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Flare & Lantern: Efficiently Swapping Horses Midstream

Summary: Flare (Spark SQL) and Lantern (TensorFlow/PyTorch) integrated for an end-to-end compiled data path. Runtime compilation and native code generation enable negligible overhead when switching between SQL and ML processing. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11899
Venue
VLDB
Year
2019
Pagerank
5.5345733e-05
Overall Rank
8,209 | 42.95%
DOI
10.14778/3352063.3352097

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
7,763 Architecting a Query Compiler for Spatial Workloads 2020 SIGMOD 5.6079812e-05
7,776 Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms 2021 VLDB 5.6051514e-05
8,468 Towards A Polyglot Framework for Factorized ML 2021 VLDB 5.4893629e-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
25 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00055280049
26 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00054892115
1,306 Weld: A Common Runtime for High Performance Data Analytics 2017 CIDR 0.00011313192
2,423 How to Architect a Query Compiler, Revisited 2018 SIGMOD 8.6700245e-05
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