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<h1 id="_1">系统设计基础<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h1>
<p><em>系统设计是构建可在大规模下可靠运行的软件的方法。本文件涵盖客户端-服务器架构、网络协议、DNS、代理、负载均衡、缓存、数据库、消息队列、一致性模型和弹性模式</em></p>
<ul>
<li>生产环境中的每一个ML系统都是一个分布式系统。推荐引擎不仅仅是模型——它是一个API服务器、一个特征存储、一个模型注册表、一个缓存层、一个消息队列和一个监控栈，所有这些通过网络进行通信。理解系统设计是区分"我训练了一个模型"和"我构建了一个产品"的关键。</li>
<li>顶级科技公司（Google、Meta、Amazon、OpenAI）的系统设计面试会测试你是否能设计这些系统。本章为你提供基础构建模块（本文件）、云基础设施（文件02）、扩展模式（文件03）、ML特定设计（文件04）和实操示例（文件05）。</li>
</ul>
<h2 id="-">客户端-服务器架构<a class="headerlink" href="#-" title="Permanent link">&para;</a></h2>
<ul>
<li>基本模式：<strong>客户端</strong>发送请求，<strong>服务器</strong>处理请求并返回响应。你的浏览器（客户端）向google.com（服务器）发送HTTP请求，服务器返回HTML。</li>
<li><strong>请求-响应模式</strong>：同步。客户端等待响应。简单但会产生瓶颈：客户端在等待时空闲，服务器必须在处理完当前请求后才能继续。</li>
<li><strong>无状态服务器</strong>：服务器不记住先前的请求。每个请求包含处理它所需的所有信息。这使得扩展变得容易：任何服务器都可以处理任何请求，因此你可以在负载均衡器后面添加更多服务器。</li>
<li><strong>有状态服务器</strong>：服务器在请求之间维护状态（例如，用户会话）。扩展更困难，因为来自同一用户的请求必须发送到同一台服务器（会话亲和性）。现代系统通过将状态存储在数据库或缓存（Redis）中来避免服务器端状态。</li>
</ul>
<h2 id="_2">网络协议<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<ul>
<li>我们在第13章（TCP/IP层、套接字）中介绍了网络知识。这里我们关注系统设计中使用的应用层协议：</li>
<li><strong>HTTP/HTTPS</strong>：Web和大多数API的协议。请求方法：GET（读取）、POST（创建/预测）、PUT（更新）、DELETE（删除）。HTTPS增加了TLS加密（第13章安全部分）。REST API（第15章文件03）基于HTTP构建。</li>
<li><strong>WebSocket</strong>：客户端和服务器之间的持久双向连接。与HTTP（请求→响应→连接关闭）不同，WebSocket保持连接打开，用于实时流式传输。用于：LLM令牌流式传输（生成时发送令牌）、实时仪表盘、聊天应用。</li>
<li><strong>gRPC</strong>：Google的RPC框架。使用Protocol Buffers（二进制序列化，比JSON小约10倍且更快），基于HTTP/2。支持流式传输（服务端、客户端、双向）。用于注重性能的内部服务间通信。Triton推理服务器（第15章）和TensorFlow Serving使用gRPC。</li>
<li><strong>Protocol Buffers</strong>：在<code>.proto</code>文件中定义消息模式：</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kd">message</span><span class="w"> </span><span class="nc">PredictRequest</span><span class="w"> </span><span class="p">{</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="w"> </span><span class="k">repeated</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="na">features</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="w"> </span><span class="kt">string</span><span class="w"> </span><span class="na">model_version</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="p">}</span>
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>
<a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="kd">message</span><span class="w"> </span><span class="nc">PredictResponse</span><span class="w"> </span><span class="p">{</span>
<a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="na">prediction</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
<a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="na">confidence</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span>
