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Tutorials

This section of the Kubernetes documentation contains tutorials. A tutorial shows how to accomplish a goal that is larger than a single task. Typically a tutorial has several sections, each of which has a sequence of steps. Before walking through each tutorial, you may want to bookmark the Standardized Glossary page for later references.

Basics

Configuration

Stateless Applications

Stateful Applications

Services

Security

What's next

If you would like to write a tutorial, see Content Page Types for information about the tutorial page type.

1 - Hello Minikube

This tutorial shows you how to run a sample app on Kubernetes using minikube. The tutorial provides a container image that uses NGINX to echo back all the requests.

Objectives

  • Deploy a sample application to minikube.
  • Run the app.
  • View application logs.

Before you begin

This tutorial assumes that you have already set up minikube. See Step 1 in minikube start for installation instructions.

You also need to install kubectl. See Install tools for installation instructions.

Create a minikube cluster

minikube start

Open the Dashboard

Open the Kubernetes dashboard. You can do this two different ways:

Open a new terminal, and run:

# Start a new terminal, and leave this running.
minikube dashboard

Now, switch back to the terminal where you ran minikube start.

If you don't want minikube to open a web browser for you, run the dashboard subcommand with the --url flag. minikube outputs a URL that you can open in the browser you prefer.

Open a new terminal, and run:

# Start a new terminal, and leave this running.
minikube dashboard --url

Now, you can use this URL and switch back to the terminal where you ran minikube start.

Create a Deployment

A Kubernetes Pod is a group of one or more Containers, tied together for the purposes of administration and networking. The Pod in this tutorial has only one Container. A Kubernetes Deployment checks on the health of your Pod and restarts the Pod's Container if it terminates. Deployments are the recommended way to manage the creation and scaling of Pods.

  1. Use the kubectl create command to create a Deployment that manages a Pod. The Pod runs a Container based on the provided Docker image.

    # Run a test container image that includes a webserver
    kubectl create deployment hello-node --image=registry.k8s.io/e2e-test-images/agnhost:2.39 -- /agnhost netexec --http-port=8080
    
  2. View the Deployment:

    kubectl get deployments
    

    The output is similar to:

    NAME         READY   UP-TO-DATE   AVAILABLE   AGE
    hello-node   1/1     1            1           1m
    
  3. View the Pod:

    kubectl get pods
    

    The output is similar to:

    NAME                          READY     STATUS    RESTARTS   AGE
    hello-node-5f76cf6ccf-br9b5   1/1       Running   0          1m
    
  4. View cluster events:

    kubectl get events
    
  5. View the kubectl configuration:

    kubectl config view
    
  6. View application logs for a container in a pod.

    kubectl logs hello-node-5f76cf6ccf-br9b5
    

    The output is similar to:

    I0911 09:19:26.677397       1 log.go:195] Started HTTP server on port 8080
    I0911 09:19:26.677586       1 log.go:195] Started UDP server on port  8081
    

Create a Service

By default, the Pod is only accessible by its internal IP address within the Kubernetes cluster. To make the hello-node Container accessible from outside the Kubernetes virtual network, you have to expose the Pod as a Kubernetes Service.

  1. Expose the Pod to the public internet using the kubectl expose command:

    kubectl expose deployment hello-node --type=LoadBalancer --port=8080
    

    The --type=LoadBalancer flag indicates that you want to expose your Service outside of the cluster.

    The application code inside the test image only listens on TCP port 8080. If you used kubectl expose to expose a different port, clients could not connect to that other port.

  2. View the Service you created:

    kubectl get services
    

    The output is similar to:

    NAME         TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
    hello-node   LoadBalancer   10.108.144.78   <pending>     8080:30369/TCP   21s
    kubernetes   ClusterIP      10.96.0.1       <none>        443/TCP          23m
    

    On cloud providers that support load balancers, an external IP address would be provisioned to access the Service. On minikube, the LoadBalancer type makes the Service accessible through the minikube service command.

  3. Run the following command:

    minikube service hello-node
    

    This opens up a browser window that serves your app and shows the app's response.

Enable addons

The minikube tool includes a set of built-in addons that can be enabled, disabled and opened in the local Kubernetes environment.

  1. List the currently supported addons:

    minikube addons list
    

    The output is similar to:

    addon-manager: enabled
    dashboard: enabled
    default-storageclass: enabled
    efk: disabled
    freshpod: disabled
    gvisor: disabled
    helm-tiller: disabled
    ingress: disabled
    ingress-dns: disabled
    logviewer: disabled
    metrics-server: disabled
    nvidia-driver-installer: disabled
    nvidia-gpu-device-plugin: disabled
    registry: disabled
    registry-creds: disabled
    storage-provisioner: enabled
    storage-provisioner-gluster: disabled
    
  2. Enable an addon, for example, metrics-server:

    minikube addons enable metrics-server
    

    The output is similar to:

    The 'metrics-server' addon is enabled
    
  3. View the Pod and Service you created by installing that addon:

    kubectl get pod,svc -n kube-system
    

    The output is similar to:

    NAME                                        READY     STATUS    RESTARTS   AGE
    pod/coredns-5644d7b6d9-mh9ll                1/1       Running   0          34m
    pod/coredns-5644d7b6d9-pqd2t                1/1       Running   0          34m
    pod/metrics-server-67fb648c5                1/1       Running   0          26s
    pod/etcd-minikube                           1/1       Running   0          34m
    pod/influxdb-grafana-b29w8                  2/2       Running   0          26s
    pod/kube-addon-manager-minikube             1/1       Running   0          34m
    pod/kube-apiserver-minikube                 1/1       Running   0          34m
    pod/kube-controller-manager-minikube        1/1       Running   0          34m
    pod/kube-proxy-rnlps                        1/1       Running   0          34m
    pod/kube-scheduler-minikube                 1/1       Running   0          34m
    pod/storage-provisioner                     1/1       Running   0          34m
    
    NAME                           TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)             AGE
    service/metrics-server         ClusterIP   10.96.241.45    <none>        80/TCP              26s
    service/kube-dns               ClusterIP   10.96.0.10      <none>        53/UDP,53/TCP       34m
    service/monitoring-grafana     NodePort    10.99.24.54     <none>        80:30002/TCP        26s
    service/monitoring-influxdb    ClusterIP   10.111.169.94   <none>        8083/TCP,8086/TCP   26s
    
  4. Check the output from metrics-server:

    kubectl top pods
    

    The output is similar to:

    NAME                         CPU(cores)   MEMORY(bytes)   
    hello-node-ccf4b9788-4jn97   1m           6Mi             
    

    If you see the following message, wait, and try again:

    error: Metrics API not available
    
  5. Disable metrics-server:

    minikube addons disable metrics-server
    

    The output is similar to:

    metrics-server was successfully disabled
    

Clean up

Now you can clean up the resources you created in your cluster:

kubectl delete service hello-node
kubectl delete deployment hello-node

Stop the Minikube cluster

minikube stop

Optionally, delete the Minikube VM:

# Optional
minikube delete

If you want to use minikube again to learn more about Kubernetes, you don't need to delete it.

Conclusion

This page covered the basic aspects to get a minikube cluster up and running. You are now ready to deploy applications.

What's next

2 - Learn Kubernetes Basics

Kubernetes Basics

This tutorial provides a walkthrough of the basics of the Kubernetes cluster orchestration system. Each module contains some background information on major Kubernetes features and concepts, and a tutorial for you to follow along.

Using the tutorials, you can learn to:

  • Deploy a containerized application on a cluster.
  • Scale the deployment.
  • Update the containerized application with a new software version.
  • Debug the containerized application.

What can Kubernetes do for you?

With modern web services, users expect applications to be available 24/7, and developers expect to deploy new versions of those applications several times a day. Containerization helps package software to serve these goals, enabling applications to be released and updated without downtime. Kubernetes helps you make sure those containerized applications run where and when you want, and helps them find the resources and tools they need to work. Kubernetes is a production-ready, open source platform designed with Google's accumulated experience in container orchestration, combined with best-of-breed ideas from the community.


2.1 - Create a Cluster

Learn about Kubernetes cluster and create a simple cluster using Minikube.

2.1.1 - Using Minikube to Create a Cluster

Learn what a Kubernetes cluster is. Learn what Minikube is. Start a Kubernetes cluster.

Objectives

  • Learn what a Kubernetes cluster is.
  • Learn what Minikube is.
  • Start a Kubernetes cluster on your computer.

Kubernetes Clusters

Kubernetes coordinates a highly available cluster of computers that are connected to work as a single unit. The abstractions in Kubernetes allow you to deploy containerized applications to a cluster without tying them specifically to individual machines. To make use of this new model of deployment, applications need to be packaged in a way that decouples them from individual hosts: they need to be containerized. Containerized applications are more flexible and available than in past deployment models, where applications were installed directly onto specific machines as packages deeply integrated into the host. Kubernetes automates the distribution and scheduling of application containers across a cluster in a more efficient way. Kubernetes is an open-source platform and is production-ready.

A Kubernetes cluster consists of two types of resources:

  • The Control Plane coordinates the cluster
  • Nodes are the workers that run applications

Summary:

  • Kubernetes cluster
  • Minikube

Kubernetes is a production-grade, open-source platform that orchestrates the placement (scheduling) and execution of application containers within and across computer clusters.


Cluster Diagram


The Control Plane is responsible for managing the cluster. The Control Plane coordinates all activities in your cluster, such as scheduling applications, maintaining applications' desired state, scaling applications, and rolling out new updates.

A node is a VM or a physical computer that serves as a worker machine in a Kubernetes cluster. Each node has a Kubelet, which is an agent for managing the node and communicating with the Kubernetes control plane. The node should also have tools for handling container operations, such as containerd or CRI-O. A Kubernetes cluster that handles production traffic should have a minimum of three nodes because if one node goes down, both an etcd member and a control plane instance are lost, and redundancy is compromised. You can mitigate this risk by adding more control plane nodes.

Control Planes manage the cluster and the nodes that are used to host the running applications.

When you deploy applications on Kubernetes, you tell the control plane to start the application containers. The control plane schedules the containers to run on the cluster's nodes. Node-level components, such as the kubelet, communicate with the control plane using the Kubernetes API, which the control plane exposes. End users can also use the Kubernetes API directly to interact with the cluster.

A Kubernetes cluster can be deployed on either physical or virtual machines. To get started with Kubernetes development, you can use Minikube. Minikube is a lightweight Kubernetes implementation that creates a VM on your local machine and deploys a simple cluster containing only one node. Minikube is available for Linux, macOS, and Windows systems. The Minikube CLI provides basic bootstrapping operations for working with your cluster, including start, stop, status, and delete.

Now that you know more about what Kubernetes is, visit Hello Minikube to try this out on your computer.

2.2 - Deploy an App

2.2.1 - Using kubectl to Create a Deployment

Learn about application Deployments. Deploy your first app on Kubernetes with kubectl.

Objectives

  • Learn about application Deployments.
  • Deploy your first app on Kubernetes with kubectl.

Kubernetes Deployments

Once you have a running Kubernetes cluster, you can deploy your containerized applications on top of it. To do so, you create a Kubernetes Deployment. The Deployment instructs Kubernetes how to create and update instances of your application. Once you've created a Deployment, the Kubernetes control plane schedules the application instances included in that Deployment to run on individual Nodes in the cluster.

Once the application instances are created, a Kubernetes Deployment controller continuously monitors those instances. If the Node hosting an instance goes down or is deleted, the Deployment controller replaces the instance with an instance on another Node in the cluster. This provides a self-healing mechanism to address machine failure or maintenance.

In a pre-orchestration world, installation scripts would often be used to start applications, but they did not allow recovery from machine failure. By both creating your application instances and keeping them running across Nodes, Kubernetes Deployments provide a fundamentally different approach to application management.

Summary:

  • Deployments
  • Kubectl

A Deployment is responsible for creating and updating instances of your application


Deploying your first app on Kubernetes


You can create and manage a Deployment by using the Kubernetes command line interface, kubectl. Kubectl uses the Kubernetes API to interact with the cluster. In this module, you'll learn the most common kubectl commands needed to create Deployments that run your applications on a Kubernetes cluster.

When you create a Deployment, you'll need to specify the container image for your application and the number of replicas that you want to run. You can change that information later by updating your Deployment; Modules 5 and 6 of the bootcamp discuss how you can scale and update your Deployments.

Applications need to be packaged into one of the supported container formats in order to be deployed on Kubernetes

For your first Deployment, you'll use a hello-node application packaged in a Docker container that uses NGINX to echo back all the requests. (If you didn't already try creating a hello-node application and deploying it using a container, you can do that first by following the instructions from the Hello Minikube tutorial).

You will need to have installed kubectl as well. If you need to install it, visit install tools.

Now that you know what Deployments are, let's deploy our first app!


kubectl basics

The common format of a kubectl command is: kubectl action resource

This performs the specified action (like create, describe or delete) on the specified resource (like node or deployment). You can use --help after the subcommand to get additional info about possible parameters (for example: kubectl get nodes --help).

Check that kubectl is configured to talk to your cluster, by running the kubectl version command.

Check that kubectl is installed and you can see both the client and the server versions.

To view the nodes in the cluster, run the kubectl get nodes command.

You see the available nodes. Later, Kubernetes will choose where to deploy our application based on Node available resources.

Deploy an app

Let’s deploy our first app on Kubernetes with the kubectl create deployment command. We need to provide the deployment name and app image location (include the full repository url for images hosted outside Docker Hub).

kubectl create deployment kubernetes-bootcamp --image=gcr.io/google-samples/kubernetes-bootcamp:v1

Great! You just deployed your first application by creating a deployment. This performed a few things for you:

  • searched for a suitable node where an instance of the application could be run (we have only 1 available node)
  • scheduled the application to run on that Node
  • configured the cluster to reschedule the instance on a new Node when needed

To list your deployments use the kubectl get deployments command:

kubectl get deployments

We see that there is 1 deployment running a single instance of your app. The instance is running inside a container on your node.

View the app

Pods that are running inside Kubernetes are running on a private, isolated network. By default they are visible from other pods and services within the same Kubernetes cluster, but not outside that network. When we use kubectl, we're interacting through an API endpoint to communicate with our application.

We will cover other options on how to expose your application outside the Kubernetes cluster later, in Module 4. Also as a basic tutorial, we're not explaining what Pods are in any detail here, it will be covered in later topics.

The kubectl proxy command can create a proxy that will forward communications into the cluster-wide, private network. The proxy can be terminated by pressing control-C and won't show any output while it's running.

You need to open a second terminal window to run the proxy.

kubectl proxy

We now have a connection between our host (the terminal) and the Kubernetes cluster. The proxy enables direct access to the API from these terminals.

You can see all those APIs hosted through the proxy endpoint. For example, we can query the version directly through the API using the curl command:

curl http://localhost:8001/version

The API server will automatically create an endpoint for each pod, based on the pod name, that is also accessible through the proxy.

First we need to get the Pod name, and we'll store it in the environment variable POD_NAME:

export POD_NAME=$(kubectl get pods -o go-template --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')
echo Name of the Pod: $POD_NAME

You can access the Pod through the proxied API, by running:

curl http://localhost:8001/api/v1/namespaces/default/pods/$POD_NAME:8080/proxy/

In order for the new Deployment to be accessible without using the proxy, a Service is required which will be explained in Module 4.

Once you're ready, move on to Viewing Pods and Nodes.

2.3 - Explore Your App

2.3.1 - Viewing Pods and Nodes

Learn how to troubleshoot Kubernetes applications using kubectl get, kubectl describe, kubectl logs and kubectl exec.

Objectives

  • Learn about Kubernetes Pods.
  • Learn about Kubernetes Nodes.
  • Troubleshoot deployed applications.

Kubernetes Pods

When you created a Deployment in Module 2, Kubernetes created a Pod to host your application instance. A Pod is a Kubernetes abstraction that represents a group of one or more application containers (such as Docker), and some shared resources for those containers. Those resources include:

  • Shared storage, as Volumes
  • Networking, as a unique cluster IP address
  • Information about how to run each container, such as the container image version or specific ports to use

A Pod models an application-specific "logical host" and can contain different application containers which are relatively tightly coupled. For example, a Pod might include both the container with your Node.js app as well as a different container that feeds the data to be published by the Node.js webserver. The containers in a Pod share an IP Address and port space, are always co-located and co-scheduled, and run in a shared context on the same Node.

Pods are the atomic unit on the Kubernetes platform. When we create a Deployment on Kubernetes, that Deployment creates Pods with containers inside them (as opposed to creating containers directly). Each Pod is tied to the Node where it is scheduled, and remains there until termination (according to restart policy) or deletion. In case of a Node failure, identical Pods are scheduled on other available Nodes in the cluster.

Summary:

  • Pods
  • Nodes
  • Kubectl main commands

A Pod is a group of one or more application containers (such as Docker) and includes shared storage (volumes), IP address and information about how to run them.


Pods overview


Nodes

A Pod always runs on a Node. A Node is a worker machine in Kubernetes and may be either a virtual or a physical machine, depending on the cluster. Each Node is managed by the control plane. A Node can have multiple pods, and the Kubernetes control plane automatically handles scheduling the pods across the Nodes in the cluster. The control plane's automatic scheduling takes into account the available resources on each Node.

Every Kubernetes Node runs at least:

  • Kubelet, a process responsible for communication between the Kubernetes control plane and the Node; it manages the Pods and the containers running on a machine.
  • A container runtime (like Docker) responsible for pulling the container image from a registry, unpacking the container, and running the application.

Containers should only be scheduled together in a single Pod if they are tightly coupled and need to share resources such as disk.


Node overview


Troubleshooting with kubectl

In Module 2, you used the kubectl command-line interface. You'll continue to use it in Module 3 to get information about deployed applications and their environments. The most common operations can be done with the following kubectl subcommands:

  • kubectl get - list resources
  • kubectl describe - show detailed information about a resource
  • kubectl logs - print the logs from a container in a pod
  • kubectl exec - execute a command on a container in a pod

You can use these commands to see when applications were deployed, what their current statuses are, where they are running and what their configurations are.

Now that we know more about our cluster components and the command line, let's explore our application.

A node is a worker machine in Kubernetes and may be a VM or physical machine, depending on the cluster. Multiple Pods can run on one Node.

Check application configuration

Let's verify that the application we deployed in the previous scenario is running. We'll use the kubectl get command and look for existing Pods:

kubectl get pods

If no pods are running, please wait a couple of seconds and list the Pods again. You can continue once you see one Pod running.

Next, to view what containers are inside that Pod and what images are used to build those containers we run the kubectl describe pods command:

kubectl describe pods

We see here details about the Pod’s container: IP address, the ports used and a list of events related to the lifecycle of the Pod.

The output of the describe subcommand is extensive and covers some concepts that we didn’t explain yet, but don’t worry, they will become familiar by the end of this bootcamp.

Note: the describe subcommand can be used to get detailed information about most of the Kubernetes primitives, including Nodes, Pods, and Deployments. The describe output is designed to be human readable, not to be scripted against.

Show the app in the terminal

Recall that Pods are running in an isolated, private network - so we need to proxy access to them so we can debug and interact with them. To do this, we'll use the kubectl proxy command to run a proxy in a second terminal. Open a new terminal window, and in that new terminal, run:

kubectl proxy

Now again, we'll get the Pod name and query that pod directly through the proxy. To get the Pod name and store it in the POD_NAME environment variable:

export POD_NAME="$(kubectl get pods -o go-template --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')"
echo Name of the Pod: $POD_NAME

To see the output of our application, run a curl request:

curl http://localhost:8001/api/v1/namespaces/default/pods/$POD_NAME:8080/proxy/

The URL is the route to the API of the Pod.

View the container logs

Anything that the application would normally send to standard output becomes logs for the container within the Pod. We can retrieve these logs using the kubectl logs command:

kubectl logs "$POD_NAME"

Note: We don't need to specify the container name, because we only have one container inside the pod.

Executing command on the container

We can execute commands directly on the container once the Pod is up and running. For this, we use the exec subcommand and use the name of the Pod as a parameter. Let’s list the environment variables:

kubectl exec "$POD_NAME" -- env

Again, it's worth mentioning that the name of the container itself can be omitted since we only have a single container in the Pod.

Next let’s start a bash session in the Pod’s container:

kubectl exec -ti $POD_NAME -- bash

We have now an open console on the container where we run our NodeJS application. The source code of the app is in the server.js file:

cat server.js

You can check that the application is up by running a curl command:

curl http://localhost:8080

Note: here we used localhost because we executed the command inside the NodeJS Pod. If you cannot connect to localhost:8080, check to make sure you have run the kubectl exec command and are launching the command from within the Pod

To close your container connection, type exit.

Once you're ready, move on to Using A Service To Expose Your App.

2.4 - Expose Your App Publicly

2.4.1 - Using a Service to Expose Your App

Learn about a Service in Kubernetes. Understand how labels and selectors relate to a Service. Expose an application outside a Kubernetes cluster.

Objectives

  • Learn about a Service in Kubernetes
  • Understand how labels and selectors relate to a Service
  • Expose an application outside a Kubernetes cluster using a Service

Overview of Kubernetes Services

Kubernetes Pods are mortal. Pods have a lifecycle. When a worker node dies, the Pods running on the Node are also lost. A ReplicaSet might then dynamically drive the cluster back to the desired state via the creation of new Pods to keep your application running. As another example, consider an image-processing backend with 3 replicas. Those replicas are exchangeable; the front-end system should not care about backend replicas or even if a Pod is lost and recreated. That said, each Pod in a Kubernetes cluster has a unique IP address, even Pods on the same Node, so there needs to be a way of automatically reconciling changes among Pods so that your applications continue to function.

A Service in Kubernetes is an abstraction which defines a logical set of Pods and a policy by which to access them. Services enable a loose coupling between dependent Pods. A Service is defined using YAML or JSON, like all Kubernetes object manifests. The set of Pods targeted by a Service is usually determined by a label selector (see below for why you might want a Service without including a selector in the spec).

Although each Pod has a unique IP address, those IPs are not exposed outside the cluster without a Service. Services allow your applications to receive traffic. Services can be exposed in different ways by specifying a type in the spec of the Service:

  • ClusterIP (default) - Exposes the Service on an internal IP in the cluster. This type makes the Service only reachable from within the cluster.
  • NodePort - Exposes the Service on the same port of each selected Node in the cluster using NAT. Makes a Service accessible from outside the cluster using <NodeIP>:<NodePort>. Superset of ClusterIP.
  • LoadBalancer - Creates an external load balancer in the current cloud (if supported) and assigns a fixed, external IP to the Service. Superset of NodePort.
  • ExternalName - Maps the Service to the contents of the externalName field (e.g. foo.bar.example.com), by returning a CNAME record with its value. No proxying of any kind is set up. This type requires v1.7 or higher of kube-dns, or CoreDNS version 0.0.8 or higher.

More information about the different types of Services can be found in the Using Source IP tutorial. Also see Connecting Applications with Services.

Additionally, note that there are some use cases with Services that involve not defining a selector in the spec. A Service created without selector will also not create the corresponding Endpoints object. This allows users to manually map a Service to specific endpoints. Another possibility why there may be no selector is you are strictly using type: ExternalName.

Summary

  • Exposing Pods to external traffic
  • Load balancing traffic across multiple Pods
  • Using labels

A Kubernetes Service is an abstraction layer which defines a logical set of Pods and enables external traffic exposure, load balancing and service discovery for those Pods.


Services and Labels

A Service routes traffic across a set of Pods. Services are the abstraction that allows pods to die and replicate in Kubernetes without impacting your application. Discovery and routing among dependent Pods (such as the frontend and backend components in an application) are handled by Kubernetes Services.

Services match a set of Pods using labels and selectors, a grouping primitive that allows logical operation on objects in Kubernetes. Labels are key/value pairs attached to objects and can be used in any number of ways:

  • Designate objects for development, test, and production
  • Embed version tags
  • Classify an object using tags


Labels can be attached to objects at creation time or later on. They can be modified at any time. Let's expose our application now using a Service and apply some labels.

Step 1: Creating a new Service

Let’s verify that our application is running. We’ll use the kubectl get command and look for existing Pods:

kubectl get pods

If no Pods are running then it means the objects from the previous tutorials were cleaned up. In this case, go back and recreate the deployment from the Using kubectl to create a Deployment tutorial. Please wait a couple of seconds and list the Pods again. You can continue once you see the one Pod running.

Next, let’s list the current Services from our cluster:

kubectl get services

We have a Service called kubernetes that is created by default when minikube starts the cluster. To create a new service and expose it to external traffic we'll use the expose command with NodePort as parameter.

kubectl expose deployment/kubernetes-bootcamp --type="NodePort" --port 8080

Let's run again the get services subcommand:

kubectl get services

We have now a running Service called kubernetes-bootcamp. Here we see that the Service received a unique cluster-IP, an internal port and an external-IP (the IP of the Node).

To find out what port was opened externally (for the type: NodePort Service) we’ll run the describe service subcommand:

kubectl describe services/kubernetes-bootcamp

Create an environment variable called NODE_PORT that has the value of the Node port assigned:

export NODE_PORT="$(kubectl get services/kubernetes-bootcamp -o go-template='{{(index .spec.ports 0).nodePort}}')"
echo "NODE_PORT=$NODE_PORT"

Now we can test that the app is exposed outside of the cluster using curl, the IP address of the Node and the externally exposed port:

curl http://"$(minikube ip):$NODE_PORT"

And we get a response from the server. The Service is exposed.

Step 2: Using labels

The Deployment created automatically a label for our Pod. With the describe deployment subcommand you can see the name (the key) of that label:

kubectl describe deployment

Let’s use this label to query our list of Pods. We’ll use the kubectl get pods command with -l as a parameter, followed by the label values:

kubectl get pods -l app=kubernetes-bootcamp

You can do the same to list the existing Services:

kubectl get services -l app=kubernetes-bootcamp

Get the name of the Pod and store it in the POD_NAME environment variable:

export POD_NAME="$(kubectl get pods -o go-template --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')"
echo "Name of the Pod: $POD_NAME"

To apply a new label we use the label subcommand followed by the object type, object name and the new label:

kubectl label pods "$POD_NAME" version=v1

This will apply a new label to our Pod (we pinned the application version to the Pod), and we can check it with the describe pod command:

kubectl describe pods "$POD_NAME"

We see here that the label is attached now to our Pod. And we can query now the list of pods using the new label:

kubectl get pods -l version=v1

And we see the Pod.

Step 3: Deleting a service

To delete Services you can use the delete service subcommand. Labels can be used also here:

kubectl delete service -l app=kubernetes-bootcamp

Confirm that the Service is gone:

kubectl get services

This confirms that our Service was removed. To confirm that route is not exposed anymore you can curl the previously exposed IP and port:

curl http://"$(minikube ip):$NODE_PORT"

This proves that the application is not reachable anymore from outside of the cluster. You can confirm that the app is still running with a curl from inside the pod:

kubectl exec -ti $POD_NAME -- curl http://localhost:8080

We see here that the application is up. This is because the Deployment is managing the application. To shut down the application, you would need to delete the Deployment as well.

Once you're ready, move on to Running Multiple Instances of Your App.

2.5 - Scale Your App

2.5.1 - Running Multiple Instances of Your App

Scale an existing app manually using kubectl.

Objectives

  • Scale an app using kubectl.

Scaling an application

Previously we created a Deployment, and then exposed it publicly via a Service. The Deployment created only one Pod for running our application. When traffic increases, we will need to scale the application to keep up with user demand.

If you haven't worked through the earlier sections, start from Using minikube to create a cluster.

Scaling is accomplished by changing the number of replicas in a Deployment

NOTE If you are trying this after the previous section , then you may have deleted the service you created, or have created a Service of type: NodePort. In this section, it is assumed that a service with type: LoadBalancer is created for the kubernetes-bootcamp Deployment.

If you have not deleted the Service created in the previous section, first delete that Service and then run the following command to create a new Service with its type set to LoadBalancer:

kubectl expose deployment/kubernetes-bootcamp --type="LoadBalancer" --port 8080


Summary:

  • Scaling a Deployment

You can create from the start a Deployment with multiple instances using the --replicas parameter for the kubectl create deployment command

Scaling overview


Scaling out a Deployment will ensure new Pods are created and scheduled to Nodes with available resources. Scaling will increase the number of Pods to the new desired state. Kubernetes also supports autoscaling of Pods, but it is outside of the scope of this tutorial. Scaling to zero is also possible, and it will terminate all Pods of the specified Deployment.

Running multiple instances of an application will require a way to distribute the traffic to all of them. Services have an integrated load-balancer that will distribute network traffic to all Pods of an exposed Deployment. Services will monitor continuously the running Pods using endpoints, to ensure the traffic is sent only to available Pods.

Scaling is accomplished by changing the number of replicas in a Deployment.


Once you have multiple instances of an application running, you would be able to do Rolling updates without downtime. We'll cover that in the next section of the tutorial. Now, let's go to the terminal and scale our application.

Scaling a Deployment

To list your Deployments, use the get deployments subcommand:

kubectl get deployments

The output should be similar to:

NAME                  READY   UP-TO-DATE   AVAILABLE   AGE
kubernetes-bootcamp   1/1     1            1           11m

We should have 1 Pod. If not, run the command again. This shows:

  • NAME lists the names of the Deployments in the cluster.
  • READY shows the ratio of CURRENT/DESIRED replicas
  • UP-TO-DATE displays the number of replicas that have been updated to achieve the desired state.
  • AVAILABLE displays how many replicas of the application are available to your users.
  • AGE displays the amount of time that the application has been running.

To see the ReplicaSet created by the Deployment, run:

kubectl get rs

Notice that the name of the ReplicaSet is always formatted as [DEPLOYMENT-NAME]-[RANDOM-STRING]. The random string is randomly generated and uses the pod-template-hash as a seed.

Two important columns of this output are:

  • DESIRED displays the desired number of replicas of the application, which you define when you create the Deployment. This is the desired state.
  • CURRENT displays how many replicas are currently running.

Next, let’s scale the Deployment to 4 replicas. We’ll use the kubectl scale command, followed by the Deployment type, name and desired number of instances:

kubectl scale deployments/kubernetes-bootcamp --replicas=4

To list your Deployments once again, use get deployments:

kubectl get deployments

The change was applied, and we have 4 instances of the application available. Next, let’s check if the number of Pods changed:

kubectl get pods -o wide

There are 4 Pods now, with different IP addresses. The change was registered in the Deployment events log. To check that, use the describe subcommand:

kubectl describe deployments/kubernetes-bootcamp

You can also view in the output of this command that there are 4 replicas now.

Load Balancing

Let's check that the Service is load-balancing the traffic. To find out the exposed IP and Port we can use the describe service as we learned in the previous part of the tutorial:

kubectl describe services/kubernetes-bootcamp

Create an environment variable called NODE_PORT that has a value as the Node port:

export NODE_PORT="$(kubectl get services/kubernetes-bootcamp -o go-template='{{(index .spec.ports 0).nodePort}}')"

echo NODE_PORT=$NODE_PORT

Next, we’ll do a curl to the exposed IP address and port. Execute the command multiple times:

curl http://"$(minikube ip):$NODE_PORT"

We hit a different Pod with every request. This demonstrates that the load-balancing is working.

