execute_policy ¶

def execute_policy(
    self,
    *,
    config_overrides: Optional[AutoScalingClientConfig] = None,
    auto_scaling_group_name: Optional[
        "capo_auto_scaling.types.xml_string_max_len255.XmlStringMaxLen255"
    ] = None,
    policy_name: Optional[
        "capo_auto_scaling.types.resource_name.ResourceName"
    ] = None,
    honor_cooldown: Optional[
        "capo_auto_scaling.types.honor_cooldown.HonorCooldown"
    ] = None,
    metric_value: Optional[
        "capo_auto_scaling.types.metric_scale.MetricScale"
    ] = None,
    breach_threshold: Optional[
        "capo_auto_scaling.types.metric_scale.MetricScale"
    ] = None,
) -> None:
    """<p>Executes the specified policy. This can be useful for testing the design of your scaling policy.</p>

    Args:
        auto_scaling_group_name: <p>The name of the Auto Scaling group.</p>
        policy_name: <p>The name or ARN of the policy.</p>
        honor_cooldown: <p>Indicates whether Amazon EC2 Auto Scaling waits for the cooldown period to complete before executing the policy.</p> <p>Valid only if the policy type is <code>SimpleScaling</code>. For more information, see <a href="https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-scaling-cooldowns.html">Scaling cooldowns for Amazon EC2 Auto Scaling</a> in the <i>Amazon EC2 Auto Scaling User Guide</i>.</p>
        metric_value: <p>The metric value to compare to <code>BreachThreshold</code>. This enables you to execute a policy of type <code>StepScaling</code> and determine which step adjustment to use. For example, if the breach threshold is 50 and you want to use a step adjustment with a lower bound of 0 and an upper bound of 10, you can set the metric value to 59.</p> <p>If you specify a metric value that doesn't correspond to a step adjustment for the policy, the call returns an error.</p> <p>Required if the policy type is <code>StepScaling</code> and not supported otherwise.</p>
        breach_threshold: <p>The breach threshold for the alarm.</p> <p>Required if the policy type is <code>StepScaling</code> and not supported otherwise.</p>

    Raises:
        capo_auto_scaling.errors.resource_contention_fault.ResourceContentionFault: <p>You already have a pending update to an Amazon EC2 Auto Scaling resource (for example, an Auto Scaling group, instance, or load balancer).</p>
        capo_auto_scaling.errors.scaling_activity_in_progress_fault.ScalingActivityInProgressFault: <p>The operation can't be performed because there are scaling activities in progress.</p>
        capo_auto_scaling.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        To execute a scaling policy
        This example executes the specified policy.

        >>> client.execute_policy(auto_scaling_group_name='my-auto-scaling-group', policy_name='my-step-scale-out-policy', breach_threshold=50.0, metric_value=59.0)
    """

    def _handler(
        req: "OperationRequest[capo_auto_scaling.types.execute_policy_type.ExecutePolicyType]",
    ) -> OperationResponse[None]:
        import capo_auto_scaling._operations.auto_scaling_2011_01_01.execute_policy

        output, http_response = (
            capo_auto_scaling._operations.auto_scaling_2011_01_01.execute_policy.execute_policy(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ExecutePolicy")
    )
    input_: capo_auto_scaling.types.execute_policy_type.ExecutePolicyType = {}
    if auto_scaling_group_name is not None:
        input_["auto_scaling_group_name"] = auto_scaling_group_name
    if policy_name is not None:
        input_["policy_name"] = policy_name
    if honor_cooldown is not None:
        input_["honor_cooldown"] = honor_cooldown
    if metric_value is not None:
        input_["metric_value"] = metric_value
    if breach_threshold is not None:
        input_["breach_threshold"] = breach_threshold

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