Data Caching System in Distributed Microservice Architecture

Currently, data caching technology is key in information systems while ensuring their speed and efficiency.It involves saving copies of data in high-speed storage, such as RAM, in order to access them later, without the need to re-request the original sources of information.The purpose of the work is to research the methods and data caching automation tools in a branched microservice architecture and the implementation of an automated caching system for web platforms with the aim of implementing stages of data caching.The object of research is the process of implementing a high-performance data caching system for a branched microservice architecture using the token ring architecture and synchronization tools between nodes.The subject of research are models, methods and data caching automation tools in a branched microservice architecture.The system is implemented using the token ring architecture and has synchronization mechanisms between nodes.To create a system model and implement caching, the Spring Boot framework and the Maven tool in the Intelijj IDEA environment were chosen.The distributed caching process is implemented using Maven, Docker, and using our own implemented data caching and synchronization mechanism for distributed microservice architecture.To implement the system, three popular programming languages: Java, Python, C# were analyzed according to four main criteria: speed, ecosystem, support for distributed systems, scalability and support for operations.Given the advantages in speed, scalability and the availability of a large number of libraries, the Java programming language was chosen.The authors conducted a study of the system performance in comparison with existing solutions based on the analysis of the speed characteristics of the caching mechanism use.The implemented high-performance data caching system in a branched microservice architecture significantly improves the quality of services that allow you to store and access data faster, reducing the load on servers and optimizing network resources.