Four MLs (and a Python)

I wrote a small command-line text processing program in four different ML-derived languages, to try to get a feel for how they compare in terms of syntax, library, and build-run cycles.

ML is a family of functional programming languages that have grown up during the past 40 years and more, with strong static typing, type inference, and eager evaluation. I tried out Standard ML, OCaml, Yeti, and F#, all compiling and running from a shell prompt on Linux.

The job was to write a utility that:

  • accepts the name of a CSV (comma-separated values) file as a command-line argument
  • reads all the lines from that file, each consisting of the same number of numeric columns
  • sums each column and prints out a single CSV line with the results
  • handles large inputs
  • fails if it finds a non-numeric column value or an inconsistent number of columns across lines (an uncaught exception is acceptable)

A toy exercise, but one that touches on file I/O, library support, string processing and numeric type conversion, error handling, and the build-invocation cycle.

I tested on a random Big Data CSV file that I had to hand; running the wc (word count) utility on it gives the size and a plausible lower bound for our program’s runtime:

$ time wc big-data.csv 
 337024 337024 315322496 big-data.csv

real 0m3.086s
user 0m3.050s
sys 0m0.037s
$

I’ve included timings throughout because I thought a couple of them were interesting, but they don’t tell us much except that none of the languages performed badly (with the slowest taking about 16 seconds on this file).

Finally I wrote the same thing in Python as well for comparison.

Practical disclaimer: If you actually have a CSV file you want to do things like this with, don’t use any of these languages. Do it with R instead, where this exercise takes three lines including file I/O. Or at least use an existing CSV-mangling library.

Here are the programs I ended up with, and my impressions.

Standard ML

Standard ML, or SML, is the oldest and “smallest” of the four and the only one to have a formal standard, fixed since 1997. Its standard library (the Basis library) is a more recent addition.

fun fold_stream f acc stream =
    case TextIO.inputLine stream of
	SOME line => fold_stream f (f (line, acc)) stream
      | NONE => acc
    
fun to_number str =
    case Real.fromString str of
	SOME r => r
      | NONE => raise Fail ("Invalid real: " ^ str)
		      
fun values_of_line line =
    let val fields = String.fields (fn c => c = #",") line in
	map to_number fields
    end

fun add_to [] values = values
  | add_to totals values =
    if length totals = length values then
	ListPair.map (Real.+) (totals, values)
    else raise Fail "Inconsistent-length rows"

fun sum_stream stream =
    fold_stream (fn (line, tot) => add_to tot (values_of_line line)) [] stream
		    
fun sum_and_print_file filename =
    let val stream = TextIO.openIn filename in
	let val result = sum_stream stream in
	    print ((String.concatWith "," (map Real.toString result)) ^ "\n")
	end;
	TextIO.closeIn stream
    end
							     
fun main () =
    case CommandLine.arguments () of [filename] => sum_and_print_file filename
      | _ => raise Fail "Exactly 1 filename must be given"

val () = main ()

(Note that although I haven’t included any type annotations, like all ML variants this is statically typed and the compiler enforces type consistency. There are no runtime type errors.)

This is the first SML program I’ve written since 23 years ago. I enjoyed writing it, even though it’s longer than I’d hoped. The Basis library doesn’t offer a whole lot, but it’s nicely structured and easy to understand. To my eye the syntax is fairly clear. I had some minor problems getting the syntax right first time—I kept itching to add end or semicolons in unnecessary places—but once written, it worked, and my first attempt was fine with very large input files.

I had fun messing around with a few different function compositions before settling on the one above, which takes the view that, since summing up a list is habitually expressed in functional languages as an application of fold, we could start with a function to apply a fold over the sequence of lines in a file.

More abstractly, there’s something delightful about writing a language with a small syntax that was fixed and standardised 18 years ago and that has more than one conforming implementation to choose from. C++ programmers (like me) have spent much of those 18 years worrying about which bits of which sprawling standards are available in which compiler. And let’s not talk about the lifespans of web development frameworks.

