Open In App

Priority Queue using Binary Heap

Improve
Improve
Improve
Like Article
Like
Save Article
Save
Share
Report issue
Report

Priority Queue is an extension of the queue with the following properties:  

  1. Every item has a priority associated with it.
  2. An element with high priority is dequeued before an element with low priority.
  3. If two elements have the same priority, they are served according to their order in the queue.

A Binary Heap is a Binary Tree with the following properties:  

  1. It is a Complete Tree. This property of Binary Heap makes them suitable to be stored in an array.
  2. A Binary Heap is either Min Heap or Max Heap.
  3. In a Min Binary Heap, the key at the root must be minimum among all keys present in Binary Heap. The same property must be recursively true for all nodes in Binary Tree.
  4. Similarly, in a Max Binary Heap, the key at the root must be maximum among all keys present in Binary Heap. The same property must be recursively true for all nodes in Binary Tree.

Operation on Binary Heap 

  • insert(p): Inserts a new element with priority p.
  • extractMax(): Extracts an element with maximum priority.
  • remove(i): Removes an element pointed by an iterator i.
  • getMax(): Returns an element with maximum priority.
  • changePriority(i, p): Changes the priority of an element pointed by i to p.

Example of A Binary Max Heap 

  • Suppose below is the given Binary Heap that follows all the properties of Binary Max Heap. 
     

 

  • Now a node with value 32 need to be insert in the above heap: To insert an element, attach the new element to any leaf. For Example A node with priority 32 can be added to the leaf of the node 7. But this violates the heap property. To maintain the heap property, shift up the new node 32
     

 

  • Shift Up Operation get node with 32 at the correct position: Swap the incorrectly placed node with its parent until the heap property is satisfied. For Example: As node 7 is less than node 32 so, swap node 7 and node 32. Then, swap node 31 and node 32.
     

 

  • ExtractMax: The maximum value is stored at the root of the tree. But the root of the tree cannot be directly removed. First, it is replaced with any one of the leaves and then removed. For Example: To remove Node 45, it is first replaced with node 7. But this violates the heap property, so move the replaced node down. For that, use shift-down operation. 
     

 

  • ShiftDown operation: Swap the incorrectly placed node with a larger child until the heap property is satisfied. For Example Node 7 is swapped with node 32 then, last it is swapped with node 31
     

 

  • ChangePriority: Let the changed element shift up or down depending on whether its priority decreased or increased. For Example: Change the priority of nodes 11 to 35, due to this change the node has to shift up the node in order to maintain the heap property.
  • Remove: To remove an element, change its priority to a value larger than the current maximum, then shift it up, and then extract it using extract max. Find the current maximum using getMax.
  • GetMax: The max value is stored at the root of the tree. To getmax, just return the value at the root of the tree.

Array Representation of Binary Heap

Since the heap is maintained in form of a complete binary tree, because of this fact the heap can be represented in the form of an array. To keep the tree complete and shallow, while inserting a new element insert it in the leftmost vacant position in the last level i.e., at the end of our array. Similarly, while extracting maximum replace the root with the last leaf at the last level i.e., the last element of the array. Below is the illustration of the same: 
 

 

Below is the program to implement Priority Queue using Binary Heap:
 

C++




// C++ code to implement priority-queue
// using array implementation of
// binary heap
 
#include <bits/stdc++.h>
using namespace std;
 
int H[50];
int size = -1;
 
// Function to return the index of the
// parent node of a given node
int parent(int i)
{
 
    return (i - 1) / 2;
}
 
// Function to return the index of the
// left child of the given node
int leftChild(int i)
{
 
    return ((2 * i) + 1);
}
 
// Function to return the index of the
// right child of the given node
int rightChild(int i)
{
 
    return ((2 * i) + 2);
}
 
// Function to shift up the node in order
// to maintain the heap property
void shiftUp(int i)
{
    while (i > 0 && H[parent(i)] < H[i]) {
 
        // Swap parent and current node
        swap(H[parent(i)], H[i]);
 
        // Update i to parent of i
        i = parent(i);
    }
}
 
// Function to shift down the node in
// order to maintain the heap property
void shiftDown(int i)
{
    int maxIndex = i;
 
    // Left Child
    int l = leftChild(i);
 
    if (l <= size && H[l] > H[maxIndex]) {
        maxIndex = l;
    }
 
    // Right Child
    int r = rightChild(i);
 
    if (r <= size && H[r] > H[maxIndex]) {
        maxIndex = r;
    }
 
    // If i not same as maxIndex
    if (i != maxIndex) {
        swap(H[i], H[maxIndex]);
        shiftDown(maxIndex);
    }
}
 
