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Nextdoor·Mobile Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Nextdoor mobile engineer interview with a tree/graph traversal problem centered on flattening nested comment threads. The follow-ups pushed into iterative rewriting which is where things got interesting.

Questions Asked (3)

Q1

Given a post with nested comment threads where each comment can have child comments at arbitrary depth, flatten the entire structure into a single array using depth-first search.

Algorithms & Data Structures
Author's notes

My recursive solution came out clean enough but I fumbled explaining why DFS specifically made sense here versus BFS.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data structure (e.g., each comment has an id, text, and children array) and the desired output order (pre-order DFS). Then implement a recursive DFS that visits a comment, appends it to the result, and recursively processes its children. Discuss iterative alternatives and edge cases like empty input or very deep nesting.

Pro tip: Mention that recursion depth could cause stack overflow for extremely deep threads, and propose an iterative solution using an explicit stack. Also, note that on mobile, you might want to flatten lazily or paginate to avoid blocking the UI thread.

1. Clarify the problem and data structure

Ask about the comment object shape (e.g., id, text, children) and the expected output order (pre-order DFS). Confirm whether the input is a single root comment or an array of top-level comments.

2. Choose DFS traversal order

Decide on pre-order (visit node, then children) since it preserves the natural reading order of comments. Explain that post-order or in-order are less intuitive for this use case.

3. Implement recursive DFS

Write a function that takes a comment, adds it to the result array, then iterates over its children and recursively calls itself. Handle the case where children is empty or undefined.

4. Discuss iterative alternative

Mention that an explicit stack can avoid recursion depth limits. Push children in reverse order to maintain pre-order traversal when popping.

5. Analyze complexity and edge cases

State O(n) time and O(h) space for recursion (h = max depth), or O(n) space for iterative. Cover edge cases: empty input, single comment, very deep nesting, and large threads.

Key Points to Mention

  • Pre-order DFS traversal to preserve comment reading order
  • Recursive vs iterative implementation and trade-offs (stack overflow risk)
  • Time complexity O(n) and space complexity O(h) for recursion
  • Handling edge cases: empty input, missing children, deep nesting
  • Mobile-specific considerations: UI thread blocking, lazy flattening, pagination
  • Potential for using a generator or yield to flatten lazily

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

Q2

How would you modify your solution to stop traversal beyond a certain depth level?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Passing a depth counter through the recursion was straightforward.

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AI HintsAI Generated

Suggested Approach

First, clarify the traversal method (BFS/DFS) and the data structure used in your original solution. Then, explain how to introduce a depth parameter or level counter to stop traversal when the limit is reached, discussing trade-offs between iterative and recursive approaches.

Pro tip: Mention that for BFS, you can track depth by processing level by level, and for DFS, you can pass depth as a parameter; also note that early termination can save memory and time, which is crucial for mobile performance.

1. Clarify the original solution

Briefly restate the traversal algorithm and data structures used, ensuring the interviewer knows the starting point.

2. Introduce depth tracking

Explain how to add a depth counter or parameter to track the current level during traversal.

3. Implement the depth limit

Describe the condition to stop traversal when depth exceeds the limit, and how to integrate it into the loop or recursion.

4. Discuss trade-offs

Compare iterative vs recursive approaches, and mention implications for time/space complexity and mobile constraints.

5. Consider edge cases

Address scenarios like depth limit 0, negative values, or when the tree is deeper than the limit.

Key Points to Mention

  • BFS: use level-order traversal with a queue and track depth by processing nodes level by level.
  • DFS: pass depth as a parameter in recursion or use a stack with depth information.
  • Early termination: stop traversal when depth limit is reached to save resources.
  • Trade-offs: recursion may cause stack overflow for deep trees; iterative is safer for mobile.
  • Mobile performance: limiting depth reduces memory and CPU usage, important for battery and responsiveness.
  • Edge cases: handle depth limit 0 (return root only) and negative limits (invalid).

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

Q3

Rewrite your recursive DFS solution iteratively using an explicit stack instead of recursion.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This tripped me up more than I expected.

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AI HintsAI Generated

Suggested Approach

Explain that you will simulate the call stack using an explicit stack data structure, pushing nodes in reverse order of recursive calls to maintain the same traversal order. Then walk through the iterative code, highlighting how you manage visited state and ensure termination.

Pro tip: Mention that iterative DFS avoids stack overflow on deep graphs, which is critical for mobile apps with limited memory; also note that the explicit stack can be more memory-efficient if you push only necessary state.

1. Identify the recursive pattern

Describe the recursive DFS: base case, recursive calls, and any state passed (e.g., node, visited set). This clarifies what needs to be simulated.

2. Choose the stack contents

Decide what to store in the stack: just nodes, or nodes plus additional state (e.g., iterator index for pre/post order). For simple traversal, nodes suffice.

3. Simulate the call stack

Push the initial node(s) onto the stack. While the stack is not empty, pop a node, process it, and push its unvisited neighbors in reverse order to mimic recursion order.

4. Handle visited state and termination

Use a visited set to avoid cycles and redundant work. Ensure the loop terminates when the stack is empty, and handle disconnected graphs by iterating over all nodes if needed.

5. Analyze trade-offs

Compare iterative vs recursive: iterative avoids stack overflow, may be more memory-efficient, but code is more verbose. Mention that recursion depth is limited by call stack size.

Key Points to Mention

  • Explicit stack replaces the call stack, preventing stack overflow on deep graphs.
  • Push neighbors in reverse order to preserve the same traversal order as recursion.
  • Use a visited set to avoid infinite loops and redundant processing.
  • Iterative DFS can be more memory-efficient on mobile devices with limited stack size.
  • Time complexity remains O(V+E), space complexity O(V) for the stack and visited set.
  • Consider edge cases: empty graph, single node, disconnected components, cycles.

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