supabase-postgres-best-practices

To optimize slow Supabase Postgres queries, you correct query, indexing, and schema anti-patterns. This skill provides concrete incorrect versus correct SQL rewrites and prioritizes indexing improvements to directly reduce filtered query latency.

Connection pooling affects Supabase performance by mitigating connection limits and preventing timeouts during production bottlenecks. Implementing proper connection management and timeouts ensures stable database concurrency under high application load.

Supabase RLS policies cause per-row performance issues when security rules trigger inefficient query execution plans. Adjusting RLS policy patterns to avoid per-row traps and applying index-aware Postgres statements resolves these security-related bottlenecks.

The best way to use EXPLAIN ANALYZE for Postgres query tuning is to diagnose production bottlenecks from monitoring data and execution plans. This skill applies prioritized rule categories to translate EXPLAIN output into corrected, index-aware SQL statements.

You can fix lock contention and concurrency issues in Supabase Postgres by applying specific locking mitigation best practices. This skill addresses concurrency anti-patterns to ensure stable data access during schema migrations and heavy transactional loads.