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
Kubernetes Basics is an in-depth interactive tutorial that helps you understand the Kubernetes system and try out some basic Kubernetes features.
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.
Note:
Only execute the instructions in Step 1, Installation. The rest is covered on this page.
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:
# Start a new terminal, and leave this running.minikube dashboard
Now, switch back to the terminal where you ran minikube start.
Note:
The dashboard command enables the dashboard add-on and opens the proxy in the default web browser. You can create Kubernetes resources on the dashboard such as Deployment and Service.
To find out how to avoid directly invoking the browser from the terminal and get a URL for the web dashboard, see the "URL copy and paste" tab.
By default, the dashboard is only accessible from within the internal Kubernetes virtual network. The dashboard command creates a temporary proxy to make the dashboard accessible from outside the Kubernetes virtual network.
To stop the proxy, run Ctrl+C to exit the process. After the command exits, the dashboard remains running in the Kubernetes cluster. You can run the dashboard command again to create another proxy to access the dashboard.
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.
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 webserverkubectl create deployment hello-node --image=registry.k8s.io/e2e-test-images/agnhost:2.39 -- /agnhost netexec --http-port=8080
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
(It may take some time for the pod to become available. If you see "0/1", try again in a few seconds.)
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
View cluster events:
kubectl get events
View the kubectl configuration:
kubectl config view
View application logs for a container in a pod (replace pod name with the one you got from kubectl get pods).
Note:
Replace hello-node-5f76cf6ccf-br9b5 in the kubectl logs command with the name of the pod from the kubectl get pods command output.
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
Note:
For more information about kubectl commands, see the kubectl overview.
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.
Warning:
The agnhost container has a /shell endpoint, which is useful for debugging, but dangerous to expose to the public internet. Do not run this on an internet-facing cluster, or a production cluster.
Expose the Pod to the public internet using the kubectl expose command:
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.
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.
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.
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.
Learn about Kubernetes cluster and create a simple cluster using Minikube.
2.1.1 - Using Minikube to Create a Cluster
Objectives
Learn what a Kubernetes cluster is.
Learn what Minikube is.
Start a Kubernetes cluster on your computer.
Kubernetes Clusters
Kubernetes is a production-grade, open-source platform that orchestrates the placement (scheduling) and execution of application containers within and across computer 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
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.
Control Planes manage the cluster and the nodes that are used to host the running applications.
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.
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.
A Deployment is responsible for creating and updating instances of your application.
Note:
This tutorial uses a container that requires the AMD64 architecture. If you are using minikube on a computer with a different CPU architecture, you could try using minikube with a driver that can emulate AMD64. For example, the Docker Desktop driver can do this.
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.
Deploying your first app on Kubernetes
Applications need to be packaged into one of the supported container formats in order to be deployed 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; Module 5 and Module 6 of the bootcamp discuss how you can scale and update your Deployments.
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 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 that 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).
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
Note:
If port 8001 is not accessible, ensure that the kubectl proxy that you started above is running in the second terminal.
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.
exportPOD_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:
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.
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.
Pods overview
Containers should only be scheduled together in a single Pod if they are tightly coupled and need to share resources such as disk.
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.
Nodes 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.
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 tutorial.
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:
exportPOD_NAME="$(kubectl get pods -o go-template --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')"echo Name of the Pod: $POD_NAME
To see the output of our application, run a curl request:
We don't need to specify the container name, because we only have one container inside the pod.
Executing commands on the container
We can execute commands directly on the container once the Pod is up and running. For this, we use the exec subcommand and use the name of the Pod as a parameter. Let’s list the environment variables:
kubectl exec"$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.
Understand how labels and selectors relate to a Service.
Expose an application outside a Kubernetes cluster.
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 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.
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.
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.
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 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:
exportNODE_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):$NODE_PORT"
Note:
If you're running minikube with Docker Desktop as the container driver, a minikube tunnel is needed. This is because containers inside Docker Desktop are isolated from your host computer.
In a separate terminal window, execute:
minikube service kubernetes-bootcamp --url
The output looks like this:
http://127.0.0.1:51082 ! Because you are using a Docker driver on darwin, the terminal needs to be open to run it.
Then use the given URL to access the app:
curl 127.0.0.1:51082
And we get a response from the server. The Service is exposed.
Step 2: Using labels
The Deployment created automatically a label for our Pod. With the describe deployment subcommand you can see the name (the key) of that label:
kubectl describe deployment
Let’s use this label to query our list of Pods. We’ll use the kubectl get pods command with -l as a parameter, followed by the label values:
kubectl get pods -l app=kubernetes-bootcamp
You can do the same to list the existing Services:
kubectl get services -l app=kubernetes-bootcamp
Get the name of the Pod and store it in the POD_NAME environment variable:
exportPOD_NAME="$(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:
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.
You can create from the start a Deployment with multiple instances using the --replicas parameter for the kubectl create deployment command.
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.
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:
Scaling is accomplished by changing the number of replicas in a Deployment.
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.
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:
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 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:
exportNODE_PORT="$(kubectl get services/kubernetes-bootcamp -o go-template='{{(index .spec.ports 0).nodePort}}')"echoNODE_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.
The output should be similar to:
Hello Kubernetes bootcamp! | Running on: kubernetes-bootcamp-644c5687f4-wp67j | v=1 Hello Kubernetes bootcamp! | Running on: kubernetes-bootcamp-644c5687f4-hs9dj | v=1 Hello Kubernetes bootcamp! | Running on: kubernetes-bootcamp-644c5687f4-4hjvf | v=1 Hello Kubernetes bootcamp! | Running on: kubernetes-bootcamp-644c5687f4-wp67j | v=1 Hello Kubernetes bootcamp! | Running on: kubernetes-bootcamp-644c5687f4-4hjvf | v=1
Note:
If you're running minikube with Docker Desktop as the container driver, a minikube tunnel is needed. This is because containers inside Docker Desktop are isolated from your host computer.
