How Does the Hadoop Mapper Generate Intermediate Key-Value Pairs?

What Is the Core Purpose of the Map Method in Hadoop MapReduce?

Understand the purpose of the Map method in Hadoop for your Big Data certification exam. Discover how the Mapper processes raw input data in parallel to generate the intermediate key-value pairs essential for distributed data processing.

Question

What is the purpose of the Map method in Hadoop?

A. To validate cluster configurations
B. To process input data and generate intermediate key-value pairs
C. To write final outputs into HDFS directly
D. To store metadata in NameNode

Answer

B. To process input data and generate intermediate key-value pairs

Explanation

In the Hadoop framework, the Map method is the first stage of the MapReduce processing model. Its primary purpose is to take raw data blocks (input splits) from HDFS, process each record independently and in parallel, and convert them into intermediate key-value pairs. These intermediate pairs are temporarily stored on local disks before being shuffled, sorted, and sent to the Reduce phase for final aggregation. The Map method does not directly write final outputs into HDFS (unless it is a Map-only job), nor does it validate cluster configurations or handle metadata storage (which is the NameNode’s responsibility).