Yesterday, Amazon Web Services (AWS) shared AWS Lambda Functions Powered By Graviton2 Processor.
At a high-level AWS claim:
- Up to 19% better performance
- At 20% lower cost
- Also applies to functions using provisioned concurrency.
All supported Lambda runtimes, including the custom runtime are supported. You just need to be careful with any binaries or functions packaged as containers to ensure they are built to support the architecture.
To help with performance testing, you can create two versions of a function, one in x86 and one for Arm. You can then use an alias with appropriate weights to distribute traffic between them. Once your test is complete, you can compare the performance difference within CloudWatch.
When migrating from x86 to Arm in production, you can use the same function version and weighted alias approach. This will allow you to slowly start ramping up, e.g. from 1%, gradually up to 100%. If something looks wrong, or you are experiencing errors, then adjust the weights down to zero to force traffic back to the x86 function.
Everything I deploy to AWS is via Cloudformation or AWS SAM. Below we will look at how to configure those to use the new architecture.
Update CLIs
If you use a CLI tool to deploy, it is probably a good idea to update to the latest one for your setup. That will ensure it's compatible with the new changes.
Template Changes
The example below shows the changes required for an AWS::Lambda::Function and an AWS::Lambda::LayerVersion. The changes are the same if you are using an AWS::Serverless::Function.
exampleLambdaFunction:
Type: AWS::Lambda::Function
Properties:
# OTHER ITEMS REMOVED FOR EXAMPLE
Layers:
- !Ref exampleLibrary
Architectures:
- arm64
exampleLibrary:
Type: AWS::Lambda::LayerVersion
Properties:
# OTHER ITEMS REMOVED FOR EXAMPLE
CompatibleArchitectures:
- arm64
Now the change is made, you are ready to deploy using your preferred mechanism, whether thats CLI or via the console.
Summary
There is really no reason not to try out the changes. They perform better, cost less, and the changes are minimal. There are also mechanisms in place by using the function versions and alias to do a gradual rollout until you have the confidence to switch over fully.
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