Efficient Sorting with Merge Sort: A Step-by-Step Guide to Algorithm and C Implementation

Explore Merge Sort in-depth, with clear C code examples, and learn why it's favored for efficient data sorting.

Efficient Sorting with Merge Sort: A Step-by-Step Guide to Algorithm and C Implementation
Photo by pine watt / Unsplash

Introduction to Merge Sort

Merge Sort is a highly efficient, comparison-based sorting algorithm known for its capability to handle large datasets. Unlike simpler algorithms such as Bubble Sort or Insertion Sort, Merge Sort follows a divide-and-conquer approach that significantly reduces the time complexity, especially noticeable as the array size increases. This blog post delves into the workings of Merge Sort, its implementation in C, and its complexities, making it easier to understand for programmers of all levels.

Understanding the Merge Sort Algorithm

Merge Sort divides the array into halves, sorts each half, and then merges the two sorted halves back together. The primary challenge and the crux of the algorithm lie in the merging process, where the elements from the two halves must be combined in a sorted manner.

Pseudo Code for Merge Sort

To understand Merge Sort, consider the following pseudo code, which breaks down the process:

MergeSort(arr[], l, r)
  If r > l
     1. Find the middle point to divide the array into two halves:
             middle m = l+ (r-l)/2
     2. Call mergeSort for the first half:
             Call mergeSort(arr, l, m)
     3. Call mergeSort for the second half:
             Call mergeSort(arr, m+1, r)
     4. Merge the two halves sorted in step 2 and 3:
             Call merge(arr, l, m, r)

Step-by-Step Explanation

  1. Divide: Find the midpoint of the array and divide it into two halves.
  2. Conquer: Recursively sort both halves of the array.
  3. Combine: Merge the two sorted halves into a single sorted array.

Implementation in C

Here's how Merge Sort can be implemented in C, with comments to explain each step:

#include <stdio.h>

// Function to merge the two halves
void merge(int arr[], int l, int m, int r) {
    int i, j, k;
    int n1 = m - l + 1;
    int n2 = r - m;

    // Create temporary arrays
    int L[n1], R[n2];

    // Copy data to temp arrays L[] and R[]
    for (i = 0; i < n1; i++)
        L[i] = arr[l + i];
    for (j = 0; j < n2; j++)
        R[j] = arr[m + 1 + j];

    // Merge the temp arrays back into arr[l..r]
    i = 0;
    j = 0;
    k = l;
    while (i < n1 && j < n2) {
        if (L[i] <= R[j]) {
            arr[k] = L[i];
        } else {
            arr[k] = R[j];

    // Copy the remaining elements of L[], if there are any
    while (i < n1) {
        arr[k] = L[i];

    // Copy the remaining elements of R[], if there are any
    while (j < n2) {
        arr[k] = R[j];

// Function to sort the elements using merge sort
void mergeSort(int arr[], int l, int r) {
    if (l < r) {
        // Same as (l+r)/2, but avoids overflow for large l and h
        int m = l + (r - l) / 2;

        // Sort first and second halves
        mergeSort(arr, l, m);
        mergeSort(arr, m + 1, r);

        merge(arr, l, m, r);

Explanation of the C Code

  • Function Definitions: Two functions are defined, merge and mergeSort. mergeSort is a recursive function that continues to split the array into halves, while merge combines those halves back into a sorted array.
  • Temporary Arrays: In the merge process, temporary arrays store the sorted halves before combining them into the main array.
  • Merging Logic: The merging process involves comparisons that efficiently combine two sorted arrays into a single sorted array.

Time and Space Complexity

Time Complexity

  • Best Case: O(n log n)
  • Average Case: O(n log n)
  • Worst Case: O(n log n)

Space Complexity

  • Space Complexity: O(n), because of the temporary arrays used for merging.

Usage of Merge Sort

Merge Sort is ideal for sorting linked lists and large arrays where high efficiency is crucial. It is stable, making it useful in scenarios where the relative order of equal elements must be maintained, such as in multi-key sorting.


Merge Sort’s divide-and-conquer approach provides it with significantly better efficiency compared to simpler, elementary sorting algorithms. It is particularly useful in applications requiring stable sorting and for large datasets where efficiency is a concern. Understanding how Merge Sort works and implementing it in programming languages like C not only boosts one's algorithm skills but also enhances problem-solving capabilities in the realm of data structures and algorithms. This thorough grasp of Merge Sort lays a solid foundation for tackling more complex problems in computer science.

Subscribe to rahulv.dev | blog

Don’t miss out on the latest issues. Sign up now to get access to the library of members-only issues.
[email protected]