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Rancher Kubernetes Engine 2, Harvester, SUSE Linux Enterprise Server 15 SP4, SAP Data Intelligence 3

SAP Data Intelligence 3 on SUSE’s Kubernetes Stack

Abstract

SAP Data Intelligence 3 is the tool set to govern big amounts of data, and it runs fully containerized. This document describes the installation and configuration of SAP Data Intelligence 3 deployed on SUSE's Kubernetes stack, including Harvester, Rancher, RKE2 and Longhorn.

Disclaimer: Documents published as part of the SUSE Best Practices series have been contributed voluntarily by SUSE employees and third parties. They are meant to serve as examples of how particular actions can be performed. They have been compiled with utmost attention to detail. However, this does not guarantee complete accuracy. SUSE cannot verify that actions described in these documents do what is claimed or whether actions described have unintended consequences. SUSE LLC, its affiliates, the authors, and the translators may not be held liable for possible errors or the consequences thereof.

Author: Dr. Ulrich Schairer, SAP Solutions Architect, SUSE
Author: Kevin Klinger, SAP Solutions Architect, SUSE
Publication Date: 2022-11-11

1 Introduction

This guide describes the on-premises installation of SAP Data Intelligence 3.3 using Harvester and Rancher Kubernetes Engine (RKE) 2. In a nutshell, the installation of SAP Data Intelligence 3.3 consists of the following steps:

  • Installing Harvester

  • Installing SUSE Rancher

  • Installing RKE 2 Kubernetes cluster on the dedicated nodes

  • Deploying SAP Data Intelligence 3.3 on RKE 2 Kubernetes cluster

  • Performing post-installation steps for SAP Data Intelligence 3.3

  • Testing the installation of SAP Data Intelligence 3.3

To have a fully supported setup, there are two Kubernetes clusters required. One runs SUSE Rancher Management server and the other runs the actual workload, which for the purpose of this guide is SAP Data Intelligence.

2 Requirements

2.1 Hardware requirements

This chapter describes the hardware requirements for installing SAP Data Intelligence 3.3 on RKE 2 on top of SUSE Linux Enterprise Server 15 SP3. Only the AMD64/Intel 64 architecture is applicable for our use case.

2.1.1 Hardware Sizing

Correct hardware sizing is very important for setting up SAP Data Intelligence 3.3 on RKE 2.

2.1.1.1 Development systems
  • Minimal hardware requirements for a generic SAP Data Intelligence 3 deployment:

    • At least 7 nodes are needed for the Kubernetes cluster.

    • Minimum sizing of the nodes needs to be as shown below:

Server RoleCountRAMCPUDisk space

Management Workstation

1

16 GiB

4

>100 GiB

Master Node

3

16 GiB

4

>120 GiB

Worker Node

4

32 GiB

8

>120 GiB

2.1.1.2 Production systems
  • Minimal hardware requirements for an SAP Data Intelligence 3 deployment for production use:

    • At least seven nodes are needed for the Kubernetes cluster.

    • Minimum sizing of the nodes needs to be as shown below:

      Server RoleCountRAMCPUDisk space

      Management Workstation

      1

      16 GiB

      4

      >100 GiB

      Master Node

      3

      16 GiB

      4

      >120 GiB

      Worker Node

      4

      64 GiB

      16

      >120 GiB

2.2 Software requirements

The following list contains the software components needed to install SAP Data Intelligence 3.3 on RKE:

  • SUSE Linux Enterprise Server 15 SP4

  • Rancher Kubernetes Engine 2

  • SAP Software Lifecycle Bridge

  • SAP Data Intelligence 3.3

  • Secure private registry for container images, for example https://documentation.suse.com/sbp/all/single-html/SBP-Private-Registry/index.html

  • Access to a storage solution providing dynamically physical volumes

  • If it is planned to use Vora’s streaming tables checkpoint store, an S3 bucket like object store is needed

  • If it is planned to enable backup of SAP Data Intelligence 3.3 during installation access to an S3-compatible object store is needed

2.3 Installation on top of Harvester

When using Harvester to provision the virtual machines for an SAP Data Intelligence installation, the hardware requirements for Harvester need to be added to the requirements of SAP Data Intelligence described at Section 2.1.1, “Hardware Sizing”.

