Concurrent Futures in Sheepdoge: How a few lines of code resulted in a 78% performance improvement

Concurrent Futures in Sheepdoge: How a few lines of code resulted in a 78% performance improvement

For the past couple of months, I’ve been working on Sheepdoge, a tool for managing your personal Unix machines with Ansible. It’s like boxen, but for Ansible.

One new sheepdoge feature I’m particularly excited about is the use of concurrent.futures during sheepdoge install. concurrent.futures provides a high-level API for executing code asynchronously, making adding thread/process based concurrency trivial. It is a standard library python module as of 3.2, and is available on python 2.7 through the futures backport.

Why concurrency for sheepdoge install?

The sheepdoge install execution path is an excellent candidate for this concurrency. At a high level, we can think of sheepdoge install like the pip install -r requirements.txt command. Both take a list of packages hosted at a remote location and install them into a specified directory on the host machine.

Examining the work profile of sheepdoge install shows it is a good theoretical candidate for parallelization. Both downloading packages from a remote location and installing them into a specified directory are I/O heavy operations.

Analysis

When we run time sheepdoge install --no-parallel, we get the following:

real 7.115s
user 0.246s
sys  0.244s

From this output, we know the sheepdoge install --no-parallel command took 7.115s to execute. However, it was only using the CPU for ~.5s. Given I ran this test on my laptop, with no other processes consuming a large amount of CPU, I conclude that the ~6.5 seconds unaccounted for by the summation of user and sys was time spent doing IO. Additionally, when run sequentially, the install command blocks during IO operations, meaning the CPU does nothing related to sheepdoge until the next IO operation completes. In other words, we’re not maximizing our utilization of computing resources.

However, if we run sheepdoge install --parallel, we can keep the CPU working while we wait for IO operations to finish. Furthermore, we can begin multiple IO operations at once, since the system is easily able to parallelize these operations. These optimizations lead to big speed improvements, as we can see from the output of time sheepdoge install --parallel.

real 1.498s
user 0.272s
sys  0.294s

As you can see, we’ve vastly decreased the time where resources sit idle, improving overall run time by 78%!

Using concurrent.futures

Adding concurrency through concurrent.futures is simple. Because each process we’re executing is independent, we only needed to change a couple lines of code and do not have to worry about locking, memory sharing, etc. Note that pup.install() is the I/O heavy method responsible for downloading the remote package and installing it in the proper location.

Without concurrent.futures:

def _execute(self):
    for pup in self._pups_to_install:
        pup.install()

With concurrent.futures:

from concurrent.futures import ThreadPoolExecutor, wait

...

def _execute(self):
    with ThreadPoolExecutor(max_workers=self._max_workers) as executor:
        install_futures = {
            executor.submit(pup.install) for pup in self._pups_to_install
        }

        wait(install_futures)

You can find further documentation for all the different ways to utilize concurrent.futures here.

Wrapping up

concurrent.futures is a great tool for speeding up I/O heavy portions of your code, and I hope this post will point you towards some places in your code base where it could make a performance difference. And if you’re interested in using or contributing to sheepdge, please get in touch!

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