Okay you can easy install application but where is the benefit compared to normal .yaml files from Kubernetes?
Can someone give me a example where it is useful to use helm and why normal Kubernetes is not sufficient?
Also a confrontation for helm and Kubernetes would be nice.
With Helm, a set of resources (read as Kubernetes manifests) logically define a release - and you need to treat this group of resources as a single unit.
A simple example on why this is necessary: Imagine an application bundle that has, let's say, 10 kubernetes objects in total. On the next release, due to the changes in the app, now 1 of the resources is not needed anymore - there are 9 objects in total. How would I roll out this new release? If I simply do kubectl apply -f new_release/, that wouldn't take care of the deletion of that 1 resource that is not needed anymore. This means, I cannot roll upgrades that doesn't need manual intervention. Helm takes care of this.
Helm also keeps a history of releases with their exact set of resources, so you can rollback to a previous release with a single command, in case things go wrong.
Also, one of the things you need often is templating your resources - imagine you want to deploy multiple instances of the same exact application. What would you do?
Kubernetes doesn't offer many options to tackle this problem - one solution is to use different namespaces: Don't specify namespace in the manifests, but give it in the command, such as kubectl apply -n my_namespace -f resources/, but what if you want to deploy two of this instances on the same namespace? Then you need some kind of name/label/selector templating, and Helm takes care of that.
These are some examples for the use cases that Helm addresses.
Related
I have an Openshift cluster and I want to move its resources to another cluster,
e.g. I have 40 Secrets, and 20 ConfigMaps, and some other resources such as deployment configs and more.
Moving these secrets and config maps manually is mind-blowing.
What is the best approach?
I would recommend trying out Monokle's Compare & Sync feature.
It allows you to visually compare the resources of two clusters and deploy resources from one to the other.
Here's a screenshot of the UI:
You can read more about how this works in the docs.
OpenShift has an "official" process for this called "Migration Toolkit for Containers (MTC)":
https://docs.openshift.com/container-platform/4.12/migration_toolkit_for_containers/about-mtc.html
Velero is also a great tool for your scenario. You can backup your namespaces with the granularity of the objects included, and restore them elsewhere with or without making changes:
https://velero.io/docs/v1.10/migration-case/
Follow these steps:
move secrets and config maps
move deployments
move services
move routes
As an example of how I'll do each step mentioned above, follow these steps for each of them:
1 - Login to the first cluster:
oc login --token="your-token-for-first-server" --server="your-first-server"
2 - Export your resources:
oc get -o yaml cm > configmaps.yaml
oc get -o yaml secrets > secrets.yaml
...
There are also some default ConfigMaps and Secrets which you don't need to copy, you can erase them after making the files.
3 - Login to the second cluster:
oc login --token="your-token-for-second-server" --server="your-second-server"
If you forget this step, you may get an error that says resource already exists, but be careful not to forget this step.
4 - Load resources to the second cluster
oc create -f configmaps.yaml
oc create -f secrets.yaml
...
There might be easier ways too, and there are a lot of information about this which is out of my knowledge.
There are also some considerations you need to aware of:
You may not need to move pods, usually they are made and controlled by other resources such as deployment configs.
In some companies, databases are managed completely separately by DBA teams, you may not need to change anything, but if your database is within your cluster, you should consider moving it's PV.
Using Helm chart or Openshift templates can help you make this kind of task so easier.
You can include templates in your GitLab CI/CD pipelines and just change your cluster URL and everything is up and running and redeploy.
In the end, if you are migrating from version 3 to 4, this article might be helpful.
I have a containerized daemon that I need to run one instance of for every thing. Each thing has a unique set of configs associated with it, but the container image is the same. The configs can be set simply as environment variables. I have a list of the configs, and I need to define the desired state as having exactly 1 pod running for each thing. What is the appropriate way to construct this in Kubernetes with or without Helm?
My understanding is that ReplicaSets and Deployments work on identical containers, in other words they would all be spun up with the same environment variables? I understand that StatefulSet may be able to represent this, but the deamons do not need to hold state really, they do not need persistent storage, they can be killed at will, so long as another with the same configs comes up soon afterwards.
One clue I was given by somebody was to use Helmfile or Helm partials. That is the extent of what they told me. I have not yet investigated whether those are appropriate or not.
You are correct saying that Deployment and ReplicaSets are running on identical containers, so the way I see it you have 2 options:
Deploy multiple deployments with different configs defined in the values file:
You can see an example here, where multiple configs are set in the values file and using {{ range }} to iterate and create multiple deployments
Iterate over you configurations names/files using scripting language of your choice and create separate release for each of your configuration via the command line for example: --set configName=
Personally, I would go with the 2nd option since multiple helm releases can harness the helm cli to better understand what is running and it's state. also, any CRUD action you would like to do would be less dangerous since the deployments are decoupled
I'm just getting started with kubernetes and setting up a cluster on AWS using kops. In many of the examples I read (and try), there will be commands like:
kubectl run my-app --image=mycompany/myapp:latest --replicas=1 --port=8080
kubectl expose deployment my=app --port=80 --type=LoadBalancer
This seems to do several things behind the scenes, and I can view the manifest files created using kubectl edit deployment, and so forth However, i see many examples where people are creating the manifest files by hand, and using commands like kubectl create -f or kubectl apply -f
Am I correct in assuming that both approaches accomplish the same goals, but that by creating the manifest files yourself, you have a finer grain of control?
