Installing CloudBees Core on Google Kubernetes Engine (GKE)

This document explains the cluster requirements, points you to the Google Cloud Platform documentation you will need to create a cluster and explains how to install CloudBees Core in your Kubernetes cluster.

Important
 The GKE cluster requirements must be satisfied before CloudBees Core can be installed.

GKE Cluster Requirements

The CloudBees Core installer requires:

  • On your local computer or a bastion host:

  • A GKE cluster running Kubernetes 1.10 (or newer) installed and configured

    • With nodes that have 1 full CPU / 1 Gb available, so nodes need at least 2 CPUs, 4 Gbs of memory

    • Must have network access to container images (public Docker Hub or a private Docker Registry)

  • The NGINX Ingress Controller installed in the cluster (v0.9.0 minimum)

    • Load balancer configured and pointing to the NGINX Ingress Controller

    • A DNS record that points to the NGINX Ingress Controllers Load balancer

    • SSL certificates (needed when you deploy CloudBees Core

  • A namespace in the cluster (provided by your admin) with permissions to create Role and RoleBinding objects

  • Kubernetes cluster Default Storage Class defined and ready to use.

Important
 Kubernetes beta releases are not supported. Use production releases.

Creating your GKE Cluster

To create a Google Kubernetes Engine (GKE) cluster refer to the official Google documentation Create a GKE cluster.

More information on administering a Google Kubernetes cluster is available from the Kubernetes Engine How-to Guides.

More information on Kubernetes concepts is available from the Kubernetes site, including:

Cluster Admin Permissions

If the kubeconfig was created automatically by gcloud then the user lacks the required permissions.

Bind the user account to the 'cluster-admin' role using:

kubectl create clusterrolebinding cluster-admin-binding  --clusterrole cluster-admin  --user $(gcloud config get-value account)

Cluster-admin (full) permission is only needed during installation, services will run using the created roles with limited privileges.

Installing Ingress Controller

CloudBees Core does not support the GKE ingress controller at this point but instead, requires the use of the NGINX Ingress Controller. This section walks through the installation using Helm.

If you are not able to use Helm, you may install the Ingress Controller manually. Then skip to the DNS Record section.

Installing Tiller

Tiller is the Kubernetes cluster’s server-side component of Helm. Perform the following to install Tiller:

  • Create a service account for Tiller

    kubectl create serviceaccount --namespace kube-system tiller

  • Give that service account admin capabilities

    kubectl create clusterrolebinding tiller-cluster-rule --clusterrole=cluster-admin --serviceaccount=kube-system:tiller

  • Install Tiller on Kubernetes cluster

    helm init --service-account tiller

Creating Ingress Controller

  • Create the Ingress Controller

    helm install --namespace ingress-nginx --name nginx-ingress stable/nginx-ingress \
             --set controller.service.externalTrafficPolicy=Local \
             --set controller.scope.enabled=true \
             --set controller.scope.namespace=cje

Creating DNS Record

Creating the Ingress Controller results in the creation of the corresponding service, along with its corresponding Load Balancer, both of which will take a few moments. You may then execute the following command to retrieve the external IP address to be used for the CloudBees Core cluster domain name. In the following example, the IP address that you want to note is 35.203.153.152.

$ kubectl get services -n ingress-nginx
NAME                            TYPE           CLUSTER-IP     EXTERNAL-IP      PORT(S)                      AGE
nginx-ingress-controller        LoadBalancer   10.3.244.187   35.203.153.152   80:30396/TCP,443:31290/TCP   3m

Create a DNS record, for the domain you want to use for CloudBees Core, pointing to the external IP address. In our example, it is 35.203.153.152.

For example, if the CloudBees Core domain name is cloudbees-core.example.com, then its A Record entry should be 35.203.153.152.

