list_job_runs ¶

def list_job_runs(
    self,
    virtual_cluster_id: "capo_emr_containers.types.resource_id_string.ResourceIdString",
    *,
    config_overrides: Optional[EMRcontainersClientConfig] = None,
    created_before: Optional["capo_emr_containers.types.date.Date"] = None,
    created_after: Optional["capo_emr_containers.types.date.Date"] = None,
    name: Optional[
        "capo_emr_containers.types.resource_name_string.ResourceNameString"
    ] = None,
    states: Optional[
        "capo_emr_containers.types.job_run_states.JobRunStates"
    ] = None,
    max_results: Optional[
        "capo_emr_containers.types.java_integer.JavaInteger"
    ] = None,
    next_token: Optional["capo_emr_containers.types.next_token.NextToken"] = None,
) -> "capo_emr_containers.types.list_job_runs_response.ListJobRunsResponse":
    """<p>Lists job runs based on a set of parameters. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS.</p>

    Args:
        virtual_cluster_id: <p>The ID of the virtual cluster for which to list the job run. </p>
        created_before: <p>The date and time before which the job runs were submitted.</p>
        created_after: <p>The date and time after which the job runs were submitted.</p>
        name: <p>The name of the job run.</p>
        states: <p>The states of the job run.</p>
        max_results: <p>The maximum number of job runs that can be listed.</p>
        next_token: <p>The token for the next set of job runs to return.</p>

    Raises:
        capo_emr_containers.errors.internal_server_exception.InternalServerException: <p>This is an internal server exception.</p>
        capo_emr_containers.errors.validation_exception.ValidationException: <p>There are invalid parameters in the client request.</p>
        capo_emr_containers.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_emr_containers.types.list_job_runs_request.ListJobRunsRequest]",
    ) -> OperationResponse[
        "capo_emr_containers.types.list_job_runs_response.ListJobRunsResponse"
    ]:
        import capo_emr_containers._operations.aws_chicago_web_service.list_job_runs

        output, http_response = (
            capo_emr_containers._operations.aws_chicago_web_service.list_job_runs.list_job_runs(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_output(
        output: "capo_emr_containers.types.list_job_runs_response.ListJobRunsResponse",
    ) -> dict[str, Any]:
        import capo_emr_containers.types.list_job_runs_response

        return capo_emr_containers.types.list_job_runs_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("ListJobRuns", redact_output=_redact_output),
    )
    input_: capo_emr_containers.types.list_job_runs_request.ListJobRunsRequest = {
        "virtual_cluster_id": virtual_cluster_id
    }
    if created_before is not None:
        input_["created_before"] = created_before
    if created_after is not None:
        input_["created_after"] = created_after
    if name is not None:
        input_["name"] = name
    if states is not None:
        input_["states"] = states
    if max_results is not None:
        input_["max_results"] = max_results
    if next_token is not None:
        input_["next_token"] = next_token

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