# AutoIndexer: A Reinforcement Learning-Enhanced Index Advisor Towards Scaling Workloads

arXiv: https://arxiv.org/abs/2507.23084 · HTML: https://mlsanity.com/paper/2507.23084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Managing engineering systems with large state and action spaces through deep reinforcement learning](https://mlsanity.com/paper/1811.02052/) (2018)
- [Lero: A Learning-to-Rank Query Optimizer](https://mlsanity.com/paper/2302.06873/) (2023)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [Axiomatic Foundations and Algorithms for Deciding Semantic Equivalences of SQL Queries](https://mlsanity.com/paper/1802.02229/) (2018)
- [Exact Reduction of Huge Action Spaces in General Reinforcement Learning](https://mlsanity.com/paper/2012.10200/) (2020)
- [The Case for Automatic Database Administration using Deep Reinforcement Learning](https://mlsanity.com/paper/1801.05643/) (2018)
- [Comprehensive and Efficient Workload Compression](https://mlsanity.com/paper/2011.05549/) (2020)
- [Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference](https://mlsanity.com/paper/2201.00354/) (2022)
- [Index Selection for NoSQL Database with Deep Reinforcement Learning](https://mlsanity.com/paper/2006.08842/) (2020)

## Abstract (arXiv)

Efficiently selecting indexes is fundamental to database performance optimization, particularly for systems handling large-scale analytical workloads. While deep reinforcement learning (DRL) has shown promise in automating index selection through its ability to learn from experience, few works address how these RL-based index advisors can adapt to scaling workloads due to exponentially growing action spaces and heavy trial and error. To address these challenges, we introduce AutoIndexer, a framework that combines workload compression, query optimization, and specialized RL models to scale index selection effectively. By operating on compressed workloads, AutoIndexer substantially lowers search complexity without sacrificing much index quality. Extensive evaluations show that it reduces end-to-end query execution time by up to 95% versus non-indexed baselines. On average, it outperforms state-of-the-art RL-based index advisors by approximately 20% in workload cost savings while cutting tuning time by over 50%. These results affirm AutoIndexer's practicality for large and diverse workloads.