H2RDF+: High-performance distributed joins over large-scale RDF graphs

The proliferation of data in RDF format calls for efficient and scalable solutions for their management. While scalability in the era of big data is a hard requirement, modern systems fail to adapt based on the complexity of the query. Current approaches do not scale well when faced with substantially complex, non-selective joins, resulting in exponential growth of execution times. In this work we present H₂RDF+, an RDF store that efficiently performs distributed Merge and Sort-Merge joins over a multiple index scheme. H₂RDF+ is highly scalable, utilizing distributed MapReduce processing and HBase indexes. Utilizing aggressive byte-level compression and result grouping over fast scans, it can process both complex and selective join queries in a highly efficient manner. Furthermore, it adaptively chooses for either single- or multi-machine execution based on join complexity estimated through index statistics. Our extensive evaluation demonstrates that H₂RDF+ efficiently answers non-selective joins an order of magnitude faster than both current state-of-the-art distributed and centralized stores, while being only tenths of a second slower in simple queries, scaling linearly to the amount of available resources.