What is Hadoop?

Hadoop is an Apache open source platform used to store, process, and analyse very large volumes of data. Hadoop is not OLAP and is written in Java (online analytical processing). Handling in batches 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.

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Modules of Hadoop

  • HDFS: Hadoop Distributed File System modules. Following the publication of Google’s paper GFS, HDFS was created. The files will be divided into blocks and kept in nodes using the distributed design, it says.
  • Yarn: The cluster is managed and job scheduling is done using yet another Resource Negotiator.
  • Map Reduce is a framework that enables Java programmes to do key-value pair-based concurrent computations on data. The Map task transforms 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, and the output from the reducer produces the required outcome.
  • Common Hadoop: These Java libraries are used by other Hadoop modules and to launch Hadoop.

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Architecture Hadoop

The HDFS, MapReduce engine, and file system are all included in the Hadoop architecture (Hadoop Distributed File System). MapReduce engines 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.

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 numerous DataNodes serve as the slaves.

Both NameNode and DataNode have sufficient capabilities to function on common machines. The HDFS software was created in Java. Therefore, 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.
  • Due to the fact that it is a single node, it could cause single point failure.
  • By carrying out actions including opening, renaming, and shutting files, it administers 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 is stored 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

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

Task Manager

  • It serves Job Tracker as a slave node.
  • It receives the task from Job Tracker and the code, then it applies the code to the text. The term “mapping tool” also applies to this process.

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.