RAFT at Work: Speeding-Up MapReduce Applications under Task and Node Failures
Summary: RAFT at Work speeds up MapReduce under task and node failures using recovery algorithms. It piggybacks checkpoints on progress, reuses intermediates, and maintains per-map input lists to stream to reducers; a web-demo compares RAFT with Hadoop. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jorge-Arnulfo Quiané-Ruiz (Saarland University)
- 2. Christoph Pinkel (Saarland University)
- 3. Jörg Schad (Saarland University)
- 4. Jens Dittrich (Saarland University)
BibTeX Citation
@inproceedings{quianeruiz_sigmod11,
title = {{RAFT at Work: Speeding-Up MapReduce Applications under Task and Node Failures}},
author = {Quiané-Ruiz, Jorge-Arnulfo and Pinkel, Christoph and Schad, Jörg and Dittrich, Jens},
series = {{SIGMOD} '11},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1989323.1989460},
url = {https://dl.acm.org/doi/10.1145/1989323.1989460},
year = {2011}
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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 |
|---|---|---|---|---|
| 660 | Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing) | 2010 | VLDB | 0.00015198804 |
| 895 | Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance | 2010 | VLDB | 0.00013357681 |
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