Implementing distributed tracing is fast becoming a fundamental expectation when building modern (distributed) systems. However, this is yet another thing for developers to learn, and configuring distributed tracing on Kubernetes is hard, right? Actually, no. Getting started with Jaeger on Kubernetes has never been easier.
The Docker infrastructure abstracts a lot of aspects of the creation of images and running them as containers, which we usually do not know about nor interact with. One of those aspects is the handling of the filesystem inside the container. This post is a case study on how we discovered that writing large amounts of data inside a container has side effects with memory caching. Initially, we thought that we had an issue with our source code, but this was never the case.
We created the Fleet Project to provide centralized GitOps-style management of a large number of Kubernetes clusters. A key design goal of Fleet is to be able to manage 1 million geographically distributed clusters. When we architected Fleet, we wanted to use a standard Kubernetes controller architecture. This meant in order to scale, we needed to prove we could scale Kubernetes much farther than we ever had.
Data services—such as caches, messaging queues, and relational databases—are the backbone of applications. And when it comes to relational databases, Postgres is a pretty popular option. Its killer feature is its versatility. Natively and through plugins, Postgres supports a wide variety of data types, formats, and programming languages, which makes it useful for all kinds of applications, including text, geospatial, graph, and more.
Kubernetes and its various APIs offer a wealth of information for monitoring and observability. In a recent webinar with the CNCF (as well as a whitepaper based on that webinar), Sensu CEO Caleb Hailey goes in-depth into the most-useful APIs for cloud-native observability. In this post, we’ll focus on the Kubernetes Events API — including why it matters and how it can add context for your observability strategy.
Kubernetes can bring a wide collection of advantages to a development organization. Properly leveraging Kubernetes can greatly improve productivity, empower you to better utilize your cloud spend, improve application stability and reliability, and more. On the flip side, if you are not properly leveraging Kubernetes, your would-be benefits become drawbacks. As a developer, this can become especially frustrating when you are focused on delivering quality code, fast.
The new Honeycomb Kubernetes agent is out! This post describes how infrastructure metrics contribute to observability, and then walks you through the steps to start sending your own Kubernetes data into Honeycomb. Follow the steps to start observing your infrastructure in production!
If you’re an SRE or on a DevOps team working with Kubernetes and containers, you’ve undoubtedly encountered network connectivity issues with your microservices and workloads. Something is broken and you’re under pressure to fix it, quickly. And so you begin the tedious, manual process of identifying the issue using the observability tools at your disposal…namely metrics and logs.
We dedicate a lot of space in our blog to the topic of monitoring. That’s because when you’re managing Kubernetes clusters, things can change quickly. It’s important that you have tools to monitor the health and resource metrics of your clusters. In Rancher 2.5, we introduced a new version of our monitoring based on the Prometheus Operator, which provides Kubernetes-native deployment and management of Prometheus and related monitoring components.
The control plane is the brain and heart of Kubernetes. All of its components are key to the proper working and efficiency of the cluster. Monitor Kubernetes control plane is just as important as monitoring the status of the nodes or the applications running inside. It may be even more important, because an issue with the control plane will affect all of the applications and cause potential outages.
