Db2une: Tuning Under Pressure via Deep Learning

Summary: Db2une automatically tunes Db2 knobs by encoding queries with QBERT (transformer-based embeddings) and driving a stability-oriented on-policy deep reinforcement learning agent to balance performance and resource use. Multi-phase, metadata-driven training leverages cost estimates and interpolation to avoid executing queries, enabling scalable tuning of large workloads and outperforming expert and state-of-the-art recommenders. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13776
Venue: VLDB
Year: 2024
Pagerank: 5.2678428e-05
Overall Rank: 9,441 | 35.23%
DOI: 10.14778/3685800.3685811

Incoming Non-self Citations Over Time

Authors

1. Alexander Bianchi (IBM; York University)
2. Andrew Chai (IBM; York University)
3. Vincent Corvinelli (IBM)
4. Parke Godfrey (IBM; York University)
5. Jarek Szlichta (IBM; York University)
6. Calisto Zuzarte (IBM)

BibTeX Citation

@article{bianchi_vldb24,
        title = {{Db2une: Tuning Under Pressure via Deep Learning}},
        author = {Bianchi, Alexander and Chai, Andrew and Corvinelli, Vincent and Godfrey, Parke and Szlichta, Jarek and Zuzarte, Calisto},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {12},
        pages = {3855--3868},
        doi = {10.14778/3685800.3685811},
        url = {https://doi.org/10.14778/3685800.3685811},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,047	SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation	2026	VLDB	5.3251649e-05
10,506	This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!	2026	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
18	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059284255
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
387	AutoAdmin "What-if" Index Analysis Utility	1998	SIGMOD	0.00019442332
465	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.0001803934
498	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00017440583
1,337	DB-BERT: A Database Tuning Tool that "Reads the Manual"	2022	SIGMOD	0.00011117488
1,344	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00011094717
1,548	Automatically Indexing Millions of Databases in Microsoft Azure SQL Database	2019	SIGMOD	0.00010392475
3,116	ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases	2021	SIGMOD	7.7390737e-05
3,173	Why You Should Run TPC-DS:A Workload Analysis	2007	VLDB	7.6664516e-05
3,338	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB	7.5068221e-05
3,587	HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements	2022	SIGMOD	7.2829345e-05
3,926	UDO: Universal Database Optimization using Reinforcement Learning	2021	VLDB	7.0128068e-05
4,671	PreQR: Pre-training Representation for SQL Understanding	2022	SIGMOD	6.5732787e-05
6,938	Guided automated learning for query workload re-optimization	2019	VLDB	5.733869e-05
7,655	Plan Stitch: Harnessing the Best of Many Plans	2018	VLDB	5.5741093e-05

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