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Integrating RStudio Server Pro with Kubernetes#

Overview#

These steps describe how to integrate RStudio Server Pro with Launcher and Kubernetes.

Info

Launcher is a new feature of RStudio Server Pro 1.2 that is only available under named user licensing. RStudio Server Pro 1.2 without Launcher is available under existing server-based licensing. For questions about using Launcher with RStudio Server Pro, please contact sales@rstudio.com.

Prerequisites#

This integration is intended to be performed on top of a base installation of RStudio Server Pro.

  • Installation of RStudio Server Pro 1.2.5 or higher
  • NFS server that is configured with RStudio Server Pro for home directory project storage
  • Kubernetes cluster:
    • Kubernetes API endpoint
    • Kubernetes cluster CA certificate
    • Access to kubectl to create namespaces, service accounts, cluster roles, and role bindings
  • Access to Docker image registry (if working within an offline environment)

Pre-Flight Configuration Checks#

Verifying active Kubernetes worker nodes

  • On a machine with kubectl configured, ensure that you have one or more worker nodes that are ready to accept pods as part of the Kubernetes cluster by running the following command:

    Terminal
    $ kubectl get nodes
    NAME STATUS ROLES AGE VERSION
    ip-172-31-12-54.ec2.internal Ready <none> 90d v1.11.5
    ip-172-31-15-141.ec2.internal Ready <none> 90d v1.11.5
    ip-172-31-18-59.ec2.internal Ready <none> 90d v1.11.5
    ip-172-31-20-112.ec2.internal Ready <none> 90d v1.11.5
    

Verifying functionality with a test deployment

  • On a machine with kubectl configured, ensure that you are able to deploy a sample application to your Kubernetes cluster by running the following command:

    Terminal
    $ kubectl create deployment hello-node --image=gcr.io/google-samples/node-hello:1.0
    

  • Confirm that the pod is running by using the following command:

    Terminal
    $ kubectl get pods
    NAME READY STATUS RESTARTS AGE
    hello-node-6d6cd9679f-mllr7 1/1 Running 0 1m
    

  • Now, you can clean up the test deployment by running the following command:

    Terminal
    $ kubectl delete deployment hello-node
    deployment.extensions "hello-node" deleted
    

Step 1. Configure RStudio Server Pro with Launcher#

  • Add the following lines to the RStudio Server Pro configuration file:

    File: /etc/rstudio/rserver.conf
    # Launcher Config
    launcher-address=127.0.0.1
    launcher-port=5559
    launcher-sessions-enabled=1
    launcher-default-cluster=Kubernetes
    launcher-sessions-callback-address=http://<SERVER-ADDRESS>:8787
    launcher-sessions-container-run-as-root=0
    launcher-sessions-create-container-user=1
    

  • It is recommended that you do the following:

    • In the launcher-sessions-callback-address setting, you should replace <SERVER-ADDRESS> with the DNS name or IP address of RStudio Server Pro.
    • You should also change the protocol and port if you are using HTTPS or a different port.

    Note

    The <SERVER-ADDRESS> needs to be reachable from the containers in Kubernetes to the instance of RStudio Server Pro.

Step 2. Configure Launcher settings and plugins#

  • Add the following lines to the Launcher configuration file:

    File: /etc/rstudio/launcher.conf
    [server]
    address=127.0.0.1
    port=5559
    server-user=rstudio-server
    admin-group=rstudio-server
    authorization-enabled=1
    thread-pool-size=4
    enable-debug-logging=1
    
    [cluster]
    name=Kubernetes
    type=Kubernetes
    

Step 3. Configure profile for Launcher Kubernetes plugin#

  • Add the following lines to the Launcher profiles configuration file:

    File: /etc/rstudio/launcher.kubernetes.profiles.conf
    [*]
    default-cpus=1
    default-mem-mb=512
    max-cpus=2
    max-mem-mb=1024
    container-images=rstudio/r-session-complete:centos7-1.3.1093-1
    default-container-image=rstudio/r-session-complete:centos7-1.3.1093-1
    allow-unknown-images=0
    

For more information on using Docker images with Launcher, refer to the Support article on Using Docker images with RStudio Server Pro, Launcher, and Kubernetes.

