I tried to automate the rolling update when the configmap changes are made. But, I am confused about how can I verify if the rolling update is successful or not. I found out the command
kubectl rollout status deployment test-app -n test
But I guess this is used when we are performing the rollback rather than for rolling update. What's the best way to know if the rolling update is successful or not?
I think it is fine,
kubectl rollout status deployments/test-app -n test can be used to verify the rollout deployment as well.
As an additional step, you can run,
kubectl rollout history deployments/test-app -n test as well.
if you need further clarity,
kubectl get deployments -o wide and check the READY and IMAGE fields.
ConfigMap generation and rolling update
I tried to automate the rolling update when the configmap changes are made
It is a good practice to create new resources instead of mutating (update in-place). kubectl kustomize is supporting this workflow:
The recommended way to change a deployment's configuration is to
create a new configMap with a new name,
patch the deployment, modifying the name value of the appropriate configMapKeyRef field.
You can deploy using Kustomize to automatically create a new ConfigMap every time you want to change content by using configMapGenerator. The old ones can be garbage collected when not used anymore.
Whith Kustomize configMapGenerator can you get a generated name.
Example
kind: ConfigMap
metadata:
name: example-configmap-2-g2hdhfc6tk
and this name get reflected to your Deployment that then trigger a new rolling update, but with a new ConfigMap and leave the old unchanged.
Deploy both Deployment and ConfigMap using
kubectl apply -k <kustomization_directory>
When handling change this way, you are following the practices called Immutable Infrastructure.
Verify deployment
To verify a successful deployment, you are right. You should use:
kubectl rollout status deployment test-app -n test
and when leaving the old ConfigMap unchanged but creating a new ConfigMap for the new ReplicaSet it is clear which ConfigMap belongs to which ReplicaSet.
Also rollback will be easier to understand since both old and new ReplicaSet use its own ConfigMap (on change of content).
Your command is fine to check if an update went through.
Now, a ConfigMap change eventually gets applied to the Pod. There is no need to do a rolling update for that. Depending on what you have passed in the ConfigMap, you probably could have restarted the service and that's it.
What's the best way to know if the rolling update is successful or not?
To check if you rolling update was executed correctly, you command works fine, you could check also if you replicas is running.
I tried to automate the rolling update when the configmap changes are made.
You could use Reloader to perform your rolling updates automatically when a configmap/secret changed.
Reloader can watch changes in ConfigMap and Secret and do rolling upgrades on Pods with their associated DeploymentConfigs, Deployments, Daemonsets and Statefulsets.
Let's explore how Reloader works in a pratical way using a nginx deployment as exxample.
First install Reloader in your cluster:
kubectl apply -f https://raw.githubusercontent.com/stakater/Reloader/master/deployments/kubernetes/reloader.yaml
You will see a new container named reloader-reloader-... this container is responsible to 'watch' your deployments and make the rolling updates when necessary.
Create a configMap with your values, in my case I'll create a my-config and set a variable called myvar.value with value hello:
kubectl create configmap my-config --from-literal=myvar.value=hello
Now, let's create a simple deployment:
apiVersion: apps/v1
kind: Deployment
metadata:
name: deployment-example
labels:
app: nginx
metadata:
annotations:
configmap.reloader.stakater.com/reload: my-config
spec:
replicas: 1
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.7.9
ports:
- containerPort: 80
env:
- name: MYVAR
valueFrom:
configMapKeyRef:
name: my-config
key: myvar.value
In this example, the nginx image will be used getting the value from my configMap and set in a variable called MYVAR.
To Reloader works, you must specify the name of your configMap in annotations, in the example above it will be:
metadata:
annotations:
configmap.reloader.stakater.com/reload: my-config
Apply the deployment example with kubectl apply -f mydeplyment-example.yaml and check the variable from the new pod.
$ kubectl exec -it $(kubectl get pods -l=app=nginx --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}') env | grep MYVAR
MYVAR=hello
Now let's change the value of the variable:
Edit configmap with kubectl edit configmap my-config, change the value of myvar.value to hi, save and close.
After save, Reloader will recreate your container and get the new value from configmap.
To check if the rolling update was executed successfully:
kubectl rollout status deployment deployment-example
Check the new value:
$ kubectl exec -it $(kubectl get pods -l=app=nginx --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}') env | grep MYVAR
MYVAR=hi
That's all!
Check the Reloader github see more options to use.
I hope it helps!
