It has been quite some time now that RWH came out (almost 3 years). I was eager to get my copy after following the incremental writing of the book online (which is, I think, one of the best ways to write a book.) What a rewarding read in the midst of all the rather academic papers a haskell student usually encounters!
It was a sturdy companion on quite some trips and I refer back to it regularly.
Still, my copy started to look pretty battered and even though most of the content is still valid, there has been an abundance of new topics in the haskell world that would be worth covering in a similar fashion.
Considering the impact RWH had (and still has,) I sincerely hope that there will be a sequel some day :)
Some of the topics for a sequel that would immediately come to my mind:
Iteratees
more on concurrent programming in haskell
merits and dangers of lazy evaluation
possibly covering some common libraries that deal with this
in particular lazy io
new ghc features (e.g. the new I/O Manager, LLVM code generator)
Memoization
..
What are the topics that the haskell community needs a RWH-style explanation for?
this is a summary of the suggestions so far:
Concepts
Iteratees / lazy IO
Arrows
ghc event manager
Techniques
generics (uniplate, syb)
metaprogramming (Template Haskell)
data structures (use of functional datastructures, designing data structures)
EDSLs (designing EDSLs)
memoization
designing with monads
best practices for imperative programming
Tools
ThreadScope
Advanced FFI tools (c2hs, using Haskell from C)
cabal
haddock
hoogle
Tuning the runtime, esp. GC flags
Djinn
Libraries
arrays and array programming (vector, repa, hmatrix)
numerics (random numbers)
parallel programming (The Par monad)
unicode and locales (text, text-icu)
parsing (attoparsec, tagsoup)
networking (snap, yesod)
web stuff (templating)
persistance (especially no-sql storage bindings)
graphics (cairo, sdl, opengl)
xml (haxml)
crypto
processors and systems stuff
Here's my take, biased towards the ecosystem.
Libraries
arrays and array programming:
vector
repa
hmatrix
numerics
random numbers
parallel programming
The Par monad
unicode and locales
text and text-icu
parsing
attoparsec
tagsoup
networking
snap and/or yesod
web stuff
templating
persistance
databases beyond hdbc
no-sql storage bindings
graphics
cairo
sdl
opengl
xml
haxml
crypto
processors and systems stuff
Techniques
generics
uniplate
syb
metaprogramming
Template Haskell
data structures
designing data structures
EDSLs
designing EDSLs
memoization
designing with monads
Tools
ThreadScope
Advanced FFI tools
c2hs
using Haskell from C
Tuning the runtime, esp. GC flags
I would love to see:
Cabal & Hoogle & Haddock (best practices for the daily code - build - test - deploy workflow)
Available datastructures and their (real world) usage, performance and space characteristics
Data Visualization
Best practices for imperative programming
Yesod & Snap
More on Database Connectivity (SQL and NoSQL)
More on Network Programming
The "More on..." might be better placed in a "Haskell Cookbook" though.
These are less "real worldy", but I'd like to see helpful introductions (and possible Real World applications?) to
Djinn
Template Haskell
Arrows
I've been meaning to ask this exact same question! I would buy RWH vol2 if it contained the items in the list so far. I would also like to real world examples for (in no particular order)
GADTs
type families
techniques for heterogeneous lists
Typeclassopedia style presentation of standard typeclasses
a fuller explanation of Edward Yang's Type Technology Tree
records / lenses
I would love to see an "RWH approach" to functional reactive programming - a RWH version of this, maybe covering Yampa or something similar. But maybe this topic is not quite "real-worldy" enough (yet)...
I am only recently new to Haskell and have only read a few chapters of this book and Programming in Haskell by Graham Hutton
However, I would have to agree with Alexander in the sense I would love to see a "Haskell Cookbook" as well as a new more updated version of RWH (As I have yet to finish this is not as important personally for me!).
Advice and sample codes to do with Dates, Generating Random Numbers and the most efficient codes to perform key algorithms (Sorting etc.) would be a great addition to any such book!
Related
I'd like to use Haskell for stochastic simulation, but I don't know how. I've read Hutton's 'Programming in Haskell', and I'm comfortable writing deterministic functional programs. However, I don't know how to start writing stochastic simulations of the sort that are easy in imperative languages like R or python. Is there a tutorial or primer on this that I could read, or can anyone provide some tips on getting started?
