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<h1>缓存系统</h1>
<h2 id="_1">概览<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h2>
<p><a class="glightbox" href="images/profile.jpg" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="" src="images/profile.jpg" /></a></p>
<h2 id="_2">缓存三大问题<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<h3 id="_3">缓存雪崩<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h3>
<p>缓存雪崩是指缓存中数据大批量到过期时间，而查询数据量巨大，引起数据库压力过大甚至down机。和缓存击穿不同的是， 缓存击穿指并发查同一条数据，缓存雪崩是不同数据都过期了，很多数据都查不到从而查数据库。</p>
<p>解决办法：</p>
<ul>
<li>缓存过期时间设置成随机</li>
<li>热点数据考虑永不过期（定时刷新）</li>
<li>使用分布式缓存，防止单点故障缓存全部丢失</li>
</ul>
<h3 id="_4">缓存穿透<a class="headerlink" href="#_4" title="Permanent link">&para;</a></h3>
<p>缓存穿透是指缓存和数据库中都没有的数据，而用户不断发起请求，如发起为id为“-1”的数据或id为特别大不存在的数据。这时的用户很可能是攻击者，攻击会导致数据库压力过大。</p>
<p>解决办法：</p>
<ul>
<li>空对象</li>
<li>布隆过滤器</li>
</ul>
<h3 id="_5">缓存击穿<a class="headerlink" href="#_5" title="Permanent link">&para;</a></h3>
<p>缓存击穿是指缓存中没有但数据库中有的数据（一般是缓存时间到期），这时由于并发用户特别多，同时读缓存没读到数据，又同时去数据库去取数据，引起数据库压力瞬间增大，造成过大压力</p>
<p>解决办法：</p>
<ul>
<li>热点数据永不过期(后台进程定时刷新)</li>
<li>加互斥锁</li>
</ul>
<h2 id="_6">缓存淘汰策略<a class="headerlink" href="#_6" title="Permanent link">&para;</a></h2>
<ul>
<li>
<p>先进先出策略 FIFO（First In，First Out）</p>
<p>如果一个数据最先进入缓存中，则应该最早淘汰掉</p>
</li>
<li>
<p>最近最少使用策略 LRU（Least Recently Used）</p>
<p>如果数据最近被访问过，那么将来被访问的几率也更高。对于循环出现的数据，缓存命中不高。实际实现时候一般可采用双向链表，将最近访问过得缓存key放在链表首部，删除尾部的缓存key，再加上hash表来记录key-value，实现快速访问缓存</p>
</li>
<li>
<p>最少使用策略 LFU（Least Frequently Used）</p>
</li>
</ul>
<p>如果一个数据在最近一段时间内使用次数很少，那么在将来一段时间内被使用的可能性也很小。对于交替出现的数据，缓存命中不高</p>
<h4 id="lru">基于链表实现 LRU 缓存淘汰算法<a class="headerlink" href="#lru" title="Permanent link">&para;</a></h4>
<p>维护一个有序单链表，越靠近链表尾部的结点是越早之前访问的。当有一个新的数据被访问时，我们从链表头开始顺序遍历链表。</p>
<p>如果此数据之前已经被缓存在链表中了，我们遍历得到这个数据对应的结点，并将其从原来的位置删除，然后再插入到链表的头部。</p>
<p>如果此数据没有在缓存链表中，又可以分为两种情况：</p>
<p>如果此时缓存未满，则将此结点直接插入到链表的头部；
如果此时缓存已满，则链表尾结点删除，将新的数据结点插入链表的头部。
现在我们来看下 m 缓存访问的时间复杂度是多少。因为不管缓存有没有满，我们都需要遍历一遍链表，所以这种基于链表的实现思路，缓存访问的时间复杂度为 O(n)。</p>
<p>实际上，我们可以继续优化这个实现思路，比如引入散列表（Hash table）来记录每个数据的位置，将缓存访问的时间复杂度降到 O(1)</p>
<h2 id="_7">缓存一致性<a class="headerlink" href="#_7" title="Permanent link">&para;</a></h2>
<p><strong>对于读是不存在缓存与数据库不一致的的情况</strong>。读的流程：</p>
<ul>
<li>如果我们的数据在缓存里边有，那么就直接取缓存的。</li>
<li>如果缓存里没有我们想要的数据，我们会先去查询数据库，然后将数据库查出来的数据写到缓存中。</li>
<li>最后将数据返回给请求</li>
</ul>
<p>对于数据库更新操作， 执行操作时候，两种选择：</p>
<ul>
<li>先操作数据库，再操作缓存</li>
<li>先操作缓存，再操作数据库</li>
</ul>
<p>操作缓存，两种方案选择：</p>
<ul>
<li>更新缓存</li>
<li>删除缓存</li>
</ul>
<p>一般我们都是采取删除缓存缓存策略的， 原因：</p>
<ul>
<li>高并发环境下，无论是先操作数据库还是后操作数据库而言，如果加上更新缓存，那就更加容易导致数据库与缓存数据不一致问题。(删除缓存直接和简单很多)</li>
<li>如果每次更新了数据库，都要更新缓存【这里指的是频繁更新的场景，这会耗费一定的性能】，倒不如直接删除掉。等再次读取时，缓存里没有，那我到数据库找，在数据库找到再写到缓存里边 (体现懒加载)</li>
</ul>
<p><strong>先删缓存，再更新数据库</strong></p>
<p>该方案会导致不一致的原因是。同时有一个请求A进行更新操作，另一个请求B进行查询操作。那么会出现如下情形:
（1）请求A进行写操作，删除缓存
（2）请求B查询发现缓存不存在
（3）请求B去数据库查询得到旧值
（4）请求B将旧值写入缓存
（5）请求A将新值写入数据库
上述情况就会导致不一致的情形出现。而且，如果不采用给缓存设置过期时间策略，该数据永远都是脏数据。</p>
<p><strong>先更新数据，再删除缓存：</strong></p>
<p>如果在高并发的场景下，出现数据库与缓存数据不一致的概率特别低，也不是没有：</p>
<ul>
<li>缓存刚好失效</li>
<li>线程 A 查询数据库，得一个旧值</li>
<li>线程 B 将新值写入数据库</li>
<li>线程 B 删除缓存</li>
<li>线程 A 将查到的旧值写入缓存</li>
</ul>
<p>解决办法：
- Cache Aside Pattern</p>
<p><a class="glightbox" href="images/cache_aside.jpg" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="" src="images/cache_aside.jpg" /></a></p>
<ul>
<li>binlog模式</li>
</ul>
<p><a class="glightbox" href="images/cache_binlog.jpg" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="" src="images/cache_binlog.jpg" /></a></p>
<h2 id="_8">资料<a class="headerlink" href="#_8" title="Permanent link">&para;</a></h2>
<ul>
<li><a href="https://jingbin.me/2019/04/13/list/">链表（上）：如何实现LRU缓存淘汰算法?</a></li>
</ul>
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最后更新:
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-iso_date">2023-10-11</span>
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