Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads
Summary: Cross-industry analysis of six production MapReduce traces reveals interactive workloads dominated by many small, query-like jobs—not just large batch jobs—and invalidates assumptions about uniform access and diurnal regularity. Proposes a TCP-like benchmark. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yanpei Chen (University of California Berkeley)
- 2. Sara Alspaugh (University of California Berkeley)
- 3. Randy Katz (University of California Berkeley)
BibTeX Citation
@article{chen_vldb12,
title = {{Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads}},
author = {Chen, Yanpei and Alspaugh, Sara and Katz, Randy},
journal = {PVLDB},
series = {{VLDB} '12},
volume = {5},
number = {12},
pages = {1802--1813},
doi = {10.14778/2367502.2367516},
url = {https://doi.org/10.14778/2367502.2367516},
year = {2012}
}
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