Understanding Multiprocessing in Python

The term 'multiprocessing' in Python refers to the ability to create and manage multiple processes that can run concurrently. This is particularly useful in circumstances where you have tasks that can be performed independently of each other and you want to make the most efficient use of your system's resources.

In Python, the multiprocessing module is used to create and manage processes that can run concurrently in different memory spaces. It bypasses the Global Interpreter Lock (GIL) by using subprocesses instead of threads, hence allows developers to fully leverage multiple processors on a machine.

Practical Application of Multiprocessing in Python

Let's consider an example. Suppose you have to download multiple files from a server. Instead of downloading them one by one in a single process (which could take a long time), you could split the task into multiple processes with each of them downloading a part of the total number of files. In Python, you can use the multiprocessing module to achieve this.

Here is a simple example:

from multiprocessing import Process

def download_file(file_name):
    # logic to download a file
    pass

file_list = ['file1', 'file2', 'file3', 'file4']

if __name__ == '__main__':
    processes = [Process(target=download_file, args=(file_name,)) for file_name in file_list]

    for p in processes:
        p.start()

    for p in processes:
        p.join()

In this code, each file download task runs in a separate process, allowing for concurrent downloading.

Best Practices in Using Multiprocessing

When using multiprocessing, there are a few best practices to consider:

1. Data Sharing - Since each process runs in its memory space, they do not share data. To share data, the multiprocessing module provides options like multiprocessing.Value() or multiprocessing.Array(). However, while using these methods to share data, exercise caution to avoid data inconsistency or corruption.

2. Avoid Too Many Processes - Creating more processes than the number of processors can lead to context switching overhead. Therefore, consider the number of processors in your machine before creating processes.

3. Error Handling - Make sure to handle errors at the child process level. Unhandled exceptions can cause the child process to terminate silently.

In conclusion, multiprocessing in Python is an advanced feature that allows for concurrent execution of tasks, resulting in more efficient use of system resources. However, it does require more cautious design and error handling compared to normal single-threaded programs.