Monolith to Microservices Decomposition

Monolith to Microservices Decomposition

Decomposing a monolith into microservices is one of the highest-impact architectural changes a team can make — but it's also one of the riskiest if done incorrectly. This guide covers proven patterns for safe, incremental decomposition using Domain-Driven Design and the Strangler Fig pattern.

When to Decompose

Decomposition is warranted when:

• Deployment bottleneck — Teams wait for each other to release
• Scaling limitations — You need to scale one component but must scale everything
• Technology constraints — Different components need different tech stacks
• Team autonomy — Teams need to own and deploy independently

Don't decompose if: You have a small team (<5 developers), the monolith works fine, or you're just following a trend.

Domain-Driven Design (DDD)

Use DDD to identify natural service boundaries:

1. Event Storming — Workshop with domain experts to identify domain events, commands, and aggregates
2. Bounded Contexts — Identify areas where the same term means different things (e.g., "Order" in sales vs shipping)
3. Context Mapping — Define relationships between bounded contexts (upstream/downstream, shared kernel, anti-corruption layer)

Strangler Fig Pattern

Incrementally replace monolith functionality with microservices:

Phase 1: Route all traffic through an API Gateway
Client → API Gateway → Monolith (all routes)

Phase 2: Extract first service behind gateway
Client → API Gateway → Monolith (most routes)
                     → User Service (/api/users)

Phase 3: Continue extracting
Client → API Gateway → Monolith (shrinking)
                     → User Service
                     → Order Service
                     → Payment Service

Phase 4: Monolith fully replaced
Client → API Gateway → User Service
                     → Order Service
                     → Payment Service
                     → Notification Service

Decomposition Steps

1. Start with the seam — Find a module with clear boundaries and minimal dependencies
2. Define the API contract — Design the service's API before extracting
3. Extract data — Each service owns its data store (hardest step)
4. Implement the service — Build, test, deploy independently
5. Route traffic — API Gateway sends requests to the new service
6. Remove from monolith — Delete the extracted code from the monolith

Data Ownership

The biggest challenge: splitting the shared database.

• Database per service — Each microservice owns its tables/schema
• No shared tables — Services communicate via APIs, not database joins
• Data duplication is OK — Eventual consistency between services is acceptable
• Event-driven sync — Publish domain events when data changes, other services consume

Common Mistakes

• Too many services too fast — Start with 2-3 services, not 50
• Distributed monolith — Services that must be deployed together aren't microservices
• Shared database — If services share a database, they're not truly independent
• Ignoring data consistency — Plan for eventual consistency from day one
• No API versioning — Breaking changes without versioning causes cascading failures

Eazy SaaS Tip: We follow the "extract the highest-value, lowest-risk service first" principle. For most applications, that's the authentication/user service. It has clear boundaries, well-defined APIs, and proving the pattern with a small service builds team confidence before tackling complex domains.