AdhocTriggerJobRequest

class openapi_client.models.adhoc_trigger_job_request.AdhocTriggerJobRequest(**data)

Bases: BaseModel

number_of_workers: Optional[StrictInt]

timeout: Optional[StrictInt]

file_processing_mode: Optional[StrictStr]

file_ids: Optional[List[StrictStr]]

from_time: Optional[StrictStr]

to_time: Optional[StrictStr]

target_dataset_id: Optional[StrictStr]

target_dataset_provided: Optional[StrictBool]

classmethod file_processing_mode_validate_enum(value)

Validates the enum

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

to_str()

Returns the string representation of the model using alias

Return type:

str

to_json()

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str)

Create an instance of AdhocTriggerJobRequest from a JSON string

Return type:

Optional[Self]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj)

Create an instance of AdhocTriggerJobRequest from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'file_ids': FieldInfo(annotation=Union[List[Annotated[str, Strict(strict=True)]], NoneType], required=False, alias='FileIds', alias_priority=2, description='List of specific file IDs to process (used with file processing mode)'), 'file_processing_mode': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='FileProcessingMode', alias_priority=2, description='The file processing mode for running BDA extraction Job'), 'from_time': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='FromTime', alias_priority=2, description='Start time for time-based processing (ISO 8601 format)'), 'number_of_workers': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='NumberOfWorkers', alias_priority=2, description='The number of worker nodes used to run data profiling job'), 'target_dataset_id': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='TargetDatasetId', alias_priority=2, description='Target dataset ID for BDA extraction output'), 'target_dataset_provided': FieldInfo(annotation=Union[Annotated[bool, Strict(strict=True)], NoneType], required=False, alias='TargetDatasetProvided', alias_priority=2, description='Flag indicating if target dataset is provided'), 'timeout': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='Timeout', alias_priority=2, description='Timeout of the data profiling job'), 'to_time': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='ToTime', alias_priority=2, description='End time for time-based processing (ISO 8601 format)')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_post_init(__context)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• __context (Any) – The context.

Return type:

None