Idiomatic Functional Programming With DRY Python

Episode • Jul 21, 2020 • 47m

Summary

Python is an intuitive and flexible language, but that versatility can also lead to problematic designs if you’re not careful. Nikita Sobolev is the CTO of Wemake Services where he works on open source projects that encourage clean coding practices and maintainable architectures. In this episode he discusses his work on the DRY Python set of libraries and how they provide an accessible interface to functional programming patterns while maintaining an idiomatic Python interface. He also shares the story behind the wemake Python styleguide plugin for Flake8 and the benefits of strict linting rules to engender good development habits. This was a great conversation about useful practices to build software that will be easy and fun to work on.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
Your host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about his work with DRY Python and Wemake Services

Interview

Introductions
How did you get introduced to Python?
Can you start by sharing your overarching philosophies or design aesthetics for writing maintainable software?
What is your process for starting a new project, beginning at the design phase?
What are some of the challenges or shortcomings that you see in the "default" way that most developers write Python?
What is DRY Python is and how does it help in addressing those concerns?
- What was your motivation for creating these projects?
There are a number of different projects that are being built under the DRY Python umbrella. Can you list the ones that are currently active and outline how they fit together?
What are some of the initial challenges that newcomers to the DRY Python libraries encounter?
How do you approach the design of the API and developer experience to make these development approaches more accessible?
What have you seen in terms of real world impact on the maintainability and extensibility of projects that you have built on top of the DRY Python components?
In addition to DRY Python you are also involved with development of the wemake-python-styleguide. Can you describe that projects goal and how it got started?
- If you make the linting too restrictive then developers are likely to just ignore or disable it. What have you found to be the right balance to which rules will fail a build and which are just informational?
- Why do you push the responsibility for things like formatting onto the developer, rather than an autoformatter such as YAPF or Black?
What are some of the other supporting technologies that you rely on during your development workflow?
What are some of the elements that you think are missing in the common toolbox for Python developers?
- What tools are we lacking entirely?
What are the cases where DRY Python is the wrong choice?
What are your goals and plans for the future of DRY Python and the various Wemake libraries?

Keep In Touch

Blog
sobolevn on GitHub

Picks

Tobias
- The Map To Everywhere
Nikita
- Russian Python Week

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Switch to the Fountain App

Open in Fountain