Difference between revisions of "Haskell programming tips"
m (→Lists are not finite maps: changed link to relevant section)
(→Partial functions like fromJust and head: link to Avoiding partial functions)
|Line 807:||Line 807:|
There is also a function which returns an existing first list element in terms of <hask>Maybe</hask>:
There is also a function which returns an existing first list element in terms of <hask>Maybe</hask>:
(See [http://www.haskell.org/pipermail/haskell-cafe/2007-March/023391.html remark].
(See [http://www.haskell.org/pipermail/haskell-cafe/2007-March/023391.html remark].
See also "[[Avoiding partial functions]]".)
== Related Links ==
== Related Links ==
Latest revision as of 12:15, 5 June 2012
- 1 Preface
- 2 Be concise
- 3 Use syntactic sugar wisely
- 4 Efficiency and infinity
- 5 Choose types properly
- 6 Miscellaneous
- 7 Related Links
This page shows several examples of how code can be improved. We try to derive general rules from them, though they cannot be applied deterministically and are a matter of taste. We all know this, 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, well-tuned functions. If you rewrite an existing library function, the reader of your code might spend a minute trying to figure out why you've done that. But if you use a standard function, the reader will either immediately understand what you've done, or can learn something new.
Avoid explicit recursion
Explicit recursion is not generally bad, but you should spend some time 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 understand the program. Thanks to higher order functions Haskell gives you very many opportunities to factor out parts of the code. If you find the function 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 in an even more general way.
Decomposing a problem this way also has the advantage that you can debug more easily. 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
This is a special case of the general principle of separating concerns. If you can write the loop over a data structure once and debug it, then there's no need to duplicate that code.
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 after 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 this for you if you pass an option like
-Wall to 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. For example 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). This 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 with a function that is present in GHC.Exts of GHC 6.10:
sortWith :: (Ord b) => (a -> b) -> [a] -> [a] sortWith 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
hasSpace (a:as) | isSpace a = True | otherwise = hasSpace as
hasSpace (a:as) = if isSpace a then True else hasSpace as
can be shortened to
hasSpace (a:as) = isSpace a || hasSpace as
(I just wanted to show the logic transform.
In the particular example you would write
any isSpace, of course.)
The same way
allPrintable (a:as) | isSpace a = False | otherwise = allPrintable as
allPrintable (a:as) = if isSpace a then False else allPrintable as
can be shortened to
allPrintable (a:as) = not (isSpace a) && allPrintable as
all (not . isSpace) is even better in this particular example.)
Use syntactic sugar wisely
People who employ syntactic sugar extensively argue that it makes their code more readable. 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 lets you remain in imperative thinking, that is it lets 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 discover 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 more easily from
List to other data structures.
map (1+) list
mapSet (1+) set
If there were 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 may feel you need parallel list comprehension.
This is unfortunately supported by GHC now,
but it is arguably 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, the
Functor class method
fmap and the
Monad based function
liftM are the same (as long as both are defined, as they should be).
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
Disclaimer: This section is NOT advising you to avoid guards. It is advising you to prefer pattern matching to guards when both are appropriate. -- 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 whether 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)
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 implementation 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
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"
Incidentally, compilers often also have problems with numerical patterns. For example, 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 when 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 criticized for hiding computational complexity and producing ambiguities, see /Discussion for details. They are subsumed by the more general Views proposal, which has unfortunately never been implemented despite being around for quite some time now.
n+k patterns are not in the Haskell 2010 standard.
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 also inefficient 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 thing 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 certainly fails 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 also works 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 appropriate fold
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
involves traversing through the first
n nodes of the list.
This is very inefficient.
If you access the elements progressively, as in
[x !! i - i | i <- [0..n]]
you should try to get rid of indexing, as in
zipWith (-) x [0..n]
If you really need random access, as in the Fourier Transform, you should switch to Arrays.
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 occurrences of objects,
you can use a
Data.Map.Map a Int.
Lists are not finite maps
Similarly, lists are not finite maps, as mentioned in 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
Avoid redundancy in data types
I often see data types with redundant fields, e.g.
data XML = Element Position Name [Attribute] [XML] | Comment Position String | Text Position String
I suggest to factor out the common field
since this lets you handle the text position the same way for all XML parts.
data XML = XML Position Part data Part = Element Name [Attribute] [XML] | Comment String | Text String
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. Useful techniques are described in Avoiding IO.
Forget about quot and rem
They complicate handling of negative dividends.
mod are almost always the better choice. If
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
rem. That is,
mod a b always wraps
a to an element from
[0..(b-1)], 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
- Daan Leijen: Division and Modulus for Computer Scientists
- Haskell-Cafe: default for quotRem in terms of divMod?
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)
head can be avoided by checking with types, that it is never empty.
There is also a function which returns an existing first list element in terms of
See also "Avoiding partial functions".)