research-article

RHEEM: enabling cross-platform data processing: may the big data be with you!

Authors:

Bertty Contreras-Rojas,

Ahmed Elmagarmid,

Sebastian Kruse,

Mourad Ouzzani,

Jorge-Arnulfo Quiané-Ruiz,

Saravanan Thirumuruganathan,

Anis TroudiAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 11, Issue 11

Pages 1414 - 1427

https://doi.org/10.14778/3236187.3236195

Published: 01 July 2018 Publication History

Abstract

Solving business problems increasingly requires going beyond the limits of a single data processing platform (platform for short), such as Hadoop or a DBMS. As a result, organizations typically perform tedious and costly tasks to juggle their code and data across different platforms. Addressing this pain and achieving automatic cross-platform data processing is quite challenging: finding the most efficient platform for a given task requires quite good expertise for all the available platforms. We present Rheem, a general-purpose cross-platform data processing system that decouples applications from the underlying platforms. It not only determines the best platform to run an incoming task, but also splits the task into subtasks and assigns each subtask to a specific platform to minimize the overall cost (e.g., runtime or monetary cost). It features (i) an interface to easily compose data analytic tasks; (ii) a novel cost-based optimizer able to find the most efficient platform in almost all cases; and (iii) an executor to efficiently orchestrate tasks over different platforms. As a result, it allows users to focus on the business logic of their applications rather than on the mechanics of how to compose and execute them. Using different real-world applications with Rheem, we demonstrate how cross-platform data processing can accelerate performance by more than one order of magnitude compared to single-platform data processing.

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 11, Issue 11

July 2018

507 pages

ISSN:2150-8097

Editors:
Sihem Amer-Yahia
University of Grenoble Alpes, CNRS
,
Jian Pei
Simon Fraser University

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VLDB Endowment

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Published: 01 July 2018

Published in PVLDB Volume 11, Issue 11

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https://dl.acm.org/doi/10.14778/3681954.3682026
Kraska TLi TMadden SMarkakis MNgom AWu ZYu G(2023)Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data MeshesProceedings of the VLDB Endowment10.14778/3611479.361152616:11(3293-3301)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611526
Park YTak BHan W(2023)QaaD (Query-as-a-Data): Scalable Execution of Massive Number of Small Queries in SparkProceedings of the ACM on Management of Data10.1145/35892791:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589279
Giatrakos NAlevizos EDeligiannakis AKlinkenberg RArtikis AFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Proactive Streaming Analytics at Scale: A Journey from the State-of-the-art to a Production PlatformProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615293(5204-5207)Online publication date: 21-Oct-2023
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Kaoudi ZQuiané-Ruiz J(2022)Unified data analyticsProceedings of the VLDB Endowment10.14778/3554821.355489815:12(3778-3781)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.14778/3554821.3554898
Kiehn FSchmidt MGlake DPanse FWingerath WWollmer BPoppinga MRitter N(2022)Polyglot data managementProceedings of the VLDB Endowment10.14778/3554821.355489115:12(3750-3753)Online publication date: 1-Aug-2022
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Winter CGiceva JNeumann TKemper A(2022)On-demand state separation for cloud data warehousingProceedings of the VLDB Endowment10.14778/3551793.355184515:11(2966-2979)Online publication date: 29-Sep-2022
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Grulich PZeuch SMarkl V(2022)BabelfishProceedings of the VLDB Endowment10.14778/3489496.348950115:2(196-210)Online publication date: 4-Feb-2022
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El-Mahgary SSoisalon-Soininen EOrponen PRönnholm PHyyppä H(2022)OVI-3: A NoSQL visual query system supporting efficient anti-joinsJournal of Intelligent Information Systems10.1007/s10844-022-00742-460:3(777-801)Online publication date: 21-Sep-2022
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