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aria-hidden="true" viewbox="0 0 24 24" data-testid="ChevronRightIcon"><path d="M10 6 8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"/></svg></div></div></div></li><li class="MuiTreeItem-root css-1oy5rnm" role="treeitem" aria-expanded="true" id="left-nav-treeview-0-11" tabindex="-1" style="margin-top:0.1875rem;margin-bottom:0.1875rem"><div class="css-khel7y MuiTreeItem-content Mui-expanded" style="width:inherit"><div class="MuiTreeItem-iconContainer"/><div class="MuiTreeItem-label"><div class="css-znl8a3"><div class="MuiBox-root css-8atqhb"><div class="MuiTypography-root MuiTypography-body1 css-1hwyipb">教程</div></div><style data-emotion="css 171pefp">.css-171pefp{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;width:1em;height:1em;display:inline-block;fill:currentColor;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;-webkit-transition:fill 200ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:fill 200ms cubic-bezier(0.4, 0, 0.2, 1) 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(min-width:0px){.css-vofosd{width:100%;}}@media (min-width:600px){.css-vofosd{width:calc(100% - 17.5rem);}}</style><div class="MuiBox-root css-vofosd"><style data-emotion="css 1ekb41w">.css-1ekb41w{width:100%;margin-left:auto;box-sizing:border-box;margin-right:auto;display:block;padding-left:16px;padding-right:16px;}@media (min-width:600px){.css-1ekb41w{padding-left:24px;padding-right:24px;}}@media (min-width:1536px){.css-1ekb41w{max-width:1536px;}}</style><div class="MuiContainer-root MuiContainer-maxWidthXl doc-content css-1ekb41w"><div class="markdown-body MuiBox-root css-0"><h1 id="tidb-高并发写入场景最佳实践" style="position:relative"><a href="#tidb-%E9%AB%98%E5%B9%B6%E5%8F%91%E5%86%99%E5%85%A5%E5%9C%BA%E6%99%AF%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5" aria-label="tidb 高并发写入场景最佳实践 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>TiDB 高并发写入场景最佳实践</h1><p>在 TiDB 的使用过程中，一个典型场景是高并发批量写入数据到 TiDB。本文阐述了该场景中的常见问题，旨在给出一个业务的最佳实践，帮助读者避免因使用 TiDB 不当而影响业务开发。</p><h2 id="目标读者" style="position:relative"><a href="#%E7%9B%AE%E6%A0%87%E8%AF%BB%E8%80%85" aria-label="目标读者 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>目标读者</h2><p>本文假设你已对 TiDB 有一定的了解，推荐先阅读 TiDB 原理相关的三篇文章（<a href="https://pingcap.com/blog-cn/tidb-internal-1/" target="_blank" referrerpolicy="no-referrer-when-downgrade">讲存储</a>，<a href="https://pingcap.com/blog-cn/tidb-internal-2/" target="_blank" referrerpolicy="no-referrer-when-downgrade">说计算</a>，<a href="https://pingcap.com/blog-cn/tidb-internal-3/" target="_blank" referrerpolicy="no-referrer-when-downgrade">谈调度</a>），以及 <a href="https://pingcap.com/blog-cn/tidb-best-practice/" target="_blank" referrerpolicy="no-referrer-when-downgrade">TiDB Best Practice</a>。</p><h2 id="高并发批量插入场景" style="position:relative"><a href="#%E9%AB%98%E5%B9%B6%E5%8F%91%E6%89%B9%E9%87%8F%E6%8F%92%E5%85%A5%E5%9C%BA%E6%99%AF" aria-label="高并发批量插入场景 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>高并发批量插入场景</h2><p>高并发批量插入的场景通常出现在业务系统的批量任务中，例如清算以及结算等业务。此类场景存在以下特点：</p><ul><li>数据量大</li><li>需要短时间内将历史数据入库</li><li>需要短时间内读取大量数据</li></ul><p>这就对 TiDB 提出了以下挑战：</p><ul><li>写入/读取能力是否可以线性水平扩展</li><li>随着数据持续大并发写入，数据库性能是否稳定不衰减</li></ul><p>对于分布式数据库来说，除了本身的基础性能外，最重要的就是充分利用所有节点能力，避免让单个节点成为瓶颈。