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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
hf7d7ee140c7e4899
Venue
SIGMOD
Year
2025
Pagerank
6.1827415e-05
Overall Rank
5,316 | 64.28%
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 12 of 12 citing papers.

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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 0.0023943337
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061067652
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035876108
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021166957
361 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020000855
406 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019050182
462 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017836105
510 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017059914
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016408613
629 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00015428007
1,065 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202293
1,195 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011574218
1,834 Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet 2024 VLDB 9.5349903e-05
2,209 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8360101e-05
2,248 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7567205e-05
2,342 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.6074783e-05
2,518 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.3532841e-05
2,837 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.9495917e-05
2,879 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9126862e-05
3,311 Cloud Analytics Benchmark 2023 VLDB 7.4364696e-05
4,191 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.7425275e-05
4,240 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7064546e-05
5,482 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.1123461e-05
5,630 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.056758e-05
5,835 Pre-training Summarization Models of Structured Datasets for Cardinality Estimation 2022 VLDB 5.9737602e-05
6,298 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8177684e-05
6,408 A Unified Transferable Model for ML-Enhanced DBMS 2022 CIDR 5.7921918e-05
7,021 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.6168049e-05
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