BasicStepScalingPolicyProps

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

Bases: object

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. Must be between 2 and 40 steps.
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: - Same as 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

ExampleMetadata:

infused

Example:

# capacity: ScalableAttribute
# cpu_utilization: cloudwatch.Metric


capacity.scale_on_metric("ScaleToCPU",
    metric=cpu_utilization,
    scaling_steps=[appscaling.ScalingInterval(upper=10, change=-1), appscaling.ScalingInterval(lower=50, change=+1), appscaling.ScalingInterval(lower=70, change=+3)
    ],

    # Change this to AdjustmentType.PercentChangeInCapacity to interpret the
    # 'change' numbers before as percentages instead of capacity counts.
    adjustment_type=appscaling.AdjustmentType.CHANGE_IN_CAPACITY
)

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:

Same as 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.

Must be between 2 and 40 steps.