How to Design a 1M RPS API: Scalable, Resilient, and Production-Ready Architecture

#systemdesign #systemarchitecture #restapi

Designing an API that handles 1 million requests per second (1M RPS) is a significant engineering challenge that pushes the boundaries of system scalability, performance, and reliability. This article presents a layered, distributed system architecture built to meet such extreme demands while maintaining low latency and high availability. It breaks down the system into key functional layers—Global Traffic Management, API Gateway, Stateless Processing, Multi-Tier Caching, Sharded Data Storage, Asynchronous Processing, and Observability—to ensure each component scales independently and operates fault-tolerantly.

We begin with user traffic management via global load balancers and CDN caches to offload redundant requests before they hit the backend. API Gateways enforce security, rate limits, and intelligent routing. Stateless API servers enable horizontal scaling, while caching at various layers drastically reduces load on core systems. For persistence, we implement sharded databases and read replicas to handle concurrent writes and massive read workloads efficiently. Time-consuming tasks are offloaded to a background processing layer using queues and worker pools. The system is equipped with circuit breakers, failover logic, auto-scalers, and monitoring pipelines to ensure resilience and rapid recovery from failures.

The architecture balances eventual vs. strong consistency, optimizes resource usage with elastic scaling, and supports cost-efficient operations at scale. This guide provides a full-stack perspective on how modern high-throughput APIs are designed to serve millions while staying performant, reliable, and observable. Whether you're building the next big platform or scaling an existing service, these patterns and principles are your foundation for internet-scale architecture. 🚀