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<a href="../../chapter_hello_algo/" class="md-nav__link ">
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序
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序
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第 0 章 &nbsp; 前言
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第 1 章 &nbsp; 初识算法
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第 2 章 &nbsp; 复杂度分析
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第 3 章 &nbsp; 数据结构
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第 4 章 &nbsp; 数组与链表
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第 5 章 &nbsp; 栈与队列
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第 6 章 &nbsp; 哈希表
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第 7 章 &nbsp; 树
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第 8 章 &nbsp; 堆
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第 11 章 &nbsp; 排序
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第 12 章 &nbsp; 分治
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第 13 章 &nbsp; 回溯
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第 14 章 &nbsp; 动态规划
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第 15 章 &nbsp; 贪心
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第 16 章 &nbsp; 附录
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<a href="../../chapter_paperbook/" class="md-nav__link ">
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纸质书
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</ol>
<p>哈希表的结构改良方法主要包括“链式地址”和“开放寻址”。</p>
<h2 id="621">6.2.1 &nbsp; 链式地址<a class="headerlink" href="#621" title="Permanent link">&para;</a></h2>
<p>在原始哈希表中，每个桶仅能存储一个键值对。「链式地址 separate chaining」将单个元素转换为链表，将键值对作为链表节点，将所有发生冲突的键值对都存储在同一链表中。图 6-5 展示了一个链式地址哈希表的例子。</p>
<p>在原始哈希表中，每个桶仅能存储一个键值对。<u>链式地址（separate chaining）</u>将单个元素转换为链表，将键值对作为链表节点，将所有发生冲突的键值对都存储在同一链表中。图 6-5 展示了一个链式地址哈希表的例子。</p>
<p><a class="glightbox" href="../hash_collision.assets/hash_table_chaining.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="链式地址哈希表" class="animation-figure" src="../hash_collision.assets/hash_table_chaining.png" /></a></p>
<p align="center"> 图 6-5 &nbsp; 链式地址哈希表 </p>
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</details>
<p>值得注意的是，当链表很长时，查询效率 <span class="arithmatex">\(O(n)\)</span> 很差。<strong>此时可以将链表转换为“AVL 树”或“红黑树”</strong>，从而将查询操作的时间复杂度优化至 <span class="arithmatex">\(O(\log n)\)</span> 。</p>
<h2 id="622">6.2.2 &nbsp; 开放寻址<a class="headerlink" href="#622" title="Permanent link">&para;</a></h2>
<p>「开放寻址 open addressing」不引入额外的数据结构，而是通过“多次探测”来处理哈希冲突，探测方式主要包括线性探测、平方探测和多次哈希等。</p>
<p><u>开放寻址（open addressing）</u>不引入额外的数据结构，而是通过“多次探测”来处理哈希冲突，探测方式主要包括线性探测、平方探测和多次哈希等。</p>
<p>下面以线性探测为例，介绍开放寻址哈希表的工作机制。</p>
<h3 id="1">1. &nbsp; 线性探测<a class="headerlink" href="#1" title="Permanent link">&para;</a></h3>
<p>线性探测采用固定步长的线性搜索来进行探测，其操作方法与普通哈希表有所不同。</p>
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<p><a class="glightbox" href="../hash_collision.assets/hash_table_open_addressing_deletion.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="在开放寻址中删除元素导致的查询问题" class="animation-figure" src="../hash_collision.assets/hash_table_open_addressing_deletion.png" /></a></p>
<p align="center"> 图 6-7 &nbsp; 在开放寻址中删除元素导致的查询问题 </p>
<p>为了解决该问题，我们可以采用「懒删除 lazy deletion」机制：它不直接从哈希表中移除元素，<strong>而是利用一个常量 <code>TOMBSTONE</code> 来标记这个桶</strong>。在该机制下，<code>None</code> 和 <code>TOMBSTONE</code> 都代表空桶，都可以放置键值对。但不同的是，线性探测到 <code>TOMBSTONE</code> 时应该继续遍历，因为其之下可能还存在键值对。</p>
<p>为了解决该问题，我们可以采用<u>懒删除（lazy deletion）</u>机制：它不直接从哈希表中移除元素，<strong>而是利用一个常量 <code>TOMBSTONE</code> 来标记这个桶</strong>。在该机制下，<code>None</code> 和 <code>TOMBSTONE</code> 都代表空桶，都可以放置键值对。但不同的是，线性探测到 <code>TOMBSTONE</code> 时应该继续遍历，因为其之下可能还存在键值对。</p>
<p>然而，<strong>懒删除可能会加速哈希表的性能退化</strong>。这是因为每次删除操作都会产生一个删除标记，随着 <code>TOMBSTONE</code> 的增加，搜索时间也会增加，因为线性探测可能需要跳过多个 <code>TOMBSTONE</code> 才能找到目标元素。</p>
<p>为此，考虑在线性探测中记录遇到的首个 <code>TOMBSTONE</code> 的索引，并将搜索到的目标元素与该 <code>TOMBSTONE</code> 交换位置。这样做的好处是当每次查询或添加元素时，元素会被移动至距离理想位置（探测起始点）更近的桶，从而优化查询效率。</p>
<p>以下代码实现了一个包含懒删除的开放寻址（线性探测）哈希表。为了更加充分地使用哈希表的空间，我们将哈希表看作一个“环形数组”，当越过数组尾部时，回到头部继续遍历。</p>
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