Malloy is an experimental language for describing data relationships and transformations. It is both a semantic modeling language and a query language that uses an existing SQL engine to execute queries. Malloy currently can connect to BigQuery, Snowflake, PostgreSQL, MySQL, Trino, or Presto, and natively supports DuckDB. We've built a Visual Studio Code extension to facilitate building Malloy data models, querying and transforming data, and creating simple visualizations and dashboards.
Click here to try Malloy in your browser!
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
.csvor.parquetfile 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 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.
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
If you would like to work on Malloy, take a look at the instructions for developing Malloy.
Here is a simple example of a Malloy query:
run: bigquery.table('malloydata-org.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 aggregateLearn more about the syntax and language features of Malloy in the Quickstart.