The Efficiency of Distributed Caching Platforms in Modern Backend Architectures: A Comparative Analysis of Redis and Hazelcast

The object of this study is two caching and distributed data storage systems — Redis and Hazelcast — which are widely used to accelerate data access in high-load applications. This article presents a comprehensive comparative analysis of these systems based on key aspects important for efficient caching: architectural features, memory management models, clustering approaches, fault tolerance mechanisms, and scalability. Special attention is given to investigating caching patterns and support for SQL-like queries. The aim of the work is to provide an in-depth analysis of the advantages and limitations of Redis and Hazelcast in the context of data caching, as well as to identify their strengths and weaknesses under different loads and usage scenarios. The methodology of the research includes a comparative analysis of Redis and Hazelcast based on key aspects, with results presented in the form of a comparative table. Performance testing of CRUD operations was also conducted using automated tests integrated into a Spring Boot application. The study shows that Redis, being a single-threaded system with fast read and write operations, is more efficient for simple, localized applications, while Hazelcast, which supports multi-threading and dynamic clustering, handles large data volumes and distributed tasks more effectively. The author's contribution to the research is a comprehensive comparative analysis of these systems, considering key characteristics such as performance, scalability, and fault tolerance, along with testing their performance in real-world scenarios. The novelty of the research lies in the detailed examination of Redis and Hazelcast for data caching in high-load applications, which will be valuable for the development and optimization of the infrastructure of high-performance distributed systems that require real-time data caching.

caching, Redis, Hazelcast, performance, CRUD operations, fault tolerance, multithreading, clustering, in-memory data storage, distributed system