</p><h2 id="tidb-数据分布原理" style="position:relative"><a href="#tidb-%E6%95%B0%E6%8D%AE%E5%88%86%E5%B8%83%E5%8E%9F%E7%90%86" aria-label="tidb 数据分布原理 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>TiDB 数据分布原理</h2><p>如果要解决以上挑战，需要从 TiDB 数据切分以及调度的原理开始讲起。这里只作简单说明，详情可参阅<a href="https://pingcap.com/blog-cn/tidb-internal-3/" target="_blank" referrerpolicy="no-referrer-when-downgrade">谈调度</a>。</p><p>TiDB 以 Region 为单位对数据进行切分，每个 Region 有大小限制（默认 96M）。Region 的切分方式是范围切分。每个 Region 会有多副本，每一组副本，称为一个 Raft Group。每个 Raft Group 中由 Leader 负责执行这块数据的读 &amp; 写（TiDB 支持 <a href="/zh/tidb/stable/follower-read">Follower-Read</a>）。Leader 会自动地被 PD 组件均匀调度在不同的物理节点上，用以均分读写压力。</p><p><img src="https://download.pingcap.com/images/docs-cn/best-practices/tidb-data-overview.png" alt="TiDB 数据概览"/></p><p>从原理上来说，只要没有业务上的写入热点（即业务写入没有 <code>AUTO_INCREMENT</code> 的主键和单调递增的索引，更多细节可参阅 <a href="https://zhuanlan.zhihu.com/p/25574778" target="_blank" referrerpolicy="no-referrer-when-downgrade">TiDB 正确使用方式</a>），依靠这个架构，TiDB 不仅具备线性扩展的读写能力，也能够充分利用分布式资源。从这一点看，TiDB 尤其适合高并发批量写入场景的业务。</p><p>但理论场景和实际情况往往存在不同。以下实例说明了热点是如何产生的。</p><h2 id="热点产生的实例" style="position:relative"><a href="#%E7%83%AD%E7%82%B9%E4%BA%A7%E7%94%9F%E7%9A%84%E5%AE%9E%E4%BE%8B" aria-label="热点产生的实例 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>热点产生的实例</h2><p>以下为一张示例表：</p><pre><code class="hljs language-sql"><style data-emotion="css 1qhimia">.css-1qhimia{margin-right:3rem;overflow:auto;}</style><div class="MuiBox-root css-1qhimia"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">TABLE</span> IF <span class="hljs-keyword">NOT</span> <span class="hljs-keyword">EXISTS</span> TEST_HOTSPOT( id <span class="hljs-type">BIGINT</span> <span class="hljs-keyword">PRIMARY</span> KEY, age <span class="hljs-type">INT</span>, user_name <span class="hljs-type">VARCHAR</span>(<span class="hljs-number">32</span>), email <span class="hljs-type">VARCHAR</span>(<span class="hljs-number">128</span>) </div><style data-emotion="css ux9q70">.css-ux9q70{text-align:center;-webkit-flex:0 0 auto;-ms-flex:0 0 auto;flex:0 0 auto;font-size:1.5rem;padding:8px;border-radius:50%;overflow:visible;color:rgba(0, 0, 0, 0.54);-webkit-transition:background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;padding:5px;font-size:1.125rem;top:0.625rem;right:2.4rem;background:transparent;border:unset;}.css-ux9q70:hover{background-color:rgba(0, 0, 0, 0.04);}@media (hover: none){.css-ux9q70:hover{background-color:transparent;}}.css-ux9q70.Mui-disabled{background-color:transparent;color:rgba(0, 0, 0, 0.26);}</style><style data-emotion="css 1rmx1rm">.css-1rmx1rm{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;text-align:center;-webkit-flex:0 0 auto;-ms-flex:0 0 auto;flex:0 0 auto;font-size:1.5rem;padding:8px;border-radius:50%;overflow:visible;color:rgba(0, 0, 0, 0.54);-webkit-transition:background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;padding:5px;font-size:1.125rem;top:0.625rem;right:2.4rem;background:transparent;border:unset;}.css-1rmx1rm::-moz-focus-inner{border-style:none;}.css-1rmx1rm.