API¶

class flask_kadabra.Kadabra(app=None, config=None)¶

This object holds ties the Flask application object to the Kadabra library. Each app object gets its own Kadabra instance, which it uses to generate a MetricsCollector for each request.

Parameters:	app (Flask) – The Flask application object to initialize. config (dict) – Dictionary of configuration to use for the `Kadabra` client API. For information on the acceptable values see Configuration.

init_app(app, config=None)¶

Configure the application to use Kadabra. Requests will have access to a MetricsCollector via the metrics attribute of Flask’s g object. You can record metrics anywhere in the context of a request like so:

...
g.metrics.add_count("userSignup", 1)
...

The metrics object will be closed and sent at the end of the request if any view that handles the request has been annotated with record_metrics.

flask_kadabra.record_metrics(func)¶

Views that are annotated with this decorator will cause any request they handle to send all metrics collected via the Kadabra client API. For example:

@api.route('/')
@record_metrics
def index():
    return 'Hello, world!'

Parameters:	func (function) – The view function to decorate.