// Function to insert a new element
// in the Binary Heap
void insert(int p)
{
    size = size + 1;
    H[size] = p;
 
    // Shift Up to maintain heap property
    shiftUp(size);
}
 
// Function to extract the element with
// maximum priority
int extractMax()
{
    int result = H[0];
 
    // Replace the value at the root
    // with the last leaf
    H[0] = H[size];
    size = size - 1;
 
    // Shift down the replaced element
    // to maintain the heap property
    shiftDown(0);
    return result;
}
 
// Function to change the priority
// of an element
void changePriority(int i, int p)
{
    int oldp = H[i];
    H[i] = p;
 
    if (p > oldp) {
        shiftUp(i);
    }
    else {
        shiftDown(i);
    }
}
 
// Function to get value of the current
// maximum element
int getMax()
{
 
    return H[0];
}
 
// Function to remove the element
// located at given index
void remove(int i)
{
    H[i] = getMax() + 1;
 
    // Shift the node to the root
    // of the heap
    shiftUp(i);
 
    // Extract the node
    extractMax();
}
 
// Driver Code
int main()
{
 
    /*         45
            /      \
           31      14
          /  \    /  \
         13  20  7   11
        /  \
       12   7
    Create a priority queue shown in
    example in a binary max heap form.
    Queue will be represented in the
    form of array as:
    45 31 14 13 20 7 11 12 7 */
 
    // Insert the element to the
    // priority queue
    insert(45);
    insert(20);
    insert(14);
    insert(12);
    insert(31);
    insert(7);
    insert(11);
    insert(13);
    insert(7);
 
    int i = 0;
 
    // Priority queue before extracting max
    cout << "Priority Queue : ";
    while (i <= size) {
        cout << H[i] << " ";
        i++;
    }
 
    cout << "\n";
 
    // Node with maximum priority
    cout << "Node with maximum priority : "
         << extractMax() << "\n";
 
    // Priority queue after extracting max
    cout << "Priority queue after "
         << "extracting maximum : ";
    int j = 0;
    while (j <= size) {
        cout << H[j] << " ";
        j++;
    }
 
    cout << "\n";
 
    // Change the priority of element
    // present at index 2 to 49
    changePriority(2, 49);
    cout << "Priority queue after "
         << "priority change : ";
    int k = 0;
    while (k <= size) {
        cout << H[k] << " ";
        k++;
    }
 
    cout << "\n";
 
    // Remove element at index 3
    remove(3);
    cout << "Priority queue after "
         << "removing the element : ";
    int l = 0;
    while (l <= size) {
        cout << H[l] << " ";
        l++;
    }
    return 0;
}


Java




// Java code to implement
// priority-queue using
// array implementation of
// binary heap
import java.util.*;
class GFG{
 
static int []H = new int[50];
static int size = -1;
 
// Function to return the index of the
// parent node of a given node
static int parent(int i)
{
  return (i - 1) / 2;
}
 
// Function to return the index of the
// left child of the given node
static int leftChild(int i)
{
  return ((2 * i) + 1);
}
 
// Function to return the index of the
// right child of the given node
static int rightChild(int i)
{
  return ((2 * i) + 2);
}
 
// Function to shift up the
// node in order to maintain
// the heap property
static void shiftUp(int i)
{
  while (i > 0 &&
         H[parent(i)] < H[i])
  {
    // Swap parent and current node
    swap(parent(i), i);
 
    // Update i to parent of i
    i = parent(i);
  }
}
 
// Function to shift down the node in
// order to maintain the heap property
static void shiftDown(int i)
{
  int maxIndex = i;
 
  // Left Child
  int l = leftChild(i);
 
  if (l <= size &&
      H[l] > H[maxIndex])
  {
    maxIndex = l;
  }
 
  // Right Child
  int r = rightChild(i);
 
  if (r <= size &&
      H[r] > H[maxIndex])
  {
    maxIndex = r;
  }
 
  // If i not same as maxIndex
  if (i != maxIndex)
  {
    swap(i, maxIndex);
    shiftDown(maxIndex);
  }
}
 
// Function to insert a
// new element in
// the Binary Heap
static void insert(int p)
{
  size = size + 1;
  H[size] = p;
 
  // Shift Up to maintain
  // heap property
  shiftUp(size);
}
 
// Function to extract
// the element with
// maximum priority
static int extractMax()
{
  int result = H[0];
 
  // Replace the value
  // at the root with
  // the last leaf
  H[0] = H[size];
  size = size - 1;
 
  // Shift down the replaced
  // element to maintain the
  // heap property
  shiftDown(0);
  return result;
}
 
