Learn how to deploy a custom MEAN application from a GitHub repository to a Kubernetes cluster in three simple steps using Bitnami's Node.js Helm chart. After showing you how to deploy your application in a Kubernetes cluster, this article also explains how to modify the source code and publish a new version in Kubernetes using the Helm CLI.
kube-rclone is a rclone mount solution for Kubernetes. It allows you to sync files and directories to and from different cloud storage providers i.e Google Drive. It creates a Daemonset across the Kubernetes cluster which mounts a volume on the hostPath that can be used with other services such as kube-plex
We’re pleased to announce the delivery of Kubernetes 1.15, our second release of 2019! Kubernetes 1.15 consists of 25 enhancements: 2 moving to stable, 13 in beta, and 10 in alpha. The main themes of this release are:
Project sustainability is not just about features. Many SIGs have been working on improving test coverage, ensuring the basics stay reliable, and stability of the core feature set and working on maturing existing features and cleaning up the backlog.
The community has been asking for continuing support of extensibility, so this cycle features more work around CRDs and API Machinery. Most of the enhancements in this cycle were from SIG API Machinery and related areas.
Running large Kubernetes clusters serving high volumes of traffic (thousands of nodes serving thousands of requests/second) requires tackling scaling challenges in both the control plane and data plane. This talk will present options that allow for performant networking when the number of nodes, services, endpoints and traffic grow in your Kubernetes cluster. Laurent and Manjot will cover how to use CNI plugins for efficient routing by not requiring overlays, how kube-proxy can be configured to handle clusters with thousands of services and endpoint and how ingress controllers can route traffic directly to pods without requiring nodeports. In addition, many of these solutions are at an early stage and the talk will dive into the issues faced and how they were addressed. Finally, the talk will discuss upcoming technologies that will allow Kubernetes to scale even further.
At Namely we’ve been running with Istio for a year now. Yes, that’s pretty much when it first came out. We had a major performance regression with a Kubernetes cluster, we wanted distributed tracing, and used Istio to bootstrap Jaeger to investigate. We immediately saw the potential of a service mesh as it relates to our infrastructure and decided to make an investment in the tool.
According to this recently completed CNCF Survey, the adoption rate of Cloud Native technologies in production is growing rapidly. Kubernetes is at the heart of this technological revolution. Naturally, the growth of cloud native technologies has been accompanied by the growth of the ecosystem that surrounds it. Of course, the complexity of cloud native technologies have increased as well. Just google for the phrase “Kubernetes is hard”, and you’ll get plenty of articles that explain this complexity problem. The best thing about the CNCF community is that problems like this can be solved by smart people building new tools to enable Kubernetes users: Projects like Knative and its Build resource extension, for example, serve to reduce complexity across a range of scenarios. Even though increasing complexity might seem like the most important issue to tackle, it is not the only challenge you face when transitioning to Cloud Native.
Kubernetes 1.14 consists of 31 enhancements: 10 moving to stable, 12 in beta, and 7 net new. The main themes of this release are extensibility and supporting more workloads on Kubernetes with three major features moving to general availability, and an important security feature moving to beta.
More enhancements graduated to stable in this release than any prior Kubernetes release. This represents an important milestone for users and operators in terms of setting support expectations. In addition, there are notable Pod and RBAC enhancements in this release, which are discussed in the “additional notable features” section below.
The first release of Kubernetes in 2019 brings a highly anticipated feature - production-level support for Windows workloads. Up until now Windows node support in Kubernetes has been in beta, allowing many users to experiment and see the value of Kubernetes for Windows containers. While in beta, developers in the Kubernetes community and Windows Server team worked together to improve the container runtime, build a continuous testing process, and complete features needed for a good user experience. Kubernetes now officially supports adding Windows nodes as worker nodes and scheduling Windows containers, enabling a vast ecosystem of Windows applications to leverage the power of our platform.
The Local Persistent Volumes feature has been promoted to GA in Kubernetes 1.14. It was first introduced as alpha in Kubernetes 1.7, and then beta in Kubernetes 1.10. The GA milestone indicates that Kubernetes users may depend on the feature and its API for production use. GA features are protected by the Kubernetes deprecation policy.
The technology world is looking for flexible IT infrastructure that will easily evolve to meet changing data and performance requirements in support of the onslaught of upcoming and lucrative use cases. Kmesh addresses data management and data sovereignty concerns while decreasing costs associated with storage and network resources.