RDF has become very popular for semantic data publishing due to its flexible and universal graph-like data model. Yet, the ever-increasing size of RDF data collections makes it more and more infeasible to store and process them on a single machine, raising the need for distributed approaches. Instead of building a standalone but closed distributed RDF store, we endorse the usage of existing infrastructures for Big Data processing, e.g. Hadoop. However, SPARQL query performance is a major challenge as these platforms are not designed for RDF processing from ground. Thus, existing Hadoop-based approaches often favor certain query pattern shape while performance drops significantly for other shapes. In this paper, we describe 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 its relational interface to execute SPARQL queries over ExtVP. We demonstrate its superior performance in comparison to state of the art SPARQL-on-Hadoop approaches using the recent WatDiv test suite. S2RDF achieves sub-second runtimes for majority of queries on a billion triples RDF graph.