Open Access   Article Go Back

Query Optimization of Big Data Using Hive

A.Vinay Kumar1 , A. Madhuri2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-9 , Page no. 135-139, Sep-2015

Online published on Oct 01, 2015

Copyright © A.Vinay Kumar , A. Madhuri . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: A.Vinay Kumar , A. Madhuri , “Query Optimization of Big Data Using Hive,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.135-139, 2015.

MLA Style Citation: A.Vinay Kumar , A. Madhuri "Query Optimization of Big Data Using Hive." International Journal of Computer Sciences and Engineering 3.9 (2015): 135-139.

APA Style Citation: A.Vinay Kumar , A. Madhuri , (2015). Query Optimization of Big Data Using Hive. International Journal of Computer Sciences and Engineering, 3(9), 135-139.

BibTex Style Citation:
@article{Kumar_2015,
author = {A.Vinay Kumar , A. Madhuri },
title = {Query Optimization of Big Data Using Hive},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2015},
volume = {3},
Issue = {9},
month = {9},
year = {2015},
issn = {2347-2693},
pages = {135-139},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=655},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=655
TI - Query Optimization of Big Data Using Hive
T2 - International Journal of Computer Sciences and Engineering
AU - A.Vinay Kumar , A. Madhuri
PY - 2015
DA - 2015/10/01
PB - IJCSE, Indore, INDIA
SP - 135-139
IS - 9
VL - 3
SN - 2347-2693
ER -

VIEWS PDF XML
2339 2226 downloads 2363 downloads
  
  
           

Abstract

Huge amounts of data are required to build internet search engines and therefore large number of machines to process this entire data. The Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of machines. The Hadoop having two modules 1. Hadoop distributed file system and 2. Map Reduce. The Hadoop distributed file system is different from the local normal file system. The HDFS can be implemented as single node cluster and multi node cluster. The large datasets are processed more efficiently by the multi node clusters. By using the hive query language on the Hadoop and increasing number of nodes the data will be processed fastest than with the fewer nodes.

Key-Words / Index Term

Big Data, HDFS, Map Reduce, Hive,Join

References

[1] Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters by YangDasdan and Hasio ,Parker Vol-8,Issue-7,1029-1040,2007
[2] J.Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In OSDI, pages 137–150, 2004,
[3] Liu Liu, Jiangtao Yin, Lixin Gao, “Efficient Social Network Data Query Processing on MapReduce” ACM August 16, 2013.
[4] Stephen Kaisler, Frank Armour, J. Alberto Espinosa, William Money, “Big Data: Issues and Challenges Moving Forward” 1530-1605/12, Jan 2013.
[5] “Hadoop Mapreduce Outline in Big Figures Analytics” IJCSE,Vol-2,Issue-9 100-104,Sep 2014.
[6] ApacheHadoop.http://hadoop.apache.org/.friday 2 Dec,14
[7] https://en.wikipedia.org/wiki/Apache_Hadoop, 25 Jan,15
[8] http://hashprompt.blogspot.in/2014/06/multi-node-hadoop-cluster-on-ubuntu-1404.html, 7 April,2015