  Database knob tuning is a critical challenge in the database community,
aiming to optimize knob values to enhance database performance for specific
workloads. DBMS often feature hundreds of tunable knobs, posing a significant
challenge for DBAs to recommend optimal configurations. Consequently, many
machine learning-based tuning methods have been developed to automate this
process. Despite the introduction of various optimizers, practical applications
have unveiled a new problem: they typically require numerous workload runs to
achieve satisfactory performance, a process that is both time-consuming and
resource-intensive. This inefficiency largely stems from the optimal
configuration often being substantially different from the default setting,
necessitating multiple iterations during tuning. Recognizing this, we argue
that an effective starting point could significantly reduce redundant
exploration in less efficient areas, thereby potentially speeding up the tuning
process for the optimizers. Based on this assumption, we introduce LLMTune, a
large language model-based configuration generator designed to produce an
initial, high-quality configuration for new workloads. These generated
configurations can then serve as starting points for various base optimizers,
accelerating their tuning processes. To obtain training data for LLMTune's
supervised fine-tuning, we have devised a new automatic data generation
framework capable of efficiently creating a large number of <workload,
configuration> pairs. We have conducted thorough experiments to evaluate
LLMTune's effectiveness with different workloads, such as TPC-H and JOB. In
comparison to leading methods, LLMTune demonstrates a quicker ability to
identify superior configurations. For instance, with the challenging TPC-H
workload, our LLMTune achieves a significant 15.6x speed-up ratio in finding
the best-performing configurations.
