start_job_run ¶

def start_job_run(
    self,
    application_id: "capo_emr_serverless.types.application_id.ApplicationId",
    client_token: "capo_emr_serverless.types.client_token.ClientToken",
    execution_role_arn: "capo_emr_serverless.types.iam_role_arn.IAMRoleArn",
    *,
    config_overrides: Optional[EMRServerlessClientConfig] = None,
    execution_iam_policy: Optional[
        "capo_emr_serverless.types.job_run_execution_iam_policy.JobRunExecutionIamPolicy"
    ] = None,
    job_driver: Optional["capo_emr_serverless.types.job_driver.JobDriver"] = None,
    configuration_overrides: Optional[
        "capo_emr_serverless.types.configuration_overrides.ConfigurationOverrides"
    ] = None,
    tags: Optional["capo_emr_serverless.types.tag_map.TagMap"] = None,
    execution_timeout_minutes: Optional[
        "capo_emr_serverless.types.duration.Duration"
    ] = None,
    name: Optional["capo_emr_serverless.types.string256.String256"] = None,
    mode: Optional["capo_emr_serverless.types.job_run_mode.JobRunMode"] = None,
    retry_policy: Optional[
        "capo_emr_serverless.types.retry_policy.RetryPolicy"
    ] = None,
) -> "capo_emr_serverless.types.start_job_run_response.StartJobRunResponse":
    """<p>Starts a job run.</p>

    Args:
        application_id: <p>The ID of the application on which to run the job.</p>
        client_token: <p>The client idempotency token of the job run to start. Its value must be unique for each request.</p>
        execution_role_arn: <p>The execution role ARN for the job run.</p>
        execution_iam_policy: <p>You can pass an optional IAM policy. The resulting job IAM role permissions will be an intersection of this policy and the policy associated with your job execution role.</p>
        job_driver: <p>The job driver for the job run.</p>
        configuration_overrides: <p>The configuration overrides for the job run.</p>
        tags: <p>The tags assigned to the job run.</p>
        execution_timeout_minutes: <p>The maximum duration, in minutes, for the job run. If the job run exceeds this duration, Amazon EMR Serverless cancels it automatically.</p> <p>For BATCH mode job runs, the maximum value is 10080 minutes (7 days) starting with Amazon EMR release 7.11. Setting a value of 0 to disable the timeout is no longer supported for BATCH mode job runs.</p>
        name: <p>The optional job run name. This doesn't have to be unique.</p>
        mode: <p>The mode of the job run when it starts.</p>
        retry_policy: <p>The retry policy when job run starts.</p>

    Raises:
        capo_emr_serverless.errors.conflict_exception.ConflictException: <p>The request could not be processed because of conflict in the current state of the resource.</p>
        capo_emr_serverless.errors.internal_server_exception.InternalServerException: <p>Request processing failed because of an error or failure with the service.</p>
        capo_emr_serverless.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource was not found.</p>
        capo_emr_serverless.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by an Amazon Web Services service.</p>
        capo_emr_serverless.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_emr_serverless.types.start_job_run_request.StartJobRunRequest]",
    ) -> OperationResponse[
        "capo_emr_serverless.types.start_job_run_response.StartJobRunResponse"
    ]:
        import capo_emr_serverless._operations.aws_toledo_web_service.start_job_run

        output, http_response = (
            capo_emr_serverless._operations.aws_toledo_web_service.start_job_run.start_job_run(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_emr_serverless.types.start_job_run_request.StartJobRunRequest",
    ) -> dict[str, Any]:
        import capo_emr_serverless.types.start_job_run_request

        return capo_emr_serverless.types.start_job_run_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("StartJobRun", redact_input=_redact_input),
    )
    input_: capo_emr_serverless.types.start_job_run_request.StartJobRunRequest = {
        "application_id": application_id,
        "client_token": client_token,
        "execution_role_arn": execution_role_arn,
    }
    if execution_iam_policy is not None:
        input_["execution_iam_policy"] = execution_iam_policy
    if job_driver is not None:
        input_["job_driver"] = job_driver
    if configuration_overrides is not None:
        input_["configuration_overrides"] = configuration_overrides
    if tags is not None:
        input_["tags"] = tags
    if execution_timeout_minutes is not None:
        input_["execution_timeout_minutes"] = execution_timeout_minutes
    if name is not None:
        input_["name"] = name
    if mode is not None:
        input_["mode"] = mode
    if retry_policy is not None:
        input_["retry_policy"] = retry_policy

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