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Comment by piemonkey | Hacker News Reader

My first thought is: Neat! Languages with different grammars, syntax, and libraries have different expressivities; my first thought is always compiler implementation in OCaml. I would love, for example, to (effortlessly!) dip into OCaml or Haskell while writing a high-performance C++ program, for example.

I am very intrigued by Lia [0] as an example for how languages can be embedded inside of one another for useful effects. See Will Crichton's great blog post for more [1].

However, I am wondering why Python and PHP were chosen. I can't think of a compelling use for having both languages simultaneously. They are both dynamic scripting languages with similar design objectives.

[0] https://github.com/willcrichton/lia [1] http://notes.willcrichton.net/the-coming-age-of-the-polyglot...

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It's possible in OCaml to use a preprocessor (eg. camlp4) to integrate another language at compile time. I did this with PG'OCaml (combining Postgres's SQL & OCaml). At compile time the SQL statements are sent to the DBMS to be "described" (syntax and type checking), and then we insert the necessary glue code to prepare the SQL and convert OCaml values to and from SQL columns and results. The result is type-safe across the two languages, and free of SQL injections. You literally cannot write non-well-formed or badly typed SQL and have the program still compile.

http://pgocaml.forge.ocamlcore.org/

What happens if somebody does an ALTER TABLE changing a column type? I think it's fair to just not deal with that case, but I'm curious if you did more than that.

Then you'll get a runtime error. However if you recompiled before running the program you'd get a compile time error, so it's probably best to recompile your source after any schema change, although bad stuff won't happen if you don't do that.

From the article:

> Depending on how one chooses to classify programming languages, Python and PHP can appear similar—most obviously, both are dynamically typed. From our perspective, however, there are a number of tricky differences: PHP has multiple global namespaces which can span multiple files, whereas Python uses one global namespace per file (semantic friction); most of PHP’s core data-structures are immutable, whereas many of Python’s are mutable (semantic and performance friction); and PHP’s sole collection data type is a mapping, whereas Python separates the notion of mappings from that of sequences (semantic and performance friction). As this may suggest, this combination of languages presents a number of design and implementation challenges which have no obvious precedent. We show that PyHyp’s solutions to these challenges allow interesting case studies to be implemented.

Why Python and PHP? There are several reasons, but the major ones were: we had to start somewhere; we had an excellent Python interpreter available to us, as well as a fairly decent PHP interpreter; and PHP and Python turn out to be rather different languages with a number of tricky challenges. We certainly look forward to other people composing together even more distinct languages (e.g. we've also done a composition of Python and Prolog http://goo.gl/p1opSl, though, compared to PyHyp, it is rather simplistic).

I'd love to see Python and R, the top two data analysis languages. Obviously this will only appeal to a subset of users, but language interoperability for data work is a hot issue right now.

Yeah I would love to see this too. Some differences:

- R has lazy evaluation semantics (with caveats), and Python is eagerly evaluated. The ggplot library in Python recently posted to HN sheds some light on these issues.

- R has like 2 or 3 class systems; Python has a C++-like class system (without static typing, but gaining it in Python 3)

- They differ in semantics with respect to closures (Python 3 changed things a bit)

- R's built-in types are all vectorized, but that might be a good thing, so you can use Python semantics for scalars and R semantics for vectors/data frames/matrices, etc ?

- Python has decorators, generators, coroutines, etc.

I tend to write my Python and R in a pretty small common subset, but yeah they are in fact quite different.

A crude R/Python composition would be fairly simple (http://goo.gl/p1opSl shows that a strict and lazy language can be crudely put together pretty easily, though you miss good performance and all the programmer-friendly features of PyHyp). Closures/generators are unlikely to be a big deal (PyHyp has good suggestions for both). Although I don't know much about R's class system(s), I expect that we can probably do OK on those. However, I have no idea how R's vectorised types might be handled -- those could be painful to deal with, or they might just fall out of the hat, and I'd have to know more about them in order to make an informed guess. However, this isn't on our roadmap at the moment, as it doesn't fit in with our current funding, unless anyone wants to change our minds!

Wow. If you can combine Python and Prolog, I can't help but feel you can combine anything. Those are pretty different!

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