sql-optimization-patterns

Use EXPLAIN ANALYZE to examine query execution plans and pinpoint performance bottlenecks. This shows how the database executes your query, revealing sequential scans, missing indexes, and inefficient JOINs that slow execution.

Choose B-Tree indexes for standard lookups, composite indexes for multi-column filters, partial indexes for subset queries, and covering indexes to eliminate table lookups. Match index type to your query patterns for optimal read performance.

N+1 occurs when one query fetches parent records and subsequent queries fetch related data per row. Resolve it by rewriting queries with efficient JOINs, using batch loading, or implementing cursor-based pagination to fetch related data in fewer round trips.

Yes. Apply query rewriting for JOINs and aggregations, add strategic indexes, use materialized views for complex aggregations, and implement partitioning for extremely large tables to maintain performance as data grows.

Avoid SELECT *, design efficient JOINs with proper filtering, use indexed columns in WHERE clauses, optimize aggregation patterns, and implement pagination for result sets. These practices reduce database load and improve application response times.