Explain concatenative languages to me like I'm an 8-year-old - programming-languages

I've read the Wikipedia article on concatenative languages, and I am now more confused than I was when I started. :-)
What is a concatenative language in stupid people terms?

In normal programming languages, you have variables which can be defined freely and you call methods using these variables as arguments. These are simple to understand but somewhat limited. Often, it is hard to reuse an existing method because you simply can't map the existing variables into the parameters the method needs or the method A calls another method B and A would be perfect for you if you could only replace the call to B with a call to C.
Concatenative language use a fixed data structure to save values (usually a stack or a list). There are no variables. This means that many methods and functions have the same "API": They work on something which someone else left on the stack. Plus code itself is thought to be "data", i.e. it is common to write code which can modify itself or which accepts other code as a "parameter" (i.e. as an element on the stack).
These attributes make this languages perfect for chaining existing code to create something new. Reuse is built in. You can write a function which accepts a list and a piece of code and calls the code for each item in the list. This will now work on any kind of data as long it's behaves like a list: results from a database, a row of pixels from an image, characters in a string, etc.
The biggest problem is that you have no hint what's going on. There are only a couple of data types (list, string, number), so everything gets mapped to that. When you get a piece of data, you usually don't care what it is or where it comes from. But that makes it hard to follow data through the code to see what is happening to it.
I believe it takes a certain set of mind to use the languages successfully. They are not for everyone.
[EDIT] Forth has some penetration but not that much. You can find PostScript in any modern laser printer. So they are niche languages.
From a functional level, they are at par with LISP, C-like languages and SQL: All of them are Turing Complete, so you can compute anything. It's just a matter of how much code you have to write. Some things are more simple in LISP, some are more simple in C, some are more simple in query languages. The question which is "better" is futile unless you have a context.

First I'm going to make a rebuttal to Norman Ramsey's assertion that there is no theory.
Theory of Concatenative Languages
A concatenative language is a functional programming language, where the default operation (what happens when two terms are side by side) is function composition instead of function application. It is as simple as that.
So for example in the SKI Combinator Calculus (one of the simplest functional languages) two terms side by side are equivalent to applying the first term to the second term. For example: S K K is equivalent to S(K)(K).
In a concatenative language S K K would be equivalent to S . K . K in Haskell.
So what's the big deal
A pure concatenative language has the interesting property that the order of evaluation of terms does not matter. In a concatenative language (S K) K is the same as S (K K). This does not apply to the SKI Calculus or any other functional programming language based on function application.
One reason this observation is interesting because it reveals opportunities for parallelization in the evaluation of code expressed in terms of function composition instead of application.
Now for the real world
The semantics of stack-based languages which support higher-order functions can be explained using a concatenative calculus. You simply map each term (command/expression/sub-program) to be a function that takes a function as input and returns a function as output. The entire program is effectively a single stack transformation function.
The reality is that things are always distorted in the real world (e.g. FORTH has a global dictionary, PostScript does weird things where the evaluation order matters). Most practical programming languages don't adhere perfectly to a theoretical model.
Final Words
I don't think a typical programmer or 8 year old should ever worry about what a concatenative language is. I also don't find it particularly useful to pigeon-hole programming languages as being type X or type Y.

After reading http://concatenative.org/wiki/view/Concatenative%20language and drawing on what little I remember of fiddling around with Forth as a teenager, I believe that the key thing about concatenative programming has to do with:
viewing data in terms of values on a specific data stack
and functions manipulating stuff in terms of popping/pushing values on the same the data stack
Check out these quotes from the above webpage:
There are two terms that get thrown
around, stack language and
concatenative language. Both define
similar but not equal classes of
languages. For the most part though,
they are identical.
Most languages in widespread use today
are applicative languages: the central
construct in the language is some form
of function call, where a function is
applied to a set of parameters, where
each parameter is itself the result of
a function call, the name of a
variable, or a constant. In stack
languages, a function call is made by
simply writing the name of the
function; the parameters are implicit,
and they have to already be on the
stack when the call is made. The
result of the function call (if any)
is then left on the stack after the
function returns, for the next
function to consume, and so on.
Because functions are invoked simply
by mentioning their name without any
additional syntax, Forth and Factor
refer to functions as "words", because
in the syntax they really are just
words.
This is in contrast to applicative languages that apply their functions directly to specific variables.
Example: adding two numbers.
Applicative language:
int foo(int a, int b)
{
return a + b;
}
var c = 4;
var d = 3;
var g = foo(c,d);
Concatenative language (I made it up, supposed to be similar to Forth... ;) )
push 4
push 3
+
pop
While I don't think concatenative language = stack language, as the authors point out above, it seems similar.

I reckon the main idea is 1. We can create new programs simply by joining other programs together.
Also, 2. Any random chunk of the program is a valid function (or sub-program).
Good old pure RPN Forth has those properties, excluding any random non-RPN syntax.
In the program 1 2 + 3 *, the sub-program + 3 * takes 2 args, and gives 1 result. The sub-program 2 takes 0 args and returns 1 result. Any chunk is a function, and that is nice!
You can create new functions by lumping two or more others together, optionally with a little glue. It will work best if the types match!
These ideas are really good, we value simplicity.
It is not limited to RPN Forth-style serial language, nor imperative or functional programming. The two ideas also work for a graphical language, where program units might be for example functions, procedures, relations, or processes.
In a network of communicating processes, every sub-network can act like a process.
In a graph of mathematical relations, every sub-graph is a valid relation.
These structures are 'concatenative', we can break them apart in any way (draw circles), and join them together in many ways (draw lines).
Well, that's how I see it. I'm sure I've missed many other good ideas from the concatenative camp. While I'm keen on graphical programming, I'm new to this focus on concatenation.