To continue with the instructions in this document, as this time, create the environmental variable, DOMAIN_NAME:

export DOMAIN_NAME=cloudbees-core.example.com

CloudBees Core Namespace

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
ingress-nginx   Active    8m
kube-public     Active    13m
kube-system     Active    13m

It is recommended to use a CloudBees Core specific namespace in the cluster with permissions to create Role and RoleBinding objects. For example, to create a 'cje' namespace, perform the following:

  • Create the namespace cje

    kubectl create namespace cje

  • Attach a label to that namespace

    kubectl label namespace cje name=cje

  • Make namespace cje the default namespace for the kubectl context

    kubectl config set-context $(kubectl config current-context) --namespace=cje

Installing CloudBees Core

SSD persistent storage considerations

Once a JENKINS_HOME volume is created, its storage type cannot be changed. To use the 'ssd' storage class for Operations Center, you will need to uncomment and set the storageClassName definition under 'volumeClaimTemplates' to 'ssd' in the cloudbees-core.yml file prior to installation.

  volumeClaimTemplates:
  - metadata:
      name: jenkins-home
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 20Gi
      storageClassName: ssd

To configure Managed Masters to use SSD disks by default, update the storage class in the cloudbees-core.yml file. Search for the commented-out section

# To allocate masters using a non-default storage class, add the following
# -Dcom.cloudbees.masterprovisioning.kubernetes.KubernetesMasterProvisioning.storageClassName=some-storage-class

Change it so that the storage class is now ssd.

-Dcom.cloudbees.masterprovisioning.kubernetes.KubernetesMasterProvisioning.storageClassName=ssd

Regional persistent disks

Google Kubernetes v1.10 provides beta access to regional persistent disks.

Regional persistent disks replicate data between two zones in the same region, and can be used similarly to regular persistent disks. In the event of a zonal outage, Kubernetes can failover workloads using the volume of the other zone. You can use regional persistent disks to build highly available solutions for Operations Center and Managed Masters' stateful workloads. Users must ensure that both the primary and failover zones are configured with enough resource capacity to run the workload.

See Google Persistent Volumes documentation for more information on how to configure a StorageClass to use regional persistent disks.

CloudBees Core installation

CloudBees Core runs on a Kubernetes cluster. Kubernetes cluster installations are configured with YAML files. The CloudBees Core installer provides a cloudbees-core.yml file that is modified for each installation.

  • Download installer

  • Unpack installer

    $ export INSTALLER=cloudbees-core_2.121.3.1_kubernetes.tgz
$ sha256sum -c $INSTALLER.sha256
$ tar xzvf $INSTALLER

  • Prepare shell variables for your installation. Replace cloudbees-core.example.com with your domain name.

    $ DOMAIN_NAME=<YOUR_DOMAIN_NAME_FOR_CLOUDBEES_CORE>

    If you do not have an available domain, you can use xip.io combined with the IP of the Ingress controller.

    $ CLOUDBEES_CORE_IP=$(kubectl -n ingress-nginx get svc ingress-nginx -o jsonpath="{.status.loadBalancer.ingress[0].ip}")
$ DOMAIN_NAME="jenkins.$CLOUDBEES_CORE_IP.xip.io"

  • Edit the cloudbees-core.yml file for your installation

    $ cd cloudbees-core_2.121.3.1_kubernetes
$ sed -e s,cloudbees-core.example.com,$DOMAIN_NAME,g < cloudbees-core.yml > tmp && mv tmp cloudbees-core.yml

  • Disable SSL redirection if you do not have SSL certificates.

    $ sed -e s,https://$DOMAIN_NAME,http://$DOMAIN_NAME,g < cloudbees-core.yml > tmp && mv tmp cloudbees-core.yml
$ sed -e s,ssl-redirect:\ \"true\",ssl-redirect:\ \"false\",g < cloudbees-core.yml > tmp && mv tmp cloudbees-core.yml

  • Run the installer

    $ kubectl apply -f cloudbees-core.yml
serviceaccount "cjoc" created
role "master-management" created
rolebinding "cjoc" created
configmap "cjoc-config" created
configmap "cjoc-configure-jenkins-groovy" created
statefulset "cjoc" created
service "cjoc" created
ingress "cjoc" created
ingress "default" created
serviceaccount "jenkins" created
role "pods-all" created
rolebinding "jenkins" created
configmap "jenkins-agent" created

  • Wait until CJOC is rolled out

    $ kubectl rollout status sts cjoc

  • Read the admin password

    $ kubectl exec cjoc-0 -- cat /var/jenkins_home/secrets/initialAdminPassword
h95pSNDaaMJzz7r2GxxCjrGQ3t

Open Operations Center

CloudBees Core is now installed, configured, and ready to run. Open the CloudBees Core URL and log in with the initial admin password. Install the CloudBees Core license and the recommended plugins.

