pg-optimization

Identify slow queries using pg_stat_statements, apply targeted indexes like partial or covering indexes, and tune execution plans via EXPLAIN ANALYZE workflows to reduce latency in Django applications.

Run EXPLAIN ANALYZE to inspect execution plans and verify improvements, while using pg_stat_statements to measure query performance metrics and diagnose real-world Django workloads.

Yes, Django 5.2 supports native connection pooling via psycopg3 and integrates with PgBouncer in transaction mode to manage database connections efficiently and reduce latency.

Use materialized views with REFRESH CONCURRENTLY for analytics workloads in Django to update aggregated data without blocking concurrent reads, ensuring measurable performance gains and safe deployment.

The best indexing strategies include partial, covering, GIN, GiST, and trigram indexes to optimize diverse PostgreSQL query patterns, ensuring measurable performance metrics and robust diagnostics for Django workloads.

PgBouncer in transaction mode restricts session-level features like prepared statements and advisory locks, requiring careful Django psycopg3 configuration to manage connections efficiently without breaking functionality.