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Stackadapt·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Stackadapt software engineer interview that went deep into graph traversal and system design. One meaty problem, lots of follow-ups about edge cases I hadn't fully thought through.

Questions Asked (1)

Q1

You have a graph where nodes represent instances of two different entity types, and edges connect related entities across types. You're given two separate functions to fetch neighbors for each entity type. Implement a traversal that runs a validation function on every node in the graph. How do you handle cycles, disconnected components, and memory pressure on large graphs?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I went straight to BFS and felt okay about it until they pushed on disconnected components.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the graph model and constraints, then propose a traversal that uses a visited set to handle cycles and disconnected components. Discuss memory trade-offs for large graphs, suggesting iterative approaches with explicit stacks/queues and possibly external storage or streaming validation.

Pro tip: Mention that you would validate nodes as you visit them and consider early termination if validation fails, which can save time and memory. Also, discuss how to handle very large graphs by processing in chunks or using disk-based storage.

1. Clarify requirements and constraints

Ask about graph size, memory limits, whether validation can be done in-place, and if the graph is static or dynamic. Confirm the two entity types and how neighbors are fetched.

2. Choose traversal strategy

Select BFS or DFS based on memory and recursion limits. Use an iterative approach with an explicit stack/queue to avoid stack overflow on deep graphs.

3. Handle cycles and disconnected components

Maintain a visited set (or hash set) to track visited nodes. Iterate over all nodes to ensure disconnected components are covered, starting a new traversal from unvisited nodes.

4. Address memory pressure

For large graphs, consider memory-efficient data structures (e.g., bitsets for visited), process nodes in batches, or use external storage. Discuss trade-offs between memory and speed.

5. Integrate validation and discuss optimizations

Run validation on each node when first visited. Consider early termination if validation fails, and mention parallelization or distributed processing for very large graphs.

Key Points to Mention

  • Use a visited set to avoid infinite loops and redundant work.
  • Iterate over all nodes to handle disconnected components.
  • Prefer iterative traversal (BFS/DFS) over recursion to avoid stack overflow.
  • For memory pressure, consider bitsets, chunking, or external storage.
  • Validate nodes as they are visited, and consider early termination.
  • Discuss trade-offs between time, memory, and complexity.

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