Introduction to Heap - Data Structure and Algorithm Tutorials

Last Updated : 23 Jul, 2025

A Heap is a special tree-based data structure with the following properties:

It is a complete binary tree (all levels are fully filled except possibly the last, which is filled from left to right).
It satisfies either the max-heap property (every parent node is greater than or equal to its children) or the min-heap property (every parent node is less than or equal to its children).

Note: This definition applies specifically to binary heaps. Other types of heaps (like Fibonacci heaps or binomial heaps) may not be complete binary trees but still maintain the heap property in their own way. So if you mean binary heap, your original statement is correct and standard.

Max-Heap

The value of the root node must be the greatest among all its descendant nodes and the same thing must be done for its left and right sub-tree also.

Min-Heap

The value of the root node must be the smallest among all its descendant nodes and the same thing must be done for its left and right sub-tree also.

Properties of Heap:

The minimum or maximum element is always at the root of the heap, allowing constant-time access.
The relationship between a parent node at index 'i' and its children is given by the formulas: left child at index 2i+1 and right child at index 2i+2 for 0-based indexing of node numbers.
As the tree is complete binary, all levels are filled except possibly the last level. And the last level is filled from left to right.
When we insert an item, we insert it at the last available slot and then rearrange the nodes so that the heap property is maintained.
When we remove an item, we swap root with the last node to make sure either the max or min item is removed. Then we rearrange the remaining nodes to ensure heap property (max or min)

Operations Supported by Heap:

Operations supported by min - heap and max - heap are same. The difference is just that min-heap contains minimum element at root of the tree and max - heap contains maximum element at the root of the tree.

Heapify: It is the process to rearrange the elements to maintain the property of heap data structure. It is done when root is removed (we replace root with the last node and then call heapify to ensure that heap property is maintained) or heap is built (we call heapify from the last internal node to root) to make sure that the heap property is maintained. This operation also takes O(log n) time.

For max-heap, itmakes sure the maximum element is the root of that binary tree and all descendants also follow the same property.
For min-heap, it balances in such a way that the minimum element is the root and all descendants also follow the same property.

Insertion: When a new element is inserted into the heap, it can disrupt the heap's properties. To restore and maintain the heap structure, a heapify operation is performed. This operation ensures the heap properties are preserved and has a time complexity of O(log n).

Examples:

Assume initially heap(taking max-heap) is as follows
8
/ \
4 5
/ \
1 2
Now if we insert 10 into the heap
8
/ \
4 5
/ \ /
1 2 10
After repeatedly comparing with the parent nodes and swapping if required, the final heap will be look like this
10
/ \
4 8
/ \ /
1 2 5

Deletion:

If we delete the element from the heap it always deletes the root element of the tree and replaces it with the last element of the tree.
Since we delete the root element from the heap it will distort the properties of the heap so we need to perform heapify operations so that it maintains the property of the heap.

It takes O(log n) time.

Example:

Assume initially heap(taking max-heap) is as follows
15
/ \
5 7
/ \
2 3
Now if we delete 15 into the heap it will be replaced by leaf node of the tree for temporary.
3
/ \
5 7
/
2
After heapify operation final heap will be look like this
7
/ \
5 3
/
2

getMax (For max-heap) or getMin (For min-heap):

It finds the maximum element or minimum element for max-heap and min-heap respectively and as we know minimum and maximum elements will always be the root node itself for min-heap and max-heap respectively. It takes O(1) time.

removeMin or removeMax:

This operation returns and deletes the maximum element and minimum element from the max-heap and min-heap respectively. In short, it deletes the root element of the heap binary tree.

Implementation of Heap Data Structure:-

The following code shows the implementation of a max-heap.

Let's understand the maxHeapify function in detail:-

maxHeapify is the function responsible for restoring the property of the Max Heap. It arranges the node i, and its subtrees accordingly so that the heap property is maintained.

Suppose we are given an array, arr[] representing the complete binary tree. The left and the right child of i^th node are in indices 2*i+1 and 2*i+2.
We set the index of the current element, i, as the ‘MAXIMUM’.
If arr[2 * i + 1] > arr[i], i.e., the left child is larger than the current value, it is set as ‘MAXIMUM’.
Similarly if arr[2 * i + 2] > arr[i], i.e., the right child is larger than the current value, it is set as ‘MAXIMUM’.
Swap the ‘MAXIMUM’ with the current element.
Repeat steps 2 to 5 till the property of the heap is restored.

