Steps to use Map Reduce in MongoDB

Look at this step-by-step guide to learn how to use MongoDB Map-Reduce. Let’s try to understand the mapReduce() using the following example:

In this example, we have five records from which we need to take out the maximum marks of each section and the keys are id, sec, marks.

{"id":1, "sec":A, "marks":80}
{"id":2, "sec":A, "marks":90}
{"id":1, "sec":B, "marks":99}
{"id":1, "sec":B, "marks":95}
{"id":1, "sec":C, "marks":90}

Here we need to find the maximum marks in each section. So, our key by which we will group documents is the sec key and the value will be marks. Inside the map function, we use emit(this.sec, this.marks) function, and we will return the sec and marks of each record(document) from the emit function. This is similar to group By MySQL.

var map = function(){emit(this.sec, this.marks)};

After iterating over each document Emit function will give back the data like this:

{"A":[80, 90]},  {"B":[99, 90]},  {"C":[90] } 

and upto this point it is what map() function does. The data given by emit function is grouped by sec key, Now this data will be input to our reduce function. Reduce function is where actual aggregation of data takes place. In our example we will pick the Max of each section like for sec A:[80, 90] = 90 (Max)  B:[99, 90] = 99 (max) , C:[90] = 90(max).

var reduce = function(sec,marks){return Array.max(marks);};

Here in reduce() function, we have reduced the records now we will output them into a new collection.{out :”collectionName”}

db.collectionName.mapReduce(map,reduce,{out :"collectionName"});

In the above query we have already defined the map, reduce. Then for checking we need to look into the newly created collection we can use the query db.collectionName.find() we get:

{"id":"A", value:90}
{"id":"B", value:99}
{"id":"C", value:90}

MongoDB Map-Reduce is a data processing programming model that helps to perform operations on large data sets and produce aggregated results. MongoDB provides the mapReduce() function to perform the map-reduce operations. This function has two main functions, i.e., map function and reduce function.

The map function is used to group all the data based on the key-value and the reduce function is used to perform operations on the mapped data. So, the data is independently mapped and reduced in different spaces and then combined in the function and the result will be saved to the specified new collection.

This mapReduce() function generally operates on large data sets. Using Map Reduce you can perform aggregation operations such as max, avg on the data using some key and it is similar to groupBy in SQL. It performs on data independently and in parallel.