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@ -170,7 +170,7 @@ policy_name:
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policy_type:
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policy_type:
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description: The policy type.
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description: The policy type.
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returned: when state present
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returned: when state present
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type: string
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type: string
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min_capacity:
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min_capacity:
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description: The minimum value to scale to in response to a scale in event. Required if I(state) is C(present).
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description: The minimum value to scale to in response to a scale in event. Required if I(state) is C(present).
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returned: when state present
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returned: when state present
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@ -197,7 +197,7 @@ step_scaling_policy_configuration:
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type: string
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type: string
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sample: "ChangeInCapacity, PercentChangeInCapacity, ExactCapacity"
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sample: "ChangeInCapacity, PercentChangeInCapacity, ExactCapacity"
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cooldown:
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cooldown:
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description: The amount of time, in seconds, after a scaling activity completes
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description: The amount of time, in seconds, after a scaling activity completes
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where previous trigger-related scaling activities can influence future scaling events
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where previous trigger-related scaling activities can influence future scaling events
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returned: when state present and the policy type is StepScaling
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returned: when state present and the policy type is StepScaling
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type: int
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type: int
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@ -462,7 +462,18 @@ def main():
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], type='str'),
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], type='str'),
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policy_type=dict(required=True, choices=['StepScaling', 'TargetTrackingScaling'], type='str'),
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policy_type=dict(required=True, choices=['StepScaling', 'TargetTrackingScaling'], type='str'),
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step_scaling_policy_configuration=dict(required=False, type='dict'),
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step_scaling_policy_configuration=dict(required=False, type='dict'),
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target_tracking_scaling_policy_configuration=dict(required=False, type='dict'),
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target_tracking_scaling_policy_configuration=dict(
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required=False,
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type='dict',
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options=dict(
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CustomizedMetricSpecification=dict(type='dict'),
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DisableScaleIn=dict(type='bool'),
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PredefinedMetricSpecification=dict(type='dict'),
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ScaleInCooldown=dict(type='int'),
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ScaleOutCooldown=dict(type='int'),
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TargetValue=dict(type='float'),
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)
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),
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minimum_tasks=dict(required=False, type='int'),
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minimum_tasks=dict(required=False, type='int'),
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maximum_tasks=dict(required=False, type='int'),
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maximum_tasks=dict(required=False, type='int'),
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override_task_capacity=dict(required=False, type=bool)
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override_task_capacity=dict(required=False, type=bool)
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@ -472,6 +483,15 @@ def main():
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connection = module.client('application-autoscaling')
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connection = module.client('application-autoscaling')
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# Remove any target_tracking_scaling_policy_configuration suboptions that are None
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policy_config_options = [
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'CustomizedMetricSpecification', 'DisableScaleIn', 'PredefinedMetricSpecification', 'ScaleInCooldown', 'ScaleOutCooldown', 'TargetValue'
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]
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if isinstance(module.params['target_tracking_scaling_policy_configuration'], dict):
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for option in policy_config_options:
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if module.params['target_tracking_scaling_policy_configuration'][option] is None:
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module.params['target_tracking_scaling_policy_configuration'].pop(option)
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if module.params.get("state") == 'present':
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if module.params.get("state") == 'present':
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# A scalable target must be registered prior to creating a scaling policy
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# A scalable target must be registered prior to creating a scaling policy
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scalable_target_result = create_scalable_target(connection, module)
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scalable_target_result = create_scalable_target(connection, module)
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