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Quantitative Analysis of Document Stored Databases

by Pradeep Soni, Narendra Singh Yadav
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 20
Year of Publication: 2015
Authors: Pradeep Soni, Narendra Singh Yadav
10.5120/20865-3587

Pradeep Soni, Narendra Singh Yadav . Quantitative Analysis of Document Stored Databases. International Journal of Computer Applications. 118, 20 ( May 2015), 37-41. DOI=10.5120/20865-3587

@article{ 10.5120/20865-3587,
author = { Pradeep Soni, Narendra Singh Yadav },
title = { Quantitative Analysis of Document Stored Databases },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 20 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number20/20865-3587/ },
doi = { 10.5120/20865-3587 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:02:18.057916+05:30
%A Pradeep Soni
%A Narendra Singh Yadav
%T Quantitative Analysis of Document Stored Databases
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 20
%P 37-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

So far relational databases are used for storing the data for the applications but now there is need to store huge amount of data to store and manage which cannot stored by relational databases. NoSQL technology over comes this problem. This research paper provides a brief introduction to NoSQL database working and comparative study between MongodB and Cassandra, Which are mostly used for big data application. The operations are performed on Ubuntu system to explore the results as distinguish between both NoSql databases. This paper shows the performance of Mongodb and Cassandra. Results proves that Cassandra is more powerful than Mongodb to load and process on big data and processing very fast as compare to Mongodb. This paper describes the functionality of Mongodb and Cassandra over the large dataset.

References
  1. Rabl, Tilmann; Sadoghi, Mohammad; Jacobsen, Hans-Arno; Villamor, Sergio Gomez-; Mulero -, Victor Muntes; Mankovskii, Serge (2012-08-27). "Solving Big Data Challenges for Enterprise Application Performance Management". VLDB. Retrieved 2013-07-25. In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes in all experiments. . . this comes at the price of high write and read latencies
  2. DataStax (2013-01-15). "About data consistency". Retrieved 2013-07-25.
  3. Ellis, Jonathan (2012-02-15). "Schema in Cassandra 1. 1". DataStax. Retrieved 2013-07-25.
  4. Ellis, Jonathan (2010-12-03). "What's new in Cassandra 0. 7: Secondary indexes". DataStax. Retrieved 2013-07-25.
  5. Ellis, Jonathan (2012-03-02). "The Schema Management Renaissance in Cassandra 1. 1". DataStax. Retrieved 2013-07-25.
  6. Lebresne, Sylvain (2012-08-05). "Coming in 1. 2: Collections support in CQL3". DataStax. Retrieved 2013-07-25.
  7. "Deploying Cassandra across Multiple Data Centers". DataStax. Retrieved 11 December 2014.
  8. J. Dean and S. Ghemawat, "Mapreduce: simplified data processing on large clusters", Commun. ACM, Pages:107-113, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

NoSql Databases Mongodb Cassandra Big Data.

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