Took me a minute to stop thinking about this as a system design thing and realize it's just binary search on a sorted list of timestamps.
Clarify requirements and constraints, then propose a design using a hash map where each key maps to a list of (timestamp, value) pairs sorted by timestamp. For set, append and keep sorted (or use binary search for insertion); for get, binary search for the largest timestamp ≤ query time. Discuss trade-offs and potential optimizations like using a balanced BST or time-series database for scalability.
Pro tip: Mention that timestamps are monotonically increasing in typical use cases, so appending to the list maintains sorted order without extra sorting. Also, discuss how to handle large-scale data with distributed storage and caching, showing awareness of Uber's scale.
Ask about expected data volume, read/write ratio, timestamp granularity, and whether timestamps are unique per key. Confirm that get should return the value at the largest timestamp ≤ given time, and what to return if no such timestamp exists.
Propose a hash map from key to a sorted list of (timestamp, value) pairs. For efficient lookup, use binary search on the list. Alternatively, consider a balanced BST (e.g., TreeMap) if insertions are frequent and out of order.
For set: if timestamps are increasing, append to the list; otherwise, insert in sorted order. For get: binary search for the largest timestamp ≤ query time and return the corresponding value, or empty if none.
Discuss time and space complexity: set O(1) amortized if appending, O(log n) for binary search insertion; get O(log n) for binary search. Space O(n) for n entries.
Address how to handle large-scale data: sharding by key, using a distributed database like Cassandra or Redis with sorted sets, and caching hot keys. Mention trade-offs between in-memory and persistent storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.