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A Caching-Based Parallel FP-Growth in Apache Spark

  • Zhicheng Cai
  • Xingyu Zhu
  • Yuehui Zheng
  • Duan Liu
  • Lei Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)

Abstract

The association-rule-based recommendation is widespread in many big data applications which need quick response to improve user experience. Spark is a widely used distributed computing platform, which accelerates the processing of large-scale distributed data. Developing appropriate distributed algorithm for Spark is essential to decrease the processing time of distributed recommendation. The existing FP-Growth in Spark is a popular parallel recommendation method but getting the best performance only when the memory of machines can accommodate all immediate Resilient Distributed DataSets (RDDs). However, memory of many practice data centers is still not large enough for large data sets. Therefore, in this paper, a caching-based parallel FP-Growth is proposed which consists of an integer-based sorting and an RDD-caching strategy to improve the efficiency. Experimental results show that the proposal decreases the execution time by 32.37% on average compared with the existing parallel FP-Growth in Spark. Furthermore, impacts of some important parameters upon the performance of the proposal are analyzed by numerous realistic experiments in Spark.

Keywords

Spark Parallel FP-Growth Caching strategy 

Notes

Acknowledgments

Zhicheng Cai is supported by the National Natural Science Foundation of China (Grant No. 61602243) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20160846). Lei Xu is supported by the National Natural Science Foundation of China (No. 61671244). Duan Liu is supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zhicheng Cai
    • 1
  • Xingyu Zhu
    • 1
  • Yuehui Zheng
    • 1
  • Duan Liu
    • 1
  • Lei Xu
    • 1
  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina

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