Google Git
Sign in
apache / iotdb-website / 58cad7010f370b058faa76922f1e0850e2372f4d / . / assets / Maintennance.html-7gNIfqNB.js
blob: 1c39283a0eed35ae1ca55ff68166296b6c676881 [file] [log] [blame]
import{_ as n,o as e,c as s,e as a}from"./app-Bx8hKGcu.js";const i={},t=a(`<h1 id="运维语句" tabindex="-1"><a class="header-anchor" href="#运维语句"><span>运维语句</span></a></h1><h2 id="查询分析" tabindex="-1"><a class="header-anchor" href="#查询分析"><span>查询分析</span></a></h2><h3 id="概述" tabindex="-1"><a class="header-anchor" href="#概述"><span>概述</span></a></h3><p>查询分析的意义在于帮助用户理解查询的执行机制和性能瓶颈，从而实现查询优化和性能提升。这不仅关乎到查询的执行效率，也直接影响到应用的用户体验和资源的有效利用。为了进行有效的查询分析，IoTDB提供了查询分析工具：Explain和Explain Analyze。</p><p>Explain工具允许用户预览查询SQL的执行计划，包括IoTDB如何组织数据检索和处理。</p><p>Explain Analyze则在此基础上增加了性能分析，完整执行SQL并展示查询执行过程中的时间和资源消耗。为IoTDB用户深入理解查询详情以及进行查询优化提供了详细的相关信息。</p><h4 id="explain" tabindex="-1"><a class="header-anchor" href="#explain"><span>Explain</span></a></h4><p>Explain命令允许用户查看SQL查询的执行计划。执行计划以算子的形式展示，描述了IoTDB会如何执行查询。Explain的输出包括了数据访问策略、过滤条件是否下推以及查询计划在不同节点的分配等信息，为用户提供了一种手段，以可视化查询的内部执行逻辑。其语法如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">EXPLAIN</span> <span class="token operator">&lt;</span>SELECT_STATEMENT<span class="token operator">&gt;</span>
</code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>其中SELECT_STATEMENT是查询相关的SQL语句。一个使用案例如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span><span class="token keyword">explain</span><span class="token punctuation">.</span><span class="token keyword">data</span><span class="token punctuation">(</span><span class="token keyword">timestamp</span><span class="token punctuation">,</span> column1<span class="token punctuation">,</span> column2<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1710494762</span><span class="token punctuation">,</span> <span class="token string">&quot;hello&quot;</span><span class="token punctuation">,</span> <span class="token string">&quot;explain&quot;</span><span class="token punctuation">)</span>
<span class="token keyword">explain</span> <span class="token keyword">select</span> <span class="token operator">*</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span><span class="token keyword">explain</span><span class="token punctuation">.</span><span class="token keyword">data</span>
</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></div><p>执行上方SQL，会得到如下结果：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------------------------------------------------+
| distribution plan|
+-----------------------------------------------------------------------+
| ┌───────────────────┐ |
| │FullOuterTimeJoin-3│ |
| │Order: ASC │ |
| └───────────────────┘ |
| ┌─────────────────┴─────────────────┐ |
| │ │ |
|┌─────────────────────────────────┐ ┌─────────────────────────────────┐|
|│SeriesScan-4 │ │SeriesScan-5 │|
|│Series: root.explain.data.column1│ │Series: root.explain.data.column2│|
|│Partition: 3 │ │Partition: 3 │|
|└─────────────────────────────────┘ └─────────────────────────────────┘|
+-----------------------------------------------------------------------+
