How Can I Optimize Database Indexing for LIKE Queries with Leading Wildcards?

Indexing Strategies for LIKE Queries

When dealing with LIKE queries, it becomes crucial to understand the limitations of B-tree indexes. While B-tree indexes excel in speeding up comparisons using operators like =, >, and BETWEEN, they face challenges with LIKE queries that start with wildcard characters. In such scenarios, the potential benefits of indexing diminish.

Considering your specific query:

SELECT name, usage_guidance, total_used_num
FROM tags
WHERE
 ( name LIKE CONCAT('%', ?, '%') OR
   usage_guidance LIKE CONCAT(?, '%') )
 AND name NOT IN ($in)
ORDER BY name LIKE CONCAT('%', ?, '%') DESC, name ASC
LIMIT 6

Both LIKE conditions start with wildcard characters, making the use of B-tree indexes impractical. However, there is a glimmer of hope with the second LIKE condition, which doesn't start with a wildcard. To improve query performance, consider creating a composite index on the columns usage_guidance and name. This allows for partial matches on usage_guidance and faster ordering based on name.

CREATE INDEX idx_tags ON tags (usage_guidance, name);

While this indexing strategy won't magically alleviate all performance concerns, it partially addresses the limitations of LIKE queries and should improve query speed noticeably. If you encounter additional challenges with this query, providing the table structure with sample data and expected output could lead to further optimizations.

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