Back to papers
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
- 12550
- Venue
- VLDB
- Year
- 2021
- Pagerank
- 5.9604983e-05
- Overall Rank
- 4,730 | 67.13%
- DOI
-
10.14778/3484224.3484236
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 21 of 21 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,105 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
7.5567226e-05 |
| 4,180 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
6.3725334e-05 |
| 5,506 |
Can Large Language Models Predict Data Correlations from Column Names? |
2023 |
VLDB |
5.4711611e-05 |
| 5,925 |
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning |
2023 |
VLDB |
5.2669029e-05 |
| 6,376 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.0861082e-05 |
| 7,676 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
4.6770108e-05 |
| 8,003 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6049527e-05 |
| 8,027 |
ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning |
2023 |
VLDB |
4.5986382e-05 |
| 8,452 |
Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual |
2022 |
SIGMOD |
4.5023516e-05 |
| 8,585 |
A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning |
2024 |
VLDB |
4.4856045e-05 |
| 9,012 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
4.4059413e-05 |
| 9,358 |
Db2une: Tuning Under Pressure via Deep Learning |
2024 |
VLDB |
4.3480651e-05 |
| 9,469 |
Database Gyms |
2023 |
CIDR |
4.3304872e-05 |
| 10,093 |
MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,217 |
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 |
4.1905499e-05 |
| 10,247 |
Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents |
2026 |
VLDB |
4.1905499e-05 |
| 10,259 |
Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink |
2026 |
VLDB |
4.1905499e-05 |
| 10,278 |
LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration |
2026 |
VLDB |
4.1905499e-05 |
| 10,313 |
DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning |
2026 |
VLDB |
4.1905499e-05 |
| 11,227 |
Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems |
2023 |
VLDB |
4.1905499e-05 |
| 11,418 |
Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications |
2022 |
VLDB |
4.1905499e-05 |
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 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 1,608 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011169837 |
| 1,727 |
QuickSel: Quick Selectivity Learning with Mixture Models |
2020 |
SIGMOD |
0.00010731889 |
| 1,816 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010438512 |
| 1,856 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010319105 |
| 2,222 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
9.2598438e-05 |
| 2,498 |
Leveraging Lock Contention to Improve OLTP Application Performance |
2016 |
VLDB |
8.6408169e-05 |
| 3,066 |
Learning a Partitioning Advisor for Cloud Databases |
2020 |
SIGMOD |
7.6255556e-05 |
| 3,144 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4844943e-05 |
| 3,905 |
A Demonstration of the OtterTune Automatic Database Management System Tuning Service |
2018 |
VLDB |
6.6430079e-05 |
| 3,943 |
Exact Cardinality Query Optimization for Optimizer Testing |
2009 |
VLDB |
6.6067351e-05 |
| 5,696 |
Exact Cardinality Query Optimization with Bounded Execution Cost |
2019 |
SIGMOD |
5.367449e-05 |
| 8,180 |
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning |
2021 |
SIGMOD |
4.5627116e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 6,376 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.0861082e-05 |
| 9,735 |
UDAO: A Next-Generation Unified Data Analytics Optimizer |
2019 |
VLDB |
4.2901665e-05 |
| 5,289 |
LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning |
2022 |
SIGMOD |
5.5790771e-05 |
| 5,834 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
5.3082111e-05 |
| 7,009 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
4.8597992e-05 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 11,352 |
DeepO: A Learned Query Optimizer |
2022 |
SIGMOD |
4.1905499e-05 |
| 4,930 |
User-Defined Operators: Efficiently Integrating Custom Algorithms into Modern Databases |
2022 |
VLDB |
5.8171482e-05 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 8,180 |
Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning |
2021 |
SIGMOD |
4.5627116e-05 |