Parallelizing user–defined functions in the ETL workflow using orchestration style sheets
Today’s ETL tools provide capabilities to develop custom code as user-defined functions (UDFs) to extend the expressiveness of the standard ETL operators. However, while this allows us to easily add new functionalities, it also comes with the risk that the custom code is not intended to be optimized...
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doaj-26067912ad9c47aabd386140ea3846f02021-09-06T19:41:09ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922019-03-01291697910.2478/amcs-2019-0005amcs-2019-0005Parallelizing user–defined functions in the ETL workflow using orchestration style sheetsAli Syed Muhammad Fawad0Mey Johannes1Thiele Maik2Faculty of Computing, Poznań University of Technology, Piotrowo 2, 60-965Poznań, PolandFaculty of Computer Science, Technical University of Dresden, Helmholtzstrasse 10, 01069, Dresden, GermanyFaculty of Computer Science, Technical University of Dresden, Helmholtzstrasse 10, 01069, Dresden, GermanyToday’s ETL tools provide capabilities to develop custom code as user-defined functions (UDFs) to extend the expressiveness of the standard ETL operators. However, while this allows us to easily add new functionalities, it also comes with the risk that the custom code is not intended to be optimized, e.g., by parallelism, and for this reason, it performs poorly for data-intensive ETL workflows. In this paper we present a novel framework, which allows the ETL developer to choose a design pattern in order to write parallelizable code and generates a configuration for the UDFs to be executed in a distributed environment. This enables ETL developers with minimum expertise in distributed and parallel computing to develop UDFs without taking care of parallelization configurations and complexities. We perform experiments on large-scale datasets based on TPC-DS and BigBench. The results show that our approach significantly reduces the effort of ETL developers and at the same time generates efficient parallel configurations to support complex and data-intensive ETL tasks.https://doi.org/10.2478/amcs-2019-0005etl workflowparallel etl operatorsparallel algorithmic skeletonsuser-defined functions |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ali Syed Muhammad Fawad Mey Johannes Thiele Maik |
spellingShingle |
Ali Syed Muhammad Fawad Mey Johannes Thiele Maik Parallelizing user–defined functions in the ETL workflow using orchestration style sheets International Journal of Applied Mathematics and Computer Science etl workflow parallel etl operators parallel algorithmic skeletons user-defined functions |
author_facet |
Ali Syed Muhammad Fawad Mey Johannes Thiele Maik |
author_sort |
Ali Syed Muhammad Fawad |
title |
Parallelizing user–defined functions in the ETL workflow using orchestration style sheets |
title_short |
Parallelizing user–defined functions in the ETL workflow using orchestration style sheets |
title_full |
Parallelizing user–defined functions in the ETL workflow using orchestration style sheets |
title_fullStr |
Parallelizing user–defined functions in the ETL workflow using orchestration style sheets |
title_full_unstemmed |
Parallelizing user–defined functions in the ETL workflow using orchestration style sheets |
title_sort |
parallelizing user–defined functions in the etl workflow using orchestration style sheets |
publisher |
Sciendo |
series |
International Journal of Applied Mathematics and Computer Science |
issn |
2083-8492 |
publishDate |
2019-03-01 |
description |
Today’s ETL tools provide capabilities to develop custom code as user-defined functions (UDFs) to extend the expressiveness of the standard ETL operators. However, while this allows us to easily add new functionalities, it also comes with the risk that the custom code is not intended to be optimized, e.g., by parallelism, and for this reason, it performs poorly for data-intensive ETL workflows. In this paper we present a novel framework, which allows the ETL developer to choose a design pattern in order to write parallelizable code and generates a configuration for the UDFs to be executed in a distributed environment. This enables ETL developers with minimum expertise in distributed and parallel computing to develop UDFs without taking care of parallelization configurations and complexities. We perform experiments on large-scale datasets based on TPC-DS and BigBench. The results show that our approach significantly reduces the effort of ETL developers and at the same time generates efficient parallel configurations to support complex and data-intensive ETL tasks. |
topic |
etl workflow parallel etl operators parallel algorithmic skeletons user-defined functions |
url |
https://doi.org/10.2478/amcs-2019-0005 |
work_keys_str_mv |
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1717766967881891840 |