What is the Difference Between HDFS and MapReduce? with Proper Definition and Brief Explanation
The main difference between HDFS and MapReduce is that HDFS is a distributed file system that provides high-performance access to application data while MapReduce is a software framework that reliably processes large volumes of data in large batches.
Big data is a collection of a large set of data. It has three main properties: volume, speed and variety. Hadoop is software that allows you to store and manage big data. It is an open source framework written in Java. Furthermore, it supports distributed processing of large data sets across groups of computers. HDFS and MapReduce are two modules in the Hadoop architecture.
Key Areas Covered
1. What is HDFS?
– Definition, Functionality
2. What is MapReduce?
– Definition, Functionality
3. What is the difference between HDFS and MapReduce?
– Comparison of key differences
Key terms
Big Data, HDFS, Map Reduce
What is HDFS?
HDFS stands for Hadoop Distributed File System . It is a Hadoop distributed file system to run on large clusters reliably and efficiently. Also, it is based on the Google File System (GFS). Additionally, it also has a list of commands to interact with the file system.
Furthermore, the HDFS works according to the master and slave architecture. The master node or name node manages the file system metadata while the slave nodes or data notes store actual data.
Figure 1: HDFS Architecture
Also, a file in an HDFS namespace is divided into multiple blocks. The data nodes store these blocks. And, the name node maps blocks to data nodes, which handle read and write operations with the file system. In addition, they perform tasks such as block creation, deletion, etc. as indicated by the name node.
What is MapReduce
MapReduce is a software framework that enables writing applications to process big data simultaneously on large pools of commodity hardware. This framework consists of a single master job tracker and slave task tracker per cluster node. The master performs resource management, scheduling jobs on slaves, monitoring and re-executing failed tasks. On the other hand, the slave task tracker executes the tasks indicated by the master and sends the status information of the tasks to the matter constantly.
Figure 2: MapReduce Summary
Additionally, there are two tasks associated with MapReduce. They are the map task and the reduce task. The Map task takes the input data and splits it into tuples of key, value pairs, while the Reduce task takes the output of a Map task as input and plugs those tuples of data into smaller tuples. Also, the map task is performed before the reduce task.
Difference between HDFS and MapReduce
Definition
HDFS is a distributed file system that reliably stores large files across machines in a large cluster. In contrast, MapReduce is a software framework for writing applications that process large amounts of data in parallel on large groups of product hardware in a reliable and fault-tolerant manner. These definitions explain the main difference between HDFS and MapReduce.
Core Functionality
Another difference between HDFS and MapReduce is that HDFS provides high performance access to data through highly scalable Hadoop clusters while MapReduce performs big data processing.
Conclusion
In short, HDFS and MapReduce are two modules in the Hadoop architecture. The main difference between HDFS and MapReduce is that HDFS is a distributed file system that provides high-performance application data access while MapReduce is a software framework that reliably processes large volumes of data in large batches. .
Reference:
1. “HDFS Architecture Guide”, Apache Hadoop, Available here.
2. “MapReduce Tutorial”, Apache Hadoop, Available here.
3. “What is Hadoop Distributed File System (HDFS)? – Definition of WhatIs.com. ”SearchDataManagement, available here.
Courtesy image:
1. “Hdfsarchitecture” By Magnai17 – Own work (CC BY-SA 4.0) via Commons Wikimedia
2. “Mapreduce Overview” By Poposhka – SVG-Edit (CC BY-SA 3.0) via Commons Wikimedia