I made the trip up to Seattle for KubeCon North America at the end of 2018 along with a bunch of us from Sumo Logic. KubeCon is a conference that specializes in all things Kubernetes and focuses on updating the world on the state of the Kubernetes ecosystem. This year’s event was massive with 8,000 attendees, and talks given by representatives from Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure to name a few big wigs that were there.

In many product development workflows, there are three main concerns: building, testing, and deployment. In this scenario, every change that is made to the code means something could accidentally go wrong, so to lessen the likelihood of this happening, developers assume many strategies to reduce incidents and bugs. One strategy is to adopt continuous integration tools (CI): used together with a source version software to verify if something has gone wrong for every update.

eBPF tracing is a broad and deep subject, and can be a bit daunting at first sight. However, when Brendan Gregg issued the dictum “Perhaps you’d like a new year’s resolution: learn eBPF!”, I figured it was as good a time as any to do something fun with it. Here at Circonus, we’ve talked about eBPF previously, so I had a starting point to look for an interesting problem to solve.

In this post, I’m going to cover some of the fundamentals of how Calico works. I really don’t like the idea that with these Kubernetes deployments, you simply grab a yaml file and deploy it, sometimes with little to no explanation of what’s actually happening. Hopefully, this post will servce to better understand what’s going on.

A few months back, I blogged about my experience arriving at Stackery after code school. Months later, each day is still interesting and challenging and I’m so glad to have decided to pursue serverless as my concentration. I credit my AWS certifications for narrowing my focus enough to lead me to this point. The serverless community puts so much emphasis on exploration and getting started on your work or experiments today that, getting some exposure to AWS, you can get started right away.

Configuration management is an essential process for DevOps teams. It allows engineers to manage dozens, hundreds, or even thousands of machines in a consistent and automated way. While there are many configuration management tools available, three of the most popular are Chef, Puppet, and Ansible. In this post, we’ll explain how these tools work and the unique benefits that they offer.

Site reliability engineering (SRE) is Google’s approach to service management where software engineers run production systems using a software engineering approach. It’s clear that Google is unique, and they usually need to tackle software bugs and errors in different and non-conventional ways. But having software engineers doing a job that is traditionally done by professionals with a systems administration background sounds impractical.

We’re very excited to announce that Ansible roles to deploy StackStorm have been promoted to major version 1.0.0!

We were only in the first week of the month-long billing period for our client’s AWS account. Already, it showed that they had exceeded the free-tier limit for SQS and had nearly exceeded it for CloudWatch too (approximately 85 per cent used). This is puzzling, because we hadn't run any data downloads for the client at all. In fact, all services had been down since before Christmas when we shut it down to work on new server CloudFormation scripts.