Creates an environment. For each application, you define one or more environments. An environment is a deployment group of AppConfig targets, such as applications in a Beta or Production environment. You can also define environments for application subcomponents such as the Web, Mobile and Back-end components for your application. You can configure Amazon CloudWatch alarms for each environment. The system monitors alarms during a configuration deployment. If an alarm is triggered, the system rolls back the configuration.
Parameters:
-
application_id
(Name)
–
-
name
(Name)
–
A name for the environment.
-
description
(Optional[Description], default:
None
)
–
A description of the environment.
-
monitors
(Optional[MonitorList], default:
None
)
–
Amazon CloudWatch alarms to monitor during the deployment process.
-
tags
(Optional[TagMap], default:
None
)
–
Metadata to assign to the environment. Tags help organize and categorize your AppConfig resources. Each tag consists of a key and an optional value, both of which you define.
Raises:
-
BadRequestException
–
The input fails to satisfy the constraints specified by an Amazon Web Services service.
-
InternalServerException
–
There was an internal failure in the AppConfig service.
-
ResourceNotFoundException
–
The requested resource could not be found.
-
ServiceQuotaExceededException
–
The number of one more AppConfig resources exceeds the maximum allowed. Verify that your environment doesn't exceed the following service quotas:
Applications: 100 max
To resolve this issue, you can delete one or more resources and try again. Or, you can request a quota increase. For more information about quotas and to request an increase, see Service quotas for AppConfig in the Amazon Web Services General Reference.
-
UnknownServiceError
–
The service returned an error code this client does not model.
Examples:
To create an environment
The following create-environment example creates an AWS AppConfig environment named Example-Environment using the application you created using create-application
>>> await client.create_environment(application_id='339ohji', name='Example-Environment')
Source code in docs_src/capo_appconfig/_services/async_app_config.py
| async def create_environment(
self,
application_id: "capo_appconfig.types.name.Name",
name: "capo_appconfig.types.name.Name",
*,
config_overrides: Optional[AsyncAppConfigClientConfig] = None,
description: Optional["capo_appconfig.types.description.Description"] = None,
monitors: Optional["capo_appconfig.types.monitor_list.MonitorList"] = None,
tags: Optional["capo_appconfig.types.tag_map.TagMap"] = None,
) -> "capo_appconfig.types.environment.Environment":
"""<p>Creates an environment. For each application, you define one or more environments. An environment is a deployment group of AppConfig targets, such as applications in a <code>Beta</code> or <code>Production</code> environment. You can also define environments for application subcomponents such as the <code>Web</code>, <code>Mobile</code> and <code>Back-end</code> components for your application. You can configure Amazon CloudWatch alarms for each environment. The system monitors alarms during a configuration deployment. If an alarm is triggered, the system rolls back the configuration.</p>
Args:
application_id: <p>The application ID.</p>
name: <p>A name for the environment.</p>
description: <p>A description of the environment.</p>
monitors: <p>Amazon CloudWatch alarms to monitor during the deployment process.</p>
tags: <p>Metadata to assign to the environment. Tags help organize and categorize your AppConfig resources. Each tag consists of a key and an optional value, both of which you define.</p>
Raises:
capo_appconfig.errors.bad_request_exception.BadRequestException: <p>The input fails to satisfy the constraints specified by an Amazon Web Services service.</p>
capo_appconfig.errors.internal_server_exception.InternalServerException: <p>There was an internal failure in the AppConfig service.</p>
capo_appconfig.errors.resource_not_found_exception.ResourceNotFoundException: <p>The requested resource could not be found.</p>
capo_appconfig.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>The number of one more AppConfig resources exceeds the maximum allowed. Verify that your environment doesn't exceed the following service quotas:</p> <p>Applications: 100 max</p> <p>To resolve this issue, you can delete one or more resources and try again. Or, you can request a quota increase. For more information about quotas and to request an increase, see <a href="https://docs.aws.amazon.com/general/latest/gr/appconfig.html#limits_appconfig">Service quotas for AppConfig</a> in the Amazon Web Services General Reference.</p>
capo_appconfig.errors.UnknownServiceError: The service returned an error code this client does not model.
Examples:
To create an environment
The following create-environment example creates an AWS AppConfig environment named Example-Environment using the application you created using create-application
>>> await client.create_environment(application_id='339ohji', name='Example-Environment')
"""
async def _handler(
req: "AsyncOperationRequest[capo_appconfig.types.create_environment_request.CreateEnvironmentRequest]",
) -> AsyncOperationResponse["capo_appconfig.types.environment.Environment"]:
import capo_appconfig._operations.amazon_app_config.create_environment
(
output,
http_response,
) = await capo_appconfig._operations.amazon_app_config.create_environment.async_create_environment(
req.options, req.input
)
return AsyncOperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("CreateEnvironment")
)
input_: capo_appconfig.types.create_environment_request.CreateEnvironmentRequest = {
"application_id": application_id,
"name": name,
}
if description is not None:
input_["description"] = description
if monitors is not None:
input_["monitors"] = monitors
if tags is not None:
input_["tags"] = tags
response = await aexecute_pipeline(
AsyncOperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
await response.response.aclose()
return response.output
|