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Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026Remote

Summary

OpenAI coding round that picked up where a previous session left off, extending a SocialNetwork class with follower/followee lookup and a real conversation about data structure tradeoffs. Pretty design-heavy for what felt like a continuation problem.

Questions Asked (2)

Q1

Given a user ID in the SocialNetwork class, implement a method that returns both the list of followers (who follows this user) and the list of followees (who this user follows).

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

The followees side was easy since we already had a forward adjacency map from part one.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data model and expected scale, then propose an adjacency-list representation with separate follower and followee maps. Implement the method to return both lists in O(1) or O(followers + followees) time, and discuss trade-offs for large-scale systems.

Pro tip: Mention that in a real system you'd likely use a graph database or a distributed store like Redis for fast lookups, and that the method should return immutable copies to avoid external mutation.

1. Clarify requirements

Ask about expected scale (millions of users?), read/write ratio, and whether the lists need to be sorted or paginated. Confirm the return type (e.g., List<Integer> or Set<Integer>).

2. Choose data structures

Propose using two hash maps: one mapping user ID to a set of follower IDs, and another mapping user ID to a set of followee IDs. This gives O(1) average-time access to each list.

3. Implement the method

Write a method that looks up the user ID in both maps and returns the corresponding sets (or lists). Handle the case where the user has no followers/followees by returning empty collections.

4. Analyze complexity and trade-offs

State that time complexity is O(1) for retrieval (or O(n) if copying to a list), and space is O(E) where E is the number of edges. Discuss alternatives like adjacency matrix (O(V^2) space) or database queries.

5. Consider scalability and extensions

Mention how this would scale with sharding, caching, or using a graph database. Also note potential need for pagination or asynchronous updates in a distributed setting.

Key Points to Mention

  • Adjacency list representation using hash maps for O(1) average-time lookups
  • Time and space complexity analysis: O(1) retrieval, O(E) space
  • Handling edge cases: non-existent user, empty follower/followee lists
  • Immutability: returning copies to prevent external modification
  • Trade-offs: in-memory vs. database, consistency vs. availability
  • Scalability: sharding, caching, and graph databases for large social networks

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

Q2

Walk through the tradeoffs between keeping only a forward adjacency map versus maintaining both forward and reverse maps for a social graph.

Technical Trade-offsData ModelingSystem Design
Author's notes

This is where the interview actually lived.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the graph's access patterns and scale, then compare the two designs on memory, write latency, and read performance. Conclude with a recommendation that balances the workload's dominant operations and consistency requirements.

Pro tip: Mention that reverse edges can be derived from forward edges via a batch job or materialized view, so you can start with forward-only and add reverse maps later if read patterns demand it—this shows you think about evolution, not just static tradeoffs.

1. Clarify requirements and scale

Ask about the graph size, read/write ratio, and whether queries like 'who follows me?' are common. This grounds the tradeoff analysis in real constraints.

2. Analyze forward-only design

Explain that forward-only uses less memory and simpler writes, but reverse queries require full scans or secondary indexes, hurting read latency.

3. Analyze bidirectional design

Describe how maintaining both maps doubles memory and write cost (two updates per edge) but makes both forward and reverse traversals O(1).

4. Compare on key dimensions

Contrast memory footprint, write amplification, read performance, and consistency complexity (e.g., atomic updates across both maps).

5. Recommend based on workload

Propose a choice: forward-only if reverse queries are rare or can be batched; bidirectional if low-latency reverse lookups are critical and write volume is manageable.

Key Points to Mention

  • Memory overhead: reverse map roughly doubles storage for edges.
  • Write amplification: each edge insertion/deletion requires two updates, increasing latency and failure surface.
  • Read performance: reverse queries become O(1) instead of O(degree) or requiring a full scan.
  • Consistency: keeping both maps in sync atomically is harder; eventual consistency may be acceptable.
  • Alternative: derive reverse edges on demand via a secondary index or batch job to avoid maintaining both maps.
  • Scalability: at social-graph scale (billions of edges), the memory and write costs of bidirectional maps are significant.

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