There's a nice self-contained paper Erwig and Kollmansberger: Functional Pearls - Probabilistic Functional Programming in Haskell on this topic. I used this as a starting point for writing a natural language processor based on Hidden Markov Models in Haskell. There's a package that is based on this paper, which also seems to provide a basic interface to R plotting.
There's also an entry on the HaskellWiki with more links to hackage. In particular, the ProbabilityMonads package might be useful for you.
http://learnyouahaskell.com/a-fistful-of-monads#the-list-monad
This small section in Learn You a Haskell talks about using the List monad and functor functions to easily deal with non-determinism. May be a little simplistic depending on what your needs are, but make good use of the tools that are already in the standard library.
So I'm starting to get a feel for what sets functional programming apart from imperative programming. So like any good convert I'm looking at things with the Haskell hammer and trying to imagine how my embedded programming work could be shaped as appropriate nails for that tool.
So that got me thinking about this question. Is the embedded environment a special case of general computing in the eyes of functional programming or is it just another form of the general case? Is the challenge all in the IO? My embedded work usually entails about 90 - 95% peripheral IO work and the last little bit of stuff being what algorithm work I can fit onto it and still make it back to my IO in time. Does that sort of work make a functional program unsuited to my needs?
Finally, if there are any projects to embedded Haskell projects you could suggest, that'd be greatly appreciated. Thanks.
There are a number of promising projects for bringing functional programming to the embedded programming world.
It seems like a common approach is to take advantage of the type safety and other correctness features of but to abandon heavyweight runtime like ghc. As a result of abandoning the run time, you give up features like garbage collection. Instead, embedded Haskell projects use embedded DSL languages that output real time C code.
Embedded projects using mix C, C++ and Haskell code, rather than being pure functional projects. The C code produced from the Haskell code is not idiomatic C code so collaborators on the project typically need to be familiar with Haskell syntax to participate.
Galois's Copilot project is one the mode extensively documented embedded Haskell projects.
http://corp.galois.com/blog/2010/9/22/copilot-a-dsl-for-monitoring-embedded-systems.html
Copilot uses the Atom DSL which seems popular
http://hackage.haskell.org/cgi-bin/hackage-scripts/package/atom-0.0.2
There is also a moderately active Google Group
https://groups.google.com/forum/#forum/fp-embedded
Personally I found Haskell.Atom quite lacking. It's not functional programming it's an EDSL in a functional language. You are limited to the constructs of that EDSL. No higher order functions, list comprehensions and all the other things that make functional programming so succinct and enjoyable. It may be fun for exceptionally small projects (like blinking a LED) but to me it seems the code you write (not only the generated C-code) will grow exponentially compared to the functionality it provides.
If you want to go the functional route I suggest reading this paper by Malcolm Wallace. It's a bit dated but at least it describes in quite a detail how to do low-level I/O, IRQ-handling and so on in a pure functional language (Gofer, a Haskell-dialect).
Update: There's also a quite new research project with the goal to make a functional systems programming language based on Haskell, Habit. Unfortunately it seems to exist mostly in theory.
I'm hoping to use either Haskell or OCaml on a new project because R is too slow. I need to be able to use support vectory machines, ideally separating out each execution to run in parallel. I want to use a functional language and I have the feeling that these two are the best so far as performance and elegance are concerned (I like Clojure, but it wasn't as fast in a short test). I am leaning towards OCaml because there appears to be more support for integration with other languages so it could be a better fit in the long run (e.g. OCaml-R).
Does anyone know of a good tutorial for this kind of analysis, or a code example, in either Haskell or OCaml?
Hal Daume has written several major machine learning algorithms during his Ph.D. (now he is an assistant professor and rising star in machine learning community)
On his web page, there are a SVM, a simple decision tree and a logistic regression all in OCaml. By reading these code, you can have a feeling how machine learning models are implemented in OCaml.
Another good example of writing basic machine learning models is Owl library for scientific and numeric computations in OCaml.
I'd also like to mention F#, a new .Net language similar to OCaml. Here's a factor graph model written in F# analyzing Chess play data. This research also has a NIPS publication.