C++

// C++ code to depict // the implementation of a max heap.  #include <bits/stdc++.h> using namespace std;  // A class for Max Heap. class MaxHeap {     // A pointer pointing to the elements     // in the array in the heap.     int* arr;      // Maximum possible size of     // the Max Heap.     int maxSize;      // Number of elements in the     // Max heap currently.     int heapSize;  public:     // Constructor function.     MaxHeap(int maxSize);      // Heapifies a sub-tree taking the     // given index as the root.     void MaxHeapify(int);      // Returns the index of the parent     // of the element at ith index.     int parent(int i)     {         return (i - 1) / 2;     }      // Returns the index of the left child.     int lChild(int i)     {         return (2 * i + 1);     }      // Returns the index of the     // right child.     int rChild(int i)     {         return (2 * i + 2);     }      // Removes the root which in this     // case contains the maximum element.     int removeMax();      // Increases the value of the key     // given by index i to some new value.     void increaseKey(int i, int newVal);      // Returns the maximum key     // (key at root) from max heap.     int getMax()     {         return arr[0];     }      int curSize()     {         return heapSize;     }      // Deletes a key at given index i.     void deleteKey(int i);      // Inserts a new key 'x' in the Max Heap.     void insertKey(int x); };  // Constructor function builds a heap // from a given array a[] // of the specified size. MaxHeap::MaxHeap(int totSize) {     heapSize = 0;     maxSize = totSize;     arr = new int[totSize]; }  // Inserting a new key 'x'. void MaxHeap::insertKey(int x) {     // To check whether the key     // can be inserted or not.     if (heapSize == maxSize) {         cout << "\nOverflow: Could not insertKey\n";         return;     }      // The new key is initially     // inserted at the end.     heapSize++;     int i = heapSize - 1;     arr[i] = x;      // The max heap property is checked     // and if violation occurs,     // it is restored.     while (i != 0 && arr[parent(i)] < arr[i]) {         swap(arr[i], arr[parent(i)]);         i = parent(i);     } }  // Increases value of key at // index 'i' to new_val. void MaxHeap::increaseKey(int i, int newVal) {     arr[i] = newVal;     while (i != 0 && arr[parent(i)] < arr[i]) {         swap(arr[i], arr[parent(i)]);         i = parent(i);     } }  // To remove the root node which contains // the maximum element of the Max Heap. int MaxHeap::removeMax() {     // Checking whether the heap array     // is empty or not.     if (heapSize <= 0)         return INT_MIN;     if (heapSize == 1) {         heapSize--;         return arr[0];     }      // Storing the maximum element     // to remove it.     int root = arr[0];     arr[0] = arr[heapSize - 1];     heapSize--;      // To restore the property     // of the Max heap.     MaxHeapify(0);      return root; }  // In order to delete a key // at a given index i. void MaxHeap::deleteKey(int i) {     // It increases the value of the key     // to infinity and then removes     // the maximum value.     increaseKey(i, INT_MAX);     removeMax(); }  // To heapify the subtree this method // is called recursively void MaxHeap::MaxHeapify(int i) {     int l = lChild(i);     int r = rChild(i);     int largest = i;     if (l < heapSize && arr[l] > arr[i])         largest = l;     if (r < heapSize && arr[r] > arr[largest])         largest = r;     if (largest != i) {         swap(arr[i], arr[largest]);         MaxHeapify(largest);     } }  // Driver program to test above functions. int main() {     // Assuming the maximum size of the heap to be 15.     MaxHeap h(15);      // Asking the user to input the keys:     int k, i, n = 6, arr[10];     cout << "Entered 6 keys:- 3, 10, 12, 8, 2, 14 \n";     h.insertKey(3);     h.insertKey(10);     h.insertKey(12);     h.insertKey(8);     h.insertKey(2);     h.insertKey(14);      // Printing the current size     // of the heap.     cout << "The current size of the heap is "          << h.curSize() << "\n";      // Printing the root element which is     // actually the maximum element.     cout << "The current maximum element is " << h.getMax()          << "\n";      // Deleting key at index 2.     h.deleteKey(2);      // Printing the size of the heap     // after deletion.     cout << "The current size of the heap is "          << h.curSize() << "\n";      // Inserting 2 new keys into the heap.     h.insertKey(15);     h.insertKey(5);     cout << "The current size of the heap is "          << h.curSize() << "\n";     cout << "The current maximum element is " << h.getMax()          << "\n";      return 0; }