My pragmatic (and subjective) definition for concatenative programming (now, you can avoid read the rest of it):
-> function composition in extreme ways (with Reverse Polish notation (RPN) syntax):
( Forth code )
: fib
dup 2 <= if
drop 1
else
dup 1 - recurse
swap 2 - recurse +
then ;
-> everything is a function, or at least, can be a function:
( Forth code )
: 1 1 ; \ define a function 1 to push the literal number 1 on stack
-> arguments are passed implicitly over functions (ok, it seems to be a definition for tacit-programming), but, this in Forth:
a b c
may be in Lisp:
(c a b)
(c (b a))
(c (b (a)))
so, it's easy to generate ambiguous code...
you can write definitions that push the xt (execution token) on stack and define a small alias for 'execute':
( Forth code )
: <- execute ; \ apply function
so, you'll get:
a b c <- \ Lisp: (c a b)
a b <- c <- \ Lisp: (c (b a))
a <- b <- c <- \ Lisp: (c (b (a)))

To your simple question, here's a subjective and argumentative answer.
I looked at the article and several related web pages. The web pages say themselves that there isn't a real theory, so it's no wonder that people are having a hard time coming up with a precise and understandable definition. I would say that at present, it is not useful to classify languages as "concatenative" or "not concatenative".
To me it looks like a term that gives Manfred von Thun a place to hang his hat but may not be useful for other programmers.
While PostScript and Forth are worth studying, I don't see anything terribly new or interesting in Manfred von Thun's Joy programming language. Indeed, if you read Chris Okasaki's paper on Techniques for Embedding Postfix Languages in Haskell you can try out all this stuff in a setting that, relative to Joy, is totally mainstream.
So my answer is there's no simple explanation because there's no mature theory underlying the idea of a concatenative language. (As Einstein and Feynman said, if you can't explain your idea to a college freshman, you don't really understand it.) I'll go further and say although studying some of these languages, like Forth and PostScript, is an excellent use of time, trying to figure out exactly what people mean when they say "concatenative" is probably a waste of your time.

You can't explain a language, just get one (Factor, preferably) and try some tutorials on it. Tutorials are better than Stack Overflow answers.

Related

What are super combinators and constant applicative forms?

I'm struggling with what Super Combinators are:
A supercombinator is either a constant, or a combinator which contains only supercombinators as subexpressions.
And also with what Constant Applicative Forms are:
Any super combinator which is not a lambda abstraction. This includes truly constant expressions such as 12, ((+) 1 2), [1,2,3] as well as partially applied functions such as ((+) 4). Note that this last example is equivalent under eta abstraction to \ x -> (+) 4 x which is not a CAF.
This is just not making any sense to me! Isn't ((+) 4) just as "truly constant" as 12? CAFs sound like values to my simple mind.
These Haskell wiki pages you reference are old, and I think unfortunately written. Particularly unfortunate is that they mix up CAFs and supercombinators. Supercombinators are interesting but unrelated to GHC. CAFs are still very much a part of GHC, and can be understood without reference to supercombinators.
So let's start with supercombinators. Combinators derive from combinatory logic, and, in the usage here, consist of functions which only apply the values passed in to one another in one or another form -- i.e. they combine their arguments. The most famous set of combinators are S, K, and I, which taken together are Turing-complete. Supercombinators, in this context, are functions built only of values passed in, combinators, and other supercombinators. Hence any supercombinator can be expanded, through substitution, into a plain old combinator.
Some compilers for functional languages (not GHC!) use combinators and supercombinators as intermediate steps in compilation. As with any similar compiler technology, the reason for doing this is to admit optimization analysis that is more easily performed in such a simplified, minimal language. One such core language built on supercombinators is Edwin Brady's epic.
Constant Applicative Forms are something else entirely. They're a bit more subtle, and have a few gotchas. The way to think of them is as an aspect of compiler implementation with no separate semantic meaning but with a potentially profound effect on runtime performance. The following may not be a perfect description of a CAF, but it'll try to convey my intuition of what one is, since I haven't seen a really good description anywhere else for me to crib from. The clean "authoritative" description in the GHC Commentary Wiki reads as follows:
Constant Applicative Forms, or CAFs for short, are top-level values
defined in a program. Essentially, they are objects that are not
allocated dynamically at run-time but, instead, are part of the static
data of the program.
That's a good start. Pure, functional, lazy languages can be thought of in some sense as a graph reduction machine. The first time you demand the value of a node, that forces its evaluation, which in turn can demand the values of subnodes, etc. One a node is evaluated, the resultant value sticks around (although it does not have to stick around -- since this is a pure language we could always keep the subnodes live and recalculate with no semantic effect). A CAF is indeed just a value. But, in the context, a special kind of value -- one which the compiler can determine has a meaning entirely dependent on its subnodes. That is to say:
foo x = ...
where thisIsACaf = [1..10::Int]
thisIsNotACaf = [1..x::Int]
thisIsAlsoNotACaf :: Num a => [a]
thisIsAlsoNotACaf = [1..10] -- oops, polymorphic! the "num" dictionary is implicitly a parameter.
thisCouldBeACaf = const [1..10::Int] x -- requires a sufficiently smart compiler
thisAlsoCouldBeACaf _ = [1..10::Int] -- also requires a sufficiently smart compiler
So why do we care if things are CAFs? Basically because sometimes we really really don't want to recompute something (for example, a memotable!) and so want to make sure it is shared properly. Other times we really do want to recompute something (e.g. a huge boring easy to generate list -- such as the naturals -- which we're just walking over) and not have it stick around in memory forever. A combination of naming things and binding them under lets or writing them inline, etc. typically lets us specify these sorts of things in a natural, intuitive way. Occasionally, however, the compiler is smarter or dumber than we expect, and something we think should only be computed once is always recomputed, or something we don't want to hang on to gets lifted out as a CAF. Then, we need to think things through more carefully. See this discussion to get an idea about some of the trickiness involved: A good way to avoid "sharing"?
[By the way, I don't feel up to it, but anyone that wants to should feel free to take as much of this answer as they want to try and integrate it with the existing Haskell Wiki pages and improve/update them]
Matt is right in that the definition is confusing. It is even contradictory. A CAF is defined as:
Any super combinator which is not a lambda abstraction. This includes
truly constant expressions such as 12, ((+) 1 2), [1,2,3] as
well as partially applied functions such as ((+) 4).
Hence, ((+) 4) is seen as a CAF. But in the very next sentence we're told it is equivalent to something that is not a CAF:
this last example is equivalent under eta abstraction to \ x -> (+) 4 x which is not a CAF.
It would be cleaner to rule out partially applied functions on the ground that they are equivalent to lambda abstractions.

