PostgreSQL performance optimization requires understanding query execution and database configuration. Start with query analysis using EXPLAIN ANALYZE to identify slow . Create appropriate indexes based on query patterns: B-tree for general use, GIN for full-text search, and GiST for geometric data. Use partial indexes for frequently filtered queries. Implement connection pooling with PgBouncer to handle concurrent connections efficiently. Configure shared_buffers to 25% of available RAM for dedicated servers. Adjust work_mem for sorting operations and maintenance_work_mem for maintenance tasks. Use VACUUM regularly to reclaim storage and update statistics. Consider partitioning large tables based on date or range. Implement query caching at the application level using Redis. Use materialized views for expensive aggregations. Optimize JOIN operations with proper indexing. Use CTEs with caution as they can be optimization fences. Implement autovacuum with appropriate thresholds. Monitor performance with pg_stat_statements and pgBadger. Consider read replicas for read-heavy workloads. Upgrade PostgreSQL version regularly for performance improvements. Use connection strings with proper timeout settings. Regular database maintenance ensures consistent performance.
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