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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">
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<h1 id="62">6.2. &nbsp; 哈希冲突<a class="headerlink" href="#62" title="Permanent link">&para;</a></h1>
<p>理想情况下,哈希函数应该为每个输入产生唯一的输出,使得 key 和 value 一一对应。而实际上,往往存在向哈希函数输入不同的 key 而产生相同输出的情况,这种情况被称为「哈希冲突 Hash Collision」。哈希冲突会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,为什么会出现哈希冲突呢?本质上看,<strong>由于哈希函数的输入空间往往远大于输出空间</strong>,因此不可避免地会出现多个输入产生相同输出的情况,即为哈希冲突。比如,输入空间是全体整数,输出空间是一个固定大小的桶(数组)的索引范围,那么必定会有多个整数同时映射到一个桶索引。</p>
<p>为了缓解哈希冲突,一方面,<strong>我们可以通过哈希表扩容来减小冲突概率</strong>。极端情况下,当输入空间和输出空间大小相等时,哈希表就等价于数组了,可谓“大力出奇迹”。</p>
<p>另一方面,<strong>考虑通过优化哈希表的表示方式以缓解哈希冲突</strong>,常见的方法有「链式地址」和「开放寻址」。</p>
<h2 id="621">6.2.1. &nbsp; 哈希表扩容<a class="headerlink" href="#621" title="Permanent link">&para;</a></h2>
<p>「负载因子 Load Factor」定义为 <strong>哈希表中元素数量除以桶槽数量(即数组大小)</strong>,代表哈希冲突的严重程度。</p>
<p><strong>负载因子常用作哈希表扩容的触发条件</strong>。比如在 Java 中,当负载因子 <span class="arithmatex">\(&gt; 0.75\)</span> 时则触发扩容,将 HashMap 大小扩充至原先的 <span class="arithmatex">\(2\)</span> 倍。</p>
<p>与数组扩容类似,<strong>哈希表扩容操作的开销很大</strong>,因为需要将所有键值对从原哈希表依次移动至新哈希表。</p>
<h2 id="622">6.2.2. &nbsp; 链式地址<a class="headerlink" href="#622" title="Permanent link">&para;</a></h2>
<p>在原始哈希表中,桶内的每个地址只能存储一个元素(即键值对)。<strong>考虑将单个元素转化成一个链表,将所有冲突元素都存储在一个链表中</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,哈希表操作方法为:</p>
<ul>
<li><strong>查询元素</strong>:先将 key 输入到哈希函数得到桶内索引,即可访问链表头结点,再通过遍历链表查找对应 value 。</li>
<li><strong>添加元素</strong>:先通过哈希函数访问链表头部,再将结点(即键值对)添加到链表头部即可。</li>
<li><strong>删除元素</strong>:同样先根据哈希函数结果访问链表头部,再遍历链表查找对应结点,删除之即可。</li>
</ul>
<p>链式地址虽然解决了哈希冲突问题,但仍存在局限性,包括:</p>
<ul>
<li><strong>占用空间变大</strong>,因为链表或二叉树包含结点指针,相比于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>为了提升操作效率,<strong>可以把「链表」转化为「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="623">6.2.3. &nbsp; 开放寻址<a class="headerlink" href="#623" title="Permanent link">&para;</a></h2>
<p>「开放寻址」不引入额外数据结构,而是通过“多次探测”来解决哈希冲突。根据探测方法的不同,主要分为 <strong>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」使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>:如果出现哈希冲突,则从冲突位置向后线性遍历(步长一般取 1 ),直到找到一个空位,则将元素插入到该空位中。</p>
<p><strong>查找元素</strong>:若出现哈希冲突,则使用相同步长执行线性查找,会遇到两种情况:</p>
<ol>
<li>找到对应元素,返回 value 即可;</li>
<li>若遇到空位,则说明查找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>。删除元素会导致桶内出现一个空位,在查找其他元素时,该空位有可能导致程序认为元素不存在(即上述第 <code>2.</code> 种情况)。因此需要借助一个标志位来标记删除元素。</li>
<li><strong>容易产生聚集</strong>。桶内被占用的连续位置越长,这些连续位置发生哈希冲突的可能性越大,从而进一步促进这一位置的“聚堆生长”,最终导致增删查改操作效率的劣化。</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」的思路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>:以相同的哈希函数顺序查找,存在两种情况:</p>
<ol>
<li>找到目标元素,则返回之;</li>
<li>到空位或已尝试所有哈希函数,说明哈希表中无此元素;</li>
</ol>
<p>相比于「线性探测」,「多次哈希」方法更不容易产生聚集,代价是多个哈希函数增加了额外计算量。</p>
<div class="admonition note">
<p class="admonition-title">工业界方案</p>
<p>Java 采用「链式地址」。在 JDK 1.8 之后HashMap 内数组长度大于 64 时,长度大于 8 的链表会被转化为「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>
</div>
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