DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning

2026-03-16

AI Summary

This work addresses the challenge of database tuning, which is hindered by a vast parameter space, reliance on manual expertise, and costly warm-up phases that impede efficient identification of critical configuration knobs. To overcome these limitations, the paper proposes a novel online, warm-up-free dynamic tuning approach that integrates recursive feature elimination with cross-validation (RFECV) to identify key parameters, employs likelihood ratio tests (LRT) to balance exploration and exploitation, and seamlessly couples this feature selection mechanism with Bayesian optimization (BO) for real-time search of optimal configurations. This method is the first to enable dynamic parameter selection without requiring an initial warm-up period and supports incorporation of prior knowledge. Experimental results demonstrate that it matches or surpasses state-of-the-art tuners across multiple benchmarks while substantially reducing tuning time and computational overhead.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Auto ML and Hyperparameter Tuning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• database tuning
• knob selection
• parameter optimization
• performance tuning
• automated tuning

Innovation

• Dynamic Knob Selection
• Online Sampling
• Recursive Feature Elimination
• Likelihood Ratio Test
• Bayesian Optimization