My first instinct was to load everything into a hash map and call it a day, but then I remembered both inputs are sorted and the interviewer kind of raised an eyebrow when I said that.
Clarify the problem constraints (e.g., memory, data size, duplicate keys) and then propose a two-pointer merge join that leverages the sorted order for O(n+m) time. Walk through the algorithm step-by-step, handle edge cases like duplicate keys and one dataset exhausted, and discuss trade-offs versus hash join.
Pro tip: Explicitly state that you're using the sorted property to achieve O(n+m) time and O(1) extra space, and mention that this is ideal for streaming or large datasets where memory is limited. This shows you understand the practical implications beyond just solving the problem.
Ask about data size, memory limits, duplicate keys, and whether the output should preserve order. Confirm that both datasets are sorted by the join key and that an inner join is required.
Initialize pointers at the start of each dataset. Compare keys: if equal, output the combined row and advance both; if one key is smaller, advance that pointer. Repeat until one dataset is exhausted.
Explain how to handle multiple rows with the same key (e.g., nested loops for matching groups) and what to do when one dataset runs out. Also consider empty datasets or missing join key.
State that time complexity is O(n+m) and space is O(1) extra (excluding output). Compare with hash join (O(n+m) time but O(n) space) and sort-merge join if data isn't sorted.
Mention how to read CSV rows, parse the join key, and output combined rows. Optionally discuss parallelization, external memory, or using a database join if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.