This is where I spent the most time and probably went too deep too fast.
Start by clarifying requirements (scale, latency, device constraints) and then walk through the pipeline stages: ingestion, transcoding to multiple bitrates, packaging, and adaptive streaming delivery. Emphasize trade-offs like encoding speed vs. quality, storage vs. cost, and how you'd handle failures and scaling.
Pro tip: Mention Roku-specific considerations like supporting HLS/DASH, device capabilities, and the importance of low-latency for live events. Also, discuss how you'd monitor and optimize the pipeline using metrics like rebuffering ratio and startup time.
Ask about scale (e.g., concurrent viewers, upload volume), latency requirements (live vs. VOD), supported devices, and budget constraints. This shapes the entire design.
Outline how videos are uploaded (e.g., S3 multipart upload, direct upload from clients) and validated (format, codec, resolution). Consider using a queue to decouple ingestion from processing.
Describe splitting the video into chunks and encoding them in parallel to multiple bitrates/resolutions using a scalable transcoding farm (e.g., AWS Elemental, FFmpeg on EC2). Discuss trade-offs between speed and quality (e.g., preset selection).
Explain how encoded chunks are packaged into streaming formats (HLS/DASH) with manifests, and stored in a CDN-friendly origin (e.g., S3 + CloudFront). Mention segment duration and manifest updates.
Detail how the player selects the appropriate bitrate based on network conditions and device capabilities, using protocols like HLS or DASH. Discuss client-side logic and server-side support (e.g., CDN caching).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: scale, video types (live/VOD), geographic distribution, and latency goals. Then propose a multi-tier CDN architecture with edge, mid-tier, and origin shielding, focusing on caching, adaptive bitrate streaming, and failover. Discuss trade-offs between cost, performance, and complexity.
Pro tip: Emphasize the importance of monitoring and real-time analytics to dynamically adjust caching and routing; mention how Roku's scale demands a hybrid approach combining owned infrastructure and third-party CDNs for resilience.
Ask about expected traffic volume, geographic regions, video formats (live vs. on-demand), latency targets, and budget constraints to tailor the design.
Outline a multi-tier CDN: edge servers for last-mile delivery, mid-tier caches for regional aggregation, and origin shield to reduce load on origin. Include DNS-based global load balancing.
Explain caching strategies: TTL, cache invalidation, and support for adaptive bitrate streaming (HLS/DASH). Discuss prefetching and popularity-based caching.
Describe auto-scaling, redundancy, and failover mechanisms. Mention anycast routing, health checks, and multi-CDN strategy for resilience.
Discuss trade-offs: cost vs. performance, cache hit ratio vs. storage, and complexity of multi-CDN. Suggest optimizations like edge computing for transcoding or personalization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope: what types of content (movies, series, live), scale (millions of titles, billions of users), and access patterns (search, browse, recommendations). Then propose a hybrid data model that separates core metadata (title, description, cast) from dynamic data (viewing rights, availability) and uses a combination of relational and NoSQL stores optimized for read-heavy workloads.
Pro tip: Emphasize the importance of a content delivery network (CDN) and caching layer for metadata to ensure low-latency responses, and discuss how to handle eventual consistency for updates like availability windows.
Ask questions to understand the number of titles, user base, read/write ratio, and latency requirements. This will drive decisions on database choices and caching strategies.
Propose a schema that captures core metadata (e.g., title, genre, release year) and dynamic attributes (e.g., licensing windows, video assets). Consider using a graph model for relationships (e.g., cast, recommendations).
Select appropriate databases: a relational DB for transactional metadata, a NoSQL store (e.g., Cassandra, DynamoDB) for high-scale read access, and a search engine (e.g., Elasticsearch) for full-text search. Discuss trade-offs.
Explain how to partition and replicate data, implement caching (e.g., Redis, CDN), and handle hot spots. Discuss read replicas and eventual consistency for availability updates.
Describe how the catalog integrates with other services (recommendations, playback) and how to evolve the schema over time (e.g., versioning, migrations).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I leaned on collaborative filtering and said something about embedding models for content similarity.
Start by clarifying requirements and constraints, then design a high-level architecture that covers data collection, candidate generation, ranking, and serving. Focus on scalability, real-time personalization, and evaluation metrics, while discussing trade-offs and potential improvements.