While FP is suitable for implementing machine learning and data mining models. But what you can get here most is NOT performance. It is right that FP supports parallel computing better than imperative languages, like C# or Java. But implementing a parallel SVM, or decision tree, has very little relation to do with the language! Parallel is parallel. The numerical optimizations behind machine learning and data mining are usually imperative, writing them pure-functionally is usually hard and less efficient. Making these sophisticated algorithms parallel is very hard task in the algorithm level, not in the language level. If you want to run 100 SVM in parallel, FP helps here. But I don't see the difficulty running 100 libsvm parallel in C++, not to consider that the single thread libsvm is more efficient than a not-well-tested haskell svm package.
Then what do FP languages, like F#, OCaml, Haskell, give?
Easy to test your code. FP languages usually have a top-level interpreter, you can test your functions on the fly.
Few mutable states. This means that passing the same parameter to a function, this function always gives the same result, thus debugging is easy in FPs.
Code is succinct. Type inference, pattern matching, closures, etc. You focus more on the domain logic, and less on the language part. So when you write the code, your mind is mainly thinking about the programming logic itself.
Writing code in FPs is fun.
The only problem I can see is that OCaml doesn't really support multicore parallelism, while GHC has excellent support and performance. If you're looking to use multiple threads of execution, on multiple calls, GHC Haskell will be a lot easier.
Secondly, the Haskell FFI is more powerful (that is, it does more with less code) than OCaml's, and more libraries are avaliable (via Hackage: http://hackage.haskell.org ) so I don't think foreign interfaces will be a deciding factor.
As far as multi-language integration goes, combining C and Haskell is remarkably easy, and I say this as someone who is (unlike dons) not really much of an expert on either. Any other language that integrates well with C shouldn't be much trickier; you can always fall back to a thin interface layer in C if nothing else. For better or worse, C is still the lingua franca of programming, so Haskell is more than acceptable for most cases.
...but. You say you're motivated by performance issues, and want to use "a functional language". From this I infer you're not previously familiar with the languages you ask about. Among Haskell's defining features are that it, by default, uses non-strict evaluation and immutable data structures--which are both incredibly useful in many ways, but it also means that optimizing Haskell for performance is often dramatically different from other languages, and well-honed instincts may lead you astray in baffling ways. You may want to browse performance-related topics on the Haskell wiki to get a feel for the issues.
Which isn't to say that you can't do what you want in Haskell--you certainly can. Both laziness and immutability can in fact be exploited for performance benefits (Chris Okasaki's thesis provides some nice examples). But be aware that there'll be a bit of a learning curve when it comes to dealing with performance.
Both Haskell and OCaml provide the lovely benefits of using an ML-family language, but for most programmers, OCaml is likely to offer a gentler learning curve and better immediate results.
It's hard to give a definitive answer on this. Haskell has the advantages that Don mentioned along with having a more powerful type system and cleaner syntax. OCaml will be easier to learn if you coming from almost any other language (this is because Haskell is as function as functional languages get), and working with mutable random access structures can be a little clunky in Haskell. You will also likely find the performance characteristics of your OCaml code more intuitive than Haskell because of Haskell's lazy evaluation.
Really, I would recommend you evaluate both if you have the time. Here are some relevant Haskell resources:
http://hackage.haskell.org/package/hslibsvm
http://hackage.haskell.org/package/HSvm
Real World Haskell: this is a great freely available book for Haskell
Learn You a Haskell: this tutorial is just plain fun to read
Oh, if you look further into Haskell be sure to sign up for the Haskell Beginners and Haskell Cafe lists. The community is friendly and eager to help out newcomers (is my bias showing?).
If speed is your prime concern then go for C. Haskell is pretty good performance wise but you are never going to get as fast as C. To my knowledge the only functional language that has bettered C in a benchmark is Stalin Scheme but that is very old and nobody really knows how it works.
I've written genetic programming libraries where performance was key and I wrote it in a functional style in C. The functional style allowed me to easily parallelise it using OMP and it scales linearly upto 8 cores within a single process. You certainly can't do that in OCaml although Haskell is improving all the time with regards to concurrency and parallelism.
