文章介绍了一种名为AutoIndexer的框架，通过结合工作负载压缩、查询优化和专门的强化学习模型，有效扩展索引选择。它显著降低了搜索复杂度，同时减少了查询执行时间，相比非索引基准降低95%，在节省工作负载成本方面优于现有强化学习索引顾问20%，并缩短了调优时间超过50%。

arXiv:2507.23084v1 Announce Type: cross Abstract: 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.