Map/Reduce Example
------------------

This is an example of how to use the mapReduce function to perform
map/reduce style aggregation on your data.

This document has been shamelessly ported from the similar
[pymongo Map/Reduce Example](http://api.mongodb.org/python/1.4%2B/examples/map_reduce.html).

Setup
-----

To start, we'll insert some example data which we can perform
map/reduce queries on:

    $ ghci -package mongoDB
    GHCi, version 6.12.1: http://www.haskell.org/ghc/  :? for help
    ...
    Prelude> :set prompt "> "
    > import Database.MongoDB
    > import Database.MongoDB.BSON
    > import Data.ByteString.Lazy.UTF8
    > c <- connect "localhost" []
    > let col = (fromString "test.mr1")
    > :{
    insertMany c col [
          (toBsonDoc [("x", BsonInt32 1),
                      ("tags", BsonArray [toBson "dog",
                                          toBson "cat"])]),
          (toBsonDoc [("x", BsonInt32 2),
                      ("tags", BsonArray [toBson "cat"])]),
          (toBsonDoc [("x", BsonInt32 3),
                      ("tags", BsonArray [toBson "mouse",
                                          toBson "cat",
                                          toBson "doc"])]),
          (toBsonDoc [("x", BsonInt32 4),
                      ("tags", BsonArray [])])
    ]
    :}

Basic Map/Reduce
----------------

Now we'll define our map and reduce functions. In this case we're
performing the same operation as in the MongoDB Map/Reduce
documentation - counting the number of occurrences for each tag in the
tags array, across the entire collection.

Our map function just emits a single (key, 1) pair for each tag in the
array:

    > :{
    let mapFn = "
    function() {\n
      this.tags.forEach(function(z) {\n
        emit(z, 1);\n
      });\n
    }"
    :}

The reduce function sums over all of the emitted values for a given
key:

    > :{
    let reduceFn = "
    function (key, values) {\n
      var total = 0;\n
      for (var i = 0; i < values.length; i++) {\n
        total += values[i];\n
      }\n
      return total;\n
    }"
    :}

Note: We can't just return values.length as the reduce function might
be called iteratively on the results of other reduce steps.

Finally, we call map_reduce() and iterate over the result collection:

    > mapReduce c col (fromString mapFn) (fromString reduceFn) [] >>= allDocs
    [[(Chunk "_id" Empty,BsonString (Chunk "cat" Empty)),
      (Chunk "value" Empty,BsonDouble 6.0)],
     [(Chunk "_id" Empty,BsonString (Chunk "doc" Empty)),
      (Chunk "value" Empty,BsonDouble 1.0)],
     [(Chunk "_id" Empty,BsonString (Chunk "dog" Empty)),
      (Chunk "value" Empty,BsonDouble 3.0)],
     [(Chunk "_id" Empty,BsonString (Chunk "mouse" Empty)),
      (Chunk "value" Empty,BsonDouble 2.0)]]

Advanced Map/Reduce
-------------------

MongoDB returns additional information in the map/reduce results. To
obtain them, use *runMapReduce*:

    > res <- runMapReduce c col (fromString mapFn) (fromString reduceFn) []
    > res
    [(Chunk "result" Empty, BsonString (Chunk "tmp.mr.mapreduce_1268105512_18" Empty)),
     (Chunk "timeMillis" Empty, BsonInt32 90),
     (Chunk "counts" Empty,
      BsonDoc [(Chunk "input" Empty,BsonInt64 8),
               (Chunk "emit" Empty,BsonInt64 12),
               (Chunk "output" Empty,BsonInt64 4)]),
     (Chunk "ok" Empty,BsonDouble 1.0)]

You can then obtain the results using *mapReduceResults*:

    > mapReduceResults c (fromString "test") res >>= allDocs
    [[(Chunk "_id" Empty,BsonString (Chunk "cat" Empty)),
      (Chunk "value" Empty,BsonDouble 6.0)],
     [(Chunk "_id" Empty,BsonString (Chunk "doc" Empty)),
      (Chunk "value" Empty,BsonDouble 1.0)],
     [(Chunk "_id" Empty,BsonString (Chunk "dog" Empty)),
      (Chunk "value" Empty,BsonDouble 3.0)],
     [(Chunk "_id" Empty,BsonString (Chunk "mouse" Empty)),
      (Chunk "value" Empty,BsonDouble 2.0)]]