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UDO: Universal Database Optimization using Reinforcement Learning

Summary: UDO applies RL to offline DB tuning across heavy (design) and light (config) parameters, evaluating via actual queries. Cost-based planner amortizes reconfigs and delays heavy-params rewards, enabling staged eval; tested on Postgres/MySQL with TPC-H/C. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h99e903cbd8a89ab1
Venue
VLDB
Year
2021
Pagerank
7.1137504e-05
Overall Rank
3,655 | 75.51%
DOI
10.14778/3484224.3484236
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb21,
        title = {{UDO: Universal Database Optimization using Reinforcement Learning}},
        author = {Wang, Junxiong and Trummer, Immanuel and Basu, Debabrota},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {13},
        pages = {3402--3414},
        doi = {10.14778/3484224.3484236},
        url = {https://doi.org/10.14778/3484224.3484236},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 23 of 23 citing papers.

Rank Citing Paper Year Venue Pagerank
2,227 GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization 2024 VLDB 8.7924681e-05
3,057 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.6865211e-05
5,377 Can Large Language Models Predict Data Correlations from Column Names? 2023 VLDB 6.1396287e-05
5,687 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0201937e-05
6,752 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.6733557e-05
7,092 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.5868637e-05
7,394 ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning 2023 VLDB 5.5202266e-05
7,997 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.3938336e-05
8,026 A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning 2024 VLDB 5.3884724e-05
8,866 Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual 2022 SIGMOD 5.2471343e-05
9,154 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.2050937e-05
9,655 Db2une: Tuning Under Pressure via Deep Learning 2024 VLDB 5.1307018e-05
9,731 Database Gyms 2023 CIDR 5.120066e-05
10,323 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 5.0241309e-05
10,324 Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents 2026 VLDB 5.0241309e-05
10,653 MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration 2026 SIGMOD 4.9605343e-05
10,800 Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink 2026 VLDB 4.9605343e-05
10,818 LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration 2026 VLDB 4.9605343e-05
10,957 MFTune: An Efficient Multi-fidelity Framework for Spark SQL Configuration Tuning 2026 VLDB 4.9605343e-05
10,982 Tuning the Lookahead Distance for PostgreSQL Asynchronous IO 2026 VLDB 4.9605343e-05
11,116 DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning 2026 VLDB 4.9605343e-05
11,790 Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems 2023 VLDB 4.9605343e-05
11,971 Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications 2022 VLDB 4.9605343e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

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

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
411 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.0001874043
1,132 Qd-tree: Learning Data Layouts for Big Data Analytics 2020 SIGMOD 0.00011873466
1,156 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011759683
1,282 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011197535
1,294 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011129451
1,609 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.0001007196
2,189 Leveraging Lock Contention to Improve OLTP Application Performance 2016 VLDB 8.8866479e-05
2,290 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.6813363e-05
2,481 Learning a Partitioning Advisor for Cloud Databases 2020 SIGMOD 8.3862675e-05
3,361 Exact Cardinality Query Optimization for Optimizer Testing 2009 VLDB 7.3621595e-05
3,443 A Demonstration of the OtterTune Automatic Database Management System Tuning Service 2018 VLDB 7.2781031e-05
5,223 Exact Cardinality Query Optimization with Bounded Execution Cost 2019 SIGMOD 6.210331e-05
8,253 Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning 2021 SIGMOD 5.353686e-05
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