query-optimization

Optimize database queries by using EXPLAIN ANALYZE to inspect execution plans, creating targeted indexes (single-column, composite, partial, or expression), and applying eager loading with joinedload or selectinload to prevent N+1 problems. Start by analyzing your slowest queries with EXPLAIN to identify bottlenecks.

The N+1 problem occurs when fetching a parent record triggers separate database queries for each related child record. Fix it in SQLAlchemy by using joinedload or selectinload to eager-load relationships in a single optimized query instead of N separate ones.

Run EXPLAIN ANALYZE on your SQL queries to see the execution plan, row counts, and timing data. This reveals which operations consume the most resources—sequential scans, index usage, joins—helping you identify where to add indexes or restructure queries for better performance.

Create single-column indexes on frequently filtered fields, composite indexes for multi-column WHERE clauses, partial indexes for conditional queries, and expression indexes for computed columns. Use EXPLAIN ANALYZE to confirm each index actually improves query plans.

Yes, enable pg_stat_statements in PostgreSQL to track query execution statistics and identify slow queries. Review metrics like total time and call count to prioritize which queries to optimize, then apply indexing or query restructuring to the most expensive ones.

Yes, query optimization works directly with SQLAlchemy ORM. Use joinedload and selectinload for eager loading, EXPLAIN ANALYZE on generated SQL, and index creation on your PostgreSQL tables to optimize ORM queries just as you would with raw SQL.