Binary Heap
A Binary Heap is a complete binary tree that stores data efficiently, allowing quick access to the maximum or minimum element, depending on the type of heap. It can either be a Min Heap or a Max Heap. In a Min Heap, the key at the root must be the smallest among all the keys in the heap, and this property must hold true recursively for all nodes. Similarly, a Max Heap follows the same principle, but with the largest key at the root.
Valid and Invalid examples of heaps
How is Binary Heap represented?
A Binary Heap is a Complete Binary Tree. A binary heap is typically represented as an array.
- The root element will be at arr[0].
- The below table shows indices of other nodes for the ith node, i.e., arr[i]:
arr[(i-1)/2] | Returns the parent node |
arr[(2*i)+1] | Returns the left child node |
arr[(2*i)+2] | Returns the right child node |
The traversal method use to achieve Array representation is Level Order Traversal. Please refer to Array Representation Of Binary Heap for details.

Operations on Heap
Refer Introduction to Min-Heap – Data Structure and Algorithm Tutorials for more
// A C++ program to demonstrate common Binary Heap Operations
#include<iostream>
#include<climits>
using namespace std;
// Prototype of a utility function to swap two integers
void swap(int *x, int *y);
// A class for Min Heap
class MinHeap
{
int *harr; // pointer to array of elements in heap
int capacity; // maximum possible size of min heap
int heap_size; // Current number of elements in min heap
public:
// Constructor
MinHeap(int capacity);
// to heapify a subtree with the root at given index
void MinHeapify(int i);
int parent(int i) { return (i-1)/2; }
// to get index of left child of node at index i
int left(int i) { return (2*i + 1); }
// to get index of right child of node at index i
int right(int i) { return (2*i + 2); }
// to extract the root which is the minimum element
int extractMin();
// Decreases key value of key at index i to new_val
void decreaseKey(int i, int new_val);
// Returns the minimum key (key at root) from min heap
int getMin() { return harr[0]; }
// Deletes a key stored at index i
void deleteKey(int i);
// Inserts a new key 'k'
void insertKey(int k);
};
// Constructor: Builds a heap from a given array a[] of given size
MinHeap::MinHeap(int cap)
{
heap_size = 0;
capacity = cap;
harr = new int[cap];
}
// Inserts a new key 'k'
void MinHeap::insertKey(int k)
{
if (heap_size == capacity)
{
cout << "\nOverflow: Could not insertKey\n";
return;
}
// First insert the new key at the end
heap_size++;
int i = heap_size - 1;
harr[i] = k;
// Fix the min heap property if it is violated
while (i != 0 && harr[parent(i)] > harr[i])
{
swap(&harr[i], &harr[parent(i)]);
i = parent(i);
}
}
// Decreases value of key at index 'i' to new_val. It is assumed that
// new_val is smaller than harr[i].
void MinHeap::decreaseKey(int i, int new_val)
{
harr[i] = new_val;
while (i != 0 && harr[parent(i)] > harr[i])
{
swap(&harr[i], &harr[parent(i)]);
i = parent(i);
}
}
// Method to remove minimum element (or root) from min heap
int MinHeap::extractMin()
{
if (heap_size <= 0)
return INT_MAX;
if (heap_size == 1)
{
heap_size--;
return harr[0];
}
// Store the minimum value, and remove it from heap
int root = harr[0];
harr[0] = harr[heap_size-1];
heap_size--;
MinHeapify(0);
return root;
}
// This function deletes key at index i. It first reduced value to minus
// infinite, then calls extractMin()
void MinHeap::deleteKey(int i)
{
decreaseKey(i, INT_MIN);
extractMin();
}
// A recursive method to heapify a subtree with the root at given index
