Building Scalable Microservices: Lessons Learned

# Building Scalable Microservices: Lessons Learned Over the past several years, I've had the opportunity to work on microservices architectures across different companies and domains. From voice AI systems to edge computing platforms, each project taught me valuable lessons about what works and what doesn't when building distributed systems. ## Start with a Monolith (Sometimes) One of the biggest mistakes I've seen is teams jumping straight into microservices without understanding their domain boundaries. If you're building a new product and the requirements are still evolving, starting with a well-structured monolith can save you months of refactoring later. The key is to build your monolith with clear module boundaries. Think of it as a "modular monolith" where each module could eventually become its own service if needed. ## Event-Driven Architecture is Your Friend When I worked on the Scale Computing fleet management system, we used Kafka for real-time telemetry streams from thousands of edge servers. The event-driven approach allowed us to: - Decouple services naturally - Handle high-volume data streams efficiently - Enable multiple consumers without impacting producers - Build a natural audit trail But event-driven systems come with complexity. You need robust monitoring, dead letter queues, and clear strategies for handling failures. ## API Gateway Patterns At multiple companies, I've implemented API Gateway patterns using AWS API Gateway, custom Node.js gateways, and even GraphQL federation. Here's what I've learned: **GraphQL Federation works great for:** - Consolidating multiple data sources - Reducing over-fetching on mobile apps - Teams that need autonomy over their schemas **REST + API Gateway is better when:** - You need fine-grained rate limiting - Legacy clients expect REST - Your team is more comfortable with HTTP verbs ## Observability is Non-Negotiable You can't manage what you can't measure. Every microservice should expose: - Health check endpoints - Metrics (latency, throughput, error rates) - Structured logs with correlation IDs - Distributed tracing Tools like DataDog, New Relic, or even open-source stacks (Prometheus + Grafana + Jaeger) are worth the investment from day one. ## Data Consistency Challenges The hardest problems in microservices aren't technical—they're about data ownership and consistency. I've seen teams struggle with: - **Distributed transactions:** Usually better to avoid them entirely - **Data duplication:** Sometimes necessary, but keep it minimal - **Eventual consistency:** Your product team needs to understand this concept My approach: Use the Saga pattern for complex workflows, embrace eventual consistency where possible, and be very explicit about service boundaries and data ownership. ## Kubernetes: Powerful but Complex I've deployed microservices on Kubernetes at multiple companies. It's incredibly powerful, but don't underestimate the operational overhead. You need: - Someone who deeply understands K8s - Proper monitoring and alerting - A strategy for config management (Helm, Kustomize, etc.) - Well-defined resource limits For smaller teams or early-stage products, managed container services like AWS ECS/Fargate or Google Cloud Run might be a better fit. ## Key Takeaways 1. **Don't over-engineer early** - Start simple, evolve as needed 2. **Invest in tooling** - Good CI/CD, monitoring, and logging save time 3. **Clear ownership** - Each service should have a clear owner and purpose 4. **Document boundaries** - Make it crystal clear which service owns what data 5. **Test strategies** - Unit tests, integration tests, and contract tests all matter Building microservices is about finding the right balance between autonomy and consistency, between flexibility and complexity. The patterns that work depend heavily on your team size, domain complexity, and operational maturity. What's your experience with microservices? I'd love to hear your thoughts.