I knew roughly where this was going once they said 'timestamp' but I fumbled the get logic for a bit.
Clarify the requirements and constraints, then propose a design using a hash map from keys to sorted lists of (timestamp, value) pairs. For set, append to the list; for get, binary search for the largest timestamp ≤ query timestamp. Discuss time/space complexity and potential optimizations like using TreeMap or balanced BST.
Pro tip: Mention that timestamps for set operations are strictly increasing, which allows appending without sorting and enables efficient binary search. Also, consider thread-safety and persistence if the system needs to scale.
Ask about constraints: timestamp range, number of operations, concurrency, persistence, and whether timestamps are unique per key. Confirm that get should return the value with the largest timestamp ≤ query timestamp.
Propose a hash map where each key maps to a list of (timestamp, value) pairs. Since timestamps for set are increasing, the list remains sorted. Alternatively, use a TreeMap for each key to allow floorKey operations.
For set: append the new (timestamp, value) to the list for the key. For get: binary search the list for the largest timestamp ≤ query timestamp; if found, return the value, else return empty string or null.
Set is O(1) amortized (append). Get is O(log n) where n is the number of timestamps for that key. Space is O(total number of set operations).
Consider using a balanced BST or TreeMap for O(log n) get and O(log n) set if timestamps are not strictly increasing. Handle edge cases: empty store, key not found, query timestamp before all timestamps, and duplicate timestamps (if allowed, decide on overwrite or keep latest).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.