Implicit garbage collection could have been added in, but it just didn't make the cut. Probably due to not just implementation complications, but also due to people not being able to come to a general consensus fast enough.
A quote from Bjarne Stroustrup himself:
I had hoped that a garbage collector
which could be optionally enabled
would be part of C++0x, but there were
enough technical problems that I have
to make do with just a detailed
specification of how such a collector
integrates with the rest of the
language, if provided. As is the case
with essentially all C++0x features,
an experimental implementation exists.
There is a good discussion of the topic here.
General overview:
C++ is very powerful and allows you to do almost anything. For this reason it doesn't automatically push many things onto you that might impact performance. Garbage collection can be easily implemented with smart pointers (objects that wrap pointers with a reference count, which auto delete themselves when the reference count reaches 0).
C++ was built with competitors in mind that did not have garbage collection. Efficiency was the main concern that C++ had to fend off criticism from in comparison to C and others.
There are 2 types of garbage collection...
Explicit garbage collection:
C++0x will have garbage collection via pointers created with shared_ptr
If you want it you can use it, if you don't want it you aren't forced into using it.
You can currently use boost:shared_ptr as well if you don't want to wait for C++0x.
Implicit garbage collection:
It does not have transparent garbage collection though. It will be a focus point for future C++ specs though.
Why Tr1 doesn't have implicit garbage collection?
There are a lot of things that tr1 of C++0x should have had, Bjarne Stroustrup in previous interviews stated that tr1 didn't have as much as he would have liked.
I'm going to order this guide by the level of skill you have in Haskell, going from an absolute beginner right up to an expert. Note that this process will take many months (years?), so it is rather long.
Absolute Beginner
Firstly, Haskell is capable of anything, with enough skill. It is very fast (behind only C and C++ in my experience), and can be used for anything from simulations to servers, guis and web applications.
However there are some problems that are easier to write for a beginner in Haskell than others. Mathematical problems and list process programs are good candidates for this, as they only require the most basic of Haskell knowledge to be able to write.
Some good guides to learning the very basics of Haskell are the Happy Learn Haskell Tutorial and the first 6 chapters of Learn You a Haskell for Great Good (or its JupyterLab adaptation). While reading these, it is a very good idea to also be solving simple problems with what you know.
Another two good resources are Haskell Programming from first principles, and Programming in Haskell. They both come with exercises for each chapter, so you have small simple problems matching what you learned on the last few pages.
A good list of problems to try is the haskell 99 problems page. These start off very basic, and get more difficult as you go on. It is very good practice doing a lot of those, as they let you practice your skills in recursion and higher order functions. I would recommend skipping any problems that require randomness as that is a bit more difficult in Haskell. Check this SO question in case you want to test your solutions with QuickCheck (see Intermediate below).
Once you have done a few of those, you could move on to doing a few of the Project Euler problems. These are sorted by how many people have completed them, which is a fairly good indication of difficulty. These test your logic and Haskell more than the previous problems, but you should still be able to do the first few. A big advantage Haskell has with these problems is Integers aren't limited in size. To complete some of these problems, it will be useful to have read chapters 7 and 8 of learn you a Haskell as well.
Beginner
After that you should have a fairly good handle on recursion and higher order functions, so it would be a good time to start doing some more real world problems. A very good place to start is Real World Haskell (online book, you can also purchase a hard copy). I found the first few chapters introduced too much too quickly for someone who has never done functional programming/used recursion before. However with the practice you would have had from doing the previous problems you should find it perfectly understandable.
Working through the problems in the book is a great way of learning how to manage abstractions and building reusable components in Haskell. This is vital for people used to object-orientated (oo) programming, as the normal oo abstraction methods (oo classes) don't appear in Haskell (Haskell has type classes, but they are very different to oo classes, more like oo interfaces). I don't think it is a good idea to skip chapters, as each introduces a lot new ideas that are used in later chapters.
