sql-optimization-patterns

To find slow SQL queries, read the EXPLAIN execution plan output to identify full table scans, nested loop joins, high cost drivers, and missing indexes. Analyzing the execution plan reveals specific bottlenecks causing high database latency under load.

The best way to choose indexes for PostgreSQL and MySQL is matching index types and column orders to specific access patterns. Select covering, partial, expression-based, or range indexes based on equality and range queries in your workload.

Yes, this query optimization approach works for both PostgreSQL and MySQL workloads. It diagnoses execution plans, indexing decisions, pagination tuning, joins, and partitioning strategies across both database platforms to reduce latency and improve scalability.

To rewrite SQL pagination and aggregation queries to reduce latency, replace offset-based pagination with keyset pagination, optimize join structures, and use materialized views for heavy aggregates. These query rewrites minimize load and improve response times.

Use partitioning or materialized views for database performance when query rewrites and standard indexing cannot handle large data volumes. Partitioning splits large tables for faster scans, while materialized views precompute aggregations to eliminate runtime bottlenecks.