Mounted ConfigMaps are updated automatically reference
To Check roll-out history used below steps
ubuntu#dlv-k8s-cluster-master:~$ kubectl rollout history deployment busybox
REVISION CHANGE-CAUSE
1 kubectl create --filename=busybox.yaml --record=true
Update image on deployment as below
$ kubectl set image deployment.app/busybox *=busybox --record
deployment.apps/busybox image updated
Check new rollout history which will list the new change cause for rollout
$ kubectl rollout history deployment busybox
REVISION CHANGE-CAUSE
1 kubectl create --filename=busybox.yaml --record=true
2 kubectl set image deployment.app/busybox *=busybox --record=true
To Rollback Deployment : use undo flag along rollout command
ubuntu#dlv-k8s-cluster-master:~$ kubectl rollout undo deployment busybox deployment.apps/busybox rolled back
ubuntu#dlv-k8s-cluster-master:~$ kubectl rollout history deployment busybox
REVISION CHANGE-CAUSE
2 kubectl set image deployment.app/busybox *=busybox --record=true
3 kubectl create --filename=busybox.```
Related
I am new to kubernete and have a bit confused about apply and rollout command. If I update the kubernete configuration file, should I use kubectl apply -f or kubectl rollout?
if I update kubernete configuration and I run kubectl apply -f, it will terminate the running pod and create a new one.
but rollout also has restart command which is used to restart the pod. so when should I use rollout restart?
The kubectl apply -f used to apply the configuration file kubernetes(where your deploy your desired application).
And kubectl rollout is used to check the above deployed application
Example
Suppose your deployment configuration file looks like this and you saved that in nginx.yaml file. Now you want deploy the nginx app from the below yaml file. so you should use kubectl apply -f nginx.yaml, and now you want to check if your application deployed successfully or not using kubectl rollout status nginx
apiVersion: apps/v1
kind: Deployment
metadata:
name: nginx
labels:
app: nginx
spec:
replicas: 3
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.14.2
ports:
- containerPort: 80
and if you updated the yaml locally and you want replace that with existing then use kubectl replace -f nginx.yaml
One major difference I can think of is kubectl apply can be used for all Kubernetes objects (Pod, Deployment, ConfigMaps, Secrets, etc.) where as the kubectl rollout applies specifically to objects that deals with some computation like Deployments, Statefulsets, etc.
Further, kubectl rollout restart is useful to restart the pods without requiring any changes in the spec fields which is not possible with kubectl apply. If we run kubectl apply with no changes in the spec fields, the pods will not get updated as there is no change to update.
Consider the scenario where some configuration (say, external certificate) is mounted to the pods as ConfigMap and any change in the ConfigMap do not cause the pods to get updated automatically. kubectl rollout restart can be useful in such scenarios to create new pods which can then read updated configurations from the ConfigMap.
Also, a important note from the docs:
Note: A Deployment's rollout is triggered if and only if the
Deployment's Pod template (that is, .spec.template) is changed, for
example if the labels or container images of the template are updated.
Other updates, such as scaling the Deployment, do not trigger a
rollout.
I have a pod in my kubernetes which needed an update to have securityContext. So generated a yaml file using -
kubectl get pod pod_name -o yaml > mypod.yaml
After updating the required securityContext and executing command -
kubectl apply -f mypod.yaml
no changes are observed in pod.
Where as a fresh newly created yaml file works perfectly fine.
new yaml file -
apiVersion: v1
kind: Pod
metadata:
name: mypod
namespace: default
spec:
securityContext:
runAsUser: 1010
containers:
- command:
- sleep
- "4800"
image: ubuntu
name: myubuntuimage
Immutable fields
In Kubernetes you can find information about Immutable fields.
A lot of fields in APIs tend to be immutable, they can't be changed after creation. This is true for example for many of the fields in pods. There is currently no way to declaratively specify that fields are immutable, and one has to rely on either built-in validation for core types, or have to build a validating webhooks for CRDs.
Why ?
There are resources in Kubernetes which have immutable fields by design, i.e. after creation of an object, those fields cannot be mutated anymore. E.g. a pod's specification is mostly unchangeable once it is created. To change the pod, it must be deleted, recreated and rescheduled.
Editing existing pod configuration
If you want to apply new config with security context using kubectl apply you will get error like below:
The Pod "mypod" is invalid: spec: Forbidden: pod updates may not change fields other than `spec.containers[*].image`, `spec.initContainers[*].image`, `spec.activeDeadlineSeconds` or `spec.tolerations` (only additions to existing tolerations)
Same output will be if you would use kubectl patch
kubectl patch pod mypod -p '{"spec":{"securityContext":{"runAsUser":1010}}}'
Also kubectl edit will not change this specific configuration
$ kubectl edit pod
Edit cancelled, no changes made.
Solution
If you need only one pod, you must delete it and create new one with requested configuration.
