Twister: a runtime for iterative MapReduce

  • Authors:
  • Jaliya Ekanayake;Hui Li;Bingjing Zhang;Thilina Gunarathne;Seung-Hee Bae;Judy Qiu;Geoffrey Fox

  • Affiliations:
  • Indiana University Bloomington;Indiana University Bloomington;Indiana University Bloomington;Indiana University Bloomington;Indiana University Bloomington;Indiana University Bloomington;Indiana University Bloomington

  • Venue:
  • Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
  • Year:
  • 2010

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Abstract

MapReduce programming model has simplified the implementation of many data parallel applications. The simplicity of the programming model and the quality of services provided by many implementations of MapReduce attract a lot of enthusiasm among distributed computing communities. From the years of experience in applying MapReduce to various scientific applications we identified a set of extensions to the programming model and improvements to its architecture that will expand the applicability of MapReduce to more classes of applications. In this paper, we present the programming model and the architecture of Twister an enhanced MapReduce runtime that supports iterative MapReduce computations efficiently. We also show performance comparisons of Twister with other similar runtimes such as Hadoop and DryadLINQ for large scale data parallel applications.