Database Indexing Strategies That Actually Matter for Web Application Performance
A slow application often traces back to a missing index causing a full table scan: invisible until you look at the actual query execution plan. Here's what to index and what not to.
The Performance Problem That Hides Behind 'The Server Feels Slow'
A significant share of the 'slow application' complaints we diagnose trace back to a database query performing a full table scan because the right index doesn't exist: invisible from the application code, invisible from the frontend, and only obvious once you look at the actual query execution plan. As table size grows, an unindexed query that felt fast in development with a few hundred rows becomes a production bottleneck at tens of thousands of rows.
Index Every Foreign Key and Every Frequent WHERE Clause Column: Then Stop
The most common under-indexing pattern is a foreign key column with no index, forcing every join through it to scan the full referenced table. The most common over-indexing pattern is indexing every column defensively. Each additional index adds write overhead and storage cost, so we index deliberately based on actual query patterns from production logs, not speculatively for hypothetical future queries.
Composite Indexes and Column Order Actually Matter
A composite index on (tenant_id, created_at) serves queries filtering by tenant_id alone or by both columns together, but doesn't efficiently serve a query filtering only by created_at: column order in a composite index has to match the actual query patterns it's meant to accelerate, which is a detail that's easy to get backwards and still technically 'have an index' without getting the performance benefit.
N+1 Query Patterns Are an Application Problem an Index Can't Fix
No amount of indexing fixes a code pattern that issues one query per row of a result set instead of a single batched query. This remains one of the most common performance issues in ORMs used without care, and it requires an application-code fix (eager loading, batched queries) rather than a database-layer fix. We catch this specifically in performance code review, not just in database tuning.
Measuring Before Optimizing, Every Time
We use actual query execution plans (EXPLAIN ANALYZE) against production-representative data volume before adding or removing any index: intuition about which queries are slow is frequently wrong, and blind index additions based on guesswork can make write-heavy workloads slower without meaningfully improving the reads they were meant to help.
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