Scale Down

To scale down the Deployment to 2 replicas, run again the scale subcommand:

kubectl scale deployments/kubernetes-bootcamp --replicas=2

List the Deployments to check if the change was applied with the get deployments subcommand:

kubectl get deployments

The number of replicas decreased to 2. List the number of Pods, with get pods:

kubectl get pods -o wide

This confirms that 2 Pods were terminated.

Once you're ready, move on to Performing a Rolling Update.

2.6 - Update Your App

2.6.1 - Performing a Rolling Update

Perform a rolling update using kubectl.

Objectives

  • Perform a rolling update using kubectl.

Updating an application

Users expect applications to be available all the time, and developers are expected to deploy new versions of them several times a day. In Kubernetes this is done with rolling updates. A rolling update allows a Deployment update to take place with zero downtime. It does this by incrementally replacing the current Pods with new ones. The new Pods are scheduled on Nodes with available resources, and Kubernetes waits for those new Pods to start before removing the old Pods.

In the previous module we scaled our application to run multiple instances. This is a requirement for performing updates without affecting application availability. By default, the maximum number of Pods that can be unavailable during the update and the maximum number of new Pods that can be created, is one. Both options can be configured to either numbers or percentages (of Pods). In Kubernetes, updates are versioned and any Deployment update can be reverted to a previous (stable) version.

Summary:

  • Updating an app

Rolling updates allow Deployments' update to take place with zero downtime by incrementally updating Pods instances with new ones.


Rolling updates overview


Similar to application Scaling, if a Deployment is exposed publicly, the Service will load-balance the traffic only to available Pods during the update. An available Pod is an instance that is available to the users of the application.

Rolling updates allow the following actions:

  • Promote an application from one environment to another (via container image updates)
  • Rollback to previous versions
  • Continuous Integration and Continuous Delivery of applications with zero downtime

If a Deployment is exposed publicly, the Service will load-balance the traffic only to available Pods during the update.


In the following interactive tutorial, we'll update our application to a new version, and also perform a rollback.


Update the version of the app

To list your Deployments, run the get deployments subcommand: kubectl get deployments

To list the running Pods, run the get pods subcommand:

kubectl get pods

To view the current image version of the app, run the describe pods subcommand and look for the Image field:

kubectl describe pods

To update the image of the application to version 2, use the set image subcommand, followed by the deployment name and the new image version:

kubectl set image deployments/kubernetes-bootcamp kubernetes-bootcamp=docker.io/jocatalin/kubernetes-bootcamp:v2

The command notified the Deployment to use a different image for your app and initiated a rolling update. Check the status of the new Pods, and view the old one terminating with the get pods subcommand:

kubectl get pods

Verify an update

First, check that the service is running, as you might have deleted it in previous tutorial step, run describe services/kubernetes-bootcamp. If it's missing, you can create it again with:

kubectl expose deployment/kubernetes-bootcamp --type="NodePort" --port 8080

Create an environment variable called NODE_PORT that has the value of the Node port assigned:

export NODE_PORT="$(kubectl get services/kubernetes-bootcamp -o go-template='{{(index .spec.ports 0).nodePort}}')"
echo "NODE_PORT=$NODE_PORT"

Next, do a curl to the exposed IP and port:

curl http://"$(minikube ip):$NODE_PORT"

Every time you run the curl command, you will hit a different Pod. Notice that all Pods are now running the latest version (v2).

You can also confirm the update by running the rollout status subcommand:

kubectl rollout status deployments/kubernetes-bootcamp

To view the current image version of the app, run the describe pods subcommand:

kubectl describe pods

In the Image field of the output, verify that you are running the latest image version (v2).

Roll back an update

Let’s perform another update, and try to deploy an image tagged with v10:

kubectl set image deployments/kubernetes-bootcamp kubernetes-bootcamp=gcr.io/google-samples/kubernetes-bootcamp:v10

Use get deployments to see the status of the deployment:

kubectl get deployments

Notice that the output doesn't list the desired number of available Pods. Run the get pods subcommand to list all Pods:

kubectl get pods

Notice that some of the Pods have a status of ImagePullBackOff.

To get more insight into the problem, run the describe pods subcommand:

kubectl describe pods

In the Events section of the output for the affected Pods, notice that the v10 image version did not exist in the repository.

To roll back the deployment to your last working version, use the rollout undo subcommand:

kubectl rollout undo deployments/kubernetes-bootcamp

The rollout undo command reverts the deployment to the previous known state (v2 of the image). Updates are versioned and you can revert to any previously known state of a Deployment.

Use the get pods subcommand to list the Pods again:

kubectl get pods

Four Pods are running. To check the image deployed on these Pods, use the describe pods subcommand:

kubectl describe pods

The Deployment is once again using a stable version of the app (v2). The rollback was successful.

Remember to clean up your local cluster

kubectl delete deployments/kubernetes-bootcamp services/kubernetes-bootcamp

3 - Configuration

3.1 - Example: Configuring a Java Microservice

3.1.1 - Externalizing config using MicroProfile, ConfigMaps and Secrets

In this tutorial you will learn how and why to externalize your microservice’s configuration. Specifically, you will learn how to use Kubernetes ConfigMaps and Secrets to set environment variables and then consume them using MicroProfile Config.

Before you begin

Creating Kubernetes ConfigMaps & Secrets

There are several ways to set environment variables for a Docker container in Kubernetes, including: Dockerfile, kubernetes.yml, Kubernetes ConfigMaps, and Kubernetes Secrets. In the tutorial, you will learn how to use the latter two for setting your environment variables whose values will be injected into your microservices. One of the benefits for using ConfigMaps and Secrets is that they can be re-used across multiple containers, including being assigned to different environment variables for the different containers.

ConfigMaps are API Objects that store non-confidential key-value pairs. In the Interactive Tutorial you will learn how to use a ConfigMap to store the application's name. For more information regarding ConfigMaps, you can find the documentation here.

Although Secrets are also used to store key-value pairs, they differ from ConfigMaps in that they're intended for confidential/sensitive information and are stored using Base64 encoding. This makes secrets the appropriate choice for storing such things as credentials, keys, and tokens, the former of which you'll do in the Interactive Tutorial. For more information on Secrets, you can find the documentation here.

Externalizing Config from Code

Externalized application configuration is useful because configuration usually changes depending on your environment. In order to accomplish this, we'll use Java's Contexts and Dependency Injection (CDI) and MicroProfile Config. MicroProfile Config is a feature of MicroProfile, a set of open Java technologies for developing and deploying cloud-native microservices.

CDI provides a standard dependency injection capability enabling an application to be assembled from collaborating, loosely-coupled beans. MicroProfile Config provides apps and microservices a standard way to obtain config properties from various sources, including the application, runtime, and environment. Based on the source's defined priority, the properties are automatically combined into a single set of properties that the application can access via an API. Together, CDI & MicroProfile will be used in the Interactive Tutorial to retrieve the externally provided properties from the Kubernetes ConfigMaps and Secrets and get injected into your application code.

Many open source frameworks and runtimes implement and support MicroProfile Config. Throughout the interactive tutorial, you'll be using Open Liberty, a flexible open-source Java runtime for building and running cloud-native apps and microservices. However, any MicroProfile compatible runtime could be used instead.

Objectives

  • Create a Kubernetes ConfigMap and Secret
  • Inject microservice configuration using MicroProfile Config

Example: Externalizing config using MicroProfile, ConfigMaps and Secrets

Start Interactive Tutorial

3.2 - Updating Configuration via a ConfigMap

This page provides a step-by-step example of updating configuration within a Pod via a ConfigMap and builds upon the Configure a Pod to Use a ConfigMap task.
At the end of this tutorial, you will understand how to change the configuration for a running application.
This tutorial uses the alpine and nginx images as examples.

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:

You need to have the curl command-line tool for making HTTP requests from the terminal or command prompt. If you do not have curl available, you can install it. Check the documentation for your local operating system.

Objectives

  • Update configuration via a ConfigMap mounted as a Volume
  • Update environment variables of a Pod via a ConfigMap
  • Update configuration via a ConfigMap in a multi-container Pod
  • Update configuration via a ConfigMap in a Pod possessing a Sidecar Container

Update configuration via a ConfigMap mounted as a Volume

Use the kubectl create configmap command to create a ConfigMap from literal values:

kubectl create configmap sport --from-literal=sport=football

Below is an example of a Deployment manifest with the ConfigMap sport mounted as a volume into the Pod's only container.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: configmap-volume
  labels:
    app.kubernetes.io/name: configmap-volume
spec:
  replicas: 3
  selector:
    matchLabels:
      app.kubernetes.io/name: configmap-volume
  template:
    metadata:
      labels:
        app.kubernetes.io/name: configmap-volume
    spec:
      containers:
        - name: alpine
          image: alpine:3
          command:
            - /bin/sh
            - -c
            - while true; do echo "$(date) My preferred sport is $(cat /etc/config/sport)";
              sleep 10; done;
          ports:
            - containerPort: 80
          volumeMounts:
            - name: config-volume
              mountPath: /etc/config
      volumes:
        - name: config-volume
          configMap:
            name: sport

Create the Deployment:

kubectl apply -f https://k8s.io/examples/deployments/deployment-with-configmap-as-volume.yaml

Check the pods for this Deployment to ensure they are ready (matching by selector):

kubectl get pods --selector=app.kubernetes.io/name=configmap-volume

You should see an output similar to:

NAME                                READY   STATUS    RESTARTS   AGE
configmap-volume-6b976dfdcf-qxvbm   1/1     Running   0          72s
configmap-volume-6b976dfdcf-skpvm   1/1     Running   0          72s
configmap-volume-6b976dfdcf-tbc6r   1/1     Running   0          72s

On each node where one of these Pods is running, the kubelet fetches the data for that ConfigMap and translates it to files in a local volume. The kubelet then mounts that volume into the container, as specified in the Pod template. The code running in that container loads the information from the file and uses it to print a report to stdout. You can check this report by viewing the logs for one of the Pods in that Deployment:

# Pick one Pod that belongs to the Deployment, and view its logs
kubectl logs deployments/configmap-volume

You should see an output similar to:

Found 3 pods, using pod/configmap-volume-76d9c5678f-x5rgj
Thu Jan  4 14:06:46 UTC 2024 My preferred sport is football
Thu Jan  4 14:06:56 UTC 2024 My preferred sport is football
Thu Jan  4 14:07:06 UTC 2024 My preferred sport is football
Thu Jan  4 14:07:16 UTC 2024 My preferred sport is football
Thu Jan  4 14:07:26 UTC 2024 My preferred sport is football

Edit the ConfigMap:

kubectl edit configmap sport

In the editor that appears, change the value of key sport from football to cricket. Save your changes. The kubectl tool updates the ConfigMap accordingly (if you see an error, try again).

Here's an example of how that manifest could look after you edit it:

apiVersion: v1
data:
  sport: cricket
kind: ConfigMap
# You can leave the existing metadata as they are.
# The values you'll see won't exactly match these.
metadata:
  creationTimestamp: "2024-01-04T14:05:06Z"
  name: sport
  namespace: default
  resourceVersion: "1743935"
  uid: 024ee001-fe72-487e-872e-34d6464a8a23

You should see the following output:

configmap/sport edited

Tail (follow the latest entries in) the logs of one of the pods that belongs to this Deployment:

kubectl logs deployments/configmap-volume --follow

After few seconds, you should see the log output change as follows:

Thu Jan  4 14:11:36 UTC 2024 My preferred sport is football
Thu Jan  4 14:11:46 UTC 2024 My preferred sport is football
Thu Jan  4 14:11:56 UTC 2024 My preferred sport is football
Thu Jan  4 14:12:06 UTC 2024 My preferred sport is cricket
Thu Jan  4 14:12:16 UTC 2024 My preferred sport is cricket

When you have a ConfigMap that is mapped into a running Pod using either a configMap volume or a projected volume, and you update that ConfigMap, the running Pod sees the update almost immediately.
However, your application only sees the change if it is written to either poll for changes, or watch for file updates.
An application that loads its configuration once at startup will not notice a change.

Update environment variables of a Pod via a ConfigMap

Use the kubectl create configmap command to create a ConfigMap from literal values:

kubectl create configmap fruits --from-literal=fruits=apples

Below is an example of a Deployment manifest with an environment variable configured via the ConfigMap fruits.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: configmap-env-var
  labels:
    app.kubernetes.io/name: configmap-env-var
spec:
  replicas: 3
  selector:
    matchLabels:
      app.kubernetes.io/name: configmap-env-var
  template:
    metadata:
      labels:
        app.kubernetes.io/name: configmap-env-var
    spec:
      containers:
        - name: alpine
          image: alpine:3
          env:
            - name: FRUITS
              valueFrom:
                configMapKeyRef:
                  key: fruits
                  name: fruits
          command:
            - /bin/sh
            - -c
            - while true; do echo "$(date) The basket is full of $FRUITS";
                sleep 10; done;
          ports:
            - containerPort: 80

Create the Deployment:

kubectl apply -f https://k8s.io/examples/deployments/deployment-with-configmap-as-envvar.yaml

Check the pods for this Deployment to ensure they are ready (matching by selector):

kubectl get pods --selector=app.kubernetes.io/name=configmap-env-var

You should see an output similar to:

NAME                                 READY   STATUS    RESTARTS   AGE
configmap-env-var-59cfc64f7d-74d7z   1/1     Running   0          46s
configmap-env-var-59cfc64f7d-c4wmj   1/1     Running   0          46s
configmap-env-var-59cfc64f7d-dpr98   1/1     Running   0          46s

The key-value pair in the ConfigMap is configured as an environment variable in the container of the Pod. Check this by viewing the logs of one Pod that belongs to the Deployment.

kubectl logs deployment/configmap-env-var

You should see an output similar to:

Found 3 pods, using pod/configmap-env-var-7c994f7769-l74nq
Thu Jan  4 16:07:06 UTC 2024 The basket is full of apples
Thu Jan  4 16:07:16 UTC 2024 The basket is full of apples
Thu Jan  4 16:07:26 UTC 2024 The basket is full of apples

Edit the ConfigMap:

kubectl edit configmap fruits

In the editor that appears, change the value of key fruits from apples to mangoes. Save your changes. The kubectl tool updates the ConfigMap accordingly (if you see an error, try again).

Here's an example of how that manifest could look after you edit it:

apiVersion: v1
data:
  fruits: mangoes
kind: ConfigMap
# You can leave the existing metadata as they are.
# The values you'll see won't exactly match these.
metadata:
  creationTimestamp: "2024-01-04T16:04:19Z"
  name: fruits
  namespace: default
  resourceVersion: "1749472"

You should see the following output:

configmap/fruits edited

Tail the logs of the Deployment and observe the output for few seconds:

# As the text explains, the output does NOT change
kubectl logs deployments/configmap-env-var --follow

Notice that the output remains unchanged, even though you edited the ConfigMap:

Thu Jan  4 16:12:56 UTC 2024 The basket is full of apples
Thu Jan  4 16:13:06 UTC 2024 The basket is full of apples
Thu Jan  4 16:13:16 UTC 2024 The basket is full of apples
Thu Jan  4 16:13:26 UTC 2024 The basket is full of apples

You can trigger that replacement. Perform a rollout for the Deployment, using kubectl rollout:

# Trigger the rollout
kubectl rollout restart deployment configmap-env-var

# Wait for the rollout to complete
kubectl rollout status deployment configmap-env-var --watch=true

Next, check the Deployment:

kubectl get deployment configmap-env-var

You should see an output similar to:

NAME                READY   UP-TO-DATE   AVAILABLE   AGE
configmap-env-var   3/3     3            3           12m

Check the Pods:

kubectl get pods --selector=app.kubernetes.io/name=configmap-env-var

The rollout causes Kubernetes to make a new ReplicaSet for the Deployment; that means the existing Pods eventually terminate, and new ones are created. After few seconds, you should see an output similar to:

NAME                                 READY   STATUS        RESTARTS   AGE
configmap-env-var-6d94d89bf5-2ph2l   1/1     Running       0          13s
configmap-env-var-6d94d89bf5-74twx   1/1     Running       0          8s
configmap-env-var-6d94d89bf5-d5vx8   1/1     Running       0          11s

View the logs for a Pod in this Deployment:

# Pick one Pod that belongs to the Deployment, and view its logs
kubectl logs deployment/configmap-env-var

You should see an output similar to the below:

Found 3 pods, using pod/configmap-env-var-6d9ff89fb6-bzcf6
Thu Jan  4 16:30:35 UTC 2024 The basket is full of mangoes
Thu Jan  4 16:30:45 UTC 2024 The basket is full of mangoes
Thu Jan  4 16:30:55 UTC 2024 The basket is full of mangoes

This demonstrates the scenario of updating environment variables in a Pod that are derived from a ConfigMap. Changes to the ConfigMap values are applied to the Pod during the subsequent rollout. If Pods get created for another reason, such as scaling up the Deployment, then the new Pods also use the latest configuration values; if you don't trigger a rollout, then you might find that your app is running with a mix of old and new environment variable values.

Update configuration via a ConfigMap in a multi-container Pod

Use the kubectl create configmap command to create a ConfigMap from literal values:

kubectl create configmap color --from-literal=color=red

Below is an example manifest for a Deployment that manages a set of Pods, each with two containers. The two containers share an emptyDir volume that they use to communicate. The first container runs a web server (nginx). The mount path for the shared volume in the web server container is /usr/share/nginx/html. The second helper container is based on alpine, and for this container the emptyDir volume is mounted at /pod-data. The helper container writes a file in HTML that has its content based on a ConfigMap. The web server container serves the HTML via HTTP.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: configmap-two-containers
  labels:
    app.kubernetes.io/name: configmap-two-containers
spec:
  replicas: 3
  selector:
    matchLabels:
      app.kubernetes.io/name: configmap-two-containers
  template:
    metadata:
      labels:
        app.kubernetes.io/name: configmap-two-containers
    spec:
      volumes:
        - name: shared-data
          emptyDir: {}
        - name: config-volume
          configMap:
            name: color
      containers:
        - name: nginx
          image: nginx
          volumeMounts:
            - name: shared-data
              mountPath: /usr/share/nginx/html
        - name: alpine
          image: alpine:3
          volumeMounts:
            - name: shared-data
              mountPath: /pod-data
            - name: config-volume
              mountPath: /etc/config
          command:
            - /bin/sh
            - -c
            - while true; do echo "$(date) My preferred color is $(cat /etc/config/color)" > /pod-data/index.html;
              sleep 10; done;

Create the Deployment:

kubectl apply -f https://k8s.io/examples/deployments/deployment-with-configmap-two-containers.yaml

Check the pods for this Deployment to ensure they are ready (matching by selector):

kubectl get pods --selector=app.kubernetes.io/name=configmap-two-containers

You should see an output similar to:

NAME                                        READY   STATUS    RESTARTS   AGE
configmap-two-containers-565fb6d4f4-2xhxf   2/2     Running   0          20s
configmap-two-containers-565fb6d4f4-g5v4j   2/2     Running   0          20s
configmap-two-containers-565fb6d4f4-mzsmf   2/2     Running   0          20s

Expose the Deployment (the kubectl tool creates a Service for you):

kubectl expose deployment configmap-two-containers --name=configmap-service --port=8080 --target-port=80

Use kubectl to forward the port:

kubectl port-forward service/configmap-service 8080:8080 & # this stays running in the background

Access the service.

curl http://localhost:8080

You should see an output similar to:

Fri Jan  5 08:08:22 UTC 2024 My preferred color is red

Edit the ConfigMap:

kubectl edit configmap color

In the editor that appears, change the value of key color from red to blue. Save your changes. The kubectl tool updates the ConfigMap accordingly (if you see an error, try again).

Here's an example of how that manifest could look after you edit it:

apiVersion: v1
data:
  color: blue
kind: ConfigMap
# You can leave the existing metadata as they are.
# The values you'll see won't exactly match these.
metadata:
  creationTimestamp: "2024-01-05T08:12:05Z"
  name: color
  namespace: configmap
  resourceVersion: "1801272"
  uid: 80d33e4a-cbb4-4bc9-ba8c-544c68e425d6

Loop over the service URL for few seconds.

# Cancel this when you're happy with it (Ctrl-C)
while true; do curl --connect-timeout 7.5 http://localhost:8080; sleep 10; done

You should see the output change as follows:

Fri Jan  5 08:14:00 UTC 2024 My preferred color is red
Fri Jan  5 08:14:02 UTC 2024 My preferred color is red
Fri Jan  5 08:14:20 UTC 2024 My preferred color is red
Fri Jan  5 08:14:22 UTC 2024 My preferred color is red
Fri Jan  5 08:14:32 UTC 2024 My preferred color is blue
Fri Jan  5 08:14:43 UTC 2024 My preferred color is blue
Fri Jan  5 08:15:00 UTC 2024 My preferred color is blue

Update configuration via a ConfigMap in a Pod possessing a sidecar container

The above scenario can be replicated by using a Sidecar Container as a helper container to write the HTML file.
As a Sidecar Container is conceptually an Init Container, it is guaranteed to start before the main web server container.
This ensures that the HTML file is always available when the web server is ready to serve it.
Please see Enabling sidecar containers to utilize this feature.

If you are continuing from the previous scenario, you can reuse the ConfigMap named color for this scenario.
If you are executing this scenario independently, use the kubectl create configmap command to create a ConfigMap from literal values:

kubectl create configmap color --from-literal=color=blue

Below is an example manifest for a Deployment that manages a set of Pods, each with a main container and a sidecar container. The two containers share an emptyDir volume that they use to communicate. The main container runs a web server (NGINX). The mount path for the shared volume in the web server container is /usr/share/nginx/html. The second container is a Sidecar Container based on Alpine Linux which acts as a helper container. For this container the emptyDir volume is mounted at /pod-data. The Sidecar Container writes a file in HTML that has its content based on a ConfigMap. The web server container serves the HTML via HTTP.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: configmap-sidecar-container
  labels:
    app.kubernetes.io/name: configmap-sidecar-container
spec:
  replicas: 3
  selector:
    matchLabels:
      app.kubernetes.io/name: configmap-sidecar-container
  template:
    metadata:
      labels:
        app.kubernetes.io/name: configmap-sidecar-container
    spec:
      volumes:
        - name: shared-data
          emptyDir: {}
        - name: config-volume
          configMap:
            name: color
      containers:
        - name: nginx
          image: nginx
          volumeMounts:
            - name: shared-data
              mountPath: /usr/share/nginx/html
      initContainers:
        - name: alpine
          image: alpine:3
          restartPolicy: Always
          volumeMounts:
            - name: shared-data
              mountPath: /pod-data
            - name: config-volume
              mountPath: /etc/config
          command:
            - /bin/sh
            - -c
            - while true; do echo "$(date) My preferred color is $(cat /etc/config/color)" > /pod-data/index.html;
              sleep 10; done;

Create the Deployment:

kubectl apply -f https://k8s.io/examples/deployments/deployment-with-configmap-and-sidecar-container.yaml

Check the pods for this Deployment to ensure they are ready (matching by selector):

kubectl get pods --selector=app.kubernetes.io/name=configmap-sidecar-container

You should see an output similar to:

NAME                                           READY   STATUS    RESTARTS   AGE
configmap-sidecar-container-5fb59f558b-87rp7   2/2     Running   0          94s
configmap-sidecar-container-5fb59f558b-ccs7s   2/2     Running   0          94s
configmap-sidecar-container-5fb59f558b-wnmgk   2/2     Running   0          94s

Expose the Deployment (the kubectl tool creates a Service for you):

kubectl expose deployment configmap-sidecar-container --name=configmap-sidecar-service --port=8081 --target-port=80

Use kubectl to forward the port:

kubectl port-forward service/configmap-sidecar-service 8081:8081 & # this stays running in the background

Access the service.

curl http://localhost:8081

You should see an output similar to:

Sat Feb 17 13:09:05 UTC 2024 My preferred color is blue

Edit the ConfigMap:

kubectl edit configmap color

In the editor that appears, change the value of key color from blue to green. Save your changes. The kubectl tool updates the ConfigMap accordingly (if you see an error, try again).

Here's an example of how that manifest could look after you edit it:

apiVersion: v1
data:
  color: green
kind: ConfigMap
# You can leave the existing metadata as they are.
# The values you'll see won't exactly match these.
metadata:
  creationTimestamp: "2024-02-17T12:20:30Z"
  name: color
  namespace: default
  resourceVersion: "1054"
  uid: e40bb34c-58df-4280-8bea-6ed16edccfaa

Loop over the service URL for few seconds.

# Cancel this when you're happy with it (Ctrl-C)
while true; do curl --connect-timeout 7.5 http://localhost:8081; sleep 10; done

You should see the output change as follows:

Sat Feb 17 13:12:35 UTC 2024 My preferred color is blue
Sat Feb 17 13:12:45 UTC 2024 My preferred color is blue
Sat Feb 17 13:12:55 UTC 2024 My preferred color is blue
Sat Feb 17 13:13:05 UTC 2024 My preferred color is blue
Sat Feb 17 13:13:15 UTC 2024 My preferred color is green
Sat Feb 17 13:13:25 UTC 2024 My preferred color is green
Sat Feb 17 13:13:35 UTC 2024 My preferred color is green

Update configuration via an immutable ConfigMap that is mounted as a volume

An example manifest for an Immutable ConfigMap is shown below.

apiVersion: v1
data:
  company_name: "ACME, Inc." # existing fictional company name
kind: ConfigMap
immutable: true
metadata:
  name: company-name-20150801

Create the Immutable ConfigMap:

kubectl apply -f https://k8s.io/examples/configmap/immutable-configmap.yaml

Below is an example of a Deployment manifest with the Immutable ConfigMap company-name-20150801 mounted as a volume into the Pod's only container.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: immutable-configmap-volume
  labels:
    app.kubernetes.io/name: immutable-configmap-volume
spec:
  replicas: 3
  selector:
    matchLabels:
      app.kubernetes.io/name: immutable-configmap-volume
  template:
    metadata:
      labels:
        app.kubernetes.io/name: immutable-configmap-volume
    spec:
      containers:
        - name: alpine
          image: alpine:3
          command:
            - /bin/sh
            - -c
            - while true; do echo "$(date) The name of the company is $(cat /etc/config/company_name)";
              sleep 10; done;
          ports:
            - containerPort: 80
          volumeMounts:
            - name: config-volume
              mountPath: /etc/config
      volumes:
        - name: config-volume
          configMap:
            name: company-name-20150801

Create the Deployment:

kubectl apply -f https://k8s.io/examples/deployments/deployment-with-immutable-configmap-as-volume.yaml

Check the pods for this Deployment to ensure they are ready (matching by selector):

kubectl get pods --selector=app.kubernetes.io/name=immutable-configmap-volume

You should see an output similar to:

NAME                                          READY   STATUS    RESTARTS   AGE
immutable-configmap-volume-78b6fbff95-5gsfh   1/1     Running   0          62s
immutable-configmap-volume-78b6fbff95-7vcj4   1/1     Running   0          62s
immutable-configmap-volume-78b6fbff95-vdslm   1/1     Running   0          62s

The Pod's container refers to the data defined in the ConfigMap and uses it to print a report to stdout. You can check this report by viewing the logs for one of the Pods in that Deployment:

# Pick one Pod that belongs to the Deployment, and view its logs
kubectl logs deployments/immutable-configmap-volume

You should see an output similar to:

Found 3 pods, using pod/immutable-configmap-volume-78b6fbff95-5gsfh
Wed Mar 20 03:52:34 UTC 2024 The name of the company is ACME, Inc.
Wed Mar 20 03:52:44 UTC 2024 The name of the company is ACME, Inc.
Wed Mar 20 03:52:54 UTC 2024 The name of the company is ACME, Inc.

Create a new immutable ConfigMap by using the manifest shown below:

apiVersion: v1
data:
  company_name: "Fiktivesunternehmen GmbH" # new fictional company name
kind: ConfigMap
immutable: true
metadata:
  name: company-name-20240312
kubectl apply -f https://k8s.io/examples/configmap/new-immutable-configmap.yaml

You should see an output similar to:

configmap/company-name-20240312 created

Check the newly created ConfigMap:

kubectl get configmap

You should see an output displaying both the old and new ConfigMaps:

NAME                    DATA   AGE
company-name-20150801   1      22m
company-name-20240312   1      24s

Modify the Deployment to reference the new ConfigMap.

Edit the Deployment:

kubectl edit deployment immutable-configmap-volume

In the editor that appears, update the existing volume definition to use the new ConfigMap.

volumes:
- configMap:
    defaultMode: 420
    name: company-name-20240312 # Update this field
  name: config-volume

You should see the following output:

deployment.apps/immutable-configmap-volume edited

This will trigger a rollout. Wait for all the previous Pods to terminate and the new Pods to be in a ready state.

Monitor the status of the Pods:

kubectl get pods --selector=app.kubernetes.io/name=immutable-configmap-volume
NAME                                          READY   STATUS        RESTARTS   AGE
immutable-configmap-volume-5fdb88fcc8-29v8n   1/1     Running       0          13s
immutable-configmap-volume-5fdb88fcc8-52ddd   1/1     Running       0          14s
immutable-configmap-volume-5fdb88fcc8-n5jx4   1/1     Running       0          15s
immutable-configmap-volume-78b6fbff95-5gsfh   1/1     Terminating   0          32m
immutable-configmap-volume-78b6fbff95-7vcj4   1/1     Terminating   0          32m
immutable-configmap-volume-78b6fbff95-vdslm   1/1     Terminating   0          32m

You should eventually see an output similar to:

NAME                                          READY   STATUS    RESTARTS   AGE
immutable-configmap-volume-5fdb88fcc8-29v8n   1/1     Running   0          43s
immutable-configmap-volume-5fdb88fcc8-52ddd   1/1     Running   0          44s
immutable-configmap-volume-5fdb88fcc8-n5jx4   1/1     Running   0          45s

View the logs for a Pod in this Deployment:

# Pick one Pod that belongs to the Deployment, and view its logs
kubectl logs deployment/immutable-configmap-volume

You should see an output similar to the below:

Found 3 pods, using pod/immutable-configmap-volume-5fdb88fcc8-n5jx4
Wed Mar 20 04:24:17 UTC 2024 The name of the company is Fiktivesunternehmen GmbH
Wed Mar 20 04:24:27 UTC 2024 The name of the company is Fiktivesunternehmen GmbH
Wed Mar 20 04:24:37 UTC 2024 The name of the company is Fiktivesunternehmen GmbH

Once all the deployments have migrated to use the new immutable ConfigMap, it is advised to delete the old one.

kubectl delete configmap company-name-20150801 

Summary

Changes to a ConfigMap mounted as a Volume on a Pod are available seamlessly after the subsequent kubelet sync.

Changes to a ConfigMap that configures environment variables for a Pod are available after the subsequent rollout for the Pod.

Once a ConfigMap is marked as immutable, it is not possible to revert this change (you cannot make an immutable ConfigMap mutable), and you also cannot make any change to the contents of the data or the binaryData field. You can delete and recreate the ConfigMap, or you can make a new different ConfigMap. When you delete a ConfigMap, running containers and their Pods maintain a mount point to any volume that referenced that existing ConfigMap.