</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><p>不难看出，IoTDB分别通过两个SeriesScan节点去获取column1和column2的数据，最后通过fullOuterTimeJoin将其连接。</p><h4 id="explain-analyze" tabindex="-1"><a class="header-anchor" href="#explain-analyze"><span>Explain Analyze</span></a></h4><p>Explain Analyze是IOTDB查询引擎自带的性能分析SQL，与Explain不同，它会执行对应的查询计划并统计执行信息，可以用于追踪一条查询的具体性能分布，用于对资源进行观察，进行性能调优与异常分析。</p><p>与IoTDB常用的排查手段相比，Explain Analyze没有部署负担，同时能够针对单条sql进行分析，能够更好定位问题：</p><table><thead><tr><th style="text-align:left;">方法</th><th style="text-align:left;">安装难度</th><th style="text-align:left;">对业务的影响</th><th style="text-align:left;">支持分布式</th><th style="text-align:left;">单条sql分析</th></tr></thead><tbody><tr><td style="text-align:left;">Arthas抽样</td><td style="text-align:left;">需要在机器上下载并运行文件：部分内网无法直接安装Arthas ；且安装后，有时需要重启应用</td><td style="text-align:left;">CPU 抽样可能会影响线上业务的响应速度</td><td style="text-align:left;">否</td><td style="text-align:left;">线上业务通常都有多种查询负载，整体的监控指标以及抽样结果，只能反映所有查询的整体负载和耗时情况，无法对单条慢sql做耗时分析</td></tr><tr><td style="text-align:left;">监控面板</td><td style="text-align:left;">需要开启监控服务并部署grafana，且开源用户没有监控面板</td><td style="text-align:left;">记录指标会带来额外耗时</td><td style="text-align:left;">是</td><td style="text-align:left;">与Arthas相同</td></tr><tr><td style="text-align:left;">Explain Analyze</td><td style="text-align:left;">无需安装，启动IoTDB即可</td><td style="text-align:left;">只会影响当前分析的单条查询，对线上其他负载无影响</td><td style="text-align:left;">是</td><td style="text-align:left;">可以对单条sql进行追踪分析</td></tr></tbody></table><p>其语法如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">EXPLAIN</span> <span class="token keyword">ANALYZE</span> <span class="token punctuation">[</span>VERBOSE<span class="token punctuation">]</span> <span class="token operator">&lt;</span>SELECT_STATEMENT<span class="token operator">&gt;</span>
</code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>其中SELECT_STATEMENT对应需要分析的查询语句；默认情况下，为了尽可能简化结果，EXPLAIN ANALYZE会省略部分信息，指定VERBOSE可以用来输出这些信息。</p><p>在EXPLAIN ANALYZE的结果集中，会包含如下信息</p><figure><img src="https://alioss.timecho.com/docs/img/image.png" alt="image.png" tabindex="0" loading="lazy"><figcaption>image.png</figcaption></figure><p>其中，QueryStatistics包含查询层面进的统计信息，主要包含规划解析阶段耗时，Fragment元数据等信息。</p><p>FragmentInstance是IoTDB在一个节点上查询计划的封装，每一个节点都会在结果集中输出一份Fragment信息，主要包含FragmentStatistics和算子信息。</p><p>其中，FragmentStastistics包含Fragment的统计信息，包括总实际耗时（墙上时间），所涉及到的TsFile，调度信息等情况。在一个Fragment的信息输出同时会以节点树层级的方式展示该Fragment下计划节点的统计信息，主要包括：</p><ul><li>CPU运行时间</li><li>输出的数据行数</li><li>指定接口被调用的次数</li><li>所占用的内存</li><li>节点专属的定制信息</li></ul><p>下面是Explain Analyze的一个样例:</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span><span class="token keyword">explain</span><span class="token punctuation">.</span><span class="token keyword">analyze</span><span class="token punctuation">.</span><span class="token keyword">data</span><span class="token punctuation">(</span><span class="token keyword">timestamp</span><span class="token punctuation">,</span> column1<span class="token punctuation">,</span> column2<span class="token punctuation">,</span> column3<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1710494762</span><span class="token punctuation">,</span> <span class="token string">&quot;hello&quot;</span><span class="token punctuation">,</span> <span class="token string">&quot;explain&quot;</span><span class="token punctuation">,</span> <span class="token string">&quot;analyze&quot;</span><span class="token punctuation">)</span>
<span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span><span class="token keyword">explain</span><span class="token punctuation">.</span><span class="token keyword">analyze</span><span class="token punctuation">.</span><span class="token keyword">data</span><span class="token punctuation">(</span><span class="token keyword">timestamp</span><span class="token punctuation">,</span> column1<span class="token punctuation">,</span> column2<span class="token punctuation">,</span> column3<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1710494862</span><span class="token punctuation">,</span> <span class="token string">&quot;hello2&quot;</span><span class="token punctuation">,</span> <span class="token string">&quot;explain2&quot;</span><span class="token punctuation">,</span> <span class="token string">&quot;analyze2&quot;</span><span class="token punctuation">)</span>
