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: BasicStepScalingPolicyProps

Parameters:

metric (IMetric) – Metric to scale on.
scaling_steps (Sequence[Union[ScalingInterval, Dict[str, Any]]]) – 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

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

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

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

metric: Metric to scale on.

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.

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

scaling_steps

The intervals for scaling.

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

scaling_target: The scaling target.