Image Optimization Service

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design an image optimization CDN that resizes, compresses, and converts images on-the-fly for web performance.
• •Design for a peak load target around 2,894 RPS (including burst headroom).
• •Requests/day: ~50,000,000
• •Unique transformations/day: ~10,000,000
• •Transformation latency (cache miss): < 1 second
• •Cache hit ratio: > 90%
• •Edge PoPs: ~30

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

• •Storage: Use object storage for large blobs and keep metadata/authorization separate in the API tier.
• •CDN: Serve static and cacheable content from edge and keep origin strictly for misses and dynamic requests.
• •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.
• •Media Processing: Split ingest, transform, and delivery into independent stages with async orchestration.

4) Reliability and Failure Strategy

• •Enforce lifecycle policies, retention tiers, and checksum validation.
• •Define cache keys and purge workflows before launch to avoid stale/global outages.
• •Apply strict input validation and backward-compatible versioning.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Store original media durably and make transforms replayable.

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

• •Storage: Object storage is cheap and durable, but random low-latency reads are weaker than databases/caches.
• •CDN: Long TTL improves latency/cost; short TTL improves freshness.
• •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.
• •Media Processing: Pre-processing improves playback UX, but requires substantial compute/storage budget.

Practical Notes

• •Cache key = hash(origin_url + transformation_params + Accept header). Store transformed images in a tiered cache (memory → SSD → object store).
• •Process images with libvips (fast C library) - 10× faster than ImageMagick for most operations.
• •Origin shielding: edge miss → shield cache → worker (transform) → origin. Shield collapses duplicate requests for the same transformation.

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

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •Media processing is handled by background workers so user-facing latency stays low.

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.