The monotonic timestamp constraint is the key thing to notice early.
Use a queue (or deque) to store (timestamp, value) pairs, and maintain a running sum of values in the window. On insert, add the new pair and update the sum; then remove from the front all pairs with timestamp < current_timestamp - window_size, subtracting their values from the sum. For get_avg, return sum / queue length (or 0 if empty).
Pro tip: Clarify whether the window is inclusive of the current timestamp and whether get_avg should be called with the same timestamp as the last insert. Also, mention that the non-decreasing timestamps guarantee the queue remains sorted, so we can efficiently evict old entries.
Ask about window inclusivity, behavior when no values are in the window, and whether get_avg can be called with a timestamp earlier than the last insert. Confirm that timestamps are non-decreasing.
Select a queue (e.g., collections.deque in Python) to store (timestamp, value) pairs in insertion order, and maintain a running sum variable to avoid recomputing the sum each time.
Append the new (timestamp, value) to the queue and add value to the running sum. Then, while the queue is not empty and the front timestamp is outside the window (i.e., < current_timestamp - window_size), remove it and subtract its value from the sum.
If the queue is empty, return 0 (or appropriate sentinel). Otherwise, return the running sum divided by the number of elements in the queue.
Explain that each element is inserted and removed at most once, so amortized O(1) time per operation and O(n) space. Walk through an example with multiple inserts and get_avg calls to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: update method should modify value or refresh timestamp by ID. Then propose a data structure that supports efficient lookup and update, such as a hash map for O(1) average update, or a balanced BST for O(log n) worst-case. Discuss tradeoffs including time complexity, memory overhead, and concurrency considerations.
Pro tip: Mention that O(1) hash map updates degrade to O(n) in worst-case due to collisions, while O(log n) structures like balanced BSTs provide predictable performance and ordered traversal, which can be valuable for range queries or timestamp ordering.
Confirm what 'update' entails: modifying value, refreshing timestamp, or both. Ask about expected read/write patterns and concurrency needs.
Suggest a hash map (ID -> record) for O(1) average update, or a balanced BST (e.g., red-black tree) keyed by ID for O(log n) update. Mention hybrid approaches like hash map + linked list for LRU.
Compare O(1) vs O(log n): hash map offers faster average updates but no ordering and worst-case O(n); BST provides ordered operations and stable O(log n) but higher constant factors.
Discuss memory overhead, cache performance, concurrency (locking vs lock-free), and whether ordering by timestamp is needed for queries.
Choose based on use case: if updates dominate and order isn't needed, hash map; if range queries or ordered traversal matter, BST.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.