2.3.1 Harvester hardware requirements

A full list of requirements for Harvester can be found at https://docs.harvesterhci.io/v1.0/install/requirements

2.3.2 Development systems

The recommended setup for a Harvester cluster to be used for development has the following requirements:

Server RoleCountRAMCPUDisk space

Harvester Node

3

32 GiB

8

>140 GiB

Adding the hardware requirements of SAP Data Intelligence as described in chapter Section 2.1.1.1, “Development systems”, the following hardware is required to run an SAP Data Intelligence cluster on top of Harvester for development:

Server RoleCountRAMCPUDisk space

Harvester Node

3

288 GiB

72

>1360 GiB

2.3.3 Production systems

The recommended setup for a Harvester cluster to be used in production has the following requirements:

Server RoleCountRAMCPU"Disk space"Disk speed

Harvester Node

3

64 GiB

16

>500 GiB

>5000 IOPs

Adding the hardware requirements of SAP Data Intelligence as described in chapter Section 2.1.1.2, “Production systems”, the following hardware is required to run an SAP Data Intelligence cluster on top of Harvester in production:

Server RoleCountRAMCPUDisk space

Harvester Node

3

512 GiB

128

>2440 GiB

3 Preparations

Important
Important

Make sure that the Harvester version fits the SUSE Rancher version. A support matrix can be found here: https://docs.harvesterhci.io/v1.0/rancher/rancher-integration#rancher—​harvester-support-matrix

4 Installing SUSE Rancher Harvester

4.1 Introduction

Harvester is the open source Hyper Converged Infrastructure (HCI) solution running on Kubernetes, Longhorn and Kubevirt. Harvester provides the ability to provision, manage and run virtual machines.

4.2 Prerequistites

Depending on the purpose of the Harvester installation (development, testing or productive use), one or more (virtual) machines are needed. At the time of writing, the system architecture is x86_64 only. For the Harvester deployment, the following information should be handy:

  • IP addresses for the hosts running Harvester

  • IP address to be used as management address

  • Gateway address

  • IP address of name server

  • Access to a time server

  • Access to the Internet (for airgapped installations see: https://docs.harvesterhci.io/v1.1/airgap)

For more information, see the product documentation for Harvester: https://docs.harvesterhci.io/v1.1/

4.3 Preparing the installation

Before the installation of Harvester can be started, the following steps should be performed:

4.4 Installing Harvester

The installation of Harvester is straight forward:

  • Boot the machines dedicated to the Harvester cluster from the installation media.

  • After booting the machine, a guided setup leads you through the installation.

002 Harvester Install 1
Figure 1: Choose Installation Mode
  • Provide the following information:

    • Device where the installation is targeted to

003 Harvester Installation 2
Figure 2: Installation target
  • Host name

  • IP address

  • Network interface to be used

  • Gateway

004 Harvester Installation 3
Figure 3: Network configuration
  • DNS servers

005 Harvester Installation 4
Figure 4: DNS configuration
  • Management IP address

006 Harvester Installation 5
Figure 5: Configure Management IP address
  • Define cluster token. This is needed when joining other Harvester nodes.

007 Harvester Installation 6
Figure 6: Define clustertoken
  • Set the node shell access password. Default user is "rancher".

008 Harvester Installation 7
Figure 7: Set password for node access
  • Configure the time server.

009 Harvester Installation 8
Figure 8: Timehost configuration
  • Proxy servers (optional) are being entered.

Finally, a review panel is displayed.

010 Harvester Installation 9
Figure 9: Review installation settings

Confirm the configuration. The installation will start.

When the installation is finished, you will see the following screen:

012 Harvester Installation 11
Figure 10: Installation finished

This means that Harvester is up and running. Be patient as it can take some minutes.

For more installation options, see the Harvester documentation at https://docs.harvesterhci.io/v1.1

For productive environments, it is recommended to set up a Harvester cluster consisting of at least three nodes (or a higher odd number). To join nodes to the existing Harvester installation, simply select "Join existing Harvester cluster" after booting the node from the installation media.

