Haskell programming tips
- 1 Preface
- 2 Be concise
- 3 Use syntactic sugar wisely
- 4 Efficiency and infinity
- 5 Choose types properly
- 6 Miscellaneous
This page shows several examples of how code can be improved. We try to derive general rules from them, though they can not be applied deterministicly and are a matter of taste. We all know that, please don't add "this is disputable" to each item!
Instead, you can now add "this is disputable" on /Discussion and change this page only when some sort of consensus is reached.
Don't reinvent the wheel
The standard libraries are full of useful functions, possibly too full. If you rewrite an existing function, the reader wonders what the difference to the standard function is. But if you use a standard function, the reader may learn something new and useful. If you have problems finding an appropriate list function, try this guide:
Avoid explicit recursion
Explicit recursion is not generally bad, but you should spend some time on trying to find a more declarative implementation using higher order functions.
raise :: Num a => a -> [a] -> [a] raise _  =  raise x (y:ys) = x+y : raise x ys
because it is hard for the reader to find out how much of the list is processed and on which values the elements of the output list depend. Just write
raise x ys = map (x+) ys
raise x = map (x+)
and the reader knows that the complete list is processed and that each output element depends only on the corresponding input element.
If you don't find appropriate functions in the standard library, extract a general function. This helps you and others understanding the program. Haskell is very good at factoring out parts of the code. If you find it very general, put it in a separate module and re-use it. It may appear in the standard libraries later, or you may later find that it is already there.
Decomposing a problem this way has also the advantage that you can debug easier. If the last implementation of
raise does not show the expected behaviour, you can inspect
map (I hope it is correct :-) ) and the invoked instance of
Could this be stated more generally? It seems to me this is a special case of the general principle of separating concerns: iteration over a collection vs operating on elements of a collection should apply. If you can write the loop over a data structure (list, tree, whatever) once and debug it, then you don't need to duplicate that code over and over (at least in haskell), so your code can follow the principle of Wiki:OnceAndOnlyOnce ; Wiki:OnceAndOnlyOnce is a lot harder in languages that don't provide a certain level of functional programming support (i.e. Java requires copy and paste programming, the delegate C# syntax is clumsy but workable - using it is almost Wiki:GoldPlating).
Another example: The function
count counts the number of elements
which fulfill a certain property,
i.e. the elements for which the predicate
I found the following code (but convoluted in a more specific function) in a Haskell program
count :: (a -> Bool) -> [a] -> Int count _  = 0 count p (x:xs) | p x = 1 + count p xs | otherwise = count p xs
which you won't like any longer if you become aware of
count p = length . filter p
Only introduce identifiers you need
Here is some advice that is useful for every language, including scientific prose
Introduce only identifiers you use.
The compiler will check that you if you pass an option like
-Wall for GHC.
In an expression like
[a | i <- [1..m]]
a might be a horrible complex expression it is not easy to see,
a really does not depend on
replicate m a
is certainly better here.
Remember the zero
Don't forget that zero is a natural number. Recursive definitions become more complicated if the recursion anchor is not chosen properly. As an example I have chosen the function
tupel presented in DMV-Mitteilungen 2004/12-3, Jürgen Bokowski: Haskell, ein gutes Werkzeug der Diskreten Mathematik (Haskell, a good tool for discrete mathematics). It is also a good example of how to avoid guards.
tuples :: Int -> [a] -> [[a]] tuples r l | r == 1 = [[el] | el <- l] | length l == r = [l] | otherwise = (map ([head l] ++) (tuples (r-1) (tail l))) ++ tuples r (tail l)
Do you have an idea what it does?
