# Merge Sort Algorithm

Other names:
mergesort

## Quick reference

Complexity
Worst case time
Best case time
Average case time
Space

#### Strengths:

• Fast. Merge sort runs in , which scales well as n grows.
• Parallelizable. Merge sort breaks the input into chunks, each of which can be sorted at the same time in parallel.

#### Weaknesses:

• Space. Merge sort takes up extra space, including space for the recursive call stack.

## The High-Level Idea

Merge sort is a recursive algorithm that works like this:

1. split the input in half
2. sort each half by recursively using this same process
3. merge the sorted halves back together

Like all recursive algorithms, merge sort needs a base case. Here, the base case is an input array with one item. A one-element array is already sorted.

## Splitting the Input

Here's a visual representation of merge sort repeatedly breaking the input array in half:

At the bottom, we've got a bunch of "sorted" one-item arrays that we want to combine into one large sorted array.

## Merging sorted arrays

Combining two one-item arrays is easy—the smaller item goes first and the larger item goes second.

Now, how do we merge two of these two-item arrays?

That's a bit trickier, and it definitely requires more than one comparison.

Let's come up with a more general algorithm that can merge two sorted arrays of any size.

Take this example:

The smallest item has to be the first item in either the first array or the second array. So, we compare the first element in both arrays and place the smaller of the two at the front of the merged array.

We can keep repeating this process, skipping over any items that we've already added to the merged array. At each step, we'll just compare the earliest unmerged items from both arrays and add the smaller one.

Once we exhaust one array, we can just append any remaining items from the other one to the end of the merged array.

Using this more general approach, we can easily combine our two-element arrays into four-element arrays, combine those into eight-element arrays, and so on, until all the items are in a single, sorted array.

## Implementation

Here's how we'd code it up:

private static int[] combineSortedArrays(int[] arrayOne, int[] arrayTwo) { int arrayOneIndex = 0; int arrayTwoIndex = 0; int mergedArrayIndex = 0; int[] mergedArray = new int[arrayOne.length + arrayTwo.length]; // both arrays have some items left in them. while (arrayOneIndex < arrayOne.length && arrayTwoIndex < arrayTwo.length) { // choose the smaller of the two items and add it to the // merged array. if (arrayOne[arrayOneIndex] <= arrayTwo[arrayTwoIndex]) { mergedArray[mergedArrayIndex] = arrayOne[arrayOneIndex]; arrayOneIndex += 1; } else { mergedArray[mergedArrayIndex] = arrayTwo[arrayTwoIndex]; arrayTwoIndex += 1; } mergedArrayIndex += 1; } // grab any lingering items in the first array after we've // exhausted the second array while (arrayOneIndex < arrayOne.length) { mergedArray[mergedArrayIndex] = arrayOne[arrayOneIndex]; mergedArrayIndex += 1; arrayOneIndex += 1; } // grab any lingering items in the second array after we've // exhausted the first array while (arrayTwoIndex < arrayTwo.length) { mergedArray[mergedArrayIndex] = arrayTwo[arrayTwoIndex]; mergedArrayIndex += 1; arrayTwoIndex += 1; } return mergedArray; } public static int[] mergeSort(int[] theArray) { // base case: single element array if (theArray.length <= 1) { return theArray; } // split the input in half int middleIndex = theArray.length / 2; int[] left = Arrays.copyOfRange(theArray, 0, middleIndex); int[] right = Arrays.copyOfRange(theArray, middleIndex, theArray.length); // sort each half int[] leftSorted = mergeSort(left); int[] rightSorted = mergeSort(right); // merge the sorted halves return combineSortedArrays(leftSorted, rightSorted); }

## Execution Order

We described merge sort as two steps: a splitting step and a merge step.

In reality, these steps are interleaved through recursive calls.

When we split the input into two arrays, we'll actually finish sorting the left half (splitting it all the way down to one-element arrays and merging them back together) before we even touch the right half.

Then, we'll do the same thing with the right half before combining both sorted halves together to get the full sorted array.

Understanding the actual execution order is important for thinking through the space cost on the call stack.

## Time Complexity

Look at each row in the execution diagram:

Each row in the diagram is time.

1. In the upper half, we do some constant-time work for each item by copying the item from the input array into a smaller array.
2. In the bottom half, we do some constant-time work for each item by copying the item from a smaller, sorted array into the larger, merged array.

How many rows are there in the diagram? In the top half, we're dividing n in half until we get down to 1. Then, in the bottom half we do the reverse—multiplying by two until we get to n. That's divisions followed by multiplications.. Adding the two together, we've got levels.

So overall, merge sort takes time.

## Space Complexity

When combining two sorted arrays into a single bigger one, we need scratch space to hold the merged result.

Since the arrays we'll be combining have items, we'll need space in total.

But, don't we need less than space for some of the merges?

Yes. For instance, when we're merging two arrays with one element each, that requires space.

But, by the time we get to the final merge, we'll have two halves that each have n/2 items, so we'll need space for the combined result.

Do we really need that scratch space? Can't we just be more clever about how we merge the sorted arrays?

Try for yourself! Turns out you can't really do the merge in space without bumping up the time complexity.

If you look carefully, our implementation uses more space () than is strictly necessary.

In the arguments for each recursive call, we're passing in a copy of the input. And, since we might have recursive calls before we hit the base case, that's copies of the input.

We did this to keep the implementation nice and tidy (and not distract from the key intuition behind merge sort).

But with some cleverness, you can get it down to extra space. Try it. :)

## What's next?

If you're ready to start applying these concepts to some problems, check out our mock coding interview questions.

They mimic a real interview by offering hints when you're stuck or you're missing an optimization.

. . .