Problem
The system started to hit serious scaling limitations as traffic increased.
Key issues
- Monolithic structure caused deployment bottlenecks
- Database contention during peak traffic
- Tight coupling between core services
- Limited observability in distributed flows
Solution
We introduced an incremental migration strategy instead of rewriting the system.
Architecture changes
- Domain-driven decomposition into services
- Event-driven architecture using Kafka
- Caching layer for hot paths (Redis)
- Async processing pipelines for heavy workloads
- Improved observability (metrics + tracing)
Result
The system became stable under production load and significantly easier to scale.
Outcomes
- Reduced latency under load
- Improved system resilience
- Zero-downtime deployments
- Clear service boundaries for scaling
Deep dive
Key engineering principles applied:
- Prefer evolution over rewrite
- Design for failure (not uptime assumption)
- Make system observable by default
- Decouple via events, not APIs