Models and Database Document Configuration

Oct 13, 2025 by recoveryshake.com 26 views

Challenge: Building a Robust CRUD Application with FastAPI, Pydantic, and Beanie

Overview

The objective of this challenge is to complete the logic for a CRUD app that manages products. You will work within the app directory to implement features and logic that include:

Validating domain-specific rules (such as ensuring product prices end with .99).
Implementing robust error handling at both the business logic and API layers.
Supporting dynamic configuration management through a CLI interface.

This hands-on experience is designed to sharpen your backend development skills and improve your proficiency with key Python frameworks and libraries.

Project Structure

The challenge repository is organized into several key files within the app directory, including:

actions.py – Contains the business logic functions (e.g., create_product).
schemas.py – Houses the Pydantic schemas for request validation.
models.py – Defines the data models used by the application.
documents.py – Manages the database document configuration (using Beanie).
exceptions.py – Declares custom exceptions for robust error handling.
dependencies.py – Provides dependency injection for API endpoints.
api.py – Implements the FastAPI routes.

Although the file contents are pre-implemented, your main tasks are to refine and complete specific logic inside these files.

Core Requirements

Models and Database Document Configuration

Objective:
Implement a Nested Model Structure:

Create a generic Category model alongside the Product model. Both models should include name and description fields.

Challenge: Implement the price_ends_with_99 validator to ensure that every product price ends with .99.

Examples: Valid prices include 799.99 and 1299.99.
Purpose: This validation enforces a standard monetary format for product pricing.

Robust Error Handling in Product Creation

Objective: Enhance the create_product action to ensure robust error handling:

Wrap the action using a helper function (run_action) that is responsible for catching and re-raising exceptions.
Challenge: Explicitly raise an InternalServerError with a clear and informative error message if the product insertion fails or if the result is invalid.

Coordinating API Endpoint Error Handling

Objective: Ensure the API endpoint that handles product creation:

Catches Exceptions: Capture any exceptions raised by internal actions, such as those thrown by custom exceptions (e.g., APIException).
Converts to HTTP Exceptions: Translate these exceptions into proper HTTP responses with suitable status codes and detailed error messages.
Outcome: Clients interacting with the API receive meaningful feedback if product creation fails, improving the user experience and debuggability.

Testing Strategy

Unit Testing: The challenge repository includes unit tests to validate your implementation.
Optional: You are encouraged to write your own test suite if needed. This will help ensure that the logic meets the specified requirements and handles edge cases appropriately.

Useful Resources

These resources will be invaluable as you navigate through the implementation details and ensure adherence to best practices.

Additional Tips

Commit Conventions: Follow proper Git commit conventions throughout your development process. Small, focused commits can help maintain a clean history and ease debugging.
Dynamic Configuration: Ensure your application supports flexible configuration management via the CLI. This can be useful for deploying the app in different environments.
Code Organization: Work primarily within the app directory. Use the provided file structure to guide your implementation and maintain consistency.

Conclusion

This challenge offers a comprehensive exercise in building a maintainable, user-friendly product management service using modern Python frameworks. By focusing on domain-specific validations, robust error handling, and effective API design, you'll gain valuable experience that translates to real-world backend development tasks.

Implementation Steps

Model and Database Document Configuration

Create a generic Category model with name and description fields.
Update the Product model to include:
- name and description fields.
- A price field constrained to be a float greater than 0 and less than 100,000.
- Implement the price_ends_with_99 validator to ensure the price always ends with .99 (e.g., 799.99, 1299.99).

CRUD Actions (Business Logic in `actions.py`)

Create Product
- Validate input data using Pydantic schemas.
- Insert the new product into the database.
- Enhance error handling: if insertion fails or results are invalid, raise an InternalServerError with a clear error message.
Read Product
- Implement logic to retrieve product details by ID.
Update Product
- Implement logic to update product details.
Delete Product
- Implement logic to remove a product by ID.

API Endpoints (in `api.py`)

Product Creation Endpoint
- Accept and validate request payload using Pydantic schemas.
- Call the create_product action.
- Catch exceptions (including custom APIException) and translate them into proper HTTP responses with suitable status codes and error messages.
Other CRUD Endpoints
- Create endpoints for reading, updating, and deleting products.
- Ensure each endpoint has proper error handling and returns meaningful responses.

