StepScalingPolicyProps

class aws_cdk.aws_autoscaling.StepScalingPolicyProps(*, metric, scaling_steps, adjustment_type=None, cooldown=None, estimated_instance_warmup=None, evaluation_periods=None, metric_aggregation_type=None, min_adjustment_magnitude=None, auto_scaling_group)

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. Default: Default cooldown period on your AutoScalingGroup
estimated_instance_warmup (Optional[Duration]) – Estimated time until a newly launched instance can send metrics to CloudWatch. Default: Same as the cooldown
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. 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
auto_scaling_group (IAutoScalingGroup) – The auto scaling group.

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_autoscaling as autoscaling
import aws_cdk.aws_cloudwatch as cloudwatch
import aws_cdk.core as cdk

# auto_scaling_group: autoscaling.AutoScalingGroup
# metric: cloudwatch.Metric

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

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

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

Attributes

adjustment_type

How the adjustment numbers inside ‘intervals’ are interpreted.

Default:: ChangeInCapacity

auto_scaling_group: The auto scaling group.

cooldown

Grace period after scaling activity.

Default:: Default cooldown period on your AutoScalingGroup

estimated_instance_warmup

Estimated time until a newly launched instance can send metrics to CloudWatch.

Default:: Same as the cooldown

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.

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.