Java

// Java code to depict // the implementation of a max heap. import java.util.Arrays; import java.util.Scanner;  public class MaxHeap {     // A pointer pointing to the elements     // in the array in the heap.     int[] arr;      // Maximum possible size of     // the Max Heap.     int maxSize;      // Number of elements in the     // Max heap currently.     int heapSize;      // Constructor function.     MaxHeap(int maxSize) {         this.maxSize = maxSize;         arr = new int[maxSize];         heapSize = 0;     }      // Heapifies a sub-tree taking the     // given index as the root.     void MaxHeapify(int i) {         int l = lChild(i);         int r = rChild(i);         int largest = i;         if (l < heapSize && arr[l] > arr[i])             largest = l;         if (r < heapSize && arr[r] > arr[largest])             largest = r;         if (largest != i) {             int temp = arr[i];             arr[i] = arr[largest];             arr[largest] = temp;             MaxHeapify(largest);         }     }      // Returns the index of the parent     // of the element at ith index.     int parent(int i) {         return (i - 1) / 2;     }      // Returns the index of the left child.     int lChild(int i) {         return (2 * i + 1);     }      // Returns the index of the     // right child.     int rChild(int i) {         return (2 * i + 2);     }      // Removes the root which in this     // case contains the maximum element.     int removeMax() {         // Checking whether the heap array         // is empty or not.         if (heapSize <= 0)             return Integer.MIN_VALUE;         if (heapSize == 1) {             heapSize--;             return arr[0];         }          // Storing the maximum element         // to remove it.         int root = arr[0];         arr[0] = arr[heapSize - 1];         heapSize--;          // To restore the property         // of the Max heap.         MaxHeapify(0);          return root;     }      // Increases value of key at     // index 'i' to new_val.     void increaseKey(int i, int newVal) {         arr[i] = newVal;         while (i != 0 && arr[parent(i)] < arr[i]) {             int temp = arr[i];             arr[i] = arr[parent(i)];             arr[parent(i)] = temp;             i = parent(i);         }     }      // Returns the maximum key     // (key at root) from max heap.     int getMax() {         return arr[0];     }      int curSize() {         return heapSize;     }      // Deletes a key at given index i.     void deleteKey(int i) {         // It increases the value of the key         // to infinity and then removes         // the maximum value.         increaseKey(i, Integer.MAX_VALUE);         removeMax();     }      // Inserts a new key 'x' in the Max Heap.     void insertKey(int x) {         // To check whether the key         // can be inserted or not.         if (heapSize == maxSize) {             System.out.println("\nOverflow: Could not insertKey\n");             return;         }          // The new key is initially         // inserted at the end.         heapSize++;         int i = heapSize - 1;         arr[i] = x;          // The max heap property is checked         // and if violation occurs,         // it is restored.         while (i != 0 && arr[parent(i)] < arr[i]) {             int temp = arr[i];             arr[i] = arr[parent(i)];             arr[parent(i)] = temp;             i = parent(i);         }     }      // Driver program to test above functions.     public static void main(String[] args) {         // Assuming the maximum size of the heap to be 15.         MaxHeap h = new MaxHeap(15);          // Asking the user to input the keys:         int k, i, n = 6;         System.out.println("Entered 6 keys:- 3, 10, 12, 8, 2, 14 \n");         h.insertKey(3);         h.insertKey(10);         h.insertKey(12);         h.insertKey(8);         h.insertKey(2);         h.insertKey(14);          // Printing the current size         // of the heap.         System.out.println("The current size of the heap is "                 + h.curSize() + "\n");          // Printing the root element which is         // actually the maximum element.         System.out.println("The current maximum element is " + h.getMax()                 + "\n");          // Deleting key at index 2.         h.deleteKey(2);          // Printing the size of the heap         // after deletion.         System.out.println("The current size of the heap is "                 + h.curSize() + "\n");          // Inserting 2 new keys into the heap.         h.insertKey(15);         h.insertKey(5);         System.out.println("The current size of the heap is "                 + h.curSize() + "\n");         System.out.println("The current maximum element is " + h.getMax()                 + "\n");     } }

