Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization

Summary: Reqo: a learning-based query optimization cost model that jointly attacks plan generation, plan representation, and plan selection. Key novelty: explainable subgraph attribution for plan hints, Bi-GNN+GRU tree encoding, and uncertainty-aware learning-to-rank for robust cost estimation. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID: 7706
Venue: SIGMOD
Year: 2026
Pagerank: 5.093636e-05
Overall Rank: 10,492 | 28.02%
DOI: 10.1145/3786689

Incoming Non-self Citations Over Time

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

Authors

1. Baoming Chang (University of Ottawa)
2. Amin Kamali (University of Ottawa)
3. Verena Kantere (University of Ottawa)

BibTeX Citation

@inproceedings{chang_sigmod26,
        title = {{Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization}},
        author = {Chang, Baoming and Kamali, Amin and Kantere, Verena},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786689},
        url = {https://dl.acm.org/doi/10.1145/3786689},
        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 22 of 22 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
154	Neo: A Learned Query Optimizer	2019	VLDB	0.00028726181
378	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019638121
388	Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors	2009	VLDB	0.00019410042
465	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.0001803934
1,122	Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation	2022	VLDB	0.0001209124
1,832	DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems	2021	VLDB	9.6607418e-05
1,876	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.5717543e-05
1,987	Plan Bouquets: Query Processing without Selectivity Estimation	2014	SIGMOD	9.3517129e-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
3,173	Why You Should Run TPC-DS:A Workload Analysis	2007	VLDB	7.6664516e-05
4,368	Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process	2022	SIGMOD	6.7393882e-05
4,434	LEON: A New Framework for ML-Aided Query Optimization	2023	VLDB	6.7079088e-05
4,470	Kepler: Robust Learning for Faster Parametric Query Optimization	2023	SIGMOD	6.6817353e-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
6,088	How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks	2025	SIGMOD	5.9813965e-05
10,881	Robust Plan Evaluation based on Approximate Probabilistic Machine Learning	2025	VLDB	5.093636e-05

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