# MapReduce as a monad

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## Latest revision as of 18:22, 31 October 2011

## Contents |

## [edit] 1 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 JAVA-based HADOOP framework, which is very complex and somewhat temperamental. Moreover, it is necessary to write HADOOP-specific 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## [edit] 2 Initial Approach

### [edit] 2.1 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 functionbind
- The implementation is naturally parallel
- Making a MapReduce program is trivial:

### [edit] 2.2 Details

Full details of the implementation and sample code can be found here. I'll just give highlights here.

#### [edit] 2.2.1 Generalised mappers / reducers

One can generalise MapReduce a bit, so that each stage (map, reduce, etc) becomes a function of signature

where

#### [edit] 2.2.2 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.

See Parametrized monads.

Then clearly the generalised mapper/reducer above can be written as a#### [edit] 2.2.3 Implementation details

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

Now we can write a wrapper function

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

*do not need to know anything about the way MapReduce is implemented*. So a standard MapReduce job becomes

mapReduce state = runMapReduce mr state

where

mr = return () >>= wrapMR mapper >>= wrapMR reducer

I have tested the implementation with the standard word-counter mapper and reducer, and it works perfectly (full code is available via the link above).

## [edit] 3 The monad transformer approach

Define the monad transformer typewith operations

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

Then we show in this paper that:

- withMapReduce = MapReduceT [](>>=) = bind nub
- For a suitable choice of the standardpmonad isStatewhereMapReduceT Hom

- 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

## [edit] 4 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)