Rabbit: Retrieval-Augmented Generation Enables Better Automatic Database Knob Tuning

The large language model (LLM)-based knob tuning method has attracted considerable attention due to its excellent in-context learning ability and generalizability. However, the existing LLM-based tuning methods do not effectively harmonize multi-source external knowledge, leading to missed opportunities for enhanced knob tuning. In light of this, we propose Rabbit, a novel approach that leverages Retrieval-augmented generation to enhance database knob tuning tools, which seamlessly integrates structured historical tuning experience with graph-encoded static knowledge. First, we introduce an experience-driven knob selection strategy, enhanced by dependency-aware external knowledge integration, to systematically select key knobs. Second, we develop a cutting-edge multi-agent knob domain pruning method, which ensures the reduced search space remains compact yet effective. Finally, we leverage the few-shot capabilities of LLMs to act as surrogate models, enabling rapid exploration of the pruned search space, followed by incremental optimization that expands the search space using historical insights. Moreover, we also design an adaptive strategy to transition between these two search spaces, striking an optimal balance between exploration and exploitation. Extensive experiments on well-established bench-marks demonstrate that Rabbit outperforms the state-of-the-art methods in both effectiveness and efficiency, pointing to a new paradigm for this area.