Cleaning up

Terminate the kubectl port-forward commands in case they are running.

Delete the resources created during the tutorial:

kubectl delete deployment configmap-volume configmap-env-var configmap-two-containers configmap-sidecar-container immutable-configmap-volume
kubectl delete service configmap-service configmap-sidecar-service
kubectl delete configmap sport fruits color company-name-20240312

kubectl delete configmap company-name-20150801 # In case it was not handled during the task execution

3.3 - Configuring Redis using a ConfigMap

This page provides a real world example of how to configure Redis using a ConfigMap and builds upon the Configure a Pod to Use a ConfigMap task.

Objectives

  • Create a ConfigMap with Redis configuration values
  • Create a Redis Pod that mounts and uses the created ConfigMap
  • Verify that the configuration was correctly applied.

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:

To check the version, enter kubectl version.

Real World Example: Configuring Redis using a ConfigMap

Follow the steps below to configure a Redis cache using data stored in a ConfigMap.

First create a ConfigMap with an empty configuration block:

cat <<EOF >./example-redis-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: example-redis-config
data:
  redis-config: ""
EOF

Apply the ConfigMap created above, along with a Redis pod manifest:

kubectl apply -f example-redis-config.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes/website/main/content/en/examples/pods/config/redis-pod.yaml

Examine the contents of the Redis pod manifest and note the following:

  • A volume named config is created by spec.volumes[1]
  • The key and path under spec.volumes[1].configMap.items[0] exposes the redis-config key from the example-redis-config ConfigMap as a file named redis.conf on the config volume.
  • The config volume is then mounted at /redis-master by spec.containers[0].volumeMounts[1].

This has the net effect of exposing the data in data.redis-config from the example-redis-config ConfigMap above as /redis-master/redis.conf inside the Pod.

apiVersion: v1
kind: Pod
metadata:
  name: redis
spec:
  containers:
  - name: redis
    image: redis:5.0.4
    command:
      - redis-server
      - "/redis-master/redis.conf"
    env:
    - name: MASTER
      value: "true"
    ports:
    - containerPort: 6379
    resources:
      limits:
        cpu: "0.1"
    volumeMounts:
    - mountPath: /redis-master-data
      name: data
    - mountPath: /redis-master
      name: config
  volumes:
    - name: data
      emptyDir: {}
    - name: config
      configMap:
        name: example-redis-config
        items:
        - key: redis-config
          path: redis.conf

Examine the created objects:

kubectl get pod/redis configmap/example-redis-config 

You should see the following output:

NAME        READY   STATUS    RESTARTS   AGE
pod/redis   1/1     Running   0          8s

NAME                             DATA   AGE
configmap/example-redis-config   1      14s

Recall that we left redis-config key in the example-redis-config ConfigMap blank:

kubectl describe configmap/example-redis-config

You should see an empty redis-config key:

Name:         example-redis-config
Namespace:    default
Labels:       <none>
Annotations:  <none>

Data
====
redis-config:

Use kubectl exec to enter the pod and run the redis-cli tool to check the current configuration:

kubectl exec -it redis -- redis-cli

Check maxmemory:

127.0.0.1:6379> CONFIG GET maxmemory

It should show the default value of 0:

1) "maxmemory"
2) "0"

Similarly, check maxmemory-policy:

127.0.0.1:6379> CONFIG GET maxmemory-policy

Which should also yield its default value of noeviction:

1) "maxmemory-policy"
2) "noeviction"

Now let's add some configuration values to the example-redis-config ConfigMap:

apiVersion: v1
kind: ConfigMap
metadata:
  name: example-redis-config
data:
  redis-config: |
    maxmemory 2mb
    maxmemory-policy allkeys-lru    

Apply the updated ConfigMap:

kubectl apply -f example-redis-config.yaml

Confirm that the ConfigMap was updated:

kubectl describe configmap/example-redis-config

You should see the configuration values we just added:

Name:         example-redis-config
Namespace:    default
Labels:       <none>
Annotations:  <none>

Data
====
redis-config:
----
maxmemory 2mb
maxmemory-policy allkeys-lru

Check the Redis Pod again using redis-cli via kubectl exec to see if the configuration was applied:

kubectl exec -it redis -- redis-cli

Check maxmemory:

127.0.0.1:6379> CONFIG GET maxmemory

It remains at the default value of 0:

1) "maxmemory"
2) "0"

Similarly, maxmemory-policy remains at the noeviction default setting:

127.0.0.1:6379> CONFIG GET maxmemory-policy

Returns:

1) "maxmemory-policy"
2) "noeviction"

The configuration values have not changed because the Pod needs to be restarted to grab updated values from associated ConfigMaps. Let's delete and recreate the Pod:

kubectl delete pod redis
kubectl apply -f https://raw.githubusercontent.com/kubernetes/website/main/content/en/examples/pods/config/redis-pod.yaml

Now re-check the configuration values one last time:

kubectl exec -it redis -- redis-cli

Check maxmemory:

127.0.0.1:6379> CONFIG GET maxmemory

It should now return the updated value of 2097152:

1) "maxmemory"
2) "2097152"

Similarly, maxmemory-policy has also been updated:

127.0.0.1:6379> CONFIG GET maxmemory-policy

It now reflects the desired value of allkeys-lru:

1) "maxmemory-policy"
2) "allkeys-lru"

Clean up your work by deleting the created resources:

kubectl delete pod/redis configmap/example-redis-config

What's next

4 - Security

Security is an important concern for most organizations and people who run Kubernetes clusters. You can find a basic security checklist elsewhere in the Kubernetes documentation.

To learn how to deploy and manage security aspects of Kubernetes, you can follow the tutorials in this section.

4.1 - Apply Pod Security Standards at the Cluster Level

Pod Security is an admission controller that carries out checks against the Kubernetes Pod Security Standards when new pods are created. It is a feature GA'ed in v1.25. This tutorial shows you how to enforce the baseline Pod Security Standard at the cluster level which applies a standard configuration to all namespaces in a cluster.

To apply Pod Security Standards to specific namespaces, refer to Apply Pod Security Standards at the namespace level.

If you are running a version of Kubernetes other than v1.30, check the documentation for that version.

Before you begin

Install the following on your workstation:

This tutorial demonstrates what you can configure for a Kubernetes cluster that you fully control. If you are learning how to configure Pod Security Admission for a managed cluster where you are not able to configure the control plane, read Apply Pod Security Standards at the namespace level.

Choose the right Pod Security Standard to apply

Pod Security Admission lets you apply built-in Pod Security Standards with the following modes: enforce, audit, and warn.

To gather information that helps you to choose the Pod Security Standards that are most appropriate for your configuration, do the following:

  1. Create a cluster with no Pod Security Standards applied:

    kind create cluster --name psa-wo-cluster-pss
    

    The output is similar to:

    Creating cluster "psa-wo-cluster-pss" ...
    ✓ Ensuring node image (kindest/node:v1.30.0) 🖼
    ✓ Preparing nodes 📦
    ✓ Writing configuration 📜
    ✓ Starting control-plane 🕹️
    ✓ Installing CNI 🔌
    ✓ Installing StorageClass 💾
    Set kubectl context to "kind-psa-wo-cluster-pss"
    You can now use your cluster with:
    
    kubectl cluster-info --context kind-psa-wo-cluster-pss
    
    Thanks for using kind! 😊
    
  2. Set the kubectl context to the new cluster:

    kubectl cluster-info --context kind-psa-wo-cluster-pss
    

    The output is similar to this:

    Kubernetes control plane is running at https://127.0.0.1:61350
    
    CoreDNS is running at https://127.0.0.1:61350/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
    
    To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.
    
  3. Get a list of namespaces in the cluster:

    kubectl get ns
    

    The output is similar to this:

    NAME                 STATUS   AGE
    default              Active   9m30s
    kube-node-lease      Active   9m32s
    kube-public          Active   9m32s
    kube-system          Active   9m32s
    local-path-storage   Active   9m26s
    
  4. Use --dry-run=server to understand what happens when different Pod Security Standards are applied:

    1. Privileged

      kubectl label --dry-run=server --overwrite ns --all \
      pod-security.kubernetes.io/enforce=privileged
      

      The output is similar to:

      namespace/default labeled
      namespace/kube-node-lease labeled
      namespace/kube-public labeled
      namespace/kube-system labeled
      namespace/local-path-storage labeled
      
    2. Baseline

      kubectl label --dry-run=server --overwrite ns --all \
      pod-security.kubernetes.io/enforce=baseline
      

      The output is similar to:

      namespace/default labeled
      namespace/kube-node-lease labeled
      namespace/kube-public labeled
      Warning: existing pods in namespace "kube-system" violate the new PodSecurity enforce level "baseline:latest"
      Warning: etcd-psa-wo-cluster-pss-control-plane (and 3 other pods): host namespaces, hostPath volumes
      Warning: kindnet-vzj42: non-default capabilities, host namespaces, hostPath volumes
      Warning: kube-proxy-m6hwf: host namespaces, hostPath volumes, privileged
      namespace/kube-system labeled
      namespace/local-path-storage labeled
      
    3. Restricted

      kubectl label --dry-run=server --overwrite ns --all \
      pod-security.kubernetes.io/enforce=restricted
      

      The output is similar to:

      namespace/default labeled
      namespace/kube-node-lease labeled
      namespace/kube-public labeled
      Warning: existing pods in namespace "kube-system" violate the new PodSecurity enforce level "restricted:latest"
      Warning: coredns-7bb9c7b568-hsptc (and 1 other pod): unrestricted capabilities, runAsNonRoot != true, seccompProfile
      Warning: etcd-psa-wo-cluster-pss-control-plane (and 3 other pods): host namespaces, hostPath volumes, allowPrivilegeEscalation != false, unrestricted capabilities, restricted volume types, runAsNonRoot != true
      Warning: kindnet-vzj42: non-default capabilities, host namespaces, hostPath volumes, allowPrivilegeEscalation != false, unrestricted capabilities, restricted volume types, runAsNonRoot != true, seccompProfile
      Warning: kube-proxy-m6hwf: host namespaces, hostPath volumes, privileged, allowPrivilegeEscalation != false, unrestricted capabilities, restricted volume types, runAsNonRoot != true, seccompProfile
      namespace/kube-system labeled
      Warning: existing pods in namespace "local-path-storage" violate the new PodSecurity enforce level "restricted:latest"
      Warning: local-path-provisioner-d6d9f7ffc-lw9lh: allowPrivilegeEscalation != false, unrestricted capabilities, runAsNonRoot != true, seccompProfile
      namespace/local-path-storage labeled
      

From the previous output, you'll notice that applying the privileged Pod Security Standard shows no warnings for any namespaces. However, baseline and restricted standards both have warnings, specifically in the kube-system namespace.

Set modes, versions and standards

In this section, you apply the following Pod Security Standards to the latest version:

  • baseline standard in enforce mode.
  • restricted standard in warn and audit mode.

The baseline Pod Security Standard provides a convenient middle ground that allows keeping the exemption list short and prevents known privilege escalations.

Additionally, to prevent pods from failing in kube-system, you'll exempt the namespace from having Pod Security Standards applied.

When you implement Pod Security Admission in your own environment, consider the following:

  1. Based on the risk posture applied to a cluster, a stricter Pod Security Standard like restricted might be a better choice.

  2. Exempting the kube-system namespace allows pods to run as privileged in this namespace. For real world use, the Kubernetes project strongly recommends that you apply strict RBAC policies that limit access to kube-system, following the principle of least privilege. To implement the preceding standards, do the following:

  3. Create a configuration file that can be consumed by the Pod Security Admission Controller to implement these Pod Security Standards:

    mkdir -p /tmp/pss
    cat <<EOF > /tmp/pss/cluster-level-pss.yaml
    apiVersion: apiserver.config.k8s.io/v1
    kind: AdmissionConfiguration
    plugins:
    - name: PodSecurity
      configuration:
        apiVersion: pod-security.admission.config.k8s.io/v1
        kind: PodSecurityConfiguration
        defaults:
          enforce: "baseline"
          enforce-version: "latest"
          audit: "restricted"
          audit-version: "latest"
          warn: "restricted"
          warn-version: "latest"
        exemptions:
          usernames: []
          runtimeClasses: []
          namespaces: [kube-system]
    EOF
    
  4. Configure the API server to consume this file during cluster creation:

    cat <<EOF > /tmp/pss/cluster-config.yaml
    kind: Cluster
    apiVersion: kind.x-k8s.io/v1alpha4
    nodes:
    - role: control-plane
      kubeadmConfigPatches:
      - |
        kind: ClusterConfiguration
        apiServer:
            extraArgs:
              admission-control-config-file: /etc/config/cluster-level-pss.yaml
            extraVolumes:
              - name: accf
                hostPath: /etc/config
                mountPath: /etc/config
                readOnly: false
                pathType: "DirectoryOrCreate"
      extraMounts:
      - hostPath: /tmp/pss
        containerPath: /etc/config
        # optional: if set, the mount is read-only.
        # default false
        readOnly: false
        # optional: if set, the mount needs SELinux relabeling.
        # default false
        selinuxRelabel: false
        # optional: set propagation mode (None, HostToContainer or Bidirectional)
        # see https://kubernetes.io/docs/concepts/storage/volumes/#mount-propagation
        # default None
        propagation: None
    EOF
    
  5. Create a cluster that uses Pod Security Admission to apply these Pod Security Standards:

    kind create cluster --name psa-with-cluster-pss --config /tmp/pss/cluster-config.yaml
    

    The output is similar to this:

    Creating cluster "psa-with-cluster-pss" ...
     ✓ Ensuring node image (kindest/node:v1.30.0) 🖼
     ✓ Preparing nodes 📦
     ✓ Writing configuration 📜
     ✓ Starting control-plane 🕹️
     ✓ Installing CNI 🔌
     ✓ Installing StorageClass 💾
    Set kubectl context to "kind-psa-with-cluster-pss"
    You can now use your cluster with:
    
    kubectl cluster-info --context kind-psa-with-cluster-pss
    
    Have a question, bug, or feature request? Let us know! https://kind.sigs.k8s.io/#community 🙂
    
  6. Point kubectl to the cluster:

    kubectl cluster-info --context kind-psa-with-cluster-pss
    

    The output is similar to this:

    Kubernetes control plane is running at https://127.0.0.1:63855
    CoreDNS is running at https://127.0.0.1:63855/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
    
    To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.
    
  7. Create a Pod in the default namespace:

    apiVersion: v1
     kind: Pod
     metadata:
       name: nginx
     spec:
       containers:
         - image: nginx
           name: nginx
           ports:
             - containerPort: 80
     
    kubectl apply -f https://k8s.io/examples/security/example-baseline-pod.yaml
    

    The pod is started normally, but the output includes a warning:

    Warning: would violate PodSecurity "restricted:latest": allowPrivilegeEscalation != false (container "nginx" must set securityContext.allowPrivilegeEscalation=false), unrestricted capabilities (container "nginx" must set securityContext.capabilities.drop=["ALL"]), runAsNonRoot != true (pod or container "nginx" must set securityContext.runAsNonRoot=true), seccompProfile (pod or container "nginx" must set securityContext.seccompProfile.type to "RuntimeDefault" or "Localhost")
    pod/nginx created
    

Clean up

Now delete the clusters which you created above by running the following command:

kind delete cluster --name psa-with-cluster-pss
kind delete cluster --name psa-wo-cluster-pss

What's next

4.2 - Apply Pod Security Standards at the Namespace Level

Pod Security Admission is an admission controller that applies Pod Security Standards when pods are created. It is a feature GA'ed in v1.25. In this tutorial, you will enforce the baseline Pod Security Standard, one namespace at a time.

You can also apply Pod Security Standards to multiple namespaces at once at the cluster level. For instructions, refer to Apply Pod Security Standards at the cluster level.

Before you begin

Install the following on your workstation:

Create cluster

  1. Create a kind cluster as follows:

    kind create cluster --name psa-ns-level
    

    The output is similar to this:

    Creating cluster "psa-ns-level" ...
     ✓ Ensuring node image (kindest/node:v1.30.0) 🖼 
     ✓ Preparing nodes 📦  
     ✓ Writing configuration 📜 
     ✓ Starting control-plane 🕹️ 
     ✓ Installing CNI 🔌 
     ✓ Installing StorageClass 💾 
    Set kubectl context to "kind-psa-ns-level"
    You can now use your cluster with:
    
    kubectl cluster-info --context kind-psa-ns-level
    
    Not sure what to do next? 😅  Check out https://kind.sigs.k8s.io/docs/user/quick-start/
    
  2. Set the kubectl context to the new cluster:

    kubectl cluster-info --context kind-psa-ns-level
    

    The output is similar to this:

    Kubernetes control plane is running at https://127.0.0.1:50996
    CoreDNS is running at https://127.0.0.1:50996/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
    
    To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.
    

Create a namespace

Create a new namespace called example:

kubectl create ns example

The output is similar to this:

namespace/example created

Enable Pod Security Standards checking for that namespace

  1. Enable Pod Security Standards on this namespace using labels supported by built-in Pod Security Admission. In this step you will configure a check to warn on Pods that don't meet the latest version of the baseline pod security standard.

    kubectl label --overwrite ns example \
       pod-security.kubernetes.io/warn=baseline \
       pod-security.kubernetes.io/warn-version=latest
    
  2. You can configure multiple pod security standard checks on any namespace, using labels. The following command will enforce the baseline Pod Security Standard, but warn and audit for restricted Pod Security Standards as per the latest version (default value)

    kubectl label --overwrite ns example \
      pod-security.kubernetes.io/enforce=baseline \
      pod-security.kubernetes.io/enforce-version=latest \
      pod-security.kubernetes.io/warn=restricted \
      pod-security.kubernetes.io/warn-version=latest \
      pod-security.kubernetes.io/audit=restricted \
      pod-security.kubernetes.io/audit-version=latest
    

Verify the Pod Security Standard enforcement

  1. Create a baseline Pod in the example namespace:

    kubectl apply -n example -f https://k8s.io/examples/security/example-baseline-pod.yaml
    

    The Pod does start OK; the output includes a warning. For example:

    Warning: would violate PodSecurity "restricted:latest": allowPrivilegeEscalation != false (container "nginx" must set securityContext.allowPrivilegeEscalation=false), unrestricted capabilities (container "nginx" must set securityContext.capabilities.drop=["ALL"]), runAsNonRoot != true (pod or container "nginx" must set securityContext.runAsNonRoot=true), seccompProfile (pod or container "nginx" must set securityContext.seccompProfile.type to "RuntimeDefault" or "Localhost")
    pod/nginx created
    
  2. Create a baseline Pod in the default namespace:

    kubectl apply -n default -f https://k8s.io/examples/security/example-baseline-pod.yaml
    

    Output is similar to this:

    pod/nginx created
    

The Pod Security Standards enforcement and warning settings were applied only to the example namespace. You could create the same Pod in the default namespace with no warnings.

Clean up

Now delete the cluster which you created above by running the following command:

kind delete cluster --name psa-ns-level

What's next

4.3 - Restrict a Container's Access to Resources with AppArmor

FEATURE STATE: Kubernetes v1.4 [beta]

This page shows you how to load AppArmor profiles on your nodes and enforce those profiles in Pods. To learn more about how Kubernetes can confine Pods using AppArmor, see Linux kernel security constraints for Pods and containers.

Objectives

  • See an example of how to load a profile on a Node
  • Learn how to enforce the profile on a Pod
  • Learn how to check that the profile is loaded
  • See what happens when a profile is violated
  • See what happens when a profile cannot be loaded

Before you begin

AppArmor is an optional kernel module and Kubernetes feature, so verify it is supported on your Nodes before proceeding:

  1. AppArmor kernel module is enabled -- For the Linux kernel to enforce an AppArmor profile, the AppArmor kernel module must be installed and enabled. Several distributions enable the module by default, such as Ubuntu and SUSE, and many others provide optional support. To check whether the module is enabled, check the /sys/module/apparmor/parameters/enabled file:

    cat /sys/module/apparmor/parameters/enabled
    Y
    

    The kubelet verifies that AppArmor is enabled on the host before admitting a pod with AppArmor explicitly configured.

  2. Container runtime supports AppArmor -- All common Kubernetes-supported container runtimes should support AppArmor, including containerd and CRI-O. Please refer to the corresponding runtime documentation and verify that the cluster fulfills the requirements to use AppArmor.

  3. Profile is loaded -- AppArmor is applied to a Pod by specifying an AppArmor profile that each container should be run with. If any of the specified profiles are not loaded in the kernel, the kubelet will reject the Pod. You can view which profiles are loaded on a node by checking the /sys/kernel/security/apparmor/profiles file. For example:

    ssh gke-test-default-pool-239f5d02-gyn2 "sudo cat /sys/kernel/security/apparmor/profiles | sort"
    
    apparmor-test-deny-write (enforce)
    apparmor-test-audit-write (enforce)
    docker-default (enforce)
    k8s-nginx (enforce)
    

    For more details on loading profiles on nodes, see Setting up nodes with profiles.

Securing a Pod

AppArmor profiles can be specified at the pod level or container level. The container AppArmor profile takes precedence over the pod profile.

securityContext:
  appArmorProfile:
    type: <profile_type>

Where <profile_type> is one of:

  • RuntimeDefault to use the runtime's default profile
  • Localhost to use a profile loaded on the host (see below)
  • Unconfined to run without AppArmor

See the API Reference for the full details on the AppArmor profile API.

To verify that the profile was applied, you can check that the container's root process is running with the correct profile by examining its proc attr:

kubectl exec <pod_name> -- cat /proc/1/attr/current

The output should look something like this:

cri-containerd.apparmor.d (enforce)

Example

This example assumes you have already set up a cluster with AppArmor support.

First, load the profile you want to use onto your Nodes. This profile blocks all file write operations:

#include <tunables/global>

profile k8s-apparmor-example-deny-write flags=(attach_disconnected) {
  #include <abstractions/base>

  file,

  # Deny all file writes.
  deny /** w,
}

The profile needs to loaded onto all nodes, since you don't know where the pod will be scheduled. For this example you can use SSH to install the profiles, but other approaches are discussed in Setting up nodes with profiles.

# This example assumes that node names match host names, and are reachable via SSH.
NODES=($(kubectl get nodes -o name))

for NODE in ${NODES[*]}; do ssh $NODE 'sudo apparmor_parser -q <<EOF
#include <tunables/global>

profile k8s-apparmor-example-deny-write flags=(attach_disconnected) {
  #include <abstractions/base>

  file,

  # Deny all file writes.
  deny /** w,
}
EOF'
done

Next, run a simple "Hello AppArmor" Pod with the deny-write profile:

apiVersion: v1
kind: Pod
metadata:
  name: hello-apparmor
spec:
  securityContext:
    appArmorProfile:
      type: Localhost
      localhostProfile: k8s-apparmor-example-deny-write
  containers:
  - name: hello
    image: busybox:1.28
    command: [ "sh", "-c", "echo 'Hello AppArmor!' && sleep 1h" ]
kubectl create -f hello-apparmor.yaml

You can verify that the container is actually running with that profile by checking /proc/1/attr/current:

kubectl exec hello-apparmor -- cat /proc/1/attr/current

The output should be:

k8s-apparmor-example-deny-write (enforce)

Finally, you can see what happens if you violate the profile by writing to a file:

kubectl exec hello-apparmor -- touch /tmp/test
touch: /tmp/test: Permission denied
error: error executing remote command: command terminated with non-zero exit code: Error executing in Docker Container: 1

To wrap up, see what happens if you try to specify a profile that hasn't been loaded:

kubectl create -f /dev/stdin <<EOF
apiVersion: v1
kind: Pod
metadata:
  name: hello-apparmor-2
spec:
  securityContext:
    appArmorProfile:
      type: Localhost
      localhostProfile: k8s-apparmor-example-allow-write
  containers:
  - name: hello
    image: busybox:1.28
    command: [ "sh", "-c", "echo 'Hello AppArmor!' && sleep 1h" ]
EOF
pod/hello-apparmor-2 created

Although the Pod was created successfully, further examination will show that it is stuck in pending:

kubectl describe pod hello-apparmor-2
Name:          hello-apparmor-2
Namespace:     default
Node:          gke-test-default-pool-239f5d02-x1kf/10.128.0.27
Start Time:    Tue, 30 Aug 2016 17:58:56 -0700
Labels:        <none>
Annotations:   container.apparmor.security.beta.kubernetes.io/hello=localhost/k8s-apparmor-example-allow-write
Status:        Pending
... 
Events:
  Type     Reason     Age              From               Message
  ----     ------     ----             ----               -------
  Normal   Scheduled  10s              default-scheduler  Successfully assigned default/hello-apparmor to gke-test-default-pool-239f5d02-x1kf
  Normal   Pulled     8s               kubelet            Successfully pulled image "busybox:1.28" in 370.157088ms (370.172701ms including waiting)
  Normal   Pulling    7s (x2 over 9s)  kubelet            Pulling image "busybox:1.28"
  Warning  Failed     7s (x2 over 8s)  kubelet            Error: failed to get container spec opts: failed to generate apparmor spec opts: apparmor profile not found k8s-apparmor-example-allow-write
  Normal   Pulled     7s               kubelet            Successfully pulled image "busybox:1.28" in 90.980331ms (91.005869ms including waiting)

An Event provides the error message with the reason, the specific wording is runtime-dependent:

  Warning  Failed     7s (x2 over 8s)  kubelet            Error: failed to get container spec opts: failed to generate apparmor spec opts: apparmor profile not found 

Administration

Setting up Nodes with profiles

Kubernetes 1.30 does not provide any built-in mechanisms for loading AppArmor profiles onto Nodes. Profiles can be loaded through custom infrastructure or tools like the Kubernetes Security Profiles Operator.

The scheduler is not aware of which profiles are loaded onto which Node, so the full set of profiles must be loaded onto every Node. An alternative approach is to add a Node label for each profile (or class of profiles) on the Node, and use a node selector to ensure the Pod is run on a Node with the required profile.

Authoring Profiles

Getting AppArmor profiles specified correctly can be a tricky business. Fortunately there are some tools to help with that:

  • aa-genprof and aa-logprof generate profile rules by monitoring an application's activity and logs, and admitting the actions it takes. Further instructions are provided by the AppArmor documentation.
  • bane is an AppArmor profile generator for Docker that uses a simplified profile language.

To debug problems with AppArmor, you can check the system logs to see what, specifically, was denied. AppArmor logs verbose messages to dmesg, and errors can usually be found in the system logs or through journalctl. More information is provided in AppArmor failures.

Specifying AppArmor confinement

AppArmor profile within security context

You can specify the appArmorProfile on either a container's securityContext or on a Pod's securityContext. If the profile is set at the pod level, it will be used as the default profile for all containers in the pod (including init, sidecar, and ephemeral containers). If both a pod & container AppArmor profile are set, the container's profile will be used.

An AppArmor profile has 2 fields:

type (required) - indicates which kind of AppArmor profile will be applied. Valid options are:

Localhost
a profile pre-loaded on the node (specified by localhostProfile).
RuntimeDefault
the container runtime's default profile.
Unconfined
no AppArmor enforcement.

localhostProfile - The name of a profile loaded on the node that should be used. The profile must be preconfigured on the node to work. This option must be provided if and only if the type is Localhost.

What's next

Additional resources:

4.4 - Restrict a Container's Syscalls with seccomp

FEATURE STATE: Kubernetes v1.19 [stable]

Seccomp stands for secure computing mode and has been a feature of the Linux kernel since version 2.6.12. It can be used to sandbox the privileges of a process, restricting the calls it is able to make from userspace into the kernel. Kubernetes lets you automatically apply seccomp profiles loaded onto a node to your Pods and containers.

Identifying the privileges required for your workloads can be difficult. In this tutorial, you will go through how to load seccomp profiles into a local Kubernetes cluster, how to apply them to a Pod, and how you can begin to craft profiles that give only the necessary privileges to your container processes.

Objectives

  • Learn how to load seccomp profiles on a node
  • Learn how to apply a seccomp profile to a container
  • Observe auditing of syscalls made by a container process
  • Observe behavior when a missing profile is specified
  • Observe a violation of a seccomp profile
  • Learn how to create fine-grained seccomp profiles
  • Learn how to apply a container runtime default seccomp profile

Before you begin

In order to complete all steps in this tutorial, you must install kind and kubectl.

The commands used in the tutorial assume that you are using Docker as your container runtime. (The cluster that kind creates may use a different container runtime internally). You could also use Podman but in that case, you would have to follow specific instructions in order to complete the tasks successfully.

This tutorial shows some examples that are still beta (since v1.25) and others that use only generally available seccomp functionality. You should make sure that your cluster is configured correctly for the version you are using.

The tutorial also uses the curl tool for downloading examples to your computer. You can adapt the steps to use a different tool if you prefer.

Download example seccomp profiles

The contents of these profiles will be explored later on, but for now go ahead and download them into a directory named profiles/ so that they can be loaded into the cluster.

{
    "defaultAction": "SCMP_ACT_LOG"
}

{
    "defaultAction": "SCMP_ACT_ERRNO"
}

{
    "defaultAction": "SCMP_ACT_ERRNO",
    "architectures": [
        "SCMP_ARCH_X86_64",
        "SCMP_ARCH_X86",
        "SCMP_ARCH_X32"
    ],
    "syscalls": [
        {
            "names": [
                "accept4",
                "epoll_wait",
                "pselect6",
                "futex",
                "madvise",
                "epoll_ctl",
                "getsockname",
                "setsockopt",
                "vfork",
                "mmap",
                "read",
                "write",
                "close",
                "arch_prctl",
                "sched_getaffinity",
                "munmap",
                "brk",
                "rt_sigaction",
                "rt_sigprocmask",
                "sigaltstack",
                "gettid",
                "clone",
                "bind",
                "socket",
                "openat",
                "readlinkat",
                "exit_group",
                "epoll_create1",
                "listen",
                "rt_sigreturn",
                "sched_yield",
                "clock_gettime",
                "connect",
                "dup2",
                "epoll_pwait",
                "execve",
                "exit",
                "fcntl",
                "getpid",
                "getuid",
                "ioctl",
                "mprotect",
                "nanosleep",
                "open",
                "poll",
                "recvfrom",
                "sendto",
                "set_tid_address",
                "setitimer",
                "writev",
                "fstatfs",
                "getdents64",
                "pipe2",
                "getrlimit"
            ],
            "action": "SCMP_ACT_ALLOW"
        }
    ]
}

Run these commands:

mkdir ./profiles
curl -L -o profiles/audit.json https://k8s.io/examples/pods/security/seccomp/profiles/audit.json
curl -L -o profiles/violation.json https://k8s.io/examples/pods/security/seccomp/profiles/violation.json
curl -L -o profiles/fine-grained.json https://k8s.io/examples/pods/security/seccomp/profiles/fine-grained.json
ls profiles

You should see three profiles listed at the end of the final step:

audit.json  fine-grained.json  violation.json

Create a local Kubernetes cluster with kind

For simplicity, kind can be used to create a single node cluster with the seccomp profiles loaded. Kind runs Kubernetes in Docker, so each node of the cluster is a container. This allows for files to be mounted in the filesystem of each container similar to loading files onto a node.

apiVersion: kind.x-k8s.io/v1alpha4
kind: Cluster
nodes:
- role: control-plane
  extraMounts:
  - hostPath: "./profiles"
    containerPath: "/var/lib/kubelet/seccomp/profiles"

Download that example kind configuration, and save it to a file named kind.yaml:

curl -L -O https://k8s.io/examples/pods/security/seccomp/kind.yaml

You can set a specific Kubernetes version by setting the node's container image. See Nodes within the kind documentation about configuration for more details on this. This tutorial assumes you are using Kubernetes v1.30.

