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
    ...
    Prelude> :set prompt "> "
    > :set -XOverloadedStrings
    > import Database.MongoDB
    > import Data.CompactString ()
    > conn <- newConnPool 1 (host "127.0.0.1")
    > let run act = access safe Master conn $ use (Database "test") act
    > :{
    run $ insertMany "mr1" [
          ["x" =: 1, "tags" =: ["dog", "cat"]],
          ["x" =: 2, "tags" =: ["cat"]],
          ["x" =: 3, "tags" =: ["mouse", "cat", "dog"]],
          ["x" =: 4, "tags" =: ([] :: [String])]
    ]
    :}

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 = Javascript [] "
    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 = Javascript [] "
    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 run mapReduce, results by default will be return in an array in the result document (inlined):

    > run $ runMR' (mapReduce "mr1" mapFn reduceFn)
    Right [ results: [[ _id: "cat", value: 3.0],[ _id: "dog", value: 2.0],[ _id: "mouse", value: 1.0]], timeMillis: 379, counts: [ input: 4, emit: 6, reduce: 2, output: 3], ok: 1.0]

Inlining only works if result set < 16MB. An alternative to inlining is outputing to a collection. But what to do if there is data already in the collection from a previous run of the same MapReduce? You have three alternatives in the MRMerge data type: Replace, Merge, and Reduce. See its documentation for details. To output to a collection, set the mOut field in MapReduce.

	> run $ runMR' (mapReduce "mr1" mapFn reduceFn) {rOut = Output Replace "mr1out" Nothing}
	Right [ result: "mr1out", timeMillis: 379, counts: [ input: 4, emit: 6, reduce: 2, output: 3], ok: 1.0]

You can now query the mr1out collection to see the result, or run another MapReduce on it! A shortcut for running the map-reduce then querying the result collection right away is `runMR`.

	> run $ rest =<< runMR (mapReduce "mr1" mapFn reduceFn) {rOut = Output Replace "mr1out" Nothing}
	Right [[ _id: "cat", value: 3.0],[ _id: "dog", value: 2.0],[ _id: "mouse", value: 1.0]]