database-optimizer

Start by running EXPLAIN ANALYZE to inspect the execution plan and identify costly operations like sequential scans or nested loops. Then rewrite the query, add composite or covering indexes matching the JOIN and filter columns, and validate improvements with benchmarking tools like pgbench.

Detect N+1 patterns through ORM query logging or application profiling, then resolve them with eager loading, batch queries, or JOIN optimization. For GraphQL, apply DataLoader patterns with query batching and field-level caching.

Yes, it covers cloud-specific tuning including RDS Performance Insights, Aurora indexing and serverless patterns, Azure SQL intelligent performance, and DynamoDB GSI optimization. It also addresses cloud cost optimization through reserved capacity and storage tiering.

Design indexes based on actual query patterns rather than indexing every column. Use composite indexes with correct column ordering, partial indexes for filtered queries, and specialized types like GIN for JSONB or full-text search, while managing index bloat over time.

Denormalize when read patterns justify it and benchmarking shows normalization causes expensive JOINs on hot paths. Balance the performance gain against increased write complexity and storage cost, and document the rationale with measured impact.