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community.general/plugins/modules/scaleway_function.py

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#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Scaleway Serverless function management module
#
# Copyright (c) 2022, Guillaume MARTINEZ <lunik@tiwabbit.fr>
# GNU General Public License v3.0+ (see LICENSES/GPL-3.0-or-later.txt or https://www.gnu.org/licenses/gpl-3.0.txt)
# SPDX-License-Identifier: GPL-3.0-or-later
from __future__ import absolute_import, division, print_function
__metaclass__ = type
DOCUMENTATION = '''
---
module: scaleway_function
short_description: Scaleway Function management
version_added: 6.0.0
author: Guillaume MARTINEZ (@Lunik)
description:
- This module manages function on Scaleway account.
extends_documentation_fragment:
- community.general.scaleway
- community.general.scaleway_waitable_resource
requirements:
- passlib[argon2] >= 1.7.4
options:
state:
type: str
description:
- Indicate desired state of the function.
default: present
choices:
- present
- absent
namespace_id:
type: str
description:
- Function namespace identifier.
required: true
region:
type: str
description:
- Scaleway region to use (for example C(fr-par)).
required: true
choices:
- fr-par
- nl-ams
- pl-waw
name:
type: str
description:
- Name of the function.
required: true
description:
description:
- Description of the function.
type: str
default: ''
min_scale:
description:
- Minimum number of replicas for the function.
type: int
max_scale:
description:
- Maximum number of replicas for the function.
type: int
environment_variables:
description:
- Environment variables of the function.
- Injected in function at runtime.
type: dict
default: {}
secret_environment_variables:
description:
- Secret environment variables of the function.
- Updating thoses values will not output a C(changed) state in Ansible.
- Injected in function at runtime.
type: dict
default: {}
runtime:
description:
- Runtime of the function
- See U(https://www.scaleway.com/en/docs/compute/functions/reference-content/functions-lifecycle/) for all available runtimes
type: str
required: true
memory_limit:
description:
- Resources define performance characteristics of your function.
- They are allocated to your function at runtime.
type: int
function_timeout:
description:
- The length of time your handler can spend processing a request before being stopped.
type: str
handler:
description:
- The C(module-name.export) value in your function.
type: str
privacy:
description:
- Privacy policies define whether a function can be executed anonymously.
- Choose C(public) to enable anonymous execution, or C(private) to protect your function with an authentication mechanism provided by the Scaleway API.
type: str
default: public
choices:
- public
- private
redeploy:
description:
- Redeploy the function if update is required.
type: bool
default: false
'''
EXAMPLES = '''
- name: Create a function
community.general.scaleway_function:
namespace_id: '{{ scw_function_namespace }}'
region: fr-par
state: present
name: my-awesome-function
runtime: python3
environment_variables:
MY_VAR: my_value
secret_environment_variables:
MY_SECRET_VAR: my_secret_value
register: function_creation_task
- name: Make sure function is deleted
community.general.scaleway_function:
namespace_id: '{{ scw_function_namespace }}'
region: fr-par
state: absent
name: my-awesome-function
'''
RETURN = '''
function:
description: The function information.
returned: when I(state=present)
type: dict
sample:
cpu_limit: 140
description: Function used for testing scaleway_function ansible module
domain_name: fnansibletestfxamabuc-fn-ansible-test.functions.fnc.fr-par.scw.cloud
environment_variables:
MY_VAR: my_value
error_message: null
handler: handler.handle
http_option: ""
id: ceb64dc4-4464-4196-8e20-ecef705475d3
max_scale: 5
memory_limit: 256
min_scale: 0
name: fn-ansible-test
namespace_id: 82737d8d-0ebb-4d89-b0ad-625876eca50d
privacy: public
region: fr-par
runtime: python310
runtime_message: ""
secret_environment_variables:
- key: MY_SECRET_VAR
value: $argon2id$v=19$m=65536,t=1,p=2$tb6UwSPWx/rH5Vyxt9Ujfw$5ZlvaIjWwNDPxD9Rdght3NarJz4IETKjpvAU3mMSmFg
status: created
timeout: 300s
'''
from copy import deepcopy
from ansible_collections.community.general.plugins.module_utils.scaleway import (
SCALEWAY_REGIONS, scaleway_argument_spec, Scaleway,
scaleway_waitable_resource_argument_spec, resource_attributes_should_be_changed,
SecretVariables
)
from ansible.module_utils.basic import AnsibleModule
STABLE_STATES = (
"ready",
"created",
"absent"
)
VERIFIABLE_MUTABLE_ATTRIBUTES = (
"description",
"min_scale",
"max_scale",
"environment_variables",
"runtime",
"memory_limit",
"timeout",
"handler",
"privacy",
"secret_environment_variables"
)
MUTABLE_ATTRIBUTES = VERIFIABLE_MUTABLE_ATTRIBUTES + (
"redeploy",
)
def payload_from_wished_fn(wished_fn):
payload = {
"namespace_id": wished_fn["namespace_id"],
"name": wished_fn["name"],
"description": wished_fn["description"],
"min_scale": wished_fn["min_scale"],
"max_scale": wished_fn["max_scale"],
"runtime": wished_fn["runtime"],
"memory_limit": wished_fn["memory_limit"],
