Handling traffic spikes & viral load

Traffic spikes (launch, viral event, attack) can overwhelm the system. Handling them involves: absorbing load (caching, CDN, queues), limiting load (rate limiting, backpressure), scaling (horizontal auto-scale), and degrading gracefully when capacity is exceeded.

Layers of protection

flowchart TB Traffic[Spike in traffic] --> CDN[CDN / edge cache] CDN --> Limit[Rate limit / throttle] Limit --> Scale[Auto-scale app tier] Scale --> Queue[Queue for async work] Queue --> DB[(DB)] Limit -->|Over capacity| Degrade[Graceful degradation]

Strategies

Cache aggressively — Static and cacheable API responses at edge and app layer to reduce origin load.
Rate limiting — Per user/IP/key to cap abuse and give fair share; return 429 when over limit.
Queue non-critical work — E.g. send email, analytics; process from queue so web tier stays responsive.
Auto-scaling — Scale out app (and optionally DB read replicas) on CPU/request count; scale in when load drops.
Graceful degradation — When overloaded, serve reduced features or cached content instead of failing everything.

sequenceDiagram participant U as Users participant LB as Load Balancer participant App as App (scaled) participant Cache as Cache U->>LB: Spike LB->>App: Forward (within limit) App->>Cache: Hit cache when possible Cache-->>App: Reduce DB load App-->>U: 200 or 429 if over limit

Design for spikes: stateless app tier, horizontal scaling, caching, and queues. Use rate limiting and circuit breakers to protect downstream. Plan for “viral” events with load tests and runbooks.