Python Multi-threading: Unlock the Secrets of Concurrent Programming

In Python, you can use the threading module to create and manage multiple threads (smaller units of a program that can run concurrently). This can be useful for improving the performance of certain types of programs by allowing them to perform multiple tasks in parallel.

To create a new thread in Python, you need to define a function that will serve as the thread’s “target”. Then, you can create a Thread object, pass the target function as an argument, and call the start() method to start the thread. Here’s an example of how to create and launch a new Python thread:

import threading

def my_function():
    # code to be executed by the thread
    print("Running in a new thread")

# create a new thread
thread = threading.Thread(target=my_function)

# start the thread
thread.start()

You can also pass additional arguments to the target function by using the args argument of the Thread constructor. For example:

import threading

def my_function(arg1, arg2):
    # code to be executed by the thread
    print("Running in a new thread with arguments:", arg1, arg2)

# create a new thread
thread = threading.Thread(target=my_function, args=(1, 2))

# start the thread
thread.start()

In addition to the start() method, the Thread class provides several other methods for controlling and managing threads, such as join(), which waits for a thread to finish execution, and is_alive(), which returns True if the thread is currently running.

It’s important to note that Python’s threading module is built on top of the lower-level thread module, which is built on top of the even lower-level _thread module. These modules all use the same underlying thread implementation, but the threading module provides a higher-level and easier-to-use interface. It’s generally recommended to use the threading module for most multi-threading tasks in Python.

Multiple threads within a process shares the same data space as the main thread, making it easier to share information and communicate than if they were separate processes.

Threads, also known as light-weight processes, do not require significant memory overhead and are therefore less expensive than processes.

A thread has a start, an execution sequence, and an end. It has an instruction pointer that maintains track of where it is in its context at any given time.

• It is possible to anticipate it (interrupted)
• It can be put on hold (also known as sleeping) while other threads are running, which is known as yield.

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