aws-lambda-power-tuning VS sst

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.

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!

> But if you have a sufficiently large enough API surface, doing one lambda per endpoint comes with a lot of pain as well. Packaging and deploying all of those artifacts can be very time consuming, especially if you have a naive approach that does a full rebuild/redeploy every time the pipeline runs.

Yeah, thankfully SST [0] does the heavy lifting for me. I've tried most of the solutions out there and SST was where I was the happiest. Right now I do 1 functions per endpoint. I structure my code like url paths mostly, 1 stack per final folder, so that the "users" folder maps to "/users/*" and inside I have get/getAll/create/update/delete files that map to GET X/id, GET X, POST X, POST X/id, DELETE/id. It works out well, it's easy to reason about, and deploys (a sizable a backend) in about 10min on GitHub Actions (which I'm going to swap out probably for something faster).

I agree with the secrets/permissions aspect and I like that it's stupid-simple for me to attach secrets/permissions at a low level if I want.

I use NodeJS and startup isn't horrible and once it's up the requests as very quick. For my needs, an the nature of the software I'm writing, lambda makes a ton of sense (mostly never used, but when it's used it's used heavily and needs to scale up high).

[0] https://sst.dev

We will start by building a project with SST that provisions an API Gateway, a Lambda, and an S3 bucket. Once implemented, we'll look into testing for concurrent write conflicts or exceeding capacity limits.

I recently deployed a node websocket server using the SST Service construct. Until this point my stack had been functions and buckets. While I had no users 😢, I also had no costs 🤡.

Most my personal and side-business projects have very spiky load or just low load in general. Because of that I love using AWS Lambda as my backend since it scales to 0 and scales to whatever you have your limits set at.

I use SST [0] for my backend with NodeJS (TypeScript) and Vue (Quasar) for my frontend. For my database I use either Postgres or DynamoDB if the fit is right (Single Table Design is really neat). For Postgres I like Neon [1] though their recent pricing changes make it less appealing.

[0] https://sst.dev

[1] https://neon.tech

Yup. Entire core business product for a succeeding startup, though it's a small team of contributors (<10), and a much smaller platform team. Serverless backend started in 2018. Been a blessing in many regards, but it has its warts (often related to how new this architecture is, and of course we've made our own mistakes along the way).

I really like the model of functions decoupled through events. Big fan of that. It's very flexible and iterative. Keep that as your focus and it's great. Be careful of duplicating config, look for ways to compose/reuse (duh, but definitely a lesson learnt) and same with CI, structure your project so it can use something off-the-shelf like serverless-compose. Definitely monorepo/monolith it, I'd be losing my mind with 100-150 repos/"microservices" with a team this size. If starting now I'd maybe look at SST framework[0] because redeploying every change during development gets old fast

I couldn't go back to any other way to be honest, for cloud-heavy backends at least. By far the most productive I've ever been

Definitely has its warts though, it's not all roses.

[0] http://sst.dev

Along the way, you find an excellent framework, SST. Which is much faster than CDK and provides a better DX1. Here is how you then define your MultiPipelineStack.

SST is a powerful framework that simplifies the development of serverless applications. It offers a straightforward and opinionated approach to defining serverless apps using TypeScript. Built on top of AWS CDK, SST handles the complexity of setting up your serverless infrastructure automatically. SST is an open-source framework and is completely free to use.

SST is an open-source framework designed to facilitate the development and deployment of Serverless stacks on AWS. It operates under the hood by integrating with Amazon CDK. However, its primary benefit is in allowing us to concentrate on creating resources using familiar languages like TypeScript, treating them as Infrastructure as Code (IaC).