Database Indexing Done Right: Why Your "Optimized" Queries Might Be Slower Than Before

Adding indexes does not always improve query performance; incorrect indexing can significantly slow down queries. Database indexes are separate data structures that store sorted copies of specific columns and pointers to table rows, acting as roadmaps for faster data retrieval. B-Tree indexes, commonly used in PostgreSQL and MySQL, organize data in a sorted, hierarchical tree structure, suitable for range queries and sorting. Hash indexes are faster for exact matches but ineffective for range queries. Indexing high-cardinality columns (unique values) speeds up queries, while indexing low-cardinality columns can slow them down due to excessive random I/O. Indexes increase INSERT, UPDATE, and DELETE times and consume storage space, impacting write performance. A smart indexing strategy involves indexing primary keys, foreign keys, frequently queried high-cardinality columns, and composite indexes for multi-column queries. It's essential to avoid indexing low-cardinality columns, small tables, rarely used columns, and frequently updated columns unless necessary. Measuring query performance with `EXPLAIN ANALYZE` before and after indexing helps determine the actual impact. Strategic indexing, focusing on high-cardinality columns and considering write performance, results in faster queries and efficient resource usage.