"""State module for managing Amazon Application Autoscaling scalable target."""
import copy
import re
from dataclasses import field
from dataclasses import make_dataclass
from typing import Any
from typing import Dict
__contracts__ = ["resource"]
[docs]async def present(
hub,
ctx,
name: str,
service_namespace: str,
scaling_resource_id: str,
scalable_dimension: str,
resource_id: str = None,
min_capacity: int = None,
max_capacity: int = None,
role_arn: str = None,
suspended_state: make_dataclass(
"SuspendedState",
[
("DynamicScalingInSuspended", bool, field(default=None)),
("DynamicScalingOutSuspended", bool, field(default=None)),
("ScheduledScalingSuspended", bool, field(default=None)),
],
) = None,
) -> Dict[str, Any]:
"""Registers or updates a scalable target.
A scalable target is a resource that Application Auto Scaling can scale
out and scale in. Scalable targets are uniquely identified by the combination of resource ID, scalable
dimension, and namespace.
When you register a new scalable target, you must specify values for minimum and
maximum capacity. Current capacity will be adjusted within the specified range when scaling starts. Application
Auto Scaling scaling policies will not scale capacity to values that are outside of this range.
After you register a scalable target, you do not need to register it again to use other Application Auto Scaling
operations. To see which resources have been registered, use DescribeScalableTargets. You can also view the
scaling policies for a service namespace by using DescribeScalableTargets. If you no longer need a scalable
target, you can deregister it by using DeregisterScalableTarget.
To update a scalable target, specify the
parameters that you want to change. Include the parameters that identify the scalable target: resource ID,
scalable dimension, and namespace. Any parameters that you don't specify are not changed by this update request.
.. Note::
If you call the RegisterScalableTarget API to update an existing scalable target, Application Auto Scaling
retrieves the current capacity of the resource. If it is below the minimum capacity or above the maximum
capacity, Application Auto Scaling adjusts the capacity of the scalable target to place it within these bounds,
even if you don't include the MinCapacity or MaxCapacity request parameters.
Args:
name(str):
An Idem name of the resource.
resource_id(str, Optional):
An identifier of the resource in the provider. Defaults to None.
service_namespace(str):
The namespace of the Amazon Web Services service that provides the resource. For a resource
provided by your own application or service, use custom-resource instead.
scaling_resource_id(str):
The identifier of the resource that is associated with the scalable target. This string consists
of the resource type and unique identifier.
* ECS service - The resource type is service and the
unique identifier is the cluster name and service name. Example: service/default/sample-webapp.
* Spot Fleet - The resource type is spot-fleet-request and the unique identifier is the Spot Fleet
request ID. Example: spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE.
* EMR cluster - The resource type is instancegroup and the unique identifier is the cluster ID and instance
group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ig-1791Y4E1L8YI0.
* AppStream 2.0 fleet - The resource type is fleet and the unique identifier is the fleet name.
Example: fleet/sample-fleet.
* DynamoDB table - The resource type is table and the unique identifier is the table name.
Example: table/my-table.
* DynamoDB global secondary index - The resource type is index and the
unique identifier is the index name. Example: table/my-table/index/my-table-index.
* Aurora DB cluster - The resource type is cluster and the unique identifier is the cluster name. Example:
cluster:my-db-cluster.
* SageMaker endpoint variant - The resource type is variant and the
unique identifier is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering.
* Custom resources are not supported with a resource type.
This parameter must specify the OutputValue from the CloudFormation template stack used to access
the resources. The unique identifier is defined by the service provider.
More information is available in our GitHub repository.
* Amazon Comprehend document classification endpoint - The resource type and unique
identifier are specified using the endpoint ARN.
Example: arn:aws:comprehend:us-west-2:123456789012:document-classifier-endpoint/EXAMPLE.
* Amazon Comprehend entity recognizer endpoint - The resource type and unique identifier are specified
using the endpoint ARN.
Example: arn:aws:comprehend:us-west-2:123456789012:entity-recognizer-endpoint/EXAMPLE.
* Lambda provisioned concurrency - The resource type is function and the unique identifier is the
function name with a function version or alias name suffix that is not $LATEST. Example:
function:my-function:prod or function:my-function:1.
* Amazon Keyspaces table - The resource type is table and the unique identifier is the table name.
Example: keyspace/mykeyspace/table/mytable.
* Amazon MSK cluster - The resource type and unique identifier are specified using the cluster ARN.
Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo-cluster-1
/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5.