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-1rmx1rm{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-1rmx1rm:hover{background-color:rgba(0, 0, 0, 0.04);}@media (hover: none){.css-1rmx1rm:hover{background-color:transparent;}}.css-1rmx1rm.Mui-disabled{background-color:transparent;color:rgba(0, 0, 0, 0.26);}</style><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-1rmx1rm" tabindex="0" type="button" aria-label="Toggle code wrap"><style data-emotion="css 1cw4hi4">.css-1cw4hi4{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;width:1em;height:1em;display:inline-block;fill:currentColor;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;-webkit-transition:fill 200ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:fill 200ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;font-size:inherit;}</style><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="WrapTextIcon"><path d="M4 19h6v-2H4v2zM20 5H4v2h16V5zm-3 6H4v2h13.25c1.1 0 2 .9 2 2s-.9 2-2 2H15v-2l-3 3 3 3v-2h2c2.21 0 4-1.79 4-4s-1.79-4-4-4z"/></svg></button><style data-emotion="css 1e2dcm1">.css-1e2dcm1{z-index:1500;pointer-events:none;}</style><style data-emotion="css okvapm">.css-okvapm{z-index:1500;pointer-events:none;}</style><style data-emotion="css rnn29l">.css-rnn29l{text-align:center;-webkit-flex:0 0 auto;-ms-flex:0 0 auto;flex:0 0 auto;font-size:1.5rem;padding:8px;border-radius:50%;overflow:visible;color:rgba(0, 0, 0, 0.54);-webkit-transition:background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;padding:5px;font-size:1.125rem;top:0.625rem;right:0.625rem;background:transparent;border:unset;}.css-rnn29l:hover{background-color:rgba(0, 0, 0, 0.04);}@media (hover: none){.css-rnn29l:hover{background-color:transparent;}}.css-rnn29l.Mui-disabled{background-color:transparent;color:rgba(0, 0, 0, 0.26);}</style><style data-emotion="css fxo539">.css-fxo539{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;text-align:center;-webkit-flex:0 0 auto;-ms-flex:0 0 auto;flex:0 0 auto;font-size:1.5rem;padding:8px;border-radius:50%;overflow:visible;color:rgba(0, 0, 0, 0.54);-webkit-transition:background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 150ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;padding:5px;font-size:1.125rem;top:0.625rem;right:0.625rem;background:transparent;border:unset;}.css-fxo539::-moz-focus-inner{border-style:none;}.css-fxo539.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-fxo539{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-fxo539:hover{background-color:rgba(0, 0, 0, 0.04);}@media (hover: none){.css-fxo539:hover{background-color:transparent;}}.css-fxo539.Mui-disabled{background-color:transparent;color:rgba(0, 0, 0, 0.26);}</style><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-fxo539" tabindex="0" type="button" aria-label="copy"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="ContentCopyIcon"><path d="M16 1H4c-1.1 0-2 .9-2 2v14h2V3h12V1zm3 4H8c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z"/></svg></button></code></pre><p>这个表的结构非常简单，除了 <code>id</code> 为主键以外，没有额外的二级索引。