GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints

Summary: GenJoin treats optimization as a conditional generative plan-to-plan task, learning from random subplan hints to shrink the search space. Outperforms PostgreSQL and state-of-the-art methods on two real-world benchmarks across diverse workloads, with rigorous ML evaluation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 7512
Venue: SIGMOD
Year: 2026
Pagerank: 5.093636e-05
Overall Rank: 10,316 | 29.23%
DOI: 10.1145/3749165

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Pavel Sulimov (Zurich University of Applied Sciences)
2. Claude Lehmann (Zurich University of Applied Sciences)
3. Kurt Stockinger (Zurich University of Applied Sciences)

BibTeX Citation

@inproceedings{sulimov_sigmod26,
        title = {{GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints}},
        author = {Sulimov, Pavel and Lehmann, Claude and Stockinger, Kurt},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3749165},
        url = {https://dl.acm.org/doi/10.1145/3749165},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 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
53	Eddies: Continuously Adaptive Query Processing	2000	SIGMOD	0.00041071971
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035838391
154	Neo: A Learned Query Optimizer	2019	VLDB	0.00028726181
323	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021264788
378	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019638121
513	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00017190574
1,241	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	0.00011521639
1,988	FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation	2021	VLDB	9.3501502e-05
2,355	QueryFormer: A Tree Transformer Model for Query Plan Representation	2022	VLDB	8.7022189e-05
2,420	Lero: A Learning-to-Rank Query Optimizer	2023	VLDB	8.605257e-05
2,620	Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation	2021	VLDB	8.3363963e-05
2,762	Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection	2022	VLDB	8.1539867e-05
2,844	Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction	2022	VLDB	8.0608767e-05
2,991	FactorJoin: A New Cardinality Estimation Framework for Join Queries	2023	SIGMOD	7.8880723e-05
3,051	Towards a Hands-Free Query Optimizer through Deep Learning	2019	CIDR	7.8121919e-05
3,516	LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans	2023	VLDB	7.3524442e-05
4,434	LEON: A New Framework for ML-Aided Query Optimization	2023	VLDB	6.7079088e-05
4,929	AutoSteer: Learned Query Optimization for Any SQL Database	2023	VLDB	6.4423294e-05
5,277	FASTgres: Making Learned Query Optimizer Hinting Effective	2023	VLDB	6.2859099e-05
5,573	Eraser: Eliminating Performance Regression on Learned Query Optimizer	2024	VLDB	6.1682747e-05
5,712	Sample-Efficient Cardinality Estimation Using Geometric Deep Learning	2024	VLDB	6.1123894e-05
6,271	Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective	2024	VLDB	5.9326197e-05
6,462	PilotScope: Steering Databases with Machine Learning Drivers	2024	VLDB	5.8717744e-05
8,040	PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!	2021	VLDB	5.5018396e-05

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