* Amazon ElastiCache replication group - The resource type is replication-group and the unique identifier
is the replication group name. Example: replication-group/mycluster.
* Neptune cluster - The resource type is cluster and the unique identifier is the cluster name. Example:
cluster:mycluster.
scalable_dimension(str):
The scalable dimension associated with the scalable target. This string consists of the service
namespace, resource type, and scaling property.
* ecs:service:DesiredCount - The desired task count of an ECS service.
* elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group.
* ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet.
* appstream:fleet:DesiredCapacity - The desired capacity of an AppStream 2.0 fleet.
* dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table.
* dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table.
* dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index.
* dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index.
* rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster.
Available for Aurora MySQL-compatible edition and Aurora PostgreSQL- compatible edition.
* sagemaker:variant:DesiredInstanceCount -
The number of EC2 instances for an SageMaker model endpoint variant.
* custom-resource:ResourceType:Property -
The scalable dimension for a custom resource provided by your own application or service.
* comprehend:document-classifier-endpoint:DesiredInferenceUnits -
The number of inference units for an Amazon Comprehend document classification endpoint.
* comprehend:entity-recognizer-endpoint:DesiredInferenceUnits -
The number of inference units for an Amazon Comprehend entity recognizer endpoint.
* lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function.
* cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table.
* cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table.
* kafka:broker-storage:VolumeSize -
The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster.
* elasticache:replication-group:NodeGroups -
The number of node groups for an Amazon ElastiCache replication group.
* elasticache:replication-group:Replicas -
The number of replicas per node group for an Amazon ElastiCache replication group.
* neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster.
min_capacity(int, Optional):
The minimum value that you plan to scale in to. When a scaling policy is in effect, Application
Auto Scaling can scale in (contract) as needed to the minimum capacity limit in response to
changing demand. This property is required when registering a new scalable target.
For certain resources, the minimum value allowed is 0. This includes Lambda provisioned concurrency, Spot
Fleet, ECS services, Aurora DB clusters, EMR clusters, and custom resources. For all other
resources, the minimum value allowed is 1. Defaults to None.
max_capacity(int, Optional):
The maximum value that you plan to scale out to. When a scaling policy is in effect, Application
Auto Scaling can scale out (expand) as needed to the maximum capacity limit in response to
changing demand. This property is required when registering a new scalable target.
Although you can specify a large maximum capacity, note that service quotas may impose lower limits. Each
service has its own default quotas for the maximum capacity of the resource. If you want to
specify a higher limit, you can request an increase. For more information, consult the
documentation for that service. For information about the default quotas for each service, see
Service Endpoints and Quotas in the Amazon Web Services General Reference. Defaults to None.
role_arn(str, Optional):
This parameter is required for services that do not support service-linked roles (such as Amazon
EMR), and it must specify the ARN of an IAM role that allows Application Auto Scaling to modify
the scalable target on your behalf.
If the service supports service-linked roles, Application
Auto Scaling uses a service-linked role, which it creates if it does not yet exist. For more
information, see Application Auto Scaling IAM roles. Defaults to None.
suspended_state(dict[str, Any], Optional):
An embedded object that contains attributes and attribute values that are used to suspend and
resume automatic scaling. Setting the value of an attribute to true suspends the specified
scaling activities. Setting it to false (default) resumes the specified scaling activities.
Suspension Outcomes
* For DynamicScalingInSuspended, while a suspension is in effect, all
scale-in activities that are triggered by a scaling policy are suspended.
* For DynamicScalingOutSuspended, while a suspension is in effect, all scale-out activities that are
triggered by a scaling policy are suspended.
* For ScheduledScalingSuspended, while a suspension is in effect,
all scaling activities that involve scheduled actions are suspended.
For more information, see Suspending and resuming scaling in the Application Auto Scaling User Guide.
Defaults to None.
* DynamicScalingInSuspended (bool, Optional):
Whether scale in by a target tracking scaling policy or a step scaling policy is suspended. Set
the value to true if you don't want Application Auto Scaling to remove capacity when a scaling
policy is triggered. The default is false.
* DynamicScalingOutSuspended (bool, Optional):
Whether scale out by a target tracking scaling policy or a step scaling policy is suspended. Set
the value to true if you don't want Application Auto Scaling to add capacity when a scaling
policy is triggered. The default is false.
* ScheduledScalingSuspended (bool, Optional):
Whether scheduled scaling is suspended. Set the value to true if you don't want Application Auto
Scaling to add or remove capacity by initiating scheduled actions. The default is false.
