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JavaScript
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JavaScript
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搜索框上开始键入关键字,就可以看到联想词了。\n\n![](https://gitee.com/yanglbme/resource/raw/master/doocs-md/juejin.gif)\n\n在上图示例中,我们可以看到,输入关键字 `juej`,Google 搜索会联想到“掘金”、“掘金小册”、“绝句”等等,好处就是,我们无须输入完整的关键字即可轻松完成针对这些 topics 的搜索。\n\n谷歌搜索的自动补全功能对于使用移动设备的用户来说特别有用,用户可以轻松在难以键入的小屏幕上完成搜索。当然,对于移动设备用户和台式机用户而言,这都节省了大量的时间。根据 Google 官方报告,自动补全功能可以减少大约 25% 的打字,累积起来,预计每天可以节省 200 多年的打字时间。是的,每天!\n\n> 注意,本文所提到的“**联想词**”与“**预测**”,是同一个意思。\n\n## 基于“预测”而非“建议”\nGoogle 官方将自动补全功能称之为“预测”,而不是“建议”,为什么呢?其实是有充分理由的。自动补全功能是为了**帮助用户完成他们打算进行的搜索**,而不是建议用户要执行什么搜索。\n\n那么,Google 是如何确定这些“预测”的?其实,Google 会根据趋势搜索 [trends](https://trends.google.com/trends/?geo=US) 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。\n\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。\n\n![](https://gitee.com/yanglbme/resource/raw/master/doocs-md/juex.gif)\n\n## 为什么看不到某些联想词?\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:\n\n- 这个关键字不是热门字词;\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;\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)。\n\n## 为什么会看到某些不当的联想词?\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。\n\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。\n\n![](https://gitee.com/yanglbme/resource/raw/master/doocs-md/report.gif)\n\n## 如何实现自动补全算法?\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。\n\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。\n\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。\n\n### 词汇表实现\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。\n\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。\n\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。\n\n### 前缀树实现\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。\n\n![](https://gitee.com/yanglbme/resource/raw/master/doocs-md/pretree.png)\n\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。\n\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。\n\n### 有限状态自动机(DFA)实现\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。\n\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。\n\n<center>\n<img src="https://gitee.com/yanglbme/resource/raw/master/doocs-md/dfa.gif" style="width: 50%;"></center>\n\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。\n\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。\n\n### 一些扩展\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。\n\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。\n\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;\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;\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。\n\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。\n\n#### 推荐阅读\n- [阿里又一个 20k+ stars 开源项目诞生,恭喜 fastjson!](https://mp.weixin.qq.com/s/RNKDCK2KoyeuMeEs6GUrow)\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)\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)\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)\n\n---\n\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。\n\n<center>\n <img src="https://gitee.com/yanglbme/resource/raw/master/doocs-md/qrcode.png" style="width: 100px;">\n</center>\n\n',r=n;o.default=r},"56d7":function(e,o,t){"use strict";var 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