NDLI: Towards Scalable One-Pass Analytics Using MapReduce

Content Provider	IEEE Xplore Digital Library
Author	Mazur, E. Boduo Li Yanlei Diao Shenoy, P.
Copyright Year	2011
Abstract	An integral part of many data-intensive applications is the need to collect and analyze enormous datasets efficiently. Concurrent with such application needs is the increasing adoption of MapReduce as a programming model for processing large datasets using a cluster of machines. Current MapReduce systems, however, require the data set to be loaded into the cluster before running analytical queries, and thereby incur high delays to start query processing. Furthermore, existing systems are geared towards batch processing. In this paper, we seek to answer a fundamental question: what architectural changes are necessary to bring the benefits of the MapReduce computation model to incremental, one-pass analytics, i.e., to support stream processing and online aggregation? To answer this question, we first conduct a detailed empirical performance study of current MapReduce implementations including Hadoop and MapReduce Online using a variety of workloads. By doing so, we identify several drawbacks of existing systems for one-pass analytics. Based on the insights from our study, we conclude by listing key design requirements and arguing for architectural changes of MapReduce systems to overcome their current limitations and fully embrace incremental one-pass analytics and showing promising preliminary results.
Starting Page	1102
Ending Page	1111
File Size	1249954
Page Count	10
File Format	PDF
ISBN	9781612844251
ISSN	15302075
DOI	10.1109/IPDPS.2011.251
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault tolerance Computational modeling Fault tolerant systems Benchmark testing Parallel processing Sorting Load modeling
Content Type	Text
Resource Type	Article

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