Скачать или смотреть Sharing and Updating a List Between Processes and Threads in Python

Explore how to effectively share and update a list between multiple processes and threads in Python's multiprocessing environment. Learn key adjustments and best practices for smoother inter-process communication!
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Sharing and Updating a List Between Processes and Threads in Python

In Python, dealing with concurrent programming through the use of processes and threads can present unique challenges, especially when it comes to sharing and updating data. A common scenario involves needing to share and update a list among the main process and sub-processes or threads. However, many programmers often run into the issue of duplicating data rather than sharing it effectively. This guide aims to clarify these challenges and provide a step-by-step solution.

The Problem Statement

When attempting to share and update a list between the main process (main thread) and sub-processes (sub-threads), you might encounter unexpected behaviors. Specifically, it can occur that the main thread and sub-process create separate copies of the list, which you initially intended to share globally.

Consider the following example code that outlines this scenario:

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

In this scenario, even though Manager should allow for shared lists, there are common pitfalls that lead to issues when trying to access and manipulate the list across different threads and processes.

Understanding the Solution

Key Adjustments

To correctly share your list among the processes and threads, follow these adjustments:

Initialize the Managed List Correctly
When assigning a value to l, ensure that both the creation of the managed list and initialization with values happens simultaneously. Modify the list initialization like so:

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

Manage Sub-Lists Individually
Each inner list also needs to be a managed list to allow for shared updates:

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

Output Example

With these changes, when you run the process, you can expect to see increment operations performed correctly across threads and processes.

You can print the lists differently:

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

Understanding the Limitations

Using Manager() can impose a significant performance hit because it runs a TCP server in a separate process to handle updates. This can often be up to 100 times slower than using basic lists.

Given the nature of multiprocessing, it is sometimes better to use alternatives like:

Queues: Ideal for passing data back to the main process without shared structures.

Shared Arrays: Allow for a form of shared memory whilst being aware of limitations.

Conclusion

Sharing data structures between processes and threads in Python requires careful management to avoid duplications and ensure efficient use of resources. By properly initializing managed lists and understanding their operational constraints, you can improve concurrency management in your applications.

Taking the time to structure your approach will lead to more reliable, efficient inter-process communication in your Python projects. Happy coding!