MongoDB中group() mapReduce() aggregate()之比较

对于SQL而言，如果从users表里查询每个team所有成员的number，查询语句如下：

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1. SELECT?team,?no?FROM?users?GROUP?BY?team?????????????????????????????(1)??

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但是对于Mongodb而言，实现这样的功能，则比较复杂。

从mongodb2.2之后，有了三个function可以实现这个功能，他们按照产生的顺序，分别是group(), mapReduce()和aggregate().

他们之间的区别有哪些呢？参照stack overflow上讨论http://stackoverflow.com/questions/12337319/mongodb-aggregation-comparison-group-group-and-mapreduce整理如下：

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1.?????db.collection.group().

定义为:

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1. Db.collection.group(??
2. ????????????key,??
3. ????????????reduce,??
4. ????????????initial,??
5. ????????????keyf,??
6. ????????????cond,??
7. ????????????finalize).??

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特征为：

Simple syntax and functionality for grouping .. analogous to?GROUP BY?in SQL.Returns result set inline (as an array of grouped items).Implemented using the JavaScript engine; custom?reduce()?functions can be written in JavaScript.Current LimitationsWill not group into a result set with more than 10,000 keys.Results must fit within the limitations of a BSON document (currently 16Mb).Takes a read lock and does not allow any other threads to execute JavaScript while it is running.Does not work with sharded collections.

Ex: 如果需要实现语句1的功能，实现如下：

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1. db.users.group({key:?{team:?1},?initial:?{members:?[]},?reduce:?function(cur,?result){result.members.push(cur.no);}});??

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2.?????db.collection.mapReduce().

据说增加mapreduce是为了迎合mapreduce的流行。

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1. db.collection.mapReduce(??
2. ?????????????????????????<mapfunction>,??
3. ????????????????????????<reducefunction>,??
4. ?????????????????????????{??
5. ???????????????????????????out:?<collection>,??
6. ???????????????????????????query:?<document>,??
7. ???????????????????????????sort:?<document>,??
8. ???????????????????????????limit:?<number>,??
9. ???????????????????????????finalize:?<function>,??
10. ???????????????????????????scope:?<document>,??
11. ???????????????????????????jsMode:?<boolean>,??
12. ???????????????????????????verbose:?<boolean>??
13. ?????????????????????????}??
14. ???????????????????????)??

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特征为：

Implements the?MapReduce model?for processing large data sets.Can choose from one of several?output options?(inline, new collection, merge, replace, reduce)MapReduce functions are written in JavaScript.Supports non-sharded and sharded input collections.Can be used for incremental aggregation over large collections.MongoDB 2.2 implements much better support for?sharded map reduce output.Current LimitationsThere is a JavaScript lock so a mongod server can only execute one JavaScript function at a point in time .. however, most steps of the MapReduce are very short so locks can be yielded frequently.MapReduce functions can be difficult to debug. You can use?print()?and?printjson()?to include diagnostic output in the?mongod?log.MapReduce is generally not intuitive for programmers trying to translate relational query aggregation experience.

由于需要用到js engine，所以速度是比较慢的，具体的可以参照http://technicaldebt.com/?p=1157

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Ex: 如果需要实现语句1的功能，实现如下：

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1. var?map?=?function(){?emit(this.team,?this.no);?};???
2. var?reduce?=?function(key,?value){?return?{team:?key,?members:?value};?};??
3. db.users.mapReduce(map,?reduce,?{out:?"team_member"});??

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3.?????db.collection.aggregate().

For simplertasks, mapReduce is big hammer. And avoid overhead of JavaScript engine, alsoselect matching subdocuments and arrays. Aggregate framework is implementedwithpipelinein C++.

Pipeline?定义的操作有:

$match? query predicate as a filter.

$project? use a sample document todetermine the shape of the result.

$unwind? hands out array elements oneat a time.

$group? aggregates items into bucketsdefined by a key.

$sort? sort document.

$limit? allow the specified number ofdocuments to pass

$skip? skip over the specified numberof documents.

特征如下：

New feature in the MongoDB 2.2.0 production release (August, 2012).Designed with specific goals of improving performance and usability.Returns result set inline.Supports non-sharded and sharded input collections.Uses a "pipeline" approach where objects are transformed as they pass through a series of pipeline operators such as matching, projecting, sorting, and grouping.Pipeline operators need not produce one output document for every input document: operators may also generate new documents or filter out documents.Using projections you can add computed fields, create new virtual sub-objects, and extract sub-fields into the top-level of results.Pipeline operators can be repeated as needed (for example, multiple?$project?or?$groupsteps.Current LimitationsResults are returned inline, so are limited to the maximum document size supported by the server (16Mb)Doesn't support as many output options as MapReduceLimited to?operators and expressions?supported by the Aggregation Framework (i.e. can't write custom functions)Newest server feature for aggregation, so has more room to mature in terms of documentation, feature set, and usage.

Ex: 如果需要实现语句1的功能，实现如下：

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1. db.users.aggregate({$project:?{team:?1,?no:?1}},?{$group:?{?_id:?"$team",?memebers:?{$addToSet:?"$no"}}});??

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Refs:

http://docs.mongodb.org/manual/aggregation/#Aggregation-Examples

http://docs.mongodb.org/manual/reference/method/db.collection.group/

http://technicaldebt.com/?p=1157

http://stackoverflow.com/questions/12337319/mongodb-aggregation-comparison-group-group-and-mapreduce

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