Database Indexing Mistakes That Are Quietly Killing Your Query Performance
Category: Data Engineering
By Akanni Dorcas · 2026-07-27
The fastest databases aren’t built by chance. They’re carefully tuned over time, with every index serving a clear purpose
Your database might not be slow because it has too much data. It might be slow because it's using the wrong indexes.
You’ve optimised your application code. You’ve upgraded your servers. You’ve increased memory and CPU resources. Yet your application still feels sluggish.
Pages take longer to load. Reports that once finished in seconds now take minutes. APIs become slower as your user base grows, and your database server seems to be working harder than ever.
Sound familiar? For many engineering teams, the culprit isn’t the database itself. It’s the way the database is indexed.
Indexes are one of the most powerful performance tools available, but they’re also one of the most misunderstood. Used correctly, they can reduce query times dramatically. Used poorly, they can slow down your database, increase storage costs, and even make write operations less efficient.
Let’s look at some of the most common indexing mistakes that quietly damage database performance and, more importantly, how to avoid them.
What Is a Database Index?
Think of a database index as the index at the back of a book.
Without it, you’d have to flip through every page to find the information you're looking for. With an index, you can jump directly to the relevant section in seconds.
Databases work in much the same way.
Instead of scanning every row in a table, an index helps the database locate matching records much more efficiently. The larger your dataset becomes, the more valuable a well-designed index is.
However, not every column should be indexed, and more indexes don’t automatically mean better performance.
Mistake #1: Not Indexing Frequently Queried Columns
One of the most common mistakes is failing to create indexes on columns that are searched repeatedly.
Imagine an e-commerce application where customers constantly search for products by category or SKU. If those columns aren’t indexed, the database may scan millions of records every time someone performs a search.
As traffic grows, these table scans become increasingly expensive.
The solution is simple: identify the columns most commonly used in WHERE, JOIN, ORDER BY, and GROUP BY clauses and ensure they’re properly indexed.
Your database should spend its time finding data, not searching every row to locate it.
Mistake #2: Creating Too Many Indexes
If too few indexes are bad, surely adding more must be better. Not quite. Every index consumes storage space and must be updated whenever data is inserted, modified, or deleted.
Imagine a customer table with ten different indexes. Every time a new customer registers, the database has to update all ten indexes before completing the transaction.
The result? Slower write performance.
Effective indexing is about balance. Create indexes that support your most important queries, but avoid indexing every column simply because you can.
Mistake #3: Ignoring Composite Indexes
Many developers create separate indexes for individual columns without considering how queries actually work.
Suppose your application frequently searches for orders using both customer_id and order_date. Two separate indexes may help, but a composite index covering both columns in the correct order is often significantly faster.
Composite indexes are designed for queries that filter or sort using multiple columns together. Understanding your query patterns is far more valuable than creating indexes one column at a time.
Mistake #4: Never Reviewing Old Indexes
Applications evolve. Queries change. Features are added. But indexes often remain exactly as they were years earlier.
Over time, databases accumulate indexes that are no longer used by any application. These unnecessary indexes consume storage, increase maintenance overhead, and slow down write operations without providing any performance benefit.
Regular index audits help identify unused or duplicate indexes that can safely be removed. Sometimes improving database performance isn't about adding new indexes. It's about removing old ones.
Mistake #5: Forgetting to Analyse Query Execution Plans
Many developers assume an index is being used simply because it exists. That’s a risky assumption.
Databases decide whether or not to use an index based on query structure, table size, and data distribution. In some cases, the database may ignore an index entirely if it believes another approach is faster.
Execution plans reveal exactly how the database processes a query. They show whether indexes are being used, where table scans occur, and which operations consume the most time.
Before guessing what’s causing slow performance, examine the execution plan. The database is often telling you exactly what’s wrong.
Optimisation Is an Ongoing Process
Database performance isn't something you optimise once and forget.
As your application grows, query patterns change. Tables become larger, new features introduce additional workloads, and yesterday's perfect indexing strategy may no longer be the best choice.
Regular performance monitoring helps identify slow queries before they affect users.
Many organisations schedule periodic database health checks, reviewing execution plans, index usage, query response times, and storage growth to ensure their systems continue performing efficiently.
The best-performing databases aren’t the ones with the most indexes. They’re the ones with the right indexes.
Final Thoughts
A slow database doesn't always need more hardware. Often, it simply needs smarter indexing.
By indexing frequently queried columns, avoiding unnecessary indexes, using composite indexes where appropriate, reviewing outdated indexes, and analysing execution plans regularly, you can dramatically improve query performance without changing your application code.
The fastest databases aren’t built by chance. They’re carefully tuned over time, with every index serving a clear purpose.
A few well-designed indexes can turn queries that take seconds into queries that complete in milliseconds, creating a faster experience for users and a more scalable foundation for your application.