#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2017 Google
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
# ----------------------------------------------------------------------------
#
# *** AUTO GENERATED CODE *** Type: MMv1 ***
#
# ----------------------------------------------------------------------------
#
# This file is automatically generated by Magic Modules and manual
# changes will be clobbered when the file is regenerated.
#
# Please read more about how to change this file at
# https://www.github.com/GoogleCloudPlatform/magic-modules
#
# ----------------------------------------------------------------------------
from __future__ import absolute_import, division, print_function
__metaclass__ = type
################################################################################
# Documentation
################################################################################
ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ["preview"], 'supported_by': 'community'}
DOCUMENTATION = '''
---
module: gcp_compute_autoscaler
description:
- Represents an Autoscaler resource.
- Autoscalers allow you to automatically scale virtual machine instances in managed
instance groups according to an autoscaling policy that you define.
short_description: Creates a GCP Autoscaler
author: Google Inc. (@googlecloudplatform)
requirements:
- python >= 2.6
- requests >= 2.18.4
- google-auth >= 1.3.0
options:
state:
description:
- Whether the given object should exist in GCP
choices:
- present
- absent
default: present
type: str
name:
description:
- Name of the resource. The name must be 1-63 characters long and match the regular
expression `[a-z]([-a-z0-9]*[a-z0-9])?` which means the first character must
be a lowercase letter, and all following characters must be a dash, lowercase
letter, or digit, except the last character, which cannot be a dash.
required: true
type: str
description:
description:
- An optional description of this resource.
required: false
type: str
autoscaling_policy:
description:
- 'The configuration parameters for the autoscaling algorithm. You can define
one or more of the policies for an autoscaler: cpuUtilization, customMetricUtilizations,
and loadBalancingUtilization.'
- If none of these are specified, the default will be to autoscale based on cpuUtilization
to 0.6 or 60%.
required: true
type: dict
suboptions:
min_num_replicas:
description:
- The minimum number of replicas that the autoscaler can scale down to. This
cannot be less than 0. If not provided, autoscaler will choose a default
value depending on maximum number of instances allowed.
required: false
type: int
aliases:
- minReplicas
max_num_replicas:
description:
- The maximum number of instances that the autoscaler can scale up to. This
is required when creating or updating an autoscaler. The maximum number
of replicas should not be lower than minimal number of replicas.
required: true
type: int
aliases:
- maxReplicas
cool_down_period_sec:
description:
- The number of seconds that the autoscaler should wait before it starts collecting
information from a new instance. This prevents the autoscaler from collecting
information when the instance is initializing, during which the collected
usage would not be reliable. The default time autoscaler waits is 60 seconds.
- Virtual machine initialization times might vary because of numerous factors.
We recommend that you test how long an instance may take to initialize.
To do this, create an instance and time the startup process.
required: false
default: '60'
type: int
aliases:
- cooldownPeriod
mode:
description:
- Defines operating mode for this policy.
- 'Some valid choices include: "OFF", "ONLY_UP", "ON"'
required: false
default: 'ON'
type: str
scale_in_control:
description:
- Defines scale in controls to reduce the risk of response latency and outages
due to abrupt scale-in events .
required: false
type: dict
suboptions:
max_scaled_in_replicas:
description:
- A nested object resource.
required: false
type: dict
suboptions:
fixed:
description:
- Specifies a fixed number of VM instances. This must be a positive
integer.
required: false
type: int
percent:
description:
- Specifies a percentage of instances between 0 to 100%, inclusive.
- For example, specify 80 for 80%.
required: false
type: int
time_window_sec:
description:
- How long back autoscaling should look when computing recommendations
to include directives regarding slower scale down, as described above.
required: false
type: int
cpu_utilization:
description:
- Defines the CPU utilization policy that allows the autoscaler to scale based
on the average CPU utilization of a managed instance group.
required: false
type: dict
suboptions:
utilization_target:
description:
- The target CPU utilization that the autoscaler should maintain.
- Must be a float value in the range (0, 1]. If not specified, the default
is 0.6.
- If the CPU level is below the target utilization, the autoscaler scales
down the number of instances until it reaches the minimum number of
instances you specified or until the average CPU of your instances reaches
the target utilization.
- If the average CPU is above the target utilization, the autoscaler scales
up until it reaches the maximum number of instances you specified or
until the average utilization reaches the target utilization.
required: false
type: str
aliases:
- target
predictive_method:
description:
- 'Indicates whether predictive autoscaling based on CPU metric is enabled.