将数据写入该表的语句如下，<code>id</code> 通过随机数离散生成：</p><pre><code class="hljs language-sql"><div class="MuiBox-root css-1qhimia"><span class="hljs-keyword">SET</span> SESSION cte_max_recursion_depth <span class="hljs-operator">=</span> <span class="hljs-number">1000000</span>; <span class="hljs-keyword">INSERT</span> <span class="hljs-keyword">INTO</span> TEST_HOTSPOT <span class="hljs-keyword">SELECT</span> n, <span class="hljs-comment">-- ID</span> RAND()<span class="hljs-operator">*</span><span class="hljs-number">80</span>, <span class="hljs-comment">-- 0 到 80 之间的随机数</span> CONCAT(<span class="hljs-string">'user-'</span>,n), CONCAT( <span class="hljs-type">CHAR</span>(<span class="hljs-number">65</span> <span class="hljs-operator">+</span> (RAND() <span class="hljs-operator">*</span> <span class="hljs-number">25</span>) <span class="hljs-keyword">USING</span> ascii), <span class="hljs-comment">-- 65 到 65+25 之间的随机数，转换为一个 A-Z 字符</span> <span class="hljs-string">'-user-'</span>, <span class="hljs-string">'@example.com'</span> (<span class="hljs-keyword">WITH</span> <span class="hljs-keyword">RECURSIVE</span> nr(n) <span class="hljs-keyword">AS</span> (<span class="hljs-keyword">SELECT</span> <span class="hljs-number">1</span> <span class="hljs-comment">-- 从 1 开始 CTE</span> <span class="hljs-keyword">UNION</span> <span class="hljs-keyword">ALL</span> <span class="hljs-keyword">SELECT</span> n <span class="hljs-operator">+</span> <span class="hljs-number">1</span> <span class="hljs-comment">-- 每次循环 n 增加 1</span> <span class="hljs-keyword">FROM</span> nr <span class="hljs-keyword">WHERE</span> n <span class="hljs-operator">&lt;</span> <span class="hljs-number">1000000</span> <span class="hljs-comment">-- 当 n 为 1_000_000 时停止循环</span> ) <span class="hljs-keyword">SELECT</span> n <span class="hljs-keyword">FROM</span> nr </div><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-1rmx1rm" tabindex="0" type="button" aria-label="Toggle code wrap"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="WrapTextIcon"><path d="M4 19h6v-2H4v2zM20 5H4v2h16V5zm-3 6H4v2h13.25c1.1 0 2 .9 2 2s-.9 2-2 2H15v-2l-3 3 3 3v-2h2c2.21 0 4-1.79 4-4s-1.79-4-4-4z"/></svg></button><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-fxo539" tabindex="0" type="button" aria-label="copy"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="ContentCopyIcon"><path d="M16 1H4c-1.1 0-2 .9-2 2v14h2V3h12V1zm3 4H8c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z"/></svg></button></code></pre><p>负载是短时间内密集地执行以上写入语句。</p><p>以上操作看似符合理论场景中的 TiDB 最佳实践，业务上没有热点产生。只要有足够的机器，就可以充分利用 TiDB 的分布式能力。要验证是否真的符合最佳实践，可以在实验环境中进行测试。</p><p>部署拓扑 2 个 TiDB 节点，3 个 PD 节点，6 个 TiKV 节点。请忽略 QPS，因为测试只是为了阐述原理，并非 benchmark。</p><p><img src="https://download.pingcap.com/images/docs-cn/best-practices/QPS1.png" alt="QPS1"/></p><p>客户端在短时间内发起了“密集”的写入，TiDB 收到的请求是 3K QPS。理论上，压力应该均摊给 6 个 TiKV 节点。但是从 TiKV 节点的 CPU 使用情况上看，存在明显的写入倾斜（tikv - 3 节点是写入热点）：</p><p><img src="https://download.pingcap.com/images/docs-cn/best-practices/QPS2.png" alt="QPS2"/></p><p><img src="https://download.pingcap.com/images/docs-cn/best-practices/QPS3.png" alt="QPS3"/></p><p><a href="/zh/tidb/stable/grafana-tikv-dashboard">Raft store CPU</a> 为 <code>raftstore</code> 线程的 CPU 使用率，通常代表写入的负载。在这个场景下 tikv-3 为 Raft Leader，tikv-0 和 tikv-1 是 Raft 的 Follower，其他的 TiKV 节点的负载几乎为空。