RBAC

Setting up Helm for Kubernetes (with RBAC) and Deploying Your First Chart

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I was pointed to Helm the other day and decided to have a quick look at it. I tasked myself with setting it up in a sandbox environment and deploying a pre-packaged application (a.k.a chart, or helm package) into my Kubernetes sandbox environment.

Helm 101

The best way to think about Helm is as a ‘package manager for Kubernetes’. You install Helm as a cli tool (It’s written in Golang) and all the operations it provides to you, you’ll find are very similar to those of common package managers like npm etc…

Helm has a few main concepts.

  • As mentioned above, a ‘Chart’ is a package for Helm. It contains the resource definitions required to run an app/tool/service on a Kubernetes cluster.
  • A ‘Repository’ is where charts are stored and shared from
  • A ‘Release’ is an instance of a chart running in your Kubernetes cluster. You can create multiple releases for multiple instances of your app/tool/service.

More info about Helm and it’s concepts can be found on the Helm Quickstart guide. If however, you wish to get stuck right in, read on…

This is a quick run-down of the tasks involved in setting it up and deploying a chart (I tried out kube-slack to provide slack notifications for failed kubernetes operations in my sandbox environment to my slack channel).

Setting up Helm

Download and unzip the latest Helm binary for your OS. I’m using Windows so I grabbed that binary, unblocked it, and put in a folder found in my path. Running a PowerShell session I can simply type:

helm

Helm executes and provides a list of possible options.

Before you continue with initialising Helm, you should create a service account in your cluster that Helm will use to manage releases across namespaces (or in a particular namespace you wish it to operate in). For testing its easiest to set up the service account to use the default built-in “cluster-admin” role. (To be more secure you should set up Tiller to have restricted permissions and even restrict it based on namespace too).

To setup the basic SA with the cluster-admin role, you’ll need a ClusterRoleBinding to go with the SA. Here is the config you need to set both up.

apiVersion: v1
kind: ServiceAccount
metadata:
  name: tillersa
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
  name: tillersa-clusterrolebinding
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-admin
subjects:
  - kind: ServiceAccount
    name: tillersa
    namespace: kube-system

Run kubectl create and point to this config to set up the SA and ClusterRoleBinding:

kubectl create -f .\tillersa-and-cluster-rolebinding.yaml

Now you can do a helm initialisation.

helm init --service-account tillersa --tiller-namespace kube-system

If all went well, you’ll get a message stating it was initialised and setup in your cluster.

Run:

kubectl get pods -n=kube-system

and you should see your new tiller-deploy pod running.

Deploying Charts with Helm

Run helm list to see that you currently have no chart releases deployed.

helm list

You can search the public Helm repository for charts (applications/tools/etc) that you can now easily deploy into your cluster.

helm search

Search for ‘grafana’ with helm. We’ll deploy that to the cluster in this example.

helm search grafana

Next up you might want to inspect and discover more about the chart you’re going to install. This is useful to see what sort of configuration parameters you can pass to it to customise it to your requirements.

helm inspect grafana

Choose a namespace in your cluster to deploy to and a service type for Grafana (to customise it slightly to your liking) and then run the following, replacing the service.type and service.port values for your own. For example you could use a ClusterIP service instead of LoadBalancer like I did:

helm install --name sean-grafana-release stable/grafana --set service.type=LoadBalancer --set service.port=8088 --namespace sean-dev

Helm will report back on the deployment it started for your release.

The command is not synchronous so you can run helm status to report on the status of a release.

helm status sean-grafana-release

Check on deployments in your namespace with kubectl or the Kubernetes dashboard and you should find Grafana running happily along.

In my case I used a LoadBalancer service, so my cluster being AWS based spun up an ELB to front Grafana. Checking the ELB endpoint on port 8088 as I specified in my Helm install command sure enough shows my new Grafana app’s login page.

The chart ensures all the necessary components are setup and created in your cluster to run Grafana. Things like the deployment, the service, service account, secrets, etc..

In this case the chart outputs instructions on how to retrieve your Grafana admin password for login. You can see how to get that in the output of your release.

Tidy Up

To clean up and delete your release simply do:

helm delete sean-grafana-release

Concluding

Done!

There is plenty more to explore with helm. If you wish to change your helm configuration with helm init, look into using the –upgrade parameter. helm reset can be used to remove Helm from your cluster and there are many many more options and scenarios that could be covered.

