research-article

A comparison of join algorithms for log processing in MaPreduce

Authors:

Jignesh M. Patel,

Eugene J. Shekita,

Yuanyuan TianAuthors Info & Claims

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

Pages 975 - 986

https://doi.org/10.1145/1807167.1807273

Published: 06 June 2010 Publication History

Abstract

The MapReduce framework is increasingly being used to analyze large volumes of data. One important type of data analysis done with MapReduce is log processing, in which a click-stream or an event log is filtered, aggregated, or mined for patterns. As part of this analysis, the log often needs to be joined with reference data such as information about users. Although there have been many studies examining join algorithms in parallel and distributed DBMSs, the MapReduce framework is cumbersome for joins. MapReduce programmers often use simple but inefficient algorithms to perform joins. In this paper, we describe crucial implementation details of a number of well-known join strategies in MapReduce, and present a comprehensive experimental comparison of these join techniques on a 100-node Hadoop cluster. Our results provide insights that are unique to the MapReduce platform and offer guidance on when to use a particular join algorithm on this platform.

References

[1]

http://www.slideshare.net/cloudera/hw09-data-processing-in-the-enterprise.

[2]

http://www.slideshare.net/cloudera/hw09-large-scale-transaction-analysis.

[3]

http://open.blogs.nytimes.com/2007/11/01/self-service-prorated-super-computing-fun.

[4]

http://www.slideshare.net/cloudera/hw09-hadoop-based-data-mining-platform-for-the-telecom-industry.

[5]

http://wiki.apache.org/hadoop/PoweredBy.

[6]

http://developer.yahoo.net/blogs/theater/archives/2009/06/hadoop summit hadoop and the enterprise.html.

[7]

http://www.slideshare.net/prasadc/hive-percona-2009.

[8]

http://hadoop.apache.org/.

[9]

http://research.yahoo.com/files/facebook-hadoop-summit.pdf.

[10]

http://hadoop.apache.org/hive/.

[11]

http://www.jaql.org.

[12]

Teradata: DBC/1012 data base computer concepts and facilities, Teradata Corp., Document No. C02-0001-00, 1984.

[13]

P. A. Bernstein and N. Goodman. Full reducers for relational queries using multi-attribute semijoins. In Symp. On Comp. Network, 1979.

[14]

P. A. Bernstein, N. Goodman, E. Wong, C. L. Reeve, and J. B. Rothnie Jr. Query processing in a system for distributed databases (SDD-1). ACM Transactions on Database Systems, 6(4):602--625, 1981.

Digital Library

[15]

R. Chaiken, B. Jenkins, P.-Å. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. SCOPE: Easy and efficient parallel processing of massive data sets. PVLDB, 1(2):1265--1276, 2008.

Digital Library

[16]

J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In OSDI, 2004.

Digital Library

[17]

D. J. DeWitt and J. Gray. Parallel database systems: The future of high performance database systems. Commun. ACM, 35(6), 1992.

Digital Library

[18]

D. J. DeWitt and M. Stonebraker. MapReduce: A major step backwards. Blog post at The Database Column, 17 January 2008.

[19]

S. Ghemawat, H. Gobioff, and S.-T. Leung. The Google file system. In SOSP, 2003.

Digital Library

[20]

G. Graefe. Query evaluation techniques for large databases. ACM Comput. Surv., 25(2), 1993.

Digital Library

[21]

J. Hammerbacher. Managing a large Hadoop cluster. Presentation, Facebook Inc., May 2008.

[22]

P. Mishra and M. H. Eich. Join processing in relational databases. ACM Comput. Surv., 24(1), 1992.

Digital Library

[23]

C. Olston, B. Reed, A. Silberstein, and U. Srivastava. Automatic optimization of parallel dataflow programs. In USENIX Annual Technical Conference, pages 267--273, 2008.

Digital Library

[24]

C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig latin: A not-so-foreign language for data processing. In SIGMOD, pages 1099--1110, 2008.

Digital Library

[25]

A. Pavlo, E. Paulson, A. Rasin, D. J. Abadi, D. J. Dewitt, S. Madden, and M. Stonebraker. A comparison of approaches to large-scale data analysis. In SIGMOD, 2009.

Digital Library

[26]

D. A. Schneider and D. J. DeWitt. A performance evaluation of four parallel join algorithms in a shared-nothing multiprocessor environment. In SIGMOD, 1989.

Digital Library

[27]

H.-C. Yang, A. Dasdan, R.-L. Hsiao, and D. S. Parker. Map-reduce-merge: simplified relational data processing on large clusters. In SIGMOD, pages 1029--1040, 2007.

Digital Library

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Index Terms

A comparison of join algorithms for log processing in MaPreduce
1. Information systems

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cover image ACM Conferences

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

Copyright © 2010 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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Published: 06 June 2010

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

June 6 - 10, 2010

Indiana, Indianapolis, USA

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https://doi.org/10.3233/SW-223201
Cheng LWang YJhaveri RWang QMao Y(2023)Toward Network-Aware Query Execution Systems in Large DatacentersIEEE Transactions on Network and Service Management10.1109/TNSM.2023.327316620:4(4494-4504)Online publication date: Dec-2023
https://doi.org/10.1109/TNSM.2023.3273166
Zhang TQiu HCastellano GRifai MChen CPianese F(2023)System Log Parsing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3222417(1-20)Online publication date: 2023
https://doi.org/10.1109/TKDE.2022.3222417
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