Archive

Posts Tagged ‘Pivotal’

Using Helm and Dynamic PersistentVolumes with Multi-AZ PKS on vSphere

01/17/2019 Comments off

So, you’ve installed PKS and created a PKS cluster.  Excellent!  Now what?

We want to use helm charts to deploy applications.  Many of the charts use PersistentVolumes, so getting PVs set up is our first step.

There are a couple of complicating factors to be aware of when it comes to PVs in a multi-AZ/multi-vSphere-Cluster environment.  First, you probably have cluster-specific datastores – particularly if you are using Pivotal Ready Architecture and VSAN.  These datastores are not suitable for PersistentVolumes consumed by applications deployed to our Kubernetes cluster.  To work-around this, we’ll need to provide some shared block storage to each host in each cluster.  Probably the simplest way to do this is with an NFS share.

Prerequisites:

Common datastore; NFS share or iSCSI

In production, you’ll want a production-quality fault-tolerant solution for NFS or iSCSI, like Dell EMC Isilon. For this proof-of-concept, I’m going to use an existing NFS server, create a volume and share it to the hosts in the three vSphere clusters where the PKS workload VMs will run.  In this case, the NFS datastore is named “sharednfs” ’cause I’m creative like that.  Make sure that your hosts have adequate permissions to the share.  Using VMFS on iSCSI is supported, just be aware that you may need to cable-up additional NICs if yours are already consumed by N-VDS and/or VSAN.

Workstation Prep

We’ll need a handful of command-line tools, so make sure your workstation has the PKS CLI and Kubectl CLI from Pivotal and you’ve downloaded and extracted Helm.

PKS Cluster
We’ll want to provision a cluster using the PKS CLI tool.  This document assumes that your cluster was provisioned successfully, but nothing else has been done to it.  For my environment, I configured the “medium” plan to use 3 Masters and 3 Workers in all three AZs, then created the cluster with the command

pks create-cluster pks1cl1 --external-hostname cl1.pks1.lab13.myenv.lab --plan "medium" --num-nodes "3"


Logged-in
Make sure you’re logged into the Kubernetes cluster. In PKS, the easiest way to do this is via the PKS cli:

pks login -a api.pks1.lab13.myenv.lab -u pksadmin -p my_password --skip-ssl-validation
pks cluster pks1cl1
pks get-credentials pks1cl1
kubectl config use-context pks1cl1
kubectl get nodes -o wide

Where “pks1cl1″ is replaced by your cluster’s name,”api.pks1.lab13.myenv.lab” is replaced by the FQDN to your PKS API server, “pksadmin” is replaced by the username with admin rights to PKS and “my_password” is replaced with that account’s password.

Procedure:

  1. Create storageclass
    • Create storageclass spec yaml. Note that the file is named storageclass-nfs.yml and we’re naming the storage class itself “nfs”:
      kind: StorageClass
      apiVersion: storage.k8s.io/v1
      metadata:
        name: nfs
        annotations:
          storageclass.kubernetes.io/is-default-class: "true"
      provisioner: kubernetes.io/vsphere-volume
      parameters:
        diskformat: thin
        datastore: sharednfs
        fstype: ext3
      

    • Apply the yml with kubectl

      kubectl create -f storageclass-nfs.yml

    • Create a sample PVC (Persistent Volume Claim). Note that the file is names pvc-sample.yml, the PVC name is “pvc-sample” and uses the “nfs” storageclass we created above. This step is not absolutely necessary, but will help confirm we can use the storage.
      kind: PersistentVolumeClaim
      apiVersion: v1
      metadata:
        name: pvc-sample
        annotations:
          volume.beta.kubernetes.io/storage-class: nfs
      spec:
        accessModes:
          - ReadWriteOnce
        resources:
          requests:
            storage: 1Gi
        storageClassName: nfs
      
    • Apply the yml with kubectl

      kubectl create -f pvc-sample.yml


      If you’re watching vSphere closely, you’ll see a VMDK created in the kubevols folder of the NFS datastore

    • Check that the PVC was created with

      kubectl get pvc

      and

      kubectl describe pvc pvc-sample

    • Remove sample PVC with

      kubectl delete -f pvc-sample

  2. Configure Helm and Tiller
    • Create Service Account for tiller with
      apiVersion: v1
      kind: ServiceAccount
      metadata:
        name: tiller
        namespace: kube-system
      ---
      apiVersion: rbac.authorization.k8s.io/v1beta1
      kind: ClusterRoleBinding
      metadata:
        name: tiller
      roleRef:
        apiGroup: rbac.authorization.k8s.io
        kind: ClusterRole
        name: cluster-admin
      subjects:
        - kind: ServiceAccount
          name: tiller
          namespace: kube-system
      
    • Apply the service account yml with Kubectl

      kubectl create -f rbac-config.yml

    • Initialize helm and tiller with

      helm init --service-account tiller

    • Check that tiller is ready

      helm version


      Look for a version number for the version; note that it might take a few seconds for tiller in the cluster to get ready.

  3. Deploy sample helm chart
    • Update helm local chart repository. We do this so that we can be sure that helm can reach the public repo and to cache teh latest information to our local repo.

      helm repo update


      If this step results in a certificate error, you may have to add the cert to the trusted certificates on the workstation.

    • Install helm chart with ingress enabled. Here, I’ve selected the Dokuwiki app. The command below will enable ingress, so we can access it via routable IP and it will use the default storageclass we configured earlier.

      helm install --name dokuwiki \
      --set ingress.enabled="true",dokuwikiUsername=admin,dokuwikiPassword=password \
      stable/dokuwiki

      Edit – April 23 2019 – Passing the credentials in here makes connecting easier later.

