The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Hadoop++: making a yellow elephant run like a cheetah (without it even noticing)
Abstract
MapReduce is a computing paradigm that has gained a lot of attention in recent years from industry and research. Unlike parallel DBMSs, MapReduce allows non-expert users to run complex analytical tasks over very large data sets on very large clusters and clouds. However, this comes at a price: MapReduce processes tasks in a scan-oriented fashion. Hence, the performance of Hadoop --- an open-source implementation of MapReduce --- often does not match the one of a well-configured parallel DBMS. In this paper we propose a new type of system named Hadoop++: it boosts task performance without changing the Hadoop framework at all (Hadoop does not even 'notice it'). To reach this goal, rather than changing a working system (Hadoop), we inject our technology at the right places through UDFs only and affect Hadoop from inside . This has three important consequences: First, Hadoop++ significantly outperforms Hadoop. Second, any future changes of Hadoop may directly be used with Hadoop++ without rewriting any glue code. Third, Hadoop++ does not need to change the Hadoop interface. Our experiments show the superiority of Hadoop++ over both Hadoop and HadoopDB for tasks related to indexing and join processing.
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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
Dryad: distributed data-parallel programs from sequential building blocks
Isard, M., Budiu, M., Yu, Y., Birrell, A., & Fetterly, D. (2007) · Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007 · doi:10.1145/1272996.1273005 · Cited by 2,432
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
Hive: a warehousing solution over a map-reduce framework
Thusoo, A., Sarma, J. S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., & Murthy, R. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687553.1687609 · Cited by 1,556
MapReduce: a flexible data processing tool
Dean, J., & Ghemawat, S. (2010) · Communications of the ACM · doi:10.1145/1629175.1629198 · Cited by 1,182
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
HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads
Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D., Silberschatz, A., & Rasin, A. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687731 · Cited by 857
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
Condie, T., Conway, N., Alvaro, P., Hellerstein, J. M., Elmeleegy, K., & Sears, R. (2010) · doi:10.5555/1855711.1855732 · Cited by 673
Cited by
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
Interactive analytical processing in big data systems: a cross-industry study of MapReduce workloads
Chen, Y., Alspaugh, S., & Katz, R. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2367502.2367519 · Cited by 469
Metadata
- Authors
- Dittrich, J., Quiané-Ruiz, J.-A., Jindal, A., Kargin, Y., Setty, V., & Schad, J.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2010-09
- Volume
- 3
- Issue
- 1-2
- Pages
- 515-529
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W2035543557
- Cited by
- 364
- References
- 17
- FWCI
- 128.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dittrich, J., Quiané-Ruiz, J.-A., Jindal, A., Kargin, Y., Setty, V., & Schad, J. (2010). Hadoop++: making a yellow elephant run like a cheetah (without it even noticing). Proceedings of the VLDB Endowment. https://doi.org/10.14778/1920841.1920908
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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.
