Google·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Apr 2026

Summary

Google SWE interview focused entirely on a waitlist system design with OOP flavor. Pretty interesting problem but the concurrency part at the end threw me off more than I expected.

Questions Asked (1)

Design a Waitlist system class that supports adding a user to the back, removing a user from any position, popping the front user, and querying a user's position or presence. Walk through the data structures, class design, edge cases, and concurrency concerns.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with a doubly linked list plus a hashmap keyed on userId pointing to the node.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a design using a doubly linked list for O(1) removals and a hash map for O(1) lookups. Discuss edge cases and concurrency, and compare trade-offs with alternative structures like balanced trees or skip lists.

Pro tip: Emphasize that the combination of a doubly linked list and hash map is a classic pattern for O(1) operations, but also discuss how to handle concurrency with fine-grained locking or lock-free techniques to show depth.

1. Clarify Requirements

Ask about expected operations, performance requirements, concurrency needs, and whether the waitlist is bounded or unbounded.

2. Choose Data Structures

Propose a doubly linked list for O(1) insertions/removals and a hash map for O(1) lookups, explaining why this combination meets the requirements.

3. Design Class Interface

Define methods: add(user), remove(user), popFront(), getPosition(user), contains(user), and discuss return types and error handling.

4. Handle Edge Cases

Cover empty list operations, duplicate additions, removing non-existent users, and popping from an empty waitlist.

5. Address Concurrency

Discuss thread-safety using locks (e.g., per-node or global) or lock-free approaches, and trade-offs between simplicity and scalability.

Key Points to Mention

Doubly linked list enables O(1) removal from any position when given a node reference.
Hash map provides O(1) average-case lookup for user presence and position.
Position query can be O(1) if we store position in the node and update on insert/remove, but that adds complexity; alternatively, O(n) traversal.
Concurrency: use a global lock for simplicity, or fine-grained locks for better performance; consider lock-free with atomic pointers.
Edge cases: empty list, duplicate users, removing non-existent users, and concurrent modifications.
Trade-offs: alternative structures like balanced BST (O(log n) operations) or skip list, but linked list + hash map is optimal for given operations.

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