Scalable Ecommerce Platform Architecture
Re-architect a monolithic ecommerce platform to handle 10x traffic growth, reduce deployment risk, and enable independent scaling of order, inventory, and customer systems.
Business Context
D2C brand experiencing hypergrowth. Monolith was a deployment bottleneck—every release required full regression. Black Friday outages were costing millions.
Scale & Constraints
50K orders/hour peak, 10M SKU catalog, sub-200ms API response times, 99.99% availability during peak events, gradual migration (no big-bang rewrite).
Architectural Role
Solution architect for platform modernization. Defined service boundaries, migration strategy, and established architectural guardrails for autonomous teams.
System Design
Domain-driven decomposition: Order Context, Inventory Context, Customer Context, Fulfillment Context. Event-driven integration via Kafka. CQRS for order queries. Saga pattern for distributed transactions. API Gateway for external traffic. Service mesh for internal communication.
Key Architectural Decisions
- ✓Strangler pattern over rewrite—reduced risk, enabled incremental value
- ✓Event sourcing for orders—full audit trail, replay capability for reconciliation
- ✓Separate read models (CQRS)—optimized for different access patterns
- ✓Choreography over orchestration for most flows—reduced single points of failure
Trade-offs
- ⚠Accepted eventual consistency for inventory—optimistic UI with compensation
- ⚠Higher operational complexity—invested heavily in observability
- ⚠Some data duplication across services—accepted for autonomy
Technologies
Measurable Outcomes
- →Zero outages during Black Friday (vs 3 previous year)
- →75% reduction in deployment lead time
- →10x capacity headroom with current infrastructure
- →Independent team deployments (12 teams, 50+ deploys/week)
Lessons Learned
"Service boundaries should follow organizational boundaries. Every time we drew service boundaries that didn't match team structures, we created coordination overhead that slowed everyone down."