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| <h1 id="原理">原理</h1> |
| |
| <p>通常来说，Pegasus的数据会存储3个副本。对于每个partition，正常情况下应当都有一主两备3个replica提供服务。</p> |
| |
| <p>但是，集群不可避免会发生节点宕机、网络异常、心跳失联等情况，造成副本丢失，对服务可用性产生影响。副本丢失的程度会影响读写的能力（在<a href="rebalance#概念篇">负载均衡</a>里也有介绍）：</p> |
| <ul> |
| <li>一主两备都可用：partition完全健康，可以<strong>正常读和写</strong>。</li> |
| <li>一主一备可用：根据PacificA一致性协议，同样可以<strong>安全地读和写</strong>。</li> |
| <li>只有一主可用：此时<strong>不可写</strong>，但是<strong>可读</strong>。</li> |
| <li>全部不可用：此时<strong>不可读写</strong>。这种情况我们称之为<strong>DDD</strong>，即Dead-Dead-Dead的缩写，表示3个副本都不可用。</li> |
| </ul> |
| |
| <p>以上情况中，除了<strong>全部不可用的DDD状态</strong>，其他情况下MetaServer都能自动补充副本，并最终恢复至完全健康状态。但是如果partition进入DDD状态，MetaServer就无法对其进行自动恢复，需要进行人工干预。</p> |
| |
| <p><a href="https://github.com/XiaoMi/rdsn/issues/80">这个讨论</a>中给出了进入DDD状态的例子。实际上，<strong>只要某个partition进入DDD状态，且LastDrop的最后两个节点中有一个节点无法正常启动，就会进入需要人工干预的DDD状态</strong>。而在线上集群多个节点的起起停停过程中，这种情况是很容易出现的。</p> |
| |
| <p>可以通过Shell工具的<code class="language-plaintext highlighter-rouge">ls -d</code>命令查看健康状况，如果<code class="language-plaintext highlighter-rouge">read_unhealthy</code>的个数大于0，就表示有partition进入了DDD状态。</p> |
| |
| <h1 id="ddd诊断工具">DDD诊断工具</h1> |
| |
| <p>从v1.11.0版本开始，Pegasus在Shell工具中提供了<code class="language-plaintext highlighter-rouge">ddd_diagnose</code>命令，以支持DDD自动诊断功能。</p> |
| |
| <p>命令用法：</p> |
| <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>ddd_diagnose [-g|--gpid appid|appid.pidx] [-d|--diagnose] [-a|--auto_diagnose] |
| [-s|--skip_prompt] [-o|--output file_name] |
| </code></pre></div></div> |
| |
| <p>参数说明：</p> |
| <ul> |
| <li><code class="language-plaintext highlighter-rouge">-g</code>：指定app_id或者partition_id，譬如<code class="language-plaintext highlighter-rouge">-g 1</code>或者<code class="language-plaintext highlighter-rouge">-g 1.3</code>；如果不指定，则对所有表执行操作。</li> |
| <li><code class="language-plaintext highlighter-rouge">-d</code>：进入诊断模式；如果不指定，则只显示DDD情况，但不进行诊断。</li> |
| <li><code class="language-plaintext highlighter-rouge">-a</code>：开启自动诊断，即在保证数据一致性前提下，如果诊断工具能寻找到合适的备份作为该partition的主备份，则自动将其设置为primary，完成数据恢复，无需人工干预。</li> |
| <li><code class="language-plaintext highlighter-rouge">-s</code>：避免交互模式；如果不指定，则在进行诊断的过程中可能会要求用户输入信息，以完成选择、确认、或者信息补充。</li> |
| <li><code class="language-plaintext highlighter-rouge">-o</code>：将结果输出到指定文件。</li> |
| </ul> |
| |
| <p>使用示例（如果看不清楚，请在单独的页面中打开图片）：</p> |
| |
| <p><img src="/assets/images/ddd-diagnose.png" alt="ddd-diagnose.png" class="img-responsive" /></p> |
| |
