Database Paper Browser

Back to papers

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
11898
Venue
VLDB
Year
2019
Pagerank
4.5509332e-05
Overall Rank
8,248 | 42.63%
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
7,925 Architecting a Query Compiler for Spatial Workloads 2020 SIGMOD 4.6153403e-05
8,094 Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms 2021 VLDB 4.5867812e-05
8,595 Towards A Polyglot Framework for Factorized ML 2021 VLDB 4.4889397e-05
Previous Page 1 / 1 Next

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
60 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00064439773
66 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00061639801
1,750 Weld: A Common Runtime for High Performance Data Analytics 2017 CIDR 0.00010683647
2,838 How to Architect a Query Compiler, Revisited 2018 SIGMOD 8.0408472e-05
Previous Page 1 / 1 Next

Semantically Similar Papers