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Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Stripe SWE screen focused on transaction processing, the kind of question that looks straightforward but has enough moving parts to trip you up if you're not careful about the details.

Questions Asked (1)

Q1

Given a stream of transaction records (each with an id, timestamp, and payload like sender, receiver, amount), sort them by timestamp oldest-first and build hashmap-based summaries grouping transactions by user with aggregates like count, total amount, and last seen. Discuss time complexity and how you'd handle timestamp ties.

Algorithms & Data StructuresTechnical Trade-offsData Modeling
Author's notes

The sort part was fine, O(n log n), said it out loud and moved on.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: whether the stream is bounded or unbounded, and whether sorting is necessary before aggregation. Then propose an approach that sorts by timestamp (using a stable sort and a tie-breaker like id) and builds a hashmap keyed by user to accumulate aggregates, discussing time and space complexity. Finally, address timestamp ties and potential scalability concerns.

Pro tip: Mention that if the stream is unbounded, sorting the entire stream is impractical; instead, consider windowed aggregation or using a priority queue for streaming top-k, showing awareness of real-world constraints.

1. Clarify requirements and constraints

Ask whether the stream is bounded or unbounded, the expected volume, and if sorting is strictly required before aggregation. Clarify what 'last seen' means and how ties should be handled.

2. Design the sorting strategy

Propose sorting by timestamp using a stable sort (e.g., TimSort) and define a deterministic tie-breaker, such as transaction id or arrival order, to ensure consistency.

3. Build the hashmap-based aggregation

Iterate through sorted transactions, using a hashmap keyed by user (sender/receiver) to accumulate count, total amount, and last seen timestamp. Update aggregates efficiently in O(1) per transaction.

4. Analyze time and space complexity

State that sorting takes O(n log n) time, aggregation takes O(n) time, and space is O(n) for sorting plus O(u) for the hashmap where u is the number of unique users.

5. Address timestamp ties and scalability

Explain how ties are resolved (e.g., by id) and discuss alternatives for unbounded streams, such as windowed processing or using a priority queue for streaming aggregation.

Key Points to Mention

  • Stable sorting and deterministic tie-breaking (e.g., by transaction id) to handle timestamp ties.
  • Time complexity: O(n log n) for sorting, O(n) for aggregation; space complexity: O(n) for sorting and O(u) for the hashmap.
  • Hashmap keyed by user with aggregates: count, total amount, last seen timestamp (and possibly first seen).
  • Handling both sender and receiver as users, updating aggregates for each.
  • Scalability considerations for unbounded streams: windowing, external sorting, or streaming algorithms.
  • Potential optimizations: using a balanced BST or priority queue if only top-k users are needed, or if the stream is already partially sorted.

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