ActiveUserTokensEvent

pydantic model gafaelfawr.events.ActiveUserTokensEvent

Current count of the number of active user tokens.

Notes

This is really a proper metric that is measured periodically, not an event. For now, Gafaelfawr uses the event system to log this metric since that’s the system we have in place. If we later have a proper metrics system for storing measurements, this should move to that.

Parameters:

data (Any)

Show JSON schema
{
   "title": "ActiveUserTokensEvent",
   "description": "Current count of the number of active ``user`` tokens.\n\nNotes\n-----\nThis is really a proper metric that is measured periodically, not an\nevent. For now, Gafaelfawr uses the event system to log this metric since\nthat's the system we have in place. If we later have a proper metrics\nsystem for storing measurements, this should move to that.",
   "type": "object",
   "properties": {
      "count": {
         "description": "Number of unexpired user tokens",
         "title": "Active user tokens",
         "type": "integer"
      }
   },
   "required": [
      "count"
   ]
}

Fields:
field count: int [Required]

Number of unexpired user tokens

asdict()

Returns this model in dictionary form. This method differs from pydantic’s dict by converting all values to their Avro representation. It also doesn’t provide the exclude, include, by_alias, etc. parameters that dict provides.

Return type:

Dict[str, Any]

classmethod fake(**data)

Creates a fake instance of the model.

Attributes:

data: Dict[str, Any] represent the user values to use in the instance

Parameters:

data (Any)

Return type:

TypeVar(CT, bound= AvroBaseModel)

classmethod generate_dataclass()
Return type:

Type[TypeVar(CT, bound= AvroBaseModel)]

classmethod json_schema(*args, **kwargs)
Parameters:
Return type:

str

serialize(serialization_type='avro')

Overrides the base AvroModel’s serialize method to inject this class’s standardization factory method

Parameters:

serialization_type (Literal['avro', 'avro-json'], default: 'avro')

Return type:

bytes

to_dict()
Return type:

Dict[str, Any]

validate_avro()

Validate that instance matches the avro schema

Return type:

bool

classmethod validate_structure()

Do runtime validation of fields.

Make sure all of the fields are compatible with the backing datastore (InfluxDB at the moment).

Return type:

None