lamby VS aws-embedded-metrics-node

AWS Serverless Hero Ken Collins maintains a very popular Rails-Lambda package. After some discussion, he added the capability to track Proactive Initializations and came to a similar conclusion - in his case after a 3-day test using Ruby with a custom runtime, 80% of initializations were proactive:

This means the Monitoring with CloudWatch is just half the picture. But how much is your application potentially benefiting from proactive inits? Since Lamby v5.1.0, you can now find out easily using CloudWatch Metrics. To turn metrics on, enable the config like so:

After years of smashing Cloud & Rails together, I've come up with an idea. Better than an idea, a working specification! One where us Rails & Lambda enthusiasts can once again "console into" our "servers" and execute CLI tasks like migrations or interact via our beloved IRB friend, the Rails console. Today, I would like to present, the Lambda Console project. An open specification proposal for any AWS Lambda runtime to adopt.

> Does this mean you have a cron job just pinging the serverless function every 3 minutes? I'm curious how much this adds on to your costs. It means that the whole "don't pay for non-usage" thing is not quite true, but maybe it's still significantly cheaper than running an EC2 instance or whatnot. I'm curious about the cost calculation here.

Yes, specifically it kicks off a Lambda function that does a parallel GET to our website at a special endpoint that has a 100ms "wait" and basic DB call. This keeps the lambda process alive/in-memory.

To keep a function alive costs ~125ms (100ms wait + 25ms full func roundtrip). every 3 minutes. ~0.041% of 1x CPU time. Our website server costs are tiny and lower for Staging and UAT. Benefit - can scale to 1000x (AWS Limit) servers at the speed of your cold start time.

> Another thing I'm curious about, since you have a container-based deployment, did you compare with Fargate?

Yes we use Fargate for our core product which is built in Rails before containers could be deployed in Lambda. Rails works fine on Lambda[0] but the transition cost wasn't worth it for us. Fargate is great, but as you point out it is expensive if your application isn't a user heavy one like ours. To be highly available, we always have a minimum of 2 online but we're a b2b application so our night usage, 10pm-6am is zero. But I have 2 machines just sitting there. This is why i love Lambda >> Fargate.

Also, scalaing Fargate machines is slow if you get a traffic spike.

[0] https://github.com/rails-lambda/lamby

If you are curious to learn more about how Rails & Lambda work together, check out our Lamby project. The architecture of Lambda Containers works so well with Rails since our framework distills everything from HTTP, Jobs, Events, & WebSocket connections down to amazing CMD Docker contract. The architecture above at the proxy layer was easy to build and connect up to our single delegate function, Lamby.cmd. Shown below.

Today I am happy to announce that Lamby (Simple Rails & AWS Lambda Integration using Rack) now demonstrates just how easy it is to use multi-platform arm64 images on AWS Lambda. If this sounds interesting to you, jump right into our Quick Start guide and deploy a new Rails 7 on Ruby 3.2 Ubuntu image to see it for yourself.

(I have not actually used this myself). The folks over at CustomInk maintain Lamby, a project to run Rails in a quickly-bootable Lambda environment. Might be worth checking out, if you otherwise do enjoy working with Rails: https://lamby.custominktech.com

In any case you can try out https://github.com/customink/lamby which is a gem responsible to allow to run a ror app native on aws lambda.

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.