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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Google software engineering interview focused on a single system design coding question about building a waitlist data structure. The follow-up discussion went deeper than I expected, pushing into priority queues and estimated wait times.

Questions Asked (1)

Q1

Design a waitlist data structure that supports adding a person, removing a person, getting their current position, popping the next person to be served, and returning the current size. What data structures would you use and why?

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

I jumped straight to a plain array and immediately regretted it once they asked about remove().

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., expected operations, concurrency, memory). Then propose a hybrid data structure: a doubly linked list for O(1) insertion/removal and a hash map for O(1) lookup of nodes. Explain how each operation works and analyze time/space complexity, mentioning trade-offs and possible optimizations.

Pro tip: Mention that this is essentially an LRU cache without the eviction policy, and discuss how you would handle concurrency (e.g., locks or lock-free) if the waitlist is accessed by multiple threads.

1. Clarify Requirements

Ask about expected operations, frequency, concurrency, and memory constraints. Confirm that position lookup and removal by person are needed.

2. Propose Data Structures

Suggest a doubly linked list to maintain order and a hash map (dictionary) mapping person to their node for O(1) access.

3. Detail Operations

Explain how each operation (add, remove, get position, pop, size) is implemented using the proposed structures, ensuring O(1) time for most.

4. Analyze Complexity and Trade-offs

Discuss time and space complexity, compare with alternatives (e.g., array, balanced tree), and mention trade-offs like memory overhead vs. speed.

5. Address Edge Cases and Extensions

Cover concurrency, persistence, or additional operations (e.g., moving a person to the end) and how to handle them.

Key Points to Mention

  • Doubly linked list for O(1) insertion and removal given a node reference.
  • Hash map for O(1) lookup of a person's node by ID.
  • Position retrieval can be O(1) if each node stores its position, but updates on insert/remove may require O(n) unless using a different structure (e.g., order-statistic tree).
  • Time complexity: add O(1), remove O(1), get position O(1) with position tracking or O(n) without, pop O(1), size O(1).
  • Space complexity: O(n) for both structures.
  • Concurrency considerations: use locks or concurrent data structures if multiple threads access the waitlist.

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