Snorkel AI rejection report.
The interview problem was to implement a SnapshotMap. The prompt gave a fairly long background and API requirements, so the first step was to extract the core data model and operation semantics from the business description. The basic interfaces were put(k, v), get(k), and delete(k), extended with take_snapshot() and get(k, snap_id) to read a key's value from a historical snapshot.
The key was to satisfy additional performance and storage constraints. Values that had not changed between snapshots could not be stored repeatedly. All operations had to be sublinear relative to the entire map size. The scale was about one million keys and ten snapshots, with only about 1% of the keys changing in each snapshot.
So the problem was essentially a Versioned Key-Value Store / Snapshot Data Structure. The core idea was to avoid copying the entire map for each snapshot and record only the keys that changed, for example by maintaining a version history of key -> [(snapshot_id, value)]. When reading a historical snapshot, use the version number to find the most recent modification before that snapshot. This takes advantage of the small number of keys changing each time and avoids storing large amounts of duplicate data.
The problem type could be classified as Data Structure Design + HashMap + Versioning + Binary Search / Persistence. The interview mainly tested extracting requirements, API semantics, space optimization, complexity analysis, and choosing a data structure to fit the workload, rather than a complicated algorithm itself.
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