029 Harvester Installation 28
Figure 11: Join Harvester cluster

For the installation workflow described, the following information is needed in addition:

  • the management VIP

  • the cluster token

030 Harvester Installation 29
Figure 12: Harvester VIP
031 Harvester Installation 30
Figure 13: Cluster token

4.5 Accessing the management UI

The Harvester HCI is managed via a Web UI:

  • Use the management (VIP) address to access the Harvester UI via an Internet browser. Next, set up the administrative account for Harvester.

013 Harvester Installation 12
Figure 14: First Welcome
  • After logging in, the Harvester Cluster overview dashboard is displayed.

014 Harvester Installation 13
Figure 15: Harvester dashboard

5 Installing SUSE Rancher

5.1 Preparation

5.1.1 Installing Helm

The easiest option to install Helm is to run:

# curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

5.1.2 Installing RKE2

To install RKE2, the script provided at https://get.rke2.io can be used as follows:

# curl -sfL https://get.rke2.io | sh -

After the script finished, start the RKE2 components and run:

# systemctl enable rke2-server --now

To verify the installation, run the following command:

# /var/lib/rancher/rke2/bin/kubectl --kubeconfig /etc/rancher/rke2/rke2.yaml get nodes

For convenience, the kubectl binary can be added to the $PATH and the given kubeconfig can be set via an environment variable:

# export PATH=$PATH:/var/lib/rancher/rke2/bin/
# export KUBECONFIG=/etc/rancher/rke2/rke2.yaml

5.2 Installing SUSE Rancher

To install SUSE Rancher, you need to add the related Helm repository. To achieve that, use the following command:

$ helm repo add rancher-stable https://releases.rancher.com/server-charts/stable

As a next step, create the cattle-system namespace in Kubernetes as follows:

$ kubectl create namespace cattle-system

The Kubernetes cluster is now ready for the installation of SUSE Rancher:

$ helm install rancher rancher-stable/rancher \
    --namespace cattle-system \
    --set hostname=<your.domain.com> \
    --set replicas=3

During the rollout of SUSE Rancher, you can monitor the progress using the following command:

$ kubectl -n cattle-system rollout status deploy/rancher

When the deployment is done, you can access the SUSE Rancher cluster at https://<your.domain.com>. Here you will also find a description about how to log in for the first time.

6 Using Harvester and SUSE Rancher together

Important
Important

If not done already, make sure the desired Harvester installation is compatible with your SUSE Rancher setup: https://docs.harvesterhci.io/v1.0/rancher/rancher-integration#rancher—​harvester-support-matrix

6.1 Connecting Harvester with SUSE Rancher

To connect Harvester with SUSE Rancher, the first step is to access Rancher. The menu in the upper left corner allows you to open the Virtualization Management tab.

Rancher Virtualization Management
Figure 16: Rancher Virtualization Management

The only option available here should be the "Import Existing" button. Click this button.

Rancher Create Harvester Cluster
Figure 17: Create Harvester Cluster

On the next screen, enter a name for the Harvester cluster.

Rancher Pending Harvester Cluster
Figure 18: Create Harvester Cluster

After clicking the "Create" button, three steps to be executed on the Harvester cluster are shown.

6.2 Provisioning virtual machines with RKE2

After SUSE Rancher and Harvester are connected, virtual machines can be provisioned using SUSE Rancher. To do so, access SUSE Rancher and click "Create" in the home tab. Select the option "Harvester" and make sure that RKE2/K3s is selected.

Next, the "Cluster: Create Harvester" page is loaded.

The first step you perform here is to set the cluster name. The "Machine Pools" section specifies the number of machines to be provisioned, their Kubernetes role, and further VM specific parameters.

The "Cluster Configuration" section allows you to set some Kubernetes-specific parameters. Here, select the Kubernetes version to fulfill the requirements of the desired workload.

