The follow/unfollow and snap parts felt manageable.
Start by clarifying requirements and scale, then propose a design that separates the mutable current graph from immutable snapshots. Use a versioned adjacency list with copy-on-write or persistent data structures to enable efficient snapshots, and design the recommendation API to compute mutual follower counts using snapshot data, optimizing with caching or precomputation.
Pro tip: Emphasize the trade-offs between snapshot frequency, memory usage, and query latency; propose a hybrid approach like periodic full snapshots with incremental deltas to balance cost and performance.
Ask about expected number of users, follow operations per second, snapshot frequency, query patterns, and latency requirements. This informs data structure and storage choices.
Propose a versioned adjacency list where each node's followers/followees are stored with timestamps or version numbers. Use persistent data structures (e.g., immutable maps) or copy-on-write for snapshots.
Describe how to create point-in-time snapshots efficiently: either full copy (if scale allows), or using persistent data structures that share structure, or logging changes and replaying to a snapshot version.
Outline an algorithm to find friends-of-friends and rank by mutual follower count. Use the snapshot to compute intersections of followee sets, and optimize with caching or precomputed counts for frequent queries.
Compare approaches: memory vs. snapshot speed, query latency vs. consistency. Suggest optimizations like incremental snapshots, lazy evaluation, or approximate counts for large scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.