Hyracks: A flexible and extensible foundation for data-intensive computing

  • Authors:

  • Vinayak Borkar;Michael Carey;Raman Grover;Nicola Onose;Rares Vernica

  • Affiliations:

  • Computer Science Department, University of California, Irvine, 92697, USA;Computer Science Department, University of California, Irvine, 92697, USA;Computer Science Department, University of California, Irvine, 92697, USA;Computer Science Department, University of California, Irvine, 92697, USA;Computer Science Department, University of California, Irvine, 92697, USA

  • Venue:
  • ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
  • Year:
  • 2011

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Abstract

Hyracks is a new partitioned-parallel software platform designed to run data-intensive computations on large shared-nothing clusters of computers. Hyracks allows users to express a computation as a DAG of data operators and connectors. Operators operate on partitions of input data and produce partitions of output data, while connectors repartition operators' outputs to make the newly produced partitions available at the consuming operators. We describe the Hyracks end user model, for authors of dataflow jobs, and the extension model for users who wish to augment Hyracks' built-in library with new operator and/or connector types. We also describe our initial Hyracks implementation. Since Hyracks is in roughly the same space as the open source Hadoop platform, we compare Hyracks with Hadoop experimentally for several different kinds of use cases. The initial results demonstrate that Hyracks has significant promise as a next-generation platform for data-intensive applications.