Floor and Ceil in BST in DSA Java - Time & Space Complexity

Time Complexity: Floor and Ceil in BST

O(h)

Understanding Time Complexity

We want to know how long it takes to find the floor or ceil value in a Binary Search Tree (BST).

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

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

public int floor(Node root, int key) {
    int floor = -1;
    while (root != null) {
        if (root.data == key) return root.data;
        if (root.data > key) root = root.left;
        else {
            floor = root.data;
            root = root.right;
        }
    }
    return floor;
}

This code finds the floor value (largest value <= key) in a BST by moving left or right.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Traversing down the tree nodes one by one.
• How many times: At most once per tree level, moving left or right until a leaf or match is found.

How Execution Grows With Input

As the tree grows taller, the number of steps to find floor or ceil grows roughly with the tree height.

Input Size (n)	Approx. Operations
10	Up to 4 steps (if tree height ~4)
100	Up to 7 steps (if tree height ~7)
1000	Up to 10 steps (if tree height ~10)

Pattern observation: The steps grow with the height of the tree, which depends on how balanced the tree is.

Final Time Complexity

Time Complexity: O(h)

This means the time depends on the height of the BST, moving down one level at a time.

Common Mistake

[X] Wrong: "Finding floor or ceil always takes O(log n) time because BSTs are balanced."

[OK] Correct: Not all BSTs are balanced; if the tree is skewed, height can be as large as n, making the time O(n).

Interview Connect

Understanding how tree height affects search time helps you explain and improve BST operations clearly in interviews.

Self-Check

What if the BST is perfectly balanced? How would the time complexity change?

Practice

1. What does the floor of a value in a Binary Search Tree (BST) represent?

easy

A. The greatest value in the BST less than or equal to the given value

B. The smallest value in the BST greater than or equal to the given value

C. The root value of the BST

D. The smallest value in the BST less than the given value

Solution

1. Step 1: Understand the definition of floor in BST

   The floor of a value is the greatest value in the BST that is less than or equal to the given value.

2. Step 2: Compare with options

   The greatest value in the BST less than or equal to the given value matches this definition exactly, while others describe different concepts.

3. Final Answer:

   The greatest value in the BST less than or equal to the given value -> Option A

4. Quick Check:

   Floor = greatest ≤ value [OK]

Hint: Floor is largest value ≤ target in BST [OK]

Common Mistakes:

• Confusing floor with ceil
• Thinking floor is smallest value
• Assuming floor is always the root

2. Which of the following is the correct Java method signature to find the ceil of a value in a BST node class?

easy

A. public int findCeil(Node root, int key)

B. int findCeil(Node root, int key) void

C. public void findCeil(Node root, int key) int

D. findCeil(Node root, int key) public int

Solution

1. Step 1: Recall Java method signature syntax

   In Java, a method signature includes access modifier, return type, method name, and parameters in parentheses.

2. Step 2: Check each option

   public int findCeil(Node root, int key) follows correct syntax: public int findCeil(Node root, int key). Others have incorrect order or missing return types.

3. Final Answer:

   public int findCeil(Node root, int key) -> Option A

4. Quick Check:

   Java method = access + return + name(params) [OK]

Hint: Java method: access + return type + name(params) [OK]

Common Mistakes:

• Placing return type after method name
• Omitting access modifier
• Using void when return type is int

3. Given the BST below and key = 14, what is the floor and ceil?

       15
      /  \
    10    20
   /  \     \
  8   12    25

medium

A. Floor = 15, Ceil = 20

B. Floor = 12, Ceil = 15

C. Floor = 10, Ceil = 15

D. Floor = 12, Ceil = 20

Solution

1. Step 1: Find floor of 14 in BST

   Floor is greatest value ≤ 14. Values ≤14 are 8,10,12. Greatest is 12.

2. Step 2: Find ceil of 14 in BST

   Ceil is smallest value ≥ 14. Values ≥14 are 15,20,25. Smallest is 15.

3. Final Answer:

   Floor = 12, Ceil = 15 -> Option B

4. Quick Check:

   Floor ≤14 = 12, Ceil ≥14 = 15 [OK]

Hint: Floor ≤ key, Ceil ≥ key in BST [OK]

Common Mistakes:

• Choosing floor or ceil outside BST values
• Mixing floor and ceil values
• Ignoring BST property when searching

4. Consider this Java method to find floor in BST:

int floor(Node root, int key) {
  if (root == null) return -1;
  if (root.data == key) return root.data;
  if (root.data > key) return floor(root.left, key);
  int floorValue = floor(root.right, key);
  return (floorValue <= key && floorValue != -1) ? floorValue : root.data;
}

What is the bug in this code?

medium

A. It always searches left subtree

B. It does not check for null root

C. It returns -1 when key is found

D. It returns root.data even if floorValue is -1 and root.data > key

Solution

1. Step 1: Analyze the return condition when root.data > key

   If root.data > key, method searches left subtree correctly.

2. Step 2: Analyze return when root.data < key

   It gets floorValue from right subtree. If floorValue is -1 (no floor found), it returns root.data even if root.data > key, which is wrong.

3. Step 3: Identify bug

   Returning root.data without checking if root.data ≤ key causes incorrect floor when floorValue is -1.

4. Final Answer:

   It returns root.data even if floorValue is -1 and root.data > key -> Option D

5. Quick Check:

   Check root.data ≤ key before returning [OK]

Hint: Check root.data ≤ key before returning floor [OK]

Common Mistakes:

• Ignoring invalid floorValue -1
• Returning root.data without condition
• Not handling null root properly

5. You want to find both floor and ceil of a key in a BST efficiently. Which approach is best?

hard

A. Convert BST to sorted array, then binary search twice

B. Traverse BST twice: once for floor, once for ceil

C. Traverse BST once, updating floor and ceil during search

D. Use BFS traversal to find floor and ceil

Solution

1. Step 1: Understand BST properties for floor and ceil

   BST allows searching floor and ceil by comparing key with node values and moving left or right.

2. Step 2: Evaluate approaches

   Traversing once and updating floor and ceil during search is efficient (O(h)), better than traversing twice or converting to array.

3. Step 3: Choose best approach

   Traverse BST once, updating floor and ceil during search is best as it uses BST property and single traversal.

4. Final Answer:

   Traverse BST once, updating floor and ceil during search -> Option C

5. Quick Check:

   Single BST traversal updates floor & ceil [OK]

Hint: Update floor and ceil in one BST traversal [OK]

Common Mistakes:

• Doing two separate traversals
• Converting BST to array unnecessarily
• Using BFS which ignores BST order