Continuously Managing Microservice Granularity: An Evidence-Based Industrial Approach

  • Yan Justino CESAR SCHOOL
  • Carlos Eduardo da Silva Sheffield Hallam University
  • Rafael Batista Duarte CESAR SCHOOL

Resumo


Defining and managing appropriate service granularity remains a recurring challenge in the design and evolution of microservices-based systems, directly affecting modularity, maintenance, and operational efficiency. This paper presents early empirical evidence supporting Granulify, a continuous granularity-management approach that dynamically adjusts service boundaries throughout the system lifecycle. Although still preliminary, the results already point to tangible benefits in modularity, maintainability, and operational cost, underscoring the method’s industrial applicability. The proposal is being validated through the reengineering of a real-world investment management platform from a major financial institution in Brazil. The analysed platform manages over 40 million transactions monthly and supports more than 250K internal users across investment, compliance, and trading domains. Preliminary results indicate patterns of architectural fragmentation, productivity impacts, and signs of granularity saturation. We believe that continuous application of this approach will contribute to more informed architectural decisions, balancing modularity, maintenance effort, and operational costs. This ongoing research aims to consolidate Granulify as a practical solution to support teams in the evolutionary management of granularity in microservices architectures.

Palavras-chave: Microservices, Service Granularity, Continuous Architecture, Software Maintenance, Software Evolution, Case Study, Granularity Management Approach

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Publicado
22/09/2025
JUSTINO, Yan; SILVA, Carlos Eduardo da; DUARTE, Rafael Batista. Continuously Managing Microservice Granularity: An Evidence-Based Industrial Approach. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE (SBES), 39. , 2025, Recife/PE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 226-236. ISSN 2833-0633. DOI: https://doi.org/10.5753/sbes.2025.9913.