I started with the upload path and worked forward, which felt natural but I think I spent too long on chunked/resumable uploads before getting to the more interesting stuff.
Start by clarifying requirements and scale (e.g., daily uploads, concurrent viewers, global reach), then design the core components: upload service, transcoding pipeline, storage, CDN, and streaming. Focus on trade-offs between consistency, latency, and cost, and explain how you would handle failures and scale each component.
Pro tip: Emphasize the transcoding pipeline's use of a message queue and worker pool for scalability, and discuss adaptive bitrate streaming (e.g., HLS/DASH) as a key to handling diverse network conditions.
Ask about expected upload volume, video sizes, concurrent viewers, geographic distribution, and latency/consistency requirements. Define functional and non-functional requirements.
Sketch the main components: upload service, transcoding service, storage (object store), metadata DB, CDN, and streaming service. Explain data flow from upload to playback.
Detail the transcoding pipeline: how videos are chunked, queued (e.g., Kafka), processed by workers, and stored in multiple formats/resolutions. Discuss fault tolerance and retries.
Explain how videos are served via CDN, using adaptive bitrate streaming (HLS/DASH). Discuss caching strategies, edge locations, and handling of popular vs. long-tail content.
Address scaling bottlenecks (e.g., metadata DB, transcoding workers), failure handling, and cost optimization. Discuss trade-offs like consistency vs. availability, and storage vs. compute.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Resumable uploads I knew cold, chunk offsets tracked server-side with a session ID.
Start by clarifying requirements (file size, concurrency, consistency needs) and then design a two-part solution: a resumable upload protocol (e.g., chunked uploads with unique upload IDs and offset tracking) and a playback progress store (e.g., a key-value store with periodic updates). Emphasize trade-offs between consistency, latency, and cost, and how you'd handle failures and scale.
Pro tip: Mention that you'd use a client-generated upload ID and store chunk metadata in a distributed store like Cassandra or DynamoDB, and for playback progress, use a write-behind cache with periodic flushes to reduce database load. This shows you think about both correctness and efficiency at scale.
Ask about expected file sizes, number of concurrent uploads, consistency requirements for playback progress, and whether users can upload from multiple devices.
Propose chunked uploads with a unique upload ID, storing received chunk metadata (e.g., in a database) and allowing the client to query the last received offset to resume.
Use a key-value store (e.g., Redis, DynamoDB) to store user-video progress, with periodic updates from the client and a write-behind strategy to balance consistency and performance.
Discuss handling upload failures (retries, idempotency), concurrent uploads from multiple devices (last-write-wins or versioning), and ensuring progress is not lost.
Explain how to scale the solution (sharding, CDN for uploads, caching) and optimize for cost and latency (e.g., batching progress updates).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CDN was the obvious first move and I said it immediately.
Start by clarifying the requirements: what 'low startup latency' means (e.g., time to first frame), the scale (concurrent streams, QPS), and the streaming use case (live video, data pipeline, etc.). Then propose a layered architecture that minimizes cold-start delays through techniques like pre-warming, edge caching, and adaptive bitrate, while discussing trade-offs between latency, cost, and consistency.
Pro tip: Emphasize that low startup latency is not just about faster servers but about reducing round trips and data transfer; mention specific Meta-scale optimizations like QUIC, edge computing, and predictive prefetching to show depth.
Ask questions to understand the expected startup latency target, scale (e.g., millions of concurrent streams), and the nature of the streaming data (live vs. on-demand). This ensures your design is tailored to the problem.
Propose a layered streaming architecture: ingestion, processing, and delivery. Highlight components like edge servers, CDN, and load balancers that can reduce latency by bringing content closer to users.
Analyze where startup latency occurs: connection establishment, authentication, manifest fetching, initial buffer fill, etc. Discuss how each can be optimized (e.g., TLS 1.3, QUIC, pre-warmed connections).
Detail specific techniques: edge caching, predictive prefetching, adaptive bitrate starting at low quality, and parallelizing handshakes. Explain how they reduce time to first byte/frame.
Address trade-offs: cost of edge resources vs. latency, consistency vs. availability, and complexity of maintaining global state. Explain how your design scales horizontally and handles failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Didn't expect this framing as a comparison.
Compare the two ingestion pipelines by contrasting their core requirements: Netflix's controlled, high-quality batch ingestion of licensed content versus YouTube's scalable, real-time processing of user-generated uploads. Structure your answer around data flow stages—acquisition, validation, transcoding, storage, and delivery—highlighting how each platform's design choices reflect their business needs and constraints.
Pro tip: Emphasize the trade-offs between quality control and scalability, and mention how Netflix's pre-processing enables a superior viewing experience while YouTube's on-the-fly processing handles unpredictability. This shows you understand that system design is about balancing competing priorities.
Start by outlining the key differences: Netflix deals with a known, finite catalog of professionally produced content, while YouTube handles massive volumes of unpredictable user-generated content. Discuss implications for latency, quality, and scale.
Walk through the common stages: acquisition (content delivery vs. direct upload), validation (format checks, copyright scanning), transcoding (pre-transcode to multiple resolutions vs. on-demand transcoding), and storage (centralized vs. distributed).
Explain how Netflix uses a controlled batch pipeline with extensive pre-processing (e.g., per-title encoding, DRM) while YouTube uses a scalable, real-time pipeline with automated content ID and adaptive streaming.
Compare trade-offs: Netflix prioritizes quality and consistency, YouTube prioritizes speed and scale. Mention optimizations like Netflix's Open Connect CDN and YouTube's use of machine learning for transcoding decisions.
Conclude by summarizing how each approach suits its platform's goals, and briefly relate to Meta's context (e.g., handling both professional and user-generated content at scale).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.