Started fine with the base case in my head, then they asked about one-to-many and I had to stop and rethink the whole thing.
Start by clarifying the requirements and edge cases, then propose a hash-based join that groups processor rows by key while preserving their original order. Walk through the algorithm step-by-step, analyze time and space complexity, and discuss trade-offs such as memory usage and handling of duplicate keys.
Pro tip: Emphasize that preserving the processor file's row order is critical for deterministic output and often required for downstream systems; mention that using a hash map with lists naturally maintains insertion order if you append during the initial scan.
Ask about expected input sizes, whether keys are unique in the customer file, how to handle missing matches, and if the output should be sorted or preserve processor order. Confirm that one output row per match is desired.
Propose building a hash map from processor key to a list of processor rows, where the list preserves the original order of appearance. This allows O(1) average lookup per customer row.
Iterate through the processor file once to populate the hash map. Then iterate through the customer file, and for each customer row, look up the key and emit a combined row for each processor row in the list, in order.
State that time complexity is O(P + C + M) where P is processor rows, C is customer rows, and M is total matches; space is O(P) for the hash map. Discuss alternatives like sorting if memory is constrained.
Mention handling of missing keys (emit customer row with nulls or skip), duplicate keys in customer file, and ensuring stable order. Suggest writing unit tests for these scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.