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<a class="post-title-link" href="/2018/01/29/Flink-分布式快照的设计-流程/" itemprop="url">Flink-分布式快照的设计-流程</a></h1>
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<p>Flink 通过 barrier 来协调 checkpoint 的时机，我们在介绍【Flink 网络栈】的时候已经有介绍：对于一个拓扑结构，只有上游算子 checkpoint 完成，下游算子的 checkpoint 才能开始并有意义，又因为下游算子的消费速率并不统一【有的 channel 快，有的 channel 慢】，barrier 就是这样一种协调上下游算子的机制。<br><img src="/2018/01/29/Flink-分布式快照的设计-流程/stream_barriers.svg" alt="stream_barriers" title="stream_barriers"><br>
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<p>分布式快照是整个 Flink 计算框架中非常核心的模块，Flink 的 checkpoint、状态存储都依赖于其分布式快照；不仅框架自身借助于 chandy-lamda 算法，实现了算子状态的快照和恢复，也对用户暴露了一套简洁的状态存储的 API，用户无需关心快照自身的容错/扩展/一致性，这些 Flink 都已对用户透明；由于分布式快照这块的设计比较复杂，因此将拆成两篇文章来介绍，本篇文章主要介绍分布式快照存储部分的设计，下一篇会介绍快照的流程和细节。</p>
<p>代码参考版本: 1.5-SNAPSHOT。</p>
<h2 id="核心模块"><a href="#核心模块" class="headerlink" title="核心模块"></a>核心模块</h2><p>Flink 的分布式快照存储部分设计抽象出了大致 5 个层次：</p>
<ul>
<li>最底层是快照的物理存储，包括内存和文件系统两种形式</li>
<li>再上层是 CheckpointStreamFactory：封装了具体的存储交互，也就是内存/文件系统读写</li>
<li>再上层是 StateBackend：封装了工作状态的存储逻辑，包括内存和 RocksDB 两种形式</li>
<li>再上层是 KeyedStateBackend：封装了快照的读写细节，快照分区策略等</li>
<li>再上层是 State：封装了与 KeyedStateBackend 交互时状态的 val 序列化/反序列化 等逻辑</li>
</ul>
<p><em>还有一个 StateContext 比较特殊，它不提供快照功能，只提供临时的状态读写，下面会讲到</em><br>核心模块的总体交互图:<br><img src="/2017/12/24/Flink-分布式快照的设计-存储/checkpoint-sys-interact.png" alt="checkpoint-sys-interact" title="checkpoint-sys-interact"><br>
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本文将介绍 flink 1.3.1 版本 WindowOperator 的设计，主要涉及核心的抽象，角色功能以及之间的交互，因为 WindowOperator 的状态存储用到了 flink 的状态存储，所以这里会略带涉及状态存储的知识，具体细节会单独开一篇文章介绍。
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本文将介绍 flink 1.2 的 0.9.0 版本 kafka connector，主要讲解消费模型，快照策略/恢复等，尤其是为什么 flink kafka connector 借助于 flink 内核的分布式快照算法做到了 exact-once 语义
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