mongodb mapReduce aggregate

//函数准备：
> function map(){
	emit(this.name,{count:1});
  }

> function reduce(key,value){
      var result = {count:0};
      for(var i=0;i<value.length;i++){
         result.count += value[i].count;
      }
      result.ext = "haha";
      return result;
  }

//例子1-1：
> db.person.find()
{ "_id" : ObjectId("566e89f8382a8419511d02ab"), "name" : "hxc", "age" : 20 }
{ "_id" : ObjectId("566e8a0c382a8419511d02ac"), "name" : "hxc", "age" : 24 }
{ "_id" : ObjectId("566e8a1c382a8419511d02ad"), "name" : "zjl", "age" : 34 }
{ "_id" : ObjectId("566e8a1c382a8419511d02ad"), "name" : "zjl", "age" : 26 }
{ "_id" : ObjectId("566e8a32382a8419511d02ae"), "name" : "xx", "age" : 32 }
{ "_id" : ObjectId("566e8a42382a8419511d02af"), "name" : "abcdweb", "age" : 32 }
{ "_id" : ObjectId("566e8a52382a8419511d02b0"), "name" : "abcdweb", "age" : 32 }
{ "_id" : ObjectId("566e8a5f382a8419511d02b1"), "name" : "xx", "age" : 32 }
> db.person.mapReduce(map,reduce,{"out":"collection"})
{
        "result" : "collection",
        "timeMillis" : 285,
        "counts" : {
                "input" : 8,
                "emit" : 8,
                "reduce" : 4,		//此值为4
                "output" : 4
        },
        "ok" : 1
}
> db.collection.find()
{ "_id" : "abcdweb", "value" : { "count" : 2, "ext" : "haha" } }
{ "_id" : "hxc", "value" : { "count" : 2, "ext" : "haha" } }
{ "_id" : "xx", "value" : { "count" : 2, "ext" : "haha" } }
{ "_id" : "zjl", "value" : { "count" : 2, "ext" : "haha" } }

//例子1-2：
> db.person.find()
{ "_id" : ObjectId("566e8a1c382a8419511d02ad"), "name" : "zjl", "age" : 34 }
{ "_id" : ObjectId("566e8a32382a8419511d02ae"), "name" : "xx", "age" : 32 }
{ "_id" : ObjectId("566e8a5f382a8419511d02b1"), "name" : "xx", "age" : 32 }
{ "_id" : ObjectId("566e8fdd382a8419511d02b2"), "name" : "zjl", "age" : 26 }
{ "_id" : ObjectId("566e90a4382a8419511d02b3"), "name" : "hxc", "age" : 20 }
{ "_id" : ObjectId("566e90bd382a8419511d02b4"), "name" : "abcdweb", "age" : 32 }
> db.person.mapReduce(map,reduce,{"out":"collection"})
{
        "result" : "collection",
        "timeMillis" : 271,
        "counts" : {
                "input" : 6,
                "emit" : 6,
                "reduce" : 2,		//此值为2
                "output" : 4
        },
        "ok" : 1
}
> db.collection.find()
//留意，首两行记录没有“ext”字段，表明该两组并没有执行reduce函数；value为emit的value
{ "_id" : "abcdweb", "value" : { "count" : 1 } }
{ "_id" : "hxc", "value" : { "count" : 1 } }
{ "_id" : "xx", "value" : { "count" : 2, "ext" : "haha" } }
{ "_id" : "zjl", "value" : { "count" : 2, "ext" : "haha" } }
>

总结：map是拿来分组的，具体实现通过emit，emit的key则为分组字段，其value的集合（即数组）会传给reduce函数执行，若value集合长度小于2，则该分组不会传到reduce函数执行（因为reduce的字面意思是“简化”，是将集合数据进行精简，统计获取一个结果；而若集合只有一条数据，根本就不需要统计了）

 

更多用法，参考：

MongoDB:MapReduce基础及实例

MongoDB MapReduce学习笔记

 

Aggregate

db.article.aggregate(

{ $project : {

title : 1,

doctoredPageViews : { $add: ["$pageViews", 10] }	//留意：$add后面是中括号

}});
	
{ $project : { title : 1 , page_views : "$pageViews" , bar : "$other.foo" }}

//注意操作符的顺序
db.user.aggregate(	
 [
   
    {
      $group: {
      	_id: "$User",
      	totalCount: {$sum: "$Count"}
      }
    },
    { 
     $project : {
        _id: 0,
        name : "$_id",
        totalCount: "$totalCount"
     }
    }
  ]
);

