QuackBerry API Framework

A containerized modern, scalable, and asynchronous API framework with GraphQL support, leveraging Docker, FastAPI, Strawberry, Pydantic, DuckDB, Starlette, and Uvicorn.

Architecture Diagram

Features

• Containerized application using Docker for ease of deployment and scalability
• GraphQL support using Strawberry
• REST API endpoints using FastAPI
• Type definitions and validation using Pydantic
• Data access and querying using DuckDB
• Asynchronous functionality with Uvicorn
• CORS middleware for handling cross-origin requests
• Starlette for rate limiting middleware for protecting against excessive requests
• Logging utility for application logging

Project Structure

Project Directory: /quackberry
- requirements.txt
- Dockerfile
- .env
- main.py
+ src/
|  + middleware/
|  |  - cors_middleware.py
|  |  - __init__.py
|  |  - ratelimit.py
|  + utils/
|  |  - logging.py
|  |  - __init__.py
|  + graphql_api/
|  |  + types/
|  |  |  - example_type.py
|  |  |  - example_sql_type.py
|  |  + queries/
|  |  |  - example_query.py
|  |  |  - example_sql_query.py
|  |  - __init__.py
|  |  - graphql_schema.py
|  + api/
|  |  - __init__.py
|  |  - schemas.py
|  + data/
|  |  - dataaccess.py
|  |  - __init__.py

Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/quackberry.git
   

2. Install the dependencies:

   pip install -r requirements.txt
   

3. Configure the environment variables in the .env file.

   SSL_KEY=/path/to/your/ssl_key.pem
   SSL_CERT=/path/to/your/ssl_cert.pem
   DELTA_TABLE_PATHS=/path/to/delta_table
   

4. Run the application:

   python main.py
   

   The application will be accessible at http://localhost:8000, and the GraphQL endpoint will be available at http://localhost:8000/graphql.

REST API Endpoints

• /example: Retrieve example data with pagination and filtering support.
  • Query Parameters:
    • start (optional): The starting index of the data (default: 0).
    • limit (optional): The maximum number of items to retrieve (default: 100, max: 1000).
    • filters (optional): Field-value pairs for filtering the data (format: field1=value1,field2=value2).

GraphQL Queries

• exampleQuery: Retrieve example data with filtering support.
  • Arguments:
    • start (optional): The starting index of the data (default: 0).
    • limit (optional): The maximum number of items to retrieve (default: 100).
    • filters (optional): Field-value pairs for filtering the data (format: field1=value1,field2=value2).

Customization

• Update the Pydantic schemas in src/api/schemas.py to define your API request/response models.
• Modify the data access functions in src/data/dataaccess.py to interact with your database or data source.
• Define your GraphQL types in src/graphql_api/types/ and implement the corresponding queries in src/graphql_api/queries/.
• Customize the Starlette middleware in src/middleware/ to add additional functionality or modify existing behaviors.

Deployment

The application is containerized using Docker for easy deployment and scalability. Update the Dockerfile as needed and build the Docker image:

docker build -t duckberry .

Run the Docker container:

docker run -p 8000:8000 duckberry

The application can be deployed to various platforms like Kubernetes, Azure App Services, or AWS App Runner for scalability and managed infrastructure.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.