SCALLA: A Platform for Scalable One-Pass Analytics Using MapReduce

  • Authors:

  • Boduo Li;Edward Mazur;Yanlei Diao;Andrew McGregor;Prashant Shenoy

  • Affiliations:

  • University of Massachusetts Amherst;University of Massachusetts Amherst;University of Massachusetts Amherst;University of Massachusetts Amherst;University of Massachusetts Amherst

  • Venue:
  • ACM Transactions on Database Systems (TODS)
  • Year:
  • 2012

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Abstract

Today’s one-pass analytics applications tend to be data-intensive in nature and require the ability to process high volumes of data efficiently. MapReduce is a popular programming model for processing large datasets using a cluster of machines. However, the traditional MapReduce model is not well-suited for one-pass analytics, since it is geared towards batch processing and requires the dataset to be fully loaded into the cluster before running analytical queries. This article examines, from a systems standpoint, what architectural design changes are necessary to bring the benefits of the MapReduce model to incremental one-pass analytics. Our empirical and theoretical analyses of Hadoop-based MapReduce systems show that the widely used sort-merge implementation for partitioning and parallel processing poses a fundamental barrier to incremental one-pass analytics, despite various optimizations. To address these limitations, we propose a new data analysis platform that employs hash techniques to enable fast in-memory processing, and a new frequent key based technique to extend such processing to workloads that require a large key-state space. Evaluation of our Hadoop-based prototype using real-world workloads shows that our new platform significantly improves the progress of map tasks, allows the reduce progress to keep up with the map progress, with up to 3 orders of magnitude reduction of internal data spills, and enables results to be returned continuously during the job.