FOSS：面向查询优化器的自学习“医生”
FOSS: A Self-Learned Doctor for Query Optimizer
AI总结：
针对现有深度学习查询优化方法训练效率低、搜索空间受限的问题，提出基于深度强化学习的FOSS框架，结合非对称优势模型构建模拟环境自学习优化，在多基准测试中延迟性能优于现有方法，较PostgreSQL最高提速8.33倍。
摘要：
已有诸多研究利用深度学习解决数据库系统中的查询优化问题，这些方法要么以自底向上的方式从零学习构建执行计划，要么通过提示引导传统优化器的计划生成行为。尽管这类方法已取得一定成效，但仍面临训练效率低或计划搜索空间受限的挑战。为应对上述挑战，我们提出FOSS——一种基于深度强化学习的新型查询优化框架。FOSS从传统优化器生成的初始计划启动优化流程，通过一系列动作逐步修正计划中的次优节点。此外，我们设计了一种非对称优势模型用于评估两个计划之间的优势差，并将该模型与传统优化器集成，构建出模拟环境。借助这一模拟环境，FOSS能够实现自我引导，快速生成大量高质量的模拟经验，随后从这些经验中学习以提升自身优化能力。我们在Join Order Benchmark（连接顺序基准测试）、TPC-DS和Stack Overflow数据集上评估了FOSS的性能。实验结果表明，FOSS在延迟性能上优于现有最优方法；与PostgreSQL相比，FOSS在不同基准测试中的总延迟加速比可达1.15倍至8.33倍。
英文摘要：
Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks.
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