There is a very unique way of indexing in Hadoop. Once the data is stored as per the block size, the HDFS will keep on storing the last part of the data which specifies the location of the next part of the data.
As of my last knowledge update in January 2022, Hadoop Distributed File System (HDFS) does not use traditional indexing mechanisms like those found in relational databases. HDFS is designed for large-scale distributed storage of data, and it typically relies on a block-based storage model.
In HDFS, data is divided into blocks, and each block is stored across multiple nodes in the Hadoop cluster for fault tolerance. The data within a block is not indexed in the traditional sense, and instead, the system relies on the concept of data locality. Data locality means that computation is performed on the same nodes where the data resides, minimizing data movement across the network.
While HDFS itself does not have built-in indexing mechanisms, higher-level processing frameworks built on top of Hadoop, such as Apache Hive or Apache HBase, may implement indexing to improve query performance. These frameworks may use techniques like indexing, partitioning, and pruning to optimize data retrieval. Keep in mind that the specifics can vary depending on the framework being used.
It’s always a good idea to check the latest documentation for the specific Hadoop ecosystem components you are working with, as developments and features may have changed since my last update in January 2022.