The performance optimization method of like fuzzy query in MySQL In actual development, it is often necessary to use MySQL for fuzzy query. However, with the increase of data volume and the complexity of query conditions, the performance problems of like fuzzy query have gradually emerged. This article will introduce some performance optimization methods of like fuzzy query in MySQL to help developers improve query efficiency.

In MySQL, a fuzzy query (LIKE) is a very common operation to find records that contain specific substrings.

However, with the increase of data volume and the complexity of query conditions, the performance problems of LIKE fuzzy query gradually appear.

This article will introduce some performance optimization methods of LIKE fuzzy query in MySQL to help developers improve query efficiency.

I. Avoid using the wildcard% before LIKE mode.

Use wildcards before LIKE mode %It will cause a full table scan, which will seriously affect the query performance.

Therefore, try to avoid using the wildcard `% before the LIKE pattern, and use the index or full-text index for optimization.

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Example:.

-- 不推荐的做法
SELECT * FROM users WHERE name LIKE '%john';

-- 推荐的做法
SELECT * FROM users WHERE name LIKE 'john%';

II. Use indexes to speed up fuzzy queries.

Using indexes before LIKE mode can greatly improve the performance of fuzzy queries.

However, if the wildcards in the LIKE pattern do not start, the index cannot be optimized.

Therefore, you need to pay attention to the position of the LIKE pattern and the use of wildcards when using the index.

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Example:.

-- 创建索引
CREATE INDEX idx_name ON users(name);

-- 使用索引进行查询
SELECT * FROM users WHERE name LIKE 'john%';

3. Avoid excessive use of LIKE mode.

Excessive use of LIKE mode will cause the database to frequently scan the full table, which will affect the query performance.

Therefore, when designing query statements, the use of LIKE mode should be minimized, or other more efficient query methods should be adopted.

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Example:.

-- 不推荐的做法
SELECT * FROM users WHERE name LIKE '%john%' OR email LIKE '%john%';

-- 推荐的做法
SELECT * FROM users WHERE name LIKE 'john%' OR email LIKE 'john%';

IV. Use regular expressions instead of LIKE patterns.

Compared with the LIKE pattern, the regular expression has stronger matching ability and flexibility, and can better meet the complex query needs.

However, the performance of regular expressions is relatively low, so you need to weigh the pros and cons when using them.

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Example:.

-- 使用正则表达式进行查询
SELECT * FROM users WHERE name REGEXP '^john';

V. Full text search using full text index.

For scenarios that require full-text search, you can use MySQL's full-text indexing function.

Full-text indexing can significantly improve the efficiency of text search.

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Example:.

-- 创建全文索引
ALTER TABLE articles ADD FULLTEXT(title, content);

-- 使用全文索引进行查询
SELECT * FROM articles WHERE MATCH(title, content) AGAINST('search term');

VI. Partition table and sub-database sub-table.

When the amount of data is very large, you can consider using partition tables or sub-database sub-tables to improve query performance.

The partitioned table can divide the large table into multiple small tables, and the sub-database sub-table can distribute the data to multiple database instances.

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Example:.

-- 创建分区表
CREATE TABLE orders (
    id INT,
    order_date DATE,
    amount DECIMAL(10,2),
    PRIMARY KEY(id, order_date)
) PARTITION BY RANGE (YEAR(order_date)) (
    PARTITION p0 VALUES LESS THAN (2010),
    PARTITION p1 VALUES LESS THAN (2015),
    PARTITION p2 VALUES LESS THAN (2020)
);

VII. Cache result set.

For some high-frequency and infrequently changing query results, caching techniques can be used to reduce the pressure on the database.

For example, a cache system such as Redis can be used to store query results.

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Example:.

import redis
import pymysql

# 连接到Redis
r = redis.Redis(host='localhost', port=6379, db=0)

# 检查缓存中是否有结果
cached_result = r.get('user_query_result')
if cached_result:
    result = cached_result.decode('utf-8')
else:
    # 连接到MySQL数据库
    connection = pymysql.connect(host='localhost', user='root', password='password', database='test')
    cursor = connection.cursor()
    cursor.execute("SELECT * FROM users WHERE name LIKE 'john%'")
    result = cursor.fetchall()
    # 将结果存入缓存
    r.set('user_query_result', str(result))
    cursor.close()
    connection.close()

Summarize.

By reasonably selecting the query method and optimizing the query statement, the query performance and response speed of MySQL can be effectively improved.

In actual development, the appropriate optimization method should be selected according to specific business scenarios and data characteristics.

I hope this article can help you better apply these optimization techniques in actual development and improve database query efficiency.