supabase-postgres-best-practices

To optimize PostgreSQL query performance in Supabase, you analyze existing schemas and query metrics to generate tailored SQL adjustments. This includes identifying missing indexes, designing composite or partial indexes, and applying schema refinements to reduce slow queries and improve throughput.

Row Level Security (RLS) affects PostgreSQL performance by adding policy checks to query execution. You can optimize RLS by implementing security rules with performance in mind, ensuring your policies leverage appropriate indexes to prevent full table scans during data access.

The best way to partition large tables in PostgreSQL is to select appropriate data types and apply careful primary and foreign key strategies. Partitioning divides large tables into smaller, manageable pieces to accelerate query performance and ensure scalable schemas.

To minimize database deadlocks, you should use consistent locking orders, advisory locks, and SKIP LOCKED patterns in your SQL queries. These concurrency management techniques prevent transaction blocking and improve parallel processing reliability.

You use pg_stat_statements and EXPLAIN ANALYZE for SQL optimization by setting up database monitoring to track query execution statistics. These tools expose performance bottlenecks in your PostgreSQL setup, guiding concrete SQL tuning recommendations and schema adjustments.