2015/04/01 by E. Preston Carman Jr., E. Preston Carman, Carman, E. Preston +9 · 9 citations
Computer Science · #Cloud Computing and Resource Management #Computer science #Databases (cs.DB) #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Operating system #Parallel Computing and Optimization Techniques #Scalability #World Wide Web #XML #XQuery #cs.DB
paper · pdf · doi:10.48550/arxiv.1504.00331
published in arXiv (Cornell University) (Cornell University)
arxiv created 2015/04/01 · openalex publication_date 2015/04/01 · arxiv updated 2015/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.