KnobCF: Uncertainty-Aware Knob Tuning

Yu Yan, Junfang Huang, Hongzhi Wang, Jian Geng, Kaixin Zhang, Tao Yu

Abstract: The knob tuning aims to optimize database performance by searching for the most effective knob configuration under a certain workload. Existing works suffer from two significant problems. First, there exist multiple useless evaluations of knob tuning even with diverse searching methods because of the different sensitivities of knobs on a certain workload. Second, the single evaluation of knob configurations may bring overestimation or underestimation because of query performance uncertainty. To solve the above problems, we propose a query uncertainty-aware knob classifier, called ${\sf KnobCF}$, to enhance knob tuning. Our method has three contributions: (1) We propose uncertainty-aware configuration estimation to improve the tuning process. (2) We design a few-shot uncertainty estimator that requires no extra data collection, ensuring high efficiency in practical tasks. (3) We provide a flexible framework that can be integrated into existing knob tuners and DBMSs without modification. Our experiments on four open-source benchmarks demonstrate that our method effectively reduces useless evaluations and improves the tuning results. Especially in TPCC, our method achieves competitive tuning results with only 60% to 70% time consumption compared to the full workload evaluations.

External IDs:doi:10.1109/tkde.2025.3608030