// This method assumes that the subtrees are already heapified
void MinHeap::MinHeapify(int i)
{
int l = left(i);
int r = right(i);
int smallest = i;
if (l < heap_size && harr[l] < harr[i])
smallest = l;
if (r < heap_size && harr[r] < harr[smallest])
smallest = r;
if (smallest != i)
{
swap(&harr[i], &harr[smallest]);
MinHeapify(smallest);
}
}
// A utility function to swap two elements
void swap(int *x, int *y)
{
int temp = *x;
*x = *y;
*y = temp;
}
// Driver program to test above functions
int main()
{
MinHeap h(11);
h.insertKey(3);
h.insertKey(2);
h.deleteKey(1);
h.insertKey(15);
h.insertKey(5);
h.insertKey(4);
h.insertKey(45);
cout << h.extractMin() << " ";
cout << h.getMin() << " ";
h.decreaseKey(2, 1);
cout << h.getMin();
return 0;
}
// Java program for the above approach
import java.util.*;
// A class for Min Heap
class MinHeap {
// To store array of elements in heap
private int[] heapArray;
// max size of the heap
private int capacity;
// Current number of elements in the heap
private int current_heap_size;
// Constructor
public MinHeap(int n) {
capacity = n;
heapArray = new int[capacity];
current_heap_size = 0;
}
// Swapping using reference
private void swap(int[] arr, int a, int b) {
int temp = arr[a];
arr[a] = arr[b];
arr[b] = temp;
}
// Get the Parent index for the given index
private int parent(int key) {
return (key - 1) / 2;
}
// Get the Left Child index for the given index
private int left(int key) {
return 2 * key + 1;
}
// Get the Right Child index for the given index
private int right(int key) {
return 2 * key + 2;
}
// Inserts a new key
public boolean insertKey(int key) {
if (current_heap_size == capacity) {
// heap is full
return false;
}
// First insert the new key at the end
int i = current_heap_size;
heapArray[i] = key;
current_heap_size++;
// Fix the min heap property if it is violated
while (i != 0 && heapArray[i] < heapArray[parent(i)]) {
swap(heapArray, i, parent(i));
i = parent(i);
}
return true;
}
// Decreases value of given key to new_val.
// It is assumed that new_val is smaller
// than heapArray[key].
public void decreaseKey(int key, int new_val) {
heapArray[key] = new_val;
while (key != 0 && heapArray[key] < heapArray[parent(key)]) {
swap(heapArray, key, parent(key));
key = parent(key);
}
}
// Returns the minimum key (key at
// root) from min heap
public int getMin() {
return heapArray[0];
}
// Method to remove minimum element
// (or root) from min heap
public int extractMin() {
if (current_heap_size <= 0) {
return Integer.MAX_VALUE;
}
if (current_heap_size == 1) {
current_heap_size--;
return heapArray[0];
}
// Store the minimum value,
// and remove it from heap
int root = heapArray[0];
heapArray[0] = heapArray[current_heap_size - 1];
current_heap_size--;
MinHeapify(0);
return root;
}
// This function deletes key at the
// given index. It first reduced value
// to minus infinite, then calls extractMin()
public void deleteKey(int key) {
decreaseKey(key, Integer.MIN_VALUE);
extractMin();
}
// A recursive method to heapify a subtree
// with the root at given index
// This method assumes that the subtrees
// are already heapified
private void MinHeapify(int key) {
int l = left(key);
int r = right(key);
int smallest = key;
if (l < current_heap_size && heapArray[l] < heapArray[smallest]) {
smallest = l;
}
if (r < current_heap_size && heapArray[r] < heapArray[smallest]) {
smallest = r;
}
if (smallest != key) {
swap(heapArray, key, smallest);
MinHeapify(smallest);
}
}
// Increases value of given key to new_val.
// It is assumed that new_val is greater
// than heapArray[key].