Within the scope of this guide it is recommended to deploy three master nodes (roles: etcd & Control Plane) and four worker nodes (roles: Worker) for SAP Data Intelligence. Check Section 2.1.1, “Hardware Sizing” to fill out the machine specific configurations to meet the requirements for the given purpose and Section 6.2, “Provisioning virtual machines with RKE2” to get an overview how to roll out an RKE2 cluster using Harvester.

When all machines are provisioned and the RKE2 cluster is up and running, a storage must be made available for SAP Data Intelligence. SUSE offers Longhorn which is a validated storage for SAP Data Intelligence workloads. The next chapter describes how to set up Longhorn.

Important
Important

SAP Data Intelligence requires an S3 compatible storage for its backups, which is not delivered by Longhorn. Check the related SAP Note to get an overview of the supported storage solutions together with RKE2.

7 Installing Longhorn

This chapter details the minimum requirements to install Longhorn and describes three different ways for the installation. For more details, visit https://longhorn.io/docs/1.3.1/deploy/install/

7.1 Requirements

Before Longhorn can be installed on a Kubernetes cluster, all nodes must have the open-iscsi package installed, and the ISCSI daemon needs to be started. To do so, run:

# zypper in -y open-iscsi
# systemctl iscsid enable --now

To make sure a node is prepared for Longhorn, you can use the following script to check:

$ curl -sSfL https://raw.githubusercontent.com/longhorn/longhorn/v1.2.4/scripts/environment_check.sh | bash

7.2 Installing Longhorn using SUSE Rancher

Up-to-date and detailed instructions how to install Longhorn using SUSE Rancher can be found at https://longhorn.io/docs/1.3.1/deploy/install/install-with-rancher/

7.3 Installing Longhorn using Helm

To install Longhorn using Helm, run the following commands:

$ helm repo add longhorn https://charts.longhorn.io
$ helm repo update
$ helm install longhorn longhorn/longhorn --namespace longhorn-system --create-namespace

These commands will add the Longhorn Helm charts to the list of Helm repositories, update the Helm repository and execute the installation of Longhorn.

7.4 Installing Longhorn using kubectl

You can install Longhorn using kubectl with the following command:

$ kubectl apply -f https://raw.githubusercontent.com/longhorn/longhorn/v{lh_version}/deploy/longhorn.yaml

7.5 Exposing Longhorn UI by creating an Ingress with Basic Authentication

  • Create a basic auth file:

    $ USER=<USERNAME_HERE>; \
      PASSWORD=<PASSWORD_HERE>; \
      echo "${USER}:$(openssl passwd -stdin -apr1 <<< ${PASSWORD})" >> auth
  • Create a secret from the file auth:

    $ kubectl -n longhorn-system create secret generic basic-auth --from-file=auth
  • Create the Ingress with basic authentication:

    $ cat <<EOF > longhorn-ingress.yaml
    apiVersion: networking.k8s.io/v1beta1
    kind: Ingress
    metadata:
      name: longhorn-ingress
      namespace: longhorn-system
      annotations:
        # type of authentication
        nginx.ingress.kubernetes.io/auth-type: basic
        # prevent the controller from redirecting (308) to HTTPS
        nginx.ingress.kubernetes.io/ssl-redirect: 'false'
        # name of the secret that contains the user/password definitions
        nginx.ingress.kubernetes.io/auth-secret: basic-auth
        # message to display with an appropriate context why the authentication is required
        nginx.ingress.kubernetes.io/auth-realm: 'Authentication Required '
    spec:
      rules:
      - http:
          paths:
          - path: /
            backend:
              serviceName: longhorn-frontend
              servicePort: 80
    EOF
    
    $ kubectl -n longhorn-system apply -f longhorn-ingress.yaml

For more details, visit https://longhorn.io/docs/1.3.1/deploy/accessing-the-ui/longhorn-ingress/

8 Installing SAP Data Intelligence 3.3

This section describes the installation of SAP Data Intelligence 3.3 on an RKE 2-powered Kubernetes cluster.

8.1 Preparation

The following steps need to be executed before the deployment of SAP Data Intelligence 3.3 can start:

  • Create a namespace for SAP Data Intelligence 3.3.

  • Create an access to a secure private registry.

