Check if Binary Tree is Balanced in DSA Go - Time & Space Complexity

Time Complexity: Check if Binary Tree is Balanced

O(n²)

Understanding Time Complexity

We want to know how long it takes to check if a binary tree is balanced.

Specifically, how the time grows as the tree gets bigger.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

func isBalanced(root *TreeNode) bool {
    if root == nil {
        return true
    }
    leftHeight := height(root.Left)
    rightHeight := height(root.Right)
    if abs(leftHeight - rightHeight) > 1 {
        return false
    }
    return isBalanced(root.Left) && isBalanced(root.Right)
}

func height(node *TreeNode) int {
    if node == nil {
        return 0
    }
    left := height(node.Left)
    right := height(node.Right)
    if left > right {
        return left + 1
    }
    return right + 1
}
    

This code checks if a binary tree is balanced by comparing heights of subtrees recursively.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls to height inside isBalanced.
• How many times: For each node, height is called multiple times, causing repeated subtree height calculations.

How Execution Grows With Input

As the tree size grows, the number of recursive calls grows quickly because height is recalculated many times.

Input Size (n)	Approx. Operations
10	About 100 calls
100	About 10,000 calls
1000	About 1,000,000 calls

Pattern observation: The operations grow roughly with the square of the number of nodes.

Final Time Complexity

Time Complexity: O(n²)

This means the time to check balance grows very fast as the tree gets bigger, roughly proportional to the square of the number of nodes.

Common Mistake

[X] Wrong: "The function runs in linear time because it visits each node once."

[OK] Correct: The height function is called repeatedly for the same nodes, causing many extra visits and making the total work much larger.

Interview Connect

Understanding this helps you explain why some recursive solutions can be inefficient and how to improve them, a key skill in interviews.

Self-Check

"What if we modified the code to calculate height and balance in a single recursive pass? How would the time complexity change?"

Practice

1. What does it mean for a binary tree to be balanced?

easy

A. The height difference between left and right subtrees of every node is at most 1

B. All nodes have two children

C. The tree has the same number of nodes on left and right

D. The tree is a complete binary tree

Solution

1. Step 1: Understand the balance condition

   A balanced binary tree means for every node, the height difference between its left and right subtree is at most 1.

2. Step 2: Compare options with definition

   The height difference between left and right subtrees of every node is at most 1 matches the definition exactly. Other options describe different tree properties.

3. Final Answer:

   The height difference between left and right subtrees of every node is at most 1 -> Option A

4. Quick Check:

   Balanced tree = height difference ≤ 1 [OK]

Hint: Balance means height difference ≤ 1 at every node [OK]

Common Mistakes:

• Confusing balanced with complete tree
• Thinking balanced means equal number of nodes
• Assuming all nodes must have two children

2. Which Go function signature correctly represents a function to check if a binary tree is balanced?

easy

A. func isBalanced(root *TreeNode) bool

B. func isBalanced(root TreeNode) int

C. func checkBalance(root *TreeNode) string

D. func balanced(root *TreeNode) void

Solution

1. Step 1: Identify correct parameter and return types

   The function should take a pointer to the root node and return a boolean indicating balanced or not.

2. Step 2: Match options with correct signature

   func isBalanced(root *TreeNode) bool matches: input is *TreeNode pointer, output is bool. Others have wrong return types or parameter types.

3. Final Answer:

   func isBalanced(root *TreeNode) bool -> Option A

4. Quick Check:

   Input pointer, output bool = correct signature [OK]

Hint: Check function returns bool and takes *TreeNode pointer [OK]

Common Mistakes:

• Passing TreeNode by value instead of pointer
• Returning int or string instead of bool
• Using void (no return) for a check function

3. What will the output be for the following Go code snippet that checks if a binary tree is balanced?

package main
import "fmt"

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

func height(root *TreeNode) int {
    if root == nil {
        return 0
    }
    leftHeight := height(root.Left)
    rightHeight := height(root.Right)
    if leftHeight == -1 || rightHeight == -1 || abs(leftHeight-rightHeight) > 1 {
        return -1
    }
    if leftHeight > rightHeight {
        return leftHeight + 1
    }
    return rightHeight + 1
}

func isBalanced(root *TreeNode) bool {
    return height(root) != -1
}

func abs(x int) int {
    if x < 0 {
        return -x
    }
    return x
}

func main() {
    root := &TreeNode{Val: 1}
    root.Left = &TreeNode{Val: 2}
    root.Right = &TreeNode{Val: 3}
    root.Left.Left = &TreeNode{Val: 4}
    root.Left.Left.Left = &TreeNode{Val: 5}
    fmt.Println(isBalanced(root))
}

medium

A. Compilation error

B. true

C. false

D. Runtime panic

Solution

1. Step 1: Analyze tree structure and height function

   The tree is left-heavy: left subtree has height 3, right subtree height 1 at root. Difference is 2 > 1, so height returns -1.

2. Step 2: Check isBalanced return value

   isBalanced returns height(root) != -1, which is false here because height returned -1.

3. Final Answer:

   false -> Option C

4. Quick Check:

   Height difference > 1 means balanced = false [OK]

Hint: Height returns -1 if unbalanced; check != -1 for balanced [OK]

Common Mistakes:

• Assuming tree is balanced without checking height difference
• Confusing return values of height function
• Ignoring left-heavy subtree depth

4. Identify the bug in this Go function that checks if a binary tree is balanced:

func isBalanced(root *TreeNode) bool {
    if root == nil {
        return false
    }
    leftHeight := height(root.Left)
    rightHeight := height(root.Right)
    if abs(leftHeight - rightHeight) > 1 {
        return false
    }
    return isBalanced(root.Left) && isBalanced(root.Right)
}

medium

A. Uses incorrect logical operator in return

B. Returns false for empty tree instead of true

C. Missing base case for leaf nodes

D. Does not check height difference correctly

Solution

1. Step 1: Check base case for empty tree

   Function returns false if root is nil, but empty tree is balanced, so should return true.

2. Step 2: Verify other logic correctness

   Height difference check and recursive calls are correct. Only base case return is wrong.

3. Final Answer:

   Returns false for empty tree instead of true -> Option B

4. Quick Check:

   Empty tree is balanced, so base case must return true [OK]

Hint: Empty tree is balanced; base case must return true [OK]

Common Mistakes:

• Returning false for nil root
• Not checking both subtrees recursively
• Confusing logical AND with OR in recursion

5. Given a binary tree, how can you efficiently check if it is balanced without recalculating heights multiple times?

hard

A. Calculate height of left and right subtrees separately for each node

B. Check balance only at the root node

C. Use breadth-first traversal to count nodes at each level

D. Use a bottom-up recursion that returns height or -1 if unbalanced

Solution

1. Step 1: Understand naive approach inefficiency

   Calculating height separately for each node causes repeated work, leading to O(n²) time.

2. Step 2: Use bottom-up recursion for efficiency

   Return height if balanced, else -1 to stop early. This avoids repeated height calculations, achieving O(n) time.

3. Final Answer:

   Use a bottom-up recursion that returns height or -1 if unbalanced -> Option D

4. Quick Check:

   Bottom-up recursion avoids repeated height calls [OK]

Hint: Return -1 on unbalanced during height calc to stop early [OK]

Common Mistakes:

• Recalculating heights multiple times causing slow code
• Checking balance only at root misses subtree imbalance
• Using BFS which doesn't help with height difference