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How Hadoop Clusters Break
July-Aug. 2013 (vol. 30 no. 4)
pp. 88-94
Ariel Rabkin, Princeton University
Randy Howard Katz, University of California, Berkeley
This article describes an examination of a sample of several hundred support tickets for the Hadoop ecosystem, a widely used group of big data storage and processing systems; a taxonomy of errors and how they are addressed by supporters; and the misconfigurations that are the dominant cause of failures. Some design "antipatterns" and missing platform features contribute to these problems. Developers can use various methods to build more robust distributed systems, thereby helping users and administrators prevent some of these rough edges.
Index Terms:
Cluster approximation,Information management,Data handling,Data storage systems,Software development,Software reliability,Analytical models,system administration,reliability,distributed systems,cloud computing,big data
Citation:
Ariel Rabkin, Randy Howard Katz, "How Hadoop Clusters Break," IEEE Software, vol. 30, no. 4, pp. 88-94, July-Aug. 2013, doi:10.1109/MS.2012.73
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