LIBRA: Lightweight Data Skew Mitigation in MapReduce

IEEE Transactions on Parallel and Distributed Systems(2015)

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摘要
MapReduce is an effective tool for parallel data processing. One significant issue in practical MapReduce applications is data skew: the imbalance in the amount of data assigned to each task. This causes some tasks to take much longer to finish than others and can significantly impact performance. This paper presents LIBRA, a lightweight strategy to address the data skew problem among the reducers...
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关键词
Indexes,Parallel processing,Sampling methods,Distributed databases,Approximation methods,Semantics,Delays
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