基于强化学习的数据库多属性索引推荐

MIRA:database multi-attribute index recommendation based on reinforcement learning

传统关系型数据库通过人工方式进行索引推荐,已无法适应日益增长的数据需求,而机器学习技术可以有效地解决数据库索引选择问题。针对以往仅在静态数据库下进行索引推荐且无法及时更新索引配置的局限性,提出了一种基于强化学习算法实现为数据库数据动态变化情况下的一组工作负载推荐最佳多属性索引配置的方法(multi-attribute index intelligent recommendation approach,MIRA)。在公开的TPC-H数据集上的实验结果表明,该方法不仅能有效地为一组工作负载推荐最佳的索引配置,而且优于自定义的比较基线和相关强化学习方法。

Traditional relational databases use the artificial way to achieve index recommendations that can no longer meet the growing requirement of data,and using machine learning technology can effectively solve the index optimization problem of databases.The limitation that only recommended the index in a static database and couldn’t update the index configuration in time,this paper proposed a multi-attribute index intelligent recommendation approach(MIRA)to recommend multi-attri-bute index configurations for a set of workloads using reinforcement learning under databases with dynamically changing data.Experimental results on TPC-H datasets show that MIRA can effectively recommend the optimal index configuration for workload,and outperforms the defined comparison baselines and related reinforcement learning methods.