Start by clarifying requirements and scale (e.g., daily uploads, concurrent viewers, global reach), then design the core pipeline: upload, transcoding, storage, and streaming. Finally, layer on user-facing features like comments, likes, and recommendations, discussing trade-offs and data models at each stage.
Pro tip: Emphasize observability and monitoring from the start, as Datadog values these deeply; mention how you'd track metrics like upload success rate, transcoding latency, and CDN cache hit ratio to ensure system health.
Ask questions to understand expected scale (e.g., daily uploads, concurrent viewers, geographic distribution) and key functional/non-functional requirements. This ensures the design meets realistic constraints.
Outline the upload process (resumable uploads, chunking), transcoding (job queue, worker pool, multiple formats/resolutions), and storage (object storage, CDN). Discuss trade-offs like cost vs. latency.
Explain how videos are streamed (adaptive bitrate streaming, HLS/DASH) and delivered via CDN. Cover edge caching, geo-distribution, and handling of popular vs. long-tail content.
Detail data models and services for comments, likes, and recommendations. Discuss scalability (e.g., sharding, caching) and consistency trade-offs (e.g., eventual consistency for likes).
Summarize key trade-offs (e.g., consistency vs. availability, cost vs. performance) and how you'd monitor the system (metrics, logging, tracing) to ensure reliability and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.