See Administering CloudBees Core for further information.

Auto-scaling nodes

Google Kubernetes Engine supports node auto-scaling by enabling the option in the GKE console.

Go to GKE console - Select your cluster - click on 'EDIT' - Under "Node Pools" set "Autoscaling" to "on" - Adjust autoscaling limits by setting "Minimum size" and "Maximum size"

Auto-scaling considerations

While scaling up functionality is straightforward, scaling down is potentially more problematic. Scaling down involve moving workload to different nodes if the node to reclaim has still some utilization but is below the reclamation threshold. Moving agent workload would potentially mean build interruption (failed build) and moving Operations Center/Managed Master workload would mean downtime.

Distinct node pools

One way to deal with scaling down is to treat each workload differently by using separate node pools and thus apply different logic to control the scaling down.

Managed Master and Operations Center workload

By assigning Managed Master and Operations Center workload to a dedicated pool, the scaling down of nodes can be prevented by restricting eviction of Managed Master or Operations Center deployments. Scale up will happen normally when resources need to be increased in order to deploy additional Managed Masters, but scale down will only happen when the nodes are free of Operations Center or Managed Master workload. This might be acceptable since masters are meant to be stable and permanent, meaning that they are not ephemeral but long lived.

This is achieved by adding the following annotation to Operations Center and Managed Masters: "cluster-autoscaler.kubernetes.io/safe-to-evict": "false"

For Operations Center, the annotation is added to the cloudbees-core.yml in the CJOC "StatefulSet" definition under "spec - template - metadata - annotations"

apiVersion: "apps/v1beta1"
kind: "StatefulSet"
metadata:
  name: cjoc
  labels:
    com.cloudbees.cje.type: cjoc
    com.cloudbees.cje.tenant: cjoc
spec:
  serviceName: cjoc
  replicas: 1
  updateStrategy:
    type: RollingUpdate
  template:
    metadata:
      annotations:
          cluster-autoscaler.kubernetes.io/safe-to-evict: "false"

For Managed Master, the annotation is added in the configuration page under the 'Advanced Configuration - YAML' parameter. The YAML snippet to add would look like:

kind: StatefulSet
spec:
  template:
    metadata:
      annotations:
          cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
Agent workload

By assigning Jenkins agent workload to a dedicated pool, the scaling could be handled by the default logic. Since agents are Pods that are not backed by a Kubernetes controller, they prevent scale down of nodes until no pods are running on a particular node. This prevents nodes to be reclaimed while agents are running and agent to be interrupted even though the autoscaler is below its reclamation threshold.

In order to create a dedicated pool for agent workload, we need to prevent other types of workload to be deployed on the dedicated pool nodes. This is accomplished by tainting the dedicated pool nodes. Then to allow scheduling of agent workload on the dedicated pool nodes, the agent pod will use a corresponding taint tolerations and a node selector.

When nodes are created dynamically by the Kubernetes autoscaler, they need to be created with the proper taint and label.

In the Google console, the taint and label can be specified when creating the NodePool:

gke nodepool label taint

The first parameter will automatically add the label workload=build to the newly created nodes. This label will then be used as the NodeSelector for the agent. The second parameter will automatically add the nodeType=build:NoSchedule taint to the node.

The agent template will then need to add the corresponding 'toleration' to allow the scheduling of agent workload on those nodes.

agent toleration selector

For Pipelines, 'toleration' can be added to podTemplate using the yaml parameter as follows:

    def label = "mypodtemplate-${UUID.randomUUID().toString()}"
    def nodeSelector = "workload=build"
    podTemplate(label: label, yaml: """
    apiVersion: v1
    kind: Pod
    spec:
      tolerations:
      - key: nodeType
        operator: Equal
        value: build
        effect: NoSchedule
    """, nodeSelector: nodeSelector, containers: [
      containerTemplate(name: 'maven', image: 'maven:3.3.9-jdk-8-alpine', ttyEnabled: true, command: 'cat')
    ]) {
      node(label) {
        stage('Run maven') {
          container('maven') {
            sh 'mvn --version'
          }
        }
      }
    }

Upgrading CloudBees Core

To upgrade to a newer version of CloudBees Core, follow the same process as the installation process.

