**1. Overview**

In this short tutorial, we’ll see how we can efficiently merge sorted arrays using a heap.

**2. The Algorithm**

Since our problem statement is to use a heap to merge the arrays, we’ll use a min-heap to solve our problem. A min-heap is nothing but a **binary tree in which the value of each node is smaller than the values of its child nodes**.

Usually, the min-heap is implemented using an array in which the array satisfies specific rules when it comes to finding the parent and children of a node.

For an array *A[]* and an element at index *i*:

*A[(i-1)/2]* will return its parent
*A[(2*i)+1]* will return the left child
*A[(2*i)+2]* will return the right child

Here’s a picture of min-heap and its array representation:

Let’s now create our algorithm that merges a set of sorted arrays:

- Create an array to store the results, with the size determined by adding the length of all the input arrays.
- Create a second array of size equal to the number of input arrays, and populate it with the first elements of all the input arrays.
- Transform the previously created array into a min-heap by applying the min-heap rules on all nodes and their children.
- Repeat the next steps until the result array is fully populated.
- Get the root element from the min-heap and store it in the result array.
- Replace the root element with the next element from the array in which the current root is populated.
- Apply min-heap rule again on our min-heap array.

Our algorithm has a **recursive flow to create the min-heap, **and we have to **visit all the elements of the input arrays**.

The time complexity of this algorithm is *O(k log n)*, where *k* is the total number of elements in all the input arrays, and *n *is the total number of sorted arrays.

Let’s now see a sample input and the expected result after running the algorithm so that we can gain a better understanding of the problem. So for these arrays:

`{ { 0, 6 }, { 1, 5, 10, 100 }, { 2, 4, 200, 650 } }`

The algorithm should return a result array:

`{ 0, 1, 2, 4, 5, 6, 10, 100, 200, 650 }`

**3. Java Implementation**

Now that we have a basic understanding of what a min-heap is and how the merge algorithm works, let’s look at the Java implementation. We’ll use two classes — one to represent the heap nodes and the other to implement the merge algorithm.

**3.1. Heap Node Representation**

Before implementing the algorithm itself, let’s create a class that represents a heap node. This will store the node value and two supporting fields:

```
public class HeapNode {
int element;
int arrayIndex;
int nextElementIndex = 1;
public HeapNode(int element, int arrayIndex) {
this.element = element;
this.arrayIndex = arrayIndex;
}
}
```

Note that we’ve purposefully omitted the *getters* and *setters *here to keep things simple. We’ll use the *arrayIndex* property to store the index of the array in which the current heap node element is taken. And we’ll use the *nextElementIndex* property to store the index of the element that we’ll be taking after moving the root node to the result array.

Initially, the value of *nextElementIndex* will be *1*. We’ll be incrementing its value after replacing the root node of the min-heap.

**3.2. Min-Heap Merge Algorithm**

Our next class is to represent the min-heap itself and to implement the merge algorithm:

```
public class MinHeap {
HeapNode[] heapNodes;
public MinHeap(HeapNode heapNodes[]) {
this.heapNodes = heapNodes;
heapifyFromLastLeafsParent();
}
int getParentNodeIndex(int index) {
return (index - 1) / 2;
}
int getLeftNodeIndex(int index) {
return (2 * index + 1);
}
int getRightNodeIndex(int index) {
return (2 * index + 2);
}
HeapNode getRootNode() {
return heapNodes[0];
}
// additional implementation methods
}
```

Now that we’ve created our min-heap class, let’s add a method that will heapify a subtree where the root node of the subtree is at the given index of the array:

```
void heapify(int index) {
int leftNodeIndex = getLeftNodeIndex(index);
int rightNodeIndex = getRightNodeIndex(index);
int smallestElementIndex = index;
if (leftNodeIndex < heapNodes.length
&& heapNodes[leftNodeIndex].element < heapNodes[index].element) {
smallestElementIndex = leftNodeIndex;
}
if (rightNodeIndex < heapNodes.length
&& heapNodes[rightNodeIndex].element < heapNodes[smallestElementIndex].element) {
smallestElementIndex = rightNodeIndex;
}
if (smallestElementIndex != index) {
swap(index, smallestElementIndex);
heapify(smallestElementIndex);
}
}
```

When we use an array to represent a min-heap, the last leaf node will always be at the end of the array. So when transforming an array into a min-heap by calling the *heapify() *method iteratively, we only need to start the iteration from the last leaf’s parent node:

```
void heapifyFromLastLeafsParent() {
int lastLeafsParentIndex = getParentNodeIndex(heapNodes.length);
while (lastLeafsParentIndex >= 0) {
heapify(lastLeafsParentIndex);
lastLeafsParentIndex--;
}
}
```

Our next method will do the actual implementation of our algorithm. For our better understanding, let’s split the method into two parts and see how it works:

```
int[] merge(int[][] array) {
// transform input arrays
// run the minheap algorithm
// return the resulting array
}
```

The first part transforms the input arrays into a heap node array that contains all the first array’s elements and finds the resulting array’s size:

```
HeapNode[] heapNodes = new HeapNode[array.length];
int resultingArraySize = 0;
for (int i = 0; i < array.length; i++) {
HeapNode node = new HeapNode(array[i][0], i);
heapNodes[i] = node;
resultingArraySize += array[i].length;
}
```

And the next part populates the result array by implementing the steps 4, 5, 6, and 7 of our algorithm:

```
MinHeap minHeap = new MinHeap(heapNodes);
int[] resultingArray = new int[resultingArraySize];
for (int i = 0; i < resultingArraySize; i++) {
HeapNode root = minHeap.getRootNode();
resultingArray[i] = root.element;
if (root.nextElementIndex < array[root.arrayIndex].length) {
root.element = array[root.arrayIndex][root.nextElementIndex++];
} else {
root.element = Integer.MAX_VALUE;
}
minHeap.heapify(0);
}
```

**4. Testing the Algorithm**

Let’s now test our algorithm with the same input we mentioned previously:

```
int[][] inputArray = { { 0, 6 }, { 1, 5, 10, 100 }, { 2, 4, 200, 650 } };
int[] expectedArray = { 0, 1, 2, 4, 5, 6, 10, 100, 200, 650 };
int[] resultArray = MinHeap.merge(inputArray);
assertThat(resultArray.length, is(equalTo(10)));
assertThat(resultArray, is(equalTo(expectedArray)));
```

**5. Conclusion**

In this tutorial, we learned how we can efficiently merge sorted arrays using min-heap.

The example we’ve demonstrated here can be found over on GitHub.