MapReduce with MongoDB and Python

 1 安装使用MongoDB

    a) 下载MongoDB, 请注意，32bit只能存2GB的内容（32-bit builds are limited to around 2GB of data）。

    b）配置好mongodb.config， 然后命令行：Mongod.exe --config /path/to/your/mongodb.config就可以了。

    c) 下载pymongo, 后面用python来写测试程序。

    请参阅：The Little MongoDB Book, (pdf)。

2 MapReduce

Map/reduce in MongoDB is useful for batch processing of data and aggregation operations. It is similar in spirit to using something like Hadoop with all input coming from a collection and output going to a collection. Often, in a situation where you would have used GROUP BY in SQL, map/reduce is the right tool in MongoDB.

参见MongoDB网站上对MapReduce的介绍。Map/reduce 流程如下：

 

3 例子

以单词统计为例说明。输入文本是Obama的演讲词，可以看看里面里面单词的使用频率。如下图:

 

MongoDB 运行客户端用JS脚本。

Map程序为：

Reduce程序为：

 

客户端程序为：

from pymongo import Connection
from pymongo.code import Code


#'''
#Open a connection to MongoDb (localhost)
connection =  Connection()
db = connection.test

#Remove any existing data
db.texts.remove()

#Insert the data 
lines = open('2009-obama.txt').readlines()
[db.texts.insert({'text': line}) for line in lines]

#Load map and reduce functions
map = Code(open('wordMap.js','r').read())
reduce = Code(open('wordReduce.js','r').read())


#Run the map-reduce query
results = db.texts.map_reduce(map, reduce, "collection_name")

#Print the results
for result in results.find():
    print result['_id'] , result['value']['count']

运行结果为：

 

文章代码可以在这里下载。

 

参见：MapReduce with MongoDB and Python 以及这里。