The question started deceptively simple and then they just kept pulling the thread.
Start by clarifying requirements and scale, then walk through the end-to-end pipeline: upload, processing, storage, delivery, and playback. Focus on key design decisions and trade-offs at each stage, emphasizing scalability, reliability, and cost-efficiency.
Pro tip: Demonstrate awareness of real-world constraints by discussing how to handle failures gracefully and optimize for the 80/20 of traffic (e.g., popular videos), showing you understand both technical and business aspects.
Ask questions to understand functional and non-functional requirements: expected upload volume, video sizes, playback latency, global reach, budget constraints, and consistency needs.
Sketch the main components: upload service, transcoding pipeline, storage layers (hot/cold), CDN, metadata database, and playback service. Explain how they interact.
Detail the upload process (resumable, chunked), transcoding (parallel, adaptive bitrate), storage (object store, tiering), and delivery (CDN, caching). Discuss trade-offs like consistency vs. availability.
Explain how to scale each component (sharding, replication, auto-scaling) and ensure reliability (redundancy, failover, monitoring). Address bottlenecks and mitigation strategies.
Summarize key decisions and their trade-offs (e.g., cost vs. latency). Mention potential optimizations like edge computing or AI-driven encoding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a path-based layout in object storage with video ID as the top-level key, then subfolders per codec and resolution.
Start by clarifying the requirements: video types, scale, access patterns, and delivery needs. Then propose a hierarchical object storage structure that organizes assets by video ID, variant type, and version, with a clear naming convention and metadata strategy. Finally, discuss how manifests tie everything together for adaptive streaming and how to handle lifecycle and consistency.
Pro tip: Emphasize that object storage is not a filesystem—design keys for efficient listing and retrieval, and consider using a CDN with signed URLs for secure delivery. Also, mention that manifests should be immutable and versioned to avoid cache invalidation issues.
Ask about video types (e.g., movies, user-generated clips), expected scale (number of videos, variants), access patterns (streaming, download), and any compliance or lifecycle needs.
Propose a key structure like /videos/{videoId}/{version}/{codec}/{resolution}/segment_{n}.ts for segments, and /videos/{videoId}/{version}/manifest.m3u8 for manifests. Use a consistent naming convention.
Store metadata (codec, resolution, bitrate, duration) in a separate database or as object metadata. Use a catalog to map videoId to available variants and manifests.
Explain how master manifests reference variant playlists, and how they are generated and stored. Ensure manifests are immutable and versioned to support caching and rollback.
Discuss lifecycle policies (e.g., move old versions to cold storage), CDN integration, and security (signed URLs, access controls). Mention consistency considerations for uploads and updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the high-level pipeline stages: ingest, segmentation, encoding, packaging, and delivery. Then explain how you generate per-codec and per-resolution variants using a scalable, parallelized approach, and finally describe how HLS/DASH manifests reference these variants to enable adaptive streaming. Emphasize trade-offs in quality, latency, and cost.
Pro tip: Mention that you use a just-in-time packaging approach to avoid duplicating storage for each protocol, and highlight the importance of monitoring and ABR heuristics for real-world performance.
Describe how the source video is ingested, validated, and pre-processed (e.g., demuxing, normalization) before segmentation.
Explain how the video is split into segments (e.g., 2-10 seconds) and encoded into multiple resolutions and codecs (e.g., H.264, H.265, AV1) using parallel workers.
Detail how encoded segments are packaged into HLS/DASH formats, and how manifests (m3u8/MPD) are generated to list variants and segments.
Discuss how manifests are delivered to clients, and how players use them to switch between variants based on network conditions (ABR).
Highlight trade-offs: encoding cost vs. quality, storage vs. just-in-time packaging, latency vs. segment size, and codec support vs. compression efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Treated this as an async side job triggered after the first successful transcode.
Start by clarifying requirements and scale, then propose a decoupled, event-driven architecture where thumbnail generation is triggered asynchronously by pipeline events. Focus on idempotency, caching, and failure handling to ensure reliability and efficiency.
Pro tip: Emphasize idempotency and deduplication by using a content hash as the cache key, and discuss how you would handle poison messages with a dead-letter queue to avoid blocking the pipeline.
Ask about expected throughput, image types, size constraints, latency SLAs, and storage/retrieval patterns. This ensures your design meets actual needs.
Outline a stateless service that consumes jobs from a queue, generates thumbnails using a library like libvips, and stores results in object storage. Include caching and idempotency.
