My first instinct was to just do a DFS and use a map to index children by key, which is the right move.
Clarify the merge semantics: nodes are matched by key at the same position, with the second tree's value taking precedence, and children merged recursively. Then design a recursive function that traverses both trees simultaneously, handling cases where nodes exist in only one tree, and analyze time and space complexity.
Pro tip: Discuss how to handle duplicate keys among siblings—either assume uniqueness or define a deterministic rule, and mention that the merge is not commutative, which affects caching and idempotency.
Ask about key uniqueness among siblings, behavior when keys match but children differ, and whether input trees can be modified. Confirm that the second tree's value overrides when keys match.
Write a function that takes two nodes (or null) and returns a merged node. If one node is null, return the other. If both exist, create a new node with the second's value and recursively merge their children.
Use a hash map to index children by key for O(1) lookup, then iterate through the union of keys to merge matched children and include unmatched ones.
State time complexity O(N + M) where N and M are total nodes, and space O(N + M) for the output. Discuss whether to mutate inputs or create new nodes, and the impact on memory.
Walk through a simple example with matching and non-matching nodes to verify correctness, and consider edge cases like empty trees or deep recursion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.