Would I then have to be creating Service, ReplicationController, and Pod specs myself?
Lastly, if you create the manifest files yourself, how do people generally structure their projects as far as storing these files? Are they simply in a directory alongside the project they are deploying?
The fundamental question is how to apply all of the K8s objects into the k8s cluster. There are several ways to do this job.
Using Generators (Run, Expose)
Using Imperative way (Create)
Using Declarative way (Apply)
All of the above ways have a different purpose and simplicity. For instance, If you want to check quickly whether the container is working as you desired then you might use Generators .
If you want to version control the k8s object then it's better to use declarative way which helps us to determine the accuracy of data in k8s objects.
Deployment, ReplicaSet and Pods are different layers which solve different problems.All of these concepts provide flexibility to k8s.
Pods: It makes sure that related containers are together and provide efficiency.
ReplicaSet: It makes sure that k8s cluster has desirable replicas of the pods
Deployment: It makes sure that you can have different version of Pods and provide the capability to rollback to the previous version
Lastly, It depends on use case how you want to use these concepts or methodology. It's not about which is good or which is bad.
There is a little more nuance to the difference between apply and create than what is already mentioned here. Kubectl create can be used imperatively on the command line or declaratively against a manifest file.
Kubectl apply is used declaratively against a manifest file. You can't use kubectl apply imperatively.
One key difference is when you already have an object and you want to update something. Even if you used a manifest file with kubectl create, you will get an error when you use kubectl create again to update the same resource. But, if you use kubectl apply, you will not get an error. It will update the resource without any issues.
So, the convention is to use kubectl apply to create AND update resources, kubectl create is used to create resources, and kubectl run is used to create a pod with a specific image, namespace, etc. for experimentation and testing with the --dry-run=client option.
After some intense google and SO search i couldn't find any document that mentions both rolling update and set image, and can stress the difference between the two.
Can anyone shed light? When would I rather use either of those?
EDIT: It's worth mentioning that i'm already working with deployments (rather than replication controller directly) and that I'm using yaml configuration files. It would also be nice to know if there's a way to perform any of those using configuration files rather than direct commands.
In older k8s versions the ReplicationController was the only resource to manage a group of replicated pods. To update the pods of a ReplicationController you use kubectl rolling-update.
Later, k8s introduced the Deployment which manages ReplicaSet resources. The Deployment could be updated via kubectl set image.
Working with Deployment resources (as you already do) is the preferred way. I guess the ReplicationController and its rolling-update command are mainly still there for backward compatibility.
UPDATE: As mentioned in the comments:
To update a Deployment I used kubectl patch as it could also change things like adding new env vars whereas kubectl set image is rather limited and can only change the image version. Also note, that patch can be applied to all k8s resources and is not restricted to be used with a Deployment.
Later, I shifted my deployment processes to use helm - a really neat and k8s native package management tool. Can highly recommend to have a look at it.
I've got a local deployment system that is mirroring our production system. Both are deployed by calling kubectl apply -f deployments-and-services.yaml
I'm tagging all builds with the current git hash, which means that for clean deploys to GKE, all the services have a new docker image tag which means that apply will restart them, but locally to minikube the tag is often not changing which means that new code is not run. Before I was working around this by calling kubectl delete and then kubectl create for deploying to minikube, but as the number of services I'm deploying has increased, that is starting to stretch the dev cycle too far.
Ideally, I'd like a better way to tell kubectl apply to restart a deployment rather than just depending on the tag?
I'm curious how people have been approaching this problem.
Additionally, I'm building everything with bazel which means that I have to be pretty explicit about setting up my build commands. I'm thinking maybe I should switch to just delete/creating the one service I'm working on and leave the others running.
But in that case, maybe I should just look at telepresence and run the service I'm dev'ing on outside of minikube all together? What are best practices here?
I'm not entirely sure I understood your question but that may very well be my reading comprehension :)
In any case here's a few thoughts that popped up while reading this (again not sure what you're trying to accomplish)
Option 1: maybe what you're looking for is to scale down and back up, i.e. scale your deployment to say 0 and then back up, given you're using configmap and maybe you only want to update that, the command would be kubectl scale --replicas=0 -f foo.yaml and then back to whatever
Option 2: if you want to apply the deployment and not kill any pods for example, you would use the cascade=false (google it)
Option 3: lookup the rollout option to manage deployments, not sure if it works on services though
Finally, and that's only me talking, share some more details like which version of k8s are you using? maybe provide an actual use case example to better describe the issue.
Kubernetes, only triggers a deployment when something has changed, if you have image pull policy to always you can delete your pods to get the new image, if you want kube to handle the deployment you can update the kubernetes yaml file to container a constantly changing metadata field (I use seconds since epoch) which will trigger a change. Ideally you should be tagging your images with unique tags from your CI/CD pipeline with the commit reference they have been built from. this gets around this issue and allows you to take full advantage of the kubernetes rollback feature.