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Big data- hadoop -MapReduce
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Term Paper
Final-Review
GMR Institute of Technology
An Autonomous Institute Affiliated to JNTUK, Kakinada
1
Department of Computer Science Engineering
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Performance analysis of MapReduce
task in Big data using Hadoop
2November 13, 2016
TITLE
by
M. S. V. S. K .Avadhani
(14341A05A4)
Under the Guidance and supervision Of
Mrs. K . Jayasri
Assistant Professor
Department Of Computer Science Engineering
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ABSTRACT
Big Data is a huge amount of data that cannot be managed by the traditional
data management system.
There can be three forms of data, structured form, unstructured form and semi
structured form. Most of the part of big data is in unstructured form.
Unstructured data is difficult to handle.
Hadoop is a technological answer to Big Data.
The Apache Hadoop project provides better tools and techniques to handle this
huge amount of data.
A Hadoop Distributed File System (HDFS) for storage and the MapReduce
techniques for processing the data.
This paper discusses the work done on Hadoop by applying a number of files
as input to the system and then analysing the performance of the Hadoop .
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GMRInstituteofTechnology,Rajam ABSTRACT(contd..)
Besides it discusses the behaviour of the map method and the
reduce method with increasing number of files and the amount of
bytes written and read by these tasks.
oKeywords:
Big data
Hadoop
HDFS
MapReduce.
4November 13, 2016
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Hadoop
• Hadoop is an open-source framework that
allows to store and process big data in a
distributed environment across clusters of
commodity hardware.
• Storing HDFS(Hadoop Distributed File System)
• Processing MapReduce
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• HDFS:
Specially designed file system for storing
huge data sets in cluster of commodity
hardware with streaming access pattern.
5 services :
Name node
Secondary node
Job tracker
Data node
Task tracker
7November 13, 2016
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• MapReduce:
MapReduce is a processing technique and a program
model for distributed computing based on java.
The MapReduce algorithm contains two important
tasks, namely Map and Reduce.
Map stage : The map or mapper’s job is to process the
input data.
Reduce stage : This stage is the combination of
the Shuffle stage and the Reduce stage.
9November 13, 2016
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• The performance of the
MapReduce task on the basis of
the byte written, File bytes read,
Reduce input records, have been
recorded in the beside Table.
• Number of bytes written by the
Map Reduce task does not
increase with the rate at which the
number of files is increasing.
11November 13, 2016
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The reason is that when the reduce function reduces the map output
it just combines the output of map reduce like in example two time how is saved
with only a single value increase by one.
12November 13, 2016
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Conclusion:
We have analyzed the performance of the map reduce task
with the increase number of files. We have used the word count application
of the Map reduce for this analysis. The output shows that the Bytes written
do not increase in the same proportion as compared to the amount of files
increase.
13November 13, 2016
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GMRInstituteofTechnology,Rajam REFERENCES
•[1] Shankar Ganesh Manikandan, Siddarth Ravi , “Big Data Analysis using Apache
Hadoop”, IEEE,2014
•[2] Ankita Saldhi, Abhinav Goel”,” Big Data Analysis Using Hadoop Cluster”,
IEEE,2014
•[3] Amrit Pal, Pinki Agrawal, Kunal Jain, Kunal Jain, ”A Performance Analysis of
MapReduce Task with Large Number of Files Dataset in Big Data Using Hadoop”,
2014 Fourth International Conference on Communication Systems and Network
Technologies
•[4] Aditya B. Patel, Manashvi Birla, Ushma Nair,” “Big Data Problem Using Hadoop
and Map Reduce”, NIRMA UNIVERSITY INTERNATIONAL CONFERENCE ON
ENGINEERING, NUiCONE -2012
14November 13, 2016
