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

EasyChair Preprint no. 8841

11 pagesDate: September 18, 2022


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.

Keyphrases: Artificial Intelligence, Big Data, Hadoop

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Elham Azhir and Mehdi Hosseinzadeh and Faheem Khan and Amir Mosavi},
  title = {Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark},
  howpublished = {EasyChair Preprint no. 8841},

  year = {EasyChair, 2022}}
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