The downside of using C was that it took me months to finally find all the bugs and stop the core dumps which was extremely challenging because of the concurrency. Haskell would probably have caught 90% of those bugs on the first compilation.
So speed at any cost ? Looking back I'd wish I'd used Haskell as I could stand it to be 2 - 3 times slower if I'd saved over a month in development time.
While dons is correct that multicore parallelism at the thread level is better supported in Haskell, it sounds like you could live with process level parallelism (from your phrase: ideally separating out each execution to run in parallel.) which is supported quite well in OCaml. Keith pointed out that Haskell has a more powerful type system, but it can also be said that OCaml has a more powerful module system than Haskell.
As others have pointed out, OCaml's learning curve will be lower than Haskell's; you'll likely be more productive more quickly in OCaml. That said, learning OCaml is a great stepping-stone towards learning Haskell because many of the underlying concepts are very similar, so you could always migrate to Haskell later and find a lot of things familiar there. And as you pointed out, there is an OCaml-R bridge.
As an examples of Haskell and Ocaml in machine learning see stuff at Hal Daume and Lloyd Allison homepages. IMO it's is much more straightforward to achieve C++-like performance in Ocaml, than in Haskell. Through, as already said, Haskell has much nicer community (packages, tools and support), syntax&features (i.e. FFI, probability monads via typeclasses) and parallel programming support.
Having revamped OCaml-R, I've got a few comments to make on integrating OCaml and R. It might be worthwile to use OCaml to call R code, it works, but is not yet exactly straightforward. So using it to pilot R is worthwile. Integrating R functionality much more thoroughly is still cumbersome as, for example, much remains to be done to export R's type system and data to OCaml in a seamless way (you will have work to do). Moreover, the interaction of R's GC and OCaml's GC is a delicate point: you free n values in O(n^2) time, which isn't nice (to solve this point, you either need a more flexible R API, as far as I understand it, or to implement a GC in the binding itself as a big R array for proper interaction between GCs).
In a nutshell, I'd go for the "pilot R from OCaml" approach.
Contributions on the GC interaction layer and on mapping R datatypes to OCaml are most welcome.
You may want to take a look at this : http://www.haskell.org/pipermail/haskell-cafe/2010-May/077243.html
Late answer but a machine learning library in Haskell is available here : https://github.com/mikeizbicki/HLearn
This library implements various ML algorithms who are designed to have a much faster cross-validation than the usual implementations. It is based on the following paper Algebraic classifiers: a generic approach to fast cross-validation,
online training, and parallel training. The authors claims a 400x speed-up compared to the same task in Weka.
for haskell, consider checking hasktorch (which I managed to use for my AI thesis). for ocaml there seem to be tensorflow bindings.
As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance.
Closed 11 years ago.
Locked. This question and its answers are locked because the question is off-topic but has historical significance. It is not currently accepting new answers or interactions.
I am wondering if I should continue to learn OCaml or switch to F# or Haskell.
Here are the criteria I am most interested in:
Longevity
Which language will last longer? I don't want to learn something that might be abandoned in a couple years by users and developers.
Will Inria, Microsoft, University of Glasgow continue to support their respective compilers for the long run?
Practicality
Articles like this make me afraid to use Haskell. A hash table is the best structure for fast retrieval. Haskell proponents in there suggest using Data.Map which is a binary tree.
I don't like being tied to a bulky .NET framework unless the benefits are large.
I want to be able to develop more than just parsers and math programs.
Well Designed
I like my languages to be consistent.
Please support your opinion with logical arguments and citations from articles. Thank you.
Longevity
Haskell is de facto the dominant language of functional-programming research. Haskell 98 will last for many more years in stable form, and something called Haskell may last 10 to 30 years---although the language will continue to evolve. The community has a major investment in Haskell and even if the main GHC developers are hit by a bus tomorrow (the famous "bus error in Cambridge" problem), there are plenty of others who can step up to the plate. There are also other, less elaborate compilers.
Caml is controlled by a small group at INRIA, the French national laboratory. They also have a significant investment, Others are also invested in Caml, and the code is open source, and the compiler is not too complicated, so that too will be maintained for a long time. I predict Caml will be much more stable than Haskell, as the INRIA folks appear no longer to be using it as a vehicle for exploring new language ideas (or at least they are doing so at a smaller rate than in the past).
