Off-by-one on range boundaries
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Move from brute-force thinking to an efficient approach using array strategy.
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 <= 1002 <= logs.length <= 5000 <= function_id < n0 <= timestamp <= 109"end" log for each "start" log.Problem summary: 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.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: Array · Stack
2 ["0:start:0","1:start:2","1:end:5","0:end:6"]
1 ["0:start:0","0:start:2","0:end:5","0:start:6","0:end:6","0:end:7"]
2 ["0:start:0","0:start:2","0:end:5","1:start:6","1:end:6","0:end:7"]
Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #636: Exclusive Time of Functions
class Solution {
public int[] exclusiveTime(int n, List<String> logs) {
int[] ans = new int[n];
Deque<Integer> stk = new ArrayDeque<>();
int pre = 0;
for (var log : logs) {
var parts = log.split(":");
int i = Integer.parseInt(parts[0]);
int cur = Integer.parseInt(parts[2]);
if (parts[1].charAt(0) == 's') {
if (!stk.isEmpty()) {
ans[stk.peek()] += cur - pre;
}
stk.push(i);
pre = cur;
} else {
ans[stk.pop()] += cur - pre + 1;
pre = cur + 1;
}
}
return ans;
}
}
// Accepted solution for LeetCode #636: Exclusive Time of Functions
func exclusiveTime(n int, logs []string) []int {
ans := make([]int, n)
stk := []int{}
pre := 0
for _, log := range logs {
parts := strings.Split(log, ":")
i, _ := strconv.Atoi(parts[0])
cur, _ := strconv.Atoi(parts[2])
if parts[1][0] == 's' {
if len(stk) > 0 {
ans[stk[len(stk)-1]] += cur - pre
}
stk = append(stk, i)
pre = cur
} else {
ans[stk[len(stk)-1]] += cur - pre + 1
stk = stk[:len(stk)-1]
pre = cur + 1
}
}
return ans
}
# Accepted solution for LeetCode #636: Exclusive Time of Functions
class Solution:
def exclusiveTime(self, n: int, logs: List[str]) -> List[int]:
stk = []
ans = [0] * n
pre = 0
for log in logs:
i, op, t = log.split(":")
i, cur = int(i), int(t)
if op[0] == "s":
if stk:
ans[stk[-1]] += cur - pre
stk.append(i)
pre = cur
else:
ans[stk.pop()] += cur - pre + 1
pre = cur + 1
return ans
// Accepted solution for LeetCode #636: Exclusive Time of Functions
struct Solution;
enum Action {
Start,
End,
}
impl Solution {
fn exclusive_time(n: i32, logs: Vec<String>) -> Vec<i32> {
let n = n as usize;
let mut stack: Vec<usize> = vec![];
let mut res: Vec<i32> = vec![0; n];
let mut prev = 0;
for log in logs {
let mut it = log.split(':');
let id: usize = it.next().unwrap().parse::<usize>().unwrap();
let action: Action = if it.next().unwrap() == "start" {
Action::Start
} else {
Action::End
};
let val: i32 = it.next().unwrap().parse::<i32>().unwrap();
match action {
Action::Start => {
if let Some(&top) = stack.last() {
res[top] += val - prev;
}
prev = val;
stack.push(id);
}
Action::End => {
if let Some(top) = stack.pop() {
res[top] += val + 1 - prev;
prev = val + 1;
}
}
}
}
res
}
}
#[test]
fn test() {
let n = 2;
let logs = vec_string!["0:start:0", "1:start:2", "1:end:5", "0:end:6"];
let res = vec![3, 4];
assert_eq!(Solution::exclusive_time(n, logs), res);
}
// Accepted solution for LeetCode #636: Exclusive Time of Functions
function exclusiveTime(n: number, logs: string[]): number[] {
const ans: number[] = Array(n).fill(0);
let pre = 0;
const stk: number[] = [];
for (const log of logs) {
const [i, op, cur] = log.split(':');
if (op[0] === 's') {
if (stk.length) {
ans[stk.at(-1)!] += +cur - pre;
}
stk.push(+i);
pre = +cur;
} else {
ans[stk.pop()!] += +cur - pre + 1;
pre = +cur + 1;
}
}
return ans;
}
Use this to step through a reusable interview workflow for this problem.
For each element, scan left (or right) to find the next greater/smaller element. The inner scan can visit up to n elements per outer iteration, giving O(n²) total comparisons. No extra space needed beyond loop variables.
Each element is pushed onto the stack at most once and popped at most once, giving 2n total operations = O(n). The stack itself holds at most n elements in the worst case. The key insight: amortized O(1) per element despite the inner while-loop.
Review these before coding to avoid predictable interview regressions.
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Wrong move: Pushing without popping stale elements invalidates next-greater/next-smaller logic.
Usually fails on: Indices point to blocked elements and outputs shift.
Fix: Pop while invariant is violated before pushing current element.