Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning

Summary: UDO is a unified offline tuner optimizing txn code variants, indexes, and DB parameters for workloads via reinforcement learning. It differentiates heavy vs light parameters, delays rewards for costly changes, and uses a cost-aware planner to amortize expensive data-structure creation, validated with real queries on Postgres/MySQL (TPC-H/TPC-C). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6036
Venue: SIGMOD
Year: 2021
Pagerank: 4.5663204e-05
Overall Rank: 8,180 | 43.10%
DOI: 10.1145/3448016.3452754

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,240	Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation	2021	VLDB	6.3318228e-05
4,913	UDO: Universal Database Optimization using Reinforcement Learning	2021	VLDB	5.8316231e-05
8,026	ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning	2023	VLDB	4.6030518e-05
8,082	Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems	2022	SIGMOD	4.5905454e-05
8,103	Grep: A Graph Learning Based Database Partitioning System	2023	SIGMOD	4.5852201e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
782	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00016729063
2,020	Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms	2020	VLDB	9.762624e-05
2,513	Leveraging Lock Contention to Improve OLTP Application Performance	2016	VLDB	8.6178149e-05

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