</p><p>从 PD 的监控中也可以证明热点的产生：</p><p><img src="https://download.pingcap.com/images/docs-cn/best-practices/QPS4.png" alt="QPS4"/></p><h2 id="热点问题产生的原因" style="position:relative"><a href="#%E7%83%AD%E7%82%B9%E9%97%AE%E9%A2%98%E4%BA%A7%E7%94%9F%E7%9A%84%E5%8E%9F%E5%9B%A0" aria-label="热点问题产生的原因 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>热点问题产生的原因</h2><p>以上测试并未达到理论场景中最佳实践，因为刚创建表的时候，这个表在 TiKV 中只会对应为一个 Region，范围是：</p><pre><code class="hljs language-plaintext"><div class="MuiBox-root css-1qhimia">[CommonPrefix + TableID, CommonPrefix + TableID + 1) </div><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-1rmx1rm" tabindex="0" type="button" aria-label="Toggle code wrap"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="WrapTextIcon"><path d="M4 19h6v-2H4v2zM20 5H4v2h16V5zm-3 6H4v2h13.25c1.1 0 2 .9 2 2s-.9 2-2 2H15v-2l-3 3 3 3v-2h2c2.21 0 4-1.79 4-4s-1.79-4-4-4z"/></svg></button><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-fxo539" tabindex="0" type="button" aria-label="copy"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="ContentCopyIcon"><path d="M16 1H4c-1.1 0-2 .9-2 2v14h2V3h12V1zm3 4H8c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z"/></svg></button></code></pre><p>短时间内大量数据会持续写入到同一个 Region 上。</p><p><img src="https://download.pingcap.com/images/docs-cn/best-practices/tikv-Region-split.png" alt="TiKV Region 分裂流程"/></p><p>上图简单描述了这个过程，随着数据持续写入，TiKV 会将一个 Region 切分为多个。但因为首先发起选举的是原 Leader 所在的 Store，所以新切分好的两个 Region 的 Leader 很可能还会在原 Store 上。新切分好的 Region 2，3 上，也会重复之前发生在 Region 1 上的过程。也就是压力会密集地集中在 TiKV-Node 1 上。</p><p>在持续写入的过程中，PD 发现 Node 1 中产生了热点，会将 Leader 均分到其他的 Node 上。如果 TiKV 的节点数多于副本数的话，TiKV 会尽可能将 Region 迁移到空闲的节点上。这两个操作在数据插入的过程中，也能在 PD 监控中得到印证：</p><p><img src="https://download.pingcap.com/images/docs-cn/best-practices/QPS5.png" alt="QPS5"/></p><p>在持续写入一段时间后，整个集群会被 PD 自动地调度成一个压力均匀的状态，到那个时候整个集群的能力才会真正被利用起来。在大多数情况下，以上热点产生的过程是没有问题的，这个阶段属于表 Region 的预热阶段。</p><p>但是对于高并发批量密集写入场景来说，应该避免这个阶段。</p><h2 id="热点问题的规避方法" style="position:relative"><a href="#%E7%83%AD%E7%82%B9%E9%97%AE%E9%A2%98%E7%9A%84%E8%A7%84%E9%81%BF%E6%96%B9%E6%B3%95" aria-label="热点问题的规避方法 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>热点问题的规避方法</h2><p>为了达到场景理论中的最佳性能，可跳过这个预热阶段，直接将 Region 切分为预期的数量，提前调度到集群的各个节点中。</p><p>TiDB 在 v3.0.x 以及 v2.1.13 后支持一个叫 <a href="/zh/tidb/stable/sql-statement-split-region">Split Region</a> 的新特性。这个特性提供了新的语法：</p><pre><code class="hljs language-sql"><div class="MuiBox-root css-1qhimia">SPLIT <span class="hljs-keyword">TABLE</span> table_name [INDEX index_name] <span class="hljs-keyword">BETWEEN</span> (lower_value) <span class="hljs-keyword">AND</span> (upper_value) REGIONS region_num </div><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-1rmx1rm" tabindex="0" type="button" aria-label="Toggle code wrap"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="WrapTextIcon"><path d="M4 19h6v-2H4v2zM20 5H4v2h16V5zm-3 6H4v2h13.25c1.1 0 2 .9 2 2s-.9 2-2 2H15v-2l-3 3 3 3v-2h2c2.21 0 4-1.79 4-4s-1.79-4-4-4z"/></svg></button><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-fxo539" tabindex="0" type="button" aria-label="copy"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="ContentCopyIcon"><path d="M16 1H4c-1.1 0-2 .9-2 2v14h2V3h12V1zm3 4H8c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z"/></svg></button></code></pre><pre><code class="hljs language-sql"><div class="MuiBox-root css-1qhimia">SPLIT <span class="hljs-keyword">TABLE</span> table_name [INDEX index_name] <span class="hljs-keyword">BY</span> (value_list) [, (value_list)] </div><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-1rmx1rm" tabindex="0" type="button" aria-label="Toggle