Understanding Min Heap vs Max Heap
February 5, 2021
A heap is a treebased data structure that allows access to the minimum and maximum element in the tree in constant time. The constant time taken is Big O(1). This is regardless of the data stored in the heap.
There are two types of heaps: Minheap and Maxheap. A minheap is used to access the minimum element in the heap whereas the Maxheap is used when accessing the maximum element in the heap.
Prerequisites
To follow along with this article, you should have the following:

Node.js installed on your computer.

Basic knowledge of JavaScript.
Overview
Setting up the project
To set up the project, clone this GitHub repository. After you clone it, you will find two folders: start and final. In this article, we will be working on the start folder but in case you encounter an error, feel free to check out the final folder for the final code.
Minheap
In a minheap, the parent or root node is usually lesser in value than the children nodes. The least element is accessed within constant time since it is at index 1
.
Based on the figure below, at every level, the smallest number is the parent node.
Implementation
When illustrating a minheap, we use a treebased structure. But when stored in memory, we use an arraybased structure. Consider the figure below showing the treebased and memorybased representation.
In a minheap, the first element is null
and then the following formula is used in arranging the elements:

Parent node:
i

Left node:
i * 2

Right node:
i * 2 + 1

At any node you can find the parent by
i / 2

i
is the index in the array.
For the minheap, we will insert an element, get the lowest element, and remove an element.
Inserting an element
While inserting an element in a minheap, we use heap sort algorithm.
The algorithm works by first pushing the element to be inserted at the end of the array and then traversing through to find the correct position for the element.
In the minHeap.js
file, under insert()
function, we add up the following functionality to insert an element:
function insert(node) {
heap.push(node);
if (heap.length > 1) {
let current = heap.length  1;
while (current > 1 && heap[Math.floor(current / 2)] > heap[current]) {
//swapping values
[heap[Math.floor(current / 2)], heap[current]] = [
heap[current],
heap[Math.floor(current / 2)],
];
current = Math.floor(current / 2);
}
}
return;
}
//testing functionality
insert(10);
insert(90);
insert(36);
insert(5);
insert(1);
console.log(heap.slice(1));
Expected output:
[ 1, 5, 36, 90, 10 ]
From the function above:

Push the element to the end of the array.

Check if the number of elements in the array is more than one. If they are, follow the steps below.

Get the index of the inserted element.

Loop through the array checking if there is a parent greater than the inserted element.

If it exists, swap them.
Retrieving the minimum element
With a minheap data structure, the minimum element is at index 1
.
In the same file, under getMin()
function, we add up the functionalities:
function getMin(){
return heap[1];
};
//testing functionality
insert(10);
insert(90);
insert(36);
insert(5);
insert(1);
console.log(getMin());
Expected output:
1
From the above code snippets, we get the minimum element stored at index 1
.
Removing an element
Removing an element from a minheap data structure consists of the following steps:

Removing the least element first.

Adjusting the minheap to retain the order.
In the same file, under remove()
, we add up the functionalities:
function remove() {
if (heap.length > 2) {
//assign last value to first index
heap[1] = heap[heap.length  1];
//remove the last value
heap.splice(heap.length  1);
if (heap.length === 3) {
if (heap[1] > heap[2]) {
//swap them
[heap[1], heap[2]] = [heap[2], heap[1]];
}
return;
}
//get indexes
let parent_node = 1;
let left_node = parent_node * 2;
let right_node = parent_node * 2 + 1;
while (heap[left_node] && heap[right_node]) {
//parent node greater than left child node
if (heap[parent_node] > heap[left_node]) {
//swap the values
[heap[parent_node], heap[left_node]] = [
heap[left_node],
heap[parent_node],
];
}
//parent node greater than right child node
if (heap[parent_node] > heap[right_node]) {
// swap
[heap[parent_node], heap[right_node]] = [
heap[right_node],
heap[parent_node],
];
}
if (heap[left_node] > heap[right_node]) {
//swap
[heap[left_node], heap[right_node]] = [
heap[right_node],
heap[left_node],
];
}
parent_node += 1;
left_node = parent_node * 2;
right_node = parent_node * 2 + 1;
}
//incase right child index is undefined.
if (heap[right_node] === undefined && heap[left_node] < heap[parent_node]) {
//swap.
[heap[parent_node], heap[left_node]] = [
heap[left_node],
heap[parent_node],
];
}
}
// if there are only two elements in the array.
else if (heap.length === 2) {
// remove the 1st index value
heap.splice(1, 1);
} else {
return null;
}
return;
}
//testing functionality
insert(10);
insert(90);
insert(36);
insert(5);
insert(1);
remove();
console.log(heap.slice(1));
Expected output
[ 5, 10, 36, 90 ]
From the function above:

Check if the array has more than two elements. If it does not, remove the element in the first index. If it does, continue with the steps below.

Assign the last value to the first index.

Remove the last value from the array.

Check if the array has three elements remaining. If it is
true
, check if the first element is greater than the second element. Swap them if the condition is satisfied. If there are more than three elements, continue with the steps below. 
Define the index of the parent node, left node, and right node.

Loop through the array that have both the left child value and right child value. Where the parent value is greater than the left child value or right child value, swap them. If the left node value is greater than the right node value, swap them as well.

