moto/moto/applicationautoscaling/models.py

from __future__ import unicode_literals
from moto.core import BaseBackend, BaseModel
from moto.ecs import ecs_backends
from .exceptions import AWSValidationException
from collections import OrderedDict
from enum import Enum, unique
import time
import uuid


@unique
class ServiceNamespaceValueSet(Enum):
    APPSTREAM = "appstream"
    RDS = "rds"
    LAMBDA = "lambda"
    CASSANDRA = "cassandra"
    DYNAMODB = "dynamodb"
    CUSTOM_RESOURCE = "custom-resource"
    ELASTICMAPREDUCE = "elasticmapreduce"
    EC2 = "ec2"
    COMPREHEND = "comprehend"
    ECS = "ecs"
    SAGEMAKER = "sagemaker"


@unique
class ScalableDimensionValueSet(Enum):
    CASSANDRA_TABLE_READ_CAPACITY_UNITS = "cassandra:table:ReadCapacityUnits"
    CASSANDRA_TABLE_WRITE_CAPACITY_UNITS = "cassandra:table:WriteCapacityUnits"
    DYNAMODB_INDEX_READ_CAPACITY_UNITS = "dynamodb:index:ReadCapacityUnits"
    DYNAMODB_INDEX_WRITE_CAPACITY_UNITS = "dynamodb:index:WriteCapacityUnits"
    DYNAMODB_TABLE_READ_CAPACITY_UNITS = "dynamodb:table:ReadCapacityUnits"
    DYNAMODB_TABLE_WRITE_CAPACITY_UNITS = "dynamodb:table:WriteCapacityUnits"
    RDS_CLUSTER_READ_REPLICA_COUNT = "rds:cluster:ReadReplicaCount"
    RDS_CLUSTER_CAPACITY = "rds:cluster:Capacity"
    COMPREHEND_DOCUMENT_CLASSIFIER_ENDPOINT_DESIRED_INFERENCE_UNITS = (
        "comprehend:document-classifier-endpoint:DesiredInferenceUnits"
    )
    ELASTICMAPREDUCE_INSTANCE_FLEET_ON_DEMAND_CAPACITY = (
        "elasticmapreduce:instancefleet:OnDemandCapacity"
    )
    ELASTICMAPREDUCE_INSTANCE_FLEET_SPOT_CAPACITY = (
        "elasticmapreduce:instancefleet:SpotCapacity"
    )
    ELASTICMAPREDUCE_INSTANCE_GROUP_INSTANCE_COUNT = (
        "elasticmapreduce:instancegroup:InstanceCount"
    )
    LAMBDA_FUNCTION_PROVISIONED_CONCURRENCY = "lambda:function:ProvisionedConcurrency"
    APPSTREAM_FLEET_DESIRED_CAPACITY = "appstream:fleet:DesiredCapacity"
    CUSTOM_RESOURCE_RESOURCE_TYPE_PROPERTY = "custom-resource:ResourceType:Property"
    SAGEMAKER_VARIANT_DESIRED_INSTANCE_COUNT = "sagemaker:variant:DesiredInstanceCount"
    EC2_SPOT_FLEET_REQUEST_TARGET_CAPACITY = "ec2:spot-fleet-request:TargetCapacity"
    ECS_SERVICE_DESIRED_COUNT = "ecs:service:DesiredCount"


class ApplicationAutoscalingBackend(BaseBackend):
    def __init__(self, region, ecs):
        super(ApplicationAutoscalingBackend, self).__init__()
        self.region = region
        self.ecs_backend = ecs
        self.targets = OrderedDict()
        self.policies = {}

    def reset(self):
        region = self.region
        ecs = self.ecs_backend
        self.__dict__ = {}
        self.__init__(region, ecs)

    @property
    def applicationautoscaling_backend(self):
        return applicationautoscaling_backends[self.region]

    def describe_scalable_targets(
        self, namespace, r_ids=None, dimension=None,
    ):
        """ Describe scalable targets. """
        if r_ids is None:
            r_ids = []
        targets = self._flatten_scalable_targets(namespace)
        if dimension is not None:
            targets = [t for t in targets if t.scalable_dimension == dimension]
        if len(r_ids) > 0:
            targets = [t for t in targets if t.resource_id in r_ids]
        return targets

    def _flatten_scalable_targets(self, namespace):
        """ Flatten scalable targets for a given service namespace down to a list. """
        targets = []
        for dimension in self.targets.keys():
            for resource_id in self.targets[dimension].keys():
                targets.append(self.targets[dimension][resource_id])
        targets = [t for t in targets if t.service_namespace == namespace]
        return targets

