middy VS aws-embedded-metrics-node

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.

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.