FP-Hadoop: Efficient Execution of Parallel Jobs Over Skewed Data
Summary: FP-Hadoop addresses reduce-side skew with an intermediate-reduce phase that dynamically partitions intermediate-value blocks among workers, parallelizing even a single hot key. A prototype reports up to 10× faster reduce time and 5× faster overall execution than Hadoop. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Miguel Liroz-Gistau (INRIA; Laboratory of Computer Science, Robotics and Microelectronics of Montpellier)
- 2. Reza Akbarinia (INRIA; Laboratory of Computer Science, Robotics and Microelectronics of Montpellier)
- 3. Patrick Valduriez (INRIA; Laboratory of Computer Science, Robotics and Microelectronics of Montpellier)
BibTeX Citation
@article{lirozgistau_vldb15,
title = {{FP-Hadoop: Efficient Execution of Parallel Jobs Over Skewed Data}},
author = {Liroz-Gistau, Miguel and Akbarinia, Reza and Valduriez, Patrick},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {12},
pages = {1856},
doi = {10.14778/2824032.2824085},
url = {https://doi.org/10.14778/2824032.2824085},
year = {2015}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,319 | SkewTune: Mitigating Skew in MapReduce Applications | 2012 | SIGMOD | 0.00011175005 |
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