What is Python Multithreading and How Does It Work?

Multiple threads and multiprocessing are the two most common ways to achieve concurrency and parallelization; however, not many developers understand the difference between them and cannot effectively choose which one to use and when.

In this article we are going to teach you what Python Multithreading is and how to decide when to use and how to implement it.

But, to start, we must understand the basic concepts of the main components of Multithreading. So, let's start from the beginning.

What is a thread?

A thread is an independent stream of execution. It can be seen as an individual lightweight component of a process that can run in parallel. Threading is a function usually provided by the operating system. There can be multiple threads in a process, sharing the same memory space, meaning they share the code to be executed and the variables declared in the program.

To better understand this, consider an example of the programs running on your laptop right now. You are probably reading this article with several tabs open in your browser. Meanwhile, you have the Spotify desktop application open to listen to music. Now, the browser and the Spotify desktop application are like two separate processes that can use multiple processes or threads to achieve parallelism.

If we understand this foundation, we can move on to level number two:

What is multithreading?

Multithreading is a fundamental concept in programming that involves the simultaneous execution of multiple threads of execution in a program. Instead of having a single thread executing tasks sequentially, multithreading allows several tasks to be performed concurrently.

Each thread has its independent flow of execution, providing the possibility of performing simultaneous operations and thus improving the performance and responsiveness of a program. It is a technique used to enhance the performance and efficiency of programs.

So, in general:

Multithreading is based on the idea that a single process can execute multiple tasks in parallel. Instead of running one task at a time, tasks are divided into threads so they can run concurrently. In a world where things need to be ready fast, his helps save time and cover many needs simultaneously. 

Each thread has its execution stack and can perform independent operations. This allows the program to make the most of system resources and processing power.

Multithreading also helps improve the responsiveness of programs. If one task takes a long time, the rest of the threads can continue running without interruptions.

Threads can communicate with each other and share resources through shared variables and objects. It is important to consider synchronization and mutual exclusion to avoid concurrency issues.

The implementation of multithreading can be done through specific language libraries or interfaces. Some languages like Java and C++ have native support for multithreading.

Multithreading is especially useful in applications that require handling multiple simultaneous tasks such as web servers, video games, or real-time data analysis applications.

Proper thread management is crucial to avoid issues like race conditions and deadlocks. It is important to design programs carefully and pay attention to synchronization and mutual exclusion.

How to create threads in Python

There are two main ways to create a thread in Python. The first involves extending the Thread class and overriding its run method where the code to be executed in the thread is specified. The second involves directly instantiating a Thread and specifying the code to be executed.

Let's focus on this second option, as it can be a bit simpler to start with.

To create a thread, we will instantiate the Thread class to obtain an object. The class constructor can take several arguments. They are as follows:

Target. It is the main argument and must be a reference to an invocable object, such as a function.

Args. It is a tuple with all the arguments that you want to provide to the invocable object specified in the target.

Kwargs. It is a dictionary with all the named arguments that you want to provide to the target.

Name. It is a text string with the name you want to give to the thread for identification. By default, a name will be created in the format Thread-N.

So, to create a thread, simply call the constructor of Thread specifying a name, an invocable, which can be a function, and the parameters for that invocable.

When to use Multithreading in Python?

Using multithreading can be a great choice if you want to boost the performance of your application.

Below are some use cases where you can apply Multithreading:

1. Performing I/O bound tasks

2. Multithreading can provide high performance while performing I/O bound tasks such as reading from or writing to files, network sockets, or databases.

3. Parallel Processing

4. If your application requires heavy computation, like processing a large number of images or audio files, you can split the work among multiple threads to speed up the processing time.

5. Asynchronous Programming

6. Multithreading is frequently used in conjunction with Asynchronous Programming techniques such as callbacks, promises, and coroutines. By using multiple threads, you can execute multiple asynchronous operations concurrently, improving the overall performance of your application.

Benefits of Multithreading in Python

It ensures the effective utilization of computer system resources.

Multithreaded applications are more responsive.

It shares resources and its state with sub-threads, making it more economical.

It makes the multiprocessor architecture more effective due to parallelism.

It reduces the time required by executing multiple threads at the same time.

The system would be less in need of too much memory to store multiple threads.

Multithreading is a very useful technique for time-saving and performance improvement of an application. Therefore, multithreading enables more than one user at a time, and it also manages multiple requests from the same user. At Avalith, we can cover all your project’s needs! If you don’t have the time or resources but you think this could be useful, hire our full time team of experts and get it solved now!