HaLoop: : efficient iterative data processing on large clusters

  • Bu Y
  • Howe B
  • Balazinska M
 et al. 
  • 3


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


The growing demand for large-scale data mining and data anal- ysis applications has led both industry and academia to design new types of highly scalable data-intensive computing platforms. MapReduce and Dryad are two popular platforms in which the dataflow takes the form of a directed acyclic graph of operators. These platforms lack built-in support for iterative programs, which arise naturally in many applications including data mining, web ranking, graph analysis, model fitting, and so on. This paper presents HaLoop, a modified version of the Hadoop MapReduce framework that is designed to serve these applications. HaLoop not only extends MapReduce with programming support for it- erative applications, it also dramatically improves their efficiency by making the task scheduler loop-aware and by adding various caching mechanisms. We evaluated HaLoop on real queries and real datasets. Compared with Hadoop, on average, HaLoop reduces query runtimes by 1.85, and shuffles only 4% of the data between mappers and reducers.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Yingyi Bu

  • Bill Howe

  • Magdalena Balazinska

  • Michael D. Ernst

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free