Gain insights into clean architecture using Python. Delve into layered systems, design efficient applications, and integrate web components, error handling, and databases with test-driven development practices.

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Python Execution

Flask

Flask + Postgres

Python Test

Clean architecture is a software design methodology that separates the processes of an application into discrete steps instead of presenting them as “spaghetti code.” With the versatility of Python, software engineers can build performant, modular applications quickly using the Flask framework.

This course will help you build better applications using the principles of clean architecture. You’ll start with an introduction to data flow in clean systems and learn about the advantages of layered architectures. Next, you’ll learn system components and the connections between application layers. You’ll then design and build a clean system using Python by layering components in guided coding exercises. Finally, you’ll learn how to add functional components for the web, handle errors, and work with external databases using both Postgres and MongoDB. At each step, you’ll use test-driven development practices to ensure robust functionality.

Clean Architecture in Python

## Build a web application

Now that we’ve successfully containerized the tests, we can devise a production-ready setup of the whole application, running both a web server and a database in Docker containers.

To run a production-ready infrastructure, we need to put a WSGI server in front of the web framework and a web server in front of it. We’ll also need to run a database container that we’ll initialize only once.

The steps toward achieving a production-ready configuration aren’t complicated, and the final setup won’t be too different from what we already did for the tests. We need to follow these steps:

1. Create a JSON configuration with environment variables suitable for production.
2. Create a suitable configuration for Docker Compose, and configure the containers.
3. Add commands to `manage.py` that allow us to control the processes.


### Step 1: Create the JSON configuration file
We’ve created the `config/production.json` file, which is similar to the one we created for the tests.


# Build a web application

Now that we’ve successfully containerized the tests, we can devise a production-ready setup of the whole application, running both a web server and a database in Docker containers.

To run a production-ready infrastructure, we need to put a WSGI server in front of the web framework and a web server in front of it. We’ll also need to run a database container that we’ll initialize only once.

The steps toward achieving a production-ready configuration aren’t complicated, and the final setup won’t be too different from what we already did for the tests. We need to follow these steps:

1. Create a JSON configuration with environment variables suitable for production.
2. Create a suitable configuration for Docker Compose, and configure the containers.
3. Add commands to `manage.py` that allow us to control the processes.


## Step 1: Create the JSON configuration file
We’ve created the `config/production.json` file, which is similar to the one we created for the tests.


Let’s learn to configure our system for production in clean architecture.

Get Started

A Day in the Life of a Clean System

Components of Clean Architecture

A Simple Coding Example

Add a Web Application

Error Management

Integration with a Real External System Postgres

Integration with a Real External System MongoDB

Run a Production-ready System

Appendix

Build a Web Stack

Build a web application

Step 1: Create the JSON configuration file