Containerized Python Development – Part 3
This is the last part in the series of blog posts showing how to set up and optimize a containerized Python development environment. The first part covered how to containerize a Python service and the best development practices for it. The second part showed how to easily set up different components that our Python application needs and how to easily manage the lifecycle of the overall project with Docker Compose.
In this final part, we review the development cycle of the project and discuss in more details how to apply code updates and debug failures of the containerized Python services. The goal is to analyze how to speed up these recurrent phases of the development process such that we get a similar experience to the local development one.
Applying Code Updates
In general, our containerized development cycle consists of writing/updating code, building, running and debugging it.
For the building and running phase, as most of the time we actually have to wait, we want these phases to go pretty quick such that we focus on coding and debugging.
We now analyze how to optimize the build phase during development. The build phase corresponds to image build time when we change Continue reading












