Database Management Systems (DBMS) are crucial for efficient data management and access control, but their administration remains challenging for Database Administrators (DBAs). Tuning, in particular, is known to be difficult. Modern systems have many tuning parameters, but only a subset significantly impacts performance. Focusing on these influential parameters reduces the search space and optimizes performance. Current methods rely on costly warm-up phases and human expertise to identify important tuning parameters. In this paper, we present DOT, a dynamic knob selection and online sampling DBMS tuning algorithm. DOT uses Recursive Feature Elimination with Cross-Validation (RFECV) to prune low-importance tuning parameters and a Likelihood Ratio Test (LRT) strategy to balance exploration and exploitation. For parameter search, DOT uses a Bayesian Optimization (BO) algorithm to optimize configurations on-the-fly, eliminating the need for warm-up phases or prior knowledge (although existing knowledge can be incorporated). Experiments show that DOT achieves matching or outperforming performance compared to state-of-the-art tuners while substantially reducing tuning overhead.

翻译：数据库管理系统（DBMS）对于高效的数据管理与访问控制至关重要，但其管理对数据库管理员（DBA）而言仍具挑战性，其中调优尤为困难。现代系统拥有众多调优参数，但仅其中一部分对性能有显著影响。聚焦于这些关键参数可缩小搜索空间并优化性能。现有方法依赖于成本高昂的预热阶段和人工经验来识别重要调优参数。本文提出DOT，一种动态参数选择与在线采样的DBMS调优算法。DOT采用带交叉验证的递归特征消除（RFECV）来剪枝低重要性调优参数，并利用似然比检验（LRT）策略来平衡探索与利用。在参数搜索方面，DOT采用贝叶斯优化（BO）算法实时优化配置，从而无需预热阶段或先验知识（但现有知识可被纳入）。实验表明，与最先进的调优器相比，DOT在显著降低调优开销的同时，实现了相当或更优的性能。