This is the main question and it ate the whole session.
Start by clarifying requirements and scale (DAU, QPS, latency, personalization depth), then design the API contract and data model before diving into the feed generation pipeline. Propose a layered architecture with a precomputed candidate pool, a lightweight ranking/scoring service, and a caching layer to meet low-latency pagination. Discuss trade-offs between freshness, personalization, and cost, and cover failure modes and monitoring.
Pro tip: Emphasize that cursor-based pagination must be stable and consistent under concurrent writes; use a snapshot or versioned cursor to avoid duplicates/skips. Also, precompute and cache feed pages for active users to reduce online ranking cost, but have a fallback for cold users.
Ask about DAU, peak QPS, latency SLA, page size, personalization signals, and freshness requirements. Establish assumptions for the design.
Specify request/response schema, cursor semantics (opaque, versioned), and metadata fields (CDN URL, thumbnail, caption, etc.). Decide on cursor encoding (e.g., base64 of timestamp+video_id+version).
Outline candidate generation (e.g., from follow graph, trending, embeddings), ranking (ML model or heuristic), and filtering (dedup, safety). Consider precomputation vs. online ranking.
Explain how to handle pagination with a cursor: use a snapshot of the ranked list or a stable sort key. Discuss strategies for consistency (e.g., versioned cursors, read-your-writes).
Propose caching layers (CDN for media, Redis for feed pages), sharding, and async precomputation. Discuss trade-offs between freshness and cost, and failure handling.
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Bloom filter was the obvious answer and I said it fast, but then they asked what happens when the filter gives a false positive and you skip a video the user actually hasn't seen.
Start by clarifying the scale and requirements, then propose a two-tiered approach: an in-memory cache for session-level deduplication and a persistent store (like a database or Redis) for long-term deduplication. Discuss trade-offs between accuracy, latency, and storage, and how to handle edge cases like cache eviction and eventual consistency.
Pro tip: Mention that you'd use a probabilistic data structure like a Bloom filter for long-term deduplication to save memory, but combine it with a fallback exact check to avoid false positives. This shows you understand the trade-offs between memory efficiency and accuracy.
Ask about scale (number of users, videos, watch events per day), latency requirements, and whether deduplication should be exact or approximate. Also clarify if deduplication is per user or per device.
For within a single session, use an in-memory data structure like a hash set or LRU cache to track watched video IDs. This is fast and simple, but ephemeral.
For longer time windows, use a persistent store. Consider a key-value store like Redis with TTL for recent history, and a database for longer retention. Discuss sharding by user ID for scalability.
Discuss memory vs. accuracy (e.g., Bloom filters), cache eviction policies, handling multiple devices, and eventual consistency. Also consider how to handle new videos and resetting deduplication after a certain period.
Recap the proposed solution, emphasizing how it meets the requirements and handles scale. Mention monitoring and potential optimizations.
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Start by outlining the high-level CDN architecture for video delivery, emphasizing scalability and low latency. Then dive into access control using signed URLs, explaining the signing process, validation, and security considerations. Finally, discuss trade-offs and how you'd handle edge cases like token expiration and revocation.
Pro tip: Mention that signed URLs should be generated server-side with short TTLs and include IP restrictions when possible, and highlight the importance of using a CDN that supports token authentication natively to avoid custom crypto at the edge.
Explain how you'd choose a CDN (e.g., multi-CDN for redundancy), configure caching rules for video segments (e.g., long TTL for popular content, shorter for live), and optimize for global low-latency delivery.
Describe the signed URL mechanism: generate a URL with an expiration timestamp and HMAC signature using a secret key, and validate it at the CDN edge to grant or deny access.
Detail the signing process (e.g., using a library like AWS CloudFront signed URLs or custom HMAC) and how the CDN validates the signature and expiration, including handling clock skew.
Discuss key management (rotating secrets), preventing hotlinking, and scaling signature generation with a stateless service. Mention using short TTLs and IP restrictions for sensitive content.
Compare signed URLs vs. signed cookies vs. token-based auth, and discuss trade-offs like added latency from validation, complexity, and cost of CDN features.
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I defaulted to the classic celebrity problem argument: push-on-write breaks for creators with millions of followers because you're writing to millions of feed caches on every upload.
Start by defining the product context (e.g., Snapchat's ephemeral, social graph-heavy feed) and then compare push vs. pull across dimensions like latency, scalability, cost, and complexity. Conclude with a hybrid recommendation that leverages the strengths of both approaches, tailored to the product's specific access patterns and constraints.
Pro tip: Emphasize that the choice isn't binary—real-world systems often use a hybrid (e.g., push for active users, pull for inactive) and that the decision should be driven by metrics like fan-out ratio and read/write patterns. Show awareness of Snapchat's unique aspects, such as ephemeral content and close-friend networks, which can influence the trade-offs.
Ask about the product's scale, user engagement patterns, and latency requirements to ground the discussion. For Snapchat, consider ephemeral content, friend graph size, and read-heavy vs. write-heavy patterns.
Briefly explain push (fan-out on write) and pull (fan-out on read) with examples. Highlight that push precomputes feeds, while pull computes on demand.
Analyze trade-offs across key dimensions: latency (push is faster for reads), scalability (pull handles high fan-out better), cost (push uses more storage, pull uses more compute), and complexity (push requires robust write pipeline, pull requires efficient read aggregation).
Relate trade-offs to Snapchat's features: ephemeral stories, friend updates, and Discover content. Discuss how push might suit active users with small friend lists, while pull could handle celebrities or inactive users.
Recommend a hybrid approach, such as push for active users and pull for others, or using a write-ahead log with caching. Justify based on trade-offs and product goals.
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