Writing code is only one piece of creating good software. Code reviews are an important step in the process of building applications that are maintainable and sustainable. In this episode On Freund shares his thoughts on the myriad purposes that code reviews serve, as well as exploring some of the patterns and anti-patterns that grow up around a seemingly simple process.
Do you want to try out some of the tools and applications that you heard about on Podcast.__init__? Do you have a side project that you want to share with the world? With Linode’s managed Kubernetes platform it’s now even easier to get started with the latest in cloud technologies. With the combined power of the leading container orchestrator and the speed and reliability of Linode’s object storage, node balancers, block storage, and dedicated CPU or GPU instances, you’ve got everything you need to scale up. Go to pythonpodcast.com/linode today and get a $100 credit to launch a new cluster, run a server, upload some data, or… And don’t forget to thank them for being a long time supporter of Podcast.__init__!
- Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
- 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 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. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Your host as usual is Tobias Macey and today I’m interviewing On Freund about the intricacies and importance of code reviews
- How did you get introduced to Python?
- Can you start by giving us your description of what a code review is?
- What is the purpose of the code review?
- At face value a code review appears to be a simple task. What are some of the subtleties that become evident with time and experience?
- What are some of the ways that code reviews can go wrong?
- What are some common anti-patterns that get applied to code reviews?
- What are the elements of code review that are useful to automate?
- What are some of the risks/bad habits that can result from overdoing automated checks/fixes or over-reliance on those tools in code reviews?
- identifying who can/should do a review for a piece of code
- how to use code reviews as a teaching tool for new/junior engineers
- how to use code reviews for avoiding siloed experience/promoting cross-training
- PR templates for capturing relevant context
- What are the most interesting, innovative, or unexpected ways that you have seen code reviews used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while leading and supporting engineering teams?
- What are some resources that you recommend for anyone who wants to learn more about code review strategies and how to use them to scale their teams?
Keep In Touch
- Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.
- 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 email@example.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
- Code Review
- Home Assistant
- Trunk-based Development
- Git Flow
- Pair Programming
- Feature Flags
- KPI == Key Performance Indicator
- MIT Open Learning Engineering Handbook
- PEP Repository