Start by clarifying requirements and scale, then walk through the end-to-end pipeline from upload to playback, focusing on key components like transcoding, CDN, ABR, and DRM. Dive deep into 2-3 areas (e.g., transcoding and recommendations) while maintaining a holistic view, and discuss trade-offs at each stage.
Pro tip: Emphasize how you would handle failures and ensure low latency at scale, and proactively discuss cost optimization for storage and CDN—these are often overlooked but critical for a Netflix-like system.
Clarify functional and non-functional requirements: number of users, upload volume, global distribution, latency targets, and DRM needs. Estimate scale (e.g., petabytes of storage, millions of concurrent streams).
Outline the main components: upload service, transcoding pipeline, metadata store, CDN, streaming server, user profile service, recommendation engine, search service, and DRM system. Sketch data flow from upload to playback.
Choose 2-3 areas to detail: e.g., transcoding (parallel chunked processing, format selection), CDN (multi-tier caching, edge servers), ABR (HLS/DASH, bitrate ladders), and DRM (encryption, license delivery). Discuss data models for metadata and user profiles.
Explain how each component scales (sharding, replication, caching) and handles failures (redundancy, retries). Discuss trade-offs: latency vs. cost, consistency vs. availability, and build vs. buy for CDN/DRM.
Summarize the design, mention monitoring (QoE metrics like startup time, buffering ratio), and suggest future improvements (e.g., AI-driven recommendations, edge computing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.