Better solution is to use resource which will make sure to fulfil some own requirements, like Deployment. After change of the current configuration, deployment will create new Replicaset which will create new pods with new configuration.
by updating the PodTemplateSpec of the Deployment. A new ReplicaSet is created and the Deployment manages moving the Pods from the old ReplicaSet to the new one at a controlled rate. Each new ReplicaSet updates the revision of the Deployment.
Is this a valid imperative command for creating job?
kubectl create job my-job --image=busybox
I see this in https://kubernetes.io/docs/reference/generated/kubectl/kubectl-commands. But the command is not working. I am getting error as bellow:
Error: unknown flag: --image
What is the correct imperative command for creating job?
Try this one
kubectl create cronjob my-job --schedule="0,15,30,45 * * * *" --image=busy-box
What you have should work, though it not recommended as an approach anymore. I would check what version of kubectl you have, and possibly upgrade it if you aren't using the latest.
That said, the more common approach these days is to write a YAML file containing the Job definition and then run kubectl apply -f myjob.yaml or similar. This file-driven approach allowed for more natural version control, editing, review, etc.
Using correct value for --restart field on "kubectl run" will result run command to create an deployment or job or cronjob
--restart='Always': The restart policy for this Pod. Legal values [Always, OnFailure, Never]. If set to 'Always'
a deployment is created, if set to 'OnFailure' a job is created, if set to 'Never', a regular pod is created. For the
latter two --replicas must be 1. Default 'Always', for CronJobs `Never`.
Use "kubectl run" for creating basic kubernetes job using imperatively command as below
master $ kubectl run nginx --image=nginx --restart=OnFailure --dry-run -o yaml > output.yaml
Above should result an "output.yaml" as below example, you can edit this yaml for advance configurations as needed and create job by "kubectl create -f output.yaml or if you just need basic job then remove --dry-run option from above command and you will get basic job created.
apiVersion: batch/v1
kind: Job
metadata:
creationTimestamp: null
labels:
run: nginx
name: nginx
spec:
template:
metadata:
creationTimestamp: null
labels:
run: nginx
spec:
containers:
- image: nginx
name: nginx
resources: {}
restartPolicy: OnFailure
status: {}
I took the CKA exam and I needed to work with Daemonsets for quite a while there. Since it is much faster to do everything with kubectl instead of creating yaml manifests for k8s resources, I was wondering if it is possible to create Daemonset resources using kubectl.
I know that it is NOT possible to create it using regular kubectl create daemonset at least for now. And there is no description of it in the documentation. But maybe there is a way to do that in some different way?
The best thing I could do right now is to create Deployment first like kubectl create deployment and edit it's output manifest. Any options here?
The fastest hack is to create a deployment file using
kubectl create deploy nginx --image=nginx --dry-run -o yaml > nginx-ds.yaml
Now replace the line kind: Deployment with kind: DaemonSet in nginx-ds.yaml and remove the line replicas: 1
However, the following command will give a clean daemonset manifest considering that "apps/v1" is the api used for DaemonSet in your cluster
kubectl create deploy nginx --image=nginx --dry-run -o yaml | \
sed '/null\|{}\|replicas/d;/status/,$d;s/Deployment/DaemonSet/g' > nginx-ds.yaml
You have your nginx DaemonSet.
CKA allows access to K8S documentation. So, it should be possible to get a sample YAML for different resources from there. Here is the one for the Daemonset from K8S documentation.
Also, not sure if the certification environment has access to resources in the kube-system namespace. If yes, then use the below command to get a sample yaml for Daemonset.
kubectl get daemonsets kube-flannel-ds-amd64 -o yaml -n=kube-system > daemonset.yaml
It's impossible. At least for Kubernetes 1.12. The only option is to get a sample Daemonset yaml file and go from there.
The fastest way to create
kubectl create deploy nginx --image=nginx --dry-run -o yaml > nginx-ds.yaml
Now replace the line kind: Deployment with kind: DaemonSet in nginx-ds.yaml and remove the line replicas: 1 , strategy {} and status {} as well.
