Demystifying HDFS: Understanding the Basics of Hadoop Distributed File System in Big Data
Big data has become an integral part of modern-day businesses, and with the increasing amount of data generated every day, it is essential to have a robust and scalable storage system that can handle massive amounts of data. Hadoop Distributed File System (HDFS) is an open-source distributed file system that has gained a lot of popularity in big data applications due to its reliability, fault-tolerance, and scalability characteristics.
In this article, we will explore the basics of HDFS, its architecture, and how it works.
Introduction to HDFS
HDFS is a distributed file system that is designed to store and manage large datasets across multiple nodes in a cluster. It is part of the Apache Hadoop project, which is an open-source framework used for distributed processing of large datasets. The primary purpose of HDFS is to store vast amounts of data reliably and efficiently and provide high-throughput access to that data.
HDFS Architecture
HDFS follows a master-slave architecture, where there is a single NameNode that acts as the master and manages the entire file system namespace. The NameNode keeps track of the location of all the data blocks in the cluster and manages the metadata about files, directories, and their permissions.
On the other hand, there are multiple DataNodes that act as slaves and hold the actual data blocks. The DataNodes are responsible for serving read and write requests from clients and responding to the instructions given by the NameNode. The DataNodes also perform block recovery and replication in case of hardware failures.
Working of HDFS
When a user wants to write a file to HDFS, they first need to connect to the NameNode and request a data location for the file. The NameNode then returns the location of the DataNodes that will store the file’s data blocks. The user can then transmit the data blocks to the DataNodes.
Similarly, when the user wants to read the file, they connect to the NameNode and request the file’s location. The NameNode returns the location of the data blocks, and the user can then retrieve the data from the respective DataNodes.
HDFS also supports replication of data blocks to ensure fault-tolerance and high availability. By default, HDFS stores three replicas of each block, and the replicas are stored on different DataNodes to ensure redundancy.
Conclusion
HDFS is an essential component of the Hadoop ecosystem, and it is widely used in big data applications due to its scalability, fault-tolerance, and reliability characteristics. In this article, we have explored the basics of HDFS, its architecture, and how it works. We hope this article has helped demystify HDFS and given you a better understanding of Hadoop Distributed File System. As businesses continue to generate more and more data, it is essential to have a storage system like HDFS that can handle large datasets and provide high-throughput access to that data.
