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Maintaining laziness

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== Checking laziness ==
 
== Checking laziness ==
   
undefined, cycles
+
If you want to check whether a function is lazy enough, you may feed it with undefined values.
  +
An undefined value can be <hask>undefined</hask>, <hask>error "reason"</hask>, 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.
  +
  +
Examples:
  +
Check whether <hask>filter</hask> is lazy:
  +
<haskell>
  +
filter even [0..]
  +
filter even ([0..5] ++ undefined)
  +
</haskell>
  +
If the <hask>filter</hask> 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 <hask>False</hask> but by leading to undefined results,
  +
either explicit <hask>undefined</hask> or an infinite loop.
  +
<haskell>
  +
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
  +
</haskell>
   
unit tests
 
   
 
== Laziness breakers ==
 
== Laziness breakers ==

Revision as of 23:23, 28 December 2008

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.

Contents

1 Checking laziness

If you want to check whether a function is lazy enough, you may feed it with undefined values.

An undefined value can be
undefined
,
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.

Examples:

Check whether
filter
is lazy:
filter even [0..]
filter even ([0..5] ++ undefined)
If the
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, either explicit
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


2 Laziness breakers

2.1 Maybe, Either, Exceptions

Wadler's force function

The following looks cumbersome:

let (Just x) = y
in  Just x
It looks like a complicated expression for
y
, with an added danger of failing unrecoverably when
y
is not
Just
.

...

parsers - leave Maybe where no Maybe is required

2.2 Early decision

Be aware that the following two expression are not equivalent.

-- less lazy
if b then f x else f y
-- more lazy
f (if b then x else y)
It is
if undefined then f x else f y
is
undefined
, whereas
f (if b then x else y)
if
f undefined
,

which is a difference in non-strict semantics.

Consider e.g.
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.

It is
null (if undefined then [x] else y:ys)
again
undefined
, but we like to have it evaluated to
False
. Here we need lazy pattern matching as provided by
let
.
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 catched 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))


2.3 Strict pattern matching in a recursion

Consider the
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 http://www.haskell.org/pipermail/libraries/2004-October/002645.html failed on infinite lists]. What happened? The reason was a 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. Formally
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
.
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
z
. 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
undefined
.

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
partition
.

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.

2.4 List reversal

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.

3 Alternatives

From the above issues you see that it laziness is a fragile thing. Only one moment where you do not pay attention and a function, carefully developed with laziness in mind, is no longer lazy, when you call it. The type system can almost not help you hunting laziness breakers and there is little support by debuggers. Thus detection of laziness breakers, often requires understanding of a large portion of code, which is against the idea of modularity. Maybe for your case you might prefer a different idiom, that achieves the same goals in a safer way. See e.g. the Enumerator and iteratee pattern.