大数据环境下数据库性能调优的实践与挑战  

Practice and Challenge of Database Performance Tuning in Big Data Environment

作　　者：张清春 ZHANG Qingchun(Fujian Agricultural Vocational and Technical College,Fuzhou 350007,China)

机构地区：[1]福建农业职业技术学院,福州350007

出　　处：《高科技与产业化》2024年第11期85-86,共2页High-Technology & Commercialization

摘　　要：随着大数据时代的来临,数据库性能调优在大数据环境下变得尤为关键。本文围绕大数据环境下数据库性能调优展开实践与挑战的研究。首先,通过深入分析大数据场景下数据库性能瓶颈,提出了一系列有效的性能调优策略。其次,论文探讨了在大规模数据处理和高并发访问情境下数据库性能的关键优化技术。然后,重点分析了在分布式存储和处理框架下数据库调优的挑战,并提出相应的解决方案。最后,通过实际案例验证了本文提出的调优策略的实用性和有效性。With the advent of the big data era,database performance optimization has become particularly critical in big data environments.This article focuses on researching the practice and challenges of database performance optimization in big data environments.Firstly,through in-depth analysis of the performance bottlenecks of databases in big data scenarios,a series of effective performance optimization strategies are proposed.Secondly,the paper explores the key optimization techniques for database performance in large-scale data processing and high concurrent access scenarios.Then,it focuses on the challenges of database optimization under the framework of distributed storage and processing,and proposes corresponding solutions.Finally,practical cases are used to verify the practicality and effectiveness of the optimization strategies proposed in this article.

关 键 词：大数据 数据库性能调优 分布式存储 高并发访问 性能瓶颈 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]