Difference between logic programming and functional programming

I have been reading many articles trying to understand the difference between functional and logic programming, but the only deduction I have been able to make so far is that logic programming defines programs through mathematical expressions. But such a thing is not associated with logic programming.
I would really appreciate some light being shed on the difference between functional and logic programming.
I wouldn't say that logic programming defines programs through mathematical expressions; that sounds more like functional programming. Logic programming uses logic expressions (well, eventually logic is math).
In my opinion, the major difference between functional and logic programming is the "building blocks": functional programming uses functions while logic programming uses predicates. A predicate is not a function; it does not have a return value. Depending on the value of it's arguments it may be true or false; if some values are undefined it will try to find the values that would make the predicate true.
Prolog in particular uses a special form of logic clauses named Horn clauses that belong to first order logic; Hilog uses clauses of higher order logic.
When you write a prolog predicate you are defining a horn clause:
foo :- bar1, bar2, bar3. means that foo is true if bar1, bar2 and bar3 is true.
note that I did not say if and only if; you can have multiple clauses for one predicate:
foo:-
bar1.
foo:-
bar2.
means that foo is true if bar1 is true or if bar2 is true
Some say that logic programming is a superset of functional programming since each function could be expressed as a predicate:
foo(x,y) -> x+y.
could be written as
foo(X, Y, ReturnValue):-
ReturnValue is X+Y.
but I think that such statements are a bit misleading
Another difference between logic and functional is backtracking. In functional programming once you enter the body of the function you cannot fail and move to the next definition. For example you can write
abs(x) ->
if x>0 x else -x
or even use guards:
abs(x) x>0 -> x;
abs(x) x=<0 -> -x.
but you cannot write
abs(x) ->
x>0,
x;
abs(x) ->
-x.
on the other hand, in Prolog you could write
abs(X, R):-
X>0,
R is X.
abs(X, R):-
R is -X.
if then you call abs(-3, R), Prolog would try the first clause, and fail when the execution reaches the -3 > 0 point but you wont get an error; Prolog will try the second clause and return R = 3.
I do not think that it is impossible for a functional language to implement something similar (but I haven't used such a language).
All in all, although both paradigms are considered declarative, they are quite different; so different that comparing them feels like comparing functional and imperative styles. I would suggest to try a bit of logic programming; it should be a mind-boggling experience. However, you should try to understand the philosophy and not simply write programs; Prolog allows you to write in functional or even imperative style (with monstrous results).
In a nutshell:
In functional programming, your program is a set of function definitions. The return value for each function is evaluated as a mathematical expression, possibly making use of passed arguments and other defined functions. For example, you can define a factorial function, which returns a factorial of a given number:
factorial 0 = 1 // a factorial of 0 is 1
factorial n = n * factorial (n - 1) // a factorial of n is n times factorial of n - 1
In logic programming, your program is a set of predicates. Predicates are usually defined as sets of clauses, where each clause can be defined using mathematical expressions, other defined predicates, and propositional calculus. For example, you can define a 'factorial' predicate, which holds whenever second argument is a factorial of first:
factorial(0, 1). // it is true that a factorial of 0 is 1
factorial(X, Y) :- // it is true that a factorial of X is Y, when all following are true:
X1 is X - 1, // there is a X1, equal to X - 1,
factorial(X1, Z), // and it is true that factorial of X1 is Z,
Y is Z * X. // and Y is Z * X
Both styles allow using mathematical expressions in the programs.
First, there are a lot of commonalities between functional and logic programming. That is, a lot of notions developed in one community can also be used in the other. Both paradigms started with rather crude implementations and strive towards purity.
But you want to know the differences.
So I will take Haskell on the one side and Prolog with constraints on the other. Practically all current Prolog systems offer constraints of some sort, like B, Ciao, ECLiPSe, GNU, IF, Scryer, SICStus, SWI, YAP, XSB. For the sake of the argument, I will use a very simple constraint dif/2 meaning inequality, which was present even in the very first Prolog implementation - so I will not use anything more advanced than that.
What functional programming is lacking
The most fundamental difference revolves around the notion of a variable. In functional programming a variable denotes a concrete value. This value must not be entirely defined, but only those parts that are defined can be used in computations. Consider in Haskell:
> let v = iterate (tail) [1..3]
> v
[[1,2,3],[2,3],[3],[],*** Exception: Prelude.tail: empty list
After the 4th element, the value is undefined. Nevertheless, you can use the first 4 elements safely:
> take 4 v
[[1,2,3],[2,3],[3],[]]
Note that the syntax in functional programs is cleverly restricted to avoid that a variable is left undefined.
In logic programming, a variable does not need to refer to a concrete value. So, if we want a list of 3 elements, we might say:
?- length(Xs,3).
Xs = [_A,_B,_C].
In this answer, the elements of the list are variables. All possible instances of these variables are valid solutions. Like Xs = [1,2,3]. Now, lets say that the first element should be different to the remaining elements:
