Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Google SWE coding round, one meaty design-and-implement question about a restaurant waitlist that ate up most of the session. The follow-up extension changed the whole problem and I had to think on my feet.

Questions Asked (2)

Design and implement a restaurant waitlist system supporting two operations: add(party_size, arrival_time) to enqueue a party, and allocate(table_size) to seat and remove the earliest-arriving party whose size exactly matches the given table size.

Algorithms & Data StructuresSystem Design

Author's notes

My first instinct was to just throw everything in a list and scan linearly, which would have been fine to say out loud but felt embarrassing to lead with at Google.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a data structure that supports efficient add and allocate operations. Discuss trade-offs between different approaches, such as using a hash map of queues versus a balanced BST, and analyze time and space complexity. Finally, outline the implementation details and consider edge cases.

Pro tip: Mention that you would use a hash map from party size to a queue of arrival times, but also consider whether arrival times are globally unique and if you need to maintain global order for other operations. This shows you think about real-world constraints and scalability.

1. Clarify Requirements

Ask about expected operation frequency, constraints on party sizes, whether arrival times are unique, and if there are additional operations like cancel or check availability.

2. Choose Data Structures

Propose a hash map mapping party size to a queue (FIFO) of arrival times. Discuss alternatives like a balanced BST keyed by (party size, arrival time) for ordered operations.

3. Analyze Complexity

For the hash map + queue approach, add is O(1) and allocate is O(1) amortized. For BST, both are O(log n). Compare trade-offs in terms of simplicity and additional operations.

4. Implement Core Operations

Write pseudocode for add: append arrival time to the queue for the given party size. For allocate: if queue for table size exists and non-empty, pop the earliest arrival time and return it; else return null.

5. Handle Edge Cases and Extensions

Discuss handling of empty queues, non-existent party sizes, and potential extensions like waiting time estimation or priority for larger parties.

Key Points to Mention

Use a hash map from party size to a FIFO queue of arrival times for O(1) operations.
Consider using a balanced BST (e.g., TreeMap) if you need to support operations like finding the earliest party across all sizes or range queries.
Analyze time and space complexity: O(1) for add and allocate with hash map + queue, O(n) space.
Discuss trade-offs: hash map is simpler and faster for exact match, but BST offers ordered operations at the cost of O(log n) time.
Mention edge cases: empty queue, party size not present, and concurrency if multiple threads.
Suggest possible optimizations: using a doubly linked list for O(1) removal if needed, or a min-heap if arrival times are not unique.

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

Extend the allocate operation so that a table of a given size can seat the earliest-arriving party whose size is less than or equal to the table size, not just an exact match.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is where I slowed down.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: we need to find the earliest-arriving party with size ≤ table size. Then, propose a data structure that efficiently supports this operation, such as a balanced BST or a segment tree over party sizes, and analyze its time complexity. Finally, discuss trade-offs and potential optimizations.

Pro tip: Mention that you would use a TreeMap (or equivalent) to store parties by arrival time and a segment tree to query the smallest size ≥ table size, showing awareness of both time and space trade-offs.

1. Clarify requirements

Confirm that 'earliest-arriving' means the party that arrived first among those with size ≤ table size, and that we need to find such a party for each allocate call.

2. Choose data structures

Propose maintaining a data structure keyed by party size to quickly find the smallest size ≥ table size, and another to track arrival order, such as a queue or timestamp.

3. Design algorithm

Outline an algorithm: for a given table size, query the size-based structure for the smallest size ≥ table size, then among parties of that size, pick the one with the earliest arrival time.

4. Analyze complexity

State the time complexity for allocate (e.g., O(log n) with a balanced BST) and space complexity, and compare with naive approaches.

5. Discuss trade-offs and edge cases

Mention potential optimizations, handling of no available party, and how the solution scales with many allocate calls.

Key Points to Mention

Use a balanced binary search tree (e.g., TreeMap) to store parties by size for efficient floor/ceiling queries.
Maintain a separate structure (e.g., queue or priority queue) to track arrival order for parties of the same size.
Time complexity: O(log n) per allocate operation with the proposed data structures.
Space complexity: O(n) to store all parties.
Edge case: if no party fits, return null or indicate failure.
Trade-off: using a segment tree over possible sizes can give O(log n) but may require more space if sizes are sparse.

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