  Vector data management systems (VDMSs) have become an indispensable
cornerstone in large-scale information retrieval and machine learning systems
like large language models. To enhance the efficiency and flexibility of
similarity search, VDMS exposes many tunable index parameters and system
parameters for users to specify. However, due to the inherent characteristics
of VDMS, automatic performance tuning for VDMS faces several critical
challenges, which cannot be well addressed by the existing auto-tuning methods.
In this paper, we introduce VDTuner, a learning-based automatic performance
tuning framework for VDMS, leveraging multi-objective Bayesian optimization.
VDTuner overcomes the challenges associated with VDMS by efficiently exploring
a complex multi-dimensional parameter space without requiring any prior
knowledge. Moreover, it is able to achieve a good balance between search speed
and recall rate, delivering an optimal configuration. Extensive evaluations
demonstrate that VDTuner can markedly improve VDMS performance (14.12% in
search speed and 186.38% in recall rate) compared with default setting, and is
more efficient compared with state-of-the-art baselines (up to 3.57 times
faster in terms of tuning time). In addition, VDTuner is scalable to specific
user preference and cost-aware optimization objective. VDTuner is available
online at https://github.com/tiannuo-yang/VDTuner.
