DBScholar

Back to papers

How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks

Summary: Systematic evaluation of Learned Cost Models for query optimization across main tasks: join ordering, access path, and operator choice. Seven LCMs vs traditional models; surprisingly, traditional cost models often beat LCMs, guiding future work. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7295
Venue
SIGMOD
Year
2025
Pagerank
5.9813965e-05
Overall Rank
6,088 | 58.24%
DOI
10.1145/3725309

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{heinrich_sigmod25,
        title = {{How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks}},
        author = {Heinrich, Roman and Luthra, Manisha and Wehrstein, Johannes and Kornmayer, Harald and Binnig, Carsten},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725309},
        url = {https://dl.acm.org/doi/10.1145/3725309},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 28 of 28 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 0.0024089429
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
401 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019092557
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
624 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00015683402
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
2,355 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7022189e-05
2,408 Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet 2024 VLDB 8.6154404e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,543 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.4445934e-05
2,723 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.2049453e-05
2,812 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0979597e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
3,838 Cloud Analytics Benchmark 2023 VLDB 7.0823175e-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
5,712 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.1123894e-05
5,767 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.0945741e-05
5,792 Pre-training Summarization Models of Structured Datasets for Cardinality Estimation 2022 VLDB 6.0871213e-05
6,271 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.9326197e-05
6,357 A Unified Transferable Model for ML-Enhanced DBMS 2022 CIDR 5.9020843e-05
6,921 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.7388557e-05
Previous Page 1 / 1 Next

Semantically Similar Papers