SpawnSuccessEvent

pydantic model controller.events.SpawnSuccessEvent

A lab spawn succeeded.

Parameters:

data (Any)

Show JSON schema

{
   "title": "SpawnSuccessEvent",
   "description": "A lab spawn succeeded.",
   "type": "object",
   "properties": {
      "image": {
         "description": "Docker reference for the lab image",
         "title": "Lab image",
         "type": "string"
      },
      "cpu_limit": {
         "description": "Kubernetes pod limit of CPU equivalents",
         "title": "Lab CPU limit",
         "type": "number"
      },
      "memory_limit": {
         "description": "Kubernetes pod limit of memory in bytes",
         "title": "Lab memory limit",
         "type": "integer"
      },
      "username": {
         "description": "User who spawned a lab",
         "title": "Username",
         "type": "string"
      },
      "elapsed": {
         "description": "How long the spawn took before Kubernetes resources were ready. This does not include the startup time of the lab itself.",
         "format": "duration",
         "title": "Duration of spawn",
         "type": "string"
      }
   },
   "required": [
      "image",
      "cpu_limit",
      "memory_limit",
      "username",
      "elapsed"
   ]
}

Fields:

• cpu_limit ()

• elapsed (timedelta)

• image ()

• memory_limit ()

• username (str)

field cpu_limit: float [Required]

Kubernetes pod limit of CPU equivalents

field elapsed: timedelta [Required]

How long the spawn took before Kubernetes resources were ready. This does not include the startup time of the lab itself.

field image: str [Required]

Docker reference for the lab image

field memory_limit: int [Required]

Kubernetes pod limit of memory in bytes

field username: str [Required]

User who spawned a lab

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:

AvroBaseModel

classmethod json_schema(*args, **kwargs)

Parameters:

• args (Any)

• kwargs (Any)

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