Valid values are: - NONE (default). No predictive method is used. The
autoscaler scales the group to meet current demand based on real-time
metrics.'
- "- OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability
by monitoring daily and weekly load patterns and scaling out ahead of
anticipated demand."
required: false
default: NONE
type: str
custom_metric_utilizations:
description:
- Configuration parameters of autoscaling based on a custom metric.
elements: dict
required: false
type: list
aliases:
- metric
suboptions:
metric:
description:
- The identifier (type) of the Stackdriver Monitoring metric.
- The metric cannot have negative values.
- The metric must have a value type of INT64 or DOUBLE.
required: true
type: str
aliases:
- name
utilization_target:
description:
- The target value of the metric that autoscaler should maintain. This
must be a positive value. A utilization metric scales number of virtual
machines handling requests to increase or decrease proportionally to
the metric.
- For example, a good metric to use as a utilizationTarget is U(www.googleapis.com/compute/instance/network/received_bytes_count).
- The autoscaler will work to keep this value constant for each of the
instances.
required: false
type: str
aliases:
- target
utilization_target_type:
description:
- Defines how target utilization value is expressed for a Stackdriver
Monitoring metric.
- 'Some valid choices include: "GAUGE", "DELTA_PER_SECOND", "DELTA_PER_MINUTE"'
required: false
type: str
aliases:
- type
load_balancing_utilization:
description:
- Configuration parameters of autoscaling based on a load balancer.
required: false
type: dict
suboptions:
utilization_target:
description:
- Fraction of backend capacity utilization (set in HTTP(s) load balancing
configuration) that autoscaler should maintain. Must be a positive float
value. If not defined, the default is 0.8.
required: false
type: str
aliases:
- target
target:
description:
- URL of the managed instance group that this autoscaler will scale.
- 'This field represents a link to a InstanceGroupManager resource in GCP. It
can be specified in two ways. First, you can place a dictionary with key ''selfLink''
and value of your resource''s selfLink Alternatively, you can add `register:
name-of-resource` to a gcp_compute_instance_group_manager task and then set
this target field to "{{ name-of-resource }}"'
required: true
type: dict
zone:
description:
- URL of the zone where the instance group resides.
required: true
type: str
project:
description:
- The Google Cloud Platform project to use.
type: str
auth_kind:
description:
- The type of credential used.
type: str
required: true
choices:
- application
- machineaccount
- serviceaccount
- accesstoken
- impersonation
service_account_contents:
description:
- The contents of a Service Account JSON file, either in a dictionary or as a
JSON string that represents it.
type: jsonarg
service_account_file:
description:
- The path of a Service Account JSON file if serviceaccount is selected as type.
type: path
service_account_email:
description:
- An optional service account email address if machineaccount is selected and
the user does not wish to use the default email.
- Required service account to impersonate if impersonation is selected.
type: str
access_token:
description:
- An OAuth2 access token if credential type is accesstoken.
type: str
scopes:
description:
- Array of scopes to be used
type: list
elements: str
env_type:
description:
- Specifies which Ansible environment you're running this module within.
- This should not be set unless you know what you're doing.
- This only alters the User Agent string for any API requests.
type: str
notes:
- 'API Reference: U(https://cloud.google.com/compute/docs/reference/rest/v1/autoscalers)'
- 'Autoscaling Groups of Instances: U(https://cloud.google.com/compute/docs/autoscaler/)'
- for authentication, you can set service_account_file using the C(GCP_SERVICE_ACCOUNT_FILE)
env variable.
- for authentication, you can set service_account_contents using the C(GCP_SERVICE_ACCOUNT_CONTENTS)
env variable.
- For authentication, you can set service_account_email using the C(GCP_SERVICE_ACCOUNT_EMAIL)
env variable.
- For authentication, you can set access_token using the C(GCP_ACCESS_TOKEN)
env variable.
- For authentication, you can set auth_kind using the C(GCP_AUTH_KIND) env variable.
- For authentication, you can set scopes using the C(GCP_SCOPES) env variable.
- Environment variables values will only be used if the playbook values are not set.
- The I(service_account_email) and I(service_account_file) options are mutually exclusive.
