XML data partitioning schemes for parallel holistic twig joins

Machdi, Imam; Amagasa, Toshiyuki; Kitagawa, Hiroyuki

doi:10.1108/17440080910968445

Cited by 6 publications

(14 citation statements)

References 18 publications

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“…The main objective is to partition streams of XML nodes, which are the underlying data structure for holistic twig joins, and to provide ways of refining partitions for achieving workload balance in the distribution. The first part of XML data partitioning method is basically derived from GMX [16]. We enhance the partitioning methods of GMX in the second part.…”

Section: Xml Data Partitioning Methodsmentioning

confidence: 99%

“…GMX [16] introduces the XML data partitioning scheme for holistic twig joins. The conceptual view of the data partitioning method is shown in the grid metadata model that codes the relationships among XML documents, query patterns, query-path patterns, and streams of XML nodes.…”

Section: Related Workmentioning

confidence: 99%

“…The main objective is to improve the performance of our parallel query processing system for both inter query parallelism and intra query parallelism. Basically, we adopt the existing technique of XML data partitioning called Grid Metadata for XML [16] with an enhancement technique called streamsbased data partitioning for both distributions, but, with different strategies.…”

Section: Introductionmentioning

confidence: 99%

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XML data partitioning strategies to improve parallelism in parallel holistic twig joins

Machdi

Amagasa

Kitagawa

2009

Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication

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Parallel XML query processing systems that process numerous queries over large heterogeneous XML documents often experience under-performance due to workload imbalance and low CPU/system utilization, because conventional partitioning strategies cannot serve well for state-of-the-art query processing algorithms, such as holistic twig joins. Consequently, partitioning and distributing heterogeneous XML documents onto a parallel cluster system have lead to such an intricacy issue for maintaining good query performance. In this paper, we propose XML data partitioning strategies that are able to alleviate system performance degradation due to workload imbalance, especially for parallel holistic twig joins processing. The proposed XML data partitioning strategies aim at improving workload balance on both static data distribution and dynamic data distribution. In the first strategy we refine an XML partition having a high cost by series of XML data partition refinements with various levels of granularities from document, query, and subquery, up to node streams. The selection of the granularity level for refining a high cost partition is contextually dependent on the overall workload balance in the system. In the second strategy for dynamic data distribution, we dynamically handle low system utilization when there are many idle nodes in the system. We propose an XML data redistribution approach by partitioning XML data on the fly at the stream nodes-based granularity.

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Section: Xml Data Partitioning Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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XML data partitioning strategies to improve parallelism in parallel holistic twig joins

Machdi

Amagasa

Kitagawa

2009

Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication

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“…Combining the approaches shown here with techniques that replicate the most heavily loaded fragments (e.g. the techniques described by Machdi et al [95,96]) might result in additional performance gains in certain circumstances. Further, it might be possible to extend the definition of vertical fragmentation to take into account the position of a node in the document in addition to the type of the node.…”

Section: Possible Directions For Future Workmentioning

confidence: 99%

“…Focusing on a specific query evaluation strategy using holistic twig joins (first proposed by Bruno et al [32]), Machdi et al [95,96] describe a technique that can be used to cluster both documents and queries to enable parallel query evaluation. Unlike the fragmentation model used in this work, their model replicates data as necessary.…”

