The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Shark: SQL and rich analytics at scale
Abstract
Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g. iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100X faster than Apache Hive, and machine learning programs more than 100X faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, and the fine-grained fault tolerance properties that such engine provides. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.
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Cites
Advances in neural information processing systems 7
Cios, K., & Shields, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,059
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
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J. H., McCauley, M., Franklin, M. J. M., Shenker, S. J., & Stoica, I. G. (2012) · Cited by 3,622
Self-citationPregel: a system for large-scale graph processing
Malewicz, G., Austern, M. H., Bik, A. J. C., Dehnert, J. C., Horn, I., Leiser, N., & Czajkowski, G. (2010) · Proceedings of the 2010 ACM SIGMOD International Conference on Management of data · doi:10.1145/1807167.1807184 · Cited by 3,515
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
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., & Hellerstein, J. M. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2212351.2212354 · Cited by 1,653
(2011) · doi:10.5555/1972457.1972488 · Cited by 1,619
Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
Zaharia, M., Borthakur, D., Sen Sarma, J., Elmeleegy, K., Shenker, S., & Stoica, I. (2010) · Proceedings of the 5th European conference on Computer systems · doi:10.1145/1755913.1755940 · Cited by 1,393
Self-citationMap-Reduce for Machine Learning on Multicore
Chu, C.-T., Kim, S. K., Lin, Y.-A., Yu, Y., Bradski, G., Ng, A. Y., & Olukotun, K. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0040 · Cited by 1,229
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
Cited by
Apache Spark: A unified engine for big data processing
Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., et al. (2016) · Communications of the ACM · doi:10.1145/2934664 · Cited by 2,757
Self-citationSocial big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846
“his framework is particularly well suited to machine learning algoithms [[29]].”
BackgroundRevealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632
A survey on the development status and application prospects of knowledge graph in smart grids
Wang, J., Wang, X., Ma, C., & Kou, L. (2021) · IET Generation, Transmission & Distribution · doi:10.1049/gtd2.12040 · Cited by 111
A distributed frequent itemset mining algorithm using Spark for Big Data analytics
Zhang, F., Liu, M., Gui, F., Shen, W., Shami, A., & Ma, Y. (2015) · Cluster Computing · doi:10.1007/s10586-015-0477-1 · Cited by 87
Metadata
- Authors
- Xin, R. S., Rosen, J., Zaharia, M., Franklin, M. J., Shenker, S., & Stoica, I.
- Venue
- Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
- Publisher
- ACM
- Published
- 2013-06-22
- Pages
- 13-24
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2139072600
- Cited by
- 369
- References
- 35
- FWCI
- 56.43
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xin, R. S., Rosen, J., Zaharia, M., Franklin, M. J., Shenker, S., & Stoica, I. (2013). Shark: SQL and rich analytics at scale. Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. https://doi.org/10.1145/2463676.2465288
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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.
