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Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Stripe SWE interview with a streaming data problem that sounds deceptively simple until you start thinking about state management across pairs. The sliding window angle is what makes it interesting, and I don't think I nailed the eviction logic on the first pass.

Questions Asked (1)

Q1

You're given a stream of transaction log records, each with a timestamp, merchant ID, status code, and count. For every unique (merchant, status code) pair, track a sliding time-window sum of error events. Emit a TRIGGER when the cumulative count first hits a configured threshold T, and emit a RESOLVE when it falls back below T as old records age out. Records arrive in timestamp order. Implement this.

Algorithms & Data StructuresSystem Design
Author's notes

My first instinct was to reach for a hashmap keyed on the pair and just keep a running total, which obviously doesn't account for the sliding part at all.

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AI HintsAI Generated

Suggested Approach

Clarify requirements and edge cases, then design a solution using a sliding window per (merchant, status) pair with a deque and running sum. Explain how to emit TRIGGER and RESOLVE events when the sum crosses the threshold, and discuss time/space complexity and potential optimizations.

Pro tip: Explicitly discuss how to handle late or out-of-order records, as real-world streams often have delays; mention using a watermark or allowed lateness to maintain correctness.

1. Clarify Requirements

Ask about window size, threshold T, definition of 'error events', and whether records can be late or out-of-order. Confirm that only error status codes contribute to the sum.

2. Design Data Structures

For each (merchant, status) pair, maintain a deque of (timestamp, count) for records within the window and a running sum of error counts. Use a hash map to index these structures.

3. Process Stream and Emit Events

For each record, add to the appropriate deque and update sum. Evict expired records from the front. After each update, check if sum crosses T and emit TRIGGER or RESOLVE accordingly.

4. Analyze Complexity and Optimize

Discuss O(1) amortized time per record and O(N) space. Consider optimizations like lazy deletion or bucketed timestamps for high-throughput scenarios.

5. Test and Validate

Walk through examples, including edge cases like exactly hitting T, multiple triggers/resolves, and late records. Verify correctness and discuss potential pitfalls.

Key Points to Mention

  • Sliding window implementation using a deque and running sum for O(1) amortized updates.
  • Event emission logic: TRIGGER when sum >= T and previously < T; RESOLVE when sum < T and previously >= T.
  • Handling out-of-order or late records with watermarks or allowed lateness.
  • Time and space complexity analysis: O(1) per record, O(N) space for active windows.
  • Scalability considerations: partitioning by merchant, using distributed stream processing frameworks.
  • Edge cases: empty windows, multiple status codes, threshold changes, and concurrent updates.

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