Spark SQL: Relational Data Processing in Spark
Summary: Relational processing integrated into Spark via DataFrame API, unifying SQL queries with Spark's functional workflow. Catalyst, a Scala-based extensible optimizer, enables composable rules, code generation, JSON schema inference, and federation to databases. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michael Armbrust (Databricks)
- 2. Reynold S. Xin (Databricks)
- 3. Cheng Lian (Databricks)
- 4. Yin Huai (Databricks)
- 5. Davies Liu (Databricks)
- 6. Joseph K. Bradley (Databricks)
- 7. Xiangrui Meng (Databricks)
- 8. Tomer Kaftan (University of California Berkeley)
- 9. Michael J. Franklin (Databricks; University of California Berkeley)
- 10. Ali Ghodsi (Databricks)
- 11. Matei Zaharia (Databricks; Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{armbrust_sigmod15,
title = {{Spark SQL: Relational Data Processing in Spark}},
author = {Armbrust, Michael and Xin, Reynold S. and Lian, Cheng and Huai, Yin and Liu, Davies and Bradley, Joseph K. and Meng, Xiangrui and Kaftan, Tomer and Franklin, Michael J. and Ghodsi, Ali and Zaharia, Matei},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2742797},
url = {https://dl.acm.org/doi/10.1145/2723372.2742797},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 50 of 221 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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