What are query optimization techniques?

Query optimization is the process of improving the performance of a database query. Key techniques include:

1. Indexing – Create B‑tree or bitmap indexes on columns used in WHERE, JOIN, ORDER BY, or GROUP BY clauses to reduce full table scans.
2. Statistics and Cost‑Based Optimizer – Keep table and index statistics up to date so the optimizer can estimate cardinality and choose the cheapest plan.
3. Query Rewriting – Simplify expressions, remove unnecessary subqueries, use EXISTS instead of IN, and push predicates to the earliest stage.
4. Join Order and Join Algorithms – Let the optimizer choose the best join order; consider using hash joins for large tables and nested loop joins for small tables.
5. Partitioning – Partition large tables on a frequently filtered column to limit the data scanned.
6. Materialized Views – Pre‑compute expensive aggregations or joins and refresh them periodically.
7. Parallel Execution – Enable parallel query execution where supported to utilize multiple CPU cores.
8. Caching – Use query result caching or in‑memory tables for frequently accessed data.
9. Use of Hints – In some engines, hints can force a specific join type or index usage when the optimizer’s choice is suboptimal.
10. Avoiding Cartesian Products – Ensure proper join conditions to prevent accidental cross joins.

Applying these techniques reduces I/O, CPU usage, and memory consumption, leading to faster query response times.