# λ-Tune: Harnessing Large Language Models for Automated Database System Tuning

arXiv: https://arxiv.org/abs/2411.03500 · HTML: https://mlsanity.com/paper/2411.03500/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2024.

## Built on by (most cited first, 19 of 19)

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## Abstract (arXiv)

We introduce λ-Tune, a framework that leverages Large Language Models (LLMs) for automated database system tuning. The design of λ-Tune is motivated by the capabilities of the latest generation of LLMs. Different from prior work, leveraging LLMs to extract tuning hints for single parameters, λ-Tune generates entire configuration scripts, based on a large input document, describing the tuning context. λ-Tune generates alternative configurations, using a principled approach to identify the best configuration, out of a small set of candidates. In doing so, it minimizes reconfiguration overheads and ensures that evaluation costs are bounded as a function of the optimal run time. By treating prompt generation as a cost-based optimization problem, λ-Tune conveys the most relevant context to the LLM while bounding the number of input tokens and, therefore, monetary fees for LLM invocations. We compare λ-Tune to various baselines, using multiple benchmarks and PostgreSQL and MySQL as target systems for tuning, showing that λ-Tune is significantly more robust than prior approaches.