In a separate terminal window, execute:
minikube service kubernetes-bootcamp --url
The output looks like this:
http://127.0.0.1:51082 ! Because you are using a Docker driver on darwin, the terminal needs to be open to run it.
Then use the given URL to access the app:
curl 127.0.0.1:51082
Scale Down
To scale down the Deployment to 2 replicas, run again the scale subcommand:
Rolling updates allow Deployments' update to take place with zero downtime by incrementally updating Pods instances with new ones.
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.
Rolling updates overview
If a Deployment is exposed publicly, the Service will load-balance the traffic only to available Pods during the update.
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
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:
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
To check the image deployed on the running 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.
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/v1kind:Deploymentmetadata:name:configmap-volumelabels:app.kubernetes.io/name:configmap-volumespec:replicas:3selector:matchLabels:app.kubernetes.io/name:configmap-volumetemplate:metadata:labels:app.kubernetes.io/name:configmap-volumespec:containers:- name:alpineimage:alpine:3command:- /bin/sh- -c- while true; do echo "$(date) My preferred sport is $(cat /etc/config/sport)";sleep 10; done;ports:- containerPort:80volumeMounts:- name:config-volumemountPath:/etc/configvolumes:- name:config-volumeconfigMap:name:sport
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 logskubectl 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:v1data:sport:cricketkind: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:sportnamespace:defaultresourceVersion:"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:
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.
Note:
The total delay from the moment when the ConfigMap is updated to the moment when new keys are projected to the Pod can be as long as kubelet sync period. Also check Mounted ConfigMaps are updated automatically.
Update environment variables of a Pod via a ConfigMap
Use the kubectl create configmap command to create a ConfigMap from literal values:
apiVersion:apps/v1kind:Deploymentmetadata:name:configmap-env-varlabels:app.kubernetes.io/name:configmap-env-varspec:replicas:3selector:matchLabels:app.kubernetes.io/name:configmap-env-vartemplate:metadata:labels:app.kubernetes.io/name:configmap-env-varspec:containers:- name:alpineimage:alpine:3env:- name:FRUITSvalueFrom:configMapKeyRef:key:fruitsname:fruitscommand:- /bin/sh- -c- while true; do echo "$(date) The basket is full of $FRUITS";sleep 10; done;ports:- containerPort:80
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:v1data:fruits:mangoeskind: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:fruitsnamespace:defaultresourceVersion:"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 changekubectl 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
Note:
Although the value of the key inside the ConfigMap has changed, the environment variable in the Pod still shows the earlier value. This is because environment variables for a process running inside a Pod are not updated when the source data changes; if you wanted to force an update, you would need to have Kubernetes replace your existing Pods. The new Pods would then run with the updated information.
You can trigger that replacement. Perform a rollout for the Deployment, using kubectl rollout:
# Trigger the rolloutkubectl rollout restart deployment configmap-env-var # Wait for the rollout to completekubectl 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
Note:
Please wait for the older Pods to fully terminate before proceeding with the next steps.
View the logs for a Pod in this Deployment:
# Pick one Pod that belongs to the Deployment, and view its logskubectl 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/v1kind:Deploymentmetadata:name:configmap-two-containerslabels:app.kubernetes.io/name:configmap-two-containersspec:replicas:3selector:matchLabels:app.kubernetes.io/name:configmap-two-containerstemplate:metadata:labels:app.kubernetes.io/name:configmap-two-containersspec:volumes:- name:shared-dataemptyDir:{}- name:config-volumeconfigMap:name:colorcontainers:- name:nginximage:nginxvolumeMounts:- name:shared-datamountPath:/usr/share/nginx/html- name:alpineimage:alpine:3volumeMounts:- name:shared-datamountPath:/pod-data- name:config-volumemountPath:/etc/configcommand:- /bin/sh- -c- while true; do echo "$(date) My preferred color is $(cat /etc/config/color)" > /pod-data/index.html;sleep 10; done;
# this stays running in the backgroundkubectl port-forward service/configmap-service 8080:8080 &
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:v1data:color:bluekind: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:colornamespace:configmapresourceVersion:"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.
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/v1kind:Deploymentmetadata:name:configmap-sidecar-containerlabels:app.kubernetes.io/name:configmap-sidecar-containerspec:replicas:3selector:matchLabels:app.kubernetes.io/name:configmap-sidecar-containertemplate:metadata:labels:app.kubernetes.io/name:configmap-sidecar-containerspec:volumes:- name:shared-dataemptyDir:{}- name:config-volumeconfigMap:name:colorcontainers:- name:nginximage:nginxvolumeMounts:- name:shared-datamountPath:/usr/share/nginx/htmlinitContainers:- name:alpineimage:alpine:3restartPolicy:AlwaysvolumeMounts:- name:shared-datamountPath:/pod-data- name:config-volumemountPath:/etc/configcommand:- /bin/sh- -c- while true; do echo "$(date) My preferred color is $(cat /etc/config/color)" > /pod-data/index.html;sleep 10; done;
# this stays running in the backgroundkubectl port-forward service/configmap-sidecar-service 8081:8081 &
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:v1data:color:greenkind: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:colornamespace:defaultresourceVersion:"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
Note:
Immutable ConfigMaps are especially used for configuration that is constant and is not expected to change over time. Marking a ConfigMap as immutable allows a performance improvement where the kubelet does not watch for changes.
If you do need to make a change, you should plan to either:
change the name of the ConfigMap, and switch to running Pods that reference the new name
replace all the nodes in your cluster that have previously run a Pod that used the old value
restart the kubelet on any node where the kubelet previously loaded the old ConfigMap
apiVersion:apps/v1kind:Deploymentmetadata:name:immutable-configmap-volumelabels:app.kubernetes.io/name:immutable-configmap-volumespec:replicas:3selector:matchLabels:app.kubernetes.io/name:immutable-configmap-volumetemplate:metadata:labels:app.kubernetes.io/name:immutable-configmap-volumespec:containers:- name:alpineimage:alpine:3command:- /bin/sh- -c- while true; do echo "$(date) The name of the company is $(cat /etc/config/company_name)";sleep 10; done;ports:- containerPort:80volumeMounts:- name:config-volumemountPath:/etc/configvolumes:- name:config-volumeconfigMap:name:company-name-20150801
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 logskubectl 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.
