Malloy

Malloy is an experimental language for describing data relationships and transformations. It is both a semantic modeling language and a querying language that runs queries against a relational database. Malloy currently supports BigQuery and Postgres, as well as querying Parquet and CSV files via DuckDB.

Click here to try Malloy in your browser!

Installing Malloy

The easiest way to try Malloy is with our VS Code Extension, which provides a place to create Malloy models, execute queries, get help, and more. VS Code is a text editor and IDE (integrated development environment) that runs on your desktop or in your browser. A few ways to install the extension:

• I already have VS Code
• I use BigQuery and Google Cloud.
• I have a Github account and want to try Malloy on a .csv or .parquet file in a repository.

To get to know the Malloy language, follow our Quickstart.

Note: The Malloy VSCode Extension tracks a small amount of anonymous usage data. You can opt out in the extension settings. Learn more.

Join the Community

• Join our Malloy Slack Community! Use this community to ask questions, meet other Malloy users, and share ideas with one another.
• Use GitHub issues in this Repo to provide feedback, suggest improvements, report bugs, and start new discussions.

Resources

Documentation:

• Malloy Language - A quick introduction to the language
• eCommerce Example Analysis - a walkthrough of the basics on an ecommerce dataset (BigQuery public dataset)
• Modeling Walkthrough - introduction to modeling via the Iowa liquor sales public data set (BigQuery public dataset)

YouTube - Watch demos / walkthroughs of Malloy

Contributing

If you would like to work on Malloy, take a look at the instructions for developing Malloy.

To report security issues please see our security policy.

Malloy is not an officially supported Google product.

Syntax Example

Here is a simple example of a Malloy query:

run: bigquery.table('malloy-data.faa.flights') -> {
  where: origin ? 'SFO'
  group_by: carrier
  aggregate:
    flight_count is count()
    average_flight_time is flight_time.avg()
}

In SQL this would be expressed:

SELECT
   carrier,
   COUNT(*) as flight_count,
   AVG(flight_time) as average_flight_time
FROM `malloy-data.faa.flights`
WHERE origin = 'SFO'
GROUP BY carrier
ORDER BY flight_count desc         -- malloy automatically orders by the first aggregate

Learn more about the syntax and language features of Malloy in the Quickstart.