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SpongeFiles: mitigating data skew in mapreduce using distributed memory

Authors:

Khaled Elmeleegy,

Christopher Olston,

Benjamin ReedAuthors Info & Claims

SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

Pages 551 - 562

https://doi.org/10.1145/2588555.2595634

Published: 18 June 2014 Publication History

Abstract

Data skew is a major problem for data processing platforms like MapReduce. Skew causes worker tasks to spill to disk what they cannot fit in memory, which slows down the task and the overall job. Moreover, performance of other jobs sharing same disk degrades. In many cases, this situation occurs even as the cluster has plenty of spare memory it is just not used evenly. We introduce SpongeFiles, a novel distributed-memory abstraction tailored to data processing environments like MapReduce. A SpongeFile is a logical byte array, comprised of large chunks that can be stored in a variety of locations in the cluster. Spilled data goes to SpongeFiles, which route it to the nearest location with sufficient capacity (local memory, remote memory, local disk, or remote disk as a last resort). By enabling memory-sapped nodes to tap into the spare capacity of their neighbors, SpongeFiles minimize expensive disk spilling, thereby improving performance. In our experiments with Hadoop and Pig, SpongeFiles reduce overall job runtimes by up to 55% and by up to 85% under disk contention.

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Cited By

Ge YTian YYu ZZhang W(2023)Memory sharing for handling memory overload on physical machines in cloud data centersJournal of Cloud Computing10.1186/s13677-023-00405-x12:1Online publication date: 28-Feb-2023
https://doi.org/10.1186/s13677-023-00405-x
Wang TLiu HJin H(2023)Efficient Remote Memory Paging for Disaggregated Memory SystemsAlgorithms and Architectures for Parallel Processing10.1007/978-3-031-22677-9_1(1-20)Online publication date: 11-Jan-2023
https://doi.org/10.1007/978-3-031-22677-9_1
Kocharyan AEkane BTeabe BTran GAstsatryan HHagimont D(2022)A Remote Memory Sharing System for Virtualized Computing InfrastructuresIEEE Transactions on Cloud Computing10.1109/TCC.2020.301808910:3(1532-1542)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TCC.2020.3018089
Show More Cited By

Index Terms

SpongeFiles: mitigating data skew in mapreduce using distributed memory
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures

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Published In

cover image ACM Conferences

SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

June 2014

1645 pages

ISBN:9781450323765

DOI:10.1145/2588555

General Chairs:
Curtis Dyreson
Utah State University, USA
,
Feifei Li
University of Utah, USA
,
Program Chair:
M. Tamer Özsu
University of Waterloo, Canada

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2014

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handling data skew

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SIGMOD/PODS'14

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SIGMOD/PODS'14: International Conference on Management of Data

June 22 - 27, 2014

Utah, Snowbird, USA

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SIGMOD '14 Paper Acceptance Rate 107 of 421 submissions, 25%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Ge YTian YYu ZZhang W(2023)Memory sharing for handling memory overload on physical machines in cloud data centersJournal of Cloud Computing10.1186/s13677-023-00405-x12:1Online publication date: 28-Feb-2023
https://doi.org/10.1186/s13677-023-00405-x
Wang TLiu HJin H(2023)Efficient Remote Memory Paging for Disaggregated Memory SystemsAlgorithms and Architectures for Parallel Processing10.1007/978-3-031-22677-9_1(1-20)Online publication date: 11-Jan-2023
https://doi.org/10.1007/978-3-031-22677-9_1
Kocharyan AEkane BTeabe BTran GAstsatryan HHagimont D(2022)A Remote Memory Sharing System for Virtualized Computing InfrastructuresIEEE Transactions on Cloud Computing10.1109/TCC.2020.301808910:3(1532-1542)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TCC.2020.3018089
Song WYang YEo JSeo JKim JLee SLee GUm TCho HChun B(2021)Apache Nemo: A Framework for Optimizing Distributed Data ProcessingACM Transactions on Computer Systems10.1145/346814438:3-4(1-31)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3468144
DAIKOKU HKAWASHIMA HTATEBE O(2019)Skew-Aware Collective Communication for MapReduce ShufflingIEICE Transactions on Information and Systems10.1587/transinf.2019PAP0019E102.D:12(2389-2399)Online publication date: 1-Dec-2019
https://doi.org/10.1587/transinf.2019PAP0019
Mishra SSethi NChinmay A(2019)Various Data Skewness Methods in the Hadoop Environment2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)10.1109/ICRAECC43874.2019.8994979(1-4)Online publication date: Mar-2019
https://doi.org/10.1109/ICRAECC43874.2019.8994979
Bindschaedler LMalicevic JSchiper NGoel AZwaenepoel WOliveira RFelber PHu Y(2018)Rock you like a hurricaneProceedings of the Thirteenth EuroSys Conference10.1145/3190508.3190532(1-15)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3190508.3190532
Meena KTayal D(2018)Partition Tuning based Bagging technique to Skew Handling2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)10.1109/ICICCT.2018.8473202(244-250)Online publication date: Apr-2018
https://doi.org/10.1109/ICICCT.2018.8473202
Kim MCho H(2018)Popularity-based covering sets for energy proportionality in shared-nothing clustersThe Journal of Supercomputing10.1007/s11227-017-2197-174:5(1885-1910)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11227-017-2197-1
Shang FChen XYan CLi LZhao Y(2018)The bandwidth-aware backup task scheduling strategy using SDN in HadoopCluster Computing10.1007/s10586-018-1736-8Online publication date: 15-Jan-2018
https://doi.org/10.1007/s10586-018-1736-8
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