Who knows what a company will do? If F# is successful, Microsoft could support it for 20 years. If it is not successful, they could pull the plug in 2012. I can't guess and won't try.
Practicality
A hash table is the best structure for fast retrieval. Haskell proponents in there suggest using Data.Map which is a binary tree.
It depends on what you are searching. When your keys are strings, ternary search trees are often faster than hash tables. When your keys are integers, Okasaki and Gill's binary Patricia trees are competitive with hashing. If you really want to, you can build a hash table in Haskell using the IO monad, but it's rare to need to.
I think there will always be a performance penalty for lazy evaluation. But "practical" is not the same as "as fast as possible". The following are true about performance:
It is easiest to predict the time and space behavior of a Caml program.
F# is in the middle (who really knows what .NET and the JIT will do?).
It is hardest to predict the time and space behavior of Haskell programs.
Haskell has the best profiling tools, and in the long run, this is what yields the best performance.
I want to be able to develop more than just parsers and math programs.
For an idea of the range of what's possible in Haskell, check out the xmonad window manager and the vast array ofpackages at hackage.haskell.org.
I don't like being tied to a bulky .NET framework unless the benefits are large.
I can't comment:
Well Designed
I like my languages to be consistent.
Some points on which to evaluate consistency:
Haskell's concrete syntax is extremely well designed; I'm continually impressed at the good job done by the Haskell committee. OCaml syntax is OK but suffers by comparison. F# started from Caml core syntax and has many similarities.
Haskell and OCaml both have very consistent stories about operator overloading. Haskell has a consistent and powerful mechanism you can extend yourself. OCaml has no overloading of any kind.
OCaml has the simplest type system, especially if you don't write objects and functors (which many Caml programmers don't, although it seems crazy to me not to write functors if you're writing ML). Haskell's type system is ambitious and powerful, but it is continually being improved, which means there is some inconsistency as a result of history. F# essentially uses the .NET type system, plus ML-like Hindley-Milner polymorphism (See question "What is Hindley-Milner".)
OCaml is not quite consistent on whether it thinks variants should be statically typed or dynamically typed, so it provides both ("algebraic data types" and "polymorphic variants"). The resulting language has a lot of expressive power, which is great for experts, but which construct to use is not always obvious to the amateur.
OCaml's order of evaluation is officially undefined, which is a poor design choice in a language with side effects. Worse, the implementations are inconsistent: the bytecoded virtual machine uses one order and the native-code compiler uses the other.
Should you learn F# or Haskell if you know OCaml?
I believe the answer is certainly yes, ideally you should learn all three languages because each one has something to offer but F# is the only one with a significant future so, if you can only feasibly learn one language, learn F# by reading my Visual F# 2010 for Technical Computing book or subscribing to our The F#.NET Journal.
Longevity
Microsoft committed to supporting F# when they released it as part of Visual Studio 2010 in April. So F# is guaranteed a rosy future for at least a few years. With a powerful combination of practically-important features like a high performance native-code REPL, high-level constructs for parallelism built-in to .NET 4 and a production-quality IDE mode, F# is a long way ahead of any other functional programming language in terms of real world applicability now. Frankly, nobody is even working on anything that might be able to compete with F# in the near future. My own open source HLVM project is an attempt to do so but it is far from ready.
In contrast, both OCaml and Haskell are being developed in extremely unproductive directions. This has been killing OCaml for several years now and I expect Haskell to follow suit over the next few years. Most former professional OCaml and Haskell programmers already moved on to F# (e.g. Credit Suisse, Flying Frog Consultancy) and most of the rest will doubtless migrate to more practical alternatives such as Clojure and Scala in the near future.
Specifically, OCaml's QPL license prevents anyone else from fixing its growing number of fundamental design flaws (16Mb string and array limits on 32-bit machines, no shared-memory parallelism, no value types, parametric polymorphism via type erasure, interpreted REPL, cumbersome FFI etc.) because they must distribute derivative works only in the form of patches to the original and the Debian package maintainers refuse to acknowledge an alternative upstream. The new features being added to the language, such as first-class modules in OCaml 3.12, are nowhere near as valuable as multicore capability would have been.
