These facts clarify that the Binary Heap is the best data structure for priority queue implementation. Hence, building a self-balancing BST’s cost O(NlogN) where binary heap just costs you O(n). Whereas, the Binary search trees use pointers to implement front and rear nodes, which takes up more space in memory. Since binary heap utilizes arrays, there is always a better locality of reference, and operations become more cache-friendly. To answer this question, you need to explore memory management in the case of both data structures. But, which approach is the best to implement a priority queue? These approaches cost you O(logn) for insertion and deletion and O(1) for peek operation. They are shown in the table below:īinary heap and binary tree provide almost similar complexities. Three approaches implement a priority queue in data structure with a complexity less than O(n^2). Representation of the linked queue below displays how it will insert element 45 in a priority queue.ĭifferent Implementation Strategies for Priority Queue However, this insertion will cost you O(N). Here, it will compare element 45 with each element inside the queue. But what if the element is significantly larger than all the nodes of a queue?įor instance, say you want to insert a node consisting of element 45. This particular scenario of insertion seems perfect as it doesn’t cost you more time. The diagram above shows how it will insert the new node consisting of elements in a linked queue. Additionally, it will allow you to have O(1) time-complexity during deletion. But, what if you have to insert a new node into the linked queue consisting of value 2? Since 2 is smaller than the element at the front (head) node, insertion from the front will be more efficient.

It arranges all these elements according to priority. Hence, you will only learn about the representation of the priority queue using a linked list.Ĭonsider a linked queue having 3 data elements 3, 17, 43, respectively.

Because of this complexity, implementing a priority queue using arrays is not an ideal approach. In general, processing each element will further cost you O(n^2). You can also implement a priority queue using arrays, however, if you consider array implementation of the priority queue, then inserting elements into the sorted array will cost you O(n). But, if you carry the N comparisons for each insertion, time-complexity will become O(N^2). The image above shows how it maintains the priority during insertion in a queue. Thus, you should maintain the lowest element at the front node. The element with the least value has the highest property. Consider you have to insert 7, 2, 45, 32, and 12 in a priority queue. Now, understand these properties with the help of an example. If multiple elements have the same priority, it does their removal from the queue according to the FCFS principle.It will delete the element with higher priority before the element with lower priority.Every element of this queue must be comparable.Every element has a certain priority assigned to it.Priority queue in a data structure is an extension of a linear queue that possesses the following properties: The priority of elements determines the order of removal in a queue, i.e., the element with higher priority will leave the queue first, whereas the element with the lowest priority at last. It behaves similar to a linear queue except for the fact that each element has some priority assigned to it. Thus, it is highly used in sorting algorithms. The priority queue in data structure resembles the properties of the hospital emergency queue.

In this queue, the priority depends on the medical condition of the patients. In this queue of patients, the patient with the most critical situation is the first in a queue, and the patient who doesn’t need immediate medical attention will be the last. The hospital emergency queue is an ideal real-life example of a priority queue. To understand it better, first analyze the real-life scenario of a priority queue. Priority Queue is an abstract data type that performs operations on data elements per their priority. Introduction to Priority Queue in Data Structure

Priority queue time complexity