Corpus ID: 16322742

Optimizing Shuffle Performance in Spark

@inproceedings{Davidson2013OptimizingSP,
  title={Optimizing Shuffle Performance in Spark},
  author={A. Davidson},
  year={2013}
}
  • A. Davidson
  • Published 2013
  • Spark [6] is a cluster framework that performs in-memory computing, with the goal of outperforming disk-based engines like Hadoop [2]. As with other distributed data processing platforms, it is common to collect data in a manyto-many fashion, a stage traditionally known as the shuffle phase. In Spark, many sources of inefficiency exist in the shuffle phase that, once addressed, potentially promise vast performance improvements. In this paper, we identify the bottlenecks in the execution of the… CONTINUE READING

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