<a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="p">}</span>
<a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>
<a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="kd">service</span><span class="w"> </span><span class="n">ModelService</span><span class="w"> </span><span class="p">{</span>
<a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="w"> </span><span class="k">rpc</span><span class="w"> </span><span class="n">Predict</span><span class="p">(</span><span class="n">PredictRequest</span><span class="p">)</span><span class="w"> </span><span class="k">returns</span><span class="w"> </span><span class="p">(</span><span class="n">PredictResponse</span><span class="p">);</span>
<a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a><span class="p">}</span>
</code></pre></div>
<ul>
<li>该模式被编译成任何语言（Python、C++、Go、Java）的客户端和服务端代码。类型安全、向后兼容性和高性能都自然具备。</li>
</ul>
<h2 id="dns">DNS<a class="headerlink" href="#dns" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>DNS</strong>（域名系统）将人类可读的名称转换为IP地址（第13章）。对于系统设计，DNS还提供：</li>
<li><strong>通过DNS的负载均衡</strong>：为同一域名返回不同的IP地址，将流量分布到多个服务器。简单但粒度较粗（DNS结果会被缓存数分钟到数小时，因此流量不会快速重新平衡）。</li>
<li><strong>地理路由</strong>：根据客户端位置返回最近数据中心的IP。东京的用户获得日本数据中心；伦敦的用户获得欧洲数据中心。</li>
<li><strong>故障转移</strong>：如果服务器宕机，DNS停止返回其IP。新客户端连接到健康的服务器。但缓存的DNS条目意味着某些客户端会继续访问已宕机的服务器持续数分钟（TTL问题）。</li>
</ul>
<h2 id="_3">代理<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>代理</strong>是客户端和服务器之间的中介：</li>
<li><strong>反向代理</strong>（在服务器前面）：客户端连接到代理，代理将请求转发给后端服务器。客户端不知道哪个服务器处理了请求。<strong>Nginx</strong>和<strong>HAProxy</strong>是标准的反向代理。它们提供：负载均衡（分发请求）、SSL终止（在代理处解密HTTPS，向后端发送明文HTTP）、缓存、速率限制和压缩。</li>
<li><strong>API网关</strong>：一种专门用于API的反向代理。处理身份验证、速率限制、请求路由（不同路径→不同服务）和API版本管理。<strong>Kong</strong>、<strong>AWS API Gateway</strong>和<strong>Envoy</strong>是常见选择。</li>
<li>对于ML服务：API网关位于模型服务器前面。它验证API密钥、对免费用户进行速率限制、将<code>/v1/predict</code>路由到模型服务器A、将<code>/v2/predict</code>路由到模型服务器B，并收集使用指标。</li>
</ul>
<h2 id="_4">负载均衡<a class="headerlink" href="#_4" title="Permanent link">&para;</a></h2>
<ul>
<li>当你拥有多台服务器时，<strong>负载均衡器</strong>将传入请求分布到它们之间。</li>
</ul>
<p><img alt="负载均衡器将传入请求分布到多个后端服务器" src="../../images/load_balancer.svg" /></p>
<ul>
<li><strong>算法</strong>：<ul>
<li><strong>轮询</strong>：按顺序发送请求到服务器（1, 2, 3, 1, 2, 3...）。简单、公平，但不考虑服务器负载。</li>
<li><strong>最少连接</strong>：发送到活动连接最少的服务器。适用于处理时间可变的请求（有些LLM请求生成10个令牌，有些生成1000个）。</li>
<li><strong>加权轮询</strong>：容量更大的服务器获得更多请求。拥有80 GB GPU内存的服务器处理的请求量是40 GB的两倍。</li>
<li><strong>一致性哈希</strong>：对请求键进行哈希运算，映射到特定服务器。相同的键始终发送到相同的服务器。适用于：缓存（同一用户的请求命中同一缓存）、会话亲和性和前缀缓存（第17章：具有相同系统提示词的请求发送到具有该提示词KV缓存的服务器）。</li>
</ul>
</li>
<li><strong>L4 vs L7负载均衡</strong>：<ul>
<li><strong>L4</strong>（传输层）：基于IP和端口路由。快速但无法检查请求内容。</li>
