Apache VXQuery: A Scalable XQuery Implementation by E. Preston Carman Jr., Till Westmann, Vinayak R. Borkar, Michael J. Carey, Vassilis J. Tsotras.
Abstract:
The wide use of XML for document management and data exchange has created the need to query large repositories of XML data. To efficiently query such large data collections and take advantage of parallelism, we have implemented Apache VXQuery, an open-source scalable XQuery processor. The system builds upon two other open-source frameworks — Hyracks, a parallel execution engine, and Algebricks, a language agnostic compiler toolbox. Apache VXQuery extends these two frameworks and provides an implementation of the XQuery specifics (data model, data-model dependent functions and optimizations, and a parser). We describe the architecture of Apache VXQuery, its integration with Hyracks and Algebricks, and the XQuery optimization rules applied to the query plan to improve path expression efficiency and to enable query parallelism. An experimental evaluation using a real 500GB dataset with various selection, aggregation and join XML queries shows that Apache VXQuery performs well both in terms of scale-up and speed-up. Our experiments show that it is about 3x faster than Saxon (an open-source and commercial XQuery processor) on a 4-core, single node implementation, and around 2.5x faster than Apache MRQL (a MapReduce-based parallel query processor) on an eight (4-core) node cluster.
Are you looking for more “pop” in your XQueries? Apache VXQuery may be the answer.
Suitable for the Edgar dataset and the OpenStreetMap dataset.
This maybe what finally pushes me over the edge in choosing between a local cluster or pursuing one of the online options. Just looks too interesting to not want to play with it.