Note:
Once a ConfigMap is marked as immutable, it is not possible to revert this change nor to mutate the contents of the data or the binaryData field. In order to modify the behavior of the Pods that use this configuration, you will create a new immutable ConfigMap and edit the Deployment to define a slightly different pod template, referencing the new ConfigMap.
Create a new immutable ConfigMap by using the manifest shown below:
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 logskubectl 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
3.2 - 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:
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.
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:
This section is relevant for people adopting a new built-in sidecar containers feature for their workloads.
Sidecar container is not a new concept as posted in the blog post. Kubernetes allows running multiple containers in a Pod to implement this concept. However, running a sidecar container as a regular container has a lot of limitations being fixed with the new built-in sidecar containers support.
FEATURE STATE:Kubernetes v1.33 [stable] (enabled by default: true)
Objectives
Understand the need for sidecar containers
Be able to troubleshoot issues with the sidecar containers
Understand options to universally "inject" sidecar containers to any workload
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 1.29.
To check the version, enter kubectl version.
Sidecar containers overview
Sidecar containers are secondary containers that run along with the main application container within the same Pod. These containers are used to enhance or to extend the functionality of the primary app container by providing additional services, or functionalities such as logging, monitoring, security, or data synchronization, without directly altering the primary application code. You can read more in the Sidecar containers concept page.
The concept of sidecar containers is not new and there are multiple implementations of this concept. As well as sidecar containers that you, the person defining the Pod, want to run, you can also find that some addons modify Pods - before the Pods start running - so that there are extra sidecar containers. The mechanisms to inject those extra sidecars are often mutating webhooks. For example, a service mesh addon might inject a sidecar that configures mutual TLS and encryption in transit between different Pods.
While the concept of sidecar containers is not new, the native implementation of this feature in Kubernetes, however, is new. And as with every new feature, adopting this feature may present certain challenges.
This tutorial explores challenges and solutions that can be experienced by end users as well as by authors of sidecar containers.
Benefits of a built-in sidecar container
Using Kubernetes' native support for sidecar containers provides several benefits:
You can configure a native sidecar container to start ahead of init containers.
The built-in sidecar containers can be authored to guarantee that they are terminated last. Sidecar containers are terminated with a SIGTERM signal once all the regular containers are completed and terminated. If the sidecar container isn’t gracefully shut down, a SIGKILL signal will be used to terminate it.
With Jobs, when Pod's restartPolicy: OnFailure or restartPolicy: Never, native sidecar containers do not block Pod completion. With legacy sidecar containers, special care is needed to handle this situation.
Also, with Jobs, built-in sidecar containers would keep being restarted once they are done, even if regular containers would not with Pod's restartPolicy: Never.
The SidecarContainersfeature gate is in beta state starting from Kubernetes version 1.29 and is enabled by default. Some clusters may have this feature disabled or have software installed that is incompatible with the feature.
When this happens, the Pod may be rejected or the sidecar containers may block Pod startup, rendering the Pod useless. This condition is easy to detect as the Pod simply gets stuck on initialization. However, it is often unclear what caused the problem.
Here are the considerations and troubleshooting steps that one can take while adopting sidecar containers for their workload.
Ensure the feature gate is enabled
As a very first step, make sure that both API server and Nodes are at Kubernetes version v1.29 or later. The feature will break on clusters where Nodes are running earlier versions where it is not enabled.
Note
The feature can be enabled on nodes with the version 1.28. The behavior of built-in sidecar container termination was different in version 1.28, and it is not recommended to adjust the behavior of a sidecar to that behavior. However, if the only concern is the startup order, the above statement can be changed to Nodes running version 1.28 with the feature gate enabled.
You should ensure that the feature gate is enabled for the API server(s) within the control plane and for all nodes.
One of the ways to check the feature gate enablement is to run a command like this:
For API Server:
kubectl get --raw /metrics | grep kubernetes_feature_enabled | grep SidecarContainers
For the individual node:
kubectl get --raw /api/v1/nodes/<node-name>/proxy/metrics | grep kubernetes_feature_enabled | grep SidecarContainers
If you experience issues when validating the feature, it may be an indication that one of the 3rd party tools or mutating webhooks are broken.
When the SidecarContainers feature gate is enabled, Pods gain a new field in their API. Some tools or mutating webhooks might have been built with an earlier version of Kubernetes API.
If tools pass unknown fields as-is using various patching strategies to mutate a Pod object, this will not be a problem. However, there are tools that will strip out unknown fields; if you have those, they must be recompiled with the v1.28+ version of Kubernetes API client code.
The way to check this is to use the kubectl describe pod command with your Pod that has passed through mutating admission. If any tools stripped out the new field (restartPolicy:Always), you will not see it in the command output.
If you hit an issue like this, please advise the author of the tools or the webhooks use one of the patching strategies for modifying objects instead of a full object update.
Note
Mutating webhook may update Pods based on some conditions. Thus, sidecar containers may work for some Pods and fail for others.
Automatic injection of sidecars
If you are using software that injects sidecars automatically, there are a few possible strategies you may follow to ensure that native sidecar containers can be used. All strategies are generally options you may choose to decide whether the Pod the sidecar will be injected to will land on a Node supporting the feature or not.
Mark Pods that land to nodes supporting sidecars. You can use node labels and node affinity to mark nodes supporting sidecar containers and Pods landing on those nodes.
Check Nodes compatibility on injection. During sidecar injection, you may use the following strategies to check node compatibility:
query node version and assume the feature gate is enabled on the version 1.29+
query node prometheus metrics and check feature enablement status
there may be other custom ways to detect nodes compatibility.
Develop a universal sidecar injector. The idea of a universal sidecar injector is to inject a sidecar container as a regular container as well as a native sidecar container. And have a runtime logic to decide which one will work. The universal sidecar injector is wasteful, as it will account for requests twice, but may be considered as a workable solution for special cases.