To build and run it I used the MLton native-code compiler:

$ time mlton -output sum-sml sum.sml

real 0m2.295s
user 0m2.160s
sys 0m0.103s
$ time ./sum-sml big-data.csv 
150.595368855,68.9467923856,[...]

real 0m16.383s
user 0m16.370s
sys 0m0.027s
$

The executable was a 336K native binary with dependencies on libm, libgmp, and libc. Although the compiler has a good reputation, this was (spoiler alert!) the slowest of these language examples both to build and to run. I also tried the PolyML compiler, with which it took less than a tenth of a second to compile but 26 seconds to run, and Moscow ML, which was also fast to compile but much slower to run.

OCaml

OCaml is a more recent language, from the same root but with a more freewheeling style. It seems to have more library support than SML and, almost certainly, more users. I started taking an interest in it recently because of its use in the Mirage OS unikernel project—but of these examples it’s the one in which I’m least confident in my ability to write idiomatic code.

open Str

let read_line chan =
  try Some (input_line chan)
  with End_of_file -> None

let rec fold_channel f acc chan =
  match read_line chan with
  | Some line -> fold_channel f (f line acc) chan
  | None -> acc

let values_of_line line =
  let fields = Str.split (Str.regexp ",") line in
  List.map float_of_string fields
  
let add_to totals values =
  match totals with
  | [] -> values
  | _  ->
     if List.length totals = List.length values then
       List.map2 (+.) totals values
     else failwith "Inconsistent-length rows"

let sum_channel chan =
  let folder line tot = add_to tot (values_of_line line) in
  fold_channel folder [] chan
	      
let sum_and_print_file filename =
  let chan = open_in filename in
  (let result = sum_channel chan in
   print_string ((String.concat "," (List.map string_of_float result)) ^ "\n");
   close_in chan)

let main () =
  match Sys.argv with
  | [| _; filename |] -> sum_and_print_file filename
  | _ -> failwith "Exactly 1 filename must be given"
    
let () = main ()

I’m in two minds about this code. I don’t much like the way it looks and reads. Syntax-wise there are an awful lot of lets; I prefer the way SML uses fun for top-level function declarations and saves let for scoped bindings. OCaml has a more extensive but scruffier library than SML and although there’s lots of documentation, I didn’t find it all that simple to navigate—as a result I’m not sure I’m using the most suitable tools here. There is probably a shorter simpler way. And my first attempt didn’t work for long files: caught out by the fact that input_line throws an exception at end of file (ugh), I broke tail-recursion optimisation by adding an exception handler.

On the other hand, writing this after the SML and Yeti versions, I found it very easy to write syntax that worked, even when I wasn’t quite clear in my head what the syntax was supposed to look like. (At one point I started to worry that the compiler wasn’t working, because it took no time to run and printed no errors.)

I didn’t spot at first that OCaml ships with separate bytecode and optimising native-code compilers, so my first tests seemed a bit slow. In fact it was very fast indeed:

$ time ocamlopt -o sum-ocaml str.cmxa sum.ml

real 0m0.073s
user 0m0.063s
sys 0m0.003s
$ time ./sum-ocaml big-data.csv 
150.595368855,68.9467923856,[...]

real 0m7.761s
user 0m7.740s
sys 0m0.027s
$

The OCaml native binary was 339K and depended only on libm, libdl, and libc.

Yeti

Yeti is an ML-derived language for the Java virtual machine. I’ve written about it a couple of times before.

valuesOfLine line =
    map number (strSplit "," line);
    
addTo totals row =
    if empty? totals then array row
    elif length totals == length row then array (map2 (+) totals row)
    else failWith "Inconsistent-length rows"
    fi;

rowsOfFile filename =
    readFile filename "UTF-8"
        do handle: map valuesOfLine (handle.lines ()) done;

sumFile filename =
    fold addTo (array []) (rowsOfFile filename);

sumAndPrintFile filename =
    println (strJoin "," (map string (sumFile filename)));

case (list _argv) of
     [filename]: sumAndPrintFile filename;
     _: failWith "Exactly 1 filename must be given";
esac

I love Yeti’s dismissive approach to function and binding declaration syntax—no let or fun keywords at all. Psychologically, this is great when you’re staring at an empty REPL prompt trying to decide where to start: no syntax to forget, the first thing you need to type is whatever it is that you want your function to produce.

