mongodb aggregate mapReduce and group-WorkNote
> db.emp.mapReduce( function() { emit(this.department,1); }, function(key,values) { return Array.sum(values) }, { out:"depart_summary" } ).find(){ "_id" : "Development", "value" : 3 }
{ "_id" : "HR", "value" : 2 }
{ "_id" : "Planning", "value" : 1 }
{ "_id" : "Sales", "value" : 2 }
利用内置的sum函数返回每个部门的人数
> db.emp.mapReduce( function() { emit(this.department,this.salary); }, function(key,values) {return Array.avg(values) }, { out:"depart_summary" } ).find()
{ "_id" : "Development", "value" : 7166.666666666667 }
{ "_id" : "HR", "value" : 5250 }
{ "_id" : "Planning", "value" : 5000 }
{ "_id" : "Sales", "value" : 7000 }
利用内置的avg函数返回每个部门的工资平均数
> db.emp.mapReduce( function() { emit(this.department,this.salary); }, function(key,values) {return Array.avg(values).toFixed(2) }, { out:"depart_summary" } ).find()
{ "_id" : "Development", "value" : "7166.67" }
{ "_id" : "HR", "value" : "5250.00" }
{ "_id" : "Planning", "value" : 5000 }
{ "_id" : "Sales", "value" : "7000.00" }
> 保留两位小数
> db.emp.mapReduce( function() { emit(this.department,this.salary); }, function(key,values) {return Array.sum(values) }, { out:"depart_summary" } ).find()
{ "_id" : "Development", "value" : 21500 }
{ "_id" : "HR", "value" : 10500 }
{ "_id" : "Planning", "value" : 5000 }
{ "_id" : "Sales", "value" : 14000 }
>利用内置的sum函数返回每个部门的工资总和
> db.emp.mapReduce( function() { emit(this.department,{count:1}); }, function(key,values) { var sum=0; values.forEach(function(val){sum+=val.count}); return sum; }, { out:"depart_summary" } ).find()
{ "_id" : "Development", "value" : 3 }
{ "_id" : "HR", "value" : 2 }
{ "_id" : "Planning", "value" : { "count" : 1 } }
{ "_id" : "Sales", "value" : 2 }
>手工计算每个部门的员工总数
> db.emp.mapReduce( function() { emit(this.department,{salct:this.salary,count:1}); }, function(key,values) { var res={salct:0,sum:0}; values.forEach(function(val){res.sum+=val.count;res.salct+=val.salct}); return res; }, { out:"depart_summary" } ).find()
{ "_id" : "Development", "value" : { "salct" : 21500, "sum" : 3 } }
{ "_id" : "HR", "value" : { "salct" : 10500, "sum" : 2 } }
{ "_id" : "Planning", "value" : { "salct" : 5000, "count" : 1 } }
{ "_id" : "Sales", "value" : { "salct" : 14000, "sum" : 2 } }
>手工计算每个部门的员工总数和工资总数
> db.emp.mapReduce( function() { emit(this.department,{salct:this.salary,count:1}); }, function(key,values) { var res={salct:0,sum:0}; values.forEach(function(val){res.sum+=val.count;res.salct+=val.salct}); return res.salct/res.sum; }, { out:"depart_summary" } ).find()
{ "_id" : "Development", "value" : 7166.666666666667 }
{ "_id" : "HR", "value" : 5250 }
{ "_id" : "Planning", "value" : { "salct" : 5000, "count" : 1 } }
{ "_id" : "Sales", "value" : 7000 }
>手工计算每个部门的工资平均值
> db.emp.mapReduce( function() { emit(this.department,this.salary); }, function(key,values) {return Array.avg(values) }, { out:"depart_summary" } ).find({value:{$gt:5000}})
{ "_id" : "Development", "value" : 7166.666666666667 }
{ "_id" : "HR", "value" : 5250 }
{ "_id" : "Sales", "value" : 7000 }
将分组计算后的值进行过滤显示，只显示工资平均数大于5000的部门
> db.emp.mapReduce( function() { emit(this.department,this.salary); }, function(key,values) {return Array.avg(values) }, { out:"depart_summary" } ).find({value:{$gt:5000}}).sort({value:1})
{ "_id" : "HR", "value" : 5250 }
{ "_id" : "Sales", "value" : 7000 }
{ "_id" : "Development", "value" : 7166.666666666667 }
将分组计算后的值进行排序，默认为升序
> db.emp.mapReduce( function() { emit(this.department,this.salary); }, function(key,values) {return Array.avg(values) }, { out:"depart_summary" } ).find({value:{$gt:5000}}).sort({value:-1})
{ "_id" : "Development", "value" : 7166.666666666667 }
{ "_id" : "Sales", "value" : 7000 }
{ "_id" : "HR", "value" : 5250 }
> 将分组计算后的值进行排序，手工指定降序
> db.emp.mapReduce( function() { emit(this.department,this.salary); }, function(key,values) {return Array.avg(values) }, { out:"depart_summary" } ).find({value:{$gt:5000}}).sort({value:-1}).limit(2)
{ "_id" : "Development", "value" : 7166.666666666667 }
{ "_id" : "Sales", "value" : 7000 }
> 将分组计算后的值进行降序排序后，取其中的两个值
> db.emp.mapReduce( function() { emit(this.department,{count:1}); }, function(key,values) { var sum=0; values.forEach(function(val){sum+=val.count}); return sum; }, { out:"depart_summary",query:{age:{$gt:25}} } ).find()
{ "_id" : "Development", "value" : { "count" : 1 } }
{ "_id" : "HR", "value" : { "count" : 1 } }
{ "_id" : "Sales", "value" : { "count" : 1 } }
> 分组前过滤数据，然后再分组计算
> db.emp.mapReduce( function() { emit(this.department,{count:1}); }, function(key,values) { var sum=0; values.forEach(function(val){sum+=val.count}); return sum; }, { out:"depart_summary",query:{age:{$gt:22}},sort:{age:1} } ).find()
{ "_id" : "Development", "value" : 2 }
{ "_id" : "HR", "value" : 2 }
{ "_id" : "Planning", "value" : { "count" : 1 } }
{ "_id" : "Sales", "value" : 2 }
> 分组前过滤数据，并排序，然后再分组计算 (本示例无意义)
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