Section: Fragmentation Based On Query Workloadsmentioning

confidence: 99%

Distributed XML Query Processing

Özsu

Kling

2010

Database and XML Technologies

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While centralized query processing over collections of XML data stored at a single site is a well understood problem, centralized query evaluation techniques are inherently limited in their scalability when presented with large collections (or a single, large document) and heavy query workloads. In the context of relational query processing, similar scalability challenges have been overcome by partitioning data collections, distributing them across the sites of a distributed system, and then evaluating queries in a distributed fashion, usually in a way that ensures locality between (sub-)queries and their relevant data. This thesis presents a suite of query evaluation techniques for XML data that follow a similar approach to address the scalability problems encountered by XML query evaluation.Due to the significant differences in data and query models between relational and XML query processing, it is not possible to directly apply distributed query evaluation techniques designed for relational data to the XML scenario. Instead, new distributed query evaluation techniques need to be developed. Thus, in this thesis, an end-to-end solution to the scalability problems encountered by XML query processing is proposed.Based on a data partitioning model that supports both horizontal and vertical fragmentation steps (or any combination of the two), XML collections are fragmented and distributed across the sites of a distributed system. Then, a suite of distributed query evaluation strategies is proposed. These query evaluation techniques ensure locality between each fragment of the collection and the parts of the query corresponding to the data in this fragment. Special attention is paid to scalability and query performance, which is achieved by ensuring a high degree of parallelism during distributed query evaluation and by avoiding access to irrelevant portions of the data.For maximum flexibility, the suite of distributed query evaluation techniques proposed in this thesis provides several alternative approaches for evaluating a given query over a given distributed collection. Thus, to achieve the best performance, it is necessary to predict and compare the expected performance of each of these alternatives. In this work, this is accomplished through a query optimization technique based on a distribution-aware cost model. The same cost model is also used to fine-tune the way a collection is fragmented to the demands of the query workload evaluated over this collection. v To evaluate the performance impact of the distributed query evaluation techniques proposed in this thesis, the techniques were implemented within a production-quality XML database system. Based on this implementation, a thorough experimental evaluation was performed. The results of this evaluation confirm that the distributed query evaluation techniques introduced here lead to significant improvements in query performance and scalability both when compared to centralized techniques and when compared to existing distributed query evaluation tec...

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Parallel holistic twig joins on a multi‐core system

Machdi

Amagasa

Kitagawa

2010

International Journal of Web Information Systems

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Purpose -The purpose of this paper is to propose general parallelism techniques for holistic twig join algorithms to process queries against Extensible Markup Language (XML) databases on a multi-core system. Design/methodology/approach -The parallelism techniques comprised data and task parallelism. As for data parallelism, the paper adopted the stream-based partitioning for XML to partition XML data as the basis of parallelism on multiple CPU cores. The XML data partitioning was performed in two levels. The first level was to create buckets for creating data independence and balancing loads among CPU cores; each bucket was assigned onto a CPU core. Within each bucket, the second level of XML data partitioning was performed to create finer partitions for providing finer parallelism. Each CPU core performed the holistic twig join algorithm on each finer partition of its own in parallel with other CPU cores. In task parallelism, the holistic twig join algorithm was decomposed into two main tasks, which were pipelined to create parallelism. The first task adopted the data parallelism technique and their outputs were transferred to the second task periodically. Since data transfers incurred overheads, the size of each data transfer needed to be estimated cautiously for achieving optimal performance. Findings -The data and task parallelism techniques contribute to good performance especially for queries having complex structures and/or higher values of query selectivity. The performance of data parallelism can be further improved by task parallelism. Significant performance improvement is attained by queries having higher selectivity because more outputs computed by the second task is performed in parallel with the first task.Research limitations/implications -The proposed parallelism techniques primarily deals with executing a single long-running query for intra-query parallelism, partitioning XML data on-the-fly, and allocating partitions on CPU cores statically. During the parallel execution, presumably there are no such dynamic XML data updates. Practical implications -The effectiveness of the proposed parallel holistic twig joins relies fundamentally on some system parameter values that can be obtained from a benchmark of the system platform. Originality/value -The paper proposes novel techniques to increase parallelism by combining techniques of data and task parallelism for achieving high performance. To the best of the author's knowledge, this is the first paper of parallelizing the holistic twig join algorithms on a multi-core system.

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XML data partitioning schemes for parallel holistic twig joins

Cited by 6 publications

References 18 publications

XML data partitioning strategies to improve parallelism in parallel holistic twig joins

XML data partitioning strategies to improve parallelism in parallel holistic twig joins

Distributed XML Query Processing

Parallel holistic twig joins on a multi‐core system

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