Docker Best Practices For Python In Production – Episode 222
Docker is a useful technology for packaging and deploying software to production environments, but it also introduces a different set of complexities that need to be understood. In this episode Itamar Turner-Trauring shares best practices for running Python workloads in production using Docker. He also explains some of the security implications to be aware of and digs into ways that you can optimize your build process to cut down on wasted developer time. If you are using Docker, thinking about using it, or just heard of it recently then it is worth your time to listen and learn about some of the cases you might not have considered.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? Check out Linode at linode.com/podcastinit or use the code podcastinit2019 and get a $20 credit to try out their fast and reliable Linux virtual servers. They’ve got lightning fast networking and SSD servers with plenty of power and storage to run whatever you want to experiment on.
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- Your host as usual is Tobias Macey and today I’m interviewing Itamar Turner-Trauring about what you need to know about running Python workloads in Docker
- How did you get introduced to Python?
- For anyone who is unfamiliar with it, can you describe what Docker is and the benefits that it can provide?
- What was your motivation for dedicating so much time and energy to the specific area of using Docker for Python production usage?
- What are some of the common issues that developers and operations engineers run into when dealing with Docker and its build system?
- What are some of the issues that are specific to Python that you have run into when using Docker?
- How does the ecosystem for Python in containers compare to other languages that you are familiar with?
- What are some of the security issues that engineers are likely to run into when using some of the advice and pre-existing containers that are publicly available?
- One of the issues that you call out is the speed of container builds. What are some of the contributing factors that lead to such slow packaging times?
- Can you talk through some of the aspects of multi-layer packages and useful ways to take proper advantage of them?
- There have been some recent projects that attempt to work around the shortcomings of the Dockerfile itself. What are your thoughts on that overall effort and any specific tools that you have experimented with?
- When is Docker the wrong choice for a production environment?
- What are some useful alternatives to Docker, for Python specifically and for software distribution in general that you have had good luck with?