One of Haskell's main features is non-strict semantics, which in is implemented by lazy evaluation in all popular Haskell compilers. However many Haskell libraries found on Hackage are implemented just as if Haskell would be a strict language. This leads to unnecessary inefficiencies, memory leaks and, we suspect, unintended semantics. In this article we want to go through some techniques on how to check lazy behaviour on functions, examples of typical constructs which break laziness without need, and finally we want to link to techniques that may yield the same effect without laziness.
If you want to check whether a function is lazy enough, you may feed it with undefined values.
An undefined value can be
error "reason", or an infinite loop.
The latter one has the advantage that it cannot be hidden by some hacks like "catching" the error in the IO monad.
filter is lazy:
filter even [0..] filter even ([0..5] ++ undefined)
filter function is lazy
then it keeps generating elements in the first case
and it outputs a prefix of the output list, before breaking because of the undefined, in the second case.
An automated unit test can check whether infinite or corrupted input data produces correct prefixes.
Those tests usually do not fail by returning
False but by leading to undefined results,
undefined or an infinite loop.
testFilter0 = filter even [0..100] `isPrefixOf` filter even [0..] testFilter1 = filter even [0..100] `isPrefixOf` filter even ([0..102]++undefined) testFilter2 = let x = filter even [0..] !! 100 in x==x testFilter3 = let x = filter even ([0..102]++undefined) !! 50 in x==x
Maybe, Either, Exceptions
Some laziness breakers are visible in type signatures:
decodeUTF8 :: [Word8] -> Either Message String
Either type signals that the function marks decoding failure by using the
Left constructor of
This function cannot be lazy, because when you access the first character of the result,
it must already be computed, whether the result is
For this decision, the complete input must be decoded.
A better type signature is
decodeUTF8 :: [Word8] -> (Maybe Message, String)
String contains as much characters as could be decoded
Maybe Message gives the reason for the stop of the decoding.
Nothing means the input was completely read,
Just msg means the decoding was aborted for the reason described in
If you touch the first element of the pair, the complete decodings is triggered, thus laziness is broken.
This means you should first process the
String and look at
Maybe Message afterwards.
Instead of the unspecific pair type you should use the special type for asynchronous exceptions as found in the explicit exception package.
Especially in parsers you may find a function, called Wadler's force function. It works as follows:
force y = let Just x = y in Just x
It looks like a complicated expression for
with an added danger of failing unrecoverably when
y is not
Its purpose is to use the lazy pattern matching of
and to show to the runtime system, that we expect that
y is always a
Then the runtime system need not to wait until it can determine the right constructor but it can proceed immediately.
This way a function can be made lazy, also if it returns
It can however fail, if later it turns out, that
y is actually
Using force like functions is sometimes necessary,
but should be avoided for data types with more than one constructor.
It is better to use an interim data type with one constructor and lift to the multi-constructor datatype when needed.
Consider parsers of type
StateT [Word8] Maybe a.
Now consider the parser combinator
many :: StateT [Word8] Maybe a -> StateT [Word8] Maybe [a]
which parses as many elements of type
a as possible.
It shall be lazy and thus must be infallible and must not use the
It shall just return an empty list, if parsing of one element fails.
A quick hack would be to define
many using a
It would be better to show by the type, that
many cannot fail:
many :: StateT [Word8] Maybe a -> StateT [Word8] Identity [a]
Be aware that the following two expressions are not equivalent.
-- less lazy if b then f x else f y -- more lazy f (if b then x else y)
if undefined then f x else f y is
f (if b then x else y) is
which is a difference in non-strict semantics.
if b then 'a':x else 'a':y.
It is common source of too much strictness to make decisions too early and thus duplicate code in the decision branches. Intuitively spoken, the bad thing about code duplication (stylistic questions put aside) is, that the run-time system cannot see that in the branches some things are equal and do it in common before the critical decision. Actually, the compiler and run-time system could be "improved" to do so, but in order to keep things predictable, they do not do so. Even more, this behaviour is required by theory, since by pushing decisions to the inner of an expression you change the semantics of the expression. So we return to the question, what the programmer actually wants.
Now, do you think this expression
if b then [x] else y:ys
is maximally lazy?
It seems so, but actually it is not. In both branches we create non-empty lists, but the run-time system cannot see this.
null (if undefined then [x] else y:ys) again
but we like to have it evaluated to
Here we need lazy pattern matching as provided by
let z:zs = if b then [x] else y:ys in z:zs
This expression always returns the constructor
(:) and thus
null knows that the list is not empty.
However, this is a little bit unsafe, because the
let z:zs may fail if in the branches of
if there is an empty list.
This error can only caught at run-time which is bad.
We can avoid it using the single constructor pair type.
let (z,zs) = if b then (x,) else (y,ys) in z:zs
which can be abbreviated to
uncurry (:) (if b then (x,) else (y,ys))
Another example is the
In the Haskell 98 report the implementation
inits :: [a] -> [[a]] inits  = [] inits (x:xs) = [] ++ map (x:) (inits xs)
However you find that
inits undefined is undefined,
inits always should return the empty list as first element.
The following implementation does exactly this:
inits :: [a] -> [[a]] inits xt =  : case xt of  ->  x:xs -> map (x:) (inits xs)
See also the article on base cases and identities.
I do not know whether the following example can be simplified. In this form it occured in a real application, namely the HTTP package.
