RaSQL: Greater Power and Performance for Big Data Analytics with Recursive-aggregate-SQL on Spark
Summary: RaSQL extends Spark SQL with Recursive-aggregate-SQL for declarative recursive queries with aggregates. A novel fixpoint-based compiler maps RaSQL to a single fixpoint operator; optimized runtime yields superior performance vs Giraph, GraphX, Myria. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiaqi Gu (University of California Los Angeles)
- 2. Yugo H. Watanabe (University of California Los Angeles)
- 3. William A. Mazza (University of Naples)
- 4. Alexander Shkapsky (Workday)
- 5. Mohan Yang (Google)
- 6. Ling Ding (University of California Los Angeles)
- 7. Carlo Zaniolo (University of California Los Angeles)
BibTeX Citation
@inproceedings{gu_sigmod19,
title = {{RaSQL: Greater Power and Performance for Big Data Analytics with Recursive-aggregate-SQL on Spark}},
author = {Gu, Jiaqi and Watanabe, Yugo H. and Mazza, William A. and Shkapsky, Alexander and Yang, Mohan and Ding, Ling and Zaniolo, Carlo},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3324959},
url = {https://dl.acm.org/doi/10.1145/3299869.3324959},
year = {2019}
}
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