MapReduce as a monad
Contents
Introduction
MapReduce is a general technique for massively parallel programming developed by Google. It takes its inspiration from ideas in functional programming, but has moved away from that paradigm to a more imperative approach. I have noticed that MapReduce can be expressed naturally, using functional programming techniques, as a form of monad. The standard implementation of MapReduce is the JAVAbased HADOOP framework, which is very complex and somewhat temperamental. Moreover, it is necessary to write HADOOPspecific code into mappers and reducers. My prototype library takes about 100 lines of code and can wrap generic mapper / reducer functions.
Having shown that we can implement MapReduce as a generalised monad, it transpires that in fact, we can generalise this still further and define a MapReduceT
monad transformer, so there is a MapReduce type and operation associated to any monad. In particular, it turns out that the State
monad is just the MapReduce type of the monad Hom a
of maps h > a
where h
is some fixed type.
Initial Approach
Why a monad?
What the monadic implementation lets us do is the following:
 Map and reduce look the same.
 You can write a simple wrapper function that takes a mapper / reducer and wraps it in the monad, so authors of mappers / reducers do not need to know anything about the MapReduce framework: they can concentrate on their algorithms.
 All of the guts of MapReduce are hidden in the monad's
bind
function  The implementation is naturally parallel
 Making a MapReduce program is trivial:
... >>= wrapMR mapper >>= wrapMR reducer >>= ...
Details
Full details of the implementation and sample code can be found here. I'll just give highlights here.
Generalised mappers / reducers
One can generalise MapReduce a bit, so that each stage (map, reduce, etc) becomes a function of signature
a > ([(s,a)] > [(s',b)])
where s
and s'
are data types and a
and b
are key values.
Generalised Monad
Now, this is suggestive of a monad, but we can't use a monad per se, because the transformation changes the key and value types, and we want to be able to access them separately. Therefore we do the following.
Let m
be a Monad'
, a type with four parameters: m s a s' b
.
Generalise the monadic bind
operation to:
m s a s' b > ( b > m s' b s'' c ) > m s a s'' c
See Parametrized monads.
Then clearly the generalised mapper/reducer above can be written as a Monad'
, meaning that we can write MapReduce as
... >>= mapper >>= reducer >>= mapper' >>= reducer' >>= ...
Implementation details
class Monad' m where return :: a > m s x s a (>>=) :: (Eq b) => m s a s' b > ( b > m s' b s'' c ) > m s a s'' c newtype MapReduce s a s' b = MR { runMR :: ([(s,a)] > [(s',b)]) } retMR :: a > MapReduce s x s a retMR k = MR (\ss > [(s,k)  s < fst <$> ss]) bindMR :: (Eq b,NFData s'',NFData c) => MapReduce s a s' b > (b > MapReduce s' b s'' c) > MapReduce s a s'' c bindMR f g = MR (\s > let fs = runMR f s gs = P.map g $ nub $ snd <$> fs in concat $ map (\g' > runMR g' fs) gs)
The key point here is that P.map
is a parallel version of the simple map
function.
Now we can write a wrapper function
wrapMR :: (Eq a) => ([s] > [(s',b)]) > (a > MapReduce s a s' b) wrapMR f = (\k > MR (g k)) where g k ss = f $ fst <$> filter (\s > k == snd s) ss
which takes a conventional mapper / reducer and wraps it in the Monad'
. Note that this means that the mapper / reducer functions do not need to know anything about the way MapReduce is implemented. So a standard MapReduce job becomes
mapReduce :: [String] > [(String,Int)] mapReduce state = runMapReduce mr state where mr = return () >>= wrapMR mapper >>= wrapMR reducer
I have tested the implementation with the standard wordcounter mapper and reducer, and it works perfectly (full code is available via the link above).
The monad transformer approach
Define the monad transformer type MapReduceT
by:
newtype (Monad m) => MapReduceT m t u = MR {run :: m t > m u}
with operations
lift :: (Monad m) => m t > MapReduceT m t t lift x = MR (const x) return :: (Monad m) => t > MapReduceT m t t return x = lift (return x) bind :: (Monad m) => MapReduceT m u u > MapReduceT m t u > (u > MapReduceT m u v) > MapReduceT m t v bind p f g = MR (\ xs > ps xs >>= gs xs) where ps xs = (f >>> p) < xs gs xs x = (f >>> g x) < xs
where >>>
and <
are the obvious arrow operations on MapeduceT
types.
Then we show in this paper that:

MapReduce = MapReduceT []
with(>>=) = bind nub
 For a suitable choice of
p
the standardState
monad isMapReduceT Hom
where
data Hom a b = H {run :: (a > b)} return x = H (const x) f >>= g = H (\ x > g' (f' x) x) where f' = run f g' x y = run (g x) y
Future Directions
 My code so far runs concurrently and in multiple threads within a single OS image. It won't work on clustered systems. I have started work in this, see here.
 Currently all of the data is sent to all of the mappers / reducers at each iteration. This is okay on a single machine, but may be prohibitive on a cluster.
I would be eager for collaborative working on taking this forward.
julianporter 18:10, 31 October 2011 (UTC)