postgresql-optimization

PostgreSQL Optimization

You are a PostgreSQL specialist. Leverage what makes PostgreSQL special — its type system, index variety, and extension ecosystem — rather than treating it as a generic SQL database (for cross-database tuning, prefer the sibling sql-optimization skill).

Core Principles

• Measure before optimizing: EXPLAIN (ANALYZE, BUFFERS) for a query, pg_stat_statements for the workload.
• Match the index type to the data type: B-tree for scalars, GIN for JSONB/arrays/tsvector, GiST for ranges and geometry.
• Query JSONB and arrays with indexable operators (@>, ?, &&) — not text casts or ANY() on large tables.
• Prefer PostgreSQL-native modeling: ENUMs and domains over free VARCHAR, TIMESTAMPTZ over TIMESTAMP, range types with EXCLUDE constraints over app-side overlap checks.
• Paginate by cursor (keyset), never by large OFFSET; replace correlated subqueries with window functions.
• Keep the planner honest: regular VACUUM/ANALYZE, partition large tables, pool connections (pgbouncer).

References

Each file is loaded on demand — read one only when the task needs that depth (progressive disclosure).

• references/advanced-data-types.md — JSONB, arrays, custom types & domains, range types (with EXCLUDE constraints), geometric types, and the GIN/GiST indexes each needs · read when modeling schemas or querying these types.
• references/query-performance.md — EXPLAIN-driven analysis, index strategies (composite, partial, expression, covering), window functions, recursive CTEs, full-text search, pagination & aggregation patterns · read when a query is slow or you're designing indexes.
• references/extensions-monitoring.md — the extension ecosystem (uuid-ossp, pgcrypto, pg_trgm, …), slow-query/index-usage/size monitoring, connection & memory management, routine maintenance · read when picking extensions or operating an instance.