Learned Query Rewrite System using Monte Carlo Tree Search
To address challenges in classic query rewrite strategies (e.g., topdown rewrite from the root node), we propose a policy tree based query rewrite framework, where the root is the input query and each node is a rewritten query from its parent. We aim to explore the tree nodes in the \tree to find the optimal rewrite query. We propose to use Monte Carlo Tree Search to explore the policy tree, which navigates the policy tree to efficiently get the optimal node. Moreover, we propose a learning-based model to estimate the expected performance improvement of each rewritten query, which guides the tree search more accurately. We also propose a parallel algorithm that can explore the tree search in parallel in order to improve the performance.
Online Demo
http://rewrite_demo.dbmind.cn/
Co-Contributors: Jiesi Liu, Jianming Wu, Xinning Zhang
Before you run
You need to configure java and maven environment properly.
Citing LearnedRewrite
@article{DBLP:journals/pvldb/ZhouLCF21,
author = {Xuanhe Zhou and
Guoliang Li and
Chengliang Chai and
Jianhua Feng},
title = {A Learned Query Rewrite System using Monte Carlo Tree Search},
journal = {Proc. {VLDB} Endow.},
volume = {15},
number = {1},
pages = {46--58},
year = {2021},
url = {http://www.vldb.org/pvldb/vol15/p46-li.pdf},
timestamp = {Tue, 11 Jan 2022 18:01:10 +0100},
biburl = {https://dblp.org/rec/journals/pvldb/ZhouLCF21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}