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O processamento de consultas em grandes bases de dados XML ainda um problema desafiador, especialmente porque no existe um modelo e nem uma lgebra padro para o processamento eficiente de consultas a esse tipo de dados. Na literatura, autores sugerem fragmentar fisicamente e distribuir o banco de dados, para que as consultas possam ser executadas em paralelo. Entretanto, a fragmentao fsica projetada para beneficiar um conjunto de consultas frequentes. Dessa forma, aplicaes que requerem o processamento de consultas ad hoc sob bases de dados XML, tais como as OLAP, apresentam baixo desempenho ao utilizar esse tipo de fragmentao. Para resolver este problema, trabalhos anteriores propem o uso da fragmentao virtual, que consiste em replicar o banco de dados em vrios ns de processamento e definir a fragmentao dos dados em tempo de execuo da consulta. Porm, a Fragmentao Virtual Simples pode sofrer com desbalanceamento de carga na presena de distoro de dados, o que impacta negativamente no tempo de processamento da consulta. Para superar esta limitao e aumentar o desempenho do processamento de consultas, propomos nesta tese trs estratgias para o processamento paralelo de consultas XML ad hoc: Fragmentao Virtual Simples com balanceamento de carga Mestre-Escravo, Fragmentao Virtual Adaptativa e Fragmentao Virtual Adaptativa Dinmica. Alm disso, implementamos um prottipo de software para a arquitetura de um processador paralelo intraconsultas que utiliza as diferentes estratgias de fragmentao virtual em um cluster de banco de dados XML sem compartilhamento de recursos. Resultados de nossa avaliao experimental com o benchmark TPC-H XML mostraram acelerao sublinear na maioria das consultas executadas com estas estratgias de fragmentao virtual em um cluster de computadores. Em particular, a Fragmentao Virtual Adaptativa Dinmica se mostrou como a mais adequada para o processamento eficiente de consultas XML ad hoc de alto custo, acelerando o tempo de processamento dessas consultas em at 6,2 vezes no cenrio com 16 ns quando comparado ao ambiente centralizado.
Palavras-chave: Balanceamento de carga; Fragmentao virtual; Processamento paralelo de consultas; XML; XQuery.
Abstract
Efficient query processing in large XML datasets is still a challenging problem, especially because there is no standard model and algebra to handle XML data and drive query optimization. In the literature, authors suggest to physically fragment and distribute the database, so that queries can be executed in parallel to increase performance. However, physical fragmentation is designed to benefit a set of frequent queries, and applications that require ad-hoc queries over XML datasets, such as OLAP, usually present poor performance in such settings. To solve this problem, previous work has proposed the use of virtual partitioning, which consists in replicating the dataset in several processing nodes and defining data partition at query runtime. However, Simple Virtual Partitioning may suffer from load imbalance in the presence of data skew, which negatively impacts the query processing time. To overcome this limitation and improve query processing performance, in this thesis, we contribute by proposing three skew-aware strategies for the parallel processing of ad-hoc XML queries: Master-Slave Simple XML Virtual Partitioning, Adaptive XML Virtual Partitioning and Dynamic Adaptive XML Virtual Partitioning. In addition, we implemented a prototype for an intra-query parallelism architecture that applies different strategies of virtual partitioning in a shared nothing XML database cluster. Experimental evaluation results with the TPC-H XML benchmark show sublinear speedup in most of the queries executed with these virtual partitioning strategies in a cluster. In particular, the Dynamic Adaptive XML Virtual Partitioning is the most adequate approach for heavyweight queries, speeding up the query processing time up to 6.2 times in a scenario with 16 processing nodes when compared to a single node.
Keywords: Load balance; Parallel query processing; Virtual partitioning; XML; XQuery.
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