  • Create a default storage class.

  • Download and install SAP SLC Bridge.

  • Download the stack.xml file for provisioning the DI 3.3 installation.

  • Check if the nfsd and nfsv4 kernel modules are loaded and/or loadable on the Kubernetes nodes.

8.1.1 Creating namespace for SAP Data Intelligence 3.3 in the Kubernetes cluster

Log in to your management workstation and create the namespace in the Kubernetes cluster where DI 3.3 will be deployed.

$ kubectl create ns <NAMESPACE for DI 31>
$ kubectl get ns

8.1.2 Creating cert file to access the secure private registry

Create a file named cert that contains the SSL certificate chain for the secure private registry. This imports the certificates into SAP Data Intelligence 3.3.

$ cat CA.pem > cert
$ kubectl -n <NAMESPACE for DI 31> create secret generic cmcertificates --from-file=cert

8.2 Creating default storage class

To install SAP Data Intelligence 3.3, a default storage class is needed to provision the installation with physical volumes (PV). Below find an example for a ceph/rbd based storage class that uses the CSI.

  • Create the yaml files for the storage class. Contact your storage admin to get the required information.

  • Create config-map:

    $ cat << EOF > csi-config-map.yaml
    ---
    apiVersion: v1
    kind: ConfigMap
    data:
      config.json: |-
        [
          {
            "clusterID": "<ID of your ceph cluster>",
            "monitors": [
              "<IP of Monitor 1>:6789",
              "<IP of Monitor 2>:6789",
              "<IP of Monitor 3>:6789"
            ]
          }
        ]
    metadata:
      name: ceph-csi-config
    EOF
  • Create a secret to access the storage:

    $ cat << EOF > csi-rbd-secret.yaml
    ---
    apiVersion: v1
    kind: Secret
    metadata:
      name: csi-rbd-secret
      namespace: default
    stringData:
      userID: admin
      userKey: AQCR7htglvJzBxAAtPN0YUeSiDzyTeQe0lveDQ==
    EOF
  • Download the file:

    $ curl -LO https://raw.githubusercontent.com/ceph/ceph-csi/master/deploy/rbd/kubernetes/csi-rbdplugin-provisioner.yaml
  • Download the file:

    $ curl -LO https://raw.githubusercontent.com/ceph/ceph-csi/master/deploy/rbd/kubernetes/csi-rbdplugin.yaml
  • Create a pool on the Ceph storage where the PVs will be created, and insert the pool name and the Ceph cluster ID:

    $ cat << EOF > csi-rbd-sc.yaml
    ---
    apiVersion: storage.k8s.io/v1
    kind: StorageClass
    metadata:
       name: csi-rbd-sc
    provisioner: rbd.csi.ceph.com
    parameters:
       clusterID: <your ceph cluster id>
       pool: <your pool>
       csi.storage.k8s.io/provisioner-secret-name: csi-rbd-secret
       csi.storage.k8s.io/provisioner-secret-namespace: default
       csi.storage.k8s.io/node-stage-secret-name: csi-rbd-secret
       csi.storage.k8s.io/node-stage-secret-namespace: default
    reclaimPolicy: Delete
    mountOptions:
       - discard
    EOF
  • Create config for encryption. This is needed, else the deployment of the CSI driver for ceph/rbd will fail.

    $ cat << EOF > kms-config.yaml
    ---
    apiVersion: v1
    kind: ConfigMap
    data:
      config.json: |-
        {
          },
          "vault-tokens-test": {
              "encryptionKMSType": "vaulttokens",
              "vaultAddress": "http://vault.default.svc.cluster.local:8200",
              "vaultBackendPath": "secret/",
              "vaultTLSServerName": "vault.default.svc.cluster.local",
              "vaultCAVerify": "false",
              "tenantConfigName": "ceph-csi-kms-config",
              "tenantTokenName": "ceph-csi-kms-token",
              "tenants": {
                  "my-app": {
                      "vaultAddress": "https://vault.example.com",
                      "vaultCAVerify": "true"
                  },
                  "an-other-app": {
                      "tenantTokenName": "storage-encryption-token"
                  }
              }
           }
        }
    metadata:
      name: ceph-csi-encryption-kms-config
    EOF
  • Deploy the ceph/rbd CSI and storage class:

