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list_batch_job_restart_points

list_batch_job_restart_points(
    application_id: Identifier,
    execution_id: Identifier,
    *,
    config_overrides: Optional[m2ClientConfig] = None,
    auth_secrets_manager_arn: Optional[AuthSecretsManagerArn] = None,
) -> ListBatchJobRestartPointsResponse

Lists all the job steps for a JCL file to restart a batch job. This is only applicable for Micro Focus engine with versions 8.0.6 and above.

Parameters:

  • application_id (Identifier) –

    The unique identifier of the application.

  • execution_id (Identifier) –

    The unique identifier of the batch job execution.

  • auth_secrets_manager_arn (Optional[AuthSecretsManagerArn], default: None ) –

    The Amazon Web Services Secrets Manager containing user's credentials for authentication and authorization for List Batch Job Restart Points operation.

Raises:

Source code in docs_src/capo_m2/_services/m2.py
def list_batch_job_restart_points(
    self,
    application_id: "capo_m2.types.identifier.Identifier",
    execution_id: "capo_m2.types.identifier.Identifier",
    *,
    config_overrides: Optional[m2ClientConfig] = None,
    auth_secrets_manager_arn: Optional[
        "capo_m2.types.auth_secrets_manager_arn.AuthSecretsManagerArn"
    ] = None,
) -> "capo_m2.types.list_batch_job_restart_points_response.ListBatchJobRestartPointsResponse":
    """<p>Lists all the job steps for a JCL file to restart a batch job. This is only applicable for Micro Focus engine with versions 8.0.6 and above.</p>

    Args:
        application_id: <p>The unique identifier of the application.</p>
        execution_id: <p>The unique identifier of the batch job execution.</p>
        auth_secrets_manager_arn: <p>The Amazon Web Services Secrets Manager containing user's credentials for authentication and authorization for List Batch Job Restart Points operation.</p>

    Raises:
        capo_m2.errors.access_denied_exception.AccessDeniedException: <p>The account or role doesn't have the right permissions to make the request.</p>
        capo_m2.errors.conflict_exception.ConflictException: <p>The parameters provided in the request conflict with existing resources.</p>
        capo_m2.errors.internal_server_exception.InternalServerException: <p>An unexpected error occurred during the processing of the request.</p>
        capo_m2.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource was not found.</p>
        capo_m2.errors.throttling_exception.ThrottlingException: <p>The number of requests made exceeds the limit.</p>
        capo_m2.errors.validation_exception.ValidationException: <p>One or more parameters provided in the request is not valid.</p>
        capo_m2.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_m2.types.list_batch_job_restart_points_request.ListBatchJobRestartPointsRequest]",
    ) -> OperationResponse[
        "capo_m2.types.list_batch_job_restart_points_response.ListBatchJobRestartPointsResponse"
    ]:
        import capo_m2._operations.aws_supernova_control_plane_service.list_batch_job_restart_points

        output, http_response = (
            capo_m2._operations.aws_supernova_control_plane_service.list_batch_job_restart_points.list_batch_job_restart_points(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListBatchJobRestartPoints")
    )
    input_: capo_m2.types.list_batch_job_restart_points_request.ListBatchJobRestartPointsRequest = {
        "application_id": application_id,
        "execution_id": execution_id,
    }
    if auth_secrets_manager_arn is not None:
        input_["auth_secrets_manager_arn"] = auth_secrets_manager_arn

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