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Big Data Analytics in Java with PCJ Library: Performance Comparison with Hadoop

  • Marek Nowicki
  • Magdalena Ryczkowska
  • Łukasz Górski
  • Piotr Bala
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10778)

Abstract

The focus of this article is to present Big Data analytics using Java and PCJ library. The PCJ library is an award-winning library for development of parallel codes using PGAS programming paradigm. The PCJ can be used for easy implementation of the different algorithms, including ones used for Big Data processing. In this paper, we present performance results for standard benchmarks covering different types of applications from computational intensive, through traditional map-reduce up to communication intensive. The performance is compared to one achieved on the same hardware but using Hadoop. The PCJ implementation has been used with both local file system and HDFS. The code written with the PCJ can be developed much faster as it requires a smaller number of libraries used. Our results show that applications developed with the PCJ library are much faster compare to Hadoop implementation.

Keywords

Big Data Java Parallel computing Hadoop 

Notes

Acknowledgments

The authors would like to thank CHIST-ERA consortium for financial support under HPDCJ project. The Polish contribution is financed through NCN grant 2014/14/Z/ST6/00007. The performance tests have been performed using ICM University of Warsaw computational facilities.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Marek Nowicki
    • 1
  • Magdalena Ryczkowska
    • 1
    • 2
  • Łukasz Górski
    • 1
    • 2
  • Piotr Bala
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceNicolaus Copernicus UniversityTorunPoland
  2. 2.Interdisciplinary Centre for Mathematical and Computational ModelingUniversity of WarsawWarsawPoland

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