Selecting appropriate values for the configurable parameters of Database Management Systems (DBMS) to improve performance is a significant challenge. Recent machine learning (ML)-based tuning systems have shown strong potential, but their practical adoption is often limited by the high tuning cost. This cost arises from two main factors: (1) the system needs to evaluate a large number of configurations to identify a satisfactory one, and (2) for each configuration, the system must execute the entire target workload on the DBMS, which is both time-consuming. Existing studies have primarily addressed the first factor by improving sample efficiency, that is, by reducing the number of configurations evaluated. However, the second factor, improving runtime efficiency by reducing the time required for each evaluation, has received limited attention and remains an underexplored direction. We develop WAter, a runtime-efficient and workload-adaptive tuning system that finds near-optimal configurations at a fraction of the tuning cost compared with state-of-the-art methods. We divide the tuning process into multiple time slices and evaluate only a small subset of queries from the workload in each slice. Different subsets are evaluated across slices, and a runtime profile is used to dynamically identify more representative subsets for evaluation in subsequent slices. At the end of each time slice, the most promising configurations are evaluated on the original workload to measure their actual performance. Evaluations demonstrate that WAter identifies the best-performing configurations with up to 73.5% less tuning time and achieves up to 16.2% higher performance than the best-performing alternative.
Figures & tables
Figure 1. Tuning Time Breakdown (Percentage)
Figure 2. Overview of Knob Tuning Paradigm
Target Workload
Comp. Unit
Strategy
WAter
OLAP
Query
Dynamic
SCompression
OLTP
Time slice
Static
Table 1. Main Difference between WAter and SCompression
Figure 3. Tuning Subsets VS Tuning the Original Workload
Figure 4. Overview of the Components in the WAter System
Figure 5. WAter Tuning Workflow
θ1
θ2
θ3
q1
H[q1,θ1]
H[q1,θ2]
H[q1,θ3]
q2
H[q2,θ1]
H[q2,θ3]
q3
H[q3,θ1]
q4
H[q4,θ1]
H[q4,θ2]
H[q4,θ3]
Table 2. A Toy Example of History
Workload
Queries
Templates
Tables
Columns
TPC-DS* (sf=1)
88
88
24
237
JOB (5.2GB)
113
113
21
38
TPC-H (sf=10)
22
22
8
55
TPC-H×10 (sf=10)
220
22
8
55
Table 3. Summary of Workloads
Figure 6. Performance on different benchmarks (bottom-left is better)
Figure 7. Performance on different benchmarks (SMAC-based) (bottom-left is better)
Figure 8. Performance under different scale factors
Figure 9. Performance on different machine
Figure 13
Figure 12. Robustness under under concurrent and LLM-generated workloads
Database Management Systems (DBMS) are crucial for efficient data management and access control, but their administration remains challenging for Database Administrators (DBAs). Tuning, in particular, is known to be difficult. Modern systems have many tuning parameters, but only a subset significantly impacts performance. Focusing on these influential parameters reduces the search space and optimizes performance. Current methods rely on costly warm-up phases and human expertise to identify important tuning parameters. In this paper, we present DOT, a dynamic knob selection and online sampling DBMS tuning algorithm. DOT uses Recursive Feature Elimination with Cross-Validation (RFECV) to prune low-importance tuning parameters and a Likelihood Ratio Test (LRT) strategy to balance exploration and exploitation. For parameter search, DOT uses a Bayesian Optimization (BO) algorithm to optimize configurations on-the-fly, eliminating the need for warm-up phases or prior knowledge (although existing knowledge can be incorporated). Experiments show that DOT achieves matching or outperforming performance compared to state-of-the-art tuners while substantially reducing tuning overhead.
Yifan Wang, Debabrota Basu, Pierre Bourhis +2
Orange / Inria / Univ. Lille, France France · Inria / Univ. Lille / CNRS, France France · CNRS / Univ. Lille / Inria, France France +2
Documentation has long guided computer system tuning by distilling expert knowledge into per-parameter recommendations. Yet such guides capture only what experts conclude, discarding how they reason. This fundamental gap manifests in three concrete deficiencies: documentation grows stale as software evolves, fails under heterogeneous workloads, and ignores inter-parameter dependencies. We propose shifting from static documentation to dynamic action for system tuning. We introduce PerfEvolve, which translates expert tuning methodologies into executable skills that equip LLM-based agents to perform version-consistency verification, workload-specific profiling, and multi-parameter joint optimization. Evaluated on PostgreSQL under TPC-C and TPC-H benchmarks, PerfEvolve outperforms state-of-the-art documentation-driven tuning baselines by up to 35.2%. The tool is available at https://github.com/ISCAS-OSLab/PerfEvolve.
Hongyu Lin, Mingyu Li, Weichen Zhang +4
Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Key Laboratory of System Software (Chinese Academy of Sciences) +3
Benchmark workloads are extremely important to the database management research community, especially as more machine learning components are integrated into database systems. Here, we propose a Bayesian optimization technique to automatically search for difficult benchmark queries, significantly reducing the amount of manual effort usually required. In preliminary experiments, we show that our approach can generate queries with more than double the optimization headroom compared to existing benchmarks.