I started with the naive approach, full copy of the graph per snapshot, which works but blows up in space if you have millions of users and frequent snapshots.
Start by clarifying requirements: snapshot frequency, expected query patterns, and consistency needs. Then propose a versioned edge model where each follow/unfollow is stored with a timestamp or version, and snapshots are represented as a cutoff point. For point queries, use binary search on the edge history to check if A followed B at the snapshot time.
Pro tip: Discuss the trade-off between storage overhead and query latency: keeping full history enables efficient point queries but uses more space, while periodic full snapshots save space but make point queries slower. Choose based on read/write ratio and snapshot frequency.
Ask about snapshot frequency, query patterns (point vs. range), consistency guarantees, and scale (number of users, edges, snapshots).
Propose storing each follow/unfollow as a versioned edge with a timestamp or version number. Snapshots are represented as a cutoff version or timestamp.
For a given snapshot and users A and B, retrieve the edge history for (A,B) and binary search for the latest event before the snapshot time to determine the follow state.
Discuss optimizations like indexing, caching, or periodic snapshots. Compare storage vs. query latency trade-offs and choose based on requirements.
Consider concurrent updates, snapshot consistency, and scalability. Mention how to handle missing edges or out-of-order events.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.