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Looker
Usage

The Usage Panel is a Looker-created dashboard that presents usage information about your Looker instance. Admins can use the data to better understand how their users utilize the application.

The i__looker Model

All of the information in the Usage panel is based on a LookML model called i__looker. Understanding that model can help you build useful, custom reports around the usage of your Looker instance and information about saved Looks and Dashboards. See our Creating Looker Usage and Metadata Reports page to learn more.

Usage Dashboard

The Usage dashboard is accessed from the Admin page of Looker:

You can download or schedule the Usage dashboard just as any other dashboard.

Also, you can drill into metrics and elements like any other dashboard:

Query by Source Tile

The Query by Source tile, which is located at the top of the Usage page, includes information about the number of queries run from different sources within Looker. The possible sources are:

Context Comments for SQL Queries

Looker admins can enable the experimental Context Comments feature in Looker Labs. When the Context Comments feature is enabled, Looker will automatically add a comment to the beginning of outgoing SQL queries. The comments are added to queries from Explores, SQL Runner, the API, and filter suggestions.

Context comments are added in the following format:

-- Query Context '{ "user_id":<user ID>,"history_id":<history ID>,
"instance_slug":"<Looker instance number>","model":"<model name>","explore":"<explore name>"}'

The comments provide the following information:

New in 5.16, SQL comments include the query’s Looker instance ID, model name, and Explore name.

The context comments are added to outgoing SQL commands right before the SQL is sent to the database. This prevents the comments from affecting the caching of Looker queries, but it also means that you cannot see the added comments in most places in Looker (such as the Queries page or i__looker).

Instead, you’ll see these comments in your database logs, which is useful for security and auditing. You might also be able to query the comments in SQL Runner:

This example is for Amazon Redshift. See the documentation for your SQL dialect to determine the command you’d need to run.

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