    $ kubectl apply -f csi-config-map.yaml
    $ kubectl apply -f csi-rbd-secret.yaml
    $ kubectl apply -f \
      https://raw.githubusercontent.com/ceph/ceph-csi/master/deploy/rbd/kubernetes/csi-provisioner-rbac.yaml
    $ kubectl apply -f \
      https://raw.githubusercontent.com/ceph/ceph-csi/master/deploy/rbd/kubernetes/csi-nodeplugin-rbac.yaml
    $ kubectl apply -f csi-rbdplugin-provisioner.yaml
    $ kubectl apply -f csi-rbdplugin.yaml
    $ kubectl apply -f csi-rbd-sc.yaml
    $ kubectl apply -f kms-config.yaml
    $ kubectl patch storageclass csi-rbd-sc \
      -p '{"metadata": {"annotations":{"storageclass.kubernetes.io/is-default-class":"true"}}}'
  • Check your storage class:

    $ kubectl get sc
    NAME                   PROVISIONER        RECLAIMPOLICY   VOLUMEBINDINGMODE   ALLOWVOLUMEEXPANSION   AGE
    csi-rbd-sc (default)   rbd.csi.ceph.com   Delete          Immediate           false                  103m

8.3 Downloading the SLC Bridge

The SLC Bridge can be obtained:

Download the SLC Bridge software to the management workstation.

8.4 Installing the SLC Bridge

Rename the SLC Bridge binary to slcb and make it executable. Deploy the SLC Bridge to the Kubernetes cluster.

$ mv SLCB01_XX-70003322.EXE slcb
$ chmod 0700 slcb
$ export KUBECONFIG=<KUBE_CONFIG>
$ ./slcb init

During the interactive installation, the following information is needed:

  • URL of secure private registry

  • Choose expert mode

  • Choose NodePort for the service

Take a note of the service port of the SLC Bridge. It is needed for the installation of SAP Data Intelligence 3.3 or for the reconfiguration of DI 3.3, for example to enable backup. If you forgot to note it down, the following command will list the service port:

$ kubectl -n sap-slcbridge get svc

8.5 Creating and downloading Stack XML for the SAP Data Intelligence installation

Follow the steps described in the chapter Install SAP Data Intelligence with SLC Bridge in a Cluster with Internet Access of the SAP Data Intelligence 3.3 Installation Guide.

8.5.1 Creating Stack XML

You can create the Stack XML via the SAP Maintenance Planner. Access the tool via https://support.sap.com/en/alm/solution-manager/processes-72/maintenance-planner.html. Go to the Maintenance Planner at https://apps.support.sap.com/sap/support/mp published on the SAP Web site and generate a Stack XML file with the container image definitions of the SAP Data Intelligence release that you want to install. Download the Stack XML file to a local directory. Copy stack.xml to the management workstation.

8.6 Running the installation of SAP Data Intelligence

The installation of SAP Data Intelligence 3.3 is invoked by:

$ export KUBECONFIG=<path to kubeconfig>
$ ./slcb execute --useStackXML MP_Stack_XXXXXXXXXX_XXXXXXXX_.xml --url https://<node>:<service port>/docs/index.html

This starts an interactive process for configuring and deploying SAP Data Intelligence 3.3.

The table below lists some parameters available for an SAP Data Intelligence 3.3 installation:

ParameterConditionRecommendation

Kubernetes Namespace

Always

set to namespace created beforehand

Installation Type

installation or update

either

Container Registry

Always

add the uri for the secure private registry

Checkpoint Store Configuration

installation

whether to enable Checkpoint Store

Checkpoint Store Type

if Checkpoint Store is enabled

use S3 object store from SES

Checkpoint Store Validation

if Checkpoint is enabled

Object store access will be verified

Container Registry Settings for Pipeline Modeler

optional

used if a second container registry is used

StorageClass Configuration

optional, needed if a different StorageClass is used for some components

leave the default

Default StorageClass

detected by SAP Data Intelligence installer

The Kubernetes cluster shall have a storage class annotated as default SC

Enable Kaniko Usage

optional if running on Docker

enable

Container Image Repository Settings for SAP Data Intelligence Modeler

mandatory

 

