smlsharp VS empirical-lang

Another relevant software category is the statistical analysis languages, including SAS, Stata and SPSS.

Old-school SAS included only two data types (floats and character strings), but allowed for SQL and sequential data-steps to live together. Persistence was baked in. The floats could be used to represent dates, datetimes and other formats. I particularly appreciated being able to use macros to define a data-step view to split the follow-up for an individual from a table. Such a view could then be collapsed using SQL. SAS also allows for using variables. More recently, R tools such as dplyr have brought together data-frames and relational operations. However, I miss the sequential coding in SAS, using macros as higher-level tools to define the logic, including corner cases.

For strictly typed records, I have always wanted to spend more time with SML# [0] this allows for record updating, with close ties to SQL -- an under-appreciated version of SML.

[0] https://github.com/smlsharp/smlsharp

I'm interested in languages experimenting with integrating tables/dataframes and the relational model as native parts of the language

With how much of the code I've written that's been around representing entities and doing CRUD on collections of them it seems a bit wild not to have native "Table" types.

There's an HN user who has a neat language called "Empirical" based on this, and there's an active research language called "Flix" that has this as well. APL/K and other array langs feature a similar concept as I understand it.

https://www.empirical-soft.com/

https://flix.dev/

The most used in finance is a few functions in pandas, like merge_asof(). It's pretty industry standard now.

https://pandas.pydata.org/docs/reference/api/pandas.merge_as...

The biggest open-source thing I've ever written is Empirical, a language for time-series analysis.

https://www.empirical-soft.com

I'm also top 1% on Stack Overflow. A lot of companies have found me through there.

https://stackoverflow.com/users/478288/chrisaycock

I started Empirical with the goal of "q like Haskell". The end result went in a radically different direction, but the guiding light has always been to have a statically typed language where tables and queries are a first-class operation.

The source code is publicly available under AGPL with the Commons Clause:

https://github.com/empirical-soft/empirical-lang

Some of the OP's observations are why I wrote Empirical [1].

- I wanted static types that felt dynamic, which meant being able to infer types from a file as long as the file path can be determined at compile time.

- I wanted Dataframes to be a first-class container since the high-level of abstraction leads to great performance when doing analytics.

Therefore, I created a language with statically typed Dataframes. Empirical can infer a CSV's type at compile time, namely because performs compile-time function evaluation automatically. The result is a language that runs easily in a REPL but can be used for long-running scripts without worrying about common programming errors.

[1] https://www.empirical-soft.com

I wanted to hit the runtime repeatedly with as little overhead as possible. So VVM has no type look-up, multiple operands per instruction, and a cache-efficient IR. The sum() operand for the above example is invoked directly in a loop almost as fast as hand-written C++.

VVM has its own assembly language [2]. I have blog post that explains some of the design choices [3].

[1] https://github.com/empirical-soft/empirical-lang/tree/master...

[2] https://github.com/empirical-soft/empirical-lang/tree/master...

[3] https://www.empirical-soft.com/2020/09/03/a-tour-of-the-vect...