Start by clarifying the problem: confirm the bucket thresholds, whether buckets are inclusive, and the expected output format. Then outline a two-phase approach: first deduplicate by user-movie pair using a hash map to keep the max percent watched, then iterate over the deduplicated pairs to count how many fall into each bucket. Discuss trade-offs between memory usage and streaming vs. batch processing.
Pro tip: Mention that in a real streaming scenario, you might need to handle late-arriving events or out-of-order data, so a simple hash map might not suffice; consider using a windowed aggregation or a stateful stream processor like Flink. Also, clarify if the buckets are cumulative (e.g., at least 30%) or exclusive ranges, as this drastically changes the counting logic.
Ask about bucket definitions (inclusive/exclusive, cumulative), handling of ties, missing data, and whether the stream is bounded or unbounded. Confirm the output format (e.g., map of bucket to count).
Use a hash map keyed by (userID, movieID) to store the maximum percent watched seen so far. Explain that this ensures O(1) average update per event and O(U*M) space, where U*M is the number of unique pairs.
Based on clarified bucket definitions, write a function that maps a percent watched to a bucket. For example, if buckets are [0-30), [30-50), [50-80), [80-100], use conditional checks or binary search for efficiency.
Iterate over the deduplicated pairs and increment the count for the corresponding bucket. Use an array or hash map to accumulate counts.
Address memory constraints for large streams: consider approximate algorithms (e.g., count-min sketch) if exact counts are not required, or partitioning by userID to process in parallel. Mention time complexity: O(N) for N events, plus O(P) for P unique pairs.
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Clarify the input format (e.g., a dictionary mapping bucket names to counts) and confirm that sorting should be descending by count. Then implement a sort using a custom comparator or key function, and extract only the bucket names. Discuss time complexity and potential tie-breaking rules.
Pro tip: Mention that if counts are large or buckets are numerous, a counting sort or bucket sort by count could be more efficient than comparison-based sorting, but for typical interview constraints, Python's Timsort is fine. Also, ask about tie-breaking to show attention to detail.
Confirm the data structure (e.g., dictionary of bucket names to counts) and that output should be a list of names sorted by count descending. Ask about tie-breaking (e.g., alphabetical order).
Decide between sorting the items by count using a key function or using a heap if only top k are needed. For full sort, use built-in sort with key=lambda x: -count or reverse=True.
Sort the items, then iterate to collect only the bucket names into a new list. Ensure the sort is stable if tie-breaking matters.
State time complexity O(n log n) for comparison sort, space O(n). Discuss edge cases: empty input, all counts equal, negative counts (if possible).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.