?- length(Xs,3), Xs = [X|Ys], maplist(dif(X), Ys).
Xs = [X,_A,_B], Ys = [_A,_B], dif(X,_B), dif(X,_A).
Later on, we might demand that the elements in Xs are all equal. Before I write it out, I will try it alone:
?- maplist(=(_),Xs).
Xs = []
; Xs = [_A]
; Xs = [_A,_A]
; Xs = [_A,_A,_A]
; Xs = [_A,_A,_A,_A]
; ... .
See that the answers contain always the same variable? Now, I can combine both queries:
?- length(Xs,3), Xs = [X|Ys], maplist(dif(X), Ys), maplist(=(_),Xs).
false.
So what we have shown here is that there is no 3 element list where the first element is different to the other elements and all elements are equal.
This generality has permitted to develop several constraint languages which are offered as libraries to Prolog systems, the most prominent are CLPFD and CHR.
There is no straight forward way to get similar functionality in functional programming. You can emulate things, but the emulation isn't quite the same.
What logic programming is lacking
But there are many things that are lacking in logic programming that make functional programming so interesting. In particular:
Higher-order programming: Functional programming has here a very long tradition and has developed a rich set of idioms. For Prolog, the first proposals date back to the early 1980s, but it is still not very common. At least ISO Prolog has now the homologue to apply called call/2, call/3 ....
Lambdas: Again, it is possible to extend logic programming in that direction, the most prominent system is Lambda Prolog. More recently, lambdas have been developed also for ISO Prolog.
Type systems: There have been attempts, like Mercury, but it has not caught on that much. And there is no system with functionality comparable to type classes.
Purity: Haskell is entirely pure, a function Integer -> Integer is a function. No fine print lurking around. And still you can perform side effects. Comparable approaches are very slowly evolving.
There are many areas where functional and logic programming more or less overlap. For example backtracking and lazyness and list comprehensions, lazy evaluation and freeze/2, when/2, block. DCGs and monads. I will leave discussing these issues to others...
Logic programming and functional programming use different "metaphors" for computation. This often affects how you think about producing a solution, and sometimes means that different algorithms come naturally to a functional programmer than a logic programmer.
Both are based on mathematical foundations that provide more benefits for "pure" code; code that doesn't operate with side effects. There are languages for both paradigms that enforce purity, as well as languages that allow unconstrained side effects, but culturally the programmers for such languages tend to still value purity.
I'm going to consider append, a fairly basic operation in both logical and functional programming, for appending a list on to the end of another list.
In functional programming, we might consider append to be something like this:
append [] ys = ys
append (x:xs) ys = x : append xs ys
While in logic programming, we might consider append to be something like this:
append([], Ys, Ys).
append([X|Xs], Ys, [X|Zs]) :- append(Xs, Ys, Zs).
These implement the same algorithm, and even work basically the same way, but they "mean" something very different.
The functional append defines the list that results from appending ys onto the end of xs. We think of append as a function from two lists to another list, and the runtime system is designed to calculate the result of the function when we invoke it on two lists.
The logical append defines a relationship between three lists, which is true if the third list is the elements of the first list followed by the elements of the second list. We think of append as a predicate that is either true or false for any 3 given lists, and the runtime system is designed to find values that will make this predicate true when we invoke it with some arguments bound to specific lists and some left unbound.
The thing that makes logical append different is you can use it to compute the list that results from appending one list onto another, but you can also use it to compute the list you'd need to append onto the end of another to get a third list (or whether no such list exists), or to compute the list to which you need to append another to get a third list, or to give you two possible lists that can be appended together to get a given third (and to explore all possible ways of doing this).
While equivalent in that you can do anything you can do in one in the other, they lead to different ways of thinking about your programming task. To implement something in functional programming, you think about how to produce your result from the results of other function calls (which you may also have to implement). To implement something in logic programming, you think about what relationships between your arguments (some of which are input and some of which are output, and not necessarily the same ones from call to call) will imply the desired relationship.
Prolog, being a logical language, gives you free backtracking, it's pretty noticeable.
To elaborate, and I precise that I'm in no way expert in any of the paradigms, it looks to me like logical programming is way better when it comes to solving things. Because that's precisely what the language does (that appears clearly when backtracking is needed for example).
I think the difference is this:
imperative programming=modelling actions
function programming=modelling reasoning
logic programming =modelling knowledge
choose what fits your mind best
functional programming:
when 6PM, light on.
logic programming:
when dark, light on.

