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Templating Shuffles
Summary: TeShu: a unified, extensible shuffle service that represents optimization choices as parameterized shuffle templates capturing application, workload, and data‑center variability. Templates are instantiated via efficient sampling to quickly find near‑optimal shuffles.
(summarized by gpt-5-mini on Feb 09 2026)
Paper ID
heb09b2135a608d03
Venue
CIDR
Year
2023
Pagerank
4.9793485e-05
Overall Rank
11,676 | 21.50%
DOI
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(CC BY 4.0)
Incoming Non-self Citations Over Time
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BibTeX Citation
@inproceedings{zhang_cidr23,
address = {Amsterdam, Netherlands},
series = {{CIDR} '23},
title = {{Templating Shuffles}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Zhang, Qizhen and Wu, Jiacheng and Chen, Ang and Liu, Vincent and Loo, Boon Thau},
year = {2023}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
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Rank
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Pagerank
3
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Spark SQL: Relational Data Processing in Spark 2015
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One Trillion Edges: Graph Processing at Facebook-Scale 2015
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415
SystemML: Declarative Machine Learning on Spark 2016
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Starling: A Scalable Query Engine on Cloud Functions 2020
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NUMA-aware algorithms: the case of data shuffling 2013
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1,763
Lambada: Interactive Data Analytics on Cold Data Using Serverless Cloud Infrastructure 2020
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Understanding the Effect of Data Center Resource Disaggregation on Production DBMSs 2020
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Big Data Analytics with Datalog Queries on Spark 2016
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Boxer: Data Analytics on Network-enabled Serverless Platforms 2021
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AdaptDB: Adaptive Partitioning for Distributed Joins 2017
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