Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

OpenAI SWE interview with a system design coding problem that had a weird constraint I didn't see coming. The problem itself wasn't crazy hard but the restriction tripped me up more than I expected.

Questions Asked (1)

Design and implement a cluster message aggregation service where messages arrive from multiple nodes, get grouped by a key like cluster ID, and the system returns combined results. All logic must live in a single class with no separate helper classes. Walk through your data model and explain how aggregation and lookup would hold up at scale.

System DesignData ModelingAlgorithms & Data Structures

Author's notes

The single-class constraint is the part that got me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a single class that encapsulates the data model, aggregation logic, and lookup operations. Explain how the design scales by discussing data structures, concurrency, and distribution strategies, and walk through a concrete example.

Pro tip: Emphasize the trade-offs between different data structures and partitioning strategies, and show awareness of real-world constraints like network latency and failure handling.

1. Clarify Requirements

Ask about message volume, latency requirements, consistency needs, and failure scenarios to scope the problem appropriately.

2. Design the Data Model

Define how messages are stored and grouped by cluster ID, choosing appropriate in-memory structures (e.g., hash maps, trees) and considering serialization for persistence.

3. Implement Aggregation Logic

Describe how messages are combined (e.g., sum, count, average) and how the single class manages state and updates efficiently.

4. Address Scalability

Discuss partitioning, sharding, replication, and concurrency control to handle increasing load and ensure high availability.

5. Walk Through an Example

Trace a sample message flow from arrival to aggregation and lookup, highlighting how the design performs under scale.

Key Points to Mention

Choice of data structures (e.g., hash map for O(1) average lookup, balanced tree for ordered aggregation)
Partitioning strategy (e.g., consistent hashing) to distribute clusters across nodes
Concurrency handling (e.g., locks, actor model, or lock-free structures) for thread safety
Fault tolerance and replication to avoid single points of failure
Trade-offs between latency and consistency (e.g., eventual vs strong consistency)
Monitoring and metrics for performance and health

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