Use of the MapReduce function for MongoDB aggregation operations (11)

MapReduce is popular with the concept of "big data".

In fact, the concept of MapReduce is very simple,

Functionally, a group operation that is equivalent to an RDBMS

What are the real strengths of MapReduce?

A: Distributed, when the data is very large, like Google, there are n multi-data centers,

The data are not at one end of the Earth, with group grasp.

Since the group does not support distributed, the computing power of a single server is bound to be limited.

While Maprecuce supports distributed, supporting a large number of servers to work simultaneously,

Use brute force to count.

Maprecuce's working process:

①map:

This is called the mapping function, which calls emit (Key,value), and the collection is grouped according to the key you specify.

②reduce:

This is called the simplification function, the data grouped by the map is grouped to simplify, note: The key in reduce (Key,value) is emit in the key,value for the emit grouping of emit (value), the array (the same group) of the data, the operation .

Calculate the total inventory for each column with MapReduce

Map function

var map = function () {

Emit (This.cat_id,this.goods_number);

}

var reduce = function (cat_id,numbers) {

return array.sum (numbers);

}

Db.goods.mapReduce (map,reduce,{out: ' Res '});

Results are saved to "res"

Db.res.find ()

#用mapReduce计算每个栏目下商品的平均价格

var map = function () {

Emit (This.cat_id,this.shop_price);

}

var reduce = function (cat_id,values) {

return Array.avg (values);

}

Db.goods.mapReduce (map,reduce,{out: ' Res '});

Use of the MapReduce function for MongoDB aggregation operations (11)