Understanding multiprocess.Pool.map Within a Class in Python

---

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---

The Problem: The Unexpected Output

You may find yourself in a situation where you expect your multiprocessing code to yield results that match your logic. For example:

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

In this scenario, the output is 0 instead of the anticipated 30. Let's break down why this is happening.

Understanding the Behavior of Multiprocessing

The Nature of the Processes

When you create a multiprocessing pool with pool = Pool(processes=4), each worker in the pool runs in its own separate memory space. This means that:

Using print statements helps us to analyze what’s happening:

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

Fluctuating Outputs

You see variations like i=0 and i=1 because of how tasks are chunked together by the map function. The default chunksize can lead to the same process hitting the run method multiple times, resulting in the odd outputs you observed.

Properly Managing State in Multiprocessing

Adjusting the Chunksize

To gain better control over how tasks are allocated, you can specify the chunksize. Using a chunksize=1 or chunksize=30 can yield different outcomes:

Chunksize = 1: Each process handles one task, but this does not allow shared updates.

Chunksize = 30: One process handles all tasks, which means no parallel execution occurs, but updates accumulate in the original instance.

Here's how you can adjust the chunksize:

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

A More Effective Design

To maintain the overall count while leveraging multiprocessing, consider using shared memory or other designs. Here's a new approach that uses a multiprocess-safe variable, which we will cover below.

Example of a Shared Counter

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

Conclusion

The key takeaway? Use shared values with locks to safely manage communal state across worker processes in a multiprocessing environment.

By implementing these strategies, you can harness the full power of Python's multiprocessing capabilities while retaining control over your class's state.