struct Kubernetes::HPAScalingRules

Overview

HPAScalingRules configures the scaling behavior for one direction via scaling Policy Rules and a configurable metric tolerance. Scaling Policy Rules are applied after calculating DesiredReplicas from metrics for the HPA. They can limit the scaling velocity by specifying scaling policies. They can prevent flapping by specifying the stabilization window, so that the number of replicas is not set instantly, instead, the safest value from the stabilization window is chosen. The tolerance is applied to the metric values and prevents scaling too eagerly for small metric variations. (Note that setting a tolerance requires the beta HPAConfigurableTolerance feature gate to be enabled.)

Included Modules

Defined in:

generated/models/io_k8s_api_autoscaling_v2.cr

Constructors

Macro Summary

Instance Method Summary

Constructor Detail

def self.new(ctx : YAML::ParseContext, node : YAML::Nodes::Node) #

def self.new(pull : ::JSON::PullParser) #

def self.new(*, __pull_for_json_serializable pull : ::JSON::PullParser) #

def self.new(*, __context_for_yaml_serializable ctx : YAML::ParseContext, __node_for_yaml_serializable node : YAML::Nodes::Node) #

Macro Detail

macro field(name, type = nil, **options, &block) #

Helper macro for defining fields with automatic camelCase conversion


Instance Method Detail

def policies : Array(HPAScalingPolicy) | Nil #

policies is a list of potential scaling polices which can be used during scaling. If not set, use the default values: - For scale up: allow doubling the number of pods, or an absolute change of 4 pods in a 15s window. - For scale down: allow all pods to be removed in a 15s window.


def policies=(policies : Array(HPAScalingPolicy) | Nil) #

policies is a list of potential scaling polices which can be used during scaling. If not set, use the default values: - For scale up: allow doubling the number of pods, or an absolute change of 4 pods in a 15s window. - For scale down: allow all pods to be removed in a 15s window.


def select_policy : String | Nil #

selectPolicy is used to specify which policy should be used. If not set, the default value Max is used.


def select_policy=(select_policy : String | Nil) #

selectPolicy is used to specify which policy should be used. If not set, the default value Max is used.


def stabilization_window_seconds : Int32 | Nil #

stabilizationWindowSeconds is the number of seconds for which past recommendations should be considered while scaling up or scaling down. StabilizationWindowSeconds must be greater than or equal to zero and less than or equal to 3600 (one hour). If not set, use the default values: - For scale up: 0 (i.e. no stabilization is done). - For scale down: 300 (i.e. the stabilization window is 300 seconds long).


def stabilization_window_seconds=(stabilization_window_seconds : Int32 | Nil) #

stabilizationWindowSeconds is the number of seconds for which past recommendations should be considered while scaling up or scaling down. StabilizationWindowSeconds must be greater than or equal to zero and less than or equal to 3600 (one hour). If not set, use the default values: - For scale up: 0 (i.e. no stabilization is done). - For scale down: 300 (i.e. the stabilization window is 300 seconds long).


def tolerance : Quantity | Nil #

tolerance is the tolerance on the ratio between the current and desired metric value under which no updates are made to the desired number of replicas (e.g. 0.01 for 1%). Must be greater than or equal to zero. If not set, the default cluster-wide tolerance is applied (by default 10%). For example, if autoscaling is configured with a memory consumption target of 100Mi, and scale-down and scale-up tolerances of 5% and 1% respectively, scaling will be triggered when the actual consumption falls below 95Mi or exceeds 101Mi. This is an beta field and requires the HPAConfigurableTolerance feature gate to be enabled.


def tolerance=(tolerance : Quantity | Nil) #

tolerance is the tolerance on the ratio between the current and desired metric value under which no updates are made to the desired number of replicas (e.g. 0.01 for 1%). Must be greater than or equal to zero. If not set, the default cluster-wide tolerance is applied (by default 10%). For example, if autoscaling is configured with a memory consumption target of 100Mi, and scale-down and scale-up tolerances of 5% and 1% respectively, scaling will be triggered when the actual consumption falls below 95Mi or exceeds 101Mi. This is an beta field and requires the HPAConfigurableTolerance feature gate to be enabled.