A Hierarchical Approach to Maximizing MapReduce Efficiency

Abstract

MapReduce has been widely recognized for its elastic scalability and fault tolerance, with the efficiency being relatively disregarded, which, however, is equally important in "pay-as-you-go" cloud systems such as Amazon's Elastic Map Reduce. This paper argues that there are multiple levels of data locality and parallelism in typical multicore clusters that affect performance. By characterizing the performance limitations of typical Map Reduce applications on multi-core based Hadoop clusters, we show that current JVM-based runtime (i.e., Task Worker) fails to exploit data locality and task parallelism at single-node level. Based on the study, we extend Hadoop with a hierarchical Map Reduce model and seamlessly integrate an efficient multicore Map Reduce runtime to Hadoop, resulting in a system we called Azwraith. Such a hierarchical scheme enables Map Reduce applications to explore locality and parallelism at both cluster level and single-node level. To reuse data across job boundary, we also extend Azwraith with an effective in-memory cache scheme that significantly reduces networking and disk traffics. Performance evaluation on a small-scale cluster show that, Azwraith, combined with the optimizations, outperforms Hadoop from 1.4x to 3.5x.