architect VS aws-lambda-power-tuning

I had been running dockeri.co with https://arc.codes/ for pennies a month.

Then, one month, I got a ~$500 bill out of no where.

Docker had changed an api causing my service to return 5xx errors all month. Each error was individually logged to CloudWatch - which racked up a ~$500 bill.

I moved to Cloudflare Workers that day and haven’t moved back.

We use https://github.com/architect/architect to test, provision and deploy the functional web app via GitHub Actions (…whenever they work ;). For the UI/UX we work with https://tailwindui.com and paid them for their great work.

I use https://arc.codes/ for deploying to AWS Lambda/API Gateway. It does a really good job with Remix and NestJS and is easy enough. I like that all I have to do is give a very simple config, and it builds the apps, zips the function code, uploads all my static assets, and then generates and deploys the CloudFormation. I am curious to migrate off as I do have to do some workarounds and it doesn't seem to have a ton of traction.

Not sure why this is downvoted, Fastify is quite popular and the 'generator for everything' approach of Koa didn't really take off.

Architect serverless (https://arc.codes) is pretty good for serverless.

Architect is a heavily opinionated framework for building FWA's, Functional Web Apps. It uses AWS SAM under the hood but provides a layer on top with simplified abstractions that lets developers define and use AWS infrastructure without necessarily knowing what service is backing their "events" construct.

I work for vercel but I highly recommend a host like us because we make it a lot easier to manage a lambda environment and being a lot more to the table (cdn, edge functions, etc). If you want to go your own I really like architect https://arc.codes too. It really depends on your traffic and application patterns but cold starts can be virtually nil.

It uses: - Remix for the frontend - Architect for the backend

Alternately, use Architect. Architect is a 3rd party developer experience that builds on top of AWS SAM. Architect includes a TypeScript plugin.

For us at Begin and Architect, tape has been in use for several years. tape has a stable and straightforward API, routine maintenance updates, and outputs TAP, making it really versatile. While TAP is legible, it's not the most human-readable format. Fortunately, several TAP reporters can help display results for developers. Until recently, Begin's TAP reporter of choice was tap-spec. Sadly tap-spec wasn't kept up to date and npm began reporting vulnerabilities.

Sometimes, changing services, like opting for HTTP over REST API Gateway, leveraging tools like Lambda Powertuning to optimize functions, or reducing a CloudWatch log retention and changing log level, can lead to significant savings.

In case of not enabling SnapStart for the Lambda function we observed that increasing memory reduces the warm execution time for our use case especially for p>90. As adding more memory to the Lambda function is also a cost factor, the sweet spot between cold and warm start time and cost is somewhere between 768 and 1204 MB memory setting for the Lambda function for our use case. You can use AWS Lambda Power Tuning for very nice visualisations.

The aws lambda power tuning tool helps optimise the Lambda performance and cost in a data-driven manner. Let's try it out:

For Lambda, a very useful tool to help optimise is the AWS Lambda Power Tuning tool, released by Alex Casalboni, Developer Advocate at AWS: https://github.com/alexcasalboni/aws-lambda-power-tuning

Lambda memory affects not only the CPU performance and and host execution priority, but also network performance. Be wary though as the price scales linearly. You can use a tool like Lambda Power Tuning to find the sweet spot for your application. https://github.com/alexcasalboni/aws-lambda-power-tuning

This tool, which is open source and available here, takes the form of a Step Function that is deployed on your AWS account. The purpose of this Step Function is to run your lambda with different memory configurations several times and output a comparison in the form of a graph (or JSON) to try to find the optimal balance between cost and execution time. There are three possible optimization modes: cost, execution time, or a "balanced" mode where it tries to find a balance between the two.

The AWS Documentation's Memory and Computing Power page is a good starting point. To avoid configuring it manually, it's worth checking out AWS Lambda Power Tuning, which will help you find the sweet spot.

Utilizing tools such as AWS X-Ray, AWS Lambda Power Tuning, and AWS Lambda Powertools tracer utility is recommended. Read more about it here.

Install the AWS SAM CLI in your local environment. Configure your AWS credentials (requires AWS CLI installed): $ aws configure Clone this git repository: $ git clone https://github.com/alexcasalboni/aws-lambda-power-tuning.git Build the Lambda layer and any other dependencies (Docker is required): $ cd ./aws-lambda-power-tuning $ sam build -u sam build -u will run SAM build using a Docker container image that provides an environment similar to that which your function would run in. SAM build in-turn looks at your AWS SAM template file for information about Lambda functions and layers in this project. Once the build has completed you should see output that states Build Succeeded. If not there will be error messages providing guidance on what went wrong. Deploy the application using the SAM deploy "guided" mode: $ sam deploy -g

Use AWS Lambda Power Tuning to balance cost and performance.