I got the basic join working fine but fumbled on the left join part for longer than I should've.
First, clarify the current join logic and data structures, then outline how to modify it to a left join by iterating over the customer rows and looking up matches in the processor data. Emphasize preserving order and handling missing matches by filling with empty strings, and discuss trade-offs like using a hash map for efficiency.
Pro tip: Mention that you would add tests for edge cases like duplicate keys, empty processor file, and missing keys to ensure correctness, and consider memory implications if datasets are large.
Review the current join implementation to identify how it iterates over rows, accesses keys, and combines columns. Determine if it uses nested loops or a hash map for lookups.
Plan to iterate over each customer row, look up the matching processor row by key, and if found, merge columns; if not, use empty strings for processor columns. Ensure the output order matches the customer file order.
Build a hash map (dictionary) from the processor dataset keyed by the join key to achieve O(1) average lookup time. Handle duplicate keys by deciding on a strategy (e.g., first match or list of matches) based on requirements.
For each customer row without a match, append empty strings for all processor columns. Ensure the output CSV structure remains consistent with the original join output.
Write tests covering normal cases, missing keys, duplicate keys, and empty processor file. Verify that row order is preserved and empty strings are correctly inserted.
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