aws-lambda-runtime-interface-emulator VS serverless-application-model

FWIW, Lambda takes the opposite of your assertion: there are function entrypoints and the HTTP or gRPC or Stdin is an implementation detail; one can see that in practice via the golang lambda "bootstrap" shim <https://pkg.go.dev/github.com/aws/[email protected]/lamb...> which is invoked by the Runtime Interface Emulator <https://github.com/aws/aws-lambda-runtime-interface-emulator...>

I don't have the links to Azure's or GCP's function emulation framework, but my recollection is that they behave similarly, for similar reasons

Another option I explored was the AWS Lambda Runtime Interface Emulator. This is an official AWS tool that lets you run Lambda inside a Docker container, mimicking the AWS runtime. It's particularly useful if you're deploying container-based Lambdas.

Enter the aws-lambda-runtime-interface-emulator. This tool will allow us to emulate the AWS Lambda Runtime API locally (I suspect that the AWS SAM uses this tool under the hood as well). The aws-lambda-runtime-interface-emulator is designed to be used with Docker. Still, nothing stops us from containerizing our code for the sake of development and then, whenever we are ready, proceeding with deployment how we wish to. This local workflow is a bit more involved than the previous one, but it might be a valid alternative for those not using AWS SAM or are already using containers to deploy their Lambdas.

There is a special tool to test AWS Lambda images locally. It's called AWS Lambda Runtime Interface Emulator (RIE). You have two options: include RIE in your image or install it locally. We don't need it in the production image, so let's choose the second option. We will download binary locally and mount it to our image if we need to test it.

Suggestions:

1. If you are building APIs and using Lambda functions as targets from an API Gateway API, look into libraries like serverless-wsgi (Python) or wai-handler-hal (Haskell) that translate between API Gateway request/response payloads and some kind of ecosystem-native representation. Then as long as you're writing code where all state gets persisted outside of the request/response cycle, you can develop locally as if you were writing for a more normal deploy environment.

2. Look into the lambda runtime interface emulator ( https://github.com/aws/aws-lambda-runtime-interface-emulator... ). This lets you send invoke requests to a fake listener and locally test the lambda more easily. While the emulator is provided in the AWS container base images, you don't need to run it inside a container if you're deploying with zip files. (AWS-provided container images automatically enable the emulator if not running in a lambda runtime environment, and using docker for port remapping is nice. But not at all required.)

3. Get really good at capturing all requests to external services, and mocking them out for local testing. Whether this is with free monads, effect systems, gateway classes will depend on your language and library choices.

A simple Google search found it: a tool called SAM — Serverless Application Model — something that could read the YAML, package the application, and deploy the whole thing: function, gateway, code, all of it 📦🚀.

Move to CDK or SAM.

In this case, I used the AWS Lambda Web Adapter and SAM (Serverless Application Model) to deploy a simple server application built with the Go web framework Echo via Lambda Function URLs.

In this post, I will share WebRTC.ventures' best practices in automating the deployment of AI-powered voice assistants for Amazon Connect, moving beyond manual, click-by-click setups to a robust, scalable Infrastructure as Code (IaC) approach. We’ll explore how to manage both static and dynamic resources, leverage tools like Terraform and AWS Serverless Application Model (SAM), and even set up an automated deployment pipeline using Github Actions to deploy your conversational IVR solutions efficiently.

My first stop was AWS SAM (Serverless Application Model), which is AWS's official tool for local development. With SAM, you can invoke a Lambda function on your machine using sam local invoke, or even spin up a local API Gateway using sam local start-api. This means you can test API calls before deploying to AWS, which is super useful.

To expose your FastAPI endpoints using AWS API Gateway, you will use AWS Serverless Application Model (AWS SAM). AWS SAM simplifies the process of building and deploying serverless applications on AWS by providing a simplified syntax for defining AWS resources such as Lambda functions, API Gateway, IAM roles, and other related services, all within a template.yaml file.

AWS SAM GitHub Repository

This further enables you design your infrastructure visually, generate Infrastructure as Code and deploy it using AWS SAM (AWS Serverless Application Model) https://aws.amazon.com/serverless/sam/.

Right now I’m working on a project where REST API is built using AWS lambdas as request handlers. The whole thing uses AWS SAM to define lambdas, layers and connect it to Api Gateway in nice template.yaml file.

Container images (Dockerfiles), infrastructure as code with AWS CloudFormation, AWS Serverless Application Model (SAM), or HashiCorp Terraform can all be generated, updated, or optimized using AI-assisted tools.