Content Delivery Network

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a CDN with edge caching, cache invalidation, origin shielding, and real-time purge support.
• •Design for a peak load target around 57,870 RPS (including burst headroom).
• •Edge locations: ~50 globally
• •Requests/day (total): ~1,000,000,000
• •Cached content: ~100 TB
• •Cache hit ratio target: > 95%
• •Purge propagation: < 30 seconds globally

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •CDN: Serve static and cacheable content from edge and keep origin strictly for misses and dynamic requests.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •Storage: Use object storage for large blobs and keep metadata/authorization separate in the API tier.
• •Monitoring: Instrument golden signals (latency, traffic, errors, saturation) per tier and per tenant/domain.
• •Geo Distribution: Route users to nearest region/edge while keeping write-consistency boundaries explicit.

4) Reliability and Failure Strategy

• •Define cache keys and purge workflows before launch to avoid stale/global outages.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Enforce lifecycle policies, retention tiers, and checksum validation.
• •Alert on user-impact SLOs, not only infrastructure metrics.
• •Design region failover and data residency controls as first-class requirements.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •CDN: Long TTL improves latency/cost; short TTL improves freshness.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •Storage: Object storage is cheap and durable, but random low-latency reads are weaker than databases/caches.
• •Monitoring: Deep observability speeds incident response but raises ingestion and tooling costs.
• •Geo Distribution: Global latency improves, but cross-region consistency and operations become harder.

Practical Notes

• •Cache key = HTTP method + URL + Vary headers. Use consistent hashing to distribute content across cache servers at each edge.
• •Origin shielding: an edge miss goes to the nearest shield POP. Only the shield talks to the customer's origin - this collapses thundering herds.
• •Purge: publish a purge message to a pub/sub system (Kafka, Redis Streams). All edges subscribe and invalidate matching keys.

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Web Clients -> DNS -> CDN Edge -> Load Balancer -> API Service -> Primary SQL DB -> Read Model DB -> Redis Cache

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •Monitoring and logs are wired in from day one for rapid incident triage.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.