Container Registry for Pipeline Modeler

optional

Needed if a different container registry is used for the pipeline modeler images

Loading NFS Modules

optional

Make sure that nfsd and nfsv4 kernel modules are loaded on all worker nodes

Additional Installer Parameters

optional

 

For more details about input parameters for an SAP Data Intelligence 3.3 installation, visit the section Required Input Parameters of the SAP Data Intelligence Installation Guide.

8.7 Post-installation tasks

After the installation workflow is successfully finished, you need to carry out some additional tasks:

  • Obtain or create an SSL certificate to securely access the SAP Data Intelligence installation:

    • Create a certificate request using openssl, for example:

      $ openssl req -newkey rsa:2048 -keyout <hostname>.key -out <hostname>.csr
    • Decrypt the key:

      $ openssl rsa -in <hostname>.key -out decrypted-<hostname>.key
    • Let a CA sign the <hostname>.csr You will receive a <hostname>.crt.

    • Create a secret from the certificate and the key in the SAP Data Intelligence 3 namespace:

      $ export NAMESPACE=<{di} 3 namespace>
      $ kubectl -n $NAMESPACE create secret tls vsystem-tls-certs --key  decrypted-<hostname>.key--cert <hostname>.crt
  • Deploy an nginx-ingress controller:

    • For more information, see https://kubernetes.github.io/ingress-nginx/deploy/#bare-metal.

    • Create the nginx-ingress controller as a nodePort service according to the Ingress nginx documentation:

      $ kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/controller-v0.46.0/deploy/static/provider/baremetal/deploy.yaml
    • Determine the port the nginx controller is redirecting HTTPS to:

      $ kubectl -n ingress-nginx get svc ingress-nginx-controller

      The output should be similar to the below:

      kubectl -n ingress-nginx get svc ingress-nginx-controller
      NAME                       TYPE       CLUSTER-IP    EXTERNAL-IP   PORT(S)                      AGE
      ingress-nginx-controller   NodePort   10.43.86.90   <none>        80:31963/TCP,443:{di_version}06/TCP   53d

      In our example here, the TLS port is be 3.306. Note the port IP down as you will need it to access the SAP Data Intelligence installation from the outside.

  • Create an Ingress to access the SAP Data Intelligence installation:

    $ cat <<EOF > ingress.yaml
    apiVersion: extensions/v1beta1
    kind: Ingress
    metadata:
      annotations:
        kubernetes.io/ingress.class: nginx
        nginx.ingress.kubernetes.io/force-ssl-redirect: "true"
        nginx.ingress.kubernetes.io/secure-backends: "true"
        nginx.ingress.kubernetes.io/backend-protocol: HTTPS
        nginx.ingress.kubernetes.io/proxy-body-size: "0"
        nginx.ingress.kubernetes.io/proxy-buffer-size: 16k
        nginx.ingress.kubernetes.io/proxy-connect-timeout: "30"
        nginx.ingress.kubernetes.io/proxy-read-timeout: "1800"
        nginx.ingress.kubernetes.io/proxy-send-timeout: "1800"
      name: vsystem
    spec:
      rules:
      - host: "<hostname FQDN must match SSL certificate"
        http:
          paths:
          - backend:
              serviceName: vsystem
              servicePort: 8797
            path: /
      tls:
      - hosts:
        - "<hostname FQDN must match SSL certificate>"
        secretName: vsystem-tls-certs
    EOF
    $ kubectl apply -f ingress.yaml
  • Connecting to https://hostname:<ingress service port> brings up the SAP Data Intelligence login dialog.

8.8 Testing the SAP Data Intelligence 3 installation

Finally, the SAP Data Intelligence 3 installation should be verified with some very basic tests:

  • Log in to SAP Data Intelligence’s launchpad

  • Create example pipeline

  • Create ML Scenario

  • Test machine learning

  • Download vctl

For details, see the SAP Data Intelligence 3 Installation Guide

9 Maintenance tasks

This section provides some tips about what should and could be done to maintain the Kubernetes cluster, the operating system and the SAP Data Intelligence 3 deployment.