Error Handling and Exception Management

Wrap critical business logic in helper functions (e.g., run_action) to catch exceptions.
Explicitly handle errors in CRUD actions by raising InternalServerError (or other custom exceptions) with informative messages.
Ensure the API layer properly converts these exceptions into HTTP exceptions.

Testing and Validation

Run the provided unit tests to ensure the implementation meets the requirements.
Optionally write additional tests to cover edge cases in CRUD operations, validation, and error handling.

Dynamic Configuration Management

Support flexible configuration management through CLI options for different deployment environments.

Code Quality and Git Commit Conventions

Follow proper Git commit conventions by making small, focused commits.
Maintain clear code structure and documentation within the app directory.
Challenge: Building a Robust CRUD Application with FastAPI, Pydantic, and Beanie - Q&A

Q: What is the main objective of this challenge?

A: The main objective of this challenge is to complete the logic for a CRUD app that manages products, focusing on domain-specific validations, robust error handling, and effective API design.

Q: What are the key Python libraries used in this challenge?

A: The key Python libraries used in this challenge are:

FastAPI: A modern, fast (high-performance), web framework for building APIs with Python 3.7+.
Pydantic: A library for building robust, fast, and scalable data models and APIs.
Beanie: A Python Object-Document Mapping (ODM) library that provides a simple and efficient way to interact with databases.

Q: What is the project structure of this challenge?

A: The project structure of this challenge is organized into several key files within the app directory, including:

actions.py: Contains the business logic functions (e.g., create_product).
schemas.py: Houses the Pydantic schemas for request validation.
models.py: Defines the data models used by the application.
documents.py: Manages the database document configuration (using Beanie).
exceptions.py: Declares custom exceptions for robust error handling.
dependencies.py: Provides dependency injection for API endpoints.
api.py: Implements the FastAPI routes.

Q: What are the core requirements of this challenge?

A: The core requirements of this challenge are:

Models and Database Document Configuration: Implement a Nested Model Structure and the price_ends_with_99 validator.
Robust Error Handling in Product Creation: Enhance the create_product action to ensure robust error handling.
Coordinating API Endpoint Error Handling: Ensure the API endpoint that handles product creation catches exceptions and translates them into proper HTTP responses.

Q: What is the testing strategy for this challenge?

A: The testing strategy for this challenge includes:

Unit Testing: Run the provided unit tests to ensure the implementation meets the requirements.
Optional: Write additional tests to cover edge cases in CRUD operations, validation, and error handling.

Q: What are the useful resources for this challenge?

A: The useful resources for this challenge are:

FastAPI Documentation: A comprehensive guide to building APIs with FastAPI.
Pydantic Documentation: A guide to building robust, fast, and scalable data models and APIs with Pydantic.
Beanie ODM Documentation: A guide to using Beanie for Object-Document Mapping.
UV Documentation: A guide to using UV for asynchronous programming.

Q: What are the additional tips for this challenge?

A: The additional tips for this challenge are:

Commit Conventions: Follow proper Git commit conventions throughout your development process.
Dynamic Configuration: Ensure your application supports flexible configuration management via the CLI.
Code Organization: Work primarily within the app directory and use the provided file structure to guide your implementation.

Q: What is the conclusion of this challenge?

A: This challenge offers a comprehensive exercise in building a maintainable, user-friendly product management service using modern Python frameworks. By focusing on domain-specific validations, robust error handling, and effective API design, you'll gain valuable experience that translates to real-world backend development tasks.

Q: What are the implementation steps for this challenge?

A: The implementation steps for this challenge include:

Model and Database Document Configuration: Create a generic Category model and update the Product model to include the price_ends_with_99 validator.
CRUD Actions (Business Logic in actions.py): Implement the create_product, read_product, update_product, and delete_product actions.
API Endpoints (in api.py): Implement the product creation endpoint and other CRUD endpoints.
Error Handling and Exception Management: Wrap critical business logic in helper functions to catch exceptions and translate them into proper HTTP responses.
Testing and Validation: Run the provided unit tests and write additional tests to cover edge cases.
Dynamic Configuration Management: Support flexible configuration management through CLI options.
Code Quality and Git Commit Conventions: Follow proper Git commit conventions and maintain clear code structure and documentation.