Build Heap from Array Heapify in DSA Go - Time & Space Complexity

Time Complexity: Build Heap from Array Heapify

O(n)

Understanding Time Complexity

We want to understand how long it takes to build a heap from an array using heapify.

Specifically, how the work grows as the array size increases.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

func buildHeap(arr []int) {
    n := len(arr)
    for i := n/2 - 1; i >= 0; i-- {
        heapify(arr, n, i)
    }
}

func heapify(arr []int, n, i int) {
    largest := i
    left := 2*i + 1
    right := 2*i + 2

    if left < n && arr[left] > arr[largest] {
        largest = left
    }
    if right < n && arr[right] > arr[largest] {
        largest = right
    }
    if largest != i {
        arr[i], arr[largest] = arr[largest], arr[i]
        heapify(arr, n, largest)
    }
}

This code builds a max heap from an unsorted array by calling heapify from the bottom up.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The heapify function called on each non-leaf node.
• How many times: The for loop runs about n/2 times, and each heapify can recurse down the tree.

How Execution Grows With Input

Heapify work depends on the height of the tree at each node, which varies.

Input Size (n)	Approx. Operations
10	About 20-30 operations
100	About 300-400 operations
1000	About 4000-5000 operations

Pattern observation: The total work grows roughly linearly with input size, not quadratically.

Final Time Complexity

Time Complexity: O(n)

This means building a heap from an array takes time proportional to the number of elements.

Common Mistake

[X] Wrong: "Heapify runs on n/2 nodes and each takes O(log n), so total is O(n log n)."

[OK] Correct: Most heapify calls are on nodes near the bottom with small height, so total work adds up to O(n), not O(n log n).

Interview Connect

Understanding this helps you explain why heap construction is efficient and prepares you for questions on priority queues and sorting.

Self-Check

"What if we called heapify starting from the root down to the leaves instead of bottom-up? How would the time complexity change?"

Practice

1. What is the main purpose of the heapify process when building a heap from an array?

easy

A. To rearrange the array elements to satisfy the heap property

B. To sort the array in ascending order

C. To find the maximum element in the array

D. To reverse the array elements

Solution

1. Step 1: Understand heap property

   A heap is a special tree-based structure where each parent node is greater (max-heap) or smaller (min-heap) than its children.

2. Step 2: Role of heapify

   The heapify process rearranges elements to ensure this property holds for every subtree starting from a given node.

3. Final Answer:

   To rearrange the array elements to satisfy the heap property -> Option A

4. Quick Check:

   Heapify = Rearrange for heap property [OK]

Hint: Heapify fixes subtree to maintain heap order [OK]

Common Mistakes:

• Confusing heapify with sorting
• Thinking heapify finds max element only
• Assuming heapify reverses array

2. Which of the following is the correct way to call heapify on a node index i in Go when building a max heap from an array arr of size n?

easy

A. heapify(n, arr, i)

B. heapify(i, arr, n)

C. heapify(arr, i, n)

D. heapify(arr, n, i)

Solution

1. Step 1: Understand heapify function parameters

   Heapify usually takes the array, size of heap, and the index to heapify as parameters in that order.

2. Step 2: Match parameter order

   heapify(arr, n, i) matches the correct order: array, size, index.

3. Final Answer:

   heapify(arr, n, i) -> Option D

4. Quick Check:

   heapify(arr, n, i) is correct call [OK]

Hint: Remember: heapify(array, size, index) [OK]

Common Mistakes:

• Mixing parameter order
• Passing index before array
• Confusing size and index positions

3. Given the array [3, 9, 2, 1, 4, 5], what is the array after building a max heap using heapify from the bottom up?

medium

A. [5, 4, 9, 1, 3, 2]

B. [9, 4, 5, 3, 1, 2]

C. [9, 4, 5, 1, 3, 2]

D. [9, 3, 5, 1, 4, 2]

Solution

1. Step 1: Start heapify from last non-leaf node

   Last non-leaf node index = n/2 -1 = 6/2 -1 = 2. Heapify nodes 2, 1, 0 in order.

2. Step 2: Heapify index 2

   Node 2 value=2, children 5 (index 5). Swap 2 and 5. Array becomes [3, 9, 5, 1, 4, 2].

3. Step 3: Heapify index 1

   Node 1 value=9, children 1 and 4. 9 is largest, no change.

4. Step 4: Heapify index 0

   Node 0 value=3, children 9 and 5. Swap 3 and 9. Array becomes [9, 3, 5, 1, 4, 2]. Heapify index 1 again: 3 with children 1 and 4, swap 3 and 4. Final array: [9, 4, 5, 1, 3, 2].

5. Final Answer:

   [9, 4, 5, 1, 3, 2] -> Option C

6. Quick Check:

   Bottom-up heapify gives [9,4,5,1,3,2] [OK]

Hint: Heapify bottom-up swaps to fix largest child first [OK]

Common Mistakes:

• Not heapifying children after swap
• Starting heapify from wrong index
• Confusing max-heap with min-heap

4. Consider this Go code snippet to build a max heap from array arr. What is the error?

func buildHeap(arr []int) {
    n := len(arr)
    for i := n / 2; i >= 0; i-- {
        heapify(arr, n, i)
    }
}

medium

A. heapify function is missing the array size parameter

B. The loop should start from n/2 - 1, not n/2

C. The loop should increment i, not decrement

D. The loop condition should be i > 0, not i >= 0

Solution

1. Step 1: Identify correct start index for heapify

   Heapify starts from last non-leaf node at index n/2 - 1, not n/2.

2. Step 2: Analyze loop range

   Starting at n/2 includes one extra index which is a leaf node, unnecessary and may cause incorrect behavior.

3. Final Answer:

   The loop should start from n/2 - 1, not n/2 -> Option B

4. Quick Check:

   Start heapify at n/2 - 1 [OK]

Hint: Start heapify at last non-leaf node: n/2 - 1 [OK]

Common Mistakes:

• Starting loop at n/2 instead of n/2 - 1
• Loop increment instead of decrement
• Wrong loop condition excluding index 0

5. You have an array [4, 10, 3, 5, 1]. After building a max heap using heapify, you want to insert a new element 15 and maintain the max heap property. Which sequence of operations correctly maintains the heap?

hard

A. Append 15, then bubble up the new element until heap property is restored

B. Append 15, then heapify only the root node

C. Append 15, then heapify from last non-leaf node down to root

D. Replace root with 15, then heapify entire array

Solution

1. Step 1: Understand insertion in heap

   To insert, add element at end, then restore heap by bubbling up (swapping with parent if larger).

2. Step 2: Why bubble up?

   Heapify from bottom up is for building heap initially; for insertion, bubbling up fixes heap efficiently.

3. Final Answer:

   Append 15, then bubble up the new element until heap property is restored -> Option A

4. Quick Check:

   Insert + bubble up maintains heap [OK]

Hint: Insert at end, bubble up to fix heap [OK]

Common Mistakes:

• Using heapify from root after insertion
• Heapifying only root node
• Replacing root without adjusting heap