My first instinct was to just do a nested loop and i had to stop myself.
First, clarify the problem requirements and constraints, such as file sizes, memory limits, and whether the files are sorted. Then, propose an efficient algorithm using hash maps to track object IDs and their associated client IDs from both files, ensuring you handle duplicates and distinct clients correctly. Finally, discuss time and space complexity and potential optimizations.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that if the files are too large to fit in memory, you could use external sorting or a distributed approach like MapReduce. This shows you think beyond the basic algorithm.
Ask about file sizes, memory limits, whether files are sorted, and if timestamps matter. This ensures you design an appropriate solution.
Use a hash map to aggregate client IDs per object ID from both files, then filter object IDs that have at least two distinct client IDs. Consider using a set for distinct clients.
State the time complexity (O(N+M) where N and M are the number of records in each file) and space complexity (O(K) where K is the number of unique object IDs).
Mention handling large files with external sorting or streaming, and edge cases like duplicate records or missing fields.
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