How to Handle Variables in Multiple multiprocessing.Pool Instances in Python

Learn how to effectively manage variables shared between different `multiprocessing.Pool` instances in Python to avoid common errors.
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Understanding Variable Sharing in Python's multiprocessing.Pool

When working with Python's multiprocessing.Pool, you may run into a common issue regarding variable scope. In particular, you might find yourself asking, how to treat a variable in a second multiprocessing.Pool when the variable originated from the first Pool? This can be a confusing point, especially when errors arise due to variable accessibility.

The Problem

Suppose you have a variable, dfs, which you want to manipulate in a second Pool after originally generating it in the first Pool. You may encounter errors indicating that this variable is not defined. Here's a simplified version of what your code might look like:

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

When you run this code, you may receive a NameError, as shown in the error traceback:

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

Why Does This Happen?

This issue arises because global variables in the main process are not automatically accessible in worker processes spawned by a Pool. Each worker starts with its own memory space, so global variables need special handling.

The Solution

To solve this issue, you can use an initializer function that is called once for each worker process. This function can take the necessary variables as arguments and make them accessible to the workers.

Steps to Implement the Solution

Define an Initializer Function:
Create a function that accepts the variable you want to share and set it as a global variable within the worker processes.

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

Modify mp_dosomething:
Update your second Pool initialization to include the initializer function and pass the dfs variable as an argument.

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

Run the Code:
Now, when you run your script, the dfs variable will be accessible within the second Pool's worker functions, allowing you to manipulate data without encountering a NameError.

Conclusion

By using an initializer function in multiprocessing.Pool, you can effectively manage shared variables across different pool instances in Python. This technique helps you avoid common pitfalls, allowing for smoother multiprocessing in your Python applications.

Feel free to implement this pattern in your own projects to ensure proper variable ownership and accessibility!