I went straight to a deque and felt good about it for the basic case.
Start by clarifying requirements and assumptions, then propose a design using a deque (or queue) to store timestamp-value pairs and maintain a running sum for O(1) amortized insert and get_avg. Discuss trade-offs such as memory usage, concurrency, and handling out-of-order timestamps, and consider edge cases like empty window or duplicate timestamps.
Pro tip: Emphasize that eviction must happen before every operation to keep the window consistent, and mention that using a running sum avoids O(n) scans, but be prepared to discuss how to handle out-of-order inserts (e.g., using a balanced BST or sorted list) if the interviewer pushes for it.
Ask about timestamp ordering (monotonic?), window definition (inclusive/exclusive), and whether get_avg should consider only samples within the last W seconds from the given timestamp or from the latest timestamp. Confirm if timestamps are unique and if out-of-order inserts are allowed.
Propose a deque (double-ended queue) to store (timestamp, value) pairs in chronological order, plus a running sum of values. Explain that this gives O(1) amortized time for insert and get_avg when timestamps are monotonic.
Describe the eviction process: before any operation, remove from the front of the deque all samples with timestamp < current_timestamp - W, subtracting their values from the running sum. Ensure this is done for both insert and get_avg.
Discuss handling empty window (return 0 or None), duplicate timestamps, and out-of-order inserts. For out-of-order, suggest alternatives like a balanced BST or sorted list with O(log n) insert, or a min-heap for eviction if order is not needed for average.
State time and space complexity: O(1) amortized per operation for monotonic timestamps, O(n) space. Discuss trade-offs: memory vs. speed, concurrency (thread-safety), and whether to use a lock or lock-free approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the semantics of 'current time' for the update operation—whether it should use the actual system time or the timestamp of the last inserted sample. Then, describe how the update operation interacts with the eviction policy, ensuring that the updated sample's timestamp is refreshed appropriately to reflect its new recency. Finally, discuss the data structure modifications needed to support efficient updates and evictions.
Pro tip: Mention that using the last inserted timestamp as 'current time' maintains consistency with the insertion order and avoids introducing external time dependencies, which is often preferred in streaming systems. Also, highlight the trade-off between updating the timestamp (which affects eviction order) versus keeping the original timestamp (which may cause immediate eviction).
Decide whether 'current time' refers to the system clock at the moment of update or the timestamp of the most recent insertion. Explain the implications of each choice on eviction behavior.
Specify that update changes the value of an existing sample and may also refresh its timestamp to the chosen 'current time', effectively treating it as a new insertion for eviction purposes.
Describe how to modify the underlying data structure (e.g., a hash map for O(1) access plus a min-heap or balanced BST for eviction) to support efficient updates and maintain ordering.
Explain that after updating, the eviction step should remove samples older than the new 'current time' minus the window, and that the updated sample's new timestamp affects its eviction eligibility.
Address scenarios like updating a non-existent sample, updating an already evicted sample, and the performance impact of timestamp updates on eviction order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.