Deploying Single Lambda Function From CI/CD pipeline - aws-lambda

Question

I am dealing an infrastructure and trying to figure it out how to deploy just single lambda from CI/CD pipeline.
Let's say in a repo you have 20 lambdas, and you made change for one single lambda, instead of deploying all of them i just want to deploy the changed one so cut out the deployment time.
I've got an idea like checking difference from git and figure it out which ones are changed, and do deployment only that part of functionality, but it surely doesn't seem right way to do it. Believing there is more proper way to do it.
I am using terraform for now (moving to serverless framework) i know that terraform and serverless framework holds a state on s3 bucket. However on my case when i run it through pipelines, eventhogh there is a terraform state and there is no change on the state, it still deploys the whole thing as far as realised (i might be wrong). I just want to get clear my mind to see how people does this with their pipline.

Answer 1

Since you seem to be asking about both Terraform and Serverless Framework here, I'm assuming you're looking for a general answer rather than specifically how this would be solved with a particular tool.
One way to solve this problem is to decouple your build process from your deploy process by adding a version selection mechanism in between. This just means that somewhere in your system you have a value that can be written by your build process and read by your deploy process which indicates what is the "current" artifact for each of your Lambda functions.
When your build process completes successfully, it can write the information about the artifact it built into the appropriate location, and then trigger your deployment process. Your deployment process will then read the artifact information and use it to decide what to deploy.
If you have made no changes to the current artifact metadata for a particular function then the deploy process can see that and not do anything. If a particular artifact is flawed in some way and you only notice once it's deployed, you can potentially set the artifact metadata back to the previous one and re-run the deployment process to roll back. If you choose a data store that retains historical versions, you'll also have a log of changes to the current artifact which might be useful to understand circumstances that lead to an incident.
Without getting into specifics it's hard to say more about this. For Terraform in particular, the artifact metadata store ought to be something that Terraform can read using a data source. To show a real example I'm going to just arbitrarily choose AWS SSM Parameter Store as a location for that artifact metadata store:
data "aws_ssm_parameter" "foo" {
name = "FooFunctionArtifact"
}
locals {
# For this example, we'll assume that the stored parameter is a JSON
# string shaped like this:
# {
# "s3_bucket": "awesomecorp-app-artifacts"
# "s3_key": "/awesomeapp/v1.2.0/function.zip"
# }
foo_artifact = jsondecode(data.aws_ssm_parameter.foo)
}
resource "aws_lambda_function" "foo" {
function_name = "foo"
s3_bucket = local.foo_artifact.s3_bucket
s3_key = local.foo_artifact.s3_key
# etc, etc
}
The technical details of this will vary a lot depending on your technology choices. If you don't use Terraform then you'll either use a feature similar to data sources in your other tool or you'd write some wrapper glue code that can itself retrieve the necessary information and pass it into the tool as an argument.
The main thing, regardless of technology choices, is that there is an explicit record somewhere of what is the latest artifact for each function, which is updated by your build step and read by your deploy step. This pattern can apply to other artifact types too, such as AMIs for EC2, docker images, etc.

Answer 2

Seems you have added label of terraform, serverless-framework (I called it sls), and aws-lambda. So all of them work for you.
terraform - Terraform itself will care of the differences which lambda need be updated. But it is not lambda friendly if you need install related packages.
serverless framework (sls) - it is good to use to manage lambda functions, but as side effect, it has to be managed with api gateway together. I am not sure if sls team has fix this issue or not. Need some confirmations.
SLS will take care of installing related packages.
The bad part is, sls can't diff the resources to be deployed and to be planned.
cloudformation - that's AWS owned Infrastructure as Code (IaC) tool to manage aws resources, you should be fine to use it to manage the lambda resource. you will get same issues as Terraform that you have to install the related packages before deploy the stack.
Bad part is, cfn (cloudformation) doesn't have diff feature as well, furtherly, it doesn't have proper tools to manage its aws cli commands, you have to use others, such as shell scriping, Ansible or even Terraform to manage coudformation templates updates.
aws cdk - The newest way is using aws-cdk, it does have the diff feature cdk diff which is mostly suitable for your current job, but it is very new project, a lot of features are still waiting to be developed.
You can take these and think with your team's skill sets. Always choice the tools, which you and your team are most confident.