The core logic isn't hard: use a map to track the max timestamp per event ID, then sort.
Start by clarifying the problem: confirm the data types, whether timestamps are comparable, and if the result should be sorted by event ID. Then propose an efficient solution using a hash map to track the latest timestamp per event ID, followed by sorting the unique event IDs. Discuss time and space complexity, and consider edge cases like duplicate timestamps or empty input.
Pro tip: Mention that you would handle ties (same timestamp for same ID) by keeping one arbitrarily, but ask the interviewer if a specific tie-breaking rule is preferred. Also, note that if the input is already sorted by event ID, you can avoid the final sort, showing awareness of input characteristics.
Ask about input size, data types, whether timestamps are unique per ID, and if the output must be sorted by event ID. Confirm if in-place modification is allowed or if a new list is expected.
Use a hash map (dictionary) to map event ID to the event with the latest timestamp. This allows O(1) average-time updates. For sorting, you can either sort the keys or collect values and sort by ID.
Traverse the list once, and for each event, compare its timestamp with the stored one for that ID. If it's later, update the map; otherwise, ignore. This ensures only the latest event per ID is kept.
Extract the values from the hash map, sort them by event ID ascending, and return the resulting list. If the input was already sorted by ID, you can skip sorting and just filter duplicates while preserving order.
State that time complexity is O(n + k log k) where n is number of events and k is number of unique IDs, and space is O(k). Discuss edge cases: empty list, all unique IDs, all same ID, timestamps equal, and negative timestamps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.