sql-optimization-patterns

Use EXPLAIN to analyze query execution plans and identify bottlenecks like sequential scans or missing indexes. EXPLAIN shows where queries spend time, revealing whether to add indexes, rewrite the query, or restructure joins to reduce cost.

Composite, functional, partial, and covering indexes target different query patterns. Composite indexes optimize multi-column filters; covering indexes eliminate table lookups; partial indexes reduce size for filtered datasets; functional indexes speed computed columns.

N+1 occurs when one query loads a parent row, then separate queries load each child. Rewrite using joins, subqueries, or batch loading to fetch related data in one or two queries instead of one-plus-many, dramatically reducing database round trips.

Use keyset pagination or LIMIT with OFFSET on indexed columns instead of scanning full tables. Keyset pagination fetches only needed rows by comparing previous result boundaries, avoiding expensive sorts and full-table scans.

Yes. Measure execution time, row counts examined, and index usage before and after changes. Re-run EXPLAIN and performance tests to confirm improvements are measurable and sustainable across varied data volumes.

Design indexes during schema creation by analyzing expected query patterns, not retroactively. Identify high-frequency filters, joins, and sorts in your application queries, then create supporting indexes to prevent slow queries from deployment.