QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning
Summary: QTune uses query featurization and a Double-State DDPG actor–critic model to jointly exploit SQL features and database state for configuration tuning. It supports query-, workload-, and cluster-level tuning across three DBMSs, outperforming prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Guoliang Li (Tsinghua University)
- 2. Xuanhe Zhou (Tsinghua University)
- 3. Shifu Li (Huawei)
- 4. Bo Gao (Huawei)
BibTeX Citation
@article{li_vldb19,
title = {{QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning}},
author = {Li, Guoliang and Zhou, Xuanhe and Li, Shifu and Gao, Bo},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {2118--2130},
doi = {10.14778/3352063.3352129},
url = {https://doi.org/10.14778/3352063.3352129},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 33 of 83 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 86 | Automatic Database Management System Tuning Through Large-scale Machine Learning | 2017 | SIGMOD | 0.00035316107 |
| 334 | An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning | 2019 | SIGMOD | 0.00020875082 |
| 347 | Tuning Database Configuration Parameters with iTuned | 2009 | VLDB | 0.00020651582 |
| 465 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.0001803934 |
| 568 | SageDB: A Learned Database System | 2019 | CIDR | 0.0001641553 |
| 781 | Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering | 2002 | VLDB | 0.00014085674 |
| 7,493 | Human-in-the-loop Data Integration | 2017 | VLDB | 5.6046905e-05 |
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