aws-lambda-power-tuning VS aws-lambda-java-libs

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.

FaaS is specifically focused on building and running applications as a set of independent functions or microservices. Major cloud providers like AWS (Lambda), Microsoft Azure (Functions), and Google Cloud (Cloud Functions) offer FaaS platforms that allow developers to write and deploy individual functions without managing the underlying infrastructure.

So AWS Lambda is basically a serverless computing service that is offered by AWS. It enables developers to run the code in response to various events. It protects the developers from the pain of managing the servers. Using a serverless execution model helps the developers to handle provision, manage and scale the servers automatically. Through this approach the developers can fully focus on writing the code instead of dealing with other aspects.

The first product that popularized the term “serverless” was AWS Lambda, which is both the prototypical and archetypical function as a service provider. It also has a great name, which pings back to its envisioned place in the cloud of the future. In computer programming, a lambda, often referred to as a lambda function or lambda expression, is a concise way to represent an anonymous function, which is a function without a name. The concept originates from lambda calculus in mathematical logic and has been adopted by many programming languages, each with its own syntax and characteristics.

You can build a custom config rules in 2 ways, using AWS Lambda and CloudFormation Guard. Lambda gives you a lot of flexibility, but it also brings complexity of maintaining. CloudFormation Guard is a bit more lightweight in that regard. Yes, you still need to maintain the logic to determine when your resource is compliant or not. But you need to do this in both cases, thus my go to preference is CloudFormation Guard.

AWS Lambda: https://aws.amazon.com/lambda/

This blog details how you can use some key serverless components from AWS like Amazon Eventbridge, AWS Lambda, and Simple Notification Service to setup a system that will monitor your site (which can be running anywhere) and send emails, text messages, slack messages, and more when the reachability status of your site changes.

I use the OpenWeather API to receive the current temperature data by providing the latitude and longitude coordinates as query strings in the request. A Lambda function invokes the weather API and sends the current temperature value as a custom metric to CloudWatch. I then graph the temperature values on a dashboard and activate an alarm when the temperature sinks below zero degrees Celsius.

After two years, I moved to a Web3 startup where I was given a lead software engineer role. This new role gave me more hands-on experience with AWS, where I've learned to implement serverless technologies like Lambda and DynamoDB.

I discussed a way to control access to endpoints using JSON web tokens and a Lambda authorizer earlier.