<span class="token keyword">insert</span> <span class="token keyword">into</span> root<span class="token punctuation">.</span><span class="token keyword">explain</span><span class="token punctuation">.</span><span class="token keyword">analyze</span><span class="token punctuation">.</span><span class="token keyword">data</span><span class="token punctuation">(</span><span class="token keyword">timestamp</span><span class="token punctuation">,</span> column1<span class="token punctuation">,</span> column2<span class="token punctuation">,</span> column3<span class="token punctuation">)</span> <span class="token keyword">values</span><span class="token punctuation">(</span><span class="token number">1710494962</span><span class="token punctuation">,</span> <span class="token string">&quot;hello3&quot;</span><span class="token punctuation">,</span> <span class="token string">&quot;explain3&quot;</span><span class="token punctuation">,</span> <span class="token string">&quot;analyze3&quot;</span><span class="token punctuation">)</span>
<span class="token keyword">explain</span> <span class="token keyword">analyze</span> <span class="token keyword">select</span> column2 <span class="token keyword">from</span> root<span class="token punctuation">.</span><span class="token keyword">explain</span><span class="token punctuation">.</span><span class="token keyword">analyze</span><span class="token punctuation">.</span><span class="token keyword">data</span> <span class="token keyword">order</span> <span class="token keyword">by</span> column1
</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></div><p>得到输出如下：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-------------------------------------------------------------------------------------------------+
| Explain Analyze|
+-------------------------------------------------------------------------------------------------+
|Analyze Cost: 1.739 ms |
|Fetch Partition Cost: 0.940 ms |
|Fetch Schema Cost: 0.066 ms |
|Logical Plan Cost: 0.000 ms |
|Logical Optimization Cost: 0.000 ms |
|Distribution Plan Cost: 0.000 ms |
|Fragment Instances Count: 1 |
| |
|FRAGMENT-INSTANCE[Id: 20240315_115800_00030_1.2.0][IP: 127.0.0.1][DataRegion: 4][State: FINISHED]|
| Total Wall Time: 25 ms |
| Cost of initDataQuerySource: 0.175 ms |
| Seq File(unclosed): 0, Seq File(closed): 1 |
| UnSeq File(unclosed): 0, UnSeq File(closed): 0 |
| ready queued time: 0.280 ms, blocked queued time: 2.456 ms |
| [PlanNodeId 10]: IdentitySinkNode(IdentitySinkOperator) |
| CPU Time: 0.780 ms |
| output: 1 rows |
| HasNext() Called Count: 3 |
| Next() Called Count: 2 |
| Estimated Memory Size: : 1245184 |
| [PlanNodeId 5]: TransformNode(TransformOperator) |
| CPU Time: 0.764 ms |
| output: 1 rows |
| HasNext() Called Count: 3 |
| Next() Called Count: 2 |
| Estimated Memory Size: : 1245184 |
| [PlanNodeId 4]: SortNode(SortOperator) |
| CPU Time: 0.721 ms |
| output: 1 rows |
| HasNext() Called Count: 3 |
| Next() Called Count: 2 |
| sortCost/ns: 1125 |
| sortedDataSize: 272 |
| prepareCost/ns: 610834 |
| [PlanNodeId 3]: FullOuterTimeJoinNode(FullOuterTimeJoinOperator) |
| CPU Time: 0.706 ms |
| output: 1 rows |
| HasNext() Called Count: 5 |
