Design And Implementation Of A NoSQL-based Video Traffic Data Management System

With the advent of the cloud era,the number of online video users has grown dramatically,and the network operators put forward the concept of the big video era.In the context of the big video era,video traffic visualization and management issues faced by operators is particularly significant.With the geometric growth of traffic data,the traditional relational data management technology in the scalability of the challenges encountered,it is difficult to complete the task of large data analysis.From this point of view,this paper proposes a video traffic management system based on non-relational database,which can effectively solve a series of problems such as large data acquisition,storage,analysis and visual display.The system has a distributed architecture and single architecture to meet the needs of different operators.The system runs under Huawei FusionInsight cloud platform.First of all,this paper analyzes the requirements of the system,completed the system architecture design,functional design,and gives the basic class diagram.Then the system is divided into query engine,data management,data storage,data acquisition module for detailed design,and then focused on the design of the overall function of the system,the module design ideas.In the system implementation section,the experimental environment is summarized,and the experimental results of the query engine and data storage module are summarized and analyzed.Also it introduced the development environment and realization process of data management module,and shows some of the implementation code.Finally,in the test part,selected part of the selection results for functional testing,reliability testing,analyzed,summed up some noteworthy problems.This paper used the study of large data technology,combined with Huawei video intelligent operation and maintenance platform development example,can greatly meet the needs of operators and large video era of development law,and ignificantly improve the data analysis and storage performance of the intelligent operation and maintenance system.