The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A comparison of join algorithms for log processing in MaPreduce
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003) · ACM SIGOPS Operating Systems Review · doi:10.1145/1165389.945450 · Cited by 4,984
Pig latin: a not-so-foreign language for data processing
Olston, C., Reed, B., Srivastava, U., Kumar, R., & Tomkins, A. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376726 · Cited by 1,730
Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003) · Proceedings of the nineteenth ACM symposium on Operating systems principles · doi:10.1145/945445.945450 · Cited by 1,423
Parallel database systems: the future of high performance database systems
DeWitt, D., & Gray, J. (1992) · Communications of the ACM · doi:10.1145/129888.129894 · Cited by 1,291
Query evaluation techniques for large databases
Graefe, G. (1993) · ACM Computing Surveys · doi:10.1145/152610.152611 · Cited by 1,262
A comparison of approaches to large-scale data analysis
Pavlo, A., Paulson, E., Rasin, A., Abadi, D. J., DeWitt, D. J., Madden, S., & Stonebraker, M. (2009) · Proceedings of the 2009 ACM SIGMOD International Conference on Management of data · doi:10.1145/1559845.1559865 · Cited by 1,071
SCOPE: easy and efficient parallel processing of massive data sets
Chaiken, R., Jenkins, B., Larson, P.-Å., Ramsey, B., Shakib, D., Weaver, S., & Zhou, J. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1454159.1454166 · Cited by 727
Map-reduce-merge: simplified relational data processing on large clusters
Yang, H.-C., Dasdan, A., Hsiao, R.-L., & Parker, D. S. (2007) · Proceedings of the 2007 ACM SIGMOD international conference on Management of data · doi:10.1145/1247480.1247602 · Cited by 715
Query processing in a system for distributed databases (SDD-1)
Bernstein, P. A., Goodman, N., Wong, E., Reeve, C. L., & Rothnie, J. B. (1981) · ACM Transactions on Database Systems · doi:10.1145/319628.319650 · Cited by 493
Cited by
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial
Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014) · IEEE Access · doi:10.1109/access.2014.2332453 · Cited by 1,099
Hadoop GIS: a high performance spatial data warehousing system over mapreduce
Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., & Saltz, J. (2013) · Proceedings of the VLDB Endowment · doi:10.14778/2536222.2536227 · Cited by 593
IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges
Cai, H., Xu, B., Jiang, L., & Vasilakos, A. V. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2619369 · Cited by 588
Metadata
- Authors
- Blanas, S., Patel, J. M., Ercegovac, V., Rao, J., Shekita, E. J., & Tian, Y.
- Venue
- Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
- Publisher
- ACM
- Published
- 2010-06-06
- Pages
- 975-986
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2043099794
- Cited by
- 414
- References
- 11
- FWCI
- 28.78
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A comparison of join algorithms for log processing in MaPreduce. https://app.cochat.ai/papers/10.1145/1807167.1807273
- The paper
- Blanas, S., Patel, J. M., Ercegovac, V., Rao, J., Shekita, E. J., & Tian, Y. (2010). A comparison of join algorithms for log processing in MaPreduce. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. https://doi.org/10.1145/1807167.1807273
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
