list_instance_fleets ¶

def list_instance_fleets(
    self,
    *,
    config_overrides: Optional[EMRClientConfig] = None,
    cluster_id: Optional["capo_emr.types.cluster_id.ClusterId"] = None,
    marker: Optional["capo_emr.types.marker.Marker"] = None,
) -> "capo_emr.types.list_instance_fleets_output.ListInstanceFleetsOutput":
    """<p>Lists all available details about the instance fleets in a cluster.</p> <note> <p>The instance fleet configuration is available only in Amazon EMR releases 4.8.0 and later, excluding 5.0.x versions.</p> </note>

    Args:
        cluster_id: <p>The unique identifier of the cluster.</p>
        marker: <p>The pagination token that indicates the next set of results to retrieve.</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.
    """

    def _handler(
        req: "OperationRequest[capo_emr.types.list_instance_fleets_input.ListInstanceFleetsInput]",
    ) -> OperationResponse[
        "capo_emr.types.list_instance_fleets_output.ListInstanceFleetsOutput"
    ]:
        import capo_emr._operations.elastic_map_reduce.list_instance_fleets

        output, http_response = (
            capo_emr._operations.elastic_map_reduce.list_instance_fleets.list_instance_fleets(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListInstanceFleets")
    )
    input_: capo_emr.types.list_instance_fleets_input.ListInstanceFleetsInput = {}
    if cluster_id is not None:
        input_["cluster_id"] = cluster_id
    if marker is not None:
        input_["marker"] = marker

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