BasicStepScalingPolicyProps

class aws_cdk.aws_autoscaling.BasicStepScalingPolicyProps(*, metric, scaling_steps, adjustment_type=None, cooldown=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.
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

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)
    ],

    # 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

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.