Скачать или смотреть Understanding Python's Multiprocessing: How to Handle Iterable Fields

Discover how to effectively manage iterable fields in Python's multiprocessing. Learn why updates to lists are overlooked and how to implement elegant solutions for non-static methods.
---
This video is based on the question https://stackoverflow.com/q/67469233/ asked by the user 'Skullsploder' ( https://stackoverflow.com/u/15885363/ ) and on the answer https://stackoverflow.com/a/67471287/ provided by the user 'Booboo' ( https://stackoverflow.com/u/2823719/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How do I make Python respect iterable fields when multiprocessing?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding Python's Multiprocessing: How to Handle Iterable Fields

When working with Python's multiprocessing module—particularly the concurrent.futures component—developers often encounter perplexing behaviors, especially when handling iterable fields like lists or dictionaries within classes. In this guide, we'll unpack why updates to such fields may not be processed as expected and provide you with clear solutions to maintain effective data sharing across processes.

The Problem: Why Don't Updates to Lists Work?

Suppose you have a class with some iterable fields (like lists), and you are using the ProcessPoolExecutor to run methods from that class in parallel. You might expect that changes to any of the class's attributes would be reflected in the child processes. For example:

[[See Video to Reveal this Text or Code Snippet]]

In the above example, you might write something like this to modify data_list and data_number before invoking the executor. However, when you run your code, you notice strange results. This can be frustrating, especially when results for integers behave as expected, but lists do not.

Unpacking the Issue: Class vs. Instance Attributes

At first glance, this issue seems related to how Python is expected to share data across processes. However, the real reason lies in how class and instance attributes are handled differently. Let's clarify this with some key points:

Class Attributes vs. Instance Attributes:

Class attributes (like data_list and data_number in the Thing class) are shared across all instances of the class.

When you use self.data_number, you inadvertently create or modify an instance attribute instead of changing the class attribute.

Pickling Mechanism:

In multiprocessing, your object (in this case, an instance of Thing) gets pickled and then sent to child processes.

If you change self.data_number, you create an instance attribute in that process. Since that specific instance attribute isn't part of the class's defined state, default values are retained in the pickled class in all other processes.

Why Lists Seem Broken:

When you access self.data_list, you're still referring to the original class attribute, which is unaffected by changes made directly to its contents when using the instance method approach.

Hence, when foo tries to calculate based on data_list, it finds its original state unchanged.

The Solution: Clarify Class Structure

To achieve consistent behavior across processes, you should directly access class attributes instead of using instance attributes. Here's how you can modify your bar function to correctly reference the class attribute:

[[See Video to Reveal this Text or Code Snippet]]

By doing this, you're explicitly accessing the class variable data_number, ensuring that the shared state is kept intact across all processes.

Final Thoughts

This behavior can be a typical pitfall in the world of parallel programming with Python, especially when using the concurrent.futures module. Understanding the distinction between class and instance attributes can save a lot of debugging time. Here are some takeaways:

Always be clear about whether you're changing class or instance attributes.

Remember that multiprocessing involves pickling objects, which can lead to different states in child processes.

By following these principles, you can ensure that your multiprocessing applications behave as intended, allowing for more efficient and predictable data management.

Happy coding!