aws-lambda-power-tuning VS aws-sam-cli

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.

aws-sam-cli-linux

Does anyone have any success with API Gateway/Lamba for local environment with hot-reloading? I was playing around with “sam local start-api” a few weeks ago with along with CDK but it doesn’t seem like hot-reloading is supported, and need to manually restart for code changes to reflect, which is currently stopping me from trying out the API Gateway/Lamba combo further due to inefficient DX. Here’s a GitHub issue about it: https://github.com/aws/aws-sam-cli/issues/901

Mostly due to lack of proper documentation it was difficult to get the HTTPApi syntax correct. The yaml file is very picky for CORS configuration on an HTTPApi. Luckily I found this github issue which allowed me to troubleshoot my yaml file.

No, it's not just muscle memory. It doesn't work with podman. That's what I'm talking about https://github.com/aws/aws-sam-cli/issues/1668

AWS SAM CLI with the support of Typescript is still in Beta. If you want more advanced features maybe you should not use it. Lately, in version 1.49.0 External and Loader properties have been added. If you want to share your feedback and/or requests this issue has been created.

I spent a while creating a custom runtime to use the native-image agent to harvest config from my Lambda, and then patching sam cli to work in Docker user networks... after pushing through all that, I still didn't get a working build - the native-image compiler just doesn't support everything (or didn't support my feature set, at the time - there has been a new release since then). In my case I think it was Guice that defeated me.

SAM support for TypeScript has been lagging for some time, but a pull request was just merged that should change that. It looks like we'll be able to add an aws_sam key in the package.json file to enable building within the SAM engine as part of sam build. Although this PR has been merged to aws-lambda-builders, the engine behind sam build, it will still need to be added to aws-sam-cli and released (to much fanfare, one expects) before it can be used with SAM.

I am installing SAM CLI on windows and its super simple. All I need to do is Click on this MSI installer.

sem-version java 8 wget https://github.com/aws/aws-sam-cli/releases/latest/download/aws-sam-cli-linux-x86_64.zip unzip aws-sam-cli-linux-x86_64.zip -d sam-installation sudo ./sam-installation/install checkout cd java-app-backend cache restore build-java-app-backend-$SEMAPHORE_GIT_BRANCH sam deploy --no-confirm-changeset --stack-name serverless-web-application-java-backend --capabilities CAPABILITY_AUTO_EXPAND CAPABILITY_IAM