How Can I Optimize Multi-Column LIKE Queries in MySQL for Better Performance?

Optimizing Multi-Column LIKE Queries for MySQL Performance

In MySQL, frequent LIKE queries can impede performance. Consider the query: SELECT x, y, z FROM table WHERE x LIKE '%text%' OR y LIKE '%text%' OR z LIKE '%text%'. How can we accelerate such queries without incurring significant disk usage or performance penalties during data manipulation?

Limitations of Indexes

Traditional indexing approaches prove ineffective for LIKE queries with wildcards at the beginning of search terms. Indexes facilitate quick access to data by indexing specific characters from the left of a field. In the case of LIKE '%text%', the variable number of characters preceding "text" hinders index utilization.

Full Text Search (FTS)

Instead of relying on indexes, MySQL offers FTS for MyISAM tables. FTS streamlines textual searches by indexing words within columns. This method is highly efficient for LIKE queries with wildcards at both ends.

Non-MyISAM Tables

For tables using non-MyISAM storage engines, a custom indexing system can be implemented. This involves creating a separate index table where words are associated with corresponding table IDs.

MySQL 5.6

Starting with MySQL 5.6, FTS became available for InnoDB tables. This offers a suitable alternative for users who require InnoDB's advantages.

Consequences

While FTS significantly improves LIKE query performance, it comes with potential drawbacks:

• Disk Usage: FTS indexes can increase database file sizes due to the storage of word lists.
• Data Manipulation Performance: Indexing every word in certain columns can impact the speed of INSERT and DELETE operations in those columns.

Before implementing FTS, consider the trade-off between search speed and disk usage/data manipulation performance. For applications where fast LIKE queries are paramount, FTS is a valuable optimization technique.

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