Work

RBOIRA: Integrating Rules and Reinforcement Learning to Improve Index Recommendation

Year: 2023

Type: article

Abstract: INTRODUCTION: The index is one of the most effective ways to improve the database query performance. The expert-based index recommendation approach cannot adjust the index configuration in real time. At the same time, reinforcement learning can automatically update the index and improve the recommended configuration by leveraging expert experience.OBJECTIVES: This paper proposes the RBOIRA, which combines rules and reinforcement learning to recommend the optimal index configuration for a set of workloads in a dynamic database.METHODS: Firstly, RBOIRA designed three heuristic rules for pruning index candidates. Secondly, it uses reinforcement learning to recommend the optimal index configuration for a set of workloads in the database. Finally, we conducted extensive experiments to evaluate RBOIRA using the TPC-H database benchmark.RESULTS: RBOIRA recommends index configurations with superior performance compared to the baselines we define and other reinforcement learning methods used in related work and also has robustness in different database sizes.

Source: ICST Transactions on Scalable Information Systems

DOI: 10.4108/eetsis.3822

Authors: Wenbo Yu, Jinguo You, Xiangyu Niu, Jianfeng He, Yunwei Zhang

Institution: Kunming University of Science and Technology

Language: English

Topic: Advanced Database Systems and Queries

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Award: 大数据联机分析处理智能化技术研究