StepScalingPolicyProps

class aws_cdk.aws_autoscaling.StepScalingPolicyProps(*, metric, scaling_steps, adjustment_type=None, cooldown=None, datapoints_to_alarm=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. 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. Default: Default cooldown period on your AutoScalingGroup
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. Must be less than or equal to evaluationPeriods. Default: - Same as evaluationPeriods
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. 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
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 as cdk
from aws_cdk import aws_autoscaling as autoscaling
from aws_cdk import aws_cloudwatch as cloudwatch

# 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),
    datapoints_to_alarm=123,
    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

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. Must be less than or equal to evaluationPeriods.

Default:

Same as evaluationPeriods

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.

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.