  • Download installer

  • Unpack installer

  • Edit the cloudbees-core.yml file for your installation to match the previous changes made during initial installation

  • Run the installer

    $ kubectl apply -f cloudbees-core.yml

  • Wait until CJOC is rolled out

    $ kubectl rollout status sts cjoc

Once the new version of Operations Center is rolled out, you can log in to Operations Center again and upgrade the managed masters. See Upgrading Managed Masters for further information.

Removing CloudBees Core

If you need to remove CloudBees Core from Kubernetes, use the following steps:

  • Delete all masters from Operations Center

  • Stop Operations Center

    kubectl scale statefulsets/cjoc --replicas=0

  • Delete CloudBees Core

    kubectl delete -f cloudbees-core.yml

  • Delete remaining pods and data

    kubectl delete pod,statefulset,pvc,ingress,service -l com.cloudbees.cje.tenant

  • Delete services, pods, persistent volume claims, etc.

    kubectl delete svc --all
kubectl delete statefulset --all
kubectl delete pod --all
kubectl delete ingress --all
kubectl delete pvc --all

Additional Topics

Manual Installation of Ingress Controller

If you are not able to use Helm, you will need to manually install the NGINX Ingress Controller. See the NGINX controller install guide for installation instruction.

These instructions will deploy the NGINX Ingress Controller to a namespace named ingress-nginx.

  • Deploy NGINX Ingress Controller

    kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/master/deploy/mandatory.yaml

  • Deploy the service creating the Load Balancer

    kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/master/deploy/provider/cloud-generic.yaml

Go to DNS Record section to continue with CloudBees Core installation.

Tip
 More information on the NGINX Controller installation.

HTTPS Setup

To setup the NGINX ingress controller to support SSL termination, see the GKE Reference Architecture TLS Termination at Ingress chapter.

HTTPS Load Balancer

As an alternative, SSL termination can be setup at the Google Load Balancer level. In order to do that, a new/additional load balancer needs to be created since the load balancer created during the installation of the NGINX ingress controller is a TCP load balancer and does not support HTTPS termination.

Information about Setting up HTTP(S) load balancing can be found at HTTP(S) Load Balancing

1) Get the NGINX controller service port

The new load balancer will be connected to the NGINX controller. We need first to get information about the current controller.

Get the service port number for TCP port 80. (nginx_service_port_80 = 30622 in the example below)

$ kubectl get svc -n ingress-nginx ingress-nginx
NAME            TYPE           CLUSTER-IP     EXTERNAL-IP      PORT(S)                                      AGE
ingress-nginx   LoadBalancer   10.23.243.53   35.196.134.177   80:30622/TCP,443:30216/TCP,50000:31462/TCP   27d

2) Create a new load balancer

To create a new load balancer, go to the GCE Network services console

  • Click on 'Create a load balancer'

  • Select 'HTTPS Load Balancer'

    • Give it a name

    • Select 'Backend configuration' → 'Backend services' → 'Create a backend service'

      • Give it a name

      • Select the instance group of your cluster

      • Set the port number to the ingress controller service port (nginx_service_port_80 we got previously)

      • Under 'Healthchecks', select the healthcheck for the ingress controller service port (nginx_service_port_80)

      • Click on 'Create'

    • Select 'Frontend configuration'

      • Give it a name

      • Select Protocol HTTPS

      • Under 'IP Address' select 'Create IP Address' to create a new static IP for the load balancer

      • Under 'Certificates' Select your domain certificate if already uploaded or create a new certificate

        • If creating a new certificate, upload the various parts of the certificate (information on how to create an SSL certificates )

      • Click 'Done'

    • Click 'Create'

3) Remove load balancing for port 80 and 443 from NGINX load balancer (Optional)

Now that HTTPS access has been configured, you can remove access to the CJE cluster for port 80 and 443 via the NGINX load balancer.

