duckQL 🐥

duckQL is simple JSON-based notation for some SQL dialects (PostgreSQL, MariaDB, MySQL) based on pydantic library.

Example

Here is a simple example of library usage. For more examples please visit project page.

from duckql import Query, Property, Comparision, Constant


my_query = Query(
    entity='users',
    properties=[
        Property(name='users.name'),
        Property(name='users.surname')
    ],
    conditions=Comparision(
        properties=[
            Property(name='users.age'),
            Constant(value=15)
        ],
        operation=Comparision.Operation.GREATER_EQUAL
    )
)

{
  "obj": "structures.Query",
  "entity": "users",
  "properties": [
    {
      "obj": "properties.Property",
      "name": "users.name"
    },
    {
      "obj": "properties.Property",
      "name": "users.surname"
    }
  ],
  "conditions": {
    "obj": "structures.Comparision",
    "properties": [
      {
        "obj": "properties.Property",
        "name": "users.age"
      },
      {
        "obj": "properties.Constant",
        "value": "15"
      }
    ],
    "operation": "gte"
  }
}

SELECT users.name, users.surname FROM users WHERE (users.age >= 15);

Development

Project is using poetry and documentation is generated by MkDocs. If you want to generate documentation you need to follow these simple steps inside of your virtual environment:

1. poetry install
2. mkdocs build

Inside of your virtual environment, you can also use mkdocs serve to create temporary auto-reload http server with live docs.

We use pytest for unit tests and flake8 for code-style validation. You can execute tests inside of pipenv shell using these commands:

• pytest -v .: executes unit tests
• pytest -v --flake8 .: flake8 code-style tests

License

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Made with ☕️ and ❤️ by Jakub Dubec & BACKBONE s.r.o.