I jumped straight to a hash map approach and the interviewer nudged me toward using the sorted property.
Clarify the exact join semantics and output format, then propose a two-pointer merge join that exploits the sorted order of both datasets. Walk through the algorithm step by step, handling duplicate keys and unmatched rows, and analyze time and space complexity.
Pro tip: Explicitly discuss how you would handle duplicate join keys in the processor dataset, since a naive two-pointer approach can miss matches if not advanced correctly. Mention that you'd validate the join column exists in both headers and decide on a consistent output schema before coding.
Confirm the join column name, output column order, handling of duplicate keys, and whether headers should be included in the output. Ask about memory constraints to decide between streaming and in-memory approaches.
Since both datasets are sorted, use a two-pointer merge join for O(n+m) time and O(1) extra space (excluding output). If sorted order weren't guaranteed, consider hash join or sorting first.
Iterate through customer rows; for each, advance the processor pointer while processor key < customer key. If keys match, emit joined row(s); otherwise emit customer row with empty processor fields.
For duplicate processor keys, emit one output row per matching processor row. Ensure every customer row appears at least once, with empty strings for unmatched processor columns.
State time complexity O(n+m) and space O(1) auxiliary. Discuss trade-offs: two-pointer requires sorted input; hash join is O(n+m) time but O(m) space and doesn't require sorting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.