Go to the GCE Network services console

  • Select the NGINX load balancer

  • Click 'Edit'

  • Select 'Frontend configuration'

    • Delete the frontend configuration for port 80,443

  • Click 'Update'

Adding Client Masters

Occasionally administrators need to connect existing masters to a CloudBees Core cluster. Existing masters connected to a CloudBees Core cluster are called "Client Masters" to distinguish them from Managed Masters. A master running on Windows is one example that requires a Client Master.

Configure Load Balancer

The load balancer routes traffic from the public internet into the Kubernetes cluster. The standard installation opens the http port (80) and the https port (443). Port 50000 must be opened and must route traffic to the Kubernetes internal port.

Go to the GCE console under Network services - Load balancing

  • select the load balancer

  • click on 'EDIT'

  • select 'Frontend configuration'

  • add a mapping for port TCP:50000 to the IP of the load balancer

Then under VPC Network - Firewall rules edit the cluster firewall rule that has currently port 80 and 443 opened to 0.0.0.0/0 and add port 50000 to the rule.

Configure NGINX Ingress Controller

In GKE, the load balancer does not allow to forward a port to a different port. To overcome this, we can reconfigure NGINX to act as a TCP proxy for the jnlp port.

Modify the NGINX 'tcp-services' config map to enable NGINX to proxy port 50000 as a TCP stream.

nginx-config-map.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: tcp-services
  namespace: ingress-nginx
data:
  50000: "cje/cjoc:50000"

Apply the config map changes:

$ kubectl apply -f nginx-config-map.yaml

Then add port 50000 as an exposed port for the NGINX controller. Go to the GKE console under Workloads

  • select the nginx-ingress-controller

  • click on 'EDIT'

  • under 'ports' add a containerPort 50000 named 'jnlp'

        ports:
        - containerPort: 50000
          name: jnlp
          protocol: TCP

  • save

Then add a service port for port 50000 to the 'ingress-nginx' service.

Go to the GKE console under Discovery & load balancing

  • select the 'ingress-nginx' load balancer service

  • click on 'EDIT'

  • under 'ports' add a 'jnlp' 50000 targetPort

  ports:
  - name: jnlp
    port: 50000
    protocol: TCP
    targetPort: jnlp

  • save

Test Connection

You can confirm that Operations Center is ready to receive external JNLP requests with the following command:

$ curl $DOMAIN_NAME:50000
Jenkins-Agent-Protocols: Diagnostic-Ping, JNLP4-connect, MultiMaster, OperationsCenter2, Ping
Jenkins-Version: 2.107.1.2
Jenkins-Session: b02dc475
Client: 10.20.4.12
Server: 10.20.5.10
Remoting-Minimum-Version: 2.60

Continue installation

Once ports and security are correctly configured in your cloud and on your Client Master, continue the instructions in Adding Client Masters.

Adding JNLP Agents

To provide connectivity for JNLP agents, the master must be configured to "Allow external agents". If the master is not configured as such, edit the master configuration, enable "Allow external agents and then restart the master.

The "Allow external agents" will create a Kubernetes Service of type NodePort for the jnlp port. The NodePort exposed port can be retrieve by looking at the master service.

For example if the master name is 'master-1', the NodePort service will be called 'master-1-jnlp'. In the example below, the jnlp exposed port (JNLP_NODE_PORT) for 'master-1' is 32075 and the master jnlp port (JNLP_MASTER_PORT) is 50004

$ kubectl get svc master-1-jnlp
NAME            TYPE       CLUSTER-IP     EXTERNAL-IP   PORT(S)           AGE
master-1-jnlp   NodePort   10.23.248.32   <none>        50004:32075/TCP   2h

Configure Load Balancer

The load balancer routes traffic from the public internet into the Kubernetes cluster. In order for the jnlp agent to connect to the master, the jnlp node port must be opened and traffic routed to it on the load balancer.