| <p>上图是使用<code class="language-plaintext highlighter-rouge">ddd_diagnose</code>命令时的输出，我们通过红色的箭头标识依次进行说明：</p> |
| <ul> |
| <li>1：当前正在诊断的partition id。</li> |
| <li>2：该partition在zookeeper上持久化的<code class="language-plaintext highlighter-rouge">ballot</code>和<code class="language-plaintext highlighter-rouge">last_committed_decree</code>信息，但是由于持久化不是实时的，该值可能小于实际值。</li> |
| <li>3：dropped列表，列举曾经服务过该partition的节点的状态信息，重点关注： |
| <ul> |
| <li>alive：该节点是否可用。</li> |
| <li>ballot：该partition在该节点上replica的实际<code class="language-plaintext highlighter-rouge">ballot</code>；如果为-1，表示该partition在该节点上不存在数据。</li> |
| <li>last_committed：该partition在该节点上replica的实际<code class="language-plaintext highlighter-rouge">last_committed_decree</code>。</li> |
| <li>last_prepared：该partition在该节点上replica的实际<code class="language-plaintext highlighter-rouge">last_prepared_decree</code>。</li> |
| <li>最后如果有<code class="language-plaintext highlighter-rouge"><==</code>，表示该节点是 <strong>最后一个变得不可用</strong>（the latest） 还是 <strong>倒数第二个变得不可用</strong>（the secondary latest）。</li> |
| </ul> |
| </li> |
| <li>4：last_drops列表，记录节点变得不可用的时间顺序。</li> |
| <li>5：ddd_reason，表示该partition变成DDD状态的原因。</li> |
| <li>6：recommanded_primary，诊断工具在保证数据一致性的前提下推荐的新primary；如果无法给出，则为<code class="language-plaintext highlighter-rouge">none</code>。</li> |
| <li>7：如果第6步给出了推荐节点，则提示用户采取下一步操作（如果指定了<code class="language-plaintext highlighter-rouge">-a</code>或者<code class="language-plaintext highlighter-rouge">-s</code>选项，则不会进入这一步，相当于总是自动选择y）： |
| <ul> |
| <li>y：采用该推荐节点作为新的primary。</li> |
| <li>n：不采用该推荐节点，而是让用户选择其他的节点。</li> |
| <li>s：忽略对该partition的诊断。</li> |
| </ul> |
| </li> |
| <li>8：如果第6步没有给出推荐节点或者第7步选择了n，则提示用户输入新的节点作为primary。</li> |
| <li>9：生成propose命令，发送给MetaServer，将节点指定为新的primary，对该partition进行恢复。</li> |
| <li>10：收到propose命令的回复，<code class="language-plaintext highlighter-rouge">ERR_OK</code>表示执行成功。</li> |
| <li>11：显示当前已经完成的进度，分子为已经完成诊断的个数，分母为需要进行诊断的总个数。</li> |
| </ul> |
| |
| <p><strong>推荐用法</strong>：</p> |
| <ul> |
| <li><code class="language-plaintext highlighter-rouge">ddd_diagnose -d -a</code>，即开启自动诊断，对于无法自动完成诊断的partition，通过与用户交互来获得人工干预。这是最简单省心的用法，在大部分情况下都能自动完成恢复过程，无需人工干预。</li> |
| </ul> |
| |
| <p>在无法完成自动诊断的情况下，会进入上图中的第8步，需要用户输入新的节点作为primary。那么，在dropped列表的众多节点中，如何选择最合适的节点作为primary呢？我们的建议是：</p> |
| <ul> |
| <li><strong>在所有alive为true的节点中，选择<code class="language-plaintext highlighter-rouge">last_prepared</code>值最大的节点</strong>，因为这样能尽可能多地恢复数据，减少数据丢失的可能性。</li> |
| </ul> |
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