Step 4. Provision and configure NFS server#

Shared home directory storage via NFS is required for configurations of RStudio Server Pro and Launcher. RStudio Server Pro stores project data for each user in their respective home directory.

  • Perform the following steps in your environment:

    • Provision an NFS server that exports the /home directory. We recommend configuring an NFS server on a machine that runs separately from RStudio Server Pro and Launcher.
    • On the machine with RStudio Server Pro and Launcher, mount the NFS share at /home.

    Note

    Similar to any NFS configuration, all machines (e.g., the machine with the NFS server and the machine with RStudio Server Pro and Launcher) should have the same users with matching user IDs and group IDs to avoid permission or ownership issues across NFS client machines.

Step 5. Configure NFS mounts for Launcher#

  • Add the following lines to the Launcher mounts configuration file, which is the NFS server and mount path that will be used by the containers to mount the home directory for each user:

    File: /etc/rstudio/launcher-mounts
    # Required home directory mount for RSP, Launcher, and Kubernetes
    Host: <NFS-IP-ADDRESS>
    Path: /home/{USER}
    MountPath: /home/{USER}
    ReadOnly: false
    Cluster: Kubernetes
    

  • Replace <NFS-IP-ADDRESS> with the IP address of your NFS server.

  • The Path and MountPath contain the special variable {USER} to indicate that the user’s name will be substituted when the container starts, so there is no need to change that variable in this configuration file.
  • The Path is the source directory of the mount, i.e., the home directory path within NFS server. Please replace it with the correct path if it is something different than /home/.
  • The MountPath is the path within the container that the home directory will be mounted to. It must match how the home directory is mounted on the RSP server. Please replace it with the correct path if it is something different than /home/.

Note

Shared home directory storage via NFS is required for configurations of RStudio Server Pro and Launcher. Therefore, the configuration section shown above is required in the /etc/rstudio/launcher-mounts configuration file for RStudio Server Pro and Launcher to function with Kubernetes.

Additional NFS mounts can be added to this same configuration file to make other read-only or read-write file storage mounts available within remote session containers.

Step 6. Create Kubernetes resources for Launcher sessions and jobs#

  • Run the following commands in a terminal to create the rstudio namespace and required service account, cluster role, and role bindings:

    Terminal
    $ kubectl create namespace rstudio
    
    $ kubectl create serviceaccount job-launcher --namespace rstudio
    
    $ kubectl create rolebinding job-launcher-admin \
        --clusterrole=cluster-admin \
        --group=system:serviceaccounts:rstudio \
        --namespace=rstudio
    
    $ kubectl create clusterrole job-launcher-clusters \
        --verb=get,watch,list \
        --resource=nodes
    
    $ kubectl create clusterrolebinding job-launcher-list-clusters \
        --clusterrole=job-launcher-clusters \
        --group=system:serviceaccounts:rstudio
    

    (Alternative) Using a custom role instead of the cluster-admin role

    The default steps above use the cluster-admin role on the Kubernetes cluster. If you are unable to use the cluster-admin role, then you can use a custom role that has full access to the rstudio namespace.

    In this case, you can run the following commands in a terminal to create the rstudio namespace and required service account, custom cluster role, and role bindings:

    Terminal
    $ kubectl create namespace rstudio
    
    $ kubectl create serviceaccount job-launcher --namespace rstudio
    
    # Create a role with full access to the rstudio namespace
    $ kubectl create role rstudio-full-access \
        --verb='*' \
        --resource='*.*' \
        --namespace=rstudio
    
    # Bind the new role to the service account
    $ kubectl create rolebinding job-launcher-admin \
        --role=rstudio-full-access \
        --group=system:serviceaccounts:rstudio \
        --namespace=rstudio
    
    $ kubectl create clusterrole job-launcher-clusters \
        --verb=get,watch,list \
        --resource=nodes
    
    $ kubectl create clusterrolebinding job-launcher-list-clusters \
        --clusterrole=job-launcher-clusters \
        --group=system:serviceaccounts:rstudio
    

    (Optional) Perform these steps if your Kubernetes cluster doesn't have impersonation enabled

    With the default configuration of most Kubernetes distributions, the above steps should be sufficient enough to allow Launcher session containers to run as the end user who created the session. If your Kubernetes cluster does not have impersonation enabled, then you can use a custom cluster role and role binding that allow for impersonation.

