FastQL - FastAPI GraphQL Playground 🔧

Generate a FullStack playground using FastAPI and GraphQL and Ariadne ⚡

This Repository is based on this Article Getting started with GraphQL in Python with FastAPI and Ariadne, Read Article to know how to use it.

Overview 📌

• FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.
• GraphQL used to create a schema to describe all the possible data that clients can query through that service. A GraphQL schema is made up of object types, which define which kind of object you can request and what fields it has.
• Ariadne is a Python library for implementing GraphQL servers using schema-first approach.

Features

• Full Docker integration (Docker based).
• Docker Compose integration and optimization for local development.
• Production ready Python web server using Uvicorn and Gunicorn.
• GraphQL playground based on Graphene and Ariadne.
• Docker Compose integration and optimization for local development.
• Production ready Python web server using Uvicorn.
• Secure password hashing by default.
• JWT token authentication.
• SQLAlchemy database integration using PostgreSQL.
• Alembic migrations for database schema.
• rabbitMQ (asynchronous) message broker.
• API tests based on Pytest, integrated with Docker, so you can test the full API interaction, independent on the database.

Getting Started

Project setup

# clone the repo
$ git clone https://github.com/obytes/fastql.git

# move to the project folder
$ cd fastql

Running the Docker Container

• We have the Dockerfile created in above section. Now, we will use the Dockerfile to create the image of the FastAPI app and then start the FastAPI app container.
• Using a preconfigured Makefile tor run the Docker Compose:

# Pull the latest image
$ make pull

# Build the image
$ make build

# Run the container
$ make start

Testing

While i use HTTPX an HTTP client for Python 3, to test the API, most of the tests are using a live log thats why need before to run a server using uvicorn and migrate the database, then you will have the ability to run the tests. To have a clean environment, recommended to use Docker for that, when you start the containers try to open the application container and then run pytest to test the API.

License

This project is licensed under the terms of the MIT license.