// Heapify from the given key
public void increaseKey(int key, int new_val) {
heapArray[key] = new_val;
MinHeapify(key);
}
// Changes value on a key
public void changeValueOnAKey(int key, int new_val) {
if (heapArray[key] == new_val) {
return;
}
if (heapArray[key] < new_val) {
increaseKey(key, new_val);
} else {
decreaseKey(key, new_val);
}
}
}
// Driver Code
class MinHeapTest {
public static void main(String[] args) {
MinHeap h = new MinHeap(11);
h.insertKey(3);
h.insertKey(2);
h.deleteKey(1);
h.insertKey(15);
h.insertKey(5);
h.insertKey(4);
h.insertKey(45);
System.out.print(h.extractMin() + " ");
System.out.print(h.getMin() + " ");
h.decreaseKey(2, 1);
System.out.print(h.getMin());
}
}
// This code is contributed by rishabmalhdijo
# A Python program to demonstrate common binary heap operations
# Import the heap functions from python library
from heapq import heappush, heappop, heapify
# heappop - pop and return the smallest element from heap
# heappush - push the value item onto the heap, maintaining
# heap invarient
# heapify - transform list into heap, in place, in linear time
# A class for Min Heap
class MinHeap:
# Constructor to initialize a heap
def __init__(self):
self.heap = []
def parent(self, i):
return (i-1)/2
# Inserts a new key 'k'
def insertKey(self, k):
heappush(self.heap, k)
# Decrease value of key at index 'i' to new_val
# It is assumed that new_val is smaller than heap[i]
def decreaseKey(self, i, new_val):
self.heap[i] = new_val
while(i != 0 and self.heap[self.parent(i)] > self.heap[i]):
# Swap heap[i] with heap[parent(i)]
self.heap[i] , self.heap[self.parent(i)] = (
self.heap[self.parent(i)], self.heap[i])
# Method to remove minimum element from min heap
def extractMin(self):
return heappop(self.heap)
# This function deletes key at index i. It first reduces
# value to minus infinite and then calls extractMin()
def deleteKey(self, i):
self.decreaseKey(i, float("-inf"))
self.extractMin()
# Get the minimum element from the heap
def getMin(self):
return self.heap[0]
# Driver pgoratm to test above function
heapObj = MinHeap()
heapObj.insertKey(3)
heapObj.insertKey(2)
heapObj.deleteKey(1)
heapObj.insertKey(15)
heapObj.insertKey(5)
heapObj.insertKey(4)
heapObj.insertKey(45)
print heapObj.extractMin(),
print heapObj.getMin(),
heapObj.decreaseKey(2, 1)
print heapObj.getMin()
# This code is contributed by Nikhil Kumar Singh(nickzuck_007)
// C# program to demonstrate common
// Binary Heap Operations - Min Heap
using System;
// A class for Min Heap
class MinHeap{
// To store array of elements in heap
public int[] heapArray{ get; set; }
// max size of the heap
public int capacity{ get; set; }
// Current number of elements in the heap
public int current_heap_size{ get; set; }
// Constructor
public MinHeap(int n)
{
capacity = n;
heapArray = new int[capacity];
current_heap_size = 0;
}
// Swapping using reference
public static void Swap<T>(ref T lhs, ref T rhs)
{
T temp = lhs;
lhs = rhs;
rhs = temp;
}
// Get the Parent index for the given index
public int Parent(int key)
{
return (key - 1) / 2;
}
// Get the Left Child index for the given index
public int Left(int key)
{
return 2 * key + 1;
}
// Get the Right Child index for the given index
public int Right(int key)
{
return 2 * key + 2;
}
// Inserts a new key
public bool insertKey(int key)
{
if (current_heap_size == capacity)
{
// heap is full
return false;
}
// First insert the new key at the end
int i = current_heap_size;
heapArray[i] = key;
current_heap_size++;
// Fix the min heap property if it is violated
while (i != 0 && heapArray[i] <
heapArray[Parent(i)])
{
Swap(ref heapArray[i],
ref heapArray[Parent(i)]);
i = Parent(i);
}
return true;
}
// Decreases value of given key to new_val.
// It is assumed that new_val is smaller
// than heapArray[key].
public void decreaseKey(int key, int new_val)
{
heapArray[key] = new_val;
while (key != 0 && heapArray[key] <
heapArray[Parent(key)])
{
Swap(ref heapArray[key],
ref heapArray[Parent(key)]);
key = Parent(key);
}
}
// Returns the minimum key (key at
// root) from min heap
public int getMin()
{
return heapArray[0];
}
// Method to remove minimum element
// (or root) from min heap
public int extractMin()
{
if (current_heap_size <= 0)
{
return int.MaxValue;
}
if (current_heap_size == 1)
{
current_heap_size--;
return heapArray[0];
}
// Store the minimum value,
// and remove it from heap
int root = heapArray[0];
heapArray[0] = heapArray[current_heap_size - 1];
current_heap_size--;
MinHeapify(0);
return root;
}
// This function deletes key at the
// given index. It first reduced value
// to minus infinite, then calls extractMin()
public void deleteKey(int key)
{
decreaseKey(key, int.MinValue);
extractMin();
}
// A recursive method to heapify a subtree
// with the root at given index
// This method assumes that the subtrees
// are already heapified
public void MinHeapify(int key)
{
int l = Left(key);
int r = Right(key);
int smallest = key;
if (l < current_heap_size &&
heapArray[l] < heapArray[smallest])
{
smallest = l;
}
if (r < current_heap_size &&
heapArray[r] < heapArray[smallest])
{
smallest = r;
}
if (smallest != key)
{
Swap(ref heapArray[key],
ref heapArray[smallest]);
MinHeapify(smallest);
}
}
// Increases value of given key to new_val.
