| The localization of basic software is one of the directions to achieve high-level technological self-reliance.Databases are an important component of basic software,and currently over 90% of databases used in China are supplied by foreign corporations.In the trend of domestication of databases,Huawei Technologies Co.,Ltd.independently developed a domestic relational database,openGauss,which has supported the domestication of databases for some enterprises in different fields such as finance,energy,and industry.The competition in the database market is the competition of the database industry ecology,and the database visualization tool is an important part of the database ecology.Currently,openGauss’ s supporting tools and ecology lack a database visualization tool.Based on Huawei Technologies Co.,Ltd.’s “Kunpeng Crowdsourcing Plan: openGauss Database Visualization Project” released at the end of 2021,this thesis studies,designs,and implements a database visualization tool that supports openGauss databases and can present data in multiple dimensions.The main work of this thesis is as follows:(1)In the front-end part,the “Data Source Addition Module,” “Data Source Filtering Module,” “Visualization Chart Display Module,” and “Request Forwarding Module”are designed and implemented.It supports custom import and filtering of openGauss databases and their derivative data sources in a visual way.It provides nine types of interactive and displayable visualization chart types,including bar charts,line charts,area charts,scatter plots,combination charts,histograms,pie charts,radar charts,and 3D scatter plots,solving the visualization problem of openGauss database query operations and data display.(2)In the back-end part,the “Database Connection Processing Module,” “File Processing Module,” “Database Processing Module,” and “Data Processing Module” are designed and implemented.The back-end executes the database connection business,file parsing business,database query business,and data processing business that are difficult to handle in the front-end,thereby achieving decoupling of front-end and back-end code and improving tool operation efficiency.(3)Two optimization schemes,“Segmented Storage Optimization” and “Mark-Sweep Optimization,” are adopted and implemented for big data scenarios to improve the backend file storage speed and front-end response speed.An advanced component library called element-ui is used to optimize the front-end interface,ensuring consistency,feedback,efficiency,and controllability of the tool.A Java library called dynamic-datasource is encapsulated to solve the problems of dynamically removing database connection pools,starting without database connection pools,and manually switching database connection pools that are difficult to handle in traditional Spring Boot projects.(4)The implemented openGauss database visualization tool is tested on Windows and MacOS operating systems,including core functional testing of front-end modules,unit testing of back-end modules,and performance testing of optimization schemes.The results show that each module’s functions run normally,and unit testing covers 85% of the back-end code.The two optimization schemes adopted can reduce the execution time of some operations by more than 30% in the case of a large amount of data. |
