aws-lambda-power-tuning VS middy

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.

In many cases, you will have to write the same authorization code in multiple functions. For example, you might want to check that the user is in the requested organization. You can share this code in a middleware. If you are using AWS Lambda, you can rely on middy.

Adding the is-test flag to our object metadata gave us our way of passing some kind of test context into our workload. The next step was to make the Lambda Function capable of discovering the context and then using that to control how it behaves under test. For this we used Middy.

I also decided to use the middy library to add CORS management to our lambda function. This will allow us to call our lambda function from our frontend, without having to worry about CORS.

Is there any equivalent to Node based https://middy.js.org/ for Golang?

And if your requirements were to change at a later date, it’s straightforward to swap out SSM Parameter Store with Secrets Manager there and then. Especially if you’re accessing the relevant service through a middleware layer such as Middy for javascript Lambda functions.

This function uses the Middy middleware engine to handle unhandled errors and add CORS headers in the response.

I mean I'm literally building an AWS lambda function that outputs HTML when it's called via API Gateway. So someone hits https://mydomain.com/mycoolpage, then the MyCoolPage AWS Lambda function is executed and outputs whatever.

If you're interested, I use https://middy.js.org/ as a middleware engine for my AWS lambda functions which I find helpful.

I use the open sourced serverless framework for doing deploys https://www.serverless.com/

middy for lambda-side middleware

That is it from the CDK side. Now let us create the handler and retrieve that secret. I like to use middy which describes itself as "stylish Node.js middleware engine for AWS Lambda". It offers some helpful middlewares like ssm which will help us retrieve and cache values from SSM Parameter Store. (Middy provides various other official middlewares including one for Secrets Manager.) I prefer a middleware for this because it keeps the code for retrieving the secret out of your handler which should deal with actual business logic.