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

The `filters` parameter that we want to add to the use case would allow the caller to add conditions with which the user would be able to narrow the results of the model list operation. The caller would do this by using a notation like `&lt;attribute&gt;__&lt;operator&gt;`. For example, specifying `filters={'price__lt': 100}` should return all the results with a `price` less than `100`.

Since the `Room` model has many attributes, the number of possible filters is very high. For simplicity’s sake, we’ll consider the following cases:
- The attribute code supports only `__eq`, which finds the room with the specified code if it exists.
- The attribute price supports `__eq`, `__lt`, and `__gt`.
- No other attributes can be used in the filters.


The core idea here is that requests are customized for use cases, so they can contain the logic that validates the arguments used to instantiate them. The request is valid or invalid before it reaches the use case. So, it isn’t the responsibility of the latter to check if the input values have proper values or a proper format.
This also means that building a request may result in two different objects: one valid and one invalid. For this reason, we’ll split the existing `RoomListRequest` class into `RoomListValidRequest` and `RoomListInvalidRequest`. This creates a factory function that returns the proper object.

# Update test case for no filters
The first thing to do is to change the existing tests so they may use the factory. This is done in the code below:

Let’s learn how to validate the user’s request using filters.

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

Request Validation