Migrating from a monolith to microservices is one of the most impactful — and mishandled — architecture decisions companies make. Here's a pragmatic, sequence-driven approach that actually works.
The Monolith Is Not the Enemy
Before beginning any microservices migration, teams must answer one question honestly: does our monolith create real problems that microservices will solve? Many healthy monoliths have been unnecessarily decomposed, creating distributed systems complexity without corresponding benefit. Microservices are the right answer when you have: independent scaling requirements across bounded domains, multiple teams working on the same codebase with deployment conflicts, technology diversity needs, or specific availability requirements for different system components.
The Strangler Fig Pattern
Named after the strangler fig tree that grows around a host tree and eventually replaces it, the Strangler Fig pattern is the safest migration strategy. Rather than rewriting the monolith, you build new microservices that handle individual features as the API proxy routes traffic to the new service. The monolith shrinks incrementally as services are extracted. Benefits: zero-downtime migration, rollback is trivial (just reroute), and you learn from each extraction before tackling the next.
Service Decomposition: Domain-Driven Design
How do you decide where to cut? Domain-Driven Design (DDD) provides the answer through bounded contexts. Identify subdomains (user management, order processing, inventory, billing) and define clear ownership and data boundaries. The rule of avoiding shared databases between services is paramount — services that share a database are a distributed monolith, not a microservices architecture.
The Data Migration Challenge
Data is the hardest part of microservices migration. A monolith typically has one large database with joins across all tables. Each microservice needs its own data store. The migration sequence: first, identify service boundaries in the code; second, create logical ownership of data tables per service; third, introduce abstraction layers so the monolith accesses data through service interfaces rather than direct SQL joins; finally, physically separate the databases.
Expect this to take 3–6 months for a moderately complex monolith. Rushing it creates data consistency nightmares that take years to untangle.
Observability from Day One
Distributed systems are dramatically harder to debug than monoliths. A 500ms latency that took 10 minutes to diagnose in a monolith can take hours in microservices without proper tracing. Implement distributed tracing (Jaeger or AWS X-Ray) before extracting your first service. Every service must emit structured logs, expose metrics, and participate in trace propagation. This is not optional.
The Organizational Dimension
Conway's Law states that system architecture mirrors the communication structure of the organization that builds it. Microservices succeed when aligned with team structure: one team owns one service. If two teams must coordinate for every deployment, you have not achieved microservices independence. The migration must be organizational as much as technical.
Michael designs and implements enterprise-scale cloud infrastructure, with deep expertise in multi-cloud strategies, DevOps, and resilient system architecture.