As a beta feature, you can configure Kubernetes to use the profile that the container runtime prefers by default, rather than falling back to Unconfined. If you want to try that, see enable the use of RuntimeDefault as the default seccomp profile for all workloads before you continue.

Once you have a kind configuration in place, create the kind cluster with that configuration:

kind create cluster --config=kind.yaml

After the new Kubernetes cluster is ready, identify the Docker container running as the single node cluster:

docker ps

You should see output indicating that a container is running with name kind-control-plane. The output is similar to:

CONTAINER ID        IMAGE                  COMMAND                  CREATED             STATUS              PORTS                       NAMES
6a96207fed4b        kindest/node:v1.18.2   "/usr/local/bin/entr…"   27 seconds ago      Up 24 seconds       127.0.0.1:42223->6443/tcp   kind-control-plane

If observing the filesystem of that container, you should see that the profiles/ directory has been successfully loaded into the default seccomp path of the kubelet. Use docker exec to run a command in the Pod:

# Change 6a96207fed4b to the container ID you saw from "docker ps"
docker exec -it 6a96207fed4b ls /var/lib/kubelet/seccomp/profiles
audit.json  fine-grained.json  violation.json

You have verified that these seccomp profiles are available to the kubelet running within kind.

Create a Pod that uses the container runtime default seccomp profile

Most container runtimes provide a sane set of default syscalls that are allowed or not. You can adopt these defaults for your workload by setting the seccomp type in the security context of a pod or container to RuntimeDefault.

Here's a manifest for a Pod that requests the RuntimeDefault seccomp profile for all its containers:

apiVersion: v1
kind: Pod
metadata:
  name: default-pod
  labels:
    app: default-pod
spec:
  securityContext:
    seccompProfile:
      type: RuntimeDefault
  containers:
  - name: test-container
    image: hashicorp/http-echo:1.0
    args:
    - "-text=just made some more syscalls!"
    securityContext:
      allowPrivilegeEscalation: false

Create that Pod:

kubectl apply -f https://k8s.io/examples/pods/security/seccomp/ga/default-pod.yaml
kubectl get pod default-pod

The Pod should be showing as having started successfully:

NAME        READY   STATUS    RESTARTS   AGE
default-pod 1/1     Running   0          20s

Delete the Pod before moving to the next section:

kubectl delete pod default-pod --wait --now

Create a Pod with a seccomp profile for syscall auditing

To start off, apply the audit.json profile, which will log all syscalls of the process, to a new Pod.

Here's a manifest for that Pod:

apiVersion: v1
kind: Pod
metadata:
  name: audit-pod
  labels:
    app: audit-pod
spec:
  securityContext:
    seccompProfile:
      type: Localhost
      localhostProfile: profiles/audit.json
  containers:
  - name: test-container
    image: hashicorp/http-echo:1.0
    args:
    - "-text=just made some syscalls!"
    securityContext:
      allowPrivilegeEscalation: false

Create the Pod in the cluster:

kubectl apply -f https://k8s.io/examples/pods/security/seccomp/ga/audit-pod.yaml

This profile does not restrict any syscalls, so the Pod should start successfully.

kubectl get pod audit-pod
NAME        READY   STATUS    RESTARTS   AGE
audit-pod   1/1     Running   0          30s

In order to be able to interact with this endpoint exposed by this container, create a NodePort Service that allows access to the endpoint from inside the kind control plane container.

kubectl expose pod audit-pod --type NodePort --port 5678

Check what port the Service has been assigned on the node.

kubectl get service audit-pod

The output is similar to:

NAME        TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
audit-pod   NodePort   10.111.36.142   <none>        5678:32373/TCP   72s

Now you can use curl to access that endpoint from inside the kind control plane container, at the port exposed by this Service. Use docker exec to run the curl command within the container belonging to that control plane container:

# Change 6a96207fed4b to the control plane container ID and 32373 to the port number you saw from "docker ps"
docker exec -it 6a96207fed4b curl localhost:32373
just made some syscalls!

You can see that the process is running, but what syscalls did it actually make? Because this Pod is running in a local cluster, you should be able to see those in /var/log/syslog on your local system. Open up a new terminal window and tail the output for calls from http-echo:

# The log path on your computer might be different from "/var/log/syslog"
tail -f /var/log/syslog | grep 'http-echo'

You should already see some logs of syscalls made by http-echo, and if you run curl again inside the control plane container you will see more output written to the log.

For example:

Jul  6 15:37:40 my-machine kernel: [369128.669452] audit: type=1326 audit(1594067860.484:14536): auid=4294967295 uid=0 gid=0 ses=4294967295 pid=29064 comm="http-echo" exe="/http-echo" sig=0 arch=c000003e syscall=51 compat=0 ip=0x46fe1f code=0x7ffc0000
Jul  6 15:37:40 my-machine kernel: [369128.669453] audit: type=1326 audit(1594067860.484:14537): auid=4294967295 uid=0 gid=0 ses=4294967295 pid=29064 comm="http-echo" exe="/http-echo" sig=0 arch=c000003e syscall=54 compat=0 ip=0x46fdba code=0x7ffc0000
Jul  6 15:37:40 my-machine kernel: [369128.669455] audit: type=1326 audit(1594067860.484:14538): auid=4294967295 uid=0 gid=0 ses=4294967295 pid=29064 comm="http-echo" exe="/http-echo" sig=0 arch=c000003e syscall=202 compat=0 ip=0x455e53 code=0x7ffc0000
Jul  6 15:37:40 my-machine kernel: [369128.669456] audit: type=1326 audit(1594067860.484:14539): auid=4294967295 uid=0 gid=0 ses=4294967295 pid=29064 comm="http-echo" exe="/http-echo" sig=0 arch=c000003e syscall=288 compat=0 ip=0x46fdba code=0x7ffc0000
Jul  6 15:37:40 my-machine kernel: [369128.669517] audit: type=1326 audit(1594067860.484:14540): auid=4294967295 uid=0 gid=0 ses=4294967295 pid=29064 comm="http-echo" exe="/http-echo" sig=0 arch=c000003e syscall=0 compat=0 ip=0x46fd44 code=0x7ffc0000
Jul  6 15:37:40 my-machine kernel: [369128.669519] audit: type=1326 audit(1594067860.484:14541): auid=4294967295 uid=0 gid=0 ses=4294967295 pid=29064 comm="http-echo" exe="/http-echo" sig=0 arch=c000003e syscall=270 compat=0 ip=0x4559b1 code=0x7ffc0000
Jul  6 15:38:40 my-machine kernel: [369188.671648] audit: type=1326 audit(1594067920.488:14559): auid=4294967295 uid=0 gid=0 ses=4294967295 pid=29064 comm="http-echo" exe="/http-echo" sig=0 arch=c000003e syscall=270 compat=0 ip=0x4559b1 code=0x7ffc0000
Jul  6 15:38:40 my-machine kernel: [369188.671726] audit: type=1326 audit(1594067920.488:14560): auid=4294967295 uid=0 gid=0 ses=4294967295 pid=29064 comm="http-echo" exe="/http-echo" sig=0 arch=c000003e syscall=202 compat=0 ip=0x455e53 code=0x7ffc0000

You can begin to understand the syscalls required by the http-echo process by looking at the syscall= entry on each line. While these are unlikely to encompass all syscalls it uses, it can serve as a basis for a seccomp profile for this container.

Delete the Service and the Pod before moving to the next section:

kubectl delete service audit-pod --wait
kubectl delete pod audit-pod --wait --now

Create a Pod with a seccomp profile that causes violation

For demonstration, apply a profile to the Pod that does not allow for any syscalls.

The manifest for this demonstration is:

apiVersion: v1
kind: Pod
metadata:
  name: violation-pod
  labels:
    app: violation-pod
spec:
  securityContext:
    seccompProfile:
      type: Localhost
      localhostProfile: profiles/violation.json
  containers:
  - name: test-container
    image: hashicorp/http-echo:1.0
    args:
    - "-text=just made some syscalls!"
    securityContext:
      allowPrivilegeEscalation: false

Attempt to create the Pod in the cluster:

kubectl apply -f https://k8s.io/examples/pods/security/seccomp/ga/violation-pod.yaml

The Pod creates, but there is an issue. If you check the status of the Pod, you should see that it failed to start.

kubectl get pod violation-pod
NAME            READY   STATUS             RESTARTS   AGE
violation-pod   0/1     CrashLoopBackOff   1          6s

As seen in the previous example, the http-echo process requires quite a few syscalls. Here seccomp has been instructed to error on any syscall by setting "defaultAction": "SCMP_ACT_ERRNO". This is extremely secure, but removes the ability to do anything meaningful. What you really want is to give workloads only the privileges they need.

Delete the Pod before moving to the next section:

kubectl delete pod violation-pod --wait --now

Create a Pod with a seccomp profile that only allows necessary syscalls

If you take a look at the fine-grained.json profile, you will notice some of the syscalls seen in syslog of the first example where the profile set "defaultAction": "SCMP_ACT_LOG". Now the profile is setting "defaultAction": "SCMP_ACT_ERRNO", but explicitly allowing a set of syscalls in the "action": "SCMP_ACT_ALLOW" block. Ideally, the container will run successfully and you will see no messages sent to syslog.

The manifest for this example is:

apiVersion: v1
kind: Pod
metadata:
  name: fine-pod
  labels:
    app: fine-pod
spec:
  securityContext:
    seccompProfile:
      type: Localhost
      localhostProfile: profiles/fine-grained.json
  containers:
  - name: test-container
    image: hashicorp/http-echo:1.0
    args:
    - "-text=just made some syscalls!"
    securityContext:
      allowPrivilegeEscalation: false

Create the Pod in your cluster:

kubectl apply -f https://k8s.io/examples/pods/security/seccomp/ga/fine-pod.yaml
kubectl get pod fine-pod

The Pod should be showing as having started successfully:

NAME        READY   STATUS    RESTARTS   AGE
fine-pod   1/1     Running   0          30s

Open up a new terminal window and use tail to monitor for log entries that mention calls from http-echo:

# The log path on your computer might be different from "/var/log/syslog"
tail -f /var/log/syslog | grep 'http-echo'

Next, expose the Pod with a NodePort Service:

kubectl expose pod fine-pod --type NodePort --port 5678

Check what port the Service has been assigned on the node:

kubectl get service fine-pod

The output is similar to:

NAME        TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
fine-pod    NodePort   10.111.36.142   <none>        5678:32373/TCP   72s

Use curl to access that endpoint from inside the kind control plane container:

# Change 6a96207fed4b to the control plane container ID and 32373 to the port number you saw from "docker ps"
docker exec -it 6a96207fed4b curl localhost:32373
just made some syscalls!

You should see no output in the syslog. This is because the profile allowed all necessary syscalls and specified that an error should occur if one outside of the list is invoked. This is an ideal situation from a security perspective, but required some effort in analyzing the program. It would be nice if there was a simple way to get closer to this security without requiring as much effort.

Delete the Service and the Pod before moving to the next section:

kubectl delete service fine-pod --wait
kubectl delete pod fine-pod --wait --now

Enable the use of RuntimeDefault as the default seccomp profile for all workloads

FEATURE STATE: Kubernetes v1.27 [stable]

To use seccomp profile defaulting, you must run the kubelet with the --seccomp-default command line flag enabled for each node where you want to use it.

If enabled, the kubelet will use the RuntimeDefault seccomp profile by default, which is defined by the container runtime, instead of using the Unconfined (seccomp disabled) mode. The default profiles aim to provide a strong set of security defaults while preserving the functionality of the workload. It is possible that the default profiles differ between container runtimes and their release versions, for example when comparing those from CRI-O and containerd.

Some workloads may require a lower amount of syscall restrictions than others. This means that they can fail during runtime even with the RuntimeDefault profile. To mitigate such a failure, you can:

  • Run the workload explicitly as Unconfined.
  • Disable the SeccompDefault feature for the nodes. Also making sure that workloads get scheduled on nodes where the feature is disabled.
  • Create a custom seccomp profile for the workload.

If you were introducing this feature into production-like cluster, the Kubernetes project recommends that you enable this feature gate on a subset of your nodes and then test workload execution before rolling the change out cluster-wide.

You can find more detailed information about a possible upgrade and downgrade strategy in the related Kubernetes Enhancement Proposal (KEP): Enable seccomp by default.

Kubernetes 1.30 lets you configure the seccomp profile that applies when the spec for a Pod doesn't define a specific seccomp profile. However, you still need to enable this defaulting for each node where you would like to use it.

If you are running a Kubernetes 1.30 cluster and want to enable the feature, either run the kubelet with the --seccomp-default command line flag, or enable it through the kubelet configuration file. To enable the feature gate in kind, ensure that kind provides the minimum required Kubernetes version and enables the SeccompDefault feature in the kind configuration:

kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
  - role: control-plane
    image: kindest/node:v1.28.0@sha256:9f3ff58f19dcf1a0611d11e8ac989fdb30a28f40f236f59f0bea31fb956ccf5c
    kubeadmConfigPatches:
      - |
        kind: JoinConfiguration
        nodeRegistration:
          kubeletExtraArgs:
            seccomp-default: "true"        
  - role: worker
    image: kindest/node:v1.28.0@sha256:9f3ff58f19dcf1a0611d11e8ac989fdb30a28f40f236f59f0bea31fb956ccf5c
    kubeadmConfigPatches:
      - |
        kind: JoinConfiguration
        nodeRegistration:
          kubeletExtraArgs:
            seccomp-default: "true"        

If the cluster is ready, then running a pod:

kubectl run --rm -it --restart=Never --image=alpine alpine -- sh

Should now have the default seccomp profile attached. This can be verified by using docker exec to run crictl inspect for the container on the kind worker:

docker exec -it kind-worker bash -c \
    'crictl inspect $(crictl ps --name=alpine -q) | jq .info.runtimeSpec.linux.seccomp'
{
  "defaultAction": "SCMP_ACT_ERRNO",
  "architectures": ["SCMP_ARCH_X86_64", "SCMP_ARCH_X86", "SCMP_ARCH_X32"],
  "syscalls": [
    {
      "names": ["..."]
    }
  ]
}

What's next

You can learn more about Linux seccomp:

5 - Stateless Applications

5.1 - Exposing an External IP Address to Access an Application in a Cluster

This page shows how to create a Kubernetes Service object that exposes an external IP address.

Before you begin

  • Install kubectl.
  • Use a cloud provider like Google Kubernetes Engine or Amazon Web Services to create a Kubernetes cluster. This tutorial creates an external load balancer, which requires a cloud provider.
  • Configure kubectl to communicate with your Kubernetes API server. For instructions, see the documentation for your cloud provider.

Objectives

  • Run five instances of a Hello World application.
  • Create a Service object that exposes an external IP address.
  • Use the Service object to access the running application.

Creating a service for an application running in five pods

  1. Run a Hello World application in your cluster:

    apiVersion: apps/v1
    kind: Deployment
    metadata:
      labels:
        app.kubernetes.io/name: load-balancer-example
      name: hello-world
    spec:
      replicas: 5
      selector:
        matchLabels:
          app.kubernetes.io/name: load-balancer-example
      template:
        metadata:
          labels:
            app.kubernetes.io/name: load-balancer-example
        spec:
          containers:
          - image: gcr.io/google-samples/node-hello:1.0
            name: hello-world
            ports:
            - containerPort: 8080
    
    kubectl apply -f https://k8s.io/examples/service/load-balancer-example.yaml
    

    The preceding command creates a Deployment and an associated ReplicaSet. The ReplicaSet has five Pods each of which runs the Hello World application.

  2. Display information about the Deployment:

    kubectl get deployments hello-world
    kubectl describe deployments hello-world
    
  3. Display information about your ReplicaSet objects:

    kubectl get replicasets
    kubectl describe replicasets
    
  4. Create a Service object that exposes the deployment:

    kubectl expose deployment hello-world --type=LoadBalancer --name=my-service
    
  5. Display information about the Service:

    kubectl get services my-service
    

    The output is similar to:

    NAME         TYPE           CLUSTER-IP     EXTERNAL-IP      PORT(S)    AGE
    my-service   LoadBalancer   10.3.245.137   104.198.205.71   8080/TCP   54s
    
  6. Display detailed information about the Service:

    kubectl describe services my-service
    

    The output is similar to:

    Name:           my-service
    Namespace:      default
    Labels:         app.kubernetes.io/name=load-balancer-example
    Annotations:    <none>
    Selector:       app.kubernetes.io/name=load-balancer-example
    Type:           LoadBalancer
    IP:             10.3.245.137
    LoadBalancer Ingress:   104.198.205.71
    Port:           <unset> 8080/TCP
    NodePort:       <unset> 32377/TCP
    Endpoints:      10.0.0.6:8080,10.0.1.6:8080,10.0.1.7:8080 + 2 more...
    Session Affinity:   None
    Events:         <none>
    

    Make a note of the external IP address (LoadBalancer Ingress) exposed by your service. In this example, the external IP address is 104.198.205.71. Also note the value of Port and NodePort. In this example, the Port is 8080 and the NodePort is 32377.

  7. In the preceding output, you can see that the service has several endpoints: 10.0.0.6:8080,10.0.1.6:8080,10.0.1.7:8080 + 2 more. These are internal addresses of the pods that are running the Hello World application. To verify these are pod addresses, enter this command:

    kubectl get pods --output=wide
    

    The output is similar to:

    NAME                         ...  IP         NODE
    hello-world-2895499144-1jaz9 ...  10.0.1.6   gke-cluster-1-default-pool-e0b8d269-1afc
    hello-world-2895499144-2e5uh ...  10.0.1.8   gke-cluster-1-default-pool-e0b8d269-1afc
    hello-world-2895499144-9m4h1 ...  10.0.0.6   gke-cluster-1-default-pool-e0b8d269-5v7a
    hello-world-2895499144-o4z13 ...  10.0.1.7   gke-cluster-1-default-pool-e0b8d269-1afc
    hello-world-2895499144-segjf ...  10.0.2.5   gke-cluster-1-default-pool-e0b8d269-cpuc
    
  8. Use the external IP address (LoadBalancer Ingress) to access the Hello World application:

    curl http://<external-ip>:<port>
    

    where <external-ip> is the external IP address (LoadBalancer Ingress) of your Service, and <port> is the value of Port in your Service description. If you are using minikube, typing minikube service my-service will automatically open the Hello World application in a browser.

    The response to a successful request is a hello message:

    Hello Kubernetes!
    

Cleaning up

To delete the Service, enter this command:

kubectl delete services my-service

To delete the Deployment, the ReplicaSet, and the Pods that are running the Hello World application, enter this command:

kubectl delete deployment hello-world

What's next

Learn more about connecting applications with services.

5.2 - Example: Deploying PHP Guestbook application with Redis

This tutorial shows you how to build and deploy a simple (not production ready), multi-tier web application using Kubernetes and Docker. This example consists of the following components:

  • A single-instance Redis to store guestbook entries
  • Multiple web frontend instances

Objectives

  • Start up a Redis leader.
  • Start up two Redis followers.
  • Start up the guestbook frontend.
  • Expose and view the Frontend Service.
  • Clean up.

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:

Your Kubernetes server must be at or later than version v1.14. To check the version, enter kubectl version.

Start up the Redis Database

The guestbook application uses Redis to store its data.

Creating the Redis Deployment

The manifest file, included below, specifies a Deployment controller that runs a single replica Redis Pod.

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis-leader
  labels:
    app: redis
    role: leader
    tier: backend
spec:
  replicas: 1
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
        role: leader
        tier: backend
    spec:
      containers:
      - name: leader
        image: "docker.io/redis:6.0.5"
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
        ports:
        - containerPort: 6379
  1. Launch a terminal window in the directory you downloaded the manifest files.

  2. Apply the Redis Deployment from the redis-leader-deployment.yaml file:

    kubectl apply -f https://k8s.io/examples/application/guestbook/redis-leader-deployment.yaml
    
  3. Query the list of Pods to verify that the Redis Pod is running:

    kubectl get pods
    

    The response should be similar to this:

    NAME                           READY   STATUS    RESTARTS   AGE
    redis-leader-fb76b4755-xjr2n   1/1     Running   0          13s
    
  4. Run the following command to view the logs from the Redis leader Pod:

    kubectl logs -f deployment/redis-leader
    

Creating the Redis leader Service

The guestbook application needs to communicate to the Redis to write its data. You need to apply a Service to proxy the traffic to the Redis Pod. A Service defines a policy to access the Pods.

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: v1
kind: Service
metadata:
  name: redis-leader
  labels:
    app: redis
    role: leader
    tier: backend
spec:
  ports:
  - port: 6379
    targetPort: 6379
  selector:
    app: redis
    role: leader
    tier: backend
  1. Apply the Redis Service from the following redis-leader-service.yaml file:

    kubectl apply -f https://k8s.io/examples/application/guestbook/redis-leader-service.yaml
    
  2. Query the list of Services to verify that the Redis Service is running:

    kubectl get service
    

    The response should be similar to this:

    NAME           TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)    AGE
    kubernetes     ClusterIP   10.0.0.1     <none>        443/TCP    1m
    redis-leader   ClusterIP   10.103.78.24 <none>        6379/TCP   16s
    

Set up Redis followers

Although the Redis leader is a single Pod, you can make it highly available and meet traffic demands by adding a few Redis followers, or replicas.

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis-follower
  labels:
    app: redis
    role: follower
    tier: backend
spec:
  replicas: 2
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
        role: follower
        tier: backend
    spec:
      containers:
      - name: follower
        image: us-docker.pkg.dev/google-samples/containers/gke/gb-redis-follower:v2
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
        ports:
        - containerPort: 6379
  1. Apply the Redis Deployment from the following redis-follower-deployment.yaml file:

    kubectl apply -f https://k8s.io/examples/application/guestbook/redis-follower-deployment.yaml
    
  2. Verify that the two Redis follower replicas are running by querying the list of Pods:

    kubectl get pods
    

    The response should be similar to this:

    NAME                             READY   STATUS    RESTARTS   AGE
    redis-follower-dddfbdcc9-82sfr   1/1     Running   0          37s
    redis-follower-dddfbdcc9-qrt5k   1/1     Running   0          38s
    redis-leader-fb76b4755-xjr2n     1/1     Running   0          11m
    

Creating the Redis follower service

The guestbook application needs to communicate with the Redis followers to read data. To make the Redis followers discoverable, you must set up another Service.

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: v1
kind: Service
metadata:
  name: redis-follower
  labels:
    app: redis
    role: follower
    tier: backend
spec:
  ports:
    # the port that this service should serve on
  - port: 6379
  selector:
    app: redis
    role: follower
    tier: backend
  1. Apply the Redis Service from the following redis-follower-service.yaml file:

    kubectl apply -f https://k8s.io/examples/application/guestbook/redis-follower-service.yaml
    
  2. Query the list of Services to verify that the Redis Service is running:

    kubectl get service
    

    The response should be similar to this:

    NAME             TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
    kubernetes       ClusterIP   10.96.0.1       <none>        443/TCP    3d19h
    redis-follower   ClusterIP   10.110.162.42   <none>        6379/TCP   9s
    redis-leader     ClusterIP   10.103.78.24    <none>        6379/TCP   6m10s
    

Set up and Expose the Guestbook Frontend

Now that you have the Redis storage of your guestbook up and running, start the guestbook web servers. Like the Redis followers, the frontend is deployed using a Kubernetes Deployment.

The guestbook app uses a PHP frontend. It is configured to communicate with either the Redis follower or leader Services, depending on whether the request is a read or a write. The frontend exposes a JSON interface, and serves a jQuery-Ajax-based UX.

Creating the Guestbook Frontend Deployment

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: apps/v1
kind: Deployment
metadata:
  name: frontend
spec:
  replicas: 3
  selector:
    matchLabels:
        app: guestbook
        tier: frontend
  template:
    metadata:
      labels:
        app: guestbook
        tier: frontend
    spec:
      containers:
      - name: php-redis
        image: us-docker.pkg.dev/google-samples/containers/gke/gb-frontend:v5
        env:
        - name: GET_HOSTS_FROM
          value: "dns"
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
        ports:
        - containerPort: 80
  1. Apply the frontend Deployment from the frontend-deployment.yaml file:

    kubectl apply -f https://k8s.io/examples/application/guestbook/frontend-deployment.yaml
    
  2. Query the list of Pods to verify that the three frontend replicas are running:

    kubectl get pods -l app=guestbook -l tier=frontend
    

    The response should be similar to this:

    NAME                        READY   STATUS    RESTARTS   AGE
    frontend-85595f5bf9-5tqhb   1/1     Running   0          47s
    frontend-85595f5bf9-qbzwm   1/1     Running   0          47s
    frontend-85595f5bf9-zchwc   1/1     Running   0          47s
    

Creating the Frontend Service

The Redis Services you applied is only accessible within the Kubernetes cluster because the default type for a Service is ClusterIP. ClusterIP provides a single IP address for the set of Pods the Service is pointing to. This IP address is accessible only within the cluster.

If you want guests to be able to access your guestbook, you must configure the frontend Service to be externally visible, so a client can request the Service from outside the Kubernetes cluster. However a Kubernetes user can use kubectl port-forward to access the service even though it uses a ClusterIP.

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: v1
kind: Service
metadata:
  name: frontend
  labels:
    app: guestbook
    tier: frontend
spec:
  # if your cluster supports it, uncomment the following to automatically create
  # an external load-balanced IP for the frontend service.
  # type: LoadBalancer
  #type: LoadBalancer
  ports:
    # the port that this service should serve on
  - port: 80
  selector:
    app: guestbook
    tier: frontend
  1. Apply the frontend Service from the frontend-service.yaml file:

    kubectl apply -f https://k8s.io/examples/application/guestbook/frontend-service.yaml
    
  2. Query the list of Services to verify that the frontend Service is running:

    kubectl get services
    

    The response should be similar to this:

    NAME             TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
    frontend         ClusterIP   10.97.28.230    <none>        80/TCP     19s
    kubernetes       ClusterIP   10.96.0.1       <none>        443/TCP    3d19h
    redis-follower   ClusterIP   10.110.162.42   <none>        6379/TCP   5m48s
    redis-leader     ClusterIP   10.103.78.24    <none>        6379/TCP   11m
    

Viewing the Frontend Service via kubectl port-forward

  1. Run the following command to forward port 8080 on your local machine to port 80 on the service.

    kubectl port-forward svc/frontend 8080:80
    

    The response should be similar to this:

    Forwarding from 127.0.0.1:8080 -> 80
    Forwarding from [::1]:8080 -> 80
    
  2. load the page http://localhost:8080 in your browser to view your guestbook.

Viewing the Frontend Service via LoadBalancer

If you deployed the frontend-service.yaml manifest with type: LoadBalancer you need to find the IP address to view your Guestbook.

  1. Run the following command to get the IP address for the frontend Service.

    kubectl get service frontend
    

    The response should be similar to this:

    NAME       TYPE           CLUSTER-IP      EXTERNAL-IP        PORT(S)        AGE
    frontend   LoadBalancer   10.51.242.136   109.197.92.229     80:32372/TCP   1m
    
  2. Copy the external IP address, and load the page in your browser to view your guestbook.

Scale the Web Frontend

You can scale up or down as needed because your servers are defined as a Service that uses a Deployment controller.

  1. Run the following command to scale up the number of frontend Pods:

    kubectl scale deployment frontend --replicas=5
    
  2. Query the list of Pods to verify the number of frontend Pods running:

    kubectl get pods
    

    The response should look similar to this:

    NAME                             READY   STATUS    RESTARTS   AGE
    frontend-85595f5bf9-5df5m        1/1     Running   0          83s
    frontend-85595f5bf9-7zmg5        1/1     Running   0          83s
    frontend-85595f5bf9-cpskg        1/1     Running   0          15m
    frontend-85595f5bf9-l2l54        1/1     Running   0          14m
    frontend-85595f5bf9-l9c8z        1/1     Running   0          14m
    redis-follower-dddfbdcc9-82sfr   1/1     Running   0          97m
    redis-follower-dddfbdcc9-qrt5k   1/1     Running   0          97m
    redis-leader-fb76b4755-xjr2n     1/1     Running   0          108m
    
  3. Run the following command to scale down the number of frontend Pods:

    kubectl scale deployment frontend --replicas=2
    
  4. Query the list of Pods to verify the number of frontend Pods running:

    kubectl get pods
    

    The response should look similar to this:

    NAME                             READY   STATUS    RESTARTS   AGE
    frontend-85595f5bf9-cpskg        1/1     Running   0          16m
    frontend-85595f5bf9-l9c8z        1/1     Running   0          15m
    redis-follower-dddfbdcc9-82sfr   1/1     Running   0          98m
    redis-follower-dddfbdcc9-qrt5k   1/1     Running   0          98m
    redis-leader-fb76b4755-xjr2n     1/1     Running   0          109m
    

Cleaning up

Deleting the Deployments and Services also deletes any running Pods. Use labels to delete multiple resources with one command.

  1. Run the following commands to delete all Pods, Deployments, and Services.

    kubectl delete deployment -l app=redis
    kubectl delete service -l app=redis
    kubectl delete deployment frontend
    kubectl delete service frontend
    

    The response should look similar to this:

    deployment.apps "redis-follower" deleted
    deployment.apps "redis-leader" deleted
    deployment.apps "frontend" deleted
    service "frontend" deleted
    
  2. Query the list of Pods to verify that no Pods are running:

    kubectl get pods
    

    The response should look similar to this:

    No resources found in default namespace.
    

What's next

6 - Stateful Applications

6.1 - StatefulSet Basics

This tutorial provides an introduction to managing applications with StatefulSets. It demonstrates how to create, delete, scale, and update the Pods of StatefulSets.

Before you begin

Before you begin this tutorial, you should familiarize yourself with the following Kubernetes concepts:

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:

You should configure kubectl to use a context that uses the default namespace. If you are using an existing cluster, make sure that it's OK to use that cluster's default namespace to practice. Ideally, practice in a cluster that doesn't run any real workloads.

It's also useful to read the concept page about StatefulSets.

Objectives

StatefulSets are intended to be used with stateful applications and distributed systems. However, the administration of stateful applications and distributed systems on Kubernetes is a broad, complex topic. In order to demonstrate the basic features of a StatefulSet, and not to conflate the former topic with the latter, you will deploy a simple web application using a StatefulSet.

After this tutorial, you will be familiar with the following.

