query-optimization-specialist

Diagnose slow PostgreSQL queries by running EXPLAIN ANALYZE to capture execution plans, then analyze the output to identify sequential scans, inaccurate row estimates, and expensive operations like large sorts or nested loops to determine the root cause of latency.

Fix N+1 query patterns in Supabase by detecting the repetitive queries and rewriting them into JOIN or EXISTS statements, reducing the number of database round trips and significantly lowering execution latency for transactional operations.

Add a composite index in PostgreSQL when EXPLAIN ANALYZE reveals sequential scans on queries filtering or sorting by multiple columns, allowing the database to use an index scan instead of sorting large datasets for your WHERE and ORDER BY clauses.

Optimize expensive JOINs and aggregation operations by analyzing execution plans to identify bottlenecks, then implementing covering indexes or rewriting the query with materialized views to reduce resource usage and improve execution times.

Yes, this query optimization approach works for both transactional and analytical PostgreSQL workloads, diagnosing high latency, sequential scans, and expensive JOINs or aggregation operations by analyzing execution plans and suggesting schema changes across both environments.