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Merging, Folding, Monoids, and Foldable

December 2, 2013

I recently had need to merge a collection of sorted lists. The task is simple enough, but as usual, in Haskell it was particularly pleasant, and lead to some interesting discoveries about folding and monoids. The code is available here, if you want to fiddle with it as you read.

Start with simply merging two (presumed) sorted lists. This code is about as clear as it could be:

merge2 :: Ord a => [a] -> [a] -> [a]
merge2 [] bs = bs
merge2 as [] = as
merge2 as@(a:at) bs@(b:bt) | a <= b    = a : merge2 at bs
                           | otherwise = b : merge2 as bt

To merge multiple lists, we can use a fold, which makes quick work of the task:

mergeLinear :: Ord a => [[a]] -> [a]
mergeLinear = foldr merge2 []

This works, but is isn’t optimal. The nature of foldr is such that the combing operation must be done in linear order down the list. The type signature demands it, as each element of the list can only be combined with a previous partial result:

foldr :: (a -> b -> b) -> b -> [a] -> b

Thus, the merges are performed in a linear cascade:


When a value is produced from the merge, on average, half the cascade will be traversed finding the next value. We can do better.

Because merge2 is an associative operation, the merges could instead be arranged in a tree:


The number of applications of merge2 is about the same: n−1 vs. n. If the combining operation was something like (+), then there would be no significant difference between these two approaches. However, the iterative operation of merge2 is going traverse parts of structure of the computation repeatedly, and so it makes a rather big difference, traversing only log2 n nodes to find the next value each time, rather than n/2. (We’ll measure this empirically, below.)

Let’s create this kind of fold:

mapPairs :: (a -> a -> b) -> (a -> b) -> [a] -> [b]
mapPairs pair single = go
    go []       = []
    go [a]      = [single a]
    go (a:b:cs) = pair a b : go cs

foldPairwise :: (a -> a -> a) -> a -> [a] -> a
foldPairwise _ z []  = z
foldPairwise _ _ [a] = a
foldPairwise f z as  = foldPairwise f z $ mapPairs f id as

And finally:

mergePairwise :: Ord a => [[a]] -> [a]
mergePairwise = foldPairwise merge2 []

This works, is just as elegant as the linear version, and is indeed more efficient.

Notice that the first two arguments to foldPairwise are similar to the two components of a monoid. And we have already noted that merge2 is associative. This suggests that we recast this kind of a fold in terms of Monoid:

foldPairwiseM :: Monoid a => [a] -> a
foldPairwiseM []  = mempty
foldPairwiseM [a] = a
foldPairwiseM as  = foldPairwiseM $ mapPairs mappend id as

And now, creating a Monoid for merging, we get:

newtype Merge a = Merge { merged :: [a] }
  deriving (Eq, Ord, Read, Show)

instance Ord a => Monoid (Merge a)
    mempty = Merge []
    (Merge a) `mappend` (Merge b) = Merge $ a `merge2` b

mergePairwiseM :: Ord a => [[a]] -> [a]
mergePairwiseM = merged . foldPairwiseM . map Merge

So, if our combining operation is part of a Monoid, then we can use the associativity to create a different kind of fold, one which does not combine in a simple linear cascade. For operations which will traverse the structure of the computation multiple times, like merging, this can be a significant advantage (log2 n vs. n).

There is already a type class that abstracts folding: Foldable. We can encode our pairwise folding strategy as an instance:

newtype PairwiseFoldingList a = PairwiseFoldingList [a]
  deriving (Eq, Ord, Read, Show)

instance Foldable PairwiseFoldingList where
    foldMap f (PairwiseFoldingList as) = foldPairwiseM $ map f as

Notice that it is implemented with foldMap, rather than the alternative foldr. This is because foldr and related forms must fold in a linear cascade due to their type signatures. Only fold and foldMap can implement a fold that takes advantage of associativity. (Note: foldr1 and foldl1 don’t presume associative combining functions, even though their type signatures admit them.)

This leads to an interesting observation: If an instance of Foldable doesn’t implement foldMap then it looses the opportunity (if it has any) for a more efficient fold when a Monoid is involved.

I looked through the entire Haskell Platform and found several Foldable instances that didn’t implement foldMap but would gain if they did: HashMap, HashSet, Seq, and Tree could all exploit their tree structure, and would only improve their performance. Array, Vector, and [] could each use the pairwise strategy. It isn’t clear if these are worth it in all cases: There is some overhead in foldPairwiseM over a simple foldr. Perhaps approaches like PairwiseFoldingList are best for these three.

