← Snowflake Interview Insights
My first instinct was to build the effective tree explicitly and then measure height separately.
Use a single post-order DFS that returns the height of the effective subtree rooted at each node, considering deletions. When a node is deleted, return the maximum height among its children's effective subtrees (without adding 1), effectively splicing them to the nearest non-deleted ancestor. For non-deleted nodes, return 1 plus the maximum height from children.
Pro tip: Clarify that the height of a tree with no nodes is -1 (or 0, depending on convention) and handle the case where the root is deleted and all nodes are deleted. Also, mention that the DFS should be iterative to avoid stack overflow for deep trees, but recursive is acceptable if the interviewer allows.
Confirm the definition of height (number of edges or nodes) and the splicing behavior. Clarify that deleted nodes are removed and their children attach to the nearest non-deleted ancestor.
Define a recursive function that returns the effective height of the subtree rooted at the current node. For a deleted node, return the maximum height among its children's results (no +1). For a non-deleted node, return 1 + max(children's results).
If a node has no children, return 0 if non-deleted, or -1 if deleted (to indicate no contribution). Handle the case where the root is deleted and all nodes are deleted, returning -1 or 0 as appropriate.
Write the code, then walk through a small example with a deleted chain to verify correctness. Consider iterative DFS if recursion depth is a concern.
State that the time complexity is O(n) and space is O(h) for recursion stack. Mention that no extra data structures are needed beyond the recursion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.