"timeout": wished_fn["timeout"],
"handler": wished_fn["handler"],
"privacy": wished_fn["privacy"],
"redeploy": wished_fn["redeploy"],
"environment_variables": wished_fn["environment_variables"],
"secret_environment_variables": SecretVariables.dict_to_list(wished_fn["secret_environment_variables"])
}
return payload
def absent_strategy(api, wished_fn):
changed = False
fn_list = api.fetch_all_resources("functions")
fn_lookup = dict((fn["name"], fn)
for fn in fn_list)
if wished_fn["name"] not in fn_lookup:
return changed, {}
target_fn = fn_lookup[wished_fn["name"]]
changed = True
if api.module.check_mode:
return changed, {"status": "Function would be destroyed"}
api.wait_to_complete_state_transition(resource=target_fn, stable_states=STABLE_STATES, force_wait=True)
response = api.delete(path=api.api_path + "/%s" % target_fn["id"])
if not response.ok:
api.module.fail_json(msg='Error deleting function [{0}: {1}]'.format(
response.status_code, response.json))
api.wait_to_complete_state_transition(resource=target_fn, stable_states=STABLE_STATES)
return changed, response.json
def present_strategy(api, wished_fn):
changed = False
fn_list = api.fetch_all_resources("functions")
fn_lookup = dict((fn["name"], fn)
for fn in fn_list)
payload_fn = payload_from_wished_fn(wished_fn)
if wished_fn["name"] not in fn_lookup:
changed = True
if api.module.check_mode:
return changed, {"status": "A function would be created."}
# Creation doesn't support `redeploy` parameter
del payload_fn["redeploy"]
# Create function
api.warn(payload_fn)
creation_response = api.post(path=api.api_path,
data=payload_fn)
if not creation_response.ok:
msg = "Error during function creation: %s: '%s' (%s)" % (creation_response.info['msg'],
creation_response.json['message'],
creation_response.json)
api.module.fail_json(msg=msg)
api.wait_to_complete_state_transition(resource=creation_response.json, stable_states=STABLE_STATES)
response = api.get(path=api.api_path + "/%s" % creation_response.json["id"])
return changed, response.json
target_fn = fn_lookup[wished_fn["name"]]
decoded_target_fn = deepcopy(target_fn)
decoded_target_fn["secret_environment_variables"] = SecretVariables.decode(decoded_target_fn["secret_environment_variables"],
payload_fn["secret_environment_variables"])
patch_payload = resource_attributes_should_be_changed(target=decoded_target_fn,
wished=payload_fn,
verifiable_mutable_attributes=VERIFIABLE_MUTABLE_ATTRIBUTES,
mutable_attributes=MUTABLE_ATTRIBUTES)
if not patch_payload:
return changed, target_fn
changed = True
if api.module.check_mode:
return changed, {"status": "Function attributes would be changed."}
fn_patch_response = api.patch(path=api.api_path + "/%s" % target_fn["id"],
data=patch_payload)
if not fn_patch_response.ok:
api.module.fail_json(msg='Error during function attributes update: [{0}: {1}]'.format(
fn_patch_response.status_code, fn_patch_response.json['message']))
api.wait_to_complete_state_transition(resource=target_fn, stable_states=STABLE_STATES)
response = api.get(path=api.api_path + "/%s" % target_fn["id"])
return changed, response.json
state_strategy = {
"present": present_strategy,
"absent": absent_strategy
}
def core(module):
SecretVariables.ensure_scaleway_secret_package(module)
region = module.params["region"]
wished_function = {
"state": module.params["state"],
"namespace_id": module.params["namespace_id"],
"name": module.params["name"],
"description": module.params['description'],
"min_scale": module.params['min_scale'],
"max_scale": module.params['max_scale'],
"runtime": module.params["runtime"],
"memory_limit": module.params["memory_limit"],
"timeout": module.params["function_timeout"],
"handler": module.params["handler"],
"privacy": module.params["privacy"],
"redeploy": module.params["redeploy"],
"environment_variables": module.params['environment_variables'],
"secret_environment_variables": module.params['secret_environment_variables']
}
api = Scaleway(module=module)
api.api_path = "functions/v1beta1/regions/%s/functions" % region
changed, summary = state_strategy[wished_function["state"]](api=api, wished_fn=wished_function)
module.exit_json(changed=changed, function=summary)
def main():
argument_spec = scaleway_argument_spec()
argument_spec.update(scaleway_waitable_resource_argument_spec())
argument_spec.update(dict(
state=dict(type='str', default='present', choices=['absent', 'present']),
namespace_id=dict(type='str', required=True),
region=dict(type='str', required=True, choices=SCALEWAY_REGIONS),
name=dict(type='str', required=True),
description=dict(type='str', default=''),
min_scale=dict(type='int'),
max_scale=dict(type='int'),
runtime=dict(type='str', required=True),
memory_limit=dict(type='int'),
function_timeout=dict(type='str'),
handler=dict(type='str'),
privacy=dict(type='str', default='public', choices=['public', 'private']),
redeploy=dict(type='bool', default=False),
environment_variables=dict(type='dict', default={}),
secret_environment_variables=dict(type='dict', default={}, no_log=True)
))
module = AnsibleModule(
argument_spec=argument_spec,
supports_check_mode=True,
)
core(module)
if __name__ == '__main__':
main()