One way would be on start of a native sidecar container detect the node version and exit immediately if the version does not support the sidecar feature.
Consider a runtime feature detection design:
Define an empty dir so containers can communicate with each other
Inject an init container, let's call it NativeSidecar with restartPolicy=Always.
NativeSidecar must write a file to an empty directory indicating the first run and exit immediately with exit code 0.
NativeSidecar on restart (when native sidecars are supported) checks that file already exists in the empty dir and changes it - indicating that the built-in sidecar containers are supported and running.
Inject regular container, let's call it OldWaySidecar.
OldWaySidecar on start checks the presence of a file in an empty dir.
If the file indicates that the NativeSidecar is NOT running, it assumes that the sidecar feature is not supported and works assuming it is the sidecar.
If the file indicates that the NativeSidecar is running, it either does nothing and sleeps forever (in the case when Pod’s restartPolicy=Always) or exits immediately with exit code 0 (in the case when Pod’s restartPolicy!=Always).
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
Note
This tutorial applies only for new clusters.
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.
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.
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'.
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
Use --dry-run=server to understand what happens when different Pod Security Standards are applied:
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:
Based on the risk posture applied to a cluster, a stricter Pod Security Standard like restricted might be a better choice.
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:
Create a configuration file that can be consumed by the Pod Security Admission Controller to implement these Pod Security Standards:
Creating cluster "psa-with-cluster-pss" ... ✓ Ensuring node image (kindest/node:v1.33.0) 🖼 ✓ 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 🙂
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'.
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
Run a shell script to perform all the preceding steps at once:
Create a Pod Security Standards based cluster level Configuration
Create a file to let API server consume this configuration
Create a cluster that creates an API server with this configuration
Set kubectl context to this new cluster
Create a minimal pod yaml file
Apply this file to create a Pod in the new cluster
4.2 - Apply Pod Security Standards at the Namespace Level
Note
This tutorial applies only for new clusters.
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.
Creating cluster "psa-ns-level" ... ✓ Ensuring node image (kindest/node:v1.33.0) 🖼 ✓ 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/
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
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
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 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
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
Run a shell script to perform all the preceding steps all at once.
Create kind cluster
Create new namespace
Apply baseline Pod Security Standard in enforce mode while applying restricted Pod Security Standard also in warn and audit mode.
Create a new pod with the following pod security standards applied
4.3 - Restrict a Container's Access to Resources with AppArmor
FEATURE STATE:Kubernetes v1.31 [stable] (enabled by default: true)
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:
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.
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.
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:
Prior to Kubernetes v1.30, AppArmor was specified through annotations. Use the documentation version selector to view the documentation with this deprecated API.
AppArmor profiles can be specified at the pod level or container level. The container AppArmor profile takes precedence over the pod profile.
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:
The profile needs to be 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 node -o jsonpath='{.items[*].status.addresses[?(.type == "Hostname")].address}'))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:
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 1.33 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
Caution:
Prior to Kubernetes v1.30, AppArmor was specified through annotations. Use the documentation version selector to view the documentation with this deprecated API.
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.
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.
Note:
It is not possible to apply a seccomp profile to a container running with privileged: true set in the container's securityContext. Privileged containers always run as Unconfined.
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.
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.
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 v1.33.
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.
Note:
If you have the seccompDefaultconfiguration enabled, then Pods use the RuntimeDefault seccomp profile whenever no other seccomp profile is specified. Otherwise, the default is Unconfined.
Here's a manifest for a Pod that requests the RuntimeDefault seccomp profile for all its containers:
apiVersion:v1kind:Podmetadata:name:default-podlabels:app:default-podspec:securityContext:seccompProfile:type:RuntimeDefaultcontainers:- name:test-containerimage:hashicorp/http-echo:1.0args:- "-text=just made some more syscalls!"securityContext:allowPrivilegeEscalation:false
apiVersion:v1kind:Podmetadata:name:audit-podlabels:app:audit-podspec:securityContext:seccompProfile:type:LocalhostlocalhostProfile:profiles/audit.jsoncontainers:- name:test-containerimage:hashicorp/http-echo:1.0args:- "-text=just made some syscalls!"securityContext:allowPrivilegeEscalation:false
Note:
Older versions of Kubernetes allowed you to configure seccomp behavior using annotations. Kubernetes 1.33 only supports using fields within .spec.securityContext to configure seccomp, and this tutorial explains that approach.
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.
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.
apiVersion:v1kind:Podmetadata:name:violation-podlabels:app:violation-podspec:securityContext:seccompProfile:type:LocalhostlocalhostProfile:profiles/violation.jsoncontainers:- name:test-containerimage:hashicorp/http-echo:1.0args:- "-text=just made some syscalls!"securityContext:allowPrivilegeEscalation:false
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.
apiVersion:v1kind:Podmetadata:name:fine-podlabels:app:fine-podspec:securityContext:seccompProfile:type:LocalhostlocalhostProfile:profiles/fine-grained.jsoncontainers:- name:test-containerimage:hashicorp/http-echo:1.0args:- "-text=just made some syscalls!"securityContext:allowPrivilegeEscalation:false
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-defaultcommand 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.
Note:
Enabling the feature will neither change the Kubernetes securityContext.seccompProfile API field nor add the deprecated annotations of the workload. This provides users the possibility to rollback anytime without actually changing the workload configuration. Tools like crictl inspect can be used to verify which seccomp profile is being used by a container.
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 1.33 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 1.33 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:
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:
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
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.
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:
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:
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:
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
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.
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
Note:
This manifest file creates a Service named redis-leader with a set of labels that match the labels previously defined, so the Service routes network traffic to the Redis Pod.
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.
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/guestbookapiVersion:v1kind:Servicemetadata:name:redis-followerlabels:app:redisrole:followertier:backendspec:ports:# the port that this service should serve on- port:6379selector:app:redisrole:followertier:backend
Apply the Redis Service from the following redis-follower-service.yaml file:
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
Note:
This manifest file creates a Service named redis-follower with a set of labels that match the labels previously defined, so the Service routes network traffic to the Redis Pod.