Language features helpful for writing quines (self-printing programs)?

OK, for those who have never encountered the term, a quine is a "self-replicating" computer program. To be more specific, one which - upon execution - produces a copy of its own source code as its only output.
The quines can, of course, be developed in many programming languages (but not all); but some languages are obviously more suited to producing quines than others (to clearly understand the somewhat subjective-sounding "more suited", look at a Haskell example vs. C example in the Wiki page - and I provide my more-objective definition below).
The question I have is, from programming language perspective, what language features (either theoretical design ones or syntax sugar) make the language more suitable/helpful for writing quines?
My definition of "more suitable" is "quines are easier to write" and "are shorter/more readable/less obfuscated". But you're welcome to add more criteria that are at least somewhat objective.
Please note that this question explicitly excludes degenerate cases, like a language which is designed to contain "print_a_quine" primitive.
I am not entirely sure, so correct me if anyone of you knows better.
I agree with both other answers, going further by explaining, that a quine is this:
Y g
where Y is a Y fixed-point combinator (or any other fixed-point combinator), which means in lambda calculus:
Y g = g(Y g)
now, it is quite apparent, that we need the code to be data and g be a function which will print its arguments.
So to summarize we need for constructing such a quines functions, printing function, fixed-point combinator and call-by-name evaluation strategy.
The smallest language that satisfies this conditions is AFAIK Zot from the Iota and Jot family.
Languages like the Io Programming Language and others allow the treating of code as data. In tree walking systems, this typically allows the language implementer to expose the abstract syntax tree as a first class citizen. In the case of Io, this is what it does. Being object oriented, the AST is modelled around Message objects, and a special sentinel is created to represent the currently executing message; this sentinel is called thisMessage. thisMessage is a full Message like any other, and responds to the print message, which prints it to the screen. As a result, the shortest quine I've ever been able to produce in any language, has come from Io and looks like this:
thisMessage print
Anyway, I just couldn't help but sharing this with you on this subject. The above certainly makes writing quines easy, but not doing it this way certainly doesn't preclude easily creating a quine.
I'm not sure if this is useful answer from a practical point of view, but there is some useful theory in computability theory. In particular fixed points and Kleene's recursion theorem can be used for writing quines. Apparently, the theory can be used for writing quine in LISP (as the wikipedia page shows).

Does functional programming mandate new naming conventions?

