How Can I Optimize PostgreSQL LIKE Query Performance?

PostgreSQL LIKE query performance difference analysis

PostgreSQL’s LIKE operator is known for its resource-intensive operations, with significant response time differences. This problem persists even when there is a btree index on the relevant field (which usually facilitates LIKE queries).

Solution Analysis

Some suggest using Full Text Search (FTS) to solve this problem, but this does not work for LIKE operations. The best way is to take advantage of a specific index type:

Trigram Index

Install the pg_trgm module to create GIN or GiST triple indexes. Triplets capture a sequence of three characters in a word, supporting LIKE and ILIKE modes. For values ​​less than three characters, an index scan is still used to ensure performance.

Prefix Matching

Use the ^@ operator introduced in PostgreSQL 11. This operator efficiently performs prefix matching without wildcard prefixes. However, its use may be limited until further optimizations are made in future PostgreSQL versions.

COLLATE "C" and text_pattern_ops

COLLATE "C" creates an index using C collation, providing prefix matching similar to the ^@ operator. Alternatively, you can use text_pattern_ops or varchar_pattern_ops to create a btree index that provides the best performance for left-anchored patterns (that is, patterns without leading wildcards).

Other notes

• Use the lower() or uppercase() function to convert case to optimize case-sensitive queries.
• For more complex pattern matching scenarios, consider using regular expressions (e.g., SIMILAR TO).
• Analyze your table to determine whether the indexes you created are being used effectively.

By using appropriate indexing techniques, you can significantly improve the performance of LIKE queries in PostgreSQL, reduce response time differences and ensure consistent performance.

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