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.
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.
Note:
Some cloud providers, like Google Compute Engine or Google Kubernetes Engine, support external load balancers. If your cloud provider supports load balancers and you want to use it, uncomment type: LoadBalancer.
# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbookapiVersion:v1kind:Servicemetadata:name:frontendlabels:app:guestbooktier:frontendspec:# if your cluster supports it, uncomment the following to automatically create# an external load-balanced IP for the frontend service.# type: LoadBalancer#type: LoadBalancerports:# the port that this service should serve on- port:80selector:app:guestbooktier:frontend
Apply the frontend Service from the frontend-service.yaml file:
If you deployed the frontend-service.yaml manifest with type: LoadBalancer you need to find the IP address to view your Guestbook.
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
Copy the external IP address, and load the page in your browser to view your guestbook.
Note:
Try adding some guestbook entries by typing in a message, and clicking Submit. The message you typed appears in the frontend. This message indicates that data is successfully added to Redis through the Services you created earlier.
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.
Run the following command to scale up the number of frontend Pods:
kubectl scale deployment frontend --replicas=5
Query the list of Pods to verify the number of frontend Pods running:
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.
Note:
This tutorial assumes that your cluster is configured to dynamically provision PersistentVolumes. You'll also need to have a default StorageClass. If your cluster is not configured to provision storage dynamically, you will have to manually provision two 1 GiB volumes prior to starting this tutorial and set up your cluster so that those PersistentVolumes map to the PersistentVolumeClaim templates that the StatefulSet defines.
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.
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 READY AGE web 2/2 37s
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 runningkubectl get pods --watch -l app=nginx
To configure the integer ordinal assigned to each Pod in a StatefulSet, see Start ordinal.
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 shellnslookup web-0.nginx
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 finishedkubectl 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 runningkubectl 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 shellnslookup web-0.nginx
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
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 "$(hostname)" > /usr/share/nginx/html/index.html'; donefor i in 0 1; do kubectl exec -i -t "web-$i" -- curl http://localhost/; done
web-0 web-1
Note:
If you instead see 403 Forbidden responses for the above curl command, you will need to fix the permissions of the directory mounted by the volumeMounts (due to a bug when using hostPath volumes), by running:
for i in 0 1; do kubectl exec web-$i -- chmod 755 /usr/share/nginx/html; done
before retrying the curl command above.
In one terminal, watch the StatefulSet's Pods:
# End this watch when you've reached the end of the section.# At the start of "Scaling a StatefulSet" you'll start a new watch.kubectl get pod --watch -l app=nginx
In a second terminal, delete all of the StatefulSet's Pods:
kubectl delete pod -l app=nginx
pod "web-0" deleted pod "web-1" deleted
Examine the output of the kubectl get command in the first terminal, and wait for all of the Pods to transition to Running and Ready.
# This should already be runningkubectl get pod --watch -l app=nginx
NAME READY STATUS RESTARTS AGE web-0 0/1 ContainerCreating 0 0s NAME READY STATUS RESTARTS AGE web-0 1/1 Running 0 2s web-1 0/1 Pending 0 0s web-1 0/1 Pending 0 0s web-1 0/1 ContainerCreating 0 0s web-1 1/1 Running 0 34s
Verify the web servers continue to serve their hostnames:
for i in 0 1; do kubectl exec -i -t "web-$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 StatefulSetkubectl 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 runningkubectl get pod --watch -l app=nginx
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 StatefulSetkubectl 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 runningkubectl 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.
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":"registry.k8s.io/nginx-slim:0.24"}]'
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 minutekubectl get pod -l app=nginx --watch
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 01 2; do kubectl get pod "web-$p" --template '{{range $i, $c := .spec.containers}}{{$c.image}}{{end}}'; echo; done
All the Pods in the StatefulSet are now running the previous container image.
Note:
You can also use kubectl rollout status sts/<name> to view the status of a rolling update to a StatefulSet
Staging an update
You can split updates to a StatefulSet that uses the RollingUpdate strategy into partitions, by specifying .spec.updateStrategy.rollingUpdate.partition.
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# StatefulSetkubectl 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:0.21"}]'
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 healthykubectl 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:0.24
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 StatefulSetkubectl 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 runningkubectl 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:0.21
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 runningkubectl get pod -l app=nginx --watch
kubectl get pod web-1 --template '{{range $i, $c := .spec.containers}}{{$c.image}}{{end}}'
registry.k8s.io/nginx-slim:0.24
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 runningkubectl get pod -l app=nginx --watch
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", "rollingUpdate": null}}}'
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.
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 watchkubectl 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.
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 runningkubectl 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 sectionkubectl 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 runningkubectl 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.
Note:
Although a cascading delete removes a StatefulSet together with its Pods, the cascade does not delete the headless Service associated with the StatefulSet. You must delete the nginx Service manually.
kubectl delete service nginx
service "nginx" deleted
Recreate the StatefulSet and headless Service one more time:
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).
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 tokubectl get pod -l app=nginx --watch
You also need to 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.
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.
Warning:
This deployment is not suitable for production use cases, as it uses single instance WordPress and MySQL Pods. Consider using WordPress Helm Chart to deploy WordPress in production.
Note:
The files provided in this tutorial are using GA Deployment APIs and are specific to kubernetes version 1.9 and later. If you wish to use this tutorial with an earlier version of Kubernetes, please update the API version appropriately, or reference earlier versions of this tutorial.
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:
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.
Warning:
In local clusters, the default StorageClass uses the hostPath provisioner. hostPath volumes are only suitable for development and testing. With hostPath volumes, your data lives in /tmp on the node the Pod is scheduled onto and does not move between nodes. If a Pod dies and gets scheduled to another node in the cluster, or the node is rebooted, the data is lost.
Note:
If you are bringing up a cluster that needs to use the hostPath provisioner, the --enable-hostpath-provisioner flag must be set in the controller-manager component.
