Re:Types for Tables: A Language Design Benchmark
by sideeffffect on Saturday December 11, 2021 @07:28PM
(#29525739)
> Empirical is the only system I know of that can infer types while staying static.
F#'s Type Providers can do that too.
https://docs.microsoft.com/en-us/dotnet/fsharp/tutorials/typ...
Re:Types for Tables: A Language Design Benchmark
by chrisaycock on Saturday December 11, 2021 @08:10PM
(#29526069)
F# is how I learned about type providers:
http://fsprojects.github.io/FSharp.Data/library/CsvProvider....
But look at that syntax:
CsvProvider<"file.csv">.Load("file.csv")
The file name is listed twice! Conceivably a macro could hide that, but we'd still have the issue that the string has to be written fully. I.e., I can't just call a function to generate the string.F# has Literals that mitigate some of this, but my understanding is that they are pretty limited:
https://github.com/fsharp/fslang-suggestions/issues/539
On the other hand, Empirical can perform automatic compile-time function evaluation on any legal expression, including across variables, user-defined functions, user-defined datatypes, etc.
func filename(n):
let value = String(n + 1)
return "/path/to/" + value + ".csv"
end
load(filename(7))
I could put the filename in a Dataframe embedded in my source, then reference the row/column I want. Empirical's automatic CTFE handles it.
Re:Types for Tables: A Language Design Benchmark
by chusk3 on Saturday December 11, 2021 @11:22PM
(#29527122)
Nitpick here = the `Load` member takes a path to _any_ file of the same schema as the sample, but you can also just use the sample at runtime with the use of `Load()`. So it doesn't have to be as redundant as you've shown here, the flexibility is all about loading other instances of the same data shape.
As for load(), it is a macro whose expansion includes inferring the schema of the table by sampling the first few lines of the file. This is a type provider.
Now contrast that logic against something like Apache Spark. It's written in a statically typed language (Scala), but its table semantics are dynamically typed! That's because Spark holds the schema as a runtime value and addresses column names as strings. Spark's mechanism for inferring type means it can't maintain static typing.
Empirical is the only system I know of that can infer types while staying static.