deno-lambda VS aws-embedded-metrics-node

Using Deno with AWS Lambda functions requires a custom runtime. You can build your own runtime or use one that already exists. If you’re concerned about security, I suggest maintaining a copy of an existing runtime and carefully inspecting updates or creating your own runtime. For this proof of concept, I deployed the Serverless Application Repository (SAR) app for Deno into my AWS account. I used the included Lambda layer and the provided.al2 Lambda runtime to create my Deno Lambda function. I created a file called index.ts with some basic JavaScript code that makes requests to two different websites and returns the HTTP status code of the response or a caught error. I then updated the function’s configuration to reference the exported handler function.

I'm doing a simple PoC / testing with Deno using https://github.com/hayd/deno-lambda and specifically the CDK instructions.

As I had mentioned, it requires using a Lambda Layer. See: https://github.com/hayd/deno-lambda/blob/2d90756a0f18b57f16e...

Using your own image (i.e. without using the base AWS image with the layer) you'd get even worse cold start times.

Decorators and TypeScript aren't supported out of the box in Lambda (without using deno) so we'll also need a transpilation step if we go this route. Fortunately this is a mostly solved problem for AWS CDK, AWS SAM and Serverless Framework users. If you want or need to roll your own, esbuild is a great place to start and seems to be the bundler of choice for this purpose.

In addition to the stuff posted here, there's the deno-lambda project for deploying to AWS Lambda

Cloud hosting support. Deno runs fine in a container, but lambda/cloud function support is difficult on most providers. deno-lambda exists but it only applies to AWS and can't be used with all CICD tools. Deno Deploy also exists but it's pretty new.

Lamby will now publish CloudWatch Embedded Metrics in the Lamby namespace with a custom dimension for each application's name. Captured metrics include counts for Cold Starts vs. Proactive Initializations. Here is an example running sum of 3 days of data for a large Rails application in the us-east-1 region.

I was looking into CloudWatch Embedded Metrics - which is a format that converts logs into cloudwatch metrics automatically.

Often when it comes to metrics, we think about CPU, latency and other operational metrics and AWS services usually provide those out of the box. This kind of thinking can be flawed when we end up having to use 3rd parties such as google analytics to infer critical business events. A simpler solution is to have the application emit a metric when a business event (say a customer signup) occurs. We have a few options for doing this: We can use aws-sdk, we can use the aws-embedded-metrics lib and now we can use Powertools Metrics. Which is the best? Let's see.

Let's explore the next core utility in Lambda Powertools, the Metrics utility. This utility lets you easily push metrics to CloudWatch by taking care of all the necessary boilerplate. It works asynchronously by using Amazon CloudWatch Embedded Metrics Format, by logging the metrics to stdout. It also aggregates all metrics from each invocation to save on the number of calls to CloudWatch.

To make it easier to create such an object, AWS has provided libraries for Node.js, Python, Java, and .NET. The above example using the AWS SDK can now be written as follows:

Custom metrics have a pricing structure which can be expensive. Embedded Metrics Format can help manage the cost and is supported by Lambda Powertools TypeScript. Again, the docs here are pretty good, so no need for me to break it down. Instead let's look at the experience. I've added a custom metric of "collectionSuccess" to my collectionSuccess function. In my hypothetical app, some payments wind up in collections and here I'm marking whether or not the collection resulted in a payment.

You can use metrics to aid debugging by adding them to your dashboards that we talked about earlier. It’s also possible to add custom metrics, and there are many libraries and tools (e.g. node, python, etc) which can help you do this.