I recently started studying functional programming using Haskell and came upon this article on the official Haskell wiki: How to read Haskell.
The article claims that short variable names such as x, xs, and f are fitting for Haskell code, because of conciseness and abstraction. In essence, it claims that functional programming is such a distinct paradigm that the naming conventions from other paradigms don't apply.
What are your thoughts on this?
In a functional programming paradigm, people usually construct abstractions not only top-down, but also bottom-up. That means you basically enhance the host language. In this kind of situations I see terse naming as appropriate. The Haskell language is already terse and expressive, so you should be kind of used to it.
However, when trying to model a certain domain, I don't believe succinct names are good, even when the function bodies are small. Domain knowledge should reflect in naming.
Just my opinion.
In response to your comment
I'll take two code snippets from Real World Haskell, both from chapter 3.
In the section named "A more controlled approach", the authors present a function that returns the second element of a list. Their final version is this:
tidySecond :: [a] -> Maybe a
tidySecond (_:x:_) = Just x
tidySecond _ = Nothing
The function is generic enough, due to the type parameter a and the fact we're acting on a built in type, so that we don't really care what the second element actually is. I believe x is enough in this case. Just like in a little mathematical equation.
On the other hand, in the section named "Introducing local variables", they're writing an example function that tries to model a small piece of the banking domain:
lend amount balance = let reserve = 100
newBalance = balance - amount
in if balance < reserve
then Nothing
else Just newBalance
Using short variable name here is certainly not recommended. We actually do care what those amounts represent.
I think if the semantics of the arguments are clear within the context of the code then you can get away with short variable names. I often use these in C# lambdas for the same reason. However if it is ambiguous, you should be more explicit with naming.
map :: (a->b) -> [a] -> [b]
map f [] = []
map f (x:xs) = f x : map f xs
To someone who hasn't had any exposure to Haskell, that might seem like ugly, unmaintainable code. But most Haskell programmers will understand this right away. So it gets the job done.
var list = new int[] { 1, 2, 3, 4, 5 };
int countEven = list.Count(n => n % 2 == 0)
In that case, short variable name seems appropriate.
list.Aggregate(0, (total, value) => total += value);
But in this case it seems more appropriate to name the variables, because it isn't immediately apparent what the Aggregate is doing.
Basically, I believe not to worry too much about convention unless it's absolutely necessary to keep people from screwing up. If you have any choice in the matter, use what makes sense in the context (language, team, block of code) you are working, and will be understandable by someone else reading it hours, weeks or years later. Anything else is just time-wasting OCD.
I think scoping is the #1 reason for this. In imperative languages, dynamic variables, especially global ones need to be named properly, as they're used in several functions. With lexical scoping, it's clear what the symbol is bound to at compile time.
Immutability also contributes to this to some extent- in traditional languages like C/ C++/ Java, a variable can represent different data at different points in time. Therefore, it needs to be given a name to give the programmer an idea of its functionality.
Personally, I feel that features features like first-class functions make symbol names pretty redundant. In traditional languages, it's easier to relate to a symbol; based on its usage, we can tell if it's data or a function.
I'm studying Haskell now, but I don't feel that its naming conventions is so very different. Of course, in Java you're hardly to find a names like xs. But it is easy to find names like x in some mathematical functions, i, j for counters etc. I consider such names to be perfectly appropriate in right context. xs in Haskell is appropriate only generic functions over lists. There's a lot of them in Haskell, so this name is wide-spread. Java doesn't provide easy way to handle such a generic abstractions, that's why names for lists (and lists themselves) are usually much more specific, e.g. lists or users.
I just attended a number of talks on Haskell with lots of code samples. As longs as the code dealt with x, i and f the naming didn't bother me. However, as soon as we got into heavy duty list manipulation and the like I found the three letters or so names to be a lot less readable than I prefer.
To be fair a significant part of the naming followed a set of conventions, so I assume that once you get into the lingo it will be a little easier.
Fortunately, nothing prevents us from using meaningful names, but I don't agree that the language itself somehow makes three letter identifiers meaningful to the majority of people.
When in Rome, do as the Romans do
(Or as they say in my town: "Donde fueres, haz lo que vieres")
Anything that aids readability is a good thing - meaningful names are therefore a good thing in any language.
I use short variable names in many languages but they're reserved for things that aren't important in the overall meaning of the code or where the meaning is clear in the context.
I'd be careful how far I took the advice about Haskell names
My Haskell practice is only of mediocre level, thus, I dare to try to reply only the second, more general part of Your question:
"In essence, it claims that functional programming is such a distinct paradigm that the naming conventions from other paradigms don't apply."
I suspect, the answer is "yes", but my motivation behind this opinion is restricted only on experience in just one single functional language. Still, it may be interesting, because this is an extremely minimalistic one, thus, theoretically very "pure", and underlying a lot of practical functional languages.
I was curios how easy it is to write practical programs on such an "extremely" minimalistic functional programming language like combinatory logic.
Of course, functional programming languages lack mutable variables, but combinatory logic "goes further one step more" and it lacks even formal parameters. It lacks any syntactic sugar, it lacks any predefined datatypes, even booleans or numbers. Everything must be mimicked by combinators, and traced back to the applications of just two basic combinators.
Despite of such extreme minimalism, there are still practical methods for "programming" combinatory logic in a neat and pleasant way. I have written a quine in it in a modular and reusable way, and it would not be nasty even to bootstrap a self-interpreter on it.
For summary, I felt the following features in using this extremely minimalistic functional programming language:
There is a need to invent a lot of auxiliary functions. In Haskell, there is a lot of syntactic sugar (pattern matching, formal parameters). You can write quite complicated functions in few lines. But in combinatory logic, a task that could be expressed in Haskell by a single function, must be replaced with well-chosen auxiliary functions. The burden of replacing Haskell syntactic sugar is taken by cleverly chosen auxiliary functions in combinatory logic. As for replying Your original question: it is worth of inventing meaningful and catchy names for these legions of auxiliary functions, because they can be quite powerful and reusable in many further contexts, sometimes in an unexpected way.
Moreover, a programmer of combinatory logic is not only forced to find catchy names of a bunch of cleverly chosen auxiliary functions, but even more, he is forced to (re)invent whole new theories. For example, for mimicking lists, the programmer is forced to mimick them with their fold functions, basically, he has to (re)invent catamorphisms, deep algebraic and category theory concepts.
I conjecture, several differences can be traced back to the fact that functional languages have a powerful "glue".
In Haskell, meaning is conveyed less with variable names than with types. Being purely functional has the advantage of being able to ask for the type of any expression, regardless of context.
I agree with a lot of the points made here about argument naming but a quick 'find on page' shows that no one has mentioned Tacit programming (aka pointfree / pointless). Whether this is easier to read may be debatable so it's up to you & your team, but definitely worth a thorough consideration.
No named arguments = No argument naming conventions.

