I was recently wondering if multithreading in Python was possible. After some research, I figured out how multithreading works in Python and this article will help you easily achieve the results you want.
Is Python a multithreaded language? Yes. The Python programming language supports multithreading. In Python, we can multithreading by importing the module ‘threading’ and utilizing the class ‘Thread.’
Stay tuned if you want to learn how multithreading works in Python. But, before we get there, there are a few keywords to understand about multithreading. To comprehend what multithreading is, we must first grasp what multitasking is.
Also read: Introduction to Python threading
Introduction to Multitasking
Multitasking is the execution of multiple tasks simultaneously over a set period in computing. Users can run multiple applications or software simultaneously on modern operating systems.
Nowadays, we all have machines with CPUs that have several cores. As a result, numerous jobs execute concurrently.
Some processes can run in the background while people perform other things in the foreground.
For example, you may download things on the Internet while listening to music and playing a game on your system. In this case, multiple tasks occur concurrently, which qualifies as multitasking.
In general, we may classify multitasking into two types. Multitasking can be process-based (multiprocessing) or thread-based (multithreading) (multithreading).
Introduction to Multiprocessing
Multiprocessing refers to the operation of two or more processes on the same operating system simultaneously. In this example, we will call the smallest unit, a process. It is a system’s capacity to support more than one processor or distribute duties among them.
Downloading a file is a different procedure, as is listening to music, and playing games is a separate process. All of these processes occur at the same time.
Introduction to Multithreading in Python
A single procedure may include several tasks that must complete. All of these jobs are separate components of the same process. Let’s call these distinct tasks, threads.
A thread is a self-contained flow of execution within a process. Multiple threads can exist in a single process. A thread is usually a lightweight process. We can create parallelism by separating a process into many threads.
A thread is the shortest sequence of programmed instructions that may be handled separately by an operating system scheduler.
Each thread in a process is responsible for a certain job. Consider the situation of playing the Pro Evolution Soccer (PES) game.
This game is a single procedure with several activities that may be completed simultaneously, such as moving the ball, changing the direction, playing music, and so on. There are so many things going on at the same time.
The capacity of a CPU to offer many threads of execution concurrently, as enabled by the operating system, is referred to as multithreading.
Multithreading is extremely useful for both saving time and improving performance. We can’t use it in every case though. Multithreading helps only when many tasks must be completed that are not interdependent.
Code Implementation of Multithreading in Python
Let’s have a look at how we can implement multithreading in Python.
Importing threading library
To accomplish multithreading in Python, we must first import the threading package. Before importing this module, you must first install it in your Python environment.
Fortunately, Python’s standard library already has a threading module. You do not need to install it on your PC manually. You are ready to proceed if you have Python installed on your PC.
One may use one of the following commands to import the threading module.
Let us now look at using Python to generate several threads and implement multithreading.
Create A Basic Code to Multithread
Before we get there, let’s look at a Python program with two classes: ‘JournalDev’ and ‘Python.’
Look into this program.
We have two classes with comparable methods. When we construct the object and execute the run function in the first class, it outputs ‘JournalDev’ three times. When we do the same for the second class, the word ‘Python’ appears three times.
class JournalDev: def run(self): for i in range(3): print("JournalDev") class Python: def run(self): for i in range(3): print("Python") obj1 = JournalDev() obj2 = Python() obj1.run() obj2.run()
If we execute this program, the result will be as follows.
JournalDev JournalDev JournalDev Python Python Python
So, this software operates in the main thread. There is a main thread in every program. It performs both procedures sequentially in this case.
With the help of Threading Library
However, we will need multithreading. That implies we have to perform both methods at the same time. We may accomplish this by utilizing Python’s threading module and Thread class.
Let’s see what we can come up with.
from threading import * class JournalDev(Thread): def run(self): for i in range(3): print("JournalDev") class Python(Thread): def run(self): for i in range(3): print("Python") obj1 = JournalDev() obj2 = Python() obj1.start() obj2.start()
We’ve now changed the sample program to include multithreading. We also utilized the Thread class for each class to operate as separate threads.
Another difference is that we did not call the run function directly. Instead, we utilized the start method, which is the mechanism when threading is implemented.
The run function is immediately invoked in the background when we use the start method.
By default, we now have the primary thread. When we call the start procedures, two-child threads, thread1, and thread2, are created. Both of these topics will be active at the same time.
After making these changes, we obtain the output shown below.
JournalDevPython JournalDev JournalDev Python Python
Now we can observe that multithreading occurs while the program executes.
We can see that certain words are grouped, such as ‘JournaldevPython’ even though ‘Journaldev’ and ‘Python’ are independent terms.
A collision caused this. It’s because the processor is too fast, and two threads are attempting to execute on the CPU simultaneously.
The operating system has schedulers, which define the time for execution. In this example, the software executes the instructions more than once at one particular time instance.
We may avoid this by introducing a little delay to our software by utilizing the sleep technique. To do this, we must import the sleep function from the time module to provide a slight delay between the execution of the two threads.
from threading import * from time import sleep class JournalDev(Thread): def run(self): for i in range(3): print("JournalDev") sleep(1) class Python(Thread): def run(self): for i in range(3): print("Python") sleep(1) obj1 = JournalDev() obj2 = Python() obj1.start() sleep(0.3) obj2.start()
Now we can see that the threads are running concurrently, as seen by the output.
You’ve now seen a lot of what Python threading has to offer, as well as some instances of how to write threaded programs and solve issues with them. This is how the Python programming language handles multithreading.
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