I'd seen this problem before and still shipped two bugs.
Start by clarifying requirements: fixed window size, unique IDs, strictly increasing timestamps, and average of scores within the active window. Then design a data structure that supports efficient insertion and eviction, such as a queue (or deque) combined with a running sum to achieve O(1) amortized time per operation. Finally, discuss edge cases like empty window and rounding.
Pro tip: Mention that since timestamps are strictly increasing, you can use a simple queue instead of a more complex data structure, and maintain a running sum to avoid recalculating the average each time. Also, explicitly state how you handle rounding (e.g., using round-half-up or standard rounding) to show attention to detail.
Confirm the meaning of 'fixed window size' (time-based or count-based?), the range of scores, and that timestamps are strictly increasing. Ask about expected operations (e.g., add score, get average) and any performance requirements.
Select a queue (or deque) to store entries in timestamp order, and maintain a running sum of scores. This allows O(1) amortized insertion and eviction, and O(1) average retrieval.
Implement addScore(id, score, timestamp): enqueue the new entry, update sum, and evict all entries with timestamp <= current timestamp - windowSize. Implement getAverage(): return sum / count, rounded to 2 decimals, or 0 if empty.
Address empty window (return 0 or null?), rounding method (e.g., using round half up), and potential integer overflow in sum. Discuss how to handle duplicate IDs (should be unique, but what if not?).
State time complexity: O(1) amortized per operation, space O(window size). Walk through a small example to verify correctness, including eviction and average calculation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where you have to think about your data structure choice.
First, clarify the data model and the definition of the active window (e.g., based on the original timestamp). Then, design an update method that checks the current time against the window, modifies the score only if active, and preserves the original timestamp and ordering. Discuss how to maintain ordering efficiently, possibly using a sorted data structure or by not changing the timestamp.
Pro tip: Mention that you would keep the original timestamp immutable and use it for ordering, and consider using a balanced BST or skip list to allow efficient updates while maintaining order. Also, discuss handling edge cases like exactly at the boundary of the active window.
Ask questions to confirm the definition of 'active window' (e.g., based on original timestamp or last update), what 'score' represents, and whether ordering is by timestamp or score. Confirm that the original timestamp must not change.
Propose a data structure that stores conversations with their original timestamp, score, and possibly an expiration time. Ensure the structure supports efficient lookup and ordered traversal.
Write pseudocode for the update method: check if the conversation is still within the active window (e.g., current time <= original timestamp + window duration). If active, update the score; otherwise, do nothing. Ensure the original timestamp remains unchanged.
Explain how ordering is preserved: if ordering is by original timestamp, no reordering is needed. If ordering is by score, discuss how to update the position efficiently (e.g., using a balanced tree or by removing and reinserting).
State the time complexity of the update operation and discuss edge cases such as exactly at the boundary, concurrent updates, and handling of expired conversations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the definition of percentile (e.g., nearest-rank vs. linear interpolation) and the constraints of the active window (size, update frequency). Then propose an efficient algorithm, such as maintaining a sorted data structure or using a selection algorithm, and discuss trade-offs between accuracy and performance.
Pro tip: Mention that for streaming data, exact percentiles can be expensive, so consider approximate algorithms like t-digest or reservoir sampling if the window is large or updates are frequent. Also, confirm whether the percentile should be computed over all conversations or only those with scores.
Ask about the definition of percentile (e.g., nearest-rank, linear interpolation), the size of the active window, update frequency, and whether scores are integers or floats. Confirm if the window is a sliding time window or a fixed-size buffer.
For small windows, sort the scores and pick the p-th percentile. For large or streaming windows, consider a selection algorithm (Quickselect) or an approximate method (t-digest, reservoir sampling) if exactness is not critical.
If the window updates frequently, maintain a balanced BST or a Fenwick tree over score buckets to support insertions, deletions, and percentile queries in O(log n) time. For approximate methods, maintain a sketch data structure.
Address empty window, p=0 or p=100, and duplicate scores. Test with small examples and compare against a brute-force sort to ensure correctness.
Explain the trade-off between exactness and performance, and how the choice scales with window size and update rate. Mention potential optimizations like caching or incremental computation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.