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I went with the usual breakdown: video upload pipeline, transcoding, CDN distribution, metadata storage.
Start by clarifying requirements and scale (e.g., daily active users, uploads, views, storage). Then design the system in layers: client, CDN, API, services, storage, and data processing, focusing on trade-offs for video upload, transcoding, storage, and delivery. Emphasize scalability, availability, and cost-efficiency, and tie decisions to Snowflake's data cloud strengths where relevant.
Pro tip: Proactively discuss trade-offs between consistency and latency, and how you'd handle hot videos (e.g., caching, CDN, replication) and long-tail content (e.g., cold storage, tiered storage). This shows you think about real-world constraints beyond just functional requirements.
Ask questions to understand functional (upload, watch, search, recommendations) and non-functional (latency, availability, durability) requirements. Estimate scale: e.g., 2B users, 500 hours uploaded per minute, 1B hours watched daily.
Sketch the main components: clients (web/mobile), CDN, load balancers, API gateway, microservices (user, video metadata, transcoding, recommendation), and storage (object store, databases, cache). Explain data flow for upload and playback.
Pick 2-3 areas to detail: video upload and transcoding pipeline (chunked upload, message queue, parallel transcoding, DAG), storage and serving (CDN, adaptive bitrate streaming, tiered storage), and metadata/database design (sharding, replication, consistency).
Discuss how to scale each layer (horizontal scaling, partitioning, caching), ensure reliability (replication, failover, monitoring), and make trade-offs (e.g., strong vs eventual consistency, cost vs latency, pre-compute vs on-demand).
Mention how to handle analytics (view counts, recommendations) using batch and stream processing, and how Snowflake's data cloud could integrate for large-scale analytics and ML.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the cache requirements and constraints based on the previously designed system, then choose an appropriate caching strategy (e.g., write-through, write-back) and eviction policy (e.g., LRU, LFU). Implement the cache with thread-safety and scalability in mind, and discuss trade-offs such as consistency, latency, and memory overhead.
Pro tip: Demonstrate awareness of Snowflake's cloud-native, multi-tenant environment by discussing how caching can be distributed and isolated per tenant, and how to handle cache invalidation across nodes.
Ask questions to understand the system's read/write patterns, data size, latency requirements, and consistency needs. Confirm whether the cache should be in-memory, distributed, or persistent.
Select a caching pattern (e.g., cache-aside, write-through) and eviction policy (e.g., LRU, LFU) based on access patterns and consistency requirements. Justify your choices with trade-offs.
Outline the data structures (e.g., hash map + doubly linked list for LRU) and synchronization mechanisms (e.g., locks, sharding) to ensure thread-safety and high concurrency.
Write pseudocode or actual code for get, put, and evict operations, handling edge cases like cache misses, updates, and evictions. Ensure O(1) time complexity for basic operations.
Discuss how to scale the cache (e.g., sharding, replication) and maintain consistency (e.g., invalidation protocols, TTL). Mention monitoring and metrics for cache performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.