MongoDB Map Reduce -FAQs

What is map reducing in MongoDB?

Map-Reduce in MongoDB condenses large data into aggregated results by applying a custom function to each document (map phase) and then aggregating the emitted key-value pairs (reduce phase).

Is MapReduce still used in MongoDB?

According to official MongoDB documentation, map-reduce is deprecated in MongoDB 5.0, and instead, you should use an aggregation pipeline.

What is the use of MapReduce?

The use of MapReduce in MongoDB is to condense large volumes of data into aggregated results by applying a custom function to each input document (map phase) and then aggregating the emitted key-value pairs (reduce phase).

What is better than MapReduce?

Aggregation pipeline is considered better than MapReduce in MongoDB. The aggregation pipeline provides better performance and usability compared to MapReduce operations.

What is the disadvantage of MapReduce?

The disadvantage of MapReduce in MongoDB is that it does not support joins like a relational database. It stores key names for each value pair, leading to data redundancy and increased memory usage.

What is the difference between Hadoop and MapReduce?

Hadoop is the overall open-source framework for distributed processing and analysis of big data sets, while MapReduce is a specific software framework within Hadoop for writing applications to process large amounts of data

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.