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Efficiently Implementing Priority Queue in Python


Python Stacks, Queues, and Priority Queues in Practice

Queues are an essential data structure used in various algorithms, such as games, artificial intelligence, satellite navigation, and task scheduling. As one of the fundamental abstract data types, understanding queues is crucial for computer science students and software engineers alike. In this tutorial, we will explore different types of queues, implement them in Python, and use them to solve practical problems. Additionally, we will cover Python’s thread-safe, asynchronous, and interprocess queues, as well as integrating Python with distributed message queue brokers.

Learning About the Types of Queues

Before diving into the implementation of queues in Python, let’s first understand the different types of queues:

Queue: First-In, First-Out (FIFO)

A queue follows the First-In, First-Out (FIFO) principle, where the item that enters the queue first will be the first one to leave. It is similar to a line at a grocery store, where the first person in line will be the first to be served.

Stack: Last-In, First-Out (LIFO)

In contrast to a queue, a stack operates on the Last-In, First-Out (LIFO) principle. The item that enters the stack last will be the first one to be removed. It is similar to stacking plates, where the last plate placed on top is the first one to be used.

Deque: Double-Ended Queue

A deque, short for double-ended queue, allows items to be added or removed from both ends. It offers more flexibility compared to a queue or stack and can be used in various scenarios.

Priority Queue: Sorted From High to Low

A priority queue stores items with associated priorities, where the highest priority item is always at the front of the queue. When retrieving items, they are ordered from high to low priority. This type of queue is useful when dealing with tasks that require different levels of priority or urgency.

Implementing Queues in Python

Now that we understand the types of queues, let’s explore how to implement them in Python:

Representing FIFO and LIFO Queues With a Deque

Python provides a built-in deque data structure that can be used to represent both FIFO and LIFO queues. It allows for efficient insertion and deletion of elements at both ends.

Building a Queue Data Type

If you want to build your own queue data type from scratch, you can use a list and define methods to enqueue (add) and dequeue (remove) elements. This approach gives you more control over the operations and allows for customization.

Building a Stack Data Type

Similar to building a queue, you can create a stack data type using a list. The methods to push (add) and pop (remove) elements from the stack can be implemented accordingly.

Representing Priority Queues With a Heap

Python’s heapq module provides functions to create a binary heap, which can be used to implement a priority queue. A binary heap guarantees that the item with the highest priority is always at the top.

Building a Priority Queue Data Type

If you prefer building your own priority queue data type, you can combine a list and a dictionary to store items with their associated priorities. You can then define methods to insert, remove, and retrieve items based on their priorities.

Handling Corner Cases in Your Priority Queue

When implementing a priority queue, it is essential to handle corner cases, such as empty queues, duplicate priorities, and updating priorities of existing items. By addressing these cases, you can ensure the correctness and reliability of your priority queue implementation.

Refactoring the Code Using a Mixin Class

To avoid code duplication when implementing different types of queues, you can use a mixin class. A mixin class allows you to define common methods and share them among multiple classes. This approach enhances code reusability and improves maintainability.

Using Queues in Practice

After implementing queues in Python, let’s apply them to solve practical problems:

Sample Data: Road Map of the United Kingdom

To demonstrate the usage of queues, we will use a sample dataset representing a road map of the United Kingdom. This dataset will serve as the basis for various search algorithms.

Object Representation of the Cities and Roads

We will create object representations of the cities and roads in the United Kingdom. This representation will allow us to perform search algorithms and find optimal paths between cities.

Breadth-First Search Using a FIFO Queue

Breadth-first search is a graph traversal algorithm that explores all the vertices of a graph in breadth-first order. We will use a FIFO queue to implement this algorithm and find the shortest path between two cities in the United Kingdom.

Shortest Path Using Breadth-First Traversal

By utilizing the breadth-first traversal algorithm, we can find the shortest path between any two cities in the United Kingdom. This approach is efficient and guarantees finding the optimal path.

Depth-First Search Using a LIFO Queue

In contrast to breadth-first search, depth-first search explores as far as possible along a particular branch before backtracking. We will use a LIFO queue to implement this algorithm and explore the depths of the graph.

Dijkstra’s Algorithm Using a Priority Queue

Dijkstra’s algorithm is a popular graph search algorithm used to find the shortest path between nodes with weighted edges. We will utilize a priority queue to implement this algorithm and calculate the optimal path in terms of distance.

Using Thread-Safe Queues

Python provides built-in thread-safe queue implementations that can be used in multi-threaded applications:


The queue.Queue class is a thread-safe implementation of a FIFO queue. It allows multiple threads to safely enqueue and dequeue items from the queue without data corruption or race conditions.


Similar to queue.Queue, the queue.LifoQueue class is a thread-safe implementation of a LIFO queue. It ensures that the most recently added item is the first one to be retrieved.


The queue.PriorityQueue class is a thread-safe implementation of a priority queue. It guarantees that items are retrieved in order of their priority, ensuring correct ordering in multi-threaded scenarios.

Using Asynchronous Queues

Python’s asyncio module provides asynchronous queue implementations that can be used in asynchronous programming:


The asyncio.Queue class is an asynchronous implementation of a FIFO queue. It can be used in asynchronous programs to exchange data safely between coroutines.


Similar to asyncio.Queue, the asyncio.LifoQueue class is an asynchronous implementation of a LIFO queue. It supports the same functionality as the FIFO queue but follows the LIFO principle.


The asyncio.PriorityQueue class is an asynchronous implementation of a priority queue. It is useful in scenarios where different coroutines need to communicate with different levels of priority.

Using multiprocessing.Queue for Interprocess Communication (IPC)

Python’s multiprocessing module provides a built-in queue implementation for interprocess communication:

Reversing an MD5 Hash on a Single Thread

We will demonstrate how to use the multiprocessing.Queue for interprocess communication by implementing a program to reverse an MD5 hash. This program distributes the workload evenly across multiple processes.

Distributing Workload Evenly in Chunks

To effectively utilize multiprocessing, we will distribute the workload evenly in chunks. This approach ensures that each process handles a fair share of the workload, leading to improved performance.

Communicating in Full-Duplex Mode

We will explore how to use the multiprocessing.Queue to enable full-duplex communication between processes. This allows processes to send and receive messages simultaneously, enhancing interprocess communication.

Killing a Worker With the Poison Pill

To gracefully terminate a worker process, we will utilize the concept of a “poison pill.” This technique involves sending a special message to the worker, signaling that it should stop processing.

Analyzing the Performance of Parallel Execution

We will analyze the performance of parallel execution by measuring the execution time of our program on a single thread and with multiple processes. This analysis will demonstrate the benefits of multiprocessing for certain tasks.

Integrating Python With Distributed Message Queues

Python can be integrated with popular distributed message queue brokers using specific libraries:

RabbitMQ: pika

We will explore how to integrate Python with RabbitMQ, a popular message queue broker, using the pika library. This integration enables communication between different processes or systems.

Redis: redis

Redis is another widely used distributed message queue broker. We will learn how to connect Python with Redis and leverage its functionalities using the redis library.

Apache Kafka: kafka-python3

Apache Kafka is a distributed streaming platform that can be used as a distributed message queue. We will integrate Python with Apache Kafka using the kafka-python3 library.


Queues are a fundamental data structure used in various algorithms and software systems. Understanding different types of queues and implementing them in Python can greatly enhance your problem-solving skills and improve the efficiency of your applications. Additionally, Python provides built-in thread-safe, asynchronous, and interprocess queue implementations, as well as libraries for integrating with popular distributed message queue brokers. By utilizing queues effectively, you can tackle complex problems and achieve better scalability in your projects.