# WAter: A Workload-Adaptive Knob Tuning System based on Workload Compression

arXiv: https://arxiv.org/abs/2603.28809 · HTML: https://mlsanity.com/paper/2603.28809/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 67 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [LlamaTune: Sample-Efficient DBMS Configuration Tuning](https://mlsanity.com/paper/2203.05128/) (2022)
- [Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics](https://mlsanity.com/paper/1801.05613/) (2018)
- [QUITE: A Query Rewrite System Beyond Rules with LLM Agents](https://mlsanity.com/paper/2506.07675/) (2025)
- [SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads](https://mlsanity.com/paper/2507.06192/) (2025)
- [ResQ: Realistic Performance-Aware Query Generation](https://mlsanity.com/paper/2602.02999/) (2026)

## Abstract (arXiv)

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.