Tutorial Playlist

Data Structure Tutorial


Arrays in Data Structures: A Guide With Examples

Lesson - 1

All You Need to Know About Two-Dimensional Arrays

Lesson - 2

All You Need to Know About a Linked List in a Data Structure

Lesson - 3

The Complete Guide to Implement a Singly Linked List

Lesson - 4

The Ultimate Guide to Implement a Doubly Linked List

Lesson - 5

The Fundamentals for Understanding Circular Linked List

Lesson - 6

The Ultimate Guide To Understand The Differences Between Stack And Queue

Lesson - 7

Implementing Stacks in Data Structures

Lesson - 8

Your One-Stop Solution for Stack Implementation Using Array

Lesson - 9

Your One-Stop Solution for Queue Implementation Using Array

Lesson - 10

Your One-Stop Solution to Learn Depth-First Search(DFS) Algorithm From Scratch

Lesson - 11

Your One-Stop Solution for Stack Implementation Using Linked-List

Lesson - 12

The Definitive Guide to Understand Stack vs Heap Memory Allocation

Lesson - 13

All You Need to Know About Linear Search Algorithm

Lesson - 14

All You Need to Know About Breadth-First Search Algorithm

Lesson - 15

A One-Stop Solution for Using Binary Search Trees in Data Structure

Lesson - 16

The Best Tutorial to Understand Trees in Data Structure

Lesson - 17

A Complete Guide to Implement Binary Tree in Data Structure

Lesson - 18

A Holistic Look at Using AVL Trees in Data Structures

Lesson - 19

All You Need to Know About Tree Traversal in Data Structure

Lesson - 20

The Best Guide You’ll Ever Need to Understand B-Tree in Data Structure

Lesson - 21

The Best Guide You'll Ever Need to Understand Spanning Tree in Data Structure

Lesson - 22

The Best and Easiest Way to Understand an Algorithm

Lesson - 23

Your One-Stop Solution to Understand Shell Sort Algorithm

Lesson - 24

Your One-Stop Solution to Quick Sort Algorithm

Lesson - 25

The Most Useful Guide to Learn Selection Sort Algorithm

Lesson - 26

Everything You Need to Know About Radix Sort Algorithm

Lesson - 27

Everything You Need to Know About the Counting Sort Algorithm

Lesson - 28

Everything You Need to Know About the Merge Sort Algorithm

Lesson - 29

Insertion Sort Algorithm: One-Stop Solution That Will Help You Understand Insertion Sort

Lesson - 30

Everything You Need to Know About the Bubble Sort Algorithm

Lesson - 31

The Best Guide You’ll Ever Need to Understand Bucket Sort Algorithm

Lesson - 32

Your One-Stop Solution to Understand Recursive Algorithm in Programming

Lesson - 33

The Definitive Guide to Understanding Greedy Algorithm

Lesson - 34

Your One-Stop Solution to Understand Backtracking Algorithm

Lesson - 35

The Fundamentals of the Bellman-Ford Algorithm

Lesson - 36

Your One-Stop Solution for Graphs in Data Structures

Lesson - 37

The Best Guide to Understand and Implement Solutions for Tower of Hanoi Puzzle

Lesson - 38

A Simplified and Complete Guide to Learn Space and Time Complexity

Lesson - 39

All You Need to Know About the Knapsack Problem : Your Complete Guide

Lesson - 40

The Fibonacci Series: Mathematical and Programming Interpretation

Lesson - 41

The Holistic Look at Longest Common Subsequence Problem

Lesson - 42

The Best Article to Understand What Is Dynamic Programming

Lesson - 43

A Guide to Implement Longest Increasing Subsequence Using Dynamic Programming

Lesson - 44

A Holistic Guide to Learn Stop Solution Using Dynamic Programming

Lesson - 45

One Stop Solution to All the Dynamic Programming Problems

Lesson - 46

Understanding the Fundamentals of Binomial Distribution

Lesson - 47

Here’s All You Need to Know About Minimum Spanning Tree in Data Structures

Lesson - 48

Understanding the Difference Between Array and Linked List

Lesson - 49

The Best Article Out There to Understand the B+ Tree in Data Structure

Lesson - 50

A Comprehensive Look at Queue in Data Structure

Lesson - 51

Your One-Stop Solution to Understand Coin Change Problem

Lesson - 52

The Best Way to Understand the Matrix Chain Multiplication Problem

Lesson - 53

Your One-Stop Solution to Learn Floyd-Warshall Algorithm for Using Dynamic Programming

Lesson - 54

The Best Tutorial You'll Ever Need for Queue Implementation Using Linked List

Lesson - 55
Your One-Stop Solution to Quick Sort Algorithm

Tony Hoare, a British computer scientist, invented the QuickSort algorithm in 1959. The name "Quick-sort" stems from the fact that it can sort a list of data elements substantially faster (twice or three times faster) than any other sorting method. 

