RankPQO: Learning-to-Rank for Parametric Query Optimization
Summary: RankPQO: hybrid enumeration altering cardinalities and join orders to generate compact, diverse plan sets for parametric queries. Uses learning-to-rank (vs. latency regression) to robustly pick plans under varying bindings; in PostgreSQL: up to 2.57x optimizer speedup and 1.36x over baselines. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Songsong Mo (Nanyang Technological University)
- 2. Yue Zhao (Nanyang Technological University)
- 3. Zhifeng Bao (RMIT University)
- 4. Quanqing Xu (OceanBase, Ant Group)
- 5. Chuanhui Yang (OceanBase, Ant Group)
- 6. Gao Cong (Nanyang Technological University)
BibTeX Citation
@article{mo_vldb25,
title = {{RankPQO: Learning-to-Rank for Parametric Query Optimization}},
author = {Mo, Songsong and Zhao, Yue and Bao, Zhifeng and Xu, Quanqing and Yang, Chuanhui and Cong, Gao},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {3},
pages = {863--875},
doi = {10.14778/3712221.3712248},
url = {https://doi.org/10.14778/3712221.3712248},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,720 | APQO: An Adaptive Framework for Parametric Query Optimization | 2026 | SIGMOD | 5.1349531e-05 |
| 10,924 | TATA: An Efficient Framework for Task Transfer in Query Plan Representation | 2026 | VLDB | 4.9793485e-05 |
| 10,938 | Towards Industrial-Scale Parametric Query Optimization | 2026 | VLDB | 4.9793485e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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