Let's strip the guards and forget about list comprehension.
tuples :: Int -> [a] -> [[a]] tuples 1 l = map (:) l tuples r l = if r == length l then [l] else let t = tail l in map (head l :) (tuples (r-1) t) ++ tuples r t
What about tuples with zero elements? We can add the pattern
tuples 0 _ = []
but then we can also omit the pattern for 1-tuples.
tuples :: Int -> [a] -> [[a]] tuples 0 _ = [] tuples r l = if r == length l then [l] else let t = tail l in map (head l :) (tuples (r-1) t) ++ tuples r t
What about the case
r > length l? Sure, no reason to let
head fail - in that case there is no tuple, thus we return an empty list. Again, this saves us one special case.
tuples :: Int -> [a] -> [[a]] tuples 0 _ = [] tuples r l = if r > length l then  else let t = tail l in map (head l :) (tuples (r-1) t) ++ tuples r t
We have learnt above that
length is evil! What about
tuples :: Int -> [a] -> [[a]] tuples 0 _ = [] tuples _  =  tuples r (x:xs) = map (x :) (tuples (r-1) xs) ++ tuples r xs
? It is no longer necessary to compute the length of
l again and again. The code is easier to read and it covers all special cases, including
tuples (-1) [1,2,3]!
length test can worsen performance dramatically in some cases, like
tuples 24 [1..25]. We could also use
null (drop (r-1) l) instead of
length l < r, which works for infinite lists. See also below.
You can even save one direction of recursion
by explicit computation of the list of all suffixes provided by
You can do this with do notation
tuples :: Int -> [a] -> [[a]] tuples 0 _ = [] tuples r xs = do y:ys <- tails xs map (y:) (tuples (r-1) ys)
(=<<) in the list monad is
concatMap, we can also write this as follows.
Where in the previous version the pattern
y:ys filtered out the last empty suffix
we have to do this manually now with
tuples :: Int -> [a] -> [[a]] tuples 0 _ = [] tuples r xs = concatMap (\(y:ys) -> map (y:) (tuples (r-1) ys)) (init (tails xs))
The list of all suffixes could be generated with
but this ends with a "Prelude.tail: empty list".
tails generates the suffixes in the same order but aborts properly.
More generally, Base cases and identities
Don't overuse lambdas
Like explicit recursion, using explicit lambdas isn't a universally bad idea, but a better solution often exists. For example, Haskell is quite good at currying. Don't write
zipWith (\x y -> f x y) map (\x -> x + 42)
zipWith f map (+42)
also, instead of writing
-- sort a list of strings case insensitively sortBy (\x y -> compare (map toLower x) (map toLower y))
comparing p x y = compare (p x) (p y) sortBy (comparing (map toLower))
which is both clearer and re-usable.
Actually, starting with GHC-6.6 you do not need to define
comparing, since it is already in module
(Just a remark for this special example: We can avoid multiple evaluations of the conversions.
sortKey :: (Ord b) => (a -> b) -> [a] -> [a] sortKey f x = map snd (sortBy (comparing fst) (zip (map f x) x))
As a rule of thumb, once your expression becomes too long to easily be point-freed, it probably deserves a name anyway. Lambdas are occasionally appropriate however, e.g. for control structures in monadic code (in this example, a control-structure "foreach2" which most languages don't even support.):
foreach2 xs ys f = zipWithM_ f xs ys linify :: [String] -> IO () linify lines = foreach2 [1..] lines $ \lineNr line -> do unless (null line) $ putStrLn $ shows lineNr $ showString ": " $ show line
Bool is a regular type
Logic expressions are not restricted to guards and
Avoid verbosity like in
isEven n | mod n 2 == 0 = True | otherwise = False
since it is the same as
isEven n = mod n 2 == 0
Use syntactic sugar wisely
People who employ syntactic sugar extensively argue that their code becomes more readable by it. The following sections show several examples where less syntactic sugar is more readable.
It is argued that a special notation is often more intuitive than a purely functional expression. But the term "intuitive notation" is always a matter of habit. You can also develop an intuition for analytic expressions that don't match your habits at the first glance. So why not making a habit of less sugar sometimes?
List comprehension let you remain in imperative thinking, that is it let you think in variables rather than transformations. Open your mind, discover the flavour of the pointfree style!
[toUpper c | c <- s]
map toUpper s
[toUpper c | s <- strings, c <- s]
where it takes some time for the reader
to find out which value depends on what other value
and it is not so clear how many times
the interim values
c are used.
