Heap vs Binary Search Tree (BST)

In this post, we will learn the difference between Heap and Binary Search Tree (BST) in Java. This is a frequently asked question in Java interviews for beginners. Let's dive into it.

Difference between Heap and Binary Search Tree (BST) in Java

Feature Heap Binary Search Tree (BST)
Structure Heap is a specialized tree-based data structure where the key of each node is either greater than or equal to (max heap) or less than or equal to (min-heap) the keys of its children. BST is a binary tree where the left child of a node has a key less than the node's key, and the right child has a key greater than the node's key.
Balancedness Heaps are usually not balanced; they are binary trees with some specific ordering properties. BSTs can be balanced or unbalanced, depending on the order of insertions and deletions. Balanced BSTs (e.g., AVL trees, Red-Black trees) maintain height balance for efficient operations.
Main Operations Heap supports efficient insertion, deletion (extract-max or extract-min), and finding the maximum or minimum element in O(log n) time complexity. BST supports efficient search, insertion, and deletion operations in O(log n) time complexity for balanced trees. For unbalanced BSTs, the time complexity may degrade to O(n) in the worst case.
Use Cases Heaps are commonly used in priority queues and heap-based sorting algorithms like Heap Sort. BSTs are used for fast searching and dynamic data sets where elements are inserted and removed frequently, such as in database indexing and symbol tables.
Implementation Heaps are usually implemented as arrays with heapification algorithms to maintain the heap property. BSTs are implemented using linked data structures (nodes with left and right pointers) or array representation (e.g., array-based BSTs).
In-order Traversal Heap does not guarantee in-order traversal; it only satisfies the heap property. BST's in-order traversal visits the nodes in ascending order (sorted order) if the tree is balanced.
Time Complexity for Searching Heap has O(n) time complexity for searching an element as there is no specific order among the elements in each level. Balanced BST has O(log n) time complexity for searching, as it halves the search space in each step.
Heap Property The heap property ensures that the root node has the highest (max heap) or lowest (min-heap) key among all the nodes in the heap. BST property ensures that the left subtree of a node contains keys less than the node's key, and the right subtree contains keys greater than the node's key.
Example Heap Example BST Example
In summary, heaps are specialized tree-based data structures with specific ordering properties, used mainly for priority queues and heap sorting. On the other hand, binary search trees are general-purpose binary trees with elements ordered based on their keys, used for efficient searching, insertion, and deletion operations. The choice between a heap and a BST depends on the specific requirements of the application or problem at hand.