My recursive solution came out clean enough but I fumbled explaining why DFS specifically made sense here versus BFS.
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.
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.
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.
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.
Mention that an explicit stack can avoid recursion depth limits. Push children in reverse order to maintain pre-order traversal when popping.
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.
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Passing a depth counter through the recursion was straightforward.
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.
Briefly restate the traversal algorithm and data structures used, ensuring the interviewer knows the starting point.
Explain how to add a depth counter or parameter to track the current level during traversal.
Describe the condition to stop traversal when depth exceeds the limit, and how to integrate it into the loop or recursion.
Compare iterative vs recursive approaches, and mention implications for time/space complexity and mobile constraints.
Address scenarios like depth limit 0, negative values, or when the tree is deeper than the limit.
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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.
Describe the recursive DFS: base case, recursive calls, and any state passed (e.g., node, visited set). This clarifies what needs to be simulated.
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.
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.
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.
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.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.