Efficient Distributed SPARQL Queries on Apache Spark
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 8
Abstract
RDF is a widely-accepted framework for describing metadata in the web due to its simplicity and universal graph-like data model. Owing to the abundance of RDF data, existing query techniques are rendered unsuitable. To this direction, we adopt the processing power of Apache Spark to load and query a large dataset much more quickly than classical approaches. In this paper, we have designed experiments to evaluate the performance of several queries ranging from single attribute selection to selection, filtering and sorting multiple attributes in the dataset. We further experimented with the performance of queries using distributed SPARQL query on Apache Spark GraphX and studied different stages involved in this pipeline. The execution of distributed SPARQL query on Apache Spark GraphX helped us study its performance and gave insights into which stages of the pipeline can be improved. The query pipeline comprised of Graph loading, Basic Graph Pattern and Result calculating. Our goal is to minimize the time during graph loading stage in order to improve overall performance and cut the costs of data loading.
Authors and Affiliations
Saleh Albahli
Keywords
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- EP ID EP626847
- DOI 10.14569/IJACSA.2019.0100874
- Views 111
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