Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection
Summary: Hybrid query optimization combines learning-generated hints with cost-based completion to produce candidate plans. An uncertainty-aware runtime model selects among them, improving robustness on dynamic workloads and cutting PostgreSQL total latency 25% and tail latency 65%. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiang Yu (Tsinghua University)
- 2. Chengliang Chai (Tsinghua University)
- 3. Guoliang Li (Tsinghua University)
- 4. Jiabin Liu (Tsinghua University)
BibTeX Citation
@article{yu_vldb22,
title = {{Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection}},
author = {Yu, Xiang and Chai, Chengliang and Li, Guoliang and Liu, Jiabin},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {13},
pages = {3924--3936},
doi = {10.14778/3565838.3565846},
url = {https://doi.org/10.14778/3565838.3565846},
year = {2022}
}
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