LeetCode #2715 — EASY

Timeout Cancellation

Build confidence with an intuition-first walkthrough focused on core interview patterns fundamentals.

The Problem

Problem Statement

Given a function fn, an array of arguments args, and a timeout t in milliseconds, return a cancel function cancelFn.

After a delay of cancelTimeMs, the returned cancel function cancelFn will be invoked.

setTimeout(cancelFn, cancelTimeMs)

Initially, the execution of the function fn should be delayed by t milliseconds.

If, before the delay of t milliseconds, the function cancelFn is invoked, it should cancel the delayed execution of fn. Otherwise, if cancelFn is not invoked within the specified delay t, fn should be executed with the provided args as arguments.

Example 1:

Input: fn = (x) => x * 5, args = [2], t = 20
Output: [{"time": 20, "returned": 10}]
Explanation: 
const cancelTimeMs = 50;
const cancelFn = cancellable((x) => x * 5, [2], 20);
setTimeout(cancelFn, cancelTimeMs);

The cancellation was scheduled to occur after a delay of cancelTimeMs (50ms), which happened after the execution of fn(2) at 20ms.

Example 2:

Input: fn = (x) => x**2, args = [2], t = 100
Output: []
Explanation: 
const cancelTimeMs = 50;
const cancelFn = cancellable((x) => x**2, [2], 100);
setTimeout(cancelFn, cancelTimeMs);

The cancellation was scheduled to occur after a delay of cancelTimeMs (50ms), which happened before the execution of fn(2) at 100ms, resulting in fn(2) never being called.

Example 3:

Input: fn = (x1, x2) => x1 * x2, args = [2,4], t = 30
Output: [{"time": 30, "returned": 8}]
Explanation: 
const cancelTimeMs = 100;
const cancelFn = cancellable((x1, x2) => x1 * x2, [2,4], 30);
setTimeout(cancelFn, cancelTimeMs);

The cancellation was scheduled to occur after a delay of cancelTimeMs (100ms), which happened after the execution of fn(2,4) at 30ms.

Constraints:

fn is a function
args is a valid JSON array
1 <= args.length <= 10
20 <= t <= 1000
10 <= cancelTimeMs <= 1000

Step 01

Brute Force Baseline

Problem summary: Given a function fn, an array of arguments args, and a timeout t in milliseconds, return a cancel function cancelFn. After a delay of cancelTimeMs, the returned cancel function cancelFn will be invoked. setTimeout(cancelFn, cancelTimeMs) Initially, the execution of the function fn should be delayed by t milliseconds. If, before the delay of t milliseconds, the function cancelFn is invoked, it should cancel the delayed execution of fn. Otherwise, if cancelFn is not invoked within the specified delay t, fn should be executed with the provided args as arguments.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: General problem-solving

Example 1

(x) => x * 5
[2]
20
50

Example 2

(x) => x**2
[2]
100
50

Example 3

(x1, x2) => x1 * x2
[2,4]
30
100

Step 02

Core Insight

What unlocks the optimal approach

No official hints in dataset. Start from constraints and look for a monotonic or reusable state.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2715: Timeout Cancellation
// Auto-generated Java example from ts.
class Solution {
    public void exampleSolution() {
    }
}
// Reference (ts):
// // Accepted solution for LeetCode #2715: Timeout Cancellation
// function cancellable(fn: Function, args: any[], t: number): Function {
//     const timer = setTimeout(() => fn(...args), t);
//     return () => {
//         clearTimeout(timer);
//     };
// }
// 
// /**
//  *  const result = []
//  *
//  *  const fn = (x) => x * 5
//  *  const args = [2], t = 20, cancelT = 50
//  *
//  *  const start = performance.now()
//  *
//  *  const log = (...argsArr) => {
//  *      const diff = Math.floor(performance.now() - start);
//  *      result.push({"time": diff, "returned": fn(...argsArr))
//  *  }
//  *
//  *  const cancel = cancellable(log, args, t);
//  *
//  *  const maxT = Math.max(t, cancelT)
//  *
//  *  setTimeout(() => {
//  *     cancel()
//  *  }, cancelT)
//  *
//  *  setTimeout(() => {
//  *     console.log(result) // [{"time":20,"returned":10}]
//  *  }, maxT + 15)
//  */

