update_job_queue async ¶

async def update_job_queue(
    self,
    *,
    config_overrides: Optional[AsyncBatchClientConfig] = None,
    job_queue: Optional["capo_batch.types.string.String"] = None,
    state: Optional["capo_batch.types.jq_state.JQState"] = None,
    scheduling_policy_arn: Optional["capo_batch.types.string.String"] = None,
    priority: Optional["capo_batch.types.integer.Integer"] = None,
    compute_environment_order: Optional[
        "capo_batch.types.compute_environment_orders.ComputeEnvironmentOrders"
    ] = None,
    service_environment_order: Optional[
        "capo_batch.types.service_environment_orders.ServiceEnvironmentOrders"
    ] = None,
    job_state_time_limit_actions: Optional[
        "capo_batch.types.job_state_time_limit_actions.JobStateTimeLimitActions"
    ] = None,
) -> "capo_batch.types.update_job_queue_response.UpdateJobQueueResponse":
    """<p>Updates a job queue.</p>

    Args:
        job_queue: <p>The name or the Amazon Resource Name (ARN) of the job queue.</p>
        state: <p>Describes the queue's ability to accept new jobs. If the job queue state is <code>ENABLED</code>, it can accept jobs. If the job queue state is <code>DISABLED</code>, new jobs can't be added to the queue, but jobs already in the queue can finish.</p>
        scheduling_policy_arn: <p>Amazon Resource Name (ARN) of the fair-share scheduling policy. Once a job queue is created, the fair-share scheduling policy can be replaced but not removed. The format is <code>aws:<i>Partition</i>:batch:<i>Region</i>:<i>Account</i>:scheduling-policy/<i>Name</i> </code>. For example, <code>aws:aws:batch:us-west-2:123456789012:scheduling-policy/MySchedulingPolicy</code>.</p>
        priority: <p>The priority of the job queue. Job queues with a higher priority (or a higher integer value for the <code>priority</code> parameter) are evaluated first when associated with the same compute environment. Priority is determined in descending order. For example, a job queue with a priority value of <code>10</code> is given scheduling preference over a job queue with a priority value of <code>1</code>. All of the compute environments must be either EC2 (<code>EC2</code> or <code>SPOT</code>) or Fargate (<code>FARGATE</code> or <code>FARGATE_SPOT</code>). EC2 and Fargate compute environments can't be mixed.</p>
        compute_environment_order: <p>Details the set of compute environments mapped to a job queue and their order relative to each other. This is one of the parameters used by the job scheduler to determine which compute environment runs a given job. Compute environments must be in the <code>VALID</code> state before you can associate them with a job queue. All of the compute environments must be either EC2 (<code>EC2</code> or <code>SPOT</code>) or Fargate (<code>FARGATE</code> or <code>FARGATE_SPOT</code>). EC2 and Fargate compute environments can't be mixed.</p> <note> <p>All compute environments that are associated with a job queue must share the same architecture. Batch doesn't support mixing compute environment architecture types in a single job queue.</p> </note>
        service_environment_order: <p>The order of the service environment associated with the job queue. Job queues with a higher priority are evaluated first when associated with the same service environment.</p>
        job_state_time_limit_actions: <p>The set of actions that Batch perform on jobs that remain at the head of the job queue in the specified state longer than specified times. Batch will perform each action after <code>maxTimeSeconds</code> has passed. (<b>Note</b>: The minimum value for maxTimeSeconds is 600 (10 minutes) and its maximum value is 86,400 (24 hours).)</p>

    Raises:
        capo_batch.errors.client_exception.ClientException: <p>These errors are usually caused by a client action. One example cause is using an action or resource on behalf of a user that doesn't have permissions to use the action or resource. Another cause is specifying an identifier that's not valid.</p>
        capo_batch.errors.server_exception.ServerException: <p>These errors are usually caused by a server issue.</p>
        capo_batch.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        To update a job queue
        This example disables a job queue so that it can be deleted.

        >>> await client.update_job_queue(state='DISABLED', job_queue='GPGPU')
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_batch.types.update_job_queue_request.UpdateJobQueueRequest]",
    ) -> AsyncOperationResponse[
        "capo_batch.types.update_job_queue_response.UpdateJobQueueResponse"
    ]:
        import capo_batch._operations.aws_batch_v20160810.update_job_queue

        (
            output,
            http_response,
        ) = await capo_batch._operations.aws_batch_v20160810.update_job_queue.async_update_job_queue(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateJobQueue")
    )
    input_: capo_batch.types.update_job_queue_request.UpdateJobQueueRequest = {}
    if job_queue is not None:
        input_["job_queue"] = job_queue
    if state is not None:
        input_["state"] = state
    if scheduling_policy_arn is not None:
        input_["scheduling_policy_arn"] = scheduling_policy_arn
    if priority is not None:
        input_["priority"] = priority
    if compute_environment_order is not None:
        input_["compute_environment_order"] = compute_environment_order
    if service_environment_order is not None:
        input_["service_environment_order"] = service_environment_order
    if job_state_time_limit_actions is not None:
        input_["job_state_time_limit_actions"] = job_state_time_limit_actions

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