GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization (2311.03157v2)

Abstract: Modern database management systems (DBMS) expose hundreds of configurable knobs to control system behaviours. Determining the appropriate values for these knobs to improve DBMS performance is a long-standing problem in the database community. As there is an increasing number of knobs to tune and each knob could be in continuous or categorical values, manual tuning becomes impractical. Recently, automatic tuning systems using machine learning methods have shown great potentials. However, existing approaches still incur significant tuning costs or only yield sub-optimal performance. This is because they either ignore the extensive domain knowledge available (e.g., DBMS manuals and forum discussions) and only rely on the runtime feedback of benchmark evaluations to guide the optimization, or they utilize the domain knowledge in a limited way. Hence, we propose GPTuner, a manual-reading database tuning system. Firstly, we develop a LLM-based pipeline to collect and refine heterogeneous knowledge, and propose a prompt ensemble algorithm to unify a structured view of the refined knowledge. Secondly, using the structured knowledge, we (1) design a workload-aware and training-free knob selection strategy, (2) develop a search space optimization technique considering the value range of each knob, and (3) propose a Coarse-to-Fine Bayesian Optimization Framework to explore the optimized space. Finally, we evaluate GPTuner under different benchmarks (TPC-C and TPC-H), metrics (throughput and latency) as well as DBMS (PostgreSQL and MySQL). Compared to the state-of-the-art approaches, GPTuner identifies better configurations in 16x less time on average. Moreover, GPTuner achieves up to 30% performance improvement (higher throughput or lower latency) over the best-performing alternative.

• The paper proposes GPTuner, a system that leverages GPT-based manual reading to incorporate domain knowledge into Bayesian optimization for DBMS tuning.
• It employs a coarse-to-fine optimization framework and workload-aware knob selection to efficiently refine configuration parameters.
• Empirical evaluations demonstrate that GPTuner outperforms state-of-the-art methods, achieving up to 16 times faster tuning with improved throughput and latency.

Analyzing Database Tuning with GPTuner: Optimizing DBMS Performance through GPT-Guided Bayesian Optimization

The paper "GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization" explores an innovative approach to the longstanding problem of database management system (DBMS) configuration. The tuning of hundreds of adjustable parameters, or "knobs," within DBMSs like PostgreSQL and MySQL has traditionally been a significant challenge due to the sheer volume of options and their varied nature (continuous or categorical values). As manual tuning becomes impractical, particularly in cloud environments with diverse configurations, there’s a pressing need for effective automatic tuning systems.

The authors introduce GPTuner, a system leveraging advanced machine learning techniques, particularly LLMs like GPT-4, to harness existing domain knowledge embedded within DBMS manuals and forums. This integration into Bayesian Optimization processes aims to significantly reduce configuration time and improve system performance metrics such as throughput and latency.

Key Contributions and Methods

1. LLM-Based Knowledge Integration: The paper presents a novel LLM-based pipeline to extract and refine heterogeneous knowledge from various sources, forming what they term a "Tuning Lake." This collection of structured domain knowledge is crucial for guiding the optimization process.
2. Workload-Aware Knob Selection: By employing LLM analysis, the GPTuner system enhances the selection process of DBMS knobs. It factors in system-level, workload-level, query-level, and knob-level influences, enabling a more targeted tuning approach.
3. Search Space Optimization: The solution optimizes search spaces based on domain knowledge, incorporating advanced strategies such as Region Discard, Tiny Feasible Space, and Virtual Knob Extension to focus on promising value ranges and handle special cases effectively.
4. Coarse-to-Fine Bayesian Optimization Framework: GPTuner introduces a two-stage optimization approach that initially explores a coarse-grained discrete space highly informed by domain knowledge before exploring a fine-grained, more exhaustive search. This framework is designed to streamline the tuning process and deliver high-performance configurations within fewer iterations compared to conventional methods.
5. Empirical Evaluation and Performance: The empirical validation against state-of-the-art methods like DB-BERT, SMAC, and RL-based approaches demonstrates GPTuner's superior performance. It identifies optimal configurations up to 16 times faster and achieves considerable improvements in DBMS performance metrics.

Evaluation and Implications

GPTuner outperforms existing techniques by efficiently leveraging domain-informed strategies, reducing the burden of exhaustive search in high-dimensional spaces traditionally required in DBMS tuning. This results in significant reductions in computational costs and time, making it a robust tool for scenarios involving complex database architectures and cloud environments.

The research opens new avenues for integrating sophisticated NLP models within system optimization processes, suggesting that future developments could further refine LLM integration to enhance the breadth and accuracy of machine-inferred domain knowledge. This could lead to more advanced self-tuning systems capable of adapting to evolving application requirements without extensive manual intervention.

In conclusion, GPTuner represents a substantive advancement in the automated configuration of DBMSs, offering tangible improvements in performance tuning and paving the way for broader applications of LLMs within database management and other complex systems. As AI and machine learning technologies progress, the principles demonstrated by GPTuner could be extended to other domains requiring efficient, knowledge-driven optimization solutions.