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

TL;DR

This paper evaluates the performance of query plan recommendation using Apache Hadoop and Apache Spark, demonstrating that Spark provides significantly faster clustering of query datasets, enhancing scalability in large-scale query optimization.

Contribution

It introduces a MapReduce-based clustering approach for query plan recommendation and compares the performance of Hadoop and Spark frameworks.

Findings

Spark outperforms Hadoop with an average 2x speedup.

Parallel query clustering improves scalability for large datasets.

Efficient clustering reduces query optimization time.

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.