[2603.28809] WAter: A Workload-Adaptive Knob Tuning System based on Workload Compression

Computer Science > Databases arXiv:2603.28809 (cs) [Submitted on 28 Mar 2026] Title:WAter: A Workload-Adaptive Knob Tuning System based on Workload Compression Authors:Yibo Wang, Jiale Lao, Chen Zhang, Cehua Yang, Jianguo Wang, Mingjie Tang View a PDF of the paper titled WAter: A Workload-Adaptive Knob Tuning System based on Workload Compression, by Yibo Wang and 5 other authors View PDF HTML (experimental) Abstract:Selecting appropriate values for the configurable parameters of Database Management Systems (DBMS) to improve performance is a significant challenge. Recent machine learning (ML)-based tuning systems have shown strong potential, but their practical adoption is often limited by the high tuning cost. This cost arises from two main factors: (1) the system needs to evaluate a large number of configurations to identify a satisfactory one, and (2) for each configuration, the system must execute the entire target workload on the DBMS, which is both time-consuming. Existing studies have primarily addressed the first factor by improving sample efficiency, that is, by reducing the number of configurations evaluated. However, the second factor, improving runtime efficiency by reducing the time required for each evaluation, has received limited attention and remains an underexplored direction. We develop WAter, a runtime-efficient and workload-adaptive tuning system that finds near-optimal configurations at a fraction of the tuning cost compared with state-of-the-art metho...