Open source software adoption continues to grow within enterprises (even for legacy applications), beyond just startups and born-in-the-cloud software. In this second part of our Kafka monitoring series (see the first part discussing Kafka metrics to monitor), we’ll take a look at some open source tools available to monitor Kafka clusters. We’ll explore what it takes to install, configure, and actually use each tool in a meaningful way.
Monitoring Kafka is a tricky task. As you can see in the first chapter, Kafka Key Metrics to Monitor, the setup, tuning, and operations of Kafka require deep insights into performance metrics such as consumer lag, I/O utilization, garbage collection and many more. Sematext provides an excellent alternative to other Kafka monitoring tools because it’s quick and simple to use.
When I started Checkly, all the typical SaaS things around billing, credit cards and prorating confused the hell out of me. I understood them from an intellectual point of view, but not really from an implementation point of view.
When developing new applications, it’s typical to run into a variety of competitors with similar goals. This is one reason why it is not enough to create an application without ways to monitor and manage it. In fact, usually market leaders emerge rather quickly. So who are these market leaders?
Slow websites are annoying, right? We sure think so. One common solution is to introduce a caching proxy like Varnish to help cache pages and reduce your server load. The good news is, if you have Oh Dear!, you can let those 2 work together.
Since it was first introduced in 2002, Microsoft’s .NET Framework has garnered a robust user base that includes organizations like UPS, Stack Overflow, and Jet.com. And now, thanks to the rise of the .NET Core runtime, this high-performance framework also supports cross-platform development. To provide deeper visibility into all of these environments, we are pleased to announce that Datadog APM and distributed tracing are generally available for .NET Framework and .NET Core applications.
This article is a follow up to Native Kubernetes Monitoring, Part One. In this chapter we’ll finish the two remaining demos for the other built-in tools, Probes and Horizontal Pod Autoscaler (HPA).
Amazon Elastic Container Service for Kubernetes, or Amazon EKS, is a hosted Kubernetes platform that is managed by AWS. Put another way, EKS is Kubernetes-as-a-service, with AWS hosting and managing the infrastructure needed to make your cluster highly available across multiple availability zones. EKS is distinct from Amazon Elastic Container Service (ECS), which is Amazon’s proprietary container orchestration service for running and managing Docker containers.
Working in C#, as with any language, you’ll soon find that that you need to do some debugging. With C#, thankfully, due to a plethora of debugging tools and techniques, troubleshooting is usually straightforward.
In Part 1 of this series, we looked at key metrics for tracking the performance and health of your EKS cluster. Recall that these EKS metrics fall into three general categories: Kubernetes cluster state metrics, resource metrics (at the node and container level), and AWS service metrics. In this post, we will go over methods for accessing these categories of metrics, broken down by where they are generated.