Some projects were started in an attempt to save OCaml but they proved to be too little too late. The parallel GC is practically useless and David Teller quit the batteries included project (although it has been picked up and released in a cut-down form). Consequently, OCaml has gone from being the most popular functional language in 2007 to severe decline today, with caml-list traffic down over 50% since 2007.
Haskell has fewer industrial users than OCaml and, although it does have multicore support, it is still being developed in a very unproductive direction. Haskell is developed almost entirely by two people at Microsoft Research in Cambridge (UK). Despite the fact that purely functional programming is bad for performance by design, they are continuing to try to develop solutions for parallel Haskell aimed at multicores when the massive amounts of unnecessary copying it incurs hits the memory wall and destroys any hope of scalable parallelism on a multicore.
The only major user of Haskell in industry is Galois with around 30 full-time Haskell programmers. I doubt they will let Haskell die completely but that does not mean they will develop it into a more generally-useful language.
Practicality
I wrote the article you cited about hash tables. They are a good data structure. Other people have referred to purely functional alternatives like ternary trees and Patricia trees but these are usually ~10× slower than hash tables in practice. The reason is simply that cache misses dominate performance concerns today and trees incur an extra O(log n) pointer indirections.
My personal preference is for optional laziness and optional purity because both are generally counter productive in the real world (e.g. laziness makes performance and memory consumption wildly unpredictable and purity severely degrades average-case performance and makes interoperability a nightmare). I am one of the only people earning a living entirely from functional programming through my own company. Suffice to say, if I thought Haskell were viable I would have diversified into it years ago but I keep choosing not to because I do not believe it is commercially viable.
You said "I don't like being tied to a bulky .NET framework unless the benefits are large". The benefits are huge. You get a production-quality IDE, a production-quality JIT compiler that performs hugely-effective optimizations like type-specializing generics, production-quality libraries for everything from GUI programming (see Game of Life in 32 lines of F#) to number crunching. But the real benefit of .NET, at least for me, is that you can sell the libraries that you write in F# and earn lots of money. Nobody has ever succeeded selling libraries to OCaml and Haskell programmers (and I am one of the few people to have tried) but F# libraries already sell in significant quantities. So the bulky .NET framework is well worth it if you want to earn a living by writing software.
Well designed
These languages are all well designed but for different purposes. OCaml is specifically designed for writing theorem provers and Haskell is specifically designed for researching Haskell. F# was designed to address all of the most serious practical problems with OCaml and Haskell such as poor interoperability, lack of concurrent garbage collection and lack of mature modern libraries like WPF in order to bring a productive modern language to a large audience.
This wasn't one of your criteria but have you considered job availability? Haskell currently list 144 jobs on indeed, Ocaml list 12 and C# list 26,000. These numbers are not perfect but I bet you that once F# ships it won't be long before it blows past Haskell and Ocaml in the number of job listings.
So far every programming language included in Visual Studios has thousands of job listings for it. Seems to me that if you want the best chance to use a functional programming language as your day job then F# will soon be it.
Longevity
No one can predict the future, but
OCaml and Haskell have been surving well for a number of years, which bodes well for their future
when F# ships with VS2010, MS will have legal obligations to support it for at least 5 years
Practicality
Perf: I don't have enough first-hand experience with Haskell, but based on second-hand and third-hand info, I think OCaml or F# are more pragmatic, in the sense that I think it is unlikely you'll be able to get the same run-time perf in Haskell that you do in OCaml of F#.
Libraries: Easy access to the .Net Framework is a huge benefit of F#. You can view it as being "tied to this bulky thing" if you like, but don't forget that "you have access to a huge bulky library of often incredibly useful stuff". The 'connectivity' to .Net is one of the big selling points for F#. F# is younger and so has fewer third-party libraries, but there is already e.g. FsCheck, FParsec, Fake, and a bunch of others, in addition to the libraries "in the box" on .Net.
Tooling: I don't have enough personal experience to compare, but I think the VS integration with F# is superior to anything you'll find for OCaml/Haskell today (and F# will continue to improve a bit here over the next year).
Change:
F# is still changing as it approaches its first supported release in VS2010, so there are some breaking changes to language/library you may have to endure in the near future.
