Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Stripe coding screen for a software engineer role. One problem, the whole time, and it was deceptively tricky to get right. The sliding window part was straightforward enough but the event deduplication logic is where things get messy if you're not careful.

Questions Asked (1)

Given transaction logs sorted by timestamp, each with a merchant ID, status code, error code, and count field: monitor each (merchant_id, status_code) pair using a rolling window. For each log entry at time t, the rolling count is the sum of all counts for that pair within [t - window_size + 1, t]. Emit a TRIGGER event when the rolling count crosses from below threshold to at or above it, and a RESOLVE event when it drops back below. No duplicate consecutive events per pair. Return all events in chronological order.

Algorithms & Data StructuresSystem Design

Author's notes

The sliding window itself wasn't the hard part.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a solution using a sliding window with a deque or two pointers to maintain the rolling sum per (merchant_id, status_code) pair. Track the current state (below/above threshold) for each pair to emit TRIGGER and RESOLVE events only on state transitions, ensuring no duplicate consecutive events and chronological output.

Pro tip: Emphasize the importance of handling out-of-order timestamps and the need for a data structure that efficiently supports both adding new entries and removing expired ones, such as a deque per pair. Also, mention that you would confirm whether the window is inclusive of both endpoints and how ties are handled.

1. Clarify requirements and edge cases

Ask about window size definition, threshold crossing semantics (inclusive/exclusive), handling of out-of-order timestamps, and whether multiple events can occur at the same timestamp.

2. Design data structures

Use a hash map to store per-pair state: a deque of (timestamp, count) for the window, the current rolling sum, and the current state (below/above threshold).

3. Process logs in chronological order

For each log entry, update the rolling sum by adding the new count and removing entries older than t - window_size + 1. Then check if the sum crosses the threshold and emit events accordingly.

4. Emit events and maintain state

If the sum transitions from below to at/above threshold, emit TRIGGER; if from at/above to below, emit RESOLVE. Update the state and ensure no duplicate consecutive events.

5. Analyze complexity and optimize

Discuss time complexity O(N) with amortized O(1) per log entry, and space O(N) in worst case. Mention potential optimizations like using a balanced BST for out-of-order handling.

Key Points to Mention

Use of sliding window with deque for efficient addition and removal of counts.
State tracking per (merchant_id, status_code) to emit events only on threshold crossings.
Handling of out-of-order timestamps and potential need for a priority queue or balanced tree.
Ensuring chronological output by processing logs in timestamp order and emitting events as they occur.
Edge cases: window size larger than data, threshold exactly equal to sum, multiple events at same timestamp.
Complexity analysis: O(N) time, O(N) space, and discussion of trade-offs.

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