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RStudio Server Pro 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 Server Pro on a single server#

In this configuration, RStudio Server Pro 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 Server Pro as a cluster#

In this configuration, RStudio Server Pro 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 Server Pro with an external cluster#

In this configuration, RStudio Server Pro 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 Server Pro entirely in Kubernetes#

In this configuration, RStudio Server Pro 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