'''
EXAMPLES = '''
- name: create a network
google.cloud.gcp_compute_network:
name: network-instancetemplate
project: "{{ gcp_project }}"
auth_kind: "{{ gcp_cred_kind }}"
service_account_file: "{{ gcp_cred_file }}"
state: present
register: network
- name: create a address
google.cloud.gcp_compute_address:
name: address-instancetemplate
region: us-central1
project: "{{ gcp_project }}"
auth_kind: "{{ gcp_cred_kind }}"
service_account_file: "{{ gcp_cred_file }}"
state: present
register: address
- name: create a instance template
google.cloud.gcp_compute_instance_template:
name: "{{ resource_name }}"
properties:
disks:
- auto_delete: 'true'
boot: 'true'
initialize_params:
source_image: projects/ubuntu-os-cloud/global/images/family/ubuntu-1604-lts
machine_type: n1-standard-1
network_interfaces:
- network: "{{ network }}"
access_configs:
- name: test-config
type: ONE_TO_ONE_NAT
nat_ip: "{{ address }}"
project: "{{ gcp_project }}"
auth_kind: "{{ gcp_cred_kind }}"
service_account_file: "{{ gcp_cred_file }}"
state: present
register: instancetemplate
- name: create a instance group manager
google.cloud.gcp_compute_instance_group_manager:
name: "{{ resource_name }}"
base_instance_name: test1-child
instance_template: "{{ instancetemplate }}"
target_size: 3
zone: us-central1-a
project: "{{ gcp_project }}"
auth_kind: "{{ gcp_cred_kind }}"
service_account_file: "{{ gcp_cred_file }}"
state: present
register: igm
- name: create a autoscaler
google.cloud.gcp_compute_autoscaler:
name: test_object
zone: us-central1-a
target: "{{ igm }}"
autoscaling_policy:
max_num_replicas: 5
min_num_replicas: 1
cool_down_period_sec: 60
cpu_utilization:
utilization_target: 0.5
project: test_project
auth_kind: serviceaccount
service_account_file: "/tmp/auth.pem"
state: present
'''
RETURN = '''
id:
description:
- Unique identifier for the resource.
returned: success
type: int
creationTimestamp:
description:
- Creation timestamp in RFC3339 text format.
returned: success
type: str
name:
description:
- Name of the resource. The name must be 1-63 characters long and match the regular
expression `[a-z]([-a-z0-9]*[a-z0-9])?` which means the first character must be
a lowercase letter, and all following characters must be a dash, lowercase letter,
or digit, except the last character, which cannot be a dash.
returned: success
type: str
description:
description:
- An optional description of this resource.
returned: success
type: str
autoscalingPolicy:
description:
- 'The configuration parameters for the autoscaling algorithm. You can define one
or more of the policies for an autoscaler: cpuUtilization, customMetricUtilizations,
and loadBalancingUtilization.'
- If none of these are specified, the default will be to autoscale based on cpuUtilization
to 0.6 or 60%.
returned: success
type: complex
contains:
minNumReplicas:
description:
- The minimum number of replicas that the autoscaler can scale down to. This
cannot be less than 0. If not provided, autoscaler will choose a default value
depending on maximum number of instances allowed.
returned: success
type: int
maxNumReplicas:
description:
- The maximum number of instances that the autoscaler can scale up to. This
is required when creating or updating an autoscaler. The maximum number of
replicas should not be lower than minimal number of replicas.
returned: success
type: int
coolDownPeriodSec:
description:
- The number of seconds that the autoscaler should wait before it starts collecting
information from a new instance. This prevents the autoscaler from collecting
information when the instance is initializing, during which the collected
usage would not be reliable. The default time autoscaler waits is 60 seconds.
- Virtual machine initialization times might vary because of numerous factors.
We recommend that you test how long an instance may take to initialize. To
do this, create an instance and time the startup process.
returned: success
type: int
mode:
description:
- Defines operating mode for this policy.
returned: success
type: str
scaleInControl:
description:
- Defines scale in controls to reduce the risk of response latency and outages
due to abrupt scale-in events .
returned: success
type: complex
contains:
maxScaledInReplicas:
description:
- A nested object resource.
returned: success
type: complex
contains:
fixed:
description:
- Specifies a fixed number of VM instances. This must be a positive
integer.
returned: success
type: int
percent:
description:
- Specifies a percentage of instances between 0 to 100%, inclusive.
- For example, specify 80 for 80%.
returned: success
type: int
timeWindowSec:
description:
- How long back autoscaling should look when computing recommendations to
include directives regarding slower scale down, as described above.
returned: success
type: int
cpuUtilization:
description:
- Defines the CPU utilization policy that allows the autoscaler to scale based
on the average CPU utilization of a managed instance group.
returned: success
type: complex
contains:
utilizationTarget:
description:
- The target CPU utilization that the autoscaler should maintain.
