What is Hadoop: Architecture, Modules and Advantages

Advantages of Hadoop

Introduction

In this blog, we will discuss Hadoop Architecture, Advantages of Hadoop and its modules.

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What is Hadoop

Hadoop is an Apache open-source system used to store, process, and analyse extraordinarily large volumes of data. Hadoop is not OLAP and is written in Java (online analytical processing). Work in groups or offline is done with it. Facebook, Yahoo, Google, Twitter, LinkedIn, and many other sites use it. In addition, scaling up only requires adding nodes to the cluster.

HDFS: Hadoop Distributed File System modules. 

Google published its paper on GFS, and HDFS was created as a result. It specifies that the files will be divided into blocks and kept in nodes via the distributed architecture.

  • Yarn: Yet Another Resource Mediator is utilised for cluster and job timing.
  • Map Reduce is a framework that enables Java programmes to do key-value pair-based parallel analyses on data. The Map task turns input data into a data collection that can be calculated as a Key value pair. The reduce task uses the output from the Map task, which results in the output of the reducer, which then produces the successful result.
  • These Java libraries are known as Hadoop Common and are utilised by all other Hadoop modules.

Hadoop Architecture

The HDFS, MapReduce engine, and file system are all part of the Hadoop architecture (Hadoop Distributed File System). MapReduce devices come in two flavours: MR1 and MR2.

A single master and numerous slave nodes make up a Hadoop cluster. DataNode and TaskTracker are on the slave node, whereas Job Tracker, Task Tracker, NameNode, and DataNode are on the master node.

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Distributed File System for Hadoop

A distributed file system for Hadoop is called the Hadoop Distributed File System (HDFS). A master/slave architecture is present. In this architecture, a single NameNode serves as the master, and many DataNodes serve as the slaves.

Both NameNode and DataNode have sufficient capabilities to run on common systems. The HDFS software was built in Java. So, the NameNode and DataNode software can readily run on any machine that supports the Java language.

NameNode

  • There is only one master server in the HDFS cluster.
  • Since it is a single node, it could cause single-point failure.
  • By carrying out actions including opening, updating, and closing files, it manages the file system namespace.
  • The system’s architecture is made simpler as a result.

DataNode

  • There are numerous DataNodes in the HDFS cluster.
  • Multiple data blocks are present in each DataNode.
  • Data stores in these data blocks.
  • The read and write requests from the file system’s clients must be handled by DataNode.
  • The block is created, deleted, and replicated as directed by the NameNode.

Career Tracker

  • Taking MapReduce jobs from users and processing the data using NameNode are the responsibilities of the Job Tracker.
  • It responds by giving Job Tracker metadata.

Task Manager

  • It runs Job Tracker as a slave node.
  • It gets the task from Job Tracker and the code. Then it applies the code to the file. This method is also known as a mapper.

Advantages of Hadoop:

  • Fast: The data in HDFS are map and spread across the cluster, which speeds up retrieval. Even the processing tools for the data are frequently on the same servers, which speeds up processing. It can process terabytes of data in a matter of minutes and petabytes in a matter of hours.
  • Scalable: The Hadoop cluster may be expand by by adding new nodes.
  • Cost-Effective: Hadoop is far cheaper than typical relational database systems because it is open source and employs cheap hardware to store data.
  • Resilient against failure: HDFS has the ability to copy data over the network, allowing Hadoop to use the other copy of the data in the event of a node failure or other network issue. Data are typically replicate three times, but the number of times is variable.

Conclusion

So far, we have enhanced the Advantages of Hadoop, Hadoop Architecture and its modules. FITA Academy’s Big Data Training in Coimbatore will enhance your technical skills in Big Data Platform.

Also, Read: Hadoop Interview Questions with Answers