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Handling Highly Contended OLTP Workloads Using Fast Dynamic Partitioning
Summary: Strife clusters OLTP txns into disjoint, conflict-free batches, executing most without concurrency control. Residuals run under CC; clustering uses random sampling with a concurrent union-find to partition batches, enabling adaptation and 2x throughput.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h6ce0b1d9f5d393fe
Venue
SIGMOD
Year
2020
Pagerank
7.7087933e-05
Overall Rank
3,048 | 79.52%
DOI
10.1145/3318464.3389764
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{prasaad_sigmod20,
title = {{Handling Highly Contended OLTP Workloads Using Fast Dynamic Partitioning}},
author = {Prasaad, Guna and Cheung, Alvin and Suciu, Dan},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389764},
url = {https://dl.acm.org/doi/10.1145/3318464.3389764},
year = {2020}
}
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H-Store: A High-Performance, Distributed Main Memory Transaction Processing System 2008
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Schism: a Workload-Driven Approach to Database Replication and Partitioning 2010
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Data-Oriented Transaction Execution 2010
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Staring into the Abyss: An Evaluation of Concurrency Control with One Thousand Cores 2015
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Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems 2012
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FOEDUS: OLTP Engine for a Thousand Cores and NVRAM 2015
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ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads 2016
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