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Decagon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jul 2026Remote

Summary

Decagon SWE interview had a sliding window design problem that looked deceptively clean on the surface but had a bunch of decisions baked in that you had to call out explicitly.

Questions Asked (1)

Q1

Design a class that takes a window length at initialization and supports two operations: recording a conversation event with an ID, timestamp, and score (1 to 5), and returning the average score of all conversations whose timestamp falls within the active window relative to the latest seen timestamp.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

The core question wasn't that hard but the part that tripped me up was when they asked me to state my time semantics upfront.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: the window is relative to the latest timestamp, and we need average score of events within that window. Then propose a data structure like a deque (or balanced BST) to maintain events in the window, and discuss how to efficiently compute the average. Finally, analyze time and space complexity and consider edge cases.

Pro tip: Emphasize that the window is dynamic and based on the latest timestamp, so you must evict old events as new ones arrive. Mention that maintaining a running sum allows O(1) average retrieval, but eviction may require O(1) amortized time with a deque.

1. Clarify requirements and constraints

Ask about the definition of 'active window': is it inclusive? What if timestamps are out of order? What should be returned if no events are in the window? Confirm that the window slides based on the latest timestamp seen.

2. Choose appropriate data structures

Propose using a deque (double-ended queue) to store events in timestamp order, along with a running sum of scores. Alternatively, consider a balanced BST if timestamps are not monotonic, but note that the problem implies events arrive in order.

3. Design the record operation

When recording an event, append it to the deque, add its score to the running sum, and then remove all events from the front whose timestamp is outside the window relative to the new latest timestamp. Update the sum accordingly.

4. Design the getAverage operation

Return the running sum divided by the number of events in the deque. Handle the case of an empty deque by returning 0 or throwing an exception as appropriate.

5. Analyze complexity and edge cases

Discuss time complexity: O(1) amortized per record (each event added and removed once), O(1) for getAverage. Space O(W) where W is max events in window. Mention edge cases: out-of-order timestamps, duplicate timestamps, window length zero, and large window.

Key Points to Mention

  • Use a deque to maintain events in the window and a running sum for O(1) average.
  • Evict events with timestamp < latest_timestamp - window_length (or <= depending on inclusivity).
  • Time complexity: O(1) amortized per operation; space O(W).
  • Handle empty window by returning 0 or a sentinel value.
  • Consider if timestamps are not monotonically increasing; if so, a different structure like a balanced BST or segment tree may be needed.
  • Discuss trade-offs: deque is simple and efficient for monotonic timestamps, but if out-of-order events are allowed, more complex structures are required.

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