- Must be a float value in the range (0, 1]. If not specified, the default
is 0.6.
- If the CPU level is below the target utilization, the autoscaler scales
down the number of instances until it reaches the minimum number of instances
you specified or until the average CPU of your instances reaches the target
utilization.
- If the average CPU is above the target utilization, the autoscaler scales
up until it reaches the maximum number of instances you specified or until
the average utilization reaches the target utilization.
returned: success
type: str
predictiveMethod:
description:
- 'Indicates whether predictive autoscaling based on CPU metric is enabled.
Valid values are: - NONE (default). No predictive method is used. The
autoscaler scales the group to meet current demand based on real-time
metrics.'
- "- OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability
by monitoring daily and weekly load patterns and scaling out ahead of
anticipated demand."
returned: success
type: str
customMetricUtilizations:
description:
- Configuration parameters of autoscaling based on a custom metric.
returned: success
type: complex
contains:
metric:
description:
- The identifier (type) of the Stackdriver Monitoring metric.
- The metric cannot have negative values.
- The metric must have a value type of INT64 or DOUBLE.
returned: success
type: str
utilizationTarget:
description:
- The target value of the metric that autoscaler should maintain. This must
be a positive value. A utilization metric scales number of virtual machines
handling requests to increase or decrease proportionally to the metric.
- For example, a good metric to use as a utilizationTarget is U(www.googleapis.com/compute/instance/network/received_bytes_count).
- The autoscaler will work to keep this value constant for each of the instances.
returned: success
type: str
utilizationTargetType:
description:
- Defines how target utilization value is expressed for a Stackdriver Monitoring
metric.
returned: success
type: str
loadBalancingUtilization:
description:
- Configuration parameters of autoscaling based on a load balancer.
returned: success
type: complex
contains:
utilizationTarget:
description:
- Fraction of backend capacity utilization (set in HTTP(s) load balancing
configuration) that autoscaler should maintain. Must be a positive float
value. If not defined, the default is 0.8.
returned: success
type: str
target:
description:
- URL of the managed instance group that this autoscaler will scale.
returned: success
type: dict
zone:
description:
- URL of the zone where the instance group resides.
returned: success
type: str
'''
################################################################################
# Imports
################################################################################
from ansible_collections.google.cloud.plugins.module_utils.gcp_utils import (
navigate_hash,
GcpSession,
GcpModule,
GcpRequest,
remove_nones_from_dict,
replace_resource_dict,
)
import json
import time
################################################################################
# Main
################################################################################
def main():
"""Main function"""
module = GcpModule(
argument_spec=dict(
state=dict(default='present', choices=['present', 'absent'], type='str'),
name=dict(required=True, type='str'),
description=dict(type='str'),
autoscaling_policy=dict(
required=True,
type='dict',
options=dict(
min_num_replicas=dict(type='int', aliases=['minReplicas']),
max_num_replicas=dict(required=True, type='int', aliases=['maxReplicas']),
cool_down_period_sec=dict(default=60, type='int', aliases=['cooldownPeriod']),
mode=dict(default='ON', type='str'),
scale_in_control=dict(
type='dict',
options=dict(
max_scaled_in_replicas=dict(type='dict', options=dict(fixed=dict(type='int'), percent=dict(type='int'))),
time_window_sec=dict(type='int'),
),
),
cpu_utilization=dict(
type='dict', options=dict(utilization_target=dict(type='str', aliases=['target']), predictive_method=dict(default='NONE', type='str'))
),
custom_metric_utilizations=dict(
type='list',
elements='dict',
aliases=['metric'],
options=dict(
metric=dict(required=True, type='str', aliases=['name']),
utilization_target=dict(type='str', aliases=['target']),
utilization_target_type=dict(type='str', aliases=['type']),
),
),
load_balancing_utilization=dict(type='dict', options=dict(utilization_target=dict(type='str', aliases=['target']))),
),
),
target=dict(required=True, type='dict'),
zone=dict(required=True, type='str'),
)
)
if not module.params['scopes']:
