The scope of this thing is genuinely wild.
Start by clarifying functional and non-functional requirements, then design a scalable architecture that separates metadata storage from blob storage, using CDN for global delivery. Focus on trade-offs around consistency, cost, and latency, and highlight ML-specific components like thumbnail/transcode generation and search indexing.
Pro tip: Emphasize how ML models (e.g., for thumbnail selection or content moderation) integrate into the pipeline, and discuss how to handle failures gracefully with retries and dead-letter queues.
Ask about expected QPS, storage size, latency requirements, consistency needs, and budget constraints. Define core entities: users, photos, videos, metadata.
Propose a microservices-based design with separate services for upload, download, metadata, and processing. Use object storage (e.g., S3) for blobs, a distributed database (e.g., Cassandra) for metadata, and a CDN for delivery.
Detail upload flow (pre-signed URLs, chunked uploads), download flow (CDN, signed URLs), deletion (soft delete, async cleanup), listing (pagination, indexing), and search (Elasticsearch with owner/metadata filters).
Explain how to generate thumbnails and transcodes using asynchronous workers (e.g., AWS Lambda, Kubernetes jobs). Discuss ML models for auto-tagging, content moderation, and thumbnail selection, and how to handle model versioning and retraining.
Address partitioning, replication, caching, and quota enforcement. Discuss trade-offs between consistency and availability, cost of storage vs. compute, and how to handle hot spots and failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.