At re:Invent in December, Amazon announced the AWS Graviton2 processor and its forthcoming availability powering Amazon EC2 M6g instances. While the first-generation Graviton processor that powered A1 instances was better suited to less compute-intensive workloads, this processor is intended to offer AWS customers a compelling alternative to conventional x86-powered instances on both performance and cost.
“Sociotechnical” — I learned this word from Liz Fong-Jones recently, and it immediately entered my daily lexicon. You know exactly what it means as soon as you hear it, and then you wonder how you ever lived without it. Our systems are sociotechnical systems. This is why technical problems are never just technical problems, and why social problems are never just social problems. I work on a company, Honeycomb, which develops next-gen observability tooling.
To have Observability is to have the ability to understand your system’s internal state based on signals and externally-visible output. Honeycomb’s approach to Observability is to strive toward this: every feature of the product attempts to move closer to a unified vision of figuring out what your system did, and how it got there.
Have you ever experienced a time when someone on your team takes off to recharge or takes unplanned downtime away from work? It may feel a bit scary as workloads shift, priorities change and the team has to pick up some slack. We all need to recharge and in some org’s, it’s imperative. The covering for each other builds trust among the team which is invaluable when you’re in the trenches daily, working hard.
What do a test engineer and a DevOps or SRE team member have in common? The reality is that different teams need to proactively understand what is happening in production at critical milestones along the software engineering delivery cycle. In the words of Abby Bangser, senior test engineer at Moo, “Testing has so much in common with Ops and SRE teams. We need to ask interesting questions of production. We need no more debates whether a bug gets fixed.
New eGuide takes a closer look at Prometheus, ELK and Jaeger:
Feature flags are great and serve us in so many ways. However, we do not love long-lived feature flags. They lead to more complicated code, and when we inevitably default them to be true for all our users, they lead to unused sections of code. In other words, tech debt. How do we stay on top of this? Find out how Honeycomb’s trigger alerts proactively tell you to go ahead and clean up that feature flag tech debt!
(This is the first post by our new head of Customer Success, Irving.) Sampling is a must for applications at scale; it’s a technique for reducing the burden on your infrastructure and telemetry systems by only keeping data on a statistical sample of requests rather than 100% of requests. Large systems may produce large volumes of similar requests which can be de-duplicated.
We’re big fans of AWS Lambda at Honeycomb. As you may have read, we recently made some major improvements to our storage engine by leveraging Lambda to process more data in less time. Making a change to a complex system like our storage engine is daunting, but can be made less so with good instrumentation and tracing. For this project, that meant getting instrumentation out of Lambda and into Honeycomb.
