Google·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Google SWE interview with a waitlist/seating system design question. The problem looked like a clean OOP exercise until the constraints hit and you realized a plain list wasn't going to cut it.

Questions Asked (1)

Design a restaurant waitlist system that supports adding parties, canceling them, seating them by table capacity in arrival order, and querying a party's current position, all with better-than-linear time per operation.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was a plain linked list and I said so out loud, which was fine for addParty and cancelParty but then seatTable came up and I realized scanning the whole list each time was not going to fly for 200k ops.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a data structure that supports O(log n) operations. Use a balanced BST or skip list for the waitlist, and a segment tree or Fenwick tree for efficient position queries. Discuss trade-offs and handle edge cases like cancellations and table capacity matching.

Pro tip: Demonstrate awareness of real-world constraints: parties may cancel, tables have varying capacities, and the system must be fair (FIFO). Mention that a simple linked list would be O(n) for position queries, so a tree-based structure is needed.

1. Clarify Requirements

Ask about expected scale, operations per second, and whether parties can be seated out of order if a larger table is available. Confirm that position query is 1-indexed from the front.

2. Choose Data Structures

Propose a balanced BST (e.g., Red-Black Tree) or skip list to store parties ordered by arrival time. For position queries, augment each node with subtree size to compute rank in O(log n).

3. Handle Seating by Capacity

Maintain a separate structure (e.g., min-heap or balanced BST) for available tables keyed by capacity. When seating, find the smallest table that fits the party, then remove the party from the waitlist.

4. Support Cancellations

Allow O(log n) deletion from the waitlist by party ID. Use a hash map from party ID to node pointer for direct access.

5. Analyze Complexity and Trade-offs

Explain that all operations are O(log n) due to tree operations. Discuss alternatives like Fenwick tree over arrival indices, and trade-offs between memory and speed.

Key Points to Mention

Use of augmented balanced BST (or order-statistic tree) for O(log n) insert, delete, and rank queries.
Separate data structure for tables (e.g., min-heap by capacity) to efficiently find suitable table.
Hash map for O(1) lookup of party by ID to support cancellation.
Handling of edge cases: empty waitlist, no available table, party larger than any table.
Trade-offs: memory overhead of augmentation vs. speed; alternative use of Fenwick tree with coordinate compression.
Fairness: FIFO order must be preserved for seating, but position query reflects current order after cancellations.

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