------+-------------------------------------+ |
| |1970-01-01T08:00:00.001+08:00| 2| 2| 16.0| |
| |1970-01-01T08:00:00.005+08:00| 7| 3| 36.0| |
| |1970-01-01T08:00:00.009+08:00| 10| 2| 60.0| |
| +-----------------------------+-----------------------------------------------+-------------------------------------------+-------------------------------------+ |
| </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><h3 id="会话分段聚合" tabindex="-1"><a class="header-anchor" href="#会话分段聚合"><span>会话分段聚合</span></a></h3><p><code>GROUP BY SESSION</code>可以根据时间列的间隔进行分组，在结果集的时间列中，时间间隔小于等于设定阈值的数据会被分为一组。例如在工业场景中，设备并不总是连续运行，<code>GROUP BY SESSION</code>会将设备每次接入会话所产生的数据分为一组。<br> 其语法定义如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token keyword">session</span><span class="token punctuation">(</span>timeInterval<span class="token punctuation">)</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><ul><li>timeInterval</li></ul><p>设定的时间差阈值，当两条数据时间列的差值大于该阈值，则会给数据创建一个新的分组。</p><p>下图为<code>group by session</code>下的一个分组示意图</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/UserGuide/Process-Data/GroupBy/groupBySession.jpeg"><h4 id="使用注意事项-2" tabindex="-1"><a class="header-anchor" href="#使用注意事项-2"><span>使用注意事项</span></a></h4><ol><li>对于一个分组，默认Time列输出分组的开始时间，查询时可以使用select <code>__endTime</code>的方式来使得结果输出分组的结束时间。</li><li>与<code>ALIGN BY DEVICE</code>搭配使用时会对每个device进行单独的分组操作。</li><li>当前暂不支持与<code>GROUP BY LEVEL</code>搭配使用。</li></ol><p>对于下面的原始数据，给出几个查询样例。</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------+-----------+--------+------+ |
| | Time| Device|temperature|hardware|status| |
| +-----------------------------+-----------------+-----------+--------+------+ |
| |1970-01-01T08:00:01.000+08:00|root.ln.wf02.wt01| 35.7| 11| false| |
| |1970-01-01T08:00:02.000+08:00|root.ln.wf02.wt01| 35.8| 22| true| |
| |1970-01-01T08:00:03.000+08:00|root.ln.wf02.wt01| 35.4| 33| false| |
| |1970-01-01T08:00:04.000+08:00|root.ln.wf02.wt01| 36.4| 44| false| |
| |1970-01-01T08:00:05.000+08:00|root.ln.wf02.wt01| 36.8| 55| false| |
| |1970-01-01T08:00:10.000+08:00|root.ln.wf02.wt01| 36.8| 110| false| |
| |1970-01-01T08:00:20.000+08:00|root.ln.wf02.wt01| 37.8| 220| true| |
| |1970-01-01T08:00:30.000+08:00|root.ln.wf02.wt01| 37.5| 330| false| |
| |1970-01-01T08:00:40.000+08:00|root.ln.wf02.wt01| 37.4| 440| false| |
| |1970-01-01T08:00:50.000+08:00|root.ln.wf02.wt01| 37.9| 550| false| |
| |1970-01-01T08:01:40.000+08:00|root.ln.wf02.wt01| 38.0| 110| false| |
| |1970-01-01T08:02:30.000+08:00|root.ln.wf02.wt01| 38.8| 220| true| |
| |1970-01-01T08:03:20.000+08:00|root.ln.wf02.wt01| 38.6| 330| false| |
| |1970-01-01T08:04:20.000+08:00|root.ln.wf02.wt01| 38.4| 440| false| |
| |1970-01-01T08:05:20.000+08:00|root.ln.wf02.wt01| 38.3| 550| false| |
| |1970-01-01T08:06:40.000+08:00|root.ln.wf02.wt01| null| 0| null| |
| |1970-01-01T08:07:50.000+08:00|root.ln.wf02.wt01| null| 0| null| |
| |1970-01-01T08:08:00.000+08:00|root.ln.wf02.wt01| null| 0| null| |
| |1970-01-02T08:08:01.000+08:00|root.ln.wf02.wt01| 38.2| 110| false| |
| |1970-01-02T08:08:02.000+08:00|root.ln.wf02.wt01| 37.5| 220| true| |
| |1970-01-02T08:08:03.000+08:00|root.ln.wf02.wt01| 37.4| 330| false| |
| |1970-01-02T08:08:04.000+08:00|root.ln.wf02.wt01| 36.8| 440| false| |
| |1970-01-02T08:08:05.000+08:00|root.ln.wf02.wt01| 37.4| 550| false| |
| +-----------------------------+-----------------+-----------+--------+------+ |
| </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></div><p>可以按照不同的时间单位设定时间间隔，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> __endTime<span class="token punctuation">,</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><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">session</span><span class="token punctuation">(</span><span class="token number">1</span>d<span class="token punctuation">)</span> |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>得到如下结果：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------------------+------------------------------------+---------------------------------+-------------------------------+ |
| | Time| __endTime|count(root.ln.wf02.wt01.temperature)|count(root.ln.wf02.wt01.hardware)|count(root.ln.wf02.wt01.status)| |
| +-----------------------------+-----------------------------+------------------------------------+---------------------------------+-------------------------------+ |
| |1970-01-01T08:00:01.000+08:00|1970-01-01T08:08:00.000+08:00| 15| 18| 15| |
