create_configured_table ¶

def create_configured_table(
    self,
    name: "capo_cleanrooms.types.display_name.DisplayName",
    table_reference: "capo_cleanrooms.types.table_reference.TableReference",
    allowed_columns: "capo_cleanrooms.types.allowed_column_list.AllowedColumnList",
    analysis_method: "capo_cleanrooms.types.analysis_method.AnalysisMethod",
    *,
    config_overrides: Optional[CleanRoomsClientConfig] = None,
    description: Optional[
        "capo_cleanrooms.types.table_description.TableDescription"
    ] = None,
    selected_analysis_methods: Optional[
        "capo_cleanrooms.types.selected_analysis_methods.SelectedAnalysisMethods"
    ] = None,
    tags: Optional["capo_cleanrooms.types.tag_map.TagMap"] = None,
) -> "capo_cleanrooms.types.create_configured_table_output.CreateConfiguredTableOutput":
    """<p>Creates a new configured table resource.</p>

    Args:
        name: <p>The name of the configured table.</p>
        description: <p>A description for the configured table.</p>
        table_reference: <p>A reference to the table being configured.</p>
        allowed_columns: <p>The columns of the underlying table that can be used by collaborations or analysis rules.</p>
        analysis_method: <p>The analysis method allowed for the configured tables.</p> <p> <code>DIRECT_QUERY</code> allows SQL queries to be run directly on this table.</p> <p> <code>DIRECT_JOB</code> allows PySpark jobs to be run directly on this table.</p> <p> <code>MULTIPLE</code> allows both SQL queries and PySpark jobs to be run directly on this table.</p>
        selected_analysis_methods: <p> The analysis methods to enable for the configured table. When configured, you must specify at least two analysis methods.</p>
        tags: <p>An optional label that you can assign to a resource when you create it. Each tag consists of a key and an optional value, both of which you define. When you use tagging, you can also use tag-based access control in IAM policies to control access to this resource.</p>

    Raises:
        capo_cleanrooms.errors.access_denied_exception.AccessDeniedException: <p>Caller does not have sufficient access to perform this action.</p>
        capo_cleanrooms.errors.conflict_exception.ConflictException: <p>Updating or deleting a resource can cause an inconsistent state.</p>
        capo_cleanrooms.errors.internal_server_exception.InternalServerException: <p>Unexpected error during processing of request.</p>
        capo_cleanrooms.errors.resource_not_found_exception.ResourceNotFoundException: <p>Request references a resource which does not exist.</p>
        capo_cleanrooms.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>Request denied because service quota has been exceeded.</p>
        capo_cleanrooms.errors.throttling_exception.ThrottlingException: <p>Request was denied due to request throttling.</p>
        capo_cleanrooms.errors.validation_exception.ValidationException: <p>The input fails to satisfy the specified constraints.</p>
        capo_cleanrooms.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_cleanrooms.types.create_configured_table_input.CreateConfiguredTableInput]",
    ) -> OperationResponse[
        "capo_cleanrooms.types.create_configured_table_output.CreateConfiguredTableOutput"
    ]:
        import capo_cleanrooms._operations.aws_bastion_control_plane_service_lambda.create_configured_table

        output, http_response = (
            capo_cleanrooms._operations.aws_bastion_control_plane_service_lambda.create_configured_table.create_configured_table(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateConfiguredTable")
    )
    input_: capo_cleanrooms.types.create_configured_table_input.CreateConfiguredTableInput = {
        "name": name,
        "table_reference": table_reference,
        "allowed_columns": allowed_columns,
        "analysis_method": analysis_method,
    }
    if description is not None:
        input_["description"] = description
    if selected_analysis_methods is not None:
        input_["selected_analysis_methods"] = selected_analysis_methods
    if tags is not None:
        input_["tags"] = tags

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