Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark
This is an incremental improvement for database optimization systems, addressing scalability in query clustering.
The paper tackled the problem of slow clustering for query plan recommendation by using Apache Hadoop and Apache Spark in a MapReduce-based method, resulting in Apache Spark achieving an average speedup of 2x over Hadoop.
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.