An XML Data Placement Strategy for Distributed XML Storage and Parallel Query

2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies Pub Date : 2011-10-20 DOI:10.1109/PDCAT.2011.19

Jing Zhang, B. Lang, Yawei Duan

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引用次数: 7

Abstract

Since there has been significant amount of XML documents generated in various application domains, efficient XML management has become an important problem. Distributed XML storage and parallel query based on Map Reduce can be an effective solution to this problem. As XML data placement strategy is a key factor of parallel system performance, in this paper we present an XML placement strategy, which is Query Workload Estimation based XML Placement strategy (QWEXP) for efficient distributed XML storage and parallel query. To achieve query workload balance, it partitions XML based on query workload estimation which is calculated by XML structure without knowing of user queries, considering that in common application scenarios user queries are unknown in advance. The partitioned XML segments are around an XML storage unit W0, to support scalability of parallel XML database. Finally segments are distributed to each processing node evenly to ensure workload balance on parallel query execution. Experimental results have shown that QWEXP promotes the speedup and scale up properties of parallel XML system greatly.

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面向分布式XML存储和并行查询的XML数据放置策略

由于在各种应用程序域中生成了大量XML文档，因此有效的XML管理已成为一个重要问题。分布式XML存储和基于Map Reduce的并行查询可以有效地解决这一问题。考虑到XML数据放置策略是影响并行系统性能的关键因素，本文提出了一种基于查询工作量估计的XML放置策略(QWEXP)，以实现高效的分布式XML存储和并行查询。为了实现查询工作负载均衡，在不知道用户查询情况的情况下，根据XML结构计算的查询工作负载估计对XML进行分区，考虑到在常见的应用场景中，用户的查询是事先未知的。分区的XML段围绕XML存储单元W0，以支持并行XML数据库的可伸缩性。最后，将段均匀地分布到各个处理节点，以确保并行查询执行时的工作负载平衡。实验结果表明，QWEXP极大地提高了并行XML系统的加速性能和可扩展性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies

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