Optimization Design And Implementation Of E-commerce Platform Server Performance

With the popularity of the Internet and the rapid development of computer technology,network communication technology,and big data technology,all walks of life pay more attention to data.In order to more effectively analyze business data to obtain customers and realize data,many small and medium-sized Internet companies have built their own network service platforms based on enterprise business needs.During this period,it is often necessary to query and analyze a large number of user data,add and delete in the database Among the four most basic operations,modification and query,query is one of the most frequently used operations,so query operation is often used as an important indicator to measure the performance of the database.How to improve the query efficiency of the database,that is,query optimization is now important at home and abroad research direction.This article focuses on the problems of low connection query efficiency and poor system operation efficiency in actual projects,improves and optimizes traditional genetic algorithms,and focuses on optimizing the database architecture on the basis of restructuring the overall architecture of the system.First of all,to optimize the distributed database query genetic algorithm for local search slow,easy to premature and poor search efficiency in the later period of optimization to improve the problem,through the introduction of distributed genetic selection algorithm based on simulated annealing algorithm into this paper,improve population diversity and It has better pertinence;by introducing the genetic crossover operation based on multi-probability model into this article,it effectively improves the problem of poor optimization effect in the later stage of traditional crossover operation,and accelerates the algorithm convergence;by applying the genetic mutation operation based on the dynamic parameter control method The introduction of this paper improves the traditional mutation operation in the late stage,which can not take into account the diversity of the population while maintaining a good local search ability;by using the combination of the maximum number of iterations and the degree of population convergence as the termination condition of the genetic algorithm,the population optimal solution More scientifically.Through comparative experiments,it is proved that the improved genetic selection operation,cross operation and mutation operation all have better adaptability and the improved genetic algorithm improves the performance of distributed database multi-connection query to a certain extent.At the same time,in view of the problems of low performance of the original system database architecture and low resource allocation efficiency,the system architecture was optimized by reorganizing the database and allocating tables and resources and effectively improving the system data query performance.Query responses were tested on different pages of the original system and the new system and the same page under different concurrent requests,which proved that the data query performance of the new system was better than the data query performance of the original system.