Haskell or Standard ML for beginners? [closed]

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I'm going to be teaching a lower-division course in discrete structures. I have selected the text book Discrete Structures, Logic, and Computability in part because it contains examples and concepts that are conducive to implementation with a functional programming language. (I also think it's a good textbook.)
I want an easy-to-understand FP language to illustrate DS concepts and that the students can use. Most students will have had only one or two semesters of programming in Java, at best. After looking at Scheme, Erlang, Haskell, Ocaml, and SML, I've settled on either Haskell or Standard ML. I'm leaning towards Haskell for the reasons outlined below, but I'd like the opinion of those who are active programmers in one or the other.
Both Haskell and SML have pattern matching which makes describing a recursive algorithm a cinch.
Haskell has nice list comprehensions that match nicely with the way such lists are expressed mathematically.
Haskell has lazy evaluation. Great for constructing infinite lists using the list comprehension technique.
SML has a truly interactive interpreter in which functions can be both defined and used. In Haskell, functions must be defined in a separate file and compiled before being used in the interactive shell.
SML gives explicit confirmation of the function argument and return types in a syntax that's easy to understand. For example: val foo = fn : int * int -> int. Haskell's implicit curry syntax is a bit more obtuse, but not totally alien. For example: foo :: Int -> Int -> Int.
Haskell uses arbitrary-precision integers by default. It's an external library in SML/NJ. And SML/NJ truncates output to 70 characters by default.
Haskell's lambda syntax is subtle -- it uses a single backslash. SML is more explicit. Not sure if we'll ever need lambda in this class, though.
Essentially, SML and Haskell are roughly equivalent. I lean toward Haskell because I'm loving the list comprehensions and infinite lists in Haskell. But I'm worried that the extensive number of symbols in Haskell's compact syntax might cause students problems. From what I've gathered reading other posts on SO, Haskell is not recommended for beginners starting out with FP. But we're not going to be building full-fledged applications, just trying out simple algorithms.
What do you think?
Edit: Upon reading some of your great responses, I should clarify some of my bullet points.
In SML, there's no syntactic distinction between defining a function in the interpreter and defining it in an external file. Let's say you want to write the factorial function. In Haskell you can put this definition into a file and load it into GHCi:
fac 0 = 1
fac n = n * fac (n-1)
To me, that's clear, succinct, and matches the mathematical definition in the book. But if you want to write the function in GHCi directly, you have to use a different syntax:
let fac 0 = 1; fac n = n * fac (n-1)
When working with interactive interpreters, from a teaching perspective it's very, very handy when the student can use the same code in both a file and the command line.
By "explicit confirmation of the function," I meant that upon defining the function, SML right away tells you the name of the function, the types of the arguments, and the return type. In Haskell you have to use the :type command and then you get the somewhat confusing curry notation.
One more cool thing about Haskell -- this is a valid function definition:
fac 0 = 1
fac (n+1) = (n+1) * fac n
Again, this matches a definition they might find in the textbook. Can't do that in SML!
Much as I love Haskell, here are the reasons I would prefer SML for a class in discrete math and data structures (and most other beginners' classes):
Time and space costs of Haskell programs can be very hard to predict, even for experts. SML offers much more limited ways to blow the machine.
Syntax for function defintion in an interactive interpreter is identical to syntax used in a file, so you can cut and paste.
Although operator overloading in SML is totally bogus, it is also simple. It's going to be hard to teach a whole class in Haskell without having to get into type classes.
Student can debug using print. (Although, as a commenter points out, it is possible to get almost the same effect in Haskell using Debug.Trace.trace.)
Infinite data structures blow people's minds. For beginners, you're better off having them define a stream type complete with ref cells and thunks, so they know how it works:
datatype 'a thunk_contents = UNEVALUATED of unit -> 'a
| VALUE of 'a
type 'a thunk = 'a thunk_contents ref
val delay : (unit -> 'a) -> 'a thunk
val force : 'a thunk -> 'a
Now it's not magic any more, and you can go from here to streams (infinite lists).
Layout is not as simple as in Python and can be confusing.
There are two places Haskell has an edge:
In core Haskell you can write a function's type signature just before its definition. This is hugely helpful for students and other beginners. There just isn't a nice way to deal with type signatures in SML.
Haskell has better concrete syntax. The Haskell syntax is a major improvement over ML syntax. I have written a short note about when to use parentheses in an ML program; this helps a little.
Finally, there is a sword that cuts both ways:
Haskell code is pure by default, so your students are unlikely to stumble over impure constructs (IO monad, state monad) by accident. But by the same token, they can't print, and if you want to do I/O then at minumum you have to explain do notation, and return is confusing.
On a related topic, here is some advice for your course preparation: don't overlook Purely Functional Data Structures by Chris Okasaki. Even if you don't have your students use it, you will definitely want to have a copy.
We teach Haskell to first years at our university. My feelings about this are a bit mixed. On the one hand teaching Haskell to first years means they don't have to unlearn the imperative style. Haskell can also produce very concise code which people who had some Java before can appreciate.
Some problems I've noticed students often have:
Pattern matching can be a bit difficult, at first. Students initially had some problems seeing how value construction and pattern matching are related. They also had some problems distinguishing between abstractions. Our exercises included writing functions that simplify arithmetic expression and some students had difficulty seeing the difference between the abstract representation (e.g., Const 1) and the meta-language representation (1).
