What is InputSplit in Hadoop? Explain.

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When a Hadoop job runs, it splits input files into chunks and assigns each split to a mapper for processing. It is called the InputSplit.

In Hadoop, an InputSplit is a logical division of the input data that is fed into a MapReduce job. It represents a chunk of the input data that is processed by an individual Mapper in a distributed computing environment. InputSplits are the basic units of work in a MapReduce job, and each InputSplit is processed by a separate Mapper task.

Here are some key points about InputSplits in Hadoop:

  1. Logical Division of Input Data: InputSplits represent a logical division of the input data rather than a physical division. This logical division allows the framework to parallelize the processing of data by assigning different InputSplits to different Mapper tasks.
  2. Size of InputSplits: The size of an InputSplit is a configurable parameter and is generally determined by the underlying storage system (such as HDFS). Hadoop tries to create InputSplits such that each split contains roughly the same amount of data.
  3. RecordReader: Each InputSplit is associated with a RecordReader, which is responsible for reading and processing the data within that split. The RecordReader is specific to the data format (e.g., text, sequence files) and provides a way for Mappers to iterate over the records in the split.
  4. Parallel Processing: The division of input data into InputSplits enables parallel processing, as each Mapper processes a separate InputSplit independently. This parallelism is one of the key factors contributing to the scalability of Hadoop MapReduce jobs.
  5. InputFormat: The InputFormat in Hadoop defines how the input data is split into InputSplits and how these splits are processed by the Mappers. Different InputFormats are available for handling various types of input data, such as TextInputFormat for plain text files and SequenceFileInputFormat for binary sequence files.

In summary, an InputSplit in Hadoop is a logical representation of a portion of the input data that is processed independently by a Mapper in a MapReduce job. It enables parallelization of the processing, contributing to the efficiency and scalability of Hadoop distributed computing.