用强化学习自动调优机器学习索引，性能提升近20倍。
A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach
- 采用定制深度强化学习动态调整索引参数
- 相比默认设置，运行时间降98%，吞吐量增17倍
- 适合需要自适应调优的数据库系统开发者
学习型索引结构（LIS）通过机器学习模型优化数据索引，显著提升数据管理效率。然而，设计时常面临关键权衡，导致难以在特定工作负载下找到最优平衡。现有方法或依赖人工密集调参，或采用基于启发式规则的固定配置，难以持续实现最佳性能。本文提出LITune框架，实现学习型索引的端到端自动调优。LITune采用自适应训练流程，结合定制深度强化学习（DRL）方法，确保调优过程稳定高效。为应对在线调优中的长期动态变化，进一步引入实时更新机制O2系统，有效捕捉状态转移，动态适应数据分布与工作负载变化。实验表明，针对选定的LIS实例，LITune相较默认参数设置可实现最高98%的运行时间降低和17倍的吞吐量提升，显著优于现有调优方法，具备在真实场景中推广的潜力。
原文摘要 · Abstract (English)
Learned Index Structures (LIS) have significantly advanced data management by leveraging machine learning models to optimize data indexing. However, designing these structures often involves critical trade-offs, making it challenging for both designers and end-users to find an optimal balance tailored to specific workloads and scenarios. While some indexes offer adjustable parameters that demand intensive manual tuning, others rely on fixed configurations based on heuristic auto-tuners or expert knowledge, which may not consistently deliver optimal performance. This paper introduces LITune, a novel framework for end-to-end automatic tuning of Learned Index Structures. LITune employs an adaptive training pipeline equipped with a tailor-made Deep Reinforcement Learning (DRL) approach to ensure stable and efficient tuning. To accommodate long-term dynamics arising from online tuning, we further enhance LITune with an on-the-fly updating mechanism termed the O2 system. These innovations allow LITune to effectively capture state transitions in online tuning scenarios and dynamically adjust to changing data distributions and workloads, marking a significant improvement over other tuning methods. Our experimental results demonstrate that LITune achieves up to a 98% reduction in runtime and a 17-fold increase in throughput compared to default parameter settings given a selected Learned Index instance. These findings highlight LITune's effectiveness and its potential to facilitate broader adoption of LIS in real-world applications.
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