Scaling to Millions (One-Pager)
This is a quick-read checklist for designing systems that can scale from a single server to millions of users.
Useful for interviews and real-world scaling decisions.
1. Start Simple → Scale Later
- Begin with a monolith: single server with app + DB + cache.
- Scale step by step, don’t overengineer too early.
2. Vertical Scaling
- Increase CPU, RAM, disk of existing server.
- Simple, quick fix.
- Limits reached quickly → move to horizontal scaling.
3. Horizontal Scaling
- Add more servers → load balance traffic.
- Requires stateless app servers (session externalized).
- Use load balancers (Nginx, HAProxy, ELB).
4. Database Scaling
- Replication → primary for writes, replicas for reads.
- Sharding → partition data by userID/orderID.
- Indexes → speed up queries but add write cost.
- Caching → Redis/Memcached to reduce DB load.
5. Caching & CDN
- App caching → Redis/Memcached (session, hot data).
- Database query caching → results stored in cache.
- CDN caching → static files, images, videos at edge.
- Watch out for cache invalidation & hot keys.
6. Asynchronous Processing
- Offload heavy tasks (email, video processing).
- Use message queues (Kafka, RabbitMQ, SQS).
- Workers consume jobs asynchronously.
7. Observability & Reliability
- Monitoring → metrics dashboards (Prometheus, Datadog).
- Logging → centralized logs (ELK, Splunk).
- Tracing → distributed tracing (Jaeger, OpenTelemetry).
- High availability → replication, failover, multi-region.
8. Security & Cost
- Secure endpoints (OAuth2, JWT, TLS).
- DDoS protection, rate limiting.
- Optimize infra costs: autoscaling, spot instances, caching.
Quick Checklist
✅ Monolith → Vertical Scaling → Horizontal Scaling.
✅ Replication + Sharding for DB.
✅ Caching + CDN.
✅ Async processing with queues.
✅ Observability (metrics, logs, tracing).
✅ Reliability (replication, failover).
✅ Security (auth, encryption, rate limits).
✅ Cost awareness.
Interview Tip
In interviews, when asked “How do you scale to millions?”:
- Walk through this checklist step by step.
- Start simple → scale progressively.
- Call out trade-offs at each stage.