Structured query language query join optimization by using rademacher averages and mapreduce algorithms

Query optimization involves identifying and implementing the most effective and efficient methods and strategies to enhance the performance of queries. This is achieved by intelligently utilizing system resources and considering various performance metrics. Table joining optimization involves optimizing the process of combining two or more tables within a database. Structured query language (SQL) optimization is the progress of utilizing SQL queries in the possible way to achieve fast and accurate database results. SQL optimization is critical to decreasing the no of queries in research description framework (RDF) and the time for processing a huge number of relatable data. In this paper, four new algorithms are proposed such as hash-join, sort-merge, rademacher averages and mapreduce for the progress of SQL query join optimization. The proposed model is evaluated and tested using waterloo sparql diversity test suite (WatDiv) and lehigh university benchmark (LUBM) benchmark datasets in terms of time execution. The results represented that the proposed method achieved an enhanced performance of less execution time for various queries such as Q3 of 5362, Q8 of 5921, Q9 of 5854 and Q10 of 5691 milliseconds. The proposed gives better performance than other existing methods like hybrid database-map reduction system (AQUA+) and join query processing (JQPro).