In the world of container orchestration, Kubernetes has emerged as the go-to platform for managing and scaling applications. One of the key features that make Kubernetes so powerful is its ability to intelligently schedule pods across nodes in a cluster. Node affinity is a crucial concept in this scheduling process, allowing developers to influence where pods are placed based on node characteristics.

As a developer working with Linux systems, containers, or Kubernetes, it's crucial to understand process termination signals, particularly SIGKILL and SIGTERM. This comprehensive guide will explore these signals, their differences, and their implications in various environments. We'll delve into best practices, common scenarios, and advanced considerations to help you manage process termination effectively in your applications.

If you've encountered the dreaded "exit code 137" error message while working with Docker, Kubernetes, or other containerized environments, you're not alone. This error can be frustrating and difficult to troubleshoot, but understanding its causes and solutions can help you keep your applications running smoothly. This comprehensive guide will delve into the intricacies of error code 137, its common scenarios, and strategies to resolve it.

Kubernetes has revolutionized how developers deploy, manage, and scale their applications. One of its key features is the ability to scale deployments seamlessly. This article explores various aspects of using the kubectl scale deployment command, including how to scale deployments up and down, scale all deployments in a namespace, managing replica sets, and more.

We all know that managing multiple Kubernetes clusters and their resources can be challenging. However, kubectl offers several context and namespace commands to simplify this process. This comprehensive guide will walk you through using various kubectl commands to manage your Kubernetes environments more efficiently.

Ever since I joined StackState almost three years ago, I knew we were onto something. Something that could change the way engineers observe, troubleshoot, and optimize their environments. Something that would transform your understanding of your entire infrastructure. As CEO for the last 18 months, I've seen our platform evolve into the next generation, making it truly powerful for our users.

When it comes to using (desktop) software, especially in tech, there's always that icebreaker you can use to determine whether a prospect is a more "visual" or a more "textual" user. It’s definitely not a black-or-white debate, but it's always interesting to see just how differently we’re wired as individuals at a cognitive level. In this blog post, we'll assume you lean towards being a more "visual" type of person.

As modern applications and IT infrastructures become increasingly complex, the need for effective monitoring and management tools has never been more critical. Application Performance Monitoring (APM) is a comprehensive approach that provides visibility into application performance, availability, and user experience. APM is an important tool for platform engineers and developers who are tasked with ensuring that applications run smoothly and efficiently and meet end-user needs.

Aligning IT infrastructure with business processes is paramount in today's digital landscape. This article explores how organizations can elevate their architectural modeling by integrating ArchiMate's flow diagrams, which are initially manually created, with the dynamic, auto-discovered components from StackState's end-to-end observability.

Once upon a time, in a tech company, an engineering team had the freedom to independently deploy updates and new features to a Kubernetes cluster. This autonomy helped speed up development but also led to unforeseen issues with resource management. The platform team responsible for maintaining the cluster's health noticed an increase in orphaned Persistent Volumes (PV), a piece of plug-in storage independent of other Kubernetes cluster pods.