Moving to microservices solves certain scalability problems but creates new operational ones. Observability, circuit breaker, distributed tracing: here is what nobody tells you upfront.
A monolith goes down: you check a single log. A microservice goes down: you need to know which one, why, and whether others are impacted by cascade. The operational complexity of microservices is often underestimated during migration. It demands practices, tools and a culture that many teams don't yet have when they start.
Observability rests on three pillars: logs, metrics and traces. Logs must be centralized (ELK Stack, Datadog, Loki) and structured in JSON to be queryable. Metrics (Prometheus + Grafana) give an overall view of system health. Distributed traces (OpenTelemetry, Jaeger) allow you to follow a request across multiple services — essential for diagnosing abnormal latency. Without these three layers, you are flying blind.
Resilience is implemented with several patterns. The Circuit Breaker (Resilience4j, Hystrix) cuts the circuit to a degraded service to prevent failure cascades. Retry with exponential backoff retries transient calls without overloading the target service. The Bulkhead isolates resources per service so an overload doesn't impact others. Systematic Timeout on every network call prevents indefinitely blocked threads. These patterns are not optional in production.
- Set up OpenTelemetry from the very first service
- Centralize your logs with a common correlation ID
- Implement Circuit Breaker on all inter-service calls
- Define timeouts on every HTTP client