update_dataset ¶

def update_dataset(
    self,
    dataset_arn: "capo_personalize.types.arn.Arn",
    schema_arn: "capo_personalize.types.arn.Arn",
    *,
    config_overrides: Optional[PersonalizeClientConfig] = None,
) -> "capo_personalize.types.update_dataset_response.UpdateDatasetResponse":
    """<p>Update a dataset to replace its schema with a new or existing one. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/updating-dataset-schema.html">Replacing a dataset's schema</a>. </p>

    Args:
        dataset_arn: <p>The Amazon Resource Name (ARN) of the dataset that you want to update.</p>
        schema_arn: <p>The Amazon Resource Name (ARN) of the new schema you want use.</p>

    Raises:
        capo_personalize.errors.invalid_input_exception.InvalidInputException: <p>Provide a valid value for the field or parameter.</p>
        capo_personalize.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is in use.</p>
        capo_personalize.errors.resource_not_found_exception.ResourceNotFoundException: <p>Could not find the specified resource.</p>
        capo_personalize.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_personalize.types.update_dataset_request.UpdateDatasetRequest]",
    ) -> OperationResponse[
        "capo_personalize.types.update_dataset_response.UpdateDatasetResponse"
    ]:
        import capo_personalize._operations.amazon_personalize.update_dataset

        output, http_response = (
            capo_personalize._operations.amazon_personalize.update_dataset.update_dataset(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateDataset")
    )
    input_: capo_personalize.types.update_dataset_request.UpdateDatasetRequest = {
        "dataset_arn": dataset_arn,
        "schema_arn": schema_arn,
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output