9.1 Backup

It is good practice to keep backups of all relevant data to be able to restore the environment in case of a failure. To perform regular backups, follow the instructions as outlined in the respective documentation below:

9.2 Upgrade or update

This section explains how you can keep your installation of SAP Data Intelligence, RKE 2 and SUSE Linux Enterprise Server up-to-date.

9.2.1 Updating the operating system

To obtain updates for SUSE Linux Enterprise Server 15 SP4, the installation must be registered either to SUSE Customer Center, an SMT or RMT server, or SUSE Manager with a valid subscription.

  • SUSE Linux Enterprise Server 15 SP4 can be updated on the command line using zypper:

    $ sudo zypper ref -s
    $ sudo zypper lu
    $ sudo zypper patch
  • Other methods for updating SUSE Linux Enterprise Server 15 SP4 are described in the product documentation.

If an update requires a reboot of the server, make sure that this can be done safely.

  • For example, block access to SAP Data Intelligence, and drain and cordon the Kubernetes node before rebooting:

    $ kubectl edit ingress <put in some dummy port>
    $ kubectl drain <node>
  • Check the status of the node:

    $kubectl get node <node>

    The node should be marked as not schedulable.

  • On RKE 2 master nodes, run the command:

    $ sudo systemctl stop rke2-server
  • On RKE 2 worker nodes, run the command:

    $ sudo systemctl stop rke2-agent
  • Update SUSE Linux Enterprise Server 15 SP4:

    $ ssh node
    $ sudo zypper patch
  • Reboot the nodes if necessary or start the appropriate RKE 2 service.

    • On master nodes, run the command:

      $ sudo systemctl start rke2-server
    • On worker nodes, run the command:

      $ sudo systemctl start rke2-agent
  • Check if the respective nodes are back and uncordon them.

    $ kubectl get nodes
    $ kubectl uncordon <node>

11 GNU Free Documentation License

Copyright © 2000, 2001, 2002 Free Software Foundation, Inc. 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

0. PREAMBLE

The purpose of this License is to make a manual, textbook, or other functional and useful document "free" in the sense of freedom: to assure everyone the effective freedom to copy and redistribute it, with or without modifying it, either commercially or noncommercially. Secondarily, this License preserves for the author and publisher a way to get credit for their work, while not being considered responsible for modifications made by others.

This License is a kind of "copyleft", which means that derivative works of the document must themselves be free in the same sense. It complements the GNU General Public License, which is a copyleft license designed for free software.

We have designed this License in order to use it for manuals for free software, because free software needs free documentation: a free program should come with manuals providing the same freedoms that the software does. But this License is not limited to software manuals; it can be used for any textual work, regardless of subject matter or whether it is published as a printed book. We recommend this License principally for works whose purpose is instruction or reference.

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10. FUTURE REVISIONS OF THIS LICENSE

The Free Software Foundation may publish new, revised versions of the GNU Free Documentation License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. See http://www.gnu.org/copyleft/.

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ADDENDUM: How to use this License for your documents

Copyright (c) YEAR YOUR NAME.
   Permission is granted to copy, distribute and/or modify this document
   under the terms of the GNU Free Documentation License, Version 1.2
   or any later version published by the Free Software Foundation;
   with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
   A copy of the license is included in the section entitled “GNU
   Free Documentation License”.

If you have Invariant Sections, Front-Cover Texts and Back-Cover Texts, replace the “ with…​Texts.” line with this:

with the Invariant Sections being LIST THEIR TITLES, with the
   Front-Cover Texts being LIST, and with the Back-Cover Texts being LIST.

If you have Invariant Sections without Cover Texts, or some other combination of the three, merge those two alternatives to suit the situation.

If your document contains nontrivial examples of program code, we recommend releasing these examples in parallel under your choice of free software license, such as the GNU General Public License, to permit their use in free software.

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