Exploring Tracing in Microservice Applications: Leveraging Zipkin for Enhanced Observability

Okpako Marvis, Olanriwaju Babatunde and Eguavoen Victor Osasu
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Okpako Marvis: Departmant of Computer Science, Wellspring University, Nigeria
Olanriwaju Babatunde: Departmant of Computer Science, Wellspring University, Nigeria
Eguavoen Victor Osasu: Departmant of Computer Science, Wellspring University, Nigeria

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 11, 384-395

Abstract: Microservices architecture has gained prominence due to its scalability and modularity, but its distributed nature complicates observability, performance monitoring, and troubleshooting. This study explores the integration of Zipkin, an open-source distributed tracing tool, into microservice architectures to enhance observability. The primary objective is to investigate how Zipkin can be used to trace service interactions, identify latency issues, and optimize system performance in microservices. The research methodology involves implementing Zipkin in a Job Microservice Application comprising three core services: Job, Company, and Review Microservices, built using Java Spring Boot, PostgreSQL, and Docker. The services are integrated with Zipkin to track request flows and analyze system behavior. Performance and functionality tests were conducted using REST APIs to evaluate the effectiveness of Zipkin’s tracing capabilities. Results show that Zipkin significantly improves system observability, enabling developers to pinpoint performance bottlenecks and resolve issues more efficiently. The integration of distributed tracing reduced debugging time and enhanced performance monitoring across services. In conclusion, Zipkin provides valuable insights into service interactions in microservice environments, making it an effective tool for optimizing performance and troubleshooting. The study recommends further exploration of advanced sampling strategies and integration with other monitoring tools to enhance scalability in large-scale microservices systems.

Date: 2024
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