I recognized the min-heap skeleton pretty fast, but the same-timestamp collapsing part tripped me up for a minute.
Use a min-heap to merge the K sorted lists, but when the smallest timestamp is found, collect all records with that timestamp from the heap and from the current heads of all lists, combine them, and emit one record. This ensures duplicates are aggregated before output. Complexity is O(N log K) time and O(K) space, where N is total records.
Pro tip: Emphasize that you must drain all lists of the current minimum timestamp before emitting, and discuss how to handle ties efficiently without degrading to O(N log N).
Confirm that lists are sorted by timestamp, records have a timestamp and value, and combining means summing values. Ask about duplicate timestamps within a single list and empty lists.
Initialize a min-heap with the first record from each non-empty list. Repeatedly extract the minimum timestamp, then gather all records with that timestamp from the heap and from the next elements of the lists they came from.
Sum the values of all gathered records, emit the combined record, and push the next record from each list that contributed a record with that timestamp back into the heap.
Time: O(N log K) because each record is pushed and popped once, and heap size is at most K. Space: O(K) for the heap, plus O(1) for aggregation variables.
Mention that if timestamps are dense, a bucket or counting approach might be faster, but the heap approach is general and optimal for comparison-based merging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.