Decagon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026Remote

Summary

Decagon coding screen, one question about a sliding window data structure. Pretty straightforward on the surface but the eviction logic tripped me up a bit.

Questions Asked (1)

Design a class that accepts a window size on initialization and supports inserting timestamped score records and returning the average score of all records currently within the active window. Both methods should evict stale records on every call.

Algorithms & Data StructuresSystem Design

Author's notes

The insert and average parts were fine, I had a deque in mind pretty quickly.

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AI HintsAI Generated

Suggested Approach

Use a deque to store timestamped records in chronological order, and maintain a running sum of scores. On each insert or query, evict records older than the window by comparing timestamps to the current time minus window size, then update the sum and return the average.

Pro tip: Clarify whether the window is inclusive or exclusive and whether timestamps are strictly increasing; this shows attention to edge cases and can prevent off-by-one errors.

1. Clarify requirements and assumptions

Ask about window semantics (inclusive/exclusive), timestamp ordering, and whether scores can be negative or zero. Confirm that eviction should happen on every method call.

2. Choose data structures

Select a deque (double-ended queue) to store records in order, and a running sum variable to avoid recomputing the average from scratch.

3. Design eviction logic

On each insert or query, remove records from the front of the deque while their timestamp is older than (current time - window size). Update the running sum accordingly.

4. Implement insert and getAverage

For insert, append the new record and add its score to the sum. For getAverage, return sum / count if count > 0, else 0 (or handle empty case as specified).

5. Analyze complexity and edge cases

State that both operations are O(1) amortized due to deque operations, and discuss edge cases like empty window, out-of-order timestamps, and large windows.

Key Points to Mention

Use of deque for O(1) amortized eviction and insertion
Maintaining a running sum to achieve O(1) average retrieval
Eviction condition: timestamp < current_time - window_size
Handling empty window (return 0 or null as appropriate)
Assumption about timestamp ordering (if not sorted, consider alternative approaches)
Time and space complexity analysis

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