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Querying very large RDF data sets in an efficient manner requires asophisticated distribution strategy. Several innovative solutions have recentlybeen proposed for optimizing data distribution with predefined query workloads.This paper presents an in-depth analysis and experimental comparison of fiverepresentative and complementary distribution approaches. For achieving fairexperimental results, we are using Apache Spark as a common parallel computingframework by rewriting the concerned algorithms using the Spark API. Sparkprovides guarantees in terms of fault tolerance, high availability andscalability which are essential in such systems. Our different implementationsaim to highlight the fundamental implementation-independent characteristics ofeach approach in terms of data preparation, load balancing, data replicationand to some extent to query answering cost and performance. The presentedmeasures are obtained by testing each system on one synthetic and onereal-world data set over query workloads with differing characteristics anddifferent partitioning constraints.

On the Evaluation of RDF Distribution Algorithms Implemented over Apache Spark