Well Designed
Haskell is definitely beautiful and consistent. I don't know enough OCaml but my hunch is it is similarly attractive. I think that F# is 'bigger' than either of those, which means more dusty corners and inconsistencies (largely as a result of mediating the impedence mismatch between FP and .Net), but overall F# still feels 'clean' to me, and the inconsistencies that do exist are at least well-reasoned/intentioned.
Overall
In my opinion you will be in 'good shape' knowing any of these three languages well. If you know a big long-term project you want to use it for, one may stand out, but I think many of the skills will be transferable (more easily between F# and OCaml than to/from Haskell, but also more easily among any of these three than with, say, Java).
There's no simple answer to that question, but here are some things to consider:
Haskell and OCaml are both mature languages with strong implementations. Actually, there are multiple good implementations of Haskell, but I don't think that's a major point in its favor for your purpose.
F# is much younger, and who can predict where Microsoft will decide to take it? How you feel about that depends more on how you feel about Microsoft than anything anyone can tell you about programming languages.
OCaml (or ML in general), is a good practical language choice that supports doing cool functional stuff without forcing you to work in a way that might be uncomfortable. You get the full benefit of things like algebraic data types, pattern matching, type inference, and everybody else's favorite stuff. Oh, and objects.
Haskell gives you all that (except objects, pretty much), but also more or less forces you to rethink everything you think you know about programming. This might be a very good thing, if you're looking to learn something new, but it might be more than you want to bite off. I say this as someone who is only maybe halfway along the path to being a productive, happy Haskell programmer.
Both OCaml and Haskell are being used to write lots of different kinds of programs, not just compilers and AI or whatever. Google is your friend.
One last note: OCaml gives you hashtable, but it's hardly sensible to use it in code if you really want to embrace functional programming. Persistent trees (like Data.Map) are really the right solution for Haskell, and have lots of nice properties, which is one of the cool things to learn about when you pick up Haskell.
F# and OCaml are very similar in syntax, though, obviously, F# is better with .NET.
Which one you learn or use should be dependent on which platform you are aiming for.
In VS2010 F# is going to be included, and since it compiles to .NET bytecode, it can be used on a windows OS that supports the .NET version you used for it. This will give you a large area, but there are limits, currently with F# that OCaml don't have, in that F# appears not to take advantage of all the processors on a machine, but, that is probably due to F# still being developed, and this may be a feature that isn't as important yet.
There are other functional languages, such as Erlang that you could look at, but, basically, if you are strong in one FP language then you should be able to pick up another fairly quickly, so, just pick one that you like and try to develop interesting and challenging applications in it.
Eventually language writers will find a way to get OO languages to work well with multi-cores and FP may fall to the wayside again, but, that doesn't appear to be happening anytime soon.
This is not directly related to the OP's question as to whether or not to learn F#, but rather, an example of real world OCaml usage in the financial sector:
http://ocaml.janestreet.com/?q=node/61
Very interesting talk.
In terms of longevity, it's very difficult to judge popularity of languages, but just doing a quick check on here, these are the numbers of questions tagged with the appropriate (functional) language :-
2672 Scala,
1936 Haskell,
1674 F#,
1126 Clojure,
709 Scheme,
332 OCaml
I'd say this was a good indication of which languages people are actively learning at the moment and therefore might be a good indication of which ones might be popular in the next few years.
I know of several functional languages - F#, Lisp and its dialects, R, and more. However, as I've never used any of them (although the three I mentioned are on my "to-learn" list), I was wondering about the pros/cons of the various functional languages out there. Are there significant pros/cons, both in learning the language and in any real-world applications of said language?
Haskell is "extreme" (lazy, pure), has active users, lots of documentation, and makes runnable applications.
SML is "less extreme" (strict, impure), has active users, formal specification, many implementations (SML/NJ, Mlton, Moscow ML, etc.). Implementations vary on how applications are deployed wrt the runtimes.
OCaml is ML with attitude. It has an object orientation, active users, documentation, add ons, and makes runnable applications.
Erlang is concurrent, strict, pure (mostly), and supports distributed apps. It needs a runtime installed separately, so deployment is different from the languages that make runnable binaries.
F# is similar to OCaml with Microsoft backing and .NET libraries.