<li><strong>L7</strong>（应用层）：基于HTTP路径、标头或正文内容路由。可以将<code>/api/chat</code>路由到聊天服务器，将<code>/api/embed</code>路由到嵌入服务器。较慢但更灵活。</li>
</ul>
</li>
</ul>
<h2 id="_5">缓存<a class="headerlink" href="#_5" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>缓存</strong>将频繁访问的数据存储在快速存储层（内存）中，以避免重新计算或重新获取。</li>
</ul>
<p><img alt="缓存旁路模式：先检查缓存，未命中时从数据库获取并存储到缓存中以备下次使用" src="../../images/cache_aside_pattern.svg" /></p>
<ul>
<li><strong>缓存模式</strong>：<ul>
<li><strong>缓存旁路</strong>（惰性加载）：应用程序先检查缓存。未命中时，从数据库获取、存储到缓存并返回。最常见的模式。</li>
<li><strong>直写</strong>：每次写入同时写入缓存和数据库。确保缓存始终是最新的，但会减慢写入速度。</li>
<li><strong>回写</strong>：写入只进入缓存；缓存异步刷新到数据库。写入最快，但若缓存刷新前崩溃则有数据丢失风险。</li>
</ul>
</li>
<li><strong>驱逐策略</strong>（当缓存满时）：<ul>
<li><strong>LRU</strong>（最近最少使用）：驱逐最长时间未被访问的条目。最常见的策略。</li>
<li><strong>LFU</strong>（最不频繁使用）：驱逐访问次数最少的条目。当某些条目持续受欢迎时效果更好。</li>
<li><strong>TTL</strong>（生存时间）：条目在固定时长后过期。用于会过时的数据（模型预测缓存5分钟，特征值缓存1小时）。</li>
</ul>
</li>
<li><strong>CDN</strong>（内容分发网络）：用于静态内容（图片、JavaScript、CSS）的全球分布式缓存。遍布100多个地点的服务器从离用户最近的位置提供缓存内容。对于ML：模型权重可以缓存在CDN上以实现快速下载。</li>
<li><strong>Redis</strong>：标准的键值内存缓存/数据库。支持字符串、列表、集合、有序集合、哈希和流。亚毫秒级延迟。用于：缓存模型预测、存储会话数据、速率限制（统计每个用户每分钟的请求数）和实时特征服务。</li>
<li>对于ML服务：缓存重复输入的预测结果。如果很多用户问"法国的首都是什么？"，计算一次答案然后提供缓存结果。对于聊天机器人工作负载，缓存命中率通常为20-40%，按比例降低GPU成本。</li>
</ul>
<h2 id="_6">数据库<a class="headerlink" href="#_6" title="Permanent link">&para;</a></h2>
<h3 id="sql">SQL（关系型）<a class="headerlink" href="#sql" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>SQL数据库</strong>（PostgreSQL、MySQL）以包含行和列的形式存储数据。表之间的关系通过外键表示。查询使用SQL。<strong>ACID</strong>保证：<ul>
<li><strong>原子性</strong>：事务要么完全完成，要么完全回滚。没有部分更新。</li>
<li><strong>一致性</strong>：数据库从一个有效状态转换到另一个有效状态。约束条件（唯一键、外键）始终得到满足。</li>
<li><strong>隔离性</strong>：并发事务不互相干扰。</li>
<li><strong>持久性</strong>：已提交的数据在崩溃后仍然存在（在确认前写入磁盘）。</li>
</ul>
</li>
<li>SQL数据库擅长：具有关系的有结构数据、复杂查询（联接、聚合）、严格的一致性要求和数据完整性。</li>
</ul>
<h3 id="nosql">NoSQL<a class="headerlink" href="#nosql" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>NoSQL数据库</strong>为了可扩展性和灵活性而牺牲了一些ACID保证：<ul>
<li><strong>键值存储</strong>（Redis、DynamoDB）：最简单的模型。按键快速查找。用于缓存、会话存储和特征存储。</li>
<li><strong>文档存储</strong>（MongoDB、Firestore）：存储类似JSON的文档。灵活的模式（每个文档可以有不同字段）。用于用户资料、产品目录和配置。</li>
<li><strong>列族存储</strong>（Cassandra、HBase）：针对写入密集型工作负载和时间序列数据进行了优化。用于事件日志、指标和分析。</li>
<li><strong>图数据库</strong>（Neo4j）：存储节点和边。针对遍历查询进行了优化。用于社交网络、知识图谱和推荐系统。</li>
<li><strong>向量数据库</strong>（Pinecone、Milvus、Weaviate、FAISS）：存储高维嵌入并支持近似最近邻（ANN）搜索。对于语义搜索、RAG（检索增强生成）和推荐系统至关重要。</li>
</ul>
</li>
</ul>
<h3 id="cap">CAP定理<a class="headerlink" href="#cap" title="Permanent link">&para;</a></h3>
<ul>
<li>在分布式数据库中，最多只能满足三个属性中的两个：<ul>
<li><strong>一致性</strong>：每次读取都返回最新的写入。</li>
<li><strong>可用性</strong>：每个请求都会收到响应（即使某些节点宕机）。</li>
<li><strong>分区容忍性</strong>：系统在网络分区（节点无法通信）时仍能继续运行。</li>
</ul>
</li>
</ul>
