Non-Linear Data StructuresBeginner LevelStudy Time: 15 mins

Tree Traversals Data Structure

Tree Traversals refer to search operations that visit each node in a binary tree exactly once. Depth-First Search (DFS) traversals include Preorder (Root-L-R), Inorder (L-Root-R), and Postorder (L-R-Root).

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Binary Tree Traversal Step-by-Step Trace

Sample Input:Binary Tree: Root=4, Left=2 (leaves 1,3), Right=6 (leaves 5,7)
Step 1

Preorder Traversal (Root -> Left -> Right)

Visit root node 4 first, recurse to left subtree [2, 1, 3], then right subtree [6, 5, 7].

Output Sequence: [4, 2, 1, 3, 6, 5, 7]
Preorder processes parent nodes before visiting child subtrees.
Step 2

Inorder Traversal (Left -> Root -> Right)

Recurse to leftmost leaf 1, visit parent 2, visit 3, visit root 4, then right subtree [5, 6, 7].

Output Sequence: [1, 2, 3, 4, 5, 6, 7]
Inorder traversal over a Binary Search Tree produces perfectly sorted output!
Step 3

Postorder Traversal (Left -> Right -> Root)

Recurse to leaves [1, 3], visit parent 2, visit leaves [5, 7], visit parent 6, visit root 4 last.

Output Sequence: [1, 3, 2, 5, 7, 6, 4]
Postorder processes all child dependencies before parent evaluation.

Real-world analogy: Tracing Tree Nodes

Preorder
Inorder
Postorder
Hover over items for details

Imagine visiting houses in a cul-de-sac. Preorder announces the visit at the entrance. Inorder processes the house after checking the left lane. Postorder announces the visit when exiting the street.

  • Preorder= Visit Root, traverse Left, traverse Right.
  • Inorder= Traverse Left, visit Root, traverse Right. Yields fully sorted output for BSTs!
  • Postorder= Traverse Left, traverse Right, visit Root.

Operations explained

Complexity analysis

Scenario Simulator: Dataset Size (N = 500)
Presets:
N = 10N = 10,000
Complexity Curve MapTree Traversals: O(log N) Tree Height Time
Time (t)Size N (500)N = 500O(N²)O(N log N)O(N)O(log N)O(1)Tree Traversals
Time Steps (N=500)

~9 steps (Logarithmic)

O(log N) Tree Height Time

Space Footprint

O(N) Tree Nodes Space

Memory growth rate as N expands.

Performance Rank
Optimal Tier

Relative efficiency rating for large N.

OperationTime ComplexitySpace Complexity
Inorder TraversalO(N)O(H)
Preorder TraversalO(N)O(H)
Postorder TraversalO(N)O(H)

Code implementations

function inorder(root) {
  if (!root) return;
  inorder(root.left);
  console.log(root.val);
  inorder(root.right);
}

Real-world applications

Case 01

Expression Parsing

Converts syntax trees into postfix/prefix code formats.

Case 02

Sorted Code Export

Outputs binary tree contents in sorted order using inorder traversal.

Interview questions

Topic Knowledge Test & Assessment

10 Questions Assessment

Tree Traversals Knowledge & Skill Test

Validate your conceptual understanding, operation mechanics, and core concepts of Tree Traversals.