After implementing microservices at scale for three different companies, I've learned which patterns actually deliver on their promises and which ones create more problems than they solve.
The Reality Check
Let's be honest: microservices aren't a silver bullet. They solve certain problems while creating others. But when implemented correctly with proven patterns, they can significantly improve your system's scalability, maintainability, and team autonomy.
Pattern 1: Database per Service
This is foundational. Each microservice owns its data completely. No shared databases, no direct database access between services. This pattern enables true service autonomy and prevents the dreaded distributed monolith.
"The biggest mistake I see teams make is sharing databases between services. It couples your services tighter than sharing code." - Martin Fowler
Implementation Strategy
Start with logical separation within the same database cluster, then physically separate as services mature. Use database views and read replicas for cross-service queries during transition periods.
Pattern 2: API Gateway
A single entry point for all client requests. The API Gateway handles cross-cutting concerns like authentication, rate limiting, and request routing.
We use Kong in production, and it's been rock solid. The key is keeping business logic out of the gateway—it should only handle technical concerns.
Pattern 3: Circuit Breaker
When services fail (and they will), circuit breakers prevent cascading failures. We implement this pattern using Hystrix for Java services and go-circuit-breaker for Go services.
Configuration That Works
- Error threshold: 50% over 20 requests
- Timeout: 30 seconds
- Sleep window: 60 seconds
Pattern 4: Event-Driven Architecture
Services communicate through events rather than direct API calls for non-critical operations. This reduces coupling and improves resilience.
We use Apache Kafka for event streaming. The key insight is distinguishing between commands (synchronous) and events (asynchronous).
Anti-Patterns to Avoid
Distributed Monolith
Services that can't be deployed independently are worse than a monolith. They have all the complexity of distributed systems with none of the benefits.
Chatty Services
Services that make multiple synchronous calls to other services for a single operation. This creates latency bottlenecks and tight coupling.
Lessons Learned
Three years ago, I thought microservices were about technology. Now I understand they're primarily about organizational structure and team autonomy. Conway's Law is real—your architecture will mirror your organization.
Start with a monolith, identify bounded contexts, then extract services when team boundaries become clear. Don't start with microservices unless you have a compelling reason and the organizational maturity to support them.
What's Next?
In my next post, I'll dive deep into service mesh patterns and when they're worth the complexity. We've been evaluating Istio vs Linkerd for the past six months, and the results might surprise you.