BasicStepScalingPolicyProps
- class aws_cdk.aws_autoscaling.BasicStepScalingPolicyProps(*, 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)
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: ChangeInCapacitycooldown (
Optional
[Duration
]) – Grace period after scaling activity. Default: Default cooldown period on your AutoScalingGroupdatapoints_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 forevaluationPeriods
is the N value. Only has meaning ifevaluationPeriods != 1
. Must be less than or equal toevaluationPeriods
. Default: - Same asevaluationPeriods
estimated_instance_warmup (
Optional
[Duration
]) – Estimated time until a newly launched instance can send metrics to CloudWatch. Default: Same as the cooldownevaluation_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. IfdatapointsToAlarm
is not set, then all data points in the evaluation period must meet the criteria to trigger a scaling action. Default: 1metric_aggregation_type (
Optional
[MetricAggregationType
]) – Aggregation to apply to all data points over the evaluation periods. Only has meaning ifevaluationPeriods != 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:
# auto_scaling_group: autoscaling.AutoScalingGroup worker_utilization_metric = cloudwatch.Metric( namespace="MyService", metric_name="WorkerUtilization" ) auto_scaling_group.scale_on_metric("ScaleToCPU", metric=worker_utilization_metric, scaling_steps=[autoscaling.ScalingInterval(upper=10, change=-1), autoscaling.ScalingInterval(lower=50, change=+1), autoscaling.ScalingInterval(lower=70, change=+3) ], evaluation_periods=10, datapoints_to_alarm=5, # Change this to AdjustmentType.PERCENT_CHANGE_IN_CAPACITY to interpret the # 'change' numbers before as percentages instead of capacity counts. adjustment_type=autoscaling.AdjustmentType.CHANGE_IN_CAPACITY )
Attributes
- adjustment_type
How the adjustment numbers inside ‘intervals’ are interpreted.
- Default:
ChangeInCapacity
- 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 toevaluationPeriods
.- 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.