Analysis of Microservices Granularity: Effectiveness of Architectural Approaches

Modern information systems require scalable architectures for processing big data and ensuring availability. Microservice architecture, based on decomposing applications into autonomous services focused on business functions, addresses these challenges. However, the optimal granularity of microservices impacts performance, scalability, and manageability. Suboptimal decomposition leads to anti-patterns, such as excessive fineness or cosmetic microservice architecture, complicating maintenance. The aim of the study is a comparative analysis of methods for determining the granularity of microservices to identify approaches that provide a balance of performance, flexibility, and manageability in high-load systems. The object of the study is the microservice architecture of high-load systems. The subject of the research is the comparison of granularity methods, including monolith, DDD, Data-Driven Approach, Monolith to Microservices, and their impact on the system. The study employs an experimental approach, including the implementation of a Task Manager application in four architectural configurations. Load testing was conducted using Apache JMeter under a load of 1000 users. Performance metrics (response time, throughput, CPU), availability, scalability, security, and consistency were collected via Prometheus and processed to calculate averages and standard deviations. The scientific novelty lies in the development of a methodology for comparative analysis of decomposition methods using unified metrics adapted for high-load systems, setting this study apart from works that focus on qualitative assessments. The results of the experiment showed that the monolithic architecture provides the minimum response time (0.76 s) and high throughput (282.5 requests/s) under a load of 1000 users, but is limited in scalability. The Data-Driven Approach ensures data consistency, DDD is effective for complex domains, while Monolith to Microservices demonstrates low performance (response time 15.99 s) due to the overload of the authorization service. A limitation of the study is the use of a single host system (8 GB RAM), which may restrict the scalability of the experiment. The obtained data are applicable for designing architectures of high-load systems. It is recommended to optimize network calls in DDD (based on response time of 1.07 s), data access in Data-Driven (response time of 5.49 s), and to carefully plan decomposition for Monolith to Microservices to reduce the load on services (response time of 15.99 s).

microservice architecture, granularity of microservices, Domain-Driven Design, Data-Driven Approach, Monolith to Microservices, performance, scalability, data consistency, experimental analysis, high-load systems