    • Confirm that the app was deployed
      helm list
      kubectl get pods -n default
      kubectl get services -n default


      From the get services results, make a note of the external IP address – in the example above, it’s 192.13.6.73

    • Point a browser at the external address from the previous step and marvel at your success in deploying Dokuwiki via helm to Kubernetes!
      If you want to actually login to your Dokuwiki instance, first obtain the password for the user account with this command:

      kubectl get secret -n default dokuwiki-dokuwiki \
      -o jsonpath="{.data.dockuwiki-password}" | base64 --decode

      Then login with username “user” and that password.

       

      Edit – 04/23/19 – Login with the username and password you included in the helm install command

  4. Additional info
    • View Persistent Volume Claims with

      kubectl get pvc -n default


      This will list the PVCs and the volumes in the “default” namespace. Note the volume corresponds to the name of the VMDK on the datastore.

    • Load-Balancer
      Notice that since we are leveraging the NSX-T Container Networking Interface and enabled the ingress when we installed dokuwiki, a load-balancer in NSX-T was automatically created for us to point to the application.

This took me some time to figure out; had to weed through a lot of documentation – some of which contradicted itself and quite a bit of trial-and-error. I hope this helps save someone time later!

Advertisements

Automating PKS Upgrades

05/22/2018 Comments off

Last night, Pivotal announced new versions of PKS and Harbor, so I thought it’s time to simplify the upgrade process. Here is a concourse pipeline that essentially aggregates the upgrade-tile pipeline so that PKS and Harbor are upgraded in one go.

What it does:

  1. Runs on a schedule – you set the time and days it may run
  2. Downloads the latest version of PKS and Harbor from Pivnet- you set the major.minor version range
  3. Uploads the PKS and Harbor releases to your BOSH director
  4. Determines whether the new release is missing a stemcell, downloads it from PivNet and uploads it to BOSH director
  5. Stages the tiles/releases
  6. Applies changes

What you need:

  1. A working Concourse instance that is able to reach the Internet to pull down the binaries and repo
  2. The fly cli and credentials for your Concourse.
  3. A token from your PivNet account
  4. An instance of PKS 1.0.2 or 1.0.3 AND Harbor 1.4.x deployed on Ops Manager
  5. Credentials for your Ops Manager
  6. (optional) A token from your GitHub account

How to use the pipeline:

  1. Download params.yml and pipeline.yml from here.
  2. Edit the params.yml by replacing the values in double-parentheses with the actual value. Each line has a bit explaining what it’s expecting.  For example, ((ops_mgr_host)) becomes opsmgr.pcf1.domain.local
    • Remove the parens
    • If you have a GitHub Token, pop that value in, otherwise remove ((github_token))
    • The current pks_major_minor_version regex will get the latest 1.0.x.  If you want to pin it to a specific version, or when PKS 1.1.x is available, you can make those changes here.
    • The ops_mgr_usr and ops_mgr_pwd credentials are those you use to logon to Ops Manager itself.  Typically set when the Ops Manager OVA is deployed.
    • The schedule params should be adjusted to a convenient time to apply the upgrade.  Remember that in addition to the PKS Service being offline (it’s a singleton) during the upgrade, your Kubernetes clusters may be affected if you have the “Upgrade all Clusters” errand set to run in the PKS configuration, so schedule wisely!

  3. Open your cli and login to concourse with fly

    fly -t concourse login -c http://concourse.domain.local:8080 -u username -p password

  4. Set the new pipeline. Here, I’m naming the pipeline “PKS_Upgrade”. You’ll pass the pipeline.yml with the “-c” param and your edited params.yml with the “-l” param

    fly -t concourse sp -p PKS_Upgrade -c pipeline.yml -l params.yml

    Answer “y” to “Apply Configuration”…

  5. Unpause the pipeline so it can run when in the scheduled window

    fly -t concourse up -p PKS_Upgrade

  6. Login to the Concourse web to see our shiny new pipeline!

    If you don’t want to deal with the schedule and simply want it to upgrade on-demand, use the pipeline-nosched.yml instead of pipeline.yml, just be aware that when you unpause the pipeline, it’ll start doing its thing.  YMMV, but for me, it took about 8 minutes to complete the upgrade.

Behind the scenes
It’s not immediately obvious how the pipeline does what it does. When I first started out, I found it frustrating that there just isn’t much to the pipeline itself. To that end, I tried making pipelines that were entirely self-contained. This was good in that you can read the pipeline and see everything it’s doing; plus it can be made to run in an air-gapped environment. The downside is that there is no separation, one error in any task and you’ll have to edit the whole pipeline file.
As I learned a little more and poked around in what others were doing, it made sense to split the “tasks” out, keep them in a GitHub public repo and pull it down to run on-demand.

Pipelines generally have two main sections; resources and jobs.
Resources are objects that are used by jobs. In this case, the binary installation files, a zip of the GitHub repo and the schedule are resources.
Jobs are (essentially) made up of plans and plans have tasks.
Each task in most pipelines uses another source yml. This task.yml will indicate which image concourse should build a container from and what it should do on that container (typically, run a script). All of these task components are in the GitHub repo, so when the pipeline job runs, it clones the repo and runs the appropriate task script in a container built on an image pulled from dockerhub.

More info
I’ve got a several pipelines in the repo.   Some of them do what they’re supposed to. 🙂 Most of them are derived from others’ work, so many thanks to Pivotal Services and Sabha Parameswaran