Consider the following action of the
Control.Monad.RWS which fetches a certain number of elements from a list.
The state of the monad is the input list we fetch the elements from.
The reader part provides an element which means that the input is consumed.
It is returned as singleton when the caller tries to read from a completely read input.
The writer allows to log some information, however the considered action does not output something to the log.
getN :: Int -> RWS a [Int] [a] [a] getN n = do input <- get if null input then asks (:) else let (fetched,rest) = splitAt n input in put rest >> return fetched
As we learned as good imperative programmers, we only call
splitAt when the input is non-empty,
that is, only if there is something to fetch.
This works even more many corner cases, but not in the following one.
getN does obviously not log something (i.e. it does not call
it requires to read the input in order to find out, that nothing was logged:
*Test> (\(_a,_s,w) -> w) $ runRWS (getN 5) '\n' undefined *** Exception: Prelude.undefined
The problem is again, that
if checks the emptiness of the input,
which is undefined, since the input is undefined.
Thus we must ensure, that the invoked monadic actions are run independent from the input.
Only this way, the run-time system can see that the logging stream is never touched.
We start refactoring by calling
put independently from
It works as well for empty lists, since
splitAt will just return empty lists in this case.
getN :: Int -> RWS a [Int] [a] [a] getN n = do input <- get let (fetched,rest) = splitAt n input put rest if null input then asks (:) else return fetched
This doesn't resolve the problem. There is still a choice between
We have to pull out
ask as well.
getN :: Int -> RWS a [Int] [a] [a] getN n = do input <- get let (fetched,rest) = splitAt n input put rest endOfInput <- ask return $ if null input then [endOfInput] else fetched
Now things work as expected:
*Test> (\(_a,_s,w) -> w) $ runRWS (getN 5) '\n' undefined 
We learn from this example, that sometimes in Haskell it is more efficient to call functions that are not needed under some circumstances.
Always remind, that the do notation looks only imperative, but it is not imperative.
endOfInput is only evaluated if the end of the input is really reached.
Thus, the call
ask does not mean that there is actually an action performed between
Strict pattern matching in a recursion
partition function which sorts elements, that match a predicate, into one list and the non-matching elements into another list.
This function should also work on infinite lists,
but the implementation shipped with GHC up to 6.2 failed on infinite lists.
The reason was too strict pattern matching.
Let's first consider the following correct implementation:
partition :: (a -> Bool) -> [a] -> ([a], [a]) partition p = foldr (\x ~(y,z) -> if p x then (x : y, z) else (y, x : z)) (,)
The usage of
foldr seems to be reserved for advanced programmers.
foldr runs from the end to the start of the list.
However, how can this work if there is a list without an end?
That can be seen when applying the definition of
foldr :: (a -> b -> b) -> b -> [a] -> b foldr _ b  = b foldr f b (a:as) = f a (foldr f b as)
Now we expand this once for an infinite input list, we get
partition p (a:as) = (\ ~(y,z) -> if p a then (a:y, z) else (y, a:z)) (foldr ... (,) as)
We see that the whether
a is prepended to the first or the second list,
does only depend on
p a, and neither on
y nor on
The laziness annotation
~ is crucial, since it tells, intuitively spoken,
that we can rely on the recursive call of
foldr to return a pair and not
Omitting it, would require the evaluation of the whole input list before the first output element can be determined.
This fails for infinite lists and is inefficient for finite lists, and that was the bug in former implementations of
Btw. by the expansion you also see, that it would not help to omit the tilde and apply the above 'force' trick to the 'if-then-else' expression.
Any use of the list function
reverse should alert you,
since when you access the first element of a reversed list, then all nodes of the input list must be evaluated and stored in memory.
Think twice whether it is really needed.
The article Infinity and efficiency shows how to avoid list reversal.
Input and Output
In general functions output of lazily generated data is no problem, whereas lazily reading data requires a sort of a hack and thus caution. Consider the nice program
readFile "source" >>= writeFile "target"
which copies the file
source to the file
target with constant memory consumption,
readFile reads the data lazily and
writeFile writes it as it comes in.
However it fails badly, when a file shall updated in-place:
readFile "text" >>= writeFile "text" . map toUpper
This would work only when
readFile would be strict,
that is it would read the file contents to memory before returning.
readFile needs certain hacks:
- The function
unsafeInterleaveIOis needed for defering the calls to
hGetCharuntil the characters are actually needed.
- Exceptions that occur while reading the file are raised in the code that writes the result of processing the file content to somewhere. I.e. the exceptions produced by
readFilecan occur in code that has nothing to do with file reading and there is no warning, that they might occur there.
- The file must be closed after it is no longer needed. The documentation says, that the file is put into a semi-closed state. Maybe this means, it uses Weak Reference which lets the garbage collector close the file, once no reference to data of the file exists anymore. However, the garbage collector never works immediately, but in phases. It may be that the file remains open for a long time, maybe until the program exits.
From the above issues you see that laziness is a fragile thing. Make one mistake and a function, carefully developed with laziness in mind, is no longer lazy. The type system will rarely help you hunting laziness breakers, and there is little support by debuggers.
Thus detecting laziness breakers will often requires understanding of a large portion of code, which is against the idea of modularity.
Maybe for your case you will prefer a different idiom, that achieves the same goals in a safer way. See e.g. the Enumerator and iteratee pattern.