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Went straight to a max-heap and sorted by total duration per worker.
Clarify the input format (e.g., list of employee records with employee ID and duration) and define 'total time' as the sum of durations per employee. Then use a hash map to aggregate total time per employee, and a min-heap of size N to efficiently find the top N employees, or sort the aggregated list if N is large relative to the number of employees.
Pro tip: Discuss trade-offs between sorting and heap-based selection, and mention that for very large datasets, a distributed approach (e.g., MapReduce) might be needed. Also, consider edge cases like ties and invalid data.
Ask about input format, definition of 'total time', handling of ties, and expected size of data. Confirm whether N is small or large relative to the number of employees.
Use a hash map to accumulate total time per employee by iterating through the records. This gives O(M) time where M is number of records.
If N is small, use a min-heap of size N to keep track of the top N employees while iterating through the aggregated map. If N is large, sort the aggregated list in descending order and take the first N.
Decide how to break ties (e.g., by employee ID or arbitrary). Ensure the method handles cases where there are fewer than N employees, or when N is zero.
State time and space complexity: O(M + K log N) with heap (K = number of unique employees) or O(M + K log K) with sorting. Mention potential optimizations like using a quickselect algorithm for O(K) average time.
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