计算机应用研究2023，Vol.40Issue(12)：3742-3746,3765,6.DOI:10.19734/j.issn.1001-3695.2023.03.0146
基于多线程并行强化学习的数据库索引推荐
Database index recommendation based on multi-thread parallel reinforcement learning
摘要
Abstract
Indexing is an important method to improve database performance.At present,with the development of reinforce-ment learning algorithm,there are a series of methods to solve the index recommendation problem by reinforcement learning.Aiming at the problem that the existing deep reinforcement learning index recommendation algorithm has long training time and unstable training,this paper proposed an index recommendation algorithm based on A2C(advantage actor-critical),called PRELIA(parallel compensation learning index advisor).In order to improve the accuracy and efficiency of index selection and reduce the occupation of index space,the algorithm added the characteristic matrix of the number of rows scanned by load index and normalized the reward value.Experimental results on different data sets show that the proposed algorithm can gua-rantee the index recommendation quality equivalent to that of the compared algorithms,while the recommended index occupies less storage space,and the training time is more than 4 times longer than that of the baseline algorithms.关键词
数据库/索引推荐/强化学习/查询优化Key words
database/index recommendations/reinforcement learning/query optimization分类
信息技术与安全科学引用本文复制引用
牛祥虞,游进国,虞文波..基于多线程并行强化学习的数据库索引推荐[J].计算机应用研究,2023,40(12):3742-3746,3765,6.基金项目
国家自然科学基金资助项目(62062046) （62062046）
CCF信息系统开放资助项目(HZ2021F0055A) （HZ2021F0055A）
