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Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges
Summary: Tutorial-style survey of DRL-based join order selection: outlines JOS fundamentals, limits of traditional optimizers, DRL preliminaries, and a taxonomy/analysis of recent DRL methods with their strengths and weaknesses. Includes two open-source demos, empirical comparisons, and a roadmap of research challenges and open problems to guide practical DRL JOS development.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13220
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 4.9004921e-05
- Overall Rank
- 6,862 | 52.31%
- DOI
-
10.14778/3611540.3611576
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
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 |
| 1 |
Access Path Selection in a Relational Database Management System |
1979 |
SIGMOD |
0.0040465394 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 979 |
Rapid Bushy Join-order Optimization with Cartesian Products |
1996 |
SIGMOD |
0.00014871114 |
| 1,344 |
Dynamic Programming Strikes Back |
2008 |
SIGMOD |
0.00012477274 |
| 1,621 |
Adaptive Optimization of Very Large Join Queries |
2018 |
SIGMOD |
0.00011105663 |
| 1,825 |
Analysis of Two Existing and One New Dynamic Programming Algorithm for the Generation of Optimal Bushy Join Trees without Cross Products |
2006 |
VLDB |
0.00010392367 |
| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,222 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
9.2598438e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 3,466 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.0645718e-05 |
| 3,729 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8078013e-05 |
| 4,464 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1552798e-05 |
| 5,787 |
Machine Learning for Databases |
2021 |
VLDB |
5.3256401e-05 |
| 6,060 |
Efficient Massively Parallel Join Optimization for Large Queries* |
2022 |
SIGMOD |
5.2271244e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 4,734 |
Query Simplification: Graceful Degradation for Join-Order Optimization |
2009 |
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5.9553181e-05 |
| 2,222 |
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2019 |
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| 1,621 |
Adaptive Optimization of Very Large Join Queries |
2018 |
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| 4,464 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
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| 11,710 |
Improving Join Reorderability with Compensation Operators |
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SIGMOD |
4.1905499e-05 |
| 5,787 |
Machine Learning for Databases |
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VLDB |
5.3256401e-05 |
| 3,658 |
Towards a Hands-Free Query Optimizer through Deep Learning |
2019 |
CIDR |
6.8700949e-05 |
| 8,149 |
Efficiently Computing Join Orders with Heuristic Search |
2023 |
SIGMOD |
4.5715614e-05 |
| 8,027 |
ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning |
2023 |
VLDB |
4.5986382e-05 |
| 11,300 |
Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning |
2023 |
VLDB |
4.1905499e-05 |