Apache VXQuery：一个可伸缩的XQuery实现
Apache VXQuery: A Scalable XQuery Implementation
摘要：
XML在文档管理和数据交换中的广泛使用，产生了查询大型XML数据存储库的需求。为了高效地查询如此大的数据汇集并利用并行性，我们实现了Apache VXQuery，这是一个开源的可扩展的XQuery处理器。该系统建立在另外两个开源框架之上--Hyracs，一个并行执行引擎，和Algebricks，一个语言无关的编译器工具箱。ApacheVXQuery扩展了这两个框架，并提供了XQuery细节（数据模型、依赖于数据模型的函数和优化以及解析器）的实现。我们描述了Apache VXQuery的体系结构，它与Hyracks和Algebricks的集成，以及适用于查询计划的XQuery优化规则，以提高路径表达式的效率，并使查询并行。使用一个真实的500 GB数据集进行的实验评估显示，Apache VXQuery在扩展和加速方面都表现良好。我们的实验表明，在4核单节点实现上，它比Saxon（一种开源商业XQuery处理器）快3倍，在8（4核）节点集群上比Apache MRQL（一种基于MapReduce的并行查询处理器）快2.5倍。
英文摘要：
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.
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