Microservices are not always the answer. I've seen teams rewrite working monoliths into microservices and end up with a distributed monolith that's harder to debug and slower to deploy. Before splitting services, ask: do different parts of your system have genuinely different scaling needs? Do separate teams own separate domains? If the answer is no, a well-structured monolith is faster and simpler.
That said, when you do need microservices, Spring Boot is excellent for the job. Here are the patterns that have served me well in production.
In a microservices setup, services need to find each other without hardcoded URLs. Netflix Eureka (included in Spring Cloud) handles this. Each service registers itself on startup:
# application.yml
spring:
application:
name: order-service
eureka:
client:
service-url:
defaultZone: http://eureka-server:8761/eurekaOther services then call order-service by name, not by IP. Eureka handles routing, load balancing across instances, and health-check-based removal of dead instances.
Expose a single URL to clients. Spring Cloud Gateway routes requests to the right service, handles auth token validation, rate limiting, and request logging. This means your frontend never knows about your internal service topology:
JWT validation happens once at the gateway — downstream services trust the request and skip re-validation, keeping them fast.
Services call other services. If the payment-service is slow, you don't want the order-service to queue up thousands of waiting threads. Resilience4j's circuit breaker detects failure rates and "opens" the circuit after a threshold — requests fail fast with a fallback response instead of timing out.
I set thresholds at 50% failure rate over a 10-second window, with a 30-second wait before trying to close the circuit. This keeps degraded services from taking down their callers.
A single user request can span 5 services. Without tracing, debugging a slow request means checking logs in 5 places. Spring Boot integrates with Micrometer Tracing (formerly Sleuth) + Zipkin to give each request a trace ID that propagates across service boundaries. One search in Zipkin shows the full request waterfall and exactly which service caused the latency.
This has saved hours of debugging time in production. Add it from day one — retrofitting is painful.