Python Multithreading

In common usage, multitasking is an ability to perform several tasks at the same time. Technically, it allows a system to function with the setting of pre-planned operation together. For example, you are getting a movie on your machine, playing a game, constantly listening to music, etc.

All these multiple tasks are performed by the same Operating System in sync. The Python multithreading process allows saving time and increases productivity.

Benefits Of Multithreading

Multithreading can primarily improve the pace of computation on multicore or multiprocessor systems because every processor or core manages a distinct thread concurrently.

It allows any activity to remain active. While one thread waits for data, the other works on the GUI at the same time. This system continues for both single-processor or multiprocessor systems.

Every thread of a means has access to its extended variables. If an extended variable shifts in one thread, it’s apparent to additional threads too. A thread can additionally have limited variables.

Definition Of A Thread?

A thread is essentially an independent course of execution. A single job can consist of various threads. Every thread in a system performs a particular job. For instance, when you are operating a game, say, FIFA on your machine, while a whole activity is a single means, it comprises many threads retained for playing the action, for taking data from the users, for running the emulator synchronously, etc. Everything internally is a separate thread responsible for bringing out these various tasks in the identical program.

All processes have an individual thread that is constantly running. This is a key thread. This key thread actually builds the child threads. The child thread is additionally initiated by the key thread.

In this blog, we will show how the working of the contemporary running thread. Through this, I hope you will get a clear understanding of how threads work. Additionally, we will deep dive into how Multithreading in Python works overall as well.

How To Run Parallel Tasks In Python Using Threads?

As we all know, any Python program runs as a single-threaded process. With the support threads, we can achieve the concurrency & parallelism to any Python task which can help the developer to run any task in an optimal way.

This approach can help to do any kind of a task like scraping the website, writing utilities like fetching the records, copying the object(s), sending messages, etc. This post will explain why threads are important, how to implement the threads, and some tips about implementing threads.

What Are Python Threads? And Why Is It Needed?

Threading is a technique by which we can achieve the parallelism in Python. By default, the threading feature is provided by the operating system.

Because it runs in the same process, they can share the same memory space with each other. They are very lightweight & not required much overhead as compared to processes.

How To Implement Thread?

There are two modules supported by Python.

  • threading (Python 3)
  • _thread (Python 2)

We are using threading for our example.

#!/usr/bin/Python 3

import threading
import time

exitFlag = 0

class CustomThread (threading.Thread):
def __init__(self, name):
threading.Thread.__init__(self)
self.name = name
def run(self):
print("Starting the thread" + self.name)
my_task(self.name, 5)
print("Exiting the thread" + self.name)
def my_task(threadName, counter):
while counter:
if exitFlag:
threadName.exit()
time.sleep(5)
print("Executing my_task by thread {} at {}".format(threadName, time.ctime()))
counter -= 1

# Create new threads
thread1 = CustomThread("Custom Thread-1")
thread2 = CustomThread("Custom Thread-2")

# Start new Threads
thread1.start() # Calling run method
thread2.start() # Calling run method
thread1.join() # Waits for to terminate
thread2.join() # Waits for to terminate
print ("Exiting from main thread")

We Developed A Proof Pilot With The Support Of Python Technology

Well, there are many ways available but we prefer to implement thread like semaphore from the threading module.

#!/usr/bin/Python 3

import threading

def my_task(semaphore, my_obj):
semaphore.acquire(blocking=False)
print("Executing my_task, performing test on {}".format(my_obj))
semaphore.release() # Release the thread

semaphore = threading.Semaphore(5) # Creating 5 threads
My_collection = [“one”, “two”, “three”, “four”, “five”, “six”, “seven”, “eight”, “nine”, “ten” ]
custom_threads = [threading.Thread(target=my_task, args=(semaphore, my_obj )) for my_obj in my_collection]
for thread in custom_threads:
thread.start()

With the support of threading, we can easily execute complicated tasks without creating process overhead.

Sandeep Natoo

Head Of Emerging Trend

Sandeep is a highly vigorous Machine learning expert with over 12+ work of experience with developing heterogeneous systems in the IT sector. He is an expert in building Java integrated web applications and Python data analysis stack. He has been known for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing valuable prediction with a good eye for detail. He is highly optimistic and avid nature, for various challenges is his major strength.

Get in touch for a detailed discussion.

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