LeetCode #3386 — EASY

Button with Longest Push Time

Build confidence with an intuition-first walkthrough focused on array fundamentals.

The Problem

Problem Statement

You are given a 2D array events which represents a sequence of events where a child pushes a series of buttons on a keyboard.

Each events[i] = [index_i, time_i] indicates that the button at index index_i was pressed at time time_i.

The array is sorted in increasing order of time.
The time taken to press a button is the difference in time between consecutive button presses. The time for the first button is simply the time at which it was pressed.

Return the index of the button that took the longest time to push. If multiple buttons have the same longest time, return the button with the smallest index.

Example 1:

Input: events = [[1,2],[2,5],[3,9],[1,15]]

Output: 1

Explanation:

Button with index 1 is pressed at time 2.
Button with index 2 is pressed at time 5, so it took 5 - 2 = 3 units of time.
Button with index 3 is pressed at time 9, so it took 9 - 5 = 4 units of time.
Button with index 1 is pressed again at time 15, so it took 15 - 9 = 6 units of time.

Example 2:

Input: events = [[10,5],[1,7]]

Output: 10

Explanation:

Button with index 10 is pressed at time 5.
Button with index 1 is pressed at time 7, so it took 7 - 5 = 2 units of time.

Constraints:

1 <= events.length <= 1000
events[i] == [index_i, time_i]
1 <= index_i, time_i <= 10⁵
The input is generated such that events is sorted in increasing order of time_i.

Step 01

Brute Force Baseline

Problem summary: You are given a 2D array events which represents a sequence of events where a child pushes a series of buttons on a keyboard. Each events[i] = [indexi, timei] indicates that the button at index indexi was pressed at time timei. The array is sorted in increasing order of time. The time taken to press a button is the difference in time between consecutive button presses. The time for the first button is simply the time at which it was pressed. Return the index of the button that took the longest time to push. If multiple buttons have the same longest time, return the button with the smallest index.

Baseline thinking

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

Pattern signal: Array

Example 1

[[1,2],[2,5],[3,9],[1,15]]

Example 2

[[10,5],[1,7]]

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 #3386: Button with Longest Push Time
class Solution {
    public int buttonWithLongestTime(int[][] events) {
        int ans = events[0][0], t = events[0][1];
        for (int k = 1; k < events.length; ++k) {
            int i = events[k][0], t2 = events[k][1], t1 = events[k - 1][1];
            int d = t2 - t1;
            if (d > t || (d == t && ans > i)) {
                ans = i;
                t = d;
            }
        }
        return ans;
    }
}

// Accepted solution for LeetCode #3386: Button with Longest Push Time
func buttonWithLongestTime(events [][]int) int {
	ans, t := events[0][0], events[0][1]
	for k, e := range events[1:] {
		i, t2, t1 := e[0], e[1], events[k][1]
		d := t2 - t1
		if d > t || (d == t && i < ans) {
			ans, t = i, d
		}
	}
	return ans
}

# Accepted solution for LeetCode #3386: Button with Longest Push Time
class Solution:
    def buttonWithLongestTime(self, events: List[List[int]]) -> int:
        ans, t = events[0]
        for (_, t1), (i, t2) in pairwise(events):
            d = t2 - t1
            if d > t or (d == t and i < ans):
                ans, t = i, d
        return ans

// Accepted solution for LeetCode #3386: Button with Longest Push Time
// Rust example auto-generated from java 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 (java):
// // Accepted solution for LeetCode #3386: Button with Longest Push Time
// class Solution {
//     public int buttonWithLongestTime(int[][] events) {
//         int ans = events[0][0], t = events[0][1];
//         for (int k = 1; k < events.length; ++k) {
//             int i = events[k][0], t2 = events[k][1], t1 = events[k - 1][1];
//             int d = t2 - t1;
//             if (d > t || (d == t && ans > i)) {
//                 ans = i;
//                 t = d;
//             }
//         }
//         return ans;
//     }
// }

// Accepted solution for LeetCode #3386: Button with Longest Push Time
function buttonWithLongestTime(events: number[][]): number {
    let [ans, t] = events[0];
    for (let k = 1; k < events.length; ++k) {
        const [i, t2] = events[k];
        const d = t2 - events[k - 1][1];
        if (d > t || (d === t && i < ans)) {
            ans = i;
            t = d;
        }
    }
    return ans;
}

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.