MapReduce Based Analysis of Sample Applications Using Hadoop - Ghazi, Mohd Rehan; Raghava, N. S. (Applications of Computing and Communication Technologies, 2018)
The rate of increase of structured, semi-structured and unstructured data is very high. To discover hidden information from different types of data is a big challenge. The two techniques, word frequency count and string matching, are applied on a single node and multi node cluster with an input data set. The results are analyzed and compared by varying MapReduce configuration of both. In this paper we have tested that for a MapReduce job how changing the number of mappers and reducers can significantly affect performance. Further, it is analyzed how Hadoop invokes number of mappers/reducers depending upon the input size and Hadoop Distributed File System (HDFS) block size. The outcome of research analysis for heterogeneous cluster configurations indicates the prospective of the framework, as well as of mappers and reducers that affect its performance.