Go to the GCE console under Network services - Load balancing

  • select the load balancer

  • click on 'EDIT'

  • select 'Frontend configuration'

  • add a mapping for port TCP:<JNLP_NODE_PORT> to the IP of the load balancer

Then under VPC Network - Firewall rules edit the cluster firewall rule that has currently port 80 and 443 opened to 0.0.0.0/0 and add port <JNLP_NODE_PORT> to the rule.

Test Connection

You can confirm that Master is ready to receive external JNLP requests with the following command:

$ curl $DOMAIN_NAME:$JNLP_MASTER_PORT
Jenkins-Agent-Protocols: Diagnostic-Ping, JNLP4-connect, OperationsCenter2, Ping
Jenkins-Version: 2.138.3.1
Jenkins-Session: f4e6410a
Client: 0:0:0:0:0:0:0:1
Server: 0:0:0:0:0:0:0:1
Remoting-Minimum-Version: 3.4

Continue installation

Once the jnlp port is correctly configured in your cloud, you can then create a new 'node' in your master under 'Manage Jenkins → Manage Nodes'.

NOTE that the node should be configured with: - Launch method: 'Launch agent via Web Start' - Tunnel connection through (under "Advanced"): LOAD_BALANCER_IP:JNLP_NODE_PORT

Then follow the instruction, given after you save the node configuration, to launch the agent.

Using Kaniko with CloudBees Core

Introducing Kaniko

Kaniko is a utility that creates container images from a Dockerfile. The image is created inside a container or Kubernetes cluster, which allows users to develop Docker images without using Docker or requiring a privileged container.

Since Kaniko doesn’t depend on the Docker daemon and executes each command in the Dockerfile entirely in the userspace, it enables building container images in environments that can’t run the Docker daemon, such as a standard Kubernetes cluster.

The remainder of this chapter provides a brief overview of Kaniko and illustrates using it in CloudBees Core with a Declarative Pipeline.

How does Kaniko work?

Kaniko looks for the Dockerfile file in the Kaniko context. The Kaniko context can be a GCS storage bucket, an S3 storage bucket, or local directory. In the case of either a GCS or S3 storage bucket, the Kaniko context must be a compressed tar file. Next, if the context contains a compressed tar file, then Kaniko expands it. Otherwise, it starts to read the Dockerfile.

Kaniko then extracts the filesystem of the base image using the FROM statement in the Dockerfile. It then executes each command in the Dockerfile. After each command completes, Kaniko captures filesystem differences. Next, it applies these differences, if there are any, to the base image and updates image metadata. Lastly, Kaniko publishes the newly created image to the desired Docker registry.

Security

Kaniko runs as an unprivileged container. Kaniko still needs to run as root to be able to unpack the Docker base image into its container or execute RUN Dockerfile commands that require root privileges.

Primarily, Kaniko offers a way to build Docker images without requiring a container running with the privileged flag, or by mounting the Docker socket directly.

Note
 Additional security information can be found under the Security section of the Kaniko documentation. Also, this blog article on unprivileged container builds provides a deep dive on why Docker build needs root access.

Kaniko parameters

Kaniko has two key parameters. They are the Kaniko context and the image destination. Kaniko context is the same as Docker build context. It is the path Kaniko expects to find the Dockerfile in and any supporting files used in the creation of the image. The destination parameter is the Docker registry where the Kaniko will publish the images. Currently, Kaniko supports hub.docker.com, GCR, and ECR as the Docker registry.

In addition to these parameters, Kaniko also needs a secret containing the authorization details required to push the newly created image to the Docker registry.

Kaniko debug image

The Kaniko executor image uses scratch and doesn’t contain a shell. The Kaniko project also provides a debug image, gcr.io/kaniko-project/executor:debug, this image consists of the Kaniko executor image with a busybox shell.

Note
 For more details on using the Debug Image, see Debug Image section of the Kaniko documenation.

Pipeline example

This example illustrates using Kaniko to build a Docker image from a Git repository and pushing the resulting image to a private Docker registry.

