Priority Queue is a data structure that holds objects in a specific order. This order is decided by the priority of the object. The priority of an object can be anything like, highest to lowest value, earliest deadline first or any other user-defined criteria. A priority queue is different from a regular queue where all objects have the same order. In a priority queue, each object has some priority associated with it and the order in which objects are stored depends on their priorities. priority queues are used in a lot of applications like CPU scheduling, Disk Scheduling, Operating Systems, etc. They are also used in graph algorithms like Dijkstra’s algorithm, Prim’s algorithm, etc.
What is a Priority Queue?
A priority queue is a data structure that allows you to store and retrieve data items in order of priority. Priority queues are often used in applications where data items must be processed in order of importance, such as in a hospital emergency room or an air traffic control system.
Priority queues can be implemented using a variety of different data structures, such as arrays, linked lists, or heaps. The most commonly used implementation is the binary heap, which offers both efficient insertion and deletion of data items.
When inserting a new data item into a priority queue, the item is given a priority value. The higher the priority value, the more important the item is. When deleting an item from the queue, the item with the highest priority value is always deleted first.
Priority queues have a number of applications in computer science and engineering. They are often used in scheduling algorithms, such as CPU scheduling and disk scheduling. Priority queues are also used in many graph algorithms, such as Dijkstra's algorithm and Prim's algorithm.
Characteristics of a Priority Queue
A priority queue is a data structure that allows you to store data according to a priority. The most common way to implement a priority queue is with a heap. A heap is a tree-based data structure where the root node is the element with the highest priority. The elements of the tree are organized so that the element with the next highest priority is always stored in the left child node, and the element with the next lowest priority is always stored in the right child node.
When you insert an element into a priority queue, it is added to the end of the tree. The tree is then rearranged so that the element with the highest priority is always at the root. This rearrangement is called “heapifying” the tree.
The main advantage of using a priority queue is that it allows you to efficiently find and remove the element with the highest priority. This operation, known as “extracting the maximum,” can be performed in O(log n) time, where n is the number of elements in the queue.
There are two main types of priority queues: min-priority queues and max-priority queues. In a min-priority queue, elements are added to the queue according to their minimum value. That is, elements with smaller values have higher priorities than elements with larger values. In a max-priority queue, elements are added to the queue according to their maximum value. That is, elements with larger values have higher priorities than elements
Types of Priority Queues
There are two types of priority queues: min-priority queues and max-priority queues.
In a min-priority queue, the element with the smallest key is always at the front of the queue. In a max-priority queue, the element with the largest key is always at the front of the queue.
Priority queues can be implemented using an array or a linked list.
When using an array, we need to keep track of both the size of the array and the current number of elements in it. This is because, unlike a linked list, an array does not have dynamic sizing. As such, when we enqueue an element onto a full array, we need to resize it first.
To implement a priority queue using a linked list, we need to create a node class that has two fields: a key field and a data field. The key field will hold the priority value of the node and the data field will hold the actual data element.
We also need to create a head pointer that points to the first node in the list and a tail pointer that points to the last node in the list.
To enqueue an element into our priority queue, we first need to compare its key with that of the head node. If it is smaller (in the case of a min-priority queue) or larger (in the case of a max-priority queue), then we can simply insert it before head and update head pointer
Implementing a Priority Queue
A priority queue is a data structure that allows you to store and retrieve elements in order of importance. Priority queues are often used in applications such as scheduling, where the most important tasks are given the highest priority.
There are two main types of priority queues: min-priority queues and max-priority queues. Min-priority queues give the smallest element the highest priority, while max-priority queues give the largest element the highest priority.
Priority queues can be implemented using an array or a linked list. The array implementation is more efficient, but the linked list implementation is easier to understand.
To implement a priority queue using an array, you will need to keep track of two things: the size of the array and the current position of the elements. The size of the array will determine how many elements can be stored in the queue. The current position will keep track of where each element is located in the array.
To insert an element into a priority queue, you first need to find its correct position in the array. To do this, you compare the element to all of the other elements in the array until you find its correct position. Once you have found its correct position, you insert it into that position and shift all of the other elements down one spot in the array.
To remove an element from a priority queue, you simply remove it from its current position in the array and shift all of the other elements up one spot in the array.
Applications of Priority Queues
There are a number of applications for priority queues in data structure and computer science. One example is in scheduling algorithms, where tasks are assigned priorities and processed according to those priorities. Another example is in graph theory, where priority queues can be used to implement shortest path algorithms.
Priority queues can also be used to implement other data structures, such as heaps. Heaps are a type of data structure that can be used to store data in a way that allows for quick retrieval of the maximum (or minimum) element. Priority queues can also be used to efficiently process large amounts of data by storing the data in an order that allows for quick access to the most important elements.
Conclusion
In this article, we have looked at the priority queue data structure: what it is, its characteristics, types and how to implement it. Priority queues are a very important data structure with a wide range of applications. I hope that this article has given you a good understanding of them so that you can start using them in your own programs. If you wish to know more about these concepts and feel to study them better, you can go with Skillslash’s Data Science Course In Delhi and learn in-depth about the topic and become a successful professional in this field. Skillslash also offers Data science course in Nagpur with placement guarantee, . Skillslash can help you get into it with its Data science course in Mangalore. Get in touch with the support team to know more.
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