The Best of Both Worlds: Big Data Programming with Both Productivity and Performance
Summary: Productive, high-performance big-data programming via Husky; multi-pattern support (MapReduce, GAS, vertex-centric, async ML). PyHusky/ScHusky provide Python/Scala frontends for declarative DAGs, lazily evaluated, with a scheduler moving hot paths to native Husky. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Fan Yang (Chinese University of Hong Kong)
- 2. Yuzhen Huang (Chinese University of Hong Kong)
- 3. Yunjian Zhao (Chinese University of Hong Kong)
- 4. Jinfeng Li (Chinese University of Hong Kong)
- 5. Guanxian Jiang (Chinese University of Hong Kong)
- 6. James Cheng (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{yang_sigmod17,
title = {{The Best of Both Worlds: Big Data Programming with Both Productivity and Performance}},
author = {Yang, Fan and Huang, Yuzhen and Zhao, Yunjian and Li, Jinfeng and Jiang, Guanxian and Cheng, James},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3058735},
url = {https://dl.acm.org/doi/10.1145/3035918.3058735},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,693 | FlexPS: Flexible Parallelism Control in Parameter Server Architecture | 2018 | VLDB | 8.247004e-05 |
| 3,858 | A General and Efficient Querying Method for Learning to Hash | 2018 | SIGMOD | 7.067591e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 2,615 | NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion | 2014 | VLDB | 8.340186e-05 |
| 3,789 | Husky: Towards a More Efficient and Expressive Distributed Computing Framework | 2016 | VLDB | 7.1240627e-05 |
| 8,270 | LFTF: A Framework for Efficient Tensor Analytics at Scale | 2017 | VLDB | 5.4574671e-05 |
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