Critical path duration
A data pipeline has n tasks (0..n-1), each with an integer `duration`, and `deps` `[a, b]` meaning task b cannot start until task a finishes. Tasks with no unmet dependency can run in parallel. Return the minimum total wall-clock time to finish all tasks (the length of the critical path), or -1 if the dependencies contain a cycle.
Implement
critical_path_length(n: int, durations: list[int], deps: list[list[int]]) → intExamples
in
[4,[3,2,5,1],[[0,1],[0,2],[1,3],[2,3]]]out9What a strong answer looks like
State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
0:00 of about 30 min
solution.py
InputExpectedGot
[4,[3,2,5,1],[[0,1],[0,2],[1,3],[2,3]]]9not run yetsample