What Was Built
A developer documented building a job portal backend using Spring Boot microservices—three services (Job, Company, Review), each with isolated databases, orchestrated via Spring Cloud Gateway, Eureka service discovery, OpenFeign for sync calls, and RabbitMQ for async messaging. Standard stuff: Docker containers, Kubernetes manifests, centralized config via Spring Cloud Config Server.
The architecture follows established patterns. API Gateway routes requests, Eureka handles service discovery, OpenFeign manages inter-service HTTP calls. When fetching job details with company info, the Job Service calls Company Service synchronously. For decoupled operations, RabbitMQ handles event-driven flows.
The Recurring Question
This tutorial format appears regularly because the patterns are well-established—Spring Boot holds roughly 50% of Java backend market share. What's less discussed: when this complexity pays off.
Microservices enable independent scaling and failure isolation. They also introduce distributed tracing requirements, increased debugging overhead, and operational complexity that monoliths avoid. The developer's learnings—"inter-service latency, debugging, and configuration management"—aren't incidental. They're the tax.
Enterprise architects face this calculation constantly. A modular monolith with clear bounded contexts often suffices until scale demands distribution. Premature microservices adoption creates operational burden without proportional benefit.
What's Missing
The tutorial omits resilience patterns—no Resilience4j circuit breakers, bulkheads, or retry logic. Production microservices need these. When Company Service goes down, does Job Service fail gracefully or cascade?
Also absent: security between services, observability depth (Prometheus/Grafana mentioned but not implemented), and database migration strategies (Flyway referenced in similar tutorials but not here).
The Pattern Recognition
Spring Boot microservices tutorials proliferate because the ecosystem makes them achievable. Spring Cloud abstracts complexity—Eureka registration happens with annotations, Config Server centralizes properties.
The real test isn't building the demo. It's knowing when not to. If your traffic doesn't justify independent scaling, if your team can't manage distributed systems, if debugging across service boundaries slows development—the monolith might be the sophisticated choice.
Worth noting: Kubernetes native service discovery increasingly challenges Eureka's relevance. If you're already running K8s, adding Eureka creates redundancy.
What This Means in Practice
For teams evaluating architecture: microservices aren't inherently superior. They're a specific solution to specific problems—scale, team autonomy, independent deployment. The operational overhead is real. The tutorial demonstrates the how. Enterprise teams still need to answer the why.