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Difference between input split and block in Hadoop MapReduce?

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Camparison between input split and a block in Hadoop MapReduce?


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HDFS Block-

Block is a continuous location on the hard drive where data is stored. In general, FileSystem stores data as a collection of blocks. In the same way, HDFS stores each file as blocks. The Hadoop application is responsible for distributing the data block across multiple nodes.

Input Split in Hadoop-
The data to be processed by an individual Mapper is represented by InputSplit. The split is divided into records and each record (which is a key-value pair) is processed by the map. The number of map tasks is equal to the number of InputSplits. Initially, the data for MapReduce task is stored in input files and input files typically reside in HDFS. InputFormat is used to define how these input files are split and read. InputFormat is responsible for creating InputSplit.

MapReduce InputSplit vs Blocks in Hadoop
InputSplit vs Block Size in Hadoop-

• Block – The default size of the HDFS block is 128 MB which we can configure as per our requirement. All blocks of the file are of the same size except the last block, which can be of same size or smaller. The files are split into 128 MB blocks and then stored into Hadoop FileSystem.
• InputSplit – By default, split size is approximately equal to block size. InputSplit is user defined and the user can control split size based on the size of data in MapReduce program.
Data Representation in Hadoop Blocks vs InputSplit-
• Block – It is the physical representation of data. It contains a minimum amount of data that can be read or write.
• InputSplit – It is the logical representation of data present in the block. It is used during data processing in MapReduce program or other processing techniques. InputSplit doesn’t contain actual data, but a reference to the data