用强化学习优化数据库索引，大幅降低查询耗时。
AutoIndexer: A Reinforcement Learning-Enhanced Index Advisor Towards Scaling Workloads
- 通过压缩工作负载和专用强化学习模型降低搜索复杂度
- 查询执行时间最多减少95%，比现有方法节省20%成本
- 适合处理大规模多样化的分析型数据库工作负载
高效选择索引是数据库性能优化的核心，尤其在处理大规模分析型工作负载时。尽管深度强化学习（DRL）在通过经验学习自动选择索引方面展现出潜力，但很少有工作解决其在扩展工作负载下的适应性问题，主要受限于动作空间指数级增长和大量试错开销。为此，我们提出AutoIndexer框架，融合工作负载压缩、查询优化与专用强化学习模型，有效实现索引选择的可扩展性。通过在压缩后的工作负载上运行，AutoIndexer显著降低搜索复杂度，同时保持较高索引质量。大量评估显示，其相比无索引基线可将端到端查询执行时间最多减少95%；平均而言，在工作负载成本节约上优于当前最先进的基于RL的索引建议器约20%，且调优时间减少超50%。这些结果验证了AutoIndexer在大规模多样化工作负载中的实用性。
原文摘要 · Abstract (English)
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.
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