Microservices Migration: What Nobody Tells You

Everyone talks about the benefits of microservices. Few talk about the migration. Having gone through it at multiple scales — enterprise financial systems and my own startup platform — here's what nobody tells you.

The Monolith Isn't the Enemy

Before you migrate, ask yourself: do you actually need microservices? Most applications don't. A well-structured monolith can serve millions of users. The real question is about organizational scale and deployment independence.

Signs you might need microservices:

• Multiple teams stepping on each other's code
• Different components need different scaling strategies
• You need independent deployment cycles
• Regulatory requirements demand service isolation

Signs you probably don't:

• You have a small team (under 10 engineers)
• Your app has tightly coupled business logic
• You're optimizing prematurely
• "Everyone else is doing it"

Lesson 1: Start with the Data

The hardest part of any microservices migration is data decomposition. When you split a monolith, you're really splitting the database. And that's where the pain lives.

What worked for us:

1. Identify bounded contexts using Domain-Driven Design
2. Create read replicas first — let services read from shared DB
3. Gradually extract writes into service-owned databases
4. Use events for eventual consistency between services

Lesson 2: You Need an API Gateway Yesterday

The moment you have two services, you need a gateway. We learned this the hard way when our frontend was making 12 different API calls to render one page.

Our setup:

• Spring Cloud Gateway for routing and rate limiting
• Service discovery with Consul
• Circuit breakers with Resilience4j
• Distributed tracing with OpenTelemetry

Lesson 3: Observability is Non-Negotiable

In a monolith, you grep the logs. In microservices, a single user request might touch 8 services. Without proper observability, debugging is impossible.

Our observability stack:

• Structured logging with correlation IDs
• Distributed tracing (every request gets a trace ID)
• Health checks and readiness probes
• Centralized metrics dashboards

Lesson 4: The Network is Not Reliable

Microservices turn function calls into network calls. Networks fail. Services go down. Timeouts happen. You need to design for failure from day one.

Patterns that saved us:

• Circuit breakers — stop cascading failures
• Retries with backoff — handle transient errors
• Bulkheads — isolate failure domains
• Graceful degradation — serve stale data rather than errors

What I'd Do Differently

1. Invest in a service template early — every new service should start from a well-configured template with logging, health checks, CI/CD, and Docker setup baked in
2. Don't split too small — we over-decomposed early on and paid the operational tax
3. Event-driven from the start — we retrofitted events later, which was painful
4. Contract testing — Pact or similar for API contracts between services

The Takeaway

Microservices aren't a silver bullet. They're a trade-off: you gain deployment independence and team autonomy, but you pay with operational complexity. Go in with your eyes open, invest in infrastructure early, and always remember that the goal is serving users — not having the most services.