Pro tip: Emphasize the importance of cold-start and diversity in recommendations, and how you would measure success beyond click-through rate, such as watch time and user retention.
Ask about scale (users, videos), latency requirements, data available (user interactions, video metadata), and business goals (engagement, retention).
Outline components: data ingestion, feature store, model training, candidate generation, ranking, and serving. Consider batch vs. real-time processing.
Discuss collaborative filtering, content-based filtering, and hybrid approaches. Mention matrix factorization, deep learning models (e.g., two-tower), and contextual bandits.
Explain how to handle large-scale data with distributed systems (e.g., Spark, Kafka) and low-latency serving (e.g., caching, precomputed recommendations).
Describe offline metrics (precision, recall, NDCG) and online A/B testing. Discuss monitoring, feedback loops, and handling cold-start.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Resume playback sounds easy until you think about concurrent sessions.
Start by clarifying requirements such as scale, latency, and consistency needs, then propose a high-level architecture with a central playback state service and client-side caching. Discuss trade-offs between consistency, availability, and performance, and explain how you would handle conflicts and offline scenarios.
Pro tip: Emphasize the importance of idempotent updates and conflict resolution strategies (e.g., last-write-wins with timestamps or versioning) to ensure a seamless user experience across devices. Also, mention how you would handle edge cases like simultaneous playback on multiple devices.
Ask questions to understand scale (number of users, devices), latency requirements, consistency needs (strong vs eventual), and offline support. This shows you consider non-functional aspects before diving into design.
Propose a client-server architecture with a central playback state service (e.g., using a database like DynamoDB or Cassandra) and client-side caching. Outline how devices read and write playback state via APIs.
Define the data model (e.g., user_id, content_id, position, timestamp, device_id) and API endpoints (e.g., GET/PUT /playback-state). Discuss how to handle updates and reads efficiently.
Explain how to sync across devices: use periodic polling or push notifications. For conflicts, use last-write-wins with timestamps or version vectors, and ensure idempotent updates to avoid duplicates.
Discuss trade-offs: consistency vs latency, cost of storage, and complexity. Address edge cases like offline playback, simultaneous updates, and device-specific behaviors (e.g., Roku's suspend/resume).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the streaming architecture and content protection requirements, then walk through integrating DRM at each stage: content preparation, license acquisition, playback, and key management. Emphasize industry standards like Widevine, PlayReady, and FairPlay, and how they fit into Roku's ecosystem.
Pro tip: Demonstrate awareness of Roku's specific DRM support (e.g., PlayReady for Roku devices) and the importance of robust key rotation and secure license delivery to prevent piracy.
Ask about content types, target devices, and existing streaming pipeline to tailor DRM integration. Confirm whether multi-DRM support is needed for cross-platform compatibility.
Select appropriate DRM systems (e.g., Widevine, PlayReady, FairPlay) based on device support. Integrate DRM into the packaging process using tools like Shaka Packager or Bento4 to encrypt content and generate manifests.
Design a secure license server that authenticates users, issues licenses, and enforces policies (e.g., rental expiration). Ensure license requests are over HTTPS and integrate with CDN for scalability.
On the client (e.g., Roku app), use the appropriate DRM APIs to request licenses, decrypt content, and handle playback. Implement error handling for license failures and fallback mechanisms.
Set up logging and analytics to detect unauthorized access. Plan for key rotation, DRM system updates, and compliance with studio requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the read/write ratio, latency requirements, and consistency needs. Then propose a layered architecture with caching, read replicas, and denormalization to offload reads from the primary datastore. Finally, discuss trade-offs like cache invalidation, replication lag, and cost.
Pro tip: Quantify the read/write ratio and estimate cache hit rates to justify design choices. Also, mention that you'd monitor cache effectiveness and adjust TTLs based on access patterns.
Ask about the read/write ratio, acceptable latency, consistency requirements, and data size. This shapes the entire design.
Introduce caching (e.g., Redis, CDN) and read replicas to serve reads without hitting the primary database. Consider denormalization for frequent queries.
Ensure writes go to the primary database and propagate to replicas/caches. Discuss strategies like write-through or write-behind caching.
Explain how to handle cache invalidation (TTL, event-based) and replication lag. Mention trade-offs between consistency and availability.
Discuss horizontal scaling of caches and replicas, and monitoring metrics like cache hit rate, replication delay, and read latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.