aws-lambda-power-tuning VS dynamodb-toolbox

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.

One of them was that it had originally been coded in JavaScript. Although Jeremy rewrote the source code in TypeScript in 2020, it didn't handle type inference, a feature that I eventually came to implement myself in the v0.4.

If you have in mind features that I missed, or would like to see some of the ones I mentioned prioritised, please comment this article and/or create an issue or open a discussion on the official repo with the v1 label 👍

For instance, here is an example of the same UpdateCommand with one of those wrappers, DynamoDB-Toolbox:

It's been mature enough for at least four years.

1. Not an issue for me. Connection reuse support in Lambda is quite good

2. NoSQL is a good skill to keep your in bucket anyhow. DynamoDB is a different approach, but much of the same tenets you'll find in other NoSQL databases still apply. Using tools like dynamodb-toolbox [1] help greatly with paradigm shifts into Dynamo.

3. True. Ask yourself how much this matters. How likely is it that you'll need to support another cloud provider for a single product? In 20 years I've seen a platform provider switch exactly once. And DynamoDB can be exported easily.

4. Nope. But there are things to learn about cold starts; how to structure code, where to initialize things, which things should be singletons, etc.

5. Depends on the situations and needs. The right tool for the right job, if you will. I've written GraphQL servers that run on lambda which serve 300k users daily. I've also done the same using Fargate/ECS et al. Much of the decisions revolve around complexity of execution and cost factors (e.g. the cost and complexity of running lambda's to process data often versus a Fargate service). You're getting into Software Architecture now.

6. Again, depends on the situation. You'll need to start thinking about what individual services/components/things are doing, what they need, and how they need to run. Gather that information, and then start cost comparisons using the pricing tools the provider has.

[1] https://github.com/jeremydaly/dynamodb-toolbox

Note that all these entities will belong to the same DynamoDB table. We define only one partition key and one sort key for this table: both of type string. Key is the values we provide for these keys. As you can imagine, this quickly can become a bit tangled mess. Therefore I recommend to express this 'schema' (e.g. of what types of keys we lay over our base table) in code. Later in this article I will show how this can be accomplished using the DynamoDB Toolbox framework.

I use single table design with app sync through this library: https://github.com/jeremydaly/dynamodb-toolbox

Serverless Cloud further reduces the cognitive load on developers by automatically applying best practices for APIs, data, storage, CDNs, and more. For example, Jeremy Daly, GM of Serverless Cloud, created the DynamoDB Toolbox to help developers implement the best practices of single table design for Amazon DynamoDB. Serverless Data bakes in those best practices for you and provides a simple interface to manage your data. Similarly, your API endpoint will automatically have a sufficient amount of computational resources by simply using the api interface of our SDK. Serverless Cloud continuously adapts the best configuration for your application, letting you focus on solving business problems, not infrastructure ones.

I like the idea, especially since I found libraries like https://github.com/jeremydaly/dynamodb-toolbox or https://github.com/sensedeep/dynamodb-onetable not elastic or up to date enough for me and reverted to raw AWS SDK. I look forward to AWS SDK v3 support in your typings!