After a while you will get to chapter 14, the dreaded monads chapter (dum dum dummmm). Almost everyone who learns Haskell has trouble understanding monads, due to how abstract the concept is. I can't think of any concept in another language that is as abstract as monads are in functional programming. Monads allows many ideas (such as IO operations, computations that might fail, parsing,...) to be unified under one idea. So don't feel discouraged if after reading the monads chapter you don't really understand them. I found it useful to read many different explanations of monads; each one gives a new perspective on the problem. Here is a very good list of monad tutorials. I highly recommend the All About Monads, but the others are also good.
Also, it takes a while for the concepts to truly sink in. This comes through use, but also through time. I find that sometimes sleeping on a problem helps more than anything else! Eventually, the idea will click, and you will wonder why you struggled to understand a concept that in reality is incredibly simple. It is awesome when this happens, and when it does, you might find Haskell to be your favorite imperative programming language :)
To make sure that you are understanding Haskell type system perfectly, you should try to solve 20 intermediate haskell exercises. Those exercises using fun names of functions like "furry" and "banana" and helps you to have a good understanding of some basic functional programming concepts if you don't have them already. Nice way to spend your evening with a bunch of papers covered with arrows, unicorns, sausages and furry bananas.
Intermediate
Once you understand Monads, I think you have made the transition from a beginner Haskell programmer to an intermediate haskeller. So where to go from here? The first thing I would recommend (if you haven't already learnt them from learning monads) is the various types of monads, such as Reader, Writer and State. Again, Real world Haskell and All about monads gives great coverage of this. To complete your monad training learning about monad transformers is a must. These let you combine different types of Monads (such as a Reader and State monad) into one. This may seem useless to begin with, but after using them for a while you will wonder how you lived without them.
Now you can finish the real world Haskell book if you want. Skipping chapters now doesn't really matter, as long as you have monads down pat. Just choose what you are interested in.
With the knowledge you would have now, you should be able to use most of the packages on cabal (well the documented ones at least...), as well as most of the libraries that come with Haskell. A list of interesting libraries to try would be:
Parsec: for parsing programs and text. Much better than using regexps. Excellent documentation, also has a real world Haskell chapter.
QuickCheck: A very cool testing program. What you do is write a predicate that should always be true (eg length (reverse lst) == length lst
). You then pass the predicate the QuickCheck, and it will generate a lot of random values (in this case lists) and test that the predicate is true for all results. See also the online manual.
HUnit: Unit testing in Haskell.
gtk2hs: The most popular gui framework for Haskell, lets you write gtk applications.
happstack: A web development framework for Haskell. Doesn't use databases, instead a data type store. Pretty good docs (other popular frameworks would be snap and yesod).
Also, there are many concepts (like the Monad concept) that you should eventually learn. This will be easier than learning Monads the first time, as your brain will be used to dealing with the level of abstraction involved. A very good overview for learning about these high level concepts and how they fit together is the Typeclassopedia.
Applicative: An interface like Monads, but less powerful. Every Monad is Applicative, but not vice versa. This is useful as there are some types that are Applicative but are not Monads. Also, code written using the Applicative functions is often more composable than writing the equivalent code using the Monad functions. See Functors, Applicative Functors and Monoids from the learn you a haskell guide.
Foldable,Traversable: Typeclasses that abstract many of the operations of lists, so that the same functions can be applied to other container types. See also the haskell wiki explanation.
Monoid: A Monoid is a type that has a zero (or mempty) value, and an operation, notated <>
that joins two Monoids together, such that x <> mempty = mempty <> x = x
and x <> (y <> z) = (x <> y) <> z
. These are called identity and associativity laws. Many types are Monoids, such as numbers, with mempty = 0
and <> = +
. This is useful in many situations.
Arrows: Arrows are a way of representing computations that take an input and return an output. A function is the most basic type of arrow, but there are many other types. The library also has many very useful functions for manipulating arrows - they are very useful even if only used with plain old Haskell functions.
Arrays: the various mutable/immutable arrays in Haskell.
ST Monad: lets you write code with a mutable state that runs very quickly, while still remaining pure outside the monad. See the link for more details.
FRP: Functional Reactive Programming, a new, experimental way of writing code that handles events, triggers, inputs and outputs (such as a gui). I don't know much about this though. Paul Hudak's talk about yampa is a good start.
