After architecting Node.js microservice systems for 50+ production environments, we've distilled our hardest-won lessons on state management, observability, and resilient design patterns.
Start With the Right Boundaries
The single most common mistake in microservice adoption is decomposing by technical layer (auth service, database service, notification service) instead of by business domain (user management, order processing, inventory). Domain-driven decomposition means each service owns its data, its business logic, and its API contract. When services share databases, you haven't built microservices — you've built a distributed monolith with network latency. We use event storming sessions with stakeholders to identify bounded contexts before writing a single line of infrastructure code.
Event-Driven Over Request-Driven
Synchronous HTTP calls between microservices create tight coupling and cascading failures. When Service A calls Service B, which calls Service C, and C goes down, your entire chain fails. We've moved overwhelmingly toward event-driven architectures using Apache Kafka or AWS EventBridge. Services publish domain events ('OrderPlaced', 'PaymentProcessed', 'InventoryReserved') and interested services consume them asynchronously. This pattern provides natural decoupling, built-in audit trails, and the ability to replay events for debugging or data migration. The tradeoff is eventual consistency, which requires careful UX design to communicate processing states to users.
Observability Is Not Optional
In a monolith, you grep the logs. In a microservice architecture with 15 services across 3 availability zones, you need distributed tracing, structured logging, and metric aggregation from day one — not as an afterthought. We standardise on OpenTelemetry for instrumented tracing, Prometheus for metrics, and Grafana for visualisation. Every service emits structured JSON logs with correlation IDs that thread through the entire request lifecycle. The investment in observability typically pays for itself within the first production incident it helps resolve in minutes instead of hours.
The Circuit Breaker Pattern
Network partitions, slow responses, and service failures are not edge cases in distributed systems — they are the normal operating condition. Every inter-service call should be wrapped in a circuit breaker pattern (we use Opossum for Node.js) with configurable thresholds for failure rate, timeout, and recovery. When a downstream service is struggling, the circuit opens and returns a fallback response immediately instead of queuing up requests that will eventually time out. Combined with bulkhead patterns that isolate connection pools per downstream service, this prevents a single failing dependency from consuming all available resources.
Deployment & Testing Strategy
Microservices multiply your deployment surface area. We enforce a strict contract: every service must have a Dockerfile, a health check endpoint, a Helm chart, and a contract test suite. Contract tests (using Pact) verify that the API contracts between producer and consumer services remain compatible without requiring integration environments. Deployments use blue-green or canary strategies with automated rollback triggers based on error rate thresholds. The goal is that any service can be deployed independently, at any time, by any team member, with confidence.