Merge Sort Made Simple: A Deep Dive Into The Efficient Sorting Algorithm

Merge Sort is a divide-and-conquer sorting algorithm. It splits an array into halves, sorts each half, and then merges the sorted halves back together. Unlike simpler, more intuitive sorting methods such as Bubble Sort or Insertion Sort, Merge Sort excels in its ability to efficiently manage large volumes of data with its time complexity of O(nlog n) in all cases—best, average, and worst.

In order to understand the Merge Sort algorithm, we first have to understand the Merge operation.

The Merge Operation

How it works

Merge operation example

The merge operation is a pivotal element of the Merge Sort algorithm. This operation entails combining two already sorted subarrays into a single, larger sorted subarray. We begin with two sorted subarrays, designated as the left and right subarrays.

The merge operation unfolds as follows:

  1. Initiate two pointers, each pointing to the start of the left and right subarrays, respectively.
  2. Compare the elements currently pointed to by the pointers in the left and right subarrays.
  3. Select the smaller of the two elements and transfer it to the next position in the merged subarray, subsequently advancing the pointer in the subarray from which the element was chosen.
  4. Continue the process of comparison and selection (steps 2 and 3) until all elements in both subarrays have been merged into the larger subarray.
  5. Should one of the subarrays be completely merged before the other, directly append the remaining elements of the non-exhausted subarray to the merged subarray.

Runtime Complexity

Each element from both subarrays is visited exactly once during the merge. If the size of the two subarrays is n_1​ and n_2​ respectively, the total number of elements is n=n_1+n_2​. The merge operation performs n comparisons. This implies that the time complexity of a single merge operation is O(n), where n is the total number of elements in the two subarrays being merged.

Space Complexity

For a single merge operation that combines two sorted subarrays into a new array, the space complexity is directly proportional to the total number of elements in the two subarrays being merged. If the size of the two subarrays is n_1​ and n_2​ respectively, the total number of elements is n=n_1+n_2​. Therefore, the space required for the new array that holds the merged result is O(n).

Merge Sort

How it works

The beauty in Merge Sort lies in breaking down a seemingly complex task into smaller, more manageable pieces, and then combining those pieces back together in a sorted manner.

Division and Conquest

  1. The Divide: Imagine starting with a disorganized array of numbers. The first step in Merge Sort isn’t to sort anything; it’s to divide. We split the array in half, over and over, until each piece is so small it consists of a single element. Why? Because a single element is inherently sorted. It’s a bit like dismantling a jigsaw puzzle into individual pieces.
  2. The Conquest: With these single-element arrays, we’ve reached a critical turning point. Now, we begin the process of merging. At this stage, every “array” is sorted, as it’s just one piece. The real magic starts as we apply the Merge Operation, combining these atomic pieces into larger sorted arrays.

From Singles to Sorted Array

  1. Pairwise Merging: We take two single-element arrays and merge them. This isn’t just about putting them together; it’s about maintaining order. Thanks to the Merge Operation, we know how to do this efficiently and effectively.
  2. Growth Through Merging: After the initial merge, we don’t just have random arrays. We have small, sorted arrays. We continue this process, merging these sorted arrays into larger ones. Each step is careful, considered, and maintains the sorted order, thanks to the Merge Operation.
  3. The Final Merge: Ultimately, all these pieces come back together. The final merge isn’t just a culmination; it’s the moment the entire array becomes sorted. From many, one sorted whole emerges.

Think of it as building a Lego castle. You start with small, sorted sections, and carefully combine them. Bit by bit, your castle grows, until it’s fully built and beautifully organized.

Why use Merge Sort?

Merge Sort offers several compelling advantages:

  • Stable Sorting: It maintains the original order of equal elements, which is crucial for certain applications. Stable sorting means that if two items are equal, they stay in the same order as they were before sorting. Imagine you have a list of students with their scores: Alice (90), Bob (75), Charlie (90). If you sort them by score in a stable way, Alice and Charlie both have 90, but Alice stays before Charlie because she was listed first originally.
  • Consistent Performance: Its time complexity of O(n log n) is guaranteed, making it predictable and reliable.


def merge_sort(arr):
    if len(arr) <= 1:
        return arr

    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])

    return merge(left, right)

def merge(left, right):
    merged, i, j = [], 0, 0

    while i < len(left) and j < len(right):
        if left[i] < right[j]:
            i += 1
            j += 1

    # Append any remaining elements

    return merged

This Python code implements the Merge Sort algorithm through two main functions: merge_sort and merge.

  • merge_sort: Recursively divides the input array into smaller halves until each contains a single element, then merges these halves in a sorted manner using the merge function. It achieves this by first dividing the array and then combining the sorted arrays, leveraging recursion for both steps.
  • merge: Takes two sorted arrays as input and merges them into a single sorted array. It compares elements from both arrays, appending the smaller one to a new array, and continues this process until all elements are sorted and merged. Remaining elements from either array (if any) are appended to the end.

Complexity Analysis

Time Complexity (Best Case)O(n log n)
Time Complexity (Average Case)O(n log n)
Time Complexity (Worst Case)O(n log n)
Space ComplexityO(n)
Merge Sort complexity summary table.

Runtime Complexity

  • Divide Phase: The divide phase of Merge Sort takes O(log n) time because it involves repeatedly halving the array until each piece is just one element. This halving process can occur log_2(n) times—for example, an array of 8 elements can only be halved 3 times (8 to 4 to 2 to 1). Thus, the number of divisions directly corresponds to the logarithm of the array’s size, defining the O(log n) complexity.
  • Merge Phase: In each level of recursion, merging the divided arrays back together requires touching each element once per level of recursion, contributing a linear component, n, to the overall complexity.
  • Combining these phases, the total runtime complexity is O(n log n), which holds true for the best, average, and worst-case scenarios. This consistency is one of Merge Sort’s strengths, avoiding the worst-case pitfalls of algorithms like Quick Sort.

Space Complexity

The space complexity of Merge Sort is O(n) because it creates temporary arrays to merge sorted subsections before combining them into a final sorted array. At each merge step, a new array is created to hold the merged and sorted elements from the two smaller subsections being combined. Since the merging process happens at every level of the sort and involves the entire dataset over the course of the algorithm, the space needed for these temporary arrays is proportional to the size of the input array. Thus, the total space required for these operations grows linearly with the input size, leading to an O(n) space complexity.


Merge Sort is a powerful, efficient sorting algorithm that employs a divide-and-conquer strategy to organize data. It works by recursively dividing the array into smaller parts until each part consists of a single element, then merges these parts back together in a sorted manner. This process ensures a stable sort with a time complexity of O(nlog n) across all cases—best, average, and worst. While Merge Sort is exceptionally efficient and predictable, it requires additional space proportional to the array size (O(n)), due to the temporary arrays used during the merge process.