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Stackadapt·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026Remote

Summary

Coding round at StackAdapt for a software engineering role. One meaty design-plus-implementation question that took up the whole session, and it had more moving parts than it looked like at first glance.

Questions Asked (1)

Q1

Design and implement a time-windowed key-value store that only retains entries from the most recent window of N milliseconds. It needs to support Get(key), Put(key, value), and GetAverage() over valid entries, and you should discuss data structure choices and time complexity tradeoffs.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I went straight to HashMap for the key-value lookup and thought I was done with the structure in two minutes.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then propose a data structure like a hash map combined with a time-ordered queue or min-heap to track expiration. Discuss lazy vs eager eviction, and analyze time complexity for each operation, including GetAverage. Finally, consider optimizations and trade-offs for different scenarios.

Pro tip: Mention that GetAverage can be maintained in O(1) by tracking the sum and count of valid entries, updating them on insertions and evictions. This shows you think about efficiency beyond the basic operations.

1. Clarify Requirements

Ask about expected read/write ratio, whether GetAverage is called frequently, and if the window is fixed or sliding. Confirm that entries expire strictly after N milliseconds from insertion.

2. Propose Data Structures

Suggest a hash map for O(1) key access and a time-ordered structure (e.g., deque or min-heap) for expiration. Explain how they work together to support Get, Put, and eviction.

3. Handle Expiration Strategy

Discuss lazy vs eager eviction. Lazy eviction checks timestamps on access, while eager eviction proactively removes expired entries. Choose based on read/write patterns and memory constraints.

4. Analyze Time Complexity

Break down time complexity for each operation: Get O(1), Put O(1) amortized, GetAverage O(1) if maintained, and eviction O(1) or O(log n) depending on structure. Discuss trade-offs.

5. Optimize and Discuss Trade-offs

Consider concurrency, memory overhead, and alternative structures like balanced BST or skip list. Mention how GetAverage can be computed in O(1) by tracking sum and count.

Key Points to Mention

  • Use a hash map for O(1) key-value access and a deque or min-heap for time-ordered expiration.
  • Lazy eviction vs eager eviction: trade-offs in time and memory.
  • Maintain running sum and count for O(1) GetAverage, updating on insertions and evictions.
  • Time complexity: Get O(1), Put O(1) amortized, GetAverage O(1), eviction O(1) or O(log n).
  • Consider concurrency and thread-safety if the store is accessed by multiple threads.
  • Discuss memory overhead and potential optimizations like batching or periodic cleanup.

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