I went with a deque of (timestamp, conversation_id, score) tuples sorted by arrival time, which felt right.
Start by clarifying requirements: what operations are needed (record score, get rolling average), how the time window is configured, and expected data volume. Then propose a data structure like a queue (or deque) combined with a running sum to achieve O(1) amortized time for both operations, and analyze the trade-offs versus alternatives like a circular buffer or a balanced BST.
Pro tip: Mention that you would use a timestamp-based eviction strategy (e.g., removing entries older than the window) and discuss how to handle edge cases like out-of-order timestamps or empty windows. This shows attention to real-world robustness.
Ask about the expected number of records, the granularity of timestamps, whether the window is fixed or sliding, and if scores can be updated or deleted. This ensures you design the right solution.
Propose a queue (or deque) to store (timestamp, score) pairs, along with a running sum of scores within the window. Explain why this gives O(1) amortized time for adding and O(1) for querying the average.
Describe the addScore method: append the new score, update the running sum, and evict expired entries from the front while their timestamp is outside the window. Describe getAverage: return sum / count if count > 0, else 0 or null.
State that both operations are O(1) amortized time (each element is added and removed once) and O(n) space where n is the maximum number of records in the window. Compare with alternatives like a sorted list (O(log n) insert) or a heap (O(log n) for eviction).
Mention potential issues: out-of-order timestamps, clock skew, and concurrency. Suggest solutions like using a monotonic clock, buffering out-of-order entries, or adding locks for thread safety.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.