Attempts to cancel an environment deployment on an UpdateEnvironment action, if the deployment is IN_PROGRESS. For more information, see Update an environment in the Proton User guide.
The following list includes potential cancellation scenarios.
-
If the cancellation attempt succeeds, the resulting deployment state is CANCELLED.
-
If the cancellation attempt fails, the resulting deployment state is FAILED.
-
If the current UpdateEnvironment action succeeds before the cancellation attempt starts, the resulting deployment state is SUCCEEDED and the cancellation attempt has no effect.
Parameters:
-
environment_name
(ResourceName)
–
The name of the environment with the deployment to cancel.
Raises:
Source code in docs_src/capo_proton/_services/proton.py
| def cancel_environment_deployment(
self,
environment_name: "capo_proton.types.resource_name.ResourceName",
*,
config_overrides: Optional[ProtonClientConfig] = None,
) -> "capo_proton.types.cancel_environment_deployment_output.CancelEnvironmentDeploymentOutput":
"""<p>Attempts to cancel an environment deployment on an <a>UpdateEnvironment</a> action, if the deployment is <code>IN_PROGRESS</code>. For more information, see <a href="https://docs.aws.amazon.com/proton/latest/userguide/ag-env-update.html">Update an environment</a> in the <i>Proton User guide</i>.</p> <p>The following list includes potential cancellation scenarios.</p> <ul> <li> <p>If the cancellation attempt succeeds, the resulting deployment state is <code>CANCELLED</code>.</p> </li> <li> <p>If the cancellation attempt fails, the resulting deployment state is <code>FAILED</code>.</p> </li> <li> <p>If the current <a>UpdateEnvironment</a> action succeeds before the cancellation attempt starts, the resulting deployment state is <code>SUCCEEDED</code> and the cancellation attempt has no effect.</p> </li> </ul>
Args:
environment_name: <p>The name of the environment with the deployment to cancel.</p>
Raises:
capo_proton.errors.access_denied_exception.AccessDeniedException: <p>There <i>isn't</i> sufficient access for performing this action.</p>
capo_proton.errors.conflict_exception.ConflictException: <p>The request <i>couldn't</i> be made due to a conflicting operation or resource.</p>
capo_proton.errors.internal_server_exception.InternalServerException: <p>The request failed to register with the service.</p>
capo_proton.errors.resource_not_found_exception.ResourceNotFoundException: <p>The requested resource <i>wasn't</i> found.</p>
capo_proton.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
capo_proton.errors.validation_exception.ValidationException: <p>The input is invalid or an out-of-range value was supplied for the input parameter.</p>
capo_proton.errors.UnknownServiceError: The service returned an error code this client does not model.
"""
def _handler(
req: "OperationRequest[capo_proton.types.cancel_environment_deployment_input.CancelEnvironmentDeploymentInput]",
) -> OperationResponse[
"capo_proton.types.cancel_environment_deployment_output.CancelEnvironmentDeploymentOutput"
]:
import capo_proton._operations.aws_proton20200720.cancel_environment_deployment
output, http_response = (
capo_proton._operations.aws_proton20200720.cancel_environment_deployment.cancel_environment_deployment(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
def _redact_output(
output: "capo_proton.types.cancel_environment_deployment_output.CancelEnvironmentDeploymentOutput",
) -> dict[str, Any]:
import capo_proton.types.cancel_environment_deployment_output
return (
capo_proton.types.cancel_environment_deployment_output.redact_sensitive(
output
)
)
interceptors_, options_ = self.operation_options(
config_overrides,
OperationLogging(
"CancelEnvironmentDeployment", redact_output=_redact_output
),
)
input_: capo_proton.types.cancel_environment_deployment_input.CancelEnvironmentDeploymentInput = {
"environment_name": environment_name
}
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|