MySQL 查相似度通常涉及到字符串比较和模糊匹配。在 MySQL 中,可以使用 LIKE
、REGEXP
等操作符来进行模糊匹配,但这些方法在处理大量数据时效率较低。为了更高效地查找相似度,可以使用全文索引(Full-Text Index)和相似度算法(如 Levenshtein 距离)。
FULLTEXT
索引类型,适用于全文搜索。假设我们有一个 articles
表,包含 title
和 content
字段:
CREATE TABLE articles (
id INT AUTO_INCREMENT PRIMARY KEY,
title TEXT,
content TEXT
);
ALTER TABLE articles ADD FULLTEXT(title, content);
插入一些示例数据:
INSERT INTO articles (title, content) VALUES
('Introduction to MySQL', 'MySQL is a popular relational database management system.'),
('Advanced MySQL', 'Learn advanced techniques for MySQL optimization.'),
('Introduction to PostgreSQL', 'PostgreSQL is another powerful relational database.');
进行全文搜索:
SELECT * FROM articles WHERE MATCH(title, content) AGAINST('MySQL');
MySQL 不直接支持 Levenshtein 距离计算,但可以通过自定义函数实现:
DELIMITER //
CREATE FUNCTION LEVENSHTEIN(s1 VARCHAR(255), s2 VARCHAR(255))
RETURNS INT
DETERMINISTIC
BEGIN
DECLARE s1_len, s2_len, i, j, c, c_temp, cost INT;
DECLARE s1_char CHAR(1);
DECLARE cv0, cv1 VARBINARY(256);
SET s1_len = CHAR_LENGTH(s1), s2_len = CHAR_LENGTH(s2), cv1 = 0x00, j = 1, i = 1, c = 0;
IF s1 = s2 THEN
RETURN 0;
ELSEIF s1_len = 0 THEN
RETURN s2_len;
ELSEIF s2_len = 0 THEN
RETURN s1_len;
ELSE
WHILE j <= s2_len DO
SET cv1 = CONCAT(cv1, UNHEX(HEX(j))), j = j + 1;
END WHILE;
WHILE i <= s1_len DO
SET s1_char = SUBSTRING(s1, i, 1), c = i, cv0 = UNHEX(HEX(c)), j = 1;
WHILE j <= s2_len DO
SET cost = IF(s1_char = SUBSTRING(s2, j, 1), 0, 1), c = c + cost;
SET c_temp = CONV(HEX(SUBSTRING(cv1, j, 1)), 16, 10) + cost;
IF c > c_temp THEN
SET c = c_temp;
END IF;
SET c_temp = CONV(HEX(SUBSTRING(cv1, j + 1, 1)), 16, 10) + 1;
IF c > c_temp THEN
SET c = c_temp;
END IF;
SET cv0 = CONCAT(cv0, UNHEX(HEX(c))), j = j + 1;
END WHILE;
SET cv1 = cv0, i = i + 1;
END WHILE;
END IF;
RETURN c;
END //
DELIMITER ;
使用自定义函数进行相似度查询:
SELECT title, LEVENSHTEIN('MySQL', title) AS distance FROM articles WHERE LEVENSHTEIN('MySQL', title) <= 2;
通过以上方法,可以在 MySQL 中高效地进行相似度查询,并解决常见的性能问题。
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