Note:
If you have a Kubernetes cluster running on Google Kubernetes Engine, please follow this guide.
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.
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.
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.
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.
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
Verify that a PersistentVolume got dynamically provisioned.
kubectl get pvc
Note:
It can take up to a few minutes for the PVs to be provisioned and bound.
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
Verify that the Pod is running by running the following command:
kubectl get pods
Note:
It can take up to a few minutes for the Pod's Status to be RUNNING.
The response should be like this:
NAME READY STATUS RESTARTS AGE wordpress-mysql-1894417608-x5dzt 1/1 Running 0 40s
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
Note:
Minikube can only expose Services through NodePort. The EXTERNAL-IP is always pending.
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
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.
Warning:
Do not leave your WordPress installation on this page. If another user finds it, they can set up a website on your instance and use it to serve malicious content.
Either install WordPress by creating a username and password or delete your instance.
Cleaning up
Run the following command to delete your Secret, Deployments, Services and PersistentVolumeClaims:
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.
Note:
Cassandra and Kubernetes both use the term node to mean a member of a cluster. In this tutorial, the Pods that belong to the StatefulSet are Cassandra nodes and are members of the Cassandra cluster (called a ring). When those Pods run in your Kubernetes cluster, the Kubernetes control plane schedules those Pods onto Kubernetes Nodes.
When a Cassandra node starts, it uses a seed list to bootstrap discovery of other nodes in the ring. This tutorial deploys a custom Cassandra seed provider that lets the database discover new Cassandra Pods as they appear inside your Kubernetes cluster.
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
Caution:
Minikube defaults to 2048MB of memory and 2 CPU. Running Minikube with the default resource configuration results in insufficient resource errors during this tutorial. To avoid these errors, start Minikube with the following settings:
minikube start --memory 5120 --cpus=4
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:apps/v1kind:StatefulSetmetadata:name:cassandralabels:app:cassandraspec:serviceName:cassandrareplicas:3selector:matchLabels:app:cassandratemplate:metadata:labels:app:cassandraspec:terminationGracePeriodSeconds:500containers:- name:cassandraimage:gcr.io/google-samples/cassandra:v13imagePullPolicy:Alwaysports:- containerPort:7000name:intra-node- containerPort:7001name:tls-intra-node- containerPort:7199name:jmx- containerPort:9042name:cqlresources:limits:cpu:"500m"memory:1Girequests:cpu:"500m"memory:1GisecurityContext:capabilities:add:- IPC_LOCKlifecycle:preStop:exec:command:- /bin/sh- -c- nodetool drainenv:- name:MAX_HEAP_SIZEvalue:512M- name:HEAP_NEWSIZEvalue:100M- name:CASSANDRA_SEEDSvalue:"cassandra-0.cassandra.default.svc.cluster.local"- name:CASSANDRA_CLUSTER_NAMEvalue:"K8Demo"- name:CASSANDRA_DCvalue:"DC1-K8Demo"- name:CASSANDRA_RACKvalue:"Rack1-K8Demo"- name:POD_IPvalueFrom:fieldRef:fieldPath:status.podIPreadinessProbe:exec:command:- /bin/bash- -c- /ready-probe.shinitialDelaySeconds:15timeoutSeconds: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-datamountPath:/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 pdvolumeClaimTemplates:- metadata:name:cassandra-dataspec:accessModes:["ReadWriteOnce"]storageClassName:fastresources:requests:storage:1Gi---kind:StorageClassapiVersion:storage.k8s.io/v1metadata:name:fastprovisioner:k8s.io/minikube-hostpathparameters:type:pd-ssd
Create the Cassandra StatefulSet from the cassandra-statefulset.yaml file:
# Use this if you are able to apply cassandra-statefulset.yaml unmodifiedkubectl apply -f https://k8s.io/examples/application/cassandra/cassandra-statefulset.yaml
# Use this if you needed to modify cassandra-statefulset.yaml locallykubectl apply -f cassandra-statefulset.yaml
Validating the Cassandra StatefulSet
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.
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
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.
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/v1kind:StatefulSetmetadata:creationTimestamp:2016-08-13T18:40:58Zgeneration:1labels:app:cassandraname:cassandranamespace:defaultresourceVersion:"323"uid:7a219483-6185-11e6-a910-42010a8a0fc0spec:replicas:3
Change the number of replicas to 4, and then save the manifest.
The StatefulSet now scales to run with 4 Pods.
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.
Warning:
Depending on the storage class and reclaim policy, deleting the PersistentVolumeClaims may cause the associated volumes to also be deleted. Never assume you'll be able to access data if its volume claims are deleted.
Run the following commands (chained together into a single command) to delete everything in the Cassandra StatefulSet:
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.
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.
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 01 2; do kubectl exec zk-$i -- hostname; done
The StatefulSet controller provides each Pod with a unique hostname based on its ordinal index. The hostnames take the form of <statefulset name>-<ordinal index>. Because the replicas field of the zk StatefulSet is set to 3, the Set's controller creates three Pods with their hostnames set to zk-0, zk-1, and zk-2.
zk-0 zk-1 zk-2
The servers in a ZooKeeper ensemble use natural numbers as unique identifiers, and store each server's identifier in a file called myid in the server's data directory.
To examine the contents of the myid file for each server use the following command.
for i in 01 2; doecho"myid zk-$i";kubectl exec zk-$i -- cat /var/lib/zookeeper/data/myid; done
Because the identifiers are natural numbers and the ordinal indices are non-negative integers, you can generate an identifier by adding 1 to the ordinal.
myid zk-0 1 myid zk-1 2 myid zk-2 3
To get the Fully Qualified Domain Name (FQDN) of each Pod in the zk StatefulSet use the following command.
for i in 01 2; do kubectl exec zk-$i -- hostname -f; done
The zk-hs Service creates a domain for all of the Pods, zk-hs.default.svc.cluster.local.
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.
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.
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.
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.
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.
When a Pod in the zkStatefulSet 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.
