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Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark

Author

Listed:
  • Elham Azhir

    (Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
    Research and Development Center, Mobile Telecommunication Company of Iran, Tehran 1991954168, Iran)

  • Mehdi Hosseinzadeh

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Faculty of Natural Sciences, Duy Tan University, Da Nang 550000, Vietnam
    Computer Science, University of Human Development, Sulaymaniyah 0778-6, Iraq)

  • Faheem Khan

    (Department of Computer Engineering, Gachon University, Seongnam 13120, Korea)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
    Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, 81243 Bratislava, Slovakia)

Abstract

Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of 2x.

Suggested Citation

  • Elham Azhir & Mehdi Hosseinzadeh & Faheem Khan & Amir Mosavi, 2022. "Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark," Mathematics, MDPI, vol. 10(19), pages 1-11, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3517-:d:926004
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    Citations

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    Cited by:

    1. Nahla Mohammed Elzein & Mazlina Abdul Majid & Ibrahim Abaker Targio Hashem & Ashraf Osman Ibrahim & Anas W. Abulfaraj & Faisal Binzagr, 2023. "JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join Technique," Mathematics, MDPI, vol. 11(5), pages 1-20, March.

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