Database knob tuning is a critical challenge in the database community,
aiming to optimize knob values to enhance database performance for specific
workloads. DBMS often feature hundreds of tunable knobs, posing a significant
challenge for DBAs to recommend optimal configurations. Consequently, many
machine learning-based tuning methods have been developed to automate this
process. Despite the introduction of various optimizers, practical applications
have unveiled a new problem: they typically require numerous workload runs to
achieve satisfactory performance, a process that is both time-consuming and
resource-intensive. This inefficiency largely stems from the optimal
configuration often being substantially different from the default setting,
necessitating multiple iterations during tuning. Recognizing this, we argue
that an effective starting point could significantly reduce redundant
exploration in less efficient areas, thereby potentially speeding up the tuning
process for the optimizers. Based on this assumption, we introduce LLMTune, a
large language model-based configuration generator designed to produce an
initial, high-quality configuration for new workloads. These generated
configurations can then serve as starting points for various base optimizers,
accelerating their tuning processes. To obtain training data for LLMTune's
supervised fine-tuning, we have devised a new automatic data generation
framework capable of efficiently creating a large number of <workload,
configuration> pairs. We have conducted thorough experiments to evaluate
LLMTune's effectiveness with different workloads, such as TPC-H and JOB. In
comparison to leading methods, LLMTune demonstrates a quicker ability to
identify superior configurations. For instance, with the challenging TPC-H
workload, our LLMTune achieves a significant 15.6x speed-up ratio in finding
the best-performing configurations.

基于大型语言模型的配置生成器 LLMTune 加速了优化器的调优过程，通过提供高质量的起始配置，避免了对不高效区域的冗余探索，从而在发现最佳配置方面取得显著的加速效果。

LLMTune：利用大型语言模型加速数据库参数调优

LLMTune: Accelerate Database Knob Tuning with Large Language Models

Black-box optimization (BBO) has a broad range of applications, including
automatic machine learning, experimental design, and database knob tuning.
However, users still face challenges when applying BBO methods to their
problems at hand with existing software packages in terms of applicability,
performance, and efficiency. This paper presents OpenBox, an open-source BBO
toolkit with improved usability. It implements user-friendly inferfaces and
visualization for users to define and manage their tasks. The modular design
behind OpenBox facilitates its flexible deployment in existing systems.
Experimental results demonstrate the effectiveness and efficiency of OpenBox
over existing systems. The source code of OpenBox is available at
this https URL

本文介绍了 OpenBox，这是一种黑盒优化工具包，适用于自动机器学习、实验设计和数据库参数调整等应用。OpenBox 工具包的模块化设计使其灵活地部署在现有系统中，并且它实现了易用的界面和可视化，实验结果表明 OpenBox 工具包的效率更高于现有系统。