Explore further with the helm command to see available commands and do some digging.

Next up for me I’ll be looking at converting one of my personal applications into a chart that I can deploy into Kubernetes.

Custom Kubernetes Webhook Token Authentication with Github (a NodeJS implementation)

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Introduction

Recently I was tasked with setting up a couple of new Kubernetes clusters for a team of developers to begin transitioning an older .NET application over to .NET Core 2.0. Part of my this work lead me down the route of trying out some different authentication strategies.

I ended on RBAC being a good solution for our needs allowing for nice role based permission flexibility, but still needed a way of handling authentication for users of the Kubernetes clusters. One of the options I looked into here was to use Kubernetes’ support for webhook token authentication.

Webhook token authentication allows a remote service to authenticate with the cluster, meaning we could hand off some of the work / admin overhead to another service that implements part of the solution already.

Testing Different Solutions

I found an interesting post about setting up Github with a custom webhook token authentication integration and tried that method out. It works quite nicely and some good benefits as discussed in the post linked before, but summarised below:

  • All developers on the team already have their own Github accounts.
  • Reduces admin overhead as users can generate their own personal tokens in their Github account and can manage (e.g. revoke/re-create) their own tokens.
  • Flexible as tokens can be used to access Kubernetes via kubectl or the Dashboard UI from different machines
  • An extra one I thought of – Github teams could potentially be used to group users / roles in Kubernetes too (based on team membership)

As mentioned before, I tried out this custom solution which was written in Go and was excited about the potential customisation we could get out of it if we wanted to expand on the solution (see my last bullet point above).

However, I was still keen to play around with Kubernetes’ Webhook Token Authentication a bit more and so decided to reimplement this solution in a language I am more familiar with. .NET Core would have been a good candidate, but I didn’t have a lot of time at hand and thought doing this in NodeJS would be quicker.

With that said, I rewrote the Github Webhook Token Authenticator service in NodeJS using a nice lightweight node alpine base image and set things up for Docker builds. Basically readying it for deployment into Kubernetes.

Implementing the Webhook Token Authenticator service in NodeJS

The Webhook Token Authentication Service simply implements a webhook to verify tokens passed into Kubernetes.

On the Kubernetes side you just need to deploy the DaemonSet with this authenticator docker image, run your API servers with RBAC enabled

Create a DaemonSet to run the NodeJS webhook service on all relevant master nodes in your cluster.

Here is the DaemonSet configuration already setup to point to the correct docker hub image.

Deploy it with:

kubectl create -f .\daemonset.yaml

Use the following configurations to start your API servers with:

authentication-token-webhook-config-file
authentication-token-webhook-cache-ttl

Update your cluster spec to add a fileAsset entry and also point to the authentication token webhook config file that will be put in place by the fileAsset using the kubeAPIServer config section.

You can get the fileAsset content in my Github repository here.

Here is how the kubeAPIServer and fileAssets sections should look once done. (I’m using kops to apply these configurations to my cluster in this example).

You can then set up a ClusterRole and ClusterRoleBinding along with usernames that match your users’ actual Github usernames to set up RBAC permissions. (Going forward it would be great to hook up the service to work with Github teams too.)

Here is an example ClusterRole that provides blanket admin access as a simple example.

kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: youradminsclusterrole
rules:
  - apiGroups: ["*"]
    resources: ["*"]
    verbs: ["*"]
  - nonResourceURLs: ["*"]
    verbs: ["*"]

Hook up the ClusterRole with a ClusterRoleBinding like so (pointing the user parameter to the name of your github user account you’re binding to the role):

kubectl create clusterrolebinding yourgithubusernamehere-admin-binding --clusterrole=youradminsclusterrole --user=yourgithubusernamehere

Don’t forget to create yourself (in your Github account), a personal access token. Update your .kube config file to use this token as the password, or login to the Kubernetes Dashboard UI and select “Token” as the auth method and drop your token in there to sign in.

The auth nodes running in the daemonset across cluster API servers will handle the authentication via your newly configured webhook authentication method, go over to Github, check that the token belongs to the user in the ClusterRoleBinding (of the same github username) and then use RBAC to allow access to the resources specified in your ClusterRole that you bound that user to. Job done!

For more details on how to build the NodeJS Webhook Authentication Docker image and get it deployed into Kubernetes, or to pull down the code and take a look, feel free to check out the repository here.