<p><img alt="CAP定理：由于网络分区不可避免，需要在CP（一致）或AP（可用）之间选择" src="../../images/cap_theorem.svg" /></p>
<ul>
<li>由于分布式系统中网络分区不可避免，真正的选择是<strong>CP</strong>（一致但在分区期间可能不可用——如PostgreSQL） vs <strong>AP</strong>（可用但在分区期间可能返回过期数据——如Cassandra、DynamoDB）。</li>
<li>对于ML：特征存储通常选择AP（稍微过期的特征值也比无法预测要好）。模型注册表选择CP（提供错误的模型版本是灾难性的）。</li>
</ul>
<h3 id="_7">分片<a class="headerlink" href="#_7" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>分片</strong>将数据库拆分到多台机器上。每个分片持有数据的一个子集。</li>
<li><strong>哈希分片</strong>：对键进行哈希运算以确定分片。<code>shard = hash(user_id) % num_shards</code>。分布均匀但不支持范围查询。</li>
<li><strong>范围分片</strong>：每个分片持有一个键范围（用户A-G在分片1，H-N在分片2）。支持范围查询但可能产生热点（如果很多用户名字以"S"开头）。</li>
<li><strong>重新分片问题</strong>：添加分片会使哈希映射失效。<strong>一致性哈希</strong>最小化数据移动：添加第n个分片时，只有约1/n的键需要移动。</li>
</ul>
<h3 id="_8">数据库索引<a class="headerlink" href="#_8" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>索引</strong>是一种加速查询的数据结构，代价是额外的存储空间和较慢的写入速度。没有索引时，查询会扫描每一行（<span class="arithmatex">\(O(n)\)</span>）。有索引时，可以在<span class="arithmatex">\(O(\log n)\)</span>时间内找到目标。</li>
<li><strong>B树索引</strong>（默认）：一种平衡树（第13章、第14章），其中每个节点包含多个键和指针。B树对缓存友好（宽节点适合缓存行）并支持范围查询（<code>WHERE age BETWEEN 20 AND 30</code>）。大多数SQL数据库使用B树。</li>
<li><strong>哈希索引</strong>：使用哈希函数将键映射到行位置。<span class="arithmatex">\(O(1)\)</span>查找但不支持范围查询。用于精确匹配查找（<code>WHERE id = 12345</code>）。</li>
<li><strong>复合索引</strong>：对多个列的索引。<code>CREATE INDEX ON users(country, city)</code> 加速按国家或按国家+城市筛选的查询，但不能加速仅按城市的查询（最左边的列必须在查询中）。</li>
<li><strong>权衡</strong>：每个索引都会加速读取但减慢写入（每次插入/更新/删除都必须更新索引）并占用存储空间（每个索引约占表大小的10-30%）。不要索引所有内容——只索引你经常查询的列。</li>
<li><strong>对于ML系统</strong>：特征存储的在线数据库需要在实体键（user_id、item_id）上建立索引以实现快速特征查找。实验跟踪数据库需要在（experiment_id、metric_name）上建立索引以实现仪表盘查询。</li>
</ul>
<h3 id="api">API设计<a class="headerlink" href="#api" title="Permanent link">&para;</a></h3>
<ul>
<li>系统通过API进行通信。良好的API设计使系统可用、可进化和可调试：</li>
<li><strong>REST约定</strong>：使用名词表示资源（<code>/users</code>、<code>/models</code>），HTTP方法表示操作（GET=读取、POST=创建、PUT=更新、DELETE=删除），状态码表示结果（200=OK、201=已创建、400=错误请求、404=未找到、429=被限流、500=服务器错误）。</li>
<li><strong>分页</strong>：对于返回列表的端点，永远不要一次返回所有结果。使用基于游标的分页（<code>GET /items?cursor=abc&amp;limit=50</code>）或基于偏移量的分页（<code>GET /items?offset=100&amp;limit=50</code>）。对于大数据集，基于游标的分页更高效（基于偏移量的分页需要跳过行）。</li>
<li><strong>版本管理</strong>：在API路径前加上版本前缀（<code>/v1/predict</code>、<code>/v2/predict</code>）。这样可以在不破坏现有客户端的情况下演进API。客户端按照自己的节奏迁移到v2；v1被弃用但在流量下降之前不会删除。</li>
<li><strong>错误响应</strong>：返回结构化的错误信息：</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="p">{</span>
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="w"> </span><span class="nt">&quot;error&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="w"> </span><span class="nt">&quot;code&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;INVALID_INPUT&quot;</span><span class="p">,</span>
