Kepler: Robust Learning for Faster Parametric Query Optimization
Summary: Kepler: end-to-end learning-based parametric query optimization that bypasses unreliable cost models. Row Count Evolution perturbs sub-plans; candidates are evaluated by actual executions, and uncertainty-aware ML predicts the fastest plan for PostgreSQL speedups. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Lyric Doshi (Google)
- 2. Vincent Zhuang (Google)
- 3. Gaurav Jain (Google)
- 4. Ryan Marcus (University of Pennsylvania)
- 5. Haoyu Huang (Google)
- 6. Deniz Altınbüken (Google)
- 7. Eugene Brevdo (Google)
- 8. Campbell Fraser (Google)
BibTeX Citation
@inproceedings{doshi_sigmod23,
title = {{Kepler: Robust Learning for Faster Parametric Query Optimization}},
author = {Doshi, Lyric and Zhuang, Vincent and Jain, Gaurav and Marcus, Ryan and Huang, Haoyu and Altınbüken, Deniz and Brevdo, Eugene and Fraser, Campbell},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588963},
url = {https://dl.acm.org/doi/10.1145/3588963},
year = {2023}
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