// Function to change the priority
// of an element
static void changePriority(int i,
                           int p)
{
  int oldp = H[i];
  H[i] = p;
 
  if (p > oldp)
  {
    shiftUp(i);
  }
  else
  {
    shiftDown(i);
  }
}
 
// Function to get value of
// the current maximum element
static int getMax()
{
  return H[0];
}
 
// Function to remove the element
// located at given index
static void remove(int i)
{
  H[i] = getMax() + 1;
 
  // Shift the node to the root
  // of the heap
  shiftUp(i);
 
  // Extract the node
  extractMax();
}
   
static void swap(int i, int j)
{
  int temp= H[i];
  H[i] = H[j];
  H[j] = temp;
}
 
// Driver Code
public static void main(String[] args)
{
 
  /*           45
            /        \
           31      14
          /  \    /  \
         13  20  7   11
        /  \
       12   7
    Create a priority queue shown in
    example in a binary max heap form.
    Queue will be represented in the
    form of array as:
    45 31 14 13 20 7 11 12 7 */
 
  // Insert the element to the
  // priority queue
  insert(45);
  insert(20);
  insert(14);
  insert(12);
  insert(31);
  insert(7);
  insert(11);
  insert(13);
  insert(7);
 
  int i = 0;
 
  // Priority queue before extracting max
  System.out.print("Priority Queue : ");
  while (i <= size)
  {
    System.out.print(H[i] + " ");
    i++;
  }
 
  System.out.print("\n");
 
  // Node with maximum priority
  System.out.print("Node with maximum priority : " +
                    extractMax() + "\n");
 
  // Priority queue after extracting max
  System.out.print("Priority queue after " +
                   "extracting maximum : ");
  int j = 0;
  while (j <= size)
  {
    System.out.print(H[j] + " ");
    j++;
  }
 
  System.out.print("\n");
 
  // Change the priority of element
  // present at index 2 to 49
  changePriority(2, 49);
  System.out.print("Priority queue after " +
                   "priority change : ");
  int k = 0;
  while (k <= size)
  {
    System.out.print(H[k] + " ");
    k++;
  }
 
  System.out.print("\n");
 
  // Remove element at index 3
  remove(3);
  System.out.print("Priority queue after " +
                   "removing the element : ");
  int l = 0;
  while (l <= size)
  {
    System.out.print(H[l] + " ");
    l++;
  }
}
}
 
// This code is contributed by 29AjayKumar


Python3




# Python3 code to implement priority-queue
# using array implementation of
# binary heap
 
H = [0]*50
size = -1
   
# Function to return the index of the
# parent node of a given node
def parent(i) :
 
    return (i - 1) // 2
   
# Function to return the index of the
# left child of the given node
def leftChild(i) :
 
    return ((2 * i) + 1)
   
# Function to return the index of the
# right child of the given node
def rightChild(i) :
 
    return ((2 * i) + 2)
     
# Function to shift up the 
# node in order to maintain 
# the heap property
def shiftUp(i) :
 
    while (i > 0 and H[parent(i)] < H[i]) :
           
        # Swap parent and current node
        swap(parent(i), i)
       
        # Update i to parent of i
        i = parent(i)
         
# Function to shift down the node in
# order to maintain the heap property
def shiftDown(i) :
 
    maxIndex = i
       
    # Left Child
    l = leftChild(i)
       
    if (l <= size and H[l] > H[maxIndex]) :
     
        maxIndex = l
       
    # Right Child
    r = rightChild(i)
       
    if (r <= size and H[r] > H[maxIndex]) :
     
        maxIndex = r
       
    # If i not same as maxIndex
    if (i != maxIndex) :
     
        swap(i, maxIndex)
        shiftDown(maxIndex)
         
# Function to insert a 
# new element in 
# the Binary Heap
def insert(p) :
     
    global size
    size = size + 1
    H[size] = p
       
    # Shift Up to maintain 
    # heap property
    shiftUp(size)
   
# Function to extract 
# the element with
# maximum priority
def extractMax() :
     
    global size
    result = H[0]
       
    # Replace the value 
    # at the root with 
    # the last leaf
    H[0] = H[size]
    size = size - 1
       
    # Shift down the replaced 
    # element to maintain the 
    # heap property
    shiftDown(0)
    return result
   
# Function to change the priority
# of an element
def changePriority(i,p) :
 
    oldp = H[i]
    H[i] = p
       
    if (p > oldp) :
     
        shiftUp(i)
  
    else :
     
        shiftDown(i)
   
# Function to get value of 
# the current maximum element
def getMax() :
  
    return H[0]
   