module.params['scopes'] = ['https://www.googleapis.com/auth/compute']
state = module.params['state']
kind = 'compute#autoscaler'
fetch = fetch_resource(module, self_link(module), kind)
changed = False
if fetch:
if state == 'present':
if is_different(module, fetch):
update(module, self_link(module), kind)
fetch = fetch_resource(module, self_link(module), kind)
changed = True
else:
delete(module, self_link(module), kind)
fetch = {}
changed = True
else:
if state == 'present':
fetch = create(module, collection(module), kind)
changed = True
else:
fetch = {}
fetch.update({'changed': changed})
module.exit_json(**fetch)
def create(module, link, kind):
auth = GcpSession(module, 'compute')
return wait_for_operation(module, auth.post(link, resource_to_request(module)))
def update(module, link, kind):
auth = GcpSession(module, 'compute')
return wait_for_operation(module, auth.put(link, resource_to_request(module)))
def delete(module, link, kind):
auth = GcpSession(module, 'compute')
return wait_for_operation(module, auth.delete(link))
def resource_to_request(module):
request = {
u'kind': 'compute#autoscaler',
u'zone': module.params.get('zone'),
u'name': module.params.get('name'),
u'description': module.params.get('description'),
u'autoscalingPolicy': AutoscalerAutoscalingpolicy(module.params.get('autoscaling_policy', {}), module).to_request(),
u'target': replace_resource_dict(module.params.get(u'target', {}), 'selfLink'),
}
return_vals = {}
for k, v in request.items():
if v or v is False:
return_vals[k] = v
return return_vals
def fetch_resource(module, link, kind, allow_not_found=True):
auth = GcpSession(module, 'compute')
return return_if_object(module, auth.get(link), kind, allow_not_found)
def self_link(module):
return "https://compute.googleapis.com/compute/v1/projects/{project}/zones/{zone}/autoscalers/{name}".format(**module.params)
def collection(module):
return "https://compute.googleapis.com/compute/v1/projects/{project}/zones/{zone}/autoscalers".format(**module.params)
def return_if_object(module, response, kind, allow_not_found=False):
# If not found, return nothing.
if allow_not_found and response.status_code == 404:
return None
# If no content, return nothing.
if response.status_code == 204:
return None
try:
module.raise_for_status(response)
result = response.json()
except getattr(json.decoder, 'JSONDecodeError', ValueError):
module.fail_json(msg="Invalid JSON response with error: %s" % response.text)
if navigate_hash(result, ['error', 'errors']):
module.fail_json(msg=navigate_hash(result, ['error', 'errors']))
return result
def is_different(module, response):
request = resource_to_request(module)
response = response_to_hash(module, response)
# Remove all output-only from response.
response_vals = {}
for k, v in response.items():
if k in request:
response_vals[k] = v
request_vals = {}
for k, v in request.items():
if k in response:
request_vals[k] = v
return GcpRequest(request_vals) != GcpRequest(response_vals)
# Remove unnecessary properties from the response.
# This is for doing comparisons with Ansible's current parameters.
def response_to_hash(module, response):
return {
u'id': response.get(u'id'),
u'creationTimestamp': response.get(u'creationTimestamp'),
u'name': module.params.get('name'),
u'description': response.get(u'description'),
u'autoscalingPolicy': AutoscalerAutoscalingpolicy(response.get(u'autoscalingPolicy', {}), module).from_response(),
u'target': response.get(u'target'),
}
def async_op_url(module, extra_data=None):
if extra_data is None:
extra_data = {}
url = "https://compute.googleapis.com/compute/v1/projects/{project}/zones/{zone}/operations/{op_id}"
combined = extra_data.copy()
combined.update(module.params)
return url.format(**combined)
def wait_for_operation(module, response):
op_result = return_if_object(module, response, 'compute#operation')
if op_result is None:
return {}
status = navigate_hash(op_result, ['status'])
wait_done = wait_for_completion(status, op_result, module)
return fetch_resource(module, navigate_hash(wait_done, ['targetLink']), 'compute#autoscaler')
def wait_for_completion(status, op_result, module):
op_id = navigate_hash(op_result, ['name'])
op_uri = async_op_url(module, {'op_id': op_id})
while status != 'DONE':
raise_if_errors(op_result, ['error', 'errors'], module)
time.sleep(1.0)
op_result = fetch_resource(module, op_uri, 'compute#operation', False)
status = navigate_hash(op_result, ['status'])
return op_result
def raise_if_errors(response, err_path, module):
errors = navigate_hash(response, err_path)
if errors is not None:
module.fail_json(msg=errors)
class AutoscalerAutoscalingpolicy(object):