Furthermore, if your students are supposed to write list processing functions themselves, be careful pointing out the difference between the patterns
[]
[x]
(x:xs)
[x:xs]
Depending on how much functional programming you want to teach them on the way, you may just give them a few library functions and let them play around with that.
We didn't teach our students about anonymous functions, we simply told them about where clauses. For some tasks this was a bit verbose, but worked well otherwise. We also didn't tell them about partial applications; this is probably quite easy to explain in Haskell (due to its form of writing types) so it might be worth showing to them.
They quickly discovered list comprehensions and preferred them over higher-order functions like filter, map, zipWith.
I think we missed out a bit on teaching them how to let them guide their thoughts by the types. I'm not quite sure, though, whether this is helpful to beginners or not.
Error messages are usually not very helpful to beginners, they might occasionally need some help with these. I haven't tried it myself, but there's a Haskell compiler specifically targeted at newcomers, mainly by means of better error messages: Helium
For the small programs, things like possible space leaks weren't an issue.
Overall, Haskell is a good teaching language, but there are a few pitfalls. Given that students feel a lot more comfortable with list comprehensions than higher-order functions, this might be the argument you need. I don't know how long your course is or how much programming you want to teach them, but do plan some time for teaching them basic concepts--they will need it.
BTW,
# SML has a truly interactive
interpreter in which functions can be
both defined and used. In Haskell,
functions must be defined in a
separate file and compiled before
being used in the interactive shell.
Is inaccurate. Use GHCi:
Prelude> let f x = x ^ 2
Prelude> f 7
49
Prelude> f 2
4
There are also good resources for Haskell in education on the haskell.org edu. page, with experiences from different teachers. http://haskell.org/haskellwiki/Haskell_in_education
Finally, you'll be able to teach them multicore parallelism just for fun, if you use Haskell :-)
Many universities teach Haskell as a first functional language or even a first programming language, so I don't think this will be a problem.
Having done some of the teaching on one such course, I don't agree that the possible confusions you identify are that likely. The most likely sources of early confusion are parsing errors caused by bad layout, and mysterious messages about type classes when numeric literals are used incorrectly.
I'd also disagree with any suggestion that Haskell is not recommended for beginners starting out with FP. It's certainly the big bang approach in ways that strict languages with mutation aren't, but I think that's a very valid approach.
SML has a truly interactive interpreter in which functions can be both defined and used. In Haskell, functions must be defined in a separate file and compiled before being used in the interactive shell.
While Hugs may have that limitation, GHCi does not:
$ ghci
GHCi, version 6.10.1: http://www.haskell.org/ghc/ :? for help
Loading package ghc-prim ... linking ... done.
Loading package integer ... linking ... done.
Loading package base ... linking ... done.
Prelude> let hello name = "Hello, " ++ name
Prelude> hello "Barry"
"Hello, Barry"
There's many reasons I prefer GHC(i) over Hugs, this is just one of them.
SML gives explicit confirmation of the function argument and return types in a syntax that's easy to understand. For example: val foo = fn : int * int -> int. Haskell's implicit curry syntax is a bit more obtuse, but not totally alien. For example: foo :: Int -> Int -> Int.
SML has what you call "implicit curry" syntax as well.
$ sml
Standard ML of New Jersey v110.69 [built: Fri Mar 13 16:02:47 2009]
- fun add x y = x + y;
val add = fn : int -> int -> int
Essentially, SML and Haskell are roughly equivalent. I lean toward Haskell because I'm loving the list comprehensions and infinite lists in Haskell. But I'm worried that the extensive number of symbols in Haskell's compact syntax might cause students problems. From what I've gathered reading other posts on SO, Haskell is not recommended for beginners starting out with FP. But we're not going to be building full-fledged applications, just trying out simple algorithms.
I like using Haskell much more than SML, but I would still teach SML first.
Seconding nominolo's thoughts, list comprehensions do seem to slow students from getting to some higher-order functions.
If you want laziness and infinite lists, it's instructive to implement it explicitly.
Because SML is eagerly evaluated, the execution model is far easier to comprehend, and "debugging via printf" works a lot better than in Haskell.
SML's type system is also simpler. While your class likely wouldn't use them anyways, Haskell's typeclasses are still an extra bump to get over -- getting them to understand the 'a versus ''a distinction in SML is tough enough.
Most answers were technical, but I think you should consider at least one that is not: Haskell (as OCaml), at this time, has a bigger community using it in a wider range of contexts. There's also a big database of libraries and applications written for profit and fun at Hackage. That may be an important factor in keeping some of your students using the language after your course is finished, and maybe trying other functional languages (like Standard ML) later.
I am amazed you are not considering OCaml and F# given that they address so many of your concerns. Surely decent and helpful development environments are a high priority for learners? SML is way behind and F# is way ahead of all other FPLs in that respect.
Also, both OCaml and F# have list comprehensions.
Haskell. I'm ahead in my algos/theory class in CS because of the stuff I learned from using Haskell. It's such a comprehensive language, and it will teach you a ton of CS, just by using it.
However, SML is much easier to learn. Haskell has features such as lazy evaluation and control structures that make it much more powerful, but with the cost of a steep(ish) learning curve. SML has no such curve.
That said, most of Haskell was unlearning stuff from less scientific/mathematic languages such as Ruby, ObjC, or Python.

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