GraphQL Python MVP (Graphene + AWS)

This is a Minimal Viable Product (MVP) project developed to demonstrate integrating GraphQL with Python in an AWS Serverless environment. The main goal is to practice implementing "Code-First" schemas using reference technologies and to deploy infrastructure as code (IaC).

🏗️ Project Architecture

    graph TD;
        A[Client / Notebook] -- HTTP POST --> B[AWS Lambda Function URL];
        B --> C[FastAPI + Mangum];
        C --> D[Graphene Engine];
        D -- Boto3 Resolver --> E[(Amazon DynamoDB)];
        
        subgraph Local Development;
        F[Uvicorn] --> D;
    end;

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🚀 Key Features

• GraphQL Engine: Implemented with Graphene following a Code-First approach.

• Web Framework: FastAPI for efficient asynchronous execution.

• Infrastructure: Fully deployed using Terraform.

• Cloud Native: Runs on AWS Lambda with persistence in DynamoDB.

• Dual Mode: Supports local execution on Linux (mocking) and real cloud deployment.

🛠️ Technologies Used

• Language / Core: Python 3.11 + Graphene API, FastAPI, Ariadne (ASGI)
• Infrastructure: Terraform (AWS)
• Services: Lambda, DynamoDB, IAM
• Tools: Boto3, Mangum, Uvicorn

📦 Local Installation & Run

Clone and configure the environment:

git clone <tu-repo-url>
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Run the development server:

uvicorn app:app --host 0.0.0.0 --port 8000

Open http://localhost:8000 in your browser to access the GraphQL Playground.

☁️ Deployment to AWS

To deploy this project to your own AWS account:

Prepare the package:

pip install --target ./package -r requirements.txt
cd package && zip -r ../function.zip . && cd ..
zip function.zip app.py

Terraform:

terraform init
terraform apply

📈 Next Steps (Roadmap)

This project is a foundation I plan to extend with the following features:

• Implement relationships (User -> Items).

• Add authentication via AWS Cognito or JWT.

• Configure CI/CD with GitHub Actions for automated deployments.

• Advanced error handling and custom validations in Graphene.