Microservices vs. Monolith: When to Break It Apart
“Microservices” has become the industry buzzword. Recruiters love it, CTOs dream of it, and developers… well, we suffer for it.
Having built both the “Enterprise Inventory System” (a Monolith) and the “Microservices E-commerce Platform”, I’ve seen the good, the bad, and the distributed transactions.
The Case for the Monolith
A monolith is a single deployable unit. Your UI, API, and Background Jobs live in one JAR file.
Pros:
- Simple Deployment: CI/CD pipeline is just
mvn package && docker build. - Debugging: You can trace a request from Controller to Database in one IDE window.
- Performance: No network latency between “Order Service” and “User Service”. It’s just a method call.
Cons:
- Scaling: You have to scale the whole app. If image processing is slow, you spin up 5 more instances of everything.
- Coupling: Spaghetti code is easier to create.
The Microservices Promise
You break your application into distinct, loosely coupled services (e.g., Product Service, Order Service, Payment Service), communicating via REST or RabbitMQ.
Pros:
- Independent Scaling: Scale the Payment Service significantly during Black Friday without touching the Product Service.
- Tech Freedom: Write the Analytics Service in Python while the Core is in Java.
- Fault Isolation: If the Recommendation Engine crashes, the user can still check out.
Cons:
- Distributed Complexity: What happens if the Order succeeds but the Payment fails? You now need Sagas and distributed transactions.
- DevOps Nightmare: You are managing 20 databases and 20 pipelines instead of one.
When to Switch?
Don’t start with microservices. Start with a Modular Monolith. Keep your code separated by packages. If you can’t build a modular monolith, building microservices will just lead to a “Distributed Monolith” — the worst of both worlds.
Break it apart only when:
- Organizational scale requires it (50+ devs working on the same repo).
- Specific components have widely different resource requirements (CPU vs Memory).