Multithreading in Python 3

A program or process's smallest unit is called a thread, and it can run on its own or as part of a schedule set by the Operating System. Multitasking in a computer system is achieved by dividing a process into threads by an operating system. A string is a lightweight cycle that guarantees the execution of the interaction independently on the framework. When multiple processors are running on a program in Python 3, each processor runs simultaneously to carry out its own tasks..

Multithreading in Python 3

Python Multithreading

Multithreading is a stringing procedure in Python programming to run various strings simultaneously by quickly exchanging between strings with a central processor help (called setting exchanging). In addition, it makes it possible to share its data space with the primary threads of a process, making it easier than with individual processes to share information and communicate with other threads. The goal of multithreading is to complete multiple tasks at the same time, which improves application rendering and performance.

Note: The Python Global Interpreter Lock (GIL) allows running a single thread at a time, even the machine has multiple processors.

Benefits of Using Python for Multithreading

The following are the advantages of using Python for multithreading:

It guarantees powerful usage of PC framework assets.
Applications with multiple threads respond faster.
It is more cost-effective because it shares resources and its state with sub-threads (child).
It makes the multiprocessor engineering more viable because of closeness.
By running multiple threads simultaneously, it cuts down on time.
To store multiple threads, the system does not require a lot of memory.

When to use Multithreading in Python?

It is an exceptionally valuable strategy for efficient and working on the presentation of an application. Programmers can run multiple subtasks of an application at the same time by using multithreading. It lets threads talk to the same processor and share resources like files, data, and memory. In addition, it makes it easier for the user to continue running a program even when a portion of it is blocked or too long.

How to achieve multithreading in Python?

There are two main modules of multithreading used to handle threads in Python.

The thread module
The threading module

Thread modules

It is started with Python 3, designated as obsolete, and can only be accessed with _thread that supports backward compatibility.

Syntax:

To implement the thread module in Python, we need to import a thread module and then define a function that performs some action by setting the target with a variable.

Thread.py

import thread # import the thread module
import time # import time module

def cal_sqre(num): # define the cal_sqre function
    print(" Calculate the square root of the given number")
    for n in num: 
        time.sleep(0.3) # at each iteration it waits for 0.3 time
        print(' Square is : ', n * n)

def cal_cube(num): # define the cal_cube() function
    print(" Calculate the cube of  the given number")
    for n in num: 
        time.sleep(0.3) # at each iteration it waits for 0.3 time
        print(" Cube is : ", n * n *n)

arr = [4, 5, 6, 7, 2] # given array

t1 = time.time() # get total time to execute the functions
cal_sqre(arr) # call cal_sqre() function
cal_cube(arr) # call cal_cube() function

print(" Total time taken by threads is :", time.time() - t1) # print the total time

Output:

Calculate the square root of the given number
 Square is:  16
 Square is:  25
 Square is:  36
 Square is:  49
 Square is:  4
 Calculate the cube of the given number
 Cube is:  64
 Cube is:  125
 Cube is:  216
 Cube is:  343
 Cube is:  8
 Total time taken by threads is: 3.005793809890747

Threading Modules

The threading module is a high-level implementation of multithreading used to deploy an application in Python. To use multithreading, we need to import the threading module in Python Program.

Thread Class Methods

Methods	Description
start()	A start() method is used to initiate the activity of a thread. And it calls only once for each thread so that the execution of the thread can begin.
run()	A run() method is used to define a thread's activity and can be overridden by a class that extends the threads class.
join()	A join() method is used to block the execution of another code until the thread terminates.

Follow the given below steps to implement the threading module in Python Multithreading:

1. Import the threading module

Create a new thread by importing the threading module, as shown.

Syntax:

A threading module is made up of a Thread class, which is instantiated to create a Python thread.

2. Declaration of the thread parameters: It contains the target function, argument, and kwargs as the parameter in the Thread() class.

Target: It defines the function name that is executed by the thread.
Args: It defines the arguments that are passed to the target function name.

For example:

import threading
def print_hello(n):
print("Hello, how old are you ", n)
t1 = threading.Thread( target = print_hello, args =(18, ))

In the above code, we invoked the print_hello() function as the target parameter. The print_hello() contains one parameter n, which passed to the args parameter.

3. Start a new thread: To start a thread in Python multithreading, call the thread class's object. The start() method can be called once for each thread object; otherwise, it throws an exception error.

Syntax:

t1.start()
t2.start()

4. Join method: It is a join() method used in the thread class to halt the main thread's execution and waits till the complete execution of the thread object. When the thread object is completed, it starts the execution of the main thread in Python.

Joinmethod.py

import threading
def print_hello(n):
	Print("Hello, how old are you? ", n)
T1 = threading.Thread( target = print_hello, args = (20, ))
T1.start()
T1.join()
Print("Thank you")

Output:

Hello, how old are you? 20
Thank you

The join() method stops the main thread from running when the above program is run and waits until the thread t1 has finished running. The main thread begins its execution once the t1 has completed successfully.

Note: If we do not use the join() method, the interpreter can execute any print statement inside the Python program. Generally, it executes the first print statement because the interpreter executes the lines of codes from the program's start.

5. Synchronizing Threads in Python

It is a thread synchronization mechanism that makes sure that no two threads can run the same part of the program at the same time to access shared resources. Critical sections could be used to describe the situation. To avoid the critical section condition, in which two threads cannot simultaneously access resources, we employ a race condition.

Let's write a program to use the threading module in Python Multithreading.

Threading.py

import time # import time module
import threading
from threading import *
def cal_sqre(num): # define a square calculating function
    print(" Calculate the square root of the given number")
    for n in num: # Use for loop 
        time.sleep(0.3) # at each iteration it waits for 0.3 time
        print(' Square is : ', n * n)

def cal_cube(num): # define a cube calculating function
    print(" Calculate the cube of  the given number")
    for n in num: # for loop
        time.sleep(0.3) # at each iteration it waits for 0.3 time
        print(" Cube is : ", n * n *n)

ar = [4, 5, 6, 7, 2] # given array

t = time.time() # get total time to execute the functions
#cal_cube(ar)
#cal_sqre(ar)
th1 = threading.Thread(target=cal_sqre, args=(ar, ))
th2 = threading.Thread(target=cal_cube, args=(ar, ))
th1.start()
th2.start()
th1.join()
th2.join()
print(" Total time taking by threads is :", time.time() - t) # print the total time
print(" Again executing the main thread")
print(" Thread 1 and Thread 2 have finished their execution.")

Output:

Calculate the square root of the given number
 Calculate the cube of the given number
 Square is:  16
 Cube is:  64
 Square is:  25
 Cube is:  125
 Square is:  36
 Cube is:  216
 Square is:  49
 Cube is:  343
 Square is:  4
 Cube is:  8
 Total time taken by threads is: 1.5140972137451172
 Again executing the main thread
 Thread 1 and Thread 2 have finished their execution.

Next TopicStatic in Python

← prev next →