start_import_task ¶

def start_import_task(
    self,
    source: str,
    graph_identifier: "capo_neptune_graph.types.graph_identifier.GraphIdentifier",
    role_arn: "capo_neptune_graph.types.role_arn.RoleArn",
    *,
    config_overrides: Optional[NeptuneGraphClientConfig] = None,
    import_options: Optional[
        "capo_neptune_graph.types.import_options.ImportOptions"
    ] = None,
    fail_on_error: Optional[bool] = None,
    format: Optional["capo_neptune_graph.types.format.Format"] = None,
    parquet_type: Optional[
        "capo_neptune_graph.types.parquet_type.ParquetType"
    ] = None,
    blank_node_handling: Optional[
        "capo_neptune_graph.types.blank_node_handling.BlankNodeHandling"
    ] = None,
) -> "capo_neptune_graph.types.start_import_task_output.StartImportTaskOutput":
    """<p>Import data into existing Neptune Analytics graph from Amazon Simple Storage Service (S3). The graph needs to be empty and in the AVAILABLE state.</p>

    Args:
        fail_on_error: <p>If set to true, the task halts when an import error is encountered. If set to false, the task skips the data that caused the error and continues if possible.</p>
        source: <p>A URL identifying the location of the data to be imported. This can be an Amazon S3 path, or can point to a Neptune database endpoint or snapshot.</p>
        format: <p>Specifies the format of Amazon S3 data to be imported. Valid values are CSV, which identifies the Gremlin CSV format or OPENCYPHER, which identifies the openCypher load format.</p>
        parquet_type: <p>The parquet type of the import task.</p>
        blank_node_handling: <p>The method to handle blank nodes in the dataset. Currently, only <code>convertToIri</code> is supported, meaning blank nodes are converted to unique IRIs at load time. Must be provided when format is <code>ntriples</code>. For more information, see <a href="https://docs.aws.amazon.com/neptune-analytics/latest/userguide/using-rdf-data.html#rdf-handling">Handling RDF values</a>.</p>
        graph_identifier: <p>The unique identifier of the Neptune Analytics graph.</p>
        role_arn: <p>The ARN of the IAM role that will allow access to the data that is to be imported.</p>

    Raises:
        capo_neptune_graph.errors.conflict_exception.ConflictException: <p>Raised when a conflict is encountered.</p>
        capo_neptune_graph.errors.internal_server_exception.InternalServerException: <p>A failure occurred on the server.</p>
        capo_neptune_graph.errors.resource_not_found_exception.ResourceNotFoundException: <p>A specified resource could not be located.</p>
        capo_neptune_graph.errors.throttling_exception.ThrottlingException: <p>The exception was interrupted by throttling.</p>
        capo_neptune_graph.errors.validation_exception.ValidationException: <p>A resource could not be validated.</p>
        capo_neptune_graph.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_neptune_graph.types.start_import_task_input.StartImportTaskInput]",
    ) -> OperationResponse[
        "capo_neptune_graph.types.start_import_task_output.StartImportTaskOutput"
    ]:
        import capo_neptune_graph._operations.amazon_neptune_graph.start_import_task

        output, http_response = (
            capo_neptune_graph._operations.amazon_neptune_graph.start_import_task.start_import_task(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartImportTask")
    )
    input_: capo_neptune_graph.types.start_import_task_input.StartImportTaskInput = {
        "source": source,
        "graph_identifier": graph_identifier,
        "role_arn": role_arn,
    }
    if import_options is not None:
        input_["import_options"] = import_options
    if fail_on_error is not None:
        input_["fail_on_error"] = fail_on_error
    if format is not None:
        input_["format"] = format
    if parquet_type is not None:
        input_["parquet_type"] = parquet_type
    if blank_node_handling is not None:
        input_["blank_node_handling"] = blank_node_handling

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