DataQualityCheckRun

class openapi_client.models.data_quality_check_run.DataQualityCheckRun(**data)

Bases: BaseModel

data_quality_check_id: Optional[StrictStr]

data_quality_check_run_id: Optional[StrictStr]

data_quality_check_run_start_time: Optional[StrictStr]

data_quality_check_run_end_time: Optional[StrictStr]

message: Optional[StrictStr]

job_run_id: Optional[StrictStr]

run_status: Optional[StrictStr]

dq_constraint_results: Optional[List[DataQualityCheckRunDqConstraintResultsInner]]

auto_constraint_suggestions: Optional[List[DataQualityCheckDqConstraintsInner]]

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 DataQualityCheckRun 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 DataQualityCheckRun from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'auto_constraint_suggestions': FieldInfo(annotation=Union[List[DataQualityCheckDqConstraintsInner], NoneType], required=False, alias='AutoConstraintSuggestions', alias_priority=2), 'data_quality_check_id': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DataQualityCheckId', alias_priority=2), 'data_quality_check_run_end_time': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DataQualityCheckRunEndTime', alias_priority=2), 'data_quality_check_run_id': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DataQualityCheckRunId', alias_priority=2), 'data_quality_check_run_start_time': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DataQualityCheckRunStartTime', alias_priority=2), 'dq_constraint_results': FieldInfo(annotation=Union[List[DataQualityCheckRunDqConstraintResultsInner], NoneType], required=False, alias='DqConstraintResults', alias_priority=2), 'job_run_id': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='JobRunId', alias_priority=2), 'message': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='Message', alias_priority=2), 'run_status': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='RunStatus', alias_priority=2)}

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