Tree Traversal Inorder Left Root Right in DSA Go - Time & Space Complexity

Time Complexity: Tree Traversal Inorder Left Root Right

O(n)

Understanding Time Complexity

We want to understand how the time needed to visit all nodes in a tree grows as the tree gets bigger.

Specifically, we ask: How does the time to do an inorder traversal change with the number of nodes?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

// Inorder traversal: Left, Root, Right
func inorderTraversal(root *TreeNode) {
    if root == nil {
        return
    }
    inorderTraversal(root.Left)
    fmt.Println(root.Val)
    inorderTraversal(root.Right)
}
    

This code visits every node in a binary tree in the order: left child, node itself, then right child.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls visiting each node once.
• How many times: Exactly once per node in the tree.

How Execution Grows With Input

Each node is visited once, so the total steps grow directly with the number of nodes.

Input Size (n)	Approx. Operations
10	About 10 visits
100	About 100 visits
1000	About 1000 visits

Pattern observation: The work grows in a straight line with the number of nodes.

Final Time Complexity

Time Complexity: O(n)

This means the time to complete the traversal grows directly in proportion to the number of nodes.

Common Mistake

[X] Wrong: "The recursion makes this traversal take more than linear time because it calls itself many times."

[OK] Correct: Each node is visited only once, so the total work is still proportional to the number of nodes, not more.

Interview Connect

Understanding this traversal's time helps you explain how tree operations scale, a key skill for many coding problems.

Self-Check

"What if we changed inorder traversal to preorder traversal? How would the time complexity change?"

Practice

1. What is the order of nodes visited in an inorder traversal of a binary tree?

easy

A. Root node, Right subtree, Left subtree

B. Root node, Left subtree, Right subtree

C. Right subtree, Root node, Left subtree

D. Left subtree, Root node, Right subtree

Solution

1. Step 1: Understand inorder traversal definition

   Inorder traversal means visiting the left subtree first, then the root node, and finally the right subtree.

2. Step 2: Apply the traversal order

   Following this order ensures nodes are visited in ascending order for binary search trees.

3. Final Answer:

   Left subtree, Root node, Right subtree -> Option D

4. Quick Check:

   Inorder = Left, Root, Right [OK]

Hint: Remember: Inorder = Left, Root, Right [OK]

Common Mistakes:

• Confusing inorder with preorder or postorder
• Visiting root before left subtree
• Visiting right subtree before root

2. Which of the following Go function signatures correctly represents an inorder traversal function for a binary tree node defined as type Node struct { Val int; Left, Right *Node }?

easy

A. func inorder(root *Node) []int

B. func inorder(root Node) []int

C. func inorder(root *Node) int

D. func inorder(root Node) int

Solution

1. Step 1: Understand pointer usage in Go for trees

   Binary trees use pointers to nodes to allow recursive traversal and modification.

2. Step 2: Choose function signature returning slice of ints

   Inorder traversal returns a list of node values, so return type should be []int, and input should be pointer to Node.

3. Final Answer:

   func inorder(root *Node) []int -> Option A

4. Quick Check:

   Pointer receiver and slice return type [OK]

Hint: Use pointer receiver and return slice for traversal [OK]

Common Mistakes:

• Passing Node by value instead of pointer
• Returning single int instead of slice
• Using incorrect return type

3. What is the output of the inorder traversal for the following binary tree?

    2
   / \
  1   3

Given the Go code:

type Node struct {
    Val int
    Left, Right *Node
}

func inorder(root *Node) []int {
    if root == nil {
        return []int{}
    }
    left := inorder(root.Left)
    right := inorder(root.Right)
    return append(append(left, root.Val), right...)
}

func main() {
    root := &Node{2, &Node{1, nil, nil}, &Node{3, nil, nil}}
    result := inorder(root)
    fmt.Println(result)
}

medium

A. [3, 2, 1]

B. [2, 1, 3]

C. [1, 2, 3]

D. [1, 3, 2]

Solution

1. Step 1: Trace inorder traversal on given tree

   Visit left subtree (1), then root (2), then right subtree (3).

2. Step 2: Confirm output list order

   The traversal returns [1, 2, 3] as expected for inorder.

3. Final Answer:

   [1, 2, 3] -> Option C

4. Quick Check:

   Inorder traversal output = [1, 2, 3] [OK]

Hint: Inorder visits left, root, then right [OK]

Common Mistakes:

• Appending root before left subtree
• Appending right subtree before root
• Confusing preorder or postorder output

4. The following Go code attempts inorder traversal but produces incorrect output. What is the bug?

func inorder(root *Node) []int {
    if root == nil {
        return []int{}
    }
    left := inorder(root.Left)
    right := inorder(root.Right)
    return append(left, right...)
    return append(result, root.Val)
}

medium

A. The root value is appended after the return statement, so never executed

B. The function should return int, not []int

C. The base case should return nil, not empty slice

D. The left subtree is not traversed

Solution

1. Step 1: Analyze return statements in function

   The first return statement returns append(left, right...), so the second return is unreachable.

2. Step 2: Identify missing root value append

   Root value is never appended because code after first return is ignored.

3. Final Answer:

   The root value is appended after the return statement, so never executed -> Option A

4. Quick Check:

   Code after return is unreachable [OK]

Hint: Check for unreachable code after return [OK]

Common Mistakes:

• Ignoring unreachable code after return
• Not appending root value in correct order
• Confusing base case return value

5. Given a binary tree where some nodes have only one child, how does inorder traversal help in printing the nodes in sorted order if the tree is a binary search tree? Consider the tree:

      5
     / \
    3   8
     \   \
      4   10

What is the inorder traversal output?

hard

A. [5, 3, 4, 8, 10]

B. [3, 4, 5, 8, 10]

C. [4, 3, 5, 10, 8]

D. [3, 5, 4, 8, 10]

Solution

1. Step 1: Apply inorder traversal on given tree

   Visit left subtree: node 3, then its right child 4, then root 5, then right subtree nodes 8 and 10.

2. Step 2: List nodes in inorder sequence

   Sequence is 3, 4, 5, 8, 10 which is sorted ascending order.

3. Final Answer:

   [3, 4, 5, 8, 10] -> Option B

4. Quick Check:

   Inorder traversal of BST gives sorted nodes [OK]

Hint: Inorder traversal of BST prints nodes sorted [OK]

Common Mistakes:

• Ignoring nodes with single child
• Visiting root before left subtree
• Mixing preorder or postorder with inorder