Tutorialspoint
Problem
Solution
Submissions
Back

Priority Queue Using a Heap

Certification: Intermediate Level Accuracy: 100% Submissions: 1 Points: 15

Implement a PriorityQueue class that supports the following operations: insert, get_highest_priority, extract_highest_priority, change_priority, and size.

Example 1
    

  • Input: 
pq = PriorityQueue()
    
pq.insert("Task A", 3)
    
pq.insert("Task B", 1)
    
pq.insert("Task C", 5)
    
highest = pq.get_highest_priority()
    
extracted = pq.extract_highest_priority()
    • 

  • Output: 
highest = "Task C"
extracted = "Task C"
    • 

  • Explanation: 
      

    • Step 1: Create a new PriorityQueue.
      • 

    • Step 2: Insert "Task A" with priority 3.
      • 

    • Step 3: Insert "Task B" with priority 1.
      • 

    • Step 4: Insert "Task C" with priority 5.
      • 

    • Step 5: Get the highest priority item without removing it, which is "Task C" (priority 5).
      • 

    • Step 6: Extract the highest priority item, which is "Task C" (priority 5), and remove it from the queue.
      • 

    

    •
Example 2
    

  • Input: 
pq = PriorityQueue()
    
pq.insert("Process 1", 10)
    
pq.insert("Process 2", 5)
    
pq.change_priority("Process 2", 15)
    
highest = pq.extract_highest_priority()
    • 

  • Output: highest = "Process 2"
    • 

  • Explanation: 
      

    • Step 1: Create a new PriorityQueue.
      • 

    • Step 2: Insert "Process 1" with priority 10.
      • 

    • Step 3: Insert "Process 2" with priority 5.
      • 

    • Step 4: Change the priority of "Process 2" from 5 to 15.
      • 

    • Step 5: Extract the highest priority item, which is now "Process 2" (priority 15), and remove it from the queue.
      • 

    

    •
Constraints
    

  • 0 ≤ number of elements ≤ 10^5
    • 

  • -10^9 ≤ priority ≤ 10^9
    • 

  • Elements can be of any type, but must be hashable
    • 

  • Time Complexity: 
      
  
    • insert: O(log n)
      • 
  
    • get_highest_priority: O(1)
      • 
  
    • extract_highest_priority: O(log n)
      • 
  
    • change_priority: O(log n)
      • 
  
    • size: O(1)
      • 

    • 

  • Space Complexity: O(n) where n is the number of elements
    •
HeapPriority QueueTech MahindraShopify
Editorial

Login to view the detailed solution and explanation for this problem.

My Submissions
All Solutions
Refresh
Lang Status Date Code
You do not have any submissions for this problem.
Refresh
User Lang Status Date Code
No submissions found.

Please Login to continue
Solve Problems

 
 
 
Output Window
Clear All Apply Filters

Trending

Others

SIGN IN

Don't have an account? Register

Hint
Solution

Solution Hints

    
  
  • Use a binary max-heap to efficiently maintain the highest priority element
    • 
  
  • Keep a separate map from values to their indices in the heap for efficient priority changes
    • 
  
  • Implement the "heapify" operation to restore heap property after modifications
    • 
  
  • Handle edge cases like empty heaps or non-existent elements
    • 
  
  • Consider using the heapq module in Python, but modify it for a max-heap since heapq is a min-heap by default
    •

Steps to solve by this approach:

 Step 1: Design a data structure with a heap array and a map to track element positions

 Step 2: Implement helper methods for heap operations (swap, bubble up, bubble down)

 Step 3: Implement insert() by adding the element to the end and bubbling up

 Step 4: Implement extract_highest_priority() by swapping the top with the last element and bubbling down

 Step 5: Implement change_priority() by updating the priority and restoring the heap property

Submitted Code :