Serverless applications handle version control through various strategies that allow developers to manage and deploy code securely and efficiently. One common method is using versioning features provided by cloud platforms. For instance, AWS Lambda allows developers to create additional versions of their functions every time they update the code. Each version is assigned a unique ARN (Amazon Resource Name), making it easy to reference and manage different iterations of the function. This process helps in maintaining stability as older versions can still be invoked even when a new version is deployed, allowing for controlled updates and rollbacks if needed.
Another approach is to integrate serverless applications with version control systems, like Git. By developing your code within a repository, you can track changes, branch out for new features, and revert to previous code if an issue arises. Tooling around Continuous Integration and Continuous Deployment (CI/CD) pipelines plays a significant role here. For example, when a developer pushes code to the repository, automated solutions such as GitHub Actions or AWS CodePipeline can build and deploy the serverless application, ensuring that the latest version is always available while still allowing for proper versioning practices. This not only streamlines the deployment process but also adds a layer of reliability by backing changes with reproducible builds.
Lastly, many serverless frameworks, like the Serverless Framework, offer built-in support for managing versions and stages. Developers can utilize commands that enable them to deploy their applications in multiple environments, such as testing, staging, and production, each running different versions of the code. This provides clarity and control over what version is actively running in each environment and allows teams to collaborate more effectively. By implementing both versioning through cloud services and integrating with source control and CI/CD tools, serverless applications can be maintained with a high degree of control and predictability.