From bc7751dc023ff79bdc78b726da03aa00685c50dd Mon Sep 17 00:00:00 2001 From: yanglbme Date: Mon, 4 Nov 2019 10:02:33 +0800 Subject: [PATCH] feat: update default-content.md --- assets/default-content.md | 121 ++++++++++++++++++++++------------- assets/images/dfa.png | Bin 0 -> 20260 bytes assets/images/feedback.gif | Bin 0 -> 194687 bytes assets/images/juej.gif | Bin 0 -> 69015 bytes assets/images/juex.gif | Bin 0 -> 58234 bytes assets/images/prefixtree.png | Bin 0 -> 51771 bytes 6 files changed, 76 insertions(+), 45 deletions(-) create mode 100644 assets/images/dfa.png create mode 100644 assets/images/feedback.gif create mode 100644 assets/images/juej.gif create mode 100644 assets/images/juex.gif create mode 100644 assets/images/prefixtree.png diff --git a/assets/default-content.md b/assets/default-content.md index 2272771..87117ed 100644 --- a/assets/default-content.md +++ b/assets/default-content.md @@ -1,55 +1,86 @@ -## 文章编辑示例 +# Google 搜索的即时自动补全功能究竟是如何“工作”的? +> Google 搜索自动补全功能的强大,相信不少朋友都能感受到,它帮助我们更快地“补全”我们所要输入的搜索关键字。那么,它怎么知道我们要输入什么内容?它又是如何工作的?在这篇文章里,我们一起来看看。 -### 简介 -一款自动将 Markdown 文章自动渲染成微信公众号文章样式的工具。 +## 使用自动补全 +Google 搜索的自动补全功能可以在 Google 搜索应用的大多数位置使用,包括 [Google](https://www.google.com/) 主页、适用于 IOS 和 Android 的 Google 应用,我们只需要在 Google 搜索框上开始键入关键字,就可以看到联想词了。 -### 功能 -- 支持序号列表和圆点列表,解决了样式会被重置的问题 -- 外链会自动转换为参考文献索引,并且附在文章末尾 -- 支持多种字体和样式 -- 支持日语注音假名、汉语拼音样式 -- 支持不同于微信的代码配色方案 -- 支持编辑内容自动保存、预览同步滚动等常见功能 +![](./images/juej.gif) -### 关于 Markdown -1. Markdown 是一种轻量级标记语言,能将文本换成有效的 XHTML(或者HTML) 文档 -2. Markdown 强大之处,在于可以用一套格式,在所有支持 Markdown 的编辑器中转换成发布样式,做到最大化兼容,不需要担心复制到不同编辑器中样式被破坏 -3. 正如你右侧看到的这样,Markdown 被转换成了微信支持的样式,同样你可以在一字不改的情况下,在 Github 等平台上转换类似的样式 -4. 学习 Markdown 的语法,可以查看 [Markdown 语法入门手册](https://www.w3cschool.cn/markdownyfsm/markdownyfsm-odm6256r.html) +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。 -### 图片 -``` -![desc](img_url) -``` +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! -### 代码块 -```java -public class Main { - public static void main(String[] args) { - System.out.println("Hello, World!"); - } -} -``` +> 注意,本文所提到的“**联想词**”与“**预测**”,是同一个意思。 -### 内联代码 -inline code `{code: 0}` +## 基于“预测”而非“建议” +Google 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。 -### 表格 -| Header 1 | Header 2 | -| --- | --- | -| Key 1 | Value 1 | -| Key 2 | Value 2 | -| Key 3 | Value 3 | +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。 -### 超链接 -如果是公众号文章的超链接,是可以点击打开的,但其他链接都无法点击,所以这里使用类似于文献的底部引用。 +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。 -例如: +![](./images/juex.gif) -- [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) -- [Google 搜索的即时自动补全功能究竟是如何“工作”的?](https://mp.weixin.qq.com/s/YlMISSc3Sn890BzTLytcLA) -- [厉害了,原来 Redisson 这么好用!](https://mp.weixin.qq.com/s/lpZ7eRdImy0MyTEVH68HYw) -- [一文带你搞懂 “缓存策略”](https://mp.weixin.qq.com/s/47A_iXY_nArURwUTPHr2IQ) -- [Java Getter/Setter “防坑指南”](https://mp.weixin.qq.com/s/TZqcAw7NTlcvU-p930-eHA) -- [太棒了,GitHub Review 代码能力小升级](https://mp.weixin.qq.com/s/Lok0epqn91Q51ygZo_FLkg) -- [巧用 Redis Hyperloglog,轻松统计 UV 数据](https://mp.weixin.qq.com/s/w1r-M6YVvQSfUtzO_xe44Q) +## 为什么我们看不到某些联想词? +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: + +- 这个关键字不是热门字词; +- 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; +- 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)。 + +## 为什么我们会看到某些不当的联想词? +Google 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。 + +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。 + +![](./images/feedback.gif) + +## 如何实现自动补全算法? +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。 + +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。 + +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。 + +### 词汇表实现 +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。 + +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。 + +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。 + +### 前缀树实现 +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。 + +![](./images/prefixtree.png) + +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。 + +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。 + +### 有限状态自动机(DFA)实现 +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。 + +有没有一种方法可以更加节省存储空间呢?有的,那就是 DFA。 + +![](./images/dfa.png) + +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。 + +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。 + +### 一些扩展 +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。 + +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。 + +- 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; +- 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; +- 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。 + +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。 + +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。 + + +![](./images/qrcode.jpg) \ No newline at end of file diff --git a/assets/images/dfa.png b/assets/images/dfa.png new file mode 100644 index 0000000000000000000000000000000000000000..248df5ababf3f3a81ff1a728110ac00e37526f80 GIT binary patch literal 20260 zcmX_I2RPOJ`%XnR*(-ZHwz7%HI8lyy%RYzA zt^NsA{IGg*;QuvGok8jC!0(y5&d$!dM{|uqXMsUMXP2&!nl}4vX9b<+^zmw4CB)HD zZw)xAb^KKPefTx)L7MS9M_b?Cwzm8GTMG#Z2~-kQ$@9@4Ki>26J3{c^cr;sY{~^=d z+?#KZrXz&o#b_FY4~?m2{lI*L^rsD;S=N;36FC2qzllH8C-vNs{;3V&yI=E7MZq zMiS6anN?ch{)!Gd`dg+FO%d=D(bnnmY41diiL_=_uSj+M=7%v z8cQoUFvwOO5h8Q&|8tD z1SwuOZ(jIx5v{pA@VU&SoiZRGK*^nql$2&!0#;h*z`zjmd}JkRvHO{%)0eFe zif8$atjK64pY6r)`NAr=G-aIi-@l7w`cUjwxV2tgj_`T%gm6}-XdH>5XwYf(_x-pu 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