The disadvantage of losing let and fun is that Yeti needs semicolons to separate bindings. It also makes for a visually rather irregular source file.

As OCaml is like a pragmatic SML, so Yeti seems like a pragmatic OCaml. It provides some useful tools for a task like this one. Although the language is eagerly evaluated, lazy lists have language support and are interchangeable with standard lists, so the standard library can expose the lines of a text file as a lazy list making a fold over it very straightforward. The default map and map2 functions produce lazy lists.

Unfortunately, this nice feature then bit me on the bottom in my first draft, as the use of a lazy map2 in line 6 blew the stack with large inputs (why? not completely sure yet). The standard library has an eager map as well as a lazy one but lacks an eager map2, so I fixed this by converting the number row to an array (arguably the more natural type for it).

The Yeti compiler runs very quickly and compiles to Java .class files. With a small program like this, I would usually just invoke it and have the compiler build and run it in one go:

$ time yc ./sum.yeti big-data.csv 
150.59536885458684,68.9467923856445,[...]

real 0m14.440s
user 0m26.867s
sys 0m0.423s
$

Those timings are interesting, because this is the only example to use more than one processor—the JVM uses a second thread for garbage collection. So it took more time than the MLton binary, but finished quicker…

F♯

F♯ is an ML-style language developed at Microsoft and subsequently open-sourced, with a substantial level of integration with the .NET platform and libraries.

let addTo totals row =
    match totals with
    | [||] -> row
    | _ ->
       if Array.length totals = Array.length row then
         Array.map2 (+) totals row
       else failwith "Inconsistent-length rows"

let sumOfFields fields =
    let rows = Seq.map (Array.map float) fields in
    Seq.fold addTo [||] rows

let fieldsOfFile filename = 
    seq { use s = System.IO.File.OpenText(filename)
          while not s.EndOfStream do yield s.ReadLine().Split ',' }

let sumAndPrintFile filename =
    let result = fieldsOfFile filename |> sumOfFields in
    printfn "%s" (String.concat "," (Array.map string result))

[<EntryPoint>]
let main argv = 
    match argv with
    | [|filename|] -> (sumAndPrintFile filename; 0)
    | _ -> failwith "Exactly 1 filename must be given"

F♯ also has language support for lazy lists, but with different syntax (they’re called sequences) and providing a Python-style yield keyword to generate them via continuations. The sequence generator here came from one of the example tutorials.

A lot of real F♯ code looks like it’s mostly plugging together .NET calls, and there are a lot of capital letters going around, but the basic functional syntax is almost exactly OCaml. It’s interesting that the fundamental unit of text output seems to be the formatted print (printfn). I gather F♯ programmers are fond of their |> operator, so I threw in one of those.

I’m running Linux so I used the open source edition of the F♯ compiler:

$ time fsharpc -o sum-fs.exe sum.fs
F# Compiler for F# 3.1 (Open Source Edition)
Freely distributed under the Apache 2.0 Open Source License

real 0m2.115s
user 0m2.037s
sys 0m0.063s
$ time ./sum-fs.exe big-data.csv 
150.595368854587,68.9467923856445,[...]

real 0m13.944s
user 0m13.863s
sys 0m0.070s
$

The compiler produced a mere 7680-byte .NET assembly, that of course (like Yeti) requires a substantial managed runtime. Performance seems pretty good.

Python

Python is not an ML-like language; I include it just for comparison.

import sys

def add_to(totals, values):
    n = len(totals)
    if n == 0:
        return values
    elif n == len(values):
        return [totals[i] + values[i] for i in range(n)]
    else:
        raise RuntimeError("Inconsistent-length rows")
        
def add_line_to(totals, line):
    values = [float(s) for s in line.strip().split(',')]
    return add_to(totals, values)

def sum_file(filename):
    f = open(filename, 'r')
    totals = []
    for line in f:
        totals = add_line_to(totals, line)
    f.close()
    return totals

if __name__ == '__main__':
    if len(sys.argv) != 2:
        raise RuntimeError("Exactly 1 filename must be given")
    result = sum_file(sys.argv[1])
    print(','.join([str(v) for v in result]))

Feels odd having to use the return keyword again, after using languages in which one just leaves the result at the end of the function.

