  Knob tuning plays a crucial role in optimizing databases by adjusting knobs
to enhance database performance. However, traditional tuning methods often
follow a Try-Collect-Adjust approach, proving inefficient and
database-specific. Moreover, these methods are often opaque, making it
challenging for DBAs to grasp the underlying decision-making process.
  The emergence of large language models (LLMs) like GPT-4 and Claude-3 has
excelled in complex natural language tasks, yet their potential in database
knob tuning remains largely unexplored. This study harnesses LLMs as
experienced DBAs for knob-tuning tasks with carefully designed prompts. We
identify three key subtasks in the tuning system: knob pruning, model
initialization, and knob recommendation, proposing LLM-driven solutions to
replace conventional methods for each subtask.
  We conduct extensive experiments to compare LLM-driven approaches against
traditional methods across the subtasks to evaluate LLMs' efficacy in the knob
tuning domain. Furthermore, we explore the adaptability of LLM-based solutions
in diverse evaluation settings, encompassing new benchmarks, database engines,
and hardware environments. Our findings reveal that LLMs not only match or
surpass traditional methods but also exhibit notable interpretability by
generating responses in a coherent ``chain-of-thought'' manner. We further
observe that LLMs exhibit remarkable generalizability through simple
adjustments in prompts, eliminating the necessity for additional training or
extensive code modifications.
  Drawing insights from our experimental findings, we identify several
opportunities for future research aimed at advancing the utilization of LLMs in
the realm of database management.
