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给到我们这些“预测”。简单来说,哪个热门、哪个搜索频率高,就更可能推给我们。当然,这也与我们当前所处的位置以及我们的搜索历史相关。\r\n\r\n另外,这些“预测”也会随着我们键入的关键字的变更而更改。例如,当我们把键入的关键字从 `juej` 更改为 `juex` 时,与“掘金”相关的预测会“消失”,同时,与“觉醒”、“决心”相关联的词会出现。\r\n\r\n![](https://gitee.com/yanglbme/resource/raw/master/doocs-md/juex.gif)\r\n\r\n## 为什么看不到某些联想词?\r\n\r\n如果我们在输入某个关键字时看不到联想词,那么表明 Google 的算法可能检测到:\r\n\r\n- 这个关键字不是热门字词;\r\n- 搜索的字词太新了,我们可能需要等待几天或几周才能看到联想词;\r\n- 这是一个侮辱性或敏感字词,这个搜索字词违反了 Google 的相关政策。更加详细的情况,可以了解 [Google 搜索自动补全政策](https://support.google.com/websearch/answer/7368877)。\r\n\r\n## 为什么会看到某些不当的联想词?\r\n\r\nGoogle 拥有专门设计的系统,可以自动捕获不适当的预测结果而不显示出来。然而,Google 每天需要处理数十亿次搜索,这意味着 Google 每天会显示数十亿甚至上百亿条预测。再好的系统,也可能存在缺陷,不正确的预测也可能随时会出现。\r\n\r\n我们作为 Google 搜索的用户,如果认定某条预测违反了相关的搜索自动补全政策,可以进行举报反馈,点击右下角“**举报不当的联想查询**”并勾选相关选项即可。\r\n\r\n![](https://gitee.com/yanglbme/resource/raw/master/doocs-md/report.gif)\r\n\r\n## 如何实现自动补全算法?\r\n\r\n目前,Google 官方似乎并没有公开搜索自动补全的算法实现,但是业界在这方面已经有了不少研究。\r\n\r\n一个好的自动补全器必须是快速的,并且在用户键入下一个字符后立即更新联想词列表。**自动补全器的核心是一个函数,它接受输入的前缀,并搜索以给定前缀开头的词汇或语句列表**。通常来说,只需要返回少量的数目即可。\r\n\r\n接下来,我们先从一个简单且低效的实现开始,并在此基础上逐步构建更高效的方法。\r\n\r\n### 词汇表实现\r\n\r\n一个**简单粗暴的实现方式**是:顺序查找词汇表,依次检查每个词汇,看它是否以给定的前缀开头。\r\n\r\n但是,此方法需要将前缀与每个词汇进行匹配检查,若词汇量较少,这种方式可能勉强行得通。但是,如果词汇量规模较大,效率就太低了。\r\n\r\n一个**更好的实现方式是**:让词汇按字典顺序排序。借助二分搜索算法,可以快速搜索有序词汇表中的前缀。由于二分搜索的每一步都会将搜索的范围减半,因此,总的搜索时间与词汇表中单词数量的对数成正比,即时间复杂度是 `O(log N)`。二分搜索的性能很好,但有没有更好的实现呢?当然有,往下看。\r\n\r\n### 前缀树实现\r\n\r\n通常来说,许多词汇都以相同的前缀开头,比如 `need`、`nested` 都以 `ne` 开头,`seed`、`speed` 都以 `s` 开头。要是为每个单词分别存储公共前缀似乎很浪费。\r\n\r\n![](https://gitee.com/yanglbme/resource/raw/master/doocs-md/pretree.png)\r\n\r\n前缀树是一种利用公共前缀来加速补全速度的数据结构。前缀树在节点树中排列一组单词,单词沿着从根节点到叶子节点的路径存储,树的层次对应于前缀的字母位置。\r\n\r\n前缀的补全是顺着前缀定义的路径来查找的。例如,在上图的前缀树中,前缀 `ne` 对应于从子节点取左边缘 `N` 和唯一边缘 `E` 的路径。然后可以通过继续遍历从 `E` 节点可以达到的所有叶节点来生成补全列表。在图中,`ne` 的补全可以是两个分支:`-ed` 和 `-sted`。