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The use of data structures is vital in the field of computer programming, especially when it comes to storing, managing, organizing data quickly and efficiently. Every developer must consider understanding data structure thoroughly as it can significantly improve their skillset. Min and max heap implementation are an important part of data structure and everyone must consider learning about them. 

Understanding what Heap is

Heap, at its core, is an advanced tree based data structure that programmers primarily use for implementing and sorting queues. Heaps are binary trees and here are their primary features:

  • The entry level in heaps is filled, with the leaf nodes being the exception
  • All nodes have 2 children maximum
  • Each node is located at the far left, meaning that each child is at their parent’s left

Heaps utilize binary trees in order to steer clear of holes present in the array. Binary trees are trees in which every node has two children and every node is full, with leaf nodes being the only exception as they are empty. Heaps are created on their property. The property essentially compares parents with their child node keys. 

Remember, heaps aren’t sorted all the time, and they follow a key condition where the smallest or largest element is present on the root node, depending on whether it is a Min or Max Heap.  

Benefits and Drawbacks of Heaps


  • You can access variables globally
  • Heaps come in quite handy for finding the greatest and minimum numbers
  • Heaps tend to be incredibly flexible and you can remove or allocate them in any order you please


  • Heaps require far more execution time in comparison to stacks
  • The computation time required by heaps is generally on the higher side
  • Managing memory can be quite challenging with heap memory. The reason for this is that heap is use all over the world. 

Heap Data Structure Applications

Heaps tend to be incredibly efficient when it comes to finding the max or min element present in arrays. They are also useful in selection algorithms and stats. The time complexity to use heap for gaining maximum or minimum value is O(1)O(1).

Programmers design priority queues based on the structures of heap. It takes around O(log(n))O(log(n)) for inserting  and deleting every elements situated in the priority queue with maximum efficiency. 

Priority queues (heap implemented) are often found in algorithms such as:

  • The heapsort algorithm
  • Dijkstra’s algorithm
  • Prim’s algorithm

Essential Heap Operations

Mentioned below are the vital operations people use whenever incorporating heap data structures.

getMax(): This operation helps return maximum value in heap

size:  The size operation is used for returning the heap’s size

extract: Extract helps to return an item’s value, followed by deleting it from heap

delete: Delete is used for removing items for a heap

insert: This command items to heap, and maintains its property.

heapify: Heapify rearranges heaps elements for maintaining heap’s property.

Steps to Build Max Heap

Every element present in max heap tends to act according to max heap property, meaning that the parent node’s key is greater than the child node key’s every time. Follow these steps to build max heap the right way:

    • Form a new node at the leap’s initial root
    • Give it a value
    • After assigning a value, compare the parent and child node’s values
  • In case the parent is lower than either child, swap the nodes
  • Repeat the step until the biggest element reaches the parent nodes of the root

You can also follow these steps when incorporating new elements in a heap. Remember, no matter which type of operation you are carrying out on max heap, maintaining heap property is vital. 

Steps for Deleting/Removing Nodes in Max Heap

The steps mentioned below will help you remove or delete max heap nodes effectively:

  • Take the last child node and move it to the root’s last level
  • Compare the children and parent nodes
  • If the parent’s value is lower than the nodes of their child, it would be best to swap them, followed by repeating the process until you satisfy the heap. 

As mentioned earlier, you must familiarize yourself with the various data structures and understand the best way to approach complex coding related questions confidently. Doing so will also help you get a clear idea of min and max heap implementation, ensuring you can use it without any hassle.

Steps to Build Min Heap

Elements present in min heap generally act in accordance with the property of min heap, which is vastly different to how max heap works. Remember, the parent node’s key is always lesser than the child node’s key. The following steps can help you create a min heap:

  • Form a brand new child node at the lest level, which is the heap’s end
  • Incorporate the new key on the node 
  • Start moving the child up, making sure you satisfy by heap property by reaching the root node

Steps to Delete or Remove Rood Node in Min Heap

  • Proceed by simply deleting the root node
  • Move the last child’s key to the root
  • Perform a comparative analysis between the node and its children
  • If the parent’s value is higher than the child nodes, make sure to swap them , following by repeating the process until you satisfy the heap property

Why Heaps are Important

While understanding how min and max heap implementation works is well and good, you must also learn the reason why heaps are so important. First off, programmers have been making use of heap in job scheduling operating systems to ensure employers can call people according to priority. You will also find heaps in various heap sort algorithms for the implementation of priority queues. Dijkstra’s algorithm also utilizes heap to determine short paths.