MapReduce 可能计算非常复杂的聚合逻辑，非常灵活，然而，MapReduce十分慢，不应该用于实时的数据分析中。MapReduce可能在多台Server上并行执行，每台Server只负责实现一部分wordload，最初将wordload发送到Master Server上合并，计算出最终的后果集，返回客户端。
MapReduce的根本思维，如下图所示：
在这个例子中，咱们以一个求和为例。首先执行Map阶段，把一个大工作拆分成若干个小工作，每个小工作运行在不同的节点上，从而反对分布式计算，这个阶段叫做Map（如蓝框所示）；每个小工作输入的后果再进行二次计算，最初失去后果55，这个阶段叫做Reduce（如红框所示）。
应用MapReduce形式计算聚合，次要分为三步：Map，Shuffle（拼凑）和Reduce，Map和Reduce须要显式定义，shuffle由MongoDB来实现。
- Map：将操作映射到每个doc，产生Key和Value
- Shuffle：依照Key进行分组，并将key雷同的Value组合成数组
- Reduce：把Value数组化简为单值
咱们以上面的测试数据（员工数据）为例，来为大家演示。
db.emp.insert([{_id:7369,ename:'SMITH' ,job:'CLERK' ,mgr:7902,hiredate:'17-12-80',sal:800,comm:0,deptno:20},{_id:7499,ename:'ALLEN' ,job:'SALESMAN' ,mgr:7698,hiredate:'20-02-81',sal:1600,comm:300 ,deptno:30},{_id:7521,ename:'WARD' ,job:'SALESMAN' ,mgr:7698,hiredate:'22-02-81',sal:1250,comm:500 ,deptno:30},{_id:7566,ename:'JONES' ,job:'MANAGER' ,mgr:7839,hiredate:'02-04-81',sal:2975,comm:0,deptno:20},{_id:7654,ename:'MARTIN',job:'SALESMAN' ,mgr:7698,hiredate:'28-09-81',sal:1250,comm:1400,deptno:30},{_id:7698,ename:'BLAKE' ,job:'MANAGER' ,mgr:7839,hiredate:'01-05-81',sal:2850,comm:0,deptno:30},{_id:7782,ename:'CLARK' ,job:'MANAGER' ,mgr:7839,hiredate:'09-06-81',sal:2450,comm:0,deptno:10},{_id:7788,ename:'SCOTT' ,job:'ANALYST' ,mgr:7566,hiredate:'19-04-87',sal:3000,comm:0,deptno:20},{_id:7839,ename:'KING' ,job:'PRESIDENT',mgr:0,hiredate:'17-11-81',sal:5000,comm:0,deptno:10},{_id:7844,ename:'TURNER',job:'SALESMAN' ,mgr:7698,hiredate:'08-09-81',sal:1500,comm:0,deptno:30},{_id:7876,ename:'ADAMS' ,job:'CLERK' ,mgr:7788,hiredate:'23-05-87',sal:1100,comm:0,deptno:20},{_id:7900,ename:'JAMES' ,job:'CLERK' ,mgr:7698,hiredate:'03-12-81',sal:950,comm:0,deptno:30},{_id:7902,ename:'FORD' ,job:'ANALYST' ,mgr:7566,hiredate:'03-12-81',sal:3000,comm:0,deptno:20},{_id:7934,ename:'MILLER',job:'CLERK' ,mgr:7782,hiredate:'23-01-82',sal:1300,comm:0,deptno:10}]);（案例一）求员工表中，每种职位的人数
var map1=function(){emit(this.job,1)}var reduce1=function(job,count){return Array.sum(count)}db.emp.mapReduce(map1,reduce1,{out:"mrdemo1"})（案例二）求员工表中，每个部门的工资总和
var map2=function(){emit(this.deptno,this.sal)}var reduce2=function(deptno,sal){return Array.sum(sal)}db.emp.mapReduce(map2,reduce2,{out:"mrdemo2"})（案例三）Troubleshoot the Map Function
定义本人的emit函数：var emit = function(key, value) {print("emit");print("key: " + key + " value: " + tojson(value));}测试一条数据：emp7839=db.emp.findOne({_id:7839})map2.apply(emp7839)输入以下后果：emitkey: 10 value: 5000测试多条数据：var myCursor=db.emp.find()while (myCursor.hasNext()) { var doc = myCursor.next(); print ("document _id= " + tojson(doc._id)); map2.apply(doc); print();}（案例四）Troubleshoot the Reduce Function
一个简略的测试案例var myTestValues = [ 5, 5, 10 ];var reduce1=function(key,values){return Array.sum(values)}reduce1("mykey",myTestValues)测试：Reduce的value蕴含多个值测试数据：薪水、奖金：var myTestObjects = [ { sal: 1000, comm: 5 }, { sal: 2000, comm: 10 }, { sal: 3000, comm: 15 } ];开发reduce办法：var reduce2=function(key,values) { reducedValue = { sal: 0, comm: 0 }; for(var i=0;i<values.length;i++) { reducedValue.sal += values[i].sal; reducedValue.comm += values[i].comm; } return reducedValue;}测试：reduce2("aa",myTestObjects)