Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Stripe coding round, one meaty data-processing problem that looked simple until you started thinking about edge cases in the join logic.

Questions Asked (1)

Implement a function that performs an inner join on two CSV-like datasets keyed by a given field name. Rows should only appear in the output if the key exists in both datasets, the output row should merge the two matching rows without duplicating the key column, and the ordering from the first dataset should be preserved. The function also accepts a flag to control behavior on unmatched rows.

Algorithms & Data StructuresAPI & IntegrationsTechnical Trade-offs

Author's notes

Spent the first few minutes just talking through what an inner join actually means in this context before touching any code, which I think helped.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: input format, key field, flag semantics, and edge cases. Then outline an algorithm that builds a hash map from the second dataset for O(1) lookups, iterates through the first dataset to preserve order, and merges rows while handling the key column and unmatched rows according to the flag. Finally, discuss trade-offs like memory usage, time complexity, and potential optimizations.

Pro tip: Explicitly discuss how you would handle duplicate keys in either dataset, as this is a common ambiguity in join operations and shows attention to detail. Also, mention that you would write unit tests covering edge cases like empty datasets, missing keys, and the flag behavior.

1. Clarify requirements and edge cases

Ask questions to understand the input format (e.g., CSV parsing, data types), the meaning of the flag (e.g., include unmatched rows from which side?), and how to handle duplicate keys, missing fields, and empty datasets.

2. Design the algorithm

Propose building a hash map from the second dataset keyed by the join field for O(1) lookups, then iterate through the first dataset to preserve order. For each row, check if the key exists in the map and merge rows accordingly, handling the key column to avoid duplication.

3. Handle the flag and unmatched rows

Explain how the flag controls behavior: if true, include unmatched rows from the first dataset (left join) or both (full outer join); if false, only include matches (inner join). Specify how to represent missing values (e.g., nulls or empty strings).

4. Analyze complexity and trade-offs

State time complexity O(n + m) and space complexity O(m) for the hash map. Discuss alternatives like sorting both datasets and merging (O(n log n + m log m) time, O(1) extra space) and when each might be preferable.

5. Discuss testing and edge cases

Mention writing unit tests for scenarios like no matches, all matches, duplicate keys, empty inputs, and flag variations. Also consider performance with large datasets and potential memory optimizations.

Key Points to Mention

Hash map for efficient lookups: O(1) average time per lookup, overall O(n + m) time complexity.
Preserving order from the first dataset by iterating through it sequentially.
Merging rows without duplicating the key column: exclude the key from the second dataset when merging.
Flag semantics: clarify whether it includes unmatched rows from the first dataset only (left join) or both (full outer join).
Handling duplicate keys: decide whether to produce multiple output rows (cartesian product) or take the first/last match.
Edge cases: empty datasets, missing key field, null values, and large datasets requiring memory considerations.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.