  • How to create a StatefulSet
  • How a StatefulSet manages its Pods
  • How to delete a StatefulSet
  • How to scale a StatefulSet
  • How to update a StatefulSet's Pods

Creating a StatefulSet

Begin by creating a StatefulSet (and the Service that it relies upon) using the example below. It is similar to the example presented in the StatefulSets concept. It creates a headless Service, nginx, to publish the IP addresses of Pods in the StatefulSet, web.

apiVersion: v1
kind: Service
metadata:
  name: nginx
  labels:
    app: nginx
spec:
  ports:
  - port: 80
    name: web
  clusterIP: None
  selector:
    app: nginx
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: web
spec:
  serviceName: "nginx"
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: registry.k8s.io/nginx-slim:0.8
        ports:
        - containerPort: 80
          name: web
        volumeMounts:
        - name: www
          mountPath: /usr/share/nginx/html
  volumeClaimTemplates:
  - metadata:
      name: www
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 1Gi

You will need to use at least two terminal windows. In the first terminal, use kubectl get to watch the creation of the StatefulSet's Pods.

# use this terminal to run commands that specify --watch
# end this watch when you are asked to start a new watch
kubectl get pods --watch -l app=nginx

In the second terminal, use kubectl apply to create the headless Service and StatefulSet:

kubectl apply -f https://k8s.io/examples/application/web/web.yaml
service/nginx created
statefulset.apps/web created

The command above creates two Pods, each running an NGINX webserver. Get the nginx Service...

kubectl get service nginx
NAME      TYPE         CLUSTER-IP   EXTERNAL-IP   PORT(S)   AGE
nginx     ClusterIP    None         <none>        80/TCP    12s

...then get the web StatefulSet, to verify that both were created successfully:

kubectl get statefulset web
NAME      DESIRED   CURRENT   AGE
web       2         1         20s

Ordered Pod creation

A StatefulSet defaults to creating its Pods in a strict order.

For a StatefulSet with n replicas, when Pods are being deployed, they are created sequentially, ordered from {0..n-1}. Examine the output of the kubectl get command in the first terminal. Eventually, the output will look like the example below.

# Do not start a new watch;
# this should already be running
kubectl get pods --watch -l app=nginx
NAME      READY     STATUS    RESTARTS   AGE
web-0     0/1       Pending   0          0s
web-0     0/1       Pending   0         0s
web-0     0/1       ContainerCreating   0         0s
web-0     1/1       Running   0         19s
web-1     0/1       Pending   0         0s
web-1     0/1       Pending   0         0s
web-1     0/1       ContainerCreating   0         0s
web-1     1/1       Running   0         18s

Notice that the web-1 Pod is not launched until the web-0 Pod is Running (see Pod Phase) and Ready (see type in Pod Conditions).

Later in this tutorial you will practice parallel startup.

Pods in a StatefulSet

Pods in a StatefulSet have a unique ordinal index and a stable network identity.

Examining the Pod's ordinal index

Get the StatefulSet's Pods:

kubectl get pods -l app=nginx
NAME      READY     STATUS    RESTARTS   AGE
web-0     1/1       Running   0          1m
web-1     1/1       Running   0          1m

As mentioned in the StatefulSets concept, the Pods in a StatefulSet have a sticky, unique identity. This identity is based on a unique ordinal index that is assigned to each Pod by the StatefulSet controller.
The Pods' names take the form <statefulset name>-<ordinal index>. Since the web StatefulSet has two replicas, it creates two Pods, web-0 and web-1.

Using stable network identities

Each Pod has a stable hostname based on its ordinal index. Use kubectl exec to execute the hostname command in each Pod:

for i in 0 1; do kubectl exec "web-$i" -- sh -c 'hostname'; done
web-0
web-1

Use kubectl run to execute a container that provides the nslookup command from the dnsutils package. Using nslookup on the Pods' hostnames, you can examine their in-cluster DNS addresses:

kubectl run -i --tty --image busybox:1.28 dns-test --restart=Never --rm

which starts a new shell. In that new shell, run:

# Run this in the dns-test container shell
nslookup web-0.nginx

The output is similar to:

Server:    10.0.0.10
Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

Name:      web-0.nginx
Address 1: 10.244.1.6

nslookup web-1.nginx
Server:    10.0.0.10
Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

Name:      web-1.nginx
Address 1: 10.244.2.6

(and now exit the container shell: exit)

The CNAME of the headless service points to SRV records (one for each Pod that is Running and Ready). The SRV records point to A record entries that contain the Pods' IP addresses.

In one terminal, watch the StatefulSet's Pods:

# Start a new watch
# End this watch when you've seen that the delete is finished
kubectl get pod --watch -l app=nginx

In a second terminal, use kubectl delete to delete all the Pods in the StatefulSet:

kubectl delete pod -l app=nginx
pod "web-0" deleted
pod "web-1" deleted

Wait for the StatefulSet to restart them, and for both Pods to transition to Running and Ready:

# This should already be running
kubectl get pod --watch -l app=nginx
NAME      READY     STATUS              RESTARTS   AGE
web-0     0/1       ContainerCreating   0          0s
NAME      READY     STATUS    RESTARTS   AGE
web-0     1/1       Running   0          2s
web-1     0/1       Pending   0         0s
web-1     0/1       Pending   0         0s
web-1     0/1       ContainerCreating   0         0s
web-1     1/1       Running   0         34s

Use kubectl exec and kubectl run to view the Pods' hostnames and in-cluster DNS entries. First, view the Pods' hostnames:

for i in 0 1; do kubectl exec web-$i -- sh -c 'hostname'; done
web-0
web-1

then, run:

kubectl run -i --tty --image busybox:1.28 dns-test --restart=Never --rm

which starts a new shell.
In that new shell, run:

# Run this in the dns-test container shell
nslookup web-0.nginx

The output is similar to:

Server:    10.0.0.10
Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

Name:      web-0.nginx
Address 1: 10.244.1.7

nslookup web-1.nginx
Server:    10.0.0.10
Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local

Name:      web-1.nginx
Address 1: 10.244.2.8

(and now exit the container shell: exit)

The Pods' ordinals, hostnames, SRV records, and A record names have not changed, but the IP addresses associated with the Pods may have changed. In the cluster used for this tutorial, they have. This is why it is important not to configure other applications to connect to Pods in a StatefulSet by the IP address of a particular Pod (it is OK to connect to Pods by resolving their hostname).

Discovery for specific Pods in a StatefulSet

If you need to find and connect to the active members of a StatefulSet, you should query the CNAME of the headless Service (nginx.default.svc.cluster.local). The SRV records associated with the CNAME will contain only the Pods in the StatefulSet that are Running and Ready.

If your application already implements connection logic that tests for liveness and readiness, you can use the SRV records of the Pods ( web-0.nginx.default.svc.cluster.local, web-1.nginx.default.svc.cluster.local), as they are stable, and your application will be able to discover the Pods' addresses when they transition to Running and Ready.

If your application wants to find any healthy Pod in a StatefulSet, and therefore does not need to track each specific Pod, you could also connect to the IP address of a type: ClusterIP Service, backed by the Pods in that StatefulSet. You can use the same Service that tracks the StatefulSet (specified in the serviceName of the StatefulSet) or a separate Service that selects the right set of Pods.

Writing to stable storage

Get the PersistentVolumeClaims for web-0 and web-1:

kubectl get pvc -l app=nginx

The output is similar to:

NAME        STATUS    VOLUME                                     CAPACITY   ACCESSMODES   AGE
www-web-0   Bound     pvc-15c268c7-b507-11e6-932f-42010a800002   1Gi        RWO           48s
www-web-1   Bound     pvc-15c79307-b507-11e6-932f-42010a800002   1Gi        RWO           48s

The StatefulSet controller created two PersistentVolumeClaims that are bound to two PersistentVolumes.

As the cluster used in this tutorial is configured to dynamically provision PersistentVolumes, the PersistentVolumes were created and bound automatically.

The NGINX webserver, by default, serves an index file from /usr/share/nginx/html/index.html. The volumeMounts field in the StatefulSet's spec ensures that the /usr/share/nginx/html directory is backed by a PersistentVolume.

Write the Pods' hostnames to their index.html files and verify that the NGINX webservers serve the hostnames:

for i in 0 1; do kubectl exec "web-$i" -- sh -c 'echo "$(hostname)" > /usr/share/nginx/html/index.html'; done

for i in 0 1; do kubectl exec -i -t "web-$i" -- curl http://localhost/; done
web-0
web-1

In one terminal, watch the StatefulSet's Pods:

# End this watch when you've reached the end of the section.
# At the start of "Scaling a StatefulSet" you'll start a new watch.
kubectl get pod --watch -l app=nginx

In a second terminal, delete all of the StatefulSet's Pods:

kubectl delete pod -l app=nginx
pod "web-0" deleted
pod "web-1" deleted

Examine the output of the kubectl get command in the first terminal, and wait for all of the Pods to transition to Running and Ready.

# This should already be running
kubectl get pod --watch -l app=nginx
NAME      READY     STATUS              RESTARTS   AGE
web-0     0/1       ContainerCreating   0          0s
NAME      READY     STATUS    RESTARTS   AGE
web-0     1/1       Running   0          2s
web-1     0/1       Pending   0         0s
web-1     0/1       Pending   0         0s
web-1     0/1       ContainerCreating   0         0s
web-1     1/1       Running   0         34s

Verify the web servers continue to serve their hostnames:

for i in 0 1; do kubectl exec -i -t "web-$i" -- curl http://localhost/; done
web-0
web-1

Even though web-0 and web-1 were rescheduled, they continue to serve their hostnames because the PersistentVolumes associated with their PersistentVolumeClaims are remounted to their volumeMounts. No matter what node web-0and web-1 are scheduled on, their PersistentVolumes will be mounted to the appropriate mount points.

Scaling a StatefulSet

Scaling a StatefulSet refers to increasing or decreasing the number of replicas (horizontal scaling). This is accomplished by updating the replicas field. You can use either kubectl scale or kubectl patch to scale a StatefulSet.

Scaling up

Scaling up means adding more replicas. Provided that your app is able to distribute work across the StatefulSet, the new larger set of Pods can perform more of that work.

In one terminal window, watch the Pods in the StatefulSet:

# If you already have a watch running, you can continue using that.
# Otherwise, start one.
# End this watch when there are 5 healthy Pods for the StatefulSet
kubectl get pods --watch -l app=nginx

In another terminal window, use kubectl scale to scale the number of replicas to 5:

kubectl scale sts web --replicas=5
statefulset.apps/web scaled

Examine the output of the kubectl get command in the first terminal, and wait for the three additional Pods to transition to Running and Ready.

# This should already be running
kubectl get pod --watch -l app=nginx
NAME      READY     STATUS    RESTARTS   AGE
web-0     1/1       Running   0          2h
web-1     1/1       Running   0          2h
NAME      READY     STATUS    RESTARTS   AGE
web-2     0/1       Pending   0          0s
web-2     0/1       Pending   0         0s
web-2     0/1       ContainerCreating   0         0s
web-2     1/1       Running   0         19s
web-3     0/1       Pending   0         0s
web-3     0/1       Pending   0         0s
web-3     0/1       ContainerCreating   0         0s
web-3     1/1       Running   0         18s
web-4     0/1       Pending   0         0s
web-4     0/1       Pending   0         0s
web-4     0/1       ContainerCreating   0         0s
web-4     1/1       Running   0         19s

The StatefulSet controller scaled the number of replicas. As with StatefulSet creation, the StatefulSet controller created each Pod sequentially with respect to its ordinal index, and it waited for each Pod's predecessor to be Running and Ready before launching the subsequent Pod.

Scaling down

Scaling down means reducing the number of replicas. For example, you might do this because the level of traffic to a service has decreased, and at the current scale there are idle resources.

In one terminal, watch the StatefulSet's Pods:

# End this watch when there are only 3 Pods for the StatefulSet
kubectl get pod --watch -l app=nginx

In another terminal, use kubectl patch to scale the StatefulSet back down to three replicas:

kubectl patch sts web -p '{"spec":{"replicas":3}}'
statefulset.apps/web patched

Wait for web-4 and web-3 to transition to Terminating.

# This should already be running
kubectl get pods --watch -l app=nginx
NAME      READY     STATUS              RESTARTS   AGE
web-0     1/1       Running             0          3h
web-1     1/1       Running             0          3h
web-2     1/1       Running             0          55s
web-3     1/1       Running             0          36s
web-4     0/1       ContainerCreating   0          18s
NAME      READY     STATUS    RESTARTS   AGE
web-4     1/1       Running   0          19s
web-4     1/1       Terminating   0         24s
web-4     1/1       Terminating   0         24s
web-3     1/1       Terminating   0         42s
web-3     1/1       Terminating   0         42s

Ordered Pod termination

The control plane deleted one Pod at a time, in reverse order with respect to its ordinal index, and it waited for each Pod to be completely shut down before deleting the next one.

Get the StatefulSet's PersistentVolumeClaims:

kubectl get pvc -l app=nginx
NAME        STATUS    VOLUME                                     CAPACITY   ACCESSMODES   AGE
www-web-0   Bound     pvc-15c268c7-b507-11e6-932f-42010a800002   1Gi        RWO           13h
www-web-1   Bound     pvc-15c79307-b507-11e6-932f-42010a800002   1Gi        RWO           13h
www-web-2   Bound     pvc-e1125b27-b508-11e6-932f-42010a800002   1Gi        RWO           13h
www-web-3   Bound     pvc-e1176df6-b508-11e6-932f-42010a800002   1Gi        RWO           13h
www-web-4   Bound     pvc-e11bb5f8-b508-11e6-932f-42010a800002   1Gi        RWO           13h

There are still five PersistentVolumeClaims and five PersistentVolumes. When exploring a Pod's stable storage, you saw that the PersistentVolumes mounted to the Pods of a StatefulSet are not deleted when the StatefulSet's Pods are deleted. This is still true when Pod deletion is caused by scaling the StatefulSet down.

Updating StatefulSets

The StatefulSet controller supports automated updates. The strategy used is determined by the spec.updateStrategy field of the StatefulSet API object. This feature can be used to upgrade the container images, resource requests and/or limits, labels, and annotations of the Pods in a StatefulSet.

There are two valid update strategies, RollingUpdate (the default) and OnDelete.

RollingUpdate

The RollingUpdate update strategy will update all Pods in a StatefulSet, in reverse ordinal order, while respecting the StatefulSet guarantees.

You can split updates to a StatefulSet that uses the RollingUpdate strategy into partitions, by specifying .spec.updateStrategy.rollingUpdate.partition. You'll practice that later in this tutorial.

First, try a simple rolling update.

In one terminal window, patch the web StatefulSet to change the container image again:

kubectl patch statefulset web --type='json' -p='[{"op": "replace", "path": "/spec/template/spec/containers/0/image", "value":"gcr.io/google_containers/nginx-slim:0.8"}]'
statefulset.apps/web patched

In another terminal, watch the Pods in the StatefulSet:

# End this watch when the rollout is complete
#
# If you're not sure, leave it running one more minute
kubectl get pod -l app=nginx --watch

The output is similar to:

NAME      READY     STATUS    RESTARTS   AGE
web-0     1/1       Running   0          7m
web-1     1/1       Running   0          7m
web-2     1/1       Running   0          8m
web-2     1/1       Terminating   0         8m
web-2     1/1       Terminating   0         8m
web-2     0/1       Terminating   0         8m
web-2     0/1       Terminating   0         8m
web-2     0/1       Terminating   0         8m
web-2     0/1       Terminating   0         8m
web-2     0/1       Pending   0         0s
web-2     0/1       Pending   0         0s
web-2     0/1       ContainerCreating   0         0s
web-2     1/1       Running   0         19s
web-1     1/1       Terminating   0         8m
web-1     0/1       Terminating   0         8m
web-1     0/1       Terminating   0         8m
web-1     0/1       Terminating   0         8m
web-1     0/1       Pending   0         0s
web-1     0/1       Pending   0         0s
web-1     0/1       ContainerCreating   0         0s
web-1     1/1       Running   0         6s
web-0     1/1       Terminating   0         7m
web-0     1/1       Terminating   0         7m
web-0     0/1       Terminating   0         7m
web-0     0/1       Terminating   0         7m
web-0     0/1       Terminating   0         7m
web-0     0/1       Terminating   0         7m
web-0     0/1       Pending   0         0s
web-0     0/1       Pending   0         0s
web-0     0/1       ContainerCreating   0         0s
web-0     1/1       Running   0         10s

The Pods in the StatefulSet are updated in reverse ordinal order. The StatefulSet controller terminates each Pod, and waits for it to transition to Running and Ready prior to updating the next Pod. Note that, even though the StatefulSet controller will not proceed to update the next Pod until its ordinal successor is Running and Ready, it will restore any Pod that fails during the update to that Pod's existing version.

Pods that have already received the update will be restored to the updated version, and Pods that have not yet received the update will be restored to the previous version. In this way, the controller attempts to continue to keep the application healthy and the update consistent in the presence of intermittent failures.

Get the Pods to view their container images:

for p in 0 1 2; do kubectl get pod "web-$p" --template '{{range $i, $c := .spec.containers}}{{$c.image}}{{end}}'; echo; done
registry.k8s.io/nginx-slim:0.8
registry.k8s.io/nginx-slim:0.8
registry.k8s.io/nginx-slim:0.8

All the Pods in the StatefulSet are now running the previous container image.

Staging an update

You can split updates to a StatefulSet that uses the RollingUpdate strategy into partitions, by specifying .spec.updateStrategy.rollingUpdate.partition.

For more context, you can read Partitioned rolling updates in the StatefulSet concept page.

You can stage an update to a StatefulSet by using the partition field within .spec.updateStrategy.rollingUpdate. For this update, you will keep the existing Pods in the StatefulSet unchanged whilst you change the pod template for the StatefulSet. Then you - or, outside of a tutorial, some external automation - can trigger that prepared update.

First, patch the web StatefulSet to add a partition to the updateStrategy field:

# The value of "partition" determines which ordinals a change applies to
# Make sure to use a number bigger than the last ordinal for the
# StatefulSet
kubectl patch statefulset web -p '{"spec":{"updateStrategy":{"type":"RollingUpdate","rollingUpdate":{"partition":3}}}}'
statefulset.apps/web patched

Patch the StatefulSet again to change the container image that this StatefulSet uses:

kubectl patch statefulset web --type='json' -p='[{"op": "replace", "path": "/spec/template/spec/containers/0/image", "value":"registry.k8s.io/nginx-slim:0.7"}]'
statefulset.apps/web patched

Delete a Pod in the StatefulSet:

kubectl delete pod web-2
pod "web-2" deleted

Wait for the replacement web-2 Pod to be Running and Ready:

# End the watch when you see that web-2 is healthy
kubectl get pod -l app=nginx --watch
NAME      READY     STATUS              RESTARTS   AGE
web-0     1/1       Running             0          4m
web-1     1/1       Running             0          4m
web-2     0/1       ContainerCreating   0          11s
web-2     1/1       Running   0         18s

Get the Pod's container image:

kubectl get pod web-2 --template '{{range $i, $c := .spec.containers}}{{$c.image}}{{end}}'
registry.k8s.io/nginx-slim:0.8

Notice that, even though the update strategy is RollingUpdate the StatefulSet restored the Pod with the original container image. This is because the ordinal of the Pod is less than the partition specified by the updateStrategy.

Rolling out a canary

You're now going to try a canary rollout of that staged change.

You can roll out a canary (to test the modified template) by decrementing the partition you specified above.

Patch the StatefulSet to decrement the partition:

# The value of "partition" should match the highest existing ordinal for
# the StatefulSet
kubectl patch statefulset web -p '{"spec":{"updateStrategy":{"type":"RollingUpdate","rollingUpdate":{"partition":2}}}}'
statefulset.apps/web patched

The control plane triggers replacement for web-2 (implemented by a graceful delete followed by creating a new Pod once the deletion is complete). Wait for the new web-2 Pod to be Running and Ready.

# This should already be running
kubectl get pod -l app=nginx --watch
NAME      READY     STATUS              RESTARTS   AGE
web-0     1/1       Running             0          4m
web-1     1/1       Running             0          4m
web-2     0/1       ContainerCreating   0          11s
web-2     1/1       Running   0         18s

Get the Pod's container:

kubectl get pod web-2 --template '{{range $i, $c := .spec.containers}}{{$c.image}}{{end}}'
registry.k8s.io/nginx-slim:0.7

When you changed the partition, the StatefulSet controller automatically updated the web-2 Pod because the Pod's ordinal was greater than or equal to the partition.

Delete the web-1 Pod:

kubectl delete pod web-1
pod "web-1" deleted

Wait for the web-1 Pod to be Running and Ready.

# This should already be running
kubectl get pod -l app=nginx --watch

The output is similar to:

NAME      READY     STATUS        RESTARTS   AGE
web-0     1/1       Running       0          6m
web-1     0/1       Terminating   0          6m
web-2     1/1       Running       0          2m
web-1     0/1       Terminating   0         6m
web-1     0/1       Terminating   0         6m
web-1     0/1       Terminating   0         6m
web-1     0/1       Pending   0         0s
web-1     0/1       Pending   0         0s
web-1     0/1       ContainerCreating   0         0s
web-1     1/1       Running   0         18s

Get the web-1 Pod's container image:

kubectl get pod web-1 --template '{{range $i, $c := .spec.containers}}{{$c.image}}{{end}}'
registry.k8s.io/nginx-slim:0.8

web-1 was restored to its original configuration because the Pod's ordinal was less than the partition. When a partition is specified, all Pods with an ordinal that is greater than or equal to the partition will be updated when the StatefulSet's .spec.template is updated. If a Pod that has an ordinal less than the partition is deleted or otherwise terminated, it will be restored to its original configuration.

Phased roll outs

You can perform a phased roll out (e.g. a linear, geometric, or exponential roll out) using a partitioned rolling update in a similar manner to how you rolled out a canary. To perform a phased roll out, set the partition to the ordinal at which you want the controller to pause the update.

The partition is currently set to 2. Set the partition to 0:

kubectl patch statefulset web -p '{"spec":{"updateStrategy":{"type":"RollingUpdate","rollingUpdate":{"partition":0}}}}'
statefulset.apps/web patched

Wait for all of the Pods in the StatefulSet to become Running and Ready.

# This should already be running
kubectl get pod -l app=nginx --watch

The output is similar to:

NAME      READY     STATUS              RESTARTS   AGE
web-0     1/1       Running             0          3m
web-1     0/1       ContainerCreating   0          11s
web-2     1/1       Running             0          2m
web-1     1/1       Running   0         18s
web-0     1/1       Terminating   0         3m
web-0     1/1       Terminating   0         3m
web-0     0/1       Terminating   0         3m
web-0     0/1       Terminating   0         3m
web-0     0/1       Terminating   0         3m
web-0     0/1       Terminating   0         3m
web-0     0/1       Pending   0         0s
web-0     0/1       Pending   0         0s
web-0     0/1       ContainerCreating   0         0s
web-0     1/1       Running   0         3s

Get the container image details for the Pods in the StatefulSet:

for p in 0 1 2; do kubectl get pod "web-$p" --template '{{range $i, $c := .spec.containers}}{{$c.image}}{{end}}'; echo; done
registry.k8s.io/nginx-slim:0.7
registry.k8s.io/nginx-slim:0.7
registry.k8s.io/nginx-slim:0.7

By moving the partition to 0, you allowed the StatefulSet to continue the update process.

OnDelete

You select this update strategy for a StatefulSet by setting the .spec.template.updateStrategy.type to OnDelete.

Patch the web StatefulSet to use the OnDelete update strategy:

kubectl patch statefulset web -p '{"spec":{"updateStrategy":{"type":"OnDelete"}}}'
statefulset.apps/web patched

When you select this update strategy, the StatefulSet controller does not automatically update Pods when a modification is made to the StatefulSet's .spec.template field. You need to manage the rollout yourself - either manually, or using separate automation.

Deleting StatefulSets

StatefulSet supports both non-cascading and cascading deletion. In a non-cascading delete, the StatefulSet's Pods are not deleted when the StatefulSet is deleted. In a cascading delete, both the StatefulSet and its Pods are deleted.

Read Use Cascading Deletion in a Cluster to learn about cascading deletion generally.

Non-cascading delete

In one terminal window, watch the Pods in the StatefulSet.

# End this watch when there are no Pods for the StatefulSet
kubectl get pods --watch -l app=nginx

Use kubectl delete to delete the StatefulSet. Make sure to supply the --cascade=orphan parameter to the command. This parameter tells Kubernetes to only delete the StatefulSet, and to not delete any of its Pods.

kubectl delete statefulset web --cascade=orphan
statefulset.apps "web" deleted

Get the Pods, to examine their status:

kubectl get pods -l app=nginx
NAME      READY     STATUS    RESTARTS   AGE
web-0     1/1       Running   0          6m
web-1     1/1       Running   0          7m
web-2     1/1       Running   0          5m

Even though web has been deleted, all of the Pods are still Running and Ready. Delete web-0:

kubectl delete pod web-0
pod "web-0" deleted

Get the StatefulSet's Pods:

kubectl get pods -l app=nginx
NAME      READY     STATUS    RESTARTS   AGE
web-1     1/1       Running   0          10m
web-2     1/1       Running   0          7m

As the web StatefulSet has been deleted, web-0 has not been relaunched.

In one terminal, watch the StatefulSet's Pods.

# Leave this watch running until the next time you start a watch
kubectl get pods --watch -l app=nginx

In a second terminal, recreate the StatefulSet. Note that, unless you deleted the nginx Service (which you should not have), you will see an error indicating that the Service already exists.

kubectl apply -f https://k8s.io/examples/application/web/web.yaml
statefulset.apps/web created
service/nginx unchanged

Ignore the error. It only indicates that an attempt was made to create the nginx headless Service even though that Service already exists.

Examine the output of the kubectl get command running in the first terminal.

# This should already be running
kubectl get pods --watch -l app=nginx
NAME      READY     STATUS    RESTARTS   AGE
web-1     1/1       Running   0          16m
web-2     1/1       Running   0          2m
NAME      READY     STATUS    RESTARTS   AGE
web-0     0/1       Pending   0          0s
web-0     0/1       Pending   0         0s
web-0     0/1       ContainerCreating   0         0s
web-0     1/1       Running   0         18s
web-2     1/1       Terminating   0         3m
web-2     0/1       Terminating   0         3m
web-2     0/1       Terminating   0         3m
web-2     0/1       Terminating   0         3m

When the web StatefulSet was recreated, it first relaunched web-0. Since web-1 was already Running and Ready, when web-0 transitioned to Running and Ready, it adopted this Pod. Since you recreated the StatefulSet with replicas equal to 2, once web-0 had been recreated, and once web-1 had been determined to already be Running and Ready, web-2 was terminated.

Now take another look at the contents of the index.html file served by the Pods' webservers:

for i in 0 1; do kubectl exec -i -t "web-$i" -- curl http://localhost/; done
web-0
web-1

Even though you deleted both the StatefulSet and the web-0 Pod, it still serves the hostname originally entered into its index.html file. This is because the StatefulSet never deletes the PersistentVolumes associated with a Pod. When you recreated the StatefulSet and it relaunched web-0, its original PersistentVolume was remounted.

Cascading delete

In one terminal window, watch the Pods in the StatefulSet.

# Leave this running until the next page section
kubectl get pods --watch -l app=nginx

In another terminal, delete the StatefulSet again. This time, omit the --cascade=orphan parameter.

kubectl delete statefulset web
statefulset.apps "web" deleted

Examine the output of the kubectl get command running in the first terminal, and wait for all of the Pods to transition to Terminating.

# This should already be running
kubectl get pods --watch -l app=nginx
NAME      READY     STATUS    RESTARTS   AGE
web-0     1/1       Running   0          11m
web-1     1/1       Running   0          27m
NAME      READY     STATUS        RESTARTS   AGE
web-0     1/1       Terminating   0          12m
web-1     1/1       Terminating   0         29m
web-0     0/1       Terminating   0         12m
web-0     0/1       Terminating   0         12m
web-0     0/1       Terminating   0         12m
web-1     0/1       Terminating   0         29m
web-1     0/1       Terminating   0         29m
web-1     0/1       Terminating   0         29m

As you saw in the Scaling Down section, the Pods are terminated one at a time, with respect to the reverse order of their ordinal indices. Before terminating a Pod, the StatefulSet controller waits for the Pod's successor to be completely terminated.

kubectl delete service nginx
service "nginx" deleted

Recreate the StatefulSet and headless Service one more time:

kubectl apply -f https://k8s.io/examples/application/web/web.yaml
service/nginx created
statefulset.apps/web created

When all of the StatefulSet's Pods transition to Running and Ready, retrieve the contents of their index.html files:

for i in 0 1; do kubectl exec -i -t "web-$i" -- curl http://localhost/; done
web-0
web-1

Even though you completely deleted the StatefulSet, and all of its Pods, the Pods are recreated with their PersistentVolumes mounted, and web-0 and web-1 continue to serve their hostnames.

Finally, delete the nginx Service...

kubectl delete service nginx
service "nginx" deleted

...and the web StatefulSet:

kubectl delete statefulset web
statefulset "web" deleted

Pod management policy

For some distributed systems, the StatefulSet ordering guarantees are unnecessary and/or undesirable. These systems require only uniqueness and identity.

You can specify a Pod management policy to avoid this strict ordering; either OrderedReady (the default), or Parallel.

OrderedReady Pod management

OrderedReady pod management is the default for StatefulSets. It tells the StatefulSet controller to respect the ordering guarantees demonstrated above.

Use this when your application requires or expects that changes, such as rolling out a new version of your application, happen in the strict order of the ordinal (pod number) that the StatefulSet provides. In other words, if you have Pods app-0, app-1 and app-2, Kubernetes will update app-0 first and check it. Once the checks are good, Kubernetes updates app-1 and finally app-2.

If you added two more Pods, Kubernetes would set up app-3 and wait for that to become healthy before deploying app-4.

Because this is the default setting, you've already practised using it.

Parallel Pod management

The alternative, Parallel pod management, tells the StatefulSet controller to launch or terminate all Pods in parallel, and not to wait for Pods to become Running and Ready or completely terminated prior to launching or terminating another Pod.

The Parallel pod management option only affects the behavior for scaling operations. Updates are not affected; Kubernetes still rolls out changes in order. For this tutorial, the application is very simple: a webserver that tells you its hostname (because this is a StatefulSet, the hostname for each Pod is different and predictable).

apiVersion: v1
kind: Service
metadata:
  name: nginx
  labels:
    app: nginx
spec:
  ports:
  - port: 80
    name: web
  clusterIP: None
  selector:
    app: nginx
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: web
spec:
  serviceName: "nginx"
  podManagementPolicy: "Parallel"
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: registry.k8s.io/nginx-slim:0.8
        ports:
        - containerPort: 80
          name: web
        volumeMounts:
        - name: www
          mountPath: /usr/share/nginx/html
  volumeClaimTemplates:
  - metadata:
      name: www
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 1Gi

This manifest is identical to the one you downloaded above except that the .spec.podManagementPolicy of the web StatefulSet is set to Parallel.

In one terminal, watch the Pods in the StatefulSet.

