Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/22649
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lucas Filho, Edson Ramiro | - |
dc.contributor.author | Cunha de Almeida, Eduardo | - |
dc.contributor.author | Scherzinger, Stefanie | - |
dc.contributor.author | Herodotou, Herodotos | - |
dc.date.accessioned | 2021-06-08T04:26:50Z | - |
dc.date.available | 2021-06-08T04:26:50Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.citation | Big Data Research, 2021, vol. 25, articl. no. 100204 | en_US |
dc.identifier.issn | 22145796 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/22649 | - |
dc.description.abstract | SQL-on-Hadoop engines such as Hive provide a declarative interface for processing large-scale data over computing frameworks such as Hadoop. The underlying frameworks contain a large number of configuration parameters that can significantly impact performance, but which are hard to tune. The problem of automatic parameter tuning has become a lively research area and several sophisticated tuning advisors have been proposed for Hadoop. In this paper, we conduct an experimental study to explore the impact of Hadoop parameter tuning on Hive. We reveal that the performance of Hive queries does not necessarily improve when using Hadoop-focused tuning advisors out-of-the-box, at least when following the current approach of applying the same tuning setup uniformly for evaluating the entire query. After extending the Hive query processing engine, we propose an alternative tuning approach and experimentally show how current Hadoop tuning advisors can now provide good and robust performance for Hive queries, as well as improved cluster resource utilization. We share our observations with the community and hope to create an awareness for this problem as well as to initiate new research on automatic parameter tuning for SQL-on-Hadoop systems. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Big Data Research | en_US |
dc.rights | © Elsevier | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | SQL-on-Hadoop | en_US |
dc.subject | Parameter tuning | en_US |
dc.subject | Self-tuning | en_US |
dc.title | Investigating Automatic Parameter Tuning for SQL-on-Hadoop Systems | en_US |
dc.type | Article | en_US |
dc.collaboration | University of Passau | en_US |
dc.collaboration | Federal University of Paraná | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | Germany | en_US |
dc.country | Brazil | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1016/j.bdr.2021.100204 | en_US |
dc.relation.volume | 25 | en_US |
cut.common.academicyear | 2020-2021 | en_US |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 2214-5796 | - |
crisitem.journal.publisher | Elsevier | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-8717-1691 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
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