From Monolith to Microservices: A Phased Approach

The Big Rewrite Trap

The most common mistake in monolith migration is the "big bang" rewrite. The plan looks reasonable on paper: freeze feature development, build the new microservices architecture, cut over when ready. Six months turns into eighteen months. The business can't wait that long. The project gets canceled, and the team goes back to the monolith—now with 18 months of accumulated technical debt.

We've never seen a big rewrite succeed for a production monolith serving real customers. The alternative is incremental migration using the Strangler Fig pattern, where you gradually replace parts of the monolith while it continues running. This isn't just safer—it's the only approach that maintains business continuity while reducing risk.

Phase 1: Assessment & Preparation

Understanding Your Monolith

Before extracting anything, you need to understand what you're working with. Most legacy monoliths lack clear boundaries because they've evolved organically over years. We use three techniques to map the architecture:

• •Static analysis: Parse the codebase to identify modules, dependencies, and call graphs
• •Runtime analysis: Instrument the application to understand actual traffic patterns and data flows
• •Domain modeling: Workshop with business stakeholders to identify bounded contexts

Establishing the Extraction Infrastructure

You need technical foundations in place before extracting your first service:

Phase 2: First Service Extraction

Choosing Your First Service

The first extraction is the hardest because you're establishing patterns. Pick something that maximizes learning while minimizing risk:

• ✓Clear bounded context: Well-defined business capability with minimal dependencies
• ✓Non-critical to start: Important enough to be representative, but not so critical that issues cause major incidents
• ✓High change frequency: Code that changes often benefits most from independent deployment
• ✓Measurable business value: Can demonstrate ROI to secure continued investment

The Extraction Process

For each service extraction, we follow a repeatable playbook:

1. 1.
   Build the service with the monolith as source of truth. The new service reads from the monolith's database (via APIs if possible, direct DB access if necessary). No writes yet—just proving the read path works.
2. 2.
   Deploy in shadow mode. Route production traffic to both monolith and microservice, but only return the monolith's response. Compare results to identify discrepancies.
3. 3.
   Gradually shift read traffic. Start routing 1% of reads to the microservice. Monitor error rates and latency. Gradually increase to 100% over days or weeks.
4. 4.
   Add write capability with dual-write pattern. Write to both monolith and microservice databases. Validate data consistency. This maintains monolith as source of truth during transition.
5. 5.
   Make the microservice authoritative. Once dual-write is stable, flip: microservice becomes source of truth, monolith reads from it. Remove monolith code only after microservice is proven stable.

Phase 3: Scaling the Migration

Building the Extraction Team

After the first service succeeds, the temptation is to parallelize extractions across multiple teams. Resist this urge initially. Instead:

• •Keep a small, dedicated migration team for the first 3-5 services
• •Document patterns, create templates, and build tooling to make extractions easier
• •Then gradually hand off individual services to feature teams for ongoing ownership
• •Migration team becomes platform team, enabling other teams to extract services independently

Handling Data Migration

Data is the hardest part of any migration. The monolith's database probably violates every rule of microservices data management: shared tables, foreign key constraints across domains, stored procedures mixing business logic across boundaries.

Our approach: accept temporary messiness to maintain momentum. Use database views, change data capture, and event-driven synchronization to keep monolith and microservice datastores consistent during transition. Perfect data isolation comes last, not first.

Common Pitfalls and How to Avoid Them

Measuring Success

Track metrics that demonstrate business value, not just technical achievement:

• ✓Deployment frequency: How often can teams ship changes? Should increase significantly for extracted services
• ✓Lead time for changes: How long from commit to production? Independent services should reduce this dramatically
• ✓Mean time to recovery: When things break, how fast can you fix them? Microservices enable faster rollbacks and isolation
• ✓Team autonomy: Can teams deploy without coordinating with others? This is the ultimate goal