Towards Industrial-Scale Parametric Query Optimization
Summary: Industrial-scale PQO via representation-based template clustering and hierarchical model sharing, avoiding per-template models while retaining plan accuracy. KL-divergence-driven online fine-tuning and plan updates handle shifting parameter distributions, validated on OceanBase workloads. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Songsong Mo (Nanyang Technological University)
- 2. Quanqing Xu (OceanBase, Ant Group)
- 3. Xuchen Ding (Nanyang Technological University)
- 4. Yue Zhao (Nanyang Technological University)
- 5. Zhifeng Bao (University of Queensland)
- 6. Chuanhui Yang (OceanBase, Ant Group)
- 7. Gao Cong (Nanyang Technological University)
BibTeX Citation
@article{mo_vldb26,
title = {{Towards Industrial-Scale Parametric Query Optimization}},
author = {Mo, Songsong and Xu, Quanqing and Ding, Xuchen and Zhao, Yue and Bao, Zhifeng and Yang, Chuanhui and Cong, Gao},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4303--4315},
doi = {10.14778/3827998.3828034},
url = {https://doi.org/10.14778/3827998.3828034},
year = {2026}
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