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Google·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
May 2026

Summary

Google SWE coding round, got a waitlist system design-ish coding problem that looked deceptively simple at first glance but had a spicy follow-up about global ordering across size buckets.

Questions Asked (1)

Q1

Design and implement a restaurant waitlist system that supports adding parties by size (FIFO per size group) and allocating tables by capacity. The follow-up requires that when a table opens up, you must seat the party that has waited the longest globally among all parties whose size fits the table, not just the longest-waiting party of a specific size.

Algorithms & Data StructuresSystem Design
Author's notes

The base version felt manageable, just a dict of queues keyed by party size.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a data structure that efficiently supports both FIFO per size group and global oldest-first allocation. For the follow-up, propose a solution that balances time complexity and implementation simplicity, such as a segment tree over party sizes or a heap with lazy deletion, and analyze trade-offs.

Pro tip: Demonstrate awareness of real-world constraints like table capacity distribution and party size distribution, and discuss how they affect data structure choice. Also, mention that you would start with a simple solution and optimize based on expected load.

1. Clarify requirements and constraints

Ask about expected number of parties, table sizes, frequency of operations, and whether parties can be split or combined. Confirm that FIFO per size group means within each party size, order is preserved.

2. Design initial data structures

Propose using a queue per party size for FIFO, and a mapping from table capacity to available tables. For allocation, iterate over sizes that fit the table and pick the oldest party.

3. Optimize for the follow-up

Introduce a global ordering mechanism, such as a segment tree over party sizes storing the oldest timestamp, or a min-heap of all waiting parties with lazy deletion when parties are seated out of order.

4. Analyze complexity and trade-offs

Compare time complexities: naive O(k) per allocation vs. segment tree O(log n) vs. heap O(log n) with lazy deletion. Discuss space and implementation complexity.

5. Handle edge cases and concurrency

Consider empty waitlist, no fitting table, multiple tables opening simultaneously, and thread safety if concurrent. Mention testing strategy.

Key Points to Mention

  • FIFO per size group: use separate queues for each party size.
  • Global oldest-first allocation: need to efficiently find the oldest party among all sizes that fit a given table capacity.
  • Segment tree over party sizes storing minimum timestamp (or maximum, depending on representation) to query oldest fitting party in O(log n).
  • Alternative: min-heap of all parties with lazy deletion, but careful with stale entries and ensuring FIFO within size groups.
  • Time complexity analysis: naive O(k) where k is number of sizes, optimized O(log n) per operation.
  • Real-world considerations: table capacity distribution, party size distribution, and potential for batching or caching.

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