Struct aws_sdk_autoscaling::types::PredictiveScalingMetricSpecification

#[non_exhaustive]
pub struct PredictiveScalingMetricSpecification {
    pub target_value: Option<f64>,
    pub predefined_metric_pair_specification: Option<PredictiveScalingPredefinedMetricPair>,
    pub predefined_scaling_metric_specification: Option<PredictiveScalingPredefinedScalingMetric>,
    pub predefined_load_metric_specification: Option<PredictiveScalingPredefinedLoadMetric>,
    pub customized_scaling_metric_specification: Option<PredictiveScalingCustomizedScalingMetric>,
    pub customized_load_metric_specification: Option<PredictiveScalingCustomizedLoadMetric>,
    pub customized_capacity_metric_specification: Option<PredictiveScalingCustomizedCapacityMetric>,
}

This structure specifies the metrics and target utilization settings for a predictive scaling policy.

You must specify either a metric pair, or a load metric and a scaling metric individually. Specifying a metric pair instead of individual metrics provides a simpler way to configure metrics for a scaling policy. You choose the metric pair, and the policy automatically knows the correct sum and average statistics to use for the load metric and the scaling metric.

Example

• You create a predictive scaling policy and specify ALBRequestCount as the value for the metric pair and 1000.0 as the target value. For this type of metric, you must provide the metric dimension for the corresponding target group, so you also provide a resource label for the Application Load Balancer target group that is attached to your Auto Scaling group.

• The number of requests the target group receives per minute provides the load metric, and the request count averaged between the members of the target group provides the scaling metric. In CloudWatch, this refers to the RequestCount and RequestCountPerTarget metrics, respectively.

• For optimal use of predictive scaling, you adhere to the best practice of using a dynamic scaling policy to automatically scale between the minimum capacity and maximum capacity in response to real-time changes in resource utilization.

• Amazon EC2 Auto Scaling consumes data points for the load metric over the last 14 days and creates an hourly load forecast for predictive scaling. (A minimum of 24 hours of data is required.)

• After creating the load forecast, Amazon EC2 Auto Scaling determines when to reduce or increase the capacity of your Auto Scaling group in each hour of the forecast period so that the average number of requests received by each instance is as close to 1000 requests per minute as possible at all times.

For information about using custom metrics with predictive scaling, see Advanced predictive scaling policy configurations using custom metrics in the Amazon EC2 Auto Scaling User Guide.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

target_value: Option<f64>

Specifies the target utilization.

Some metrics are based on a count instead of a percentage, such as the request count for an Application Load Balancer or the number of messages in an SQS queue. If the scaling policy specifies one of these metrics, specify the target utilization as the optimal average request or message count per instance during any one-minute interval.

predefined_metric_pair_specification: Option<PredictiveScalingPredefinedMetricPair>

The predefined metric pair specification from which Amazon EC2 Auto Scaling determines the appropriate scaling metric and load metric to use.

predefined_scaling_metric_specification: Option<PredictiveScalingPredefinedScalingMetric>

The predefined scaling metric specification.

predefined_load_metric_specification: Option<PredictiveScalingPredefinedLoadMetric>

The predefined load metric specification.

customized_scaling_metric_specification: Option<PredictiveScalingCustomizedScalingMetric>

The customized scaling metric specification.

customized_load_metric_specification: Option<PredictiveScalingCustomizedLoadMetric>

The customized load metric specification.

customized_capacity_metric_specification: Option<PredictiveScalingCustomizedCapacityMetric>

The customized capacity metric specification.

Implementations

impl PredictiveScalingMetricSpecification

pub fn target_value(&self) -> Option<f64>

Specifies the target utilization.

Some metrics are based on a count instead of a percentage, such as the request count for an Application Load Balancer or the number of messages in an SQS queue. If the scaling policy specifies one of these metrics, specify the target utilization as the optimal average request or message count per instance during any one-minute interval.

pub fn predefined_metric_pair_specification( &self ) -> Option<&PredictiveScalingPredefinedMetricPair>

The predefined metric pair specification from which Amazon EC2 Auto Scaling determines the appropriate scaling metric and load metric to use.

pub fn predefined_scaling_metric_specification( &self ) -> Option<&PredictiveScalingPredefinedScalingMetric>

The predefined scaling metric specification.

pub fn predefined_load_metric_specification( &self ) -> Option<&PredictiveScalingPredefinedLoadMetric>

The predefined load metric specification.

pub fn customized_scaling_metric_specification( &self ) -> Option<&PredictiveScalingCustomizedScalingMetric>

The customized scaling metric specification.

pub fn customized_load_metric_specification( &self ) -> Option<&PredictiveScalingCustomizedLoadMetric>

The customized load metric specification.

pub fn customized_capacity_metric_specification( &self ) -> Option<&PredictiveScalingCustomizedCapacityMetric>

The customized capacity metric specification.

impl PredictiveScalingMetricSpecification

pub fn builder() -> PredictiveScalingMetricSpecificationBuilder

Creates a new builder-style object to manufacture PredictiveScalingMetricSpecification.

Trait Implementations

impl Clone for PredictiveScalingMetricSpecification

fn clone(&self) -> PredictiveScalingMetricSpecification

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for PredictiveScalingMetricSpecification

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for PredictiveScalingMetricSpecification

fn eq(&self, other: &PredictiveScalingMetricSpecification) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for PredictiveScalingMetricSpecification