一：往数据库里插入数据

先生成一些简单的数据

from pymongoimport MongoClient

from randomimport randint

import datetime

client = MongoClient('localhost',27017)

db = client.get_database('229_taobao')

order = db.order_info

status = ['A','B','C']

cust_id = ['A123','B123','C123']

price = [500,200,250,300]

sku = ['mmm','nnn']

for iin range(1,100):

    items = []

    item_count =randint(2,6)

    for nin range(item_count):

        items.append({"sku":sku[randint(0,1)],"qty":randint(1,10),"price":randint(0,5)})

    new = {

    "status":status[randint(0,2)],

    "cust_id":cust_id[randint(0,2)],

    "price":price[randint(0,3)],

    "ord_date":datetime.datetime.utcnow(),

    "items":items

}

print new

order.insert_one(new)

print i

print order.estimated_document_count()

二：进入数据库进行mapreduce操作

查看数据类型

（1）查询每个cust_id 的所有price总和

map函数

var mapFunction1 = function() {

emit(this.cust_id, this.price);

};

emit函数是把数据按照cust_id和price进行分组，可以将参数传给reduce函数

reduce函数

var reduceFunction1 = function(keyCustId, valuesPrices) {

return Array.sum(valuesPrices);

};

执行mapreduce函数并把结果输入map_reduce_example中

db.order_info.mapReduce( mapFunction1, reduceFunction1, { out: "map_reduce_example" } )

查询输出的结果

（2）计算所有items 的平均库存

map函数

var mapFunction2 = function() {

for (var idx = 0; idx < this.items.length; idx++) {

var key = this.items[idx].sku;

var value = { count: 1, qty: this.items[idx].qty };

emit(key, value);

}

};

reduce函数

var reduceFunction2 = function(keySKU, countObjVals) {

    reducedVal = { count: 0, qty: 0 };

    for (var idx = 0; idx < countObjVals.length; idx++) {

        reducedVal.count += countObjVals[idx].count;

        reducedVal.qty += countObjVals[idx].qty;

}

    return reducedVal;

};

finalize函数