demonstration

Online aggregation and continuous query support in MapReduce

Authors:

Tyson Condie,

Neil Conway,

Peter Alvaro,

Joseph M. Hellerstein,

John Gerth,

Justin Talbot,

Khaled Elmeleegy,

Russell SearsAuthors Info & Claims

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Pages 1115 - 1118

https://doi.org/10.1145/1807167.1807295

Published: 06 June 2010 Publication History

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Abstract

MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, the output of each MapReduce task and job is materialized to disk before it is consumed. In this demonstration, we describe a modified MapReduce architecture that allows data to be pipelined between operators. This extends the MapReduce programming model beyond batch processing, and can reduce completion times and improve system utilization for batch jobs as well. We demonstrate a modified version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see "early returns" from a job as it is being computed. Our Hadoop Online Prototype (HOP) also supports continuous queries, which enable MapReduce programs to be written for applications such as event monitoring and stream processing. HOP retains the fault tolerance properties of Hadoop, and can run unmodified user-defined MapReduce programs.

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Ye SXu XWang YFu T(2023)Efficient Complex Aggregate Queries with Accuracy Guarantee Based on Execution Cost Model over Knowledge GraphsMathematics10.3390/math1118390811:18(3908)Online publication date: 14-Sep-2023
https://doi.org/10.3390/math11183908
Sheoran NChockchowwat SChheda AWang SVerma RPark Y(2023)A Step Toward Deep Online AggregationProceedings of the ACM on Management of Data10.1145/35892691:2(1-28)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589269
Usha DRaman R(2022)A Forensic Way to Find Solutions for Security Challenges in Cloudserver Through MapReduce TechniqueHandbook of Research on Technologies and Systems for E-Collaboration During Global Crises10.4018/978-1-7998-9640-1.ch021(330-338)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-9640-1.ch021
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Index Terms

Online aggregation and continuous query support in MapReduce
1. Information systems
  1. Information systems applications
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Abstraction, modeling and modularity

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Published In

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

1286 pages

ISBN:9781450300322

DOI:10.1145/1807167

General Chair:
Ahmed Elmagarmid
Purdue University, USA
,
Program Chair:
Divyakant Agrawal
University of California at Santa Barbara, USA

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

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Published: 06 June 2010

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Author Tag

mapreduce

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Demonstration

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SIGMOD/PODS '10

Sponsor:

SIGMOD

SIGMOD/PODS '10: International Conference on Management of Data

June 6 - 10, 2010

Indiana, Indianapolis, USA

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

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Ye SXu XWang YFu T(2023)Efficient Complex Aggregate Queries with Accuracy Guarantee Based on Execution Cost Model over Knowledge GraphsMathematics10.3390/math1118390811:18(3908)Online publication date: 14-Sep-2023
https://doi.org/10.3390/math11183908
Sheoran NChockchowwat SChheda AWang SVerma RPark Y(2023)A Step Toward Deep Online AggregationProceedings of the ACM on Management of Data10.1145/35892691:2(1-28)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589269
Usha DRaman R(2022)A Forensic Way to Find Solutions for Security Challenges in Cloudserver Through MapReduce TechniqueHandbook of Research on Technologies and Systems for E-Collaboration During Global Crises10.4018/978-1-7998-9640-1.ch021(330-338)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-9640-1.ch021
Peng JDing BWang JZeng KZhou JIves ZBonifati AEl Abbadi A(2022)One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical GuaranteesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517900(531-544)Online publication date: 10-Jun-2022
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Wang YKhan AXu XJin JHong QFu T(2022)Aggregate Queries on Knowledge Graphs: Fast Approximation with Semantic-aware Sampling2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00263(2914-2927)Online publication date: May-2022
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Zeitler ERisch T(2020)Massive scale-out of expensive continuous queriesProceedings of the VLDB Endowment10.14778/3402707.34027524:11(1181-1188)Online publication date: 3-Jun-2020
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