摘要
数据库是计算机服务中的重要基础组件,然而其运行中可能出现性能异常,影响业务服务质量.如何对数据库产生的性能异常进行诊断成为工业界与学术界的热点问题.近年来,一系列自动化的数据库异常诊断方法被相继提出,它们通过分析数据库运行状态,对数据库整体的异常类型进行判断.但随着数据规模的不断扩大,分布式数据库正成为在业界中愈受欢迎的重要解决方案.在分布式数据库中,数据库整体由多个服务器节点共同组成.现有的异常诊断方法难以有效地定位节点异常,无法识别在多节点上发生的复合异常,不能感知节点间复杂的性能影响关系,欠缺有效的诊断能力.针对上述问题,提出了一种面向分布式数据库的复合异常诊断的方法:DistDiagnosis.该方法采用复合异常图对分布式数据库的异常状态进行建模,在表示各节点异常的同时有效地捕获节点间的相关性.DistDiagnosis提出了节点相关性感知的根因异常排序方法,根据节点对数据库整体的影响力有效地定位根因异常.在国产分布式数据库OceanBase上构建了不同场景的异常测试案例.实验结果表明,该方法优于其他先进的对比方法,异常诊断的AC@1、AC@3、AC@5最高达到0.97、0.98和0.98,在各诊断场景中相较于次优方法最多提升了5.20%、5.45%和4.46%.
Databases are important foundational components in computer services.However,performance anomalies may occur during their operation,affecting business service quality.How to diagnose performance anomalies in databases has become a hot issue in industry and academia.Recently,a series of automated database anomaly diagnosis methods have been successively proposed.They analyze the runtime status of the database and determine the overall database anomaly types.However,with the continuous expansion of data scale,distributed databases are becoming an increasingly popular solution in the industry.In a distributed database,which is composed of multiple nodes,existing anomaly diagnosis methods struggle to effectively locate node anomalies,fail to identify compound anomalies across multiple nodes,and are unable to perceive the complex performance influence relationships between nodes,lacking effective diagnostic capabilities.To address these challenges,this study proposes a distributed database diagnosis method for compound anomalies,named DistDiagnosis.It models the anomalous state of distributed databases using a Compound Anomaly Graph,which not only represents anomalies at each node but also effectively captures the correlations between nodes.DistDiagnosis introduces a node correlation-aware root cause anomaly ranking method,effectively locating root cause anomalies according to the influence of nodes on the database.In this study,anomaly testing cases for various scenarios are constructed on OceanBase,a domestically developed distributed database.Experimental results show that DistDiagnosis outperforms other advanced baselines,achieving the AC@1,AC@3,and AC@5 values of 0.97,0.98,and 0.98.Compared to the second-best method,DistDiagnosis improves accuracy by up to 5.20%,5.45%,and 4.46%in each diagnostic scenario.
作者
向清风
邵蓥侠
徐泉清
杨传辉
XIANG Qing-Feng;SHAO Ying-Xia;XU Quan-Qing;YANG Chuan-Hui(School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China;Ant Group,Hangzhou 310013,China)
出处
《软件学报》
北大核心
2025年第3期1022-1039,共18页
Journal of Software
基金
国家自然科学基金(62272054,62192784)
新一代人工智能国家科技重大专项(2022ZD0116315)
北京市科技新星计划(20230484319)
小米青年学者项目。
关键词
分布式数据库
异常诊断
根因分析
distributed database
anomaly diagnosis
root cause analysis
作者简介
向清风(2000-),男,硕士生,CCF学生会员,主要研究领域为分布式系统、数据库系统与机器学习的交叉技术;通信作者:邵蓥侠(1988-),男,博士,教授,博士生导师,CCF高级会员,主要研究领域为大规模图数据高效计算与学习,并行计算框架,知识图谱分析.E-mail:shaoyx@bupt.edu.cn;徐泉清(1980-),男,博士,正高级工程师,CCF杰出会员,主要研究领域为数据库系统,分布式系统;杨传辉(1985-),男,硕士,CCF专业会员,主要研究领域为分布式系统,数据库系统.
