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Semi-join computation on distributed file systems using map-reduce-merge model

Authors:

M. Al Hajj Hassan,

M. BamhaAuthors Info & Claims

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

Pages 406 - 413

https://doi.org/10.1145/1774088.1774174

Published: 22 March 2010 Publication History

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Abstract

Semi-join is the most used technique to optimize the treatment of complex relational queries on distributed architectures. However, the overhead related to semi-join computation can be very high due to data skew and to the high cost of communication in distributed architectures. Internet search engines needs to process vast amounts of raw data every day. Hence, systems that manage such data should assure scalability, reliability and availability issues with reasonable query processing time. Hadoop and Google's File System are examples of such systems. In this paper, we present a new algorithm based on Map-Reduce-Merge model and distributed histograms for processing semi-join operations on such systems. A cost analysis of this algorithm shows that our approach is insensitive to data skew while reducing communication and disk Input/Output costs to a minimum.

References

[1]

M. Bamha. An optimal and skew-insensitive join and multi-join algorithm for ditributed architectures. In Proceedings of the International Conference on Database and Expert Systems Applications (DEXA'2005). 22--26 August, Copenhagen, Danemark, volume 3588 of Lecture Notes in Computer Science, pages 616--625. Springer-Verlag, 2005.

Digital Library

Google Scholar

[2]

M. Bamha and G. Hains. A skew insensitive algorithm for join and multi-join operation on Shared Nothing machines. In the 11th International Conference on Database and Expert Systems Applications DEXA'2000, volume 1873 of Lecture Notes in Computer Science, London, United Kingdom, 2000. Springer-Verlag.

Digital Library

Google Scholar

[3]

M. Bamha and G. Hains. A frequency adaptive join algorithm for Shared Nothing machines. Journal of Parallel and Distributed Computing Practices (PDCP), Volume 3, Number 3, pages 333--345, September 1999. Appears also in Progress in Computer Research, F. Columbus Ed. Vol. II, Nova Science Publishers, 2001.

Digital Library

Google Scholar

[4]

J. Lawrence Carter and Mark N. Wegman. Universal classes of hash functions. Journal of Computer and System Sciences, 18(2):143--154, April 1979.

Crossref

Google Scholar

[5]

Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. Bigtable: a distributed storage system for structured data. In OSDI '06: Proceedings of the 7th symposium on Operating systems design and implementation, pages 205--218, Berkeley, CA, USA, 2006. USENIX Association.

Digital Library

Google Scholar

[6]

Jeffrey Dean and Sanjay Ghemawat. Mapreduce: Simplified data processing on large clusters. In OSDI '04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, 2004.

Digital Library

Google Scholar

[7]

D. J. DeWitt, J. F. Naughton, D. A. Schneider, and S. Seshadri. Practical Skew Handling in Parallel Joins. In Proceedings of the 18th VLDB Conference, pages 27--40, Vancouver, British Columbia, Canada, 1992.

Digital Library

Google Scholar

[8]

Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The google file system. In SOSP '03: Proceedings of the nineteenth ACM symposium on Operating systems principles, pages 29--43, New York, NY, USA, 2003. ACM Press.

Digital Library

Google Scholar

[9]

Apache hadoop. http://hadoop.apache.org/core/.

Google Scholar

[10]

Ralf Lämmel. Google's mapreduce programming model --- revisited. Sci. Comput. Program., 68(3):208--237, 2007.

Digital Library

Google Scholar

[11]

S. Mohammed, B. Srinivasan, M. Bozyigit, and Phu Dung Le. Novel parallel join algorithms for grid files. In HIPC '96: Proceedings of the Third International Conference on High-Performance Computing (HiPC '96), page 144, Washington, DC, USA, 1996. IEEE Computer Society.

Digital Library

Google Scholar

[12]

Rob Pike, Sean Dorward, Robert Griesemer, and Sean Quinlan. Interpreting the data: Parallel analysis with sawzall. 2005.

