# Understand Tree In-order Traversal in Java8 min read

Many of you probably have familiar with arrays or linked-lists, we know that in those data structures, elements can be traversed linearly. But what about the binary tree? As we can presume, the binary tree is a non-linear data structure, which means within hand one item we can reach out to several other items at the same time, e.g if you have a parent node you can access it to its left and right child if existed.

Because of this, there are not just once, but several techniques can be applied to traverse the tree. The most ubiquitous ones are pre-order traversal, in-order traversal, post-order traversal and level-order traversal (Breadth First Traversal).

Firstly, for better envision, let’s draw some examples real quick:

In this article, we’re going to learn in deep about the In-order traversal in Binary Search Tree and understand how it works in a recursive manner.

## In-order Traversal Recursion in Java

What do I mean by traversing the tree?

Tree traversal is a process of exploring all the nodes in the tree only once. You can either implement in-order traversal iteratively or recursively. However, we’re interested in doing this with recursion.

With in-order traversal, the left child is explored first, next visiting the root and then visit the right child. With that in mind, we are gonna implement a recursive procedure with the following steps:

• Recursively visit the left subtree
• Visit the root node
• Recursively visit the right subtree

What do I mean by “recursively visit” left or right subtree and why it does not “visit” those positions? Let’s see another example:

The node with the value of 24 is the root of the tree, but it has 3 children on the left, which are 14, 12, and 8. Which is it going to visit first? The answer is the last one. Initially, it gets to the root node 24, then it recursively exploring the left, here in the left child 14, but here the recursion is again applied, it continues to go to the left node of 14, which is 12, then here in 12, it again traverses the left part, finds the value of 8, but here it no longer can explore deeper, then the algorithm stops, prints the value of 8, then go back for the previous call of recursion which is not yet done, print the Node of 12, then it goes back to print the Node 12, then here in 12, it exerts to attain the right child of this node but there is nothing, then it backtracks to the node 14, this procedure is continued again and again until there is nothing left.

During an inorder traversal, we visit a position between the recursive traversal of its left and right subtrees. The inorder traversal of a binary tree T can be informally viewed as visiting the nodes of T from left to right.

For now, let’s meticulously finalized the in-order traversal method with the recursive approach:

• Create a method named `inOrder(Node node)`. The recursive function keeps doing while the `node` is not null
• Recursively call `inOrder(node.left)` in its left child
• Perform the “visit” action for the position `node`
• Recursively call `inOrder(node.right)` in its right child
``````private void inOrder(Node node) {
if (node != null) {
/** recursively calls inOrder on the left sub-tree */
inOrder(node.left);
/** prints the value at the current node */
System.out.println("The current node value is " + node.element);
/** recursively calls inOrder method for the right subtree. */
inOrder(node.right);
}

}``````

Let’s recall the example I have given at the beginning of this section, put it to this method and see what happens:

``````          24
/  \
14    26
/  \    / \
12   17  25 32
/
8``````

Output:

``8, 12, 14, 17, 24, 25, 26, 32``

Here we mark `inOrder` as a private method and we gonna use a method that accepts no parameters calls `inOrder` method for public use. By doing so, the work of in-order algorithms is done internally and we don’t have to worry about the node we pass in when going outside the scope of this method:

``````public void inOrderTraversalPublic() {
inOrder(root);
}``````

The `root` variable we pass here in the `inOrderTraversalPublic` is an instance variable that is already defined in the class which contains those 2 methods. The complete code of this class is now shown below:

``````class BST<T extends Comparable<T>> {
private Node root = null;
private int nodeCount = 0;

private class Node {
T element;
Node left, right;

public Node(Node left, Node right, T element) {
this.left = left;
this.right = right;
this.element = element;
}
}
/** Utility method for adding element to the BST. */
private Node insert(Node node, T value) {
if (node == null) {
node = new Node(null, null, value);
}
else if (value.compareTo(node.element) < 0) {
node.left = insert(node.left, value);

}
else if (value.compareTo(node.element) > 0) {
node.right = insert(node.right, value);
}
else if (value.compareTo(node.element) == 0) {
return null;
}
return node;

}

root = insert(root, value);
nodeCount++;
}

/** Our work is here, the in-order traversal method in BST. */
private void inOrder(Node node) {
if (node != null) {
inOrder(node.left);
System.out.println(node.element);
inOrder(node.right);
}

}
/** method with no parameter */
public void inOrderTraversal() {
inOrder(root);
}
/** Test in-order traversal */
public static void main(String[] args){
BST tree = new BST();
tree.inOrderTraversal();
/** Output: 8, 12, 14, 17, 24, 25, 26, 32 */

}
}``````

As we implement the in-order traversal algorithm in a Binary Search Tree, hence the interesting property which is easy to notice that the output is sorted in the ascending order. Besides this, the in-order traversal algorithm can be used in binary trees to represent arithmetic expressions.

The time complexity of the in-order traversal algorithm is . Fully implementation and explanation of BST can be found here. This code is also available on Github.

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