QR Code Generator API

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a QR code generation API that creates, stores, and tracks QR codes for URLs, text, and contact cards.
• •Design for a peak load target around 100 RPS (including burst headroom).
• •API calls/day: ~50,000
• •QR codes generated/day: ~100,000
• •Generation latency: < 500 ms
• •Image sizes: 100×100 to 2000×2000 px
• •Dynamic codes tracked: ~500,000 total

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

• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •CDN: Serve static and cacheable content from edge and keep origin strictly for misses and dynamic requests.

4) Reliability and Failure Strategy

• •Apply strict input validation and backward-compatible versioning.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Define cache keys and purge workflows before launch to avoid stale/global outages.

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

• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •CDN: Long TTL improves latency/cost; short TTL improves freshness.

Practical Notes

• •QR code generation is CPU-bound but fast - a single server can generate hundreds per second. Horizontally scale with a load balancer.
• •Cache generated images by a hash of the input parameters - same request = same QR code, no regeneration needed.
• •Dynamic QR codes: the QR encodes a redirect URL (e.g., qr.factory/abc123). You control the redirect target in the database.

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 -> API Gateway -> API Service -> Redis Cache -> Primary SQL DB

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.

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.