sql-optimization-patterns

Slow SQL queries are identified using EXPLAIN or EXPLAIN ANALYZE to inspect execution plans and locate performance bottlenecks. Fix them by removing SELECT *, adding appropriate indexes, rewriting inefficient JOINs, and applying optimization patterns like covering indexes or cursor-based pagination to reduce execution cost and latency.

Indexing strategies include B-Tree indexes for general lookups, composite indexes for multi-column filters, partial indexes to narrow scope, and covering indexes that store all required columns. Selecting the right index type based on your query patterns eliminates full table scans and accelerates data retrieval.

Optimize JOINs by replacing inefficient multiple queries with single batched or joined operations. Eliminate N+1 query patterns—where one query triggers many dependent queries—by restructuring data fetching into optimized JOINS or batch operations, reducing database roundtrips and total execution time.

Partial indexes filter rows at index build time, reducing size and improving performance for queries with WHERE conditions. Expression indexes index computed columns or function results directly. Use them when queries consistently filter on specific subsets or computed values to avoid full index scans.

EXPLAIN output shows the query execution plan, including operation sequence, row counts, and cost estimates. EXPLAIN ANALYZE adds actual runtime statistics. Interpreting this plan reveals full table scans, missing indexes, and join inefficiencies—the root causes of slow queries you can then eliminate.

Cursor-based pagination uses indexed column values as pointers rather than OFFSET, which scans discarded rows. This approach scales efficiently across growing datasets by fetching only necessary rows, reducing memory consumption and query latency compared to offset-based pagination.