As the tech lead on this project, I oversaw the transformation of our legacy monolithic application—handling everything from user accounts and digital card sharing to lead capture and CRM integration—into a suite of independent microservices running on Google Cloud Platform. In this article, I’ll share both the theoretical underpinnings and the hands-on steps we took to achieve a scalable, resilient, and fast-release architecture.

1. Why We Needed to Change

When we hit our stride at large industry events, our single-instance deployment struggled:

• Traffic Spikes: QR scans and lead submissions would jump 5×, causing timeouts and memory pressure.
• Slow Releases: Even minor updates took 30–45 minutes to deploy, locking our teams out of rapid iteration.
• Cascading Failures: A bug in our CRM sync logic would sometimes stall login requests, degrading the entire user experience.

We needed a way to scale features independently, reduce blast radius of failures, and accelerate our delivery pipeline.

2. Core Principles That Guided Us

Before writing a single line of new code, we aligned on key microservices concepts:

1. Bounded Contexts: We drew clear boundaries—Auth, Card Sharing, Lead Processing, CRM Sync, and Analytics—so each team owned its domain and data.
2. Strangler Fig: We planned to incrementally replace monolith endpoints, routing a fraction of real traffic to new services, then steadily increasing until the old code could be retired.
3. Single Responsibility: Every service would do one thing well, reducing complexity and making testing and deployment straightforward.
4. Decentralized Data: Moving from one PostgreSQL instance with 30 tables to multiple Cloud SQL instances kept schemas focused and migrations safer.
5. Infrastructure as Code: We defined our GKE deployments, Helm charts, and API Gateway configs declaratively to maintain consistency across environments.

3. Our Starting Point: The Monolith

I often remind the team how our stack looked in production before migration:

Layer	Tech	Role
Front-end	React 18 + Redux	Next.js SSR for landing pages
API & Logic	Node.js 16 + Express.js	~20 000 LOC, JWT auth
Database	PostgreSQL (Cloud SQL)	Shared schema, complex migrations
Job Queue	Redis + Bull	Background jobs for emails & retries

This tight coupling meant one change could ripple everywhere.

4. Mapping Out Bounded Contexts

We held a workshop to carve out our domains:

Service	Owned Data	Responsibilities
Auth	users, roles	Signup, login, JWT generation
Card	cards, shares	QR/NFC generation, share logging
Lead	leads, events	Consuming share events, data enrichment
CRM Sync	sync_jobs	Dispatching and retrying webhooks
Analytics	metrics	Aggregating usage data, dashboards

Each service would communicate via Pub/Sub topics—CardShared, LeadCaptured, etc.—enabling asynchronous, reliable workflows.

5. Our Migration Approach

Here’s how we systematically strangled the monolith:

1. Set Up GCP API Gateway
   • Configured a single edge entry point; enforced JWT validation before traffic hit our services.
2. Extract Auth Service
   • Spun up an Express.js/TypeScript repo.
   • Migrated /signup, /login, /profile endpoints.
   • Deployed on GKE under /auth/* and toggled monolith redirects.
3. Extract Card Service
   • Ported QR/NFC logic into its own Node.js service.
   • Published CardShared events to Pub/Sub.
   • Ran 10% of share traffic through the new service, then ramped up.
4. Build Lead Service
   • Subscribed to CardShared, enriched lead data, wrote to its own Cloud SQL.
   • Ensured idempotency using unique event IDs.
5. Launch CRM Sync Service
   • Created a microservice for webhook dispatch with Redis + Bull for retry and dead-letter queues.
   • Repointed all CRM calls from the monolith to this service.
6. (Optional) Analytics Service
   • Later, we isolated reporting to a Python service reading from its own analytics database.

By the end, the monolith served only fallback traffic until we fully decommissioned each module.

6. Infrastructure and Deployment

Our stack on GCP looked like this:

• API Gateway handling routing, JWT auth, and rate limits at the edge.
• Istio Service Mesh enforcing mTLS, circuit breaking, and capturing telemetry to Cloud Monitoring.
• Cloud Build pipelines with automated testing, image builds, and Helm deployments.
• Observability via Prometheus, Grafana, and Jaeger to trace cross-service calls.

7. Testing Strategy

To ensure quality at each stage:

• Contract Tests: We used Pact to guarantee new services met monolith expectations before cutover.
• Integration Tests: Jest and supertest for HTTP endpoints; isolated Cloud SQL instances in Docker for CI.
• End-to-End Smoke Tests: Playwright against our staging cluster to validate critical flows.
• Load Testing: k6 scripts simulating thousands of share events per second to tune autoscaling.

Each build ran tests automatically, preventing regressions.

8. Lessons Learned

What Worked:

• Incremental migration minimized risk and allowed early wins.
• Pub/Sub decoupling made it easy to add new consumers (analytics, alerting).
• Service mesh policies improved security and reliability without code changes.

Challenges:

• Ensuring data consistency required careful versioning of our event schemas.
• Team coordination across multiple repos demanded robust CI/CD governance.

Conclusion

Migrating this platform was a journey of balancing theory with practical constraints. If you’re about to embark on a similar path, remember: start small, iterate quickly, and always keep your teams aligned on the end goal.