cs.DBOct 7, 2026

CATune: Structural Constraint-Aware Bayesian Optimization for DBMS Configuration Tuning

Authors: Fangping Lan, Qi Zhang, Eduard Dragut

Organizations: Temple University Philadelphia, PA, USA

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Abstract

Modern DBMSs expose hundreds of configuration knobs, resulting in a high-dimensional and heterogeneous search space that makes automated tuning costly. Existing ML-based tuning systems typically treat the configuration domain as box-constrained and rely on workload feedback to implicitly capture inter-knob relationships. However, DBMS documentation specifies deterministic knob dependency constraints, particularly ordering constraints, that characterize structurally valid regions of the configuration space. We present CATune, a constraint-aware Bayesian optimization (BO) framework that models deterministic inter-knob ordering constraints as structural components of the search domain. Instead of learning feasibility boundaries through sampled violations, CATune performs optimization within a constraint-consistent subspace. We develop a topology-aware sampling strategy that respects dependency structure during exploration and avoids the inefficiencies of post-hoc constraint handling. To enable automated constraint discovery, we further design a precision-first extraction pipeline that combines LLM-based parsing with reliability safeguards to mitigate hallucinated dependencies. Experiments on PostgreSQL and MySQL using TPC-C and TPC-H workloads show that CATune substantially improves both sample efficiency and final tuning quality across surrogate models and BO frameworks. Under default ranges, CATune reaches the baseline optimum up to 12.5x faster and improves throughput by up to 63.37%. The improvements persist under knowledge-guided reduced ranges and alternative optimization implementations. These results demonstrate that explicitly modeling system-defined deterministic ordering constraints enhances optimization robustness and system stability.

Figures & tables

Knobsuperuser_reserved_connections
DescriptionDetermines the number of connection … The value must be less than max_connections …
Constraintsuperuser_reserved_connections << max_connections
Knobmax_parallel_workers
Description… note that a setting for this value which is higher than max_worker_processes will have no effect , since …
Constraintmax_parallel_workers ≤\leq max_worker_processes
Knobmax_parallel_workers_per_gather
Table 1 . Three examples of knob dependency constraints found in PostgreSQL V13 Documentation
Figure 1 . (a) The preliminary tuning performance improvement on the TPC-C workload using 10 knobs with/without constraint-aware tuning; (b) The feasible (green) and infeasible (red) samples/region during tuning based on the ordering constraint min_wal_size << max_wal_size .
Figure 2 . System overview of CATune . CATune consists of (1) a precision-first extraction pipeline that automatically derives ordering constraints from DBMS manuals, and (2) a constraint-aware Bayesian optimization engine that explicitly integrates these structural dependencies during both exploration and exploitation.
Figure 3 . End-to-end illustration of the exploration phase. Given four ordering constraints, CATune constructs a topology graph, harmonizes heterogeneous units, and applies component-wise normalization. Topology-aware sampling then follows the dependency order to generate feasible configurations with dynamically constrained ranges, after which sampled values are mapped back to their original knob units.
Figure 5
Figure 6
MethodReturnedGeneratedInvalidTime (s)
No Rules100100990.026
Reject10015,99215,8926.841
Repair10010099*0.031
Topo10010000.089
Table 2 . Sampling efficiency for PostgreSQL tuning over 100 requested samples (Returned). Generated is the number of configurations generated, Invalid the number violating ≥1\geq 1 rule, and Time (s) the time to obtain 100 valid configurations. ∗ denotes repaired configurations.
Figure 10 . Effect of different employment strategies: proactive filtering ( CATune ) vs. passive penalization (MultiObj).

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