Solving deployment problems with Lambda Aliases

Ensuring smooth code deployments is crucial, especially when dealing with serverless architectures like AWS Lambda. Recently, I tackled a deployment challenge using Lambda aliases, and I want to share the journey with you, from the basics to implementation.

Understanding the Basics: $LATEST Version

Let's start with $LATEST. This is a special version of your Lambda function. It's essentially a placeholder that holds the latest code and environment variables. When you update your Lambda using aws lambda update-function-configuration for environment variables, and aws lambda update-function-code for code, these changes affect $LATEST.

When you're ready to lock in these changes, you "publish" a new version. This creates an immutable snapshot of $LATEST, complete with its code and configuration, and assigns it a version number.

The Problem: Inconsistent States

Consider a setup where:

• An API Lambda is triggered via API Gateway.
• An Async Lambda is triggered by SQS and EventBridge.

With $LATEST being invoked directly, deploying a new environment and code setup happens in two steps:

1. Update environment variables.
2. Update the code.

Between these 2 steps, there's a moment when $LATEST has new environment variables but old code. Let’s refer to this as an inconsistent state.

This inconsistent state can cause these significant issues:

• Compatibility Errors: If the new environment variables require updated code logic, the old code might not function correctly, leading to runtime errors.
• Configuration Mismatches: Updates in configuration settings like API endpoints might not align with old code, causing malfunctions.
• Data Processing Anomalies: Async tasks could process messages incorrectly, leading to data corruption or incomplete handling.
• Security Risks: New security settings in environment variables might not be supported by old code, exposing vulnerabilities.
• Inconsistent User Experience: User-facing applications could experience temporary breaks in functionality, affecting user trust.

Even though the inconsistency window might be small, in high-throughput environments or critical applications, these few moments can significantly impact system reliability.

The Solution: Lambda Aliases

To tackle these issues, I implemented Lambda Aliases. Here's how it works:

1. Alias as a Pointer:
2. An alias is essentially a named pointer to a specific Lambda version.
3. I configured API Gateway, SQS, and EventBridge to reference the alias rather than the function directly.
4. Deployment Process:
5. Update $LATEST with the new environment variables and code in separate steps. However, since these are affecting $LATEST only, there’s no immediate impact on the deployed function.
6. Publish a new version.
7. Update the alias to point to this newly published version.

By using an alias, the switch to the new version happens atomically. Both code and environment changes take effect just as the alias is updated. This ensures a seamless transition without any inconsistent states.

Handling Permissions

For any Lambda instances still processing tasks using the old version, permissions are crucial. They need to interact with components like SQS, even if the alias now points to the new version.

To manage this, I utilized IAM roles. By associating the necessary permissions with the Lambda execution role, I ensured that all versions have the requisite permissions.

Conclusion

Adopting Lambda Aliases for deployment streamlined the process, eliminating potential inconsistencies. By decoupling the deployment of environment variables and code updates, and utilizing aliases for atomic version switching, I achieved a smoother and more reliable deployment workflow.

This approach not only enhances stability but also adds flexibility to serverless deployments. Have you faced similar challenges in your serverless environments? Share your experiences and let's discuss!