    def register_scalable_target(self, namespace, r_id, dimension, **kwargs):
        """ Registers or updates a scalable target. """
        _ = _target_params_are_valid(namespace, r_id, dimension)
        if namespace == ServiceNamespaceValueSet.ECS.value:
            _ = self._ecs_service_exists_for_target(r_id)
        if self._scalable_target_exists(r_id, dimension):
            target = self.targets[dimension][r_id]
            target.update(**kwargs)
        else:
            target = FakeScalableTarget(self, namespace, r_id, dimension, **kwargs)
            self._add_scalable_target(target)
        return target

    def _scalable_target_exists(self, r_id, dimension):
        return r_id in self.targets.get(dimension, [])

    def _ecs_service_exists_for_target(self, r_id):
        """Raises a ValidationException if an ECS service does not exist
        for the specified resource ID.
        """
        resource_type, cluster, service = r_id.split("/")
        result, _ = self.ecs_backend.describe_services(cluster, [service])
        if len(result) != 1:
            raise AWSValidationException("ECS service doesn't exist: {}".format(r_id))
        return True

    def _add_scalable_target(self, target):
        if target.scalable_dimension not in self.targets:
            self.targets[target.scalable_dimension] = OrderedDict()
        if target.resource_id not in self.targets[target.scalable_dimension]:
            self.targets[target.scalable_dimension][target.resource_id] = target
        return target

    def deregister_scalable_target(self, namespace, r_id, dimension):
        """ Registers or updates a scalable target. """
        if self._scalable_target_exists(r_id, dimension):
            del self.targets[dimension][r_id]
        else:
            raise AWSValidationException(
                "No scalable target found for service namespace: {}, resource ID: {}, scalable dimension: {}".format(
                    namespace, r_id, dimension
                )
            )

    def put_scaling_policy(
        self,
        policy_name,
        service_namespace,
        resource_id,
        scalable_dimension,
        policy_body,
        policy_type=None,
    ):
        policy_key = FakeApplicationAutoscalingPolicy.formulate_key(
            service_namespace, resource_id, scalable_dimension, policy_name
        )
        if policy_key in self.policies:
            old_policy = self.policies[policy_name]
            policy = FakeApplicationAutoscalingPolicy(
                region_name=self.region,
                policy_name=policy_name,
                service_namespace=service_namespace,
                resource_id=resource_id,
                scalable_dimension=scalable_dimension,
                policy_type=policy_type if policy_type else old_policy.policy_type,
                policy_body=policy_body if policy_body else old_policy._policy_body,
            )
        else:
            policy = FakeApplicationAutoscalingPolicy(
                region_name=self.region,
                policy_name=policy_name,
                service_namespace=service_namespace,
                resource_id=resource_id,
                scalable_dimension=scalable_dimension,
                policy_type=policy_type,
                policy_body=policy_body,
            )
        self.policies[policy_key] = policy
        return policy

    def describe_scaling_policies(self, service_namespace, **kwargs):
        policy_names = kwargs.get("policy_names")
        resource_id = kwargs.get("resource_id")
        scalable_dimension = kwargs.get("scalable_dimension")
        max_results = kwargs.get("max_results") or 100
        next_token = kwargs.get("next_token")
        policies = [
            policy
            for policy in self.policies.values()
            if policy.service_namespace == service_namespace
        ]
        if policy_names:
            policies = [
                policy for policy in policies if policy.policy_name in policy_names
            ]
        if resource_id:
            policies = [
                policy for policy in policies if policy.resource_id in resource_id
            ]
        if scalable_dimension:
            policies = [
                policy
                for policy in policies
                if policy.scalable_dimension in scalable_dimension
            ]
        starting_point = int(next_token) if next_token else 0
        ending_point = starting_point + max_results
        policies_page = policies[starting_point:ending_point]
        new_next_token = str(ending_point) if ending_point < len(policies) else None
        return new_next_token, policies_page

    def delete_scaling_policy(
        self, policy_name, service_namespace, resource_id, scalable_dimension
    ):
        policy_key = FakeApplicationAutoscalingPolicy.formulate_key(
            service_namespace, resource_id, scalable_dimension, policy_name
        )
        if policy_key in self.policies:
            del self.policies[policy_key]
            return {}
        else:
            raise AWSValidationException(
                "No scaling policy found for service namespace: {}, resource ID: {}, scalable dimension: {}, policy name: {}".format(
                    service_namespace, resource_id, scalable_dimension, policy_name
                )
            )


