How do serverless platforms handle updates and versioning?

Serverless platforms manage updates and versioning by allowing developers to deploy new iterations of functions while maintaining backward compatibility and minimizing downtime. When you update a function—such as modifying its code, dependencies, or configuration—the platform typically creates a new version or revision. This versioning ensures that existing invocations of the function (e.g., via API endpoints or event triggers) continue to use the previous version until explicitly updated. For example, AWS Lambda lets you publish versions, which are immutable snapshots of a function. Aliases like “prod” or “dev” can point to specific versions, enabling seamless traffic routing between versions without disrupting active users. This approach decouples deployment from release, giving developers control over when and how changes go live.

Deployment strategies vary but often include gradual rollouts and rollback capabilities. Platforms like Google Cloud Functions support traffic splitting, where a percentage of requests can be routed to a new version while monitoring for errors. If issues arise, traffic can shift back to the stable version. Similarly, Azure Functions uses deployment slots (e.g., “staging” and “production”) to test updates in isolation before swapping them into live environments. These methods reduce risk by allowing incremental validation. For instance, a developer might deploy a new version with a bug fix, test it with 10% of traffic, then gradually increase the percentage once confidence grows. If a critical error occurs, reverting is as simple as pointing the alias or slot back to the previous version.

Handling dependencies and configuration is another key aspect. Serverless platforms bundle dependencies during deployment, ensuring each version is self-contained. For example, if a function relies on a specific library version, updating that dependency requires redeploying the function as a new version. Environment variables, which often store configuration like API keys, can also be version-specific. AWS Lambda allows variables to be defined at the version level, so a new version can use updated settings without affecting older ones. However, developers must ensure backward compatibility for shared resources like databases—older function versions might still rely on existing data schemas. By combining immutable versions, controlled traffic routing, and isolated configurations, serverless platforms simplify updates while maintaining stability.