# Function to remove the element
# located at given index
def Remove(i) :
 
    H[i] = getMax() + 1
       
    # Shift the node to the root
    # of the heap
    shiftUp(i)
       
    # Extract the node
    extractMax()
   
def swap(i, j) :
     
    temp = H[i]
    H[i] = H[j]
    H[j] = temp
     
# Insert the element to the
# priority queue
insert(45)
insert(20)
insert(14)
insert(12)
insert(31)
insert(7)
insert(11)
insert(13)
insert(7)
   
i = 0
   
# Priority queue before extracting max
print("Priority Queue : ", end = "")
while (i <= size) :
 
    print(H[i], end = " ")
    i += 1
   
print()
   
# Node with maximum priority
print("Node with maximum priority :" ,  extractMax())
   
# Priority queue after extracting max
print("Priority queue after extracting maximum : ", end = "")
j = 0
while (j <= size) :
 
    print(H[j], end = " ")
    j += 1
   
print()
   
# Change the priority of element
# present at index 2 to 49
changePriority(2, 49)
print("Priority queue after priority change : ", end = "")
k = 0
while (k <= size) :
 
    print(H[k], end = " ")
    k += 1
   
print()
   
# Remove element at index 3
Remove(3)
print("Priority queue after removing the element : ", end = "")
l = 0
while (l <= size) :
 
    print(H[l], end = " ")
    l += 1
     
    # This code is contributed by divyeshrabadiya07.


C#




// C# code to implement priority-queue
// using array implementation of
// binary heap
using System;
 
class GFG{
 
static int []H = new int[50];
static int size = -1;
 
// Function to return the index of the
// parent node of a given node
static int parent(int i)
{
    return (i - 1) / 2;
}
 
// Function to return the index of the
// left child of the given node
static int leftChild(int i)
{
    return ((2 * i) + 1);
}
 
// Function to return the index of the
// right child of the given node
static int rightChild(int i)
{
    return ((2 * i) + 2);
}
 
// Function to shift up the
// node in order to maintain
// the heap property
static void shiftUp(int i)
{
    while (i > 0 &&
           H[parent(i)] < H[i])
    {
         
        // Swap parent and current node
        swap(parent(i), i);
     
        // Update i to parent of i
        i = parent(i);
    }
}
 
// Function to shift down the node in
// order to maintain the heap property
static void shiftDown(int i)
{
    int maxIndex = i;
     
    // Left Child
    int l = leftChild(i);
     
    if (l <= size &&
        H[l] > H[maxIndex])
    {
        maxIndex = l;
    }
     
    // Right Child
    int r = rightChild(i);
     
    if (r <= size &&
        H[r] > H[maxIndex])
    {
        maxIndex = r;
    }
     
    // If i not same as maxIndex
    if (i != maxIndex)
    {
        swap(i, maxIndex);
        shiftDown(maxIndex);
    }
}
 
// Function to insert a
// new element in
// the Binary Heap
static void insert(int p)
{
    size = size + 1;
    H[size] = p;
     
    // Shift Up to maintain
    // heap property
    shiftUp(size);
}
 
// Function to extract
// the element with
// maximum priority
static int extractMax()
{
    int result = H[0];
     
    // Replace the value
    // at the root with
    // the last leaf
    H[0] = H[size];
    size = size - 1;
     
    // Shift down the replaced
    // element to maintain the
    // heap property
    shiftDown(0);
    return result;
}
 
// Function to change the priority
// of an element
static void changePriority(int i,
                           int p)
{
    int oldp = H[i];
    H[i] = p;
     
    if (p > oldp)
    {
        shiftUp(i);
    }
    else
    {
        shiftDown(i);
    }
}
 
// Function to get value of
// the current maximum element
static int getMax()
{
    return H[0];
}
 
// Function to remove the element
// located at given index
static void Remove(int i)
{
    H[i] = getMax() + 1;
     
    // Shift the node to the root
    // of the heap
    shiftUp(i);
     
    // Extract the node
    extractMax();
}
 
static void swap(int i, int j)
{
    int temp = H[i];
    H[i] = H[j];
    H[j] = temp;
}
 
// Driver Code
public static void Main(String[] args)
{
 
/*              45
            /     \
           31      14
          / \     / \
        13  20   7   11
       / \
      12  7
    Create a priority queue shown in
    example in a binary max heap form.
    Queue will be represented in the
    form of array as:
    45 31 14 13 20 7 11 12 7 */
 
    // Insert the element to the
    // priority queue
    insert(45);
    insert(20);
    insert(14);
    insert(12);
    insert(31);
    insert(7);
    insert(11);
    insert(13);
    insert(7);
     
    int i = 0;
     
