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

SeniorPrefer not to say

Jun 2026

Summary

System design round at OpenAI for a software engineer role. The question was a deep one about building an in-memory database from scratch, covering basically every hard problem at once. Left feeling like I only really nailed two or three of the five areas they were probing.

Questions Asked (1)

Design an in-memory database that supports CRUD operations, efficient querying, transaction isolation, a persistence and backup strategy, and horizontal scalability, all within a single-node memory-resident architecture.

System DesignTechnical Trade-offsData Modeling

Author's notes

This thing sprawls in every direction and I didn't pace myself well.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level architecture that addresses each component: CRUD, querying, transactions, persistence, and scalability. Dive into trade-offs for key design decisions, and conclude with how the system meets the stated goals.

Pro tip: Emphasize that horizontal scalability in a single-node memory-resident architecture is achieved through vertical scaling and efficient data sharding within the node, not by adding nodes. Discuss how you would handle data larger than memory, such as tiering to disk.

1. Clarify Requirements and Constraints

Ask questions to understand expected data size, query patterns, consistency needs, and performance targets. Confirm that 'single-node memory-resident' means all data fits in RAM and scaling is vertical.

2. Design Core Data Model and Storage

Choose an in-memory data structure (e.g., hash map for CRUD, skip list or B-tree for ordered queries). Discuss indexing strategies for efficient querying.

3. Implement Transaction Isolation

Select an isolation level (e.g., snapshot isolation) and describe how to implement it using versioning or locking. Explain how to handle concurrent operations.

4. Persistence and Backup Strategy

Propose write-ahead logging (WAL) for durability and periodic snapshots for backups. Discuss recovery procedures and trade-offs between performance and durability.

5. Address Scalability and Performance

Explain vertical scaling limits and optimizations like sharding within the node, caching, and efficient memory management. Discuss how to handle data larger than memory (e.g., tiering).

Key Points to Mention

Use of in-memory data structures like hash maps and skip lists for O(1) CRUD and efficient range queries.
Transaction isolation via multi-version concurrency control (MVCC) or snapshot isolation to avoid read-write conflicts.
Write-ahead logging (WAL) combined with periodic snapshots for durability and fast recovery.
Horizontal scalability within a single node through data sharding and parallel query execution.
Trade-offs between latency, throughput, and durability in persistence strategies.
Handling data larger than memory via tiering to disk or compression, while maintaining memory-resident performance.

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