OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning
Summary: OBELISK reframes offline plan management as optimizing cost-scaling knobs that steer CQO toward robust plans, instead of directly searching the plan space. Training-free closed loop: Bayesian optimization guides knob subspaces, LM reasoning proposes configs, and history-aware gating cuts redundant evaluations. (summarized by gpt-5.4-mini on May 27 2026)
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Authors
- 1. Zhicheng Pan (East China Normal University; Hong Kong Baptist University)
- 2. Wenwen Sun (East China Normal University)
- 3. Yuanjia Zhang (PingCAP)
- 4. Terence Purcell (PingCAP)
- 5. Yu Dong (PingCAP)
- 6. Chengcheng Yang (East China Normal University)
- 7. Rong Zhang (East China Normal University)
- 8. Xuan Zhou (East China Normal University)
- 9. Jianliang Xu (Hong Kong Baptist University)
BibTeX Citation
@article{pan_vldb26,
title = {{OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning}},
author = {Pan, Zhicheng and Sun, Wenwen and Zhang, Yuanjia and Purcell, Terence and Dong, Yu and Yang, Chengcheng and Zhang, Rong and Zhou, Xuan and Xu, Jianliang},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {7},
pages = {1674--1687},
doi = {10.14778/3801059.3801077},
url = {https://doi.org/10.14778/3801059.3801077},
year = {2026}
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