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Python Alpine Docker development workflow

The Python Alpine Docker development workflow utilizes the official Python images based on Alpine Linux to create lightweight and efficient containerized development environments^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

This approach separates the environment setup into a "development" target and an optimized "runtime" target for production deployment^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

container Setup

The core of the workflow is the Dockerfile, which defines the base image and the working directory^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md]. A common pattern uses python:3.9.6-alpine3.13 as the base image and sets the working directory to /work^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

To support different lifecycle stages, multi-stage builds are employed^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md]: * Development Stage: Creates a stage named dev which includes all necessary build tools and libraries for coding and debugging^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md]. * Runtime Stage: Creates a stage named runtime that copies only the necessary application artifacts (e.g., from ./src/ to /app) and sets the entry point, resulting in a smaller final image^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

Interactive Development

For writing and testing code, the workflow emphasizes running an interactive shell within the container^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

The container is launched using the docker run command with specific flags to ensure an interactive session and file synchronization^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

Command Syntax: 

docker run -it -v ${PWD}:/work -w /work customer-app sh
* -it: Allocates a pseudo-TTY and keeps stdin open, allowing for an interactive shell session^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md]. * -v ${PWD}:/work: Mounts the current local directory (${PWD}) into the container's /work directory, enabling live code editing without rebuilding the image^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md]. * -w /work: Sets the working directory inside the container to /work^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md]. * sh: Executes the shell command (Alpine Linux uses sh by default rather than bash)^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

Once inside the container, developers can execute Python scripts directly, such as running python --version to check the environment^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

Data Handling (JSON)

In this workflow, Python scripts typically perform operations on structured data like JSON^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

The workflow often involves: 1. Serialization: Converting Python dictionaries into JSON strings using json.dumps() to write data to files^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md]. 2. Deserialization: Reading data from files and parsing it back into dictionaries using json.loads()^[400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md].

  • [[JSON]]
  • [[Python]]

Sources

  • 400-devops__09-Scripting-Language__python__introduction__part-3.json__README.md