Requirements

To run this example, you need the following:

  • A Kubernetes cluster with an installation of CloudBees Core

  • A Docker account or another private Docker registry account

  • Your Docker registry credentials

  • Ability to run kubectl against your cluster

  • CloudBees Core account with permission to create the new pipeline

Steps

These are the high-level steps for this example:

  1. Create a new Kubernetes Secret.

  2. Create the Pipeline.

  3. Run the Pipeline.

Create a new Kubernetes secret

The first step is to provide credentials that Kaniko uses to publish the new image to the Docker registry. This example uses kubectl and a docker.com account.

Tip
 If you are using a private Docker registry, you can use it instead of docker.com. Just create the Kubernetes secret with the proper credentials for the private registry.

Kubernetes has a create secret command to store the credentials for private Docker registries.

Use the create secret docker-registry kubectl command to create this secret:

Kubernetes create secret command
 $ kubectl create secret docker-registry docker-credentials \ (1)
    --docker-username=<username>  \
    --docker-password=<password> \
    --docker-email=<email-address>

  1. The name of the new Kubernetes secret.

Create the Pipeline

Create a new pipeline job in CloudBees Core. In the pipeline field, paste the following Declarative Pipeline:

Sample Scripted Pipeline
def label = "kaniko-${UUID.randomUUID().toString()}"

podTemplate(name: 'kaniko', label: label, yaml: """
kind: Pod
metadata:
  name: kaniko
spec:
  containers:
  - name: kaniko
    image: gcr.io/kaniko-project/executor:debug
    imagePullPolicy: Always
    command:
    - /busybox/cat
    tty: true
    volumeMounts:
      - name: jenkins-docker-cfg
        mountPath: /kaniko/.docker
  volumes:
  - name: jenkins-docker-cfg
    projected:
      sources:
      - secret:
          name: docker-credentials (1)
          items:
            - key: .dockerconfigjson
              path: config.json
""") {
  node(label) {
    stage('Build with Kaniko') {

       git 'https://github.com/cb-jeffduska/simple-docker-example.git'
        container(name: 'kaniko', shell: '/busybox/sh') {
           withEnv(['PATH+EXTRA=/busybox']) {
            sh '''#!/busybox/sh
            /kaniko/executor --context `pwd` --destination <docker-username>/hello-kaniko:latest (2)
            '''
           }
        }
      }
    }
  }

  1. This is where the docker-credentials secret, created in the previous step, is mounted into the Kaniko Pod under /kaniko/.docker/config.json.

  2. Replace destination with your Docker username such as hello-kaniko.

Save the new Pipeline job.

Run the new Pipeline

The sample Pipeline is complete. Run the Pipeline to build the Docker image. When the pipeline is successful, a new Docker image should exist in your Docker registry. The new Docker image can be accessed via standard Docker commands such as docker pull and docker run.

Limitations

Kaniko does not use Docker to build the image, thus there is no guarantee that it will produce the same image as Docker would. In some cases, the number of layers could also be different.

Important
 Kaniko supports most Dockerfile commands, even multistage builds, but does not support all commands. See the list of Kaniko Issues to determine if there is an issue with a specific Dockerfile command. Some rare edge cases are discussed in the Limitations section of the Kaniko documentation.

Alternatives

There are many tools similar to Kaniko. These tools build container images using a variety of different approaches.

Tip
 There is a summary of these tools and others in the comparison with other tools section of the Kaniko documentation.

Here are links to a few of them:

References

This chapter is only a brief introduction into using Kaniko. In addition to the Kaniko documentation, the following is a list of helpful articles and tutorials:

Using self-signed certificates in CloudBees Core

This optional component of CloudBees Core allows to use self-signed certificates or custom root CA (Certificate Authority). It works by injecting a given set of files (certificate bundles) into all containers of all scheduled pods.

Prerequisites

Kubernetes 1.10 or later, with admission controller MutatingAdmissionWebhook enabled.

In order to check whether it is enabled for your cluster, you can run the following command:

kubectl api-versions | grep admissionregistration.k8s.io/v1beta1

The result should be:

admissionregistration.k8s.io/v1beta1

In addition, the MutatingAdmissionWebhook and ValidatingAdmissionWebhook admission controllers should be added and listed in the correct order in the admission-control flag of kube-apiserver.

Installation

This procedure requires a context with cluster-admin privilege in order to create the MutatingWebhookConfiguration.