Scala runs on the JVM and can be used as a functional language with advanced features, or as simply a souped-up Java, or both. The flexibility is cited as a drawback for learning a functional language because it's easy to slip back into imperative Java ways. Of course it is also an advantage if you want to use existing JVM libraries.
I'm not sure if your question is to functional languages in general, or differences between them. For general info on why functional:
http://paulspontifications.blogspot.com/2007/08/no-silver-bullet-and-functional.html
Why Functional Programming Matters
As far as differences between functional languages:
Distinctive traits of the functional languages
The awesome thing about functional languages is that base themselves off of the lambda calculus and other math. This results in being able to use similar algorithms and thoughts across languages more easily.
As far as which one you should learn: Pick one that will have a comfortable environment for you. For example, if you're using .NET and Visual Studio, F# is an excellent fit. (Actually, the VS integration makes F# a strong contender, period.) The book "How to Design Programs" (full text, free, online) with PLT Scheme is also a good choice.
I'm biased, but F# looks to have the biggest "real-world" potential. This is mainly because of the nice IDE/.NET integration, allowing you to fully tap .NET and OO, while keeping a lot of functional power (and extending it in ways too). Scala might be possible contender, but it's more of an OO language that has some functional features; hence Scala won't be as big a productivity gain.
Edit: Just to note JavaScript and Ruby, before someone comments on that :). Ruby is something else you could take a look at if you're doing that type of web dev, as it has a lot of functional concepts in, although not as polished as other languages.
The biggest downside is that once you see the power you can have, you won't be happy using lesser languages. This becomes a problem if you're forced to deal with people who haven't yet understood.
One final note, the only "con" is that "it's so complicated". This isn't actually true -- functional languages are often simpler -- but if you have years of C or whatnot in your brain, it can be a significant hurdle to "get" the functional concept. After it clicks, it should be relatively smooth sailing.
Lisp has a gentle learning curve. You can learn the basics in an hour, though of course it takes longer to learn idioms etc. On the down side, there are many dialects of Lisp, and it's difficult to interact with mainstream environments like Java or .NET.
I would not recommend R unless you need to do statistics. It's a strange language, and not exactly functional. You can do functional programming in R, but most people don't.
If you're familiar with the Microsoft tool stack, F# might be easy to get into. And it has a huge, well-tested library behind it, i.e. the CLR.
You can use a functional programming style in any language, though some make it easier than others. As far as that goes, you might try Python.
ML family (SML/OCaml/F#):
Pros:
Fairly simple
Have effective implementations (on the level with Java/C#)
Easily predictable resource consumption (compared to lazy languages)
Readable syntax
Strong module system
(For F#): large .Net library available
Has mutable variables
Cons:
Sometimes too simple (no typeclasses => problems with overloading)
(Except F#): standard libraries are missing some useful things
Has mutable variables :)
Cannot have infinite data structures (not lazy language)
I haven't mentioned features common to most static-typed functional languages: type inference, parametric polymorphism, higher-order functions, algrebraic data types & pattern matching.
I have learnt Haskell at the university like a pure functional languaje and I can say that's really powerful, but also I couldn't find a practical use.
However, i found this: Haskell in practice . Check it, is amazing.
The characteristics of functional paradigms sometimes are pros, and sometimes cons, depending on the situation / context.
Some of them are:
high level
lambda functions
lazy evaluation
Higher-order functions
recursion
type inference
Cite from wikipedia:
Efficiency issues
Functional programming languages have
been perceived as less efficient in
their use of CPU and memory than
imperative languages such as C and
Pascal.[26] However, for programs that
perform intensive numerical
computations, functional languages
such as OCaml and Clean are similar in
speed to C. For
programs that handle large matrices
and multidimensional databases, array
functional languages (such as J and K)
were designed with speed optimization
in mind.
Purely functional languages have a
reputation for being slower than
imperative languages.
However, immutability of data can, in
many cases, lead to execution
efficiency in allowing the compiler to
make assumptions that are unsafe in an
imperative language, vastly increasing
opportunities for inlining.
Lazy evaluation may also speed up the
program, even asymptotically, whereas
it may slow it down at most by a
constant factor (however, it may
introduce memory leaks when used
improperly).