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AdaptDB: Adaptive Partitioning for Distributed Joins
Summary: AdaptDB adaptively refines distributed table partitioning as workloads evolve. Its hyper-join matches overlapping storage blocks to avoid broad shuffles, while smooth repartitioning reduces overlap incrementally, yielding 2–3× faster TPC-H and real-world queries.
(summarized by gpt-5.6-luna on Jul 24 2026)
Paper ID
hfa66289fa1e65731
Venue
VLDB
Year
2017
Pagerank
6.5565658e-05
Overall Rank
4,533 | 69.53%
DOI
10.14778/3055540.3055549
Incoming Non-self Citations Over Time
BibTeX Citation
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@article{lu_vldb17,
title = {{AdaptDB: Adaptive Partitioning for Distributed Joins}},
author = {Lu, Yi and Shanbhag, Anil and Jindal, Alekh and Madden, Samuel},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {5},
pages = {589--600},
doi = {10.14778/3055540.3055549},
url = {https://doi.org/10.14778/3055540.3055549},
year = {2017}
}
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Showing 16 of 16 cited papers.
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Rank
Cited Paper
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Pagerank
123
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Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems
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Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing)
2010
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The End of Slow Networks: It's Time for a Redesign
2016
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2005
CIDR
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2016
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