Should I use AWS SAM for "simple" Lambda functions?

Question

I'm currently using SAM for some of my serverless applications. I also have "simpler" lambda functions that don't require an API Gateway or a more complicated integration with AWS services.

Some of these functions include S3 and DynamoDB trigger functions, or some functions that are just run periodically. I describe them as simple because they are not very large in size and don't use many services.

Because of this, I don't like the idea of using SAM for them. I would like to be able to test my functions locally still in a similar way that SAM allows. Ideally, I'd like to group all of my simple lambda functions into one git repository, then I can pull down the repo, edit, test, and deploy the new version of a function. This seems complicated with SAM. I want to avoid having many large repos for each function, which I think would end up being the case with SAM on these. I don't mind having a separate repo for larger projects, it's these small ones that are giving me a hard time.

Is there a good way to manage these small lambda functions? Or do I need to just accept using SAM for them?

Answer 1

AWS SAM is perfect for that use case! If you compare it to CloudFormation it allows you to write Lambda functions and their triggers in a much more compact way. I'm not sure what you consider complicated, but just have a look at the following example. This is a single AWS SAM template, which defines two AWS Lambda functions, one triggered by S3 events the other one with a scheduled execution every five minutes:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Globals:
  Function:
    Runtime: python3.8
Resources:
  Bucket:
    Type: AWS::S3::Bucket
  ProcessorFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: s3_processor.handler
      CodeUri: src/
      Policies: AmazonS3ReadOnlyAccess
      Events:
        PhotoUpload:
          Type: S3
          Properties:
            Bucket: !Ref Bucket
            Events: s3:ObjectCreated:*
  ScheduledFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: scheduled_function.handler
      CodeUri: src/
      Events:
        Timer:
          Type: Schedule
          Properties:
            Schedule: rate(5 minutes)

I won't include how the same definition would look like with pure CloudFormation, as it would be more than hundred lines.

You can deploy both functions using a single CloudFormation stack, which appears to me what you desire to do.

As for the code, you can see I used two different handlers, so you'd simply a s3_processor.py and a scheduled_function.py in your src/-directory and you're good to go. You can keep all of that easily in a single git-repository and enjoy the benefits of AWS SAM's local testing abilities.

If you want to have a separate CloudFormation stack per AWS Lambda function you can simply create a sub-directory in your git-repository for each AWS Lambda function.

Answer 2

You must give a look at serverless framework, 'cause as you, I don't like so much of SAM Templates, for me serverless framework is a faster and easy way to work on scenarios like this one you describe.

https://serverless.com/

I'm using it on some of my projects, it´s very simple and functional.