Scaling a Backend System to 100K RPS: An Architectural Blueprint
This article outlines a robust backend architecture designed to handle 100,000 requests per second, emphasizing strategies to prevent database overload and ensure high availability. It covers crucial system design aspects such as capacity planning, multi-layer caching, database scaling, asynchronous processing, and resilience patterns. The discussion focuses on practical considerations and trade-offs for building a high-performance, scalable distributed system.
Read original on Dev.to #architectureCore Principles for High-Scale Backend Systems
Achieving 100K RPS demands a shift from monolithic thinking to a distributed, resilient architecture. The article highlights key principles: traffic shaping, extensive caching, and graceful degradation. Capacity planning is crucial, starting with workload analysis (e.g., 90% reads, 10% writes) to drive design decisions, especially around caching strategies to offload the database.
Multi-Layer Caching Strategy
Caching is presented as the primary weapon against database overload. A multi-layer approach is recommended to serve 80-95% of reads from cache, significantly reducing direct database hits. This involves a tiered strategy combined with patterns to prevent cache stampedes and manage hot keys.
- CDN Cache: For public GET responses, static and semi-dynamic content.
- Service-side Distributed Cache: A Redis Cluster is suggested for shared, distributed caching.
- In-process Local Cache: For ultra-hot keys with very short TTLs within application instances.
Effective cache invalidation and management include using namespaced keys, TTL with jitter to prevent thundering herds, and patterns like request coalescing, soft TTLs with background refresh, and distributed locks for critical items.
Database Scaling and Asynchronous Workflows
To handle high write loads (e.g., 10K writes/sec), the database layer is protected and scaled horizontally. Read replicas handle stale-tolerant reads, while sharding partitions data to distribute write load and improve performance. Connection pooling via a DB proxy is essential to manage database connections efficiently and prevent overload.
Async Architecture for Spikes
Asynchronous processing is critical for decoupling user latency from heavy downstream work. An event stream (like Kafka or Pulsar) allows the API to quickly commit critical data and emit events, with background workers handling non-critical tasks such as emails, analytics, and notifications.
Resilience, Observability, and Advanced Touches
Building a resilient system at scale requires implementing timeouts, circuit breakers, bulkheads, rate limiting, load shedding, and graceful degradation to handle partial failures gracefully. Comprehensive observability, including tracking latency, traffic, errors, and saturation, is non-negotiable for identifying bottlenecks and ensuring system health.
- Adaptive TTL Cache: Dynamically adjust cache TTL based on key popularity and update frequency.
- Predictive Cache Warming: Pre-populate caches based on anticipated traffic patterns.
- Brownout Mode: Automatically disable non-essential features during high stress to maintain core functionality.
- Cell-Based Architecture: Isolate failures by splitting the platform into independent mini-stacks.
The article concludes with a phased launch plan, moving from a single-region setup to a multi-region, sharded, and highly resilient architecture, emphasizing that success at 100K RPS is about intelligent traffic management, caching, and robust backpressure mechanisms.