How should a solutions architect improve the CI/CD pipeline to reduce the likelihood that changes in the templates will cause downtime?
Adapt the deployment scripts to detect and report CloudFormation error conditions when performing deployments. Write test plans for a testing team to execute in a non-production environment before approving the change for production.
Implement automated testing using AWS CodeBuild in a test environment. Use CloudFormation change sets to evaluate changes before deployment. Use AWS CodeDeploy to leverage blue/green deployment patterns to allow evaluations and the ability to revert changes, if needed.
Use plugins for the integrated development environment (IDE) to check the templates for errors, and use the AWS CLI to validate that the templates are correct. Adapt the deployment code to check for error conditions and generate notifications on errors. Deploy to a test environment and execute a manual test plan before approving the change for production.
Use AWS CodeDeploy and a blue/green deployment pattern with CloudFormation to replace the user data deployment scripts. Have the operators log in to running instances and go through a manual test plan to verify the application is running as expected.
Explanations:
While adapting deployment scripts to detect CloudFormation errors and creating test plans for a testing team may improve the deployment process, it does not directly address the issue of downtime caused by resource changes in CloudFormation templates. This approach relies heavily on manual processes and does not incorporate automated testing or rollback mechanisms, which are essential for reducing downtime.
Implementing automated testing with AWS CodeBuild allows for thorough validation of changes in a test environment. Using CloudFormation change sets enables evaluation of proposed changes before deployment, minimizing risks. Additionally, leveraging AWS CodeDeploy with blue/green deployment patterns allows for safe rollouts and the ability to revert changes quickly, significantly reducing the likelihood of downtime.
While using IDE plugins and AWS CLI for template validation can help catch errors, this option lacks the automation and robust testing that would mitigate downtime effectively. Manual testing in a non-production environment does not ensure that issues are caught early in the pipeline. This approach is more reactive than proactive and may still lead to downtime.
While using AWS CodeDeploy with blue/green deployments is beneficial, relying on operators to manually verify application functionality introduces human error and delays, which could still result in downtime. This approach does not fully automate the deployment or testing process, making it less effective in preventing downtime compared to using automated testing and change sets.
From what I’ve seen, the answer is:
Implement automated testing using AWS CodeBuild in a test environment. Use CloudFormation change sets to evaluate changes before deployment. Use AWS CodeDeploy to leverage blue/green deployment patterns to allow evaluations and the ability to revert changes, if needed.