Otherwise it shows error for some required fields like this
error: error validating "nginx-ds.yaml": error validating data: [ValidationError(DaemonSet.spec): unknown field "strategy" in io.k8s.api.apps.v1.DaemonSetSpec, ValidationError(DaemonSet.status): missing required field "currentNumberScheduled" in io.k8s.api.apps.v1.DaemonSetStatus,ValidationError(DaemonSet.status): missing required field "numberMisscheduled" in io.k8s.api.apps.v1.DaemonSetStatus, ValidationError(DaemonSet.status): missing required field "desiredNumberScheduled" in io.k8s.api.apps.v1.DaemonSetStatus, ValidationError(DaemonSet.status): missing required field "numberReady" in io.k8s.api.apps.v1.DaemonSetStatus]; if you choose to ignore these errors, turn validation off with --validate=false
There is no such option to create a DaemonSet using kubectl. But still, you can prepare a Yaml file with basic configuration for a DaemonSet, e.g. daemon-set-basic.yaml, and create it using kubectl create -f daemon-set-basic.yaml
You can edit new DaemonSet using kubectl edit daemonset <name-of-the-daemon-set>. Or modify the Yaml file and apply changes by kubectl apply -f daemon-set-basic.yaml. Note, if you want to update configuration modifying file and using apply command, it is better to use apply instead of create when you create the DaemonSet.
Here is the example of a simple DaemonSet:
kind: DaemonSet
metadata:
name: fluentd-elasticsearch
labels:
k8s-app: fluentd-logging
spec:
selector:
matchLabels:
name: fluentd-elasticsearch
template:
metadata:
labels:
name: fluentd-elasticsearch
spec:
containers:
- name: fluentd-elasticsearch
image: k8s.gcr.io/fluentd-elasticsearch:1.20
You could take advantage of Kubernetes architecture to obtain definition of DaemonSet from existing cluster. Have a look at kube-proxy, which is a network component that runs on each node in your cluster.
kube-proxy is deployed as DaemonSet so you can extract its definition with below command.
$ kubectl get ds kube-proxy -n kube-system -o yaml > kube-proxy.ds.yaml
Warning!
By extracting definition of DaemonSet from kube-proxy be aware that:
You will have to do pliantly of clean up!
You will have to change apiVersion from extensions/v1beta1 to apps/v1
I used this by the following commands:
Either create Replicaset or deployment from Kubernetes imperative command
kubectl create deployment <daemonset_name> --image= --dry-run -o yaml > file.txt
Edit the kind and replace DaemonSet, remove replicas and strategy fields into it.
kubectl apply -f file.txt
During CKA examination you are allowed to access Kubernetes Documentation for DaemonSets. You could use the link and get examples of DaemonSet yaml files. However you could use the way you mentioned, change a deployment specification to DaemonSet specification. You need to change the kind to Daemonset, remove strategy, replicas and status fields. That would do.
Using command to deployment create and modifying it, one can create daemonset very quickly.
Below is one line command to create daemonset
kubectl create deployment elasticsearch --namespace=kube-system --image=k8s.gcr.io/fluentd-elasticsearch:1.20 --dry-run -o yaml | grep -v "creationTimestamp\|status" | awk '{gsub(/Deployment/, "DaemonSet"); print }'
I'm using a CI to update my kubernetes cluster whenever there's an update to an image. Whenever the image is pushed and has the latest tag it kubectl apply's the existing deployment but nothing gets updated.
this is what runs
$ kubectl apply --record --filename /tmp/deployment.yaml
My goal is when the apply is ran that a rolling deployment gets executed.
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: api
spec:
replicas: 1
template:
metadata:
labels:
app: api
spec:
containers:
- name: api
image: us.gcr.io/joule-eed41/api:latest
imagePullPolicy: Always
ports:
- containerPort: 1337
args:
- /bin/sh
- -c
- echo running api;npm start
env:
- name: NAMESPACE
valueFrom:
configMapKeyRef:
name: config
key: NAMESPACE
As others suggested, have a specific tag.
Set new image using following command
kubectl set image deployment/deployment_name deployment_name=image_name:image_tag
In your case it would be
kubectl set image deployment/api api=us.gcr.io/joule-eed41/api:0.1
As #ksholla20 mentionedm using kubectl set image is a good option for many (most?) cases.
But if you can't change the image tag consider using:
1 ) kubectl rollout restart deployment/<name>
(reference).
2 ) kubectl patch deployment <name> -p "{\"spec\":{\"template\":{\"metadata\":{\"labels\":{\"version\":\"$CURRENT_BUILD_HASH_OR_DATE\"}}}}}}" (reference)
(*) Notice that the patch command allow you to change specific properties in the deployment (or any other object chosen) like the label selector and the pod label or other properties like the value of the NAMESPACE environment variable in your example.
I've run into the same problem and none of the solutions posted so far will help. The solution is easy, but not easy to see or predict. The applied yaml will generate both a deployment and a replicaset the first time it's run. Unfortunately, applying changes to the manifest likely only replaces the replicaset, while the deployment will remain unchanged. This is a problem because some changes need to happen at the deployment level, but the old deployment hangs around. To have best results, delete the deployment and ensure all previous deployments and replicasets are deleted. Then apply the updated manifest.