Lessons Learned from Building Microservices at Scale

20 Sep 2024

After 3.5 years of building and operating microservices in production for enterprise clients, I’ve learned that successful microservices architecture is less about the technology and more about the principles you follow.

What I’ve Built

At Infosys, I’ve architected and delivered multiple microservices-based systems:

• Event-driven data integration pipelines processing millions of events
• Real-time warehouse monitoring dashboards
• Data transformation and validation services
• API gateways and orchestration layers

All deployed on Kubernetes with automated CI/CD pipelines.

Key Principles I Follow

1. Keep Services Small and Focused

Each microservice should have a single, well-defined responsibility. I’ve seen services that try to do too much become maintenance nightmares. When a service starts feeling complex, it’s time to consider splitting it.

2. Design for Failure

In distributed systems, failures are inevitable. I always:

• Implement circuit breakers for external dependencies
• Use retry mechanisms with exponential backoff
• Set up Dead Letter Queues for failed messages
• Design idempotent APIs to handle duplicate requests

3. Make Services Stateless

Stateless services are easier to scale, deploy, and recover from failures. I push all state to databases or caches, allowing any instance to handle any request.

4. Invest in Observability

You can’t fix what you can’t see. I instrument every service with:

• Structured logging with correlation IDs
• Metrics for latency, throughput, and error rates
• Distributed tracing across service boundaries
• Health checks and readiness probes

5. API Contracts are Sacred

Breaking changes to APIs can cascade across multiple services. I:

• Version APIs from day one
• Use schema validation
• Maintain backward compatibility
• Document all endpoints thoroughly

6. Automate Everything

Manual deployments don’t scale. I’ve built CI/CD pipelines that:

• Run automated tests on every commit
• Build and push Docker images
• Deploy to Kubernetes with zero downtime
• Run smoke tests post-deployment

Common Pitfalls to Avoid

Over-Engineering
Don’t build microservices if a monolith would suffice. The operational complexity of distributed systems is real.

Ignoring Data Consistency
Distributed transactions are hard. I use event sourcing and eventual consistency patterns where appropriate.

Poor Service Boundaries
Services that are too chatty or tightly coupled defeat the purpose. I design boundaries around business capabilities.

Neglecting Security
Every service is a potential attack surface. I implement OAuth/JWT authentication, encrypt data in transit, and follow the principle of least privilege.

Tools I Rely On

• Spring Boot for building robust Java microservices
• Apache Kafka for event streaming and async communication
• Docker & Kubernetes for containerization and orchestration
• Azure DevOps for CI/CD pipelines
• Postman for API testing and documentation

The Bottom Line

Microservices are powerful but complex. Success comes from following solid engineering principles, investing in automation and observability, and always designing for failure.

The journey from monolith to microservices is challenging, but the benefits—scalability, resilience, and team autonomy—make it worthwhile when done right.