We're on the fourth major revision of Malloy, a data language aiming to replace analytical SQL. Making something that is simple, complete and understandable is really hard. 'Experimental' is no longer in our description.
In software we express our ideas through tools. In data, those tools think in rectangles. From spreadsheets to the data warehouses, to do any analytical calculation, you must first go through a rectangle. Forcing data through a rectangle shapes the way we solve problems (for example, dimensional fact tables, OLAP Cubes). But really, most data isn’t rectangular
Curious about the Malloy Language? Want to try it out with no install? Check out our new Malloy Fiddle. Malloy Fiddle runs entirely in the browser using #DuckDB and WASM. Malloy Fiddle queries semantic data models. See results as tables, JSON or SQL
Give it a shot. Malloy writes the query in the twitter thread with a single database read. It computes the query, simultaneously at 4 levels of dimensionalization but only touches the disk once. No joins.
1) Nesting builds nested results (like GraphQL). This is particularly hard to do in SQL but is allows very large complex data sets to be returned in a single query.
You can pre-define calculations with 'measure:' or decalre them explicitly in a query.
> where's the windowing
It's missing with a bunch of other things (like union for example). Its coming of course. The goal is that everything represent able in SQL is represent able in Malloy.
> heck does "order by: 2" mean
Same thing as it does in SQL. We try and have reasonable defaults.
The goal here is to be able to create data models that are re-usable and compose-able and verifiable. Yeah, there is a learning curve as there is with anything powerful. There are many things expressible in Malloy that cannot be easily expressed in SQL.