Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (3): 1-16.DOI: 10.3778/j.issn.1002-8331.2304-0101

• Research Hotspots and Reviews • Previous Articles     Next Articles

Survey of Machine Learning for Database Parameter Tuning Techniques

JIANG Lulu, GAO Jintao   

  1. 1.School of Information Engineering, Ningxia University, Yinchuan 750021, China
    2.Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded by Ningxia Municipality and Ministry of Education, Yinchuan 750021, China
  • Online:2024-02-01 Published:2024-02-01



  1. 1.宁夏大学 信息工程学院,银川 750021
    2.宁夏大数据与人工智能省部共建协同创新中心,银川 750021

Abstract: Database parameter tuning techniques are facing great challenge under stringent performance requirements due to the huge data scale and complex application scenarios in big data. Traditional heuristic tuning methods or manual intervention methods are lack of the ability to handle various requirements. The development of machine learning provides a brand-new opportunity for database parameter tuning with its power in learning, reasoning, and planning. Through sufficient investigation, the evolution route of database parameter tuning technology based on machine learning is given. According to the thought of content-problem-resolution, this paper describes the traditional parameter tuning techniques and the parameter tuning techniques based on Bayesian optimization (BO) model and reinforcement learning (RL), and the future research directions are proposed as well. Hopefully, this paper can provide some valuable references for researchers in this field.

Key words: database system, parameter tuning, machine learning

摘要: 大数据时代,数据规模庞大、应用场景复杂,为满足苛刻的性能要求,面对庞杂的数据库参数,给出高质量的参数调优结果挑战巨大。传统启发式调优算法或者人工干预方法很难普适地满足各类调优需求。机器学习因其强大的学习和泛化能力被广泛应用于各种复杂场景,包括数据库参数调优。经过充分调研,给出学习式数据库参数调优技术的演化路线、依据路线,按照研究内容-存在问题-解决问题的思路,叙述传统参数调优技术、基于BO模型的参数调优技术以及基于RL的参数调优技术,展望未来的研究方向和所面临的挑战,希望为这一领域科研工作者提供有价值的参考。

关键词: 数据库系统, 参数调优, 机器学习