Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has emerged as the clear cornerstone for orchestrating containerized applications. However, alongside its numerous benefits, Kubernetes introduces its own set of complexities, particularly in monitoring and managing costs. The dynamic nature of Kubernetes — where resources can be scaled up or down in response to demand — makes traditional cost monitoring methods inadequate.

Explore the best strategies for implementing automated testing in CI/CD pipelines to streamline your development process and ensure high-quality software delivery.

In Kubernetes a static pod is a concept where you can deploy a pod that is not managed by the API server. Static pods are directly managed by the Kubelet component. The Kubelet service is deployed with the configuration path where we can add the pod manifest for the Kubelet to deploy. Even though a static pod is not managed by the API server, you can use Kubectl to list the static pod. This is because, when Kubelet deploys a static pod, it creates a mirror pod in the API server.

Calico 3.28 now has enabled VXLAN checksum offload by default for environments with the kernel version of 5.8 or above. In the past, offloading was disabled due to kernel bugs. Please keep in mind, if you are upgrading to 3.28 this change will take effect after node restarts.

In the fast-paced world of software development, efficiency is key. One of the most critical aspects of ensuring smooth and reliable software delivery is streamlining your deployment pipeline. A well-optimized deployment pipeline not only saves time and resources but also enhances the overall quality of your product. Let’s embark on a DevOps journey to explore how you can streamline your deployment pipeline effectively.

Log analysis is a very powerful feature for an application when it comes to debugging and finding out which flow is working properly in the application and which is not. In a world of containerization and cloud computing, it is essential to understand logs generated by a Docker environment to maintain healthy performing applications. In this article, we will discuss log analysis in Docker and how logging in Docker containers is different than in other applications.

Logs are useful for troubleshooting and identifying issues in applications, as they provide a record of events and activities. However, managing log data can be challenging due to the large volume of log events generated by modern applications, as well as the need to balance the level of detail in the logs and the impact on the application's performance.

I’m Romaric, the CEO and co-founder of Qovery. With 15 years of experience managing large-scale infrastructure and a passion for computer science, I’ve embarked on a journey to build an ambitious tech product designed to revolutionize the developer experience. At Qovery, we aim to simplify the path to production for developers, ensuring they can focus on writing code without worrying about the underlying infrastructure.

Troubleshooting within Kubernetes environments can be a daunting task. If we could only have a magical artificial intelligence advisor that could gather all the data about what goes on the system, and tell me what’s wrong, and even how to solve it. Wouldn’t it be nice? K8sGPT is a young open source project that uses generative AI to give Kubernetes superpowers to everyone. It recently turned a year old, and is now part of the Cloud Native Computing Foundation (CNCF).