When Ruby support on AWS Lambda was announced yesterday, I was so excited about it that I had to try it right away. We've been using Lambda for a while at Honeybadger, and I have longed to be able to write our functions in Ruby. Having played with the new Ruby support for a few hours, I'm feeling confident we'll be spending less time with Node, Go, and Python. :)

To build applications in AWS Lambda, you often need to use third party libraries and packages in your function code. Previously, these packages had to be included in a function’s deployment package. Today, Amazon Web Services released a new feature called Layers to simplify this process for Lambda developers. Layers allow you to deploy common components that you can reuse across functions, such as machine learning models, SDKs, or instrumentation libraries.

Lambda layers and runtime API are two new feature of AWS Lambda which open up fun possibilities for customizing the Lambda runtime and enable decreased duplication of code across Lambda functions. Layers lets you package up a set of files and include them in multiple functions. Runtime API provides an API for interacting with the Lambda service function lifecycle events which lets you be much more flexible about what you run in your Lambda.

AWS’s announcement of Lambda Layers means big things for those of us using serverless in production. The creation of set components that can be included with any number of Lambdas means you no longer have to zip up your application code and all its dependencies each time you deploy a serverless stack. This allows you to include dependencies that are much more bespoke to your particular serverless environment.

Since Amazon released Lambda in late 2014, the notion of serverless applications and function-as-a-service has steadily gained steam. Being able to focus on application code and simplifying infrastructure management is alluring, but traditional monitoring methods are no longer applicable. With less visibility, it becomes even more important to take advantage of the available monitoring methods. In this post, we discuss those monitoring methods, CloudWatch Metrics and CloudWatch Logs.

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The whole internet is abuzz over GitHub Actions, if by ‘whole internet’ you mean ‘the part of the internet that is obsessed with serverless ops’ and by ‘abuzz’ you mean ‘aware of‘. But Actions are a bit surprising! GitHub is a company that has famously focused on doing a single thing extremely well. As the ranks of developer-tooling SaaS companies swells by the day, you would think GitHub would have long ago joined the fray.

To make serverless architectures more observable, we’re excited to introduce the new Cloud Functions view in Datadog. You can now search, filter, and explore all your AWS Lambda functions in one central view, and dive straight into detailed performance data that is scoped to a single function. The Cloud Functions view brings together Lambda metrics and logs with distributed request traces from your functions, which are now available in Datadog thanks to our new integration with AWS X-Ray.