update_deployment_strategy
async
¶
update_deployment_strategy(
deployment_strategy_id: DeploymentStrategyId,
*,
config_overrides: Optional[AsyncAppConfigClientConfig] = None,
description: Optional[Description] = None,
deployment_duration_in_minutes: Optional[MinutesBetween0And24Hours] = None,
final_bake_time_in_minutes: Optional[MinutesBetween0And24Hours] = None,
growth_factor: Optional[GrowthFactor] = None,
growth_type: Optional[GrowthType] = None,
) -> DeploymentStrategy
Updates a deployment strategy.
Parameters:
-
deployment_strategy_id(DeploymentStrategyId) –The deployment strategy ID.
-
description(Optional[Description], default:None) –A description of the deployment strategy.
-
deployment_duration_in_minutes(Optional[MinutesBetween0And24Hours], default:None) –Total amount of time for a deployment to last.
-
final_bake_time_in_minutes(Optional[MinutesBetween0And24Hours], default:None) –The amount of time that AppConfig monitors for alarms before considering the deployment to be complete and no longer eligible for automatic rollback.
-
growth_factor(Optional[GrowthFactor], default:None) –The percentage of targets to receive a deployed configuration during each interval.
-
growth_type(Optional[GrowthType], default:None) –The algorithm used to define how percentage grows over time. AppConfig supports the following growth types:
Linear: For this type, AppConfig processes the deployment by increments of the growth factor evenly distributed over the deployment time. For example, a linear deployment that uses a growth factor of 20 initially makes the configuration available to 20 percent of the targets. After 1/5th of the deployment time has passed, the system updates the percentage to 40 percent. This continues until 100% of the targets are set to receive the deployed configuration.
Exponential: For this type, AppConfig processes the deployment exponentially using the following formula:
G*(2^N). In this formula,Gis the growth factor specified by the user andNis the number of steps until the configuration is deployed to all targets. For example, if you specify a growth factor of 2, then the system rolls out the configuration as follows:2*(2^0)2*(2^1)2*(2^2)Expressed numerically, the deployment rolls out as follows: 2% of the targets, 4% of the targets, 8% of the targets, and continues until the configuration has been deployed to all targets.
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.
-
UnknownServiceError–The service returned an error code this client does not model.
Examples:
To update a deployment strategy The following update-deployment-strategy example updates final bake time to 20 minutes in the specified deployment strategy. ::
>>> await client.update_deployment_strategy(deployment_strategy_id='1225qzk', final_bake_time_in_minutes=20)
Source code in docs_src/capo_appconfig/_services/async_app_config.py
3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 | |