Google Scholar

[13]

Konrad Stocker, Donald Kossmann, Reinhard Braumandl, Alfons Kemper, and UniversitÃd't Passau. Integrating semi-join-reducers into state-of-the-art query processors. In ICDE '01: Proceedings of the 17th International Conference on Data Engineering, page 575, Washington, DC, USA, 2001. IEEE Computer Society.

Digital Library

Google Scholar

[14]

Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, and D. Stott Parker. Map-reduce-merge: simplified relational data processing on large clusters. In SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, pages 1029--1040, New York, NY, USA, 2007. ACM.

Digital Library

Google Scholar

Cited By

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TRAN TPHAN TLAURENT AD’ORAZIO L(2020)Optimization for Large-Scale Fuzzy Joins Using Fuzzy Filters in MapReduce2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ48607.2020.9177610(1-8)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1109/FUZZ48607.2020.9177610
Chang JLai CWang M(2015)A fair scheduler using cloud computing for digital TV program recommendation systemTelecommunications Systems10.1007/s11235-014-9921-460:1(55-66)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s11235-014-9921-4
Phan TD'Orazio LRigaux P(2015)A Theoretical and Experimental Comparison of Filter-Based Equijoins in MapReduceTransactions on Large-Scale Data- and Knowledge-Centered Systems XXV - Volume 962010.1007/978-3-662-49534-6_2(33-70)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/978-3-662-49534-6_2
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Index Terms

Semi-join computation on distributed file systems using map-reduce-merge model
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

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Published In

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

March 2010

2712 pages

ISBN:9781605586397

DOI:10.1145/1774088

Conference Chairs:
Sung Y. Shin
South Dakota State University
,
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Michael Schumacher
University of Applied Sciences Western Switzerland, Switzerland
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University
,
Chih-Cheng Hung
Southern Polytechnic State University

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Published: 22 March 2010

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March 22 - 26, 2010

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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TRAN TPHAN TLAURENT AD’ORAZIO L(2020)Optimization for Large-Scale Fuzzy Joins Using Fuzzy Filters in MapReduce2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ48607.2020.9177610(1-8)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1109/FUZZ48607.2020.9177610
Chang JLai CWang M(2015)A fair scheduler using cloud computing for digital TV program recommendation systemTelecommunications Systems10.1007/s11235-014-9921-460:1(55-66)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s11235-014-9921-4
Phan TD'Orazio LRigaux P(2015)A Theoretical and Experimental Comparison of Filter-Based Equijoins in MapReduceTransactions on Large-Scale Data- and Knowledge-Centered Systems XXV - Volume 962010.1007/978-3-662-49534-6_2(33-70)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/978-3-662-49534-6_2
He WZhou MGong XHe X(2013)Massive Parallel Join in NUMA ArchitectureProceedings of the 2013 IEEE International Congress on Big Data10.1109/BigData.Congress.2013.37(219-226)Online publication date: 27-Jun-2013
https://dl.acm.org/doi/10.1109/BigData.Congress.2013.37
Chang JLai CWang MWu T(2013)A cloud-based intelligent TV program recommendation systemComputers and Electrical Engineering10.1016/j.compeleceng.2013.04.02539:7(2379-2399)Online publication date: 1-Oct-2013
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Wang LGao JWang TLi H(2012)A cost aware adaptive multiple table join evaluation in MapReduce2012 9th International Conference on Fuzzy Systems and Knowledge Discovery10.1109/FSKD.2012.6233855(2437-2441)Online publication date: May-2012
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Hains GAlrammal M(2012)Performance and Scalability of XML Query ProcessingProceedings of the 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)10.1109/CISIS.2012.18(841-846)Online publication date: 4-Jul-2012
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Lai CChang JHu CHuang YChao H(2011)CPRSFuture Generation Computer Systems10.1016/j.future.2010.10.00227:6(823-835)Online publication date: 1-Jun-2011
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Optimizing Local File Accesses for FUSE-Based Distributed Storage

Combining Joint and Semi-Join Operations for Distributed Query Processing

Towards Scalability and Data Skew Handling in GroupBy-Joins using MapReduce Model

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