| Next() Called Count: 1 |
| [PlanNodeId 7]: SeriesScanNode(SeriesScanOperator) |
| CPU Time: 1.085 ms |
| output: 1 rows |
| HasNext() Called Count: 2 |
| Next() Called Count: 1 |
| SeriesPath: root.explain.analyze.data.column2 |
| [PlanNodeId 8]: SeriesScanNode(SeriesScanOperator) |
| CPU Time: 1.091 ms |
| output: 1 rows |
| HasNext() Called Count: 2 |
| Next() Called Count: 1 |
| SeriesPath: root.explain.analyze.data.column1 |
+-------------------------------------------------------------------------------------------------+
</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 class="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><h5 id="explain-analyze分析结果中的算子压缩" tabindex="-1"><a class="header-anchor" href="#explain-analyze分析结果中的算子压缩"><span>EXPLAIN ANALYZE分析结果中的算子压缩</span></a></h5><figure><img src="https://alioss.timecho.com/docs/img/image-cyxm.png" alt="image-cyxm.png" tabindex="0" loading="lazy"><figcaption>image-cyxm.png</figcaption></figure><p>在Fragment中会输出当前节点中执行的所有节点信息，然而当一次查询涉及的序列过多时，每个节点都被输出会导致Explain Analyze返回的结果集过大，因此当相同类型的节点超过10个时，会合并当前Fragment下所有相同类型的节点。合并后统计信息也被累积，对于一些无法合并的定制信息会直接丢弃。</p><p>可以通过修改iotdb-common.properties中的配置项<code>merge_threshold_of_explain_analyze</code>来设置触发合并的节点阈值，该参数支持热加载。下面是一个触发合并后的部分结果示例：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>Analyze Cost: 143.679 ms
Fetch Partition Cost: 22.023 ms
Fetch Schema Cost: 63.086 ms
Logical Plan Cost: 0.000 ms
Logical Optimization Cost: 0.000 ms
Distribution Plan Cost: 0.000 ms
Fragment Instances Count: 2
FRAGMENT-INSTANCE[Id: 20240311_041502_00001_1.2.0][IP: 192.168.130.9][DataRegion: 14]
Total Wall Time: 39964 ms
Cost of initDataQuerySource: 1.834 ms
Seq File(unclosed): 0, Seq File(closed): 3
UnSeq File(unclosed): 0, UnSeq File(closed): 0
ready queued time: 504.334 ms, blocked queued time: 25356.419 ms
[PlanNodeId 20793]: IdentitySinkNode(IdentitySinkOperator) Count: * 1
CPU Time: 24440.724 ms
input: 71216 rows
HasNext() Called Count: 35963
Next() Called Count: 35962
Estimated Memory Size: : 33882112
[PlanNodeId 10385]: FullOuterTimeJoinNode(FullOuterTimeJoinOperator) Count: * 8
CPU Time: 41437.708 ms
input: 243011 rows
HasNext() Called Count: 41965
Next() Called Count: 41958
Estimated Memory Size: : 33882112
[PlanNodeId 11569]: SeriesScanNode(SeriesScanOperator) Count: * 1340
CPU Time: 1397.822 ms
input: 134000 rows
HasNext() Called Count: 2353
Next() Called Count: 1340
Estimated Memory Size: : 32833536
[PlanNodeId 20778]: ExchangeNode(ExchangeOperator) Count: * 7
CPU Time: 109.245 ms
input: 71891 rows
HasNext() Called Count: 1431
Next() Called Count: 1431
FRAGMENT-INSTANCE[Id: 20240311_041502_00001_1.3.0][IP: 192.168.130.9][DataRegion: 11]
Total Wall Time: 39912 ms
Cost of initDataQuerySource: 15.439 ms
Seq File(unclosed): 0, Seq File(closed): 2
UnSeq File(unclosed): 0, UnSeq File(closed): 0
ready queued time: 152.988 ms, blocked queued time: 37775.356 ms
[PlanNodeId 20786]: IdentitySinkNode(IdentitySinkOperator) Count: * 1
CPU Time: 2020.258 ms
input: 48800 rows
HasNext() Called Count: 978
Next() Called Count: 978
Estimated Memory Size: : 42336256
[PlanNodeId 20771]: FullOuterTimeJoinNode(FullOuterTimeJoinOperator) Count: * 8
CPU Time: 5255.307 ms
input: 195800 rows
HasNext() Called Count: 2455
Next() Called Count: 2448
Estimated Memory Size: : 42336256
[PlanNodeId 11867]: SeriesScanNode(SeriesScanOperator) Count: * 1680
CPU Time: 1248.080 ms
input: 168000 rows
HasNext() Called Count: 3198
Next() Called Count: 1680
Estimated Memory Size: : 41287680
......