We can, for completeness, now re-implement our two merges based on Foldable:

mergeFoldable :: (Foldable t, Ord a) => t [a] -> [a]
mergeFoldable = merged . foldMap Merge

mergeLinearF :: Ord a => [[a]] -> [a]
mergeLinearF = mergeFoldable

mergePairwiseF :: Ord a => [[a]] -> [a]
mergePairwiseF = mergeFoldable . PairwiseFoldingList

It would nice to validate that the pairwise strategy indeed performs fewer item comparisons in a merge. Our Monoid and Foldable based code makes this easy:

newtype CountedMerge a = CountedMerge { countedMerged :: [(a, Int)] }
  deriving (Eq, Ord, Read, Show)

instance Ord a => Monoid (CountedMerge a)
    mempty = CountedMerge []
    mappend = countedMerge2

countedMerge2 :: Ord a => CountedMerge a -> CountedMerge a -> CountedMerge a
countedMerge2 (CountedMerge as0) (CountedMerge bs0) = CountedMerge $ go as0 bs0
    go [] bs = bs
    go as [] = as
    go as@((a,an):at) bs@((b,bn):bt)
        | a <= b    = (a,an+1) : go at bs
        | otherwise = (b,bn+1) : go as bt

toCountedMerge :: [a] -> CountedMerge a
toCountedMerge = CountedMerge . map (\e -> (e,0))

countedMergeFoldable :: (Foldable t, Ord a) => t [a] -> [(a, Int)]
countedMergeFoldable = countedMerged . foldMap toCountedMerge

countedMergeLinearF :: Ord a => [[a]] -> [(a, Int)]
countedMergeLinearF = countedMergeFoldable

countedMergePairwiseF :: Ord a => [[a]] -> [(a, Int)]
countedMergePairwiseF = countedMergeFoldable . PairwiseFoldingList

Now we can run both the linear and pairwise versions, and see how many comparisons per item it took to merge them:

=== Comparison counts between linear and pairwise merges, sized 100
  number of trials                 =       1000
  avg. number of lists to merge    =         49.92
  avg. number of items             =       2500.06
  avg. countedMergeLinear count    =      80466.73,         24.09 per item
  avg. countedMergePairwise count  =      15302.56,          5.36 per item

=== Comparison counts between linear and pairwise merges, sized 500
  number of trials                 =         25
  avg. number of lists to merge    =        237.40
  avg. number of items             =      60507.56
  avg. countedMergeLinear count    =    9702855.80,        118.29 per item
  avg. countedMergePairwise count  =     510670.64,          7.66 per item

=== Comparison counts between linear and pairwise merges, sized 1000
  number of trials                 =          1
  avg. number of lists to merge    =        950.00
  avg. number of items             =     483556.00
  avg. countedMergeLinear count    =  231494967.00,        478.73 per item
  avg. countedMergePairwise count  =    4802432.00,          9.93 per item

You can see that the linear version makes about n/2 comparisons per item produced, whereas the pairwise version only about log2 n.

In summary:


  • • Only fold and foldMap can take advantage of associativity in the monoid to fold more efficiently.
  • • For some combining functions, like the sorted list merge, the advantage can be significant.
  • • Some standard instances of Foldable should implement foldMap for a more efficient fold strategy.

All the code for this, as well as a driver program with quick check tests can be found here.

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  1. This sounds very interesting, but I have to admit you lost me early on when you wrote

    > When a value is produced from the merge, on average, half the cascade will be traversed finding the next value.

    Can you elaborate why that is? I suppose that’s the lazy evaluation kicking in, i.e. getting the next element in order from the top merge2 call requires that all the other merge2 invocations have to process the next piece of data as well?

  2. Yes, the laziness accounts for the way I described it: When a value of the outermost merge is demanded, the heads of the two inputs are demanded and compared. On the next time ’round, one of those two heads will already be computed, so only side that produced the previous value will need to step to compute the next head.

    Think about how that works in a linear cascade vs. a tree. It might help to work out the merge of these lists by hand: [[3,13..], [2,12..], [1,11..], [4,14..], [5,15..]] each way.

    You can see the benefit if the merges were strict also: If each list were on average m long, then in the linear cascade, the innermost merge traverses m + 0 elements, then the next one traverses m + m elements, the next m + 2m… and so on. This is m*n(n+1)/2 traversals. Whereas in in the tree form we have n/2 traversals of m + m at the bottom layer, n/4 traversals of 2m + 2m, and so on. A total of log n * (n*m).

  3. I wasn’t aware of data-ordlist. Thanks for pointing it out.

    The function foldt is almost exactly the same in approach as my foldPairwise, though I factor out the utility mapPairwise.

    mergeAll in that package has somewhat different preconditions than the merges here: It assumes the list of lists is already ordered by the head element. That is, mergeAll [[3,4],[1.2]] doesn’t produce [1,2,3,4]. On the other hand, it supports an infinite list of lists, which my merges don’t.

  4. Leon Smith permalink

    To clarify, your foldPairwise appears to be identical to foldt’. foldt uses a different shape of tree in order to support the infinite list of lists case, a shape not unlike Okasaki’s random-access lists.

    Basically, it’s a (singleton element) `merge` (complete tree of merges of depth 1) `merge` (complete tree of merges of of depth 2) `merge` …

    As for all the VIP business inside mergeAll, I don’t fully understand it myself at this point in time. Henrich Apfelmus came up with that, inspired by Dave Bayer’s solution to a very similar problem. It turns out that the use of VIPs isn’t strictly required to implement `mergeAll`, as pointed out by Will Ness, however VIPs is need in order to avoid forcing significant parts of the list of lists before it is necessary to do so, as I discovered later. I mostly just served as an editor and arbitrator on the mergeAll implementation.

  5. The cool thing is that we can express merge sort (O(n*logn)) and insertion sort (O(n*n)) via pairwise and linear versions of the merge:

    mergeSort, insertionSort :: Ord a => [a] -> [a]
    mergeSort = mergePairwiseF . map return
    insertionSort = mergeLinearF . map return

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