Introduction

Hadoop Distributed File System is the foundation of the HDFS cluster (HDFS). The storage system is scalable, fault-tolerant, and rack-aware and was created for use with commodity hardware. There are various ways that HDFS sets itself apart from other distributed file systems.

A Hadoop framework enables storing massive amounts of data on node computers. You may learn about the many Hadoop parts in this article which make up the Hadoop architecture.

Join Big Data Training in Chennai to learn more about modern data analytic tools in big data.

HDFS Hadoop

The storage layer of Hadoop is known as the Hadoop Distributed File System (HDFS). Data is organised into chunks based on file size and stored across multiple servers. These blocks are then kept on slave devices and distributed at random.

The Hadoop architecture's HDFS separates vast amounts of data into several chunks.

The Hadoop Distributed File System consists of the following three parts:

• NameNode, also known as a masternode, contains metadata on the disc and in RAM.
• Has a copy of NameNode's metadata stored on the disc as a secondary NameNode.
• Blocks of actual data are stored in the slave node.

NameNode

As the master server, NameNode. There can only be one NameNode in a cluster without high availability. There could be two NameNodes in a high-availability cluster where there is no requirement for a backup NameNode.

The numerous DataNodes, their positions, the size of each block, and other metadata are stored in the NameNode. Executing file system namespace actions like opening, shutting, and renaming files and directories also helps.

Secondary NameNode

Keeping a copy of the metadata on the disc falls to the secondary NameNode server. In the event of failure, the secondary NameNode's primary function is to establish a new NameNode.

There are two NameNodes in a high availability cluster: an active one and a backup one. The backup NameNode and the secondary NameNode carry out similar tasks.

FITA Academy offers the best Big Data Online Course to enhance your technical skills in Big Data with Placement Assistance.

Rack-Based Hadoop Cluster Architecture

We know that nodes are arranged in racks in rack-aware clusters and that each rack has a separate rack switch. A core switch connects rack switches, guaranteeing that a rack won't become unavailable in the event of a switch failure.

Read and Write Mechanism for HDFS

HDFS Read and Write methods operate simultaneously. The client must contact the namenode to read or write a file in HDFS. The namenode verifies the client's rights before granting access to read or write to the data blocks.

Conclusion

It is now possible due to big data analytics businesses to make better decisions by providing useful information. The core component of the entire Hadoop ecosystem is the Hadoop Architecture. Find out more about further Big Data features with Big Data Training in Coimbatore.