Database Optimization: Beyond Adding Indexes

Adding an index is the first database optimization most developers reach for. It's often the right call, but it's also frequently applied to the wrong query or without understanding the cost. Indexes speed up reads but slow down writes and consume storage. An index on a column that's rarely filtered, or a composite index in the wrong column order, adds overhead without benefit.

The right starting point for database optimization is query analysis. Use EXPLAIN ANALYZE (PostgreSQL) or EXPLAIN (MySQL) to understand what the query planner is actually doing. A query that looks simple can produce a sequential scan on a large table because the planner estimated the index would be slower — understanding why the planner made that decision is the actual problem to solve.

N+1 queries are the most common application-layer database problem. They happen when code loads a list of records and then queries for related data for each record individually in a loop. The fix is eager loading — join the related data in the initial query or use a batch load. ORM frameworks make N+1 easy to produce accidentally and some (but not all) offer tools to detect them.

Connection management is underappreciated. Applications that open a new database connection per request — rather than using a connection pool — will hit database connection limits under any meaningful load. Monitor active connections against your pool limit and your database's max_connections setting.

For read-heavy workloads, read replicas and caching are the next level of optimization. A Redis cache in front of expensive, frequently-read queries can reduce database load dramatically. The complexity is cache invalidation — knowing when to evict stale data. That decision is worth thinking through carefully before implementing.