PostgreSQL raises this error when a regular expression pattern exceeds internal complexity limits in the POSIX regex engine. This typically occurs with deeply nested patterns, excessive alternations, or very long character classes. Simplify the regex pattern or break it into multiple smaller queries to resolve this issue.
PostgreSQL uses Henry Spencer's POSIX regular expression engine to handle regex operations through functions like ~, ~*, !~, and REGEXP_MATCH. The regex engine has built-in complexity limits (REG_ETOOBIG) to prevent catastrophic backtracking and excessive memory usage. When a regex pattern exceeds these limits—measured in the number of states and arcs the automaton must track—PostgreSQL rejects the pattern and raises this error. This is a safety mechanism to prevent regex patterns from consuming excessive CPU and memory resources during matching operations.
Break down complex patterns with many alternations into simpler, more focused patterns.
-- Instead of this deeply nested pattern:
SELECT * FROM users WHERE email ~ '(a|(b|(c|(d|(e|f)))))';
-- Use simpler patterns or join conditions:
SELECT * FROM users WHERE email ~ '[a-f]' OR email ~ 'specific_pattern';Flatter alternations with fewer levels of nesting reduce state machine complexity.
If you need to match multiple complex patterns, split the logic into multiple WHERE clauses:
-- Instead of one complex regex:
SELECT * FROM data WHERE content ~ '(very|long|complex|pattern|with|many|alternatives)';
-- Use multiple simpler queries:
SELECT * FROM data
WHERE content ~ 'very|long'
OR content ~ 'complex|pattern'
OR content ~ 'many|alternatives';This distributes the regex complexity across multiple simpler operations.
Simplify character classes that may be causing complexity issues:
-- Instead of extensive character class:
SELECT * FROM logs WHERE message ~ '[a-zA-Z0-9_@.+\\-\\s\\(\\)\\[\\]]+';
-- Use more targeted patterns:
SELECT * FROM logs WHERE message ~ '[a-zA-Z0-9]+' AND message ~ '[@.]';Breaking character classes into separate pattern checks is often more efficient.
For many use cases, LIKE patterns or PostgreSQL's full-text search is simpler and faster:
-- Instead of complex regex:
SELECT * FROM articles WHERE title ~ '(search|find|look|locate)';
-- Use LIKE (simpler and faster):
SELECT * FROM articles WHERE title ILIKE '%search%' OR title ILIKE '%find%';
-- Or use full-text search (for better relevance):
SELECT * FROM articles
WHERE to_tsvector(title) @@ to_tsquery('search | find | look | locate');Full-text search is optimized for text queries and avoids regex complexity altogether.
Before deploying complex regex patterns to production, test them in isolation:
-- Test a pattern in isolation first
SELECT 'test string' ~ 'your_pattern' AS matches;
-- Gradually increase complexity
SELECT 'test string' ~ 'simple_pattern' AS test1;
SELECT 'test string' ~ 'more|complex|pattern' AS test2;
-- Stop if you hit the complexity limitUse online regex testers (like regex101.com) with POSIX ERE mode to validate patterns before using them in PostgreSQL queries.
Understanding PostgreSQL Regex Limitations: PostgreSQL uses Henry Spencer's POSIX regex engine (ARE/ERE/BRE modes), not PCRE (Perl Compatible Regular Expressions). The complexity limits in the engine prevent pathological backtracking and memory exhaustion. Unlike some other databases, PostgreSQL does not support PCRE natively, so you are limited to POSIX syntax. If you need more advanced regex features, consider moving regex matching to the application layer using language-specific libraries like Python's re or JavaScript's RegExp, then use simpler PostgreSQL queries to pre-filter data. In PostgreSQL 12+, certain optimizations to the regex engine improved handling of larger patterns, so upgrading may help. For extremely complex pattern matching, consider storing data in separate normalized columns or using dedicated full-text search configurations instead of regex.
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