On Parallel Sorting of Data Streams

Bulánek, Jan; Yaghob, Jakub

doi:10.1007/978-3-642-32741-4_7

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…The details about the implementation of the parallel_ merge and the single_threaded_sort operators, as well as 1 http://www.ksi.mff.cuni.cz/bobox/ experiments which test the scalability of the algorithm, can be found in previous work [6].…”

Section: Parallel Sortingmentioning

confidence: 99%

“…We introduced a parallel_merge operator [6] which is able to perform the merging operation in parallel without significant serial bottleneck. The source code of the parallel sort algorithm can be expressed in a very compact form in Bobolang: input -> disp -> sort -> bcast -> merge; merge -> cons -> output; } An instance of the operator for four worker threads is shown in Figure 3.…”

Section: Parallel Sortingmentioning

confidence: 99%

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Bobolang

Bednárek

Kruliš

Yaghob

et al. 2014

Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing

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At present time, the programmers may choose from a number of streaming languages. They cover various aspects of the development process of streaming applications; however, specification of complex or runtime-dependent parts of the applications still remains a great challenge. We have analysed a large amount of requirements raised by the development of multiple data streaming parallel applications and proposed a novel language called Bobolang. It contains syntactic and semantic features which allow the programmer to naturally solve most of the problems, which we met in the design of streaming applications. The language is used to specify the structure of the whole application as well as the inner structure of each operator. Thanks to the properties of the language, Bobolang can create an optimized evaluation plan which is capable of making the best use of the available hardware resources. The language has been employed in several practical problems and it has proven itself to be a very powerful tool for the development of data-intensive parallel applications.

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Section: Parallel Sortingmentioning

confidence: 99%

Section: Parallel Sortingmentioning

confidence: 99%

Bobolang

Bednárek

Kruliš

Yaghob

et al. 2014

Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing

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“…The sort-merge join consists of two independent phases -sorting phase that sorts the input stream by join attributes and joining phase. We have utilized the highly scalable implementation of a stream sorting algorithm (Falt et al, 2012b); it is briefly described in Section 4.2…”

Section: Algorithmsmentioning

confidence: 99%

“…If one or both input streams need to be sorted, we use the approach based on algorithm described in (Falt et al, 2012b). Basically, the sorting of the stream is divided into three phases:…”

Section: Sortmentioning

confidence: 99%

Highly Scalable Sort-merge Join Algorithm for RDF Querying

Čermák

Zavoral

2013

Proceedings of the 2nd International Conference on Data Technologies and Applications

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In this paper, we introduce a highly scalable sort-merge join algorithm for RDF databases. The algorithm is designed especially for streaming systems; besides task and data parallelism, it also tries to exploit the pipeline parallelism in order to increase its scalability. Additionally, we focused on handling skewed data correctly and efficiently; the algorithm scales well regardless of the data distribution.

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On Parallel Sorting of Data Streams

Cited by 2 publications

References 13 publications

Bobolang

Bobolang

Highly Scalable Sort-merge Join Algorithm for RDF Querying

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