One API Endpoint. Four Architectures. A Journey in Scaling an Event Collector

What happens when a simple backend service meets real-world traffic?

It breaks. Sometimes slowly, sometimes spectacularly.
This is the story of how I rebuilt an event collector API — four times — to survive real-world load and scale from 3 RPS to 991 RPS, reducing response times from 28 seconds to just 17 milliseconds.

🔄 The Evolution (in 4 Attempts)

1. *Naive Design *– Direct writes to PostgreSQL
2. In-Memory Batching – Faster but fragile and risky
3. Redis Queue – Decoupled, stateless, high-performing
4. Kafka + Flink – Full event-driven architecture and real-time stream processing

Each iteration revealed a new bottleneck… and a new lesson in system design, resilience, and performance.

📖 Want the full deep-dive with architecture diagrams and metrics?
👉 Read the complete article on LinkedIn: https://www.linkedin.com/pulse/how-one-failing-api-endpoint-taught-me-everything-scale-kinikar-wmu1f