My career as a software engineer started in 2007 at Purdue University. I was working in the Linux kernel and researching how data was shuffled between the kernel and the user application layers. This was happening in huge clusters of machines that all talked to each other using OpenMPI — how supercomputers, like those at Los Alamos National Labs, operate to perform their enormous calculations around meteorology, physics, chemistry, etc.

Epsagon helps troubleshoot serverless applications. Sentry helps monitor and fix crashes in real time. In this post, Raz Lotan, Software Engineer at Epsagon, uncovers how both tools blend to make the perfect workflow for debugging serverless applications.

A little over a month ago, Jeremy Daly introduced the concept of the #serverless bubble coming out of #ServerlessNYC. In his tweet he said “if you’re in it, a serverless solution seems intuitive. If you’re not, the paradigm shift can seem overwhelming (& unapproachable).”

We read with interest a recent article from CloudBees published in The New Stack: How Culture Will Make or Break Cloud Native DevOps and have seen some highly differing views on where the adoption of DevOps is. The Cloudbees article starts by saying that “Software delivery cycles are becoming faster thanks to DevOps-backed continuous integration/continuous delivery (CI/CD) as production pipelines are increasingly ported to scale with microservices on cloud-native environments.”

AWS Lambda can use stream based services as invocation sources, essentially making your Lambda function a consumer of those streams. Stream sources include Kinesis Streams and DynamoDB streams. When you allow streams to invoke your Lambda function, Lambda will emit a CloudWatch metric called IteratorAge. In this post, we discuss what this metric is and how to fix it if it’s too high.

AWS re:Invent, the biggest cloud-computing event of the year, ended on Friday and left behind a slew of exciting new features and products for building serverless applications. Let’s summarize what was announced and how those updates can be significant for you.

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. :)

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.

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.