InputSplit is assigned with a work but doesn’t know how to access it. The record holder class is totally responsible for loading the data from its source and convert it into keys pair suitable for reading by the Mapper. The RecordReader’s instance can be defined by the Input Format.

In Hadoop, a RecordReader is a crucial component that plays a significant role in the MapReduce framework. The primary purpose of a RecordReader is to read and parse the input data stored in Hadoop’s distributed file system (HDFS) and present it to the Mapper as key-value pairs.

Here’s a breakdown of its functionality:

Input Split Handling: Hadoop divides large input data into smaller chunks called input splits. Each input split is processed by a separate mapper task. The RecordReader is responsible for reading and processing the data within a specific input split.
Decoding Input Data: The RecordReader takes the raw, often compressed, input data and decodes it into logical records. These records are typically key-value pairs that the Mapper can process. The exact structure of these records depends on the input format (e.g., TextInputFormat for plain text files, SequenceFileInputFormat for binary data in Hadoop’s SequenceFile format).
Providing Key-Value Pairs to Mapper: Once the data is decoded, the RecordReader presents it to the Mapper as key-value pairs. The Mapper then processes these pairs according to the user-defined Map function.
Customizable: Hadoop allows developers to implement custom RecordReaders to handle specific input data formats. This customization is particularly useful when dealing with non-standard or complex data sources.

In summary, the RecordReader in Hadoop is essential for breaking down and interpreting input data, making it accessible to the Map phase of the MapReduce job. It acts as an interface between the Hadoop framework and the data stored in HDFS, ensuring that the MapReduce tasks can efficiently process large-scale distributed datasets.