# Leave this watch running until the end of the section
kubectl get pod -l app=nginx --watch

In another terminal, reconfigure the StatefulSet for Parallel Pod management:

kubectl apply -f https://k8s.io/examples/application/web/web-parallel.yaml
service/nginx updated
statefulset.apps/web updated

Keep the terminal open where you're running the watch. In another terminal window, scale the StatefulSet:

kubectl scale statefulset/web --replicas=5
statefulset.apps/web scaled

Examine the output of the terminal where the kubectl get command is running. It may look something like

web-3     0/1       Pending   0         0s
web-3     0/1       Pending   0         0s
web-3     0/1       Pending   0         7s
web-3     0/1       ContainerCreating   0         7s
web-2     0/1       Pending   0         0s
web-4     0/1       Pending   0         0s
web-2     1/1       Running   0         8s
web-4     0/1       ContainerCreating   0         4s
web-3     1/1       Running   0         26s
web-4     1/1       Running   0         2s

The StatefulSet launched three new Pods, and it did not wait for the first to become Running and Ready prior to launching the second and third Pods.

This approach is useful if your workload has a stateful element, or needs Pods to be able to identify each other with predictable naming, and especially if you sometimes need to provide a lot more capacity quickly. If this simple web service for the tutorial suddenly got an extra 1,000,000 requests per minute then you would want to run some more Pods - but you also would not want to wait for each new Pod to launch. Starting the extra Pods in parallel cuts the time between requesting the extra capacity and having it available for use.

Cleaning up

You should have two terminals open, ready for you to run kubectl commands as part of cleanup.

kubectl delete sts web
# sts is an abbreviation for statefulset

You can watch kubectl get to see those Pods being deleted.

# end the watch when you've seen what you need to
kubectl get pod -l app=nginx --watch
web-3     1/1       Terminating   0         9m
web-2     1/1       Terminating   0         9m
web-3     1/1       Terminating   0         9m
web-2     1/1       Terminating   0         9m
web-1     1/1       Terminating   0         44m
web-0     1/1       Terminating   0         44m
web-0     0/1       Terminating   0         44m
web-3     0/1       Terminating   0         9m
web-2     0/1       Terminating   0         9m
web-1     0/1       Terminating   0         44m
web-0     0/1       Terminating   0         44m
web-2     0/1       Terminating   0         9m
web-2     0/1       Terminating   0         9m
web-2     0/1       Terminating   0         9m
web-1     0/1       Terminating   0         44m
web-1     0/1       Terminating   0         44m
web-1     0/1       Terminating   0         44m
web-0     0/1       Terminating   0         44m
web-0     0/1       Terminating   0         44m
web-0     0/1       Terminating   0         44m
web-3     0/1       Terminating   0         9m
web-3     0/1       Terminating   0         9m
web-3     0/1       Terminating   0         9m

During deletion, a StatefulSet removes all Pods concurrently; it does not wait for a Pod's ordinal successor to terminate prior to deleting that Pod.

Close the terminal where the kubectl get command is running and delete the nginx Service:

kubectl delete svc nginx

Delete the persistent storage media for the PersistentVolumes used in this tutorial.

kubectl get pvc
NAME        STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS   AGE
www-web-0   Bound    pvc-2bf00408-d366-4a12-bad0-1869c65d0bee   1Gi        RWO            standard       25m
www-web-1   Bound    pvc-ba3bfe9c-413e-4b95-a2c0-3ea8a54dbab4   1Gi        RWO            standard       24m
www-web-2   Bound    pvc-cba6cfa6-3a47-486b-a138-db5930207eaf   1Gi        RWO            standard       15m
www-web-3   Bound    pvc-0c04d7f0-787a-4977-8da3-d9d3a6d8d752   1Gi        RWO            standard       15m
www-web-4   Bound    pvc-b2c73489-e70b-4a4e-9ec1-9eab439aa43e   1Gi        RWO            standard       14m
kubectl get pv
NAME                                       CAPACITY   ACCESS MODES   RECLAIM POLICY   STATUS   CLAIM               STORAGECLASS   REASON   AGE
pvc-0c04d7f0-787a-4977-8da3-d9d3a6d8d752   1Gi        RWO            Delete           Bound    default/www-web-3   standard                15m
pvc-2bf00408-d366-4a12-bad0-1869c65d0bee   1Gi        RWO            Delete           Bound    default/www-web-0   standard                25m
pvc-b2c73489-e70b-4a4e-9ec1-9eab439aa43e   1Gi        RWO            Delete           Bound    default/www-web-4   standard                14m
pvc-ba3bfe9c-413e-4b95-a2c0-3ea8a54dbab4   1Gi        RWO            Delete           Bound    default/www-web-1   standard                24m
pvc-cba6cfa6-3a47-486b-a138-db5930207eaf   1Gi        RWO            Delete           Bound    default/www-web-2   standard                15m
kubectl delete pvc www-web-0 www-web-1 www-web-2 www-web-3 www-web-4
persistentvolumeclaim "www-web-0" deleted
persistentvolumeclaim "www-web-1" deleted
persistentvolumeclaim "www-web-2" deleted
persistentvolumeclaim "www-web-3" deleted
persistentvolumeclaim "www-web-4" deleted
kubectl get pvc
No resources found in default namespace.

6.2 - Example: Deploying WordPress and MySQL with Persistent Volumes

This tutorial shows you how to deploy a WordPress site and a MySQL database using Minikube. Both applications use PersistentVolumes and PersistentVolumeClaims to store data.

A PersistentVolume (PV) is a piece of storage in the cluster that has been manually provisioned by an administrator, or dynamically provisioned by Kubernetes using a StorageClass. A PersistentVolumeClaim (PVC) is a request for storage by a user that can be fulfilled by a PV. PersistentVolumes and PersistentVolumeClaims are independent from Pod lifecycles and preserve data through restarting, rescheduling, and even deleting Pods.

Objectives

  • Create PersistentVolumeClaims and PersistentVolumes
  • Create a kustomization.yaml with
    • a Secret generator
    • MySQL resource configs
    • WordPress resource configs
  • Apply the kustomization directory by kubectl apply -k ./
  • Clean up

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:

To check the version, enter kubectl version.

The example shown on this page works with kubectl 1.27 and above.

Download the following configuration files:

  1. mysql-deployment.yaml

  2. wordpress-deployment.yaml

Create PersistentVolumeClaims and PersistentVolumes

MySQL and Wordpress each require a PersistentVolume to store data. Their PersistentVolumeClaims will be created at the deployment step.

Many cluster environments have a default StorageClass installed. When a StorageClass is not specified in the PersistentVolumeClaim, the cluster's default StorageClass is used instead.

When a PersistentVolumeClaim is created, a PersistentVolume is dynamically provisioned based on the StorageClass configuration.

Create a kustomization.yaml

Add a Secret generator

A Secret is an object that stores a piece of sensitive data like a password or key. Since 1.14, kubectl supports the management of Kubernetes objects using a kustomization file. You can create a Secret by generators in kustomization.yaml.

Add a Secret generator in kustomization.yaml from the following command. You will need to replace YOUR_PASSWORD with the password you want to use.

cat <<EOF >./kustomization.yaml
secretGenerator:
- name: mysql-pass
  literals:
  - password=YOUR_PASSWORD
EOF

Add resource configs for MySQL and WordPress

The following manifest describes a single-instance MySQL Deployment. The MySQL container mounts the PersistentVolume at /var/lib/mysql. The MYSQL_ROOT_PASSWORD environment variable sets the database password from the Secret.

apiVersion: v1
kind: Service
metadata:
  name: wordpress-mysql
  labels:
    app: wordpress
spec:
  ports:
    - port: 3306
  selector:
    app: wordpress
    tier: mysql
  clusterIP: None
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: mysql-pv-claim
  labels:
    app: wordpress
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 20Gi
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: wordpress-mysql
  labels:
    app: wordpress
spec:
  selector:
    matchLabels:
      app: wordpress
      tier: mysql
  strategy:
    type: Recreate
  template:
    metadata:
      labels:
        app: wordpress
        tier: mysql
    spec:
      containers:
      - image: mysql:8.0
        name: mysql
        env:
        - name: MYSQL_ROOT_PASSWORD
          valueFrom:
            secretKeyRef:
              name: mysql-pass
              key: password
        - name: MYSQL_DATABASE
          value: wordpress
        - name: MYSQL_USER
          value: wordpress
        - name: MYSQL_PASSWORD
          valueFrom:
            secretKeyRef:
              name: mysql-pass
              key: password
        ports:
        - containerPort: 3306
          name: mysql
        volumeMounts:
        - name: mysql-persistent-storage
          mountPath: /var/lib/mysql
      volumes:
      - name: mysql-persistent-storage
        persistentVolumeClaim:
          claimName: mysql-pv-claim

The following manifest describes a single-instance WordPress Deployment. The WordPress container mounts the PersistentVolume at /var/www/html for website data files. The WORDPRESS_DB_HOST environment variable sets the name of the MySQL Service defined above, and WordPress will access the database by Service. The WORDPRESS_DB_PASSWORD environment variable sets the database password from the Secret kustomize generated.

apiVersion: v1
kind: Service
metadata:
  name: wordpress
  labels:
    app: wordpress
spec:
  ports:
    - port: 80
  selector:
    app: wordpress
    tier: frontend
  type: LoadBalancer
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: wp-pv-claim
  labels:
    app: wordpress
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 20Gi
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: wordpress
  labels:
    app: wordpress
spec:
  selector:
    matchLabels:
      app: wordpress
      tier: frontend
  strategy:
    type: Recreate
  template:
    metadata:
      labels:
        app: wordpress
        tier: frontend
    spec:
      containers:
      - image: wordpress:6.2.1-apache
        name: wordpress
        env:
        - name: WORDPRESS_DB_HOST
          value: wordpress-mysql
        - name: WORDPRESS_DB_PASSWORD
          valueFrom:
            secretKeyRef:
              name: mysql-pass
              key: password
        - name: WORDPRESS_DB_USER
          value: wordpress
        ports:
        - containerPort: 80
          name: wordpress
        volumeMounts:
        - name: wordpress-persistent-storage
          mountPath: /var/www/html
      volumes:
      - name: wordpress-persistent-storage
        persistentVolumeClaim:
          claimName: wp-pv-claim
  1. Download the MySQL deployment configuration file.

    curl -LO https://k8s.io/examples/application/wordpress/mysql-deployment.yaml
    
  2. Download the WordPress configuration file.

    curl -LO https://k8s.io/examples/application/wordpress/wordpress-deployment.yaml
    
  3. Add them to kustomization.yaml file.

    cat <<EOF >>./kustomization.yaml
    resources:
      - mysql-deployment.yaml
      - wordpress-deployment.yaml
    EOF
    

Apply and Verify

The kustomization.yaml contains all the resources for deploying a WordPress site and a MySQL database. You can apply the directory by

kubectl apply -k ./

Now you can verify that all objects exist.

  1. Verify that the Secret exists by running the following command:

    kubectl get secrets
    

    The response should be like this:

    NAME                    TYPE                                  DATA   AGE
    mysql-pass-c57bb4t7mf   Opaque                                1      9s
    
  2. Verify that a PersistentVolume got dynamically provisioned.

    kubectl get pvc
    

    The response should be like this:

    NAME             STATUS    VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS       AGE
    mysql-pv-claim   Bound     pvc-8cbd7b2e-4044-11e9-b2bb-42010a800002   20Gi       RWO            standard           77s
    wp-pv-claim      Bound     pvc-8cd0df54-4044-11e9-b2bb-42010a800002   20Gi       RWO            standard           77s
    
  3. Verify that the Pod is running by running the following command:

    kubectl get pods
    

    The response should be like this:

    NAME                               READY     STATUS    RESTARTS   AGE
    wordpress-mysql-1894417608-x5dzt   1/1       Running   0          40s
    
  4. Verify that the Service is running by running the following command:

    kubectl get services wordpress
    

    The response should be like this:

    NAME        TYPE            CLUSTER-IP   EXTERNAL-IP   PORT(S)        AGE
    wordpress   LoadBalancer    10.0.0.89    <pending>     80:32406/TCP   4m
    
  5. Run the following command to get the IP Address for the WordPress Service:

    minikube service wordpress --url
    

    The response should be like this:

    http://1.2.3.4:32406
    
  6. Copy the IP address, and load the page in your browser to view your site.

    You should see the WordPress set up page similar to the following screenshot.

    wordpress-init

Cleaning up

  1. Run the following command to delete your Secret, Deployments, Services and PersistentVolumeClaims:

    kubectl delete -k ./
    

What's next

6.3 - Example: Deploying Cassandra with a StatefulSet

This tutorial shows you how to run Apache Cassandra on Kubernetes. Cassandra, a database, needs persistent storage to provide data durability (application state). In this example, a custom Cassandra seed provider lets the database discover new Cassandra instances as they join the Cassandra cluster.

StatefulSets make it easier to deploy stateful applications into your Kubernetes cluster. For more information on the features used in this tutorial, see StatefulSet.

Objectives

  • Create and validate a Cassandra headless Service.
  • Use a StatefulSet to create a Cassandra ring.
  • Validate the StatefulSet.
  • Modify the StatefulSet.
  • Delete the StatefulSet and its Pods.

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:

To complete this tutorial, you should already have a basic familiarity with Pods, Services, and StatefulSets.

Additional Minikube setup instructions

Creating a headless Service for Cassandra

In Kubernetes, a Service describes a set of Pods that perform the same task.

The following Service is used for DNS lookups between Cassandra Pods and clients within your cluster:

apiVersion: v1
kind: Service
metadata:
  labels:
    app: cassandra
  name: cassandra
spec:
  clusterIP: None
  ports:
  - port: 9042
  selector:
    app: cassandra

Create a Service to track all Cassandra StatefulSet members from the cassandra-service.yaml file:

kubectl apply -f https://k8s.io/examples/application/cassandra/cassandra-service.yaml

Validating (optional)

Get the Cassandra Service.

kubectl get svc cassandra

The response is

NAME        TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)    AGE
cassandra   ClusterIP   None         <none>        9042/TCP   45s

If you don't see a Service named cassandra, that means creation failed. Read Debug Services for help troubleshooting common issues.

Using a StatefulSet to create a Cassandra ring

The StatefulSet manifest, included below, creates a Cassandra ring that consists of three Pods.

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: cassandra
  labels:
    app: cassandra
spec:
  serviceName: cassandra
  replicas: 3
  selector:
    matchLabels:
      app: cassandra
  template:
    metadata:
      labels:
        app: cassandra
    spec:
      terminationGracePeriodSeconds: 1800
      containers:
      - name: cassandra
        image: gcr.io/google-samples/cassandra:v13
        imagePullPolicy: Always
        ports:
        - containerPort: 7000
          name: intra-node
        - containerPort: 7001
          name: tls-intra-node
        - containerPort: 7199
          name: jmx
        - containerPort: 9042
          name: cql
        resources:
          limits:
            cpu: "500m"
            memory: 1Gi
          requests:
            cpu: "500m"
            memory: 1Gi
        securityContext:
          capabilities:
            add:
              - IPC_LOCK
        lifecycle:
          preStop:
            exec:
              command: 
              - /bin/sh
              - -c
              - nodetool drain
        env:
          - name: MAX_HEAP_SIZE
            value: 512M
          - name: HEAP_NEWSIZE
            value: 100M
          - name: CASSANDRA_SEEDS
            value: "cassandra-0.cassandra.default.svc.cluster.local"
          - name: CASSANDRA_CLUSTER_NAME
            value: "K8Demo"
          - name: CASSANDRA_DC
            value: "DC1-K8Demo"
          - name: CASSANDRA_RACK
            value: "Rack1-K8Demo"
          - name: POD_IP
            valueFrom:
              fieldRef:
                fieldPath: status.podIP
        readinessProbe:
          exec:
            command:
            - /bin/bash
            - -c
            - /ready-probe.sh
          initialDelaySeconds: 15
          timeoutSeconds: 5
        # These volume mounts are persistent. They are like inline claims,
        # but not exactly because the names need to match exactly one of
        # the stateful pod volumes.
        volumeMounts:
        - name: cassandra-data
          mountPath: /cassandra_data
  # These are converted to volume claims by the controller
  # and mounted at the paths mentioned above.
  # do not use these in production until ssd GCEPersistentDisk or other ssd pd
  volumeClaimTemplates:
  - metadata:
      name: cassandra-data
    spec:
      accessModes: [ "ReadWriteOnce" ]
      storageClassName: fast
      resources:
        requests:
          storage: 1Gi
---
kind: StorageClass
apiVersion: storage.k8s.io/v1
metadata:
  name: fast
provisioner: k8s.io/minikube-hostpath
parameters:
  type: pd-ssd

Create the Cassandra StatefulSet from the cassandra-statefulset.yaml file:

# Use this if you are able to apply cassandra-statefulset.yaml unmodified
kubectl apply -f https://k8s.io/examples/application/cassandra/cassandra-statefulset.yaml

If you need to modify cassandra-statefulset.yaml to suit your cluster, download https://k8s.io/examples/application/cassandra/cassandra-statefulset.yaml and then apply that manifest, from the folder you saved the modified version into:

# Use this if you needed to modify cassandra-statefulset.yaml locally
kubectl apply -f cassandra-statefulset.yaml

Validating the Cassandra StatefulSet

  1. Get the Cassandra StatefulSet:

    kubectl get statefulset cassandra
    

    The response should be similar to:

    NAME        DESIRED   CURRENT   AGE
    cassandra   3         0         13s
    

    The StatefulSet resource deploys Pods sequentially.

  2. Get the Pods to see the ordered creation status:

    kubectl get pods -l="app=cassandra"
    

    The response should be similar to:

    NAME          READY     STATUS              RESTARTS   AGE
    cassandra-0   1/1       Running             0          1m
    cassandra-1   0/1       ContainerCreating   0          8s
    

    It can take several minutes for all three Pods to deploy. Once they are deployed, the same command returns output similar to:

    NAME          READY     STATUS    RESTARTS   AGE
    cassandra-0   1/1       Running   0          10m
    cassandra-1   1/1       Running   0          9m
    cassandra-2   1/1       Running   0          8m
    
  3. Run the Cassandra nodetool inside the first Pod, to display the status of the ring.

    kubectl exec -it cassandra-0 -- nodetool status
    

    The response should look something like:

    Datacenter: DC1-K8Demo
    ======================
    Status=Up/Down
    |/ State=Normal/Leaving/Joining/Moving
    --  Address     Load       Tokens       Owns (effective)  Host ID                               Rack
    UN  172.17.0.5  83.57 KiB  32           74.0%             e2dd09e6-d9d3-477e-96c5-45094c08db0f  Rack1-K8Demo
    UN  172.17.0.4  101.04 KiB  32           58.8%             f89d6835-3a42-4419-92b3-0e62cae1479c  Rack1-K8Demo
    UN  172.17.0.6  84.74 KiB  32           67.1%             a6a1e8c2-3dc5-4417-b1a0-26507af2aaad  Rack1-K8Demo
    

Modifying the Cassandra StatefulSet

Use kubectl edit to modify the size of a Cassandra StatefulSet.

  1. Run the following command:

    kubectl edit statefulset cassandra
    

    This command opens an editor in your terminal. The line you need to change is the replicas field. The following sample is an excerpt of the StatefulSet file:

    # Please edit the object below. Lines beginning with a '#' will be ignored,
    # and an empty file will abort the edit. If an error occurs while saving this file will be
    # reopened with the relevant failures.
    #
    apiVersion: apps/v1
    kind: StatefulSet
    metadata:
      creationTimestamp: 2016-08-13T18:40:58Z
      generation: 1
      labels:
      app: cassandra
      name: cassandra
      namespace: default
      resourceVersion: "323"
      uid: 7a219483-6185-11e6-a910-42010a8a0fc0
    spec:
      replicas: 3
    
  2. Change the number of replicas to 4, and then save the manifest.

    The StatefulSet now scales to run with 4 Pods.

  3. Get the Cassandra StatefulSet to verify your change:

    kubectl get statefulset cassandra
    

    The response should be similar to:

    NAME        DESIRED   CURRENT   AGE
    cassandra   4         4         36m
    

Cleaning up

Deleting or scaling a StatefulSet down does not delete the volumes associated with the StatefulSet. This setting is for your safety because your data is more valuable than automatically purging all related StatefulSet resources.

  1. Run the following commands (chained together into a single command) to delete everything in the Cassandra StatefulSet:

    grace=$(kubectl get pod cassandra-0 -o=jsonpath='{.spec.terminationGracePeriodSeconds}') \
      && kubectl delete statefulset -l app=cassandra \
      && echo "Sleeping ${grace} seconds" 1>&2 \
      && sleep $grace \
      && kubectl delete persistentvolumeclaim -l app=cassandra
    
  2. Run the following command to delete the Service you set up for Cassandra:

    kubectl delete service -l app=cassandra
    

Cassandra container environment variables

The Pods in this tutorial use the gcr.io/google-samples/cassandra:v13 image from Google's container registry. The Docker image above is based on debian-base and includes OpenJDK 8.

This image includes a standard Cassandra installation from the Apache Debian repo. By using environment variables you can change values that are inserted into cassandra.yaml.

Environment variableDefault value
CASSANDRA_CLUSTER_NAME'Test Cluster'
CASSANDRA_NUM_TOKENS32
CASSANDRA_RPC_ADDRESS0.0.0.0

What's next

6.4 - Running ZooKeeper, A Distributed System Coordinator

This tutorial demonstrates running Apache Zookeeper on Kubernetes using StatefulSets, PodDisruptionBudgets, and PodAntiAffinity.

Before you begin

Before starting this tutorial, you should be familiar with the following Kubernetes concepts:

You must have a cluster with at least four nodes, and each node requires at least 2 CPUs and 4 GiB of memory. In this tutorial you will cordon and drain the cluster's nodes. This means that the cluster will terminate and evict all Pods on its nodes, and the nodes will temporarily become unschedulable. You should use a dedicated cluster for this tutorial, or you should ensure that the disruption you cause will not interfere with other tenants.

This tutorial assumes that you have configured your cluster to dynamically provision PersistentVolumes. If your cluster is not configured to do so, you will have to manually provision three 20 GiB volumes before starting this tutorial.

Objectives

After this tutorial, you will know the following.

  • How to deploy a ZooKeeper ensemble using StatefulSet.
  • How to consistently configure the ensemble.
  • How to spread the deployment of ZooKeeper servers in the ensemble.
  • How to use PodDisruptionBudgets to ensure service availability during planned maintenance.

ZooKeeper

Apache ZooKeeper is a distributed, open-source coordination service for distributed applications. ZooKeeper allows you to read, write, and observe updates to data. Data are organized in a file system like hierarchy and replicated to all ZooKeeper servers in the ensemble (a set of ZooKeeper servers). All operations on data are atomic and sequentially consistent. ZooKeeper ensures this by using the Zab consensus protocol to replicate a state machine across all servers in the ensemble.

The ensemble uses the Zab protocol to elect a leader, and the ensemble cannot write data until that election is complete. Once complete, the ensemble uses Zab to ensure that it replicates all writes to a quorum before it acknowledges and makes them visible to clients. Without respect to weighted quorums, a quorum is a majority component of the ensemble containing the current leader. For instance, if the ensemble has three servers, a component that contains the leader and one other server constitutes a quorum. If the ensemble can not achieve a quorum, the ensemble cannot write data.

ZooKeeper servers keep their entire state machine in memory, and write every mutation to a durable WAL (Write Ahead Log) on storage media. When a server crashes, it can recover its previous state by replaying the WAL. To prevent the WAL from growing without bound, ZooKeeper servers will periodically snapshot them in memory state to storage media. These snapshots can be loaded directly into memory, and all WAL entries that preceded the snapshot may be discarded.

Creating a ZooKeeper ensemble

The manifest below contains a Headless Service, a Service, a PodDisruptionBudget, and a StatefulSet.

apiVersion: v1
kind: Service
metadata:
  name: zk-hs
  labels:
    app: zk
spec:
  ports:
  - port: 2888
    name: server
  - port: 3888
    name: leader-election
  clusterIP: None
  selector:
    app: zk
---
apiVersion: v1
kind: Service
metadata:
  name: zk-cs
  labels:
    app: zk
spec:
  ports:
  - port: 2181
    name: client
  selector:
    app: zk
---
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: zk-pdb
spec:
  selector:
    matchLabels:
      app: zk
  maxUnavailable: 1
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: zk
spec:
  selector:
    matchLabels:
      app: zk
  serviceName: zk-hs
  replicas: 3
  updateStrategy:
    type: RollingUpdate
  podManagementPolicy: OrderedReady
  template:
    metadata:
      labels:
        app: zk
    spec:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: "app"
                    operator: In
                    values:
                    - zk
              topologyKey: "kubernetes.io/hostname"
      containers:
      - name: kubernetes-zookeeper
        imagePullPolicy: Always
        image: "registry.k8s.io/kubernetes-zookeeper:1.0-3.4.10"
        resources:
          requests:
            memory: "1Gi"
            cpu: "0.5"
        ports:
        - containerPort: 2181
          name: client
        - containerPort: 2888
          name: server
        - containerPort: 3888
          name: leader-election
        command:
        - sh
        - -c
        - "start-zookeeper \
          --servers=3 \
          --data_dir=/var/lib/zookeeper/data \
          --data_log_dir=/var/lib/zookeeper/data/log \
          --conf_dir=/opt/zookeeper/conf \
          --client_port=2181 \
          --election_port=3888 \
          --server_port=2888 \
          --tick_time=2000 \
          --init_limit=10 \
          --sync_limit=5 \
          --heap=512M \
          --max_client_cnxns=60 \
          --snap_retain_count=3 \
          --purge_interval=12 \
          --max_session_timeout=40000 \
          --min_session_timeout=4000 \
          --log_level=INFO"
        readinessProbe:
          exec:
            command:
            - sh
            - -c
            - "zookeeper-ready 2181"
          initialDelaySeconds: 10
          timeoutSeconds: 5
        livenessProbe:
          exec:
            command:
            - sh
            - -c
            - "zookeeper-ready 2181"
          initialDelaySeconds: 10
          timeoutSeconds: 5
        volumeMounts:
        - name: datadir
          mountPath: /var/lib/zookeeper
      securityContext:
        runAsUser: 1000
        fsGroup: 1000
  volumeClaimTemplates:
  - metadata:
      name: datadir
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 10Gi

Open a terminal, and use the kubectl apply command to create the manifest.

kubectl apply -f https://k8s.io/examples/application/zookeeper/zookeeper.yaml

This creates the zk-hs Headless Service, the zk-cs Service, the zk-pdb PodDisruptionBudget, and the zk StatefulSet.

service/zk-hs created
service/zk-cs created
poddisruptionbudget.policy/zk-pdb created
statefulset.apps/zk created

Use kubectl get to watch the StatefulSet controller create the StatefulSet's Pods.

kubectl get pods -w -l app=zk

Once the zk-2 Pod is Running and Ready, use CTRL-C to terminate kubectl.

NAME      READY     STATUS    RESTARTS   AGE
zk-0      0/1       Pending   0          0s
zk-0      0/1       Pending   0         0s
zk-0      0/1       ContainerCreating   0         0s
zk-0      0/1       Running   0         19s
zk-0      1/1       Running   0         40s
zk-1      0/1       Pending   0         0s
zk-1      0/1       Pending   0         0s
zk-1      0/1       ContainerCreating   0         0s
zk-1      0/1       Running   0         18s
zk-1      1/1       Running   0         40s
zk-2      0/1       Pending   0         0s
zk-2      0/1       Pending   0         0s
zk-2      0/1       ContainerCreating   0         0s
zk-2      0/1       Running   0         19s
zk-2      1/1       Running   0         40s

The StatefulSet controller creates three Pods, and each Pod has a container with a ZooKeeper server.

Facilitating leader election

Because there is no terminating algorithm for electing a leader in an anonymous network, Zab requires explicit membership configuration to perform leader election. Each server in the ensemble needs to have a unique identifier, all servers need to know the global set of identifiers, and each identifier needs to be associated with a network address.

Use kubectl exec to get the hostnames of the Pods in the zk StatefulSet.

for i in 0 1 2; do kubectl exec zk-$i -- hostname; done

The StatefulSet controller provides each Pod with a unique hostname based on its ordinal index. The hostnames take the form of <statefulset name>-<ordinal index>. Because the replicas field of the zk StatefulSet is set to 3, the Set's controller creates three Pods with their hostnames set to zk-0, zk-1, and zk-2.

zk-0
zk-1
zk-2

The servers in a ZooKeeper ensemble use natural numbers as unique identifiers, and store each server's identifier in a file called myid in the server's data directory.

To examine the contents of the myid file for each server use the following command.

for i in 0 1 2; do echo "myid zk-$i";kubectl exec zk-$i -- cat /var/lib/zookeeper/data/myid; done

Because the identifiers are natural numbers and the ordinal indices are non-negative integers, you can generate an identifier by adding 1 to the ordinal.

myid zk-0
1
myid zk-1
2
myid zk-2
3

To get the Fully Qualified Domain Name (FQDN) of each Pod in the zk StatefulSet use the following command.

for i in 0 1 2; do kubectl exec zk-$i -- hostname -f; done

The zk-hs Service creates a domain for all of the Pods, zk-hs.default.svc.cluster.local.

zk-0.zk-hs.default.svc.cluster.local
zk-1.zk-hs.default.svc.cluster.local
zk-2.zk-hs.default.svc.cluster.local

The A records in Kubernetes DNS resolve the FQDNs to the Pods' IP addresses. If Kubernetes reschedules the Pods, it will update the A records with the Pods' new IP addresses, but the A records names will not change.

ZooKeeper stores its application configuration in a file named zoo.cfg. Use kubectl exec to view the contents of the zoo.cfg file in the zk-0 Pod.

kubectl exec zk-0 -- cat /opt/zookeeper/conf/zoo.cfg

In the server.1, server.2, and server.3 properties at the bottom of the file, the 1, 2, and 3 correspond to the identifiers in the ZooKeeper servers' myid files. They are set to the FQDNs for the Pods in the zk StatefulSet.

clientPort=2181
dataDir=/var/lib/zookeeper/data
dataLogDir=/var/lib/zookeeper/log
tickTime=2000
initLimit=10
syncLimit=2000
maxClientCnxns=60
minSessionTimeout= 4000
maxSessionTimeout= 40000
autopurge.snapRetainCount=3
autopurge.purgeInterval=0
server.1=zk-0.zk-hs.default.svc.cluster.local:2888:3888
server.2=zk-1.zk-hs.default.svc.cluster.local:2888:3888
server.3=zk-2.zk-hs.default.svc.cluster.local:2888:3888

Achieving consensus

Consensus protocols require that the identifiers of each participant be unique. No two participants in the Zab protocol should claim the same unique identifier. This is necessary to allow the processes in the system to agree on which processes have committed which data. If two Pods are launched with the same ordinal, two ZooKeeper servers would both identify themselves as the same server.

kubectl get pods -w -l app=zk
NAME      READY     STATUS    RESTARTS   AGE
zk-0      0/1       Pending   0          0s
zk-0      0/1       Pending   0         0s
zk-0      0/1       ContainerCreating   0         0s
zk-0      0/1       Running   0         19s
zk-0      1/1       Running   0         40s
zk-1      0/1       Pending   0         0s
zk-1      0/1       Pending   0         0s
zk-1      0/1       ContainerCreating   0         0s
zk-1      0/1       Running   0         18s
zk-1      1/1       Running   0         40s
zk-2      0/1       Pending   0         0s
zk-2      0/1       Pending   0         0s
zk-2      0/1       ContainerCreating   0         0s
zk-2      0/1       Running   0         19s
zk-2      1/1       Running   0         40s

The A records for each Pod are entered when the Pod becomes Ready. Therefore, the FQDNs of the ZooKeeper servers will resolve to a single endpoint, and that endpoint will be the unique ZooKeeper server claiming the identity configured in its myid file.

zk-0.zk-hs.default.svc.cluster.local
zk-1.zk-hs.default.svc.cluster.local
zk-2.zk-hs.default.svc.cluster.local

This ensures that the servers properties in the ZooKeepers' zoo.cfg files represents a correctly configured ensemble.

server.1=zk-0.zk-hs.default.svc.cluster.local:2888:3888
server.2=zk-1.zk-hs.default.svc.cluster.local:2888:3888
server.3=zk-2.zk-hs.default.svc.cluster.local:2888:3888

When the servers use the Zab protocol to attempt to commit a value, they will either achieve consensus and commit the value (if leader election has succeeded and at least two of the Pods are Running and Ready), or they will fail to do so (if either of the conditions are not met). No state will arise where one server acknowledges a write on behalf of another.

