The core part wasn't too bad once I stopped overthinking the recursion.
Start by clarifying the data shape and edge cases (orphaned comments, missing parentId, cycles). Then describe a two-pass approach: build a map from id to comment and attach children to parents, producing a tree in O(n) time. Finally, explain how you'd render it recursively in the frontend with proper indentation and keys.
Pro tip: Mention that you'd handle orphaned comments (parentId not found) by treating them as roots or logging them, and that you'd avoid mutating the original data by creating shallow copies. This shows you think about real-world data quality and immutability.
Ask about the comment object structure (id, parentId, content, etc.), whether parentId can be null/undefined for root comments, and if there are any constraints like max depth or sorting order.
Explain a two-pass O(n) approach: first create a map of id to comment (with an empty children array), then iterate again to link each comment to its parent. Handle orphans by treating them as roots or skipping them.
Describe a recursive React component (or equivalent) that takes a comment node and renders its content, then maps over its children to render nested Comment components with increased indentation.
Discuss handling cycles (e.g., using a visited set), deep nesting (potential stack overflow, consider iterative rendering), and memoization to avoid unnecessary re-renders.
Mention creating mock data covering typical cases, orphans, and deep nesting, and verifying the rendered output matches expectations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the requirements first: the filter should show comments that match the text and all their descendants, while hiding non-matching siblings and their subtrees. Then propose a recursive tree traversal that returns a filtered subtree, and discuss trade-offs between mutating the tree and creating a new filtered structure.
Pro tip: Mention that you would preserve the original data and compute the filtered view on the fly to avoid side effects, and consider memoization or virtualization for performance with large comment trees.
Ask whether the filter is case-sensitive, whether it applies to comment text only or also author names, and how to handle empty filter (show all).
Write a function that takes a comment node and returns a filtered copy: include the node if it matches or if any descendant matches; otherwise exclude it and its subtree.
Traverse the tree once, building a new tree of matching nodes and their ancestors. Use a post-order traversal to propagate matches upward.
Compare in-place mutation vs. creating a new tree, and consider performance implications for large trees (e.g., memoization, lazy evaluation).
Walk through examples: filter matches a leaf, a parent, multiple branches; ensure hidden siblings are removed and descendants of matches are kept.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.