    // Priority queue before extracting max
    Console.Write("Priority Queue : ");
    while (i <= size)
    {
        Console.Write(H[i] + " ");
        i++;
    }
     
    Console.Write("\n");
     
    // Node with maximum priority
    Console.Write("Node with maximum priority : " +
                   extractMax() + "\n");
     
    // Priority queue after extracting max
    Console.Write("Priority queue after " +
                  "extracting maximum : ");
    int j = 0;
    while (j <= size)
    {
        Console.Write(H[j] + " ");
        j++;
    }
     
    Console.Write("\n");
     
    // Change the priority of element
    // present at index 2 to 49
    changePriority(2, 49);
    Console.Write("Priority queue after " +
                  "priority change : ");
    int k = 0;
    while (k <= size)
    {
        Console.Write(H[k] + " ");
        k++;
    }
     
    Console.Write("\n");
     
    // Remove element at index 3
    Remove(3);
    Console.Write("Priority queue after " +
                  "removing the element : ");
    int l = 0;
    while (l <= size)
    {
        Console.Write(H[l] + " ");
        l++;
    }
}
}
 
// This code is contributed by Amit Katiyar


Javascript




<script>
 
// Javascript code to implement priority-queue
// using array implementation of
// binary heap
 
var H = Array(50).fill(0);
var size = -1;
 
// Function to return the index of the
// parent node of a given node
function parent(i)
{
 
    return parseInt((i - 1) / 2);
}
 
// Function to return the index of the
// left child of the given node
function leftChild(i)
{
 
    return parseInt((2 * i) + 1);
}
 
// Function to return the index of the
// right child of the given node
function rightChild(i)
{
 
    return parseInt((2 * i) + 2);
}
 
// Function to shift up the node in order
// to maintain the heap property
function shiftUp( i)
{
    while (i > 0 && H[parent(i)] < H[i]) {
 
        // Swap parent and current node
        swap(parent(i), i);
 
        // Update i to parent of i
        i = parent(i);
    }
}
 
function swap(i, j)
{
    var temp = H[i];
    H[i] = H[j];
    H[j] = temp;
}
 
// Function to shift down the node in
// order to maintain the heap property
function shiftDown( i)
{
    var maxIndex = i;
 
    // Left Child
    var l = leftChild(i);
 
    if (l <= size && H[l] > H[maxIndex]) {
        maxIndex = l;
    }
 
    // Right Child
    var r = rightChild(i);
 
    if (r <= size && H[r] > H[maxIndex]) {
        maxIndex = r;
    }
 
    // If i not same as maxIndex
    if (i != maxIndex) {
        swap(i, maxIndex);
        shiftDown(maxIndex);
    }
}
 
// Function to insert a new element
// in the Binary Heap
function insert( p)
{
    size = size + 1;
    H[size] = p;
 
    // Shift Up to maintain heap property
    shiftUp(size);
}
 
// Function to extract the element with
// maximum priority
function extractMax()
{
    var result = H[0];
 
    // Replace the value at the root
    // with the last leaf
    H[0] = H[size];
    size = size - 1;
 
    // Shift down the replaced element
    // to maintain the heap property
    shiftDown(0);
    return result;
}
 
// Function to change the priority
// of an element
function changePriority(i, p)
{
    var oldp = H[i];
    H[i] = p;
 
    if (p > oldp) {
        shiftUp(i);
    }
    else {
        shiftDown(i);
    }
}
 
// Function to get value of the current
// maximum element
function getMax()
{
 
    return H[0];
}
 
// Function to remove the element
// located at given index
function remove(i)
{
    H[i] = getMax() + 1;
 
    // Shift the node to the root
    // of the heap
    shiftUp(i);
 
    // Extract the node
    extractMax();
}
 
// Driver Code
/*         45
        /      \
       31      14
      /  \    /  \
     13  20  7   11
    /  \
   12   7
Create a priority queue shown in
example in a binary max heap form.
Queue will be represented in the
form of array as:
45 31 14 13 20 7 11 12 7 */
// Insert the element to the
// priority queue
insert(45);
insert(20);
insert(14);
insert(12);
insert(31);
insert(7);
insert(11);
insert(13);
insert(7);
var i = 0;
// Priority queue before extracting max
document.write( "Priority Queue : ");
while (i <= size) {
    document.write( H[i] + " ");
    i++;
}
document.write( "<br>");
// Node with maximum priority
document.write( "Node with maximum priority : "
     + extractMax() + "<br>");
// Priority queue after extracting max
document.write( "Priority queue after "
     + "extracting maximum : ");
var j = 0;
while (j <= size) {
    document.write( H[j] + " ");
    j++;
}
document.write( "<br>");
 
