The latest News and Information on Containers, Kubernetes, Docker and related technologies.
In our last blog, we talked about the importance of setting memory requests when deploying applications to Kubernetes. We explained how memory requests lets you specify how much memory (RAM for short) Kubernetes should reserve for a pod before deploying it. However, this only helps your pod get deployed. What happens when your pod is running and gradually consumes more RAM over time?
Grafana is an open-source platform for metric analytics, monitoring, and visualization. In this article, we will explore the basics of Grafana and learn how to deploy it to Kubernetes. You will find specific coding examples and screenshots you can follow to deploy Grafana.
In the previous article of the series, we explained how to use Configmap generators in order to use Progressive Delivery for your configuration (and not just the container images). In this post, we will also cover another popular question: how to use Argo Rollouts with multiple services. Argo Rollouts is a Kubernetes controller that allows you to perform advanced deployment methods in a Kubernetes cluster. By default, it only supports a single service/application.
If you work with Kubernetes, you know that any number of issues can pose a serious threat to the stability and security of your deployments. One that's subtly damaging is configuration drift, which occurs when the actual state of how your system is set up — its configuration — strays from the way you defined. Configuration drift in Kubernetes can happen when people make changes manually, systems aren't synchronized properly or monitoring falls short.
Kubernetes is one of the most important and influential technologies for building and operating software today because it’s so incredibly capable. It’s flexible, available, resilient, scalable, feature-rich and backed by a global community of innovators — that’s a pretty impressive list of intangibles to apply to any particular capability.
Continuous Integration/Continuous Deployment (CI/CD), the ability to adapt swiftly to fluctuating workloads is paramount. Kubernetes, with its dynamic orchestration capabilities, offers an invaluable toolset for achieving seamless scalability. This article explores the concept of Kubernetes autoscaling and its pivotal role in optimising CI/CD pipelines.
