As per the MongoDB documentation, Map-reduce is a data processing paradigm for condensing large volumes of data into useful aggregated results. MongoDB uses mapReduce command for map-reduce operations. MapReduce is generally used for processing large data sets.

MapReduce Command

Following is the syntax of the basic mapReduce command −

>db.collection.mapReduce(
   function() {emit(key,value);},  //map function
   function(key,values) {return reduceFunction}, {   //reduce function
      out: collection,
      query: document,
      sort: document,
      limit: number
   }
)

The map-reduce function first queries the collection, then maps the result documents to emit key-value pairs, which is then reduced based on the keys that have multiple values.

In the above syntax −

• map is a javascript function that maps a value with a key and emits a key-value pair

• reduce is a javascript function that reduces or groups all the documents having the same key

• out specifies the location of the map-reduce query result

• query specifies the optional selection criteria for selecting documents

• sort specifies the optional sort criteria

• limit specifies the optional maximum number of documents to be returned

Using MapReduce

Consider the following document structure storing user posts. The document stores user_name of the user and the status of post.

{
   "post_text": "tutorialspoint is an awesome website for tutorials",
   "user_name": "mark",
   "status":"active"
}

Now, we will use a mapReduce function on our posts collection to select all the active posts, group them on the basis of user_name and then count the number of posts by each user using the following code −

>db.posts.mapReduce( 
   function() { emit(this.user_id,1); }, 
	
   function(key, values) {return Array.sum(values)}, {  
      query:{status:"active"},  
      out:"post_total" 
   }
)

The above mapReduce query outputs the following result −

{
   "result" : "post_total",
   "timeMillis" : 9,
   "counts" : {
      "input" : 4,
      "emit" : 4,
      "reduce" : 2,
      "output" : 2
   },
   "ok" : 1,
}

The result shows that a total of 4 documents matched the query (status:”active”), the map function emitted 4 documents with key-value pairs and finally the reduce function grouped mapped documents having the same keys into 2.

To see the result of this mapReduce query, use the find operator −

>db.posts.mapReduce( 
   function() { emit(this.user_id,1); }, 
   function(key, values) {return Array.sum(values)}, {  
      query:{status:"active"},  
      out:"post_total" 
   }
	
).find()

The above query gives the following result which indicates that both users tom and mark have two posts in active states −

{ "_id" : "tom", "value" : 2 }
{ "_id" : "mark", "value" : 2 }

In a similar manner, MapReduce queries can be used to construct large complex aggregation queries. The use of custom Javascript functions make use of MapReduce which is very flexible and powerful.