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Upgrade RStudio Workbench#

RStudio Workbench, formerly RStudio Server Pro

We have renamed RStudio Server Pro to RStudio Workbench. This change reflects the product’s growing support for a wide range of different development environments. RStudio Workbench enables R and Python data scientists to use their preferred IDE in a secure, scalable, and collaborative environment -- whether that is the RStudio IDE, JupyterLab, Jupyter Notebooks, or VS Code. We want RStudio Workbench to be the best single platform to support open source, code-first data science, whether your team is using R or Python.

If you perform an upgrade of RStudio Workbench, formerly RStudio Server Pro1, and an existing version of the server is currently running, then the upgrade process will also ensure that active sessions are immediately migrated to the new version.

This includes the following behavior:

  • Running R sessions are suspended so that future interactions with the server automatically launch the updated R session binary
  • Currently connected browser clients are notified that a new version is available and automatically refresh themselves.
  • The core server binary is restarted

Additional information

For more information about upgrading RStudio Workbench, navigate to section 2.1.6 Upgrading to a New Version of the Admin Guide.

For addition information about Upgrading to a later version of RStudio Server, please reference this Support Article.

Upgrading to Version 1.4+#

Requirements#

If you are using an RStudio Workbench version prior to version 1.4 and would like to upgrade RStudio Workbench to version 1.4 or greater, then there are several administrative requirements for a successful migration. For most RStudio Workbench configurations, the information in the sections below is optional.

Multiple load-balanced instances of RStudio Workbench

If you have multiple load-balanced instances of RStudio Workbench, there is a new requirement for a Postgres metadata database to upgrade to version 1.4 or greater. If you attempt to install/upgrade to version 1.4 or greater and do not have a Postgres metadata database configured, RStudio Workbench will not start. See the Database section below for more information.

Database#

Starting in RStudio Workbench version 1.4, a database is required. The product comes with a SQLite database out-of-the-box. If you are running a single-node installation of RStudio Workbench, including connecting to an external resource manager with the launcher feature, then no further configuration is required.

If you are load-balancing multiple RStudio Workbench instances, a separate Postgres database is required.

Note

The minimum supported PostgreSQL version is 9.5.

Install Type Database
Single Node, RStudio Sessions Only SQLite
Single Node, Jupyter + VS Code Sessions (Launcher) SQLite
Single Node, External Resource Manager (K8S, Slurm) SQLite
Multiple Nodes Load Balanced/HA (w/ or w/o external resource manager) Postgres

RStudio Workbench uses the database to manage and maintain the server state. If using a Postgres database, an administrator must create the database and properly configure RStudio Workbench; no further database administration is required. Understanding the RStudio Product Databases includes details on how RStudio professional products use their databases.

To update a load-balanced RStudio Workbench cluster to 1.4 or greater, you will need to:

  1. Stop all nodes in the cluster.
  2. Create the Postgres database.
  3. Edit the config files for all nodes to use the Postgres database.
  4. Install the new version of RStudio Workbench.
  5. Start the RStudio Workbench nodes.

Detailed instructions can be found in the RStudio Workbench Professional Edition - Admin guide.

VS Code#

RStudio Workbench 1.4 allows users to launch VS Code sessions in addition to RStudio, JupyterLab, and Jupyter Notebook sessions. This configuration must be enabled by an administrator.

Enabling VS Code sessions is a two step process:

  1. Install the VS Code executable.
  2. Configure RStudio Workbench to use the VS Code executable.

If RStudio Workbench is configured to run sessions locally, there is an installation utility provided by RStudio Workbench for a one-line install, including R and Python extensions.

If RStudio Workbench is configured to use an external resource manager like Kubernetes or Slurm, the VS Code executable must be installed into the relevant containers or Slurm nodes. The r-session-complete container provided by RStudio already has the VS Code executable installed.

For more details on installing and configuring VS Code in RStudio Workbench, please see the RStudio Workbench Professional Edition Admin guide.

Default Data Directory#

RStudio Workbench maintains state in the user’s home directory. In versions of RStudio below 1.4, this state was maintained in the ~/.rstudio folder, with no ability to alter the location. Starting in RStudio Workbench 1.4, this data now lives in the ~/.local/share/rstudio directory, and this location is configurable.

For most RStudio Workbench installations, the migration will be automatic and invisible to users and administrators. We strongly recommend that the data in this folder be used by the IDE alone and not manually accessed. In some cases, RStudio Workbench administrators may wish to move this data to a larger mounted drive if space is limited on the drive with user home directories.

If you wish to return the data to its prior location, or to move the data location somewhere else, the XDG_DATA_HOME or RSTUDIO_DATA_HOME environment variables in the service definition can be configured.

See the RStudio Workbench Professional Edition - Admin guide for more details on configuring this setting.

Project Sharing#

Prior to RStudio Workbench version 1.4, using the Launcher feature to enable an external resource manager and/or JupyterLab, Jupyter Notebook, and VS Code sessions required disabling the real-time project sharing feature of RStudio sessions. This is no longer the case as of version 1.4!

Please see the RStudio Workbench Professional Edition - Admin guide for details on enabling project sharing.


  1. We have renamed RStudio Server Pro to RStudio Workbench. This change reflects the product’s growing support for a wide range of different development environments. RStudio Workbench enables R and Python data scientists to use their preferred IDE in a secure, scalable, and collaborative environment -- whether that is the RStudio IDE, JupyterLab, Jupyter Notebooks, or VS Code. We want RStudio Workbench to be the best single platform to support open source, code-first data science, whether your team is using R or Python. Please see our official Announcement and review our FAQ regarding the name change from RStudio Server Pro to RStudio Workbench.