Indexes are fundamental to database performance, but too often, they’re treated as “set it and forget it” structures. As the data landscape evolves—with hybrid cloud deployments, real-time analytics, and machine learning workloads—so too must our approach to indexing.
Indeed, as technology evolves and data volumes surge, the same indexing strategies that worked well 10 or 20 years ago are probably no longer optimal. There are many reasons that this is so, ranging from changes made to improve indexing in database management systems—new types and options for indexes—and changes to the way that applications are written and supported. Let’s examine some of these issues and expose the critical need for index modernization in database applications.
The Indexes We Inherit
Let’s face it: many indexes in our environments weren’t thoughtfully designed. They were inherited, added to fix a performance issue in a hurry, or created to satisfy ad hoc access paths. Over time, as applications change and data patterns evolve, these legacy indexes can become irrelevant, redundant, or worse—harmful.
Modernization begins with awareness. Ask yourself: Why does this index exist? If you can’t answer that confidently, it’s time to dig deeper. Query workloads change. The optimal access path from a decade ago may no longer be valid today.
Redundant and Overlapping Indexes
A major modernization opportunity lies in eliminating redundant indexes. Your DBMS platform allows you to define multiple indexes that may overlap or cover the same columns in different ways. But just because you can, doesn’t mean you should.
Consider this scenario:
- One index on (LASTNAME, FIRSTNAME)
- Another on (LASTNAME)
The second index is functionally redundant. The first covers all queries the second can support—and more. Keeping both increases the cost of every insert, update, and delete operation unnecessarily.
Perhaps the second index is a unique index for a primary key. And the second one is to improve performance. In that case, you can’t just drop the second index. Instead, if your DBMS supports index INCLUDE columns, you can include the FIRSTNAME column in the second, unique index, without making it part of the actual index key. That way, using a perhaps newer feature (INCLUDE) you can remove redundance indexes and bolster performance.
Clustering and Compression Considerations
Modernization isn’t just about removal. It’s also about rebuilding smarter. Clustering indexes influence the physical data layout, and poor clustering can degrade performance. As part of modernization, review whether the clustering index still matches the predominant access path.
Also, newer index compression techniques (available in some DBMSs such as Db2) can save space and reduce I/O. If you’re still using only data page compression and haven’t explored index page compression, you’re missing out on potential CPU and storage savings—especially on high cardinality indexes with many repeated values.
Embracing Modern Index Types
Every modern DBMS provides new and improved features with each new release and version of their product. As part of these releases, many improvements have been made, offering more advanced index types and improved index maintenance features. Indexes on expression-based columns, support for larger index keys, and better inline maintenance options mean you can design smarter indexes that better serve analytical queries and transactional workloads alike.
The Role of the DBA
Modernizing indexes isn’t a one-time activity. It should be part of an ongoing index lifecycle management strategy. DBAs today must:
- Automate index analysis and pruning
- Collaborate with developers to understand query needs
- Incorporate index usage tracking into CI/CD pipelines
- Monitor the effect of new applications on existing access paths
To effectively analyze and modernize your indexing strategies you will need to capture and analyze your existing SQL to model and build more effective indexes. For this an index analysis tool can prove to be helpful to reduce manual steps and build the necessary infrastructure and scripts to effectively review your running SQL statements.
You should capture all SQL usage for a period of time that is significant enough to contain a cross-section of the most important applications and queries you run. And you will need to be able to integrate this information with details from your DBMS System Catalog and query plan details. Using this information, you can perform analysis to determine the type of indexes that exist, and that may need to be created.
Things you may need to consider include non-used indexes, indexes with no/outdated statistics, indexes with overlapping key columns, duplicate indexes, table with excess indexes, foreign keys with incomplete indexes, tables without a clustering index, and tables without any indexes at all.
After reviewing and analyzing your systems you can decide which indexes to keep and which to create. Be sure to take a baseline for a before and after comparison of the performance of your applications. Then, you can determine the impact on applications, which profited the most from the new index using access paths, CPU savings, and other "cost factors."
Indexes are not passive structures—they are active performance tools. But like any tool, they can rust with age if not maintained and modernized.
Final Thoughts
Index modernization isn’t about blindly dropping what’s old. It’s about applying data-driven analysis to ensure your indexes are aligned with current—and future—workload needs. In today’s dynamic environments, bloated and outdated index strategies can be the silent killers of performance and scalability.
As data volumes grow and business demands increase, taking a strategic approach to index modernization will be a key differentiator for high-performing database environments.