Database Indexing Best Practices for Growing Apps

Database indexing is always a tradeoff, since an index that speeds up one query can slow down writes elsewhere. The most common mistake is indexing rarely-filtered columns while ignoring the ones used in WHERE clauses and JOINs; well-chosen composite indexes usually resolve performance issues teams initially assume need a hardware upgrade.

An index that speeds up one query can slow down writes elsewhere, so indexing is always a tradeoff rather than a free performance win.

The most common mistake we see is indexing columns that are rarely filtered on while ignoring the ones actually used in WHERE clauses and JOIN conditions. Composite indexes, ordered to match your most frequent query patterns, usually deliver more benefit than many single-column indexes.

We review slow query logs early in performance audits, since a handful of well-placed indexes often resolves issues that teams initially assume require a hardware upgrade.

A concrete example: a table filtered by both customer_id and order_date in almost every query benefits far more from a single composite index on both columns, in the right order, than from two separate single-column indexes that the query planner may not combine efficiently.

A common pitfall is over-indexing a table that sees frequent writes, since every additional index has to be updated on every insert or update, which can noticeably slow down write-heavy workloads while barely improving read performance for queries that were never the bottleneck.

Index column order also matters more than many teams expect: a composite index on (a, b) speeds up queries filtering on a alone or on a and b together, but does little for queries filtering on b alone.

We treat indexing as an ongoing practice rather than a one-time setup task, revisiting slow query logs periodically as an application's actual usage patterns evolve away from what was assumed at launch.