• Database

Raj Thilak

July 6, 2026

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8 Min read

Most performance problems teams chase are not caused by underpowered servers or slow application code. They come from indexes that were added without thought, or never added at all. A single missing index can quietly turn a fast application into a sluggish one long before anyone notices it in the logs. If your team is troubleshooting slow queries this year, understanding common database indexing mistakes matters far more than throwing hardware at the problem.

The frustrating part is that indexing mistakes rarely announce themselves. A query that runs fine on a development database with a few thousand rows can behave completely differently once a production table crosses a few million rows. By the time anyone notices, the mistake has usually been sitting in the schema for months, quietly adding milliseconds to every request until those milliseconds turn into seconds.

The Missing Index Nobody Notices Until Traffic Spikes

Indexing problems rarely show up during development. Tables are small, query volume is low, and everything feels fast. The trouble starts once real traffic hits production and a query that used to run in milliseconds starts taking seconds. In most cases, the root cause is a missing index on a column used heavily in WHERE clauses, JOIN conditions, or ORDER BY statements.

This is especially common on foreign key columns. Many teams assume a foreign key constraint automatically creates an index, which is true in some databases and false in others. MySQL creates one by default, while Postgres does not index foreign key columns unless it is explicitly requested. A join that worked fine in testing can turn into a full table scan in production simply because that assumption did not hold for the database engine actually in use.

Resources like Use The Index, Luke remain some of the most practical explainers on how index structures actually behave under load, and they are worth bookmarking for any backend team that owns database performance.

The fix sounds simple. Add the index and move on. But teams that add indexes reactively, one at a time after every slow query alert, often end up with a patchwork of indexes that were never designed together, which creates a second problem down the line.

It also helps to separate two very different situations that both get labeled as slow queries. One is a query that has always been slow because it was never given the index it needed. The other is a query that used to be fast and has degraded over time as the table grew, which usually points to an index that stopped scaling well, or one that was never updated as query patterns shifted. Treating both situations the same way, by simply adding more indexes, fixes the first problem while making the second one worse.

Common Indexing Mistakes and What They Actually Cost You

Before adding or removing a single index, it helps to see the mistakes side by side with their real world impact.

When More Indexes Make Things Worse

Index bloat is one of the least discussed causes of slow application performance. Every index you add has to be updated on every insert, update, and delete on that table. Teams that respond to every slow query by adding another index eventually find that write operations have become the bottleneck instead.

This is particularly common in ecommerce and SaaS applications where product catalogs or transaction tables grow fast. A table with twelve indexes might read quickly, but every checkout, every order update, and every inventory sync now has to maintain twelve separate structures. Reviewing existing indexes with a simple frequency of use audit, and removing ones that no query has touched in months, usually recovers meaningful write throughput without any code changes.

Index bloat also shows up in less obvious places, such as backup windows and replication lag. A larger index footprint means more data to write to disk on every transaction, which slows down physical replication to read replicas and stretches nightly backup jobs. Teams that only measure query response time often miss this side effect entirely, since the pain shows up in operations rather than in the application itself.

Composite Index Column Order Matters More Than You Think

A composite index on columns A, B, and C is not interchangeable with one built in the order C, B, A. Query planners generally use a composite index efficiently only when the query filters on a matching prefix of the indexed columns. A team building an index in the wrong order often assumes the index is being used, when in reality the database is falling back to scanning far more rows than expected.

The practical fix is to order composite index columns by selectivity and by how the application actually queries the table, not by how the schema was originally designed. Equality filters generally belong before range filters within a composite index for most workloads.

It also helps to think about composite indexes as covering more than one query pattern where possible. A well ordered index built for filtering by tenant and status can often also serve a sort operation on created date, if that column is added last in the index definition. This turns one index into support for two or three common query shapes, instead of needing a separate index for each variation.

Testing Index Changes Safely Before They Reach Production

Adding or dropping an index on a large production table is not risk free. On some databases, building an index locks the table for writes unless it is created with a concurrent or online option. Testing index changes against a realistic copy of production data, rather than a small development dataset, is the only reliable way to know how long an index build will actually take and whether it changes the query plan the way you expect.

A useful habit is to run the exact query plan before and after an index change on a staging environment that mirrors production row counts. This turns index tuning from guesswork into a measurable, repeatable process, and it catches cases where a new index technically exists but the query planner still chooses not to use it.

Reading the Query Plan Instead of Guessing

Every mainstream database ships a way to see exactly how a query is being executed. Postgres users can lean on the official EXPLAIN documentation to understand whether a query is using an index scan, a sequential scan, or a bitmap heap scan, and MySQL and SQL Server offer equivalent tools.

Guessing which index is missing based on symptoms alone almost always leads to the wrong fix. Reading the actual execution plan tells you exactly which step in the query is expensive, whether that is a sort operation, a nested loop join, or a full scan that an index could eliminate entirely.

Query plans also reveal a subtler problem, cases where an index exists and is technically valid, but the planner still decides not to use it. This can happen when table statistics are out of date, when the query uses a function on an indexed column without a matching functional index, or when the planner estimates that scanning the whole table is cheaper for a particular data distribution. Running an ANALYZE or equivalent statistics refresh after large data changes fixes a surprising number of these cases without touching the index definitions at all.

Keeping Index Health Visible Over Time

Indexing is not a one time task completed during initial schema design. Application usage patterns shift, new features change which columns get filtered on, and old indexes quietly stop earning their keep. Teams that treat index review as a recurring exercise, ideally tied to quarterly performance audits, catch bloat and missing coverage long before customers notice slow pages.

Most databases expose usage statistics that make this review straightforward, showing how many times each index has actually been scanned since the last statistics reset. Cross referencing that list against index size gives a clear priority order, since a large unused index is a better candidate for removal than a small one that rarely gets touched. Building this check into a recurring maintenance task, rather than relying on someone remembering to run it, is what keeps index health from drifting quietly for months at a time.

If your engineering team needs a second set of eyes on database architecture or backend performance, the Askan Tech engineering services team works directly with platform and backend teams on exactly this kind of tuning work.

Database indexing will never be glamorous work, but it remains one of the highest leverage things a backend team can get right. A well placed index, reviewed regularly and built with the actual query patterns in mind, does more for application speed than most infrastructure upgrades combined.