There are a lot of new language features you should have a look at. I'll just list them, you can find lots of info about them from google, the haskell wikibook, the haskellwiki.org site and ghc documentation.
- Multiparameter type classes/functional dependencies
- Type families
- Existentially quantified types
- Phantom types
- GADTS
- others...
A lot of Haskell is based around category theory, so you may want to look into that. A good starting point is Category Theory for Computer Scientist. If you don't want to buy the book, the author's related article is also excellent.
Finally you will want to learn more about the various Haskell tools. These include:
- ghc (and all its features)
- cabal: the Haskell package system
- darcs: a distributed version control system written in Haskell, very popular for Haskell programs.
- haddock: a Haskell automatic documentation generator
While learning all these new libraries and concepts, it is very useful to be writing a moderate-sized project in Haskell. It can be anything (e.g. a small game, data analyser, website, compiler). Working on this will allow you to apply many of the things you are now learning. You stay at this level for ages (this is where I'm at).
Expert
It will take you years to get to this stage (hello from 2009!), but from here I'm guessing you start writing phd papers, new ghc extensions, and coming up with new abstractions.
Getting Help
Finally, while at any stage of learning, there are multiple places for getting information. These are:
- the #haskell irc channel
- the mailing lists. These are worth signing up for just to read the discussions that take place - some are very interesting.
- other places listed on the haskell.org home page
Conclusion
Well this turned out longer than I expected... Anyway, I think it is a very good idea to become proficient in Haskell. It takes a long time, but that is mainly because you are learning a completely new way of thinking by doing so. It is not like learning Ruby after learning Java, but like learning Java after learning C. Also, I am finding that my object-orientated programming skills have improved as a result of learning Haskell, as I am seeing many new ways of abstracting ideas.
Best Answer
As others have already pointed out, Haskell requires automatic, dynamic memory management: automatic memory management is necessary because manual memory management is unsafe; dynamic memory management is necessary because for some programs, the lifetime of an object can only be determined at runtime.
For example, consider the following program:
In this program, the list
[1..1000]
must be kept in memory until the user types "clear"; so the lifetime of this must be determined dynamically, and this is why dynamic memory management is necessary.So in this sense, automated dynamic memory allocation is necessary, and in practice this means: yes, Haskell requires a garbage collector, since garbage collection is the highest-performance automatic dynamic memory manager.
However...
Although a garbage collector is necessary, we might try to find some special cases where the compiler can use a cheaper memory management scheme than garbage collection. For instance, given
we might hope for the compiler to detect that
x2
can safely be deallocated whenf
returns (rather than waiting for the garbage collector to deallocatex2
). Essentially, we are asking that the compiler perform escape analysis to convert allocations in to garbage-collected heap to allocations on the stack wherever possible.This is not too unreasonable to ask for: the jhc haskell compiler does this, although GHC does not. Simon Marlow says that GHC's generational garbage collector makes escape analysis mostly unnecessary.
jhc actually uses a sophisticated form of escape analysis known as region inference. Consider
In this case, a simplistic escape analysis would conclude that
x2
escapes fromf
(because it is returned in the tuple), and hencex2
must be allocated on the garbage-collected heap. Region inference, on the other hand, is able to detect thatx2
can be deallocated wheng
returns; the idea here is thatx2
should be allocated ing
's region rather thanf
's region.Beyond Haskell
While region inference is helpful in certain cases as discussed above, it appears to be difficult to reconcile effectively with lazy evaluation (see Edward Kmett's and Simon Peyton Jones' comments). For instance, consider
One might be tempted to allocate the list
[1..n]
on the stack and deallocate it afterf
returns, but this would be catastrophic: it would changef
from using O(1) memory (under garbage collection) to O(n) memory.Extensive work was done in the 1990s and early 2000s on region inference for the strict functional language ML. Mads Tofte, Lars Birkedal, Martin Elsman, Niels Hallenberg have written a quite readable retrospective on their work on region inference, much of which they integrated into the MLKit compiler. They experimented with purely region-based memory management (i.e. no garbage collector) as well as hybrid region-based/garbage-collected memory management, and reported that their test programs ran "between 10 times faster and 4 times slower" than pure garbage-collected versions.