The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Spark SQL: Relational Data Processing in Spark
Abstract
Spark SQL is a new module in Apache Spark that integrates relational processing with Spark's functional programming API. Built on our experience with Shark, Spark SQL lets Spark programmers leverage the benefits of relational processing (e.g. declarative queries and optimized storage), and lets SQL users call complex analytics libraries in Spark (e.g. machine learning). Compared to previous systems, Spark SQL makes two main additions. First, it offers much tighter integration between relational and procedural processing, through a declarative DataFrame API that integrates with procedural Spark code. Second, it includes a highly extensible optimizer, Catalyst, built using features of the Scala programming language, that makes it easy to add composable rules, control code generation, and define extension points. Using Catalyst, we have built a variety of features (e.g. schema inference for JSON, machine learning types, and query federation to external databases) tailored for the complex needs of modern data analysis. We see Spark SQL as an evolution of both SQL-on-Spark and of Spark itself, offering richer APIs and optimizations while keeping the benefits of the Spark programming model.
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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-citationBig data preprocessing: methods and prospects
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0014-0 · Cited by 709
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation
Yuan, H., & Li, G. (2021) · Data Science and Engineering · doi:10.1007/s41019-020-00151-z · Cited by 389
VideoEdge: Processing Camera Streams using Hierarchical Clusters
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Metadata
- Authors
- Armbrust, M., Xin, R. S., Lian, C., Huai, Y., Liu, D., Bradley, J. K., Meng, X., Kaftan, T., Franklin, M. J., Ghodsi, A., & Zaharia, M.
- Venue
- Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
- Publisher
- ACM
- Published
- 2015-05-27
- Pages
- 1383-1394
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2038412523
- Cited by
- 1,253
- References
- 19
- FWCI
- 124.82
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Armbrust, M., Xin, R. S., Lian, C., Huai, Y., Liu, D., Bradley, J. K., Meng, X., Kaftan, T., Franklin, M. J., Ghodsi, A., & Zaharia, M. (2015). Spark SQL: Relational Data Processing in Spark. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. https://doi.org/10.1145/2723372.2742797
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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