Migrating a monolithic Java application to microservices is one of the most impactful — and challenging — transformations you can undertake. This article shares the practical approach I’ve refined over multiple engagements.
Why Migrate?
Before touching a single line of code, ask: why are we doing this? The most common drivers I encounter are:
- Deployment bottlenecks: A single deployable artifact blocks independent team delivery.
- Scalability constraints: You need to scale a specific module, not the entire application.
- Technology modernisation: Teams want to adopt newer frameworks or languages for specific domains.
- Organisational growth: Conway’s Law — architecture tends to mirror team structure.
“Never migrate for migration’s sake. Identify the concrete pain point and validate that microservices solve it.”
The Strangler Fig Pattern
My go-to approach is the Strangler Fig Pattern: incrementally replace monolith functionality behind a facade, leaving the monolith running until it’s fully strangled.
This lets you:
- Ship value incrementally
- Reduce risk by keeping the fallback running
- Validate each new service before extracting the next
Identifying Service Boundaries
Domain-Driven Design (DDD) gives us the best tools for finding service boundaries. I use Event Storming workshops to:
- Map all domain events with the business team
- Identify bounded contexts — areas with consistent language and ownership
- Use bounded contexts as candidate service boundaries
Practical Steps
1. Start with the API Layer
Deploy an API Gateway (AWS API Gateway, Kong, or a simple Spring Cloud Gateway) in front of the monolith. This gives you:
- A single entry point for traffic
- The ability to route selectively to new services
- A foundation for cross-cutting concerns (auth, rate limiting, logging)
2. Extract Stateless Services First
Pick a bounded context that:
- Has clear, stable APIs
- Is relatively self-contained
- Has low coupling to the rest of the monolith
Notification services, reporting modules, and authentication are often good first targets.
3. Database Decomposition
The hardest part. Never share a database between the monolith and a new service in the long run. The interim approach:
4. Embrace Eventual Consistency
With separate services comes eventual consistency. Use domain events over synchronous REST calls wherever possible:
- Publish events to a message broker (Kafka, RabbitMQ)
- Services subscribe to relevant events
- Saga pattern for distributed transactions
Key Takeaways
- Migrate iteratively — the Strangler Fig pattern is your friend.
- Define clear boundaries using DDD bounded contexts.
- Decouple the database as a separate, explicit step.
- Invest in observability early — distributed tracing (Jaeger, Zipkin) and centralised logging (ELK stack) become essential.
- Automate everything — CI/CD per service, infrastructure as code, automated testing.
The migration journey is long, but each extracted service pays dividends in team autonomy and deployment velocity. Start small, validate, and build momentum.