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<a href="https://github.com/krahets/hello-algo/tree/main/docs/chapter_hashing/hash_collision.md" title="编辑此页" class="md-content__button md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4v-2m10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1 2.1 2.1Z"/></svg>
</a>
<h1 id="72">7.2. &nbsp; 哈希冲突<a class="headerlink" href="#72" title="Permanent link">&para;</a></h1>
<p>在理想情况下,哈希函数应为每个输入生成唯一的输出,实现 key 和 value 的一一对应。然而实际上,向哈希函数输入不同的 key 却产生相同输出的情况是存在的,这种现象被称为「哈希冲突 Hash Collision」。哈希冲突可能导致查询结果错误从而严重影响哈希表的可用性。</p>
<p>那么,为何会出现哈希冲突呢?从本质上看,由于哈希函数的输入空间通常远大于输出空间,因此多个输入产生相同输出的情况是不可避免的。例如,若输入空间为全体整数,而输出空间为固定大小的数组,则必然有多个整数映射至同一数组索引。</p>
<p>为了减轻哈希冲突,一方面,<strong>可以通过扩大哈希表容量来降低冲突概率</strong>。极端情况下,当输入空间和输出空间大小相等时,哈希表等同于数组,每个 key 都对应唯一的数组索引,可谓“大力出奇迹”。</p>
<p>另一方面,<strong>可以考虑优化哈希表的表示以缓解哈希冲突</strong>,常用方法包括「链式地址 Separate Chaining」和「开放寻址 Open Addressing」。</p>
<h2 id="721">7.2.1. &nbsp; 哈希表扩容<a class="headerlink" href="#721" title="Permanent link">&para;</a></h2>
<p>哈希函数的最后一步通常是对桶数量 <span class="arithmatex">\(n\)</span> 取余,作用是将哈希值映射到桶索引范围,从而将 key 放入对应的桶中。当哈希表容量越大(即 <span class="arithmatex">\(n\)</span> 越大)时,多个 key 被分配到同一个桶中的概率就越低,冲突就越少。</p>
<p>因此,<strong>当哈希表内的冲突总体较为严重时,编程语言通常通过扩容哈希表来缓解冲突</strong>。类似于数组扩容,哈希表扩容需将所有键值对从原哈希表迁移至新哈希表,开销较大。</p>
<p>编程语言通常使用「负载因子 Load Factor」来衡量哈希冲突的严重程度<strong>定义为哈希表中元素数量除以桶数量</strong>,常作为哈希表扩容的触发条件。在 Java 中,当负载因子 <span class="arithmatex">\(&gt; 0.75\)</span> 时,系统会将 HashMap 容量扩展为原先的 <span class="arithmatex">\(2\)</span> 倍。</p>
<h2 id="722">7.2.2. &nbsp; 链式地址<a class="headerlink" href="#722" 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。<strong>链式地址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</strong></p>
<p><img alt="链式地址" src="../hash_collision.assets/hash_collision_chaining.png" /></p>
<p align="center"> Fig. 链式地址 </p>
<p>链式地址下,哈希表的操作方法包括:</p>
<ul>
<li><strong>查询元素</strong>:输入 key ,经过哈希函数得到数组索引,即可访问链表头节点,然后遍历链表并对比 key 以查找目标键值对。</li>
<li><strong>添加元素</strong>:先通过哈希函数访问链表头节点,然后将节点(即键值对)添加到链表中。</li>
<li><strong>删除元素</strong>:根据哈希函数的结果访问链表头部,接着遍历链表以查找目标节点,并将其删除。</li>
</ul>
<p>尽管链式地址法解决了哈希冲突问题,但仍存在一些局限性,包括:</p>
<ul>
<li><strong>占用空间增大</strong>,由于链表或二叉树包含节点指针,相比数组更加耗费内存空间;</li>
<li><strong>查询效率降低</strong>,因为需要线性遍历链表来查找对应元素;</li>
</ul>
<p>为了提高操作效率,<strong>可以将链表转换为「AVL 树」或「红黑树」</strong>,将查询操作的时间复杂度优化至 <span class="arithmatex">\(O(\log n)\)</span></p>
<h2 id="723">7.2.3. &nbsp; 开放寻址<a class="headerlink" href="#723" title="Permanent link">&para;</a></h2>
<p>「开放寻址」方法不引入额外的数据结构,而是通过“多次探测”来解决哈希冲突,<strong>探测方主要包括线性探测、平方探测、多次哈希</strong></p>
<h3 id="_1">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查找来解决哈希冲突。</p>
<p><strong>插入元素</strong>:若出现哈希冲突,则从冲突位置向后线性遍历(步长通常为 <span class="arithmatex">\(1\)</span> ),直至找到空位,将元素插入其中。</p>
<p><strong>查找元素</strong>:在出现哈希冲突时,使用相同步长进行线性查找,可能遇到以下两种情况。</p>
<ol>
<li>找到对应元素,返回 value 即可;</li>
<li>若遇到空位,说明目标键值对不在哈希表中;</li>
</ol>
<p><img alt="线性探测" src="../hash_collision.assets/hash_collision_linear_probing.png" /></p>
<p align="center"> Fig. 线性探测 </p>
<p>线性探测存在以下缺陷:</p>
<ul>
<li><strong>不能直接删除元素</strong>。删除元素会在数组内产生一个空位,查找其他元素时,该空位可能导致程序误判元素不存在(即上述第 <code>2.</code> 种情况)。因此,需要借助一个标志位来标记已删除元素。</li>
<li><strong>容易产生聚集</strong>。数组内连续被占用位置越长,这些连续位置发生哈希冲突的可能性越大,进一步促使这一位置的“聚堆生长”,最终导致增删查改操作效率降低。</li>
</ul>
<h3 id="_2">多次哈希<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h3>
<p>顾名思义,「多次哈希」方法是使用多个哈希函数 <span class="arithmatex">\(f_1(x)\)</span> , <span class="arithmatex">\(f_2(x)\)</span> , <span class="arithmatex">\(f_3(x)\)</span> , <span class="arithmatex">\(\cdots\)</span> 进行探测。</p>
<p><strong>插入元素</strong>:若哈希函数 <span class="arithmatex">\(f_1(x)\)</span> 出现冲突,则尝试 <span class="arithmatex">\(f_2(x)\)</span> ,以此类推,直到找到空位后插入元素。</p>
<p><strong>查找元素</strong>:在相同的哈希函数顺序下进行查找,存在以下两种情况:</p>
<ol>
<li>如果找到目标元素,则返回之;</li>
<li>若遇到空位或已尝试所有哈希函数,则说明哈希表中不存在该元素;</li>
</ol>
<p>与线性探测相比,多次哈希方法不易产生聚集,但多个哈希函数会增加额外的计算量。</p>
<div class="admonition note">
<p class="admonition-title">哈希表设计方案</p>
<p>Java 采用「链式地址」。自 JDK 1.8 以来,当 HashMap 内数组长度大于 64 且链表长度大于 8 时,链表会被转换为「红黑树」以提升查找性能。</p>
<p>Python 采用「开放寻址」。字典 dict 使用伪随机数进行探测。</p>
<p>Golang 采用「链式地址」。Go 规定每个桶最多存储 8 个键值对,超出容量则连接一个溢出桶;当溢出桶过多时,会执行一次特殊的等量扩容操作,以确保性能。</p>
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