In contrast to that
map toUpper (concat strings)
can't be clearer.
When using higher order functions you can switch easier to
data structures different from
map (1+) list
mapSet (1+) set
If there would be a standard instance for the
you could use the code
fmap (1+) pool
for both choices.
If you are not used to higher order functions for list processing
you feel like needing parallel list comprehension.
This is unfortunately supported by GHC now,
but somehow superfluous since various flavours of
zip already do a great job.
do notation is useful to express the imperative nature (e.g. a hidden state or an order of execution) of a piece of code.
Nevertheless it's sometimes useful to remember that the
do notation is explained in terms of functions.
do text <- readFile "foo" writeFile "bar" text
one can write
readFile "foo" >>= writeFile "bar"
do text <- readFile "foo" return text
can be simplified to
by a law that each Monad must fulfill.
You certainly also agree that
do text <- readFile "foobar" return (lines text)
is more complicated than
liftM lines (readFile "foobar")
By the way, in the case of
IO monad the
Functor class method
fmap and the
Monad based function
liftM are the same.
Be aware that "more complicated" does not imply "worse". If your do-expression was longer than this, then mixing do-notation and
fmap might be precisely the wrong thing to do, because it adds one more thing to think about. Be natural. Only change it if you gain something by changing it. -- AndrewBromage
Guards look like
-- Bad implementation: fac :: Integer -> Integer fac n | n == 0 = 1 | n /= 0 = n * fac (n-1)
which implements a factorial function. This example, like a lot of uses of guards, has a number of problems.
The first problem is that it's nearly impossible for the compiler to check if guards like this are exhaustive, as the guard conditions may be arbitrarily complex (Ghc will warn you if you use the
-Wall option). To avoid this problem and potential bugs through non exhaustive patterns you should use an
otherwise guard, that will match for all remaining cases:
-- Slightly improved implementation: fac :: Integer -> Integer fac n | n == 0 = 1 | otherwise = n * fac (n-1)
Another reason to prefer this one is its greater readability for humans and optimizability for compilers. Though it may not matter much in a simple case like this, when seeing an
otherwise it's immediately clear that it's used whenever the previous guard fails, which isn't true if the "negation of the previous test" is spelled out. The same applies to the compiler: It probably will be able to optimize an
otherwise (which is a synonym for
True) away but cannot do that for most expressions.
This can be done with even less sugar using
-- Less sugar (though the verbosity of if-then-else can also be considered as sugar :-) fac :: Integer -> Integer fac n = if n == 0 then 1 else n * fac (n-1)
if has its own set of problems, for example in connection with the layout rule or that nested
ifs are difficult to read. See ["Case"] how to avoid nested
But in this special case, the same can be done even more easily with pattern matching:
-- Good implementation: fac :: Integer -> Integer fac 0 = 1 fac n = n * fac (n-1)
Actually, in this case there is an even more easier to read version, which (see above) doesn't use Explicit Recursion:
-- Excellent implementation: fac :: Integer -> Integer fac n = product [1..n]
This may also be more efficient as
product might be optimized by the library-writer... In GHC, when compiling with optimizations turned on, this version runs in O(1) stack-space, whereas the previous versions run in O(n) stack-space.
Note however, that there is a difference between this version and the previous ones: When given a negative number, the previous versions do not terminate (until StackOverflow-time), while the last implemenation returns 1.
Guards don't always make code clearer. Compare
foo xs | not (null xs) = bar (head xs)
foo (x:_) = bar x
or compare the following example using the advanced pattern guards (http://www.haskell.org/ghc/docs/latest/html/users_guide/syntax-extns.html#PATTERN-GUARDS)
parseCmd ln | Left err <- parse cmd "Commands" ln = BadCmd $ unwords $ lines $ show err | Right x <- parse cmd "Commands" ln = x
with this one with no pattern guards:
parseCmd ln = case parse cmd "Commands" ln of Left err -> BadCmd $ unwords $ lines $ show err Right x -> x
or, if you expect your readers to be familiar with the
parseCmd :: -- add an explicit type signature, as this is now a pattern binding parseCmd = either (BadCmd . unwords . lines . show) id . parse cmd "Commands"
By the way, a compiler has also problems with numerical patterns. E.g. the pattern
0 in fact means
fromInteger 0, thus it involves a computation, which is uncommon for function parameter patterns. To illustrate this, consider the following example:
data Foo = Foo deriving (Eq, Show) instance Num Foo where fromInteger = error "forget it" f :: Foo -> Bool f 42 = True f _ = False
*Main> f 42 *** Exception: forget it
Only use guards if you need to, in general you should stick to pattern matching whenever possible.
