Lesson 5

Hello, aspiring programmers! Today's topic is the **"Merge Sort."** Merge Sort is a sorting technique like arranging a deck of shuffled cards in order. But for data on the Internet scale, Merge Sort outperforms your regular techniques. Today, we'll explore Merge Sort, code it in `Java`

, and analyze its speed. Ready? Let's get started!

In computer science, **Merge Sort** is a popular method to sort elements. Merge Sort uses the same 'divide-and-conquer' strategy for sorting, like the familiar **Quick Sort** algorithm. Imagine if you have one long music playlist mixed up with songs. You want to sort these songs from A to Z. That's what Merge Sort does to an array.

In the three steps of Merge Sort:

- Split the array into halves.
- Sort each half separately.
- Merge the sorted halves back together.

We will start with building a code for merging two sorted parts. The merge process makes two halves play sort and seek. It compares elements from two halves and merges so that the resulting list is sorted as well.

Let's code a `merge()`

function in `Java`

that will do just that. Note that the final variant of the merge sort function will do every operation "in place," meaning there will not be actual two arrays; we will operate parts of one array. Having this in mind, let's implement the `merge`

function to take just one array and treat its parts like separate arrays.

Java`1void merge(int arr[], int left, int mid, int right) { 2 int n1 = mid - left + 1; // Find number of elements in the left array 3 int Left[] = new int[n1]; // Define left array 4 5 int n2 = right - mid; // Find number of elements in the right array 6 int Right[] = new int[n2]; // Define right array 7 8 // Let's fill in these arrays 9 for (int i = 0; i < n1; i++) 10 Left[i] = arr[left + i]; 11 for (int j = 0; j < n2; j++) 12 Right[j] = arr[mid + 1 + j]; 13}`

So far, we've divided our original list into two halves, Left and Right.

Now, we'll sort and merge these halves:

Java`1 int i = 0, j = 0; 2 int k = left; 3 while (i < n1 && j < n2) { 4 if (Left[i] <= Right[j]) { 5 arr[k] = Left[i]; 6 i++; 7 } else { 8 arr[k] = Right[j]; 9 j++; 10 } 11 k++; 12 } 13}`

Seemingly tricky, the code is very straightforward:
We place two pointers, `i`

and `j`

, at the beginning of the `Right`

and `Left`

arrays.
We choose the smaller element, put it in the final array `arr`

, and move the corresponding pointer further.
We keep doing this until one of the pointers reaches the end of its array.

We stop the process when one of the pointers reaches the end of its array, but some elements could be left in the other array.

To handle this, let's copy the remaining elements of both arrays (if any) to the end of the resulting `arr`

array.

Java`1 2 // Copy remaining elements of Left[] if any 3 while (i < n1) { 4 arr[k] = Left[i]; 5 i++; 6 k++; 7 } 8 9 // Copy remaining elements of Right[] if any 10 while (j < n2) { 11 arr[k] = Right[j]; 12 j++; 13 k++; 14 }`

The merge section is completed. It successfully merges two halves back together in the sorted order.

Now, let's implement the method to divide the array into two halves. Coding-wise, we'll need to define a `sort()`

method to split the array and manage the merge process. We will split the array and its halves recursively until we end up with small arrays of just one element, which are naturally sorted! Next, we will merge these arrays back together into one big sorted array.

Let's start building the `sort()`

method. Initially, we'll handle the splitting part:

Java`1void sort(int arr[], int left, int right) { 2 if (left < right) { 3 int mid = (left + right) / 2; // Finding the midpoint 4 5 } 6}`

Now, we can split our list into two halves, but they still need to be sorted.

Next, we need to sort these halves and merge them together:

Java`1 sort(arr, left, mid); // Sorting the left half 2 sort(arr, mid + 1, right); // Sorting the right half 3 merge(arr, left, mid, right); // Merging the sorted halves 4 } 5}`

Phew! We've now implemented our Merge Sort algorithm in `Java`

.

In the computing world, performance matters. The less time it takes for a sorting algorithm to run, the better. Merge Sort shows good performance with the time complexity of `O(n log n),`

similar to sorting a huge deck of cards quickly. Thus, it excels when dealing with massive data sets.

Merge Sort is consistent. It's like a reliable late-night tutor that offers predictable performance, regardless of the initial order of the data input. It mimics a reliable friend who will not let down expectations.

However, it tends to use extra memory, creating new arrays during the merge process.

Great job! We've broken down Merge Sort and coded it in `Java`

. Next up, we have some exciting hands-on exercises for you. Ready to put what you've learned into practice? Let's dive into the fun part! Let's get coding!