// Change the priority of element
// present at index 2 to 49
changePriority(2, 49);
document.write( "Priority queue after "
     + "priority change : ");
var k = 0;
while (k <= size) {
    document.write( H[k] + " ");
    k++;
}
document.write( "<br>");
 
// Remove element at index 3
remove(3);
document.write( "Priority queue after "
     + "removing the element : ");
var l = 0;
while (l <= size) {
    document.write( H[l] + " ");
    l++;
}
 
// This code is contributed by noob2000.
</script>


Output

Priority Queue : 45 31 14 13 20 7 11 12 7 
Node with maximum priority : 45
Priority queue after extracting maximum : 31 20 14 13 7 7 11 12 
Priority queue after priority change : 49 20 31 13 7 7 11 12 
Priority queue after removing the element : 49 20 31 12 7 7 11 

Time Complexity: The time complexity of all the operation is O(log N) except for GetMax() which has time complexity of O(1). 
Auxiliary Space: O(N)

Method – 2

below is also a valid method to implement this priority queue using a max heap. this code is a generic method to implement a priority queue using a class-based structure. here in the implementation part, we are using a generic template (not a specific data type) so this implementation works for all data types.

C++




#include <iostream>
#include <vector>
 
using namespace std;
 
// Priority queue implementation in C++
template<typename T>
class PriorityQueue {
  private:
  vector<T> data;
 
  public:
  // Implementing Priority Queue using inbuilt available vector in C++
  PriorityQueue() {}
 
  // Element Inserting function
  void Enqueue(T item) {
    // item Insertion
    data.push_back(item);
    int ci = data.size() - 1;
 
    // Re-structure heap(Max Heap) so that after
    // addition max element will lie on top of pq
    while (ci > 0) {
      int pi = (ci - 1) / 2;
      if (data[ci] <= data[pi])
        break;
      T tmp = data[ci];
      data[ci] = data[pi];
      data[pi] = tmp;
      ci = pi;
    }
  }
 
  T Dequeue() {
    // deleting top element of pq
    int li = data.size() - 1;
    T frontItem = data[0];
    data[0] = data[li];
    data.pop_back();
 
    --li;
    int pi = 0;
 
    // Re-structure heap(Max Heap) so that after
    // deletion max element will lie on top of pq
    while (true) {
      int ci = pi * 2 + 1;
      if (ci > li)
        break;
      int rc = ci + 1;
      if (rc <= li && data[rc] < data[ci])
        ci = rc;
      if (data[pi] >= data[ci])
        break;
      T tmp = data[pi];
      data[pi] = data[ci];
      data[ci] = tmp;
      pi = ci;
    }
    return frontItem;
  }
 
  // Function which returns peek element
  T Peek() {
    T frontItem = data[0];
    return frontItem;
  }
 
  int Count() {
    return data.size();
  }
};
 
// Driver code
int main()
{
   
  // Basic functionality sample of Priority Queue
 
  // Creating priority queue which contains int in it
  PriorityQueue<int> pq;
 
  // Insert element 1 in pq
  pq.Enqueue(1);
 
  cout << "Size of pq is : " << pq.Count() <<
    " and Peek Element is : " << pq.Peek() << endl;
 
  // Insert element 10 and -8 in pq
  pq.Enqueue(10);
  pq.Enqueue(-8);
 
  cout << "Size of pq is : " << pq.Count() <<
    " and Peek Element is : " << pq.Peek() << endl;
 
  // Delete element from pq
  pq.Dequeue();
 
  cout << "Size of pq is : " << pq.Count() <<
    " and Peek Element is : " << pq.Peek() << endl;
 
  // Delete element from pq
  pq.Dequeue();
 
  cout << "Size of pq is : " << pq.Count() <<
    " and Peek Element is : " << pq.Peek() << endl;
 
  // Insert element 25 in pq
  pq.Enqueue(25);
 
  cout << "Size of pq is : " << pq.Count() <<
    " and Peek Element is : " << pq.Peek() << endl;
 
  return 0;
}


Java




import java.util.ArrayList;
import java.util.List;
 
public class GFG {
   
// Priority queue implementation in Java
static class PriorityQueue<T extends Comparable<T>> {
    private List<T> data;
 
    // Implementing Priority Queue using inbuilt available List in Java
    public PriorityQueue() {
        this.data = new ArrayList<T>();
    }
 
    // Element Inserting function
    public void Enqueue(T item) {
        // item Insertion
        data.add(item);
        int ci = data.size() - 1;
 
