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Predictable Performance for Unpredictable Workloads
Summary: Crescando delivers guaranteed latency and data freshness for unpredictable workloads via parallel in-memory scans and query-data joins. Describes the Crescando storage node, its algorithms and implementation, benchmarks showing broad workload guarantees.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h3401ac81bf3e484f
Venue
VLDB
Year
2009
Pagerank
9.375192e-05
Overall Rank
1,918 | 87.12%
DOI
10.14778/1687627.1687707
Incoming Non-self Citations Over Time
BibTeX Citation
@article{unterbrunner_vldb09,
title = {{Predictable Performance for Unpredictable Workloads}},
author = {Unterbrunner, P. and Giannikis, G. and Alonso, G. and Fauser, D. and Kossmann, D.},
journal = {PVLDB},
series = {{VLDB} '09},
doi = {10.14778/1687627.1687707},
url = {https://doi.org/10.14778/1687627.1687707},
year = {2009}
}
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Shared Workload Optimization 2014
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7.1759026e-05
4,012
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VLDB
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4,334
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4,732
MQJoin: Efficient Shared Execution of Main-Memory Joins 2016
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5,213
A Padded Encoding Scheme to Accelerate Scans by Leveraging Skew 2015
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5,658
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5,831
FluxQuery: An Execution Framework for Highly Interactive Query Workloads 2016
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AStream: Ad-hoc Shared Stream Processing 2019
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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