Multithreading in Python 3
A thread is the smallest unit of a program or process executed independently or scheduled by the Operating System. In the computer system, an Operating System achieves multitasking by dividing the process into threads. A thread is a lightweight process that ensures the execution of the process separately on the system. In Python 3, when multiple processors are running on a program, each processor runs simultaneously to execute its tasks separately.
Multithreading is a threading technique in Python programming to run multiple threads concurrently by rapidly switching between threads with a CPU help (called context switching). Besides, it allows sharing of its data space with the main threads inside a process that share information and communication with other threads easier than individual processes. Multithreading aims to perform multiple tasks simultaneously, which increases performance, speed and improves the rendering of the application.
Note: The Python Global Interpreter Lock (GIL) allows running a single thread at a time, even the machine has multiple processors.
Benefits of Multithreading in Python
Following are the benefits to create a multithreaded application in Python, as follows:
When to use Multithreading in Python?
It is a very useful technique for time-saving and improving the performance of an application. Multithreading allows the programmer to divide application tasks into sub-tasks and simultaneously run them in a program. It allows threads to communicate and share resources such as files, data, and memory to the same processor. Furthermore, it increases the user's responsiveness to continue running a program even if a part of the application is the length or blocked.
How to achieve multithreading in Python?
There are two main modules of multithreading used to handle threads in Python.
It is started with Python 3, designated as obsolete, and can only be accessed with _thread that supports backward compatibility.
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.
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
Thread Class Methods
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.
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.
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.
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.
Hello, how old are you? 20 Thank you
When the above program is executed, the join() method halts the execution of the main thread and waits until the thread t1 is completely executed. Once the t1 is successfully executed, the main thread starts its execution.
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 ensures no two threads can simultaneously execute a particular segment inside the program to access the shared resources. The situation may be termed as critical sections. We use a race condition to avoid the critical section condition, in which two threads do not access resources at the same time.
Let's write a program to use the threading module in Python Multithreading.
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.