// It is assumed that new_val is greater
// than heapArray[key].
// Heapify from the given key
public void increaseKey(int key, int new_val)
{
heapArray[key] = new_val;
MinHeapify(key);
}
// Changes value on a key
public void changeValueOnAKey(int key, int new_val)
{
if (heapArray[key] == new_val)
{
return;
}
if (heapArray[key] < new_val)
{
increaseKey(key, new_val);
} else
{
decreaseKey(key, new_val);
}
}
}
static class MinHeapTest{
// Driver code
public static void Main(string[] args)
{
MinHeap h = new MinHeap(11);
h.insertKey(3);
h.insertKey(2);
h.deleteKey(1);
h.insertKey(15);
h.insertKey(5);
h.insertKey(4);
h.insertKey(45);
Console.Write(h.extractMin() + " ");
Console.Write(h.getMin() + " ");
h.decreaseKey(2, 1);
Console.Write(h.getMin());
}
}
// This code is contributed by
// Dinesh Clinton Albert(dineshclinton)
// A class for Min Heap
class MinHeap
{
// Constructor: Builds a heap from a given array a[] of given size
constructor()
{
this.arr = [];
}
left(i) {
return 2*i + 1;
}
right(i) {
return 2*i + 2;
}
parent(i){
return Math.floor((i - 1)/2)
}
getMin()
{
return this.arr[0]
}
insert(k)
{
let arr = this.arr;
arr.push(k);
// Fix the min heap property if it is violated
let i = arr.length - 1;
while (i > 0 && arr[this.parent(i)] > arr[i])
{
let p = this.parent(i);
[arr[i], arr[p]] = [arr[p], arr[i]];
i = p;
}
}
// Decreases value of key at index 'i' to new_val.
// It is assumed that new_val is smaller than arr[i].
decreaseKey(i, new_val)
{
let arr = this.arr;
arr[i] = new_val;
while (i !== 0 && arr[this.parent(i)] > arr[i])
{
let p = this.parent(i);
[arr[i], arr[p]] = [arr[p], arr[i]];
i = p;
}
}
// Method to remove minimum element (or root) from min heap
extractMin()
{
let arr = this.arr;
if (arr.length == 1) {
return arr.pop();
}
// Store the minimum value, and remove it from heap
let res = arr[0];
arr[0] = arr[arr.length-1];
arr.pop();
this.MinHeapify(0);
return res;
}
// This function deletes key at index i. It first reduced value to minus
// infinite, then calls extractMin()
deleteKey(i)
{
this.decreaseKey(i, this.arr[0] - 1);
this.extractMin();
}
// A recursive method to heapify a subtree with the root at given index
// This method assumes that the subtrees are already heapified
MinHeapify(i)
{
let arr = this.arr;
let n = arr.length;
if (n === 1) {
return;
}
let l = this.left(i);
let r = this.right(i);
let smallest = i;
if (l < n && arr[l] < arr[i])
smallest = l;
if (r < n && arr[r] < arr[smallest])
smallest = r;
if (smallest !== i)
{
[arr[i], arr[smallest]] = [arr[smallest], arr[i]]
this.MinHeapify(smallest);
}
}
}
let h = new MinHeap();
h.insert(3);
h.insert(2);
h.deleteKey(1);
h.insert(15);
h.insert(5);
h.insert(4);
h.insert(45);
console.log(h.extractMin() + " ");
console.log(h.getMin() + " ");
h.decreaseKey(2, 1);
console.log(h.extractMin());
Output
2 4 1
Applications of Heaps
- Heap Sort: Heap Sort uses Binary Heap to sort an array in O(nLogn) time.
- Priority Queue: Priority queues can be efficiently implemented using Binary Heap because it supports insert(), delete() and extractmax(), decreaseKey() operations in O(log N) time. Binomial Heap and Fibonacci Heap are variations of Binary Heap. These variations perform union also efficiently.
- Graph Algorithms: The priority queues are especially used in Graph Algorithms like Dijkstra's Shortest Path and Prim's Minimum Spanning Tree.
- Many problems can be efficiently solved using Heaps.
See following for example. a) K'th Largest Element in an array. b) Sort an almost sorted array/ c) Merge K Sorted Arrays.
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