create_data_view ¶

def create_data_view(
    self,
    dataset_id: "capo_finspace_data.types.dataset_id.DatasetId",
    destination_type_params: "capo_finspace_data.types.data_view_destination_type_params.DataViewDestinationTypeParams",
    *,
    config_overrides: Optional[finspacedataClientConfig] = None,
    client_token: Optional[
        "capo_finspace_data.types.client_token.ClientToken"
    ] = None,
    auto_update: Optional["capo_finspace_data.types.boolean.Boolean"] = None,
    sort_columns: Optional[
        "capo_finspace_data.types.sort_column_list.SortColumnList"
    ] = None,
    partition_columns: Optional[
        "capo_finspace_data.types.partition_column_list.PartitionColumnList"
    ] = None,
    as_of_timestamp: Optional[
        "capo_finspace_data.types.timestamp_epoch.TimestampEpoch"
    ] = None,
) -> "capo_finspace_data.types.create_data_view_response.CreateDataViewResponse":
    """<p>Creates a Dataview for a Dataset.</p>

    Args:
        client_token: <p>A token that ensures idempotency. This token expires in 10 minutes.</p>
        dataset_id: <p>The unique Dataset identifier that is used to create a Dataview.</p>
        auto_update: <p>Flag to indicate Dataview should be updated automatically.</p>
        sort_columns: <p>Columns to be used for sorting the data.</p>
        partition_columns: <p>Ordered set of column names used to partition data.</p>
        as_of_timestamp: <p>Beginning time to use for the Dataview. The value is determined as epoch time in milliseconds. For example, the value for Monday, November 1, 2021 12:00:00 PM UTC is specified as 1635768000000.</p>
        destination_type_params: <p>Options that define the destination type for the Dataview.</p>

    Raises:
        capo_finspace_data.errors.conflict_exception.ConflictException: <p>The request conflicts with an existing resource.</p>
        capo_finspace_data.errors.internal_server_exception.InternalServerException: <p>The request processing has failed because of an unknown error, exception or failure.</p>
        capo_finspace_data.errors.limit_exceeded_exception.LimitExceededException: <p>A limit has exceeded.</p>
        capo_finspace_data.errors.resource_not_found_exception.ResourceNotFoundException: <p>One or more resources can't be found.</p>
        capo_finspace_data.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_finspace_data.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by an AWS service.</p>
        capo_finspace_data.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_finspace_data.types.create_data_view_request.CreateDataViewRequest]",
    ) -> OperationResponse[
        "capo_finspace_data.types.create_data_view_response.CreateDataViewResponse"
    ]:
        import capo_finspace_data._operations.aws_habanero_public_api.create_data_view

        output, http_response = (
            capo_finspace_data._operations.aws_habanero_public_api.create_data_view.create_data_view(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateDataView")
    )
    input_: capo_finspace_data.types.create_data_view_request.CreateDataViewRequest = {
        "dataset_id": dataset_id,
        "destination_type_params": destination_type_params,
    }
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token
    if auto_update is not None:
        input_["auto_update"] = auto_update
    if sort_columns is not None:
        input_["sort_columns"] = sort_columns
    if partition_columns is not None:
        input_["partition_columns"] = partition_columns
    if as_of_timestamp is not None:
        input_["as_of_timestamp"] = as_of_timestamp

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