In order to allow pattern matching against numerical types, Haskell 98 provides so-called n+k patterns, as in
take :: Int -> [a] -> [a] take (n+1) (x:xs) = x: take n xs take _ _ = 
However, they are often critizised for hiding computational complexity and producing ambiguties, see /Discussion for details. They are subsumed by the more general Views proposal, which was unfortunately never implemented despite being around for quite some time now.
Efficiency and infinity
A rule of thumb is: If a function makes sense for an infinite data structure but the implementation at hand fails for an infinite amount of data, then the implementation is probably inefficient also for finite data.
Don't ask for the length of a list when you don't need it
length x == 0
to find out if the list
x is empty.
If you write it, you force Haskell to create all list nodes. It fails on an infinite list although the expression should be evaluated to
False in this case. (Nevertheless the content of the list elements may not be evaluated.)
x == 
is faster but it requires the list
x to be of type
a is a type of class
The best to do is
atLeastfunction that only checks to see that a list is greater than the required minimum length.
atLeast :: Int -> [a] -> Bool atLeast 0 _ = True atLeast _  = False atLeast n (_:ys) = atLeast (n-1) ys
or non-recursive, but less efficient because both
take must count
atLeast :: Int -> [a] -> Bool atLeast n x = n == length (take n x)
or non-recursive but fairly efficient
atLeast :: Int -> [a] -> Bool atLeast n = if n>0 then not . null . drop (n-1) else const True
atLeast :: Int -> [a] -> Bool atLeast 0 = const True atLeast n = not . null . drop (n-1)
The same problem arises if you want to shorten a list to the length of another one by
take (length x) y
since this is inefficient for large lists
x and fails for infinite ones. But this can be useful to extract a finite prefix from an infinite list.
zipWith const y x
It should be noted that
can be replaced by
which allow the usage of Peano numbers.
Don't ask for the minimum when you don't need it
isLowerLimit checks if a number is a lower limit to a sequence.
isLowerLimit :: Ord a => a -> [a] -> Bool isLowerLimit x ys = x <= minimum ys
It fails definitely if
ys is infinite. Is this a problem?
Compare it with
isLowerLimit x = all (x<=)
This definition terminates for infinite lists, if
x is not a lower limit. It aborts immediately if an element is found which is below
x. Thus it is also faster for finite lists. Even more: It works also for empty lists.
If you want a list of lists with increasing length and constant content, don't write
map (flip replicate x) [0..]
because this needs quadratic space and run-time. If you code
iterate (x:) 
then the lists will share their suffixes and thus need only linear space and run-time for creation.
Choose the right fold
See Stack overflow for advice on which fold is appropriate for your situation.
Choose types properly
Lists are not good for everything
Lists are not arrays
Lists are not arrays, so don't treat them as such.
Frequent use of
(!!) should alarm you.
nth list element
requires to traverse through the first
n nodes of the list.
This is very inefficient.
If you access the elements progressively like in
[x !! i - i | i <- [0..n]]
you should try to get rid of indexing like in
zipWith (-) x [0..n]
If you really need random access like in the Fourier Transform
you should switch to
Lists are not sets
If you manage data sets where each object can occur only once
and the order is irrelevant,
if you use list functions like
you should think about switching to sets.
If you need multi-sets,
i.e. data sets with irrelevant order but multiple occurence of an object
you can use a
Data.Map.Map a Int.
