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)

Paper ID: 5369
Venue: SIGMOD
Year: 2017
Pagerank: 4.1945683e-05
Overall Rank: 11,782 | 18.04%
DOI: 10.1145/3035918.3058735

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,440	FlexPS: Flexible Parallelism Control in Parameter Server Architecture	2018	VLDB	8.8119143e-05
4,609	A General and Efficient Querying Method for Learning to Hash	2018	SIGMOD	6.0528541e-05

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
4	Pregel: A System for Large-Scale Graph Processing	2010	SIGMOD	0.0019005923
2,033	NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion	2014	VLDB	9.7172731e-05
4,120	Husky: Towards a More Efficient and Expressive Distributed Computing Framework	2016	VLDB	6.4364588e-05
7,951	LFTF: A Framework for Efficient Tensor Analytics at Scale	2017	VLDB	4.613363e-05

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