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편집 파일: scaleway_function.py

# -*- 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 - community.general.attributes requirements: - passlib[argon2] >= 1.7.4 attributes: check_mode: support: full diff_mode: support: none 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 V(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 V(public) to enable anonymous execution, or V(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 O(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()