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)
Incoming Non-self Citations Over Time
Authors
- 1. Roman Heinrich (German National Research Center for Information Technology; Technical University of Darmstadt)
- 2. Manisha Luthra (German National Research Center for Information Technology; Technical University of Darmstadt)
- 3. Johannes Wehrstein (Technical University of Darmstadt)
- 4. Harald Kornmayer (Baden-Wuerttemberg Cooperative State University)
- 5. Carsten Binnig (German National Research Center for Information Technology; Technical University of Darmstadt)
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.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 1,061 | Are We Ready For Learned Cardinality Estimation? | 2021 | VLDB |
| 2 | 465 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB |
| 3 | 3,162 | Efficiently Approximating Selectivity Functions using Low Overhead Regression Models | 2020 | VLDB |
| 4 | 9,215 | Deep Query Optimization | 2019 | SIGMOD |
| 5 | 6,271 | Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective | 2024 | VLDB |
| 6 | 6,921 | Rethinking Learned Cost Models: Why Start from Scratch? | 2023 | SIGMOD |
| 7 | 5,340 | Machine Learning for Databases | 2021 | VLDB |
| 8 | 2,844 | Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction | 2022 | VLDB |
| 9 | 2,762 | Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection | 2022 | VLDB |
| 10 | 11,065 | Learned Cost Models for Query Optimization: From Batch to Streaming Systems | 2025 | VLDB |