Start by restating the problem and the data structures you used (e.g., hash map for direct reports, adjacency list). Then analyze preprocessing time and space, followed by per-query time and space, using N for total employees and noting tree properties like depth (H) and branching factor (B). Finally, discuss trade-offs and possible optimizations.
Pro tip: Mention that the per-query cost depends on the subtree size of the queried node, which in the worst case (e.g., CEO) is O(N). Also, note that if queries are frequent, precomputing total reports for all nodes can reduce per-query time to O(1) at the cost of O(N) preprocessing.
Briefly describe the lookUp function and the data structures you built (e.g., hash map from manager to list of direct reports, and a map from name to node).
Building the data structures takes O(N) time and O(N) space, as each employee is processed once and stored.
For a given name, looking up direct reports is O(1) if stored, but computing total reports requires traversing the subtree, which takes O(S) time where S is the number of reports in that subtree. In the worst case (root), this is O(N).
The traversal uses O(H) space for recursion stack, where H is the height of the subtree (or O(N) in worst case for a skewed tree).
Mention that precomputing total reports for all nodes during preprocessing can reduce per-query time to O(1) but increases preprocessing time to O(N) and space to O(N). Also, note that if the tree is balanced, H = O(log N), so per-query time is O(S) but S can still be O(N).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.