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="w"> </span><span class="nt">&quot;message&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;特征&#39;user_age&#39;必须为正整数&quot;</span><span class="p">,</span>
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a><span class="w"> </span><span class="nt">&quot;details&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nt">&quot;field&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;user_age&quot;</span><span class="p">,</span><span class="w"> </span><span class="nt">&quot;value&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">-5</span><span class="p">}</span>
<a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a><span class="w"> </span><span class="p">}</span>
<a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a><span class="p">}</span>
</code></pre></div>
<h2 id="_9">消息队列<a class="headerlink" href="#_9" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>消息队列</strong>将生产者（生成工作的服务）与消费者（处理工作的服务）解耦。生产者将消息发送到队列；消费者在就绪时拉取消息。</li>
<li><strong>队列为什么重要</strong>：没有队列时，如果消费者慢或宕机，生产者会被阻塞。有了队列，生产者发送后就无需等待；队列缓冲消息，直到消费者准备好。</li>
<li><strong>Apache Kafka</strong>：一个分布式、持久化、高吞吐量的消息队列。消息存储在<strong>主题</strong>中，每个主题跨多个代理分区。消费者从分区读取，跟踪其位置（<strong>偏移量</strong>）。Kafka保证分区内的顺序，并可重播消息（日志是持久化的）。</li>
<li><strong>发布/订阅</strong>：发布者将消息发送到主题；该主题的所有订阅者都会收到一份副本。用于事件驱动架构："新模型已部署"触发监控服务、A/B测试服务和日志服务同时响应。</li>
<li>对于ML：预测请求通过HTTP到达，放入Kafka队列，由GPU工作线程处理，结果通过回调或WebSocket返回。队列缓冲突发的流量，并确保即使GPU工作线程崩溃也不会丢失请求。</li>
</ul>
<h2 id="_10">一致性模型<a class="headerlink" href="#_10" title="Permanent link">&para;</a></h2>
<ul>
<li>在分布式系统中，不同节点可能对数据有不同的视图。<strong>一致性模型</strong>定义了系统提供的保证：</li>
<li><strong>强一致性</strong>：写操作之后，所有后续读取（从任何节点）都能看到新值。易于推理但速度慢（需要在节点之间协调）。</li>
<li><strong>最终一致性</strong>：写操作之后，读取可能在某段时间内看到过期数据，但最终会看到新值。速度快（无需协调）但需要应用程序处理过期读取。</li>
<li><strong>因果一致性</strong>：如果操作A因果上先于B（例如，"写入X然后读取X"），系统保证B能看到A的结果。但不相关的操作可能以任何顺序被看到。</li>
<li><strong>读写一致性</strong>：用户始终能立即看到自己的写入，即使其他用户看到的是过期数据。大多数应用程序所需的最小一致性。</li>
</ul>
<h2 id="_11">弹性模式<a class="headerlink" href="#_11" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>速率限制</strong>：限制每个用户在时间窗口内的请求数。防止滥用并确保公平访问。使用Redis中的令牌桶或滑动窗口计数器实现。</li>
<li><strong>断路器</strong>：如果下游服务开始失败（错误率超过阈值），断路器"断开"并停止向其发送请求（立即返回回退响应）。超时后，"半开"并发送测试请求。如果测试成功，则"闭合"（恢复正常操作）。这防止了级联故障：如果特征存储宕机，模型服务器返回无特征的预测，而不是每次请求都超时。</li>
<li><strong>背压</strong>：当系统过载时，它向上游发出信号要求减速。与其接受请求然后失败，不如尽早拒绝多余的请求（返回429或503状态码）。客户端以指数退避重试。</li>
<li><strong>指数退避重试</strong>：如果请求失败，等待1秒后重试。如果再次失败，等待2秒。然后是4秒、8秒，依此类推。加入随机抖动以防止所有客户端同时重试（惊群问题）。</li>
<li><strong>幂等性</strong>：如果执行两次的效果与执行一次相同，则该操作是幂等的。<code>PUT /user/123 {"name": "Alice"}</code>是幂等的（将名称设置为"Alice"两次没问题）。<code>POST /payments</code>不是（支付两次很糟糕）。使操作幂等可确保重试是安全的。</li>
</ul>
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