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SPARQL Graph Pattern Processing with Apache Spark
Proceedings of the Fifth International Workshop on Graph Data-management Experiences & Systems - GRADES'17
A common way to achieve scalability for processing SPARQL queries is to choose MapReduce frameworks like Hadoop or Spark. Processing basic graph pattern (BGP) expressions generating large join plans over distributed data partitions is a major challenge in these frameworks. In this article, we study the use of two distributed join algorithms, partitioned join and broadcast join, for the evaluation of BGP expressions on top of Apache Spark. We compare five possible implementation and illustratedoi:10.1145/3078447.3078448 dblp:conf/grades/NaackeAC17 fatcat:yi4z2tmprzdvlkgwpebrujgkgy