| |1970-01-02T08:08:01.000+08:00|1970-01-02T08:08:05.000+08:00| 5| 5| 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></div><p>也可以和<code>HAVING</code>、<code>ALIGN BY DEVICE</code>共同使用</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> __endTime<span class="token punctuation">,</span><span class="token function">sum</span><span class="token punctuation">(</span>hardware<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>ln<span class="token punctuation">.</span>wf02<span class="token punctuation">.</span>wt01 <span class="token keyword">group</span> <span class="token keyword">by</span> <span class="token keyword">session</span><span class="token punctuation">(</span><span class="token number">50</span>s<span class="token punctuation">)</span> <span class="token keyword">having</span> <span class="token function">sum</span><span class="token punctuation">(</span>hardware<span class="token punctuation">)</span><span class="token operator">></span><span class="token number">0</span> align <span class="token keyword">by</span> device |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>得到如下结果，其中排除了<code>sum(hardware)</code>为0的部分</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------+-----------------------------+-------------+ |
| | Time| Device| __endTime|sum(hardware)| |
| +-----------------------------+-----------------+-----------------------------+-------------+ |
| |1970-01-01T08:00:01.000+08:00|root.ln.wf02.wt01|1970-01-01T08:03:20.000+08:00| 2475.0| |
| |1970-01-01T08:04:20.000+08:00|root.ln.wf02.wt01|1970-01-01T08:04:20.000+08:00| 440.0| |
| |1970-01-01T08:05:20.000+08:00|root.ln.wf02.wt01|1970-01-01T08:05:20.000+08:00| 550.0| |
| |1970-01-02T08:08:01.000+08:00|root.ln.wf02.wt01|1970-01-02T08:08:05.000+08:00| 1650.0| |
| +-----------------------------+-----------------+-----------------------------+-------------+ |
| </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></div></div><!--[--><!----><!--]--><footer class="vp-page-meta"><div class="vp-meta-item edit-link"><a href="https://github.com/apache/iotdb-docs/edit/main/src/zh/UserGuide/V1.1.x/Query-Data/Group-By.md" rel="noopener noreferrer" target="_blank" aria-label="发现错误？在 GitHub 上编辑此页" class="nav-link vp-meta-label"><!--[--><svg xmlns="http://www.w3.org/2000/svg" class="icon edit-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="edit icon"><path d="M430.818 653.65a60.46 60.46 0 0 1-50.96-93.281l71.69-114.012 7.773-10.365L816.038 80.138A60.46 60.46 0 0 1 859.225 62a60.46 60.46 0 0 1 43.186 18.138l43.186 43.186a60.46 60.46 0 0 1 0 86.373L588.879 565.55l-8.637 8.637-117.466 68.234a60.46 60.46 0 0 1-31.958 11.229z"></path><path d="M728.802 962H252.891A190.883 190.883 0 0 1 62.008 771.98V296.934a190.883 190.883 0 0 1 190.883-192.61h267.754a60.46 60.46 0 0 1 0 120.92H252.891a69.962 69.962 0 0 0-69.098 69.099V771.98a69.962 69.962 0 0 0 69.098 69.098h475.911A69.962 69.962 0 0 0 797.9 771.98V503.363a60.46 60.46 0 1 1 120.922 0V771.98A190.883 190.883 0 0 1 728.802 962z"></path></svg><!--]-->发现错误？在 GitHub 上编辑此页<span><svg class="external-link-icon" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path><polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg><span class="external-link-icon-sr-only">open in new window</span></span><!----></a></div><div class="vp-meta-item git-info"><div class="update-time"><span class="vp-meta-label">上次编辑于: </span><!----></div><!----></div></footer><nav class="vp-page-nav"><a class="route-link nav-link prev" href="/zh/UserGuide/V1.1.x/Query-Data/Where-Condition.html" aria-label="查询过滤条件"><div class="hint"><span class="arrow start"></span>上一页</div><div class="link"><!---->查询过滤条件</div></a><a class="route-link nav-link next" href="/zh/UserGuide/V1.1.x/Query-Data/Having-Condition.html" aria-label="聚合结果过滤"><div class="hint">下一页<span class="arrow end"></span></div><div class="link">聚合结果过滤<!----></div></a></nav><!----><!--[--><!----><!--]--><!--]--></main><!--]--><footer style="padding-bottom:2rem;"><span id="doc-version" style="display:none;">rel/1.1</span><p style="text-align:center;color:#909399;font-size:12px;margin:0 30px;">Copyright © 2024 The Apache Software Foundation.<br> Apache and the Apache feather logo are trademarks of The Apache Software Foundation</p><p style="text-align:center;margin-top:10px;color:#909399;font-size:12px;margin:0 30px;"><strong>Have a question?</strong> Connect with us on QQ, WeChat, or Slack. <a href="https://github.com/apache/iotdb/issues/1995">Join the community</a> now.</p></footer></div><!--]--><!--]--><!--[--><!----><!--]--><!--]--></div> |
| <script type="module" src="/assets/app-DrPcRZG6.js" defer></script> |
| </body> |
| </html> |