Python

# Python code to depict # the implementation of a max heap.  class MaxHeap:     # A pointer pointing to the elements     # in the array in the heap.     arr = []      # Maximum possible size of     # the Max Heap.     maxSize = 0      # Number of elements in the     # Max heap currently.     heapSize = 0      # Constructor function.     def __init__(self, maxSize):         self.maxSize = maxSize         self.arr = [None]*maxSize         self.heapSize = 0      # Heapifies a sub-tree taking the     # given index as the root.     def MaxHeapify(self, i):         l = self.lChild(i)         r = self.rChild(i)         largest = i         if l < self.heapSize and self.arr[l] > self.arr[i]:             largest = l         if r < self.heapSize and self.arr[r] > self.arr[largest]:             largest = r         if largest != i:             temp = self.arr[i]             self.arr[i] = self.arr[largest]             self.arr[largest] = temp             self.MaxHeapify(largest)      # Returns the index of the parent     # of the element at ith index.     def parent(self, i):         return (i - 1) // 2      # Returns the index of the left child.     def lChild(self, i):         return (2 * i + 1)      # Returns the index of the     # right child.     def rChild(self, i):         return (2 * i + 2)      # Removes the root which in this     # case contains the maximum element.     def removeMax(self):         # Checking whether the heap array         # is empty or not.         if self.heapSize <= 0:             return None         if self.heapSize == 1:             self.heapSize -= 1             return self.arr[0]          # Storing the maximum element         # to remove it.         root = self.arr[0]         self.arr[0] = self.arr[self.heapSize - 1]         self.heapSize -= 1          # To restore the property         # of the Max heap.         self.MaxHeapify(0)          return root      # Increases value of key at     # index 'i' to new_val.     def increaseKey(self, i, newVal):         self.arr[i] = newVal         while i != 0 and self.arr[self.parent(i)] < self.arr[i]:             temp = self.arr[i]             self.arr[i] = self.arr[self.parent(i)]             self.arr[self.parent(i)] = temp             i = self.parent(i)      # Returns the maximum key     # (key at root) from max heap.     def getMax(self):         return self.arr[0]      def curSize(self):         return self.heapSize      # Deletes a key at given index i.     def deleteKey(self, i):         # It increases the value of the key         # to infinity and then removes         # the maximum value.         self.increaseKey(i, float("inf"))         self.removeMax()      # Inserts a new key 'x' in the Max Heap.     def insertKey(self, x):         # To check whether the key         # can be inserted or not.         if self.heapSize == self.maxSize:             print("\nOverflow: Could not insertKey\n")             return          # The new key is initially         # inserted at the end.         self.heapSize += 1         i = self.heapSize - 1         self.arr[i] = x          # The max heap property is checked         # and if violation occurs,         # it is restored.         while i != 0 and self.arr[self.parent(i)] < self.arr[i]:             temp = self.arr[i]             self.arr[i] = self.arr[self.parent(i)]             self.arr[self.parent(i)] = temp             i = self.parent(i)   # Driver program to test above functions. if __name__ == '__main__':     # Assuming the maximum size of the heap to be 15.     h = MaxHeap(15)      # Asking the user to input the keys:     k, i, n = 6, 0, 6     print("Entered 6 keys:- 3, 10, 12, 8, 2, 14 \n")     h.insertKey(3)     h.insertKey(10)     h.insertKey(12)     h.insertKey(8)     h.insertKey(2)     h.insertKey(14)      # Printing the current size     # of the heap.     print("The current size of the heap is "           + str(h.curSize()) + "\n")      # Printing the root element which is     # actually the maximum element.     print("The current maximum element is " + str(h.getMax())           + "\n")      # Deleting key at index 2.     h.deleteKey(2)      # Printing the size of the heap     # after deletion.     print("The current size of the heap is "           + str(h.curSize()) + "\n")      # Inserting 2 new keys into the heap.     h.insertKey(15)     h.insertKey(5)     print("The current size of the heap is "           + str(h.curSize()) + "\n")     print("The current maximum element is " + str(h.getMax())           + "\n")

