How to create high-performance full-text search of MySQL data using Go language

With the rapid development of the Internet, the amount of data is increasing, and the need for fast data query is becoming more and more urgent. At present, full-text search engine is a relatively common data query method, which can be applied to various fields, such as e-commerce websites, news websites, blogs, etc. This article will introduce how to use Go language to create high-performance MySQL data full-text search.

1. What is full-text search

Full-text search (Full-Text Search) refers to the search technology that finds all text records that match specific keywords from a text. Different from traditional fuzzy search and string matching, the full-text search engine is a query method that analyzes multiple factors such as vocabulary, grammar, and semantics, which can greatly improve the efficiency and accuracy of data query.

2. Go language and MySQL

Go language is an open source programming language developed by Google and officially released in 2009. It has the characteristics of simplicity, efficiency, and concurrency, and is widely used in network programming, cloud computing, microservices and other fields. MySQL is an open source relational database with advantages such as high performance, scalability, and data security. It is one of the most common database management systems in web application development. The deep integration of Go language and MySQL can provide us with powerful data query and processing capabilities.

3. Create a MySQL full-text index

Before using MySQL for full-text search, you first need to create a full-text index in the table to be searched. Assume that the name of our data table is "article", which contains two fields: "title" and "content". We need to create a full-text index for it. The specific steps are as follows:

1. Create database connection

db, err := sql.Open("mysql", "root:password@tcp(127.0.0.1:3306)/database")
if err != nil {
    log.Fatal(err)
}
defer db.Close()

2. Execute the full-text index creation statement

_, err := db.Exec("ALTER TABLE article ADD FULLTEXT(title, content)")
if err != nil {
    log.Fatal(err)
}

After execution, you can create a full-text index on the two fields "title" and "content" so that Conduct efficient text searches.

4. Go language to implement MySQL full-text search

After creating the full-text index, we can use Go language to implement MySQL full-text search. The following is the specific implementation code.

1. Define query result structure

type Result struct {
    ID      int64  `json:"id"`
    Title   string `json:"title"`
    Content string `json:"content"`
}

We have defined three attributes, representing article ID, title and content respectively.

2. Implementing the full-text search function

func Search(keyword string) ([]Result, error) {
    db, err := sql.Open("mysql", "root:password@tcp(127.0.0.1:3306)/database")
    if err != nil {
        return nil, err
    }
    defer db.Close()

    rows, err := db.Query("SELECT id, title, content, MATCH(title, content) AGAINST(?) AS score FROM article WHERE MATCH(title, content) AGAINST(?)", keyword, keyword)
    if err != nil {
        return nil, err
    }
    defer rows.Close()

    var results []Result
    for rows.Next() {
        var id int64
        var title string
        var content string
        var score float64
        if err := rows.Scan(&id, &title, &content, &score); err != nil {
            return nil, err
        }
        results = append(results, Result{ID: id, Title: title, Content: content})
    }
    if err := rows.Err(); err != nil {
        return nil, err
    }

    return results, nil
}

This function receives a keyword as a parameter, queries the "title" and "content" fields in the "article" table, and Use the MATCH AGAINST statement to calculate the score, and finally parse the query results into the defined Result structure.

5. Optimize query performance

In practical applications, the query characteristics of full-text search are complex query statements and the search for large amounts of data, so it needs to be optimized.

1. Use connection pool

When querying multiple times, the creation and destruction of database connections will bring additional overhead. Using a connection pool can reduce this overhead and improve query efficiency. The connection pool size can be set using DB.SetMaxIdleConns and DB.SetMaxOpenConns in the "database/sql" package.

2. Build cache

Full-text search queries have high performance requirements, and cache can be used to optimize query speed. When querying, you can first obtain the result from the cache. If it does not exist, perform a database query and cache the query results. In subsequent queries, if there are results in the cache, they will be returned directly to avoid repeated queries.

3. Use paging query

When processing large amounts of data, using paging query can reduce the resources required for query and improve query efficiency. You can use the "LIMIT" and "OFFSET" keywords to paginate the query results, and set the number of items displayed on each page and the current page number.

6. Summary

Full-text search is an efficient data query method that can help us quickly search and obtain the data we need. Using the combination of Go language and MySQL, high-performance full-text search function can be achieved. In practical applications, it can also be optimized by combining connection pooling, caching, paging query and other technologies to further improve query efficiency and performance.

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