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!

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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.