def __init__(self, request, module):
self.module = module
if request:
self.request = request
else:
self.request = {}
def to_request(self):
return remove_nones_from_dict(
{
u'minNumReplicas': self.request.get('min_num_replicas'),
u'maxNumReplicas': self.request.get('max_num_replicas'),
u'coolDownPeriodSec': self.request.get('cool_down_period_sec'),
u'mode': self.request.get('mode'),
u'scaleInControl': AutoscalerScaleincontrol(self.request.get('scale_in_control', {}), self.module).to_request(),
u'cpuUtilization': AutoscalerCpuutilization(self.request.get('cpu_utilization', {}), self.module).to_request(),
u'customMetricUtilizations': AutoscalerCustommetricutilizationsArray(
self.request.get('custom_metric_utilizations', []), self.module
).to_request(),
u'loadBalancingUtilization': AutoscalerLoadbalancingutilization(self.request.get('load_balancing_utilization', {}), self.module).to_request(),
}
)
def from_response(self):
return remove_nones_from_dict(
{
u'minNumReplicas': self.request.get(u'minNumReplicas'),
u'maxNumReplicas': self.request.get(u'maxNumReplicas'),
u'coolDownPeriodSec': self.request.get(u'coolDownPeriodSec'),
u'mode': self.request.get(u'mode'),
u'scaleInControl': AutoscalerScaleincontrol(self.request.get(u'scaleInControl', {}), self.module).from_response(),
u'cpuUtilization': AutoscalerCpuutilization(self.request.get(u'cpuUtilization', {}), self.module).from_response(),
u'customMetricUtilizations': AutoscalerCustommetricutilizationsArray(
self.request.get(u'customMetricUtilizations', []), self.module
).from_response(),
u'loadBalancingUtilization': AutoscalerLoadbalancingutilization(self.request.get(u'loadBalancingUtilization', {}), self.module).from_response(),
}
)
class AutoscalerScaleincontrol(object):
def __init__(self, request, module):
self.module = module
if request:
self.request = request
else:
self.request = {}
def to_request(self):
return remove_nones_from_dict(
{
u'maxScaledInReplicas': AutoscalerMaxscaledinreplicas(self.request.get('max_scaled_in_replicas', {}), self.module).to_request(),
u'timeWindowSec': self.request.get('time_window_sec'),
}
)
def from_response(self):
return remove_nones_from_dict(
{
u'maxScaledInReplicas': AutoscalerMaxscaledinreplicas(self.request.get(u'maxScaledInReplicas', {}), self.module).from_response(),
u'timeWindowSec': self.request.get(u'timeWindowSec'),
}
)
class AutoscalerMaxscaledinreplicas(object):
def __init__(self, request, module):
self.module = module
if request:
self.request = request
else:
self.request = {}
def to_request(self):
return remove_nones_from_dict({u'fixed': self.request.get('fixed'), u'percent': self.request.get('percent')})
def from_response(self):
return remove_nones_from_dict({u'fixed': self.request.get(u'fixed'), u'percent': self.request.get(u'percent')})
class AutoscalerCpuutilization(object):
def __init__(self, request, module):
self.module = module
if request:
self.request = request
else:
self.request = {}
def to_request(self):
return remove_nones_from_dict(
{u'utilizationTarget': self.request.get('utilization_target'), u'predictiveMethod': self.request.get('predictive_method')}
)
def from_response(self):
return remove_nones_from_dict(
{u'utilizationTarget': self.request.get(u'utilizationTarget'), u'predictiveMethod': self.request.get(u'predictiveMethod')}
)
class AutoscalerCustommetricutilizationsArray(object):
def __init__(self, request, module):
self.module = module
if request:
self.request = request
else:
self.request = []
def to_request(self):
items = []
for item in self.request:
items.append(self._request_for_item(item))
return items
def from_response(self):
items = []
for item in self.request:
items.append(self._response_from_item(item))
return items
def _request_for_item(self, item):
return remove_nones_from_dict(
{u'metric': item.get('metric'), u'utilizationTarget': item.get('utilization_target'), u'utilizationTargetType': item.get('utilization_target_type')}
)
def _response_from_item(self, item):
return remove_nones_from_dict(
{u'metric': item.get(u'metric'), u'utilizationTarget': item.get(u'utilizationTarget'), u'utilizationTargetType': item.get(u'utilizationTargetType')}
)
class AutoscalerLoadbalancingutilization(object):
def __init__(self, request, module):
self.module = module
if request:
self.request = request
else:
self.request = {}
def to_request(self):
return remove_nones_from_dict({u'utilizationTarget': self.request.get('utilization_target')})
def from_response(self):
return remove_nones_from_dict({u'utilizationTarget': self.request.get(u'utilizationTarget')})
if __name__ == '__main__':
main()