At InfluxData, we love the community! Our amazing open source members are an integral part of InfluxData and have been since its founding. They’ve helped us build amazing products for time series data. This is a quick update to give you some insight into how we track metrics about our community and ensure we are building products and features that our users want to see.
Tags provide critical context for troubleshooting issues across any dimension of your environment. By applying best practices for tagging your systems, you can efficiently organize and analyze all your monitoring data, and set up automated multi alerts to streamline alerting workflows. Similar to any tags you would add to your services and infrastructure, monitor tags—tags that you apply to your monitors—are an essential feature for organizing and simplifying your workflows.
Container technologies have taken the infrastructure world by storm. Ideal for microservice architectures and environments that scale rapidly or have frequent releases, containers have seen a rapid increase in usage in recent years. But adopting Docker, containerd, or other container runtimes introduces significant complexity in terms of orchestration. That’s where Kubernetes comes into play.
As explained in Part 1 of this series, monitoring a Kubernetes environment requires a different approach than monitoring VM-based workloads or even unorchestrated containers. The good news is that Kubernetes is built around objects such as Deployments and DaemonSets, which provide long-lived abstractions on top of dynamic container workloads.
In the previous post in this series, we dug into the data you should track so you can properly monitor your Kubernetes cluster. Next, you will learn how you can start inspecting your Kubernetes metrics and logs using free, open source tools. In this post we’ll cover several ways of retrieving and viewing observability data from your Kubernetes cluster.
If you’ve read Part 3 of this series, you’ve learned how you can use different Kubernetes commands and add-ons to spot-check the health and resource usage of Kubernetes cluster objects. In this post we’ll show you how you can get more comprehensive visibility into your cluster by collecting all your telemetry data in one place and tracking it over time.
Elixir 1.10 was recently released, and with that release came a little-known, but very interesting feature—compiler tracing. This feature means that as the Elixir compiler is compiling your code, it can emit messages whenever certain kinds of things are compiled. This ability to know what’s going on when Elixir is compiling our code might seem simple, but it actually opens up a lot of doors for opportunities to build customized compile-time tooling for Elixir applications.
Big ideas need creative solutions. Creative solutions mean solving out of the box engineering problems. When you’re solving these new problems, strong error monitoring is imperative to the development cycle. You don’t want to spend all your time chasing down issues and struggling to track the source of errors.
You’re done setting up your first graph panels. You want to do more, look around the visualization settings, and discover the settings for the X and Y axes. You stumble over the configuration for a “Right Y” axis. You ask yourself, “Why on earth would I need another Y axis?” You toggle it back and forth and change some settings, yet that makes no difference to your graph. What gives? Never fear.
If you've been on the hunt for a new developer podcast, then you understand just how difficult and fruitless that pursuit can be. You can spend hours online sifting through coding podcasts, programming podcasts, and DevOps podcasts only to realize one simple thing: none of them focus on your preferred programming language! With thousands of different developer podcasts out there, the problem is magnified exponentially. Fortunately, we at Scout APM have nothing but expertise and time on our hands.