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 zkStatefulSet.
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 zkStatefulSet's Pod template contains a SecurityContext.
securityContext:runAsUser:1000fsGroup: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 zkStatefulSet is configured to use the RollingUpdate update strategy.
You can use kubectl patch to update the number of cpus allocated to the servers.
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.
In another terminal watch the Pods in the zkStatefulSet 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 zkStatefulSet specifies a liveness probe.
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.
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 zkStatefulSet.
for i in 01 2; do kubectl get pod zk-$i --template {{.spec.nodeName}}; echo""; done
All of the Pods in the zkStatefulSet are deployed on different nodes.
The requiredDuringSchedulingIgnoredDuringExecution field tells the Kubernetes Scheduler that it should never co-locate two Pods which have app label as zk in the domain defined by the topologyKey. The topologyKeykubernetes.io/hostname indicates that the domain is an individual node. Using different rules, labels, and selectors, you can extend this technique to spread your ensemble across physical, network, and power failure domains.
Surviving maintenance
In this section you will cordon and drain nodes. If you are using this tutorial on a shared cluster, be sure that this will not adversely affect other tenants.
The previous section showed you how to spread your Pods across nodes to survive unplanned node failures, but you also need to plan for temporary node failures that occur due to planned maintenance.
Use this command to get the nodes in your cluster.
kubectl get nodes
This tutorial assumes a cluster with at least four nodes. If the cluster has more than four, use kubectl cordon to cordon all but four nodes. Constraining to four nodes will ensure Kubernetes encounters affinity and PodDisruptionBudget constraints when scheduling zookeeper Pods in the following maintenance simulation.
kubectl cordon <node-name>
Use this command to get the zk-pdbPodDisruptionBudget.
kubectl get pdb zk-pdb
The max-unavailable field indicates to Kubernetes that at most one Pod from zkStatefulSet can be unavailable at any time.
NAME MIN-AVAILABLE MAX-UNAVAILABLE ALLOWED-DISRUPTIONS AGE zk-pdb N/A 1 1
In one terminal, use this command to watch the Pods in the zkStatefulSet.
kubectl get pods -w -l app=zk
In another terminal, use this command to get the nodes that the Pods are currently scheduled on.
for i in 01 2; do kubectl get pod zk-$i --template {{.spec.nodeName}}; echo""; done
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 zkStatefulSet 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.
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
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 - Cluster Management
7.1 - Running Kubelet in Standalone Mode
This tutorial shows you how to run a standalone kubelet instance.
You may have different motivations for running a standalone kubelet. This tutorial is aimed at introducing you to Kubernetes, even if you don't have much experience with it. You can follow this tutorial and learn about node setup, basic (static) Pods, and how Kubernetes manages containers.
Once you have followed this tutorial, you could try using a cluster that has a control plane to manage pods and nodes, and other types of objects. For example, Hello, minikube.
You can also run the kubelet in standalone mode to suit production use cases, such as to run the control plane for a highly available, resiliently deployed cluster. This tutorial does not cover the details you need for running a resilient control plane.
Objectives
Install cri-o, and kubelet on a Linux system and run them as systemd services.
Launch a Pod running nginx that listens to requests on TCP port 80 on the Pod's IP address.
Learn how the different components of the solution interact among themselves.
Caution:
The kubelet configuration used for this tutorial is insecure by design and should not be used in a production environment.
Before you begin
Admin (root) access to a Linux system that uses systemd and iptables (or nftables with iptables emulation).
Access to the Internet to download the components needed for the tutorial, such as:
By default, kubelet fails to start if swap memory is detected on a node. This means that swap should either be disabled or tolerated by kubelet.
Note:
If you configure the kubelet to tolerate swap, the kubelet still configures Pods (and the containers in those Pods) not to use swap space. To find out how Pods can actually use the available swap, you can read more about swap memory management on Linux nodes.
If you have swap memory enabled, either disable it or add failSwapOn: false to the kubelet configuration file.
To check if swap is enabled:
sudo swapon --show
If there is no output from the command, then swap memory is already disabled.
To disable swap temporarily:
sudo swapoff -a
To make this change persistent across reboots:
Make sure swap is disabled in either /etc/fstab or systemd.swap, depending on how it was configured on your system.
Enable IPv4 packet forwarding
To check if IPv4 packet forwarding is enabled:
cat /proc/sys/net/ipv4/ip_forward
If the output is 1, it is already enabled. If the output is 0, then follow next steps.
To enable IPv4 packet forwarding, create a configuration file that sets the net.ipv4.ip_forward parameter to 1:
sudo tee /etc/sysctl.d/k8s.conf <<EOF net.ipv4.ip_forward = 1 EOF
Note: This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
Install a container runtime
Download the latest available versions of the required packages (recommended).
There are several ways to install the CRI-O container runtime, depending on your particular Linux distribution. Although CRI-O recommends using either deb or rpm packages, this tutorial uses the static binary bundle script of the CRI-O Packaging project, both to streamline the overall process, and to remain distribution agnostic.
The script installs and configures additional required software, such as cni-plugins, for container networking, and crun and runc, for running containers.
The script will automatically detect your system's processor architecture (amd64 or arm64) and select and install the latest versions of the software packages.
Make sure that the default subnet range (10.85.0.0/16) does not overlap with any of your active networks. If there is an overlap, you can edit the file and change it accordingly. Restart the service after the change.
sudo tee /etc/kubernetes/kubelet.yaml <<EOF apiVersion: kubelet.config.k8s.io/v1beta1 kind: KubeletConfiguration authentication: webhook: enabled: false # Do NOT use in production clusters! authorization: mode: AlwaysAllow # Do NOT use in production clusters! enableServer: false logging: format: text address: 127.0.0.1 # Restrict access to localhost readOnlyPort: 10255 # Do NOT use in production clusters! staticPodPath: /etc/kubernetes/manifests containerRuntimeEndpoint: unix:///var/run/crio/crio.sock EOF
Note:
Because you are not setting up a production cluster, you are using plain HTTP (readOnlyPort: 10255) for unauthenticated queries to the kubelet's API.
