Python MongoDB MapReduce

#-*-Coding:utf-8-*-
Import OS
Import CSV
Import Pymongo
From Pymongo import mongoclient
From Bson.code Import Code
From Pymongo import mongoclient

#建立连接
Client = mongoclient (' 10.20.4.79 ', 27017)
#client = mongoclient (' 10.20.66.106 ', 27017)
db_name = ' Ta ' #数据库名
db = Client[db_name]

Insert test data:

For I in xrange (1000):
Rid=math.floor (Random.random () *10);
Price = Round (Random.random () *10,2);
If RID < 4:
Db.test.insert ({"_id": I, "user": "Joe", "Product": RID, "Price":p rice});
Elif rid>=4 and Rid<7:
Db.test.insert ({"_id": I, "user": "Josh", "Product": RID, "Price":p rice});
Else
Db.test.insert ({"_id": I, "user": "Ken", "Product": RID, "Price":p rice});

The result data is:

{"_id": 0, "price": 5.9, "Product": 9, "user": "Ken"}
{"_id": 1, "price": 7.59, "Product": 7, "User": "Ken"}
{"_id": 2, "price": 4.72, "Product": 0, "user": "Joe"}
{"_id": 3, "price": 1.35, "product": 1, "User": "Joe"}
{"_id": 4, "price": 2.31, "Product": 0, "user": "Joe"}
{"_id": 5, "price": 5.29, "Product": 5, "User": "Josh"}
{"_id": 6, "price": 3.34, "Product": 1, "User": "Joe"}
{"_id": 7, "price": 7.2, "Product": 4, "User": "Josh"}
{"_id": 8, "price": 8.1, "Product": 6, "user": "Josh"}
{"_id": 9, "price": 2.57, "Product": 3, "user": "Joe"}
{"_id": Ten, "Price": 0.54, "Product": 2, "User": "Joe"}
{"_id": One, "price": 0.66, "Product": 1, "User": "Joe"}
{"_id": "Price": 5.51, "Product": 1, "User": "Joe"}
{"_id": "Price": 3.74, "Product": 6, "user": "Josh"}
{"_id": +, "price": 4.82, "Product": 0, "user": "Joe"}
{"_id": "Price": 9.79, "Product": 3, "user": "Joe"}
{"_id": +, "price": 9.6, "Product": 5, "User": "Josh"}
{"_id": +, "price": 4.06, "Product": 7, "User": "Ken"}
{"_id": "Price": 1.37, "Product": 5, "User": "Josh"}
{"_id": +, "price": 6.77, "Product": 9, "user": "Ken"}

Test 1, how many products each user has purchased?
The SQL statement is implemented as: Select User,count (product) from the test group by user

Mapper = Code ("" "function () {Emit (This.user,{count:1})}" "")

reduce = Code ("function (key, values) {"
"var total = 0;"
"for (var i = 0; i < values.length; i++) {"
"Total + = Values[i].count;"
" }"
"Return {count:total};"
"}")

Result=db.test.map_reduce (mapper,reduce,out = ' myresults ')

　　For Doc in Db.myresults.find ():

Print doc

Test 2, query each user, buy the number of goods, the total price, and evaluate the price condition is more than 5 of the SQL implementation: Select User,count (SKU), SUM (price),

Round (sum (price)/count (SKU), 2) as Avgprice from test where prince>5 group by user

Mapper=code ("" "function () {Emit (this.user,{amount:this.price,count:1,avgprice:0})}" "")

reduce = Code ("function (key, values) {"
"Var res={amount:0,count:0,avgprice:0};"
"for (var i = 0; i < values.length; i++) "
" {"
"Res.count + = Values[i].count;"
"Res.amount + = Values[i].amount;"
" }"
"Res.avgprice = (res.amount/res.count). toFixed (2);"
"Return res;"
"}")

result = Db.test.map_reduce (mapper,reduce,out = ' myresults ', query={' price ': {' $gt ': 6}})

For Doc in Db.myresults.find ():
Print doc

Python MongoDB MapReduce