The boolean flag part is what tripped me up at first.
Start by clarifying the input data structures and the join key, then outline a hash-based join algorithm that builds a hash map from the right table and probes it with the left table. Explain how the boolean parameter controls whether unmatched left rows are filtered out or emitted with nulls for right-side columns. Finally, discuss time/space complexity and potential optimizations for large datasets.
Pro tip: Mention that you would handle duplicate keys on both sides correctly and that you'd consider memory constraints by potentially partitioning or using a sort-merge join for very large datasets. This shows you think beyond the basic algorithm and consider real-world production scenarios.
Ask about the input format (e.g., arrays of objects, database tables), the join key, and how to represent nulls for unmatched right-side columns. Confirm that the left side is the customer table and that the boolean controls inner vs. left outer join.
Propose a hash join: build a hash map from the right table keyed by the join key, then iterate over the left table. Explain why this is efficient (O(n+m) average time) and mention alternatives like nested loops or sort-merge join for context.
For each left row, look up matching right rows. If matches exist, emit combined rows. If no match and the flag is false (left outer), emit the left row with nulls for right columns; if true (inner), skip. Handle duplicate keys by emitting multiple rows per match.
State time complexity O(n+m) and space O(m) for the hash map. Discuss edge cases: empty tables, null keys, duplicate keys, and memory limits for large right tables.
Mention that for very large datasets, a partitioned hash join or sort-merge join may be better. Also note that if the right table is small, a broadcast hash join is efficient. Tie back to Stripe's scale and data processing needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.