//以多个字段分组要这样写
db.user.aggregate(	
 { $group : {
        _id : { "MyUser": "$User" ,
                  "Country":"$Country" 
                  },
       TotalCount : { $sum : "$Count" }
       }
}
);

-------------------------------------------------------------------------
数据准备：
{ 
    "_id" : ObjectId("56cd0e8b8b04c3250840e364"), 
    "title" : "MongoDB Overview", 
    "description" : "MongoDB is no sql database", 
    "by_user" : "w3cschool.cc", 
    "url" : "http://www.w3cschool.cc", 
    "tags" : [
        "mongodb", 
        "database", 
        "NoSQL"
    ], 
    "likes" : NumberInt(100)
}
{ 
    "_id" : ObjectId("56cd0e8b8b04c3250840e365"), 
    "title" : "NoSQL Overview", 
    "description" : "No sql database is very fast", 
    "by_user" : "w3cschool.cc", 
    "url" : "http://www.w3cschool.cc", 
    "tags" : [
        "mongodb", 
        "database", 
        "NoSQL"
    ], 
    "likes" : NumberInt(10)
}
{ 
    "_id" : ObjectId("56cd0e8b8b04c3250840e366"), 
    "title" : "Neo4j Overview", 
    "description" : "Neo4j is no sql database", 
    "by_user" : "Neo4j", 
    "url" : "http://www.neo4j.com", 
    "tags" : [
        "neo4j", 
        "database", 
        "NoSQL"
    ], 
    "likes" : NumberInt(750)
}
{ 
    "_id" : ObjectId("56cd20c08b04c3250840e367"), 
    "title" : "Java Overview", 
    "description" : "Java Coder", 
    "by_user" : "w3cschool.cc", 
    "url" : "http://www.java.com", 
    "tags" : [
        "javase", 
        "javaee", 
        "spring"
    ], 
    "likes" : NumberInt(300)
}


//这些操作符都是可以用多次的
db.author.aggregate([
	{
		$group: {
			_id: "$by_user",
			count: {$sum: "$likes"}
		}
	},
	{
		$project: {
			cnt: {$cond: {if: {$gte: ['$count', 500]}, then: 'a', else: 'b'}}
			
		}
	},
	{
		$project: {
			cnt: '$cnt',
			cs: {$cond: {if: {$eq: ['$cnt', 'a']}, then: '1', else: '2'}}
		}
	}
]);

//分组可以根据条件分
db.author.aggregate([
	{
		$group: {
			_id: {
			  $cond: {
			    if: {$gte: ['$likes', 150]}, 
			    then: 'a', 
			    else: 'b'
			  }
			},
			count: {$sum: "$likes"}
		}
	}
]);

例子：多个区间分组
db.author.aggregate([
	{
		$group: {
			_id: {
			  $cond: {
			    if: {$lte: ['$likes', 110]}, 
			    then: '<=110', 
			    else: '$likes'
			  }
			},
			count: {$sum: 1}
		}
	},
	{
		$group: {
			_id: {
			  $cond: {
			    if: {$lte: ['$_id', 750]}, 
			    then: '>110 and <=750', 
			    else: '$_id'
			  }
			},
			count: {$sum: '$count'}
		}
	}
]);

output：
{ "_id" : "<=110", "count" : 2 }
{ "_id" : ">110 and <=750", "count" : 2 }
-------------------------------------------------------------------------

mapreduce例子：
var map = function() { emit(this.carrier_name, {count:1});};
var reduce = function(key, values) {
            var total = 0;
            for(var i=0;i<values.length;i++){total += values[i].count;}
            return {count:total};};
var final = function(key, values){
  	return {
  		"name": key,
  		"num": values.count
  	};
 };                    
db.http_details.mapReduce(map, reduce, {"query": {"carrier_name": {$ne: "中国电信"}}, "out": "tempStat", "finalize": final, "keeptemp": false});
{ 
    "_id" : "CHINA MOBILE", 
    "value" : {
        "name" : "CHINA MOBILE", 
        "num" : 1364.0
    }
}
{ 
    "_id" : "CHN-UNICOM", 
    "value" : {
        "name" : "CHN-UNICOM", 
        "num" : 1975.0
    }
}
{ 
    "_id" : "unknown", 
    "value" : {
        "name" : "unknown", 
        "num" : 15498.0
    }
}
{ 
    "_id" : "中国移动", 
    "value" : {
        "name" : "中国移动", 
        "num" : 7711.0
    }
}
{ 
    "_id" : "中国联通", 
    "value" : {
        "name" : "中国联通", 
        "num" : 9069.0
    }
}

 MongoDB 聚合管道（Aggregation Pipeline）