Lists are not finite maps
Similarly, lists are not finite maps, as mentioned on efficiency hints.
Reduce type class constraints
Eq type class
When using functions like
nub, and so on you should be aware that they need types of the
Eq class. There are two problems: The routines might not work as expected if a processed list contains multiple equal elements and the element type of the list may not be comparable, like functions.
The following function takes the input list
xs and removes each element of
xs once from
Clear what it does? No? The code is probably more understandable
removeEach :: (Eq a) => [a] -> [[a]] removeEach xs = map (flip List.delete xs) xs
but it should be replaced by
removeEach :: [a] -> [[a]] removeEach xs = zipWith (++) (List.inits xs) (tail (List.tails xs))
since this works perfectly for function types
a and for equal elements in
Int when you don't consider integers
Before using integers for each and everything (C style)
think of more specialised types.
If only the values
1 are of interest,
try the type
If there are more but predefined choices and numeric operations aren't needed try an enumeration.
type Weekday = Int
data Weekday = Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday deriving (Eq, Ord, Enum)
It allows all sensible operations like
forbids all nonsensical ones like
You cannot accidentally mix up weekdays with numbers and
the signature of a function with weekday parameter clearly states what kind of data is expected.
If an enumeration is not appropriate
you can define a
newtype carrying the type that is closest to what you need.
E.g. if you want to associate objects with a unique identifier,
you may want to choose the type
But you don't need arithmetic and you can make this type distinct from real
Ints by defining
newtype Identifier = Identifier Int deriving Eq
Separate IO and data processing
It's not good to use the IO Monad everywhere, much of the data processing can be done without IO interaction. You should separate data processing and IO because pure data processing can be done purely functionally, that is you don't have to specify an order of execution and you don't have to worry about what computations are actually necessary. You can easily benefit from lazy evaluation if you process data purely functionally and output it by a short IO interaction.
-- import Control.Monad (replicateM_) replicateM_ 10 (putStr "foo")
is certainly worse than
putStr (concat $ replicate 10 "foo")
do h <- openFile "foo" WriteMode replicateM_ 10 (hPutStr h "bar") hClose h
can be shortened to
writeFile "foo" (concat $ replicate 10 "bar")
which also ensures proper closing of the handle
in case of failure.
A function which computes a random value
with respect to a custom distribution
distInv is the inverse of the distribution function)
can be defined via IO
randomDist :: (Random a, Num a) => (a -> a) -> IO a randomDist distInv = liftM distInv (randomRIO (0,1))
but there is no need to do so. You don't need the state of the whole world just for remembering the state of a random number generator. What about
randomDist :: (RandomGen g, Random a, Num a) => (a -> a) -> State g a randomDist distInv = liftM distInv (State (randomR (0,1)))
Forget about quot and rem
They complicate handling of negative dividends.
mod are almost always the better choice.
b > 0 then it always holds
a == b * div a b + mod a b mod a b < b mod a b >= 0
The first equation is true also for
but the two others are true only for
mod, but not for
mod a b always wraps
a to an element from
whereas the sign of
rem a b depends on the sign of
This seems to be more an issue of experience rather than one of a superior reason.
You might argue, that the sign of the dividend is more important for you, than that of the divisor.
However, I have never seen such an application,
but many uses of
mod were clearly superior.
- Conversion from a continuously counted tone pitch to the pitch class, like C, D, E etc.:
mod p 12
- Pad a list
xsto a multiple of
mnumber of elements:
xs ++ replicate (mod (- length xs) m) pad
- Conversion from a day counter to a week day:
mod n 7
- Pacman runs out of the screen and re-appears at the opposite border:
mod x screenWidth
Partial functions like
Avoid functions that fail for certain input values like
They raise errors that can only be detected at runtime.
Think about how they can be avoided by different program organization
or by choosing more specific types.
if i == Nothing then deflt else fromJust i
fromMaybe deflt i
If it is not possible to avoid
fromJust this way,
and document with an
error why you think that the value must be always
Just in your situation.
fromMaybe (error "Function bla: The list does always contains the searched value") (lookup key dict)