What Is Meant by Heap in Data Structure?


Scott Campbell

What Is Meant by Heap in Data Structure?

In the world of data structures, a heap is a specialized tree-based data structure that satisfies the heap property. A heap can be visualized as a complete binary tree, where each node holds a key value and is arranged in such a way that the key value of each node is either greater than or equal to (in a max heap) or less than or equal to (in a min heap) the key values of its children.

The Heap Property

The heap property is the fundamental characteristic of a heap data structure. It states that for every node ‘i’ in the heap, the key value at ‘i’ should be either greater than or equal to (in case of max heap) or less than or equal to (in case of min heap) the key values at its child nodes.

Heap Operations

A heap supports various operations:

  • Insertion: The process of adding an element to the heap while maintaining the heap property.
  • Deletion: The process of removing an element from the heap while maintaining the heap property.
  • Peek/Get Minimum/Maximum: Retrieving the minimum (in case of min-heap) or maximum (in case of max-heap) element from the heap without removing it.
  • Merge/Meld: Combining two heaps into one, creating a new valid heap.

Applications of Heaps

The concept of heaps finds application in various domains, including but not limited to:

  • Priority Queues: Heaps are commonly used to implement priority queues, where elements with higher priorities are dequeued before elements with lower priorities.
  • Graph Algorithms: Algorithms like Dijkstra’s shortest path algorithm and Prim’s minimum spanning tree algorithm utilize heaps to efficiently select the next node or edge based on their associated weights or costs.
  • Heap Sort: Heaps are integral to the heap sort algorithm, a comparison-based sorting algorithm that leverages the heap data structure to sort elements in ascending or descending order.


A heap is a powerful data structure that allows for efficient insertion, deletion, and retrieval of extreme values (minimum or maximum) in logarithmic time complexity. Its applications range from priority queues to graph algorithms and sorting. By understanding the heap property and mastering the operations associated with heaps, developers can optimize their code and solve complex problems more effectively.

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