Map Reduce是一种数据处理技术，可将大量数据压缩为汇总结果。
提供MongoDB mapreduce命令可以完成此任务。

让我们考虑以下示例，这些示例演示了mapreduce命令的用法。

考虑包含以下文件的汽车集合；

>db.car.insert( 
[ 
{car_id:"c1",name:"Audi",color:"Black",cno:"H110",mfdcountry:"Germany",speed:72,price:11.25}, {car_id:"c2",name:"Polo",color:"White",cno:"H111",mfdcountry:"Japan",speed:65,price:8.5}, {car_id:"c3",name:"Alto",color:"Silver",cno:"H112",mfdcountry:"San Franceco",speed:53,price:4.5},
{car_id:"c4",name:"Santro",color:"Grey",cno:"H113",mfdcountry:"Sweden",speed:89,price:3.5} ,
{car_id:"c5",name:"Zen",color:"Blue",cno:"H114",mfdcountry:"Denmark",speed:94,price:6.5} 

] )
BulkWriteResult({
	"writeErrors" : [ ],
	"writeConcernErrors" : [ ],
	"nInserted" : 5,
	"nUpserted" : 0,
	"nMatched" : 0,
	"nModified" : 0,
	"nRemoved" : 0,
	"upserted" : [ ]
})
>

现在，让我们通过对速度进行分类并将其归类为超速汽车，从而在汽车收藏分组中编写map reduce功能。

定义地图功能，如下所示

>var speedmap = function (){
var criteria;
if ( this.speed > 70 ) {
criteria = 'overspeed';
emit(criteria,this.speed);
}
};

此功能根据速度将汽车分类为超速汽车。
这里的" this"指的是当前文档，必须对其进行地图缩小处理。

使用参数键和值定义reduce函数，以计算超速汽车的平均速度为

>var avgspeed_reducemap = function(key, speed) {
   var total =0;
   for (var i = 0; i < speed.length; i++) {	
	total = total+speed[i];
   }
   return total/speed.length;
};
>

其中通过迭代循环将所有汽车的速度相加，并且将平均速度计算为所有速度与超速汽车数量的总和。

通过以以下方式调用car集合中存在的所有文档上的Map and Reduce函数来调用map reduce函数：

>var ret = db.car.mapReduce(speedmap, avgspeed_reducemap, {out: "avgspeed"});

输出在avgspeed集合中获取。
如果此集合不存在，则会创建一个新集合，否则将替换新内容。

要查看文档，请调用db.avgspeed.find()。

输出：

{ "_id" : "overspeed", "value" : 85 }

输出显示超速汽车的平均速度为85。

MongoDB Map Reduce Java示例

下面是上面的mongo shell示例的Java程序，请注意，它只是展示了Map Reduce函数的工作原理。
因此，请确保数据存在于集合中以使其达到期望的结果。

package com.theitroad.mongodb;

import java.net.UnknownHostException;

import com.mongodb.DB;
import com.mongodb.DBCollection;
import com.mongodb.DBObject;
import com.mongodb.MapReduceCommand;
import com.mongodb.MapReduceOutput;
import com.mongodb.MongoClient;

public class MongoDBMapReduce {

	public static void main(String[] args) throws UnknownHostException {

		//create an instance of client and establish the connection
		MongoClient m1 = new MongoClient();

		//get the test db,use your own
		DB db = m1.getDB("theitroad");

		//get the car collection
		DBCollection coll = db.getCollection("car");

		//map function to categorize overspeed cars
		String carMap = "function (){" + "var criteria;"
				+ "if ( this.speed > 70 ) {" + "criteria = 'overspeed';"
				+ "emit(criteria,this.speed);" + "}" + "};";

		//reduce function to add all the speed and calculate the average speed

		String carReduce = "function(key, speed) {" + "var total =0;"
				+ "for (var i = 0; i < speed.length; i++) {"
				+ "total = total+speed[i];" + "}"
				+ "return total/speed.length;" + "};";

		//create the mapreduce command by calling map and reduce functions
		MapReduceCommand mapcmd = new MapReduceCommand(coll, carMap, carReduce,
				null, MapReduceCommand.OutputType.INLINE, null);

		//invoke the mapreduce command
		MapReduceOutput cars = coll.mapReduce(mapcmd);

		//print the average speed of cars
		for (DBObject o : cars.results()) {

			System.out.println(o.toString());

		}

	}

}

上面的java程序产生以下输出。

{ "_id" : "overspeed" , "value" : 85.0}