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 are 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 the 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 inactive 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 makes use of MapReduce which is very flexible and powerful.