In the CloudBees Core binary bundle, you will find a directory named sidecar-injector. The following instructions assume this is the working directory.

Create a certificate bundle

In the following instructions, we assume you are working in the namespace where CloudBees Core is installed, and the certificate you want to install is named mycertificate.pem.

For a self-signed certificate, add the certificate itself. If the certificate has been issued from a custom root CA, add the root CA itself.

# Copy reference files locally
kubectl cp cjoc-0:etc/ssl/certs/ca-certificates.crt .
kubectl cp cjoc-0:etc/ssl/certs/java/cacerts .
# Add root CA to system certificate bundle
cat mycertificate.pem >> ca-certificates.crt
# Add root CA to java cacerts
keytool -import -noprompt -keystore cacerts -file mycertificate.pem -storepass changeit -alias service-mycertificate;
# Create a configmap with the two files above
kubectl create configmap --from-file=ca-certificates.crt,cacerts ca-bundles

Setup injector

  1. Browse to the directory where CloudBees Core archive has been unpacked, then go to sidecar-injector folder.

  2. Create a namespace to deploy the sidecar injector.

    kubectl create namespace sidecar-injector
    Note
    		 The following instructions assume the deployment is performed in the sidecar-injector namespace. If the target namespace has a different name, a global replacement needs to be done in the sidecar-injector.yaml file before proceeding

  3. Create a signed cert/key pair and store it in a Kubernetes secret that will be consumed by sidecar deployment.

    ./webhook-create-signed-cert.sh \
 --service sidecar-injector-webhook-svc \
 --secret sidecar-injector-webhook-certs \
 --namespace sidecar-injector

  4. Patch the MutatingWebhookConfiguration by set caBundle with correct value from Kubernetes cluster

    cat sidecar-injector.yaml | \
    webhook-patch-ca-bundle.sh > \
    sidecar-injector-ca-bundle.yaml
    Note
    		 In some Kubernetes deployments, it is possible that the resulting caBundle is an empty string. This means the deployment doesn’t support certificate-based authentication, but it won’t prevent using this feature.

  5. Switch to sidecar-injector namespace

    kubectl config set-context $(kubectl config current-context) --namespace=sidecar-injector

  6. Deploy resources

    kubectl create -f sidecar-injector-ca-bundle.yaml

  7. Verify everything is running

    The sidecar-inject-webhook pod should be running

    # kubectl get pods
NAME                                                  READY     STATUS    RESTARTS   AGE
sidecar-injector-webhook-deployment-bbb689d69-882dd   1/1       Running   0          5m
    # kubectl get deployment
NAME                                  DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
sidecar-injector-webhook-deployment   1         1         1            1           5m

Configure namespace

  1. Label the namespace where CloudBees Core is installed with sidecar-injector=enabled

    kubectl label namespace mynamespace sidecar-injector=enabled

  2. Check

    # kubectl get namespace -L sidecar-injector
NAME          STATUS    AGE       SIDECAR-INJECTOR
default       Active    18h
mynamespace   Active    18h       enabled
kube-public   Active    18h
kube-system   Active    18h

Verify

  1. Deploy an app in Kubernetes cluster, take sleep app as an example

    # cat <<EOF | kubectl create -f -
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: sleep
spec:
  replicas: 1
  template:
    metadata:
      labels:
        app: sleep
    spec:
      containers:
      - name: sleep
        image: tutum/curl
        command: ["/bin/sleep","infinity"]
EOF

  2. Verify injection has happened

    # kubectl get pods -o 'go-template={{range .items}}{{.metadata.name}}{{"\n"}}{{range $key,$value := .metadata.annotations}}* {{$key}}: {{$value}}{{"\n"}}{{end}}{{"\n"}}{{end}}'
sleep-d5bf9d8c9-bfglq
* com.cloudbees.sidecar-injector/status: injected

Conclusion

You are now all set to use your custom CA across your Kubernetes cluster.

To pick up the new certificate bundle, restart Operations Center and running Managed Masters. When scheduling new build agents, they will also pick up the certificate bundle and allow connection to remote endpoints using your certificates.