Understanding issue correlations: A case study of the hadoop system

Jian Huang, Xuechen Zhang, Karsten Schwan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Over the last decade, Hadoop has evolved into a widely used platform for Big Data applications. Acknowledging its wide-spread use, we present a comprehensive analysis of the solved issues with applied patches in the Hadoop ecosystem. The analysis is conducted with a focus on Hadoop's two essential components: HDFS (storage) and MapReduce (computation), it involves a total of 4218 solved issues over the last six years, covering 2180 issues from HDFS and 2038 issues from MapReduce. Insights derived from the study concern system design and development, particularly with respect to correlated issues and correlations between root causes of issues and characteristics of the Hadoop subsystems. These findings shed light on the future development of Big Data systems, on their testing, and on bug-finding tools.

Original languageEnglish (US)
Title of host publicationACM SoCC 2015 - Proceedings of the 6th ACM Symposium on Cloud Computing
EditorsMagdalena Balazinska, Michael J. Freedman, Sumita Barahmand, Shahram Ghandeharizadeh
PublisherAssociation for Computing Machinery, Inc
Pages2-15
Number of pages14
ISBN (Electronic)9781450336512
DOIs
StatePublished - Aug 27 2015
Externally publishedYes
Event6th ACM Symposium on Cloud Computing, ACM SoCC 2015 - Kohala Coast, United States
Duration: Aug 27 2015Aug 29 2015

Publication series

NameACM SoCC 2015 - Proceedings of the 6th ACM Symposium on Cloud Computing

Other

Other6th ACM Symposium on Cloud Computing, ACM SoCC 2015
Country/TerritoryUnited States
CityKohala Coast
Period8/27/158/29/15

Keywords

  • Big data
  • Bug study
  • Hadoop
  • Issue correlation

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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