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Parmar, Purvi
- Variations in Outcome for the Same Map Reduce Transitive Closure Algorithm Implemented on Different Hadoop Platforms
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1 Center for Advanced Research in Entity Resolution and Information Quality University of Arkansas at Little Rock Little Rock, Arkansas, US
1 Center for Advanced Research in Entity Resolution and Information Quality University of Arkansas at Little Rock Little Rock, Arkansas, US
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 12, No 4 (2020), Pagination: 27-34Abstract
This paper describes the outcome of an attempt to implement the same transitive closure (TC) algorithm for Apache MapReduce running on different Apache Hadoop distributions. Apache MapReduce is a software framework used with Apache Hadoop, which has become the de facto standard platform for processing and storing large amounts of data in a distributed computing environment. The research presented here focuses on the variations observed among the results of an efficient iterative transitive closure algorithm when run against different distributed environments. The results from these comparisons were validated against the benchmark results from OYSTER, an open source Entity Resolution system. The experiment results highlighted the inconsistencies that can occur when using the same codebase with different implementations of Map Reduce.Keywords
Entity Resolution, Hadoop, Mapreduce, Transitive Closure, HDFS, Cloudera, Talend.Full Text
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