brontosaurus - streamlined JSON APIs

Write small and fast API servers using:

• Sanic - for a fast async http server
• JSON Schema - for request and response validation and documentation
• JSON RPC 2.0 - for a simple surrounding format
• Python - for all your easy to read glue code

Some benefits:

• Automatic request validation
• Autogenerated API documentation in markdown
• Removes various RPC setup boilerplate
• Handles bulk requests asynchronously with no extra code
• Sets default values in requests and responses automatically
• Automatically validates responses during development/test mode
• Handles binary file uploads and responses

It is opinionated but customizable. The APIs are not resource-based, but instead use a method-call style from JSON RPC 2.0 specification (for better or worse).

View a quick code example along with its auto-generated documentation.

API and Usage

API(title, description)

Create a new instance of the API object, which can be used to register methods and run the server.

from brontosaurus import API

api = API("Example API", "This is an example API server.")

Optional keyword arguments:

• doc_path: str - path (relative to the directory where the server runs) of the generated documentation. Ignored if not in development mode.

@api.method(name, summary)

Register a method name and a short description. Used as a decorator around a function that handles the method.

The function receives the method parameters and the request headers as dictionaries.

@api.method('no_op', 'Does nothing')
def no_op(params, headers):
    pass

@api.params(json_schema: dict)

Set the JSON Schema for the parameters for a method. Used as a decorator around a method handler function. Documentation for your parameters can go inside the title and description properties in your schema.

# JSON schema for some message
message = {
    'type': 'object',
    'required': ['message'],
    'properties': {
        'message': {
            'description': 'Any message that you want',
            'type': 'string',
        }
    }
}

@api.method('log', 'Logs something in the backend')
@api.params(message)
def no_op(params, headers):
    logger.info(params)

See the JSON Schema guide for detailed information on how to write these schemas.

If you run the server (see below), you can now make the following request:

$ curl -d '{"method": "log", "params": {"message": "hello world"}}'

@api.result(json_schema: dict)

Set the JSON Schema for the result for a method, useful for documentation and tests. Used as a decorator around a method handler function.

# JSON schema for the params and the result
echo_message = {
    'type': 'object',
    'required': ['message'],
    'properties': {
        'message': {
            'type': 'string'
        }
    }
}

@api.method('echo', 'Repeat a message back to you')
@api.params(echo_message)
@api.result(echo_message)
def echo(params, headers):
    return {'message': params['message']}

Now you can make the following request:

$ curl -d '{"method": "echo", "params": {"message": "hello world"}}'
> {"jsonrpc": "2.0", "id": null, "result": {"message": "hello world"}}

@api.deprecated(msg: str)

Decorator for marking a method as deprecated. Pass in a string message that describes the reason for the deprecation and other methods the user can use instead. The method will show up as deprecated with the deprecation message in the auto-generated docs.

@api.method('hello_world')
@api.deprecated('From now we do not need to say hello anymore.')
def hello_world(params, headers):
    return 'hello world!'

@api.subpath(path, title, description, **options)

You can create multiple nested RPC APIs within a single server by using the subpath method. Each sub-path is a standalone, discrete JSON RPC API.

See a minimal example in tests/examples/paths.py.

subpath1 = api.subpath(
    path='/subpath1",
    title="My Sub-API",
    description="This is an extended description"
)

@subpath1.method('hello')
def subpath1_hello(params, header):
    return 'hello from subpath1'

Additional optional keyword arguments:

• doc_path: str - path (relative to the directory where the server runs) of the generated documentation. Ignored if not in development mode.

api.register(type_name: str, json_schema: dict)

Register a JSON schema to be displayed in the API documentation. It does not affect the API, and only returns a plain dict of the schema.

Registered schemas must have an "$id" field, which will be used as its type name in the documentation. Typically these types are prepended with the hash symbol: "#name".

login_schema = api.register('login', {
    '$id': '#login',
    'title': 'Login data',
    'type': 'object',
    'properties': {
        'password': {
            'type': 'string',
            'minLength': 7,
        },
        'email': {
            'type': 'string',
            'format': 'idn-email'
        }
    }
})

Now, when you generate the documentation by running the server, the "login" schema will appear in the documentation. Anywhere that the schema is used in a method, a link will be generated to the documentation for this type.

api.run(**kwargs)

Run the server.

In development mode, the documentation file will be generated. Also, the method results will be validated against their JSON Schemas (but not in production).

Valid keyword arguments:

• host: str - hostname to use (defaults to '0.0.0.0')
• port: int - port to use for the server (defaults to 8080)
• development: bool - whether we are in development mode (defaults to True)
• cors: bool - whether to fully enable cross origin requests (defaults to False)
• workers: int - how many async server workers to run (defaults to 2)

logger

brontosaurus comes with a logger that you can import:

from brontosaurus import logger

logger.info('Hello world')

Log messages sent to this logger will show up both in stdout and in your app.log rotating log file.

Development mode

Development mode has the following effects:

• Markdown documentation is generated for your API(s) whenever the server is started
• Response JSON structures are validated against their schemas
• Logging level is set to DEBUG

Development

Run the tests

Install poetry with pip install poetry. Install dependencies with poetry install.

Run the tests

make test

Contribution

Open an issue or PR