To scale an application and provide a reliable service, you need to understand how the application behaves when it is deployed. You can examine application performance in a Kubernetes cluster by examining the containers, pods, services, and the characteristics of the overall cluster. Kubernetes provides detailed information about an application's resource usage at each of these levels. This information allows you to evaluate your application's performance and where bottlenecks can be removed to improve overall performance.

This page shows how a Pod can use environment variables to expose information about itself to containers running in the Pod, using the downward API. You can use environment variables to expose Pod fields, container fields, or both. In Kubernetes, there are two ways to expose Pod and container fields to a running container: Environment variables, as explained in this task Volume files Together, these two ways of exposing Pod and container fields are called the downward API.

This page shows you how to run a single-instance stateful application in Kubernetes using a PersistentVolume and a Deployment. The application is MySQL. Objectives Create a PersistentVolume referencing a disk in your environment. Create a MySQL Deployment. Expose MySQL to other pods in the cluster at a known DNS name. Before you begin You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster.

Use kubectl patch to update Kubernetes API objects in place. Do a strategic merge patch or a JSON merge patch.

In this example, you will run a Kubernetes Job with multiple parallel worker processes. In this example, as each pod is created, it picks up one unit of work from a task queue, completes it, deletes it from the queue, and exits. Here is an overview of the steps in this example: Start a message queue service. In this example, you use RabbitMQ, but you could use another one. In practice you would set up a message queue service once and reuse it for many jobs.

Configure and schedule GPUs for use as a resource by nodes in a cluster.

This page shows how to perform a rollback on a DaemonSet. Before you begin You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:

kubeadm init phase enables you to invoke atomic steps of the bootstrap process. Hence, you can let kubeadm do some of the work and you can fill in the gaps if you wish to apply customization. kubeadm init phase is consistent with the kubeadm init workflow, and behind the scene both use the same code. kubeadm init phase preflight Using this command you can execute preflight checks on a control-plane node.

Production-Grade Container Orchestration

Synopsis The Kubernetes controller manager is a daemon that embeds the core control loops shipped with Kubernetes. In applications of robotics and automation, a control loop is a non-terminating loop that regulates the state of the system. In Kubernetes, a controller is a control loop that watches the shared state of the cluster through the apiserver and makes changes attempting to move the current state towards the desired state. Examples of controllers that ship with Kubernetes today are the replication controller, endpoints controller, namespace controller, and serviceaccounts controller.