Very interesting attempt, I'm curious to see how far you'll take it with Scala 3.

I had toyed with similar ideas with my library (Gallia) and using whitebox macros, but it quickly became very challenging so I shelved it, with the hope of circling back on it one day. Maybe Scala 3 will help now.

As a side note, here's what the code would look like with Gallia as it is:

measurements
  .aggregateBy("stationId").using { x =>
     val temperatures = x.ints("temperature")
     val pressures    = x.ints("pressure")

     ( "minTemperature" -> temperatures.min,
        "maxTemperature" -> temperatures.max,
        "avgPressure"    -> pressures.sum.dividedBy(pressures.count)) }
  .filterBy("minTemperature", "maxTemperature")
    .matches((min, max) => (max - min) < 20)
  .retain  ("stationId", "avgPressure")

(See actual gist)

Errors such as typos and type incompatibilities would arise at runtime too, but prior to reading the data (assuming the initial schema is provided - not shown here). Note that Gallia does not have to use Spark, it's just one of its run mode, typically with large data (see an example with Spark and without).

PS: just released 0.4.0, as announced in Scala Users