mongodb/map-reduce-example.md

86 lines
2.6 KiB
Markdown

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 "> "
> :set -XOverloadedStrings
> import Database.MongoDB
> import Data.CompactString ()
> conn <- newConnPool 1 (host "localhost")
> 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 and iterate over the result collection:
> run $ runMR (mapReduce "mr1" mapFn reduceFn) >>= rest
Right [[ _id: "cat", value: 3.0],[ _id: "dog", value: 2.0],[ _id: "mouse", value: 1.0]]
Advanced Map/Reduce
-------------------
MongoDB returns additional statistics in the map/reduce results. To
obtain them, use *runMR'* instead:
> run $ runMR' (mapReduce "mr1" mapFn reduceFn)
Right [ result: "tmp.mr.mapreduce_1276482643_7", timeMillis: 379, counts: [ input: 4, emit: 6, output: 3], ok: 1.0]
You can then obtain the results from here by quering the result collection yourself. *runMR* (above) does this for you but discards the statistics.