Request Syntax:
.. code-block:: sls
[scalable_target_id]:
aws.application_autoscaling.scalable_target.present:
- name: 'string'
- service_namespace: 'string'
- scalable_dimension: 'string'
- scaling_resource_id: 'string'
- min_capacity: 'int'
- max_capacity: 'int'
- suspended_state:
DynamicScalingInSuspended: 'Boolean'
DynamicScalingOutSuspended: 'Boolean'
ScheduledScalingSuspended: 'Boolean'
Returns:
Dict[str, Any]
Examples:
.. code-block:: sls
rds_scalable_target:
aws.application_autoscaling.scalable_target.present:
- name: rds_scalable_target
- service_namespace: rds
- scalable_dimension: rds:cluster:ReadReplicaCount
- scaling_resource_id: idem-test-rds-aurora-table
- min_capacity: 1
- max_capacity: 3
- suspended_state:
DynamicScalingInSuspended: False
DynamicScalingOutSuspended: False
ScheduledScalingSuspended: False
"""
result = dict(comment=[], old_state=None, new_state=None, name=name, result=True)
before = None
resource_updated = False
plan_state = None
if resource_id:
if not re.search(
f"^({service_namespace})/({scaling_resource_id})/({scalable_dimension})$",
resource_id,
):
result["comment"] += [
f"Incorrect aws.application_autoscaling.scalable_target resource_id: {resource_id}. Expected id {service_namespace}/{scaling_resource_id}/{scalable_dimension}"
]
result["result"] = False
return result
before = await hub.exec.aws.application_autoscaling.scalable_target.get(
ctx=ctx,
name=name,
service_namespace=service_namespace,
scaling_resource_ids=[scaling_resource_id],
scalable_dimension=scalable_dimension,
)
if not before["result"] or not before["ret"]:
result["result"] = False
result["comment"] = before["comment"]
return result
result["old_state"] = copy.deepcopy(before["ret"])
plan_state = copy.deepcopy(result["old_state"])
update_ret = await hub.tool.aws.application_autoscaling.scalable_target.update_scalable_target(
ctx=ctx,
name=name,
before=result["old_state"],
service_namespace=service_namespace,
scaling_resource_id=scaling_resource_id,
scalable_dimension=scalable_dimension,
min_capacity=min_capacity,
max_capacity=max_capacity,
role_arn=role_arn,
suspended_state=suspended_state,
)
result["comment"] = update_ret["comment"]
result["result"] = update_ret["result"]
resource_updated = bool(update_ret["ret"])
if update_ret["ret"] and ctx.get("test", False):
for modified_param in update_ret["ret"]:
plan_state[modified_param] = update_ret["ret"][modified_param]
result["comment"] += hub.tool.aws.comment_utils.would_update_comment(
resource_type="aws.application_autoscaling.scalable_target", name=name
)
else:
if ctx.get("test", False):
result["new_state"] = hub.tool.aws.test_state_utils.generate_test_state(
enforced_state={},
desired_state={
"name": name,
"service_namespace": service_namespace,
"scaling_resource_id": scaling_resource_id,
"scalable_dimension": scalable_dimension,
"min_capacity": min_capacity,
"max_capacity": max_capacity,
"role_arn": role_arn,
"suspended_state": suspended_state,
},
)
result["comment"] = hub.tool.aws.comment_utils.would_create_comment(
resource_type="aws.application_autoscaling.scalable_target", name=name
)
return result
ret = await hub.exec.boto3.client[
"application-autoscaling"
].register_scalable_target(
ctx,
ServiceNamespace=service_namespace,
ResourceId=scaling_resource_id,
ScalableDimension=scalable_dimension,
MinCapacity=min_capacity,
MaxCapacity=max_capacity,
RoleARN=role_arn,
SuspendedState=suspended_state,
)
result["result"] = ret["result"]
if not result["result"]:
result["comment"] = ret["comment"]
return result
result["comment"] = hub.tool.aws.comment_utils.create_comment(
resource_type="aws.application_autoscaling.scalable_target", name=name
)
try:
if ctx.get("test", False):
result["new_state"] = plan_state
elif (not before) or resource_updated:
after = await hub.exec.aws.application_autoscaling.scalable_target.get(
ctx=ctx,
name=name,
service_namespace=service_namespace,
scaling_resource_ids=[scaling_resource_id],
scalable_dimension=scalable_dimension,
)
if not after["result"]:
result["result"] = False
result["comment"] += after["comment"]
return result
result["new_state"] = copy.deepcopy(after["ret"])
else:
result["new_state"] = copy.deepcopy(result["old_state"])
except Exception as e:
result["comment"] += [str(e)]
result["result"] = False
return result
[docs]async def absent(
hub,
ctx,
name: str,
scaling_resource_id: str = None,
service_namespace: str = None,
scalable_dimension: str = None,
resource_id: str = None,
) -> Dict[str, Any]:
"""Deregisters an Application Auto Scaling scalable target when you have finished using it.
