RDF has become very popular for semantic data publishing due to its flexible and universal graph-like data model. Thus, the ever-increasing size of RDF data collections raises the need for scalable distributed approaches. We endorse the usage of existing infrastructures for Big Data processing like Hadoop for this purpose. Yet, SPARQL query performance is a major challenge as Hadoop is not intentionally designed for RDF processing. Existing approaches often favor certain query pattern shapes while performance drops significantly for other shapes. In this paper, we introduce a novel relational partitioning schema for RDF data called ExtVP that uses a semi-join based preprocessing, akin to the concept of Join Indices in relational databases, to efficiently minimize query input size regardless of its pattern shape and diameter. Our prototype system S2RDF is built on top of Spark and uses SQL to execute SPARQL queries over ExtVP. We demonstrate its superior performance in comparison to state of the art SPARQL-on-Hadoop approaches.