Describe how the service is triggered via events (e.g., when a new image is uploaded) and how it publishes completion events. Ensure loose coupling and backpressure handling.
Discuss retries, dead-letter queues, monitoring, and auto-scaling. Explain how to handle failures without disrupting the main pipeline.
Mention caching strategies, CDN integration, and metrics for performance. Highlight cost and latency trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a split approach: a relational store for structured metadata (title, uploader, timestamps, status) and a separate search index for full-text and faceted queries.
Start by clarifying the scale and requirements (e.g., number of videos, QPS, read/write ratio, latency SLOs) and then propose a high-level architecture that separates metadata storage from video content. Focus on the data model, partitioning strategy, and trade-offs between consistency and availability, and conclude with how you would handle hot spots and evolution.
Pro tip: Emphasize that metadata is often read-heavy and latency-sensitive, so caching and denormalization are key; also mention that you would design for idempotent writes and eventual consistency where possible to avoid bottlenecks.
Ask questions to understand the expected scale (e.g., number of videos, users, QPS), read/write patterns, latency requirements, and consistency needs. This ensures your design is grounded in realistic constraints.
Outline the core entities (e.g., Video, User, Channel, Playlist) and their relationships. Discuss key attributes, access patterns, and whether to use a relational or NoSQL store based on query needs.
Choose a storage solution (e.g., distributed SQL, wide-column store) and explain partitioning/sharding strategy (e.g., by video_id, user_id) to distribute load and enable scalability. Address replication for fault tolerance.
Discuss consistency models (strong vs. eventual) for different operations and how to handle conflicts. Propose caching layers (e.g., Redis, CDN) to reduce latency and database load for read-heavy metadata.
Explain how the schema can evolve (e.g., schema versioning, backward compatibility) and how to monitor, backup, and recover the metadata service. Mention any trade-offs made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered edge caching with a tiered CDN setup, manifest served from edge, segments pulled from origin on cache miss.
Start by clarifying requirements like scale, content type, and latency goals, then walk through the end-to-end flow from ingestion to playback. Focus on CDN architecture, caching strategies, and trade-offs between cost, performance, and reliability for high-traffic scenarios.
Pro tip: Emphasize observability and graceful degradation—show you can monitor CDN performance and handle failures without impacting viewers, which is critical for high-traffic events.
Ask about expected traffic volume, content type (live/VOD), geographic distribution, and latency/availability targets to scope the design.
Outline how content is ingested, transcoded into multiple bitrates, and stored in origin servers or object storage with redundancy.
Describe CDN selection (multi-CDN), edge caching policies, and how to route users to the nearest edge for low latency.
Explain how clients request manifests, select bitrates via ABR, and fetch segments from CDN, including failover to origin or alternate CDN.
Discuss scaling strategies like pre-warming caches, rate limiting, and using load balancers; also cover monitoring and cost optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Hot content was straightforward: aggressive CDN caching, maybe a dedicated origin cluster for the top percentile of videos by view velocity.
Start by clarifying the scale and access patterns (e.g., 80/20 rule) to justify a multi-tier caching strategy. Then describe a hot/warm/cold architecture using CDN edge caching, origin shielding, and object storage lifecycle policies, emphasizing trade-offs between latency, cost, and complexity. Finally, explain how you'd monitor and adapt the strategy over time.
Pro tip: Quantify the cost savings and latency improvements with rough numbers (e.g., 'CDN offload reduces origin egress by 90%') to show you think in terms of business impact, not just technology.
Ask about video size, popularity distribution, geographic spread, and budget constraints. Identify what 'hot' means (e.g., top 1% of videos serving 90% of requests).
Propose CDN edge caching with TTL and cache invalidation, origin shielding to reduce backend load, and in-memory caching (e.g., Redis) for metadata. Mention cache eviction policies like LRU or LFU.
Describe moving rarely accessed videos to cheaper storage tiers (e.g., S3 Infrequent Access, Glacier) using lifecycle policies based on last access time. Discuss retrieval latency and cost trade-offs.
Suggest techniques like transcoding to multiple bitrates, deduplication, compression, and using spot instances for batch processing. Mention monitoring access patterns to dynamically adjust tiers.
Propose metrics (cache hit ratio, origin load, cost per GB served) and A/B testing to validate strategy. Emphasize continuous optimization based on data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.