86 lines
2.6 KiB
Markdown
86 lines
2.6 KiB
Markdown
Map/Reduce Example
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------------------
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This is an example of how to use the mapReduce function to perform
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map/reduce style aggregation on your data.
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This document has been shamelessly ported from the similar
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[pymongo Map/Reduce Example](http://api.mongodb.org/python/1.4%2B/examples/map_reduce.html).
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Setup
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-----
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To start, we'll insert some example data which we can perform
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map/reduce queries on:
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$ ghci -package mongoDB
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GHCi, version 6.12.1: http://www.haskell.org/ghc/ :? for help
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...
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Prelude> :set prompt "> "
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> :set -XOverloadedStrings
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> import Database.MongoDB
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> import Data.CompactString ()
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> conn <- newConnPool 1 (host "localhost")
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> let run act = access safe Master conn $ use (Database "test") act
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> :{
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run $ insertMany "mr1" [
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["x" =: 1, "tags" =: ["dog", "cat"]],
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["x" =: 2, "tags" =: ["cat"]],
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["x" =: 3, "tags" =: ["mouse", "cat", "dog"]],
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["x" =: 4, "tags" =: ([] :: [String])]
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]
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:}
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Basic Map/Reduce
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----------------
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Now we'll define our map and reduce functions. In this case we're
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performing the same operation as in the MongoDB Map/Reduce
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documentation - counting the number of occurrences for each tag in the
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tags array, across the entire collection.
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Our map function just emits a single (key, 1) pair for each tag in the
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array:
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> :{
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let mapFn = Javascript [] "
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function() {\n
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this.tags.forEach(function(z) {\n
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emit(z, 1);\n
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});\n
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}"
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:}
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The reduce function sums over all of the emitted values for a given
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key:
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> :{
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let reduceFn = Javascript [] "
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function (key, values) {\n
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var total = 0;\n
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for (var i = 0; i < values.length; i++) {\n
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total += values[i];\n
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}\n
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return total;\n
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}"
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:}
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Note: We can't just return values.length as the reduce function might
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be called iteratively on the results of other reduce steps.
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Finally, we run mapReduce and iterate over the result collection:
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> run $ runMR (mapReduce "mr1" mapFn reduceFn) >>= rest
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Right [[ _id: "cat", value: 3.0],[ _id: "dog", value: 2.0],[ _id: "mouse", value: 1.0]]
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Advanced Map/Reduce
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-------------------
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MongoDB returns additional statistics in the map/reduce results. To
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obtain them, use *runMR'* instead:
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> run $ runMR' (mapReduce "mr1" mapFn reduceFn)
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Right [ result: "tmp.mr.mapreduce_1276482643_7", timeMillis: 379, counts: [ input: 4, emit: 6, output: 3], ok: 1.0]
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You can then obtain the results from here by quering the result collection yourself. *runMR* (above) does this for you but discards the statistics.
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