A Micro-benchmark Suite for Evaluating Hadoop MapReduce on High-Performance Networks

  • Dipti Shankar
  • Xiaoyi Lu
  • Md. Wasi-ur-Rahman
  • Nusrat Islam
  • Dhabaleswar K. (DK) Panda
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8807)

Abstract

Hadoop MapReduce is increasingly being used by many data-centers (e.g. Facebook, Yahoo!) because of its simplicity, productivity, scalability, and fault tolerance. For MapReduce applications, achieving low job execution time is critical. Since a majority of the existing clusters today are equipped with modern, high-speed interconnects such as InfiniBand and 10 GigE, that offer high bandwidth and low communication latency, it is essential to study the impact of network configuration on the communication patterns of the MapReduce job. However, a standardized benchmark suite that focuses on helping users evaluate the performance of the stand-alone Hadoop MapReduce component is not available in the current Apache Hadoop community. In this paper, we propose a micro-benchmark suite that can be used to evaluate the performance of stand-alone Hadoop MapReduce, with different intermediate data distribution patterns, varied key/value sizes, and data types. We also show how this micro-benchmark suite can be used to evaluate the performance of Hadoop MapReduce over different networks/protocols and parameter configurations on modern clusters. The micro-benchmark suite is designed to be compatible with both Hadoop 1.x and Hadoop 2.x.

Keywords

Big data Hadoop MapReduce Micro-benchmarks High-performance networks 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dipti Shankar
    • 1
  • Xiaoyi Lu
    • 1
  • Md. Wasi-ur-Rahman
    • 1
  • Nusrat Islam
    • 1
  • Dhabaleswar K. (DK) Panda
    • 1
  1. 1.Department of Computer Science and EngineeringThe Ohio State UniversityColumbusUSA

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