Quicksort is one of the most efficient sorting algorithms. It works by breaking an array (partition) into smaller ones and swapping (exchanging) the smaller ones, depending on a comparison with the 'pivot' element picked.

By the end of this tutorial, you will have a better understanding of the fundamental technicalities of the Quick Sort with all the necessary details along with practical implementations.

What Is the Quick Sort Algorithm?

Quicksort is a highly efficient sorting technique that divides a large data array into smaller ones. A vast array is divided into two arrays, one containing values smaller than the provided value, say pivot, on which the partition is based. The other contains values greater than the pivot value.

Now, look at the working of the Quick-Sort algorithm to understand the Quick  Sort better.

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How Does Quick Sort Work?

To sort an array, you will follow the steps below:

  1. You will make any index value in the array as a pivot.
  2. Then you will partition the array according to the pivot.
  3. Then you will recursively quicksort the left partition
  4. After that, you will recursively quicksort the correct partition.


Let's have a closer look at the partition bit of this algorithm:

  1. You will pick any pivot, let's say the highest index value.
  2. You will take two variables to point left and right of the list, excluding pivot.
  3. The left will point to the lower index, and the right will point to the higher index.
  4. Now you will move all elements which are greater than pivot to the right.
  5. Then you will move all elements smaller than the pivot to the left partition.

And this is how the QuickSort algorithm works. Now implement this algorithm through a simple C++ code.

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How to Implement the Quick Sort Algorithm?

You will be provided with an array of elements {10, 7, 8, 9, 1, 5}. You have to write a code to sort this array using the QuickSort algorithm. The final array should come out to be as {1, 5, 7, 8, 9, 10}.


// C++ implementation of QuickSort

#include <bits/stdc++.h>

using namespace std;

// A utility function to swap two elements

void swap(int* a, int* b)


int t = *a;

*a = *b;

*b = t;


/* This function takes the final pivot element, puts the pivot element in an ordered array, and places all smaller elements on the left side of the pivot, as well as all larger elements on the right of the pivot. */

int partition (int arr[], int l, int h)


int pivot = arr[h]; // pivot

int i = (l - 1); // Index of smaller element and indicates the right position of pivot found so far

for (int k = l; k <= h - 1; k++)


// When the actual element is less than the pivot

if (arr[k] < pivot)


i++; // increment index of smaller element

swap(&arr[i], &arr[k]);



swap(&arr[i + 1], &arr[h]);

return (i + 1);


//A function to implement quicksort

void quickSort(int arr[], int l, int h)


if (l < h)


//pi is a partitioning index, and 

//arr[p] is now in the correct location.

int pi = partition(arr, l, h);

// Separately sort elements before

// partition and after partition

quickSort(arr, l, pi - 1);

quickSort(arr, pi + 1, h);



/* Function to print an array */

void print_array(int arr[], int size)


int i;

for (i = 0; i < size; i++)

cout << arr[i] << " ";

cout << endl;


int main()


int arr[] = {11, 13, 16, 1, 3, 5, 9};

int n = sizeof(arr) / sizeof(arr[0]);

quickSort(arr, 0, n - 1);

cout << "Sorted array: \n";

printArray(arr, n);

return 0;



You have now explored the working of Quick Sort with a code. Now you will see some of the advantages of the Quick Sort.

What Are the Advantages of Quick Sort?

Let us discuss a few significant benefits of using Quick Sort and a few scenarios where Quick Sort is proven to be delivering the best performance.

  • It is an in-place algorithm since it just requires a modest auxiliary stack.
  • Sorting n objects takes only n (log n) time.
  • Its inner loop is relatively short.
  • After a thorough mathematical investigation of this algorithm, you can make a reasonably specific statement about performance issues.

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What Are the Disadvantages of Quick Sort?

Despite it being the quickest algorithm, Quick Sort does a few downfalls. Let us address a few significant limitations that you should be considering before you implement Quick Sort in real-time.

  • It is a recursive process. The implementation is quite tricky, mainly if recursion is not provided.
  • In the worst-case scenario, it takes quadratic (i.e., n2) time.
  • It is fragile in the sense that a slight error in implementation can go unreported and cause it to function poorly.

With this, you have come to an end of this tutorial. You will now look at what could be your next steps to master other sorting algorithms.

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Next Steps

Your next stop in mastering data structures should be the selection Sort Algorithm. Using in-place comparisons, the selection sort algorithm divides the list into two parts, with the sorted half on the left and the unsorted half on the right.

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If you have any questions or require clarification on this "Merge Sort Algorithm" tutorial, please leave them in the comments section below. Our expert team will review them and respond as soon as possible.

About the Author

Vaibhav KhandelwalVaibhav Khandelwal

Vaibhav Khandelwal is a proactive tech geek who's always on the edge of learning new technologies. He is well versed in competitive programming and possess sound knowledge of web development. He likes to read fictional and sci-fi novels and likes to play strategy games like chess.

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