// Accepted solution for LeetCode #2715: Timeout Cancellation
// Auto-generated Go example from ts.
func exampleSolution() {
}
// Reference (ts):
// // Accepted solution for LeetCode #2715: Timeout Cancellation
// function cancellable(fn: Function, args: any[], t: number): Function {
//     const timer = setTimeout(() => fn(...args), t);
//     return () => {
//         clearTimeout(timer);
//     };
// }
// 
// /**
//  *  const result = []
//  *
//  *  const fn = (x) => x * 5
//  *  const args = [2], t = 20, cancelT = 50
//  *
//  *  const start = performance.now()
//  *
//  *  const log = (...argsArr) => {
//  *      const diff = Math.floor(performance.now() - start);
//  *      result.push({"time": diff, "returned": fn(...argsArr))
//  *  }
//  *
//  *  const cancel = cancellable(log, args, t);
//  *
//  *  const maxT = Math.max(t, cancelT)
//  *
//  *  setTimeout(() => {
//  *     cancel()
//  *  }, cancelT)
//  *
//  *  setTimeout(() => {
//  *     console.log(result) // [{"time":20,"returned":10}]
//  *  }, maxT + 15)
//  */

# Accepted solution for LeetCode #2715: Timeout Cancellation
# Auto-generated Python example from ts.
def example_solution() -> None:
    return
# Reference (ts):
# // Accepted solution for LeetCode #2715: Timeout Cancellation
# function cancellable(fn: Function, args: any[], t: number): Function {
#     const timer = setTimeout(() => fn(...args), t);
#     return () => {
#         clearTimeout(timer);
#     };
# }
# 
# /**
#  *  const result = []
#  *
#  *  const fn = (x) => x * 5
#  *  const args = [2], t = 20, cancelT = 50
#  *
#  *  const start = performance.now()
#  *
#  *  const log = (...argsArr) => {
#  *      const diff = Math.floor(performance.now() - start);
#  *      result.push({"time": diff, "returned": fn(...argsArr))
#  *  }
#  *
#  *  const cancel = cancellable(log, args, t);
#  *
#  *  const maxT = Math.max(t, cancelT)
#  *
#  *  setTimeout(() => {
#  *     cancel()
#  *  }, cancelT)
#  *
#  *  setTimeout(() => {
#  *     console.log(result) // [{"time":20,"returned":10}]
#  *  }, maxT + 15)
#  */

// Accepted solution for LeetCode #2715: Timeout Cancellation
// Rust example auto-generated from ts reference.
// Replace the signature and local types with the exact LeetCode harness for this problem.
impl Solution {
    pub fn rust_example() {
        // Port the logic from the reference block below.
    }
}

// Reference (ts):
// // Accepted solution for LeetCode #2715: Timeout Cancellation
// function cancellable(fn: Function, args: any[], t: number): Function {
//     const timer = setTimeout(() => fn(...args), t);
//     return () => {
//         clearTimeout(timer);
//     };
// }
// 
// /**
//  *  const result = []
//  *
//  *  const fn = (x) => x * 5
//  *  const args = [2], t = 20, cancelT = 50
//  *
//  *  const start = performance.now()
//  *
//  *  const log = (...argsArr) => {
//  *      const diff = Math.floor(performance.now() - start);
//  *      result.push({"time": diff, "returned": fn(...argsArr))
//  *  }
//  *
//  *  const cancel = cancellable(log, args, t);
//  *
//  *  const maxT = Math.max(t, cancelT)
//  *
//  *  setTimeout(() => {
//  *     cancel()
//  *  }, cancelT)
//  *
//  *  setTimeout(() => {
//  *     console.log(result) // [{"time":20,"returned":10}]
//  *  }, maxT + 15)
//  */

// Accepted solution for LeetCode #2715: Timeout Cancellation
function cancellable(fn: Function, args: any[], t: number): Function {
    const timer = setTimeout(() => fn(...args), t);
    return () => {
        clearTimeout(timer);
    };
}

/**
 *  const result = []
 *
 *  const fn = (x) => x * 5
 *  const args = [2], t = 20, cancelT = 50
 *
 *  const start = performance.now()
 *
 *  const log = (...argsArr) => {
 *      const diff = Math.floor(performance.now() - start);
 *      result.push({"time": diff, "returned": fn(...argsArr))
 *  }
 *
 *  const cancel = cancellable(log, args, t);
 *
 *  const maxT = Math.max(t, cancelT)
 *
 *  setTimeout(() => {
 *     cancel()
 *  }, cancelT)
 *
 *  setTimeout(() => {
 *     console.log(result) // [{"time":20,"returned":10}]
 *  }, maxT + 15)
 */

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(1)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

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.