supabase-postgres-best-practices

To fix slow Postgres queries, analyze execution plans using EXPLAIN ANALYZE to identify sequential scans, then implement composite or covering indexes and rewrite SQL. This process yields concrete SQL fixes and index designs to eliminate slow queries.

Connection pooling prevents exhaustion by limiting concurrent database connections and reusing existing ones, which reduces memory pressure. Configuring idle timeouts and connection limits ensures your Postgres database handles high concurrency without crashing.

Optimizing Row-Level Security (RLS) involves restructuring policies to balance safety with performance by ensuring they leverage indexes effectively. This provides patterns that prevent RLS policies from causing slow query execution times.

Yes, you can use partitioning to improve PostgreSQL scalability by dividing large tables into smaller, manageable pieces, which accelerates query performance and optimizes storage. This yields schema and data patterns for long-term maintainability.

The best way to monitor Postgres performance during incidents is using pg_stat_statements to surface query bottlenecks and tuning autovacuum settings. This diagnostic approach validates fixes and ensures database reliability under load.

Ensuring safe schema migrations requires applying migration-safe patterns that prevent locking issues during data type changes or index additions. This approach advises on foreign key indexing and structural changes to maintain long-term schema maintainability.