Python’s concurrent.futures module

For some reason I wanted to improve performance of a small data driven Python program and tried to parallelize it. These are a few learnings to keep around for next time.

I nearly started with the very basics, defining my own threads as well as task and result queues. But then I found the very useful concurrent.futures module which provides a high-level interface to distribute tasks to both threads and processes.

I still made the mistake to start with threads. Everything worked nicely and the tool ran along with four worker threads — but every thread received 25% of CPU time and the overall runtime did not improve. I realized I had forgotten about Python’s Global Interpreter Lock (GIL).
The GIL basically prevents performance improvement using multithreading (at least of CPU-bound tasks, it is still useful for I/O). More information about the GIL:

So I had to switch to multiprocessing instead. The switch itself is really easy because it is nearly completely hidden inside concurrent.futures, I only had to replace the initialization of the ThreadPoolExecutor() with a ProcessPoolExecutor().

But with multiple processes I can no longer share variable values. Everything, including the called function itself, has to be pickled and send to the subprocess.
This required some refactoring, as I had to move the function to the module top-level (as local functions cannot be pickled) and then tried to find a good minimal set of parameters and return values in order to reduce the data transfer between the processes.

I saved my code examples for different concurrent.futures invocations as a gist for later reference: mschuett/

Along the way I also tried the asyncio module for “Asynchronous I/O, event loop, coroutines and tasks”. That one is also quite interesting, but as the name suggests it is focussed on I/O and coroutines in a single thread; functions you need for a network server. For my use case it is not useful, because asyncio does not help to utilize a second CPU core.

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