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

TL;DR

DOT introduces a novel online tuning algorithm for DBMSs that dynamically selects influential parameters and optimizes configurations without warm-up phases, outperforming existing methods in efficiency and effectiveness.

Contribution

The paper presents DOT, a new DBMS tuning approach combining dynamic knob selection with Bayesian optimization, reducing overhead and eliminating the need for warm-up phases.

Findings

DOT matches or exceeds state-of-the-art performance.

Significantly reduces tuning overhead.

Effectively identifies important tuning parameters.

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.