Building a real-time search engine with Redis and JavaScript: How to quickly retrieve articles

Building a real-time search engine using Redis and JavaScript: How to quickly retrieve articles

Introduction:
In today's Internet era, it is extremely important to quickly retrieve large amounts of data. For a website with a large number of articles, a real-time search engine can provide efficient retrieval functions, allowing users to quickly find the information they need. This article will introduce how to use Redis and JavaScript to build a real-time search engine to quickly retrieve articles.

1. Introduction to Redis
Redis is a high-performance memory-based key-value storage system that is widely used in cache, message queues, real-time statistics and other fields. It provides a wealth of data structures, such as strings, hashes, lists, sets and ordered sets, etc., which can meet the needs of various scenarios.

2. Text indexing
Before building a real-time search engine, you first need to text index the articles. Text indexing uses a specific algorithm to extract keywords from articles and build an index data structure to quickly find related articles.

1. Text segmentation
   Before indexing the article, the article needs to be segmented. Word segmentation is to cut the article into independent words according to certain rules for subsequent indexing. Common word segmentation technologies include rule-based word segmentation, statistics-based word segmentation, and machine learning-based word segmentation.

Here we use a simple word segmentation method, using spaces in the article as separators to extract each word.

function tokenize(text) {
  return text.split(" ");
}

// 示例
var text = "利用Redis和JavaScript构建实时搜索引擎";
var tokens = tokenize(text);
console.log(tokens);  // ["利用Redis和JavaScript构建实时搜索引擎"]

2. Building an inverted index
   The inverted index is a data structure that associates keywords with related articles. It can provide fast keyword search and find relevant articles. Building an inverted index requires segmenting each article into words and associating each keyword with the article.

// Redis连接
const redis = require("redis");
const client = redis.createClient();

// 文章索引
var articles = [
  { id: 1, title: "利用Redis和JavaScript构建实时搜索引擎", content: "..." },
  { id: 2, title: "使用Redis进行缓存优化", content: "..." },
  { id: 3, title: "JavaScript实现数据结构与算法", content: "..." },
  // 更多文章...
];

// 构建倒排索引
articles.forEach(function(article) {
  var tokens = tokenize(article.title + " " + article.content);
  
  tokens.forEach(function(token) {
    client.sadd("index:" + token, article.id);
  });
});

3. Search engine
With the text index, we can build a real-time search engine. The core of the real-time search engine is to match the keywords entered by the user with the inverted index to find relevant articles.

// 搜索引擎
function search(keyword) {
  var tokens = tokenize(keyword);
  
  var result = client.sinter(
    tokens.map(function(token) {
      return "index:" + token;
    })
  );
  
  return result;
}

// 示例
var keyword = "Redis 搜索引擎";
var result = search(keyword);
console.log(result);  // [1, 2]，表示找到了文章1和2

4. Real-time updates
In actual application, articles may be added, deleted or modified. In order to keep the index real-time, the index needs to be updated in time when the articles change.

// 添加文章
function addArticle(article) {
  var tokens = tokenize(article.title + " " + article.content);
  
  tokens.forEach(function(token) {
    client.sadd("index:" + token, article.id);
  });
}

// 删除文章
function removeArticle(articleId) {
  var tokens = client.smembers("index:" + articleId);
  
  tokens.forEach(function(token) {
    client.srem("index:" + token, articleId);
  });
}

// 修改文章
function updateArticle(article) {
  removeArticle(article.id);
  addArticle(article);
}

Conclusion:
This article uses Redis and JavaScript to build a simple real-time search engine. By building text index and inverted index, the function of quickly retrieving articles is realized. At the same time, when articles change, the index can be updated in real time, maintaining the real-time nature of the search engine. This real-time search engine based on Redis and JavaScript can be applied to various scenarios where a large number of articles need to be retrieved quickly, improving user experience and system response speed.

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