Sanity testing the ensemble

The most basic sanity test is to write data to one ZooKeeper server and to read the data from another.

The command below executes the zkCli.sh script to write world to the path /hello on the zk-0 Pod in the ensemble.

kubectl exec zk-0 -- zkCli.sh create /hello world
WATCHER::

WatchedEvent state:SyncConnected type:None path:null
Created /hello

To get the data from the zk-1 Pod use the following command.

kubectl exec zk-1 -- zkCli.sh get /hello

The data that you created on zk-0 is available on all the servers in the ensemble.

WATCHER::

WatchedEvent state:SyncConnected type:None path:null
world
cZxid = 0x100000002
ctime = Thu Dec 08 15:13:30 UTC 2016
mZxid = 0x100000002
mtime = Thu Dec 08 15:13:30 UTC 2016
pZxid = 0x100000002
cversion = 0
dataVersion = 0
aclVersion = 0
ephemeralOwner = 0x0
dataLength = 5
numChildren = 0

Providing durable storage

As mentioned in the ZooKeeper Basics section, ZooKeeper commits all entries to a durable WAL, and periodically writes snapshots in memory state, to storage media. Using WALs to provide durability is a common technique for applications that use consensus protocols to achieve a replicated state machine.

Use the kubectl delete command to delete the zk StatefulSet.

kubectl delete statefulset zk
statefulset.apps "zk" deleted

Watch the termination of the Pods in the StatefulSet.

kubectl get pods -w -l app=zk

When zk-0 if fully terminated, use CTRL-C to terminate kubectl.

zk-2      1/1       Terminating   0         9m
zk-0      1/1       Terminating   0         11m
zk-1      1/1       Terminating   0         10m
zk-2      0/1       Terminating   0         9m
zk-2      0/1       Terminating   0         9m
zk-2      0/1       Terminating   0         9m
zk-1      0/1       Terminating   0         10m
zk-1      0/1       Terminating   0         10m
zk-1      0/1       Terminating   0         10m
zk-0      0/1       Terminating   0         11m
zk-0      0/1       Terminating   0         11m
zk-0      0/1       Terminating   0         11m

Reapply the manifest in zookeeper.yaml.

kubectl apply -f https://k8s.io/examples/application/zookeeper/zookeeper.yaml

This creates the zk StatefulSet object, but the other API objects in the manifest are not modified because they already exist.

Watch the StatefulSet controller recreate the StatefulSet's Pods.

kubectl get pods -w -l app=zk

Once the zk-2 Pod is Running and Ready, use CTRL-C to terminate kubectl.

NAME      READY     STATUS    RESTARTS   AGE
zk-0      0/1       Pending   0          0s
zk-0      0/1       Pending   0         0s
zk-0      0/1       ContainerCreating   0         0s
zk-0      0/1       Running   0         19s
zk-0      1/1       Running   0         40s
zk-1      0/1       Pending   0         0s
zk-1      0/1       Pending   0         0s
zk-1      0/1       ContainerCreating   0         0s
zk-1      0/1       Running   0         18s
zk-1      1/1       Running   0         40s
zk-2      0/1       Pending   0         0s
zk-2      0/1       Pending   0         0s
zk-2      0/1       ContainerCreating   0         0s
zk-2      0/1       Running   0         19s
zk-2      1/1       Running   0         40s

Use the command below to get the value you entered during the sanity test, from the zk-2 Pod.

kubectl exec zk-2 zkCli.sh get /hello

Even though you terminated and recreated all of the Pods in the zk StatefulSet, the ensemble still serves the original value.

WATCHER::

WatchedEvent state:SyncConnected type:None path:null
world
cZxid = 0x100000002
ctime = Thu Dec 08 15:13:30 UTC 2016
mZxid = 0x100000002
mtime = Thu Dec 08 15:13:30 UTC 2016
pZxid = 0x100000002
cversion = 0
dataVersion = 0
aclVersion = 0
ephemeralOwner = 0x0
dataLength = 5
numChildren = 0

The volumeClaimTemplates field of the zk StatefulSet's spec specifies a PersistentVolume provisioned for each Pod.

volumeClaimTemplates:
  - metadata:
      name: datadir
      annotations:
        volume.alpha.kubernetes.io/storage-class: anything
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 20Gi

The StatefulSet controller generates a PersistentVolumeClaim for each Pod in the StatefulSet.

Use the following command to get the StatefulSet's PersistentVolumeClaims.

kubectl get pvc -l app=zk

When the StatefulSet recreated its Pods, it remounts the Pods' PersistentVolumes.

NAME           STATUS    VOLUME                                     CAPACITY   ACCESSMODES   AGE
datadir-zk-0   Bound     pvc-bed742cd-bcb1-11e6-994f-42010a800002   20Gi       RWO           1h
datadir-zk-1   Bound     pvc-bedd27d2-bcb1-11e6-994f-42010a800002   20Gi       RWO           1h
datadir-zk-2   Bound     pvc-bee0817e-bcb1-11e6-994f-42010a800002   20Gi       RWO           1h

The volumeMounts section of the StatefulSet's container template mounts the PersistentVolumes in the ZooKeeper servers' data directories.

volumeMounts:
- name: datadir
  mountPath: /var/lib/zookeeper

When a Pod in the zk StatefulSet is (re)scheduled, it will always have the same PersistentVolume mounted to the ZooKeeper server's data directory. Even when the Pods are rescheduled, all the writes made to the ZooKeeper servers' WALs, and all their snapshots, remain durable.

Ensuring consistent configuration

As noted in the Facilitating Leader Election and Achieving Consensus sections, the servers in a ZooKeeper ensemble require consistent configuration to elect a leader and form a quorum. They also require consistent configuration of the Zab protocol in order for the protocol to work correctly over a network. In our example we achieve consistent configuration by embedding the configuration directly into the manifest.

Get the zk StatefulSet.

kubectl get sts zk -o yaml
…
command:
      - sh
      - -c
      - "start-zookeeper \
        --servers=3 \
        --data_dir=/var/lib/zookeeper/data \
        --data_log_dir=/var/lib/zookeeper/data/log \
        --conf_dir=/opt/zookeeper/conf \
        --client_port=2181 \
        --election_port=3888 \
        --server_port=2888 \
        --tick_time=2000 \
        --init_limit=10 \
        --sync_limit=5 \
        --heap=512M \
        --max_client_cnxns=60 \
        --snap_retain_count=3 \
        --purge_interval=12 \
        --max_session_timeout=40000 \
        --min_session_timeout=4000 \
        --log_level=INFO"
…

The command used to start the ZooKeeper servers passed the configuration as command line parameter. You can also use environment variables to pass configuration to the ensemble.

Configuring logging

One of the files generated by the zkGenConfig.sh script controls ZooKeeper's logging. ZooKeeper uses Log4j, and, by default, it uses a time and size based rolling file appender for its logging configuration.

Use the command below to get the logging configuration from one of Pods in the zk StatefulSet.

kubectl exec zk-0 cat /usr/etc/zookeeper/log4j.properties

The logging configuration below will cause the ZooKeeper process to write all of its logs to the standard output file stream.

zookeeper.root.logger=CONSOLE
zookeeper.console.threshold=INFO
log4j.rootLogger=${zookeeper.root.logger}
log4j.appender.CONSOLE=org.apache.log4j.ConsoleAppender
log4j.appender.CONSOLE.Threshold=${zookeeper.console.threshold}
log4j.appender.CONSOLE.layout=org.apache.log4j.PatternLayout
log4j.appender.CONSOLE.layout.ConversionPattern=%d{ISO8601} [myid:%X{myid}] - %-5p [%t:%C{1}@%L] - %m%n

This is the simplest possible way to safely log inside the container. Because the applications write logs to standard out, Kubernetes will handle log rotation for you. Kubernetes also implements a sane retention policy that ensures application logs written to standard out and standard error do not exhaust local storage media.

Use kubectl logs to retrieve the last 20 log lines from one of the Pods.

kubectl logs zk-0 --tail 20

You can view application logs written to standard out or standard error using kubectl logs and from the Kubernetes Dashboard.

2016-12-06 19:34:16,236 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxn@827] - Processing ruok command from /127.0.0.1:52740
2016-12-06 19:34:16,237 [myid:1] - INFO  [Thread-1136:NIOServerCnxn@1008] - Closed socket connection for client /127.0.0.1:52740 (no session established for client)
2016-12-06 19:34:26,155 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxnFactory@192] - Accepted socket connection from /127.0.0.1:52749
2016-12-06 19:34:26,155 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxn@827] - Processing ruok command from /127.0.0.1:52749
2016-12-06 19:34:26,156 [myid:1] - INFO  [Thread-1137:NIOServerCnxn@1008] - Closed socket connection for client /127.0.0.1:52749 (no session established for client)
2016-12-06 19:34:26,222 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxnFactory@192] - Accepted socket connection from /127.0.0.1:52750
2016-12-06 19:34:26,222 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxn@827] - Processing ruok command from /127.0.0.1:52750
2016-12-06 19:34:26,226 [myid:1] - INFO  [Thread-1138:NIOServerCnxn@1008] - Closed socket connection for client /127.0.0.1:52750 (no session established for client)
2016-12-06 19:34:36,151 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxnFactory@192] - Accepted socket connection from /127.0.0.1:52760
2016-12-06 19:34:36,152 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxn@827] - Processing ruok command from /127.0.0.1:52760
2016-12-06 19:34:36,152 [myid:1] - INFO  [Thread-1139:NIOServerCnxn@1008] - Closed socket connection for client /127.0.0.1:52760 (no session established for client)
2016-12-06 19:34:36,230 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxnFactory@192] - Accepted socket connection from /127.0.0.1:52761
2016-12-06 19:34:36,231 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxn@827] - Processing ruok command from /127.0.0.1:52761
2016-12-06 19:34:36,231 [myid:1] - INFO  [Thread-1140:NIOServerCnxn@1008] - Closed socket connection for client /127.0.0.1:52761 (no session established for client)
2016-12-06 19:34:46,149 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxnFactory@192] - Accepted socket connection from /127.0.0.1:52767
2016-12-06 19:34:46,149 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxn@827] - Processing ruok command from /127.0.0.1:52767
2016-12-06 19:34:46,149 [myid:1] - INFO  [Thread-1141:NIOServerCnxn@1008] - Closed socket connection for client /127.0.0.1:52767 (no session established for client)
2016-12-06 19:34:46,230 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxnFactory@192] - Accepted socket connection from /127.0.0.1:52768
2016-12-06 19:34:46,230 [myid:1] - INFO  [NIOServerCxn.Factory:0.0.0.0/0.0.0.0:2181:NIOServerCnxn@827] - Processing ruok command from /127.0.0.1:52768
2016-12-06 19:34:46,230 [myid:1] - INFO  [Thread-1142:NIOServerCnxn@1008] - Closed socket connection for client /127.0.0.1:52768 (no session established for client)

Kubernetes integrates with many logging solutions. You can choose a logging solution that best fits your cluster and applications. For cluster-level logging and aggregation, consider deploying a sidecar container to rotate and ship your logs.

Configuring a non-privileged user

The best practices to allow an application to run as a privileged user inside of a container are a matter of debate. If your organization requires that applications run as a non-privileged user you can use a SecurityContext to control the user that the entry point runs as.

The zk StatefulSet's Pod template contains a SecurityContext.

securityContext:
  runAsUser: 1000
  fsGroup: 1000

In the Pods' containers, UID 1000 corresponds to the zookeeper user and GID 1000 corresponds to the zookeeper group.

Get the ZooKeeper process information from the zk-0 Pod.

kubectl exec zk-0 -- ps -elf

As the runAsUser field of the securityContext object is set to 1000, instead of running as root, the ZooKeeper process runs as the zookeeper user.

F S UID        PID  PPID  C PRI  NI ADDR SZ WCHAN  STIME TTY          TIME CMD
4 S zookeep+     1     0  0  80   0 -  1127 -      20:46 ?        00:00:00 sh -c zkGenConfig.sh && zkServer.sh start-foreground
0 S zookeep+    27     1  0  80   0 - 1155556 -    20:46 ?        00:00:19 /usr/lib/jvm/java-8-openjdk-amd64/bin/java -Dzookeeper.log.dir=/var/log/zookeeper -Dzookeeper.root.logger=INFO,CONSOLE -cp /usr/bin/../build/classes:/usr/bin/../build/lib/*.jar:/usr/bin/../share/zookeeper/zookeeper-3.4.9.jar:/usr/bin/../share/zookeeper/slf4j-log4j12-1.6.1.jar:/usr/bin/../share/zookeeper/slf4j-api-1.6.1.jar:/usr/bin/../share/zookeeper/netty-3.10.5.Final.jar:/usr/bin/../share/zookeeper/log4j-1.2.16.jar:/usr/bin/../share/zookeeper/jline-0.9.94.jar:/usr/bin/../src/java/lib/*.jar:/usr/bin/../etc/zookeeper: -Xmx2G -Xms2G -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=false org.apache.zookeeper.server.quorum.QuorumPeerMain /usr/bin/../etc/zookeeper/zoo.cfg

By default, when the Pod's PersistentVolumes is mounted to the ZooKeeper server's data directory, it is only accessible by the root user. This configuration prevents the ZooKeeper process from writing to its WAL and storing its snapshots.

Use the command below to get the file permissions of the ZooKeeper data directory on the zk-0 Pod.

kubectl exec -ti zk-0 -- ls -ld /var/lib/zookeeper/data

Because the fsGroup field of the securityContext object is set to 1000, the ownership of the Pods' PersistentVolumes is set to the zookeeper group, and the ZooKeeper process is able to read and write its data.

drwxr-sr-x 3 zookeeper zookeeper 4096 Dec  5 20:45 /var/lib/zookeeper/data

Managing the ZooKeeper process

The ZooKeeper documentation mentions that "You will want to have a supervisory process that manages each of your ZooKeeper server processes (JVM)." Utilizing a watchdog (supervisory process) to restart failed processes in a distributed system is a common pattern. When deploying an application in Kubernetes, rather than using an external utility as a supervisory process, you should use Kubernetes as the watchdog for your application.

Updating the ensemble

The zk StatefulSet is configured to use the RollingUpdate update strategy.

You can use kubectl patch to update the number of cpus allocated to the servers.

kubectl patch sts zk --type='json' -p='[{"op": "replace", "path": "/spec/template/spec/containers/0/resources/requests/cpu", "value":"0.3"}]'
statefulset.apps/zk patched

Use kubectl rollout status to watch the status of the update.

kubectl rollout status sts/zk
waiting for statefulset rolling update to complete 0 pods at revision zk-5db4499664...
Waiting for 1 pods to be ready...
Waiting for 1 pods to be ready...
waiting for statefulset rolling update to complete 1 pods at revision zk-5db4499664...
Waiting for 1 pods to be ready...
Waiting for 1 pods to be ready...
waiting for statefulset rolling update to complete 2 pods at revision zk-5db4499664...
Waiting for 1 pods to be ready...
Waiting for 1 pods to be ready...
statefulset rolling update complete 3 pods at revision zk-5db4499664...

This terminates the Pods, one at a time, in reverse ordinal order, and recreates them with the new configuration. This ensures that quorum is maintained during a rolling update.

Use the kubectl rollout history command to view a history or previous configurations.

kubectl rollout history sts/zk

The output is similar to this:

statefulsets "zk"
REVISION
1
2

Use the kubectl rollout undo command to roll back the modification.

kubectl rollout undo sts/zk

The output is similar to this:

statefulset.apps/zk rolled back

Handling process failure

Restart Policies control how Kubernetes handles process failures for the entry point of the container in a Pod. For Pods in a StatefulSet, the only appropriate RestartPolicy is Always, and this is the default value. For stateful applications you should never override the default policy.

Use the following command to examine the process tree for the ZooKeeper server running in the zk-0 Pod.

kubectl exec zk-0 -- ps -ef

The command used as the container's entry point has PID 1, and the ZooKeeper process, a child of the entry point, has PID 27.

UID        PID  PPID  C STIME TTY          TIME CMD
zookeep+     1     0  0 15:03 ?        00:00:00 sh -c zkGenConfig.sh && zkServer.sh start-foreground
zookeep+    27     1  0 15:03 ?        00:00:03 /usr/lib/jvm/java-8-openjdk-amd64/bin/java -Dzookeeper.log.dir=/var/log/zookeeper -Dzookeeper.root.logger=INFO,CONSOLE -cp /usr/bin/../build/classes:/usr/bin/../build/lib/*.jar:/usr/bin/../share/zookeeper/zookeeper-3.4.9.jar:/usr/bin/../share/zookeeper/slf4j-log4j12-1.6.1.jar:/usr/bin/../share/zookeeper/slf4j-api-1.6.1.jar:/usr/bin/../share/zookeeper/netty-3.10.5.Final.jar:/usr/bin/../share/zookeeper/log4j-1.2.16.jar:/usr/bin/../share/zookeeper/jline-0.9.94.jar:/usr/bin/../src/java/lib/*.jar:/usr/bin/../etc/zookeeper: -Xmx2G -Xms2G -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.local.only=false org.apache.zookeeper.server.quorum.QuorumPeerMain /usr/bin/../etc/zookeeper/zoo.cfg

In another terminal watch the Pods in the zk StatefulSet with the following command.

kubectl get pod -w -l app=zk

In another terminal, terminate the ZooKeeper process in Pod zk-0 with the following command.

kubectl exec zk-0 -- pkill java

The termination of the ZooKeeper process caused its parent process to terminate. Because the RestartPolicy of the container is Always, it restarted the parent process.

NAME      READY     STATUS    RESTARTS   AGE
zk-0      1/1       Running   0          21m
zk-1      1/1       Running   0          20m
zk-2      1/1       Running   0          19m
NAME      READY     STATUS    RESTARTS   AGE
zk-0      0/1       Error     0          29m
zk-0      0/1       Running   1         29m
zk-0      1/1       Running   1         29m

If your application uses a script (such as zkServer.sh) to launch the process that implements the application's business logic, the script must terminate with the child process. This ensures that Kubernetes will restart the application's container when the process implementing the application's business logic fails.

Testing for liveness

Configuring your application to restart failed processes is not enough to keep a distributed system healthy. There are scenarios where a system's processes can be both alive and unresponsive, or otherwise unhealthy. You should use liveness probes to notify Kubernetes that your application's processes are unhealthy and it should restart them.

The Pod template for the zk StatefulSet specifies a liveness probe.

  livenessProbe:
    exec:
      command:
      - sh
      - -c
      - "zookeeper-ready 2181"
    initialDelaySeconds: 15
    timeoutSeconds: 5

The probe calls a bash script that uses the ZooKeeper ruok four letter word to test the server's health.

OK=$(echo ruok | nc 127.0.0.1 $1)
if [ "$OK" == "imok" ]; then
    exit 0
else
    exit 1
fi

In one terminal window, use the following command to watch the Pods in the zk StatefulSet.

kubectl get pod -w -l app=zk

In another window, using the following command to delete the zookeeper-ready script from the file system of Pod zk-0.

kubectl exec zk-0 -- rm /opt/zookeeper/bin/zookeeper-ready

When the liveness probe for the ZooKeeper process fails, Kubernetes will automatically restart the process for you, ensuring that unhealthy processes in the ensemble are restarted.

kubectl get pod -w -l app=zk
NAME      READY     STATUS    RESTARTS   AGE
zk-0      1/1       Running   0          1h
zk-1      1/1       Running   0          1h
zk-2      1/1       Running   0          1h
NAME      READY     STATUS    RESTARTS   AGE
zk-0      0/1       Running   0          1h
zk-0      0/1       Running   1         1h
zk-0      1/1       Running   1         1h

Testing for readiness

Readiness is not the same as liveness. If a process is alive, it is scheduled and healthy. If a process is ready, it is able to process input. Liveness is a necessary, but not sufficient, condition for readiness. There are cases, particularly during initialization and termination, when a process can be alive but not ready.

If you specify a readiness probe, Kubernetes will ensure that your application's processes will not receive network traffic until their readiness checks pass.

For a ZooKeeper server, liveness implies readiness. Therefore, the readiness probe from the zookeeper.yaml manifest is identical to the liveness probe.

  readinessProbe:
    exec:
      command:
      - sh
      - -c
      - "zookeeper-ready 2181"
    initialDelaySeconds: 15
    timeoutSeconds: 5

Even though the liveness and readiness probes are identical, it is important to specify both. This ensures that only healthy servers in the ZooKeeper ensemble receive network traffic.

Tolerating Node failure

ZooKeeper needs a quorum of servers to successfully commit mutations to data. For a three server ensemble, two servers must be healthy for writes to succeed. In quorum based systems, members are deployed across failure domains to ensure availability. To avoid an outage, due to the loss of an individual machine, best practices preclude co-locating multiple instances of the application on the same machine.

By default, Kubernetes may co-locate Pods in a StatefulSet on the same node. For the three server ensemble you created, if two servers are on the same node, and that node fails, the clients of your ZooKeeper service will experience an outage until at least one of the Pods can be rescheduled.

You should always provision additional capacity to allow the processes of critical systems to be rescheduled in the event of node failures. If you do so, then the outage will only last until the Kubernetes scheduler reschedules one of the ZooKeeper servers. However, if you want your service to tolerate node failures with no downtime, you should set podAntiAffinity.

Use the command below to get the nodes for Pods in the zk StatefulSet.

for i in 0 1 2; do kubectl get pod zk-$i --template {{.spec.nodeName}}; echo ""; done

All of the Pods in the zk StatefulSet are deployed on different nodes.

kubernetes-node-cxpk
kubernetes-node-a5aq
kubernetes-node-2g2d

This is because the Pods in the zk StatefulSet have a PodAntiAffinity specified.

affinity:
  podAntiAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
      - labelSelector:
          matchExpressions:
            - key: "app"
              operator: In
              values:
                - zk
        topologyKey: "kubernetes.io/hostname"

The requiredDuringSchedulingIgnoredDuringExecution field tells the Kubernetes Scheduler that it should never co-locate two Pods which have app label as zk in the domain defined by the topologyKey. The topologyKey kubernetes.io/hostname indicates that the domain is an individual node. Using different rules, labels, and selectors, you can extend this technique to spread your ensemble across physical, network, and power failure domains.

Surviving maintenance

In this section you will cordon and drain nodes. If you are using this tutorial on a shared cluster, be sure that this will not adversely affect other tenants.

The previous section showed you how to spread your Pods across nodes to survive unplanned node failures, but you also need to plan for temporary node failures that occur due to planned maintenance.

Use this command to get the nodes in your cluster.

kubectl get nodes

This tutorial assumes a cluster with at least four nodes. If the cluster has more than four, use kubectl cordon to cordon all but four nodes. Constraining to four nodes will ensure Kubernetes encounters affinity and PodDisruptionBudget constraints when scheduling zookeeper Pods in the following maintenance simulation.

kubectl cordon <node-name>

Use this command to get the zk-pdb PodDisruptionBudget.

kubectl get pdb zk-pdb

The max-unavailable field indicates to Kubernetes that at most one Pod from zk StatefulSet can be unavailable at any time.

NAME      MIN-AVAILABLE   MAX-UNAVAILABLE   ALLOWED-DISRUPTIONS   AGE
zk-pdb    N/A             1                 1

In one terminal, use this command to watch the Pods in the zk StatefulSet.

kubectl get pods -w -l app=zk

In another terminal, use this command to get the nodes that the Pods are currently scheduled on.

for i in 0 1 2; do kubectl get pod zk-$i --template {{.spec.nodeName}}; echo ""; done

The output is similar to this:

kubernetes-node-pb41
kubernetes-node-ixsl
kubernetes-node-i4c4

Use kubectl drain to cordon and drain the node on which the zk-0 Pod is scheduled.

kubectl drain $(kubectl get pod zk-0 --template {{.spec.nodeName}}) --ignore-daemonsets --force --delete-emptydir-data

The output is similar to this:

node "kubernetes-node-pb41" cordoned

WARNING: Deleting pods not managed by ReplicationController, ReplicaSet, Job, or DaemonSet: fluentd-cloud-logging-kubernetes-node-pb41, kube-proxy-kubernetes-node-pb41; Ignoring DaemonSet-managed pods: node-problem-detector-v0.1-o5elz
pod "zk-0" deleted
node "kubernetes-node-pb41" drained

As there are four nodes in your cluster, kubectl drain, succeeds and the zk-0 is rescheduled to another node.

NAME      READY     STATUS    RESTARTS   AGE
zk-0      1/1       Running   2          1h
zk-1      1/1       Running   0          1h
zk-2      1/1       Running   0          1h
NAME      READY     STATUS        RESTARTS   AGE
zk-0      1/1       Terminating   2          2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Pending   0         0s
zk-0      0/1       Pending   0         0s
zk-0      0/1       ContainerCreating   0         0s
zk-0      0/1       Running   0         51s
zk-0      1/1       Running   0         1m

Keep watching the StatefulSet's Pods in the first terminal and drain the node on which zk-1 is scheduled.

kubectl drain $(kubectl get pod zk-1 --template {{.spec.nodeName}}) --ignore-daemonsets --force --delete-emptydir-data

The output is similar to this:

"kubernetes-node-ixsl" cordoned
WARNING: Deleting pods not managed by ReplicationController, ReplicaSet, Job, or DaemonSet: fluentd-cloud-logging-kubernetes-node-ixsl, kube-proxy-kubernetes-node-ixsl; Ignoring DaemonSet-managed pods: node-problem-detector-v0.1-voc74
pod "zk-1" deleted
node "kubernetes-node-ixsl" drained

The zk-1 Pod cannot be scheduled because the zk StatefulSet contains a PodAntiAffinity rule preventing co-location of the Pods, and as only two nodes are schedulable, the Pod will remain in a Pending state.

kubectl get pods -w -l app=zk

The output is similar to this:

NAME      READY     STATUS    RESTARTS   AGE
zk-0      1/1       Running   2          1h
zk-1      1/1       Running   0          1h
zk-2      1/1       Running   0          1h
NAME      READY     STATUS        RESTARTS   AGE
zk-0      1/1       Terminating   2          2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Pending   0         0s
zk-0      0/1       Pending   0         0s
zk-0      0/1       ContainerCreating   0         0s
zk-0      0/1       Running   0         51s
zk-0      1/1       Running   0         1m
zk-1      1/1       Terminating   0         2h
zk-1      0/1       Terminating   0         2h
zk-1      0/1       Terminating   0         2h
zk-1      0/1       Terminating   0         2h
zk-1      0/1       Pending   0         0s
zk-1      0/1       Pending   0         0s

Continue to watch the Pods of the StatefulSet, and drain the node on which zk-2 is scheduled.

kubectl drain $(kubectl get pod zk-2 --template {{.spec.nodeName}}) --ignore-daemonsets --force --delete-emptydir-data

The output is similar to this:

node "kubernetes-node-i4c4" cordoned

WARNING: Deleting pods not managed by ReplicationController, ReplicaSet, Job, or DaemonSet: fluentd-cloud-logging-kubernetes-node-i4c4, kube-proxy-kubernetes-node-i4c4; Ignoring DaemonSet-managed pods: node-problem-detector-v0.1-dyrog
WARNING: Ignoring DaemonSet-managed pods: node-problem-detector-v0.1-dyrog; Deleting pods not managed by ReplicationController, ReplicaSet, Job, or DaemonSet: fluentd-cloud-logging-kubernetes-node-i4c4, kube-proxy-kubernetes-node-i4c4
There are pending pods when an error occurred: Cannot evict pod as it would violate the pod's disruption budget.
pod/zk-2

Use CTRL-C to terminate kubectl.

You cannot drain the third node because evicting zk-2 would violate zk-budget. However, the node will remain cordoned.

Use zkCli.sh to retrieve the value you entered during the sanity test from zk-0.

kubectl exec zk-0 zkCli.sh get /hello

The service is still available because its PodDisruptionBudget is respected.

WatchedEvent state:SyncConnected type:None path:null
world
cZxid = 0x200000002
ctime = Wed Dec 07 00:08:59 UTC 2016
mZxid = 0x200000002
mtime = Wed Dec 07 00:08:59 UTC 2016
pZxid = 0x200000002
cversion = 0
dataVersion = 0
aclVersion = 0
ephemeralOwner = 0x0
dataLength = 5
numChildren = 0

Use kubectl uncordon to uncordon the first node.

kubectl uncordon kubernetes-node-pb41

The output is similar to this:

node "kubernetes-node-pb41" uncordoned

zk-1 is rescheduled on this node. Wait until zk-1 is Running and Ready.

kubectl get pods -w -l app=zk

The output is similar to this:

NAME      READY     STATUS    RESTARTS   AGE
zk-0      1/1       Running   2          1h
zk-1      1/1       Running   0          1h
zk-2      1/1       Running   0          1h
NAME      READY     STATUS        RESTARTS   AGE
zk-0      1/1       Terminating   2          2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Terminating   2         2h
zk-0      0/1       Pending   0         0s
zk-0      0/1       Pending   0         0s
zk-0      0/1       ContainerCreating   0         0s
zk-0      0/1       Running   0         51s
zk-0      1/1       Running   0         1m
zk-1      1/1       Terminating   0         2h
zk-1      0/1       Terminating   0         2h
zk-1      0/1       Terminating   0         2h
zk-1      0/1       Terminating   0         2h
zk-1      0/1       Pending   0         0s
zk-1      0/1       Pending   0         0s
zk-1      0/1       Pending   0         12m
zk-1      0/1       ContainerCreating   0         12m
zk-1      0/1       Running   0         13m
zk-1      1/1       Running   0         13m

Attempt to drain the node on which zk-2 is scheduled.

kubectl drain $(kubectl get pod zk-2 --template {{.spec.nodeName}}) --ignore-daemonsets --force --delete-emptydir-data

The output is similar to this:

node "kubernetes-node-i4c4" already cordoned
WARNING: Deleting pods not managed by ReplicationController, ReplicaSet, Job, or DaemonSet: fluentd-cloud-logging-kubernetes-node-i4c4, kube-proxy-kubernetes-node-i4c4; Ignoring DaemonSet-managed pods: node-problem-detector-v0.1-dyrog
pod "heapster-v1.2.0-2604621511-wht1r" deleted
pod "zk-2" deleted
node "kubernetes-node-i4c4" drained

This time kubectl drain succeeds.

