From Multiprocesing to Concurrent Futures in Python


September 12, 2020

Waiting for independent I/O can be a performance bottleneck. This can be things like downloading files, making API calls or running SQL queries. I’ve already talked about how to speed this up with multiprocessing. However it’s easy to move to the more recent concurrent.futures library which allows running on threads as well as processes, and allows handling more complicated asynchronous flows.

From the previous post suppose we have this multiprocessing code:

from multiprocessing import Pool
from urllib.request import urlretrieve

url_dests = [('', 'example.html'), ...]
with Pool(8) as p:
    p.starmap(urlretrieve, url_dests)

Turning this into futures code is very easy; we replace a multiprocessing pool with a futures PoolExecutor, and starmap(f, xs) with map(f, *zip(*xs)).

from concurrent.futures import ProcessPoolExecutor
from urllib.request import urlretrieve

url_dests = [('', 'example.html'), ...]
with ProcessPoolExecutor(max_workers=8) as p:, *zip(*url_dests))

The main difference is map, like the inbuilt Python function, wants each argument as a separate list, where starmap wants a list of argument lists. We use the standard via *zip(*) trick to convert between the two forms.

Then moving from processes to threads is as simple as replacing the ProcessPoolExecutor with a ThreadPoolExecutor. Interestingly when using this with the Pyathena library I found that the ThreadPoolExecutor didn’t seem to result in a concurrency speedup (it was as slow as executing the queries serially). I’m not yet sure why this is (something in the GIL?); but it makes me want to be more careful when switching to a ThreadPool.

This is a good starting point for more complex workflows. However if you start using a execute/wait paradigm make sure that you explicitly raise exceptions.