In Hadoop, HDFS splits huge files into small chunks that are called?

In Hadoop, when dealing with large files, the Hadoop Distributed File System (HDFS) manages these files by dividing them into smaller, manageable units known as blocks. Each block is typically 128 MB in size (though it can be configured to be larger or smaller), allowing HDFS to distribute the data across the cluster efficiently. This block-based architecture facilitates parallel processing since different blocks can be processed simultaneously on different nodes within a Hadoop cluster.

The use of blocks ensures data redundancy and fault tolerance, as each block can be replicated across multiple nodes, providing high availability. This design choice is fundamental to how HDFS functions, especially in big data scenarios, where handling large datasets effectively is crucial.

The terms segments, partitions, and chunks do not accurately describe the specific architecture of HDFS. While these terms can have meanings in different contexts or technologies, particularly in database systems or other data processing frameworks, in the specific context of HDFS, "blocks" is the correct terminology used to refer to the file pieces created during file management.