Uncordon the second node to allow zk-2 to be rescheduled.

kubectl uncordon kubernetes-node-ixsl

The output is similar to this:

node "kubernetes-node-ixsl" uncordoned

You can use kubectl drain in conjunction with PodDisruptionBudgets to ensure that your services remain available during maintenance. If drain is used to cordon nodes and evict pods prior to taking the node offline for maintenance, services that express a disruption budget will have that budget respected. You should always allocate additional capacity for critical services so that their Pods can be immediately rescheduled.

Cleaning up

  • Use kubectl uncordon to uncordon all the nodes in your cluster.
  • You must delete the persistent storage media for the PersistentVolumes used in this tutorial. Follow the necessary steps, based on your environment, storage configuration, and provisioning method, to ensure that all storage is reclaimed.

7 - Services

7.1 - Connecting Applications with Services

The Kubernetes model for connecting containers

Now that you have a continuously running, replicated application you can expose it on a network.

Kubernetes assumes that pods can communicate with other pods, regardless of which host they land on. Kubernetes gives every pod its own cluster-private IP address, so you do not need to explicitly create links between pods or map container ports to host ports. This means that containers within a Pod can all reach each other's ports on localhost, and all pods in a cluster can see each other without NAT. The rest of this document elaborates on how you can run reliable services on such a networking model.

This tutorial uses a simple nginx web server to demonstrate the concept.

Exposing pods to the cluster

We did this in a previous example, but let's do it once again and focus on the networking perspective. Create an nginx Pod, and note that it has a container port specification:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 2
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      containers:
      - name: my-nginx
        image: nginx
        ports:
        - containerPort: 80

This makes it accessible from any node in your cluster. Check the nodes the Pod is running on:

kubectl apply -f ./run-my-nginx.yaml
kubectl get pods -l run=my-nginx -o wide
NAME                        READY     STATUS    RESTARTS   AGE       IP            NODE
my-nginx-3800858182-jr4a2   1/1       Running   0          13s       10.244.3.4    kubernetes-minion-905m
my-nginx-3800858182-kna2y   1/1       Running   0          13s       10.244.2.5    kubernetes-minion-ljyd

Check your pods' IPs:

kubectl get pods -l run=my-nginx -o custom-columns=POD_IP:.status.podIPs
    POD_IP
    [map[ip:10.244.3.4]]
    [map[ip:10.244.2.5]]

You should be able to ssh into any node in your cluster and use a tool such as curl to make queries against both IPs. Note that the containers are not using port 80 on the node, nor are there any special NAT rules to route traffic to the pod. This means you can run multiple nginx pods on the same node all using the same containerPort, and access them from any other pod or node in your cluster using the assigned IP address for the pod. If you want to arrange for a specific port on the host Node to be forwarded to backing Pods, you can - but the networking model should mean that you do not need to do so.

You can read more about the Kubernetes Networking Model if you're curious.

Creating a Service

So we have pods running nginx in a flat, cluster wide, address space. In theory, you could talk to these pods directly, but what happens when a node dies? The pods die with it, and the ReplicaSet inside the Deployment will create new ones, with different IPs. This is the problem a Service solves.

A Kubernetes Service is an abstraction which defines a logical set of Pods running somewhere in your cluster, that all provide the same functionality. When created, each Service is assigned a unique IP address (also called clusterIP). This address is tied to the lifespan of the Service, and will not change while the Service is alive. Pods can be configured to talk to the Service, and know that communication to the Service will be automatically load-balanced out to some pod that is a member of the Service.

You can create a Service for your 2 nginx replicas with kubectl expose:

kubectl expose deployment/my-nginx
service/my-nginx exposed

This is equivalent to kubectl apply -f the following yaml:

apiVersion: v1
kind: Service
metadata:
  name: my-nginx
  labels:
    run: my-nginx
spec:
  ports:
  - port: 80
    protocol: TCP
  selector:
    run: my-nginx

This specification will create a Service which targets TCP port 80 on any Pod with the run: my-nginx label, and expose it on an abstracted Service port (targetPort: is the port the container accepts traffic on, port: is the abstracted Service port, which can be any port other pods use to access the Service). View Service API object to see the list of supported fields in service definition. Check your Service:

kubectl get svc my-nginx
NAME       TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)   AGE
my-nginx   ClusterIP   10.0.162.149   <none>        80/TCP    21s

As mentioned previously, a Service is backed by a group of Pods. These Pods are exposed through EndpointSlices. The Service's selector will be evaluated continuously and the results will be POSTed to an EndpointSlice that is connected to the Service using labels. When a Pod dies, it is automatically removed from the EndpointSlices that contain it as an endpoint. New Pods that match the Service's selector will automatically get added to an EndpointSlice for that Service. Check the endpoints, and note that the IPs are the same as the Pods created in the first step:

kubectl describe svc my-nginx
Name:                my-nginx
Namespace:           default
Labels:              run=my-nginx
Annotations:         <none>
Selector:            run=my-nginx
Type:                ClusterIP
IP Family Policy:    SingleStack
IP Families:         IPv4
IP:                  10.0.162.149
IPs:                 10.0.162.149
Port:                <unset> 80/TCP
TargetPort:          80/TCP
Endpoints:           10.244.2.5:80,10.244.3.4:80
Session Affinity:    None
Events:              <none>
kubectl get endpointslices -l kubernetes.io/service-name=my-nginx
NAME             ADDRESSTYPE   PORTS   ENDPOINTS               AGE
my-nginx-7vzhx   IPv4          80      10.244.2.5,10.244.3.4   21s

You should now be able to curl the nginx Service on <CLUSTER-IP>:<PORT> from any node in your cluster. Note that the Service IP is completely virtual, it never hits the wire. If you're curious about how this works you can read more about the service proxy.

Accessing the Service

Kubernetes supports 2 primary modes of finding a Service - environment variables and DNS. The former works out of the box while the latter requires the CoreDNS cluster addon.

Environment Variables

When a Pod runs on a Node, the kubelet adds a set of environment variables for each active Service. This introduces an ordering problem. To see why, inspect the environment of your running nginx Pods (your Pod name will be different):

kubectl exec my-nginx-3800858182-jr4a2 -- printenv | grep SERVICE
KUBERNETES_SERVICE_HOST=10.0.0.1
KUBERNETES_SERVICE_PORT=443
KUBERNETES_SERVICE_PORT_HTTPS=443

Note there's no mention of your Service. This is because you created the replicas before the Service. Another disadvantage of doing this is that the scheduler might put both Pods on the same machine, which will take your entire Service down if it dies. We can do this the right way by killing the 2 Pods and waiting for the Deployment to recreate them. This time the Service exists before the replicas. This will give you scheduler-level Service spreading of your Pods (provided all your nodes have equal capacity), as well as the right environment variables:

kubectl scale deployment my-nginx --replicas=0; kubectl scale deployment my-nginx --replicas=2;

kubectl get pods -l run=my-nginx -o wide
NAME                        READY     STATUS    RESTARTS   AGE     IP            NODE
my-nginx-3800858182-e9ihh   1/1       Running   0          5s      10.244.2.7    kubernetes-minion-ljyd
my-nginx-3800858182-j4rm4   1/1       Running   0          5s      10.244.3.8    kubernetes-minion-905m

You may notice that the pods have different names, since they are killed and recreated.

kubectl exec my-nginx-3800858182-e9ihh -- printenv | grep SERVICE
KUBERNETES_SERVICE_PORT=443
MY_NGINX_SERVICE_HOST=10.0.162.149
KUBERNETES_SERVICE_HOST=10.0.0.1
MY_NGINX_SERVICE_PORT=80
KUBERNETES_SERVICE_PORT_HTTPS=443

DNS

Kubernetes offers a DNS cluster addon Service that automatically assigns dns names to other Services. You can check if it's running on your cluster:

kubectl get services kube-dns --namespace=kube-system
NAME       TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)         AGE
kube-dns   ClusterIP   10.0.0.10    <none>        53/UDP,53/TCP   8m

The rest of this section will assume you have a Service with a long lived IP (my-nginx), and a DNS server that has assigned a name to that IP. Here we use the CoreDNS cluster addon (application name kube-dns), so you can talk to the Service from any pod in your cluster using standard methods (e.g. gethostbyname()). If CoreDNS isn't running, you can enable it referring to the CoreDNS README or Installing CoreDNS. Let's run another curl application to test this:

kubectl run curl --image=radial/busyboxplus:curl -i --tty --rm
Waiting for pod default/curl-131556218-9fnch to be running, status is Pending, pod ready: false
Hit enter for command prompt

Then, hit enter and run nslookup my-nginx:

[ root@curl-131556218-9fnch:/ ]$ nslookup my-nginx
Server:    10.0.0.10
Address 1: 10.0.0.10

Name:      my-nginx
Address 1: 10.0.162.149

Securing the Service

Till now we have only accessed the nginx server from within the cluster. Before exposing the Service to the internet, you want to make sure the communication channel is secure. For this, you will need:

  • Self signed certificates for https (unless you already have an identity certificate)
  • An nginx server configured to use the certificates
  • A secret that makes the certificates accessible to pods

You can acquire all these from the nginx https example. This requires having go and make tools installed. If you don't want to install those, then follow the manual steps later. In short:

make keys KEY=/tmp/nginx.key CERT=/tmp/nginx.crt
kubectl create secret tls nginxsecret --key /tmp/nginx.key --cert /tmp/nginx.crt
secret/nginxsecret created
kubectl get secrets
NAME                  TYPE                                  DATA      AGE
nginxsecret           kubernetes.io/tls                     2         1m

And also the configmap:

kubectl create configmap nginxconfigmap --from-file=default.conf

You can find an example for default.conf in the Kubernetes examples project repo.

configmap/nginxconfigmap created
kubectl get configmaps
NAME             DATA   AGE
nginxconfigmap   1      114s

You can view the details of the nginxconfigmap ConfigMap using the following command:

kubectl describe configmap  nginxconfigmap

The output is similar to:

Name:         nginxconfigmap
Namespace:    default
Labels:       <none>
Annotations:  <none>

Data
====
default.conf:
----
server {
        listen 80 default_server;
        listen [::]:80 default_server ipv6only=on;

        listen 443 ssl;

        root /usr/share/nginx/html;
        index index.html;

        server_name localhost;
        ssl_certificate /etc/nginx/ssl/tls.crt;
        ssl_certificate_key /etc/nginx/ssl/tls.key;

        location / {
                try_files $uri $uri/ =404;
        }
}

BinaryData
====

Events:  <none>

Following are the manual steps to follow in case you run into problems running make (on windows for example):

# Create a public private key pair
openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout /d/tmp/nginx.key -out /d/tmp/nginx.crt -subj "/CN=my-nginx/O=my-nginx"
# Convert the keys to base64 encoding
cat /d/tmp/nginx.crt | base64
cat /d/tmp/nginx.key | base64

Use the output from the previous commands to create a yaml file as follows. The base64 encoded value should all be on a single line.

apiVersion: "v1"
kind: "Secret"
metadata:
  name: "nginxsecret"
  namespace: "default"
type: kubernetes.io/tls
data:
  tls.crt: "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"
  tls.key: "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"

Now create the secrets using the file:

kubectl apply -f nginxsecrets.yaml
kubectl get secrets
NAME                  TYPE                                  DATA      AGE
nginxsecret           kubernetes.io/tls                     2         1m

Now modify your nginx replicas to start an https server using the certificate in the secret, and the Service, to expose both ports (80 and 443):

apiVersion: v1
kind: Service
metadata:
  name: my-nginx
  labels:
    run: my-nginx
spec:
  type: NodePort
  ports:
  - port: 8080
    targetPort: 80
    protocol: TCP
    name: http
  - port: 443
    protocol: TCP
    name: https
  selector:
    run: my-nginx
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
spec:
  selector:
    matchLabels:
      run: my-nginx
  replicas: 1
  template:
    metadata:
      labels:
        run: my-nginx
    spec:
      volumes:
      - name: secret-volume
        secret:
          secretName: nginxsecret
      - name: configmap-volume
        configMap:
          name: nginxconfigmap
      containers:
      - name: nginxhttps
        image: bprashanth/nginxhttps:1.0
        ports:
        - containerPort: 443
        - containerPort: 80
        volumeMounts:
        - mountPath: /etc/nginx/ssl
          name: secret-volume
        - mountPath: /etc/nginx/conf.d
          name: configmap-volume

Noteworthy points about the nginx-secure-app manifest:

  • It contains both Deployment and Service specification in the same file.
  • The nginx server serves HTTP traffic on port 80 and HTTPS traffic on 443, and nginx Service exposes both ports.
  • Each container has access to the keys through a volume mounted at /etc/nginx/ssl. This is set up before the nginx server is started.
kubectl delete deployments,svc my-nginx; kubectl create -f ./nginx-secure-app.yaml

At this point you can reach the nginx server from any node.

kubectl get pods -l run=my-nginx -o custom-columns=POD_IP:.status.podIPs
    POD_IP
    [map[ip:10.244.3.5]]
node $ curl -k https://10.244.3.5
...
<h1>Welcome to nginx!</h1>

Note how we supplied the -k parameter to curl in the last step, this is because we don't know anything about the pods running nginx at certificate generation time, so we have to tell curl to ignore the CName mismatch. By creating a Service we linked the CName used in the certificate with the actual DNS name used by pods during Service lookup. Let's test this from a pod (the same secret is being reused for simplicity, the pod only needs nginx.crt to access the Service):

apiVersion: apps/v1
kind: Deployment
metadata:
  name: curl-deployment
spec:
  selector:
    matchLabels:
      app: curlpod
  replicas: 1
  template:
    metadata:
      labels:
        app: curlpod
    spec:
      volumes:
      - name: secret-volume
        secret:
          secretName: nginxsecret
      containers:
      - name: curlpod
        command:
        - sh
        - -c
        - while true; do sleep 1; done
        image: radial/busyboxplus:curl
        volumeMounts:
        - mountPath: /etc/nginx/ssl
          name: secret-volume
kubectl apply -f ./curlpod.yaml
kubectl get pods -l app=curlpod
NAME                               READY     STATUS    RESTARTS   AGE
curl-deployment-1515033274-1410r   1/1       Running   0          1m
kubectl exec curl-deployment-1515033274-1410r -- curl https://my-nginx --cacert /etc/nginx/ssl/tls.crt
...
<title>Welcome to nginx!</title>
...

Exposing the Service

For some parts of your applications you may want to expose a Service onto an external IP address. Kubernetes supports two ways of doing this: NodePorts and LoadBalancers. The Service created in the last section already used NodePort, so your nginx HTTPS replica is ready to serve traffic on the internet if your node has a public IP.

kubectl get svc my-nginx -o yaml | grep nodePort -C 5
  uid: 07191fb3-f61a-11e5-8ae5-42010af00002
spec:
  clusterIP: 10.0.162.149
  ports:
  - name: http
    nodePort: 31704
    port: 8080
    protocol: TCP
    targetPort: 80
  - name: https
    nodePort: 32453
    port: 443
    protocol: TCP
    targetPort: 443
  selector:
    run: my-nginx
kubectl get nodes -o yaml | grep ExternalIP -C 1
    - address: 104.197.41.11
      type: ExternalIP
    allocatable:
--
    - address: 23.251.152.56
      type: ExternalIP
    allocatable:
...

$ curl https://<EXTERNAL-IP>:<NODE-PORT> -k
...
<h1>Welcome to nginx!</h1>

Let's now recreate the Service to use a cloud load balancer. Change the Type of my-nginx Service from NodePort to LoadBalancer:

kubectl edit svc my-nginx
kubectl get svc my-nginx
NAME       TYPE           CLUSTER-IP     EXTERNAL-IP        PORT(S)               AGE
my-nginx   LoadBalancer   10.0.162.149     xx.xxx.xxx.xxx     8080:30163/TCP        21s
curl https://<EXTERNAL-IP> -k
...
<title>Welcome to nginx!</title>

The IP address in the EXTERNAL-IP column is the one that is available on the public internet. The CLUSTER-IP is only available inside your cluster/private cloud network.

Note that on AWS, type LoadBalancer creates an ELB, which uses a (long) hostname, not an IP. It's too long to fit in the standard kubectl get svc output, in fact, so you'll need to do kubectl describe service my-nginx to see it. You'll see something like this:

kubectl describe service my-nginx
...
LoadBalancer Ingress:   a320587ffd19711e5a37606cf4a74574-1142138393.us-east-1.elb.amazonaws.com
...

What's next

7.2 - Using Source IP

Applications running in a Kubernetes cluster find and communicate with each other, and the outside world, through the Service abstraction. This document explains what happens to the source IP of packets sent to different types of Services, and how you can toggle this behavior according to your needs.

Before you begin

Terminology

This document makes use of the following terms:

NAT
Network address translation
Source NAT
Replacing the source IP on a packet; in this page, that usually means replacing with the IP address of a node.
Destination NAT
Replacing the destination IP on a packet; in this page, that usually means replacing with the IP address of a Pod
VIP
A virtual IP address, such as the one assigned to every Service in Kubernetes
kube-proxy
A network daemon that orchestrates Service VIP management on every node

Prerequisites

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:

The examples use a small nginx webserver that echoes back the source IP of requests it receives through an HTTP header. You can create it as follows:

kubectl create deployment source-ip-app --image=registry.k8s.io/echoserver:1.4

The output is:

deployment.apps/source-ip-app created

Objectives

  • Expose a simple application through various types of Services
  • Understand how each Service type handles source IP NAT
  • Understand the tradeoffs involved in preserving source IP

Source IP for Services with Type=ClusterIP

Packets sent to ClusterIP from within the cluster are never source NAT'd if you're running kube-proxy in iptables mode, (the default). You can query the kube-proxy mode by fetching http://localhost:10249/proxyMode on the node where kube-proxy is running.

kubectl get nodes

The output is similar to this:

NAME                           STATUS     ROLES    AGE     VERSION
kubernetes-node-6jst   Ready      <none>   2h      v1.13.0
kubernetes-node-cx31   Ready      <none>   2h      v1.13.0
kubernetes-node-jj1t   Ready      <none>   2h      v1.13.0

Get the proxy mode on one of the nodes (kube-proxy listens on port 10249):

# Run this in a shell on the node you want to query.
curl http://localhost:10249/proxyMode

The output is:

iptables

You can test source IP preservation by creating a Service over the source IP app:

kubectl expose deployment source-ip-app --name=clusterip --port=80 --target-port=8080

The output is:

service/clusterip exposed
kubectl get svc clusterip

The output is similar to:

NAME         TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)   AGE
clusterip    ClusterIP   10.0.170.92   <none>        80/TCP    51s

And hitting the ClusterIP from a pod in the same cluster:

kubectl run busybox -it --image=busybox:1.28 --restart=Never --rm

The output is similar to this:

Waiting for pod default/busybox to be running, status is Pending, pod ready: false
If you don't see a command prompt, try pressing enter.

You can then run a command inside that Pod:

# Run this inside the terminal from "kubectl run"
ip addr
1: lo: <LOOPBACK,UP,LOWER_UP> mtu 65536 qdisc noqueue
    link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00
    inet 127.0.0.1/8 scope host lo
       valid_lft forever preferred_lft forever
    inet6 ::1/128 scope host
       valid_lft forever preferred_lft forever
3: eth0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1460 qdisc noqueue
    link/ether 0a:58:0a:f4:03:08 brd ff:ff:ff:ff:ff:ff
    inet 10.244.3.8/24 scope global eth0
       valid_lft forever preferred_lft forever
    inet6 fe80::188a:84ff:feb0:26a5/64 scope link
       valid_lft forever preferred_lft forever

…then use wget to query the local webserver

# Replace "10.0.170.92" with the IPv4 address of the Service named "clusterip"
wget -qO - 10.0.170.92
CLIENT VALUES:
client_address=10.244.3.8
command=GET
...

The client_address is always the client pod's IP address, whether the client pod and server pod are in the same node or in different nodes.

Source IP for Services with Type=NodePort

Packets sent to Services with Type=NodePort are source NAT'd by default. You can test this by creating a NodePort Service:

kubectl expose deployment source-ip-app --name=nodeport --port=80 --target-port=8080 --type=NodePort

The output is:

service/nodeport exposed
NODEPORT=$(kubectl get -o jsonpath="{.spec.ports[0].nodePort}" services nodeport)
NODES=$(kubectl get nodes -o jsonpath='{ $.items[*].status.addresses[?(@.type=="InternalIP")].address }')

If you're running on a cloud provider, you may need to open up a firewall-rule for the nodes:nodeport reported above. Now you can try reaching the Service from outside the cluster through the node port allocated above.

for node in $NODES; do curl -s $node:$NODEPORT | grep -i client_address; done

The output is similar to:

client_address=10.180.1.1
client_address=10.240.0.5
client_address=10.240.0.3

Note that these are not the correct client IPs, they're cluster internal IPs. This is what happens:

  • Client sends packet to node2:nodePort
  • node2 replaces the source IP address (SNAT) in the packet with its own IP address
  • node2 replaces the destination IP on the packet with the pod IP
  • packet is routed to node 1, and then to the endpoint
  • the pod's reply is routed back to node2
  • the pod's reply is sent back to the client

Visually:

source IP nodeport figure 01

Figure. Source IP Type=NodePort using SNAT

To avoid this, Kubernetes has a feature to preserve the client source IP. If you set service.spec.externalTrafficPolicy to the value Local, kube-proxy only proxies proxy requests to local endpoints, and does not forward traffic to other nodes. This approach preserves the original source IP address. If there are no local endpoints, packets sent to the node are dropped, so you can rely on the correct source-ip in any packet processing rules you might apply a packet that make it through to the endpoint.

Set the service.spec.externalTrafficPolicy field as follows:

kubectl patch svc nodeport -p '{"spec":{"externalTrafficPolicy":"Local"}}'

The output is:

service/nodeport patched

Now, re-run the test:

for node in $NODES; do curl --connect-timeout 1 -s $node:$NODEPORT | grep -i client_address; done

The output is similar to:

client_address=198.51.100.79

Note that you only got one reply, with the right client IP, from the one node on which the endpoint pod is running.

This is what happens:

  • client sends packet to node2:nodePort, which doesn't have any endpoints
  • packet is dropped
  • client sends packet to node1:nodePort, which does have endpoints
  • node1 routes packet to endpoint with the correct source IP

Visually:

source IP nodeport figure 02

Figure. Source IP Type=NodePort preserves client source IP address

Source IP for Services with Type=LoadBalancer

Packets sent to Services with Type=LoadBalancer are source NAT'd by default, because all schedulable Kubernetes nodes in the Ready state are eligible for load-balanced traffic. So if packets arrive at a node without an endpoint, the system proxies it to a node with an endpoint, replacing the source IP on the packet with the IP of the node (as described in the previous section).

You can test this by exposing the source-ip-app through a load balancer:

kubectl expose deployment source-ip-app --name=loadbalancer --port=80 --target-port=8080 --type=LoadBalancer

The output is:

service/loadbalancer exposed

Print out the IP addresses of the Service:

kubectl get svc loadbalancer

The output is similar to this:

NAME           TYPE           CLUSTER-IP    EXTERNAL-IP       PORT(S)   AGE
loadbalancer   LoadBalancer   10.0.65.118   203.0.113.140     80/TCP    5m

Next, send a request to this Service's external-ip:

curl 203.0.113.140

The output is similar to this:

CLIENT VALUES:
client_address=10.240.0.5
...

However, if you're running on Google Kubernetes Engine/GCE, setting the same service.spec.externalTrafficPolicy field to Local forces nodes without Service endpoints to remove themselves from the list of nodes eligible for loadbalanced traffic by deliberately failing health checks.

Visually:

Source IP with externalTrafficPolicy

You can test this by setting the annotation:

kubectl patch svc loadbalancer -p '{"spec":{"externalTrafficPolicy":"Local"}}'

You should immediately see the service.spec.healthCheckNodePort field allocated by Kubernetes:

kubectl get svc loadbalancer -o yaml | grep -i healthCheckNodePort

The output is similar to this:

  healthCheckNodePort: 32122

The service.spec.healthCheckNodePort field points to a port on every node serving the health check at /healthz. You can test this:

kubectl get pod -o wide -l app=source-ip-app

The output is similar to this:

NAME                            READY     STATUS    RESTARTS   AGE       IP             NODE
source-ip-app-826191075-qehz4   1/1       Running   0          20h       10.180.1.136   kubernetes-node-6jst

Use curl to fetch the /healthz endpoint on various nodes:

# Run this locally on a node you choose
curl localhost:32122/healthz
1 Service Endpoints found

On a different node you might get a different result:

# Run this locally on a node you choose
curl localhost:32122/healthz
No Service Endpoints Found

A controller running on the control plane is responsible for allocating the cloud load balancer. The same controller also allocates HTTP health checks pointing to this port/path on each node. Wait about 10 seconds for the 2 nodes without endpoints to fail health checks, then use curl to query the IPv4 address of the load balancer:

curl 203.0.113.140

The output is similar to this:

CLIENT VALUES:
client_address=198.51.100.79
...

Cross-platform support

Only some cloud providers offer support for source IP preservation through Services with Type=LoadBalancer. The cloud provider you're running on might fulfill the request for a loadbalancer in a few different ways:

  1. With a proxy that terminates the client connection and opens a new connection to your nodes/endpoints. In such cases the source IP will always be that of the cloud LB, not that of the client.

  2. With a packet forwarder, such that requests from the client sent to the loadbalancer VIP end up at the node with the source IP of the client, not an intermediate proxy.

Load balancers in the first category must use an agreed upon protocol between the loadbalancer and backend to communicate the true client IP such as the HTTP Forwarded or X-FORWARDED-FOR headers, or the proxy protocol. Load balancers in the second category can leverage the feature described above by creating an HTTP health check pointing at the port stored in the service.spec.healthCheckNodePort field on the Service.

Cleaning up

Delete the Services:

kubectl delete svc -l app=source-ip-app

Delete the Deployment, ReplicaSet and Pod:

kubectl delete deployment source-ip-app

What's next

7.3 - Explore Termination Behavior for Pods And Their Endpoints

Once you connected your Application with Service following steps like those outlined in Connecting Applications with Services, you have a continuously running, replicated application, that is exposed on a network. This tutorial helps you look at the termination flow for Pods and to explore ways to implement graceful connection draining.

Termination process for Pods and their endpoints

There are often cases when you need to terminate a Pod - be it for upgrade or scale down. In order to improve application availability, it may be important to implement a proper active connections draining.

This tutorial explains the flow of Pod termination in connection with the corresponding endpoint state and removal by using a simple nginx web server to demonstrate the concept.

Example flow with endpoint termination

The following is the example of the flow described in the Termination of Pods document.

Let's say you have a Deployment containing of a single nginx replica (just for demonstration purposes) and a Service:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
  labels:
    app: nginx
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      terminationGracePeriodSeconds: 120 # extra long grace period
      containers:
      - name: nginx
        image: nginx:latest
        ports:
        - containerPort: 80
        lifecycle:
          preStop:
            exec:
              # Real life termination may take any time up to terminationGracePeriodSeconds.
              # In this example - just hang around for at least the duration of terminationGracePeriodSeconds,
              # at 120 seconds container will be forcibly terminated.
              # Note, all this time nginx will keep processing requests.
              command: [
                "/bin/sh", "-c", "sleep 180"
              ]
apiVersion: v1
kind: Service
metadata:
  name: nginx-service
spec:
  selector:
    app: nginx
  ports:
    - protocol: TCP
      port: 80
      targetPort: 80

Now create the Deployment Pod and Service using the above files:

kubectl apply -f pod-with-graceful-termination.yaml
kubectl apply -f explore-graceful-termination-nginx.yaml

Once the Pod and Service are running, you can get the name of any associated EndpointSlices:

kubectl get endpointslice

The output is similar to this:

NAME                  ADDRESSTYPE   PORTS   ENDPOINTS                 AGE
nginx-service-6tjbr   IPv4          80      10.12.1.199,10.12.1.201   22m

You can see its status, and validate that there is one endpoint registered:

kubectl get endpointslices -o json -l kubernetes.io/service-name=nginx-service

The output is similar to this:

{
    "addressType": "IPv4",
    "apiVersion": "discovery.k8s.io/v1",
    "endpoints": [
        {
            "addresses": [
                "10.12.1.201"
            ],
            "conditions": {
                "ready": true,
                "serving": true,
                "terminating": false

Now let's terminate the Pod and validate that the Pod is being terminated respecting the graceful termination period configuration:

kubectl delete pod nginx-deployment-7768647bf9-b4b9s

All pods:

kubectl get pods

The output is similar to this:

NAME                                READY   STATUS        RESTARTS      AGE
nginx-deployment-7768647bf9-b4b9s   1/1     Terminating   0             4m1s
nginx-deployment-7768647bf9-rkxlw   1/1     Running       0             8s

You can see that the new pod got scheduled.

While the new endpoint is being created for the new Pod, the old endpoint is still around in the terminating state:

kubectl get endpointslice -o json nginx-service-6tjbr

The output is similar to this:

{
    "addressType": "IPv4",
    "apiVersion": "discovery.k8s.io/v1",
    "endpoints": [
        {
            "addresses": [
                "10.12.1.201"
            ],
            "conditions": {
                "ready": false,
                "serving": true,
                "terminating": true
            },
            "nodeName": "gke-main-default-pool-dca1511c-d17b",
            "targetRef": {
                "kind": "Pod",
                "name": "nginx-deployment-7768647bf9-b4b9s",
                "namespace": "default",
                "uid": "66fa831c-7eb2-407f-bd2c-f96dfe841478"
            },
            "zone": "us-central1-c"
        },
        {
            "addresses": [
                "10.12.1.202"
            ],
            "conditions": {
                "ready": true,
                "serving": true,
                "terminating": false
            },
            "nodeName": "gke-main-default-pool-dca1511c-d17b",
            "targetRef": {
                "kind": "Pod",
                "name": "nginx-deployment-7768647bf9-rkxlw",
                "namespace": "default",
                "uid": "722b1cbe-dcd7-4ed4-8928-4a4d0e2bbe35"
            },
            "zone": "us-central1-c"

This allows applications to communicate their state during termination and clients (such as load balancers) to implement a connections draining functionality. These clients may detect terminating endpoints and implement a special logic for them.

In Kubernetes, endpoints that are terminating always have their ready status set as as false. This needs to happen for backward compatibility, so existing load balancers will not use it for regular traffic. If traffic draining on terminating pod is needed, the actual readiness can be checked as a condition serving.

When Pod is deleted, the old endpoint will also be deleted.

What's next