Validate if Tree is BST in DSA Go - Time & Space Complexity

Time Complexity: Validate if Tree is BST

O(n)

Understanding Time Complexity

We want to know how long it takes to check if a tree follows the rules of a Binary Search Tree (BST).

The question is: how does the time needed grow as the tree gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

func isBST(node *TreeNode, min, max *int) bool {
    if node == nil {
        return true
    }
    if (min != nil && node.Val <= *min) || (max != nil && node.Val >= *max) {
        return false
    }
    return isBST(node.Left, min, &node.Val) && isBST(node.Right, &node.Val, max)
}

This code checks if a binary tree is a BST by verifying each node's value is within allowed min and max limits recursively.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls visiting each node once.
• How many times: Once per node in the tree (n times for n nodes).

How Execution Grows With Input

As the tree grows, the function visits each node once, so the work grows directly with the number of nodes.

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

Pattern observation: The number of operations grows linearly with the number of nodes.

Final Time Complexity

Time Complexity: O(n)

This means the time to check the tree grows in direct proportion to the number of nodes.

Common Mistake

[X] Wrong: "The check is faster because it stops early if a node fails, so it's less than O(n)."

[OK] Correct: While it may stop early sometimes, in the worst case it must check every node, so the time complexity is still O(n).

Interview Connect

Understanding this time complexity helps you explain how efficient your tree checks are and shows you can reason about recursive algorithms clearly.

Self-Check

"What if we changed the code to use an in-order traversal and check if values are sorted? How would the time complexity change?"

Practice

1. What does it mean for a binary tree to be a Binary Search Tree (BST)?

easy

A. All nodes have the same value.

B. Every node has exactly two children.

C. The tree is balanced with equal height on both sides.

D. Every node's left subtree contains values less than the node, and right subtree contains values greater.

Solution

1. Step 1: Understand BST property

   A BST requires that for each node, all values in the left subtree are less than the node's value, and all values in the right subtree are greater.

2. Step 2: Compare options with definition

   Every node's left subtree contains values less than the node, and right subtree contains values greater. matches the BST property exactly, while others describe different tree properties.

3. Final Answer:

   Every node's left subtree contains values less than the node, and right subtree contains values greater. -> Option D

4. Quick Check:

   BST property = Every node's left subtree contains values less than the node, and right subtree contains values greater. [OK]

Hint: BST means left < node < right for every node [OK]

Common Mistakes:

• Confusing BST with balanced tree
• Thinking all nodes must have two children
• Assuming all values are equal

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

easy

A. func isBST(root *Node) bool

B. func isBST(root Node) int

C. func checkBST(root *Node) string

D. func validateBST(root Node) void

Solution

1. Step 1: Identify correct parameter and return types

   The function should accept a pointer to the root node (to avoid copying) and return a boolean indicating if the tree is BST.

2. Step 2: Match with options

   func isBST(root *Node) bool matches this: it takes *Node and returns bool. Others have wrong parameter types or return types.

3. Final Answer:

   func isBST(root *Node) bool -> Option A

4. Quick Check:

   Pointer input + bool output = func isBST(root *Node) bool [OK]

Hint: Use pointer receiver and bool return for BST check [OK]

Common Mistakes:

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

3. Given the following Go code, what will be the output?

package main

import "fmt"

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

func isBSTUtil(node *Node, min, max int) bool {
    if node == nil {
        return true
    }
    if node.Val <= min || node.Val >= max {
        return false
    }
    return isBSTUtil(node.Left, min, node.Val) && isBSTUtil(node.Right, node.Val, max)
}

func isBST(root *Node) bool {
    return isBSTUtil(root, -1000, 1000)
}

func main() {
    root := &Node{Val: 10}
    root.Left = &Node{Val: 5}
    root.Right = &Node{Val: 15}
    root.Right.Left = &Node{Val: 6}
    root.Right.Right = &Node{Val: 20}
    fmt.Println(isBST(root))
}

medium

A. false

B. true

C. Compilation error

D. Runtime panic

Solution

1. Step 1: Trace tree values against BST rules

   The node with value 6 is in the right subtree of 10 but less than 10, violating BST property.

2. Step 2: Check isBSTUtil logic

   isBSTUtil checks if node.Val is within (min, max). For node 6, min=10, max=15, but 6 <= 10 fails the condition, returning false.

3. Final Answer:

   false -> Option A

4. Quick Check:

   6 in right subtree < 10 = false [OK]

Hint: Check subtree values against min/max bounds [OK]

Common Mistakes:

• Ignoring subtree value constraints
• Assuming all nodes in right subtree are greater than root only
• Not updating min/max bounds during recursion

4. The following Go function is intended to check if a binary tree is a BST, but it has a bug. What is the bug?

func isBST(node *Node) bool {
    if node == nil {
        return true
    }
    if node.Left != nil && node.Left.Val > node.Val {
        return false
    }
    if node.Right != nil && node.Right.Val < node.Val {
        return false
    }
    return isBST(node.Left) && isBST(node.Right)
}

medium

A. It does not handle nil nodes correctly.

B. It only compares immediate children, missing deeper subtree violations.

C. It uses wrong comparison operators for BST.

D. It causes infinite recursion.

Solution

1. Step 1: Analyze comparison logic

   The function compares only node.Left.Val and node.Right.Val with node.Val, ignoring entire subtree values.

2. Step 2: Understand BST violation cases

   A violation can occur deeper in subtrees, e.g., a left subtree node greater than root, which this function misses.

3. Final Answer:

   It only compares immediate children, missing deeper subtree violations. -> Option B

4. Quick Check:

   Check all subtree nodes, not just immediate children [OK]

Hint: Check entire subtree ranges, not just direct children [OK]

Common Mistakes:

• Checking only immediate children values
• Not passing min/max bounds in recursion
• Assuming local checks guarantee global BST

5. You want to validate if a binary tree is a BST but the tree nodes can have duplicate values. Which modification to the standard BST validation logic is correct to allow duplicates only on the right subtree?

hard

A. Change the condition to allow node.Val <= max for left subtree and node.Val > max for right subtree.

B. Allow duplicates only if node.Val >= min for left subtree and node.Val < max for right subtree.

C. Allow duplicates only if node.Val > min for left subtree and node.Val <= max for right subtree.

D. Allow duplicates on both sides without changing conditions.

Solution

1. Step 1: Understand duplicate placement in BST

   Duplicates allowed only on right means left subtree nodes must be strictly less than node.Val, right subtree nodes can be equal or greater.

2. Step 2: Adjust min/max conditions accordingly

   Change violation check to (node.Val <= min || node.Val > max), enforcing node.Val > min && node.Val <= max. This allows equality to max (parent.Val in right calls) but enforces > min (parent.Val in left calls).

3. Final Answer:

   Allow duplicates only if node.Val > min for left subtree and node.Val <= max for right subtree. -> Option C

4. Quick Check:

   > min && <= max = duplicates on right only [OK]

Hint: Use > min && <= max to allow duplicates on right [OK]

Common Mistakes:

• Allowing duplicates on left subtree
• Not adjusting inequality signs properly
• Allowing duplicates on both sides without rules