Merge Sort Algorithm

Published Jan 18, 2024Updated May 15, 2024
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Merge sort is a divide-and-conquer sorting algorithm that breaks down an array into smaller arrays, sorts them, and then combines the subarrays back together to return a sorted array.

MergeSort Method


  • Base Case: If the input array data has only one element, it’s already sorted, so return.
  • Split: Divide the array into two halves, left and right, of approximately equal size.


  • Recursive Calls: Repeatedly call mergeSort on each half, (left) and (right), to create subarrays so they are sorted independently.


  • Merge: Call the merge function to merge the two sorted halves (each initially of length 1) left and right, and combine them back into the original array data in a sorted manner.

Implementation Pt.1

The following example written in Java shows an implementation of the first part of the Merge Sort Algorithm — splitting the main array into subarrays:

public static void mergeSort(int[] data){
if(data.length == 1){
int n1 = data.length/2;
int n2 = data.length - n1;
int[] left, right;
//make left and right subarrays
left = new int[n1];
right = new int[n2];
System.arraycopy(data, 0, left, 0, n1);
System.arraycopy(data, n1, right, 0, n2);
//recursively split left and right arrays
//once array is @ length 1, the left & right array will be merged
merge(data, left, right);

Merge Method

  1. Initialize:
    • Set indices l, r, and k to 0 to track the positions in left, right, and data.
  2. Compare and Merge:
    • While both left and right have elements remaining:
      • Compare the elements at left[l] and right[r].
      • Copy the smaller element into data[k] and increment the corresponding index (l or r).
      • Increment k to move to the next position in data.
  3. Copy Remaining Elements:
    • If any elements remain in left, copy them directly into the remaining positions in data.
    • Similarly, if any elements remain in right, copy them into the remaining positions in data.

Implementation Pt.2

The following example written in Java shows an implementation of the second part of Merge Sort — sorting the subarrays and merging them back into the original array:

public static void merge(int[] data, int[] left, int[] right){
//indexes of arrays left, right, and data
int l,r,k;
l = r = k = 0;
while(l < left.length && r < right.length && k < data.length){
//find min between left & right element and insert it
if(left[l] < right[r]){
data[k] = left[l];
data[k] = right[r];
//increment index of data after insertion
//if elements still remain in arrays left or right, insert them into the data
while(l < left.length){
data[k] = left[l];
while(r < right.length){
data[k] = right[r];

Time Complexity Breakdown

Overall Time Complexity: O(n log n)


  1. Divide:

    • The mergeSort function recursively divides the array into halves until each subarray has only one element.
    • This splitting process has a time complexity of O(log n) due to the repeated halving.
  2. Conquer:

    • The base case (array of size 1) requires no sorting, so its complexity is O(1).
  3. Combine:

    • The merge function merges two sorted subarrays into a single sorted array.
    • It iterates through both subarrays once, comparing elements and copying them to the final array.
    • This merging process takes O(n) time, where n is the total number of merged elements.

Overall Time Complexity:

  • The recursive calls to mergeSort create a log n-level tree.
    • The number of levels in this tree directly relates to how many times the array can be divided by 2 before reaching single-element subarrays. This is equivalent to the logarithm of the array’s size (log n). For example, an array of 8 elements would have 3 levels (log2 (8) = 3). Reference the picture below for a visual understanding.

Merge Sort

  • At each level, the merging step takes O(n) time.
  • Therefore, the overall time complexity is O(n log n), resulting from multiplying the time complexity of each level (n) by the number of levels (log n).

Benefits of Merge Sort

  • Predictable Efficiency: Merge sort maintains a time complexity of O(n log n) regardless of the initial arrangement of elements in the input array. This means it performs equally well in best-case, average-case, and worst-case scenarios.
  • Reliable for Diverse Inputs: This consistent performance makes merge sort a dependable choice for sorting various input sizes and datasets without unexpected slowdowns due to data arrangement.
  • Efficiency for Large Datasets: Its O(n log n) time complexity places it among the most efficient sorting algorithms, especially for large datasets, outperforming algorithms like bubble sort and selection sort that exhibit worse-case complexities of O(n^2).

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