International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 7, No. 11, 8 Oct. 2015
Cover page and Table of Contents: PDF (size: 262KB)
Author(s)
Pankaj Deep Kaur
1,*
Amneet Kaur
1
Sandeep Kaur
1
Index Terms
NOSQL, YCSB, HDFS
Abstract
Big data technologies like Hadoop, NoSQL, Messaging Queues etc. helps in BigData analytics, drive business growth and to take right decisions in time. These Big Data environments are very dynamic and complex; they require performance validation, root cause analysis, and tuning to ensure success. In this paper we talk about how we can analyse and test the performance of these systems. We present the important factors in a big data that are primary candidates for performance testing like data ingestion capacity and throughput, data processing capacity, simulation of expected usage, map reduce jobs and so on and suggest measures to improve performance of bigdata.
Cite This Paper
Pankaj Deep Kaur, Amneet Kaur, Sandeep Kaur, "Performance Analysis in Bigdata", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.11, pp.55-61, 2015. DOI:10.5815/ijitcs.2015.11.07
Reference
[1]http://www.cmgindia.org/wpcontent/uploads/2014/01/PerformanceTestingofNoSQLApplications.pdf.
[2]http://www.qaiglobalservices.com/conference/stc2013/PDFs/Mustufa_Batterywala.pdf.
[3]Chaudhary U and Singh H Mapreduce performance evaluation through benchmarking and stress testing on multi-node Hadoop cluster. International Journal of Computational Engineering Research (IJCER) 4:2250-3005, 2014.
[4]Dean J and Ghemawat S MapReduce simplified data processing on large clusters. Communications of the ACM 51:107-113, 2008.
[5]Dede E, Sendir B, Kuzlu P, Hartog J and Govindaraju M (2013) An Evaluation of Cassandra for Hadoop. In Cloud Computing (CLOUD) 2013 IEEE Sixth International Conference (pp. 494-501). IEEE.
[6]Dokeroglu T, Ozal S, Bayir M A, Cinar M S and Cosar A Improving the performance of Hadoop Hive by sharing scan and computation tasks. Journal of Cloud Computing 3:1-11, 2014.
[7]Jewell D, Barros R D, Diederichs S, Duijvestijn L M, Hammersley M, Hazra A, and Zolotow C Performance and Capacity Implications for Big Data. IBM Redbooks, 2014.
[8]Pavlo A, Paulson E, Rasin A and Abadi D J, DeWitt D J, Madden S, and Stonebraker M A comparison of approaches to large-scale data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (pp 165-178) ACM, 2009.
[9]Fadika Z, Dede E, Govindaraju M, & Ramakrishnan L Benchmarking mapreduce implementations for application usage scenarios. In Grid Computing (GRID) 2011 12th IEEE ACM International Conference on (pp 90-97) IEEE, 2011, September.
[10]http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data.
[11]http://www.cigniti.com/big-data-testing.
[12]http://www.bigdata-startups.com/open-source-tools/data-analysis-platforms.
[13]Nagdive A S, Tugnayat R M & Tembhurkar M P. Overview on Performance Testing Approach in Big Data. International Journal of Advanced Research in Computer Science, 5(8).
[14]Gudipadi M, Rao S, Mohan D N &Gajja N K. Bigdata: Testing approach to overcome quality challenges in Infosys lab Briefings 11(1).
[15]Abramova V, and Bernardino J “NoSQL databases: MongoDB vs cassandra” In Proceedings of the International C* Conference on Computer Science and Software Engineering pp. 14-22 ACM.
[16]http://en.wikipedia.org/wiki/big_data.
[17]Manoj V “Comparative Study of NoSQL Document, Column Store Databases and Evaluation of Cassandra” 2014 International Journal of Database Management Systems (IJDMS) Vol, 6.
[18]Abramova V, Bernardino J and Furtado P “Testing Cloud Benchmark Scalability with Cassandra” In Services (SERVICES), 2014 IEEE World Congress on pp. 434-441 IEEE.
[19]Gandini A, Gribaudo M, Knottenbelt W. J, Osman R and Piazzolla P, ‘Performance evaluation of NoSQL databases” In Computer Performance Engineering, pp 16-29.