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

SeniorPrefer not to say

Apr 2026

Summary

Stripe system design round, one meaty graph problem that took up the whole session. The problem was more interesting than I expected but I definitely underestimated how much complexity was hiding in the follow-up discussion.

Questions Asked (1)

You're given a list of user records where each field has a weight. Two users are connected if the sum of weights for their matching fields exceeds a threshold. Build this similarity graph and return all users in the same connected component as a given target user, including those reachable through multiple hops. Then discuss your approach to union-find vs BFS/DFS, and how you'd avoid the O(n²) pairwise comparison cost.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I started with the naive approach, compute every pair's match score, build the graph, run BFS from the target.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (n, field count, threshold) and then propose a two-phase solution: first, efficiently generate candidate pairs using techniques like inverted indices or locality-sensitive hashing to avoid O(n²); second, build the graph and find connected components using union-find or BFS/DFS. Compare union-find and BFS/DFS in terms of time/space complexity, implementation complexity, and suitability for dynamic updates, and justify your choice based on the scenario.

Pro tip: Mention that in production systems like Stripe, the similarity graph is often built incrementally and may need to handle updates, so union-find with path compression and union by rank is often preferred for its near-constant time operations and ability to process edges in a streaming fashion.

1. Clarify requirements and constraints

Ask about the number of users (n), number of fields, weight ranges, threshold, and whether the graph is static or dynamic. Also confirm if the target user is guaranteed to exist and if the output should include the target user.

2. Design an efficient candidate generation strategy

Propose using inverted indices per field to quickly find users sharing a field, then compute weighted sums only for those candidate pairs. Alternatively, use locality-sensitive hashing (LSH) or blocking to reduce the number of pairwise comparisons.

3. Build the similarity graph and find connected components

For each candidate pair, compute the weighted similarity; if it exceeds the threshold, add an edge. Then use union-find (with path compression and union by rank) or BFS/DFS to find all users connected to the target.

4. Compare union-find vs BFS/DFS

Discuss trade-offs: union-find is ideal for incremental edge additions and near-constant time per operation, while BFS/DFS is simpler for static graphs and can provide the actual path if needed. Mention that union-find is often more efficient for large graphs with many edges.

5. Analyze complexity and scalability

Explain that candidate generation reduces comparisons from O(n²) to O(n * average candidates per user). The overall complexity depends on the number of edges; union-find operations are nearly O(1) amortized, making the total near-linear in the number of edges.

Key Points to Mention

Inverted index or LSH to avoid O(n²) pairwise comparisons
Union-find with path compression and union by rank for efficient connected components
BFS/DFS for static graphs and path retrieval
Time and space complexity analysis: O(E α(n)) for union-find, O(V+E) for BFS/DFS
Handling dynamic updates and streaming edges with union-find
Threshold tuning and its impact on graph density and performance

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