aws-lambda-dotnet VS serverless-graphql

I saw this issue: https://github.com/aws/aws-lambda-dotnet/issues/245

Creating Lambda Project using Amazon.Lambda.Templates. I use Amazon.Lambda.Annotations because it will be similar to my previous console program, and we can use dependency injection.

Hopefully, this post will have intrigued you enough to check out the new framework, and try converting some of your existing functions into what I think is a much more readable and easier to comprehend format. Also, the development team is always interested in feedback, which you can supply in the issues folder of their repo. You can also find the design document for the annotations in this issue.

I don't think it's exactly what you're looking for, but you could try out the LambdaTestTool https://github.com/aws/aws-lambda-dotnet/tree/master/Tools/LambdaTestTool

According to this GitHub issue we need to add and configure an additional package when running a custom runtime on ARM:

I'm assuming API's are REST APIs. Those sound like excellent use cases for running as Lambda functions. If the API's are an ASP .NET Core Web API project it is possible to deploy that to Lambda using the Amazon.Lambda.AspNetCoreServer, https://github.com/aws/aws-lambda-dotnet/tree/master/Libraries/src/Amazon.Lambda.AspNetCoreServer, NuGet package. That can be a quick way to get something working and then later choose to rearchitect to a smaller more micro services architecture if necessary.

AWS provides a NuGet package (see Amazon.Lambda.AspNetCoreServer) that makes it easy to do this. It works pretty well, but instead of one copy of the API serving many clients you have many copies of the API each serving one client at a time. There are also limitations in areas like file upload and download (which vary depending upon whether this is being used with API Gateway or an Application Load Balancer).

In this article, we will use Serverless Framework and Docker Desktop to deploy Dart functions to AWS Lambda. If you haven't installed them yet, please complete the installation using the following commands or links.

Since then, the ecosystem has changed. Using the Serverless Framework makes deployment simpler. We released the managed Tracetest App making any serverless-based systems simpler to instrument and test. You can now test public-facing apps with no infra overhead!

We see some great results from using these in conjunction with frameworks such as SST or Serverless, and also some real spaghetti from people who organically proliferate 100’s of functions over time and lose track of how they relate to each other or how to update them safely across time and service. Buyer beware!

When we think about AWS serverless service, the first thing that comes to our mind is Lambda function. Yes, the quickest way to deploy this backend Express JS app to AWS is to deploy it as a Lambda function. The easiest way is using Serverless Framework.

Serverless Framework: https://www.serverless.com/

Github | Website

In this example, we're using the serverless framework to quickly set up the Lambda function along with an API gateway for the entry point. The lambda function is a simple Koa REST API with a few functional endpoints.

Naturally, there are several options available to declare your cloud resources. The options with the most popularity are the CDK, AWS CloudFormation, SST, Serverless framework, Terraform, and AWS SAM. There are others, but when talking about Infrastructure as Code (IaC), these are the ones you hear about most often.

1 - Serverless + AWS CDK + Lift: An integration that amps up the traditional Serverless Framework with Lift's static frontend construct and CDK's robust infra definition.

It's a bit easier in Python if you use tools like https://www.serverless.com/. I'm not sure if Rust has something similar yet.