What Is a Heap Tree in Data Structure?


Larry Thompson

A heap tree, also known as a binary heap, is a specialized tree-based data structure that satisfies the heap property. It is commonly used to implement priority queues and is an essential concept in computer science and programming.

What is a Heap?

A heap is a complete binary tree that satisfies the following property: for any node N, the value of N’s parent node (if it exists) is greater (or smaller) than or equal to the value of N. This property distinguishes heaps from other binary trees.

Types of Heaps

There are two types of heaps: max heaps and min heaps. In a max heap, the value of each parent node is greater than or equal to the values of its children nodes. Conversely, in a min heap, each parent node has a value that is less than or equal to the values of its children nodes.

Heap Tree Operations

Heap trees support various operations that make them efficient for certain tasks:

  • Insertion: Adding elements to a heap tree can be done by inserting them at the end and then restoring the heap property by comparing and swapping with its parent nodes.
  • Deletion: Removing elements from a heap tree involves replacing the root node with the last inserted element and then restoring the heap property by comparing and swapping with its children nodes.
  • Peek: Getting the value of the root node without removing it from the heap.
  • Merging: Combining two heaps into one, resulting in a new valid heap.
  • In addition to these basic operations,
    • Increase/Decrease key: Modifying the value of a node in the heap, maintaining the heap property.
    • Heapify: Constructing a heap from an unordered array in linear time.

Applications of Heap Trees

Heap trees find applications in a variety of algorithms and data structures, including:

  • Priority Queues: The heap property allows efficient retrieval of the highest (or lowest) priority element.
  • Heap Sort: A comparison-based sorting algorithm that uses a heap to sort elements in ascending or descending order.
  • Dijkstra’s Algorithm: A graph traversal algorithm that uses a priority queue (implemented using a min heap) to find the shortest path between two nodes in a graph with non-negative edge weights.
  • Huffman Coding: A lossless data compression algorithm that uses heaps to efficiently construct variable-length prefix codes for characters based on their frequency of occurrence.


A heap tree is an important data structure that provides efficient operations for managing elements with priority. Its ability to maintain the heap property makes it suitable for various applications, including priority queues and sorting algorithms. By understanding how heap trees work and their applications, you can leverage this powerful data structure to optimize your algorithms and improve overall performance.

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