        // Re-structure heap(Max Heap) so that after addition max element will lie on top of pq
        while (ci > 0) {
            int pi = (ci - 1) / 2;
            if (data.get(ci).compareTo(data.get(pi)) <= 0)
                break;
            T tmp = data.get(ci);
            data.set(ci, data.get(pi));
            data.set(pi, tmp);
            ci = pi;
        }
    }
 
    public T Dequeue() {
        // deleting top element of pq
        int li = data.size() - 1;
        T frontItem = data.get(0);
        data.set(0, data.get(li));
        data.remove(li);
 
        --li;
        int pi = 0;
 
        // Re-structure heap(Max Heap) so that after deletion max element will lie on top of pq
        while (true) {
            int ci = pi * 2 + 1;
            if (ci > li)
                break;
            int rc = ci + 1;
            if (rc <= li && data.get(rc).compareTo(data.get(ci)) < 0)
                ci = rc;
            if (data.get(pi).compareTo(data.get(ci)) >= 0)
                break;
            T tmp = data.get(pi);
            data.set(pi, data.get(ci));
            data.set(ci, tmp);
            pi = ci;
        }
        return frontItem;
    }
 
    // Function which returns peek element
    public T Peek() {
        T frontItem = data.get(0);
        return frontItem;
    }
 
    public int Count() {
        return data.size();
    }
}
 
// Driver code
public static void main(String[] args) {
    // Basic functionality sample of Priority Queue
 
    // Creating priority queue which contains int in it
    PriorityQueue<Integer> pq = new PriorityQueue<Integer>();
 
    // Insert element 1 in pq
    pq.Enqueue(1);
 
    System.out.println("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
 
    // Insert element 10 and -8 in pq
    pq.Enqueue(10);
    pq.Enqueue(-8);
 
    System.out.println("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
 
    // Delete element from pq
    pq.Dequeue();
 
    System.out.println("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
 
    // Delete element from pq
    pq.Dequeue();
 
    System.out.println("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
 
    // Insert element 25 in pq
    pq.Enqueue(25);
 
    System.out.println("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
}
}


Python3




import heapq
 
class PriorityQueue:
    def __init__(self):
        self._queue = []
        self._index = 0
     
    def enqueue(self, item, priority):
        heapq.heappush(self._queue, (priority, self._index, item))
        self._index += 1
     
    def dequeue(self):
        return heapq.heappop(self._queue)[-1]
     
    def peek(self):
        return self._queue[0][-1]
     
    def count(self):
        return len(self._queue)
         
# Driver code
if __name__ == "__main__":
    # Basic functionality sample of Priority Queue
 
    # Creating priority queue which contains int in it
    pq = PriorityQueue()
 
    # Insert element 1 in pq
    pq.enqueue(1, 1)
 
    print("Size of pq is : ", pq.count(), " and Peek Element is : ", pq.peek())
 
    # Insert element 10 and -8 in pq
    pq.enqueue(10, 2)
    pq.enqueue(-8, 3)
 
    print("Size of pq is : ", pq.count(), " and Peek Element is : ", pq.peek())
 
    # Delete element from pq
    pq.dequeue()
 
    print("Size of pq is : ", pq.count(), " and Peek Element is : ", pq.peek())
 
    # Delete element from pq
    pq.dequeue()
 
    print("Size of pq is : ", pq.count(), " and Peek Element is : ", pq.peek())
 
    # Insert element 25 in pq
    pq.enqueue(25, 4)
 
    print("Size of pq is : ", pq.count(), " and Peek Element is : ", pq.peek())


C#




using System;
using System.Collections.Generic;
 
class GFG {
 
    // Priority queue implementation in C#
    class PriorityQueue<T> where T : IComparable<T> {
        private List<T> data;
 
        // Implemeting Priority Queue using inbuilt available List in C#
        public PriorityQueue() {
            this.data = new List<T>();
        }
 
        // Element Inserting function
        public void Enqueue(T item) {
            // item Insertion
            data.Add(item);
            int ci = data.Count - 1;
 
            // re-structure heap(Max Heap) so that after addition max element will lie on top of pq
            while (ci > 0) {
                int pi = (ci - 1) / 2;
                if (data[ci].CompareTo(data[pi]) <= 0)
                    break;
                T tmp = data[ci]; data[ci] = data[pi]; data[pi] = tmp;
                ci = pi;
            }
        }
 
        public T Dequeue() {
            // deleting top element of pq
            int li = data.Count - 1;
            T frontItem = data[0];
            data[0] = data[li];
            data.RemoveAt(li);
 
            --li;
            int pi = 0;
 
