A Learned Query Rewrite System using Monte Carlo Tree Search
Summary: Frames SQL rewriting as policy-tree search rather than fixed-order heuristics, using Monte Carlo Tree Search to avoid rewrite-order local optima. A learned improvement estimator guides rule selection, with parallel exploration yielding superior rewrites and performance. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xuanhe Zhou (Tsinghua University)
- 2. Guoliang Li (Tsinghua University)
- 3. Chengliang Chai (Tsinghua University)
- 4. Jianhua Feng (Tsinghua University)
BibTeX Citation
@article{zhou_vldb22,
title = {{A Learned Query Rewrite System using Monte Carlo Tree Search}},
author = {Zhou, Xuanhe and Li, Guoliang and Chai, Chengliang and Feng, Jianhua},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {1},
pages = {46--58},
doi = {10.14778/3485450.3485456},
url = {https://doi.org/10.14778/3485450.3485456},
year = {2022}
}
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