Home Database Mysql Tutorial How Can I Achieve Fulltext-Like Search on InnoDB Tables in MySQL?

How Can I Achieve Fulltext-Like Search on InnoDB Tables in MySQL?

Dec 17, 2024 pm 01:52 PM

How Can I Achieve Fulltext-Like Search on InnoDB Tables in MySQL?

Searching MySQL Data with Fulltext-Like Capabilities on InnoDB

The conventional approach of using "LIKE" for string search on an InnoDB table suffers from performance limitations and the need to check each search term individually. To enhance search functionality, a solution that mimics fulltext search on InnoDB tables is desirable.

Solution: Utilizing a Separate MyISAM Table for Indexing

This approach involves creating a separate MyISAM table that mirrors the InnoDB table. The MyISAM table contains an additional column indexed with the "FULLTEXT" keyword. By populating this MyISAM table with row data, we effectively create an index that supports fast, fulltext-like search capabilities.

Implementation

Consider the following example:

-- InnoDB tables
CREATE TABLE users (id INT, name VARCHAR(255), PRIMARY KEY (id));
CREATE TABLE forums (id INT, name VARCHAR(255), PRIMARY KEY (id));
CREATE TABLE threads (id INT, subject VARCHAR(255), user_id INT, forum_id INT, PRIMARY KEY (id));

-- MyISAM fulltext table
CREATE TABLE threads_ft (id INT, subject VARCHAR(255), FULLTEXT(subject), PRIMARY KEY (id));
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Synchronizing Data

To keep the MyISAM fulltext table up to date, you can use triggers, batch updates, or any other suitable mechanism.

Performing Search Queries

Now, you can perform fulltext-like search queries:

-- Stored procedure for searching
CREATE PROCEDURE ft_search_threads(IN p_search VARCHAR(255))
BEGIN
  SELECT *
  FROM threads_ft
  WHERE MATCH(subject) AGAINST (p_search IN BOOLEAN MODE)
  ORDER BY RANK() DESC;
END;

-- Example query
CALL ft_search_threads('keyword1 keyword2 keyword3');
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Benefits

This approach offers the following advantages:

  • Fulltext-like search capabilities without the overhead of Sphinx or other external tools
  • Faster search performance compared to "LIKE" queries
  • Simplified implementation and maintenance

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