JavaScript

// JavaScript code to depict // the implementation of a max heap.  class MaxHeap {     constructor(maxSize) {         // the array in the heap.         this.arr = new Array(maxSize).fill(null);          // Maximum possible size of         // the Max Heap.         this.maxSize = maxSize;          // Number of elements in the         // Max heap currently.         this.heapSize = 0;     }      // Heapifies a sub-tree taking the     // given index as the root.     MaxHeapify(i) {         const l = this.lChild(i);         const r = this.rChild(i);         let largest = i;         if (l < this.heapSize && this.arr[l] > this.arr[i]) {             largest = l;         }         if (r < this.heapSize && this.arr[r] > this.arr[largest]) {             largest = r;         }         if (largest !== i) {             const temp = this.arr[i];             this.arr[i] = this.arr[largest];             this.arr[largest] = temp;             this.MaxHeapify(largest);         }     }      // Returns the index of the parent     // of the element at ith index.     parent(i) {         return Math.floor((i - 1) / 2);     }      // Returns the index of the left child.     lChild(i) {         return 2 * i + 1;     }      // Returns the index of the     // right child.     rChild(i) {         return 2 * i + 2;     }      // Removes the root which in this     // case contains the maximum element.     removeMax() {         // Checking whether the heap array         // is empty or not.         if (this.heapSize <= 0) {             return null;         }         if (this.heapSize === 1) {             this.heapSize -= 1;             return this.arr[0];         }          // Storing the maximum element         // to remove it.         const root = this.arr[0];         this.arr[0] = this.arr[this.heapSize - 1];         this.heapSize -= 1;          // To restore the property         // of the Max heap.         this.MaxHeapify(0);          return root;     }      // Increases value of key at     // index 'i' to new_val.     increaseKey(i, newVal) {         this.arr[i] = newVal;         while (i !== 0 && this.arr[this.parent(i)] < this.arr[i]) {             const temp = this.arr[i];             this.arr[i] = this.arr[this.parent(i)];             this.arr[this.parent(i)] = temp;             i = this.parent(i);         }     }      // Returns the maximum key     // (key at root) from max heap.     getMax() {         return this.arr[0];     }      curSize() {         return this.heapSize;     }      // Deletes a key at given index i.     deleteKey(i) {         // It increases the value of the key         // to infinity and then removes         // the maximum value.         this.increaseKey(i, Infinity);         this.removeMax();     }      // Inserts a new key 'x' in the Max Heap.     insertKey(x) {         // To check whether the key         // can be inserted or not.         if (this.heapSize === this.maxSize) {             console.log("\nOverflow: Could not insertKey\n");             return;         }          let i = this.heapSize;         this.arr[i] = x;          // The new key is initially         // inserted at the end.         this.heapSize += 1;            // The max heap property is checked         // and if violation occurs,         // it is restored.         while (i !== 0 && this.arr[this.parent(i)] < this.arr[i]) {             const temp = this.arr[i];             this.arr[i] = this.arr[this.parent(i)];             this.arr[this.parent(i)] = temp;             i = this.parent(i);         }     } }   // Driver program to test above functions.  // Assuming the maximum size of the heap to be 15. const h = new MaxHeap(15);  // Asking the user to input the keys: console.log("Entered 6 keys:- 3, 10, 12, 8, 2, 14 \n");  h.insertKey(3); h.insertKey(10); h.insertKey(12); h.insertKey(8); h.insertKey(2); h.insertKey(14);   // Printing the current size // of the heap. console.log(     "The current size of the heap is " + h.curSize() + "\n" );   // Printing the root element which is // actually the maximum element. console.log(     "The current maximum element is " + h.getMax() + "\n" );   // Deleting key at index 2. h.deleteKey(2);   // Printing the size of the heap // after deletion. console.log(     "The current size of the heap is " + h.curSize() + "\n" );   // Inserting 2 new keys into the heap. h.insertKey(15); h.insertKey(5);  console.log(     "The current size of the heap is " + h.curSize() + "\n" );  console.log(     "The current maximum element is " + h.getMax() + "\n" );  // Contributed by sdeadityasharma

Output

Entered 6 keys:- 3, 10, 12, 8, 2, 14  The current size of the heap is 6 The current maximum element is 14 The current size of the heap is 5 The current size of the heap is 7 The current maximum element is 15

Please refer the following articles for more details about Heap Data Structure

Library Implementations of Heap or Priority Queue

kartik

Improve

Article Tags :

Practice Tags :

Heap

Introduction to Heap - Data Structure and Algorithm Tutorials

Max-Heap

Min-Heap

Properties of Heap:

Operations Supported by Heap:

Deletion:

getMax (For max-heap) or getMin (For min-heap):

removeMin or removeMax:

Implementation of Heap Data Structure:-

Similar Reads

Basics & Prerequisites

Data Structures

Algorithms

Advanced

Interview Preparation

Practice Problem