Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Stripe coding round for a software engineer role, one question about implementing a join across two CSV-like datasets. Pretty algorithmic for what felt like a practical data problem.

Questions Asked (1)

Implement a join over two CSV-like datasets where one customer row can match multiple processor rows on a shared column. Walk through the algorithm, handle the one-to-many matching case, and discuss time and space complexity.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

The merge-style two-pointer approach clicked pretty quickly but I fumbled the tie-handling part for longer than I'd like to admit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the dataset sizes, memory constraints, and whether the join is in-memory or distributed. Then propose a hash join: build a hash map on the smaller dataset keyed by the join column, and probe with the larger dataset, emitting all matches for one-to-many. Finally, analyze time and space complexity, noting the trade-offs and possible optimizations.

Pro tip: Mention that if the smaller dataset doesn't fit in memory, you can partition both datasets by the join key (e.g., using hash partitioning) and perform the join partition-by-partition, which is how distributed systems like Spark handle skew.

1. Clarify requirements and constraints

Ask about dataset sizes, memory limits, whether the join is in-memory or distributed, and if the output should be sorted or streamed. This shows you consider practical constraints before diving into code.

2. Choose the join algorithm

Propose a hash join: build a hash map on the smaller dataset keyed by the join column, mapping each key to a list of rows. Then probe with the larger dataset, iterating over matches for each key.

3. Handle one-to-many matching

When building the hash map, store a list of rows for each key (or use a multimap). During probing, for each row in the larger dataset, look up the key and emit a combined row for each matching row in the list.

4. Analyze time and space complexity

Time: O(N + M) average case, where N and M are the sizes of the two datasets, assuming hash map operations are O(1). Space: O(N) for the hash map, where N is the size of the smaller dataset. Mention worst-case O(N*M) if many duplicates and no hash map, but hash join avoids that.

5. Discuss trade-offs and optimizations

Compare with sort-merge join (O(N log N + M log M) time, O(1) extra space if in-place) and nested loop join (O(N*M) time). Mention handling data skew, memory limits, and distributed joins if applicable.

Key Points to Mention

Hash join is efficient for equi-joins and handles one-to-many naturally by storing lists of values.
Time complexity: O(N + M) average case, but worst-case O(N*M) if all keys collide and lists are long; however, hash join still processes each match, so output size matters.
Space complexity: O(N) for the hash map, where N is the size of the smaller dataset; if N is too large, consider partitioning or sort-merge join.
Handling data skew: if one key has many duplicates, the corresponding list can be large; consider salting or partitioning to distribute load.
Alternative algorithms: sort-merge join (good for sorted data or limited memory) and nested loop join (simple but inefficient for large datasets).
Real-world considerations: use of external sorting or distributed processing (e.g., MapReduce, Spark) when data doesn't fit in memory.

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