Speed Up Your Python Code: Multiprocessing vs Multithreading in Python 3.8

Learn how to optimize your Python code by converting for loops to `multiprocessing` and `multithreading` in Python 3.8. This guide breaks down the process for better performance.
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Speed Up Your Python Code: Multiprocessing vs Multithreading in Python 3.8

If you've ever found yourself waiting an eternity for your Python script to complete, you're not alone. For developers, especially when dealing with tasks like data processing, performance can become a bottleneck. This guide will guide you through converting an inefficient for loop into a more efficient solution using multiprocessing or multithreading in Python 3.8.

The Problem at Hand

Suppose you're working with a for loop that retrieves information using two functions: get_dl_users and get_group_users. The code might look like this:

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While this works, you're finding that the execution time has ballooned—taking almost 15 minutes to complete. Why is this happening? In many cases, such delays can stem from the operations being executed in sequence, where each call to your functions takes considerable time.

Understanding the Solution

The solution is to implement either multiprocessing or multithreading. The right choice depends on the nature of the operations being performed:

Multithreading is ideal for I/O-bound tasks, such as network requests or reading and writing files.

Multiprocessing shines for CPU-bound tasks, like data processing or computations.

In your case—since get_dl_users and get_group_users are likely making I/O calls to LDAP—you may want to consider multithreading as your first option. However, if you find that there’s also significant data processing that’s CPU-bound in nature, multiprocessing could be beneficial as well.

Implementing Multiprocessing

To execute your functions in parallel, you can leverage the multiprocessing module in Python. Here's a simplified approach:

Import Required Modules

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Define the Number of Processes

Pick a sensible number of processes according to your system’s capabilities.

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Use a Pool to Map Your Function

Now, create a pool of workers and map your function:

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Implementing Multithreading

Import the Required Module

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Define the Number of Workers

Set this value based on your system’s threading capabilities.

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Create a ThreadPool and Map Your Function

The following example demonstrates how to use the ThreadPoolExecutor:

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Conclusion

Adopting multiprocessing or multithreading can dramatically speed up your Python script, particularly when dealing with time-consuming functions. Always consider profiling your code and experimenting with both approaches to see what yields the best results for your specific case.

Implementing these changes should help lessen the time you're spending waiting for data retrieval, so you can focus on what really matters—building amazing applications!