    After you run the above steps, run the following additional commands in a terminal to create cluster role and role binding resources that allow for impersonation:

    Terminal
    $ kubectl create clusterrole job-launcher-api \
        --verb=impersonate \
        --resource=users,groups,serviceaccounts
    
    $ kubectl create rolebinding job-launcher-impersonation \
        --clusterrole=job-launcher-api \
        --group=system:serviceaccounts:rstudio \
        --namespace=rstudio
    

Refer to the Launcher section of the RStudio Server Pro Administration Guide for more information about how Launcher creates the session user within the container.

Refer to the user impersonation section of the Kubernetes documentation for more information about authentication and impersonation in Kubernetes.

Step 7. Configure Launcher with Kubernetes#

  • Obtain the Kubernetes token for the service account in the rstudio namespace by running the following command in your terminal:

    Terminal
    $ kubectl get secret $(kubectl get serviceaccount job-launcher --namespace=rstudio -o jsonpath='{.secrets[0].name}') --namespace=rstudio -o jsonpath='{.data.token}' | base64 -d && echo
    

  • Add the following lines to the Launcher Kubernetes configuration file, (where <KUBERNETES-API-ENDPOINT> is the URL for the Kubernetes API, <KUBERNETES-CLUSTER-TOKEN> is the Kubernetes service account token from the above kubectl get secret terminal command, and <BASE-64-ENCODED-CA-CERTIFICATE> is the Base64 encoded CA certificate for the Kubernetes API):

    File: /etc/rstudio/launcher.kubernetes.conf
    api-url=<KUBERNETES-API-ENDPOINT>
    auth-token=<KUBERNETES-CLUSTER-TOKEN>
    certificate-authority=<BASE-64-ENCODED-CA-CERTIFICATE>
    

    Note

    You can typically locate these values from your Kubernetes cluster console or dashboard.

Step 8. Restart RStudio Server Pro and Launcher Services#

  • Run the following to restart services:

    Terminal
    $ sudo rstudio-server restart
    $ sudo rstudio-launcher restart
    

Step 9. Test RStudio Server Pro with Launcher and Kubernetes#

  • Run the following command to test the installation and configuration of RStudio Server Pro with Launcher and Kubernetes:

    Terminal
    $ sudo rstudio-server stop
    $ sudo rstudio-server verify-installation --verify-user=<USER>
    $ sudo rstudio-server start
    

Note

Replace <USER> with a valid username of a user that is setup to run RStudio Server Pro in your installation. You only need to run this test once for one valid user to verify that RStudio Server Pro and Launcher can successfully communicate with Kubernetes and start sessions/jobs.

For more information on using the Launcher verification tool, refer to the Troubleshooting section in the RStudio Server Pro Administration Guide .

Additional information#

Use Custom Docker Images#

You can extend or build your own custom Docker images to use with RStudio Server Pro and Kubernetes with different versions of R, R packages, or system packages.

For more information on using custom Docker images, refer to the support article on Using Docker images with RStudio Server Pro, Launcher, and Kubernetes.

Perform Additional Configuration#

For more information on configuring RStudio Server Pro and Launcher, including configuring additional shared file storage mounts, environment variables, and ports, refer to the following reference documentation:

Troubleshooting RStudio Server Pro and Kubernetes#

Refer to the documentation page on Troubleshooting Launcher and Kubernetes in RStudio Server Pro for additional information on troubleshooting RStudio Server Pro with Launcher and Kubernetes.