When to use Map-Reduce in MongoDB?

In MongoDB, you can use Map-reduce when your aggregation query is slow because data is present in a large amount and the aggregation query is taking more time to process. So using map-reduce you can perform action faster on large datasets than aggregation query. 

Map-Reduce is useful for performing complex aggregation operations that are difficult or inefficient to express using the aggregation pipeline. It provides more flexibility than the aggregation pipeline, allowing you to use custom JavaScript functions to map, reduce, and finalize the data processing

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.