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delete_resource_tree async

delete_resource_tree(
    resource_arn: Arn, *, config_overrides: Optional[AsyncforecastClientConfig] = None
) -> None

Deletes an entire resource tree. This operation will delete the parent resource and its child resources.

Child resources are resources that were created from another resource. For example, when a forecast is generated from a predictor, the forecast is the child resource and the predictor is the parent resource.

Amazon Forecast resources possess the following parent-child resource hierarchies:

  • Dataset: dataset import jobs

  • Dataset Group: predictors, predictor backtest export jobs, forecasts, forecast export jobs

  • Predictor: predictor backtest export jobs, forecasts, forecast export jobs

  • Forecast: forecast export jobs

DeleteResourceTree will only delete Amazon Forecast resources, and will not delete datasets or exported files stored in Amazon S3.

Parameters:

  • resource_arn (Arn) –

    The Amazon Resource Name (ARN) of the parent resource to delete. All child resources of the parent resource will also be deleted.

Raises:

Source code in docs_src/capo_forecast/_services/async_forecast.py
async def delete_resource_tree(
    self,
    resource_arn: "capo_forecast.types.arn.Arn",
    *,
    config_overrides: Optional[AsyncforecastClientConfig] = None,
) -> None:
    """<p>Deletes an entire resource tree. This operation will delete the parent resource and its child resources.</p> <p>Child resources are resources that were created from another resource. For example, when a forecast is generated from a predictor, the forecast is the child resource and the predictor is the parent resource.</p> <p>Amazon Forecast resources possess the following parent-child resource hierarchies:</p> <ul> <li> <p> <b>Dataset</b>: dataset import jobs</p> </li> <li> <p> <b>Dataset Group</b>: predictors, predictor backtest export jobs, forecasts, forecast export jobs</p> </li> <li> <p> <b>Predictor</b>: predictor backtest export jobs, forecasts, forecast export jobs</p> </li> <li> <p> <b>Forecast</b>: forecast export jobs</p> </li> </ul> <note> <p> <code>DeleteResourceTree</code> will only delete Amazon Forecast resources, and will not delete datasets or exported files stored in Amazon S3. </p> </note>

    Args:
        resource_arn: <p>The Amazon Resource Name (ARN) of the parent resource to delete. All child resources of the parent resource will also be deleted.</p>

    Raises:
        capo_forecast.errors.invalid_input_exception.InvalidInputException: <p>We can't process the request because it includes an invalid value or a value that exceeds the valid range.</p>
        capo_forecast.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is in use.</p>
        capo_forecast.errors.resource_not_found_exception.ResourceNotFoundException: <p>We can't find a resource with that Amazon Resource Name (ARN). Check the ARN and try again.</p>
        capo_forecast.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_forecast.types.delete_resource_tree_request.DeleteResourceTreeRequest]",
    ) -> AsyncOperationResponse[None]:
        import capo_forecast._operations.amazon_forecast.delete_resource_tree

        (
            output,
            http_response,
        ) = await capo_forecast._operations.amazon_forecast.delete_resource_tree.async_delete_resource_tree(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DeleteResourceTree")
    )
    input_: capo_forecast.types.delete_resource_tree_request.DeleteResourceTreeRequest = {
        "resource_arn": resource_arn
    }

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