            // re-structure heap(Max Heap) so that after deletion max element will lie on top of pq
            while (true) {
                int ci = pi * 2 + 1;
                if (ci > li) break;
                int rc = ci + 1;
                if (rc <= li && data[rc].CompareTo(data[ci]) < 0)
                    ci = rc;
                if (data[pi].CompareTo(data[ci]) >= 0) break;
                T tmp = data[pi]; data[pi] = data[ci]; data[ci] = tmp;
                pi = ci;
            }
            return frontItem;
        }
 
        // function which returns peek element
        public T Peek() {
            T frontItem = data[0];
            return frontItem;
        }
 
        public int Count() {
            return data.Count;
        }
    }
 
    // Driver code
    public static void Main(string[] args) {
        // basic functionality sample of Priority Queue
 
        // creating priority queue which contains int in it
        var pq = new PriorityQueue<int>();
 
        // Insert element 1 in pq
        pq.Enqueue(1);
 
        Console.WriteLine("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
 
        // Insert element 10 and -8 in pq
        pq.Enqueue(10);
        pq.Enqueue(-8);
 
        Console.WriteLine("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
 
        // Delete element from pq
        pq.Dequeue();
 
        Console.WriteLine("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
 
        // Delete element from pq
        pq.Dequeue();
 
        Console.WriteLine("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
 
        // Insert element 25 in pq
        pq.Enqueue(25);
 
        Console.WriteLine("Size of pq is : " + pq.Count() + " and Peek Element is : " + pq.Peek());
    }
}


Javascript




// Priority queue implementation in JavaScript
class PriorityQueue {
  constructor() {
    this.data = [];
  }
 
  // Element Inserting function
  enqueue(item) {
    // item Insertion
    this.data.push(item);
    let ci = this.data.length - 1;
 
    // Re-structure heap(Max Heap) so that after
    // addition max element will lie on top of pq
    while (ci > 0) {
      let pi = Math.floor((ci - 1) / 2);
      if (this.data[ci] <= this.data[pi])
        break;
      let tmp = this.data[ci];
      this.data[ci] = this.data[pi];
      this.data[pi] = tmp;
      ci = pi;
    }
  }
 
  dequeue() {
    // deleting top element of pq
    let li = this.data.length - 1;
    let frontItem = this.data[0];
    this.data[0] = this.data[li];
    this.data.pop();
 
    --li;
    let pi = 0;
 
    // Re-structure heap(Max Heap) so that after
    // deletion max element will lie on top of pq
    while (true) {
      let ci = pi * 2 + 1;
      if (ci > li)
        break;
      let rc = ci + 1;
      if (rc <= li && this.data[rc] < this.data[ci])
        ci = rc;
      if (this.data[pi] >= this.data[ci])
        break;
      let tmp = this.data[pi];
      this.data[pi] = this.data[ci];
      this.data[ci] = tmp;
      pi = ci;
    }
    return frontItem;
  }
 
  // Function which returns peek element
  peek() {
    let frontItem = this.data[0];
    return frontItem;
  }
 
  count() {
    return this.data.length;
  }
}
 
// Driver code
let pq = new PriorityQueue();
 
// Basic functionality sample of Priority Queue
 
// Insert element 1 in pq
pq.enqueue(1);
 
console.log(`Size of pq is: ${pq.count()} and Peek Element is: ${pq.peek()}`);
 
// Insert element 10 and -8 in pq
pq.enqueue(10);
pq.enqueue(-8);
 
console.log(`Size of pq is: ${pq.count()} and Peek Element is: ${pq.peek()}`);
 
// Delete element from pq
pq.dequeue();
 
console.log(`Size of pq is: ${pq.count()} and Peek Element is: ${pq.peek()}`);
 
// Delete element from pq
pq.dequeue();
 
console.log(`Size of pq is: ${pq.count()} and Peek Element is: ${pq.peek()}`);
 
// Insert element 25 in pq
pq.enqueue(25);
 
console.log(`Size of pq is: ${pq.count()} and Peek Element is: ${pq.peek()}`);


Output

Size of pq is : 1 and Peek Element is : 1
Size of pq is : 3 and Peek Element is : 10
Size of pq is : 2 and Peek Element is : 1
Size of pq is : 1 and Peek Element is : -8
Size of pq is : 2 and Peek Element is : 25

Time Complexity: O(log(n)) for EnQueue operation and O(log(n)) for Dequeue operation
Space complexity: O(n) (as we need n size array for implementation)



Last Updated : 11 Apr, 2023
Like Article
Save Article
Previous
Next
Share your thoughts in the comments
Similar Reads