System Activity pages

System Activity is a LookML model that connects to Looker's underlying application database. System Activity Explores and dashboards that are built upon this model show information about your Looker instance, which includes the following:

  • All Looks and dashboards that are saved on your instance
  • User and historical query information
  • Instance performance statistics

Both the granularity and retention of System Activity data are subject to system constraints. System Activity is designed for collecting high-volume data, and aggregating it can be used to supplement your business logs.

This data can be useful for supplementing monitoring and auditing activities, but is not intended to replace your current compliance strategy.

The text in filters run by users is accessible in System Activity and can be viewed by any user who has permission to view the System Activity model.

Take action: Modify who has view access to the System Activity models. Looker admins have access to System Activity by default. A Looker admin can grant access to System Activity to a non-admin user by giving the user the see_system_activity permission.

System Activity includes the following elements:

System Activity in embedded environments

In an embedded environment, strict restrictions prevent embedded users from rendering underlying system data, even if you grant the see_system_activity permission during single sign-on (SSO) authentication. Directly embedding System Activity dashboards for typical embedded users isn't supported.

To share System Activity insights with embedded users, you must extract the data and serve it from a customer-controlled model. You can use the following alternatives:

  • Looker API scripting: Use a Looker API client SDK, such as the Python SDK, to programmatically run queries against the system__activity model. Schedule a script to fetch daily results and write them directly to an external database.
  • Scheduled deliveries: Create a Look that's based on System Activity and schedule a daily delivery as a CSV or JSON file to an Amazon S3 bucket or SFTP server. Then, use an extract, transform, and load (ETL) pipeline to ingest the data.
  • Custom models and dashboards: Extract the data by using Looker API scripting, scheduled deliveries, or Elite System Activity, and load it into your own database, such as BigQuery. Then, create a standard LookML model on top of that data and embed dashboards that you build from the new model.