Cascading map-side joins over HBase for scalable join processing by Martin Przyjaciel-Zablocki, Alexander Schätzle, Thomas Hornung, Christopher Dorner, and Georg Lausen.
Abstract:
One of the major challenges in large-scale data processing with MapReduce is the smart computation of joins. Since Semantic Web datasets published in RDF have increased rapidly over the last few years, scalable join techniques become an important issue for SPARQL query processing as well. In this paper, we introduce the Map-Side Index Nested Loop Join (MAPSIN join) which combines scalable indexing capabilities of NoSQL storage systems like HBase, that suffer from an insufficient distributed processing layer, with MapReduce, which in turn does not provide appropriate storage structures for efficient large-scale join processing. While retaining the flexibility of commonly used reduce-side joins, we leverage the effectiveness of map-side joins without any changes to the underlying framework. We demonstrate the significant benefits of MAPSIN joins for the processing of SPARQL basic graph patterns on large RDF datasets by an evaluation with the LUBM and SP2Bench benchmarks. For most queries, MAPSIN join based query execution outperforms reduce-side join based execution by an order of magnitude.
Some topic map applications include Linked Data/RDF processing capabilities.
The salient comment here being: “For most queries, MAPSIN join based query execution outperforms reduce-side join based execution by an order of magnitude.“