The base case was fine, recursive DFS summing up file sizes as you go.
Clarify the tree structure and edge cases, then propose a recursive post-order traversal that sums file sizes and recursively adds directory children. Discuss iterative alternatives and complexity trade-offs to show depth.
Pro tip: Mention that recursion depth could be an issue for very deep file systems and offer an iterative stack-based solution as a follow-up, demonstrating awareness of production constraints.
Ask about node structure, whether sizes are non-negative, and if the tree can be very deep or large. Confirm that directories have no size of their own.
Explain that for a file node, return its size; for a directory, return the sum of recursively computed sizes of all children.
State that the algorithm visits each node once, so time complexity is O(n) and space complexity is O(h) for recursion stack, where h is tree height.
Mention iterative DFS/BFS using an explicit stack/queue to avoid recursion limits, and compare memory usage and code clarity.
Cover empty directories, single file, negative sizes (if allowed), and very deep trees. Optionally mention memoization if sizes are queried repeatedly.
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Memoizing subtree sizes on each directory node, pretty classic.
Start by clarifying the problem: repeated total-size queries on the same tree. Then propose precomputing subtree sizes once via a post-order traversal, storing them in a hash map or array, so each query becomes O(1). Discuss trade-offs like memory usage and whether the tree is static or dynamic.
Pro tip: Mention that if the tree is static, you can also use a Fenwick tree or segment tree for dynamic updates, but for pure repeated queries, precomputation is simplest and most efficient. Also, note that this pattern generalizes to other aggregate queries like sum or max.
Confirm that the tree structure is static and queries are only for total size (number of nodes). Ask if updates are expected.
Explain that a naive solution would traverse the entire subtree for each query, leading to O(n) per query and O(n*q) total time.
Suggest a one-time post-order traversal to compute and store subtree sizes for all nodes, reducing each query to O(1) lookup.
Discuss time vs. space: O(n) preprocessing and O(n) extra space for O(1) queries. Mention alternatives if updates are needed.
Note that this approach extends to other aggregate queries and is optimal for static trees with frequent queries.
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Start by clarifying the data structure and cache semantics, then discuss invalidation strategies like versioning, dependency tracking, or lazy invalidation. For deep trees, compare recursive and iterative DFS in terms of stack overflow risk, memory usage, and performance, and justify your choice based on constraints.
Pro tip: Mention that cache invalidation can be done lazily with version numbers or timestamps to avoid immediate recomputation, and that iterative DFS is often preferred in production systems to prevent stack overflow, but consider tail recursion or explicit stack with memory trade-offs.
Ask about the tree structure, cache type (e.g., in-memory, distributed), update frequency, and depth limits to tailor your answer.
Explain approaches like invalidating affected subtrees, using versioning or timestamps, or lazy invalidation with dirty flags, and their trade-offs.
Compare recursive DFS (risk of stack overflow) with iterative DFS (explicit stack, more memory but safer), and mention hybrid or tail-recursive optimizations.
Weigh performance, memory, and complexity; suggest a concrete approach, e.g., iterative DFS with lazy invalidation for deep trees.
Recap key points and ask if the interviewer wants to dive deeper into any aspect, showing collaboration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.