If there is no right node value but the parent node is greater than the left node value, swap the values.
Maxheap
In a maxheap, the parent or root node is usually greater than the children nodes. The maximum element can be accessed in constant time since it is at index 1
.
Based on the figure above, at every level, the largest number is the parent node.
Implementation
Similarly, when illustrating a maxheap we use a treebased structure but when representing in memory we use an arraybased structure. Consider the figure below showing the treebased and memorybased representation.
Similarly, in a maxheap, the first element is null
and then the following formula is used when arranging the elements:

Parent node:
i

Left node:
i * 2

Right node:
i * 2 + 1

At any node you can find the parent by
i / 2
i
is the index in the array.
For the maxheap, we will insert an element, get the largest element, and remove an element.
Inserting an element
In a maxheap, we also use the heapsort algorithm while inserting elements.
In the maxHeap.js
file, under insert()
function, we add up the following functionality to insert elements.
function insert(node) {
//insert first at the end of the array.
heap.push(node);
if (heap.length > 1) {
//get index
let current = heap.length  1;
//Loop through checking if the parent is less.
while (current > 1 && heap[Math.floor(current / 2)] < heap[current]) {
//swap
[heap[Math.floor(current / 2)], heap[current]] = [
heap[current],
heap[Math.floor(current / 2)],
];
//change the index
current = Math.floor(current / 2);
}
}
}
//testing functionality
insert(10);
insert(100);
insert(120);
insert(1000);
console.log(heap.slice(1));
Expected output:
[ 1000, 120, 100, 10 ]
From the function above:

Push the element to the end of the array.

Check if there is more than one element in the array. If there is, continue with the steps below.

Get the index of the position of the element.

Loop through the array checking if there is a parent node value less than the inserted element.

If there is, swap the values and update the index of the element in the array.
Getting the largest element
In a maxheap, getting the largest element means accessing the element at index 1
.
In the same file, under the getMax()
function, we add up the functionalities:
function getMax(){
return heap[1];
};
//testing functionality
insert(10);
insert(100);
insert(120);
insert(1000);
console.log(getMax());
Expected output:
1000
In the function above, we are returning the element stored at index 1
.
Removing an element
Removing an element from a maxheap involves the following steps:

Removing the first element which is usually the largest.

Rearranging the remaining elements in order.
In the same file, under the remove()
function, we will add up the functionalities:
function remove() {
//check if we got two elements in heap array.
if (heap.length === 2) {
//remove the Ist index value
heap.splice(1, 1);
} else if (heap.length > 2) {
//assign last value to first index
heap[1] = heap[heap.length  1];
//remove the last item
heap.splice(heap.length  1);
//check if the length is 3.
if (heap.length === 3) {
if (heap[2] > heap[1]) {
[heap[1], heap[2]] = [heap[2], heap[1]];
}
}
//setup needed indexes.
let parent_node = 1;
let left_node = parent_node * 2;
let right_node = parent_node * 2 + 1;
while (heap[left_node] && heap[right_node]) {
//parent node value is smaller than the left node value
if (heap[left_node] > heap[parent_node]) {
//swap
[heap[parent_node], heap[left_node]] = [
heap[left_node],
heap[parent_node],
];
//update the parent node index.
current = left_node;
}
if (heap[right_node] > heap[parent_node]) {
//swap
[heap[parent_node], heap[right_node]] = [
heap[right_node],
heap[parent_node],
];
//update the parent node index.
current = right_node;
}
if (heap[left_node] < heap[right_node]) {
//swap
[heap[left_node], heap[right_node]] = [
heap[right_node],
heap[left_node],
];
}
//update the left and right node
left_node = current * 2;
right_node = current * 2 + 1;
}
//no right child, but left child is greater than parent
if (heap[right_node] === undefined && heap[left_node] > heap[parent_node]) {
//swap
[heap[parent_node], heap[left_node]] = [
heap[left_node],
heap[parent_node],
];
}
} else {
return null;
}
return;
}
//testing functionality
insert(10);
insert(100);
insert(120);
insert(1000);
remove();
console.log(heap.slice(1));
From the function above:

Check if the array has more than two elements. If it does not, just remove the element in the first index. If it does, continue with the below steps.

Assign the last value to the first index.

Remove the last value from the array.

Check if the array has three elements remaining. If it is
true
, check if the element at index two is greater than the element at index one. Swap them if the condition is satisfied. If more than three elements are remaining, continue with the below steps. 
Define the indexes of the parent node, left node, and right node.

Loop through the array where there is a left node value and right node value. If the parent node value is smaller than either the left node or right node value, swap them. Also, if the left node value is smaller than the right node value, swap them.

If there is no right node value but the parent node is less than the left node value, swap the values.
Why we need heaps?
 Reduced time complexity: Linear data structures such as linked lists or arrays can access the minimum or maximum element present in Big O (n) whereas heaps can access the minimum or maximum element present in Big O (1).
This is crucial while processing large data sets. n refers to the number of data sets.
Application of heaps

They have been used in operating systems for job scheduling on a priority basis.

They are used in Heap sort algorithms to implement priority queues.

Used in the Dijkstra’s algorithm to find the shortest paths.
Conclusion
With a reduced time complexity, minheap and maxheap are efficient when processing data sets, each with its own use case and implementation.
In this article, we have covered, the minheap, the maxheap, why we need heaps, and applications of heaps.
Happy coding!!
Peer Review Contributions by: Mohan Raj
About the author
Kennedy Mwangi
Kennedy is an Information technology graduate from Karatina University. He is fluent in web development with both the frontend and backend using JavaScript. He is very passionate about Linux and Android development.