code wrap"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="WrapTextIcon"><path d="M4 19h6v-2H4v2zM20 5H4v2h16V5zm-3 6H4v2h13.25c1.1 0 2 .9 2 2s-.9 2-2 2H15v-2l-3 3 3 3v-2h2c2.21 0 4-1.79 4-4s-1.79-4-4-4z"/></svg></button><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-fxo539" tabindex="0" type="button" aria-label="copy"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="ContentCopyIcon"><path d="M16 1H4c-1.1 0-2 .9-2 2v14h2V3h12V1zm3 4H8c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z"/></svg></button></code></pre><p>但是 TiDB 并不会自动提前完成这个切分操作。原因如下：</p><p><img src="https://download.pingcap.com/images/docs-cn/best-practices/table-Region-range.png" alt="Table Region Range"/></p><p>从上图可知，根据行数据 key 的编码规则，行 ID (rowID) 是行数据中唯一可变的部分。在 TiDB 中，rowID 是一个 Int64 整型。但是用户不一定能将 Int64 整型范围均匀切分成需要的份数，然后均匀分布在不同的节点上，还需要结合实际情况。</p><p>如果行 ID 的写入是完全离散的，那么上述方式是可行的。如果行 ID 或者索引有固定的范围或者前缀（例如，只在 <code>[2000w, 5000w)</code> 的范围内离散插入数据），这种写入依然在业务上不产生热点，但是如果按上面的方式进行切分，那么有可能一开始数据仍只写入到某个 Region 上。</p><p>作为一款通用数据库，TiDB 并不对数据的分布作假设，所以开始只用一个 Region 来对应一个表。等到真实数据插入进来以后，TiDB 自动根据数据的分布来作切分。这种方式是较通用的。</p><p>所以 TiDB 提供了 <code>Split Region</code> 语法，专门针对短时批量写入场景作优化。基于以上案例，下面尝试用 <code>Split Region</code> 语法提前切散 Region，再观察负载情况。</p><p>由于测试的写入数据在正数范围内完全离散，所以用以下语句，在 Int64 空间内提前将表切分为 128 个 Region：</p><pre><code class="hljs language-sql"><div class="MuiBox-root css-1qhimia">SPLIT <span class="hljs-keyword">TABLE</span> TEST_HOTSPOT <span class="hljs-keyword">BETWEEN</span> (<span class="hljs-number">0</span>) <span class="hljs-keyword">AND</span> (<span class="hljs-number">9223372036854775807</span>) REGIONS <span class="hljs-number">128</span>; </div><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-1rmx1rm" tabindex="0" type="button" aria-label="Toggle code wrap"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="WrapTextIcon"><path d="M4 19h6v-2H4v2zM20 5H4v2h16V5zm-3 6H4v2h13.25c1.1 0 2 .9 2 2s-.9 2-2 2H15v-2l-3 3 3 3v-2h2c2.21 0 4-1.79 4-4s-1.79-4-4-4z"/></svg></button><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeSmall css-fxo539" tabindex="0" type="button" aria-label="copy"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="ContentCopyIcon"><path d="M16 1H4c-1.1 0-2 .9-2 2v14h2V3h12V1zm3 4H8c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z"/></svg></button></code></pre><p>切分完成以后，可以通过 <code>SHOW TABLE test_hotspot REGIONS;</code> 语句查看打散的情况。如果 <code>SCATTERING</code> 列值全部为 <code>0</code>，代表调度成功。</p><p>也可以通过以下 SQL 语句查看 Region 的分布。你需要将 <code>table_name</code> 替换为实际的表名。</p><pre><code class="hljs language-sql"><div class="MuiBox-root css-1qhimia"><span class="hljs-keyword">SELECT</span> p.STORE_ID, <span class="hljs-built_in">COUNT</span>(s.REGION_ID) PEER_COUNT INFORMATION_SCHEMA.TIKV_REGION_STATUS s <span class="hljs-keyword">JOIN</span> INFORMATION_SCHEMA.TIKV_REGION_PEERS p <span class="hljs-keyword">ON</span> s.REGION_ID <span class="hljs-operator">=</span> p.REGION_ID <span class="hljs-keyword">WHERE</span> TABLE_NAME <span class="hljs-operator">=</span> <span class="hljs-string">'table_name'</span> <span class="hljs-keyword">AND</span> p.is_leader <span class="hljs-operator">=</span> <span class="hljs-number">1</span> <span class="hljs-keyword">GROUP</span> <span class="hljs-keyword">BY</span> p.STORE_ID