def _target_params_are_valid(namespace, r_id, dimension):
    """ Check whether namespace, resource_id and dimension are valid and consistent with each other. """
    is_valid = True
    valid_namespaces = [n.value for n in ServiceNamespaceValueSet]
    if namespace not in valid_namespaces:
        is_valid = False
    if dimension is not None:
        try:
            valid_dimensions = [d.value for d in ScalableDimensionValueSet]
            d_namespace, d_resource_type, scaling_property = dimension.split(":")
            resource_type = _get_resource_type_from_resource_id(r_id)
            if (
                dimension not in valid_dimensions
                or d_namespace != namespace
                or resource_type != d_resource_type
            ):
                is_valid = False
        except ValueError:
            is_valid = False
    if not is_valid:
        raise AWSValidationException(
            "Unsupported service namespace, resource type or scalable dimension"
        )
    return is_valid


def _get_resource_type_from_resource_id(resource_id):
    # AWS Application Autoscaling resource_ids are multi-component (path-like) identifiers that vary in format,
    # depending on the type of resource it identifies.  resource_type is one of its components.
    #  resource_id format variations are described in
    #   https://docs.aws.amazon.com/autoscaling/application/APIReference/API_RegisterScalableTarget.html
    #  In a nutshell:
    #  - Most use slash separators, but some use colon separators.
    #  - The resource type is usually the first component of the resource_id...
    #    - ...except for sagemaker endpoints, dynamodb GSIs and keyspaces tables, where it's the third.
    #  - Comprehend uses an arn, with the resource type being the last element.

    if resource_id.startswith("arn:aws:comprehend"):
        resource_id = resource_id.split(":")[-1]
    resource_split = (
        resource_id.split("/") if "/" in resource_id else resource_id.split(":")
    )
    if (
        resource_split[0] == "endpoint"
        or (resource_split[0] == "table" and len(resource_split) > 2)
        or (resource_split[0] == "keyspace")
    ):
        resource_type = resource_split[2]
    else:
        resource_type = resource_split[0]
    return resource_type


class FakeScalableTarget(BaseModel):
    def __init__(
        self, backend, service_namespace, resource_id, scalable_dimension, **kwargs
    ):
        self.applicationautoscaling_backend = backend
        self.service_namespace = service_namespace
        self.resource_id = resource_id
        self.scalable_dimension = scalable_dimension
        self.min_capacity = kwargs["min_capacity"]
        self.max_capacity = kwargs["max_capacity"]
        self.role_arn = kwargs["role_arn"]
        self.suspended_state = kwargs["suspended_state"]
        self.creation_time = time.time()

    def update(self, **kwargs):
        if kwargs["min_capacity"] is not None:
            self.min_capacity = kwargs["min_capacity"]
        if kwargs["max_capacity"] is not None:
            self.max_capacity = kwargs["max_capacity"]
        if kwargs["suspended_state"] is not None:
            self.suspended_state = kwargs["suspended_state"]


class FakeApplicationAutoscalingPolicy(BaseModel):
    def __init__(
        self,
        region_name,
        policy_name,
        service_namespace,
        resource_id,
        scalable_dimension,
        policy_type,
        policy_body,
    ):
        self.step_scaling_policy_configuration = None
        self.target_tracking_scaling_policy_configuration = None

        if "policy_type" == "StepScaling":
            self.step_scaling_policy_configuration = policy_body
            self.target_tracking_scaling_policy_configuration = None
        elif policy_type == "TargetTrackingScaling":
            self.step_scaling_policy_configuration = None
            self.target_tracking_scaling_policy_configuration = policy_body
        else:
            raise AWSValidationException(
                "Unknown policy type {} specified.".format(policy_type)
            )

        self._policy_body = policy_body
        self.service_namespace = service_namespace
        self.resource_id = resource_id
        self.scalable_dimension = scalable_dimension
        self.policy_name = policy_name
        self.policy_type = policy_type
        self._guid = uuid.uuid4()
        self.policy_arn = "arn:aws:autoscaling:{}:scalingPolicy:{}:resource/sagemaker/{}:policyName/{}".format(
            region_name, self._guid, self.resource_id, self.policy_name
        )
        self.creation_time = time.time()

    @staticmethod
    def formulate_key(service_namespace, resource_id, scalable_dimension, policy_name):
        return "{}\t{}\t{}\t{}".format(
            service_namespace, resource_id, scalable_dimension, policy_name
        )


applicationautoscaling_backends = {}
for region_name, ecs_backend in ecs_backends.items():
    applicationautoscaling_backends[region_name] = ApplicationAutoscalingBackend(
        region_name, ecs_backend
    )