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Explain the process of data storage in Hadoop Distributed File System (HDFS) with the help of a suitable example.

HDFS is a distributed file system designed to store and manage large data sets across a cluster of machines.

It adopts a simple but effective approach to data storage:

1. Data Splitting

  • Large files are broken down into fixed-size blocks, typically 64MB or 128MB.
  • This partitioning enables parallel processing, where each block can be processed independently across different nodes in the cluster.

2. Block Replication

  • Each data block is replicated across multiple nodes in the cluster, ensuring data availability even if one node fails.
  • Replication factor is configurable, allowing for a balance between data redundancy and storage efficiency.

3. Metadata Management

  • The NameNode acts as the central authority, storing metadata about all files and blocks in the system.
  • This metadata includes block locations, replication factors, and file permissions.
  • The DataNodes store the actual data blocks and report their health status to the NameNode.

4. Data Read and Write Operations

  • Clients interact with the NameNode to locate the desired data blocks.
  • The NameNode directs the client to the DataNodes where the blocks are located.
  • Clients can then read or write data directly to the DataNodes.


Imagine you want to store a 1GB file containing weather data in HDFS.

The process would be as follows:

  1. File Splitting: The file is split into 16 blocks of 64MB each.
  2. Block Replication: Each block is replicated 3 times across different DataNodes in the cluster.
  3. Metadata Management: The NameNode stores the information about the file, including the block locations and replication factors.
  4. Data Storage: Each DataNode stores three copies of each block, resulting in a total of 48 blocks stored across the cluster.