StepScalingPolicyProps

class aws_cdk.aws_applicationautoscaling.StepScalingPolicyProps(*, metric, scaling_steps, adjustment_type=None, cooldown=None, datapoints_to_alarm=None, evaluation_periods=None, metric_aggregation_type=None, min_adjustment_magnitude=None, scaling_target)

Bases: aws_cdk.aws_applicationautoscaling.BasicStepScalingPolicyProps

Parameters
  • metric (IMetric) – Metric to scale on.

  • scaling_steps (Sequence[ScalingInterval]) – The intervals for scaling. Maps a range of metric values to a particular scaling behavior.

  • adjustment_type (Optional[AdjustmentType]) – How the adjustment numbers inside ‘intervals’ are interpreted. Default: ChangeInCapacity

  • cooldown (Optional[Duration]) – Grace period after scaling activity. Subsequent scale outs during the cooldown period are squashed so that only the biggest scale out happens. Subsequent scale ins during the cooldown period are ignored. Default: No cooldown period

  • datapoints_to_alarm (Union[int, float, None]) – The number of data points out of the evaluation periods that must be breaching to trigger a scaling action. Creates an “M out of N” alarm, where this property is the M and the value set for evaluationPeriods is the N value. Only has meaning if evaluationPeriods != 1. Default: evaluationPeriods

  • evaluation_periods (Union[int, float, None]) – How many evaluation periods of the metric to wait before triggering a scaling action. Raising this value can be used to smooth out the metric, at the expense of slower response times. If datapointsToAlarm is not set, then all data points in the evaluation period must meet the criteria to trigger a scaling action. Default: 1

  • metric_aggregation_type (Optional[MetricAggregationType]) – Aggregation to apply to all data points over the evaluation periods. Only has meaning if evaluationPeriods != 1. Default: - The statistic from the metric if applicable (MIN, MAX, AVERAGE), otherwise AVERAGE.

  • min_adjustment_magnitude (Union[int, float, None]) – Minimum absolute number to adjust capacity with as result of percentage scaling. Only when using AdjustmentType = PercentChangeInCapacity, this number controls the minimum absolute effect size. Default: No minimum scaling effect

  • scaling_target (IScalableTarget) – The scaling target.

ExampleMetadata

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
import aws_cdk.aws_applicationautoscaling as appscaling
import aws_cdk.aws_cloudwatch as cloudwatch
import aws_cdk.core as cdk

# metric: cloudwatch.Metric
# scalable_target: appscaling.ScalableTarget

step_scaling_policy_props = appscaling.StepScalingPolicyProps(
    metric=metric,
    scaling_steps=[appscaling.ScalingInterval(
        change=123,

        # the properties below are optional
        lower=123,
        upper=123
    )],
    scaling_target=scalable_target,

    # the properties below are optional
    adjustment_type=appscaling.AdjustmentType.CHANGE_IN_CAPACITY,
    cooldown=cdk.Duration.minutes(30),
    datapoints_to_alarm=123,
    evaluation_periods=123,
    metric_aggregation_type=appscaling.MetricAggregationType.AVERAGE,
    min_adjustment_magnitude=123
)

Attributes

adjustment_type

How the adjustment numbers inside ‘intervals’ are interpreted.

Default

ChangeInCapacity

Return type

Optional[AdjustmentType]

cooldown

Grace period after scaling activity.

Subsequent scale outs during the cooldown period are squashed so that only the biggest scale out happens.

Subsequent scale ins during the cooldown period are ignored.

Default

No cooldown period

See

https://docs.aws.amazon.com/autoscaling/application/APIReference/API_StepScalingPolicyConfiguration.html

Return type

Optional[Duration]

datapoints_to_alarm

The number of data points out of the evaluation periods that must be breaching to trigger a scaling action.

Creates an “M out of N” alarm, where this property is the M and the value set for evaluationPeriods is the N value.

Only has meaning if evaluationPeriods != 1.

Default

evaluationPeriods

Return type

Union[int, float, None]

evaluation_periods

How many evaluation periods of the metric to wait before triggering a scaling action.

Raising this value can be used to smooth out the metric, at the expense of slower response times.

If datapointsToAlarm is not set, then all data points in the evaluation period must meet the criteria to trigger a scaling action.

Default

1

Return type

Union[int, float, None]

metric

Metric to scale on.

Return type

IMetric

metric_aggregation_type

Aggregation to apply to all data points over the evaluation periods.

Only has meaning if evaluationPeriods != 1.

Default
  • The statistic from the metric if applicable (MIN, MAX, AVERAGE), otherwise AVERAGE.

Return type

Optional[MetricAggregationType]

min_adjustment_magnitude

Minimum absolute number to adjust capacity with as result of percentage scaling.

Only when using AdjustmentType = PercentChangeInCapacity, this number controls the minimum absolute effect size.

Default

No minimum scaling effect

Return type

Union[int, float, None]

scaling_steps

The intervals for scaling.

Maps a range of metric values to a particular scaling behavior.

Return type

List[ScalingInterval]

scaling_target

The scaling target.

Return type

IScalableTarget