Skip to main content

Fire up your API with this flamethrower

Project description

Flama

🔥 Fire up your API with this flamethrower.

CI Status Docs Status Coverage Package version PyPI - Python Version


Documentation: https://flama.perdy.io


Flama

Flama aims to bring a layer on top of Starlette to provide an easy to learn and fast to develop approach for building highly performant GraphQL and REST APIs. In the same way of Starlette is, Flama is a perfect option for developing asynchronous and production-ready services.

Among other characteristics it provides the following:

  • Generic classes for API resources that provides standard CRUD methods over SQLAlchemy tables.
  • Schema system based on Marshmallow that allows to declare the inputs and outputs of endpoints and provides a reliable way of validate data against those schemas.
  • Dependency Injection that ease the process of managing parameters needed in endpoints. Flama ASGI objects like Request, Response, Session and so on are defined as components and ready to be injected in your endpoints.
  • Components as the base of the plugin ecosystem, allowing you to create custom or use those already defined in your endpoints, injected as parameters.
  • Auto generated API schema using OpenAPI standard. It uses the schema system of your endpoints to extract all the necessary information to generate your API Schema.
  • Auto generated docs providing a Swagger UI or ReDoc endpoint.
  • Pagination automatically handled using multiple methods such as limit and offset, page numbers...

Requirements

Installation

$ pip install flama

Example

from marshmallow import Schema, fields, validate
from flama.applications import Flama
import uvicorn

# Data Schema
class Puppy(Schema):
    id = fields.Integer()
    name = fields.String()
    age = fields.Integer(validate=validate.Range(min=0))


# Database
puppies = [
    {"id": 1, "name": "Canna", "age": 6},
    {"id": 2, "name": "Sandy", "age": 12},
]


# Application
app = Flama(
    components=[],      # Without custom components
    title="Foo",        # API title
    version="0.1",      # API version
    description="Bar",  # API description
    schema="/schema/",  # Path to expose OpenAPI schema
    docs="/docs/",      # Path to expose Swagger UI docs
    redoc="/redoc/",    # Path to expose ReDoc docs
)


# Views
@app.route("/", methods=["GET"])
def list_puppies(name: str = None) -> Puppy(many=True):
    """
    description:
        List the puppies collection. There is an optional query parameter that 
        specifies a name for filtering the collection based on it.
    responses:
        200:
            description: List puppies.
    """
    return [puppy for puppy in puppies if name in (puppy["name"], None)]
    

@app.route("/", methods=["POST"])
def create_puppy(puppy: Puppy) -> Puppy:
    """
    description:
        Create a new puppy using data validated from request body and add it 
        to the collection.
    responses:
        200:
            description: Puppy created successfully.
    """
    puppies.append(puppy)
    
    return puppy


if __name__ == '__main__':
    uvicorn.run(app, host='0.0.0.0', port=8000)

Dependencies

Following Starlette philosophy Flama reduce the number of hard dependencies to those that are used as the core:

It does not have any more hard dependencies, but some of them are necessaries to use some features:

  • pyyaml - Required for API Schema and Docs auto generation.
  • apispec - Required for API Schema and Docs auto generation.
  • python-forge - Required for pagination.
  • sqlalchemy - Required for Generic API resources.
  • databases - Required for Generic API resources.

You can install all of these with pip3 install flama[full].

Credits

That library is heavily inspired by APIStar server in an attempt to bring a good amount of it essence to work with Starlette as the ASGI framework and Marshmallow as the schema system.

Contributing

This project is absolutely open to contributions so if you have a nice idea, create an issue to let the community discuss it.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

flama-0.13.3.tar.gz (39.4 kB view details)

Uploaded Source

Built Distribution

flama-0.13.3-py3-none-any.whl (46.3 kB view details)

Uploaded Python 3

File details

Details for the file flama-0.13.3.tar.gz.

File metadata

  • Download URL: flama-0.13.3.tar.gz
  • Upload date:
  • Size: 39.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.0.5 CPython/3.8.2 Linux/5.0.0-1032-azure

File hashes

Hashes for flama-0.13.3.tar.gz
Algorithm Hash digest
SHA256 e139b27cc2d525b4881c11cf628cda0528dfb43cbcc1d5a872253b04e2a973a9
MD5 ee314aeebd75a2ffb748279918ce439b
BLAKE2b-256 e06f67aa27e90f04073496093963b0d26748aa39b7f080767d7c30e2c369a636

See more details on using hashes here.

File details

Details for the file flama-0.13.3-py3-none-any.whl.

File metadata

  • Download URL: flama-0.13.3-py3-none-any.whl
  • Upload date:
  • Size: 46.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.0.5 CPython/3.8.2 Linux/5.0.0-1032-azure

File hashes

Hashes for flama-0.13.3-py3-none-any.whl
Algorithm Hash digest
SHA256 77f2f5ee72c22f27a88baa5457cca8517a4cfb5ae66589596564209d3661b625
MD5 b82e81d45bce044a4e614839daa386d5
BLAKE2b-256 e3f29be68a537bdbfe2cb39b556ba76d6fa72fcd007f93bb013f5ac78bce9b82

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page