This is compact and readable. A big difference from the above languages is invisible—it’s dynamically typed, without any compile-time type checking.

To build and run this, I just invoked Python on it:

$ time python ./sum.py ./big-data.csv 
150.59536885458684,68.9467923856445,[...]

real 0m10.939s
user 0m10.853s
sys 0m0.060s
$

That’s Python 3. Python 2 was about a second faster. I was quite impressed by this result, having expected to suffer from my decision to always return new lists of totals rather than updating the values in them.

Any conclusions?

Well, it was a fun exercise. Although I’ve written more in these languages than appears here, and read quite a bit about all of them, I’m still pretty ignorant about the library possibilities for most of them, as well as about the object support in OCaml and F♯.

I am naively impressed by the OCaml compiler. For language “feel”, it gave me the least favourable first impression but I can imagine it being pleasant to use daily.

F♯ on Linux proved unexpectedly straightforward (and fast). Could be a nice choice for web and server applications.

I have made small web and server applications using Yeti and enjoyed the experience. Being able to integrate with existing Java code is good, though of course doubly so when the available libraries in the language itself are so limited.

Standard ML has a clarity and simplicity I really like, and I’d still love to try to use it for something serious. It’s just, well, nobody else seems to—I bet quite a lot of people have learned the language as undergrads (as I did) but it doesn’t seem to be the popular choice outside it. Hardly anyone uses Yeti either, but the Java interoperability means you aren’t so dependent on other developers.

Practically speaking, for jobs like this, and where you want to run something yourself or give someone the source, there’s not much here to recommend anything other than Python. Of course I do appreciate both compile-time typechecking and (for some problems) a more functional style, which is why I’m writing this at all.

But the fact that compilers for both SML and OCaml can generate compact and quick native binaries is interesting, and Yeti and F♯ are notable for their engagement with other existing frameworks.

If you’ve any thoughts or suggestions, do leave a comment.

A release! Tony v1.0

Just a few days after my last post, I did finally manage to finish packaging the release of Tony v1.0. This followed a two-week blitz of fixing, tidying, arguing, etc., with the instigator of the Tony project, my colleague Matthias Mauch. We’re pretty happy about the results.

Tony is a program for pitch and note transcription of audio, mainly intended for precise and high-quality guided annotation of monophonic recordings—such as of unaccompanied singing. It has a fairly simple user interface, that we have put some care into designing for this specialised task.

Tony v1.0 on Linux

Tony v1.0 on Linux

Matthias (and several co-authors, but primarily he) wrote a conference paper about Tony which can tell you more about the motivation for and design of the program. If you’re interested, find that here.

Tony is free, open-source software and is available pre-compiled for Windows, OS/X, and Linux. See its project page for more.

Unreleased project pile-up

Several of the software projects I’ve been working on at the Centre for Digital Music are in need of a new release.

I ran some queries on the SoundSoftware code site, where much of my code lives, to find

  • projects I’m a member of that have seen some work (in the form of repository commits) more recently than have seen a file release, and
  • projects I’m a member of that haven’t seen a file release at all

These returned 28 and 100 projects respectively.

I feel I’ve been a bit lax.

These of course include things that aren’t releasable yet, never will be, or don’t need to be packaged up into releases for one reason or another, as well as projects I don’t actively participate in or am not responsible for making releases of. I eventually eliminated about half of the first list and 93% of the second one.

These are the ones that remained—things that could usefully be released and which I am generally responsible for releasing. Let’s see how many of these I can tidy up & release during the next few weeks:

Existing stuff that could do with a new release

New stuff that hasn’t been released yet

Some of these projects may still be marked private, in which case the links won’t work yet, but they are all planned for release so that should change eventually.

How to change your friend’s OS/X Yosemite system font to Arial

Apple made a lot of visual changes in release 10.10 (“Yosemite”) of Mac OS/X. One of the most obvious was to change the system font, as used throughout the desktop, from Lucida to Helvetica.

Helvetica.

Helvetica.

A lot of people love Helvetica, so presumably they were happy. Others were less satisfied, leading to a little rush of code and articles to help you switch the system font back to Lucida or to some other font that happens to appeal.

