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