On a single-threaded CPU, we execute a program containing n
functions. Each function has a unique ID between 0
and n-1
.
Function calls are stored in a call stack: when a function call starts, its ID is pushed onto the stack, and when a function call ends, its ID is popped off the stack. The function whose ID is at the top of the stack is the current function being executed. Each time a function starts or ends, we write a log with the ID, whether it started or ended, and the timestamp.
You are given a list logs
, where logs[i]
represents the ith
log message formatted as a string "{function_id}:{"start" | "end"}:{timestamp}"
. For example, "0:start:3"
means a function call with function ID 0
started at the beginning of timestamp 3
, and "1:end:2"
means a function call with function ID 1
ended at the end of timestamp 2
. Note that a function can be called multiple times, possibly recursively.
A function's exclusive time is the sum of execution times for all function calls in the program. For example, if a function is called twice, one call executing for 2
time units and another call executing for 1
time unit, the exclusive time is 2 + 1 = 3
.
Return the exclusive time of each function in an array, where the value at the ith
index represents the exclusive time for the function with ID i
.
Example 1:
Input: n = 2, logs = ["0:start:0","1:start:2","1:end:5","0:end:6"] Output: [3,4] Explanation: Function 0 starts at the beginning of time 0, then it executes 2 for units of time and reaches the end of time 1. Function 1 starts at the beginning of time 2, executes for 4 units of time, and ends at the end of time 5. Function 0 resumes execution at the beginning of time 6 and executes for 1 unit of time. So function 0 spends 2 + 1 = 3 units of total time executing, and function 1 spends 4 units of total time executing.
Example 2:
Input: n = 1, logs = ["0:start:0","0:start:2","0:end:5","0:start:6","0:end:6","0:end:7"] Output: [8] Explanation: Function 0 starts at the beginning of time 0, executes for 2 units of time, and recursively calls itself. Function 0 (recursive call) starts at the beginning of time 2 and executes for 4 units of time. Function 0 (initial call) resumes execution then immediately calls itself again. Function 0 (2nd recursive call) starts at the beginning of time 6 and executes for 1 unit of time. Function 0 (initial call) resumes execution at the beginning of time 7 and executes for 1 unit of time. So function 0 spends 2 + 4 + 1 + 1 = 8 units of total time executing.
Example 3:
Input: n = 2, logs = ["0:start:0","0:start:2","0:end:5","1:start:6","1:end:6","0:end:7"] Output: [7,1] Explanation: Function 0 starts at the beginning of time 0, executes for 2 units of time, and recursively calls itself. Function 0 (recursive call) starts at the beginning of time 2 and executes for 4 units of time. Function 0 (initial call) resumes execution then immediately calls function 1. Function 1 starts at the beginning of time 6, executes 1 unit of time, and ends at the end of time 6. Function 0 resumes execution at the beginning of time 6 and executes for 2 units of time. So function 0 spends 2 + 4 + 1 = 7 units of total time executing, and function 1 spends 1 unit of total time executing.
Constraints:
1 <= n <= 100
1 <= logs.length <= 500
0 <= function_id < n
0 <= timestamp <= 109
"end"
log for each "start"
log.This problem involves calculating the exclusive execution time of functions given a log of function calls and their start and end times. The key is to handle nested function calls correctly.
The most efficient way to solve this problem is using a stack to track the currently active functions. The stack keeps track of the function ID and its start time.
Algorithm:
Initialization: Create an array ans
of size n
(number of functions) to store the exclusive time for each function. Initialize all elements to 0. Create a stack stk
to store function IDs. Initialize pre
(previous timestamp) to 0.
Iterate through Logs: Process each log entry:
i
), operation (op
), and timestamp (t
).op
is "start":
t - pre
to the exclusive time of the function currently at the top of the stack (this accounts for the time spent before the new function started).i
onto the stack. Update pre
to t
.op
is "end":
t - pre + 1
to the exclusive time of the function at the top of the stack (the +1 accounts for the end timestamp being inclusive).pre
to t + 1
.Return: Return the ans
array containing the exclusive time for each function.
def exclusiveTime(n: int, logs: list[str]) -> list[int]:
ans = [0] * n
stk = []
pre = 0
for log in logs:
i, op, t = log.split(':')
i, t = int(i), int(t)
if op == 'start':
if stk:
ans[stk[-1]] += t - pre
stk.append(i)
pre = t
else:
ans[stk.pop()] += t - pre + 1
pre = t + 1
return ans
import java.util.*;
class Solution {
public int[] exclusiveTime(int n, List<String> logs) {
int[] ans = new int[n];
Deque<Integer> stk = new ArrayDeque<>();
int pre = 0;
for (String log : logs) {
String[] parts = log.split(":");
int i = Integer.parseInt(parts[0]);
int t = Integer.parseInt(parts[2]);
String op = parts[1];
if (op.equals("start")) {
if (!stk.isEmpty()) {
ans[stk.peek()] += t - pre;
}
stk.push(i);
pre = t;
} else {
ans[stk.pop()] += t - pre + 1;
pre = t + 1;
}
}
return ans;
}
}
This stack-based approach provides an efficient and clear solution to the problem, correctly handling nested function calls and calculating the exclusive time for each function.