But these people are missing the point. The substitution you want to make is not to your own desktop, but to that of your most type-conscious Mac-using Helveticaphile friend. And it is to switch the system font from Helvetica to Arial.

Delightfully, you can do this without needing admin privileges—no password required, all you need is to get control of their keyboard for a couple of minutes.

Here’s how:

1. On your own machine, provided you are also using a Mac, install Yosemite System Font Patcher and its dependencies following the instructions in its README:

$ git clone https://github.com/dtinth/YosemiteSystemFontPatcher
$ brew install fontforge --with-python

2. Retrieve and patch the Arial font files so they report themselves as system fonts:

$ cd YosemiteSystemFontPatcher
$ cp '/Library/Fonts/Arial.ttf' '/Library/Fonts/Arial Bold.ttf' .
$ bin/patch 'System Font Regular' 'Arial.ttf'
$ bin/patch 'System Font Bold' 'Arial Bold.ttf'

3. The above should have created two new font files, in the current directory, called System Arial.ttf and System Arial Bold.ttf. Make these available somewhere you can easily retrieve them on your friend’s machine (shared in the cloud or whatnot).

4. On your friend’s machine, when you see the opportunity, retrieve the System Arial.ttf and System Arial Bold.ttf files and copy them into the $HOME/Library/Fonts directory. You must be logged in as your friend to do this, as it must be their home directory whose font library you are copying into.

And you’re done. The change will only take effect for apps started after the fonts have been installed, or globally from the next login—if you want to make it happen immediately, I’m afraid you’ll have to log your friend out.

Arial.

Arial.

Now, see how long it takes them to notice. For best effect, engage them in conversation about how clean and elegant Helvetica is, with reference to their own desktop.

More on the 2015 EU VAT changes

A couple of updates since my previous post:

  • HMRC held a Q&A session on Twitter about the 2015 VAT changes. Here’s a transcript. It clarifies a lot. (I like the terse style enforced by Twitter for these answers, but some are also usefully expanded on at the end of the transcript.)
  • The digital delivery company I currently use, SendOwl, posted an excellent explanation of how they understand the rule changes and what they’re going to do to support people affected by them. It’s particularly noteworthy that they are expecting to handle the 10-year customer data retention problem as well as identifying the right VAT rate for the customer’s location. I am provisionally impressed.

What am I going to do for my own little company?

I sell three sorts of things:

  1. Developer licences for software libraries.
  2. Mac apps, both through the Apple app store and as direct sales.
  3. Windows desktop apps. These are listed on the Windows 8 Store, but it only has a link; the sale is direct.

For developer licences, I could avoid the problem by switching (back) to manual fulfilment—emailing the licence instead of completing the transaction online. The HMRC Q&A made clear that this would be enough to avoid a transaction being classified as a digital one. These sales are infrequent enough and of high enough value to make the extra work acceptable.

The Mac apps sold through the Apple app store are unaffected: Apple is the vendor and handles the VAT.

The direct sales of apps are the problem. Those of Windows desktop apps are especially a problem, because at least the Mac ones are also available in the app store. I sell very few Windows desktop apps, far too few to justify the extra work. The rational thing would be to drop them.

I don’t think I’m going to do that though, because this isn’t a very rational business—as much as anything it’s driven by a feeling that I ought to keep in touch with how software is delivered in the real world, with a vague hope that this can make me a better and more pragmatic developer in general. Or less positively, by stubbornness and pique.

So instead I’m going to get the business VAT-registered, for all its pathetically tiny income, and play along.

Update: Moments after posting this, I found this article: Meeting with HMRC and Treasury. It says, among other things, “The details on how to join VAT-MOSS without losing your UK VAT threshold will be out next week. From the initial comments it sounds pretty sensible and workable.” So I may wait-and-see a little longer.

Another update: HMRC published their suggested workaround: it doesn’t look useful. It amounts to registering for VAT anyway, using the VAT MOSS system for EU trade, and filing quarterly UK returns as normal but with zero VAT listed. It is only available if you give your primary business as “digital services” when registering for VAT. This doesn’t seem much of a simplification—you still have to track EU vs non-EU transactions even if it’s only to remove some of them from the “VAT stream”. So, I’ve submitted my VAT registration and will, at least initially, try doing the full whack.