The authentication webhook is disabled and authorization mode is set to AlwaysAllow for the purpose of this tutorial. You can learn more about authorization modes and webhook authentication to properly configure kubelet in standalone mode in your environment.
See Ports and Protocols to understand which ports Kubernetes components use.
The command line argument --kubeconfig has been intentionally omitted in the service configuration file. This argument sets the path to a kubeconfig file that specifies how to connect to the API server, enabling API server mode. Omitting it, enables standalone mode.
In standalone mode, you can run Pods using Pod manifests. The manifests can either be on the local filesystem, or fetched via HTTP from a configuration source.
Create a manifest for a Pod:
cat <<EOF > static-web.yaml apiVersion: v1 kind: Pod metadata: name: static-web spec: containers: - name: web image: nginx ports: - name: web containerPort: 80 protocol: TCP EOF
Copy the static-web.yaml manifest file to the /etc/kubernetes/manifests directory.
Find out information about the kubelet and the Pod
The Pod networking plugin creates a network bridge (cni0) and a pair of veth interfaces for each Pod (one of the pair is inside the newly made Pod, and the other is at the host level).
Query the kubelet's API endpoint at http://localhost:10255/pods:
This page covered the basic aspects of deploying a kubelet in standalone mode. You are now ready to deploy Pods and test additional functionality.
Notice that in standalone mode the kubelet does not support fetching Pod configurations from the control plane (because there is no control plane connection).
You also cannot use a ConfigMap or a Secret to configure the containers in a static Pod.
What's next
Follow Hello, minikube to learn about running Kubernetes with a control plane. The minikube tool helps you set up a practice cluster on your own computer.
A mechanism to attach authorization and policy to a subsection of the cluster.
Use of multiple namespaces is optional.
This example demonstrates how to use Kubernetes namespaces to subdivide your cluster.
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:
By default, a Kubernetes cluster will instantiate a default namespace when provisioning the cluster to hold the default set of Pods, Services, and Deployments used by the cluster.
Assuming you have a fresh cluster, you can inspect the available namespaces by doing the following:
kubectl get namespaces
NAME STATUS AGE default Active 13m
Create new namespaces
For this exercise, we will create two additional Kubernetes namespaces to hold our content.
Let's imagine a scenario where an organization is using a shared Kubernetes cluster for development and production use cases.
The development team would like to maintain a space in the cluster where they can get a view on the list of Pods, Services, and Deployments they use to build and run their application. In this space, Kubernetes resources come and go, and the restrictions on who can or cannot modify resources are relaxed to enable agile development.
The operations team would like to maintain a space in the cluster where they can enforce strict procedures on who can or cannot manipulate the set of Pods, Services, and Deployments that run the production site.
One pattern this organization could follow is to partition the Kubernetes cluster into two namespaces: development and production.
Let's create two new namespaces to hold our work.
Use the file namespace-dev.yaml which describes a development namespace:
The next step is to define a context for the kubectl client to work in each namespace. The value of "cluster" and "user" fields are copied from the current context.
By default, the above commands add two contexts that are saved into file .kube/config. You can now view the contexts and alternate against the two new request contexts depending on which namespace you wish to work against.
We have created a deployment whose replica size is 2 that is running the pod called snowflake with a basic container that serves the hostname.
kubectl get deployment
NAME READY UP-TO-DATE AVAILABLE AGE snowflake 2/2 2 2 2m
kubectl get pods -l app=snowflake
NAME READY STATUS RESTARTS AGE snowflake-3968820950-9dgr8 1/1 Running 0 2m snowflake-3968820950-vgc4n 1/1 Running 0 2m
And this is great, developers are able to do what they want, and they do not have to worry about affecting content in the production namespace.
Let's switch to the production namespace and show how resources in one namespace are hidden from the other.
kubectl config use-context prod
The production namespace should be empty, and the following commands should return nothing.
kubectl get deployment kubectl get pods
Production likes to run cattle, so let's create some cattle pods.
kubectl create deployment cattle --image=registry.k8s.io/serve_hostname --replicas=5kubectl get deployment
NAME READY UP-TO-DATE AVAILABLE AGE cattle 5/5 5 5 10s
kubectl get pods -l app=cattle
NAME READY STATUS RESTARTS AGE cattle-2263376956-41xy6 1/1 Running 0 34s cattle-2263376956-kw466 1/1 Running 0 34s cattle-2263376956-n4v97 1/1 Running 0 34s cattle-2263376956-p5p3i 1/1 Running 0 34s cattle-2263376956-sxpth 1/1 Running 0 34s
At this point, it should be clear that the resources users create in one namespace are hidden from the other namespace.
As the policy support in Kubernetes evolves, we will extend this scenario to show how you can provide different authorization rules for each namespace.
8 - Services
8.1 - 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:
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.
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 in the following yaml:
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 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.
Note:
If the service environment variables are not desired (because possible clashing with expected program ones, too many variables to process, only using DNS, etc) you can disable this mode by setting the enableServiceLinks flag to false on the pod spec.
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
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:
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
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:
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):
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.
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 ...
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.
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:
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:
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
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:
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:
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:
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:
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.
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 choosecurl localhost:32122/healthz
1 Service Endpoints found
On a different node you might get a different result:
# Run this locally on a node you choosecurl 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:
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.
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.
8.3 - 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 to 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 flow described in the Termination of Pods document.
Let's say you have a Deployment containing a single nginx replica (say just for the sake of demonstration purposes) and a Service:
apiVersion:apps/v1kind:Deploymentmetadata:name:nginx-deploymentlabels:app:nginxspec:replicas:1selector:matchLabels:app:nginxtemplate:metadata:labels:app:nginxspec:terminationGracePeriodSeconds:120# extra long grace periodcontainers:- name:nginximage:nginx:latestports:- containerPort:80lifecycle: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"]
This allows applications to communicate their state during termination and clients (such as load balancers) to implement connection 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 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.