</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 class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="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><h5 id="查询超时处理" tabindex="-1"><a class="header-anchor" href="#查询超时处理"><span>查询超时处理</span></a></h5><p>Explain Analyze本身是一种特殊的查询，当执行超时的时候，我们是无法从返回结果中获得分析结果的。为了处理该情况，使得在超时的情况下也可以通过分析结果排查超时原因，Explain Analyze提供了<strong>定时日志</strong>机制，每经过一定的时间间隔会将Explain Analyze的当前结果以文本的形式输出到专门的日志中。这样当查询超时时，就可以前往logs中查看对应的日志进行排查。</p><p>日志的时间间隔基于查询的超时时间进行计算，可以保证在超时的情况下至少会有两次的结果记录。</p>`,39),l=[t];function d(o,c){return e(),s("div",null,l)}const r=n(i,[["render",d],["__file","Maintennance.html.vue"]]),u=JSON.parse('{"path":"/zh/UserGuide/latest/User-Manual/Maintennance.html","title":"运维语句","lang":"zh-CN","frontmatter":{"description":"运维语句 查询分析 概述 查询分析的意义在于帮助用户理解查询的执行机制和性能瓶颈，从而实现查询优化和性能提升。这不仅关乎到查询的执行效率，也直接影响到应用的用户体验和资源的有效利用。为了进行有效的查询分析，IoTDB提供了查询分析工具：Explain和Explain Analyze。 Explain工具允许用户预览查询SQL的执行计划，包括IoTDB如...","head":[["link",{"rel":"alternate","hreflang":"en-us","href":"https://iotdb.apache.org/UserGuide/latest/User-Manual/Maintennance.html"}],["meta",{"property":"og:url","content":"https://iotdb.apache.org/zh/UserGuide/latest/User-Manual/Maintennance.html"}],["meta",{"property":"og:site_name","content":"IoTDB Website"}],["meta",{"property":"og:title","content":"运维语句"}],["meta",{"property":"og:description","content":"运维语句 查询分析 概述 查询分析的意义在于帮助用户理解查询的执行机制和性能瓶颈，从而实现查询优化和性能提升。这不仅关乎到查询的执行效率，也直接影响到应用的用户体验和资源的有效利用。为了进行有效的查询分析，IoTDB提供了查询分析工具：Explain和Explain Analyze。 Explain工具允许用户预览查询SQL的执行计划，包括IoTDB如..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:image","content":"https://alioss.timecho.com/docs/img/image.png"}],["meta",{"property":"og:locale","content":"zh-CN"}],["meta",{"property":"og:locale:alternate","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2024-04-08T07:45:55.000Z"}],["meta",{"property":"article:modified_time","content":"2024-04-08T07:45:55.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"运维语句\\",\\"image\\":[\\"https://alioss.timecho.com/docs/img/image.png\\",\\"https://alioss.timecho.com/docs/img/image-cyxm.png\\"],\\"dateModified\\":\\"2024-04-08T07:45:55.000Z\\",\\"author\\":[]}"]]},"headers":[{"level":2,"title":"查询分析","slug":"查询分析","link":"#查询分析","children":[{"level":3,"title":"概述","slug":"概述","link":"#概述","children":[]}]}],"git":{"createdTime":1711953645000,"updatedTime":1712562355000,"contributors":[{"name":"YangCaiyin","email":"ycycse@gmail.com","commits":1},{"name":"wanghui42","email":"105700158+wanghui42@users.noreply.github.com","commits":1}]},"readingTime":{"minutes":6.93,"words":2080},"filePathRelative":"zh/UserGuide/latest/User-Manual/Maintennance.md","localizedDate":"2024年4月1日","autoDesc":true}');export{r as comp,u as data};