The multi-match case is where I lost time.
Clarify the input formats and the expected output ordering, then propose a hash-based join that groups processor records by customer key, sorts each group by the 'order' field, and emits the customer row followed by its matches. Discuss time/space complexity and edge cases like missing matches or duplicate order values.
Pro tip: Mention that you would sort matches within each group rather than globally, and use a stable sort to preserve original order for ties—this shows attention to correctness and performance.
Ask about file sizes, whether the customer file is unique per customer, the meaning of the 'order' field, and how to handle ties or missing matches.
Propose building a hash map from the processor file keyed by customer ID, with values as lists of processor records, to enable O(1) lookups per customer.
For each customer, sort the list of matching processor records by the 'order' field, using a stable sort to maintain original order for equal 'order' values.
Iterate through the customer file, output the customer row, then immediately output each sorted match row, ensuring extra rows follow the original customer row.
Discuss time complexity (O(N + M + K log K) where K is matches per customer) and space complexity, and address edge cases like no matches, duplicate orders, and large files.
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