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)

Paper ID: 4477
Venue: SIGMOD
Year: 2011
Pagerank: -
Overall Rank: 13,487 | 6.18%
DOI: -

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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
794	Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing)	2010	VLDB	0.00016605103
953	Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance	2010	VLDB	0.00015095431

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