# GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization

arXiv: https://arxiv.org/abs/2311.03157 · HTML: https://mlsanity.com/paper/2311.03157/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2023.

## Built on by (most cited first, 29 of 29)

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- [E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model](https://mlsanity.com/paper/2404.11581/) (2024)
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- [QUITE: A Query Rewrite System Beyond Rules with LLM Agents](https://mlsanity.com/paper/2506.07675/) (2025)
- [A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation](https://mlsanity.com/paper/2410.08801/) (2024)
- [Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation](https://mlsanity.com/paper/2408.02213/) (2024)
- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
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- [In-Context Adaptation to Concept Drift for Learned Database Operations](https://mlsanity.com/paper/2505.04404/) (2025)
- [AI-Driven Research for Databases](https://mlsanity.com/paper/2604.06566/) (2026)
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- [StorageXTuner: An LLM Agent-Driven Automatic Tuning Framework for Heterogeneous Storage Systems](https://mlsanity.com/paper/2510.25017/) (2025)
- [GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered](https://mlsanity.com/paper/2603.02081/) (2026)
- [LLM-Based Test Case Generation in DBMS through Monte Carlo Tree Search](https://mlsanity.com/paper/2603.21530/) (2026)
- [DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents](https://mlsanity.com/paper/2607.22165/) (2026)
- [Couler: Unified Machine Learning Workflow Optimization in Cloud](https://mlsanity.com/paper/2403.07608/) (2024)
- [DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning](https://mlsanity.com/paper/2603.15540/) (2026)
- [TuxBot: Semantic-Aware Online OS Tuning with Large Language Models](https://mlsanity.com/paper/2605.15026/) (2026)
- [A Case for Agentic Tuning: From Documentation to Action in PostgreSQL](https://mlsanity.com/paper/2605.19988/) (2026)
- [AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies](https://mlsanity.com/paper/2608.02569/) (2026)
- [ATTUNE: Agentic Throughput Tuning for LLM Training](https://mlsanity.com/paper/2610.17528/) (2026)

## Abstract (arXiv)

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 Large Language Model (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.