def to_model(cls, rule):
kwargs = {}
kwargs["name"] = getattr(rule, "name", None)
kwargs["description"] = getattr(rule, "description", None)
# Validate trigger parameters
# Note: This must happen before we create a trigger, otherwise create trigger could fail
# with a cryptic error
trigger = getattr(rule, "trigger", {})
trigger_type_ref = trigger.get("type", None)
parameters = trigger.get("parameters", {})
validator.validate_trigger_parameters(
trigger_type_ref=trigger_type_ref, parameters=parameters
)
# Create a trigger for the provided rule
trigger_db = TriggerService.create_trigger_db_from_rule(rule)
kwargs["trigger"] = reference.get_str_resource_ref_from_model(trigger_db)
kwargs["pack"] = getattr(rule, "pack", DEFAULT_PACK_NAME)
kwargs["ref"] = ResourceReference.to_string_reference(
pack=kwargs["pack"], name=kwargs["name"]
)
# Validate criteria
kwargs["criteria"] = dict(getattr(rule, "criteria", {}))
validator.validate_criteria(kwargs["criteria"])
kwargs["action"] = ActionExecutionSpecDB(
ref=rule.action["ref"], parameters=rule.action.get("parameters", {})
)
rule_type = dict(getattr(rule, "type", {}))
if rule_type:
kwargs["type"] = RuleTypeSpecDB(
ref=rule_type["ref"], parameters=rule_type.get("parameters", {})
)
kwargs["enabled"] = getattr(rule, "enabled", False)
kwargs["context"] = getattr(rule, "context", dict())
kwargs["tags"] = TagsHelper.to_model(getattr(rule, "tags", []))
kwargs["metadata_file"] = getattr(rule, "metadata_file", None)
model = cls.model(**kwargs)
return model
def to_model(cls, rule):
kwargs = {}
kwargs['name'] = getattr(rule, 'name', None)
kwargs['description'] = getattr(rule, 'description', None)
# Validate trigger parameters
# Note: This must happen before we create a trigger, otherwise create trigger could fail
# with a cryptic error
trigger = getattr(rule, 'trigger', {})
trigger_type_ref = trigger.get('type', None)
parameters = trigger.get('parameters', {})
validator.validate_trigger_parameters(
trigger_type_ref=trigger_type_ref, parameters=parameters)
# Create a trigger for the provided rule
trigger_db = TriggerService.create_trigger_db_from_rule(rule)
kwargs['trigger'] = reference.get_str_resource_ref_from_model(
trigger_db)
kwargs['pack'] = getattr(rule, 'pack', DEFAULT_PACK_NAME)
kwargs['ref'] = ResourceReference.to_string_reference(
pack=kwargs['pack'], name=kwargs['name'])
# Validate criteria
kwargs['criteria'] = dict(getattr(rule, 'criteria', {}))
validator.validate_criteria(kwargs['criteria'])
kwargs['action'] = ActionExecutionSpecDB(ref=rule.action['ref'],
parameters=rule.action.get(
'parameters', {}))
rule_type = dict(getattr(rule, 'type', {}))
if rule_type:
kwargs['type'] = RuleTypeSpecDB(ref=rule_type['ref'],
parameters=rule_type.get(
'parameters', {}))
kwargs['enabled'] = getattr(rule, 'enabled', False)
kwargs['context'] = getattr(rule, 'context', dict())
kwargs['tags'] = TagsHelper.to_model(getattr(rule, 'tags', []))
kwargs['metadata_file'] = getattr(rule, 'metadata_file', None)
model = cls.model(**kwargs)
return model
def to_model(cls, rule):
kwargs = {}
kwargs['name'] = getattr(rule, 'name', None)
kwargs['description'] = getattr(rule, 'description', None)
# Create a trigger for the provided rule
trigger_db = TriggerService.create_trigger_db_from_rule(rule)
kwargs['trigger'] = reference.get_str_resource_ref_from_model(
trigger_db)
# Validate trigger parameters
validator.validate_trigger_parameters(trigger_db=trigger_db)
kwargs['pack'] = getattr(rule, 'pack', DEFAULT_PACK_NAME)
kwargs['ref'] = ResourceReference.to_string_reference(
pack=kwargs['pack'], name=kwargs['name'])
# Validate criteria
kwargs['criteria'] = dict(getattr(rule, 'criteria', {}))
validator.validate_criteria(kwargs['criteria'])
kwargs['action'] = ActionExecutionSpecDB(ref=rule.action['ref'],
parameters=rule.action.get(
'parameters', {}))
rule_type = dict(getattr(rule, 'type', {}))
if rule_type:
kwargs['type'] = RuleTypeSpecDB(ref=rule_type['ref'],
parameters=rule_type.get(
'parameters', {}))
kwargs['enabled'] = getattr(rule, 'enabled', False)
kwargs['tags'] = TagsHelper.to_model(getattr(rule, 'tags', []))
model = cls.model(**kwargs)
return model