To see which resources have been registered, use DescribeScalableTargets.
Deregistering a scalable target deletes the scaling policies and the scheduled actions that are associated with it.
Args:
name(str):
An Idem name of the resource.
service_namespace(str, Optional):
The namespace of the Amazon Web Services service that provides the resource. For a resource
provided by your own application or service, use custom-resource instead.
scaling_resource_id(str, Optional):
The identifier of the resource associated with the scalable target. This string consists of the
resource type and unique identifier.
* ECS service - The resource type is service and the unique identifier is the cluster name and service name.
Example: service/default/sample-webapp.
* Spot Fleet - The resource type is spot-fleet-request and the unique identifier is the Spot Fleet
request ID. Example: spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE.
* EMR cluster - The resource type is instancegroup and the unique identifier is the cluster ID and instance
group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ig-1791Y4E1L8YI0.
* AppStream 2.0 fleet - The resource type is fleet and the unique identifier is the fleet name.
Example: fleet/sample-fleet.
* DynamoDB table - The resource type is table and the unique identifier is the table name.
Example: table/my-table.
* DynamoDB global secondary index - The resource type is index and the
unique identifier is the index name. Example: table/my-table/index/my-table-index.
* Aurora DB cluster - The resource type is cluster and the unique identifier is the cluster name. Example:
cluster:my-db-cluster.
* SageMaker endpoint variant - The resource type is variant and the
unique identifier is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering.
* Custom resources are not supported with a resource type. This parameter must specify the
OutputValue from the CloudFormation template stack used to access the resources. The unique
identifier is defined by the service provider. More information is available in our GitHub
repository.
* Amazon Comprehend document classification endpoint - The resource type and unique
identifier are specified using the endpoint ARN. Example: arn:aws:comprehend:us-
west-2:123456789012:document-classifier-endpoint/EXAMPLE.
* Amazon Comprehend entity recognizer endpoint -
The resource type and unique identifier are specified using the endpoint ARN.
Example: arn:aws:comprehend:us-west-2:123456789012:entity-recognizer-endpoint/EXAMPLE.
* Lambda provisioned concurrency -
The resource type is function and the unique identifier is the
function name with a function version or alias name suffix that is not $LATEST. Example:
function:my-function:prod or function:my-function:1.
* Amazon Keyspaces table - The resource type is table and the unique identifier is the table name. Example:
keyspace/mykeyspace/table/mytable.
* Amazon MSK cluster -
The resource type and unique identifier are specified using the cluster ARN. Example:
arn:aws:kafka:us-east-1:123456789012:cluster/demo-cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5.
* Amazon ElastiCache replication group - The resource type is replication-group and the unique identifier
is the replication group name. Example: replication-group/mycluster.
* Neptune cluster - The resource type is cluster and the unique identifier is the cluster name. Example:
cluster:mycluster.
scalable_dimension(str, Optional):
The scalable dimension associated with the scalable target. This string consists of the service
namespace, resource type, and scaling property.
* ecs:service:DesiredCount - The desired task count of an ECS service.
* elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group.
* ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet.
* appstream:fleet:DesiredCapacity - The desired capacity of an AppStream 2.0 fleet.
* dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table.
* dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table.
* dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index.
* dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index.
* rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster.
Available for Aurora MySQL-compatible edition and Aurora PostgreSQL- compatible edition.
* sagemaker:variant:DesiredInstanceCount -
The number of EC2 instances for an SageMaker model endpoint variant.
* custom-resource:ResourceType:Property -
The scalable dimension for a custom resource provided by your own application or service.
* comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units
for an Amazon Comprehend document classification endpoint.
* comprehend:entity-recognizer-endpoint:DesiredInferenceUnits -
The number of inference units for an Amazon Comprehend entity recognizer endpoint.
* lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function.
* cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table.
* cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table.
* kafka:broker-storage:VolumeSize -
The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster.
* elasticache:replication-group:NodeGroups -
The number of node groups for an Amazon ElastiCache replication group.
* elasticache:replication-group:Replicas -
The number of replicas per node group for an Amazon ElastiCache replication group.
* neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster.
resource_id(str, Optional):
An identifier of the resource in the provider.
Returns:
Dict[str, Any]
Examples:
.. code-block:: sls
rds_scalable_target:
aws.application_autoscaling.scalable_target.absent:
- name: rds_scalable_target
- service_namespace: rds
- scalable_dimension: rds:cluster:ReadReplicaCount
- scaling_resource_id: idem-test-rds-aurora-table
"""
result = dict(comment=[], old_state=None, new_state=None, name=name, result=True)
if not resource_id:
result["comment"] = hub.tool.aws.comment_utils.already_absent_comment(
resource_type="aws.application_autoscaling.scalable_target", name=name
)
return result
if resource_id:
if not service_namespace or not scaling_resource_id or not scalable_dimension:
result[
"comment"
] = hub.tool.aws.comment_utils.missing_args_for_absent_comment(
resource_type="aws.application_autoscaling.scalable_target",
name=name,
args=["service_namespace", "scaling_resource_id", "scalable_dimension"],
)
result["result"] = False
return result
if not re.search(
f"^({service_namespace})/({scaling_resource_id})/({scalable_dimension})$",
resource_id,
):
result["comment"] += [
f"Incorrect aws.application_autoscaling.scalable_target resource_id: {resource_id}. Expected id {service_namespace}/{scaling_resource_id}/{scalable_dimension}"
]
result["result"] = False
return result
before = await hub.exec.aws.application_autoscaling.scalable_target.get(
ctx=ctx,
name=name,
service_namespace=service_namespace,
scaling_resource_ids=[scaling_resource_id],
scalable_dimension=scalable_dimension,
)
if not before["result"]:
result["result"] = False
result["comment"] = before["comment"]
return result
if not before["ret"]:
result["comment"] = hub.tool.aws.comment_utils.already_absent_comment(
resource_type="aws.application_autoscaling.scalable_target", name=name
)
elif ctx.get("test", False):
result["old_state"] = before["ret"]
result["comment"] = hub.tool.aws.comment_utils.would_delete_comment(
resource_type="aws.application_autoscaling.scalable_target", name=name
)
return result
else:
result["old_state"] = before["ret"]
ret = await hub.exec.boto3.client[
"application-autoscaling"
].deregister_scalable_target(
ctx,
ServiceNamespace=service_namespace,
ResourceId=scaling_resource_id,
ScalableDimension=scalable_dimension,
)
result["result"] = ret["result"]
if not result["result"]:
result["comment"] = ret["comment"]
result["result"] = False
return result
result["comment"] = result[
"comment"
] = hub.tool.aws.comment_utils.delete_comment(
resource_type="aws.application_autoscaling.scalable_target", name=name
)
return result
[docs]async def describe(hub, ctx) -> Dict[str, Dict[str, Any]]:
"""Describe the resource in a way that can be recreated/managed with the corresponding "present" function.
Gets information about the scalable targets in the specified namespace. You can filter the results using
ResourceIds and ScalableDimension.
Returns:
Dict[str, Dict[str, Any]]
Examples:
.. code-block:: bash
$ idem describe aws.application_autoscaling.scalable_target
"""
result = {}
# service_name_spaces supported by AWS. loop through all the service name spaces
# and list scaling targets of each service.
service_name_spaces = [
"ecs",
"elasticmapreduce",
"ec2",
"appstream",
"dynamodb",
"rds",
"sagemaker",
"custom-resource",
"comprehend",
"lambda",
"cassandra",
"kafka",
"elasticache",
"neptune",
]
for service_name_space in service_name_spaces:
ret = await hub.exec.boto3.client[
"application-autoscaling"
].describe_scalable_targets(ctx, ServiceNamespace=service_name_space)
if not ret["result"]:
hub.log.warning(
f"Could not describe scaling_targets for service name space {service_name_space}. {ret['comment']}"
)
continue
for scaling_target in ret["ret"]["ScalableTargets"]:
resource_id = f"{scaling_target.get('ServiceNamespace')}/{scaling_target.get('ResourceId')}/{scaling_target.get('ScalableDimension')}"
resource_translated = hub.tool.aws.application_autoscaling.conversion_utils.convert_raw_scaling_target_to_present(
ctx, raw_resource=scaling_target, idem_resource_name=resource_id
)
result[resource_id] = {
"aws.application_autoscaling.scalable_target.present": [
{parameter_key: parameter_value}
for parameter_key, parameter_value in resource_translated.items()
]
}
return result