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Quick Sort Algorithm: A Comprehensive Guide

Quick Sort Algorithm: A Comprehensive Guide

Sorting algorithms are essential for data structure in computer science. With the quick sort method, you can rely on more data than with simpler algorithms. This blog on ‘Quick Sort Algorithm’ explores the principles and real-world uses of quick sort, showcasing its details, benefits, and drawbacks.

Table of Contents

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What is the Quick Sort Algorithm?

Developed by Tony Hoare in 1960, Quick Sort is a popular sorting algorithm used to rearrange elements in a list or array. It operates by selecting a “pivot” element from the array and partitioning the other elements into two sub-arrays, with elements smaller than the pivot placed to its left and larger elements to its right. The process is then recursively applied to the sub-arrays. The efficiency of Quick Sort lies in its average-case time complexity, making it one of the fastest sorting algorithms in practice. However, it’s essential to note that the worst-case time complexity occurs when the pivot selection consistently leads to unbalanced partitions. Despite this, Quick Sort’s widespread adoption is attributed to its simplicity, adaptability, and high performance in scenarios where average-case efficiency is crucial. Implementations often involve careful pivot selection and optimization techniques to reduce potential drawbacks and ensure optimal performance across various datasets.

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How Does the Quick Sort Algorithm Work?

The Quick Sort algorithm works in three steps, which are as follows:


  • Choose a pivot element from the array. Common pivot choices include the first, last, or a random element.
  • Rearrange the elements in the array such that elements smaller than the pivot are placed to its left and elements larger than the pivot are placed to its right.
  • The pivot is now in its correct sorted position.


Recursively apply the same process to the sub-arrays on the left and right of the pivot until the base case is reached (e.g., sub-arrays with one or zero elements).

How Does the Quick Sort Algorithm Work?


As the recursion completes, the sorted subarrays are combined, resulting in a fully sorted array.


The key to QuickSort’s efficiency lies in the partitioning step, which efficiently places the pivot in its final sorted position and divides the array into two segments for further sorting. The choice of the pivot and its position during partitioning can impact the algorithm’s performance, and various strategies exist to optimize these aspects.

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Implementation of Quick Sort 

Here’s a practical implementation of the Quick Sort algorithm in the C programming language.

#include <stdio.h>
// Function to swap two elements
void swap(int* a, int* b) {
    int temp = *a;
    *a = *b;
    *b = temp;
// Function to partition the array and return the pivot index
int partition(int arr[], int low, int high) {
    int pivot = arr[high]; // Choose the last element as the pivot
    int i = low - 1; // Index of the smaller element
    for (int j = low; j < high; j++) {
        if (arr[j] <= pivot) {
            swap(&arr[i], &arr[j]);
    swap(&arr[i + 1], &arr[high]);
    return i + 1;
// Function to perform Quick Sort
void quickSort(int arr[], int low, int high) {
    if (low < high) {
        // Partition the array, arr[p] is now at the correct position
        int pivotIndex = partition(arr, low, high);
        // Recursively sort the sub-arrays
        quickSort(arr, low, pivotIndex - 1);
        quickSort(arr, pivotIndex + 1, high);
// Function to print an array
void printArray(int arr[], int size) {
    for (int i = 0; i < size; i++) {
        printf("%d ", arr[i]);
// Main function for testing the Quick Sort implementation
int main() {
    int arr[] = {12, 5, 3, 7, 2, 8, 10};
    int size = sizeof(arr) / sizeof(arr[0]);
    printf("Original array: ");
    printArray(arr, size);
    quickSort(arr, 0, size - 1);
    printf("Sorted array: ");
    printArray(arr, size);
    return 0;


Original array: 12 5 3 7 2 8 10 

Sorted array: 2 3 5 7 8 10 12 

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Complexities of Quick Sort

In the context of data structures, complexities refer to the time and space requirements associated with performing various operations on the data structure. The time complexity of an operation indicates how the execution time grows with the size of the input data, while space complexity describes the additional memory space required.

Time Complexity: It is a measure of the amount of time an algorithm takes to run as a function of the size of the input. Time complexity is typically expressed in terms of the big O notation, which provides an upper bound on the growth rate of the function.

Average CaseO(n log n)
Worst CaseO(n^2) 
Best CaseO(n log n)

Space Complexity: It is a measure of the amount of memory space required by an algorithm to execute. Space complexity is typically expressed as a function of the input size, which represents the size of the problem being solved. Higher space complexity indicates that it requires more memory and vice versa.

Space ComplexityO(log n)

Advantages and Disadvantages of Quick Sort

Understanding the advantages and disadvantages of Quick Sort algorithms is crucial for informed decision-making in computer science and software development. Below are some advantages and disadvantages of the Quick Sort algorithm:


  • Efficient for large datasets with an average time complexity of O(n log n)
  • In-place sorting requires no additional memory.
  • Good cache performance due to localized and sequential access patterns
  • Adaptable to different data distributions through pivot strategies
  • Relatively easy to implement


  • Worst-case time complexity of O(n^2) for certain datasets
  • Non-stable sorting doesn’t preserve the relative order of equal elements.
  • Efficiency heavily depends on pivot choice.
  • Not ideal for linked lists due to inefficient memory access patterns.


In conclusion, the QuickSort algorithm stands as a powerful solution for efficient data sorting, particularly in business applications where speed and adaptability are important. Its average-case time complexity makes it a good choice for handling large datasets. From a coding perspective, QuickSort’s in-place nature helps with memory conservation and is crucial for resource-intensive applications. Looking forward, QuickSort remains relevant, and ongoing research aims to enhance its adaptability, making it precisely set for future applications in the evolving field of data-centric industries, ensuring swift and reliable data processing for businesses in the years to come. 

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Is QuickSort a stable sorting algorithm?

No, QuickSort is not a stable sorting algorithm. It does not guarantee the preservation of the relative order of equal elements during the sorting process.

How does QuickSort handle duplicate elements in an array?

QuickSort may rearrange the order of equal elements, and their final order after sorting is not guaranteed. If preserving the original order of duplicates is essential, a stable sorting algorithm should be considered.

Can QuickSort be used for linked lists?

While QuickSort is typically more efficient for arrays, it can be adapted for linked lists. However, the standard recursive implementation may not be as efficient due to memory access patterns.

What is the impact of pivot selection on QuickSort's performance?

Pivot selection significantly influences QuickSort’s efficiency. Poor choices can lead to unbalanced partitions, resulting in worst-case time complexity. Various strategies, such as choosing a random pivot or using the median, can mitigate this impact.

Is QuickSort the best choice for all types of datasets?

No, QuickSort may not be ideal for nearly sorted or already sorted datasets, as it can degrade to its worst-case time complexity. In such cases, algorithms like Merge Sort might be more suitable.

Can QuickSort be implemented iteratively instead of recursively?

Yes, QuickSort can be implemented iteratively using a stack or other data structures to manage partition indices. This can be advantageous in environments where recursion is less desirable.

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About the Author

Senior Consultant Analytics & Data Science

Presenting Sahil Mattoo, a Senior Consultant Analytics & Data Science at Eli Lilly and Company is an accomplished professional with 14 years of experience across data science, analytics, and technical leadership domains, demonstrates a remarkable ability to drive business insights. Sahil holds a Post Graduate Program in Business Analytics and Business Intelligence from Great Lakes Institute of Management.