Checking for Good Pairs in JavaScript

Checking for Good Pairs In JavaScript


We often encounter problems that involve searching for specific patterns or relationships within a set of data. Today, we'll embark on a journey to explore an interesting problem that involves checking for the existence of 'good pairs' in an array. Along the way, we'll inject a dose of humor to make the learning experience more engaging.

Problem Statement

Given an array A of N integers and an integer B, we're tasked with determining whether there exists at least one 'good pair' in the array. A pair (i, j) is considered a good pair if it satisfies two conditions

  • i ≠ j (i and j are not the same element)
  • A[i] + A[j] = B (the sum of elements A[i] and A[j] equals B)

Step-by-Step Approach

Brute Force Method

The simplest approach involves iterating through the array and comparing each element with every subsequent element. For each pair of elements, we check if they satisfy the good pair conditions. If we find a good pair, we can return 'true'; otherwise, we continue iterating until the entire array has been checked.

function checkGoodPairsBruteForce(A, B) {
  for (let i = 0; i < A.length; i++) { // Iterate through the array
    for (let j = i + 1; j < A.length; j++) { // Iterate through the remaining elements
      if (i !== j && A[i] + A[j] === B) { // Check if the pair is valid
        return true; // If a good pair is found, return true
  return false; // If no good pair is found, return false

Optimized Approach

To improve efficiency, we can utilize a concept called 'hashing'. We'll create a hash set to store the unique elements encountered so far. As we iterate through the array, we'll check if the 'target' element (B - A[i]) exists in the hash set. If it does, we've found a good pair and can return 'true'; otherwise, we add the current element to the hash set.

function checkGoodPairsOptimized(A, B) {
  const hashSet = new Set(); // Initialize an empty hash set
  for (let element of A) { // Iterate through the array
    const target = B - element; // Calculate the target element (B - A[i])
    if (hashSet.has(target)) { // Check if the target element exists in the hash set
      return true; // If the target element exists, a good pair is found
    hashSet.add(element); // Add the current element to the hash set for future comparisons
  return false; // If no good pair is found after iterating the array, return false

Humorous Analogy

Imagine you're a detective tasked with finding a pair of lost shoes. The shoes are hidden within a large room filled with various objects. The brute force approach would involve examining every object in the room, one at a time, until you find the matching pair. However, a more efficient approach would be to create a mental list of the shoes you've already seen. This way, you can quickly check if the current object matches any of the shoes on your list, saving time and effort.

Code Breakdown

Let's break down the optimized code step by step

  • Initialize an empty hash set to store unique elements encountered so far.
  • Iterate through the array using a 'for...of' loop to efficiently access each element.
  • For each element, calculate the 'target' element, which is the value needed to form a good pair with the current element (B - A[i]).
  • Check if the target element exists in the hash set using the 'has' method.
  • If the target element exists, it means you've found a good pair, so return 'true'.
  • If the target element doesn't exist, add the current element to the hash set, preparing for potential future good pairs.
  • After iterating through the entire array, if no good pair was found, return 'false'.

Time Complexity

The time complexity of the optimized approach is O(N), where N is the length of the array. This is because the 'for...of' loop iterates through the array once, and the 'has' and 'add' operations of the hash set have a constant time complexity of O(1).

Space Complexity

The space complexity of the optimized approach is also O(N), as the hash set can potentially store up to N unique elements from the array.


We've successfully tackled the problem of checking for good pairs in an array, employing both the brute force and optimized approaches. Along the way, we've added a touch of humor to enhance the learning experience. Remember, data structures and algorithms are not just about formulas and codes; they're about understanding the problem, devising creative solutions, and having fun in the process. So, go forth, explore the world of data structures and algorithms, and let the power of programming bring a smile to your face!