RStudio Workbench Architectures#

Single User#

Using RStudio Desktop (OSS) on a laptop/desktop#

In this configuration, RStudio Desktop is installed on a laptop/desktop machine with Windows, macOS, or Linux and enables:

A single user to access the RStudio IDE directly on their machine

Using RStudio Server (OSS) on a single server#

In this configuration, RStudio Server is installed on a single Linux server and enables:

A single user to access the RStudio IDE via a web browser
A single RStudio session
The ability to use one version of R

Multiple users#

Using RStudio Workbench on a single server#

In this configuration, RStudio Workbench is installed on a single Linux server and enables:

Multiple users to access an RStudio IDE via a web browser
Multiple concurrent RStudio, Jupyter Notebook, or JupyterLab sessions
The ability to use multiple versions of R and Python

Using RStudio Workbench as a cluster#

In this configuration, RStudio Workbench is installed on two or more Linux servers and enables:

Multiple users to access an RStudio IDE via a web browser
Multiple concurrent RStudio, Jupyter Notebook, or JupyterLab sessions
The ability to use multiple versions of R and Python
Load balancing to provide additional computational resources to end users
High availability to provide redundancy
User's home directories to be stored on an external shared file server (typically an NFS server)

Using RStudio Workbench with an external cluster#

In this configuration, RStudio Workbench is installed on one or more Linux servers, is configured with Launcher and a Kubernetes or Slurm cluster backend, and enables:

Multiple users to access an RStudio IDE via a web browser
Multiple concurrent RStudio, Jupyter Notebook, or JupyterLab sessions
The ability to use multiple versions of R and Python
Users to run sessions and jobs on an external compute cluster
Optional configuration with high availability to provide redundancy
Storage is persisted on an external shared file server (typically an NFS server) and Postgres DB for session metadata

Using RStudio Workbench entirely in Kubernetes#

In this configuration, RStudio Workbench is installed entirely inside a Kubernetes and enables:

Multiple users to access an RStudio IDE via a web browser
Multiple concurrent RStudio, Jupyter Notebook, or JupyterLab sessions
The ability to use multiple versions of R and Python
User sessions and jobs run in isolated pods, potentially from different base images
The entire installation is managed in Kubernetes with tools like helm
Optional replicas for high availability
Storage is persisted on an external shared file server (typically an NFS server) and Postgres DB for session metadata