Skip to content

create_persistent_app_ui async

create_persistent_app_ui(
    *,
    config_overrides: Optional[AsyncEMRClientConfig] = None,
    target_resource_arn: Optional[ArnType] = None,
    emr_containers_config: Optional[EMRContainersConfig] = None,
    tags: Optional[TagList] = None,
    x_referer: Optional[String] = None,
    profiler_type: Optional[ProfilerType] = None,
) -> CreatePersistentAppUIOutput

Creates a persistent application user interface.

Parameters:

  • target_resource_arn (Optional[ArnType], default: None ) –

    The unique Amazon Resource Name (ARN) of the target resource.

  • emr_containers_config (Optional[EMRContainersConfig], default: None ) –

    The EMR containers configuration.

  • tags (Optional[TagList], default: None ) –

    Tags for the persistent application user interface.

  • x_referer (Optional[String], default: None ) –

    The cross reference for the persistent application user interface.

  • profiler_type (Optional[ProfilerType], default: None ) –

    The profiler type for the persistent application user interface.

Raises:

Source code in docs_src/capo_emr/_services/async_emr.py
async def create_persistent_app_ui(
    self,
    *,
    config_overrides: Optional[AsyncEMRClientConfig] = None,
    target_resource_arn: Optional["capo_emr.types.arn_type.ArnType"] = None,
    emr_containers_config: Optional[
        "capo_emr.types.emr_containers_config.EMRContainersConfig"
    ] = None,
    tags: Optional["capo_emr.types.tag_list.TagList"] = None,
    x_referer: Optional["capo_emr.types.string.String"] = None,
    profiler_type: Optional["capo_emr.types.profiler_type.ProfilerType"] = None,
) -> "capo_emr.types.create_persistent_app_ui_output.CreatePersistentAppUIOutput":
    """<p>Creates a persistent application user interface.</p>

    Args:
        target_resource_arn: <p>The unique Amazon Resource Name (ARN) of the target resource.</p>
        emr_containers_config: <p>The EMR containers configuration.</p>
        tags: <p>Tags for the persistent application user interface.</p>
        x_referer: <p>The cross reference for the persistent application user interface.</p>
        profiler_type: <p>The profiler type for the persistent application user interface.</p>

    Raises:
        capo_emr.errors.internal_server_exception.InternalServerException: <p>This exception occurs when there is an internal failure in the Amazon EMR service.</p>
        capo_emr.errors.invalid_request_exception.InvalidRequestException: <p>This exception occurs when there is something wrong with user input.</p>
        capo_emr.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_emr.types.create_persistent_app_ui_input.CreatePersistentAppUIInput]",
    ) -> AsyncOperationResponse[
        "capo_emr.types.create_persistent_app_ui_output.CreatePersistentAppUIOutput"
    ]:
        import capo_emr._operations.elastic_map_reduce.create_persistent_app_ui

        (
            output,
            http_response,
        ) = await capo_emr._operations.elastic_map_reduce.create_persistent_app_ui.async_create_persistent_app_ui(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreatePersistentAppUI")
    )
    input_: capo_emr.types.create_persistent_app_ui_input.CreatePersistentAppUIInput = {}
    if target_resource_arn is not None:
        input_["target_resource_arn"] = target_resource_arn
    if emr_containers_config is not None:
        input_["emr_containers_config"] = emr_containers_config
    if tags is not None:
        input_["tags"] = tags
    if x_referer is not None:
        input_["x_referer"] = x_referer
    if profiler_type is not None:
        input_["profiler_type"] = profiler_type

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output