如果在数中找不到由前缀定义的路径,则说明词汇表中不包含以该前缀开头的单词。\r\n\r\n### 有限状态自动机(DFA)实现\r\n\r\n前缀树可以有效处理公共前缀,但是,对于其他共享词部分,仍会分别存储在每个分支中。比如,后缀 `ed`、`ing`、`tion` 在英文单词中特别常见。在上一个例子中,`e`、`d` 分别存放在了每一个分支上。\r\n\r\n有没有一种方法可以更加节省存储空间呢?有的,那就是 DFA。\r\n\r\n\r\n\r\n\r\n在上面的例子中,单词 `need`、`nested`、`seed` 和 `speed` 仅由 9 个节点组成,而上一张图中的前缀树包含了 17 个节点。\r\n\r\n可以看出,最小化前缀树 DFA 可以在很大程度上减少数据结构的大小。即使词汇量很大,最小化 DFA 通常也适合在内存中存储,避免昂贵的磁盘访问是实现快速自动补全的关键。\r\n\r\n### 一些扩展\r\n\r\n上面介绍了如何利用合理的数据结构实现基本的自动补全功能。这些数据结构可以通过多种方式进行扩展,从而改善用户体验。\r\n\r\n通常,满足特定前缀的词汇可能很多,而用户界面上能够显示的却不多,我们更希望能显示最常搜索或者最有价值的词汇。这通常可以通过为词汇表中的每个单词增加一个代表单词值的**权重** `weight`,并且按照权重高低来排序自动补全列表。\r\n\r\n- 对于排序后的词汇表来说,在词汇表每个元素上增加 `weight` 属性并不难;\r\n- 对于前缀树来说,将 `weight` 存储在叶子节点中,也是很简单的一个实现;\r\n- 对于 `DFA` 来说,则较为复杂。因为一个叶子节点可以通过多条路径到达。一种解决方案是将权重关联到路径而不是叶子节点。\r\n\r\n目前有不少开源库都提供了这个功能,比如主流的搜索引擎框架 [Elasticsearch](https://www.elastic.co/products/elasticsearch)、[Solr](https://lucene.apache.org/solr/) 等,基于此,我们可以实现高效而强大的自动补全功能。\r\n\r\n#### 推荐阅读\r\n\r\n- [阿里又一个 20k+ stars 开源项目诞生,恭喜 fastjson!](https://mp.weixin.qq.com/s/RNKDCK2KoyeuMeEs6GUrow)\r\n- [刷掉 90% 候选人的互联网大厂海量数据面试题(附题解 + 方法总结)](https://mp.weixin.qq.com/s/rjGqxUvrEqJNlo09GrT1Dw)\r\n- [好用!期待已久的文本块功能究竟如何在 Java 13 中发挥作用?](https://mp.weixin.qq.com/s/kalGv5T8AZGxTnLHr2wDsA)\r\n- [2019 GitHub 开源贡献排行榜新鲜出炉!微软谷歌领头,阿里跻身前 12!](https://mp.weixin.qq.com/s/_q812aGD1b9QvZ2WFI0Qgw)\r\n\r\n---\r\n\r\n欢迎关注我的公众号“**Doocs 开源社区**”,原创技术文章第一时间推送。\r\n\r\n\r\n \r\n\r\n',v="/*\n 按Ctrl+F可格式化\n*/\n/* 一级标题样式 */\nh1 {\n}\n/* 二级标题样式 */\nh2 {\n}\n/* 三级标题样式 */\nh3 {\n}\n/* 四级标题样式 */\nh4 {\n}\n/* 图片样式 */\nimage {\n}\n/* 引用样式 */\nblockquote {\n}\n/* 引用段落样式 */\nblockquote_p {\n}\n/* 段落样式 */\np {\n}\n/* 分割线样式 */\nhr {\n}\n/* 行内代码样式 */\ncodespan {\n}\n/* 粗体样式 */\nstrong {\n}\n/* 链接样式 */\nlink {\n}\n/* 微信链接样式 */\nwx_link {\n}\n/* 有序列表样式 */\nol {\n}\n/* 无序列表样式 */\nul {\n}\n/* 列表项样式 */\nli 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