Why is Binary Heap Preferred over BST for Priority Queue?

Why is Binary Heap Preferred over BST for Priority Queue?

Last Updated : 23 Aug, 2024

A typical Priority Queue requires following operations to be efficient.

Get Top Priority Element (Get minimum or maximum)
Insert an element
Remove top priority element
Decrease Key

A Binary Heap supports above operations with following time complexities:

O(1)
O(Logn)
O(Logn)
O(Logn)

heapvsbst

A Self Balancing Binary Search Tree like AVL Tree, Red-Black Tree, etc can also support above operations with same time complexities.

Finding minimum and maximum are not naturally O(1), but can be easily implemented in O(1) by keeping an extra pointer to minimum or maximum and updating the pointer with insertion and deletion if required. With deletion we can update by finding inorder predecessor or successor.
Inserting an element is naturally O(Logn)
Removing maximum or minimum are also O(Logn)
Decrease key can be done in O(Logn) by doing a deletion followed by insertion. See this for details.

So why is Binary Heap Preferred for Priority Queue?

Since Binary Heap is implemented using arrays, there is always better locality of reference and operations are more cache friendly.
Although operations are of same time complexity, constants in Binary Search Tree are higher.
We can build a Binary Heap in O(n) time. Self Balancing BSTs require O(nLogn) time to construct.
Binary Heap doesn't require extra space for pointers.
Binary Heap is easier to implement.
There are variations of Binary Heap like Fibonacci Heap that can support insert and decrease-key in Θ(1) time

Is Binary Heap always better? Although Binary Heap is for Priority Queue, BSTs have their own advantages and the list of advantages is in-fact bigger compared to binary heap.

Searching an element in self-balancing BST is O(Logn) which is O(n) in Binary Heap.
We can print all elements of BST in sorted order in O(n) time, but Binary Heap requires O(nLogn) time.
Floor and ceil can be found in O(Logn) time.
K'th largest/smallest element be found in O(Logn) time by augmenting tree with an additional field.

Why is Binary Heap Preferred over BST for Priority Queue?

V

Vivek Gupta

Improve

Article Tags :

Practice Tags :

Similar Reads

Why is Binary Search preferred over Ternary Search?

The following is a simple recursive Binary Search function in C++ taken from here.Â C++ // CPP program for the above approach #include <bits/stdc++.h> using namespace std; // A recursive binary search function. It returns location of x in // given array arr[l..r] is present, otherwise -1 int b

Priority Queue using Binary Heap

What is a Priority Queue ?Priority Queue is an extension of the queue with the following properties: Every item has a priority associated with it.An element with high priority is dequeued before an element with low priority.If two elements have the same priority, they are served according to their o

Why Quick Sort preferred for Arrays and Merge Sort for Linked Lists?

Why is Quick Sort preferred for arrays? Below are recursive and iterative implementations of Quick Sort and Merge Sort for arrays. Recursive Quick Sort for array. Iterative Quick Sort for arrays. Recursive Merge Sort for arrays Iterative Merge Sort for arrays Quick Sort in its general form is an in-

How to implement Priority Queue - using Heap or Array?

A Priority Queue is a data structure that allows you to insert elements with a priority, and retrieve the element with the highest priority. You can implement a priority queue using either an array or a heap. Both array and heap-based implementations of priority queues have their own advantages and

Why does Queue have front but Priority-queue has top in stl?

Why does the queue have front but the priority queue has top in stl? The main difference between a queue and a priority queue is that a queue follows the FIFO (First-In-First-Out) principle, while a priority queue follows a specific priority order. In other words, the elements in a queue are process

Indexed Priority Queue with Implementation

Priority queue is a data structure in which data is stored on basis of its priority. In an Indexed Priority Queue, data is stored just like standard priority queue and along with this, the value of a data can be updated using its key. It is called "indexed" because a hash map can be used to store th

Difference Between heapq and PriorityQueue in Python

In this article, we are going to see the difference between heapq and PriorityQueue in Python. Differences between PriorityQueue and heapqPython queue PriorityQueue is thread-safe, but heapq doesn't guarantee thread safety.PriorityQueue implements locking to ensure thread safety, thus it is slower t

What is Priority Queue | Introduction to Priority Queue

A priority queue is a type of queue that arranges elements based on their priority values. Each element has a priority associated. When we add an item, it is inserted in a position based on its priority.Elements with higher priority are typically retrieved or removed before elements with lower prior

Implement the insert and delete functions on Priority queue without Array

A priority Queue is a type of queue in which every element is associated with a priority and is served according to its priority. We will use two popular data structures for implementing priority queues without arrays - Fibonacci HeapBinomial HeapFibonacci Heap:Fibonacci heap is a heap data structur

Difference between Circular Queue and Priority Queue

Queues are fundamental data structures that are used to store and manage a collection of elements. While both circular queues and priority queues are types of queues, they have distinct characteristics and applications. This article will explore the key differences between circular queues and priori