Most people think indexes always improve performance, but in reality, indexes are a double-edged sword. They can make your queries much faster, especially for reads, but if used incorrectly or excessively, they can actually hurt performance — especially for write-heavy workloads.
So, when I explain this in an interview, I usually break it down into how and why indexing can negatively impact performance, with examples from real-world experience.
First, let’s recall that indexes are separate data structures (usually B-trees) that the database maintains to make lookups faster. They help in SELECT queries by allowing the database to find rows quickly instead of scanning the entire table. But every time data in the base table changes, the corresponding index entries also need to be updated — and that’s where performance overhead comes in.
Let’s look at the main scenarios where indexing hurts performance:
1. Slower INSERT, UPDATE, and DELETE operations:
Every time we modify data, all associated indexes must also be maintained.
For example, suppose you have a Customer table with 8 indexes. When you insert one record, the database must insert one entry into the table plus 8 entries into those index structures.
Example:
INSERT INTO Customer (CustomerName, City, Email) VALUES ('Raj', 'Chennai', 'raj@gmail.com');
If the City and Email columns are indexed, the database must also update those indexes. On large, heavily indexed tables, this can slow down inserts and updates significantly.
I experienced this in a real project — an order tracking table had around 10 indexes (added by multiple developers over time). During ETL loads, inserts were taking almost 5× longer than expected. After analyzing execution plans, we dropped redundant indexes and retained only the two most used in queries. Insert speed immediately improved by about 70%.
2. Increased storage usage and maintenance overhead:
Indexes consume additional disk space — sometimes as much as or more than the base table, depending on how many columns they include.
This extra storage isn’t just about space — it also impacts backup and restore times, index rebuild operations, and I/O costs during maintenance.
For instance, in a large warehouse system, we noticed our nightly backups taking too long. Investigation showed that the indexes were consuming 2× the space of actual data. We optimized by dropping unused indexes and using filtered indexes to cover only relevant subsets of data.
3. Fragmentation and performance degradation over time:
As data changes, indexes can become fragmented, meaning the logical order of pages no longer matches the physical order on disk. This causes the database engine to read more pages than necessary, slowing down reads and writes.
I usually schedule regular index maintenance — reorganizing or rebuilding indexes weekly — to fix fragmentation. Without it, performance gradually drops even if queries don’t change.
4. Poor query optimization due to wrong or redundant indexes:
Sometimes, having too many overlapping indexes can confuse the optimizer. The optimizer may choose a suboptimal index because multiple similar ones exist, leading to inefficient execution plans.
For example, having both (City) and (City, State) indexes might cause the optimizer to pick the smaller one when the broader one would be faster for a specific query.
In one reporting system, we resolved such an issue by reviewing index usage statistics (sys.dm_db_index_usage_stats) and consolidating redundant ones.
5. Write-heavy systems suffer more than read-heavy systems:
In OLTP systems (like e-commerce or banking), write operations dominate — so too many indexes can drastically slow transaction throughput.
However, in OLAP or reporting systems (where reads dominate), more indexes are usually acceptable.
Challenges I’ve faced:
- Balancing read vs. write performance was often tricky — adding indexes to speed up reports would hurt insert-heavy ETL jobs.
- Rebuilding indexes during business hours could cause locking and blocking, especially on large tables.
I solved that by scheduling index maintenance during off-peak hours and using ONLINE=ON rebuilds in SQL Server.
Limitations and Mitigation Strategies:
- Use only necessary indexes — review regularly using DMVs or monitoring tools.
- Consider composite indexes instead of multiple single-column indexes.
- For reporting systems, use indexed views to reduce overhead on base tables.
- Apply filtered indexes to cover only active data (like current year records).
- Periodically rebuild or reorganize indexes to remove fragmentation.
In summary:
Indexes are powerful tools, but they come with trade-offs.
They speed up reads but slow down writes, increase storage, and add maintenance overhead.
In my experience, most performance issues related to indexes came not from having too few, but from having too many or poorly chosen ones.
The key is to strike a balance — analyze query patterns, monitor index usage, and design indexes strategically to achieve both fast reads and efficient writes.