MixTuner: Database-Kernel Co-Tuning via Context Transfer and Staged Search

Automatic tuning improves database performance by replacing manual trial and error with configuration search and workload measurements. Database settings govern resource use, while operating-system kernel settings determine the mechanisms available to support it. Joint tuning can align database resource use with kernel capabilities that a single-layer search leaves fixed. Selecting these settings together requires connecting differently organized configuration knowledge and managing the cost of evaluating configuration pairs. We introduce Mixtuner, a database–kernel co-tuning framework based on context transfer and staged search. A large language model first selects database knobs and suggests numerical ranges from configuration documentation. The selected knob information and functional groups then guide Linux Kconfig menu filtering and option recommendations. After the resulting kernel is built and deployed, coarse-to-fine Bayesian optimization selects database values through measurements on that fixed kernel. The grouped context connects the configuration hierarchies, while the staged schedule shares kernel preparation across repeated database trials. Across 36 MySQL and PostgreSQL TPC-C and TPC-H scenarios, Mixtuner improves over the officially recommended configurations in 35. On MySQL TPC-H, Mixtuner also achieves lower P95 latency than the state-of-the-art database-only and kernel-only baselines.