mlsanityData to 14 Oct 2026

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

arXiv 2507.23084 ↗First posted 30 Jul 2025cs.DB cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, 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.

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12 of 47 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  3. 2019Neo: A Learned Query Optimizercited by 80
  4. 2018Managing engineering systems with large state and action spaces through deep reinforcement learningcited by 32
  5. 2023Lero: A Learning-to-Rank Query Optimizercited by 26
  6. 2018Deep Reinforcement Learning for Join Order Enumerationcited by 25
  7. 2018Axiomatic Foundations and Algorithms for Deciding Semantic Equivalences of SQL Queriescited by 11
  8. 2020Exact Reduction of Huge Action Spaces in General Reinforcement Learningcited by 10
  9. 2018The Case for Automatic Database Administration using Deep Reinforcement Learningcited by 8
  10. 2020Comprehensive and Efficient Workload Compressioncited by 6
  11. 2022Toward Causal-Aware RL: State-Wise Action-Refined Temporal Differencecited by 6
  12. 2020Index Selection for NoSQL Database with Deep Reinforcement Learningcited by 1

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